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Wir schaffen Wissen – heute für morgen
24. Juni 2015PSI, 24. Juni 2015PSI,
Paul Scherrer Institut
Can the decentralised CH...
 Swiss energy system & Swiss energy strategy to 2050 objectives
 The concept of dispatchable biogenic CHPs (electricity-...
Net Imports Hydro Nuclear
Thermal Renewables
Electricity sector
Industry
Transport
Residential
Services
-2
40
25
3 1
 Ele...
Dams south, Nuclear north
24.06.2015PSI, Seite 4
Swiss grid congested lines
24.06.2015PSI, Seite 5
Source: Swissgrid
2/3 of the grid was built between 1950 and 60s with fo...
Gaseous fuel from waste biomass
Wood
Air
Heat storageEnergy conversion
with thermal
machine Electricity grid
 Combined He...
 A biogenic flexible CHP can participate in the following markets:
a) Electricity supply, on-site or distributed
 compet...
 To implement biogenic flexible CHPs in TIMES, to assess its
potential and to identify barriers and competitors we need i...
STEM-E:
 Swiss Electricity Model
 288 time slices:
4 seasons X 3 days X 24h
 Exogenous electricity
demand linked to eco...
Representation of decentralised sector
24.06.2015PSI, Seite 10
Very High Voltage
Grid Level 1
Nuclear
Hydro Dams
Pump Stor...
 To reduce complexity the focus is on heat that can be supplied by
CHPs
 Space and water heating in buildings and commer...
 Statistical estimation of consumer behaviour from surveys [1]
 Data reconciliation to minimise the deviation between ac...
 Space heating: morning peak
followed by a long day-time
plateau and a smaller evening
peak
 Water heating: sharp variat...
Representation of heat systems [2]
24.06.2015PSI, Seite 14
Heat supply option 1
Heat supply option 2
Heat supply option N
...
Avoiding technology mix in heat supply
24.06.2015PSI, Seite 15
H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23
Wood boiler...
 Primary reserves react to frequency deviations within 30 seconds
 Secondary reserves are activated just slightly after ...
 Each power plant is producing 4 additional commodities related
to the primary & secondary upward and downward reserves
...
 Each power plant eligible for participating in the balancing
markets is divided into two parts
 A capacity transfer UC ...
𝑋 𝑝,𝑡,𝑡𝑠
𝐶𝐴𝑃𝑂𝑁
= 𝑋 𝑝,𝑡,𝑡𝑠
𝐸𝐿𝐶
/(𝑐𝑎𝑝𝑎𝑐𝑡 𝑝 ∙ 𝑦𝑟𝑓𝑟𝑡,𝑡𝑠) :online capacity of process 𝑝
Downward reserve:
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
≤ 𝑋 𝑝,𝑡,𝑡...
Minimum online upward reserve ( 𝑘 ∈ 𝑃𝑈, 𝑆𝑈 )
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
𝑝𝑝 ≥ 𝐷𝐸𝑀 𝑘,𝑡,𝑡𝑠 ∙ 𝑚𝑖𝑛𝑜𝑛𝑙𝑖𝑛𝑒𝑡
All reserve from online plants when ...
 The related to this work project is currently running and we are
still integrating information from our partners regardi...
Energy service demands in PJ
Forecast of demands
24.06.2015PSI, Seite 22
0
100
200
300
400
500
600
2010 2012 2015 2020 203...
(excl. transport)
Final energy consumption in PJ
24.06.2015PSI, Seite 23
0
100
200
300
400
500
600
700
2010 2020 2030 2040...
Share of technologies in residential heat
24.06.2015PSI, Seite 24
0%
10%
20%
30%
40%
50%
60%
2010 2020 2030 2040 2050
Heat...
Operational profiles of heat technologies
24.06.2015PSI, Seite 25
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
H01
H03
H05
H07
H09
H11
...
Storage technologies for heat in services
24.06.2015PSI, Seite 26
0
1'000
2'000
3'000
4'000
5'000
6'000
1 3 5 7 9 11 13 15...
Electricity generation sector
24.06.2015PSI, Seite 27
0
2
4
6
8
10
2010 2020 2030 2040 2050
Solar
Wind
Wood &
Biogas
-10
0...
