ISRN LUTMDN/TMHP-14/5307-SE

ISSN 0282-1990

Micro-generation in local power grids

Balancing intermittency with energy storage

and demand response

Karin Hansson och Sara Olsson

Examensarbete på Civ.ingenjörsnivå

Avdelningen för energihushållning

Institutionen för Energivetenskaper

Lunds Tekniska Högskola | Lunds Universitet

Karin Hansson

Sara Olsson

Division of Efficient Energy Systems, Department of Energy Sciences

Lund University - Faculty of Engineering

2014-06-17

Micro-generation

in local power grids

Balancing intermittency with energy storage

and demand response

Föreliggande examensarbete på civilingenjörsnivå har genomförts vid Avd. för Energihushållning, Inst för

Energivetenskaper, Lunds Universitet - LTH samt vid E.ON Elnät Sverige AB i Malmö. Handledare på E.ON Elnät

Sverige AB: Alf Larsen; handledare på LU-LTH: prof. Jurek Pyrko; examinator på LU-LTH: dr Patrick Lauenburg.

Projektet har genomförts i samarbete med E.ON Elnät Sverige AB

Examensarbete på Civilingenjörsnivå

ISRN LUTMDN/TMHP-14/5307-SE

ISSN 0282-1990

© 2014 Karin Hansson och Sara Olsson samt Energivetenskaper

Energihushållning

Institutionen för Energivetenskaper

Lunds Universitet - Lunds Tekniska Högskola

Box 118, 221 00 Lund

www.energy.lth.se

i

Abstract

Global climate change has resulted in a need for an energy transition from fossil fuels

towards renewable energy sources. Small scale power production, e.g. micro-generation

from solar and wind, is an increasing part in this transition. These energy sources have a

varying power output which does not always match the demand. This intermittent power

generation poses challenges for the electricity grid which is conventionally dimensioned

according to a rather predictable load.

There are several ways to adapt the grid to these renewable and fluctuating energy sources;

namely by curtailment of the generation, reinforcements and extensions of the grid, demand

response and/or energy storage. This report has focused on how demand response and

energy storage can balance the fluctuations in a local power grid with a high penetration of

micro-generation from photovoltaics and small wind turbines. To answer this, both a

literature study and a case study of a planned city-district in Malmö, i.e. Hyllie, have been

performed.

Main results are that the load from micro-generation in a residential area will significantly

exceed the demand at certain occasions, mainly during noon in summer. If the area consists

of a mix of commercial and residential loads, the capacity limits of the grid will not be

exceeded. The most promising solutions to handle loads that exceed the capacity of a local

grid are batteries and critical peak pricing. Currently, and likely in the near future, batteries

are considerably more expensive than grid extensions. Also, the ownership of energy

storages is limited for a grid operator.

Recommendations for the future is to account for micro-generation when planning a local

grid with undiversified demand profiles as the production can exceed the demand and hence

the grid capacity.

Keywords

Micro-generation, DSO, energy storage, demand response, power variations

ii

Sammanfattning

Den globala klimatförändringen har lett till ett behov av en energiomställning från fossila till

förnybara energikällor. Småskalig elproduktion, såsom mikroproduktion från sol och vind,

spelar en allt större roll i denna omställning. Dessa energikällor ger en varierande

elproduktion som inte alltid överensstämmer med efterfrågan. Denna intermittenta

elproduktion innebär utmaningar för elnätet som konventionellt är dimensionerat enligt en

ganska förutsägbar belastning.

Det finns flera sätt att justera elnätet till dessa fluktuerande energikällor, nämligen; styra ned

produktionen, förstärka eller bygga ut elnätet, laststyrning och/eller energilager. Denna

rapport har fokuserat på hur laststyrning och energilager kan balansera variationerna i ett

lokalt elnät med en hög andel mikroproduktion från solceller och småskaliga vindkraftverk.

För att undersöka detta, har både en litteraturstudie av möjliga lösningar, samt en fallstudie

av en planerad stadsdel i Malmö, d.v.s. Hyllie, utförts.

De viktigaste resultaten från denna studie är att belastningen från mikroproduktionen i ett

bostadsområde väsentligt kan komma att överstiga efterfrågan vid vissa tillfällen,

huvudsakligen mitt på dagen under sommartid. Om området däremot består av en

blandning av bostäder och kommersiella verksamheter, kommer belastningen inte att

överskrida kapacitetsgränsen i nätet. De mest lovande lösningarna för att hantera laster som

överstiger nätkapaciteten i ett lokalt elnät är batterier och kritisk topp-prissättning. För

närvarande, och troligen inom den närmsta framtiden, är batterier betydligt dyrare än

nätutbyggnad. Dessutom är ägandet av energilager begränsat för nätägaren.

Rekommendationer för framtiden är att mikroproduktion bör tas i beaktning vid

planeringen av ett lokalt elnät med bostadslast, då produktionen i detta fall kan överstiga

nätkapaciteten.

Nyckelord

Mikroproduktion, nätägare, energilager, laststyrning, effektvariationer

iii

Preface

This report is a master thesis of 2 x 30 ECTS credits performed during the completion of the

MSc in Environmental Engineering at the Faculty of Engineering LTH. Energy system has

been the specialisation of the authors’ Master’s program. The work, which has been

performed on behalf of E.ON, is in line with the company’s strategy of cleaner and better

energy. The thesis was carried out under supervision from Alf Larsen, E.ON Elnät Sverige

AB and prof. Jurek Pyrko, Lund University.

iv

Acknowledgements

We would like to thank our supervisors Alf Larsen at E.ON and prof. Jurek Pyrko at

Lund University, Faculty of Engineering for guiding and pushing us in the right direction

during the process. We are also grateful to Anders Gustafsson and Patrik Vukalic, both at

E.ON, for assistance in understanding how the electricity grid is planned and operated.

Ingmar Leiβe has shown a great commitment and has been a valuable support concerning

all electro-technical issues. Remigiusz Pluciennik and his associates at E.DIS in Germany

and PhD Lars Henrik Hansen at DONG Energy in Denmark have hosted our study visits

and provided eye-opening experiences from other countries. We would also like to thank

Magnus Hjern, John Blomsterlind, Anna Lundsgård, Magnus Lindström and Peder Berne

at E.ON for indispensable input and data.

Last but not least, many thanks to all the staff at E.ON Elnät in Malmö for a friendly

reception and a valuable experience!

v

List of abbreviations

CAES – Compressed air energy storage

CPP – Critical peak pricing

DR – Demand response

DSO – Distribution System Operator

EV – Electric vehicle

Li-ion – Lithium ion

NaS – Sodium sulphate

Pb-acid – Lead acid

PV – Photovoltaic

RES – Renewable energy source

SEA – Swedish Electricity Act

SMES – Superconducting magnetic energy storage

SvK – Svenska Kraftnät

T&D – Transmission and distribution

TSO – Transmission System Operator

V2G – Vehicle to grid

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Content

1 Introduction ................................................................................................................................... 1

1.1 Purpose .................................................................................................................................. 1

1.2 Research questions ............................................................................................................... 2

1.3 Methods ................................................................................................................................. 2

1.4 Constrains .............................................................................................................................. 3

2 Background ................................................................................................................................... 5

2.1 Micro-generation .................................................................................................................. 5

2.1.1 PV .................................................................................................................................... 6

2.1.2 Wind power ................................................................................................................... 8

2.2 The Swedish power grid .................................................................................................... 10

2.2.1 Microgrid ..................................................................................................................... 11

2.2.2 Balance responsibility ................................................................................................ 12

2.3 Load and generation duration curve ............................................................................... 12

2.4 Power quality ...................................................................................................................... 14

3 Literature study .......................................................................................................................... 15

3.1 Energy storage possibilities ............................................................................................... 15

3.1.1 Mechanical storage ..................................................................................................... 16

3.1.2 Electrical storage ......................................................................................................... 19

3.1.3 Electrochemical storage ............................................................................................. 19

3.1.4 Chemical storage ........................................................................................................ 23

3.1.5 Ownership and regulations regarding energy storage ......................................... 23

3.1.6 Market trend for energy storage ............................................................................... 24

3.2 Demand response ............................................................................................................... 26

3.2.1 Tariffs ........................................................................................................................... 27

3.2.2 Capacity markets ........................................................................................................ 29

3.2.3 Direct load control ...................................................................................................... 31

3.2.4 Electric vehicles for demand response .................................................................... 31

3.2.5 Regulations regarding demand response ............................................................... 32

3.2.6 Market trend for demand response ......................................................................... 32

3.3 Comparison of balance methods ...................................................................................... 33

vii

3.3.1 Energy storages ........................................................................................................... 33

3.3.2 Demand response methods ....................................................................................... 40

3.4 Current situation in Germany .......................................................................................... 43

3.4.1 Die Energiewende ...................................................................................................... 43

3.4.2 The perspective from a German DSO ...................................................................... 44

3.5 Concluding remarks ........................................................................................................... 45

4 Case study of Hyllie ................................................................................................................... 47

4.1 Description of the area ....................................................................................................... 47

4.2 Method ................................................................................................................................. 47

4.2.1 Model for simulations ................................................................................................ 48

4.3 Results and analysis of simulations ................................................................................. 49

4.3.1 Scenario 1 – A conventional grid .............................................................................. 49

4.3.2 Scenario 2 – A grid with alternative solutions ....................................................... 57

4.3.3 Economic feasibility ................................................................................................... 62

4.4 Concluding remarks ........................................................................................................... 63

5 Discussion .................................................................................................................................... 65

5.1 Future prospects ................................................................................................................. 65

5.2 Uncertainty parameters ..................................................................................................... 66

5.3 Recommendations for further studies ............................................................................. 67

6 Conclusions ................................................................................................................................. 69

References ............................................................................................................................................ 70

Appendix A ......................................................................................................................................... 75

Appendix B .......................................................................................................................................... 76

Scenario 1 – A conventional grid .............................................................................................. 76

Scenario 2 – A grid with alternative solutions ....................................................................... 77

Standard deviation of the residential demand ....................................................................... 77

Appendix C ......................................................................................................................................... 78

1

1 Introduction

The global climate change is one of the greatest concerns of today and numerous measures

have been taken to reduce our emissions of greenhouse gases. In Europe, the so called

20-20-20 targets have been implemented in order to tackle the situation. These targets specify

that, until 2020, the greenhouse gas emissions shall reduce by 20%, that 20% of EUs’ energy

utilisation shall be renewable and that the energy efficiency shall increase by 20%.

Consequently, a transition from centralised fossil power production to renewable

decentralised production has been initialised.

The transition towards renewable energy sources (RES) as wind and solar, is however not

effortless. This is especially due to the intermittency, i.e. the varying abundance of these

resources. These variations cause problems in the electricity balance as the supply at all times

must match the demand. The imbalance between power generation and supply can result in

waste of renewable electricity at times of over-production and shortage, which might be

compensated by fossil power at times of under-production.

Renewable intermittent production also results in new challenges for the power grid, which

conventionally is designed to transport a rather constant electricity load from centralised

generation plants to the end-users. Intermittent, decentralised power production, e.g. micro-

generation, can cause bottlenecks in the power grid. Historically, these bottlenecks have

been solved by reinforcements in the grid, so called transmission and distribution (T&D)

upgrades. However, the increasing implementation of renewable power sources has raised

the interest in alternative solutions to accomplish the same results, which are possible by

storing energy or altering the electricity demand pattern, i.e. demand response (DR) (Eyer

and Corey, 2010).

This study has been initialised by E.ON Elnät in order to investigate and evaluate different

methods for energy storage and DR to match the local electricity demand with intermittent

micro-generation.

1.1 Purpose

The purpose of this study was to investigate: 1) of what magnitude future load variations

from micro-generation and demand can be in a local distribution grid, and 2) if energy

storage and/or DR are methods able to balance these variations in order to avoid or defer

T&D investments.

This study aims to serve as a survey for how a grid could be more flexible in order to handle

a future scenario with a high penetration of micro-generation. In order to fulfil the purposes,

the district Hyllie in Malmö has been used for simulations of load variations.

2

1.2 Research questions

The following questions have been in focus and are answered throughout the report:

How does a typical load profile look like for a local grid, with residential and

commercial activities, e.g. Hyllie, as well as a high penetration of micro-

generation?

Is the conventional electricity grid dimensioned to handle micro-generation or

does further measures have to be taken in the future?

What are the most promising sustainable energy storage possibilities for a local

grid with a high penetration of micro-generation?

Which DR method is the most suitable for peak reductions?

Are DR and/or energy storage enough to balance the intermittent micro-

generation?

Can energy storage be an economically viable solution compared to

reinforcements in the grid?

How are the solutions regulated and how does this affect the implementation of

the solutions?

1.3 Methods

In co-operation with E.ON Elnät Sverige AB, this master thesis has been accomplished at

Lund University – Faculty of Engineering LTH. In order to support the study, information

has been retrieved by semi-structured interviews with experts, as well as recent publications

within the field and at study visits to Dong Energy and IBM in Denmark and E-DIS in

Germany. These countries have been visited to gain knowledge of how a high penetration of

renewable energy affects the grid and to get an understanding of the challenges and possible

solutions to these.

The information has been processed in a literature study, consisting of a comparison of

different storage and DR technologies according to technical, financial and environmental

aspects. This results in a suggestion for a distributed system operator (DSO) of suitable

storages and DR methods for peak reduction in local grids.

To investigate of what magnitude the future load variations from micro-generation and

demand can be of in a local distribution grid a case study was performed. The district Hyllie

has been chosen for the case study as it has highly set environmental and energy ambitions

to 2020 and therefore, a high level of micro-generation is expected in the district. Hourly load

data from grid-connected photovoltaics (PVs) and multi-family dwellings were received

from E.ON Elnät Sverige AB. Production data from small-scale wind turbines has been

calculated based on wind metering from SMHI and a power curve from the turbine supplier.

Data processing was executed in Microsoft Office Excel with qualified assumptions to

visualise the load profiles and power variations that potentially can occur in a local grid in

Sweden year 2020.

3

1.4 Constrains

A macro-perspective with an economic, environmental and technical approach and a time

perspective of 2020 has been considered. The report has been conducted from the viewpoint

of a DSO, accordingly E.ON Elnät Sverige AB. Hence, not how a photovoltaic system or

wind turbine should be designed or positioned from a household’s perspective. The work is

restricted to the impact from micro-generation facilities in a local grid. A planned tax

reduction in Sweden has set the limit for the size of the studied micro-generation units.

Further, the Electricity Act and market rules in Sweden have been regarded when evaluating

balance solutions. Curtailment of renewable energy during times of excess electricity has not

been investigated further as the aim is to maximise the utilisation of renewables. Overall,

sustainable development permeates the reasoning and analysis throughout the report.

4

5

2 Background

In this section, the concept and technologies of micro-generation are explained. Also, the

structure of the Swedish power grid and the theory behind load and generation duration

curves are briefly described in order to facilitate the understanding of the following analysis.

2.1 Micro-generation

Micro-generation is commonly described as small-scale production of electricity to cover the

customers own electricity needs. In the Swedish Electricity Act, SEA (1997:857), micro-

generation is mentioned in the paragraph regarding small-scale production, 4th chap. 10 §,

and here defined as a production unit with a maximum output power of 43.5 kW and a fuse

subscription of maximum 63 A. However, a tax reduction proposal from the government of

Sweden is currently on remittance and is supposed to be decided during autumn 2014. This

proposal suggests a tax reduction of 0.6 SEK/kWh for a maximum production of 30 000

kWh/year for electricity production units with a fuse of up to 100 A (The Swedish

Government, 2014). If this becomes reality, it will mean that the producer can get a tax

reduction of up to 18 000 SEK/year. An interpretation made in this report is that the

proposed subsidy consequently will lead to that the legal definition for micro-generation will

change to 100 A which corresponds to 69 kW (according to Ohm’s law; P = U * I, where

U = 3*230 V and I = 100 A).

