Wir schaffen Wissen – heute für morgen

24. Juni 2015 PSI, 24. Juni 2015 PSI,

Paul Scherrer Institut

Can the decentralised CHP generation provide the

flexibility required to integrate intermittent RES in the

electricity system?

Evangelos Panos , Kannan Ramachandran

67th Semi-Annual ETSAP Workshop, Abou Dhabi

Swiss energy system & Swiss energy strategy to 2050 objectives

The concept of dispatchable biogenic CHPs (electricity-driven)

Extensions on Swiss Times Electricity Model (STEM-E)

Challenges in modelling

Preliminary results from model testing

Conclusions

Introduction

24.06.2015 PSI, Seite 2

Net Imports Hydro Nuclear

Thermal Renewables

Electricity sector

Industry

Transport

Residential

Services

-2

40

25

3 1

Electricity production: 66 TWh

Swiss energy system in 2013

24.06.2015 PSI, Seite 3

Final energy consumption: 896 PJ

CO2 emissions 41 Mtn:

050

100150200250300350 Renewables

Heat

Electricity

Gas

Oil

Wastes

Coal

Wood

45

17

10

5

Energy Strategy 2050 key objectives:

Enhancement of energy efficiency

Unlocking new RES (wind, solar, biomass)

Withdrawal from nuclear energy

Imports and fossil to meet residual

electricity demand

Extension of electricity grid

Dams south, Nuclear north

24.06.2015 PSI, Seite 4

Swiss grid congested lines

24.06.2015 PSI, Seite 5

Source: Swissgrid

2/3 of the grid was built between 1950 and 60s with focus on ensuring regional supplies from nearby plants

Gaseous fuel from waste biomass Wood Air

Heat storageEnergy conversion with thermal machine Electricity grid

Combined Heat and Power plant

Specifications for grid stabilisation:

Fast response power generation

Temporal independent production of

electricity and heat

A contractor (electricity utility)

could operate a CHP swarm by

remote:

Dispatchable, scalable and

decentralised power plant

Balancing power is sold at high price

levels on the market

The concept of biogenic CHP Swarm

24.06.2015 PSI, Seite 6

Running project ETH/PSI sponsored by BFE and Swisselectric Research

A biogenic flexible CHP can participate in the following markets:

a) Electricity supply, on-site or distributed

competition with power plants

competition with electricity grid price

b) Heat supply for space and water heating, on-site or distributed

competition with boilers and heat pumps

competition with district heating networks

c) Provision of grid balancing services at a grid distribution level:

competition with on-site solutions e.g. batteries

competition with services provided by pump hydro, PtG, etc.

However, the technology is resource-constrained:

it uses biogas or upgraded biogas injected to gas grid

needs access to gas pipelines

competition with other biogas uses

Assessing the potential of CHP Swarms

24.06.2015 PSI, Seite 7

To implement biogenic flexible CHPs in TIMES, to assess its

potential and to identify barriers and competitors we need in the

model at least:

Representation of decentralised power generation

High time resolution to account for demand and resource fluctuations

Introduction of dispatchability features (e.g. ramp-up constraints)

Representation of heat supply and demand sectors

Representation of electricity & heat storage technologies

Representation of alternatives, such as power-to-gas pathways

Representation of bio-methane production options

Representation of demand for balancing services

Challenges in TIMES modelling

24.06.2015 PSI, Seite 8

STEM-E:

Swiss Electricity Model

288 time slices:

4 seasons X 3 days X 24h

Exogenous electricity

demand linked to economic

activity

Electricity load profiles

Different types of power

plants

Resource potentials

Swiss Times Electricity Model

24.06.2015 PSI, Seite 9

STEM-HE:

All STEM-E plus:

