Asia-Pacific Renewable Energy Assessment › files › file... · The Asia-Pacific Renewable Energy...
89
1 Asia-Pacific Renewable Energy Assessment July 2014
Transcript of Asia-Pacific Renewable Energy Assessment › files › file... · The Asia-Pacific Renewable Energy...
1
Asia-Pacific Renewable Energy Assessment July 2014
2
Arif Syed, Shamim Ahmad, Adam Bialowas, Emma Richardson, Davin Nowakowski and Peta Nicholson
2014, Asia Pacific Renewable Energy Assessment, BREE, Canberra, July.
© Commonwealth of Australia 2014
This work is copyright, the copyright being owned by the Commonwealth of Australia. The Commonwealth of Australia has,
however, decided that, consistent with the need for free and open re-use and adaptation, public sector information should be
licensed by agencies under the Creative Commons BY standard as the default position. The material in this publication is
available for use according to the Creative Commons BY licensing protocol whereby when a work is copied or redistributed, the
Commonwealth of Australia (and any other nominated parties) must be credited and the source linked to by the user. It is
recommended that users wishing to make copies from BREE publications contact the Chief Economist, Bureau of Resources and
Energy Economics (BREE). This is especially important where a publication contains material in respect of which the copyright
is held by a party other than the Commonwealth of Australia as the Creative Commons licence may not be acceptable to those
copyright owners.
The Australian Government acting through BREE has exercised due care and skill in the preparation and compilation of the
information and data set out in this publication. Notwithstanding, BREE, its employees and advisers disclaim all liability,
including liability for negligence, for any loss, damage, injury, expense or cost incurred by any person as a result of accessing,
using or relying upon any of the information or data set out in this publication to the maximum extent permitted by law.
Asia Pacific Renewable Energy Assessment
Postal address:
Bureau of Resources and Energy Economics
GPO Box 1564
Canberra ACT 2601
Phone: +61 2 6276 1000, or 61 2 6243 7504
Email: [email protected], or [email protected]
Web: www.bree.gov.au
Acknowledgements
The authors gratefully acknowledge the assistance and comments provided on drafts of this paper by Sonya Kelly, Shari Lapthorne, Nicole Thomas and Carolyn Barton of the Department of Industry, Bruce Wilson, Wayne Calder and Renata Hasanova of BREE and Donald Chung, Bri-Mathias Hodge, Kosol Kiatreungwattana, Mackay Miller, Michael Milligan and Dan Olis of the National Renewable Energy Laboratory. Special thanks are also due to the officers from the Indian Planning Commission, Indian Ministry of Renewables and New Energy, and the Energy Research Institute of India.
Disclaimer
BREE Discussion Papers represent only the views and analyses of the authors and not necessarily those of BREE or the Department of Industry.
3
Foreword
The Asia-Pacific Renewable Energy Assessment (APREA) provides an overview of the
current and possible future costs of renewable electricity generation in selected reference
economies: China, India, Indonesia, Japan, Republic of Korea, and Australia.
BREE has undertaken an assessment of the cost of renewable electricity generation through
reviewing country specific literature and applying the Australian Energy Technology
Assessment (AETA) model. The APREA report also assesses the technical and policy issues
related to the integration of renewable electricity generation into existing electricity networks.
Knowledge of the cost of new electricity generating technology plays an important role in
determining the mix of electricity generation capacity additions that will serve growing loads
in the future. Understanding technology costs also helps to determine how new electricity
generation capacity competes against existing capacity, and the response of electricity
generators to the imposition of environmental controls on conventional pollutants or any
limitations on greenhouse gas emissions.
It can be expected that both an understanding of technology costs, as well as integration
issues and their solutions across Asia-Pacific countries will help APREA reference countries
in designing appropriate policies to deliver least cost electricity generation using a
combination of both fossil fuel and renewable technologies.
Wayne Calder
Deputy Executive Director
Bureau of Resources and Energy Economics
July 2014
4
Acronyms
AEMO Australian Energy Market Operator
AER Australian Energy Regulator
AETA Australian Energy Technology Assessment
APREA Asia-Pacific Renewable Energy Assessment
ASU Air Separation Unit
AUD Australian Dollar
BNEF Bloomberg New Energy Finance
bps basis points
BREE Bureau of Resources and Energy Economics
CEA India’s Central Electricity Authority
CEC Clean Energy Council
CEM5 Clean Energy Ministerial Forum
CERC Indian Central Electricity Regulatory Commission
CESC Clean Energy Solutions Centre
CPI Consumer Price Index
CSIRO Commonwealth Scientific and Industrial Research Organisation
Et Electricity generation in the year t
ESAA Energy Suppliers Association of Australia
Ft Fuel expenditure in the year t
FOM Fixed Operational and Maintenance costs
FIT Feed in Tariff
GDP Gross Domestic Product
GJ Gigajoule
GWEC Global Wind Energy Council
GWh Gigawatt hour
HSA Hot Sedimentary Aquifers
It Investment expenditure in the year t
IDC Interest During Construction
IEA International Energy Agency
IMO Independent Market Operator
IRENA International Renewable Energy Agency
kW Kilowatt
LCOE Levelised Cost of Energy
LRET Large Scale Renewable Energy Target
Mt Operations and maintenance expenditure in the year t
METI Japan's Ministry of Economy, Trade and Industry
MNRE India's Ministry of New and Renewable Energy
MoC India's Ministry of Coal
MoP India’s Ministry of Power
5
MTDP Medium Long Term Development Plan
MW Megawatt
MWh Megawatt hours
n Amortisation Period
NDRC China's National Development and Regulation Commission
NEA Nuclear Energy Agency
NEM National Electricity Market
NPU Japan's National Policy Unit
NREL US National Renewable Energy Laboratories
NSW New South Wales
O&M Operations and Maintenance
p.a. per annum
POWERGRID Power Grid Corporation of India Ltd
PJ Peta Joule
PV Photovoltaic
r Discount Rate
RE Renewable Electricity
REEEP Renewable Energy & Energy Efficiency Partnership
REN21 Renewable Energy Policy Network for 21st Century
RET Renewable Energy Target
Rs Indian Rupee
SGCC State Grid Corporation of China
SHAKTI Shakti Sustainable Energy Foundation in India
SWIS South West Interconnected System
USD US Dollar
VOM Variable Operational and Maintenance
WACC Weighted average cost of capital
6
Glossary
Amortisation Period: the period over which a plant must achieve its economic return.
Auxiliary Load: the internal or parasitic load from the electricity required to sustain the
operation of a plant.
Capacity Factor: the ratio of the actual output of a power plant over a period of time and its
potential output if it had operated at full nameplate capacity the entire time.
Capital Cost: also called the overnight capital cost, is the cost of delivery of a plant, not
including the cost of finance.
Direct Cost: the cost associated with all major plant, materials, minor equipment and labour
to develop a power plant to the stage of commercial operation.
Discount Rate: the rate at which future values are discounted or converted to a present
value.
Dispatchable generation: sources of electricity that can be dispatched at the request of
power grid operators.
Gross Capacity: maximum or rated generation from a power plant without losses and
auxiliary loads taken into account.
International Equipment Cost: the cost for internationally sourced equipment associated
with the project.
Labour Cost: the component of the delivery cost for a plant associated with local
(Australian) labour.
Levelised Cost of Energy: the minimum cost of energy at which a generator must sell the
produced electricity in order to achieve its desired economic return.
Local Equipment Cost: the cost of locally sourced (Australia) plant and equipment for the
project.
Net Capacity: the export capacity of a generation plant – i.e. the Gross Capacity less the
losses and auxiliary loads of the plant.
Nth-of-a-kind plant cost: All engineering, equipment, construction, testing, tooling, project
management, and other costs that are repetitive in nature and would be incurred if a plant
identical to the first plant was built.
Owner’s Cost: the costs associated with the development of a project prior to the start of
construction.
Thermal Efficiency: the ratio between the useful energy output of a generator and the input,
in energy terms.
7
Contents
Acknowledgements .................................................................................................................................. 2
Disclaimer ................................................................................................................................................ 2
Foreword .................................................................................................................................................. 3
Acronyms ................................................................................................................................................. 4
Glossary ................................................................................................................................................... 6
Executive summary .................................................................................................................................. 9
1 Introduction ......................................................................................................................................... 12
1.1 Background .................................................................................................................................. 12
1.2 Objective and scope ..................................................................................................................... 12
1.3 Methodology of the report ........................................................................................................... 13
1.4 Organisation of the report ............................................................................................................ 13
2 Renewable integration issues .............................................................................................................. 14
2.1 Integration issues ......................................................................................................................... 14
2.1.1 Technical or physical challenges .......................................................................................... 14
2.1.2 Market or policy challenges .................................................................................................. 16
2.2 Solutions to balancing Integration ............................................................................................... 17
3 Renewable electricity integration issues in APREA countries ........................................................... 19
3.1 Australia ....................................................................................................................................... 19
3.1.1 Australia’s electricity sector - overview ............................................................................... 19
3.1.2 Renewable energy policies in Australia ................................................................................ 21
3.1.3 Integration issues of renewable energy in Australia ............................................................. 23
3.2 China ............................................................................................................................................ 24
3.2.1 China’s electricity sector - overview .................................................................................... 24
3.2.2 Renewable energy policies in China ..................................................................................... 26
3.2.3 Integration issues of renewable energy in China .................................................................. 34
3.3 India ............................................................................................................................................. 41
3.3.1 India’s electricity sector - overview ...................................................................................... 41
3.3.2 Renewable energy policies in India ...................................................................................... 45
3.3.3 Integration issues of renewable energy in India .................................................................... 47
3.4 Japan ............................................................................................................................................ 49
3.4.1 Japan electricity sector - overview ........................................................................................ 49
3.4.2 Integration policies ................................................................................................................ 50
4 Levelised costs of energy estimates .................................................................................................... 54
4.1 Key findings ................................................................................................................................. 54
4.2 LCOE - concepts and definitions ................................................................................................. 55
4.3 Issues in comparing LCOE estimates across countries and sources ............................................ 56
4.4 Approach for comparing LCOE across sources and across countries ......................................... 57
4.5 Renewable energy generation costs across APREA countries..................................................... 58
4.5.1 LCOE in China ..................................................................................................................... 59
4.5.2 LCOE in India ....................................................................................................................... 61
4.5.3 LCOE in Indonesia ............................................................................................................... 63
4.5.4 LCOE in Japan ...................................................................................................................... 64
4.5.5 LCOE in South Korea ........................................................................................................... 65
4.5.6 LCOE in Australia ................................................................................................................ 66
4.6 Renewable energy generation costs across technologies ............................................................. 68
4.6.1 Wind LCOE .......................................................................................................................... 69
8
4.6.2 Solar PV LCOE ..................................................................................................................... 69
4.6.3 Hydro LCOE ......................................................................................................................... 70
4.6.4 Biomass LCOE ..................................................................................................................... 70
4.6.5 Geothermal LCOE ................................................................................................................ 70
4.6.6 Solar thermal LCOE ............................................................................................................. 71
5 Costs of renewables Integration .......................................................................................................... 72
6 Lessons and key messages for integration of renewables ................................................................... 74
Appendix A: LCOE data and assumptions from all sources .................................................................. 77
7 References ........................................................................................................................................... 84
Figures Figure 1 Australia renewable electricity generation by source and share of total electricity
generation ................................................................................................................................ 19 Figure 2 Australia’s renewable electricity generation, by energy source, 2012 .................................... 20 Figure 3 China’s renewable electricity generation by source and share of total electricity
generation ................................................................................................................................ 24 Figure 4 China’s renewable electricity generation, by energy source, 2011 ......................................... 25 Figure 5 Grid connection in China as of the end of 2010 ...................................................................... 26 Figure 6 China: onshore wind tendered prices and volumes 2003-2007 ............................................... 30 Figure 7 Wind power in China: connected and unconnected ................................................................ 35 Figure 8 Growth pattern of renewable electricity capacity in different five year plans ......................... 42 Figure 9 Share of Renewable Energy Capacity as on 31 July 2013 ...................................................... 43 Figure 10 Potential renewable resources in India (March 2012) ........................................................... 44 Figure 11 Proposed target of grid connected renewable electricity installed capacity at the end of
12th & 13
th five year plans...................................................................................................... 45
Figure 12 Japan’s renewable electricity generation by source and share of total electricity
generation .............................................................................................................................. 50 Figure 13 LCOE ranges (bar) from reference sources and BREE's estimates (black mark) for
renewable electricity technologies in APREA countries, 2013 ............................................. 59 Figure 14 LCOE estimates for RE technologies in China by sources, 2013 ......................................... 60 Figure 15 LCOE estimates for RE technologies in India by sources, 2013 ........................................... 62 Figure 16 LCOE estimates for RE technologies in Indonesia by sources, 2013 ................................... 64 Figure 17 LCOE estimates for RE technologies in Japan by sources, 2013 .......................................... 65 Figure 18 LCOE estimates for RE technologies in South Korea by sources, 2013 ............................... 66 Figure 19 LCOE estimates for RE technologies in Australia by sources, 2013 .................................... 67 Figure 20 LCOE ranges (bar) from reference sources and BREE's estimates (black mark) for RE
technologies by countries, 2013 ............................................................................................ 68 Figure 21 Integration costs for wind generation, various countries ....................................................... 73
Maps
Map 1 Australia's Major Electricity Networks ...................................................................................... 21 Map 2 Power Grid Regions of India ...................................................................................................... 41
Tables
Table A1: LCOE Estimates for RE Generation Technologies in China by Source ............................... 77 Table A2: LCOE Estimates for RE Generation Technologies in India by Source ................................ 78 Table A3: LCOE Estimates for RE Generation Technologies in Japan by Source ............................... 79 Table A4: LCOE Estimates for RE Generation Technologies in Indonesia by Source ......................... 80 Table A5: LCOE Estimates for RE Generation Technologies in South Korea by Source .................... 81 Table A6: LCOE Estimates for RE Generation Technologies in Australia by Source .......................... 82 Table A7: LCOE ranges and BREE’s LCOE estimates for RE technology in APREA countries,
2013 ...................................................................................................................................... 83
9
Executive summary
This report presents findings of BREE’s Asia-Pacific Renewable Energy Assessment
(APREA) project on renewable electricity generation technology costs and renewable
generation integration issues in the six reference countries: China, India, Indonesia, Japan,
South Korea, and Australia (APREA target countries).
This study was carried out on the basis of available regional literature and databases on the
costs of renewable electricity (RE) generation from various renewable sources mainly wind,
solar, hydro, biomass, and geothermal resources. Several international organisations,
government agencies of the APREA target countries, market analysts and local experts were
contacted and various publication and web-references in the target countries were used in
gathering technology and country-specific existing information on both the levelised cost of
energy (LCOE) estimates and policy and technical issues associated with integrating
technologies into existing energy networks. The LCOE measures are estimates of electricity
generation costs per Mega Watt hour (MWh).
Over the past decade the level of installed renewable generation capacity has been increasing
across all APREA countries. However, the investment in new capacity has typically not been
accompanied by corresponding development of the infrastructure, institutions and market
incentives necessary to support efficient integration into national power systems.
Historically electricity grids and their operational, planning and market management systems
have not been built around the need to manage growing volumes of the uncertain, intermittent
or variable supply commonly associated with a range of grid based or distributed renewable
electricity generation technologies. Technologies such as wind and solar (variable
renewables) cannot guarantee the same ‘on demand’ reliability as the traditional dispatchable
technologies (e.g. coal, gas or hydroelectric) and this can create a range of challenges, most
notably:
the need to maintain sufficient reserve capacity and fast-start reserve capacity to maintain
system balancing in the presence of variable and uncertain renewable generation.
However, the need for fast-start reserve capacity can be reduced by forecasting;
the need for grid augmentation (including better regional interconnection); and
large levels of intermittent supply can pose challenges for system reliability and may
require spinning reserve for system balancing, particularly frequency control.
Unsurprisingly experiences in integrating renewable energy vary across APREA countries.
Those with larger levels of renewable energy penetration (such as Australia and China) have
clearly observed some or all of the challenges described above while those with relatively
low levels of deployment (such as Indonesia) have yet to observe any serious difficulties. In
all cases impacts on the grid are more pronounced where renewable energy deployment is
locally or regionally concentrated.
Some of the more specific issues experienced among countries included technical constraints,
load balancing and frequency control issues (on weaker grids) imposed by limitations in
existing grid structures and capacities. There have also been operational difficulties imposed
by a general lack of capacity in forecasting renewable electricity generation and through
aspects of market design or management such as longer scheduling and dispatch periods and
10
the availability and coordination of ancillary services, including rapid ramp-up standby
capacity. Institutional factors impacting integration of renewables include uncoordinated
network planning, and a lack of national and technical standards for grid connection of
renewable electricity. Renewable energy integration issues also included institutional features
affecting incentives to invest in infrastructure and practices that facilitate renewable
integration; these include a policy focus on increasing installed renewable capacity instead of
delivered electricity, a lack of or poorly structured incentives for grid operators to invest in
grid reinforcement, interconnection and systems management and a lack of or poorly
structured incentives for adequate ancillary service provision.
None of the challenges or issues described above are technically or economically
insurmountable, and the experience globally indicates that they are not sufficient to
materially impede the overall growth in renewable energy roll-out. However, to ensure that
maximum value is being returned on these investments, while also minimising costs for
consumers, it is necessary that integration issues are managed to ensure grid stability and
avoid costly and unnecessary forced curtailments of generation.
Approaches to encourage renewable deployment and integration have varied across the
APREA target countries. The assessment of practises explored in this report finds that
important operational or infrastructure changes that can help facilitate the integration of
renewable energy include the faster scheduling and dispatch of generation, use of advanced
forecasting in fast market operations, deepening system interconnections and improving
balancing area cooperation, greater access to transmission, increased flexibility of
dispatchable generation capacity, and the use of demand response. The country studies also
reveal the need to create renewable electricity generation targets, instead of capacity
expansion targets.
Finally, while broad integration issues have been identified, the report found that integration
cost estimates are not available in current literature for any of the APREA target countries,
including Australia. This represents a significant informational deficit for accessing the
relative costs of renewable energy technologies (this could also apply to other energy
technologies), and thus, highlights the need for detailed studies aimed at developing
renewable energy integration cost estimates. Challenges faced in formulating renewable
integration cost estimates, and robust methodologies for doing so, derive from a number of
factors:
country-specific logistical challenges to renewable integration require varying suites of
strategies with differing integration cost components;
maturity of electricity system and availability of grid lines and ancillary services;
there is no definitive path for integration; there may be numerous strategies to resolve
issues associated with a given level of renewable penetration. Different paths would
involve different costs, with the outcome contingent on the interaction of institutional
features, policy setting and market forces;
integration costs are increasing with the degree of variable renewable energy penetration.
Thus, integration cost estimates would need to be marginal cost estimates conditioned on
a given level of variable renewable energy penetration;
11
some investments in network infrastructure and systems management for renewable
energy integration would eventually be necessitated by growing energy demand,
irrespective of renewable energy penetration. In this case, renewable integration simply
entails the bringing forward of these investments. Thus, for investments in this category
only the cost of bringing these investments forward should be attributed to renewable
integration; and
some renewable integration measures will improve overall electricity system efficiency
and thereby generate positive externalities for other stakeholders and market players.
Appropriately attributing costs in this context is not clear cut and depends on network
specific attributes, including market (and non-market) structures.
Developing integration cost estimates will require complex modelling that captures country
specific, or even region specific factors such as the size of the grid, feed in geographical area,
availability and flexibility of dispatchable generation and the capacity and technical
sophistication of the grid’s infrastructure and management systems.
12
1 Introduction
1.1 Background
There continues to be vigorous public debate regarding the most cost efficient choices for
supplying electricity in a manner that contributes to sustained emissions reductions while
ensuring grid stability and reliability of supply. Central to this debate is an ability to
understand the full cost of our energy options, including any associated environmental and
energy security externalities that may apply to different technology choices.
Transparent and consistently developed estimates of the cost of new electricity technologies
assists in making informed investment decisions, not only around the choice of generation
technologies, but also in relation to systems development and management. It also helps to
determine how new electricity generation capacity competes against existing capacity, and
which technologies may emerge in the future.
This report, the Asia Pacific Renewable Energy Assessment (APREA), was prepared at the
request of the Australian Government Department of Industry to provide a summary of
publicly available information on renewable electricity generation integration and generation
technology costs across six key economies in the Asia Pacific region; Australian, China,
India, Indonesia, Japan and South Korea.
Importantly, the APREA will enable each economy not only to obtain comparable LCOE
estimates of renewable generation technologies existing within each country, but also to
compare LCOE estimates across countries. The report attempts to provide an overview of
integration issues in each APREA economy and the strategies or measures that have been
employed to address emerging challenges from renewable energy deployment. Where
available the report also includes information on the reported integration costs associated
with the different approaches.
BREE has undertaken this work building on its recently published and ongoing work on costs
of electricity generation technologies for Australia (BREE 2012a, Syed 2013).
1.2 Objective and scope
The objective of the APREA project is to provide an overview of the experiences to date in
relation to the following issues related to the renewable electricity generation prevailing in
the reference countries, China, India, Indonesia, Japan, South Korea, and Australia (APREA
target countries):
1. technical and policy issues related to the integration of renewable sources of
electricity to electricity networks;
2. cost estimates for renewable electricity generation technologies using the levelised
cost of electricity generation (LCOEs) approach; and
3. where available, network and integration cost estimates for renewable sources of
electricity generation.
In doing so, the Report draws on existing sources of public and private information with a
view to distilling general conclusions along with providing a potential database on country
experiences. BREE’s research found insufficient information available on the experiences of
integration issues in Indonesia and South Korea to include sections on integration issues
13
within these countries. However, the report does contain information on the levelised cost of
energy (LCOE) estimates, which capture the costs of generation, for all the APREA target
countries. In addition, BREE’s research revealed an absence of publicly available renewable
energy integration cost estimates. A key finding of the report is the need for further detailed
studies aimed at developing renewable energy integration cost estimates. The report provides
estimates of the costs of generation (LCOE) across technologies and all APREA target
countries; however, integration cost estimates have fallen outside the scope of the report due
to a lack of publicly available information. BREE has expanded the scope of the assessment
by utilising previous BREE analysis to develop the LCOE estimates.
It is not intended that the report will provide recommendations for future action or
assessments around the effectiveness (or otherwise) of renewable energy policy or regulatory
frameworks in APREA countries.
1.3 Methodology of the report
The information presented in the report has been gathered from a wide range of existing
sources. This includes an extensive desk top literature review, feedback from relevant
renewable energy and energy market participants and research institutions in member
countries as well as international research agencies such as the International Renewable
Energy Agency, and the International Energy Agency. Information was also sought from
private consultants and industry associations with experience or knowledge relevant to the
Asia-Pacific region. Where applicable, BREE’s Australian Energy Technology Assessment
(AETA) model has also been used to substantiate the comparable LCOE information across
countries.
BREE released the AETA in July 2012 to help inform the future outlook for generation
technologies in Australia. The AETA 2013 Update provides the best available and most
up-to-date cost estimates for 40 electricity generation technologies under Australian
conditions. These costs were generated through an extensive and rigorous bottom up
engineering analysis of key component costs (capital costs, O&M costs, fuel costs, thermal
efficiency, capacity factors, emission intensity, etc.). The resulting cost estimates of the 40
technologies allow for cross-technology and over time comparisons (2012, 2020, 2025, 2030,
2040 and 2050). The method also enables a direct comparison of alternative energy
technologies in terms of cost per unit of energy (USD/MWh). The AETA report and AETA
model (downloadable) are available free of cost from BREE upon request
1.4 Organisation of the report
This study has been organised in 6 sections. Section 2 provides a brief overview of the key
renewable integration issues. Section 3 deals with renewable integration issues in the APREA
target countries. Section 4 presents the LCOE estimates for the APREA countries assembled
using various publication sources. Section 5 discusses variable renewable integration costs
and highlights gaps in this information for the APREA countries. Finally, section 6 presents
key messages for integration of renewables.
