PowerMarketJax：面向电力市场的多智能体强化学习JAX基准套件
PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets
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中文总结 AI 辅助
PowerMarketJax是一个基于JAX的电力市场多智能体强化学习基准套件，涵盖五种市场，支持GPU并行训练，实现高达33倍加速，并揭示市场设计对学习策略的显著影响。
中文摘要 AI 辅助
电力市场是多智能体强化学习（MARL）的自然试验平台，其中多个自利的参与者反复提交投标。市场出清机制随后根据电网约束和市场结算规则确定调度和价格。然而，现有的MARL环境通常聚焦于单一市场设置，实现简化的出清机制，或依赖基于CPU的优化求解器，这减慢了大规模训练并限制了对投标策略和市场行为的系统研究。我们引入了PowerMarketJax，一个涵盖五个电力市场的MARL基准套件：日前批发市场、实时平衡市场、辅助服务市场、点对点双向拍卖市场和本地灵活性市场。每个环境实现了自己的出清、定价和结算规则，同时为学习和评估提供了通用框架。我们发现，学习到的投标行为强烈依赖于市场设计：当收益需要多个智能体共同改变时，独立学习者可能错过更好的策略；当更有利可图的策略位于较低利润区域之外时；或者当更多智能体采用相同策略导致利润消失时。PowerMarketJax在JAX中实现了市场模拟和策略训练，使得整个流程能够在GPU上运行，具有1,024×1,200的并行度，覆盖环境和市场参与者，相比基于CPU的基线实现了高达33倍的加速。我们的开源基准可在以下网址获取：此https URL。
英文摘要
Power markets are a natural testbed for multi-agent reinforcement learning (MARL), where multiple self-interested participants repeatedly submit bids. A market-clearing mechanism then determines dispatch and prices subject to power grid constraints and market settlement rules. However, existing MARL environments typically focus on a single market setting, implement simplified clearing mechanisms, or rely on CPU-based optimization solvers that slow large-scale training and limit the systematic study of bidding strategies and market behavior. We introduce PowerMarketJax, a benchmark suite for MARL across five power markets: day-ahead wholesale, real-time balancing, ancillary services, peer-to-peer double auctions, and local flexibility. Each environment implements its own clearing, pricing, and settlement rules while providing a common framework for learning and evaluation. We find that learned bidding behavior depends strongly on the market design: independent learners can miss better strategies when gains require many agents to change together, when more profitable strategies lie beyond a region of lower profit, or when profits disappear as more agents adopt the same strategy. PowerMarketJax implements both market simulation and policy training in JAX, allowing the entire pipeline to run on the GPU with 1,024 X 1,200 parallelisms across both environments and market participants, achieving up to 33X speedup over CPU-based baselines. Our open-source benchmark is available at: https://github.com/powermarketjax/PowerMarketJax.
发表机构
- Nanyang Technological University（南洋理工大学）
- Cornell University（康奈尔大学）
- University of Bristol（布里斯托大学）
机构由 AI 辅助整理，请以论文原文为准。
