As the energy landscape evolves toward sustainability, the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid. One significant aspect of this issue is the notable increase in net load variability at the grid edge. Transactive energy, implemented through local energy markets, has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized, indirect demand response on a community level. Given the nature of these challenges, model-free control approaches, such as deep reinforcement learning, show promise for the decentralized automation of participation within this context. Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics, overlooking the crucial goal of reducing community-level net load variability. This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in ALEX, an economy-driven local energy market. In this setting, agents do not share information and only prioritize individual bill optimization. The study unveils a clear correlation between bill reduction and reduced net load variability in this setup. The impact on net load variability is assessed over various time horizons using metrics such as ramping rate, daily and monthly load factor, as well as daily average and total peak export and import on an open-source dataset. Agents are then benchmarked against several baselines, with their performance levels showing promising results, approaching those of a near-optimal dynamic programming benchmark.

翻译：随着能源格局向可持续发展演进，分布式能源资源的加速整合对电网的可操作性和可靠性构成了挑战。该问题的关键方面之一是电网边缘净负荷波动性的显著增加。近年来，通过本地能源市场实施的交互式能源交易作为一种有前景的解决方案，以社区层面去中心化、间接需求响应的形式应对电网挑战。鉴于这些挑战的本质，无模型控制方法（如深度强化学习）在实现该场景下参与行为的去中心化自动化方面展现出潜力。现有关于交互式能源交易与无模型控制交叉领域的研究主要关注社会经济和自发自用指标，忽视了降低社区级净负荷波动性这一关键目标。本研究通过训练一组深度强化学习智能体来自动化终端用户在ALEX（一种由经济驱动的本地能源市场）中的参与行为来弥补这一空白。在此设定中，智能体之间不共享信息，仅优先考虑个体账单优化。研究揭示了该场景下账单削减与净负荷波动性降低之间的明确关联性。基于开源数据集，通过爬坡率、日与月负荷系数、日均及总峰值输出与输入等指标，评估了不同时间尺度下对净负荷波动性的影响。随后将智能体与多个基线进行对比，其性能表现展现出可喜成果，接近近优动态规划基准的性能水平。