Energy flexibility in German industry

Transcription

1 Energy flexibility in German industry An economic model-based analysis of parallel revenue streams from batteries INSTITUTE FOR INDUSTRIAL PRODUCTION (IIP) (Prof. Dr. W. Fichtner) Fritz Braeuer, Russell McKenna, Wolf Fichtner KIT The Research University in the Helmholtz Association

2 Agenda Why energy flexibility? Battery revenue streams Research question The flex-model Results Conclusion/Outlook

3 Why energy flexibility? Integration of growing amount of renewable energy (International Electrotechnical Commission (2011)) Avoid grid extension (Reid und Julve (2016); Bolay et al. (2016)) Prices for battery storage systems (BSS) have dropped (Kairies et al. (2016)) Curtailed energy through feedin-management in GWh year total wind solar bio-mass Curtailed energy through feed-in-management in Germany (Bundesnetzagentur (2016))

4 Why energy flexibility? Integration of growing amount of renewable energy (International Electrotechnical Commission (2011)) Avoid grid extension (Reid und Julve (2016); Bolay et al. (2016)) Prices for battery storage systems (BSS) have dropped (Kairies et al. (2016)) Curtailed energy through feedin-management in GWh year total wind solar bio-mass Increasing need for energy flexibility Pseudo-flexibility through a battery storage system (BSS) Incentive for demand side management in industry Curtailed energy through feed-in-management in Germany (Bundesnetzagentur (2016))

5 Battery revenue streams Balancing control power Arbitrage trading Primary Balancing Control Secondary Balancing Control Day-ahead market Intraday market Peak shaving Increasing self-consumption Capacity prices for grid connection Individual grid prices Utilizing own energy production PV, Wind or CHP

6 Battery revenue streams Reserve power Arbitrage Ttading Primary balancing control Secondary balancing control Back-up power Day-ahead market Intraday market Peak shaving Increasing self-consumption Capacity prices for grid connection Individual grid prices Utilizing own energy production PV, Wind or CHP

7 Battery revenue streams Reserve power Arghandeh, R. et al. 201 Arbitrage Ttading Primary balancing control Secondary balancing control Back-up power Moreno, R. et al Stephan, A. et al Peak shaving Cho, J. and Kleit, A. 2015; Dowling, A. et al Day-ahead market Intraday market Increasing self-consumption Capacity prices for grid connection Individual grid prices Utilizing own energy production PV, Wind or CHP

8 Research question By following different revenue streams in parallel, can we increase the profitability of a battery storage system (BSS)?

9 The flex-model P Prod, PV, power from PV to production P Prod, BS, power form BS to production P Prod, Grid, power form grid to production P PV, BS, power from PV to BS P PV, Grid, power from PV to grid P BS, Grid, power between BS and grid P Grid, total power from or to the grid

10 Model explanation Energy demand and supply profiles Battery dispatch schedule

11 Model explanation min cccccctt vvvvvv = 168 min cccccctt vvvvvv = h=1 + xx hoooooo hoooooo h,ww cc h,ww Arbitrage Fritz Braeuer Current status: Decentralized multi energy model

12 Model explanation min cccccctt vvvvvv = 168 min cccccctt vvvvvv = h=1 + xx hoooooo hoooooo h,ww cc h,ww Arbitrage 50 min cccccctt vvvvvv = ww=1 168 h=1 + xx hoooooo hoooooo h,ww cc h,ww + PP ww PPPPPP cc ww PPPPPP Primary balancing power (PBP) Fritz Braeuer Current status: Decentralized multi energy model

13 Model explanation min cccccctt vvvvvv = 168 min cccccctt vvvvvv = h=1 + xx hoooooo hoooooo h,ww cc h,ww Arbitrage 50 min cccccctt vvvvvv = ww=1 168 h=1 + xx hoooooo hoooooo h,ww cc h,ww + PP ww PPPPPP cc ww PPPPPP Primary balancing power (PBP) min cccccctt vvvvvv = xx hoooooo hoooooo h,ww cc h,ww + PP ww PPPPPP cc ww PPPPPP + PP PPPPPPPP cc PPPPPPPP Peak shaving ww=1 h= Fritz Braeuer Current status: Decentralized multi energy model

14 Model explanation min cccccctt vvvvvv = 168 min cccccctt vvvvvv = h=1 + xx hoooooo hoooooo h,ww cc h,ww Arbitrage 50 min cccccctt vvvvvv = ww=1 168 h=1 + xx hoooooo hoooooo h,ww cc h,ww + PP ww PPPPPP cc ww PPPPPP Primary balancing power (PBP) min cccccctt vvvvvv = xx hoooooo hoooooo h,ww cc h,ww + PP ww PPPPPP cc ww PPPPPP + PP PPPPPPPP cc PPPPPPPP Peak shaving ww=1 h=1 Investment annuity: min cccccccc = cccccctt vvvvvv + IIIIvv BBBBBB AAAAnn ii,tt Fritz Braeuer Current status: Decentralized multi energy model