CHP Swarms (capacity & operation profile)
24.06.2015PSI, Seite 28
2020 2030 2040 2050
Capacity (MW) 109 185 124 56
Electri...
 CHP Swarms seems a promising technology for providing
flexibility to the electric system
 Potential parameters affectin...
References
24.06.2015PSI, Seite 30
[1] Main Sources used for obtaining the heat demand profiles:
 BFE, “Analyse des schwe...
24. Juni 2015PSI, 24. Juni 2015PSI, Seite 31
Thank you for the attention !
Dr. Evangelos Panos
Energy Economics Group in t...
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Can the decentralized CHP generation provide the flexibility required to integrate intermittent RES in the electricity system?

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Can the decentralized CHP generation provide the flexibility required to integrate intermittent RES in the electricity system?

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Can the decentralized CHP generation provide the flexibility required to integrate intermittent RES in the electricity system?

  1. 1. Wir schaffen Wissen – heute für morgen 24. Juni 2015PSI, 24. Juni 2015PSI, 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
  2. 2.  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 24.06.2015PSI, Seite 2
  3. 3. 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 24.06.2015PSI, Seite 3  Final energy consumption: 896 PJ  CO2 emissions 41 Mtn: 0 50 100 150 200 250 300 350 Renewables Heat Electricity Gas Oil Wastes Coal Wood 4 5 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
  4. 4. Dams south, Nuclear north 24.06.2015PSI, Seite 4
  5. 5. Swiss grid congested lines 24.06.2015PSI, Seite 5 Source: Swissgrid 2/3 of the grid was built between 1950 and 60s with focus on ensuring regional supplies from nearby plants
  6. 6. 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 24.06.2015PSI, Seite 6 Running project ETH/PSI sponsored by BFE and Swisselectric Research
  7. 7.  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 24.06.2015PSI, Seite 7
  8. 8.  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 24.06.2015PSI, Seite 8
  9. 9. 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 24.06.2015PSI, 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-HE2012 2014
  10. 10. Representation of decentralised sector 24.06.2015PSI, 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
  11. 11.  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 24.06.2015PSI, Seite 11 Residential Sector Single Family Multi Family Space Heating Existing buildings Space Heating New buildings Space Heating Existing buildings Space Heating New buildings Water Heating Industry Heat >500 o C (CHPs NO) Services Space Heating & Water Heating Heat Supply Technologies (boilers, resistors, heat pumps, night storage devices, Decentralised CHP) Heat <500 o C (CHPs YES )
  12. 12.  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 24.06.2015PSI, Seite 12 HDD Annual Demand Outside temperature Typical Daily Profile % of seasonal demand % of demand per typical day % demand for 288 typical hours Adjustment of electricity load curve min 𝑤1,𝑡 𝐷𝑡 − 𝐹𝑡 2 + 𝑤2,𝑡 𝑥 𝑡𝑠 − 𝑦𝑡𝑠 2 𝑡𝑠𝑡 𝐹𝑡 𝑥1 … . 𝑥 𝑛 = 0 𝐶 𝑘 𝑥1 … . 𝑥 𝑛 = 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
