can the decentralized chp generation provide the flexibility required to integrate intermittent res...
TRANSCRIPT
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