The most common systems for micro-generation are PV and smaller wind turbines, but it can

also be small combined heat and power plants (micro-CHP) as well as small hydro power

plants (Svensk Energi, 2011). The methods for micro-generation of electricity that are

relevant for this study are of renewable and intermittent nature, as can be seen in Figure 1.

A high penetration of micro-generation connected to the local grid can cause power

variations in the grid due to the weather-dependent and unpredictable production. The

variations can result in time periods of electricity scarcity or excess electricity production. In

general, the renewable intermittent energy sources with the highest excess production are PV

followed by wave power and onshore wind power (Lund, 2006).

6

Figure 1: The classification of electricity generation used in this report. After (Larsson & Ståhl, 2012, Svensk

Energi, 2011, The Swedish Government, 2014).

2.1.1 PV

Photovoltaic, abbreviated to PV, refers to the process where sunlight is converted into

electricity (Wenham et al., 2012).

The PV technology

Semiconductors, such as silicone, are common in PVs as they are stable and act insulating at

low temperatures but as good conductors at higher temperatures. When a semiconductor is

hit by light with enough energy, electrons can be released and act as charge carriers for an

electric current. This is called the photovoltaic effect and is the main principle for PV.

(Wenham et al., 2012)

The efficiency for silicon based PVs under laboratory conditions is approximately 25%.

Despite this, the commercial cells have a significantly lower efficiency which ranges between

13 – 19%. However, research continues in order to improve efficiency, lifetime and costs, but

also to develop PVs of other semiconductors and materials, such as organic polymers, i.e.

plastics. Even so, there is presently an accepted theoretical limit for the efficiencies which is

about 30%. (Wenham et al., 2012)

Power variations caused by PV

The output power from PV depends on the irradiation of the sun. The irradiation at a certain

location varies inter-annual, annual and diurnal (time of day). The inter-annual variations

are caused by the Milanković cycles, which describe the slow variations in solar irradiation at

the surface of the Earth caused by the changes of its motion around the sun and around its

own axis (Wenham et al., 2012). Annually, the production is highest in summer and lowest in

winter. Diurnally, the peak power output from PV is generally at noon. There is also a power

Renewable

Wind power

PV

Wave power

Bio power

Hydro power

Intermittent

Wind power

PV

Wave power

Micro (max. 69 kW, max. 100 A)

Wind power PV

7

variation caused by overcast, i.e. shadowing by clouds, which depends on weather

conditions.

In order to gain the highest energy output, the PVs should have an azimuth, i.e. the angular

distance from the south, of 0° and have a tilt from the horizontal plane of 90°- α, where α is

the latitude of the location (Wenham et al., 2012). Hence, the optimal PV tilt in Malmö is 35°.

To achieve a higher output during the winter season, a more vertical angle is preferable. To

modify the peak power output during the day, the azimuth angle can be altered.

In Sweden, the instantaneous power output can amount to 150 W/m2. The annual electricity

production ranges between 50 and 150 kWh/m2 (Svensk Solenergi, 2014).

Market trends

The expansion of PV has rushed forward the last 10 years, and it will most likely continue to

do so. The technology has been improved and the prices have declined, mainly as a

consequence of increased demand due to subsidies in countries such as Germany and

Denmark (Lindahl, 2013). The declining price trend of PV in Sweden is presented in Figure 2.

Figure 2: The Swedish price trend for typical turnkey PV systems (excluding VAT) reported by Swedish

installation companies (Lindahl, 2013).

Today, the only subsidy for PV in Sweden is a contribution of 35% of the installation cost. A

future tax reduction, as mentioned above, would facilitate for private customers to get a

reasonable payback of the investment. These subsidies, together with an increasing

environmental awareness as well as decreasing PV prices, increases the incentives of

producing own renewable electricity. Hence, market trends point towards that PV

installation in Sweden will most probably increase with a remarkable rate until 2020, see

Figure 3 (Lindahl, 2013). Notable is also that it is the distributed, grid-connected installations

that increases the most.

8

2.1.2 Wind power

Wind power is the conversion of wind energy into a useful form of energy, such as using

wind turbines to make electrical power

The wind turbine technology

Wind energy is the kinetic energy of air in motion, also called wind. The wind energy in an

open-air stream is proportional to the third power of the wind speed. Hence, the available

power increases eightfold when the wind speed doubles. It is thus important to place a wind

turbine at a location with high average wind speeds. Wind turbines can be classified into

horizontal axis wind turbines or vertical axis turbines, see Figure 4. The most common

design is horizontal axis wind turbines with three blades. (Manwell et al., 2009)

Figure 4: Horizontal and vertical axis wind turbines. After (EcoWatch Canada, 2013).

Small wind turbines, also referred to as distributed wind, are wind turbines installed on-site,

most often at homes or commercial buildings, which allows the facility to generate a portion

or all of the electricity demand from the wind. The definition of small wind turbines (SWTs)

has been ever changing and differs between countries. Generally, the term describes wind

Figure 3: The cumulative installed PV and yearly installed capacity trends in Sweden (Lindahl, 2013).

9

turbines with a rated power from 6 W to 300 kW. In order to cover the annual electricity

demand of a European household, a 4 kW wind turbine is needed. (World Wind Energy

Association, 2012)

Micro-generation by wind can be performed with either horizontal axis wind turbines or

vertical axis wind turbines. However, horizontal axis turbines are the most commercial and

mature technology whereas vertical axis turbines are still under development. Nevertheless,

micro-generation by vertical axis wind turbines will, in many cases, be advantageous in

comparison to horizontal axis wind turbines. This is due to the Swedish building laws, which

state that building permits are not required for small-scale turbines with a total height less

than 20 m and rotor diameter less than 3 m. As the power output is depending on the sweep

area, it is with a vertical axis turbine design possible to increase the sweep area for the same

rotor diameter by increasing the length of the turbine blades. As mentioned, for small-scale

applications, the vertical axis turbine can be favourable because of the higher power

production and the fact that it is independent of wind directions. Furthermore, they are more

silent in operation, which is a desirable feature for urban utilisation. Horizontal axis turbines,

however, are more widespread on the market than the vertical axis turbines. (Pyrko, 2014)

Power variations caused by wind power

The output power from wind turbines depends on the wind speed, which varies with the

height above the ground and the landscapes roughness due to e.g. forests and buildings etc..

Combined, these effects cause a constantly varying pattern of winds across the surface of the

Earth as well as turbulence. The wind at a certain location varies inter-annual, annual,

diurnal (time of day) and in short-term (turbulence and gusts). Inter-annual and short-term

variations of wind speed occur randomly and are therefore difficult to predict. Annually, the

available wind power is higher during winter months compared to summer months. The

diurnal variation in available wind power is typically an increase during the day and

decrease during the hours from midnight to sunrise. The largest diurnal variations occur in

spring and summer and the smallest in winter. (Manwell et al., 2009)

Market trends

Wind turbines have evolved greatly over the last 35 years and had a strong resurgence

during the 1990s with installed worldwide capacity increasing over fivefold. During this

decade, a shift towards larger, megawatt-sized, wind turbines and a growth of offshore wind

power was observed. The evolution period is however not yet over and meanwhile the best

onshore wind sites have already been exploited, the need for efficient small-scale wind

turbines increases. For a time, the expansion of wind power has mainly consisted of larger

scale turbines, but a possible increase in small-scale wind power is to be expected (Manwell

et al., 2009). Increasing fossil fuel prices, global warming and the ever-growing electricity

demand will be the three long-term drivers of the small wind industry. The World Wind

Energy Association has observed an annual 35% market increase for small wind turbines

during recent years and based on a conservative assumption, they expect the small wind

10

market to continue to grow with a rate of 20% from 2015 to 2020, see Figure 5 (World Wind

Energy Association, 2013).

Figure 5: World market forecast to 2020 for small wind turbines (World Wind Energy Association, 2013).

2.2 The Swedish power grid

The national grid in Sweden is owned by Svenska Kraftnät (SvK), which is a state-owned

company. Electricity in the voltage range of 220 – 400 kV is transferred from the largest

producers to the regional distribution grid, see Figure 6. At the regional grids, which are

owned by the larger system operators, the DSOs, e.g. E.ON Elnät Sverige AB, the electricity

is transported at 40 – 130 kV. The high voltage levels are due to the fact that losses are lower

at higher voltages. In the next step, the voltage is transformed down to the local grid with

medium voltage level; 10 – 20 kV. Thereafter in the local grid, the voltage is further lowered

to 400 V, before entering the dwellings, where the voltage reaches the customers at 230 V. To

larger electricity consumers, such as industries, the voltage can be delivered at higher

voltages. (E.ON, 2012)

11

Figure 6: The Swedish electricity grid structure. After (E.ON, 2012).

The power grid structure can be regarded as a monopoly as it is impossible for the customer

to choose which network company they want to deliver their electricity. This monopoly,

which is geographically divided between approximately 170 electricity grid operators, is due

to the national economic inefficiency in constructing and maintaining parallel infrastructure.

However, the grid operators are monitored by the Swedish Energy Markets Inspectorate

(Ei). This authority controls that the grid operators follow the Electricity Act. This includes

regulation and controlling of the revenues, but also that the fees charged by the network

company are fair and reasonable. (Swedish Energy Markets Inspectorate, 2013)

2.2.1 Microgrid

A microgrid, also commonly written µGrid, is an electricity grid that is capable of operating

in parallel with, or islanded from the existing utility’s grid. The Microgrid Exchange Group

defines a microgrid as a group of interconnected loads and distributed energy resources

within clearly defined electrical boundaries that acts as a single controllable entity with

respect to the grid. Microgrids can be seen as modern, small-scale versions of the centralised

electricity system. Various types of distributed energy resources together with customer

demand, creates varying load profiles that are balanced by energy storage systems within the

microgrid. (Fu et al., 2013)

Microgrids are often established to achieve local energy or environmental goals set by the

community. Even though, it can also be the DSO or the residents demanding for the

microgrid. A microgrid has the potential to maximise overall system efficiency, power

quality, and energy security for critical loads. Microgrids are envisioned to be

environmentally friendly and a promising way of building net zero energy communities,

12

which have the ability to supply themselves in the event of a grid outage. This is crucial for

critical infrastructures, such as hospitals, public facilities, military bases, and emergency-

response facilities (Fu et al., 2013). An example of an existing microgrid which operates in

parallel with the utility grid is UC San Diego Campus, which self-generates approximately

90% of annual demand with CHP, PV and fuel cells (UC San Diego, 2014-04-04).

Microgrids can be seen as an alternative approach to integrate small-scale distributed energy

resources into low-voltage electricity systems. According to Navigant Research, the

microgrid market corresponded to 10 billion USD in 2013 and is projected to increase to

more than 40 billion USD annually by 2020. (Fu et al., 2013)

2.2.2 Balance responsibility

It is essential that the supply at all times matches the use of electricity. It is stated in the 8th

chap. 7 - 10 §§ that an electricity consumer is obliged to ensure that there is someone who is

financially responsible for that the national electricity system is supplied with as much

electricity that is removed at the customer’s tap point, which is measured by the DSO. This

financial obligation is called balance responsibility and lies with the electricity supplier who

can take the responsible itself or assign it to another party, through an agreement with the

transmission system operator (TSO). The TSO SvK also answers for the balance

responsibility of the national system as whole.

The balance responsible electricity suppliers place orders at the Nord Pool Spot day-ahead

market (termed Elspot) for the amount of electricity that their customers are expected to use.

If the forecasts would change, supplementary trade is possible at the intra-day market Nord

Pool Elbas. If there still are, despite the efforts of the balance responsible actors, imbalances

between purchased and use of electricity, this results in a necessity of balance power

provided by SvK. The greater the imbalances are, the greater costs for the balance

responsible party. (Fritz, 2012)

Hence, planning is of utmost importance for the actors with balance responsibility.

Intermittent electricity generation from PV and wind can cause difficulties for the balance

planning due to their somewhat stochastic nature. Also, measures for DR and energy

storages can cause planning problems as it is hard to predict the consumers’ response to

different signals (Fritz, 2012).

2.3 Load and generation duration curve

A duration curve shows the number of times during a period that a certain level is exceeded.

The duration curve can have numerous applications but the ones relevant to this report are

load and generation duration curves. The curve is constructed by organising the estimated or

measured load or generation values over a period of time, in descending order, see Figure 7.

(Vaessen, 2013)

13

Figure 7: Example of a typical load duration curve for electricity consumption during one year. After (Söder,

2013).

For the DSO, the load and generation curves play an important role when dimensioning the

power grid. The cable capacity in a grid is often dimensioned after the peak power, i.e. the

maximum point of the duration curve, marked in Figure 7. However, the maximum capacity

is seldom fully utilised, which is also implied by the figure, where this capacity only is used

a few hours a year (Söder, 2013). There is also a certain “built-in” flexibility in the grid which

makes it compatible with loads over 100% of the capacity. For example, a typical cable

designed for 5 MW loads can handle 6 MW. This situation is, nevertheless, undesirable as it

shortens the life length of the cables due to wear. A benefit with the built-in flexibility is that

an investment in grid reinforcement, or a so called T&D upgrade, might not always be

necessary if there are limited loads over 100% of the cable capacity which occur seldom.

(Vukalic, 2014)

However, more flattened load and generation duration curves would be more economic

beneficial, both regarding the grid investment but also for the customer connection fees.

(Söder, 2013)

There are several ways to optimise, and thereby flatten the load and generation curve.

Curtailment of wind and solar power production, energy storage and different types of DR

are examples of ways to affect and optimise the curves. (Vaessen, 2013)

Curtailment

Curtailment of wind and solar power production can be an option when too much electricity

is generated from these sources relative to the cable capacity in the grid and the current

demand. Already today, curtailment occurs in areas with high shares of fluctuating power

generation such as offshore wind power (Klinge Jacobsen and Schröder, 2012). When

renewable energy is curtailed, the maximum point in the generation duration curve is

14

lowered. As wind and solar power are energy production sources with low greenhouse gas

emissions it is preferable to, as far as possible, avoid down-regulation of these. Therefore,

curtailment of renewable energy production is not evaluated further in this report.

Energy storage

Energy storages that can store electricity from times of excess power production and low

demand to times with low power production and high demands, e.g. on a calm evening,

could facilitate a more even and flatter load and generation duration curve (Chen et al.,

2009).

Demand response

DR can be defined as intentional alterations in the energy demand at the end users as a

response to an external signal. The concept can include both changes in the total

consumption but also the timing of the load (Albadi and El-Saadany, 2008). DR that results in

changed load timing can affect the load duration curve and possibly create a more flattened

curve with a decreased peak power.

2.4 Power quality

Apart from the previously mentioned variations in electricity production from micro-

generation, which not always coincides with the demand, the intermittency of micro-

generation can also have consequences for the power quality in the local grids.