Decentralised generation

Heat demands & load profiles

Heat supply options

Storage for electricity & heat

Power-to-gas / gas-to-power

Upgraded biogas production

Ramping constraints

Balancing services

FROM STEM-E to STEM-HE 2012 2014

Representation of decentralised sector

24.06.2015 PSI, Seite 10

Very High Voltage

Grid Level 1

Nuclear

Hydro Dams

Pump Storage

Imports

Exports

Distributed

Generation

Run-of-river hydro

Gas Turbines CC

Gas Turbines OC

Geothermal

High Voltage

Grid Level 2

Large Scale

Power Generation

Medium Voltage

Grid Level 3

Wind Farms

Solar Parks

Oil ICE

Waste Incineration

District Heating

CHPs

Wastes, Biomass

Oil

Gas

Biogas

H2

Low Voltage

Distribution Grid

Large Industries &

Commercial

CHP oil

CHP biomass

CHP gas

CHP wastes

CHP H2

Solar PV

Wind turbines

Commercial/

Residential

Generation

CHP gas

CHP biomass

CHP H2

Solar PV

Wind turbines

4 different grid voltage levels are represented Allows for compensation of RES generation that is fed into grid Similar structure with TIMES-PET (and perhaps with other models) Not always straightforward assignment of power plants to each level

To reduce complexity the focus is on heat that can be supplied by

CHPs

Space and water heating in buildings and commercial sectors

Differentiation between different types of houses

Two classes of heat in industrial sectors: <500 oC, >500 oC

Heat demand sector

24.06.2015 PSI, Seite 11

Residential Sector

Single Family Multi Family

Space Heating Existing

buildings

Space Heating

Newbuildings

Space Heating Existing

buildings

Space Heating

Newbuildings

Water Heating

Industry

Heat >500 oC

(CHPs NO)

Services

Space Heating &

Water Heating

Heat Supply Technologies(boilers, resistors, heat pumps, night storage devices, Decentralised CHP)

Heat <500 oC

(CHPs YES )

Statistical estimation of consumer behaviour from surveys [1]

Data reconciliation to minimise the deviation between actual and

calculated annual heat demand from the profiles:

Heat demands and load profiles

24.06.2015 PSI, Seite 12

HDDAnnual

DemandOutside

temperatureTypical Daily

Profile

% of seasonal demand

% of demand per typical day

% demand

for 288 typical hours

Adjustment of

electricity load curve

min 𝑤1,𝑡 𝐷𝑡 − 𝐹𝑡2 + 𝑤2,𝑡 𝑥𝑡𝑠 − 𝑦𝑡𝑠

2

𝑡𝑠𝑡

𝐹𝑡 𝑥1… . 𝑥𝑛 = 0

𝐶𝑘 𝑥1… . 𝑥𝑛 = 0

𝒘 weights, user defined

𝒚 Initial hourly profiles

𝒙 Adjusted hourly profiles

𝑭 Calculated annual heat demand

𝑫 Actual annual heat demand

𝑪 Other constraints that must hold

Single family houses

Multi family houses

Commercial

Industry

Space heating: morning peak

followed by a long day-time

plateau and a smaller evening

peak

Water heating: sharp variations

depending on use

Examples of obtained heat profiles

24.06.2015 PSI, Seite 13

Existing single family houses – Space heating in PJ

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

H0

1

H0

2

H0

3

H0

4

H0

5

H0

6

H0

7

H0

8

H0

9

H1

0

H1

1

H1

2

H1

3

H1

4

H1

5

H1

6

H1

7

H1

8

H1

9

H2

0

H2

1

H2

2

H2

3

H2

4

SPR-WK

SUM-WK

FAL-WK

WIN-WK

Existing multi family houses – Space heating in PJ

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

H01

H02

H03

H04

H05

H06

H07

H08

H09

H10

H11

H12

H13

H14

H15

H16

H17

H18

H19

H20

H21

H22

H23

H24

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

H01

H02

H03

H04

H05

H06

H07

H08

H09

H10

H11

H12

H13

H14

H15

H16

H17

H18

H19

H20

H21

H22

H23

H24

SPR-WK

SUM-WK

FAL-WK

WIN-WK

All households– Water heating in PJ

Representation of heat systems [2]

