Distributionally robust optimization for peer-to-peer energy trading considering data-driven ambiguity sets. (1st February 2023)

• Record Type: Journal Article
• Authors: Zhang, Xihai; Ge, Shaoyun; Liu, Hong; Zhou, Yue; He, Xingtang; Xu, Zhengyang
• Is Part Of: Applied energy. Volume 331(2023)
• Publication Date: 2023-02-01
• Subjects: Distributionally robust optimization -- Deep Gaussian process -- Data-driven ambiguity sets -- Peer-to-peer energy trading; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120436
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: Peer-to-peer (P2P) energy trading provides potential economic benefits to prosumers. The prosumers are responsible for managing their own resources/reserves within the energy community, especially for photovoltaic (PV). However, the intermittency of PV leaves a major issue for the optimal operation of P2P energy trading. This paper proposes a fully data-driven distributionally robust optimization (DRO) for P2P energy trading. Specifically, both the optimization approach and the ambiguity set of DRO are formed in a data-driven fashion. The proposed formulation minimizes the expected operation cost of each prosumer, which is modeled as a DRO problem considering the operational constraints. A decentralized energy negotiation mechanism and market clearing algorithm are proposed for P2P energy trading based on the alternating direction multiplier method. Furthermore, the ambiguity set is formed by deep Gaussian process under the framework of bootstrap aggregating. Finally, the equivalent linear programming reformulations of the proposed DRO model are carried out and solved in a distributed manner. Numerical results demonstrate that the proposed DRO-based approach has superior performance for handling the randomness of PV generation compared with robust optimization, stochastic programming, and other DRO variants. Graphical abstract: Highlights: The uncertainties of PV generation are quantified by implicit posterior variational inference deep Gaussian process. A novel data-driven ambiguity set is proposed for modeling P2P energy trading. The tailor-made equivalent model is derived based on the proposed ambiguity set. This is the first attempt to integrate the DRO model and machine learning approach to P2P energy trading.