  13. 13.  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 24.06.2015PSI, 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 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 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 H24SPR-WK SUM-WK FAL-WK WIN-WK All households– Water heating in PJ
  14. 14. Representation of heat systems [2] 24.06.2015PSI, Seite 14 Heat supply option 1 Heat supply option 2 Heat supply option N Heat storage Heat System 𝑎 𝑝,𝑡 ∙ 𝑋 𝑝,𝑡 𝑁𝐶𝐴𝑃 +𝑏 𝑝𝑝,𝑡 ∙ 𝑋 𝑝𝑝,𝑡 𝑁𝐶𝐴𝑃 = 𝑐1,𝑡 ∀𝑝 ∈ 𝑃𝑃𝑅𝐼 ; 𝑝𝑝 ∈ 𝑃𝑆𝐸𝐶∪𝑆𝑇𝐺 ; 𝑡 ∈ 𝑇 ; 𝑡𝑠_𝑝𝑒𝑎𝑘 ∈ 𝑇𝑆 ; 𝑎 𝑝,𝑡, 𝑏 𝑝,𝑡, 𝑐1,𝑡, 𝑐2,𝑡, 𝑐3,𝑡 const 𝑎 𝑝,𝑡 ∙ 𝑋 𝑝,𝑡 𝑁𝐶𝐴𝑃 +𝑏 𝑝𝑝,𝑡 𝑀𝐴𝑋 ∙ 𝑋 𝑝𝑝,𝑡 𝑁𝐶𝐴𝑃 ≤ 𝑐2,𝑡 𝑎 𝑝,𝑡 ∙ 𝑋 𝑝,𝑡 𝑁𝐶𝐴𝑃 +𝑏 𝑝𝑝,𝑡 𝑀𝐼𝑁 ∙ 𝑋 𝑝𝑝,𝑡 𝑁𝐶𝐴𝑃 ≥ 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 𝑋 𝑝,𝑡 𝐶𝐴𝑃 𝑝∈𝑃 𝑃𝑅𝐼 ≥ 𝑑𝑒𝑚𝑎𝑛𝑑 𝑡,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
  15. 15. Avoiding technology mix in heat supply 24.06.2015PSI, 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
  16. 16.  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 24.06.2015PSI, Seite 16 Secondary reserves Secondary reserves Tertiary reserves Source: Swissgrid
  17. 17.  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] 24.06.2015PSI, Seite 17
  18. 18.  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 ∗ 𝜎 𝐷 2 + 𝜎 𝐺 2 + 𝐾 Porting OSEMOSYS methodology 24.06.2015PSI, Seite 18 𝑋 𝑝,𝑡 𝐶𝐴𝑃 = 𝑋 𝑝𝑝,𝑡 𝐶𝐴𝑃 Power Plant p (participation in the electricity market) Power Plant pp (participation in the balancing markets) ELC Electricity 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
  19. 19. 𝑋 𝑝,𝑡,𝑡𝑠 𝐶𝐴𝑃𝑂𝑁 = 𝑋 𝑝,𝑡,𝑡𝑠 𝐸𝐿𝐶 /(𝑐𝑎𝑝𝑎𝑐𝑡 𝑝 ∙ 𝑦𝑟𝑓𝑟𝑡,𝑡𝑠) :online capacity of process 𝑝 Downward reserve: 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑘 ≤ 𝑋 𝑝,𝑡,𝑡𝑠 𝐶𝐴𝑃𝑂𝑁 ∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝 : 𝑘 ∈ 𝑃𝐷, 𝑆𝐷 Upward reserve for a fast ramping plant: (𝐦𝒂𝒙 𝒌,𝒑𝒑 ≥ 𝒔𝒕𝒂𝒃𝒍𝒆𝒐𝒑 𝒑𝒑) 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑘 ≤ 𝑋 𝑝𝑝,𝑡 𝐶𝐴𝑃 ∙ 𝑎𝑓𝑝𝑝,𝑡,𝑡𝑠 ∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝: 𝑘 ∈ 𝑃𝑈, 𝑆𝑈 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑃𝐷 + 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑆𝑈 ≤ 𝑋 𝑝,𝑡,𝑡𝑠 𝐸𝐿𝐶 𝑋 𝑝,𝑡,𝑡𝑠 𝐸𝐿𝐶 ≤ 𝑋 𝑝,𝑡,𝑡𝑠 𝐶𝐴𝑃𝑂𝑁 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝 Upward reserve for a slow ramping plant: ( 𝒎𝒂𝒙 𝒌,𝒑𝒑 ≤ 𝒔𝒕𝒂𝒃𝒍𝒆𝒐𝒑 𝒑𝒑) 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑘 ≤ 𝑋 𝑝,𝑡 𝐶𝐴𝑃𝑂𝑁 ∙ 𝑎𝑓𝑝𝑝,𝑡,𝑡𝑠 ∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝: 𝑘 ∈ 𝑃𝑈, 𝑆𝑈 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑃𝐷 + 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑆𝑈 + 𝑋 𝑝,𝑡 𝐶𝐴𝑃𝑂𝑁 ∙ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝 𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡_𝑝 ≤ 𝑋 𝑝,𝑡,𝑡𝑠 𝐸𝐿𝐶 𝑋 𝑝,𝑡,𝑡𝑠 𝐸𝐿𝐶 + 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑃𝐷 + 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑆𝑈 ≤ 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝐶𝐴𝑃𝑂𝑁 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝 Key equations for balancing services [3] 24.06.2015PSI, Seite 19