14
2 Renewable integration issues Existing grid networks around the world were designed and developed for centralised, large
power generation. Energy markets have changed rapidly over the last decade presenting new
challenges for power grids to continue to supply reliable and stable electricity. The rapid
advancement and falling costs of new energy technologies is providing energy users greater
choice than ever. As the deployment of renewables increases so does the challenge of
integrating these new forms of energy with power grids not designed to accommodate
renewable energy.
This section describes the nature of the renewable integration challenges and solutions.
Integration issues specific to each APREA country are then discussed in the following
sections.
2.1 Integration issues
Renewable generation technologies can exhibit a high degree of variation in electricity
generation, introducing greater challenges for supply and demand balancing. If variable
renewable penetration is high enough, unanticipated variation in renewable generation can
create demand and supply imbalance, influencing the frequency of electricity across the
entire grid, reducing power quality, and compromising system reliability (CEC 2012). Levels
of variable renewable penetration potentially sufficient to induce fluctuations in electricity
supply frequency are those greater than 10 to 15 per cent. These fluctuations pose challenges
for energy system management. The possible impacts of significant frequency fluctuation
include damage to the property and equipment of end users.1
Issues around managing the integration of renewable energy potentially involve the forced
curtailment2 or prevention of capacity expansion of renewable energy supply. For example,
the Clean Energy Council (CEC) recently noted that “Currently, this potential oversupply of
renewable energy is being avoided, in most cases, by fiat: electricity distributors have
created rules about how much distributed generation they believe is safe on any given feeder,
or downstream of any substation; they now routinely refuse applications to connect solar
installations large enough to breach this limit” (CEC 2012, p. 33).
The variability and uncertainty of wind and solar generation becomes relatively easier to
integrate if more flexible electricity generation capacity is available, such as open cycle gas
(OCGT), oil or hydro generators that can be speedily drawn upon when needed. Electricity
storage devices as well as demand management also help in this pursuit. However, electricity
storage is relatively underdeveloped globally, and does not currently offer commercially
viable solutions to managing variability in grid connected renewable generation.
2.1.1 Technical or physical challenges
Integration of renewable energy – especially low cost variable energy such as wind and solar
– causes challenges because grids are complex interconnected systems requiring constant
1 CEC 2012, p13
2 AEMO 2013c
15
balancing of supply and demand. Technically it is more difficult for renewable energy
systems than other sources to be integrated into the main electricity grid because of the
variability of renewable generation such as wind and solar. Variable renewable energy
systems cannot be fully controlled (Gonzalez-Longatt 2012) to generate electricity at specific
rates and times and thus, a number of policy and planning measures are required to
compensate for the (variability) in generation (AEMO 2013).
Ancillary services to maintain grid stability
Electricity from variable renewable sources such as wind and solar cannot guarantee the same
reliability of supply as dispatchable generation (coal, nuclear and gas), and create a need for
‘back-up’ capacity. Particularly in the absence of accurate forecast systems, back-up capacity
is required in the instance of unforecast falls in wind or solar generation so that other forms
of generation can take up the deficit in generation and maintain system reliability. The greater
the level of variable renewable penetration, the greater the level of back-up generation
required. These arrangements are potentially costly and may involve development of
additional investment and operating costs to run reserve power plants that can respond very
quickly to changes in energy needs.
High variability in electricity generation also creates challenges to maintaining the power
grid’s stability at mandated levels of frequency and voltage on the grid. High penetrations of
wind and solar generation add more variability to the grid than grid operators have managed
in the past, and increase demand for ancillary services used to balance energy over the grid
(IEC 2012).
For example, the Australian Energy Market Operator (AEMO 2013c) outlines the key
operational challenges and solutions to managing the expected 8.88 GW of new wind
generation forecast in the National Electricity Market (NEM) to connect to the power system
by 2020 as follows:
increased renewable penetration may lead to displacement of conventional synchronous
generation, making the control of electricity frequency difficult;
significant new wind generation can reduce existing interconnector transfer limits; and
AEMO modelling in Australia suggests around 35 per cent and 15 per cent of wind
generation from Victoria and South Australia, respectively, could be curtailed from the
grid due to network limitations.
Geographical constraints
As can be the case for conventional generators, renewable energy generators quite often find
themselves working in geographical areas where there is no demand for their full generation
capacity. This may necessitate new transmission lines from remote areas to load (demand)
areas. The cost of grid extension or enhancement to transport electricity from remote areas to
load centres may be prohibitive. Wind and solar generation plants require large areas of land
that is not available close to populous load centres.
16
Forecasting ability
Due to the variable and uncertain nature of low cost renewable electricity generation and
supply, renewable energy raises the issue of balancing demand and supply minute by minute,
and hour by hour, allowing for voltage regulation and frequency and demand forecasting
errors. The IEA (2011a) quantifies about 30 per cent potential savings in balancing cost due
to better wind forecasts.
If cost effective electricity storage devices are available, then this will diminish the need for
back-up capacity to balance demand and supply, since rapid charging and discharging of
stored electricity can be used to smooth fluctuations in frequency. “Storage can protect the
stability of the grid as a whole from the fluctuations in renewable energy output. Some
storage technologies are suitable for continuously ‘smoothing out’ this variable frequency,
again allowing for a much higher safe renewable hosting capacity limit… the mechanism for
smoothing frequency is a familiar one: rapid and small-scale injections or withdrawals of
generation and load. This can equally be done through rapid charging and discharging of
energy storage.” (CEC 2012, p.33) In the absence of sufficient storage facilities, back up
capacity in the form of gas, oil or coal will be needed to deal with wind and solar variability
to keep electricity demand and supply in balance. This is called holding operating reserves,
which may be of three main types: spinning reserves (ramping up of existing reserve plants),
supplementary reserves (ramping up of idle plants needs to be started within minutes, such as
in the case of OCGT and oil fired generation), and replacement reserves.
2.1.2 Market or policy challenges
Financial signals and encouraging an efficient generation mix
Variable renewable generation produces electricity at very low marginal cost, thus potentially
producing electricity at very low prices. If renewable generation does not match load profiles,
then there may be a tendency towards oversupply of electricity in the market e.g. as in
Australia (AEMO 2013). The challenge for grid planning is to ensure that price signals are
present to encourage the most efficient generation when it is needed the most by electricity
users. Markets that do not provide incentive for generation that can respond rapidly to
changes in the electricity market make integration of variable renewables more difficult.
Ancillary services markets that provide payments for capacity to be available in the market,
should the market require them, is one method of encouraging fast responding generation that
assists the integration of renewables.
There may be systems which already have sufficient capacities to satisfy demand at all times
at full frequency, thus integration of renewables does not require additional capacity to be
built (i.e. adequacy costs are zero, at least in the short run). The situation is different where
new generating capacities are built to satisfy new demand and additional generation needs to
be supplied in order to provide sufficient reliability. This requires modelling the optimal
generation mix that would provide the required service at the least cost.
Grid connection, extension and reinforcement
Grid connection refers to the investments in infrastructure necessary to accommodate the grid
connection of new power plants, in particular those outside the area served by the existing
17
grid such as offshore wind-power turbines. Grid connection costs depend on various factors,
such as the distance between power plant and existing grid, territory that is crossed, any
transmission capacity required, and any special needs of the plant that must be connected.
Reinforcement of the existing grid or the extension of new transmission lines from the grid
may serve objectives such as, improving the interconnections within the electricity system,
allowing for better congestion management, or improving reliability of the overall electricity
grid. The cost of extending and enhancing the grid to transport renewable energy needs to be
weighed against the ability of the renewable plant to meet electricity demand.
The National Renewable Energy Laboratory (2010) finds that the larger and more diverse an
interconnected grid system is, the more suitable the system is for renewable energy
integration.
2.2 Solutions to balancing Integration
A range of studies on integration issues have been undertaken in countries outside of those
targeted by the APREA. While it is evident there is no one single solution to resolving
integration issues, there is a range of possible solutions that can be used according to
circumstance. Some common solutions to smooth renewable integration that are found across
countries are summarised below. The set of solutions best suited to each of the APREA target
countries will depend on country specific factors, however, the solutions outlined below can
potentially be used by all APREA countries for effective renewable energy integration.
Balancing costs are a function of both the uncertainty and variability of renewables and
increase with the levels of penetration.
Enlarging balancing areas
Developing interconnections with other regions or expanding wholesale power markets are
generally the means to achieve larger balancing areas. Integration studies have consistently
found expanding access to diverse resources facilitates the integration of high penetrations of
variable renewable generation (Bird and Milligan 2012). The larger the balancing areas, the
lesser relative variability and uncertainty in both the load and renewable energy generation
will be, smoothing out differences among individual loads and generators. Larger balancing
areas can also lead to cost savings because reserves can be pooled over the entire area.
The feasibility of enlarging balancing areas by increasing interconnection with other regions
will to some extent be dependent on geography. Interconnection costs may be prohibitive due
to the distance or terrain over which infrastructure would need to be built. Balancing areas
can also potentially be expanded by improved interconnection within regions, that is, grid
reinforcement. However, grid reinforcement benefits many players thus there is a question of
equitably allocating the costs among grid users, grid operators and other participants.
Faster markets
Sub-hourly scheduling and dispatch improves system supply response efficiency, increases
reliability, and reduces the amount of reserves required to balance demand and supply in the
system. Also, faster dispatch can enable the system to access reserves from existing units at
least cost.
18
A shorter scheduling period decreases the amount of variability that may occur across supply
and demand within the scheduling period. To sustain system reliability, the proximity of the
levels of demand and supply must be maintained within the bounds necessitated by network
infrastructure. Where variability within scheduling periods is less, the system operator
requires fewer on-call ancillary services to balance demand and supply. In addition, for a
given level of ancillary services, a shorter dispatch period reduces the risk that supply and
demand imbalance disturbs system reliability, since the chance of significant deviation
between the levels of demand and supply is reduced.
Improved forecasting
The use of forecasts in grid operations can help predict the amount of wind and solar energy
available and reduce the uncertainty in the amount of generation that will be available to the
system. Thus, forecasting can reduce the required amount of fast-start reserve capacity, since
anticipated variation can be accommodated by slower (cheaper) ramp-up reserve capacity.
Forecasting is more effective in the area of wind generation, since wind flows can generally
be predicted a day in advance relatively easily. However, the achievable accuracy of
forecasting is dependent on region specific factors.
Increasing the share of rapidly dispatchable capacity
The variability and uncertainty of wind and solar generation becomes easier to manage if
more flexible electricity generation capacity is available, such as the open cycle gas (OCGT),
oil or hydro generators that can be speedily drawn upon when needed. Increased system
flexibility can be achieved through increased transmission, or the addition of flexible
resources to the system, such as more flexible generating units, storage, and demand
response. This can help in managing the added variability and uncertainty due to wind and
solar penetration.
With higher levels of renewable generation capacity, the cost of integrating renewables in the
system may be significant. Cost effective electricity storage may emerge as one effective
solution in the future (CEC 2012, CSIRO 2012). There are limits to the amount of renewable
energy capacity that any traditional electricity system can readily integrate while maintaining
stability and reliability; each system’s particular configuration of generator sizes, ramping
response times, and network design will determine the cost effective level of renewable
uptake. Storage devices may use electricity to charge them in off peak time, and may
discharge the stored electricity when the electricity is needed in peak time. This feature of
storage creates its demand to ameliorate peak periods, and provides a cushioning effect to
electricity prices. Costs of storage declined by 50 per cent between 2000 and 2013 (CEC
2012, p. 15), lending credence to the possibility that storage may become increasingly
economically viable in future.
19
3 Renewable electricity integration issues in APREA
countries
The purpose of this chapter is to provide an overview of the technical and policy issues in
Australia, China, India, and Japan in relation to the integration of renewable electricity into
the electricity networks of each country. Information on renewable energy integration from
Indonesia and South Korea is not available.
3.1 Australia
3.1.1 Australia’s electricity sector - overview
In 2012, renewable energy accounted for 9.5 per cent of total electricity generation in
Australia (BREE 2013a). Increases in renewable energy as a percentage of total energy
generation in recent years can be attributed to greater hydro utilisation after a period of
drought and a rapid expansion in wind generation (see Figure 1). Hydro power accounted for
around two thirds of total renewable electricity generation in 2012, followed by wind,
bioenergy, and solar (Figure 2). The falling cost of residential solar systems is expected to
continue to increase the deployment of renewable energy into the future.
Figure 1 Australia renewable electricity generation by source and share of total electricity
generation
Source: IEA (2013)
4
8
12
10
20
30
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
%TWh
Hydro Wind Solar Bioenergy % Renewables
20
Figure 2 Australia’s renewable electricity generation, by energy source, 2012
Source: IEA (2013)
Australia has two major electricity networks (see map 1). The eastern states of New South
Wales, Victoria, Queensland, South Australia, Tasmania, and the Australian Capital Territory
are supplied electricity by the National Electricity Market (NEM). The NEM supplies around
78 per cent of Australia's electricity generation, or around 199 TWh in 2011-12 (AER 2012;
BREE 2013a). The Perth region of Western Australia is supplied electricity by the South
West Interconnected System (SWIS) and generated approximately 18 TWh in 2011-12 (IMO
2013). There are also smaller electricity networks that supply the Northern Territory, the
Pilbara region of Western Australia, and the Mount Isa region of Queensland. These regional
and remote areas are not connected to the two major electricity networks and service just 4
per cent of the population (BREE 2013b).
The NEM is one of the world’s longest integrated electricity networks, spanning 5000
kilometres across six states, all possessing regions with diverse weather conditions and
electricity load requirements (ESAA 2010). Generators in the NEM sell electricity into a
wholesale spot market where prices are determined on a half hourly basis by levels of
demand and supply. Regulated network arrangements and semi regulated retail markets
operate at a state and territory level overseen by national energy market laws and a national
market operator and regulator. Electricity is dispatched to meet demand by the Australian
Energy Market Operator (AEMO). The Australian Energy Regulator (AER) determines
network charges and revenues in the NEM and enforces the National Electricity Law and the
National Electricity Rules that enforce standards of stability and reliability in the system.
Each state regulates its own retail electricity market and there are varying degrees of
competition in the retail markets as well as state government ownership.
The SWIS is regulated by the Economic Regulation Authority and the wholesale electricity
market is operated by the Independent Market Operator. Electricity retail and generation in
Bioenergy2 TWh
Hydro14 TWh
Solar1 TWh
Wind6 TWh
21
the SWIS is a mixture of government and non-government enterprises. The network and
transmission functions are performed by the state owned monopoly Wester Power
Corporation.
Map 1 Australia's Major Electricity Networks
Source: AER 2012
Australia’s renewable energy penetration is not distributed proportionately across its grids.
South Australia generated almost 26 per cent of its electricity from renewable energy in
2011-12, while Queensland generated only around 3 per cent (BREE 2013a). The challenge
to integrate renewable energy into Australia’s two major electricity grids varies by state with
the proportion and type of renewable energy produced.
3.1.2 Renewable energy policies in Australia
Emissions Reduction Fund
The Emissions Reduction Fund (ERF) is the centre piece of the Australian Government’s
Direct Action Plan to achieve a reduction in carbon emissions by 5 per cent below 2000
levels by 2020.
The principles guiding the design of the ERF are:
Lowest cost emissions reductions – the ERF will identify and purchase emissions
reductions at the lowest cost.
Genuine emissions reductions – the ERF will purchase emissions reductions that
make a real and additional contribution to reducing Australia’s greenhouse gas
emissions.
Streamlined administration – the ERF will make it easy for businesses to
participate.
The ERF has three main elements; crediting emissions reductions, purchasing emissions
reductions, and safeguarding emissions reductions.
22
In relation to the energy sector, the ERF will provide incentives for genuine emissions
reductions based on estimations put forward by businesses for emissions reductions
opportunities including: upgrading commercial buildings; improving the energy efficiency of
industrial facilities and domestic premises; reducing electricity generator emissions;
capturing landfill gas; reducing waste coal mine gas; and upgrading vehicles and improving
transport logistics.
The Renewable Energy Target
Currently under review, the RET is a legislative scheme that aims to encourage the additional
generation of electricity from renewable sources, reduce emissions of greenhouse gases in the
electricity sector and ensure that renewable energy sources are ecologically sustainable. The
RET was established under the Renewable Energy (Electricity) Act 2000.
The RET is designed to achieve this by creating a guaranteed market for renewable energy
deployment, using a mechanism of tradable certificates created by large-scale renewable
energy generators and owners of small-scale solar, wind, and hydro systems. Demand for
these certificates is created by placing a legal obligation on entities that buy wholesale
electricity (mainly electricity retailers), to source and surrender certificates to the
Government’s independent market operator – the Clean Energy Regulator.
The RET operates in two parts:
1. Large-scale Renewable Energy Target (LRET), and
2. Small-scale Renewable Energy Scheme (SRES).
The LRET encourages the deployment of large-scale renewable energy projects such as wind
farms, while the SRES supports the installation of small-scale systems, including solar panels
and solar water heaters. The LRET is set in annual gigawatt hour targets, rising to 41 850
GWh in 2020. The SRES is an uncapped scheme but has an implicit target of 4000 GWh. The
RET is currently scheduled to end in 2030 (Clean Energy Regulator 2012). Overall, it is
expected that the RET will ensure that at least 20 per cent of Australia’s electricity generation
comes from renewables in 2020.
Market operation
Regional half hour market pricing allows generators to make investment decisions that
maximise generation value to the whole electricity industry and include the cost of
integration. The NEM has integrated wind generation into its formal scheduling process and
wind generators can opt to provide detailed forecasts for generation to AEMO (MacGill
2009). The operator of the South West Interconnected System has yet to take up forecasting
that is as detailed as that used by AEMO in the NEM.
Standards for grid connection of small scale generation
Solar PV units on households in Australia were found to be causing low voltage levels and
tripping inverters resulting in disconnection from the network. There is a minimum DC
voltage that is required in order for the inverter to turn on and continue operation. In 2012
new standards for solar PV connections were created to allow households with solar PV units
to connect to the grid without being disconnected continually. The review of the
appropriateness of standards for new solar and wind generators connecting to the networks is
23
ongoing. Requiring variable renewable energy generation to connect to grid networks in a
less disruptive way decreased stress on grid infrastructure and operations. As regulators adapt
to new technology these requirements are being adopted around the world, including in
Australia.
Implementation of higher standards for grid connected wind and solar PV generated
electricity decreases integration issues. For instance, new wind generation in Australia is
required to be synchronous to reduce the need for inverters in the system. This requirement
decreases the need for additional complexity in the network infrastructure to accommodate
wind generation. It also decreases the cost of integrating wind generation with an
interconnected network. This measure is similar to others taken by APREA countries to ease
pressure on the network when integrating large proportions of variable generation.
3.1.3 Integration issues of renewable energy in Australia
Literature assessing the impact of high penetrations of renewable energy on Australia’s major
grid networks is still emerging. The Australian Energy Market Operator (AEMO) is
conducting ongoing work to assess the opportunities and challenges of integrating renewable
energy to Australia’s National Electricity Market (NEM). A summary of the main results
from simulation studies conducted by AEMO is presented below.
Network limits
AEMO’s simulations have showed that South Australia could potentially experience wind
generation in some periods as high as 243 per cent of local demand in 2020-21 (AEMO
2013c). In order to avoid curtailment of wind generated electricity it becomes necessary to
strengthen and increase network capacity to facilitate the import and export of electricity
between regions. Currently, a major upgrade of the interconnector that links Victoria with
South Australia is underway to integrate the high proportion of wind generation in South
Australia with the NEM. This interconnector is estimated to cost A$9.8 million in present
value but has an estimated net benefit of A$190 million over the operating life of the project
(Electranet, AEMO 2012). The cost of this network upgrade is levied on South Australian
electricity users as part of their network charges.
Varying tolerance levels
Large amounts of wind generation in the NEM may result in power system tolerance levels
varying widely. Some points in the system will experience higher tolerance levels, depending
on where the wind generation connects to the network, and some points will experience lower
tolerance levels. Some areas of Tasmania were projected to fall below the levels required for
export to the NEM for up to 60 per cent of the year in 2020–21. Under some scenarios
measures to increase fault levels (for example through increasing ancillary services), or
curtailment of generation will be required to integrate higher penetrations of renewables to
the NEM.
24
Market simulation summary of findings
AEMO (2013c) concluded that large amounts of new renewable generation (wind and solar)
in South Australia could lead to frequent power collapse events there (up to 20 per cent of the
time), as interconnector capability was insufficient to transfer power to the neighbouring state
of Victoria during times of high wind and low demand in South Australia. Projected installed
wind capacity by each state varies considerably over different scenarios modelled by AEMO
and, network congestion driven by wind generation often leads to reduced prices in South
Australia so, the value of relieving network congestion at these times is low, and the need for
network augmentation is more difficult to justify. This situation may alleviate if peak prices
are introduced for electricity generation. This may be characteristic of South Australia,
however, and may not translate to other NEM regions with larger local demands.
3.2 China
3.2.1 China’s electricity sector - overview
Between 2002 and 2011 generation of electricity from renewable sources in China grew at an
average rate of 12 per cent, increasing from 291 TWh to 814 TWh. While much of this
growth has occurred in the mature hydroelectric technology, significant growth has also been
achieved by less mature technologies such as wind energy. In 2011, approximately 17 per
cent of total electricity in China was generated from renewable energy sources (see Figure 3).
Hydro power accounted for around 86 per cent of total renewable energy generation in China
in 2011, followed by wind and bioenergy (see Figure 4).
Figure 3 China’s renewable electricity generation by source and share of total electricity
generation
Source: IEA (2013)
5
10
15
20
25
200
400
600
800
1000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
%TWh
HYDRO WIND SOLAR BIOENERGY % Renewables
25
Electricity transmission within China is highly fragmented between six regional grid clusters
(Figure 5). The State Grid Corporation of China (SGCC) manages four of those and part of
the North grid. The other part of North grid is managed by the Western Inner Mongolia Grid
Corporation; and China Southern Grid Company manages the South grid (Cheung 2011). In
2011, SGCC supplied 80 per cent of the total electricity and the other two grids - Southern
power grid and Inner Mongolia grid - supplied the remaining electricity 17 and 3 per cent of
the electricity respectively (BNEF (2013d)).
Figure 4 China’s renewable electricity generation, by energy source, 2011
Source: IEA (2013)
The Northeast China grid has access to coal, nuclear and renewable energy and can meet
local electricity demand and export electricity. The North China grid is a major load centre,
where consumption relies on local thermal power and electricity received from the Northeast
and Northwest China grids. The Northwest China grid has limited load but abundant coal,
hydro, wind and solar resources; thus requires access to a high quality transmission network
to export electricity. The Central China grid depends on the power capacity developed in
western China, and essentially a centre where western and eastern power systems are
connected. The Eastern China grid is a major load centre and receives large amounts of
electricity from the west (including thermal, hydro, wind and solar). The Southern China grid
receives electricity form the hydropower in western China.
Transmission capacity within individual regions is adequate, as evidenced by the reliability of
the network of approximately 99.9 per cent, and transmission losses of 6.5 per cent in 2010
(BNEF 2012f). However, inter-provincial transmission connections are weak, leading to grid
curtailment for variable and uncertain power such as wind and solar (BNEF 2012f). The
interconnections between the grids are important for the balancing mechanisms in order to
ease integration of renewables into the grid. Cheung (2011) notes that balancing in 2009 was
Bioenergy42 TWh
Hydro699 TWh
Solar3 TWh
Wind70 TWh
26
done at the provincial level, and not at the national level as needed: cross-regional trade was
4 per cent of total electricity production in 2009.
Figure 5 Grid connection in China as of the end of 2010
Source: reproduced from IEA and ERI
Investments in installed capacities have typically not been accompanied by corresponding
development of the infrastructure and institutions and market incentives necessary to
integrate them into local power grids. This has resulted in a relatively low share of
renewables connected to the grid (wind in particular) and high rates of energy curtailment.
For instance, in 2012 it is estimated only around 80 per cent of installed wind power capacity
was connected to the grid (up from 75 per cent in 2011 – IEA 2013a). In this section of the
report some of the issues affecting the integration of renewable energy into the Chinese grid
are examined.
3.2.2 Renewable energy policies in China
The main policy framework governing the nation-wide development and integration in China
is the Renewable Energy Law of 2005 and its 2009 amendment. Under this legislative
framework, the National Development and Regulation Commission (NDRC) and the
National Energy Administration (NEA) are the agencies responsible for implementation of
this legislation and set specific policies and guidelines for renewable energy technologies.