As stated in the Electricity Act (1997:857) 3 chap. 9 §: “the transmission of electrical power

should be of good quality”. Voltage variations, of both shorter (e.g. flicker) and longer

durations, as well as asymmetry, can be caused by power fluctuations from micro-generation

and hence lower the power quality. A poor power quality can result in wearing or breakage

of the connected electrical appliances. Despite this, a DSO cannot refuse anyone to connect

micro-generation (if they fulfil the requirements) to the grid. However, the owner of the

planned micro-generation facility has to receive permission from the DSO before the facility

can be connected to the grid. In this way, the concerned DSO can ensure that the power

quality remains at a high level even after the connection. This might cause a necessity of

reinforcements in the grid. (Svensk Energi, 2011)

15

3 Literature study

In this part of the report, the current status of the research within the field of opportunities

for stabilising intermittent power in the distribution grid will be scrutinised. The aim is to

scan the areas of energy storage and DR, in order to find viable and appropriate solutions

that can be implemented in the local grid until 2020. The literature study will begin with

brief descriptions of the possibilities and applications, as well as the legislations that regulate

them. Later on, these solutions will be further analysed regarding their viability and

sustainability. The chapter will end with a prospect from Germany and some concluding

remarks for the whole literature study.

3.1 Energy storage possibilities

Neither the technology nor the applications for using energy storages in the power grids are

novel innovations (Pieper and Rubel, 2012). However, the opportunities for energy storages

are rapidly emerging. This development have mainly been driven by the expectations for an

increased penetration of renewables and its consequences, such as the growing market for

electric vehicles (EVs), the increased interest in smart grids and DR, as well as the finical

risks coupled to T&D investments. The recent emergence of energy storage opportunities has

led to improvements in storage performance as well as cost reductions and an increased

recognition by the regulators regarding the role energy storage might play in the future

electricity market. (Eyer and Corey, 2010)

There are already many existing technologies for energy storage; some mature and some still

under development. The different technologies are in most reviewed studies classified

according to their storage method, namely:

Mechanical storage

Electrical storage

Electrochemical storage

Chemical storage

Thermal storage

The primary requirements of the energy storage technologies that are necessary to fulfil the

purpose and limitations of this report, e.g. within the time-frame of 2020, are:

Technologies that both store and deliver energy in the form of electricity

Technologies suitable for a local scale, meaning both power rating (0 - 10 MW) and

spatial measures

In Table 1, different energy storage methods are described regarding fulfilment of the

primary requirements.

16

Table 1: Energy storage possibilities and their ability to fulfil the main requirements. After (Larsson & Ståhl,

2012)

Type of storage Energy storage Fulfils primary requirements

Electricity to electricity Local scale

(<10 MW)

Mechanical Pumped hydroelectric Yes No

Compressed air energy

storage (CAES)

Yes Yes

Flywheels Yes Yes

Electrical Superconducting

magnetic energy storage

(SMES)

Yes Yes

Electrochemical Lead acid battery Yes Yes

Sodium sulphur battery

(NaS)

Yes Yes

Lithium ion battery Yes Yes

Flow battery Yes Yes

Fuel cells Yes Yes

Chemical Hydrogen Yes Yes

Methane (biogas) Yes Yes

Thermal Hot water No Yes

Phase-shift No Yes

Melting of salt No Yes

As indicated in Table 1, the thermal storages hot water, phase-shift and melting of salt are

alone unable to deliver electricity back into the electricity grid and will not be analysed

further in this report.

3.1.1 Mechanical storage

Mechanical storage systems can store energy either potentially, e.g. pumped hydroelectric

and compressed air, or kinetically, e.g. flywheels. These three storage systems will be

described in the following subsections.

Pumped hydroelectric

Pumped hydroelectric storage refers to a method where water is pumped up to a reservoir

during off-peak hours, see Figure 8. In this way, potential energy is stored. At peak hours,

the water is released back again to a lower level, passing a turbine and thus generating

electricity. This is a mature technology that has been used since the 19th century, but as the

facility requires two reservoirs situated at different levels in altitude, the technology is better

17

suited for larger storage demands, typically between 100 - 3000 MW. (Chen et al., 2009, Nair

and Garimella, 2010)

Figure 8: The pumped hydroelectric storage system (Kousksou et al., 2014).

Pumped hydroelectric is not suitable on a local scale due to its size and geographical

requirements and therefore not analysed further.

Compressed air (CAES)

The method for compressed air energy storage, or CAES, resembles the principles for gas

turbines. When the storage is charged, electricity is used for compressing air, which then is

stored in a reservoir. During discharge, the high-pressure air expands and drives a

generator, thus releasing the stored energy in the form of electricity, see Figure 9. (Cavallo,

2007)

Figure 9: The compressed air energy storage system (Kousksou et al., 2014).

Even though the principle seems rather simple, account must be taken to the heat which is

formed during the compression of the air. This both lowers the efficiency and limits the

18

capacity for safe storage (Larsson & Ståhl, 2012). To compensate the loss of efficiency, fuels

such as natural gas can be mixed with the compressed air in the discharge cycle (Ferreira et

al., 2013).

CAES is a proven technology and was first installed in 1978 in Huntorf, Germany. This

facility, which is owned by E.ON Kraftwerke, has an installed capacity of approximately 300

MW and utilises two salt caverns as reservoirs for the compressed air (E.ON, 2014).

For small-scale CAES, fabricated tanks situated above ground are proposed as more suitable

as storage reservoirs. Together with electric compressors that can be turned into generators

during discharge, an overall efficiency of approximately 50% can be reached (Ibrahim et al.,

2008). CAES of this type is however not yet a mature technology (Dunn et al., 2011).

Flywheels

In a flywheel, electricity is converted to and stored as kinetic energy, which can be released

as electricity when needed. The kinetic energy is stored in a rotating cylinder which is

supported by magnetic bearings and operates in vacuum to eliminate friction losses, see

Figure 10 (Nair and Garimella, 2010). The principles of the technology have been used for

thousands of years to store energy (Kousksou et al., 2014).

Figure 10: The flywheel energy storage system (Díaz-González et al., 2012).

Flywheels are suitable for medium to high powers discharges (kW to MW) during short

periods (seconds-minutes) with high energy efficiency in the range of 90–95% (Kousksou et

al., 2014). The major advantage of flywheels is their long life time, which makes them able of

providing several hundreds of thousands of full charge–discharge cycles (Chen et al., 2009).

Also, flywheel disposal do not have any significant environmental concerns. However, the

friction losses are high and the cost for installation and maintenance is large for flywheels

19

(Nair and Garimella, 2010). Long-term storage with flywheels is not foreseeable due to these

high friction losses.

The short discharge time of flywheels is beneficial for grid storage applications intended to

regulate and improve the power quality in the grid. Flywheels can also be used to e.g. bridge

the shift from one power source to another, for reactive power support, spinning reserve as

well as for voltage regulation. Demonstration projects prove that flywheels are applicable to

smoothing the output of wind turbine systems as well as for stabilisation in small-scale

island power grids. (Larsson & Ståhl, 2012)

3.1.2 Electrical storage

Electrical storage is either electrostatic including i.e. capacitors and super-capacitors or

magnetic/current including i.e. superconducting magnetic energy storage (SMES). In the

following section SMES is described.

Superconducting magnetic energy storage (SMES)

Magnets made of coils of superconducting cables with almost zero resistance, generally

niobiumtitane cables, can store electrical energy in a magnet field, see Figure 11. The capacity

of SMES is limited only by the rating of the power electronics. Hence, SMES can be suitable

for both smaller and larger scales. Also, the response time is quick and the lifetime, as well as

the efficiency, is high. Despite these advantages, refrigeration of the system is necessary as

the process operates at temperatures around -270°C. (Kousksou et al., 2014)

Figure 11: The principle of SMES systems (Chen et al., 2009).

3.1.3 Electrochemical storage

Electrochemical storage i.e. batteries, stores the electricity in the form of chemical energy and

are the oldest form of storage for electrical energy. The principle for regular batteries is one

or more electrochemical cells containing an electrolytic media and a positive and a negative

charged electrode. A flow of electrons occurs from the negative to the positive electrode due

to electrochemical reactions at the electrodes when the battery is discharged. However, this

process can be reversed when an external voltage is connected between the electrodes. Thus,

the battery can be recharged and used for storage applications. (Chen et al., 2009)

20

There are numerous different types of batteries. The ones that might be suitable for energy

storage in a local grid will be further described below.

Lead acid batteries

The principle of lead acid (Pb-acid) batteries is electrodes consisting of solid lead and lead

oxide. The electrolyte in which the electrodes are submerged in is sulphuric acid. During

discharge, both electrodes form lead sulphate. This technology, which has an efficiency of

about 80%, was invented already in 1859 and is the oldest and most commonly used

rechargeable electrochemical technology (Chen et al. 2009). However, the lead acid batteries

have a low energy density and a poor battery cycle life. The toxicity of lead is a major

environmental disadvantage. Despite this, there are examples where lead acid batteries are

used in power grids to prevent power shortages. The largest facility has a capacity of

40 MWh and is located in California (Larsson & Ståhl, 2012).

Even though the technology is old, there is currently ongoing research to enhance the

performance of the lead acid batteries. Some research focuses on bipolar batteries, which has

a higher battery cycle life and thus a higher tolerance for voltages that shift direction, i.e.

changes between discharge and charge state (Larsson & Ståhl, 2012). This feature can be

desirable for power grid applications as the power load is frequently shifting as a

consequence from demand and production.

Sodium sulphur batteries

The electrodes in sodium sulphur (NaS) batteries consist of liquid forms of sodium and

sulphur, separated by a solid ceramic electrolyte. When discharging, positive sodium ions

flow through the electrolyte to the sulphur, creating sodium polysulphides. To stabilise the

charge balance, electrons from the sodium flow in an external circuit, thus creating a voltage

(Divya and Østergaard, 2009). During charging of the battery, the process is reversed and the

sodium ions are released from the sulphur and can recombine with the liquid sodium. In

order for these reactions to occur, temperatures between 300 – 350 °C are required (Chen et

al., 2009). The cycle life, as well as the power density of NaS batteries, is high and the

efficiency varies between 75 – 90% (Beaudin et al., 2010).

The technology of NaS batteries is developed by the Japanese company NKG Insulators,

Ltd., who supplies battery modules with a rated power of 50 kW (NKG Insulators Ltd.,

2014). These modules can be aggregated into installations with capacities of 300 MW

(Larsson & Ståhl, 2012). According to NKG (2014), these installations only require one third

of the area that the same capacity of lead acid batteries would need.

Despite the high performance of NaS batteries, there are some drawbacks. As the process

operates at high temperatures, a heat source is required which uses some of the stored

energy and thus reduce the efficiency of the battery. Also, the battery is drawn with high

investment costs. (Beaudin et al., 2010, Chen et al., 2009)

21

There are several examples of installed facilities with NaS batteries, mainly in Japan and the

U.S., where they are used both to stabilise wind and solar farms and for peak-shaving

(Beaudin et al., 2010).

Lithium-ion batteries

The lithium-ion (Li-ion) battery consists of a cathode of lithium metal oxide and an anode of

graphitic carbon in layers. The electrolyte is lithium salts dissolved in organic carbonates. It

was Bell Labs in the 1960s that developed the technology and the first commercial lithium

ion battery were produced by Sony in 1990. In comparison with nickel–metal hybrid

batteries, lithium-ion batteries are lighter, hence able of providing more capacity and power

per volume (Chen et al., 2009). Today, Li-ion batteries have taken over a great part of the

market for small portable devices and can be found in laptops, mobile phones etc. The

advantages of the battery are the high energy density and the extremely high efficiency of

over 90% (Larsson & Ståhl, 2012).

The emergence of battery storage in the electricity grid benefits from the strong demand for

batteries in the transport sector. The high price of Li-ion batteries is despite this, the main

challenge for larger applications such as EVs and local grid applications. Another issue is the

highly flammable electrolyte, but the security questions are easier and cheaper to solve for

stationary solutions such as grid storage than for portable applications (Larsson & Ståhl,

2012). Still, due to the risk of fire or explosion, care needs to be taken to protect against over-

charge/discharge, over-current, short circuit and high temperatures (Wenham et al., 2012).

An interesting project, driven by SAFT and SatCon Power Systems in the US, concerns the

design and construction of two 100 kW Li-ion battery energy storage systems to provide

power quality for grid-connected micro-turbines (Chen et al., 2009). In the coming years for

stationary applications, Li-ion batteries in the size of 1 MW or more can be expected (Larsson

& Ståhl, 2012).

Flow batteries

In contrast to conventional batteries, flow batteries store energy in the electrolyte solutions.

The electrolyte flows through a power cell where the chemical energy is converted to

electricity. A tank with electrolyte is externally positioned and the electrolyte is usually

pumped through the cells of the reactor. The reaction is reversible, allowing the battery to be

charged, discharged and recharged. There are three different electrolytes that form the basis

of the existing designs of flow batteries currently in demonstration or in large-scale project

development; vanadium redox (VRB), zinc bromine (ZnBr) and polysulphide bromide (PSB)

batteries. (Chen et al., 2009)

The advantages with flow batteries are high capacity, long lifetime, fast response-time and

high tolerance for over-charge/discharge. The VRB flow battery is beneficial from an

environmental point of view as it have zero emissions, no charging-leakage and does not

give rise to any hazard waste materials (Larsson & Ståhl, 2012). However, the energy density

of flow batteries is rather low and therefore the majority of the development work has

22

focused on stationary applications (Chen et al., 2009). Some larger-scale projects, up to 4

MW, with flow batteries as back-up and for regulation of RES already exist around the

world. In general, the development seems to have stagnated during the last years even

though the experiences from existing projects have been successful (Larsson & Ståhl, 2012).

Fuel cells and Power to Gas

A fuel cell is charged with a fuel (hydrogen, biogas, natural gas, methanol or petrol), unlike

batteries that are charged with electricity. In addition to the fuel, an oxidant, e.g. air, chlorine

or chlorine dioxide, is required in order to generate electricity.

The technology was first discovered in 1838 and have since that been regarded as highly

potential. Even so, it has still not become competitive enough. Fuel cells are at present time a

very expensive storage technology and have a relatively low round-trip efficiency. An

advantage with fuel cells is the wide storage power range of 0 - 50 MW. (Chen et al., 2009)

To be able to store electricity from the grid as well as deliver electricity to the grid, the fuel

cell has to be reversible or combined with other technologies. For example, fuel cells in

combination with electrolysis, which converts electricity to hydrogen, can be considered as

an electrical grid storage system which both stores and delivers electricity (Ibrahim et al.,

2008).

Hydrogen based energy storage

A hydrogen fuel cell uses hydrogen and oxygen to produce electricity and water. A

reversible hydrogen fuel cell uses electricity and water to produce hydrogen and oxygen.

There are two mature and developed technologies for hydrogen storage; hydrogen

pressurisation and hydrogen adsorption in metal hybrids (Kousksou et al., 2014). Hydrogen

is efficient, clean and light but is not found naturally and therefore must be produced from

primary energy sources such as electricity. Hydrogen can be stored but due to its explosive

nature, it is difficult to handle and transport. Therefore, hydrogen storage is more suitable in

isolated areas than in everyday applications (Larsson & Ståhl, 2012).

At present, hydrogen-based energy storage systems are receiving increasing attention,

particularly regarding their integration with RES (Chen et al., 2009). Hydrogen based energy

storage is regarded as one of the most promising technologies in load shifting, proved by

several demo projects with stand-alone systems including wind and PV generation

combined with hydrogen storage.

An example is the Power to Gas demonstration plant owned by E.ON, Windgas

Falkenhagen in Germany, where surplus electricity from the nearby wind power plant is

converted to hydrogen and injected into the high-pressure natural gas grid. From the

commissioning in 2013, the Falkenhagen storage plant produces up to 360 Nm3/h of

hydrogen from about 2 MW wind power, with a total plant efficiency of 58%. The location in

Falkenhagen is strategically as there is both a high penetration of wind power as well as a

high-pressure transmission natural gas pipeline in the area. The project aims to demonstrate

23

effective storage of renewable energy in the form of WindGas within the gas grid. With this

process, it is possible to use renewable electricity when and where it is generated.