24.06.2015 PSI, Seite 14

Heat supply option 1

Heat supply option 2

Heat supply option N

Heat storage

Heat System

𝑎𝑝,𝑡 ∙ 𝑋𝑝,𝑡𝑁𝐶𝐴𝑃 +𝑏𝑝𝑝,𝑡 ∙ 𝑋𝑝𝑝,𝑡

𝑁𝐶𝐴𝑃 = 𝑐1,𝑡

∀𝑝 ∈ 𝑃𝑃𝑅𝐼 ; 𝑝𝑝 ∈ 𝑃𝑆𝐸𝐶∪𝑆𝑇𝐺 ; 𝑡 ∈ 𝑇 ; 𝑡𝑠_𝑝𝑒𝑎𝑘 ∈ 𝑇𝑆 ; 𝑎𝑝,𝑡, 𝑏𝑝,𝑡, 𝑐1,𝑡, 𝑐2,𝑡, 𝑐3,𝑡 const

𝑎𝑝,𝑡 ∙ 𝑋𝑝,𝑡𝑁𝐶𝐴𝑃 +𝑏𝑝𝑝,𝑡

𝑀𝐴𝑋 ∙ 𝑋𝑝𝑝,𝑡𝑁𝐶𝐴𝑃 ≤ 𝑐2,𝑡

𝑎𝑝,𝑡 ∙ 𝑋𝑝,𝑡𝑁𝐶𝐴𝑃 +𝑏𝑝𝑝,𝑡

𝑀𝐼𝑁 ∙ 𝑋𝑝𝑝,𝑡𝑁𝐶𝐴𝑃 ≥ 𝑐3,𝑡

Fixed capacity ratio: e.g. solar thermal and gas

boiler

Upper and lower bounds on capacity ratios: e.g.

micro CHP and gas boiler

𝑋𝑝,𝑡𝐶𝐴𝑃

𝑝∈𝑃𝑃𝑅𝐼

≥ 𝑑𝑒𝑚𝑎𝑛𝑑𝑡,𝑡𝑠_𝑝𝑒𝑎𝑘 Match demand capacity requirement with the

capacity of the primary heat supply systems

Combinations of primary and secondary heating systems is possible via

user constraints:

Heat demand

Avoiding technology mix in heat supply

24.06.2015 PSI, Seite 15

H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23

Wood boiler

Solar thermal

Gas boiler

District heating

Heat from CHP on-site

Oil boiler

Heat Pump

Electric boiler

Coal boiler

H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23

Wood boiler

Solar thermal

Gas boiler

District heating

Heat from CHP on-site

Oil boiler

Heat Pump

Electric boiler

Coal boiler

No individual technology

optimisation is applicable for

heat supply systems:

a) Go for MIP by ensuring that only

one heat system will supply a

heat demand class [2] (not

directly supported in TIMES)

OR:

b) Introduce a utilisation curve for

each heat supply system with

NCAP_AF(UP) and ACT_UPS(FX)

in accordance with the demand

curve

Primary reserves react to frequency deviations within 30 seconds

Secondary reserves are activated just slightly after the primary reserves

and maintain a balance between generation and demand within each

balancing area (duration from 1 to 60 minutes)

Tertiary reserves are called only after secondary control has been used

for a certain duration, to free the secondary reserves for other purposes

Introducing balancing services

24.06.2015 PSI, Seite 16

Secondary reserves

Secondary reserves

Tertiary reserves

Source: Swissgrid

Each power plant is producing 4 additional commodities related

to the primary & secondary upward and downward reserves

The set of the attributes of a power plant is augmented by:

Its minimum stable operation

The % of total capacity available for primary and secondary upward

and downward reserves

The ramp-up and ramp-down rates

We can also provide the % of upward reserve met by online

plants to avoid unrealistically provisions of upward reserves from

offline technologies

The additional equations for balancing services:

Can be introduced as UC (takes time to enter the constraints in EXCEL)

Can be implemented as TIMES extension in GAMS (prone to errors)

Porting OSEMOSYS methodology [3]