  20. 20. Minimum online upward reserve ( 𝑘 ∈ 𝑃𝑈, 𝑆𝑈 ) 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑘 𝑝𝑝 ≥ 𝐷𝐸𝑀 𝑘,𝑡,𝑡𝑠 ∙ 𝑚𝑖𝑛𝑜𝑛𝑙𝑖𝑛𝑒𝑡 All reserve from online plants when m𝑎𝑥 𝑘,𝑝𝑝 ≤ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝 𝑝𝑝 : 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑘 = 𝑋 𝑝,𝑡,𝑡𝑠 𝑂𝑁𝐿𝐼𝑁𝐸 𝑘 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝 Share of reserve from online plants when m𝑎𝑥 𝑘,𝑝𝑝 ≥ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝 𝑝𝑝 : 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑘 ≥ 𝑋 𝑝,𝑡,𝑡𝑠 𝑂𝑁𝐿𝐼𝑁𝐸 𝑘 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝 Upward reserve is limited by online capacity minus power output: 𝑋 𝑝,𝑡,𝑡𝑠 𝐶𝐴𝑃𝑂𝑁 − 𝑋 𝑝,𝑡,𝑡𝑠 𝐸𝐿𝐶 ≥ 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑂𝑁𝐿𝐼𝑁𝐸 𝑃𝑈 + 𝑋 𝑝𝑝,𝑡,𝑡𝑠 𝑂𝑁𝐿𝐼𝑁𝐸 𝑆𝑈 Upward reserve by online plants limited by their max contribution to upward reserve: 𝑋 𝑝,𝑡,𝑡𝑠 𝐶𝐴𝑃𝑂𝑁 ∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ≥ 𝑋 𝑝,𝑡,𝑡𝑠 𝑂𝑁𝐿𝐼𝑁𝐸 𝑘 Key equations for balancing services [3] 24.06.2015PSI, Seite 20
  21. 21.  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 24.06.2015PSI, Seite 21
  22. 22. Energy service demands in PJ Forecast of demands 24.06.2015PSI, Seite 22 0 100 200 300 400 500 600 2010 2012 2015 2020 2030 2040 2050 Residential thermal Residential electric specific Services thermal Services electric specific Industry thermal Industry electric specific
  23. 23. (excl. transport) Final energy consumption in PJ 24.06.2015PSI, 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
  24. 24. Share of technologies in residential heat 24.06.2015PSI, Seite 24 0% 10% 20% 30% 40% 50% 60% 2010 2020 2030 2040 2050 Heat pumps Heat * Gas boilers Oil boilers
  25. 25. Operational profiles of heat technologies 24.06.2015PSI, Seite 25 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23 Wood/Pelletts Gas Heatradiators Oil Heatpumps Electricboilers/resistors Solar Singlefamilyhouses 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23 Wood/Pelletts Gas Heatradiators Oil Heatpumps Electricboilers/resistors Solar Multifamilyhouses
  26. 26. Storage technologies for heat in services 24.06.2015PSI, 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 Heat Demand -120 -100 -80 -60 -40 -20 0 20 40 1 3 5 7 9 11 13 15 17 19 21 23 Thermochemical Storage of CHP heat Nightboilers
  27. 27. Electricity generation sector 24.06.2015PSI, 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
  28. 28. CHP Swarms (capacity & operation profile) 24.06.2015PSI, 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
  29. 29.  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 24.06.2015PSI, Seite 29
  30. 30. References 24.06.2015PSI, 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
  31. 31. 24. Juni 2015PSI, 24. Juni 2015PSI, Seite 31 Thank you for the attention ! Dr. Evangelos Panos Energy Economics Group in the Laboratory of Energy Analysis evangelos.panos@psi.ch

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