(Burmeister, 2014)

The main challenge for hydrogen based storage systems which deliver power at a later

moment, is the high cost of the fuel cells (Kousksou et al., 2014).

Methane based energy storage

A possible solution to make use of excess electricity is to increase the potential of biogas

(Mohseni et al., 2012). The solution is a way to avoid curtailment of renewable electricity

production. Biogas could either be stored and used in fuel cells for electricity production, or

used as renewable fuel in the transport sector, where the demand for green fuels is ever

increasing. Moreover, utilisation of biogas reduces the net emissions of carbon dioxide.

The process is executed by producing hydrogen by water electrolysis and then allowing the

hydrogen react with carbon dioxide, thus forming methane. The benefits arise from making

use of both surplus electricity and upgrading biogas by conversion of carbon dioxide,

normally seen as waste. The potential of making biogas of electricity has been studied by

Byman and Jernelius (2012). They found that the potential of biogas production in Sweden

could be doubled, from 70 TWh to 140 TWh, by utilising excess electricity and without any

addition of new raw material to the biogas digestion process.

This is a well-known technology and there are several climate advantages, but still, the

technology is not economic viable today (Svensk Vindenergi, 2013). It might be an interesting

future possibility, especially for the southern parts of Sweden where the biogas and wind

power potential is large and the power regulation resources are limited. To use this method

as a storage system in the distribution grid, it is necessary to convert the produced biogas

back into electricity again, which is possible with fuel cells or gas turbines.

3.1.4 Chemical storage

Chemical storage concerns hydrogen and methane which, in order to fulfil the primary

requirements of the report (i.e. both store and deliver electricity) have to be combined with

other technologies such as fuel cells, see subsection Fuel cells and Power to Gas under section

3.1.3 Electrochemical storage.

3.1.5 Ownership and regulations regarding energy storage

Integration of energy storage raises the question regarding both possible investors and

operators of the facilities. According to Pieper and Rubel (2012), it is most likely that an actor

from the energy sector will act as operator due to their appropriate experiences. It is also

possible for households to own and operate facilities for storage, which is commonly

discussed as the installations of residential PV increases. From a grid perspective, it is

favorable to locate the storage as close to the generation as possible if the storage is to be

used for balancing over-production (Borg, 2012). This is nevertheless regarded as the most

expensive solution in a national economic perspective as the cost for e.g. a battery is far too

24

high in comparison to using the produced energy immediately and buying the remaining

electricity demand. This suggests that the most possible operators could be municipalities

and independent power producers as well as grid operators. A potential business-case for

increasing the profitability is to use the storage for energy trading, or so called

price arbitrage. However, due to the legislations regarding ownership unbundling, which

stipulates that power production should be separated from power transmission and

distribution, DSOs have limited possibilities to operate energy storages. (Pieper and Rubel,

2012)

As the term “energy storage” is not mentioned in Swedish laws, this causes legal

uncertainties. Stated in the law are however delineations for a DSO. As mentioned above, a

DSO is not allowed to produce or trade with electricity, according to the 3rd chap. 1a § SEA

(1997:857). Exceptions to this paragraph are, even so, possible if the production is strictly

intended to cover grid losses or if it occurs temporarily in order to compensate for lack of

electricity during blackouts. This law makes it problematical for a DSO to own and operate

energy storage if the intention is to balance renewable energy and not only to improve the

power quality or to cover grid losses.

It is however possible for a third party or an energy supplier to own the energy storage and

allowing the DSO to benefit from it. An example is the energy storage owned by the energy

company Falbygdens Energi AB, or short FEAB. The storage, which is the first energy

storage in Sweden, consists of 20 Li-ion batteries with a capacity of 75 kW (ABB, 2014). The

DSO Falbygdens Energi Nät AB, is an affiliated company of FEAB, which uses the storage

for compensation of reactive power and for peak-shaving. The costs for these grid services

are regulated between the companies (Borg, 2014).

3.1.6 Market trend for energy storage

Historically and even today, it has generally been more cost-effective to invest in expanding

the grid rather than in energy storage to meet peak loads. Consequently, the energy storage

experiences are relatively poor and the attitudes of the industry are cautious. The few

present energy storage facilities, which are mainly composed of older technologies such as

pumped hydro and CAES, generally have the purpose of decreasing the vulnerability of

energy supply. Many of these storage plants were the results of the oil embargo and the

expansion of nuclear power. (Larsson & Ståhl, 2012)

The global increase in utilisation of renewable resources for electricity generation, as well as

the development of smart grids, has led to a growing trend for the application of energy

storage in power grids (Koohi-Kamali et al., 2013). Hence, the market for energy storage is

emerging and, according to Larsson & Ståhl (2012), it is estimated to amount to

approximately 10 - 25 billion USD by year 2020, see Figure 12.

25

Figure 12: The expected market trend for energy storage. The different graphs represent the expectations from

different research institutes and consulting companies; Pike Research, SBI, BCC, Visiongain and Boston

Consulting Group (Larsson & Ståhl, 2012).

Electric vehicles

A trend which highly influences the future for energy storage is the ongoing shift from

vehicles powered by fossil fuels towards transports that emit less greenhouse gases. One

candidate among others is electrification of vehicles. Latest announcements and launches of

battery EVs and plug-in hybrid vehicles suggest that a larger number of EVs could be

deployed in the coming years. The EVs are expected to increase in number in large cities due

to the limited driving range and few charging possibilities elsewhere. Consequently, the

charging posts will initially be connected to grids in cities. A study made by

Blomsterlind (2009) demonstrates that a 10% integration of EVs in Malmö by year 2020 will

not affect the power quality in the grid and that new investments in the grid are not

required.

The batteries used in EVs today are Li-ion batteries of varying sizes. An average EV needs to

be charged about 3.5 h each day at 2.3 kW, resulting in a charge of 8.2 kWh if charged from a

standard one phase wall socket, a so called slow charge (Blomsterlind, 2009). Installation of a

fast charging post requires a new grid connection and permission. Different charging speed

classes for EVs are presented in Table 2.

26

Table 2: Charging classifications for a Li-ion battery of 8 kWh (Martinsson, 2009)

Charging class Outlet type Power

(kW)

Charging time

Slow charge 10 A, 230 V, 1 phase

16 A, 230 V, 1 phase

2.3

3.7

3 h 30 min

2 h 15 min

Semi-slow charge 16 A, 400 V, 3 phase

32 A, 400 V, 3 phase

11.1

22.2

45 min

22 min

Quick charge 63 A, 400 V, 3 phase 43.6 12 min

EVs can be seen as energy storage if they transfer power both grid-to-vehicle and vehicle-to-

grid (V2G). V2G contributes to a higher integration of variable renewables than grid-to-

vehicle does, since V2G additionally increases the capacity factors of the power plants.

However, it is currently more profitable to use the electricity for transport than in the power

sector, as it competes with high taxed and expensive conventional transport fuels (Loisel et

al., 2014). An attractive option for end users is vehicle-to-building concepts, especially if

combined with decentralised intermittent renewable generation in the same building.

3.2 Demand response

DR is defined as changes in the electricity usage patterns at the end-user. Albadi and El-

Saadany (2008) has identified three different main end-user responses. The first is that

customers reduce the consumption of electricity at peak periods with high prices, by e.g.

turning down thermostats. During other periods, their electricity usage remains unchanged.

Also the second method includes reduced consumption during peak periods, but this

reduction is later compensated e.g. a delayed washing machine start. Thus, the loads are

moved in time. The third method is onsite electricity generation, e.g. micro-generation,

where the electricity consumption pattern may be unaltered from the end-users’ perspective.

This may however, include significant changes in the usage patterns from utility (e.g. the

DSOs’) perspective.

The programs in order to accomplish these responses can be classified into incentive based

programs and price based programs, see Figure 13.

27

Figure 13: DR classifications and examples of different methods. After (Albadi and El-Saadany, 2008, Aalami

et al., 2010, Pyrko, 2005).

The difference between the two main classifications, shown in Figure 13, is that the price-

based programs are based upon tariffs where the prices for using electricity vary according

to the supply cost (Aalami et al., 2010). The incentive-based programs, on the other hand,

offer the participants some sort of compensation for the DR, such as an electricity bill

discount. If the participant in an incentive based program does not follow the program, the

compensation is defaulted and the participant might be punished (Albadi and El-Saadany,

2008). In this way, the incentive-based DR programs employ more “carrot or stick” methods.

3.2.1 Tariffs

The tariffs, or pricing models, are price-based DR methods used by network operators to get

paid for grid services as well as to control the consumption of energy. This can be performed

by varying low and high prices during different times of the day, week or year. If the price

signals are strong enough, the power usage will be moved by the customers. In this way, an

indirect DR is reached. Important is that the fee for distribution has to be objective and non-

discriminatory, according to 4th chap. 1 § SEA (1997:857).

Time-of-use pricing

The time-of-use pricing offers low and high prices at different hours of the day, week or

year. The network operator can use this type of pricing to restrict the power usage in the

system in order to avoid bottlenecks and overloading of the grid. A network operator that

currently offers time-of-use pricing is Vattenfall. Their pricing model have a higher fee for

distribution during peak load hours (winter Nov-Mar, weekday Mon-Fri and daytime 06-22)

and a lower price at all other hours (Vattenfall, 2014).

28

Load pricing

Load pricing is a way to adapt the pricing to reflect the actual costs. Load pricing gives

incentives for the customer not to use a lot of electricity-driven devices at the same time. If

the customer is active, the demand profile will be evened out. For the local DSO, this means a

lowered power subscription to the overlaying network owner, hence resulting in a lower

cost. A way to design load pricing is that the customer pays an average value for the three

highest measured power peaks during each month. A disadvantage with load pricing is that

a customer with a low and even power usage most of the month and a couple of occasional

peak loads might pay more than a customer that have a high but even power usage. (Pyrko,

2005)

An analysis made by Vattenfall in 2004 studied load pricing based on experiences from

Sollentuna Energi, which has implemented load pricing. In the analysis, three different

customer groups with different fuse subscriptions, namely 16 A, 20 A and 25 A respectively,

were studied. The result was that there were no significant changes in the consumption

pattern and that manual regulation of the load had low priority for the customers. However,

according to the analysis, the potential would be higher with automatic regulation of the

customer’s load. The study also showed that it would be possible for the customer to save

approximately 500 SEK annually by lowering the monthly peak demand. (Pyrko, 2005)

Dynamic pricing

Dynamic pricing includes flexible tariffs that reflect the current or predicted load situation in

the electricity grid.

Critical peak pricing

With critical peak pricing (CPP), the variable fee for distribution is predefined with a higher

price at critical hours to stimulate decreased consumption. The consumer is informed in

advance that the price will be raised during a certain period of time and can choose to react

on the signal. CPP is beneficial as it is easy for the customers to understand. Furthermore, the

customer do not need to be aware of the price at the non-critical hours. This method does not

give any price indications about the load situation during occasions that are not defined as

critical. In Sweden, CPP has been tested in the Market Design-program, which was

initialised year 2000 by Svensk Energi, Ei and Energy Norway. The result was that 20% of the

customers (household customers) accepted the price model and in average, their power

usage was halved at critical hours (Damsgaard and Fritz, 2006).

Real-time pricing

Real-time pricing reflects the actual system and market conditions. The price is hourly

varying and exposed to the customers, who needs to be active and take decisions to move

their loads. This pricing model includes a high degree of flexibility and DR but only works

for customers that have a smart and hourly electricity meter. There is a risk of counteraction

and confusion if both the electricity supply fee and the distribution fee are real-time based.

(Pyrko, 2005)

29

3.2.2 Capacity markets

The deregulated Nordic electricity markets of today are so called energy-only markets,

meaning that the electricity producer only receives payment for the amount of delivered

energy (NEPP, 2011). Hence, all investments in production or flexible demand are based

upon the expected electricity spot prices. In order to ensure the capacity, there are currently

administrative confirmed backup reserves for power and disturbance when the market fails.

These facilities receive compensation whether they are used or not. The aim is however that

this solution will be phased out for a more market-based solution where the available

amount of capacity reserves is optimised. The current legislation regarding backup reserves

will be eliminated until 2020, and the issue is then supposed to be solved by the market

participants. It is important that these reserve markets are separated from the spot market as

the prices at the spot market otherwise would be evened out and hence lowering the

incentives for new investments, both in production and DR. (Fritz, 2012)

Capacity markets are different to the energy-only markets as it in principle means that all

installed capacity has a value, regardless if it is used or not. When the capacity is unused, the

owner is compensated for the capital cost which otherwise would have been covered by the

variable revenue. The compensation, which is based on demand and supply, is settled at an

auction. However, there are certain levels of how much installed capacity that can be

compensated. Otherwise, the electricity consumer might be forced to pay for unnecessary

capacity. (Fritz, 2012)

For a DSO, a capacity market would mean that T&D upgrade investments could be deferred

or avoided completely. This can be achieved by trading flexibility as a commodity on the

capacity market. In order to do so, the DSO must forecast the demand to see when and

where the flexible demand is needed to not overload the grid infrastructure. This can be seen

in Figure 14, where a typical residential load close to the capacity limit of the grid is

presented and where a power cut, i.e. a load reduction due to flexible demand is performed.

30

Figure 14: A load curve where a planned “power cut” is reached through a flexible demand of ∆P (Dybdal

Cajar and Hansen, 2014).

After the DSO has mapped its flexibility demand, a request for flexibility can be submitted to

the market place. An aggregator with a flexibility portfolio, containing contracts with

electricity consumers who are flexible in their demand (e.g. households, industries, EV pools

and community services), can then offer the DSO a bid for a certain flexibility capacity.

Exactly how this market should be implemented and operated is however still uncertain.

The iPower project in Denmark, which runs between 2011 and 2016, is a project aimed to

reduce the business uncertainty regarding the potentials of the flexible market system.

Currently, Dong Energy, who is a partner in the iPower project, tests the control and

operation of the market. In this pilot study, Dong Energy, which acts as both DSO and

aggregator, has flexibility contracts with medium-sized industries and community services

such as water pumping stations. The market based trading platform called FLECH, short for

FLExibilty Clearing House, which currently is under development, is intended to be used as

a market place to facilitate trade with demand flexibility between the aggregator and the

DSO, see Figure 15. (Birke et al., 2013)

31

Figure 15: The concept of FLECH. The grey arrows represent actions taking place at FLECH (Dybdal Cajar and

Hansen, 2014).

Similar to Nord Pool, the flexibility trading at FLECH is divided into two steps. In the first

step, the Capacity Reservation market, a long-term reservation is made to ensure that there

is enough flexibility to cover the DSO’s demand request. If the flexibility capacity proves to

be insufficient, the DSO might be forced to reinforce the grid. The second step constitutes of

a short-term Reserve Activation market where the activation part is scheduled and

contracted. After the operation, where the contracted flexibility should have been dispatched

by the aggregator, the actual delivered flexibility is metered and the aggregator receives

payment from the DSO. (Dybdal Cajar and Hansen, 2014)

The regional transmission organisation PJM Interconnection in the east of the U.S

implemented a capacity market called the Reliability Pricing Model in 2007. Experiences

show that DR and production can compete equally at this market. (JPM, 2014)

3.2.3 Direct load control

Direct load control is the classical incentive based DR program. It is performed by asking

customers if they are willing to participate, meaning that they accept that the DSO at times

can shut down the customer’s equipment for a compensating payment or rate discount. The

remotely controlled equipment, e.g. air conditioner, electric heater or heat pump, can be shut

down on a short notice. Some question marks remain regarding how the DSO should finance

the compensating payment and who should invest in the control equipment needed for

automatic regulation of the demand (Damsgaard and Fritz, 2006).

