Distributionally Fair Peer-to-Peer Electricity Trading

TL;DR

This paper introduces a novel optimization mechanism for peer-to-peer electricity trading that ensures distributional fairness among community members, significantly reducing unfairness in energy exchanges.

Contribution

It proposes a distributionally fair trading model based on Wasserstein distance minimization, addressing fairness issues in semi-decentralized energy communities.

Findings

Up to 70.1% reduction in unfairness achieved.

Full fairness achieved with a non-profit photovoltaic plant.

Effective in a simulated IEEE 33-bus distribution grid.

Abstract

Peer-to-peer energy trading platforms enable direct electricity exchanges between peers who belong to the same energy community. In a semi-decentralized system, a community manager adheres to grid restrictions while optimizing social welfare. However, with no further supervision, some peers can be discriminated against from participating in the electricity trades. To solve this issue, this paper proposes an optimization-based mechanism to enable distributionally fair peer-to-peer electricity trading. For the implementation of our mechanism, peers are grouped by energy poverty level. The proposed model aims to redistribute the electricity trades to minimize the maximum Wasserstein distance among the transaction distributions linked to the groups while limiting the sacrifice level with a predefined parameter. We demonstrate the effectiveness of our proposal using the IEEE 33-bus distribution grid, simulating an energy community with 1600 peers. Results indicate that up to 70.1% of unfairness can be eliminated by using our proposed model, even achieving a full elimination when including a non-profit community photovoltaic plant.