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A price decision approach for multiple multi-energy-supply microgrids considering demand response

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  • Li, Bei
  • Roche, Robin
  • Paire, Damien
  • Miraoui, Abdellatif

Abstract

Multi-energy-supply microgrids covering different types of demands are expected to play an important role in smart grids. Local generation, energy storage systems, and renewable energy sources can form load service entities, which can provide ancillary services to the utility grid and to consumers. On the other hand, microgrids can also sell energy to load service entities or the utility grid to obtain profits. But how the load service entities can decide the electricity selling price to multiple microgrids, and how the microgrids can decide the electricity selling price to load service entities are problems. In this paper, we present a price decision method for multiple microgrids considering demand response. Mixed integer linear programming is used to control the operation of each microgrid, and is also used to operate the load service entities. A genetic algorithm is used to search for the best price for each microgrid and the load service entities. The combined method is deployed in a decentralized way, namely, each microgrid runs its own operation problem. The simulation results show that the new searched price works better than a basic time of use price, which can reduce the operation cost of the whole system. The searching method is compared with a method based on the Cournot model. At last, a large system is tested, in which 4 load service entities, 16 microgrids and an IEEE30-node network are considered. In order to reduce the searching time, a neural network model is presented to estimate the operation of the whole system. Based on the neural network model, the prices are obtained, and the results show that the prices based on neural network model are better than with the time of use price.

Suggested Citation

  • Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2019. "A price decision approach for multiple multi-energy-supply microgrids considering demand response," Energy, Elsevier, vol. 167(C), pages 117-135.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:117-135
    DOI: 10.1016/j.energy.2018.10.189
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    References listed on IDEAS

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    5. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
    6. Longxi Li, 2020. "Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches," Energies, MDPI, vol. 13(12), pages 1-22, June.
    7. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    8. Zhou, Kaile & Wei, Shuyu & Yang, Shanlin, 2019. "Time-of-use pricing model based on power supply chain for user-side microgrid," Applied Energy, Elsevier, vol. 248(C), pages 35-43.
    9. Li, Bei & Miao, Hongzhi & Li, Jiangchen, 2021. "Multiple hydrogen-based hybrid storage systems operation for microgrids: A combined TOPSIS and model predictive control methodology," Applied Energy, Elsevier, vol. 283(C).
    10. Wenting Zhao & Jun Lv & Xilong Yao & Juanjuan Zhao & Zhixin Jin & Yan Qiang & Zheng Che & Chunwu Wei, 2019. "Consortium Blockchain-Based Microgrid Market Transaction Research," Energies, MDPI, vol. 12(20), pages 1-22, October.
    11. Li, Longxi, 2021. "Coordination between smart distribution networks and multi-microgrids considering demand side management: A trilevel framework," Omega, Elsevier, vol. 102(C).

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