24.06.2015 PSI, Seite 17

Each power plant eligible for participating in the balancing

markets is divided into two parts

A capacity transfer UC ensures that the capacity-related costs are

paid only once:

Demand for balancing services: 3 ∗ 𝜎𝐷2 + 𝜎𝐺

2 + 𝐾

Porting OSEMOSYS methodology

24.06.2015 PSI, Seite 18

𝑋𝑝,𝑡𝐶𝐴𝑃 = 𝑋𝑝𝑝,𝑡

𝐶𝐴𝑃

Power Plant p (participation in the electricity market)

Power Plant pp (participation in the balancing markets)

ELCElectricity demand

Primary upward reserve demand

Primary downward reserve demand

Secondary upward reserve demand

Secondary downward reserve demand

PU

PD

SU

SD

PU_DEM

PD_DEM

SU_DEM

SD_DEM

ELC_DEM

𝑋𝑝,𝑡,𝑡𝑠𝐶𝐴𝑃𝑂𝑁 = 𝑋𝑝,𝑡,𝑡𝑠

𝐸𝐿𝐶 /(𝑐𝑎𝑝𝑎𝑐𝑡𝑝 ∙ 𝑦𝑟𝑓𝑟𝑡,𝑡𝑠) :online capacity of process 𝑝

Downward reserve:

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑘 ≤ 𝑋𝑝,𝑡,𝑡𝑠

𝐶𝐴𝑃𝑂𝑁 ∙ 𝑚𝑎𝑥𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡𝑝𝑝 : 𝑘 ∈ 𝑃𝐷, 𝑆𝐷

Upward reserve for a fast ramping plant: (𝐦𝒂𝒙𝒌,𝒑𝒑 ≥ 𝒔𝒕𝒂𝒃𝒍𝒆𝒐𝒑𝒑𝒑)

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑘 ≤ 𝑋𝑝𝑝,𝑡

𝐶𝐴𝑃 ∙ 𝑎𝑓𝑝𝑝,𝑡,𝑡𝑠 ∙ 𝑚𝑎𝑥𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡𝑝𝑝: 𝑘 ∈ 𝑃𝑈, 𝑆𝑈

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑃𝐷 + 𝑋𝑝𝑝,𝑡,𝑡𝑠

𝑆𝑈 ≤ 𝑋𝑝,𝑡,𝑡𝑠𝐸𝐿𝐶

𝑋𝑝,𝑡,𝑡𝑠𝐸𝐿𝐶 ≤ 𝑋𝑝,𝑡,𝑡𝑠

𝐶𝐴𝑃𝑂𝑁 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡𝑝

Upward reserve for a slow ramping plant: ( 𝒎𝒂𝒙𝒌,𝒑𝒑 ≤ 𝒔𝒕𝒂𝒃𝒍𝒆𝒐𝒑𝒑𝒑)

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑘 ≤ 𝑋𝑝,𝑡

𝐶𝐴𝑃𝑂𝑁 ∙ 𝑎𝑓𝑝𝑝,𝑡,𝑡𝑠 ∙ 𝑚𝑎𝑥𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡𝑝𝑝: 𝑘 ∈ 𝑃𝑈, 𝑆𝑈

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑃𝐷 + 𝑋𝑝𝑝,𝑡,𝑡𝑠

𝑆𝑈 + 𝑋𝑝,𝑡𝐶𝐴𝑃𝑂𝑁 ∙ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡_𝑝 ≤ 𝑋𝑝,𝑡,𝑡𝑠

𝐸𝐿𝐶

𝑋𝑝,𝑡,𝑡𝑠𝐸𝐿𝐶 + 𝑋𝑝𝑝,𝑡,𝑡𝑠

𝑃𝐷 + 𝑋𝑝𝑝,𝑡,𝑡𝑠𝑆𝑈 ≤ 𝑋𝑝𝑝,𝑡,𝑡𝑠

𝐶𝐴𝑃𝑂𝑁 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡𝑝𝑝

Key equations for balancing services [3]