15 Model explanation min cccccctt vvvvvv = 168 min cccccctt vvvvvv = h=1 + xx hoooooo hoooooo h,ww cc h,ww Arbitrage 50 min cccccctt vvvvvv = ww=1 168 h=1 + xx hoooooo hoooooo h,ww cc h,ww + PP ww PPPPPP cc ww PPPPPP Primary balancing power (PBP) min cccccctt vvvvvv = 50 ww=1 Investment annuity: 168 h=1 min cccccccc = cccccctt vvvvvv + IIIIvv BBBBBB AAAAnn ii,tt + xx hoooooo hoooooo h,ww cc h,ww + PP ww PPPPPP cc ww PPPPPP + PP PPPPPPPP cc PPPPPPPP MILP 30 blocks of variables - 88,552 single variables 1 blocks of equations 798,053 single equations 50 discrete variables Implemented in GAMS CPLEX solver Peak shaving Fritz Braeuer Current status: Decentralized multi energy model

16 Input data Base year industrial load profiles Company Peak load in kw Yearly demand in MWh EPEX prices day-ahead hourly intraday quarterly continuous trading Prices for primary balancing power Frequency data in seconds Fixed capacity prices 10,000 /MW*a Battery parameter Capacity Power capability Lifetime Investment 1300 kwh 1300 kw 11 years 1100 /kwh

17 Exemplary two days Energy per time step in kwh 15-min time steps

18 Cumulated cost of energy per week Cumulated Cost per week in ,000-1,500-2,000-2,500-3,000-3,500 -,000 -,500-5,000 Week 1 Week 2 Time Primary Balancing Power Arbitrage All use cases

19 Annual cost in Use Case Company Peak shaving 97,687 3,82, ,310 10,371,557 37,93 Primary Balancing Power 52,273 62,190 28,736 1,200,99 26,111 Arbitrage 39,25 9,97 1,73 1,189,6 11,200 Primary + Arbitrage 33,15,073 8,700 1,183,572 5,0 All use cases 23,676 28,517-3,070 1,166,897 3,87 Peak load in kw 391 1, , Reference case (0.15 /kwh) 355,660 3,81, ,129 10,155, ,

20 Annual cost in Use Case Company Peak shaving 97,687 3,82, ,310 10,371,557 37,93 Primary Balancing Power 52,273 62,190 28,736 1,200,99 26,111 Arbitrage 39,25 9,97 1,73 1,189,6 11,200 Primary + Arbitrage 33,15,073 8,700 1,183,572 5,0 All use cases 23,676 28,517-3,070 1,166,897 3,87 Peak load in kw 391 1, , Reference case (0.15 /kwh) 355,660 3,81, ,129 10,155, ,

21 Conclusion Parallel revenue streams increase profitability of a BSS Strongly depends on: Production profile Production size vs. battery size Either primary balancing power or arbitrage trading Extreme electricity price spreads diminish the advantage of an industrial load combined with a BSS

22 Outlook Battery degradation and sizing need to be considered Grid cost need to be considered Bid sizes need to be considered Uncertainties need to be considered Aggregating industry loads need to be considered PV-Self-Production needs to be integrated Balancing power demand in more detail

23 Fritz Braeuer Research Associate Karlsruhe Institute of Technology (KIT) Institute for Industrial Production (IIP) Hertzstraße Karlsruhe Germany Phone: Fax: Web: INSTITUTE FOR INDUSTRIAL PRODUCTION (IIP) (Prof. Dr. W. Fichtner) KIT The Research University in the Helmholtz Association

24 References I International Electrotechnical Commission (2011): Electrical Energy Storage. Hg. v. International Electrotechnical Commission. Reid, Gerard; Julve, Javier (2016): Second Life-Batteries as Felxible Sotrage for Renewables Energies. Hg. v. Bundesverband Eneuerbare Energien e.v. (BEE). Bolay, Sebastian; Bullmann, Till; Hegner, Miriam (2016): Faktenpapier Energiespeicher. Rechtsrahmen, Geschäftsmodelle, Forderungen. Hg. v. BVES - Bundesverband Energiespeicher e.v. Berlin und DIHK - Deutscher Industrie- und Handelskammertag. Kairies, K.-P.; Haberschusz, D.; van Ouwerkerk, J.; Strebel, J.; Wessels, O.; Magnor, D.; Badeda, J.; Sauer, D. U. (2016): Wissenschaftliches Mess- und Evaluierungsprogramm Solarstromspeicher - Jahresbericht Shoreh, Maryam H.; Siano, Pierluigi; Shafie-khah, Miadreza; Loia, Vincenzo; Catalão, João P.S. (2016): A survey of industrial applications of Demand Response. In: Electric Power Systems Research 11, S

25 Reference II Arghandeh, Reza; Woyak, Jeremy; Onen, Ahmet; Jung, Jaesung; Broadwater, Robert P. (201): Economic optimal operation of Community Energy Storage systems in competitive energy markets. In: APPLIED ENERGY 135 (SI), S DOI: /j.apenergy Cho, Joohyun; Kleit, Andrew N. (2015): Energy storage systems in energy and ancillary markets: A backwards induction approach. In: APPLIED ENERGY 17, S DOI: /j.apenergy Dowling, Alexander W.; Kumar, Ranjeet; Zavala, Victor M. (2017): A multi-scale optimization framework for electricity market participation. In: APPLIED ENERGY 190, S DOI: /j.apenergy Rodrigo Moreno; Roberto Moreira; Goran Strbac: A MILP model for optimising multiservice portfolios of distributed energy storage. Stephan, A.; Battke, B.; Beuse, M. D.; Clausdeinken, J. H.; Schmidt, T. S. (2016): Limiting the public cost of stationary battery deployment by combing applications. In: Nature Energy 2016 (Vol. 1). In: Applied Energy

26 Appendix