The regulatory responsibilities for determining wholesale and retail electricity pricing and
project approvals belong to the NDRC, while the NEA regulates and supervises the electricity
market and is responsible for balancing electricity supply and demand3.
3 State Electricity Regulatory Commission (SERC) existed between 2004 and March 2013 and was merged into
the NEA. The merger is expected to improve progress in electricity pricing reform (BNEF 2013f). Also see Kahrl
et al 2011 on the incomplete role of the SERC due to ‘…splitting of ratemaking authority and protection of
ratepayer interests between government agencies’ (p4040).
27
The renewable energy law of 2005
The Renewable Energy Law (REL) was introduced in 2005 and was intended to set the
national framework governing China’s Renewable energy sector development. Its focus was
developing mechanisms for (Schuman and Lin, 2012; Zhang et al 2013):
1. establishing national renewable energy targets;
2. designing a framework for planning and utilization of renewable energy at the central
and regional/local levels;
3. a principle for mandatory connection and purchase policy. Under this principle, grid
companies are required to provide grid connection services and to purchase renewable
electricity from the generators (within grid companies jurisdiction areas);
4. a mechanism for renewable energy on-grid prices similar to the national feed-in tariffs
(FiTs) system, where prices for most of the renewable energy sources are set as a
premium on top of the benchmark price of the wholesale electricity price for the
(desulfurized) coal-fired power; and
5. cost sharing and funding of renewable energy. The cost-sharing mechanism is funded
by the surcharge on electricity sales (Surcharge Fund). The purpose of this fund is to
finance FiTs, grid connection projects and public renewable energy grids. A separate
funding framework was established to finance research and development, standards
setting, pilot projects, renewable energy resources assessment and rural utilisation of
renewables (Renewable Energy Development Special Fund).
In accordance with the REL, in 2007 the NDRC issued the Medium and Long Term
Development Plan (MLTD Plan) for Renewable Energy in 2007 which specified the initial
targets for the development of various sources of renewable energy up to 2020. The MLTD
Plan required the percentage of renewable energy to rise to 10 per cent of total energy
consumption by 2010 and 15 per cent by 2020. It was anticipated that an investment of CNY
2 trillion (USD 263 billion) was required to reach the 2020 goals (IEA/IRENA 2013). The
‘Mandatory Market Share’ requirement for grid companies and generators was also
established: grid companies were required to achieve 1 per cent of their total power
generation from non-hydro renewable power by 2010 and 3 per cent by 2020. Generators
with the capacity of 5 GW or more were required to have 3 per cent of their total installed
capacity from non-hydro renewable power by 2010, increasing to 8 per cent by 2020
(Schuman and Lin 2012).
The legislation framework of 2005-07 spurred installation of renewable power plants in
China: renewables, and the wind sector in particular, grew at a significant rate (Figure 14).
Wind installations were doubling each year until 2010 when growth rates slowed down due to
the integration issues, as in many cases the installation of renewables went ahead of the
available transmission capacities. Specifically, even though the REL and subsequent Full
Purchase Measures issued by SERC in 2007 called for the mandatory connection of
renewables to the grid and mandatory purchase of the electricity generated by the renewable
sources, in practice it was not followed by the grid companies (purchase); or followed with a
substantial delay (connection) (Schuman and Lin 2012, BNEF various issues, Marcelino and
Porter 2013, Martinot 2010).
In recognition of these issues, and due to the difficulties in integration of renewable energy in
particular, the 2005 REL was amended in 2009 (taking effect from 1 April 2010) to
28
incorporate substantial changes aimed at improving the renewable energy framework,
including measures to improve implementation of compulsory connection and requirements
to purchase electricity generated by renewable sources (Schuman and Lin 2012). Specifically,
the amended regulation:
1. reinforced the legally binding responsibility of grid companies to buy all renewable
electricity generation. However, the 2009 legislation limited the responsibility of grid
companies to the cases where renewable energy generators met certain technical
standards4 for connection. This way both generators and grid companies had become
mutually responsible for grid stability (Schuman and Lin 2012);
2. set the requirement to establish a priority dispatch system favouring renewable power
generation (Schuman and Lin 2012). This requirement echoed an earlier trial launched
by the NDRC in 2007 in five provinces. The trial established a priority system where
dispatch procedures favoured non-adjustable renewable energy (wind and solar)
followed by adjustable (hydro, biomass) renewable resources, prior to nuclear and
coal generators;
3. required grid companies to improve transmitting technologies and enhance grid
capacity to further facilitate the integration of electricity from renewable sources;
4. developed technical standards2 for interconnection to the grid;
5. streamlined the Renewable Energy Fund to speed up the payments for renewable
energy incentives for feed-in tariffs (FiTs); and
6. increased central government oversight of provincial and local renewable energy
development where provincial governments were required to formulate their
renewable energy development plans based on the national development plans
(Schuman and Lin 2012).
Funding of the renewable energy development
The REL of 2005 established that any additional cost of integrating electricity from
renewable energy sources should be shared among the entire electricity system. The sharing
mechanism works through the renewable energy surcharge - a fixed tariff added to the price
of each kWh of electricity sold through the grid (Schuman and Lin 2012). The total revenue
generated by the surcharge premium is then divided between power distributors and utilities
to balance the higher price they have to pay for electricity from renewable energy sources.
The initial renewable energy premium was set at CNY 0.001/kWh (US cents 0.015) in 2006.
The fast increase in the renewable energy generation resulting in fast rising costs of FiT
subsidies, brought the revision of the premium to CNY 0.004/kWh in 2009, followed by an
increase to CNY 0.008/kWh (US cents 0.12) in 2012 (IEA/IRENA policy database, Schuman
and Lin, 2012). The latest increase of the premium to 0.015CNY5 per kWh (US cents 0.24)
from 25 September 2013 is expected to assist NDRC to raise an additional 20 billion CNY6
(3.24 billion US dollars), against the reported shortfall at the end of 2011 of 10.7 billion
4 However, introduction and implementation of standards were delayed, technically until 2011 – see below on
standards development and incidents. 5 http://news.xinhuanet.com/english/china/2013-08/31/c_125287273.htm
6 BNEF (2013a) reports slightly different numbers: 1) suggests last increase was in November 2011, and 2) if
subsidy is to increase to 0.016CNY/kWh than NDRC would raise an additional 40bn CNY (6.53 bn USD)
29
CNY. The price adjustment brought by the premium rise will exclude residential and
agricultural power7. BNEF (2013d) anticipates that with this latest increase in the surcharge,
the funds available at the Renewable Energy Fund should be sufficient to finance its
operations for the 2013-15 period, but unlikely beyond that time (2016) given earlier
accumulated deficits.
The Renewable Energy Special Fund is designed for financing research and development of
mini and off-grid renewable electricity generation projects in rural and remote areas. The
surcharge subsidies collected from the grid companies are pooled together with the
Renewable Energy Development Special Fund funded by the central government budget
allocations in the Renewable Energy Development Fund (Schuman and Lin 2012). The grid
companies seek compensation from the Renewable Energy Development Fund for (a)
additional costs associated with the purchase of renewable electricity and (b) ‘reasonable’
costs associated with the connection.
The NDRC’s regulation of 2007 permits grid companies to include grid connection and other
reasonable expenses associated with the connection of the renewable power to the grid into
the power transmission costs and to retrieve these costs from the selling price. The tariffs are
distance-dependent and currently stand at 0.01CNY/kWh within 50km, 0.02CNY/kWh for
50-100km and 0.03CNY/kWh for longer distances (Ming et al 2013a).
Renewable energy tariff policy/incentives
Since 2009 the Chinese onshore wind power sector has been supported by feed-in-tariffs,
which progressed from the generation‐based tender system of 2005-09. The feed-in-tariffs for
onshore wind are essentially a premium paid over the local benchmark power tariff based on
desulphurised coal generation. The offshore wind development was formally begun in 2009
and currently operates under the auction system; unified feed-in-tariffs have not been
introduced yet. Contrary to onshore wind, biomass tariff is no longer benchmarked to the
coal-based generation prices since mid-2010. Incentives for solar power, primarily solar PV,
were also introduced in 2009 covering building‐integrated PV systems; and a Golden Sun
concession tender‐based programme for large‐scale (500 MW) grid‐connected PV plants and
off‐grid standalone PV systems. In July 2011 NDRC issued unified FiTs for solar power and
modified those in August 2013. Latest policy amendments operate based on fixed FiTs which
differ for transmission- and distribution-grid connected projects. A number of Chinese
provincial governments also offer additional incentives for renewable energy deployment,
which are generally higher than the centrally determined tariffs.
Technology specific Policy: Wind
By 2015 China expects to reach 100GW of installed wind capacity, and 200GW by 2020; the
target includes 30GW of offshore wind installations. Onshore wind technology is the most
mature of the non-hydro renewable technologies. The planning of wind farms includes
construction of some wind farms with the capacity of 1GW or more; these large scale farms
are expected to contribute 70 out of total 100GW by 2015 (GWEC 2013). Since 2008 nine
10GW farm bases were identified in the “Three Northern Areas” – the most wind abundant
7 http://news.xinhuanet.com/english/china/2013-08/31/c_125287273.htm
30
region in China (Junfeng et al 2012). By 2011 NDRC approved 8.1GW of 14.85 GW
proposed constructions of large farms, and 6.9GW were grid-connected (Junfeng et al 2012).
The development of offshore wind began in 2009, and by the end of 2011 offshore wind
power planning was complete for the coastal provinces. As of 2012, 38 projects with total
16.5GW of total capacity were at the early stages of development (Junfeng et al 2012).
However planning of offshore wind power plants often conflicts with other users of maritime
areas, therefore construction of offshore wind farms has not progressed as quickly as onshore
wind development.
Technology specific Policy: Onshore wind
The NDRC replaced the tender system, which had granted individual on‐grid prices that
varied significantly, with a fixed feed‐in tariff, differentiated by regional wind resource, in
mid‐2009 (Schuman and Lin, 2012).
Prior to 2009 onshore wind generated electricity projects were built based on the concession
tendering process or financed on a project-by-project basis following governmental approval.
The auctions information, including average contract prices and auctioned volumes for the
period 2003 – 2007, is presented in Figure 6, which shows variation in the accepted bids
between 0.4 and 0.6 CNY/kWh.
Figure 6 China: onshore wind tendered prices and volumes 2003-2007
Source: IRENA 2013
The tendering process was based on a combination of governmental ruling and elements of
market competition and was used to reduce a cost of wind power (Hu and Cheng 2013). In
2009, the NDRC introduced a four-level feed-in tariff system based on the wind endowment
in the region. Tariffs vary from CNY 0.51/kWh applied to wind projects in provinces with
larger wind resources, to CNY 0.61/kWh and are applied over a 20 year period (IEA/IRENA
1000
2000
3000
4000
5000
0.3
0.4
0.5
0.6
0.7
2003 2004 2005 2006 2007 2008 2009 2009tariffs
MWCNY/kWh
range volume
31
Renewable Energy Database). The transition from a floating project-specific pricing
mechanism to a fixed price regime was considered a positive step and definite guidance to
further development of wind power in China (Hu and Cheng 2013).
The pricing mechanism limits electricity trade as only coal-based power can be traded due to
more expensive and fixed prices for wind power (Zhao et al 2012a,b).
The local content requirement, introduced in 2003, where all newly installed wind power
turbines had to source 70 per cent8 of their components domestically, was abolished in 2009.
According to Martinot (2010), this requirement was declared as unnecessary since almost all
turbine installations in 2009 were Chinese-produced. Zhang et al (2013) suggest that the local
content requirement was criticised due to non-compliance with the WTO requirements.
Technology specific Policy: Offshore wind
The offshore wind development started in 2009 after the NDRC published the Offshore Wind
Development Plan9. According to the Plan, all coastal regions were required to establish their
own offshore Wind Development roadmaps to 2020. The Jiangsu province was the first to
submit its Offshore Wind Plan and the NEA initiated a first tender in 2010 for the total
installed capacity of 1GW with two offshore10 projects of a 300 MW capacity each and two
inter-tidal projects of a 200 MW capacity each. BNEF (BNEF 2013a) estimates that only
800MW of those projects are likely to be realised and all projects are still at the stage of
approval. Recent analysis by the China National Renewable Energy Centre (CNREC)11
confirms that the construction of four offshore projects approved in 2010 has not started. The
reasons for the delays include disagreements between political departments over the use of
sea and low levels of FiTs. CNREC reports that winning bids for the offshore wind projects
secured FiTs of 0.62-0.74 CNY/kWh, which is close to the onshore wind tariffs. However,
offshore wind developers are concerned about profitability of their projects given that the
estimate of construction and maintenance costs is approximately double compared to the
onshore wind projects. Another study by CNOOC New Energy Investment cited in BNEF
(2013a) provides approximately the same estimate and suggests that China’s tariffs for
offshore wind should be in the range of 1.00-1.20 CNY/kWh12.
8 The initial requirement was at 50 per cent of local content which was subsequently increased to 70 per cent in 2004
(Zhang et al 2013) 9 In 2010 NEA and State Oceanic Administration (SOA) jointly implemented the Interim Measure on the Management of
Offshore Wind Farm, regulating every aspect of offshore wind development. The Measures instruct allocation of offshore
wind concessions must be based on a competitive public bidding process and take into account offered prices, technical
abilities and forecasted performance results. Developers must be Chinese-funded companies or Sino-foreign joint ventures
(with at least 50 per cent Chinese ownership).The regulation also imposes a two year inactivity period from the end of the
tender process before any construction work can start (IEA/IRENA 2013 policies database). 10
According to the Development Plan for offshore wind: the Inter-tidal zone for water depth of less than 5 meters, the
offshore zone for water depth of 5 to 50 m and the deep sea zone for water depth above 50 m (IEA/IRENA 2013 policies database). 11
http://en.cnrec.info/news/wind/2013-07-04-532.html 12
including VAT; four-tier FiT according to wind profiles with different water depths and full-load utilization hours
32
Both the CNREC13 and the Global Wind Energy Council (GWEC 2013) suggest that under
these circumstances it is unlikely that China will meet its ambitious targets of 5GW and 30
GW of offshore wind target by 2015/2020.
Technology specific Policy: Solar policy
Following the experience with wind concession programs, China initiated two sets of PV
power plant concession programs in 2009 and 2010 (Ming et al 2013b). The pricing
mechanism was based on a lowest bid with the concession period for 25 years. The tariffs
applied in the concession programs were reduced from 1.09CNY/kWh in 2009 to the range of
0.73-0.99 CNY/kWh (lowest/highest bids) in October 2010. Prior to the concession solar
programs, the specific tariffs for solar power approved by the NDRC on the individual
project basis were as high as 4CNY/kWh for Inner Mongolia province (Ming et al 2013b).
In addition to provincial FiTs, nation-wide installation subsidy programs such as Golden Sun
and Building Integrated PV installation programs government were introduced in 2009.
Golden Sun provided an installation subsidy of up to 50 per cent of the installation costs for
grid-connected utility PV systems and 70 per cent of the installation costs for rural
independent systems (Productivity Commission 2011).
In July 2011, NDRC issued nation-wide FiTs for solar PV power at levels of 1-1.15
CNY/kWh, where the exact tariff depended on the approval and completion date of the
project14 (Schuman and Lin, 2012, Hu and Cheng 2013).
The latest tariffs regulation was announced in August 2013. NDRC reduced the tariffs to 0.9-
0.95 CNY/kWh for solar rich provinces and 1.00 CNY/kWh remained for other provinces
(with the subsidy commitment for 20 years15). The separation of tariffs according to the
regional solar resources endowment is in line with the currently existing onshore wind power
tariffs approach. Importantly, NDRC also announced new subsidy standards for distributed
solar power generation projects at 0.42 CNY/kWh (0.07 USD/kWh). Previously, distributed
PV units were subsidised on a project-investment basis.
Distributed solar is becoming increasingly important for the Chinese renewable energy
commitments. At the earlier stages of solar power development, grid companies, including
the State Grid Corporation of China (SGCC), were reluctant to connect distributed power to
the grid (BNEF 2012f). In October 2012 State Grid Corporation of China (SGCC) changed
its position and 1) SGCC allowed solar power generators below 6MW to be connected to the
grid; 2) waived charges associated with grid connection and 3) agreed to provide technical
assistance16. According to the researchers form the China Electric Power Research Institute,
13
http://en.cnrec.info/news/wind/2013-07-04-532.html 14 The tariff of 1.15 CNY/kWh was applied to all projects approved before July 1, 2011 and completed by December 31,
2011; and 1.00 CNY/kWh for projects approved after July 1, 2011 or not completed by December 31, 2011. FiT of 1.15
CNY/kWh was applied in Tibet for all projects.
15 http://news.xinhuanet.com/english/business/2013-08/31/c_132678835.htm 16
http://english.gov.cn/2012-10/28/content_2252786.htm
33
when compared with centralised generation, distributed generation causes less damage to the
grid and reduces transmission losses (due to shorter transmission distances)17.
The changing attitudes towards distributed solar installations in China were motivated by a
number of factors: boosting renewable energy development in China and providing a support
to the domestic solar PV manufacturing industry18. The Chinese PV manufacturing sector
was hit by falling international demand for the Chinese produced PV modules19,20 while
domestic demand was constrained due to inability to connect to the grid (BNEF 2012f).
To emphasize the role of the solar industry, in July 2013 the State Council issued detailed
guidelines to further boost the development of the solar industry which include21:
1. substantially increased solar installation targets to reach 35GW by 2015 (from 21 GW
as was announced in October 2012, and 5 GW as was set in the original 12th
five year
plan (2011-2015));
2. improved grid access for the small-scale distributed solar energy;
3. simplification of the application process (from ‘approved’ by NDRC to ‘notify’
NDRC for small-scale projects); and
4. inclusion of the electricity generated from the distributed solar projects into the
national electricity production and consumption accounting system.
Technology specific Policy: Biomass power
The installation capacity target for biomass is 30 GW by 2020. The initial FiTs for biomass
generated electricity were set at the level of CNY 0.25/kWh (USD 3.7 cents/kWh) on top of
the benchmark prices of coal-generated power, with an additional CNY 0.10/kWh for direct
combustion biomass; and were applicable over a 15 year period (Productivity Commission
2011). In 2009, the rate was increased to CNY 0.35/kWh. The final amendment was
implemented in July 2010, where NDRC increased the national FiT for biomass power to
CNY 0.75/kW (USD 11 cents/kWh) which was no longer linked to the prices of coal-
generated power (Productivity Commission 2011).
According to Hu and Cheng 2013, biomass energy in China remains at early stages of
developments with costs and technological constraints being the major obstacles for further
development. While the share of MSW incineration has been increasing over the last decade,
combustion of bagasse has been a dominant technology. Another issue is the logistics of
collection, storage and transportation of biomass supply. Traditionally, biomass power
generators were located close to the resources available, which in some cases led to
overdevelopment of biomass energy plants relative to the availability of local resources. This,
in turn, created pressures on availability of crop residual, increasing costs of electricity and
reducing the efficiency of the power plants. To tackle this issue, in 2010, NDRC issued a
17
http://english.gov.cn/2012-10/28/content_2252786.htm 18
http://english.gov.cn/2012-10/28/content_2252786.htm 19
rising overseas/US duties over alleged dumping and cut in European subsidies for green energy 20
More than 95 per cent of the Chinese manufacture photovoltaic products were exported (Hu and Cheng
2013). 21
http://news.xinhuanet.com/english/business/2013-07/20/c_132557364.htm
34
regulation which allowed operation of only one biomass power plant with the capacity of
30MW within 100 km radius in fuel rich areas (Hu and Cheng 2013).
3.2.3 Integration issues of renewable energy in China
China currently faces a number of obstacles to improving integration of renewable energy
into its electricity networks. These challenges are diverse, ranging from technical issues
associated with the geographic mismatch between resources and load centres in China, to
policy and institutional issues caused by a lack of planning and coordination in between
central and local authorities.
Grid capacities
The ability of current grid capacity to accommodate new renewable energy projects has also
proved to be a barrier to the integration of renewable energy. The rapid development of
renewable energy sources in China has strained the capacity of regional grids ability to
incorporate them. As a result there have been many instances where new projects have
experienced delays of several months before being connected to the national grid (Liao et al
2010, IRENA 2013c, Junfeng et al 2012).
Wind projects in particular have had difficulties getting connected to grids. In 2009, only 65
per cent of total installed wind power generating capacity had been connected to local power
grids. While this proportion has increased steadily, at the end of 2012 only 83 per cent of
total wind power capacity was connected to the grid. Figure 7 shows the wind installed
capacities separated by connected and unconnected volumes, percentage of unconnected
capacity and resulting average national capacity factor.
In 2010, it was estimated that 6.24 GW of wind capacity was constructed but not
commissioned, and another 4.45 GW was under construction and not ready to be
interconnected to the grid (Marcelino and Porter 2013). Furthermore, it has been argued that
these connection rates are biased upwards as they fail to include wind power generators
which have been constructed but not commissioned (Marcelino and Porter 2013).
Reports from the Chinese domestic sources also confirm that the implementation of full
purchase of all electricity generated by renewable energy measures in 2012 was poor and that
approximately about 17 per cent of the wind generation capacity was abandoned in 2012 due
to transmission and consumption problems22. The National Energy Administration (NEA)
estimated that about 20 TWh of wind power electricity was curtailed in 2012 (GWEC 2013).
22 http://news.xinhuanet.com/english/china/2013-08/27/c_125251612.htm
35
Figure 7 Wind power in China: connected and unconnected
Source: BNEF 26 November 2012, 1 August 2013
It is expected that wind integration will improve to around 95 per cent connection rates by
2015 when most of the transmission infrastructure will be completed and smart grid
technologies are implemented (BNEF 2012f). Network integration of renewable energy will
improve, with planned investment in grid extensions and strengthening and implementation
of the Renewable Portfolio Standard, a new technical standard for wind farms, and further
deployment of smart grid technology.
Coordination problems
One of the major difficulties in integrating renewable energy sources into Chinese electricity
grids has been a lack of coordination between projects approved by the state and local
governments, and between power planning and grid planning (see Zhang at al 2013, Kahrl et
al 2011).
For instance, in the early stages of developing wind power, local governments tended to
consider only the availability of resources in the area in deciding the scale and timing of grid
connections, focussing less on the long-term power market development (Zhang et al 2013).
Additionally, prior to July 2011, local government were allowed to approve wind projects
with less than 50MW capacity. Given local governments’ objectives to increase local GDP,
tax revenue and employment, the local authorities tended to split up the large scale wind
farms to keep the capacity below 50MW (Yang et al 2012). This led to a so called ‘49.5 MW’
phenomenon; where, in 2009, 111 out of 187 approved wind farms had 49.5MW capacity
(Zhang et al 2013) and an estimated 93 per cent of onshore wind farm projects were approved
by the provincial-level government (Schuman and Lin 2012).
The underlying problem was that local governments had little understanding of the wind
power industry and how it relates to the grid companies business (Junfeng et al 2012). In
addition to local wind power capacities, local authorities also relied on wind equipment
0
20
40
60
80
100
0
30
60
90
120
150
2008 2009 2010 2011 2012 2013 2014e 2015e
Cumulative grid connected unconnected
% connected national average capacity factor
36
manufacturing by bringing the industries in their jurisdiction areas and requiring wind farm
developers to use locally produced equipment23.
The flexibility allowed at the local level approval and lack of the information on the total
quantity of projects at the state level contributed to grid problems, since necessary grid
development was usually lacking when the projects were finished. The situation was
improved in mid-2011 when the approval of wind power projects had been officially
incorporated into the national power development plans and further strengthened by bringing
all projects approval rights under the National Development and Regulation Commission
(NDRC) umbrella (Zhao and Chang 2013). According to the changed regulation, only those
projects which were included in the plan, would be subsequently approved, eligible for the
FiTs (feed in tariffs) and will receive grid access (GWEC 2013).
Balancing
The lack of demand and supply balancing of the variable rates of electricity generation from
renewable sources, such as solar and wind, has also inhibited integration into the grid.
