As distributed energy resources (DERs) grow, the electricity grid faces increased net load variability at the grid edge, impacting operability and reliability. Transactive energy, facilitated through local energy markets, offers a decentralized, indirect demand response solution, with model-free control techniques, such as deep reinforcement learning (DRL), enabling automated, decentralized participation. However, existing studies largely overlook community-level net load variability, focusing instead on socioeconomic metrics. This study addresses this gap by using DRL agents to automate end-user participation in a local energy market (ALEX), where agents act independently to minimize individual energy bills. Results reveal a strong link between bill reduction and decreased net load variability, assessed across metrics such as ramping rate, load factor, and peak demand over various time horizons. Using a no-control baseline, DRL agents are benchmarked against a near-optimal dynamic programming approach. The dynamic programming benchmark achieves reductions of 22.05 percent, 83.92 percent, and 24.09 percent in daily import, export, and peak demand, respectively, while the DRL agents show comparable or superior results with reductions of 21.93 percent, 84.46 percent, and 27.02 percent. This study demonstrates the effectiveness of DRL in decentralized grid management, highlighting its scalability and near-optimal performance in reducing net load variability within community-driven energy markets.

翻译：随着分布式能源资源（DERs）的不断增长，电网在电网边缘面临日益加剧的净负荷波动性，这影响了电网的可操作性与可靠性。通过本地能源市场实现的交易能源，提供了一种去中心化、间接的需求响应解决方案，而深度强化学习（DRL）等无模型控制技术则实现了自动化、去中心化的参与。然而，现有研究大多忽视了社区层面的净负荷波动性，转而关注社会经济指标。本研究通过使用DRL智能体自动化终端用户在本地能源市场（ALEX）中的参与来弥补这一空白，其中智能体独立行动以最小化个体能源账单。结果表明，账单减少与净负荷波动性降低之间存在紧密联系，该评估基于不同时间尺度下的爬坡率、负载因子和峰值需求等指标。本研究以无控制基线为参照，将DRL智能体与一种接近最优的动态规划方法进行了性能比较。动态规划基准在每日输入、输出和峰值需求方面分别实现了22.05%、83.92%和24.09%的降低，而DRL智能体则表现出相当或更优的结果，分别降低了21.93%、84.46%和27.02%。本研究证明了DRL在去中心化电网管理中的有效性，并强调了其在社区驱动的能源市场中降低净负荷波动性方面具有可扩展性和接近最优的性能。