电力公司终于有了一个能模拟用户行为反馈的强化学习训练场，做电网调度或能源 AI 的团队可以直接用这个开源环境来优化需求响应策略，减少居民电费风险。
极端天气和波动的电力批发市场给居民用户带来巨大财务风险，但配电网级别的需求响应尚未被充分利用。DR-Gym 是一个开源、兼容 Gymnasium 的在线环境，从电力公司视角训练和评估需求响应策略。它通过状态切换的批发价格模型（校准自真实极端事件）和基于物理的建筑需求曲线，模拟定价信号与用户接受度之间的动态反馈。该环境支持可配置的多目标奖励函数，并已通过基线策略验证其可学习性。
Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs
Extreme weather and volatile wholesale electricity markets expose residential consumers to catastrophic financial risks, yet demand response at the distribution level remains an underutilized tool for grid flexibility and energy affordability. While a demand-response program can shield consumers by issuing financial credits during high-price periods, optimizing this sequential decision-making process presents a unique challenge for reinforcement learning despite the plentiful offline historical smart meter and wholesale pricing data available publicly. Offline historical data fails to capture the dynamic, interactive feedback loop between an electric utility's pricing signals and customer acceptance and adaptation to a demand-response program. To address this, we introduce DR-Gym, an open-source, online Gymnasium-compatible environment designed to train and evaluate demand-response from the electric utility's perspective. Unlike existing device-level energy simulators, our environment focuses on the market-level electric utility setting and provides a rich observational space relevant to the electric utility. The simulator additionally features a regime-switching wholesale price model calibrated to real-world extreme events, alongside physics-based building demand profiles. For our learning signal, we use a configurable, multi-objective reward function for specifying diverse learning objectives. We demonstrate through baseline strategies and data snapshots the capability of our simulator to create realistic and learnable environments.