3.2.4 Electric vehicles for demand response

As the penetration of EVs currently is low, there is no well-developed practice or strategy for

planning the time of charging (Loisel et al., 2014). Although, there are potentials of using EVs

as flexible demand since the charging is movable in time. Optimal would be if charging take

place during the night or during periods of high wind and sun inflows. Nevertheless, this is

today performed on an individual basis which depends on personal behaviour, preferences

and economics. Individual based charging patterns can for example be that many people

32

plug in their EVs when getting home from work, leading to a peak charging at evening time.

As the residential electricity consumption usually is high in the evenings, this load will

coincide with the individual based charging of EVs. To even out the predicted high

electricity withdrawal at peak load hours, the charging can be controlled by the DSO and

thus located to base load hours. An alternative is that an aggregator of EVs can implement

smart strategies for recharging batteries. The results of a German study by Loisel et al. (2014),

demonstrate positive effects of charging batteries in a controlled way. The effects included

enabling an increased penetration of renewables, especially PV, since they generate stronger

daily fluctuations compared to wind variations, which are of shorter duration.

3.2.5 Regulations regarding demand response

The expression demand response is not mentioned in SEA (1997:857). However, DR in the

form of grid tariffs is regulated in chapter 4 in SEA. The grid tariffs has to be objective and

non-discriminating, according to 4th chap. 1 § SEA. That the tariffs shall be non-

discriminating infers that all customers within the same customer-type should be offered the

same tariffs and that no particular customer should be favoured.

Since 2012, Ei regulates the DSOs’ profits by setting revenue cap during each four-year

period. This aims to provide fair tariffs for the customers as the DSOs operates under

monopoly within each grid area. (Swedish Energy Markets Inspectorate, 2012)

According to article 15.4 in the European Union’s Energy Efficiency Directive (2012/27/EU),

network tariffs shall provide signals for optimal energy infrastructure utilisation and power

savings in order to contribute to a higher overall efficiency. This can be interpreted as a legal

instrument to push for demand response.

3.2.6 Market trend for demand response

The trend for pricing models in Sweden, based on a study consisting of interviews with six

DSOs, demonstrates that load-based pricing most likely will be extensively conformed in the

coming years. At the same time, there is an ongoing change towards simpler pricing models

and fewer types of subscriptions. The differences in the pricing models between the DSOs

result in a need for a joint pricing model design standard. Other trends that the participating

DSOs identify to impact the design of future pricing models are increased micro-generation

and reduced energy consumption. (Lydén et al., 2011)

DR has been subject of discussion for a long time but has not yet been realised in larger scale

in Sweden. The actors on the energy market are generally positive to DR. The method is

mature but so far, the market and administrative structure have been insufficient. The

decision taken by the Swedish government of phasing out the backup reserves does create

the necessary basic prerequisites for further progress. Still, the political approach and the

way forward have to be clarified for a more rapidly development. Fully applied DR can in

the future balance the power variations in the range of 1 - 3 hours but it does not solve the

complete need of back up reserves. (NEPP, 2013)

33

In 2011, NEPP stated in a synthesis report regarding capacity mechanisms that there are two

ways to proceed if the aim is to create regulatory incentives for maintaining and investing in

less-frequently used generation and in DR. The two ways are either to stay to the

energy-only market design and add a strategic capacity reserve or to do a fundamental

change that affects all the actors, such as the PJM market design in the US. (NEPP, 2011)

3.3 Comparison of balance methods

Comparisons between the above described methods for balancing demand and intermittent

production are presented below. The aim is to conclude which methods that are the most

suitable for peak reductions in a local grid such as the one in Hyllie, which is further

investigated in the case study.

3.3.1 Energy storages

There are several important features to take into consideration when evaluating the

performance of the energy storages, such as technical, economic and environmental aspects.

Technical aspects

The following technical characteristics of different energy storage methods are presented in

Table 3:

Energy density - The ability to store energy in relation to the volume of the whole

storage system. This criterion is important when space is limited.

Round trip efficiency (RTE) - The relation between the energy input and output.

Power capacity - How much power that can be stored. This capacity is often larger

than the power that actually can be delivered as discharge often is incomplete

Duration - The time during which the energy storage can discharge at its rated

power.

Response time – The time it takes for the energy storage to release or absorb energy

Maturity - The progress phase the energy storage currently is in. This feature is here

graded into three different maturity levels: developing; meaning methods that are

currently under research, pilot; methods that are being tested and demonstrated and

commercial, corresponding to mature methods that already are employed.

34

Table 3: Technical characteristics for the proposed storage methods

Storage

technology

Energy

densitya

(Wh/L)

Efficiencyb

(%)

Power

capacityc

(MW)

Durationd

Response

timee

Maturityf

CAES 3 - 6 40 - 80 3 - 400 h - days 1 - 15 min Commercial

Flywheel 20 - 80 80 - 99 0.25 s - 15 min ms - s Pilot

SMES 0.2 – 2.5 85 - 99 0.1 - 10 ms - 5 min ms Pilot

Pb-acid 50 - 80 70 - 92 0 - 40 s - 3 h ms Commercial

NaS 150 - 250 75 - 90 0.05 - 8 s - h ms Commercial

Li-ion 200 - 500 85 - 90 0 - 0.1 min - h ms - s Pilot

Flow

battery

16 - 60 65 - 85 0.3 - 15 s - 10 h ms Developing

Fuel cells 500 – 3 000 20 - 70 0 - 50 s - days ms - min Developing

a After (Chen et al., 2009) b After (Ferreira et al., 2013) c, f After (Kousksou et al., 2014) d, e After (Chatzivasileiadi et al., 2013)

In order to create peak reductions of the electrical load in a local grid, the energy storage

must fulfil specific spatial requirements as the space often is limited. This is of utmost

importance when the storage is to be situated in a property, such as a residential building or

an office. It is also of great matter if the area is of urban character where land often is

expensive. With regard to spatial measures, CAES and SMES are unsuitable technologies in a

dense area due to their low energy densities, which implies larger spatial requirements.

The efficiency is rather high for all studied technologies except for fuel cells, which are

under development and still has a relatively poor efficiency. It seems unlikely that this

situation will change drastically until 2020.

As power capacities over 10 MW already have been excluded, all methods in Table 3 are in

suitable capacity ranges for a local grid. Even so, CAES, flywheels, SMES and flow batteries

are technologies that might be of too large capacity for a single property with micro-

generation as the maximum installed capacity for micro-generation, according to the

assumptions in the report, is approximately 70 kW, which thus corresponds to the maximum

need for energy storage capacity. However, for balancing a whole district, larger capacities of

a few MW are needed. For this application, even storages with lower capacities can be used if

they are aggregated into storage units.

Energy storages intended to smooth the output from renewable micro-generation requires a

duration time in the range of hours due to the diurnal nature of the generation. Hence,

35

storage technologies such as flywheels and SMES have too short duration times to be

suitable for balancing micro-generation from PV.

All technologies presented in Table 3 have quick response times which is preferable for

unpredictable power supply such as wind power or when PV modules are temporally

affected by overcasting and thus cause rapid variations of the power output. However,

concerning flywheels, both the duration of the discharge as well as the response time is short

which implies that this storage method might be more suited for short-time regulations, such

as voltage regulation.

Flow batteries and fuel cells are still under development and it is unsure whether they are

mature enough until year 2020.

Hence, considering to the technical aspects solely, Pb-acid-, NaS- and Li-ion batteries are the

most suitable storage technologies.

Economic aspects

A key for energy storages to provide economic incentives is the spread between the costs for

charging and the income that can be obtained during discharge. Both the capital costs and

other expenses for maintenance, operation and efficiency losses have to be covered by this

price spread (Pieper and Rubel, 2012). Following aspects concerning the economics of the

energy storages are presented in Table 4:

The cost – the investment cost per power capacity and the cost per energy capacity.

Maintenance – The required maintenance on a scale ranging from 1 - 5, where 1

implies a high maintenance claim and 5 implies no need.

Cycle life - The number of charge cycles the energy storage can provide.

36

Table 4: Economic characteristics for the proposed storage methods

Storage

technology

Power capacity

costa, c

(€/kW)

Energy

capacity costb

(€/kWh)

Maintenancec

Cycle lifed

(cycles)

CAES 290 - 1450 1 - 70 3 30 000+

Flywheel 250 3 620 3 1 000 000

SMES 220 7 230 2 100 000+

Pb-acid 220 290 3 500 - 1 200

NaS 720 - 2 170 220 - 360 3 2 000 - 5 000

Li-ion 2 890 1 800 5 1 000 –

10 000

Flow battery 430 - 1 080 100 - 720 1 – 3 2 000 - 13 000

Fuel cells 2 000 - 6 600 1 - 10 1 1 000 - 10 000

a, b After (Kousksou et al., 2014). USD converted and rounded off to EUR (currency 1 USD = 0.723 EUR

2014-04-25) c After (Chatzivasileiadi et al., 2013) d After (Ferreira et al., 2013)

The power capacity cost and the energy capacity cost differs widely between some

technologies, especially considering flywheels and SMES where the power cost is low

whereas the energy cost is high. The reason becomes clear when regarding the cycle life of

the technologies in Table 4. Also, SMES systems require cooling which further increases the

kWh cost.

The cycle life is a technical factor that influences the economic performance. Many cycle

lives spread the capital cost for the storage which results in a lower cost per kW. It is

important to consider the usage frequency of the planned storage when choosing storage.

The cycle life can be lowered for some technologies by improper or disrupted charging.

Flywheels show a remarkably high number of cycles which is suitable for power quality

regulations.

Regarding the power capacity cost, fuel cells and li-ion batteries are presently the most

expensive options. However, as the technologies continue to develop, e.g. that the efficiency

for fuel cell improves, these costs will probably sink and they might become economic

feasible in the future.

The required amount of maintenance is also a factor that varies between the technologies

and is of great matter as it highly affects the variable costs during the life time of the storage.

In this regard, Li-ion batteries require the least maintenance whereas SMES, flow batteries

and fuel cells requires the most.

37

Among all energy storages studied, lead-acid batteries have the most favourable economic

performance with low costs, both measured per cycle and per useful energy output.

Furthermore, the required maintenance is acceptable.

As most methods for storing electricity still are, and will most likely continue to be rather

expensive in the viable time-frame, it is important to take into account the aggregated

benefits and synergies of energy storages. Even though this report is focused on peak

shaving/valley filling to provide a more constant load even with installed intermittent micro-

generation, there are several synergies from energy storages that can provide a more positive

cost-estimate. As mentioned previously, peak shaving can create possibilities for T&D

deferral which can be seen as a profit due to the interest rate. Furthermore, distributed

storage can, according to Ferreira et al. (2013), provide other benefits in a local network, e.g.:

Demand side management – end-users can minimise their electricity costs by

installing a smaller storage to their micro-generation facility and then buy and sell

electricity at off-peak respectively peak hours

Loss reductions – the efficiency of the network increases as the usage is decreased

during peak load hours and increased during off-peak hours

Area control – preventing unintended transfer of energy from one area to another

Distributed storage might also, to a limited extent, provide contingency and black start

services. For the DSO, the synergies that are the most economic beneficial are the possible

T&D deferral, loss reductions as well as area control. With a higher level of area control, fees

for transferring energy to the overlaying grid can be reduced.

Despite the aggregated benefits that arise from energy storages distributed in the grid, the

investment costs are still high and the DSO might not be able to cover these costs with the

grid tariffs. A possible solution is that either a third party, such as an aggregator, or an end-

user connected to the grid, such as a property owner, owns and operates the energy storage.

As these actors have the legal possibility to trade with electricity, see section 3.1.5 Ownership

and regulations regarding energy storage, these revenues can provide economic incentives. As

the benefits with energy storages are still available for the DSO, the incentives for the storage

owner can increase if the DSO pays a compensation for the acquired benefits. Another

possible solution is if another company within the DSOs’ corporate group, e.g. an electricity

supplier such as E.ON Försäljning Sverige AB, owns the storage and uses it for price

arbitrage, whereas the DSO, e.g. E.ON Elnät Sverige AB rents services from the storage.

However, price arbitrage requires a differentiated, higher electricity price to become viable.

To conclude, the costs for energy storages are very high and currently, the least expensive

alternative is lead-acid batteries. However, as many technologies are under research and

development, the situation might have changed until 2020.

38

Environmental aspects

One major aspect when evaluating the sustainability of different energy storages is their

environmental performance. Otherwise, even though the electricity generation might be

sustainable and environmental friendly, this may be compensated by a high environmental

impact from the energy storage. Also, the environmental friendliness affects the public

acceptance towards energy storage installations. In Sweden and other Nordic countries, the

majority of the population prefers to pay more for sustainable energy production rather than

employing generation with high emissions (Ibrahim et al., 2008). Thus, the environmental

aspects can, to a certain limit, be regarded as more important than the economic aspects.

Even though the social aspects are not investigated further in this report, some safety aspects

must be considered in order to evaluate whether the energy storages can be situated in a

local grid area.

In Table 5, the following aspects are presented for the different storage methods:

Recyclability – The recyclability of the materials used in the storage where high

indicates that most parts of the storage are recyclable and leave small amount of

remains whereas low means poor recyclability.

Metal availability – The abundance of scarce metals measured in both the amount of

available reserves (year 2009) and years left of reserves with constant usage in the

same rate as 2009. Hence, the number of years left of one particular reserve might

change drastically due to alterations in usage.

Emissions – Emissions of greenhouse gases to the atmosphere during usage of the

storage.

Hazardous – Potential health and environmental hazards of the storage, e.g.

flammability, toxicity and magnet fields.

Overall environmental influence – The environmental impact of the storage with all

mentioned environmental aspects taken into consideration.

39

Table 5: Environmental and safety aspects for the proposed storage technologies

Storage

technology

Recyclabilitya

Metal availabilityb

(kton/years)

Emissionsc Hazardousd

Overall

environmental

influencea,d

CAES - (Not

applicable)

- (Not applicable) Yes Flammable/

Explosive

High

Flywheel High Titanium

(5 280/31.8)

No No Low

SMES - (Not

available)

Lead

(79 000/20.8)

Bismuth

(320/55.2)

Barium

(190 000/24.5)

Copper

(550 000/35.0)

Strontium

(6 800/13.3)

Yttrium

(540/60.7)

No Strong

magnet

fields

Medium

Pb-acid

battery

High Lead

(79 000/20.8)

No Flammable/

Explosive

Toxic

(Lead)

Medium

NaS

battery

High Sodium

(3 300 000/6 000)

No Flammable/

Explosive

Low

Li-ion

battery

High Lithium

(4 100/149.6)

No Flammable/

Explosive

Low

Flow

battery

Medium Vanadium

(13 000/216.7)

Zinc

(180 000/15.9)

No Toxic

(Bromine)

Medium

Fuel cells

(Hydrogen)

Medium

Nickel

(70 000/43.5)

Titanium

(5 280/31.8)

Zirconium

(51 000/37.5)

No Flammable/

Explosive

Low

a After (Chatzivasileiadi et al., 2013) b After (Beaudin et al., 2010) c After (Ferreira et al., 2013) d After (Chen et al., 2009)

40

Of the studied storage technologies, CAES has the highest environmental impact, see

Table 5. The reason is mainly the emissions of greenhouse gases during discharge, where

natural gas is combusted. Hence, by using fossil fuels in storage methods, the environmental

benefits from renewable energy production are counterbalanced. Also, utilisation of

compressed gases is drawn with explosion hazards and the storage of these gases can, in

some cases, require natural formations as caverns.

