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Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

Author

Listed:
  • Chao Huang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China)

  • Zhenyu Zhao

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Qingwen Li

    (Department of Civil Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Xiong Luo

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Long Wang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C 2 oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C 2 oDE, NDE, and EDE, respectively. Compared to C 2 oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:380-:d:1317733
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    References listed on IDEAS

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    1. Janke, Leandro & McDonagh, Shane & Weinrich, Sören & Murphy, Jerry & Nilsson, Daniel & Hansson, Per-Anders & Nordberg, Åke, 2020. "Optimizing power-to-H2 participation in the Nord Pool electricity market: Effects of different bidding strategies on plant operation," Renewable Energy, Elsevier, vol. 156(C), pages 820-836.
    2. Ali Wagdy Mohamed, 2018. "A novel differential evolution algorithm for solving constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 659-692, March.
    3. Nitsch, Felix & Deissenroth-Uhrig, Marc & Schimeczek, Christoph & Bertsch, Valentin, 2021. "Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets," Applied Energy, Elsevier, vol. 298(C).
    4. 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).
    5. Jun Dong & Xihao Dou & Dongran Liu & Aruhan Bao & Dongxue Wang & Yunzhou Zhang, 2023. "Energy Trading Strategy of Distributed Energy Resources Aggregator in Day-Ahead Market Considering Risk Preference Behaviors," Energies, MDPI, vol. 16(4), pages 1-27, February.
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