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Yuan, M., Yan, T.H. and Xu, Z.Z. (2026) Reinforcement Learning-Based Energy Management for Industrial Park with Heterogeneous Batteries under Demand Response. arXiv: 2604.03655.
https://arxiv.org/abs/2604.03655

has been cited by the following article:

• TITLE: Real-Time Demand Response Dispatch in Smart Grids: Balancing Economic Efficiency and Social Fairness via Advanced Data-Driven Approaches

  AUTHORS: Liyuan Liu, Junxiang Li, Wenjie Chen

  KEYWORDS: Demand Response, Deep Reinforcement Learning, Social Fairness, Smart Grid, Low-Carbon Economic Dispatch, Multi-Objective Optimization

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.14 No.4, April 27, 2026

  ABSTRACT: Decarbonizing modern power distribution networks requires a fundamental shift from generation-centric control to bi-directional, user-participatory mechanisms. While existing demand response (DR) frameworks prioritize total operational economy, they often neglect the disparate financial impacts on heterogeneous stakeholders, leading to potential social resistance. To address this, this study develops a multi-physics aware dispatch framework that reconciles economic objectives with cross-sectoral equity. Unlike conventional data-driven models, our approach embeds a LinDistFlow-based physical safety layer and a depth-of-discharge (DoD) battery degradation model directly into the learning environment to ensure hardware stewardship and grid stability. We employ a Soft Actor-Critic (SAC) agent optimized with Jain’s Fairness Index to dynamically allocate transition costs among residential, commercial, and industrial sectors. Simulation results on an IEEE 33-bus system demonstrate that the proposed strategy achieves zero voltage violations and a significant leap in social harmony, yielding a robust 92% fairness index with a negligible 3.2% sacrifice in system-wide efficiency.