Flywheels, NaS batteries, Li-ion batteries and hydrogen driven fuel cells account for the

lowest environmental impacts among the studied storages. Even so, there are drawbacks

also with these technologies. Several of these employ compounds which are bound to be

scarce in the coming decades with the same consumption rates as today. If these storage

methods become commercialised in larger scales, these rates will most likely increase. Thus,

the number of years we can exploit these reserves will be even further limited. On the other

hand, the recyclability of the storage compounds is medium to high, implying that all new

devices must not originate from primary reserves but from recycled materials. The second-

hand market for energy storages is also likely to grow, especially due to the increased

employment of EVs. This can also cause a reduced cost for installing energy storages such as

Li-ion batteries.

The storage methods characterised by a medium overall environmental impact in Table 5 are

SMES, Pb-acid and flow batteries. In a purely environmental perspective, SMES can be

regarded to have a low environmental impact as it is free from emissions and risks for toxic

leakage. However, SMES induces strong magnetic fields which can pose health hazards for

people living nearby, making it unsuitable for usage in an urban area. Furthermore, SMES

contains numerous compounds with limited availability, such as lead, barium and

strontium.

Both Pb-acid and flow batteries contain toxic ions of e.g. lead and bromine. These ions can

leak to the environment during handling and improper recycling, causing health hazards for

both humans and other organisms. Lead, which is a heavy metal, accumulates in organic

tissue and causes cumulative effects higher up in the ecosystem. These storage technologies

do also contain metals of poor availability.

Conclusions that can be drawn when evaluating the environmental aspects solely, are that

fuel cells, flywheels, Li-ion- and NaS-batteries are the most environmental sustainable

solutions.

3.3.2 Demand response methods

The criterions and approaches when evaluating DR methods differ in comparison to the ones

relevant for energy storage as the environmental aspect is less central and the legal aspect, as

well as the objective of the DR method, is more significant.

41

Tariffs

When comparing different tariffs it is of importance to evaluate if the tariffs will fulfil the

aim of the implementation as well as concerning the legal regulations. In this report, the aim

for the suggested tariffs is peak reductions in order to even out power variations, which is

compared in Table 6. The peak reduction is to be interpreted as demand moved from peak

hours to off-peak hours. An article made by Stromback et al. (2011) is based on results from

100 different DR pilot projects which include 290 000 residential participants worldwide. The

results for Europe are presented in Table 6. Hence, these values are based on customers with

different heating sources. Load pricing was not included in the study and hence, data has

been gathered from a study by Pyrko (2005) where the customers of one Swedish energy

company were participating.

Table 6: Potential peak reduction with different pricing models

Pricing model Peak reduction

(Without

automation)

(%)

Peak reduction

(With

automation)

(%)

Customer

financial

savings

potential

(%)

Critical peak

pricinga

24 31 6

Time-of-Usea 9 16 5

Real-Time-

Pricinga

13 9 13

Load pricingb 5 - (Not available) 10.5 - 18

a (Stromback et al., 2011) b (Pyrko, 2005)

CPP seems to reduce the demand peaks the most among the studied tariffs in Table 6. For

CPP with automation the peak reduction potential is larger than without automation due to

that customers do not always respond to the price signal if it is not automated. To 2020 all

white goods and customer electronics on the market would probably not include a chip for

automated control. Although, Hurley et al. (2013) believes that cost-effective technology for

DR from residential customers is a widespread reality until 2020.

In France, CCP has been in use since 1996 and is available for customers who subscribe for

more than 6 kW. Tempo is a CPP in France that the DSO has the right to impose, on short

notice, when they see that a critical peak is approaching. 1.2% of the grid customers in France

use this tariff, currently mainly families with electric heating that is automated controlled

after the CPP. The peak reduction due to CPP for the entire country during critical days is at

the most 300 MW. The customers are informed through an interface where different colors

correspond to different pricing levels. The highest pricing level is nine times larger than the

lowest level. Tempo requires that the customers, the day before, know what color the next

day will receive. The DSO makes this information available on the Internet at 17:30 the day

before and also sends this information by an email or SMS to the customers who have

42

registered their interest. Moreover, all the Tempo customers have a box in their home which

at 20:00 lights up red, white or blue according to the next day's price range. (Ek and

Hallgren, 2012)

To be considered furthermore, is if the customers are willing to be controlled by the DSO. If

the customer saving potential is large enough, the acceptance towards pricing tariffs with

automation possibly increases. As the customer saving potential is rather low for CPP it may

be easier to implement this pricing model without automation.

The study made by Pyrko (2005) regarding load pricing, states that this tariff only reduces

demand peaks with 5%. However, this tariff is likely to be implemented in 2016 - 2017 by

E.ON as it is regarded as objective and non-discriminatory and therefore the most promising

tariff to accomplish peak-reductions within the legal demands. This tariff necessitates hourly

metering for all participants, which is not presently installed to all customers. Hourly

metering is available on request to all customers, but multi-family dwellings might have to

use templates if they have a common meter for electricity.

Time-of-use tariffs can be suitable when customers have their own batteries (Pieper and

Rubel, 2012). The battery can be charged when the price is low and discharged when the

price is high, hence creating a business-case for the customer. Time-of-use tariffs are by E.ON

regarded as somewhat discriminating as some customers run businesses that are depending

of a certain time period of the day.

In order to reach the aim of the tariffs, it is important that the customers understand the

difference between energy and capacity and the reason to the DSO wants to change the fee

for distribution before launching a capacity based tariff. Therefore, it would be preferable if

all DSOs launch capacity tariffs at the same time in order to facilitate the information

campaigns towards the customers.

Capacity markets

Capacity markets can result in joint benefits for both the network operator and the electricity

supplier. The energy balance can be solved for the electricity supplier whereas the bottleneck

problems can be solved for the DSO. Capacity markets can also enable interactions between

DSOs and aggregators, which could facilitate a breakthrough for DR on a larger scale,

especially as one contract that includes several customers is easier to administrate than one

contract with each customer.

Evaluations made by Hurley et al. (2013) regarding projects in the U.S. demonstrate that the

advance of DR has shown the strongest progress where the participating customers have

received a firm monthly payment. This is because business models based on infrequent

events with high prices have shown to be too risky and unpredictable to rely on to give

incentives for sufficient DR participation. In order to promote both different customers and

types of loads, existence of various streams of revenue have shown to be effective, e.g. to

43

integrate renewable intermittent resources it is favourable if the revenue can reward the

speed and accuracy of the DR.

This reasoning might suggest a tariff such as a CPP with a rebate system. This means that,

during hours of critical peaks, the participating customers are paid for the consumption

quantities that they manage to avoid in comparison to their predicted levels of consumption

(Stromback et al., 2011). The number of occasions, as well as the lengths of these, can in

advance be contracted between the participant and the aggregator. Hence, a CPP with rebate

can be suitable for the capacity market. However, the study by Stromback et al. (2011) states

that CPP without rebate leads to a larger peak reduction than CPP with rebate.

Consideration thus needs to be taken to whether a large number of participants with lower

individual reductions or fewer participants with higher individual reductions are desired in

order to complete the peak reduction.

The customers’ understanding for new tariffs, e.g. CPP, might be enhanced when they are

launched in combination with a capacity market as it allows capacity to be traded as a

regular product.

Even so, contracted DR on a capacity market gives rise to the question as to what will

happen if a customer does not follow the contract during hours of DR activation. Most likely,

electricity customers will not agree to a contract if a risk of punishment exists. Therefore, it is

possible that instead the aggregator will be punished by an indemnity. Thus, it is important

that the aggregator provide adequate attractive incentives, e.g. rebates, to their participants.

Direct load control

Direct load control is a method for DR that is unsuitable for residential customers with

district heating. This is because the demand that is regulated by the DSO should preferable

not affect the residents in an undesired way as the remaining energy demand might be

strongly related to the behaviour. For example, a customer in the middle of the evening

cooking will most likely not prefer if the oven suddenly were to shut down. Due to the

unsuitability to implement direct load control to all customers, indirect load control e.g.

tariffs is to be preferred.

3.4 Current situation in Germany

Currently, Germany is the world's leading country in installed PV capacity and the third

leading country in installed wind power capacity (Masson et al., 2013, Global Wind Energy

Council, 2014). Therefore, a prospect of the situation in Germany might shed some light over

the challenges that can be expected if a high penetration of intermittent generation becomes

reality in Sweden. If not otherwise stated, the following information has been gathered from

a visit to E.DIS AG in Germany, hosted by Pluciennik (2014).

3.4.1 Die Energiewende

In Germany, a unique transition from nuclear power and fossil fuels to RES begun in 2011 in

order to enable for the country to reach its national target of 35% renewable electricity by

44

2020. This transition, known as die Energiewende, has caused a rapid change, especially

regarding nuclear power. Already a month after the decision to implement the transition,

8 GW of nuclear power was shut down. Until 2022, an additional amount of 10 GW will be

closed down. (Steele and Niemi, 2013)

The Energiewende has consequently caused a swift expansion of RES, mainly from PV and

wind. This expansion has also been driven by the feed-in tariffs, which was first introduced

in 1991 and guaranteed the producers of renewable energy a reserved price for their

electricity. This subsidy answered to 80 – 90% of the price for the end-users. As a result of the

fluctuating prices, investors became reluctant to further investments in renewable power

generation. A new regulation for renewable energy and feed-in tariffs, the EEG, was

introduced in 2000. One of the main differences from the previous regulation is that each

type of generation was given a certain feed-in tariff. Furthermore, the EEG feed-in tariff also

depends on the installed power and when the facility was applied for. The tariffs are settled

over a period of 20 years. During this period, the tariffs decrease due to the estimated cost

reductions for the facilities. Because of the long running time as well as the previous

mentioned parameters, there are numerous of different tariffs. (Steele and Niemi, 2013)

3.4.2 The perspective from a German DSO

Because of strong incentives for investments in both smaller and larger renewable electricity

generation due to the feed-in tariffs in combination with the ever decreasing technology

costs, the construction rate of new facilities is extremely high. Neuhardenberg, a PV farm in

the north-east Germany with an installed capacity of 138.9 MW, is one example of this. The

facility was completed in three weeks after the building permit was granted. These fast

construction rates pose challenges for the DSO which, according to the EEG 1 chap. 1 §,

“shall immediately and as a priority connect installations generating electricity from

renewable energy sources”. Interim solutions are however possible and necessary as the

planning and construction of new connections often require several years.

E.DIS AG, which is majority-owned by E.ON, is one of the largest DSOs in Germany and

operates the main part of the gas and electricity network in the north-east region. In this area,

renewable energy from wind and PV accounts for 73% of the total electricity generation. This

corresponds to approximately 7 000 MW installed renewable power. As the regional peak

demand amounts to 2 500 MW, the generation solely from wind and PV is three times higher

than the demand some days. Consequently, the electricity grid has to be dimensioned

according to the power production and not after the demand. The high penetration of

renewables and the requirement to connect these energy sources cause high grid fees for the

customers, even though the main part of the load originates from the electricity producers.

Another consequence with the high share of intermittent generation is the difficulties in

attracting investors to base load and back-up power plants in order to balance the fluctuating

generation.

45

As the power production in this region often generously exceeds the demand, DR is not

regarded as a solution to balance the supply. Therefore, smart meters with higher measuring

resolution for the electricity consumption have not gained a high attention. However, this is

only a regional situation and smart meters will be installed throughout Germany until 2016.

Electricity meters with a low, e.g. monthly resolution, obstruct the implementation of DR

tariffs such as CPP and load pricing as these tariffs are dependent on frequent

measurements.

In order to handle the high loads and avoid overloading the grid, E.DIS operates the grid

according to a regime where grid optimisation has the highest priority. The optimisation of

the grid includes temperature monitoring of the lines and intelligent control of the reactive

power. When grid optimisation is insufficient, reinforcements and extensions of the grid are

necessary. As mentioned, these solutions requires a substantial amount of time and

investments and as the DSO acts on a regulated market, the revenue-cap is not allowed to

include investments in the grid due to future loads. The last option in this regime is

curtailment, where generation units over 100 kW are step-wise curtailed through radio

ripple control according to a network security management program based on the current

load situation.

The load reductions caused by curtailment of the highest peak loads are not proportional to

the loss of energy. A peak reduction of 20% corresponds only to a 3% loss in energy

production in this region. Higher production losses due to curtailment are not economical

and therefore alternative solutions are under investigation. An example of this is the energy

storage project in Falkenhagen, see section 3.1.3 Electrochemical storage. Further pilot projects

with battery storage are planned. However, the costs for batteries are still high, e.g. the price

for a 10 MW Li-ion battery storage is currently 20 times higher than investing in a medium-

voltage grid extension (Schäfer, 2014).

Regarding ownership of the energy storages, the regulations are very similar to Sweden and

a DSO is only allowed to own storage to cover the distribution losses. Concerning

households, the German situation is more economic beneficial as, since 2013, customers with

their own micro-generation are granted subsidies when owning batteries. (Schäfer, 2014)

3.5 Concluding remarks

When comparing the methods for energy storage and DR, it is noticeable that different

methods are suited for different applications in different scales. On a local scale, suitable

methods aiming to reduce load variations due to micro-generation and demand are batteries,

such as NaS or Li-ion, and tariffs like CPP. NaS and Li-ion batteries are suitable because of

their favourable technical and environmental aspects. Regarding their economic qualities,

they are still very expensive. Therefore, Pb-acid batteries might be an alternative with respect

to the economic features.

46

CPP is a tariff, which in previous experiments has shown ability to reduce peak loads the

most effective, both with and without installed automation.

This tariff, in combination with a rebate that rewards peak reductions, has shown to provide

a more predictable response from the participating customers. A predictable response is

beneficial for capacity markets as the market then would be more reliable and thus provide a

higher economic safety for the actors at the capacity market, i.e. DSOs and aggregators.

47

4 Case study of Hyllie

In order to estimate the future load variations in the local grid, Hyllie in Malmö has been

studied. This is to simulate an actual worst case for the electricity grid and get an

understanding of the magnitude of power variations caused by micro-generation.

4.1 Description of the area

The city of Malmö has highly set environmental and energy ambitions for the city. In 2009

the two strategies, Miljöprogram för 2009-2020 and Energistrategi Malmö were adopted, stating

that Malmö should act to become the world leader in sustainable urban development by

2020. The district Hyllie is one of Malmö’s largest developing areas. Fully developed, the

area will include approximately 9 000 residential units and nearly as many workplaces. To

achieve the high goals in the environmental and energy strategy for Malmö, it is required

that Hyllie takes important steps and leads the transition towards a sustainable city. To reach

the ambitions for the area, a climate contract containing the common goals for Hyllie was

developed and signed by E.ON, VA SYD and the city of Malmö in 2011. The goals in the

climate contract are to be reached by 2020 and the ones that are of relevance for this report

are:

The energy supply in Hyllie shall consist of 100% renewable or recycled energy by

2020. A significant proportion of the energy is to be supplied by locally produced

renewable energy, such as solar and wind energy.

The energy flows shall rest on smart infrastructure such as smart grids.

Smart solutions for EVs shall be established in the area.

Hyllie is chosen for the case study of this report because a high penetration of micro-

generation is expected in order to achieve the goal for the district of 100% locally produced

renewable energy to 2020. As E.ON Elnät is the DSO in Hyllie, it is within their interest to

learn whether micro-generation can cause power variations that affect the electricity grid and

how these variations can be balanced by energy storage and DR. The district Hyllie is to be

seen as an example which, in the future, could represent any urban area with a high

penetration of micro-generation in a grid owned by E.ON Elnät.