Typically, ancillary services used to maintain grid reliability are mostly provided by coal and
hydro based power plants (Kahrl et al 2011). In regions which do not have sufficient
hydropower, coal based units are used for load following and peaking generation, requiring
significant cycling of those units. In terms of power system flexibility, this is a surprising
outcome as coal-fired power generation is less flexible when compared to gas or hydro based
plants, due to coal plants having longer start-up/shut-down times, higher change velocity
(thermal inertia), and much lower efficiency if not run at full capacity (Cheung 2011).
In terms of technology, both conventional and pumped hydro plants are highly suitable for
balancing purposes due to their quick start-up/shut-down and ability to operate at minimum
load; pumped can be brought online during either peak or off-peak hours. However, it is
estimated that conventional hydro stations in China are fully exploited to meet growing
electricity demand and to balance variations in thermal plant production themselves (Cheung
2011). It has been argued that there is little spare hydro capacity left to mitigate increased net
variability due to the renewable generation (Cheung 2011).
Additionally, a factor of seasonality needs to be taken into account when engaging
conventional hydro plants. Pumped hydro is less seasonally dependent and highly suitable for
balancing short-cycle variations (Cheung 2011).
The location of hydro plants relative to wind resources is another issue. The majority of
hydroelectricity resources are in Central and Southern China, while the majority of wind
farms are in the far north. To be able to balance wind resources which are located in the
north, China requires a substantial enhancement of existing power transmission lines to
connect hydro with the wind. The geographic imbalance between hydro and wind resources,
and the lack of greater interconnection amongst regional and subregional grids, constrains the
23
The process of accelerated reliance on local manufacturing was spurred by the changes in the VAT tax legislation, which eliminated ‘double taxation’ on fixed assets. On the one hand, it helped wind developers to increase investments in the wind power installations. On the other hand, that reduced the taxation base for the local governments. Therefore local authorities started to encourage local manufacturing which led to creation of excess capacities. See Wang et al 2012 for details.
37
ability of the hydropower resources to provide peaking and ancillary services (Karhl et al
2011). In Inner Mongolia, which is the most wind abundant region, there has been a trend of
reducing installed capacity of hydropower: to 9.26 per cent of the total capacity in the region
in 2009 (down from 13.02 per cent in 2006) – which further exacerbates the peak regulation
capacity of the region (Zhao et al 2012a).
Technical codes
The first technical rules for connecting wind farms were issued by the State Grid in 2005 and
the first industrial standards for wind power integration was enacted by the China Electricity
Council in February 2011 (Zhang et al 2013). In 2009 State Grid issued its own grid code for
connection of wind farms where the requirements included active power control, reactive
power and voltage regulation, LVRT24 (low voltage ride-through); together with testing
requirements and wind power forecasting (Piwko et al 2012).
However neither of the standards were set at the national level (Zhang et al 2013). From
March 2010 government initiated consultations on the Standards on Wind farm connection
which included the requirements on active power control systems, reactive power
compensation devices, LVRT capabilities, and forecasting. A regulation requiring testing for
wind turbines to be connected to the grid was issued at the end of 2010 by the NEA (Piwko et
al 2012, Junfeng et al 2012).
The first state level standards enforceable at the national level – the Wind Farm Connecting
Power Systems Technical Regulations - were approved by the National Standards Committee
in December 2011 and took effect in June 2012. The standards were focused on power grid
dispatch, wind farms, wind turbine quality, and required that all turbines be equipped with
LVRT technology to insure grid stability (Zhang et al 2013). Overall a series of 22 technical
standards have been enforced, largely involving regulating turbine power quality in order to
access the grid (BNEF 2012f).
This regulation also requires a wind farm to go through a connection testing period of around
three to six months before the final connection and generation licence will be issued by the
grid corporation (BNEF 2012f). Many installed turbines need to be replaced or retrofitted
before connecting to the grid to satisfy the regulation requirements (BNEF 2012f) at the end
of 2011, BNEF (BENF 2012e) estimated that 80 per cent of existing projects at that time
would require the technology upgrade.
Focus on increasing capacity
China’s early renewable energy policies were aimed at additional capacity of renewable
generation. For instance, the 2007 Medium Long Term Development Plan (MTDP) set
renewable portfolio standards based on installed capacity: large power producers (more than
5GW) were required to achieve a share of renewable sources installed capacity (not including
hydro) of 3 per cent by 2010 and 8 per cent by 2020.
24
LVRT is a technology that can ensure that wind turbines and large wind farms can remain online when system voltage
drops instead of tripping offline, which improves overall reliability and stability for the grid networks (Schuman and Lin
2012)
38
To meet these renewable portfolio standards, large state-owned enterprises, whose generation
portfolios were mostly based on thermal, nuclear and hydro power, rushed to exploit China’s
wind resources. By the end of 2010 more than 80 per cent of total wind installed capacity was
held by large enterprises (Yang et al 2012). The nature of these incentives contributed to
excess generation capacity compared to grid availability, and also created a downward
pressure on wind power bidding prices.
The implications of this process were two-fold. Firstly, it created entry barriers for new
investors as large companies submitted low bids in order to secure the contracts. Secondly,
this also created downward price pressure for domestic wind turbines manufacturers. In the
absence of national technical standards (see below for details) and in order to secure profits,
manufacturers tended to lower the quality of their products (Zhang et al 2013).
The lower quality of wind turbines and the lack of technical standards led to an increasing
number of wind turbine incidents. In 2010 there were 80 faults overall, and four out of those
resulted in a loss of 100-500MW and one accident lost more than 500MW. In 2011, the
number of accidents increased to 193 with 54 losing 100-500MW of power, and 12 resulting
in more than 500MW loss each (Zhang et al 2013, citing CERC 2011). The most significant
accidents were in February 2011 where 598 turbines went offline resulting in output loss of
and in April 2011 Gansu province lost 1.54 GW of power with 1278 turbines going offline
(Schuman and Lin 2012). Recent estimate by BNEF (BNEF 2012e, f) suggests that there
were 360 incidents in 2011, resulting in operational power losses of up to 3GW (BNEF
2012f).
Disincentives for grid companies
The focus on installed generation capacities was not accompanied by an equivalent set of
incentives for grid operators (Hu and Cheng 2013, Zhang et al 2013) and also lacked
emphasis on the provision of suitable national technical standards for grid connection of wind
power. As a result, grid companies were reluctant to connect and dispatch the new source of
power (Yang et al 2012). Similarly, domestic wind turbine manufacturers had poor incentives
to develop and manufacture wind turbines that meet the requirement of the electric grids
(Zhang et al 2013). As a result of these incentives, curtailment and lack of basic
interconnection have kept wind capacity factors low, declining from 23.4 per cent in 2008 to
21.6 per cent in 2012 (Figure 17).
It has been argued that grid companies have found planned PV installations challenging to
integrate as they did not consider technical problems associated with their integration, and the
expenditures by the grid companies were not subsidised (Huo and Zhang 2012). However, it
is expected that solar power will benefit from earlier experiences with the integration of wind
power and will face fewer problems. In addition, distributed solar, which is currently heavily
promoted, should present fewer problems for distributed networks, as those are already in
place and require less distance to travel. Current grid reinforcements, inspired by the wind
integration issues, should also be able to accommodate large solar installations.
39
Dispatch, pricing and trading
Historically, the dispatch system in China was based on an ‘equal shares’ basis, where
generators of a given type were allocated a roughly equal number of operating hours25.
Ideally, this was to ensure adequate revenues to recover fixed costs (Kahrl et al 2011).
However, this practice led to economic (and environmental) inefficiencies as generators with
higher heat rates may have been operating for the same number of hours as more efficient
units, thus also disturbing investment decisions (Kahrl et al 2011).
In 2007, NDRC trialled Regulation on Energy Conservation Power Generation Dispatching
(Cheung 2011) which prioritised renewable energy dispatch: non-adjustable wind, solar,
ocean energy and hydropower were to be dispatched first; followed by adjustable hydro,
biomass, geothermal; before fossil fuels and nuclear power are engaged (IEA and ERI 2011,
Cheung 2011). Five provinces participated in the trial (Kahrl et al 2011), however ‘this
system has met with technical and economic obstacles and has yet to be replicated in other
provinces’ (Gao and Li 2010, cited in Kahrl et al 2011) and was met with local resistance
(Cheung 2011). Some of these problems were related to the operation of the CHP (Combined
Heat and Power) coal fired plants in Northern China (Schuman and Lin 2012).
The fixed nature of the pricing of electricity in China also contributes negatively to renewable
energy integration. China’s on-grid electricity tariffs (or wholesale prices of selling power to
the grid) for wind energy are dependent on fixed benchmark26 electricity prices set by the
NDRC. Between 2003 and 2006, a degree of competition was introduced in the wholesale
market so that 10-20 per cent of sales were settled through a competitive bidding process.
However this practice finished with no conclusive results (BNEF 2013f).
Between September 2010 and July 2011 the Northeast Electricity Regulatory Authority –
NERA, operating in the wind abundant northeast area established a set of market-oriented
rules in order to promote large-scale utilisation of wind resources (Zhao et al 2012a). The set
of rules included trialled measures to establish legal basis for promoting power trade in the
Northeast Grid: principles of trade pricing, trade organization, information publication, trade
implementation and settlement; together with two sets of implementation guidelines and
principles to promote electricity trade across provinces with load differences. However these
policies had little effect as they conflicted with strict planning control mechanisms on wind
power (Zhao et al 2012a).
The underlying pricing system restricts large-scale wind power utilisation as current fixed
wind pricing (with 4 regional tariffs) blocks implementation of cross-provincial trading (Zhao
et al 2012a). The outcome of this framework makes wind power too expensive to be traded
between the provinces: even though wind power is much more efficient than coal-based
25
According to Cheung 2011, coal plants operate on average 5,000 hours per year, hydro 3,500 and wind 2,000 hours. Each
grid company has to make sure that plant operates the specified number of hours each year, but can reduce the load of a certain plant at some point in time and increase it later. When supply exceeds demand, load is cut evenly across all technologies. 26
Benchmark pricing is applied since 2004. Before 2004 wholesale generation tariffs were historically tied to average costs (the ‘investment recovery price’ adopted in 1983), which was modified to an ‘operational life price’ in 2001 (amortised investment costs over the expected technical (rather than financial) life of the utility – Kahrl et al 2011.
40
power, wind farms cannot engage in trading and offer lower prices (especially during the
winter nights) due to fixed power prices for wind.
On a national-wide level trade in electricity market is extremely rigid. More than 80 per cent
of trade is agreed annually (on both price and quantity) and is based on multi-year
demand/supply provincial forecasts; with the contracts negotiated among central government,
provincial governments and the grid companies (Cheung 2011). By contrast, less than 20 per
cent is traded in the spot market. This amount is strictly limited and is usually reserved in
cases of emergency. Actual trading is reviewed weekly or monthly, but tradable amounts are
capped and prices are fixed. On the other hand, demand and export capacity of a province
often evolve with time. Therefore in the situations where province has developed enough
generation capacity to meet demand – as is clearly the case for the Northern, wind-abundant
regions in China - but still have to receive electricity because of the prior agreement, it must
sell the surplus capacity to another region at lower export prices.
Further insight into power trading is provided by Zhao et al 2012a. The trans-provincial trade
in the Northeast grid is handled differently for base power and for incremental power needs.
The base power trade is (a) a planned market, (b) based on historical allocations27 and (c) is
assigned completely to power produced from thermal plants. The power trading across
provinces for the incremental part of the power exchange occurs between coal-fired plants
and grid companies. Due to different wind power prices implemented in different provinces
in China, it is difficult to exchange or trade wind power across provinces. If a coal-fired plant
wins the bid to trade the power to a different province, it does not appear reasonable for the
coal based plant to transfer the rights (of generation) to wind power producers because of
higher and fixed prices for wind power. As a result, current cross-provincial trading
arrangements in China encourage increased thermal power output rather than the expansion
of wind power within or outside the provinces.
Other barriers to trade, especially across the provinces are: large transaction costs and
transmission losses. With regard to the former, power transmission prices are set based on
bilateral negotiations and therefor costly. With regard to the latter, trans-provincial trade
involves transmission losses, and there are currently no mechanisms to compensate for those
losses. Overall there are little incentives for the grid companies to participate in the trade
(Zhao et al 2012a).
Nevertheless, the Chinese government tried more liberalised trading regimes in the past.
Since 2009 the government approved several pilots where generators could sell their
electricity directly to large industrial users in five provinces (Liaoning, Jilin, Anhui, Fujian,
Guangdong) (BNEF 2013f). However the pilot schemes were small scale with trade
effectively limited to 0.1-1.9 per cent (2011 data) of total power consumption in these
provinces28 and directed to the large energy users.
27
For three provinces sharing Northeast grid the planned allocations were established in 1974. 28
the distance between the power plants and the factories are small enough to bypass the high voltage grid.
41
3.3 India
3.3.1 India’s electricity sector - overview
The steady expansion of India’s economy, a growing population and the need to improve
living standards and reduce energy poverty have also driven strong growth in electricity
generation and consumption and a renewed focus on improving the security and quality of
supply.
India is currently the fifth largest producer of electricity in the world but is still an electricity
deficit country (MNRE 2013a). Estimates by the Indian Central Electricity Authority indicate
that the average monthly electricity generation shortfall during 2012 and 2013 was 8.7 per
cent and 4.5 per cent, respectively (CEA 2013b).
While recent economic growth rates have been moderate at between 4 and 5 per cent per
annum, in the 12th
five year plan for 2013-17 the Indian government has targeted an annual
growth rate of around 8 per cent (Planning Commission 2013). Energy security concerns and
commitment to a low carbon growth strategy led India’s 12th
five year plan (2012-2017) to
include provisions for the sustainable development of India’s electricity sector and expanding
generation capacity by around 120,500 MW (CEA 2013a). This new capacity is projected to
come from a mix of conventional (73 per cent) and renewable energy technologies (27 per
cent) (CEA 2013a).
Electricity system
India’s electricity system is comprised of five regions: the Western, the Northern, the North
Eastern, the Eastern and the Southern Region (see Map 2). India has a large and well-
connected interregional grid that is well serviced by high capacity transmission corridors. All
regions are currently operating through synchronous interconnection to seamlessly balance
inter-region flow (The Times of India 2014).
Map 2 Power Grid Regions of India
Source: http://www.mapsofindia.com/maps/india/power-grid.html accessed on 16 April 2014
42
Each region is managed by a regional control centre under which State control centres
operate. Interregional flow is coordinated by a national centre in conjunction with the
regional centres with regional trade managed through the Inter State Transmission System.
The Indian electricity market works under an open access regime where access is granted as
either long term (25 years) or short term (3 months). Long term users pay higher charges but
have higher priority. The majority of electricity has traditionally been supplied under long
term PPAs although since the formation of the market in 2004 the level of short term trading
has steadily increased. The market is settled in 15 minute intervals to facilitate more flexible
grid management.
Generation
As at August 2013 India’s total installed electricity generation capacity was around 227 GW,
which consisted of about 88 per cent conventional generation namely coal, gas, diesel,
nuclear and large hydro, and remaining 12 per cent was non-conventional or renewables
(CEA 2013a). In terms of generated energy CEA (2013a) reported that renewables
contributed around 5 per cent in 2012-13.
The strong growth in India’s renewable energy capacity over the past five-year plans is
shown in Figure 8, which shows that the installed generation capacity of wind power is
growing faster than all other renewable technologies in India.
Figure 8 Growth pattern of renewable electricity capacity in different five year plans
Source: POWERGRID (2012)
Most of the renewable electricity plants are located within seven states: Tamil Nadu; Andhra
Pradesh; Karnataka; Gujarat; Maharashtra; Rajasthan, and Himachal Pradesh. These states
are the most renewable resources rich states, and currently contribute about 80 to 90 per cent
of total installed capacity of renewable electricity in India (POWERGRID 2012). Within five
of these States renewable energy capacity comprises a significant proportion of installed
43
capacity ranging from 15 per cent in Maharashtra to around 40 per cent in Tamil Nadu (see
Figure 9).
Figure 9 Share of Renewable Energy Capacity as on 31 July 2013
Source: CEA (2013a)
While coal remains the major source of India’s electricity, access to supply has been
restricted in recent years and is likely to continue for some time. Concern over supply
sufficiency, along with the national government’s environmental goals, has emphasised the
need for further diversification of the electricity generation base, including through
harnessing of renewable energy sources like wind, solar, small hydro, biomass, and waste to
electricity.
Figure 10 illustrates that India still has a very large potential of wind (about 103 GW) and
solar (more than 100 GW) energy resources. Most of these resources are confined in southern
and western states of India (POWERGRID 2012).
44
Figure 10 Potential renewable resources in India (March 2012)
Source: POWERGRID (2012)
In the 12th
Five Year Plan the Ministry of New and Renewable Energy (MNRE) has set
targets for additional renewable (non-large hydro) electricity capacity to increase from 24.9
GW in March 2012 to 41.4 GW by FY 2017 and to 72.4 GW at the end of 13th Plan (i.e. FY
2022) as shown in Figure 11. If these targets are met then renewable electricity capacity will
grow at an average rate of 10.7 per cent per year in next five years.
45
Figure 11 Proposed target of grid connected renewable electricity installed capacity at the end
of 12th
& 13th
five year plans
Source: POWERGRID (2012)
3.3.2 Renewable energy policies in India
In order to promote the take up of renewable energy there are a mix of interlocking policies
and regulatory frameworks including fiscal incentives operating at the national and state
level.
Overall, the direction of India’s renewable energy policy is defined by two key policy
statements:
the 12th
(and subsequent) five year plan; and
the National Action Plan on Climate Change 2008.
The National Action Plan on Climate Change (NAPCC) 2008 sets out a national plan for
increasing the exploitation of India’s renewable energy resources. Under the NAPCC a
national renewable energy goal of 15 per cent of total electricity purchases by 2020 was
established. This was set at starting of 5 per cent in 2009-10 increasing by 1 per cent each
year for 10 years.
The Plan also established the National Solar Mission (also known as the Jawaharlal Nehru
National Solar Mission) which has the objective of making solar thermal electricity
commercially competitive. This includes establishment of a solar research centre, increased
international collaboration on solar technology development, strengthening of domestic
manufacturing capacity, and increased government funding and international support for the
46
development of solar technologies in India. It has set the target of deploying 20,000 MW of
grid connected solar power by 2022.
As noted previously, the Government of India’s national five year planning process defines,
in a coordinated fashion, operational targets for various forms of renewable energy as well as
setting out the critical infrastructure and institutional planning required for achieving them.
This includes the planning and development of new inter-regional high capacity green
transmission corridors and a commitment to improved institutional capacities for forecasting
and managing renewable energy generation.
At the national level these goals are operationalised through a series of key enabling pieces of
legislation and regulation namely:
the Electricity Act 2003;
the National Electricity Policy 2005;
the National Tariff Policy 2006; and
the Indian Electricity Grid Code 2010.
Collectively this framework sets out provisions for the central and state electricity
commissions to promote generation and co-generation from renewable energy sources
through three classes of intervention: setting of renewable energy tariffs; specifying
renewable purchase obligations; facilitating grid connectivity and promoting market
development. They also provide for operational actions such forecasting, scheduling and
commercial settlement arrangements for solar and wind generating plants (NREL, GTZ,
REN21 and IRADe 2010).
The most significant of the measures is the requirement for the Central and State Electricity
Regulatory Commissions (CERC and SERCs) to prescribe Renewable Energy Purchase
Obligations (RPO) for distribution licencees. Obligations, which increase incrementally each
year according to a specified schedule, are set with reference to a range of factors including
the degree of renewable energy resources and system balancing or other requirements in each
State (MoP 2013b).
As renewable energy remains more expensive (albeit increasingly less so) than conventional
technologies and fuels in most applications. SERCs are able to set differential or feed in
tariffs and other terms and conditions to favour renewable energy. Competitive tendering for
renewable energy projects matched with long term PPAs is an approach that has been
increasingly favoured by States as a way of driving down renewable energy costs. In
addition, in 2010 a market-based mechanism called Renewable Energy Certificates (REC)
was launched to address the mismatch between availability of renewable sources and the
requirement of the obligated entities to meet their Renewable Purchase Obligation (RPO).
The National Solar Mission policy framework initiated the establishment of solar purchase
obligation (SPO) starting at 0.25 per cent by 2013, going up to 3 per cent by 2022, including
a solar specific RECs scheme across all States. This policy framework is likely to enable
deployment of 20,000 MW of solar power by 2022 (SHAKTI 2013).
47
Other initiatives to support renewable energy include open access arrangements for
renewable energy plant in interstate transmission system, sharing of transmission charges,
and development of a Renewable Regulatory Fund. The Renewable regulatory fund provides
support for grid interactive renewable energy generators for deviations from the submitted
schedule. For more information on India’s renewable energy policy framework see MRNE
website http://www.mnre.gov.in/.
3.3.3 Integration issues of renewable energy in India
As is the case in countries that have a significant and rapidly growing deployment of
renewable energy India has experienced integration issues with large scale wind and solar
generation. These issues largely relate to system balancing, frequency control/voltage
stability and the need for network augmentation.
System balancing
In India the responsibility for maintaining balance in the electricity grid rests primarily at the
State level although interstate flows are managed separately through Regional Load Despatch
Centres. As noted previously most of the existing and potential renewable energy projects are
located in remote areas along the coast line, parts of the desert or hilly terrains in Northern
region of India. While these locations are far from the point of electricity use, the highly
interconnected Indian transmission network provides for good management of interregional
balancing although there are on-going challenges in ensuring that States are actively
responding to scheduled load requirements (CEA 2013).
The Central Electricity Authority report on integration issues for large scale renewable
energy projects particularly highlighted the balancing challenges faced by three States with a
high penetration of variable wind and solar energy (Tamil Nadu, Gujarat and Rajasthan). This
reported the need for each State to maintain a significant backup reserve of fossil fuel (largely
coal with some gas) and hydro capacity to support variable input from wind generators. It
also noted the need for greater regulatory support to improve flexibility and financial
incentives for conventional generators to cover costs in operating as partial cover for
renewable capacity (CEA 2013).
Tamil Nadu manages variability by backing down of up to 12 medium sized coal -generators.
While the State maintains gas and a large hydro capacity these are not used in a back-up role
due to technical and cost reasons (hydro is more valuable as a peak power provider).
The report also noted that Rajasthan can experience variations in wind generation output of
up to 1140 MW (out of an installed capacity of around 2540 MW) in a single day. Similar to
Tamil Nadu this is managed largely by backing down coal generators as well as through
varying output from two gas fired generation plants. The report also noted that wind-
generators have caused overfluxing in transformers resulting in tripping due to over-voltage
supply.
Frequency Control
Maintaining stable voltage and frequency control across the electricity grid has been a
challenge for India’s electricity grid operators for many years. A major challenge has been a
long standing tendency for sources of supply and demand to draw or supply more (or less)
48
from the grid than scheduled. Unforecasted variations in wind and solar input also add to this
challenge and can create significant reactive power flows to and from the grid.
To address these issues India’s power systems may need to enhance existing primary
frequency regulation systems for wind and large scale solar PV. India’s power systems may
also need to address the issues of harmonic voltage distortion due to the deployment of power
electronic equipment at the connection point of wind farms and solar parks in India
(POWERGRID 2012 and CEA 2013).
In order to better manage grid flows India has implemented improved scheduling into its
market operation with generators required to nominate day ahead bids on 15 minute time
blocks. Variances from actual supply and the schedule are known as Unscheduled
Interchange (UI) for which commercial settlement is required. The Indian Electricity Grid
Code (2010) required wind generators to be within +/- 30 per cent of schedule, and this
regulation was just overturned in March 2014 (live mint and The Wall Street Journal 2014 29
). For variation beyond this the generator was liable for the UI charge while UI charges for
variations within this bound were met by the State and covered by a Renewable Regulatory
Fund.
To support improved scheduling Indian electricity agencies (and the private sector) are
investing significantly to improve wind and solar forecasting with the CEA reporting that day
ahead forecasting error has been steadily improving to between 15 to 25 per cent (CEA
2013). Further action to develop a comprehensive scheme for wind and solar forecasting
stations, communication system and linked Renewable Energy Management Centres in each
State has been flagged but yet to get underway (CEA 2013).