24.06.2015 PSI, Seite 19

Minimum online upward reserve ( 𝑘 ∈ 𝑃𝑈, 𝑆𝑈 )

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑘

𝑝𝑝 ≥ 𝐷𝐸𝑀𝑘,𝑡,𝑡𝑠 ∙ 𝑚𝑖𝑛𝑜𝑛𝑙𝑖𝑛𝑒𝑡

All reserve from online plants when m𝑎𝑥𝑘,𝑝𝑝 ≤ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝𝑝𝑝 :

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑘 = 𝑋𝑝,𝑡,𝑡𝑠

𝑂𝑁𝐿𝐼𝑁𝐸𝑘 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡𝑝

Share of reserve from online plants when m𝑎𝑥𝑘,𝑝𝑝 ≥ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝𝑝𝑝 :

𝑋𝑝𝑝,𝑡,𝑡𝑠𝑘 ≥ 𝑋𝑝,𝑡,𝑡𝑠

𝑂𝑁𝐿𝐼𝑁𝐸𝑘 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡𝑝

Upward reserve is limited by online capacity minus power output:

𝑋𝑝,𝑡,𝑡𝑠𝐶𝐴𝑃𝑂𝑁 − 𝑋𝑝,𝑡,𝑡𝑠

𝐸𝐿𝐶 ≥ 𝑋𝑝𝑝,𝑡,𝑡𝑠𝑂𝑁𝐿𝐼𝑁𝐸𝑃𝑈 + 𝑋𝑝𝑝,𝑡,𝑡𝑠

𝑂𝑁𝐿𝐼𝑁𝐸𝑆𝑈

Upward reserve by online plants limited by their max contribution to

upward reserve:

𝑋𝑝,𝑡,𝑡𝑠𝐶𝐴𝑃𝑂𝑁 ∙ 𝑚𝑎𝑥𝑘,𝑝𝑝 ≥ 𝑋𝑝,𝑡,𝑡𝑠

𝑂𝑁𝐿𝐼𝑁𝐸𝑘

Key equations for balancing services [3]

24.06.2015 PSI, Seite 20

The related to this work project is currently running and we are

still integrating information from our partners regarding grid

constraints, balancing services, CHP technology characterisation

and biomass resource potentials

“Reference” scenario assumptions used to test the model:

Based on the “POM” scenario of Swiss energy strategy 2050,

implementing strong efficiency measures

Fuel prices from IEA ETP 2014, translated to Swiss border pre-tax

prices

Nuclear phase out to be completed by 2034

No CCS and no coal in electricity generation

CO2 price rises to 58 CHF/t CO2 in 2050

Solar potential: ~10 TWh, Wind potential: ~3 TWh, Hydro: ~40 TWh

Preliminary results from model testing

24.06.2015 PSI, Seite 21

Energy service demands in PJ

Forecast of demands

24.06.2015 PSI, Seite 22

0

100

200

300

400

500

600

2010 2012 2015 2020 2030 2040 2050

Residential thermal

Residential electricspecific

Services thermal

Services electric specific

Industry thermal

Industry electric specific

(excl. transport)