4.2 Method

The case study is based upon the assumption that PV and small-scale wind turbines are

installed on rooftops in Hyllie. This may be regarded as a best case in order to achieve the

goals of the climate contract but worst case for a conventionally built electricity grid.

Scenario 1 represents a conventional grid whereas scenario 2 consists of a grid with

alternative solutions (DR and energy storage). Within each scenario, both a residential case

and a total area case is simulated. A 20% penetration of EVs is considered, as smart solutions

for EVs are planned to be established in the area according to the climate contract. In

scenario 1, the EVs are considered as an uncontrollable load from the DSOs’ perspective, in

contrast to scenario 2, where the charging of the EVs is controlled.

48

As Hyllie is a developing area, there are still many uncertainties regarding the planning and

design. Therefore, a model has been constructed upon assumptions for the number and

design of the buildings, as well as the expected penetration of EVs. The model is described in

general in the following subsections. More detailed assumptions can be found in Table 8

Appendix A.

4.2.1 Model for simulations

The electricity grid in Hyllie consists in total of three loops connected to the primary medium

to low voltage (MV/LV) substation. The present large electricity consuming facilities in

Hyllie are connected separately, i.e. one of the three loops contains the shopping mall, one

contains the exhibition centre as well as the arena and one contains residential customers and

offices. Until 2020, more residential houses, as well as other facilities, are expected and even

more consumers are expected by 2030.

New grid infrastructure is to be planned and constructed in the coming years. Therefore, it is

of interest to investigate both how large the power variations can be in a loop with only

residential customers as well as for the total area.

All simulations and calculations were performed in Microsoft Office Excel 2007.

Assumptions for electricity demand

In Hyllie year 2020, the electricity demand is assumed to originate from 4 500 apartments,

4 500 workspaces, three schools, one public bath, one shopping mall (namely Emporia), one

hockey arena, one exhibition hall and one railway station. All facilities in the area are

assumed to have district heating and thus, the heating does not affect the electricity load for

these facilities.

Hourly electricity demand data from five newly built residential buildings, consisting of 95

apartments, as well as a school, all in Västra Hamnen in Malmö, were used when simulating

the load. As the computed medians of the residential electricity demand is close to the mean

values, the residential demand is normal distributed and hence, the mean value is reliable,

see Figure 38 in Appendix B.

Electricity demand data for the public bath was collected from an existing bath in Malmö.

Other demand data of existing facilities such as Emporia, the arena and offices, were based

on actual metering in Hyllie. The decrease in consumption of electricity due to expected

future energy efficiency measures is assumed to be compensated by an increase in number of

electrical appliances. Hence, the power consumption by 2020 is assumed to equal the

consumption by year 2013/2014.

According to Sköldberg et al. (2010), a 20% penetration of EVs is expected until 2020 and

consequently, considering the plans for the parking lots in Hyllie, there will be about 2 170

parking lots with EV charging possibilities. Hourly data for the charging of EVs was gained

from charging profiles, see Figure 39 and Figure 40 in Appendix C.

49

Assumptions for micro-generation

As the land in the area is owned by the municipality, the simulated micro-generation units

are located on the buildings’ rooftops. Each micro-generation unit has an installed capacity

of maximum 69 kW, see Appendix A. Hourly PV production data for July 2013 and January

2014 were collected from E.ONs smart home pilot project Hållbarheten in Malmö. The PV

modules are there positioned facing south at rails with a 25° declination. No further

shadowing due to e.g. wind turbines is considered.

In addition to the PV modules, small-scale wind turbines with rated power of 2 kW were

positioned on all rooftops to obtain a worst case future scenario. The wind turbines were

imagined to be positioned in a line with four rotor diameters in between to avoid wake

effect, i.e. lowering the power output from the turbines downstream, and shadowing of the

PV modules. Four small-scale wind turbines per residential building and ten wind turbines

per commercial building were assumed in the model. Hourly wind velocities at an altitude of

10 m for July 2013 and January 2014, at the airport Sturup near Malmö, were collected from

SMHI. Together with the power curve for the wind turbine Windon 2 kW, the hourly power

output was calculated.

Assumptions for balance solutions

The balance solutions applied in scenario 2 were ranked by firstly applying DR in the form of

CPP for all residential customers with an assumed 24% peak reduction during peak hours

and secondary, if required, peak shifting with battery storage was applied. DR was not

applied to the commercial buildings as their loads are mainly during the day and harder to

shift without affecting the business.

The battery was assumed to be charged only during excess production from the micro-

generation.

4.3 Results and analysis of simulations

This part of the report is divided into two main sections. The first is the scenario with a

conventional grid, i.e. without any of the solutions from the literature study implemented

into the grid. The second part is the scenario that represents a grid with the alternative

solutions that, according to the literature study, were the most suitable for a local grid, i.e.

batteries and CPP.

4.3.1 Scenario 1 – A conventional grid

This scenario simulates a conventional grid year 2020 with a high penetration of micro-

generation and 20% penetration of uncontrollable charged EVs.

Residential

The total load in the grid can be visualised by regarding micro-generation as a “negative”

demand. Hence, the total load duration curve can be plotted. In Figure 16, the load duration

curves with (net load) and without micro-generation (demand), are plotted for 4500

apartments with non-controllable EV parking lots in July.

50

Figure 16: The load duration curves for 4500 apartments during July.

The demand curve in Figure 16 can be regarded as a “business-as-usual” scenario, whereas

the net load curve might represent a future scenario where all residential buildings are

equipped with micro-generation. A conclusion that can be drawn is that the grid utilisation

factor changes significantly between the two scenarios. The scenario with micro-generation

results in unused grid capacity at period of times and an over-used grid at other times.

Another interesting observation is that the peak load in the scenario with micro-generation

has increased to approximately 7 MW. This is interesting as the grid historically has been

dimensioned only regarding to the predicted demand in an area.

Figure 17 shows the monthly summer load profiles for electricity demand and generation

from PV and small wind turbines, respectively.

Figure 17: Demand and micro-generation loads for 4500 apartments during July.

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In Figure 17, the fluctuations from the different loads can clearly be observed. Both the

demand and the PV profiles indicate more regular and predictable variations, in comparison

to the wind. There is a clear distinction of the demand profiles between weekdays and

weekends. The electricity production is much larger for the PV compared to the wind power

in the studied case and it is clear that the PV generation exceeds the demand at times during

a summer month.

The intermittences in Figure 17 are presented diurnally in Figure 18, which shows the

fluctuations during one day in July with high output from the micro-generation facilities.

The curve for the demand is an average summer day in July, with error bars representing the

standard deviations.

Figure 18: Demand and micro-generation loads for 4500 apartments during one day in July.

Observed in Figure 18 is that the PV generation is highest at noon. During summer time, the

demand is rather even at daytime and approximately 1 MW lower at night. This behaviour is

probably due to that a large percentage of the residents having vacation, resulting in a

smoother demand profile with lower peak demands and a higher diversity factor. The short

error bars indicate a low variation between the days. Hence, the plotted average value is

representative.

A penetration of 20% EVs where charging is uncontrollable by the DSO, causes a slightly

higher demand in the evenings but this will not affect the grid significantly.

A way to harmonise the PV generation curve with the demand is to angle the PVs towards

west and east. However, this will most likely cause a reduction in the energy output from the

PVs. A suggestion for a DSO is to provide incentives, so that the constructor positions the

PVs towards these directions. The incentives can for example be to provide a higher grid

compensation payment during mornings and evenings, which compensates for the loss in

production.

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Figure 19 presents the monthly net summer load including micro-generation, in order to

visualise to what magnitude the power variations can be in the grid.

Figure 19: The net load profile for 4500 apartments during July.

A high penetration of micro-generation will cause variations between -7 MW and 3 MW

where the negative sign implies generation exceeding the demand and thus a net export of

electricity to the grid. However, theoretically the direction of the current is not of great

importance for the grid but it might not be dimensioned for the increased load originating

from the micro-generation, in this case 4 MW. In practise, the grid can accept a slight higher

load than it is dimensioned for but this will have consequences for the lifetime of the grid

and the grid appliances due to thermal properties. The margins for over-loading decrease in

the summer as the outdoor temperature increases. Thus, the excess production from PV is

highest when the margins are low. There is however a limit for how much the grid can allow

as fuses are positioned in connection to the network stations.

In order to study how the “worst” day, i.e. the day with the highest negative net load, the

day corresponding to the peak at hour 301 in Figure 19, is presented in Figure 20.

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Figure 20: The net load profile for 4500 apartments during one day in July.

As can be seen in Figure 20, there will be hours with both excess and underage of micro-

generation. As mentioned previous, the excess load might exceed the dimensioning point of

the grid. The conventional solution in this situation is to reinforce the grid. This creates a

space for alternative solutions that can balance the load.

The corresponding curve for the day with highest micro-generation in January is displayed

in Figure 21.

Figure 21: The net load profile for 4500 apartments during one day in January.

When comparing to the summer net load, the winter load has a smaller amount of energy

that exceeds the electricity demand. Also, this load curve shows a more intermittent

behaviour, which can be explained by the wind variations, see Figure 35 in Appendix B.

These fluctuations might be hard to balance with DR due to the relatively short durations

which requires a quick response time for the end-users.

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Total area

A district like Hyllie contains a mixture of activities with both residential and commercial

buildings, resulting in different electricity demand profiles.

Figure 22 displays the load duration curves for the whole area, both with (net load) and

without micro-generation (demand).

Figure 22: The simulated load duration curve for Hyllie during July 2020.

Observed when comparing the load duration curve for the residential area in Figure 16 with

the total area in Figure 22, is that a larger part of the micro-generation is absorbed within the

studied area when it contains a mixture of demand profiles. When studying only Figure 22,

the grid seems to be not fully utilised for the situation with micro-generation and the peak

demand is lowered compared to the situation without micro-generation. To be considered is

also the winter months when the grid is fully utilised, see Figure 36, Appendix B. When

disregarding the peaks in the load duration curve, the curve representing the area with

micro-generation it is smoother than the curve representing only the demand. The peaks

might be balanced by batteries and peak pricing, hence creating an almost flat curve, which

is desirable for the DSO.

In Figure 23, the load variations for July regarding both demand and micro-generation is

visualised.

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Figure 23: Demand and micro-generation loads for Hyllie during July 2020.

The PV generation will exceed the total demand of Hyllie at some occasions, mainly in

weekends, see Figure 23. This is due to that the demand of the office buildings is very low

during weekends. Another observation is that the peaks for the demand and the PV

generation generally occur at the same time, i.e. at noon. This is a significant difference

comparing to the corresponding graph for only the residential load in Figure 17.

A day during a weekend in July with average demand and “worst case” generation is

presented in Figure 24. The demand is represented as an average during a weekend day as,

according to Figure 23, it is most likely that the “worst case” imbalance will occur then.

Figure 24: Demand and micro-generation loads for Hyllie a weekend day in July 2020.

During this day, the local electricity production exceeds the demand of the district at noon.

The expected number of EVs in the district will, when charging is uncontrollable, contribute

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to an increase of the electricity demand during daytime, consequently absorbing some of the

micro-generation.

In Figure 25, the net load profile for Hyllie during July is presented.

Figure 25: The net load profile for Hyllie during July 2020.

At some occasions, the district Hyllie will be unable to utilise all the produced electricity. At

these hours, the electricity will be transported away and utilised in another part of the grid

area. A future concern for the DSO is if all districts within a city have a high penetration of

micro-generation. If this situation occurs, the excess electricity will be transported higher up

in the grid, resulting in higher losses and penalty fees.

The net load profile for Hyllie at the “worst” day in July is presented in Figure 26.

Figure 26: The net load profile for Hyllie during one day in July 2020.

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The amount of excess energy due to micro-generation is lower for the whole area than for the

residential area only, see Figure 20. Also, the highest power peak originates from the high

demand, thus not the micro-generation. As can be seen in the load duration curve, Figure 36

Appendix B, the grid must be dimensioned to handle loads in the range of 18 MW. Hence,

the micro-generation will not, in this situation, cause any capacity problems for the DSO.

4.3.2 Scenario 2 – A grid with alternative solutions

In all figures presented in this section, a 20% penetration of EVs with DSO controllable

charging is included in the demand. The solutions for energy storage and DR is visualised

whenever balancing is possible.

Residential

The changes in the demand as a response to CPP and controllable EV charging are presented

in Figure 27 for a summer day. Even though it would be most beneficial if the EV charging

took place at noon, in order to absorb more of the micro-generation, this might not be

possible in the residential area as most people are at work at noon. Therefore, the charging is

shifted to night-time as the demand is lower then.

Figure 27: Demand and micro-generation loads with controllable EVs and CPP for one day in July 2020.

Figure 27 clearly demonstrates that neither the controllable load from EVs nor CPP is enough

to balance the micro-generation in a residential area.

As the highest demand (6 MW) is reached in January, see Figure 37, Appendix B, the grid

must be dimensioned to handle this load. As this seldom occurs, it is acceptable if the cables

have a capacity of 5 MW due to economic aspects. Figure 28 presents the same summer day

as shown in Figure 27 but in Figure 28, it is obvious that the net load will exceed this capacity

of the grid. Therefore, also energy storage has been included in the figure. To keep the load

at an acceptable level for the grid, the storage in this case needs to have a rated capacity of

0

1

2

3

4

5

6

7

8

9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

Wh

/h)

Time (h)

PV

CPP

Demand

Demand+EV

Wind

58

1.8 MW. Hence, the provided storage capacity is 1.5 MW if the efficiency of the battery is

85%.

In Figure 29, the net load for the winter day with highest production is simulated.

Figure 28: The net load profiles for loads during a summer day in July in a residential area with different

balancing solutions.

Figure 29: The net load profiles for loads during a winter day in January in a residential area with different

balancing solutions.

As shown in Figure 28, it is possible to keep the load within the capacity limits of the grid by

employing both DR in the form of CPP and a battery with a capacity of 1.5 MW. These

alternative solutions can provide a higher tolerance of unforeseen events and a lower wear of

the cables.

In winter, only DR is required in order to maintain the load within the desired range of -5 to

5 MW, see Figure 29.

-8

-6

-4

-2

0

2

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

Wh

/h)

Time (h)

Net load

CPP

Storage

-2

-1

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

Wh

/h)

Time (h)

Net load

CPP

59

The above proposed solutions result in the summer load duration curves presented in

Figure 30. In this figure, the residential demand without installed micro-generation is also

represented.

Figure 30: The load duration curves for July in a residential area with different solutions.

When comparing the load duration curves including balance solutions for a summer month,

it can be seen that CPP alone balances some intermittences while combined with battery

storage, it is possible to reduce the peak loads in a residential area. Thus, capacity margins in

the grid are provided.

Total area

The following simulations for the total area of Hyllie year 2020 include EV parking lots

divided into three segments; residential, office and shopping. Only the residential parking

lots are controllable by the DSO as the EV charging for other segments are difficult to shift

due to their operating time which is mainly during daytime.

In Figure 31, the demand for the whole area is shifted with controllable EVs and CPP, in

order to follow the micro-generation load.

-8

-6

-4

-2

0

2

4

12

54

97

39

71

21

14

51

69

19

32

17

24

12

65

28

93

13

33

73

61

38

54

09

43

34

57

48

15

05

52

95

53

57

76

01

62

56

49

67

36

97

72

1

Load

(M

Wh

/h)

Time (h)

Demand

CPP + Storage

CPP

Net load

60

Figure 31: The micro-generation and demand profile with DR for the total area during one day in July.

The residential controllable EVs are able to shift part of the demand from the evening to the

night, whereas the uncontrollable EVs increase the demand at noon, see Figure 31. CPP can

possibly absorb a part of the peak caused by micro-generation in a district with a mixture of

facilities. The load that can be shifted by CPP is illustrated in Figure 32 for a summer day and

in Figure 33 for a winter day.

