An Explainable Equity-Aware P2P Energy Trading Framework for Socio-Economically Diverse Microgrid

TL;DR

This paper presents an innovative, explainable, and adaptive energy trading framework for microgrids that enhances fairness and efficiency by integrating multi-objective optimization, cooperative game theory, and reinforcement learning.

Contribution

It introduces a novel, dynamic equity-adjustment mechanism using RL within an MILP-based energy trading framework, incorporating fairness principles and explainability for socio-economically diverse microgrids.

Findings

Peak demand reductions of up to 72.6% achieved.

The RL mechanism reduces inequality as measured by the Gini coefficient.

Framework demonstrates significant cooperative gains across scenarios.

Abstract

Fair and dynamic energy allocation in community microgrids remains a critical challenge, particularly when serving socio-economically diverse participants. Static optimization and cost-sharing methods often fail to adapt to evolving inequities, leading to participant dissatisfaction and unsustainable cooperation. This paper proposes a novel framework that integrates multi-objective mixed-integer linear programming (MILP), cooperative game theory, and a dynamic equity-adjustment mechanism driven by reinforcement learning (RL). At its core, the framework utilizes a bi-level optimization model grounded in Equity-regarding Welfare Maximization (EqWM) principles, which incorporate Rawlsian fairness to prioritize the welfare of the least advantaged participants. We introduce a Proximal Policy Optimization (PPO) agent that dynamically adjusts socio-economic weights in the optimization objective based on observed inequities in cost and renewable energy access. This RL-powered feedback loop enables the system to learn and adapt, continuously striving for a more equitable state. To ensure transparency, Explainable AI (XAI) is used to interpret the benefit allocations derived from a weighted Shapley value. Validated across six realistic scenarios, the framework demonstrates peak demand reductions of up to 72.6%, and significant cooperative gains. The adaptive RL mechanism further reduces the Gini coefficient over time, showcasing a pathway to truly sustainable and fair energy communities.