Final energy consumption in PJ

24.06.2015 PSI, Seite 23

0

100

200

300

400

500

600

700

2010 2020 2030 2040 2050

Hydrogen

Biogas

Solar

Wastes

Wood

Heat *

Coal

Natural gas

Heavy fuel oil

Light fuel oil

Electricity

Share of technologies in residential heat

24.06.2015 PSI, Seite 24

0%

10%

20%

30%

40%

50%

60%

2010 2020 2030 2040 2050

Heat pumps

Heat *

Gas boilers

Oil boilers

Operational profiles of heat technologies

24.06.2015 PSI, Seite 25

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

H0

1

H0

3

H0

5

H0

7

H0

9

H1

1

H1

3

H1

5

H1

7

H1

9

H2

1

H2

3

Wood/Pelletts

Gas

Heat radiators

Oil

Heat pumps

Electric boilers/resistors

Solar

Single family houses

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

H0

1

H0

3

H0

5

H0

7

H0

9

H1

1

H1

3

H1

5

H1

7

H1

9

H2

1

H2

3

Wood/Pelletts

Gas

Heat radiators

Oil

Heat pumps

Electric boilers/resistors

Solar

Multi family houses

Storage technologies for heat in services

24.06.2015 PSI, Seite 26

0

1'000

2'000

3'000

4'000

5'000

6'000

1 3 5 7 9 11 13 15 17 19 21 23

Total HeatDemand

-120

-100

-80

-60

-40

-20

0

20

40

1 3 5 7 9 11 13 15 17 19 21 23

ThermochemicalStorage of CHP heat

Night boilers

Electricity generation sector

24.06.2015 PSI, Seite 27

0

2

4

6

8

10

2010 2020 2030 2040 2050

Solar

Wind

Wood &Biogas

-10

0

10

20

30

40

50

60

70

80

2010 2012 2015 2020 2030 2040 2050

Solar

Wind

Geothermal

Wastes

Wood

Biogas

Gas

Nuclear

Hydro

Pump storage

Net Imports

Electricity production by fuel in TWh Electricity production from renewables in TWh

CHP Swarms (capacity & operation profile)

24.06.2015 PSI, Seite 28

2020 2030 2040 2050

Capacity (MW) 109 185 124 56

Electricity production (GWhe) 585 1200 522 415

% of total electricity production 1% 2% 1% 1%

% of decentralised thermal only electricity production 39% 80% 35% 28%

% of decentralised total electricity production 17% 16% 5% 3%

Heat production (GWhth) 838 1719 748 595

0

20

40

60

80

100

120

140

160

180

200

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

1 3 5 7 9 11 13 15 17 19 21 23

CHP Swarms MW

Solar PV (MW)

Solar PV

CHP Swarms

Winter working day

0

20

40

60

80

100

120

140

160

180

200

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

1 3 5 7 9 11 13 15 17 19 21 23

CHP Swarms MW

Solar PV (MW) Summer Sunday

CHP Swarms seems a promising technology for providing

flexibility to the electric system

Potential parameters affecting their uptake include:

Developments in the large-scale generation

Costs of bio-methane and access competition from other uses

Feed-in tariffs for biomass

Competition in heat supply from heat pumps

Storage costs for storing excess heat from CHP Swarms

Modelling challenges:

No satisfactory solution for the technology mix effect in heat sectors

Improvement of the dispatching of the power plant technologies:

crucial factor for the balancing services as well

Conclusions – Further Challenges

24.06.2015 PSI, Seite 29

References

24.06.2015 PSI, Seite 30

[1] Main Sources used for obtaining the heat demand profiles:

BFE, “Analyse des schweizerischen Energieverbrauchs 2000 - 2012 nach Verwendungszwecken“, 2013

Rossi Alessandro, “Modelling and validation of heat sinks for combined heat and power simulation: Industry”, 2013

Ayer Roman, “Modelling of heat sinks for combined heat and power simulation: Households”, 2013

Federal office of Meteorology and Climatology MeteoSwiss

Mark Hellwig "Entwicklung und Anwendung parametrisierter Standard-Lastprofile", 2003

Ulrike Jordan, Klaus Vajen, “Realistic Domestic Hot-Water Profiles in Different Time Scales”, 2001

[2] Modelling heat systems:

Merkel E., Fehrenbach D., McKenna R., Fichter W., Modelling decentralised heat supply: An application and methodological extension

in TIMEs, Energy 73 (2014), 592-605

[3] Modelling balancing services:

Welsch M., Howells M., et al., Supporting security and adequacy in future energy systems: the need to enhance long-term energy

system models to better treat issues related to variability, Int. J. Energy Res. 39 (2015), 377-396

24. Juni 2015 PSI, 24. Juni 2015 PSI, Seite 31

Thank you for the attention !

Dr. Evangelos Panos

Energy Economics Group in the Laboratory of Energy Analysis

evangelos.panos@psi.ch