Grid network augmentation
While India has a large and well-connected interregional grid further action to improve the
transferability of renewable energy power was announced in the 12th five year plan.
According to POWERGRID (2012) estimates, Indian will be require about US$7.66 billion
(Rs 425.57 billion) for the development of transmission infrastructure including real time
monitoring, control system, energy storage and establishment of renewable electricity
management centres by the end of FY2017.
Accordingly the 12th
Five Year Plan has allocated around US$5 billion to support a number
of new “green transmission corridors” to cater for 32 GW of additional renewable energy
capacity in Tamil Nadu, Gujarat, Rajasthan, Karnataka, Andhra Pradesh, Maharastra,
Himachal Pradesh and Jammu & Kashmir. These systems include both intra and inter-state
transmission and distribution lines.
Policy integration
In addition to the technical and infrastructure issues above, there are a range of
policy/regulatory issues that impact on renewable energy development in India.
29
An online Journal, “live mint”,: http://www.livemint.com/Industry/T8wtsN0wljaEvQPKcdYCpK/Indian-regulator-halts-wind-forecasting-on-inaccurate-result.html
49
The first is a need to improve synchronisation or address inconsistencies between renewable
energy policy initiatives by the central and state governments (SHAKTI 2013).
For example, renewable resource rich states are reluctant to take higher renewable purchase
obligations due to consideration of extra cost, while the resource-poor states have no
incentive to go for higher renewable purchase obligations levels. States also have different
regulations regarding technical standards such as mandating the location of the meter, which
affects the measurement of the amount of energy that is sold to the grid (REEEP 2009).
Financing also continues to be a challenge for large scale renewable deployment. Many banks
in India have reached near saturation level in their exposure to the renewable energy sector,
where the Reserve Bank of India guidelines permits banks to fix internal limits for aggregate
commitments to specific sectors so that the exposures are evenly spread over various sectors
(SHAKTI 2013). Mismatch of assets and liabilities, and cost of funding could also be a
challenge for large scale renewable electricity development.
Most renewable electricity projects in India generally have funding requirement for terms
above 10 years, while the average maturity of bank’s resources is significantly lower which
could expose banks to serious interest rate risk. Banks and financial institutions are reluctant
to finance the emerging solar energy sector due to high risk perception of the solar energy
sector and the apprehension that utilities may fail to pay the high tariff for the solar electricity
as agreed in power purchase agreement (SHAKTI 2013).
Conclusion
While there clearly are technical challenges and costs associated with integrating large scale
renewable energy in India they appear to currently manageable and not yet so significant as
to restrict levels of renewable energy development or compromise the operation of India’s
electricity system (SHAKTI 2013). That said, these challenges are projected to increase as
renewable energy projects are further deployed (CEA 2013) and there will be a need to
ensure deployment policy is synchronised or complemented with technical and regulatory
support for better grid management.
3.4 Japan
3.4.1 Japan electricity sector - overview
There has been an emphasis on the use of renewables in Japan for electricity generation,
similar to in some other key countries such as China and India. It would appear that given the
substantial share that renewables have in Japan of over 12 per cent (see Figure 12), the
overall integration costs of renewables to the electricity grid are likely to be quite substantial
for Japan. In addition, integration costs will tend to increase in line with the share of variable
renewables in total electricity generation.
50
Figure 12: Japan’s renewable electricity generation by source and share of total electricity
generation
Source: IEA (2013)
Japan is made up of ten distinct power utilities, which are only weakly interconnected and
trade little electricity. The Japanese power utilities’ ability to coordinate balancing needs is
limited by their lack of interconnection (IEA 2011). Tokyo Electric Power Company is the
leading player in the Japanese electricity market, generating 29.3 per cent of generation in
2011. Kansai Electric Power Company accounted for a further 16.5 per cent of generation
(Marketline 2012).
The IEA found that the quality of interconnection and coordination between distinct power
markets will influence the use of variable generation over a region as a whole. One
particularly salient issue that emerged from the case studies is for Japan where there are ten
different electric power areas each managed by a separate utility, there is virtually no
collaboration in balancing power needs (IEA 2011). As noted, little electricity is traded.
Japan’s wholesale electricity market is not compulsory and represented only around half of
one per cent of the total electricity volume in 2011 (Nagayama 2011).
Following the 2011 tsunami and Fukushima plant disaster, all of Japan’s 48 operating
reactors have been permanently shut down or are temporarily closed30
.
3.4.2 Integration policies
Over time renewable policy in Japan has shifted from support for renewable electricity
generation from subsidies, to by RPS (a negotiated price for electricity provided from
30
The Wall Street Journal, Japan Sees Key Role Nuclear Power, posted 25 February 2014, http://online.wsj.com/news/articles/SB10001424052702304610404579403741256563088
4
8
12
16
50
100
150
200
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
% TWh
Hydro Wind Solar Bioenergy Geothermal % Renewables
51
renewables) and finally to, a feed-in tariff (a minimum fixed price for electricity from
renewables). Japan provided support to renewables by subsidies from 1997 to promote the
use of new energy, by a) by subsidising part of the costs of private companies which
introduced new energy, b) guaranteeing the debt of financial institutions and c) subsidising
regional governments which introduce new energy.
Then the support from 2003 to 2012 was in the form of an obligation on electric power
companies to purchase at a negotiated price, a certain amount of the renewable energy
electricity.
From 2009 the surplus purchase system started which imposed an obligation on electric
power companies to purchase electricity which is generated by residential solar PV (of less
than 500kW) at a fixed price for a government guaranteed period. Following this, the feed in
tariff (FIT) scheme started in 2011, which was extended to purchase electricity which is
generated by wind, hydraulic, geothermal, and biomass. Under this scheme power companies
must accept requests from the renewable power generators to sell their electricity at this
minimum price. The Government confirms whether the renewables facility can generate
electricity stably and efficiently, with approval cancelled if the facility no longer meets these
requirements.
Since the FIT began in 2011, 24 GW of renewable facilities have been approved, though only
4 GW of these have actually started operation (Ministry of Economy, Trade and Industry
2013). Generally, solar PV facilities are rapidly introduced due to the shorter period involved
and fewer regulations for their installation.
Japan was finalising its National Green Policy Strategy in late 2012. Versions of this report
included a goal of a 30 per cent share of electricity from renewables by 2030, including
ambitious targets for installed solar PV by 2020, and also by 2030. Japan has had a feed-in-
tariff policy since 2011, however, this policy is believed to be under review (Renewable
Global Futures 2013).
For wind renewables, Japan’s environment strategy has been to triple current usage by 2030,
presumably to reduce Japan’s reliance on coal and gas-fired generation and lower the
associated greenhouse gases emitted.
Renewables in Japan
Since 2004 Japan has scaled back its subsidisation of solar PV systems. The Japanese
Government recently announced further reductions to feed in tariff rates for solar to JPY 37.8
for projects over 10kW and JPY 38 for projects under 10kW (Ministry of Economy, Trade
and Industry 2013). The government also announced the creation of a feed in tariff for
offshore wind projects to come into effect from 2014-15 (BNEF 2013).
Japan’s Ministry of Economy, Trade and Industry (METI) have noted that they don’t
have good data on network and integration costs, because the general feed in tariff
(FIT) had been just introduced in 2011.
METI have advised BREE that in terms of connecting renewable sources of energy to
the electricity grid, the easier and cheaper projects were done first in Japan. However,
52
Japan is gradually moving away from these cheaper projects to more expensive
projects, so the integration costs are increasing.
Japan is also one of the first countries to see the development of offshore wind generation. As
recently as the 13 November 2013 Japan’s first offshore wind farm commenced generation,
with the capacity to produce 1 GWh and is grid connected. Japan produced 4 559 GWh of
electricity from wind generation in 2011 and 5 160 GWh of electricity from solar PV in the
same year (IEA 2013). Japan’s government has expressed a desire to increase the proportion
of renewable energy generation in Japan, although no target has been established, following
the Fukushima nuclear disaster (Kurtenbach 2013). However, gas and coal fired generation
still dominate the energy mix as conservative options for Japan as sources of energy.
In terms of onshore wind generation, Japan plans to build a new transmission line in northern
Japan (Hokkaido and Tohoku), which is where the wind resources are the best according to
METI. Around half of the funding will come from government, with the other half from the
private sector. Japan’s Ministry of Economy, Trade and Industry is undertaking a study based
on a similar funding model to this, for additional solar PV renewables.
A key challenge for wind power generation has been the inadequacy of transmission lines in
some areas. Therefore, it is proposed that these areas with good wind conditions and
inadequate transmission lines (or a weak grid connection) be identified as special focus areas
and the provision of lines be promoted. With the good wind conditions, the power generators
should be able to obtain high generation efficiency. However, government financial support
would be required for the proposed demonstration project.
Microeconomic Reform of the Electricity Market
In April 2013, the Japanese Government announced its plan to restructure the electricity
market and ultimately make it more efficient and flexible. The reforms will include
separating generation and transmission businesses by 2014-15 to allow greater competition in
the retail and generation markets. A new energy market operator will come into operation in
2015-16 under the revision of the Electricity Business Act (BNEF 2013). Electricity licences
have increased from 37 in 2010 to 98 in 2013 driven by ongoing electricity market reform
and feed in tariffs.
Prime Minister Abe “also mentioned fuel cells and batteries for energy storage as aspects of a
move to encourage innovation in order to help integrate variable renewables into the grid”
(Regional News 2013).
Flexibility - trading between grid areas
The relatively low flexibility in the renewables power system has led to the development of
policy, such as requiring batteries in wind farms - to reduce the night time variability in
power (Morozumi 2008). The biggest issue with the variable battery storage is the cost of it,
although creating a potential fire risk is also an issue.
It was suggested in 2012 that Japan’s market for renewables was in need of competition to
bring prices down, by BNEF, which cited the cost of capital as the primary driver of the high
costs of renewables in Japan (BNEF 2012).
53
International Energy Agency Flexibility Index: Japan
In terms of flexibility the IEA has noted (BNEF 2012) that as the ten utility areas of Japan
have remained isolated, transmission among them is weak. This results in limited opportunity
for geographic and technological smoothing of electricity variability, and an inability to share
flexible capacity.
54
4 Levelised costs of energy estimates
This section focuses on assessment of the existing levelised costs of electricity generation
(LCOE) from renewable sources in the Asia Pacific region covering Australia, China, India,
Indonesia, Japan and South Korea.
This study was carried out on the basis of available regional literature and databases on cost
of renewable electricity (RE) generation from various renewable sources mainly wind, solar,
hydro, biomass, and geothermal resources. Several international organisations, government
agencies of the APREA countries, market analysts and local experts were contacted and
various publications and web-references in the target countries were used in gathering
technology and country-specific information on LCOE estimates.
The LCOE data and relevant assumptions from all available studies have been tabulated to
compare the cost of renewable electricity generation in the APREA countries. The LCOE
information for this report was sourced from:
Government agencies:
The Australian Energy Technology Assessment (AETA), Publication of the Bureau of
Resources and Energy Economics (BREE 2012), Department of Industry, Australia;
Indian Planning Commission, and Central Electricity Regulatory Commission
(CERC), India; and the National Policy Unit (NPU), and Ministry of Economy, Trade
and Industry (METI), Japan;
International organisations:
International Renewable Energy Agency (IRENA), International Energy Agency
(IEA), and Nuclear Energy Agency (NEA); and
Independent agencies:
Bloomberg New Energy Finance (BNEF), and GlobalData.
The overall information on the LCOE obtainable from various APREA country sources
differs due to the varying underlying assumptions used in the calculation of the LCOE,
especially those pertaining to discount rates, capacity factors, and plants’ economic life. This
is driven by the very country-specific nature of renewable resources and project costs.
In this report, BREE’s AETA model, which is referenced as BREE (2012a), was used to
make the LCOE estimates comparable across countries for a given technology. The AETA
model, for this analysis, uses the averages of capital costs, O&M costs and capacity factors
that were available from various reference sources for a given technology within a country.
The AETA model assumes a discount rate of 10 per cent and an economic life of plants of 30
years for all renewable technology projects across the APREA countries to make the results
simpler. This is consistent with the approach applied to the Australian projects in AETA.
Both the country and source specific results, as well as the AETA model results are provided
in this report. However, they have been clearly marked where used.
4.1 Key findings
It appears from the results that:
India and China have the lowest generation costs for most renewable energy
technologies, followed by South Korea and Australia;
55
India has some of the most competitive renewable electricity generation costs of the
APREA countries. BREE’s AETA estimates show that the LCOE in India for onshore
wind, solar PV, biomass and solar thermal electricity are the lowest as compared to other
APREA countries;
small and large hydro technologies are the low cost technologies in most countries. In
Australia, electricity generated from onshore wind and biomass resources are the lowest
cost electricity amongst all types of renewable technologies;
AETA model based cost estimates for all countries suggest that generation for each
technology is cheapest in the following countries: biomass in India, geothermal in
Indonesia, onshore wind in India, solar PV in India, solar thermal in India, and offshore
wind in China. While the AETA model does not estimate LCOE for small and large
hydro technologies, the available reference studies suggest that small hydro generation
technology is cheapest in China, and large hydro generation technology is cheapest in
South Korea.; finally
it should be noted that the AETA cost estimates are only an additional instrument in
putting together existing technology costs for all countries, in addition to using the
LCOEs from multiple sources. The AETA cost estimates assume uniform assumptions
for LCOE cost parameters across countries, which may or may not suit all
countries. Nonetheless, it does provide easy comparability of technology costs across
countries.
4.2 LCOE - concepts and definitions
Levelised cost is a frequently used technique for comparing the cost of different competing
technologies, based on a common set of assumptions. The LCOE provides a valuable tool for
policy makers in understanding the main cost drivers of electricity generation costs today and
future cost trends, and is used extensively in energy projections both in Australia and
internationally.
The LCOE is the minimum cost of energy at which a generator must sell the produced
electricity in order to breakeven. It is equivalent to the long-run marginal cost of electricity at
a given point in time because it measures the cost of producing one extra unit of electricity
with a newly constructed electricity generation plant.
The calculation of LCOE requires a significant number of inputs and assumptions. Key
factors used to calculate LCOE by technology typically include: amortisation period (i.e.
economic life of the plant), discount rate, capacity factor, fuel cost, variable and fixed O&M
(operations and maintenance) cost, and the capital cost.
AETA 2012 model
The AETA 2012 Model (BREE 2012) estimated levelised costs of energy for a set of 40
market ready and prospective electricity generation technologies (renewable and non-
renewable) for different locations in Australia. BREE engaged WorleyParsons consultancy to
develop cost estimates for Australia with the active collaboration of the Australian Energy
Market Operator (AEMO) and the Commonwealth Scientific and Industrial Research
Organisation (CSIRO).
56
The cost estimates, available for each of the 40 technologies (19 fossil fuel and 21 renewable,
including integrated technologies), were generated on a ‘bottom up’ basis that accounted for
the component costs that determine overall long-run marginal cost of electricity generation
from a utility-scale Nth kind plant. The methods used to build up the cost estimates were
applied consistently across all technologies and the key modelling assumptions are briefly
discussed in section 4.3 of this report. All the key assumptions used to generate the costs are
fully detailed in BREE (2012) report and/or the accompanying AETA model that is free to
use and available by emailing [email protected].
LCOE calculation
The formula for calculating LCOE and its component parts are defined below.
LCOE =
∑
( )
∑
( )
Where:
LCOE = Lifetime levelised electricity generation cost
It = Investment expenditure in the year t
Mt = Operations and maintenance expenditure in the year t (in calculations, other costs
(such as a carbon price) may be added in to this variable or separately)
Ft = Fuel expenditure in the year t
Et = Electricity generation in the year t
r = Discount rate
n = Amortisation period
All components costs and factors are converted into common units to develop the LCOE in
terms of $/MWh. All components costs and definitions of the variables used in the above
formula, including the caveats on the use of LCOE are discussed in BREE 2012.
4.3 Issues in comparing LCOE estimates across countries and sources
In this report and in the references cited, LCOE provides a generalised cost estimate and
does not account for site specific factors that would be encountered when constructing an
actual power plant. As a result, the costs associated with integrating a particular technology
in a specific location to a specific electricity network are not included in the LCOE. The
LCOEs for wind and photovoltaic power plants do not, typically, include energy storage, or
integration costs which often manifest across the system rather than for individual
generators.
The information on the LCOEs obtained from available sources differs greatly due to the
underlying assumptions for technologies across countries. IRENA, IEA/NEA, GlobalData
and AETA model LCOE estimates assume that different renewable technology projects are
financed at the same interest rate. BREE’s AETA model does not separate capital costs into
debt and equity financing, and assumes that the capital stack is 100 per cent debt in order to
make the results simpler for a high level analysis. In this report, the AETA model assumes
that all renewable technologies are financed at the same interest rate (10 per cent) across
57
countries, and that this interest rate is considered as the discount rate for calculating LCOE
estimates for all renewable electricity projects across the APREA countries.
Some countries offer tax or accelerated depreciation incentives for renewable electricity
projects. In this report, such incentives are not included to allow comparison across countries
for renewable electricity costs based on resource, capital costs, fuel, etc.
It becomes difficult to compare the LCOE estimates for different technologies across
countries when the financial assumptions made differ across technologies within a country or
between countries. For example, BNEF’s approach to estimating the LCOEs for Australia
applies a 70:30 debt-to-equity ratio and credit spread of 250 basis points (bps) to wind
technology and a 65:35 ratio with a 275 bps spread to solar PV technology. As a result,
BNEF estimates a WACC of 8.1 per cent for wind projects and 9.6 per cent for solar projects.
Information on financing structure was not available for all technologies across countries
from all reference sources, as such, the AETA model assumes a 10 per cent discount rate for
all renewable electricity projects across the APREA countries as discussed in earlier in this
report.
Another issue is that the estimates in the available studies were conducted in different time
periods (i.e. cost for renewable electricity have been dynamic over the last 5 years) and
sometimes in different currencies.
4.4 Approach for comparing LCOE across sources and across countries
Efforts have been made in the report to make the technology costs comparable by presenting
LCOE estimates along with technology costs by cost parameters (capital cost, operation &
maintenance costs, capacity factor, and discount rate) from various reference sources.
In estimating the comparable LCOE values for this report, BREE’s AETA model uses
averages for capital costs, O&M costs and capacity factors that were available from various
reference sources for a given technology within a country, and applies the AETA modelling
assumptions for other LCOE parameters. For this analysis, the AETA model assumes that the
capital stack is 100 per cent debt and that all renewable technologies are financed at a 10 per
cent interest rate, and considers this 10 per cent as the discount rate for calculating LCOE
estimates for all renewable electricity projects across the APREA countries. The AETA
model also assumes a 30 year amortisation period or economic life for all technologies across
the APREA countries.
The existing LCOE estimates and relevant assumptions for the renewable electricity
generation technologies have been collected from all available sources and these are
presented in Tables A1 to A6 in Appendix A of this report.
This report compares and discusses LCOE estimates and relevant assumptions for
technologies across the APREA countries and also compares LCOE across technologies by
different sources within a country. It also compares the differing LCOE assumptions for
technologies across the APREA countries as gathered from various available sources and
publishers.
This report takes the lowest and highest LCOE values from all available sources to consider
the typical LCOE ranges for each technology in the APREA countries. BREE with its AETA
58
model has also estimated the LCOE values for the selected renewable electricity
technologies.
The last column of Tables A1 to A6 in Appendix-A presents the LCOE values as estimated
by BREE using the AETA model for available renewable energy technologies across the
APREA countries. This column also shows BREE’s assumptions on key LCOE parameters
(across technologies) based on the assumptions of various reference sources as presented in
others columns of Tables A1 to A6. The AETA model does not estimate LCOE for
hydroelectricity technology. The present analysis considers the LCOE estimate of
hydroelectricity as the average of all available hydroelectricity LCOE values over different
sources. A number of charts have been used to explain and compare the LCOE results across
technologies and countries. Table A7 in Appendix A provides the summary of LCOE ranges
derived from available sources and the LCOE values (over all sources) estimated by BREE.
This report has used LCOE in 2012 U.S. dollars by applying the exchange rates to the local
currencies, applicable at the time of the publication or during the time when reports were
prepared. The units of capital costs, O&M costs and LCOE have been presented differently in
different sources. For example, some sources present O&M costs in fixed O&M ($/MW) and
variable O&M ($/MWh), some presents O&M as a percentage of capital costs, and so on.
This report has converted all fixed and variable O&M to O&M per MWh. The units used in
this report for comparing LCOE values and assumptions are as follows:
Capital costs as million USD per MW; O&M costs as USD per MWh; and the LCOE as USD
per MWh in year 2012.
4.5 Renewable energy generation costs across APREA countries
This section provides discussion on all available LCOE estimates and underlying
assumptions as presented in Tables A1 to A6 on Appendix A for the renewable energy
technologies in the APREA countries.
Figure 13 shows the LCOE ranges across individual country specific sources, as well as the
LCOE estimated by BREE using the AETA model for renewable energy generation
technologies in the APREA countries. It is observed that there are significant differences in
the LCOE ranges for different technologies in the APREA countries.
The wide range of LCOE for a certain technology within a country occurs due to many
factors; such as, the regional costs within a country, capacity utilisation factor, financing
structure, project costs, plant size, etc. This is why BREE attempted to harmonise those
LCOE estimates. It appears from Figure 13 that the electricity generation from biomass and
hydro resources is relatively cheaper than the electricity from wind, solar, and geothermal
resources in China, India, Indonesia and South Korea. In Australia, electricity from onshore
wind appears to be the cheapest among all renewables.
59
Figure 13 LCOE ranges (bar) from reference sources and BREE's estimates (black mark)31
for
renewable electricity technologies in APREA countries, 2013
Source: Adapted from BNEF (2012, 2013 & 2014), IRENA (2013a), IEA/NEA (2010), CERC (2013), NPU
(2011), METI (2011), GlobalData (2013) and AETA model (BREE 2012b).
Renewable electricity cost for each generation technology can vary significantly by country,
or even region within a country, depending on the resource availability and the local cost
structure (IRENA, 2013b). As such it requires careful analysis to draw the relative position in
terms of generation cost for the renewable technologies across the countries.
4.5.1 LCOE in China
Table A1 in Appendix A provides LCOE values and assumptions as used by various agencies
(sources) for estimating LCOE for generating electricity from onshore and offshore wind,
solar PV, small and large hydro, and biomass resources in China. The LCOE values for
renewable electricity technologies in China by sources are shown in Figure 14.
The LCOE for onshore wind electricity in China varies from source to source ranging from
$46/MWh to $126/MWh, with an average of $90/MWh. BNEF (2012a) estimates China’s
onshore wind LCOE ranging from $46 to $124/MWh and has not provided the discount rate
and O&M cost. IEA/NEA (2010) estimates LCOE for different sizes of onshore wind plant
assuming a 10 per cent discount rate and ranges for capital cost, O&M costs and capacity
factor. GlobalData (2013) applied discount rates of 5 to 8 per cent and estimates that the
onshore wind LCOE ranges from $53.3 to $66.6/MWh. Using the averages of cost
31 The black mark represents LCOE from BREE’s AETA model (2012a.b). BREE’s AETA model does not
estimate LCOE for small and large hydro technologies, where, non-black marks represent the LCOE estimates
that are collected from reference sources.
60
components of various sources, a 10 per cent discount rate and a 30 year amortisation period,
BREE (2012b) estimates the LCOE of China’s onshore wind at $90/MWh. BREE’s AETA
model derives a single estimated LCOE value because it does not assume ranges for cost
parameters.
Figure 14 LCOE estimates for RE technologies in China by sources, 2013
Source: Adapted from BNEF (2012a & 2013a), IRENA (2013a), IEA/NEA (2010), GlobalData (2013) and
AETA model (BREE 2012b). Note: bar represent the ranges of LCOE estimates that are collected from
reference sources.
The LCOE for offshore wind electricity in China ranges from $91 to $240/MWh, where
BREE estimates China’s offshore wind LCOE as $187/MWh. Capital cost and O&M costs as
used by BNEF (2013a) are more than 43 per cent higher than the averages used by BREE
(2012b). As mentioned earlier in this report, BREE’s AETA model uses a 10 per cent
discount rate, which is 2 per cent higher than that of BNEF (2013a).
