Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and effort to trade themselves on their own on short-term LEMs. Thus, machine learning algorithms are proposed to take over the bidding for households under realistic market information. We simulate a LEM on a 15 min merit-order market mechanism and deploy reinforcement learning as strategic learning for the agents. In a multi-agent simulation of 100 households including PV, micro-cogeneration, and demand shifting appliances, we show how participants in a LEM can achieve a self-sufficiency of up to 30% with trading and 41,4% with trading and demand response (DR) through an installation of only 5kWp PV panels in 45% of the households under affordable energy prices. A sensitivity analysis shows how the results differ according to the share of renewable generation and degree of demand flexibility.

中文翻译：

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当地能源市场的强化学习

当地的能源市场（LEM）非常适合应对欧洲能源转型运动的挑战。他们鼓励对可再生能源（RES）的投资，可以改善RES在能源系统中的整合，并赋予当地社区权力。但是，由于电力消耗低，居民家庭既没有专门知识，也不想花费时间和精力自行进行短期LEM交易。因此，提出了机器学习算法，以在现实的市场信息下接管住户的竞标。我们在15分钟的绩效订单市场机制上模拟LEM，并将强化学习作为代理商的战略学习。在对100个家庭的多主体模拟中，包括光伏，微型热电联产和需求转移设备，我们展示了LEM参与者如何通过在负担得起的能源下为45％的家庭安装仅5kWp的光伏电池板，在贸易中实现自给自足，达到30％，在贸易和需求响应（DR）时达到41.4％。价格。敏感性分析表明，结果根据可再生能源发电量的比例和需求灵活性的程度而有所不同。