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Journal of Electrical Engineering & Technology

, Volume 14, Issue 6, pp 2277–2287 | Cite as

Offer Curve Generation for the Energy Storage System as a Member of the Virtual Power Plant in the Day-Ahead Market

  • Sooyeon Kim
  • Wook-Hyun Kwon
  • Hyeon-Jin Kim
  • Kyemyung Jung
  • Gi Soo Kim
  • Taehyoung Shim
  • Duehee LeeEmail author
Original Article
  • 32 Downloads

Abstract

We build an offer curve for an energy storage system (ESS), which is a member of the virtual power plant (VPP) with photovoltaic (PV) modules and load. The offer curve should be built based on the optimal VPP operations while having many pairs of bidding prices and amounts in order to respond to unknown prices. Therefore, we propose the VPP operation strategy and predict the scenarios of DA electricity prices, PV outputs, and load separately by using three autoregressive models. Then, we build the offer curve for the ESS at each hour for 24 h by considering optimal VPP operations and three predicted scenarios. Finally, we verify the optimality of the VPP operation strategy by comparing it to a fixed-time strategy. We also compare the profit of our ESS offer curve to that resulting from a single offer.

Keywords

Energy storage system Offer curve Day-ahead market Virtual power plant 

List of Symbols

Indices and Index Sets

t

Index of time

k

Index of scenario

T

Set of time index

K

Set of scenario index

Parameters

\(\eta\)

Efficiency of an energy storage system (ESS)

\(SOC_{max}\)

Maximum state of charge (SOC) (\(\text {kW}\))

\(SOC_{min}\)

Minimum SOC(\(\text {kW}\))

PR

Power rating of ESS (\(\text {kW}\))

\(\pi _{t}\)

Day-ahead (DA) price at time t (\(\text {KRW/kWh}\))

\(\pi _{t}^{REC}\)

DA price applying REC at time t (\(\text {KRW/kWh}\))

\(L_{t}\)

Load at time t (\(\text {kW}\))

\(PV_{t}\)

Photovoltaic (PV) output at time t (\(\text {kW}\))

Decision Variables

\(PL_{t}\)

Power flow from PV to load at time t (\(\text {kW}\))

\(PG_{t}\)

Power flow from PV to grid at time t (\(\text {kW}\))

\(PE_{t}\)

Power flow from PV to ESS at time t (\(\text {kW}\))

\(PE_{t}^{REC}\)

Power flow from PV to ESS can be applied REC at time t (\(\text {kW}\))

\(PE_{t}^{NOT}\)

Power flow from PV to ESS can not be applied REC at time t (\(\text {kW}\))

\(GL_{t}\)

Power flow from Grid to load at time t (\(\text {kW}\))

\(GE_{t}\)

Power flow from Grid to ESS at time t (\(\text {kW}\))

\(EL_{t}\)

Power flow from ESS to load at time t (\(\text {kW}\))

\(EG_{t}\)

Power flow from ESS to grid at time t (\(\text {kW}\))

\(EG_{t}^{NOT}\)

Power flow from ESS to grid not applying REC at time t charged from PV (\(\text {kW}\))

\(EG_{t}^{REC}\)

Power flow from ESS to grid applying REC at time t charged from PV (\(\text {kW}\))

\(SOC_{t}\)

SOC of ESS at time t (\(\text {kW}\))

\(\varDelta E_{t}\)

Sum of charging and discharging amount at time t (kW)

\(QC_{t}^{da}\)

Charging amount of ESS at time t (\(\text {kW}\))

\(QD_{t}^{da}\)

Discharging amount of ESS at time t (\(\text {kW}\))

\(u_{t}^{c}\)

Binary variables for charging at time t

\(u_{t}^{d}\)

Binary variables for discharging at time t

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) (No. NRF-2017M1A2A2092209). This research was supported by Korea Electric Power Corporation (No. R18xa06-24).

References

  1. 1.
    Ellabban O, Abu-Rub H, Blaabjerg F (2014) Renewable energy resources: current status, future prospects and their enabling technology. Renew Sustain Energy Rev 39:748–764CrossRefGoogle Scholar
  2. 2.
    Mai T, Hand MM, Baldwin SF, Wiser RH, Brinkman GL, Denholm P, Arent DJ, Porro G, Sandor D, Hostick DJ, Milligan M, DeMeo EA, Bazilian M (2014) Renewable electricity futures for the united states. IEEE Trans Sustain Energy 5(2):372–378CrossRefGoogle Scholar
  3. 3.
    Du E, Zhang N, Hodge B, Wang Q, Kang C, Kroposki B, Xia Q (2018) The role of concentrating solar power toward high renewable energy penetrated power systems. IEEE Trans Power Syst 33(6):6630–6641CrossRefGoogle Scholar
  4. 4.
    He G, Chen Q, Kang C, Xia Q, Poolla K (2017) Cooperation of wind power and battery storage to provide frequency regulation in power markets. IEEE Trans Power Syst 32(5):3559–3568CrossRefGoogle Scholar
  5. 5.
    Kazemi M, Zareipour H (2018) Long-term scheduling of battery storage systems in energy and regulation markets considering battery’s lifespan. IEEE Trans Smart Grid 9(6):6840–6849CrossRefGoogle Scholar
  6. 6.
    Mohsenian-Rad H (2016) Optimal bidding, scheduling, and deployment of battery systems in california day-ahead energy market. IEEE Trans Power Syst 31(1):442–453CrossRefGoogle Scholar
  7. 7.
    KSGA (2016) A research project for development of new business of energy industries, KSGA, Tech. RepGoogle Scholar
  8. 8.
    KEMCO (2017) A study on the current status of ess expansion and promotion policy in major countries and suggestions for domestic introduction to the new climate system, KEMCO, Tech. RepGoogle Scholar
  9. 9.
    Cheng B, Powell WB (2018) Co-optimizing battery storage for the frequency regulation and energy arbitrage using multi-scale dynamic programming. IEEE Trans Smart Grid 9(3):1997–2005Google Scholar
  10. 10.
    Lee D, Shin H, Baldick R (2018) Bivariate probabilistic wind power and real-time price forecasting and their applications to wind power bidding strategy development. IEEE Trans Power Syst 33(6):6087–6097CrossRefGoogle Scholar
  11. 11.
    Son S, Han S, Roh JH, Lee D (2018) Optimal offer strategies for energy storage system integrated wind power producers in the day-ahead energy and regulation markets. J Electr Eng Technol 13(6):2236–2244Google Scholar
  12. 12.
    He G, Chen Q, Kang C, Pinson P, Xia Q (2016) Optimal bidding strategy of battery storage in power markets considering performance-based regulation and battery cycle life. IEEE Trans Smart Grid 7(5):2359–2367CrossRefGoogle Scholar
  13. 13.
    Kardakos EG, Simoglou CK, Bakirtzis AG (2016) Optimal offering strategy of a virtual power plant: a stochastic bi-level approach. IEEE Trans Smart Grid 7(2):794–806Google Scholar
  14. 14.
    Kirschen DS, Strbac G (2004) Fundamentals of power system economics, vol 1. Wiley Online Library, HobokenCrossRefGoogle Scholar
  15. 15.
    Renani YK, Ehsan M, Shahidehpour M (2017) Day-ahead self-scheduling of a transmission-constrained genco with variable generation units using the incomplete market information. IEEE Trans Sustain Energy 8(3):1260–1268CrossRefGoogle Scholar
  16. 16.
    Jamali A, Aghaei J, Esmaili M, Niknam T, Nikoobakht A, Shafie-khah M, Catalao JPS (2019) Self-scheduling approach to coordinating wind power producers with energy storage and demand response. IEEE Trans Sustain Energy 1:1–1CrossRefGoogle Scholar
  17. 17.
    Zheng Y, Zhao J, Song Y, Luo F, Meng K, Qiu J, Hill DJ (2017) Optimal operation of battery energy storage system considering distribution system uncertainty. IEEE Trans Sustain Energy 9(3):1051–1060CrossRefGoogle Scholar
  18. 18.
    Lee S, Kim H, Shin H, Kim TH, Kim W (2018) A study on the estimation of optimal ess capacity considering rec weighting scheme. Trans Kor Inst Electr Eng 67(8):1009–1018Google Scholar
  19. 19.
    Conejo AJ, Plazas MA, Espinola R, Molina AB (2005) Day-ahead electricity price forecasting using the wavelet transform and arima models. IEEE Trans Power Syst 20(2):1035–1042CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringKonkuk UniversitySeoulKorea
  2. 2.Jubix Co., Ltd.SuwonKorea
  3. 3.Seondo Electric Co., Ltd.AnsanKorea
  4. 4.Electronics and Telecommunications Research Institute (ETRI)DaejeonKorea

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