The LCOE for solar PV electricity in China ranges from $69 to $283/MWh across sources.
IRENA (2013a) estimates China’s solar PV LCOE at $191/MWh, which is about 4 per cent
higher than the BREE (2012b) estimate of $184/MWh due to relatively lower capital and
O&M costs used by BREE (2012b).
Hydroelectricity appears to be very competitive in China. LCOE values for small hydro
plants in China range from $25 to $116/MWh. IRENA (2013a) estimates the LCOE for small
hydroelectricity at $30/MWh, which lies within the range of $27.90 to $33.30/MWh as
estimated by GlobalData (2013). On the other hand, IRENA (2013a) estimates LCOE for
large hydroelectricity at $34/MWh, which lies within the range of $23.30 to $51.50/MWh as
estimated by IEA/NEA (2010). It appears that IRENA (2013a) uses a relatively low O&M
cost for both small and large hydroelectricity plants. BREE’s AETA does not provide a
LCOE for hydroelectricity generation.
61
The LCOE for biomass electricity in China ranges from $27.90 to $132/MWh, where BREE
estimates China’s biomass LCOE as $56/MWh. Biomass LCOE for China estimated by
IRENA (2013a) is the highest. The LCOE estimated by GlobalData (2013) is the lowest
among available other estimates. GlobalData (2013) uses discount rates of 5 to 7 per cent as
compared to the 10 per cent discount rate used by IRENA and BREE. Higher fuel cost is
usually a major driver for higher LCOE for biomass electricity.
The BNEF (2012a) study indicates that, in China, LCOEs vary significantly by province due
to the diversity in geography, climate and local economy. According to BNEF (2012a), Inner
Mongolia, Gansu, Jiangsu and Guangdong provinces have a significant capital cost advantage
for onshore wind technology, where wind curtailment in these provinces causes capacity
factor to be relatively low resulting in a higher LCOE for onshore wind electricity. Available
LCOE estimates suggest that the offshore wind LCOE in China is about 80 to 100 per cent
more than that of onshore wind. Annual solar radiation varies in China and affects the solar
PV capacity factor. Low capacity factor results in a higher LCOE for the solar PV in China.
Biomass incineration technology has a higher feed-in tariff than that of municipal solid waste
and landfill gas in four provinces, therefore biomass LCOE varies widely depending on
region and biomass fuel type and fuel costs in China.
4.5.2 LCOE in India
The Indian CERC (2013) report provides the levelised tariff estimates for various generation
technologies in India. The CERC’s tariff calculations are based on technology parameters
including; capital costs, O&M costs, capacity factors, plant size, and plant life. The CERC
assumes 70 per cent debt to 30 per cent equity, where the debt is financed at 13 per cent per
year and the equity at 22.4 per cent per year. The CERC derives the discount rate at 10.95 per
cent on the basis of a post-tax of 32.45 per cent (CERC 2013).
The CERC’s tariff estimates of relevant renewable electricity technologies are presented in
this report and compared with the LCOE estimates obtained from other reference sources (see
Table A2 of Appendix A).
BREE applied two modelling approaches (CERC’s tariffs model and BREE’s AETA model)
for analysing India’s LCOE estimates for the renewable generation technologies.
The purpose of applying CERC’s approach is to differentiate CERC’s levelised tariffs
estimate to LCOE estimates. For this exercise, BREE attempted to derive the levelised cost
from CERC’s levelised tariffs modelling assumptions by changing the CERC’s financial
assumptions. BREE tried to remove the return on equity component by making all capital
stocks as debt, and then estimated the LCOE by applying CERC’s assumptions (e.g. discount
rate of 10.95 per cent) in this exercise. These LCOE estimates are referenced as ‘CERC &
BREE 2013’ estimates in this report (Table A2 of Appendix A).
This approach provides India’s LCOE estimates by approximately 24 per cent lower than the
levelised tariffs given in the CERC report for most of the technologies and by about 5 per
cent lower for biomass technologies. The biomass technologies are less sensitive to changing
financial assumptions due to the fact that cost of electricity generation for this technology is
also largely influenced by fuel costs.
62
However, BREE also applied the AETA modelling assumptions as the second approach for
estimating the comparable LCOE estimates for India’s renewable energy technologies. The
AETA modelling assumptions are discussed earlier in this report, and the modelling results
are referenced as ‘AETA Model (BREE 2012)’. The LCOE estimates as derived by AETA
model for India’s renewable energy technologies are presented in Table A2 of Appendix A.
Figure 15 shows the LCOE estimates for renewable electricity technologies in India by
reference sources.
Figure 15 LCOE estimates for RE technologies in India by sources, 2013
Source: Adapted from BNEF (2012b), IRENA (2013a), CERC (2013), GlobalData (2013) and AETA model
(BREE 2012b)
The LCOE estimates for onshore wind electricity in India as carried out by different agencies
range from $50 to $150/MWh, where BREE’s AETA model (2012) estimates India’s onshore
wind LCOE as $71/MWh. BNEF (2012b) study uses relatively high discount rates and a wide
range of capacity factors, and provides the widest range for India’s onshore wind LCOE.
GlobalData (2013) estimates Indian onshore wind LCOE ranging from $50.70 to
$63.40/MWh, which is the lowest among all other available estimates. Indian CERC (2013)
estimates levelised tariffs ranging from $72.70 to $116.30/MWh, where by removing the
effects of financing assumptions, i.e. assuming the capital stock is 100 per cent debt financed,
CERC/BREE estimates the LCOE for Indian onshore wind ranging from $55.20 to
$88.40/MWh.
The LCOE estimates carried out by different agencies for Indian solar PV range from $78.10
to $220/MWh, where BREE’s AETA model estimates India’s solar PV LCOE as $146/MWh.
IRENA (2013a) assumes the highest capital cost ($3.28m/MW), which is more than double
the capital cost assumed by other agencies, and estimates the LCOE for Indian solar PV at
$220/MWh. CERC (2013) estimates the levelised tariffs at $161.8/MWh while by removing
63
the effects of financing assumptions, CERC/BREE estimates the LCOE at $122.50/MWh for
Indian solar PV electricity.
LCOE for small hydro plants in India range from $30 to $100/MWh. IRENA (2013a)
estimates India’s small hydro LCOE as $46/MWh, where they use the highest capacity factor
and the lowest O&M costs compared to other agencies. The LCOE estimated by
CERC/BREE using CERC assumption ranges from $63.50 to $75/MWh, which is about 21.5
per cent lower than the levelised tariff as estimated by CERC (2013) for small hydro in India.
The large hydro LCOE in India ranges from $44 to $81.36/MWh. IRENA (2013a) assumes
the lowest O&M costs and estimates the lowest LCOE. The available studies suggest that the
LCOE for large hydroelectricity plants in India is cheaper than for small hydroelectricity
plants, due to the effect of economies of scale in achieving a lower capital cost per MW
capacity.
LCOE for biomass electricity in India ranges from $37.70 to $160/MWh. The levelised tariff
estimated by CERC (2013) for biomass electricity ranges from $126.10 to $145.30/MWh. By
making all borrowing to be at the discount rate (10.95 per cent) and keeping CERC’s other
assumptions unchanged, CERC/BREE estimates India’s biomass LCOE ranging from
$119.30 to $138.40/MWh, which is about 4.8 to 5.5 per cent lower than CERC tariff
estimates. Biomass fuel cost is a major driver for varying LCOE for biomass electricity.
BREE’s AETA model derives India’s biomass LCOE as $43/MWh, which is much lower
than CERC’s estimate.
The LCOE estimated by CERC/BREE by using CERC’s assumptions for Indian solar thermal
electricity is $165.94/MWh, which is about 25% lower than the levelised tariff estimated by
CERC (2013), while the LCOE estimated by BREE’s AETA model for India’s solar thermal
electricity is $151/MWh. The AETA model assumes a 10 per cent discount rate, which is
lower but close to CERC’s assumption.
The abundant hydropower resources in India allow very competitive electricity generation
from small and large hydro power plants. The capital costs for biomass and onshore wind are
relatively low and the availability of wind and biomass resources in many locations within
India reduces average LCOE for wind and biomass electricity in India.
4.5.3 LCOE in Indonesia
Table A3 in Appendix A provides LCOE values along with the basic assumptions for various
renewable technologies in Indonesia as estimated by different agencies and collected from
various sources, where Figure 16 shows the LCOE values by source across technology.
In Indonesia, LCOE for solar PV ranges between sources of data from $110 to $678/MWh,
where BREE’s AETA model provides Indonesia’s solar PV LCOE as $405/MWh. The
onshore wind LCOE ranges from $86 to 390/MWh, where BREE’s AETA model provides
$168/MWh for Indonesia’s onshore wind LCOE.
The available LCOE estimates suggest that the small hydroelectricity LCOE ranges widely
from $40 to $410/MWh in Indonesia. IRENA (2013a) estimates the LCOE for large
hydroelectricity in Indonesia as $39/MWh, where no other estimate is available to compare to
this value. The LCOE for biomass electricity ranges from $52 to $230/MWh, where BREE’s
AETA model derives Indonesia’s biomass LCOE as $91/MWh. BREE’s AETA model
64
provides Indonesia’s geothermal LCOE as $57/MWh, where the available sources suggest
Indonesia’s geothermal LCOE to be from $20 to $193/MWh. It appears from the available
data that geothermal electricity is the most economic renewable electricity in Indonesia.
Figure 16 LCOE estimates for RE technologies in Indonesia by sources, 2013
Source: Adapted from BNEF (2013b & 2014a), IRENA (2013a), IEA/NEA (2010), GlobalData (2013) and
AETA model (BREE 2012b)
4.5.4 LCOE in Japan
The LCOE values and assumptions as used by various agencies for estimating LCOE for
generating electricity from onshore and offshore wind, solar PV, small and large hydro,
biomass, and geothermal resources in Japan is presented in Table A4 in Appendix A. The
LCOE values by sources across technology in Japan are shown in Figure 17.
In Japan, LCOE for onshore wind ranges from $84 to $331/MWh, where $216/MWh is
obtained by using the BREE’s AETA estimation. The offshore wind LCOE ranges from $109
to $430/MWh, where BRRE estimates Japan’s offshore wind LCOE of $283/MWh. Though
the capital costs for offshore wind are about 77 to 94 per cent more than for onshore wind in
Japan, the 50 to 60 per cent higher capacity factor of Japan’s offshore wind results in only
about a 30 per cent higher LCOE for offshore wind compared to the onshore wind in Japan.
Solar PV LCOE ranges from $241 to $785/MWh, with BREE’s estimate of $562/MWh,
which is the highest among renewable energy technologies in Japan. Relatively high capital
and O&M costs along with a low capacity factor are the main reasons for this higher solar
LCOE in Japan.
Japan’s Ministry of Economy, Trade and Industry (METI 2011) study shows that the LCOE
for Japan’s large hydroelectricity is about 45 to 52 per cent lower than the small hydro
LCOE, even though the capacity factor for large hydro is lower than for small hydro in Japan.
65
The main reason is that the large hydro plants incur significantly less O&M costs compared
to small hydro plant in Japan.
Figure 17 LCOE estimates for RE technologies in Japan by sources, 2013
Source: Adapted from BNEF (2012c & 2014b), IEA/NEA (2010), NPU (2011), METI (2011), GlobalData
(2013) and AETA model (BREE 2012b)
LCOE for Biomass electricity in Japan ranges widely due to the significant variation in
biomass fuel costs in Japan. METI (2011) estimates suggest geothermal electricity to be
competitive as compared to other sources of renewable electricity in Japan.
It is important to note that METI (2011) assumes 0, 1, 3 and 5 per cent discount rates in their
reference material for LCOE studies, while they reported the LCOE estimates of 3 per cent
discount rate for all technologies. This 3 per cent discount rate is much lower than the
discount rate used by other agencies including BREE (2012b) as presented in Table A4 in
Appendix A. AETA model (BREE 2012b) considered averages for capital costs, O&M costs
and capacity factors from available sources, and applied 10 per cent discount rate and 30
years amortisation period for estimating LCOE across all renewable electricity technologies
in Japan. As such, BREE (2012b) estimates provide higher LCOE value for Japan’s
renewable electricity technologies as compared to the METI (2011) estimates. BREE (2012b)
estimates show that the biomass electricity is the most economic renewable electricity option
in Japan followed by onshore wind electricity.
4.5.5 LCOE in South Korea
Figure 18 shows the LCOE values by sources across renewable energy technologies in South
Korea, as adapted from Table A5 in Appendix A which provides LCOE values and
assumptions of various agencies for renewable technologies in South Korea.
66
In South Korea, LCOE for onshore wind ranges from $86 to 234/MWh, while the offshore
wind LCOE ranges from $148 to 233/MWh. LCOE for solar PV varies widely ranging from
$177 to 470/MWh, with an AETA model estimated LCOE of $365/MWh. LCOEs for small
and large hydroelectricity plants are $76/MWh and $31/MWh respectively as estimated by
IRENA (2013a). Biomass LCOE in South Korea ranges from $120 to $165/MWh.
Figure 18 LCOE estimates for RE technologies in South Korea by sources, 2013
Source: Adapted from BNEF (2012d), IRENA (2013a), IEA/NEA (2010), GlobalData (2013) and AETA model
(BREE 2012b)
The available LCOE estimates suggest that the hydroelectricity is the most economic
renewable electricity in South Korea. BREE’s AETA model estimates suggest that the
average LCOE for wind is lower than the LCOE for solar PV. However, solar PV
development is growing faster than onshore and offshore wind development in South Korea.
Separate targets for solar power capacity along with high Renewable Energy Certificate
(REC) prices are the drivers for faster growth of solar power development in South Korea.
4.5.6 LCOE in Australia
LCOE values and assumptions as used by various agencies for estimating LCOE for onshore
and offshore wind, solar PV, biomass, geothermal, and solar thermal electricity in Australia
are presented in Table A6 in Appendix A. LCOE estimates for Australian renewable
electricity technologies have been sourced from BNEF (2013c), GlobalData (2013) and
AETA model (BREE 2012a). Small and large hydro LCOE for Australia is not available
from BREE’s AETA model. Differences in capital costs, O&M costs, capacity factor and
discount rates as assumed by source agencies produce the wide ranging LCOEs.
BREE’s AETA model uses its own technology specific cost component data including 10 per
cent discount rate and 30 year amortisation period in estimating LCOE for all renewable
67
energy technologies in Australia. For this report, AETA model uses solar PV non-tracking
technology for solar PV LCOE in Australia, and considers wood and wood waste as the
biomass for estimating biomass LCOE in Australia. Hot sedimentary aquifer (HSA)
technology is considered for estimating geothermal LCOE. The AETA model provides
Australia’s geothermal LCOE for the year 2020. The LCOE values by sources across
technology in Australia are shown in Figure 19.
Figure 19 LCOE estimates for RE technologies in Australia by sources, 2013
Source: Adapted from BNEF (2013c), GlobalData (2013) and AETA model (BREE 2012a)
BNEF (2013c) applied ranges of capital costs, O&M costs and capacity factors for estimating
onshore wind LCOE ranging from $76.2 to $107.6/MWh, while GlobalData (2013) applied
the discount rate ranging from 5 to 8 per cent and estimates the LCOE for onshore wind from
$63.3 to $79.1/MWh for Australia. BREE (2012a) estimates the onshore wind LCOE as
$116/MWh which is higher than the estimates of BNEF (2013c) and GlobalData (2013).
The offshore wind LCOE in Australia is $194/MWh as estimated by BREE (2012a) using
AETA model. BREE (2012a) uses higher capital cost and discount rate and estimates solar
PV LCOE of $224/MWh which is very close to the upper limit of available estimates.
Biomass LCOE ranges from $107 to $221/MWh, where BREE (2012a) estimate provides
$128/MWh. Geothermal LCOE estimate is available only from BREE (2012a) which is
$154/MWh for the year 2020. As mentioned earlier, the geothermal LCOE estimates for
Australia is available for year 2020, because it is likely that geothermal power plant will not
be commercially available in Australia before 2020. Solar thermal LCOE is available from
BNEF (2013c) and BREE (2012a) which ranges from $172 to $347/MWh.
The available LCOE estimates suggest that the onshore wind followed by biomass is the most
economic renewable electricity in Australia. According to BREE (2012a) estimates, onshore
68
wind is about 40 per cent cheaper than offshore wind in Australia. The available estimates
indicate the biomass and geothermal could be more affordable as compared to solar PV and
solar thermal technology. However, according to BNEF (2013c), since reductions in
equipment and financing costs have driven a significant decline in the LCOE of wind and
large scale solar PV since 2011 in Australia, it is forecasted that the LCOE of both of these
two technologies would continue to fall as manufacturing economies of scale and innovation
continue to reduce costs (BNEF 2013c).
4.6 Renewable energy generation costs across technologies
The appropriateness of each renewable energy technology may differ significantly depending
on location and the particular circumstances of a country. In Figure 20, the bar represents the
LCOE ranges as gathered from all available sources and the black mark represents BREE’s
estimated LCOE by countries across technologies. As the AETA model does not provide
LCOE for hydro, midpoint value of available hydro LCOEs is considered as BREE’s estimate
for hydro LCOEs for comparison purposes. This figure uses the data from Table A7 in
Appendix A.
Figure 20 LCOE ranges (bar) from reference sources and BREE's estimates (black mark)32
for
RE technologies by countries, 2013
Source: Adapted from BNEF (2012, 2013 & 2014), IRENA (2013a), IEA/NEA (2010), CERC (2013), NPU
(2011), METI (2011), GlobalData (2013) and AETA model (BREE 2012b).
32 The black mark represent LCOE from BREE’s AETA model (2012a.b). BREE’s AETA model does not
estimate LCOE for small and large hydro technologies, where, non-black marks represent the LCOE estimates
that are collected from reference sources.
69
The lowest and highest LCOE values from all available sources are taken as the typical
LCOE ranges for each technology in APREA countries. As mentioned earlier, BREE’s
AETA model assumes average value over all sources used here for the cost components
including capacity factor, while discount rate is assumed to be 10 per cent and amortisation
period of 30 years across all countries for all technologies. The LCOE derived by BREE’s
AETA model is a single value and shown as a black mark in Figure 20.
4.6.1 Wind LCOE
It appears from Figure 20 that China’s onshore wind LCOE is lower than the range of India’s
onshore wind LCOE. However, BREE’s AETA estimates show the lowest average LCOE for
onshore wind is $71/MWh in India followed by $90/MWh in China. The onshore wind
LCOE ranges widely for Indonesia and Japan, where Japan has the highest onshore wind
LCOE of $216/MWh followed by Indonesia. Offshore wind LCOE is the lowest in China
followed by Australia and South Korea. Offshore wind LCOE data is not available for India
and Indonesia. Offshore wind has higher capacity factor as compared to onshore wind, but
the offshore wind plant requires more capital cost resulting higher LCOE.
Average capital costs for wind power technology varies across countries and even regions
within a country. Rough terrain usually increases local construction costs for wind plant.
Local construction costs constitute a major share of capital costs for the wind plant in Japan.
Average capital cost for wind plant is the highest in Japan partly due to higher local
construction costs and the higher cost of locally manufactured wind turbines. Increased
development of wind projects may bring these costs down, but the rough terrain is likely to
keep costs relatively higher in Japan than other APREA countries.
Wind turbine prices in Asia Pacific region have started to fall and are likely to continue due
to the increasing entry of low cost manufacturers from emerging economy into the global
market. Therefore, the LCOE of wind is likely to fall in the near future even for the same
quality of wind resource.
4.6.2 Solar PV LCOE
The range of levelised cost for solar PV is quite wide for Indonesia, Japan and South Korea
as shown in Figure 20. Solar PV appears to be most expensive in Japan due to high capital
cost and relatively low capacity factors. Among the APREA countries, India appears to have
the lowest LCOE for solar which ranges from $78 to $220/MWh, and BREE’s AETA
estimate also suggest that the lowest average LCOE for solar PV is $146/MWh in India. A
combined effect of low cost construction (labour) and low system prices reduce the capital
costs of solar PV in India. Low capital cost partly offsets the relatively lower capacity factor.
Average capital cost of solar PV in China is about 20 per cent higher than India, though
Chinese solar plants benefits from inexpensive locally manufactured equipment.
Solar PV costs are declining rapidly due to high learning rates for PV modules and the very
rapid deployment currently being experienced. Cost reduction is likely to continue as PV
module costs decline.
70
4.6.3 Hydro LCOE
Across APREA countries Japan has the highest LCOE for hydroelectricity. LCOE for small
hydro in Japan is about 278 per cent higher than China and 172 per cent higher than India.
Higher capital cost in Japan (about 500 per cent more than China and 357 per cent more than
India) offsets Japan’s advantage of higher capacity utilisation factor for small hydroelectricity
plant.
Hydroelectricity is most affordable in China, India and South Korea, where large hydro
plants are even more economical than small hydro plant. The LCOE for small hydro appears
to be lowest in China ($46/MWh) followed by India ($64/MWh). In China, hydroelectricity
is the cheapest across all renewable technologies. Chinese government is considering lifting
the hydroelectricity tariff to the same levels as that of coal-fired electricity to attract new
investment in hydro project development.
Hydro resources provide very competitive renewable electricity in India, though the growth
of hydroelectricity in India is limited due to inadequate infrastructure for grid connectivity in
state with high potential of hydro resources.
4.6.4 Biomass LCOE
Biomass electricity appears to be very competitive in India and China. According to BREE’s
AETA estimates, the LCOE estimate for biomass electricity is $43/MWh in India which is
the lowest among all other APREA countries followed by $56/MWh in China. As mentioned
earlier, capital costs, O&M cost, and capacity factors are averaged for a given technology
within a country but not across countries in AETA modelling for this report. Capacity
utilisation depends on annual generation profile which could vary across countries for a given
technology, as such AETA model does not assume same capacity factor for a given
technology across countries.
Relatively lower capital cost and higher capacity utilisation factor reduce biomass LCOE in
India and China. Biomass LCOE ranges widely for Japan and Indonesia. AETA results
suggest that the biomass LCOE is the highest in South Korea followed by Australia. Average
capital cost for biomass electricity plant is relatively very high in South Korea and Australia.
Sustainable source and long-term supplies of low cost feedstock are important for the
economics of biomass electricity plants. Low energy density feedstock (such as woodchips or
pellets) usually increases the cost of biomass electricity which may also require significant
transportation cost.
4.6.5 Geothermal LCOE
Geothermal LCOE data are not available for China, India or South Korea. BREE’s AETA
estimates suggests that that the average LCOE for geothermal electricity in Indonesia is
$43/MWh which is the lowest among all other renewable electricity in Indonesia. Low capital
and O&M costs are the main reasons for this low geothermal LCOE in Indonesia.
Geothermal LCOE in Japan is $244/MWh which is about 467 per cent higher than Indonesia,
and 58 per cent higher than Australia. Very high capital and O&M costs are the main reasons
for the highest geothermal LCOE in Japan. Japan has very high geothermal resource
potential, though in this study, it appears from BREE’s AETA results that currently the
71
LCOE estimates of geothermal electricity in Japan is higher than the LCOE estimates of
biomass and onshore wind, but lower than the off-shore wind and solar PV technologies
within Japan. Australia has also the geothermal potential for future development.
Geothermal electricity generation can provide very competitive electricity where high quality
resources are well defined. Due to the risk of poorly performing production wells there is
always an inherent uncertainty in geothermal project development. Degradation of reservoir
may require additional production wells over the life of a project which can affect the
generator’s performance and overall generation costs. These factors tend to introduce greater
uncertainty into the development of geothermal electricity projects and may increase
financing costs, compared to other renewable electricity technologies. However, this
uncertainty factor is manageable in the markets where financing institutions have previous
experience with the industry.
4.6.6 Solar thermal LCOE Solar thermal electricity appears to be relatively expensive for India and Australia. Input data
for solar thermal LCOE is available for India and Australia. Solar thermal LCOE is not
available for other APREA countries. According to BREE’s AETA estimates, the solar
thermal LCOE in India is $151/MWh, which is the highest compared to other renewable
electricity technologies in India. Similarly, solar thermal LCOE in Australia is $287/MWh,
which appears to be the highest among other renewable electricity technologies in Australia.
