mlsanityData to 14 Oct 2026

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

arXiv 2507.18738 ↗First posted 24 Jul 2025eess.SY cs.GT cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 21 references that are in the map. First posted on arXiv 24 Jul 2025.

Builds on

3 of 21 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2023Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Pricecited by 2
  3. 2024Decentralized Equitable Energy Access in Energy Communitiescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Marketscited by 0

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  4. 2026A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs
  5. 2025Deep Reinforcement Learning-Based Bidding Strategies for Prosumers Trading in Double Auction-Based Transactive Energy Market
  6. 2025Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning
  7. 2025FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets
  8. 2026Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities