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Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads

Author

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  • Shafiekhani, Morteza
  • Ahmadi, Abdollah
  • Homaee, Omid
  • Shafie-khah, Miadreza
  • Catalão, João P.S.

Abstract

The accumulation of many production units with small capacities and transforming them into a larger entity will make them visible in electricity market. Renewable based virtual power plant (VPP) in this paper is a wide energy management system that incorporates probabilistic wind and solar units, non-renewable Distributed Generation (DG) units, and dispatchable loads. In an electricity market, a VPP optimizes its operating schedules in order to increase its economic efficiency. However, market uncertainties may influence the VPP's profit. In this paper, modelling the uncertainties is implemented by the proposed Information Gap Decision Theory (IGDT). The mentioned scheduling problem is formulated in three operation modes: risk-neutral, risk-averse and risk-seeker. The risk-neutral mode focuses on optimizing the VPP in the day-ahead market. In the risk-averse mode, the robustness function is used under low market prices. Moreover, in the risk seeker mode, an opportunity function is used under higher market prices towards higher profit results. The proposed model allows the VPP to decide on the scheduling of its components and the optimal bids to the day-ahead market. Another purpose is to investigate the role of the renewable-based VPP in minimizing emission and maximizing profit in a two-objective way. The IEEE 18-bus test system is utilized to simulate the proposed problem and analyse the results. The performance of the proposed problem is approved using different scenarios. Simulation results justify the advantages and necessities of the proposed problem.

Suggested Citation

  • Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221026281
    DOI: 10.1016/j.energy.2021.122379
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    References listed on IDEAS

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    1. Mirzaei, Mohammad Amin & Sadeghi-Yazdankhah, Ahmad & Mohammadi-Ivatloo, Behnam & Marzband, Mousa & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Integration of emerging resources in IGDT-based robust scheduling of combined power and natural gas systems considering flexible ramping products," Energy, Elsevier, vol. 189(C).
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    3. Mei, Shufan & Tan, Qinliang & Liu, Yuan & Trivedi, Anupam & Srinivasan, Dipti, 2023. "Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction," Energy, Elsevier, vol. 284(C).
    4. Zhang, Xinyue & Guo, Xiaopeng & Zhang, Xingping, 2023. "Bidding modes for renewable energy considering electricity-carbon integrated market mechanism based on multi-agent hybrid game," Energy, Elsevier, vol. 263(PA).
    5. Mostafa Darvishi & Mehrdad Tahmasebi & Ehsan Shokouhmand & Jagadeesh Pasupuleti & Pitshou Bokoro & Jwan Satei Raafat, 2023. "Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    6. Guo, Hongye & Chen, Qixin & Shahidehpour, Mohammad & Xia, Qing & Kang, Chongqing, 2022. "Bidding behaviors of GENCOs under bounded rationality with renewable energy," Energy, Elsevier, vol. 250(C).
    7. Kanjanapon Borisoot & Rittichai Liemthong & Chitchai Srithapon & Rongrit Chatthaworn, 2023. "Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators," Energies, MDPI, vol. 16(6), pages 1-19, March.
    8. Chao Huang & Zhenyu Zhao & Qingwen Li & Xiong Luo & Long Wang, 2024. "Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique," Energies, MDPI, vol. 17(2), pages 1-14, January.
    9. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    10. Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2023. "Predictive operation optimization of multi-energy virtual power plant considering behavior uncertainty of diverse stakeholders," Energy, Elsevier, vol. 280(C).
    11. Wang, Jian & Ilea, Valentin & Bovo, Cristian & Xie, Ning & Wang, Yong, 2023. "Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework," Energy, Elsevier, vol. 278(PB).
    12. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    13. Songkai Wang & Rong Jia & Chang Luo & Yuan An & Pengcheng Guo, 2022. "Spatiotemporal Complementary Characteristics of Large-Scale Wind Power, Photovoltaic Power, and Hydropower," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    14. Zhao, Kaifang & Qiu, Kai & Yan, Jian & Shaker, Mir Pasha, 2023. "Technical and economic operation of VPPs based on competitive bi–level negotiations," Energy, Elsevier, vol. 282(C).
    15. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).

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