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Optimization of Peer-to-Peer Power Trading in a Microgrid with Distributed PV and Battery Energy Storage Systems

Author

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  • Hui Huang

    (North China Electric Power University, Department of Economic Management, Beijing 102206, China)

  • Shilin Nie

    (North China Electric Power University, Department of Economic Management, Beijing 102206, China)

  • Jin Lin

    (North China Electric Power University, Department of Economic Management, Beijing 102206, China)

  • Yuanyuan Wang

    (North China Electric Power University, Department of Economic Management, Beijing 102206, China)

  • Jun Dong

    (North China Electric Power University, Department of Economic Management, Beijing 102206, China)

Abstract

Integrating distributed generation (DG) into the main grid is a challenge for the safety and stability of the grid. The application of peer-to-peer (P2P) technology in microgrids with distributed generation is expected to facilitate increased self-consumption of distributed and renewable energy, and the rise of prosumers’ monetary benefits. A P2P energy trading model in microgrids with photovoltaic (PV) distributed generation and battery energy storage systems (BESSs) is proposed in this paper. We additionally designed a P2P electricity trading mechanism based on coalition game theory. A simulation framework of this model is presented which assumed a local community with 30 households under comprehensive constraints encompassing a customer load profile, PV system, BESSs, market signals including feed-in tariffs, and retail prices. Firstly, individual customers can post orders (purchasing orders or selling orders) and exchange information in a P2P energy trading market. Secondly, the microgrid operator can validate the orders based on how to achieve the minimum overall energy consumption in microgrids and set reasonable real-time purchasing and selling prices for P2P energy transactions. Thirdly, the orders can be automatically conducted and completed at the designed optimal price. This mechanism can be a practical solution motivating individual customers to participate in P2P electricity trading, assist with electricity cost reduction, benefit from electricity supply increases, and help the grid operators to make the most economically and socially friendly decisions.

Suggested Citation

  • Hui Huang & Shilin Nie & Jin Lin & Yuanyuan Wang & Jun Dong, 2020. "Optimization of Peer-to-Peer Power Trading in a Microgrid with Distributed PV and Battery Energy Storage Systems," Sustainability, MDPI, vol. 12(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:923-:d:313418
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    References listed on IDEAS

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    Cited by:

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    3. Liu, Junhong & Long, Qinfei & Liu, Rong-Peng & Liu, Wenjie & Hou, Yunhe, 2023. "Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading," Applied Energy, Elsevier, vol. 331(C).
    4. Alaa A. F. Husain & Maryam Huda Ahmad Phesal & Mohd Zainal Abidin Ab Kadir & Ungku Anisa Ungku Amirulddin & Abdulhadi H. J. Junaidi, 2021. "A Decade of Transitioning Malaysia toward a High-Solar PV Energy Penetration Nation," Sustainability, MDPI, vol. 13(17), pages 1-16, September.
    5. Schwidtal, J.M. & Piccini, P. & Troncia, M. & Chitchyan, R. & Montakhabi, M. & Francis, C. & Gorbatcheva, A. & Capper, T. & Mustafa, M.A. & Andoni, M. & Robu, V. & Bahloul, M. & Scott, I.J. & Mbavarir, 2023. "Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    6. Hassan Khazaei & Hossein Aghamohammadloo & Milad Habibi & Mehdi Mehdinejad & Amin Mohammadpour Shotorbani, 2023. "Novel Decentralized Peer-to-Peer Gas and Electricity Transaction Market between Prosumers and Retailers Considering Integrated Demand Response Programs," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    7. Lissy Langer, 2020. "An Optimal Peer-to-Peer Market Considering Modulating Heat Pumps and Photovoltaic Systems under the German Levy Regime," Energies, MDPI, vol. 13(20), pages 1-25, October.
    8. Bogdan-Constantin Neagu & Ovidiu Ivanov & Gheorghe Grigoras & Mihai Gavrilas & Dumitru-Marcel Istrate, 2020. "New Market Model with Social and Commercial Tiers for Improved Prosumer Trading in Microgrids," Sustainability, MDPI, vol. 12(18), pages 1-43, September.
    9. Nizami, Sohrab & Tushar, Wayes & Hossain, M.J. & Yuen, Chau & Saha, Tapan & Poor, H. Vincent, 2022. "Transactive energy for low voltage residential networks: A review," Applied Energy, Elsevier, vol. 323(C).
    10. Li, Xianshan & Lu, Mingfang & Li, Fei & Xiong, Wei & Li, Zhenxing, 2022. "Prosumer energy-storage trading feasibility evaluation and price bundling," Energy, Elsevier, vol. 239(PB).
    11. Koo-Hyung Chung & Don Hur, 2020. "Towards the Design of P2P Energy Trading Scheme Based on Optimal Energy Scheduling for Prosumers," Energies, MDPI, vol. 13(19), pages 1-15, October.
    12. Zhang, Ying & Robu, Valentin & Cremers, Sho & Norbu, Sonam & Couraud, Benoit & Andoni, Merlinda & Flynn, David & Poor, H. Vincent, 2024. "Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities," Applied Energy, Elsevier, vol. 355(C).
    13. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    14. Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael, 2021. "Mixed-integer linear programming based optimization strategies for renewable energy communities," Energy, Elsevier, vol. 237(C).
    15. Mariam Gómez Sánchez & Yunesky Masip Macia & Alejandro Fernández Gil & Carlos Castro & Suleivys M. Nuñez González & Jacqueline Pedrera Yanes, 2020. "A Mathematical Model for the Optimization of Renewable Energy Systems," Mathematics, MDPI, vol. 9(1), pages 1-16, December.

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