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Optimal scheduling of a cogeneration system via Q-learning-based memetic algorithm considering demand-side response

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  • Pang, Xinfu
  • Wang, Yibao
  • Yu, Yang
  • Liu, Wei

Abstract

An optimization scheduling method for cogeneration systems based on the Q-learning-based memetic algorithm (QMA) is developed to enhance the system's wind power absorption capacity and economical operation. First, an optimal scheduling model for cogeneration systems is constructed. The model integrates time-of-use electricity pricing and heating comfort into the demand side, fully exploiting the potential of demand-side response (DR) in power generation and heating. An additional heat source (AHS), which consists of an electric boiler and heat storage tank, is used to uncouple heat and power. In addition, a QMA algorithm is designed to find the optimal operating scheme for the cogeneration system. The Q-learning algorithm is introduced to dynamically adjust crossover and mutation parameters during the global evolution stage, improving the algorithm's search capability. The Taguchi method is utilized for algorithm parameter calibration. Finally, the simulation results under various operating scenarios are compared and analyzed, verifying the feasibility and effectiveness of the proposed method. The simulation results show that DR and AHS can improve the system's economic performance and wind power utilization rate. Compared with the IMA, IPSO, MA, and IABC algorithms, the QMA algorithm reduces the average economic cost by approximately 1.18 %, 2.23 %, 5.69 %, and 14.69 %, respectively.

Suggested Citation

  • Pang, Xinfu & Wang, Yibao & Yu, Yang & Liu, Wei, 2024. "Optimal scheduling of a cogeneration system via Q-learning-based memetic algorithm considering demand-side response," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224012866
    DOI: 10.1016/j.energy.2024.131513
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    References listed on IDEAS

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    1. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2022. "Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy," Applied Energy, Elsevier, vol. 314(C).
    2. Gimelli, A. & Mottola, F. & Muccillo, M. & Proto, D. & Amoresano, A. & Andreotti, A. & Langella, G., 2019. "Optimal configuration of modular cogeneration plants integrated by a battery energy storage system providing peak shaving service," Applied Energy, Elsevier, vol. 242(C), pages 974-993.
    3. Garmabdari, R. & Moghimi, M. & Yang, F. & Lu, J., 2020. "Multi-objective optimisation and planning of grid-connected cogeneration systems in presence of grid power fluctuations and energy storage dynamics," Energy, Elsevier, vol. 212(C).
    4. He, Yi & Guo, Su & Zhou, Jianxu & Wu, Feng & Huang, Jing & Pei, Huanjin, 2021. "The many-objective optimal design of renewable energy cogeneration system," Energy, Elsevier, vol. 234(C).
    5. Lai, Wenhao & Song, Qi & Zheng, Xiaoliang & Tao, Qiong & Chen, Hualiang, 2023. "A new version of membrane search algorithm for hybrid renewable energy systems dynamic scheduling," Renewable Energy, Elsevier, vol. 209(C), pages 262-276.
    6. Wu, Danman & Bai, Jiayu & Wei, Wei & Chen, Laijun & Mei, Shengwei, 2021. "Optimal bidding and scheduling of AA-CAES based energy hub considering cascaded consumption of heat," Energy, Elsevier, vol. 233(C).
    7. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Pasdeloup, Bastien & Meyer, Patrick, 2023. "Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1296-1330.
    8. Shaheen, Abdullah M. & Ginidi, Ahmed R. & El-Sehiemy, Ragab A. & Elattar, Ehab E., 2021. "Optimal economic power and heat dispatch in Cogeneration Systems including wind power," Energy, Elsevier, vol. 225(C).
    9. La Fata, Alice & Brignone, Massimo & Procopio, Renato & Bracco, Stefano & Delfino, Federico & Barilli, Riccardo & Ravasi, Martina & Zanellini, Fabio, 2022. "An efficient Energy Management System for long term planning and real time scheduling of flexible polygeneration systems," Renewable Energy, Elsevier, vol. 200(C), pages 1180-1201.
    10. Basu, Mousumi, 2023. "Scenario-based fuel-constrained heat and power scheduling of a remote microgrid," Energy, Elsevier, vol. 277(C).
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