The capital and O&M costs for solar thermal electricity in India are less than that of
Australia.
72
5 Costs of renewables Integration Reporting on existing integration cost estimates in the APREA countries was one of the three
objectives of this project. The first two objectives, the LCOE estimates and integration issues
have been reported in the earlier sections.
The review of literature undertaken during this assessment has not revealed any study in any
APREA country, including Australia, providing estimates of the integration costs of
renewable integration.
In contrast, there are a range of studies in the United States and Europe that examine the
capacity of grids to hold higher penetrations of variable energy such as wind and solar. These
studies have also examined the need for grid improvements and other operational changes
that may be necessary to integrate higher penetrations of variable and uncertain renewable
generation and costs of integration. In addition, these studies have aimed to understand the
need for transmission improvements, interconnections, balancing area cooperation, market
design issues, and other operational changes needed to economically operate the grid under
high penetrations of variable renewable energy generation (Bird and Milligan 2012, Denholm
2010, Cochran et al 2012).
It should be noted that while variable generation requires additional reserves, it typically frees
up thermal capacity on the system to provide additional reserves. Hence, in some cases with
sufficient capacities, there may not be a need to commit too many additional reserves to cover
variability resulting from increased renewable energy.
While reviewing a collection of renewable enhancement programs in Europe and America,
Bird and Milligan (2012) found that interconnection-wide costs for integrating large amounts
of wind generation are manageable with large regional operating pools and significant market
and operational changes. In these case studies, Bird and Milligan (2012) estimated the
integration costs to be less than US$5 per megawatt-hour (MWh) for wind (roughly 5 per
cent of total levelised cost of on-shore wind technology cost in 2012), assuming large
balancing areas and fully developed regional electricity markets. The cost of integrating
intermittent energy sources into electricity grids is heavily dependent on the extent of their
share of overall electricity supply and the overall mix of generation technologies. At low
penetration levels (less than 5 per cent), integration costs are negligible, however, at wind
penetration levels of 20 per cent, wind integration costs associated with balancing could
increase the overall cost of electricity by US$9/MWh (Denholm 2010). This would roughly
amount to 9 per cent of total LCOE at 2010 prices.
As mentioned above, integration issues can be divided into three categories, each involving
costs: those that relate to transmission extension and reinforcement (not including the cost of
linking to the grid); electricity demand and supply balancing costs; and the costs associated
with the adequacy of the power system (frequency control, etc.).
A review by NREL (Cochran et al 2012) of European studies reports that the Eastern Wind
Integration and Transmission Study found that among various scenarios, the interconnection-
wide costs excluding transmission costs for integrating large amounts of wind were less than
US$5 per megawatt-hour (MWh), and the costs of managing the variability (balancing) of
wind ranged from US$2.7 to US$3.4 per MWh. For example, in Germany additional
73
balancing costs, at around 10 per cent penetration, were found to be around US$3.3/MWh
(Holttinen et al. 2009).
The IEA Grid Integration of Variable Resources (GIVAR) project seeks to better understand
the technical and market characteristics of a power system that facilitates integration of
variable renewable energy. For example, the Phase 2 report, Harnessing Variable
Renewables (IEA 2011a), proposes a tool to assess how much renewable energy can be added
to existing systems. It can be drawn that at a 20 per cent variable generation, wind energy
balancing costs range from US$1/MWh to US$7/MWh. Higher balancing costs are found in
the United Kingdom, where the availability of flexible resources is likely to be low due to
grid and market constraints. In contrast, projections for the Eastern Interconnection in the
United States in 2024, which assume optimisation measures such as balancing area
consolidation and optimal forecasting, give a midrange cost of US$3.5/MWh at a 20 per cent
wind energy share, and US$5/MWh at a 30 per cent share. Integration cost ranges are
depicted in Figure 21 below (IEA 2011a).
Figure 21 Integration costs for wind generation, various countries
Source: Reproduced from IEA 2011
74
6 Lessons and key messages for integration of renewables This report presents the existing levelised cost estimates of renewable electricity generation
technologies, and renewable integration technical and policy issues prevailing in the six Asia-
Pacific countries: China, India, Indonesia, Japan, South Korea, and Australia (APREA target
countries).
The study has made an extensive desk top literature review, made written requests to the
member countries to provide relevant information, and where applicable, used BREE’s
AETA model to substantiate the comparable information across countries. This has produced
comparable generation cost estimates of renewable technologies across the APREA target
countries. Where available this report provides summarised country information on
experiences of renewable integration. BREE’s research found insufficient information
available on the experiences of integration issues in Indonesia and South Korea to include
sections for these countries.
Renewable energy policies within the APREA target countries have tended to focus on
increasing renewable generation capacity, without accompanying policies focused on the
integration of renewable energies into existing electricity networks. There has also typically
been a policy emphasis on increasing renewable generation capacity, rather than on
increasing renewable generation deployment. In countries where the degree of variable
renewable energy penetration is low, this has not caused significant difficulty for the energy
systems as a whole. However, where the degree of variable renewable energy penetration is
relatively high, countries encountered a range of issues, including compromised network
reliability, delays in grid connection for generators, forced curtailment of renewable
generation, issues with quality generation investment construction and energy price effects.
The issues encountered with higher levels of renewable energy penetration were due to
technical constraints, load balancing and frequency control issues imposed by limitations in
existing grid structures and capacities. The limitations of existing networks were typically
exacerbated by operational difficulties imposed by a general lack of capacity in forecasting
renewable electricity generation and aspects of market design or management such as longer
scheduling and dispatch periods and the availability and coordination of ancillary services,
particularly rapid ramp-up standby capacity.
Renewable energy integration issues also included institutional factors such as:
uncoordinated network planning;
a policy focus on increasing installed renewable capacity instead of delivered
electricity;
a lack of or poorly structured incentives for grid operators to invest in infrastructure
and practices that facilitate renewable integration;
a lack of national and technical standards for grid connection of renewable electricity;
and
a lack of, or poorly structured, incentives for adequate ancillary service provision for
both system balancing and to ameliorate pricing risks to renewable energy generators
75
induced by peaks in renewable generation, where such risks threaten the profitability
of variable and uncertain renewable energy projects.
Of central concern among these is the lack of or poorly structured incentives for investment
in grid infrastructure and practices that facilitate renewable integration. Resolving any of
these issues involves policy innovation to create appropriate incentives for investment in
systems and infrastructure for renewable energy generation and deployment. How this might
best be achieved is dependent on the legislative and market environment in each of the
APREA target countries.
Countries with higher levels of renewable energy penetration have faced barriers to the
smooth and efficient integration of renewable energies. The APREA target countries have
explored a range of approaches to encourage renewable energy integration and deployment.
Technical measures included improved transmission and distribution grid technologies,
improved market operations for scheduling and dispatch, fixing standards for grid connection
of small scale renewable generation, enlarged balancing areas, load shifting, demand side
management, and storage. Policy measures included new renewable energy and electricity
market laws, mandatory renewable energy connection and purchase policies, feed-in-tariffs,
increased regulatory oversight, and electricity market rules to encourage the growth of
ancillary services in electricity markets. These measures accompanied technology specific
capacity expansion targets (roadmaps) and energy efficiency grants.
The assessment of practises explored in this report finds that important operational or
infrastructure changes that can help facilitate the integration of higher variable renewable
energy penetrations include:
the faster scheduling and dispatch of generation;
use of advanced forecasting in fast market operations;
deepening system interconnections and improving balancing area cooperation;
greater access to transmission;
increased flexibility of dispatchable generation capacity;
electricity storage; and
the use of demand response.
These measures are capable of overcoming technical issues in integrating variable renewable
energy, while addressing the needs of the grid. The extent to which any of these measures are
economically viable as levels of variable renewable energy penetration continue to rise will
depend on country specific factors such as market structure, geography, nature resource
endowments, local cost structures and existing grid practices and infrastructure.
The review of APREA country integration experiences also reveals the merits of renewable
electricity generation targets over capacity expansion targets.
The nature of experiences in the APREA target countries in integrating renewable energy
perhaps reflect the lack of transparency of the trade-offs of increased renewable energy
development. Increased transparency of the impacts of renewable energy integration to
electricity networks and other energy market stakeholders will provide governments and the
public with a greater understanding of the trade-offs of renewable energy deployment.
76
An understanding of these trade-offs will underlie an understanding of the technical measures
necessary to improve the efficiency of renewable energy deployment. This will increase the
capacity of policy makers to craft policies that appropriately incentivise investment to
facilitate renewable energy integration. However, central to accessing the relative merits of
various solutions to overcome the challenges of renewable energy integration is the
availability of comparable cost estimates.
The LCOE estimates do not capture the entirety of costs associated with increasing renewable
energy supply to existing electricity networks, since LCOE only covers plant level generation
costs.
At the time of publication of this report, there were no readily available integration cost
estimates for the APREA countries. Future development of integration cost estimates will be
important for policy makers and informed public debate. A number of challenges exist in
developing robust methodologies for estimating integration costs. These include:
the varying nature of existing electricity network infrastructure and potential renewable
energy projects pose country-specific logistical challenges to renewable energy
integration, which require varying suites of strategies with differing integration cost
components;
integration costs are increasing with the degree of variable and uncertain renewable
energy penetration due to the increasing impact of system wide effects of connecting each
additional unit of renewable energy capacity. Thus any integration cost estimates would
need to be marginal cost estimates conditioned on a given level of variable renewable
energy penetration;
there is no definitive path for integration; there may be numerous strategies to resolve
issues associated with a given level of renewable energy penetration. Different paths
would involve different costs, where the eventuating path will be contingent on the
intersection of institutional features, policy and market forces, rather than dollar costs
alone;
some renewable energy integration measures may improve overall energy system
efficiency (economic as opposed to technical) and thereby generate positive externalities
for other stakeholders and market players. Appropriately attributing costs in this context
is not clear cut and depends on network specific attributes, including market (and non-
market) structures; and
costs of variable renewable energy integration potentially vary significantly not just
between countries and between regions within a single country, but also between
renewable energy generation projects as a consequence of geographic location relative to
load centres and the terrain over which infrastructure may need to be built can be
significant in determining the cost of grid connection.
Developing methodologies to estimate integration costs that deal with the challenges outlined
above will provide the platform for developing comparable cost estimates of renewable
energy. These cost estimates will help in determining the optimal energy supply mix within
countries as they seek to reduce emissions while minimising costs.
77
Appendix A: LCOE data and assumptions from all sources Table A1: LCOE Estimates for RE Generation Technologies in China by Source
Technology &
Assumption Units
BNEF
2012
BNEF
2013c
IRENA
2013a
IEA/NEA
2010
GlobalData
2013
AETA model
BREE 2012b
Onshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 1.19-1.31 na 1.28 1.22-1.58 1.13 1.265
O&M costs USD/MWh na na 10 15.5-27.1 11.72 14.34
Capacity factor % 19-25 na 24 20-27 22 23
Discount rate % na na 10 10 5-8 10
Capacity MW 50 na >5 30-200 20 100
LCOE USD/MWh 46-124 na 79 72.0-125.8 53.3-66.6 90
Offshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 2.54 4.40 na na 2.3 3.08
O&M costs USD/MWh na 48.33 na na 21.00 34.66
Capacity factor % 30 30 na na 25 29
Discount rate % na 8 na na 5-8 10
Capacity MW 300 na na na 100 100
LCOE USD/MWh 91 - 240 177.7 na na 95.5-119.4 187
Solar PV
(fixed)
Electricity
Generation
Capital costs/Capex Million USD/MW 1.75-1.79 na 2.48 2.88 - 3.74 1.2 2.19
O&M costs USD/MWh na na 16.53 15.65-23.73 8.35 14.86
Capacity factor % 12-18 na 17.27 18 - 21 16.4 17
Discount rate % na na 10 10 5-8 10
Capacity MW 25 na 1 10 - 20 5 100
LCOE USD/MWh 99-257 na 191 186.5-282.9 69.3-86.8 184
Small
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW 1.26-1.42 na 0.98 na 1.1 na
O&M costs USD/MWh na na 0.07 na 5.23 na
Capacity factor % 28-45 na 47.58 na 36 na
Discount rate % na na 10 na 5-7 na
Capacity MW 15 na 20 na 35 na
LCOE USD/MWh 25-116 na 30 na 27.9 - 33.3 na
Large
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na na 1.03 0.89-1.58 na na
O&M costs USD/MWh na na 0.08 1.37-9.85 na na
Capacity factor % na na 45.62 34-57 na na
Discount rate % na na 10 10 na na
Capacity MW na na na 4783-18134 na na
LCOE USD/MWh na na 34 23.3-51.5 na na
Biomass
Electricity
Generation
Capital costs/Capex Million USD/MW 0.71-3.38 na 1.27 na 1.08 1.61
O&M costs USD/MWh na na 0.06 na 6.06 3.06
Capacity factor % 66-86 na 77.62 na 50.7 64
Discount rate % na na 10 na  5-7 10
Capacity MW 3-30 na 1 na 25-30 18
Fuel costs USD/MWh $40/ton na low/high na 10 5.4
LCOE USD/MWh 28-132 na 53-67 na 27.9-31.6 56
na = data is not available from the corresponding source
Source: Adapted from BNEF (2012a & 2013a), IRENA (2013a), IEA/NEA (2010), GlobalData (2013) and AETA model (BREE
2012b)
78
Table A2: LCOE Estimates for RE Generation Technologies in India by Source
Technology &
Assumption Units
BNEF
2012
BNEF
2013c
IRENA
2013a
IEA/NEA
2010
GlobalData
2013
AETA model
BREE 2012b
Onshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 1.07 1.26 1.11 1.11 1.01 1.11
O&M costs USD/MWh na 10 6.3-10.0 6.3-10.0 11.14 9.36
Capacity factor % 15-37 24 20-32 20-32 20.6 24.5
Discount rate % 14 10 10.95 10.95 5-8 10
Capacity MW na >5 1 1 10 100
LCOE USD/MWh 50-150 78 72.7-116.3 55.2- 88.4 50.7-63.4 71
Solar PV
Electricity
Generation
Capital costs/Capex Million USD/MW 1.56 3.28 1.48 1.48 1.34 1.83
O&M costs USD/MWh na 15.41 12.92 12.92 9.62 12.72
Capacity factor % 15-20 18.52 19 19 15.9 18
Discount rate % 14 10 10.95 10.95 5-8 10
Capacity MW na 1 na na 5 100
LCOE USD/MWh 150-200 220 161.8 122.47 78.1-100 146
Small
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na 1.55 1.15-1.48 1.15-1.48 1.44 na
O&M costs USD/MWh na 0.10 12.4-14.9 12.4-14.9 6.99 na
Capacity factor % na 51.6 30-45 30-45 35.3 na
Discount rate % na 10 10.95 10.95 5-7 na
Capacity MW na 20 <5MW <5MW 20 na
LCOE USD/MWh 30-100 46 81-95.4 63.5-75.0 37.3-44.6 na
Large
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na 1.44 1.06-1.35 1.06-1.35 na na
O&M costs USD/MWh na 0.11 8.9-10.4 8.9-10.4 na na
Capacity factor % na 45.78 30-45 30-45 na na
Discount rate % na 10 10.95 10.95 na na
Capacity MW na 5-25 5-25 na na
LCOE USD/MWh na 44 69.3-81.4 53.4-62.8 na na
Solar
Thermal
Electricity
Generation
Capital costs/Capex Million USD/MW na na 2.22 2.22 na 2.22
O&M costs USD/MWh na na 14.55 14.55 na 14.55
Capacity factor % na na 23 23 na 23
Discount rate % na na 10.95 10.95 na 10
Capacity MW na na 1 1 na 138
LCOE USD/MWh na na 220.04 165.94 na 151
Biomass
Electricity
Generation
Capital costs/Capex Million USD/MW 1.00 0.996 0.86 0.86 1.13 0.97
O&M costs USD/MWh na 0.05 6.69 6.69 5.80 4.8
Capacity factor % 50-90 67.27 80 80 55.7 70.6
Discount rate % 14 10 10.95 10.95 5-7 10
Capacity MW na 1 1 1 10 - 12 18
Fuel costs USD/MWh na low/high 54.7-66.2 54.68-66.19 10 5.4
LCOE USD/MWh 50-160 49-63 126.1-145.3 119.3-138.4 37.7- 44.6 43
na = data is not available from the corresponding source
Source: Adapted from BNEF (2012b), IRENA (2013a), CERC (2013), BREE (2013), GlobalData (2013) and AETA model
(BREE 2012b)
79
Table A3: LCOE Estimates for RE Generation Technologies in Japan by Source
Technology & Assumption Units
BNEF 2012
BNEF 2013c
IRENA 2013a
IEA/NEA 2010
GlobalData 2013
AETA model BREE 2012b
Onshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 2.4-2.9 0.7-1.6 na na 2.0 2.43
O&M costs USD/MWh na na na na 19.03 19.03
Capacity factor % 15-25 15-25 na na 24 22
Discount rate % 7.5-14 10-20 na na 5-8 10
Capacity MW na na na na 10 100
LCOE USD/MWh 134-380 110-390 na na 86.5-108.1 168
Solar PV
Electricity
Generation
Capital costs/Capex Million USD/MW 1.8-3.1 1.8-3.3 na na 4.5 3.5
O&M costs USD/MWh na 17-49 na na 68.49 50.7
Capacity factor % 14-20 14-20 na na 7.5 13
Discount rate % 7.5-14 na na na 5-8 10
Capacity MW na na na na 1 100
LCOE USD/MWh 121-410 110-410 na na 529.3-677.9 405
Small Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW 1.6-3.9 0.9-3.4 2.02 na na na
O&M costs USD/MWh na na 0.09 na na na
Capacity factor % 23-50 23-50 75.36 na na na
Discount rate % 7.5-14 10-20 10 na na na
Capacity MW na na 20 na na na
LCOE USD/MWh 44-318 50-410 40 na na na
Large Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na na 2.20 na na na
O&M costs USD/MWh na na 0.11 na na na
Capacity factor % na na 68.03 na na na
Discount rate % na na 10 na na na
Capacity MW na na na na na na
LCOE USD/MWh na na 39 na na na
Geothermal
Electricity
Generation
Capital costs/Capex Million USD/MW 2.1-3.8 na na na 2.23 2.71
O&M costs USD/MWh na na na na 4.24 4.24
Capacity factor % 50-75 50-75 na na 90 76
Discount rate % 7.5-14 10-20 na na 5-7 10
Capacity MW na na na na 110 10
LCOE USD/MWh 62-193 90-160 na na 20.7-25.5 57
Biomass
Electricity
Generation
Capital costs/Capex Million USD/MW 2.0-4.9 0.8-4.5 1.57 na 2.27 2.69
O&M costs USD/MWh na na 0.08 na 20.9 10.5
Capacity factor % 67-77 67-77 71.5 na 31 58
Discount rate % 7.5-14 10-20 10 na 5-7 10
Capacity MW na na 1 na 55 18
Fuel costs USD/MWh na na na na 10 5.4
LCOE USD/MWh 79-230 60-230 52-66 na 81.3-94.3 91
na = data is not available from the corresponding source
Source: Adapted from BNEF (2013b & 2014a), IRENA (2013a), IEA/NEA (2010), GlobalData (2013) and AETA model (BREE
2012b)
80
Table A4: LCOE Estimates for RE Generation Technologies in Indonesia by Source
Technology &
Assumption Units
BNEF
2012
BNEF
2013c
IRENA
2013a
IEA/NEA
2010
GlobalData
2013
AETA model
BREE 2012b
Onshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 2.6 na 2.6-4.6 2.33-4.08 1.70 2.78
O&M costs USD/MWh 31 na 31-59 30.3-53.1 18.48 34.05
Capacity factor % 22 na 20 20 21 21
Discount rate % na na na 3 5-8 10
Capacity MW 20 na 20 20 10 100
LCOE USD/MWh 172 na 189-331 115.5-201.8 84.1-105.1 216
Offshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 5.38 na na 3.30-8.16 4.25 5.12
O&M costs USD/MWh 81.8 na na 28.7-70.8 27.72 53.09
Capacity factor % 30 na na 30 35 32
Discount rate % 7.5 na na 3 5-8 10
Capacity MW 7+ na na 150 10 100
LCOE USD/MWh 430 na na 109.6-269.4 126.1-157.6 283
Solar PV
Electricity
Generation
Capital costs/Capex Million USD/MW 3.55 na 4.55-7.15 4.08-6.41 3.0 4.41
O&M costs USD/MWh 67 na 69-109 102.6-143.5 29.78 77.2
Capacity factor % 12 na 12 12 11.50 12
Discount rate % na 3 5-8 10
Capacity MW 3.3 na 1.2 1.2 1 100
LCOE USD/MWh 401 na 499-785 351.1-534.2 241.1-308.8 562
Small
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na na na 9.33-11.66 6.59 na
O&M costs USD/MWh na na na 149.3-164.5 18.48 na
Capacity factor % na na na 60 61 na
Discount rate % na na na 3 5-7 na
Capacity MW na na na 0.2 15 na
LCOE USD/MWh na na na 222.8-256.6 98.7-117.8 na
Large
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na 8.39 na 9.91 na na
O&M costs USD/MWh na 36.11 na 25.7 na na
Capacity factor % na 45 na 45 na na
Discount rate % na 10 na 3 na na
Capacity MW na 19 na 12 na na
LCOE USD/MWh na 281.5 na 123.63 na na
Geothermal Electricity Generation
Capital costs/Capex Million USD/MW na na na 8.16-10.5 na 9.33
O&M costs USD/MWh na na na 53.6-66.5 na 60.1
Capacity factor % na na na 80 na 80
Discount rate % na na na 3 na 10
Capacity MW na na na 30 na 10
LCOE USD/MWh na na na 107.3-135.3 na 244
Biomass
Electricity
Generation
Capital costs/Capex Million USD/MW na na na 3.5-4.67 2.65 3.37
O&M costs USD/MWh na na na 52.5–60.6 13.05 34.8
Capacity factor % na na na 80 57.9 69
Discount rate % na na na 3 5-7 10
Capacity MW na na na 5 30 18
Fuel costs USD/MWh na na na 127.1-283.4 10 5.4
LCOE USD/MWh na na na 202.9-375.5 53-61.1 121
na = data is not available from the corresponding source; *BNEF 2014 provides data for Offshore wind technology, and BNEF 2012 provides
data for other technologies.
