Distributionally robust optimization for peer-to-peer energy trading considering data-driven ambiguity sets

Xihai Zhang, Shaoyun Ge, Hong Liu, Yue Zhou, Xingtang He and Zhengyang Xu

Applied Energy, 2023, vol. 331, issue C, No S0306261922016932

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.

Keywords: Distributionally robust optimization; Deep Gaussian process; Data-driven ambiguity sets; Peer-to-peer energy trading (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922016932
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:331:y:2023:i:c:s0306261922016932

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.120436

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().