Figure 32: The net load profile with DR for the total area during one day in July.

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

Wh

/h)

Time (h)

Demand+EV

Demand

CPP

Wind

PV

-4

-3

-2

-1

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Load

(M

Wh

/h)

Time (h)

Net load

CPP

61

Figure 33: The net load profile with DR for the total area during one day in July.

Figures 32 and 33 show that even with a high penetration of micro-generation in the whole

area, the load from PV and small wind turbines does not result in net load greater than the

highest demand. Despite this, an over production of electricity will occur occasionally during

summer. However, this load will not exceed the capacity of the grid. Subsequently, no

energy storage is needed for peak reduction in districts with diversified demand profiles.

During winter, when the output from PV is low whereas the output from wind is higher,

batteries are unsuitable due to the short term variations in the wind pattern which cause

wearing of the battery and hence shorten the life time.

Furthermore, Figure 33 illustrates the difficulties with finding a suitable tariff that suits the

irregular wind output. A time-of-use tariff would here be hard to implement whereas a more

dynamic pricing would be more suitable. It would also be beneficial with automated devices

due to the rapid changes in the weather.

The load duration curve for Hyllie in July with CPP is presented in Figure 34.

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

Wh

/h)

Time (h)

Net load

CPP

62

Figure 34: The load duration curve for July in Hyllie with and without DR and micro-generation.

Figure 34 shows that CPP on a monthly basis, only slightly evens out the load duration

curve. Furthermore, with micro-generation during summer, the maximum peak is lowered

and a main part of the load curve is more flat.

4.3.3 Economic feasibility

In order to calculate whether battery storages with a total capacity of 1.8 MW, as needed

according to Figure 28, located downstream the MV/LV substations, are an economic

investment, it is compared with the costs for a grid upgrade. The costs for the respective

investments are presented in Table 7, where the price for reinforcement includes new cables

with a length of 5 km as well as control cables. The price for extension includes all costs

related to constructing a 5 km new loop i.e. secondary substations, trafo and cables. The

expected battery lifetimes in Table 7 are calculated based on the cycle life of the batteries, see

Table 4, and the number of cycles during one year. The number of cycles during one year is

based on the expected number of occasions that the net load in the residential area exceeds 5

MW, which is approximately 80 occasions each year.

Table 7: Investment cost and lifetime of T&D grid upgrades and different batteries

Measure T&D upgrade Battery (1.8 MW)

Reinforcementa Extensiona NaS Li-ion Pb-acid

Cost

(€)

680 000 1 120 000 1 270 000-

3 910 000

5 200 000 370 000

Lifetime

(year)

40 40 50 100 10

a After (EBR, 2013).

According to Table 7, neither NaS nor Li-ion batteries are economic options to a T&D-

upgrade when comparing only the investment cost. However, if also the lifetime is

considered, NaS is economic comparable to a grid extension. The long expected lifetime of

-4

-2

0

2

4

6

8

10

12

14

16

12

54

97

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19

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28

93

13

33

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61

38

54

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34

57

48

15

05

52

95

53

57

76

01

62

56

49

67

36

97

72

1

Load

(M

Wh

/h)

Time (h)

Demand

CPP

Net load

63

the batteries are due to the seldom usage as they are only intended to be used to reduce

peaks that exceeds the grid capacity. If they would be located in the dwellings and not

owned by the DSO, they would most likely be used more often as it would be desirable to

store all over-production, hence resulting in a shorter lifetime.

A viable alternative is Pb-acid batteries. The lifetime of these batteries is however short

which causes the annual cost to be higher than a T&D upgrade. Consequently, this is a

temporary solution that can defer a T&D upgrade. The profitability of an upgrade deferral is

however currently small due to the presently low interest rate. Even so, the future interest is

hard to predict and thus, the grid update deferral might become more profitable in the near

future. Also, the battery prices are likely to decrease due to development and market

expansion.

4.4 Concluding remarks

The conventional dimensioning of the grid infrastructure in Hyllie will be able to

support a future scenario with a maximum penetration of micro-generation, i.e. the

total load will not increase.

The business-case for balance solutions in the area of Hyllie is rather to smooth the

load duration curve with energy storage and DR in order to be able to connect more

customers to the same loop. It would then be possible to avoid investment in T&D

upgrade.

If an area of only residential structures is to be planned, consideration must be taken

to micro-generation as this has been proven to be able to cause higher loads than the

demand itself.

With the prices of today, investments in battery storages in the studied grid instead of

T&D upgrades are not economical without subsidies.

64

65

5 Discussion

In this section, the future prospects for solutions to balance micro-generation as well as the

challenges for a DSO are discussed. Furthermore uncertainty parameters are analysed. In the

end of the section recommendations for further studies are suggested.

5.1 Future prospects

The penetration of micro-generation is dependent on the development of incentives to

promote small-scale generation. Hence, it is hard to predict the magnitude of the future

power variations in the local grid. Despite this, it is important for a DSO to be prepared for a

future which is unlike the situation the past century where the grid capacity is solely based

on the demand. To prepare for an alternative future is especially important due to the long

life-time and high cost of the grid infrastructure. Moreover, grid costs will become harder to

allocate as an increased self-supply of electricity will result in that fewer consumers will use

the grid. Therefore, the future grid must be cheaper in order for the grid fees to remain at an

acceptable level, which is regulated by the revenue cap controlled by Ei. The obvious

solution is to optimise the load in order to improve the degree of utilisation.

A way to optimise the load is to provide incentives for both producers and consumers to

adjust their load in accordance with the current capacity situation in the local grid. This

incentives have to come from the DSO as they are the grid responsible party and hence

negative affected by the intermittencies. Without incentives, customers act blindly and

sometimes create problems for the network without knowing it and without having to pay

for it additionally. Hence, DR with dynamical pricing where grid-users pay for their used

capacity is an important part when optimising the degree of utilisation. In some cases, DR

will not be enough to stabilise the local grid, as for example the simulated grid with only

residential customers, see Figure 27. In these situations other alternative solutions are

required to maintain the load within the grids capacity limits.

Energy storage is one alternative that recently has gained a lot of attention. Theoretically,

batteries could be the solution for most grid related problems. However, due to the high

prices which are unlikely to sink below an acceptable level until 2020, it is more realistic to

install batteries with only the capacity of reducing the worst peaks. In reality at present,

batteries are expensive and the ownership is regulated so that it is not economical for the

DSO to invest in storage. Therefore, with today’s circumstances a suggested strategy for a

DSO is rather to send price-signals to micro-generators to invest in energy storages.

Moreover, in the future beyond 2020 with decreasing battery prices storage most likely will

become viable. Furthermore, the increasing market for EVs might ultimately lead to a

secondary market for batteries which can be either recycled or reused as peak shaving

storages. Also, the cost for batteries can be allocated to different services and actors e.g. DSO

and electricity supplier, as storages often provide synergies with several benefits.

To coordinate all flexible capacity, e.g. flexible customers, EVs and storages, as well as to

ensure that the DSO receives the acquired flexibility, a capacity market is a promising

66

solution. However, there are still many uncertainties to solve for capacity markets to become

reality. Examples of uncertainties are who would like to take the role as an aggregator, how

the flexibility deliveries can be assured and how the unpredictable renewable production

loads will be forecasted? Hence, a larger scale capacity market in Sweden seems remote until

2020.

Local balances with even load profiles may be both positive and negative for the DSO.

Beneficial is a reduced need of grid investments due to bottleneck problems and reduced

payments to the overlaying grid. On the other hand, the DSO might have to convert their

business from handling grids that distributes electricity to a disposal grid only used during

power shortage i.e. micro-grids. However, this is from a DSOs perspective and from a

customer perspective it would be favourable.

5.2 Uncertainty parameters

As always when predicting the future, there are many uncertainties that have to be regarded

when reading the report and interpreting the results.

Concerning the micro-generation, the production data for PV and wind are gathered from

different sources. The output from PV is from actual production data from one plant during

one year. However, whether the production from this year can be used as standard has not

been investigated. The output from the small wind turbines is calculated from actual hourly

wind speed data at 10 meters and the power curve for one wind turbine. Hence, the wind

speed may differ in an area like Hyllie, both due to the altitude of which the turbines are

placed at, but also due to the turbulence caused by the roughness of the area. Also, the

power curve received from the manufacturer may be exaggerated, which causes a higher

production than in reality.

Furthermore, the simulated micro-generation is based upon assumptions that all rooftops are

covered with PV and wind turbines to a very high degree. This situation is rather unlikely to

occur by 2020. On the other hand, lesser production facilities placed on rooftops might be

compensated by more generation placed on e.g. facades and the ground. Furthermore, the

legislations concerning micro-generation are under debate and it is presently rather difficult

to interpret how large a micro-generation unit is allowed to be in order to get subsidies.

The data for the demand is also from actual measurements and thus might not completely

correspond to the future demand of Hyllie. Additionally, the heating source for all facilities

is expected to be district heating. Hence, no heat pumps are accounted for in the simulations.

It is also uncertain whether the planned amount of buildings actually will be constructed

until 2020.

All data for both generation and demand are hourly measured or calculated. Hence, shorter

variations in the interval of seconds are not represented in the figures.

67

As for DR, it is difficult to predict the actual response. Therefore, the amount of peak

reduction used in the simulations may differ if implementing DR in reality.

The assumptions for EVs are based on a rather high penetration of 20%. However, the

development of the EV market is uncertain and dependant on regulatory measures as well as

the markets for other alternative fuels, e.g. biogas and hydrogen. Furthermore, it is unsure if

the EVs will act as an added demand mainly during peak hours or if they can be operated as

electricity reservoirs for the grid.

The costs both regarding T&D upgrades and energy storages are estimated costs which are

likely to change in the coming years. The costs, especially for grid extensions, are highly

dependent on the local conditions and possible rebates.

5.3 Recommendations for further studies

Simulate the effect of micro-generation with software for grid calculations e.g.

dpPower, in order to investigate the impact on the power quality as well as to be able

to implement the study further in the business and grid planning.

Study electricity load variations for a similar or rural area with electric heating or

heat pumps as the loads are different and might be more suitable for DR.

Examine where in the grid energy storage should be located to provide most benefits

to the lowest cost.

Calculate what grid fees and electricity prices that would be necessary in order to

provide enough incentives for customers to invest in energy storages or shift their

loads.

Analyse how high the grid compensation payment would have to be altered in order

for the grid customers to place their PV panels in other azimuth angels to smooth the

output profile for the area. Is this a legal possible and realistic alternative?

Investigate how the legislations, e.g. the Electricity Act, should be developed to

provide incentives and allow the DSO to operate energy storages for peak-shaving.

Study if there can be a profitable business-case within the next few years if energy

storages are co-utilised by a DSO, electricity supplier and others.

Calculate the break-even point for curtailing versus to store energy. Can curtailment

be a sustainable solution in a life cycle-perspective compared to the required energy

for manufacturing cables or energy storages?

68

69

6 Conclusions

The main conclusions from both the literature study and the case study are presented below.

•In residential areas, the load from micro-generation can exceed the capacity of the grid. Therefore, consideration have to be taken during planning. In an existing grid, reinforcements or alternative solutions must be employed.

•In areas with mixed activities, e.g. Hyllie, the grid capacity will not be exceeded even with a high penetration of micro-generation.

Is micro-generation a problem for the local grid?

•Critical peak pricing is the most promising method for demand response when considering only the potential peak reductions.

•Li-ion and NaS batteries are the most suitable energy storage methods for local peak-shaving due to their favourable technical and environmental aspects.

Suitable balance solutions

•Battery storages are not presently an economical alternative to reinforcements/extensions of the local grid.

•Demand response is a more economical solution but this is not sufficient to reduce all intermittencies.

•It is difficult for a network operator to own a storage due to the legislations. Co-operations between a network operator and other owners are possible.

Viability of the balance solutions

70

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75

Appendix A

Assumptions used for calculations in the case study of Hyllie year 2020 are presented in

Table 8.

Table 8: Assumptions for simulations of a residential area and the district Hyllie year 2020

Residential Total area

Facilities 4500 apartments 4500 apartments

4500 workspaces

3 schools

1 public bath

1 shopping mall (Emporia)

1 hockey arena

1 exhibition hall

1 railway station

Buildings 237 residential buildings

19 apartments/dwellingsa

13 office buildingsb

Rooftop area 480 m2/residential buildingc 930 m2/office buildingd

Electric vehicles 20% penetration by 2020e

Charging profilesg

0,65 cars/householdf

585 EV parking lots

2 170 EV parking lotsh

(Emporia: 1000, residential:

585, office: 585)

Micro-

generation

(Max. 69 kW)

PV PV modules at rails (25° declination) equals 65% of rooftop

area

Efficiency of 14.7% i

143.3 Wp/m2 i

0° azimuth (PV modules facing south)

312 m2/residential building 312 m2/residential building,

480 m2/other building,

1300 m2/one of the schools

Wind

power

Power curve from Windon 2 kW

Four rotor diameters between wind turbines

4 wind turbines/residential

building

10 wind turbines/commercial

building

Demand response Critical peak pricing

24% peak reductions

a based on an average for five multi-family dwellings in Västra Hamnen, Malmö.

b based on number of workspaces at one of E.ON’s offices in Malmö.

c, f After (Malmö stad, 2014).

d Based on (Skanska, 2013).

e After (Sköldberg et al., 2010).

f After (Malmö stad, 2014).

g After (Soylu, 2011).

h Based on (Caesar & Morland, 2007).

i After (Trina Solar Ltd., 2011).

76

Appendix B

Scenario 1 – A conventional grid

In Figure 35 the demand and micro-generation loads for the residential case during one day

in January is illustrated.

Residential

Figure 35: Demand and micro-generation loads for 4500 apartments during one day in January.

Total area

In Figure 36, the load duration curves with (net load) and without (demand) micro-

generation for Hyllie during January are presented.

Figure 36: The simulated load duration curve for Hyllie during January 2020.

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

Wh

/h)

Time (h)

Demand+EV

Demand

Wind

PV

0

2

4

6

8

10

12

14

16

18

20

12

54

97

39

71

21

14

51

69

19

32

17

24

12

65

28

93

13

33

73

61

38

54

09

43

34

57

48

15

05

52

95

53

57

76

01

62

56

49

67

36

97

72

1

Load

(M

Wh

/h)

Time (h)

Demand

Net load

77

Scenario 2 – A grid with alternative solutions

Residential

In Figure 37, the load duration curves with and without micro-generation, as well as with

CPP are presented for the residential area during January.

Figure 37: The load duration curves for January in a residential area with different solutions.

Standard deviation of the residential demand

Figure 38 illustrates the mean value and the standard deviations of the demand for five

residential buildings in Västra Hamnen in Malmö for one day in July.

Figure 38: Standard deviations of the electricity demand of five newly built residential buildings in Västra

Hamnen, Malmö for one day in July.

-2

-1

0

1

2

3

4

5

6

7

12

54

97

39

71

21

14

51

69

19

32

17

24

12

65

28

93

13

33

73

61

38

54

09

43

34

57

48

15

05

52

95

53

57

76

01

62

56

49

67

36

97

72

1

Load

(M

Wh

/h)

Time (h)

Demand+EV

CPP

Net load

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(kW

h/h

)

Time

78

Appendix C

Charging profiles for uncontrolled charging of EVs is shown in Figure 39 and for controlled

charging of EVs in Figure 40.

Figure 39: Charging profile for uncontrolled charging of EVs (Soylu, 2011).

Figure 40: Charging profile for smoothing off-peak charging of EVs (Soylu, 2011).