Source: Adapted from BNEF (2012c & 2014b), IEA/NEA (2010), NPU (2011), METI (2011), GlobalData (2013) and AETA model (BREE
2012b)
81
Table A5: LCOE Estimates for RE Generation Technologies in South Korea by Source
Technology &
Assumption Units
BNEF
2012
BNEF
2013c
IRENA
2013a
IEA/NEA
2010
GlobalData
2013
AETA model
BREE 2012b
Onshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 1.9-3.3 na 1.52 na 2.00 2.04
O&M costs USD/MWh na na 10 na 20.76 15.38
Capacity factor % 20-30 na 42 na 22 30
Discount rate % na na 10 na 5-8 10
Capacity MW na na > 5MW na 10 100
LCOE USD/MWh 86-234 na 101 na 94.4-118 109
Offshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW 4.3-6.1 na na na 4.0 4.6
O&M costs USD/MWh na na na na 16.31 16.31
Capacity factor % 26 - 38 na na na 28 30
Discount rate % na na na na 5-8 10
Capacity MW na na na na 30 100
LCOE USD/MWh na na na na 148.3-185.4 233
Solar PV
(fixed)
Electricity
Generation
Capital costs/Capex Million USD/MW 2.5-3.3 na 6.11 na 2.50 3.84
O&M costs USD/MWh na na 15.93 na 21.95 18.94
Capacity factor % 10-15 na 17.92 na 13 14.5
Discount rate % na na 10 na 5-8 10
Capacity MW na na 1 na 1 100
LCOE USD/MWh 182-437 na 470 na 177.7-227.6 365
Small
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na na 2.76 na na na
O&M costs USD/MWh na na 0.18 na na na
Capacity factor % na na 51.49 na na na
Discount rate % na na 10 na na na
Capacity MW na na 20 na na na
LCOE USD/MWh na na 76 na na na
Large
Hydro
Electricity
Generation
Capital costs/Capex Million USD/MW na na 2.32 na na na
O&M costs USD/MWh na na 0.15 na na na
Capacity factor % na na 53.17 na na na
Discount rate % na na 10 na na na
Capacity MW na na na na na na
LCOE USD/MWh na na 31 na na na
Biomass
Electricity
Generation
Capital costs/Capex Million USD/MW na na 4.28 na na 4.28
O&M costs USD/MWh na na 0.27 na na 0.27
Capacity factor % na na 54.79 na na 54.79
Discount rate % na na 10 na na 10
Capacity MW na na 1 na na 18
Fuel costs USD/MWh na na na na na 5.4
LCOE USD/MWh na na 151-165 na na 120
na = data is not available from the corresponding source
Source: Adapted from BNEF (2012d), IRENA (2013a), GlobalData (2013) and AETA model (BREE 2012b)
82
Table A6: LCOE Estimates for RE Generation Technologies in Australia by Source
Technology &
Assumption Units
BNEF
2012
BNEF
2013c
IRENA
2013a
IEA/NEA
2010
GlobalData
2013
AETA model
BREE 2012b
Onshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW na 1.98-2.13 na na 1.73 2.53
O&M costs USD/MWh na 15.64-18.85 na na 13.93 24.02
Capacity factor % na 30-42 na na 28.4 38
Discount rate % na 8.1 na na 5-8 10
Capacity MW na na na na 10 100
LCOE USD/MWh na 76.2-107.6 na na 63.3-79.1 116
Offshore
Wind
Electricity
Generation
Capital costs/Capex Million USD/MW na na na na na 4.45
O&M costs USD/MWh na na na na na 34.83
Capacity factor % na na na na na 40
Discount rate % na na na na na 10
Capacity MW na na na na na 100
LCOE USD/MWh na na na na na 194
Solar PV
Electricity
Generation
Capital costs/Capex Million USD/MW na 2.09 na na 2.20 3.38
O&M costs USD/MWh na 12.94-19.42 na na 14.43 13.59
Capacity factor % na 14-21 na na 17.4 21
Discount rate % na 9.60 na na 5-8 10
Capacity MW na na na na 2 100
LCOE USD/MWh na 149.5-224.7 na na 117.1-150 224
Geothermal
Electricity
Generation*
Capital costs/Capex Million USD/MW na na na na na 7.0
O&M costs USD/MWh na na na na na 27.5
Capacity factor % na na na na na 83
Discount rate % na na na na na 10
Capacity MW na na na na na 10
LCOE USD/MWh na na na na na 154
Solar
Thermal
Electricity
Generation*
Capital costs/Capex Million USD/MW na 2.27-9.50 na na na 4.92
O&M costs USD/MWh na 13.6-15.5 na na na 44.8
Capacity factor % na 21-65 na na na 23
Discount rate % na 10.2 na na na 10
Capacity MW na na na na na 138
LCOE USD/MWh na 172.3-342.7 na na na 347
Biomass
Electricity
Generation
Capital costs/Capex Million USD/MW na 4.18 na na 2.65 5.0
O&M costs USD/MWh na 24.6-41.58 na na 29.06 25.84
Capacity factor % na 40-80 na na 26 80
Discount rate % na 10.20 na na 5-7 10
Capacity MW na na na na 30 18
Fuel costs USD/MWh na 2.84-5.15 na na 10 5.4
LCOE USD/MWh na 128.5-220.9 na na 107.9-125.5 128
na = data is not available from the corresponding source
*Note: Assessed Hot Sedimentary Aquifer technology for geothermal electricity in Australia. Assessed Parabolic Through w/o
Storage technology for solar thermal electricity. AETA model does not provide Hydroelectricity costing data.
Source: Adapted from BNEF (2012b), IRENA (2013a), CERC (2013), BREE (2013), GlobalData (2013) and AETA model
(BREE 2012b)
83
Table A7: LCOE ranges and BREE’s LCOE estimates for RE technology in APREA countries, 2013
Technology Units China India Indonesia Japan South
Korea
Australia
Onshore Wind US$/MWh Range 46-126 50-150 86-390 84-331 86-234 63-116 BREE 90 71 168 216 109 116
Offshore Wind US$/MWh Range 91-240 na na 109-430 148-233 na BREE 187 na na 283 233 194
Solar PV (fixed) US$/MWh Range 69-283 78-220 110-678 241-785 177-470 117-225 BREE 184 146 405 562 365 224
Small Hydro US$/MWh Range 25-116 30-100 40-410 98-257 na na Midpoint 46 64 134 174 76 na
Large Hydro US$/MWh Range 23-52 44-82 na 123-282 na na Midpoint 36 62 39 203 31 na
Biomass (wood
waste)
US$/MWh Range 28-132 37-160 52-230 53-376 120-165 107-221 BREE 56 43 91 121 120 128
Geothermal US$/MWh Range na na 20-193 107-244 na na BREE na na 57 244 na 154
Solar Thermal US$/MWh Range na 151-220 na na na 172-347
BREE na 151 na na na 347
na = data not available for corresponding technology/country
Source: Adapted from BNEF, IRENA, IEA/NEA, GlobalData, CERC, NPU, METI, and BREE. Data on second line of each cell
represents LCOE from BREE’s AETA model
84
7 References
AEMO (Australian Energy Market Operator) 2012, Carbon price – market review, AEMO,
Melbourne, November.
AEMO (Australian Energy Market Operator) 2013a, 100 per cent renewables study-
modelling outcomes, AEMO, Melbourne, July.
AEMO (Australian Energy Market Operator) 2013b, South Australian electricity report,
AEMO, Melbourne, August.
AEMO (Australian Energy Market Operator) 2013c, Integrating renewable energy – wind
integration studies report, AEMO, September.
AER (Australian Energy Regulator) 2012, State of the energy market 2012, AER, Canberra,
December.
Bird, L. and Milligan, M (2012), Lessons from large-scale renewable energy integration
studies, Presented at the 2012 World Renewable Energy Forum, Denver, Colorado, May 3-
17.
BREE (Bureau of Resources and Energy Economics) 2013a, 2013 Australian energy
statistics, BREE, Canberra, July.
BREE (Bureau of Resources and Energy Economics) 2013b, Beyond the NEM and the SWIS:
regional and remote electricity in Australia 2011-12, BREE, Canberra, October.
BREE (Bureau of Resources and Energy Economics) 2012a, Australian Energy Technology
Assessment (AETA), Publication of the Bureau of Resources and Energy Economics,
Department of Industry, Australia
BREE (Bureau of Resources and Energy Economics) 2012b, LCOE results derived from the
Australian Energy Technology Assessment (AETA) model, Department of Industry,
Australia
BNEF (Bloomberg New Energy Finance) 2014a, Southeast Asia LCOE update, 4 February
2014.
BNEF (Bloomberg New Energy Finance) 2014b, What tariff will Japan offer offshore wind?
Japan Wind – Analyst Reaction, 15 January 2014.
BNEF (Bloomberg New Energy Finance) 2013a, H2 2013 China Market Outlook, 18 July
2013.
BNEF (Bloomberg New Energy Finance) 2013a, H2 2013 China Market Outlook, Bloomberg
New Energy Finance, 18 July 2013.
BNEF (Bloomberg New Energy Finance) 2013b, Southeast Asia LCOE update: is solar
winning the game in the region? Renewables – Asia – Research Note, 10 April 2013.
BNEF (Bloomberg New Energy Finance) 2013c, Australia LCOE update: wind cheaper than
coal and gas, Asia & Oceania – Clean Energy – Research Note, 31 January 2013.
BNEF (Bloomberg New Energy Finance) 2013d, Fire starter? The long-awaited
announcement for China PV subsidy. Bloomberg New Energy Finance, 6 September 2013.
BNEF (Bloomberg New Energy Finance) 2013e, Should China’s underperforming wind
turbines be replaced? Bloomberg New Energy Finance, 1 August.
BNEF (Bloomberg New Energy Finance) 2013f, Understanding China’s power market:
present and future, Bloomberg New Energy Finance, 30 July 2013.
BNEF (Bloomberg New Energy Finance) 2013g, H2 2013 Japan market outlook insight call,
12 December 2013, retrieved from Bloomberg New Energy Finance 16/12/2013
85
BNEF (Bloomberg New Energy Finance) 2012a, China’s provincial cost of energy: a race
between old and new, Renewable Energy – Research Note, 6 July 2012.
BNEF (Bloomberg New Energy Finance) 2012b, Understanding costs and returns for India’s
renewables, Renewables – India – Research Note, 3 July 2012.
BNEF (Bloomberg New Energy Finance) 2012c, In need of competition: Japan’s cost of
renewable energy, Renewable Energy – Japan – Research Note, 6 March 2012.
BNEF (Bloomberg New Energy Finance) 2012c, In need of competition: Japan's cost of
renewable energy. Bloomberg New Energy Finance, March
BNEF (Bloomberg New Energy Finance) 2012d, Is Korea’s renewable portfolio standard
achievable? Renewable Energy – Research Note, 23 August 2012.
BNEF (Bloomberg New Energy Finance) 2012e, Can anything be done to solve China’s grid
connection problems with wind?, Bloomberg New Energy Finance, 15 June 2012.
BNEF (Bloomberg New Energy Finance) 2012f, Transmission transition: China’s changing
of the grid, Bloomberg New Energy Finance, 26 November 2012.
CEA (Central Electricity Authority, Ministry of Power, Government of India) 2013a, Large
Scale Grid Integration of Renewable Energy Sources – Way Forward, November 2013.
http://www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf
CEA (Central Electricity Authority, Ministry of Power, Government of India) 2013b, Power
Supply Position (Revised), February 2013. http://cea.nic.in/welcome1.html
http://cea.nic.in/monthly_power_sup.html
http://cea.nic.in/reports/monthly/gm_div_rep/power_supply_position_rep/energy/Energy_2
012_12.pdf
CEC (Clean Energy Council) 2013, Clean Energy Council submission to Economic
Regulation Authority inquiry into microeconomic reform in Western Australia, CEC,
Melbourne.
CEC (Clean Energy Council) 2012, Energy storage in Australia, commercial opportunities,
barriers and policy options, Marchment Hill Consulting, Nov.
CER (Clean Energy Regulator) 2013, About the Renewable Energy Target, CER, Canberra,
January
CERC (Central Electricity Regulatory Commission) 2013, Central Electricity Regulatory
Commission Petition No. 243/SM/2012 (Suo-Motu), India
Cheung, K, 2011, Integration of Renewables: Status and challenges in China, IEA, Paris.
CNREC (China National Renewable Energy Centre) 2013, High RE scenarios for China in
2050, Renewable Energy, Volume 2, Number 2, July, 35-40.
Cochran, J., Bird, L., Heeter, J., and Douglas J. 2012, Integrating Variable Renewable Energy
in Electric Power Markets: Best Practices from International Experience, NREL/TP-
6A00-53732, April.
Denholm, P., 2010, The role of energy storage with renewable electricity generation.
National Renewable Energy Laboratory. January.
Deo, P. (Chairman, Central Electricity Regulatory Commission, India) 2009, Spoke on
National action plan on climate change and the role of electricity regulator to mark the
fifth foundation day of Pune-based World Institute of Sustainable Energy (WISE). Pune,
India. January.
Department of Finance WA 2013, Generation, Department of Finance Western Australia,
website, <http://www.finance.wa.gov.au/cms/content.aspx?id=15085> accessed
28/10/2013
86
DLA Piper (2012) Renewable Energy in the Asia Pacific: A Legal Overview, DLA Piper, 2nd
edition
Electranet, AEMO, 2012, Heywood Interconnector RIT-T PADR, AEMO, September
Elliston B. MacGill I. Diesendorf M. 2013, Least cost 100% renewable electricity in the
Australian National Electricity Market, Elsevier, Energy Policy, vol. 59, p270-282, August
2013, accessed via Science Direct database 25/10/3013
Elliston B. MacGill I. Diesendorf M. 2012, Simulation scenarios with 100% renewable
electricity in the Australian National Electricity Market, Elsevier, Energy Policy, vol. 45,
p606-613, June 2012, accessed via Science Direct database 30/05/3013
ESAA (Energy Suppliers Association of Australia) 2013, Who pays for solar?, ESAA,
Melbourne
ESAA (Energy Supply Association of Australia) 2010, Australian electricity markets, Energy
Supply Association of Australia, Melbourne, June
GlobalData (2013), GlobalData database.
Gonzalez-Longatt, F.M. 2012, Impact of synthetic inertia from wind power on the
protection/control schemes of future power systems: Simulation study, 2nd
International
Symposium on Environment Friendly Energies and Applications (EFEA), Newcastle, UK:
Northumbria University, June, p389-395
GWEC (Global Wind Energy Council) 2013, Global Wind Report Annual market update
2012, April, Brussels.
Holttinen, H; Meibom, P.; Orths, A.; van Hulle, F.; Lange, B.; O’Malley, M.; Pierik, J.;
Ummels, B.; Tande, J.O.; Estanqueiro, A.; Matos, M.; Gomez, E.; Söder, L.; Strbac, G.;
Anser Shakoor; Ricardo, J.; Smith. J.C.; Milligan, M.; Ela, E. (2009). Design and
Operation of Power Systems with Large Amounts of Wind Power. Final report, IEA WIND
Task 25, Phase One 2006–2008. Vuorimiehentie, Finland: VTT. http://www.vtt.fi/inf/pdf/tiedotteet/2009/T2493.pdf.
http://www.mapsofindia.com/maps/india/power-grid.html; Accessed on 16 April 2014.
Hu Y., and Cheng H., 2013, Development and Bottlenecks of Renewable Electricity
Generation in China: A Critical Review, Environmental Science & Technology, 47,
3044−3056.
Huo M., and Zhang D., 2012, Lessons from photovoltaic policies in China for future
development, Energy Policy, 51, 38–45.
IEA (International Energy Agency) 2013a, Renewable Energy: Medium-Term Market Report,
OECD/IEA, Paris.
IEA (International Energy Agency) 2013b, Japan: electricity and heat for 2011, IEA/OECD,
website
<http://www.iea.org/statistics/statisticssearch/report/?country=JAPAN=&product=electrici
tyandheat&year=Select>, accessed 20/12/2013
IEA (International Energy Agency) 2012, Renewable Energy: Medium-Term Market Report,
OECD/IEA, Paris.
IEA (International Energy Agency) 2011a, Harnessing variable renewables: a guide to the
balancing challenge, IEA Publications, France, p38
IEA (International Energy Agency) and ERI (Energy Research Institute) 2011, China Wind
Energy Development Roadmap 2050, OECD/IEA, Paris.
87
IEA (International Energy Agency) / IRENA (International Renewable Energy Agency) 2013,
IEA/IRENA Joint Policy and Measures Database, available at
http://www.iea.org/policiesandmeasures/renewableenergy/
IEA/NEA (International Energy Agency and Nuclear Energy Agency) 2010, Projected Costs
of Generating Electricity, 2010 Edition.
IEC (International Electrotechnical Commission) 2012, White Paper on Grid integration of
large-capacity Renewable Energy sources and use of large-capacity Electrical Energy
Storage, October, Geneva.
IMO (Independent Market Operator) 2013, Electricity statement of opportunities, IMO,
appendix 8, Perth, June
IMO (Independent Market Operator) 2006, Opportunities for renewable energy and demand
side management to participate in the wholesale energy market, IMO, Perth, August
IRENA (International Renewable Energy Agency) 2013a, IRENA Renewable Cost database
2013, Germany.
IRENA (International Renewable Energy Agency) 2013b, Renewable Power Generation Cost
in 2012: An Overview. Germany
IRENA (International Renewable Energy Agency) 2013c, Renewable Energy Auctions in
Developing Countries.
James, G. and Hayward, J., 2012, AEMO 100% renewable energy study, energy storage, EP126455,
Commonwealth Scientific and Industrial Research organisation, Canberra, September.
Junfeng L., Fengbo C., Liming Q., Hongwen X., Hu G., Xiaosheng Y., Wenqian T., Weiquan
W., and Xiuqin L., 2012, China Wind Energy Outlook, GreenPeace, GWEC and Chinese
Wind Power Association.
Kahrl F., Williams J., Jianhua D., and Junfeng H., 2011, Challenges to China’s transition to a
low carbon electricity system, Energy Policy 39, 4032-4041.
KEMA 2013, National Smart Meter Infrastructure Report, report for the former Department
of Resources Energy and Tourism, Canberra, February
King S 2013, Solar power and the electricity death price spiral, Macrobusiness, website
article, < http://www.macrobusiness.com.au/2013/10/solar-power-and-the-electricity-
death-price-spiral/> accessed 27/10/2013
Kurtenbach E., 2013, Japan starts up offshore wind farm near Fukushima, Sydney Morning
Herald, 12 November 2013
Live mint 2014, http://www.livemint.com/Industry/T8wtsN0wljaEvQPKcdYCpK/Indian-
regulator-halts-wind-forecasting-on-inaccurate-result.html, 22 May 2014
MacGill I. 2009, Electricity market design for facilitating the integration of wind energy:
experience and prospects with the Australian National Electricity Market, Energy Policy,
vol. 38, p3180-3191, retrieved from Science Direct 24/10/2013
Marcelino, M., and Porter K., 2013. Operating and Planning Electricity Grids with Variable
Renewable Generation: Review of Emerging Lessons from Selected Operational
Experiences and Desktop Studies. Washington, DC: World Bank.
Marketline (2012) Electricity in Japan, Marketline industry profile, September, retrieved
from Business Source Complete 07/11/13
Martinot E., 2010, Renewable power for China: Past, present, and future, Front. Energy
Power Eng. China, 4(3): 287–294.
88
METI (Ministry of Economy, Trade and Industry) 2011, Reference materials 1 & 2 and
Modelling Assumptions, Inspection Committee’s Report on Validation Costs, December,
Japan
Minchener, A., 2010, Developments in China’s Coal‐fired Power Sector, IEA Clean Coal
Centre.
Ming, Z., Honglin, L., Mingjuan, M., Na, L., Song, X., Liang, W., and Lilin P., 2013a,
Review on transaction status and relevant policies of southern route in China’s West-East
Power Transmission Renewable Energy, 60, 454-461.
Ming, M., Ximei L., Na, L., and Song X., 2013b, Overall review of renewable energy tariff
policy in China: Evolution, implementation, problems and countermeasures, Renewable
and Sustainable Energy Reviews, 25, 260–271.
MNRE (Ministry of New and Renewable Energy) 2013a, Development Impacts and
Sustainable Governance Aspects of Renewable Energy Projects, September 2013
MoP (Ministry of Power, Government of India) 2013a, the Electricity Act, 2003.
http://www.powermin.nic.in/acts_notification/electricity_act2003/pdf/The%20Electricity%
20Act_2003.pdf accessed on 24 October 2013.
MoP (Ministry of Power, Government of India) 2013b, National Tariff Policy 2006.
No.23/2/2005-R&R(Vol.III), January 2006
Morozumi et al., 2008, p630
Nagayama H (2011) Japanese electricity industry: recommendations for restructuring,
Elsevier, Electricity Journal, Vol. 24, Issue 10, December, retrieved from Business Source
Premier
MNRE (Ministry of New and Renewable Energy) 2013b, Jawaharlal Nehru National Solar
Mission. http://www.mnre.gov.in/solar-mission/jnnsm/introduction-2/ accessed on 25
October 2013.
NPU (National Policy Unit) 2011, Cited in BNEF publication (Japan – Analyst Reaction), 6
March 2012.
NREL (National Renewable Energy Laboratory), GTZ (German Technical Cooperation),
REN21 (Renewable Energy Policy Network for 21st Century), and IRADe (Integrated
Research and Action for Development) 2010, Indian Renewable Energy Status Report -
Background Report for DIREC 2010, October 2010.
Parliament of Australia 2013, Clean Energy Legislation (Carbon Tax Repeal) Bill 2013,
Australian Government, November
Piwko R., Meibom P., Holttinen H., Shi B., Miller N., Chi Y.,Wang W., 2012, Penetrating
Insights, IEEE Power & Energy Magazine, March/April 2012, 44 – 52.
Planning Commission (Government of India) 2013, Twelfth Five Year Plan 2013-2017:
Faster, More Inclusive and Sustainable Growth, 2013
Planning Commission (Government of India) 2008, Eleventh Five Year Plan 2007-2012:
Agriculture, Rural Development, Industry, Services, and Physical Infrastructure. 2008
POWERGRID (Power Grid Corporation of India Ltd) 2012, Report on Green Energy
Corridors - Transmission Plan for Envisaged Renewable Capacity, July 2012
Productivity Commission 2011, Appendix E China’s Electricity Generation Sector in Carbon
Emission Policies in Key Economies, Research Report, June, Canberra.
REEEP (Renewable Energy & Energy Efficiency Partnership) 2009, Policy and Regulatory
Review - Special Report on India and Indian States, 2009
89
Regional News (2013) Japan's "Third Arrow" aims for renewables, energy storage,
Renewable Energy World, Jul/Aug2013, Vol. 16 Issue 4, p20-20. 2/3p, retrieved from
Energy & Power Source 08/11/2013
REN21 (Renewable Energy Policy Network for 21st Century) 2013, Renewables 2013 Global
Status Report, 2013
REN21 (2013) Renewable Global Futures Report, REN21
Schuman S., and Lin A., 2012, China’s Renewable Energy Law and its impact on renewable
power in China: Progress, challenges and recommendations for improving
implementation, Energy Policy, 51, 89–109.
SHAKTI (Shakti Sustainable Energy Foundation) 2013, Contribution of Renewable Power
Towards Eliminating Shortages and Meeting Economic Growth Aspirations, February
Shrimali, G. 2013, In India, Renewable Energy Certificates are missing the target, February
2013. http://blogs.isb.edu/cems/2013/08/01/in-india-renewable-energy-certificates-are-
missing-the-target/ accessed 19 February 2014.
State Grid Corporation of China (SGCC) 2010, White Paper on Green Development,
available at
http://www.sgcc.com.cn/images/ywlm/socialresponsiility/whitepaper/2013/04/17/7A02C5
CDE3D3E493C6F0BE2D96183783.pdf.
Syed, A. (Bureau of Resources and Energy Economics) 2013, Australian Energy Technology
Assessment 2013 Model Update, Canberra, December
Syed, A. 2012, Australian energy projections to 2049-50, BREE, Canberra, December
The Times of India 2014, Indian power system becomes largest operating synchronous grid
in the world, The Times of India, January 2014;
http://timesofindia.indiatimes.com/india/Indian-power-system-becomes-largest-operating-
synchronous-grid-in-the-world/articleshow/28294988.cms Accessed: 16 May 2014
Wang Q., 2010, Effective policies for renewable energy – the example of China’s wind power
– lessons for China’s photovoltaic power, Renewable and Sustainable Energy Reviews, 14,
702-712.
Wang Z., Qin H., and Lewis J., 2012, China’s wind power industry: Policy support,
technological achievements, and emerging challenges, Energy Policy 51, 80–88.
Yang M., Patiño-Echeverri D., and Yang F., 2012, Wind power generation in China:
Understanding the mismatch between capacity and generation, Renewable Energy, 41,
145-151.
Zhang S., Andrews-Speed Ph., and Zhao X., 2013, Political and institutional analysis of the
success and failures of China’s wind policy in Energy Policy 56 (2013) 331-340.
Zhao X., Wang F., and Wang M., 2012a, Large scale utilization of wind power in China:
Obstacles of conflict between market and planning, Energy Policy 48, 222-232.
Zhao X., Zhang S., Yang R., Wang M., 2012b Constraints on the effective utilization of wind
power in China: An illustration from the northeast China grid, Renewable and Sustainable
Energy Reviews, 16, 4508–4514.
Zhao X., Wu, L., Zhang S., 2013, Joint environmental and economic power dispatch
considering wind power integration: Empirical analysis from Liaoning Province of China,
Renewable Energy, 52, 260-265
Zhao Z., and Chang R., 2013, How to implement a wind power project in China? -
Management procedure and model study, Renewable Energy, 50, 950-958.
