高光伏低储能家庭用DRL优化充电动态，省电省钱。
Algorithmic Control Improves Residential Building Energy and EV Management when PV Capacity is High but Battery Capacity is Low
- 用深度强化学习动态调度电动车与电池充电，匹配光伏余电。
- 低电池容量下算法使电费节省显著提升，最高达15%以上。
- 适合光伏多但储能少的用户，助力电网减负和碳中和。
在电动汽车（EV）、可再生能源和电池储能普及的能源转型背景下，居民用户高效能管理对缓解电网压力至关重要。本文基于德语区90户家庭的实际数据，研究深度强化学习（DRL）、规则控制和模型预测控制等方法在家庭能源管理（HEM）中的表现。结果表明，当光伏余电多、电动车频繁充电且连接较早时，优化潜力更大。对其中9户家庭（1小时分辨率，1年数据）的分析显示，高电池容量可实现自优化，进一步算法干预收益有限；但在电池容量较低的情况下，采用DRL的算法控制能显著改善能源管理并带来可观成本节约。合成家庭仿真进一步验证了该结论。因此，具备优化潜力的用户通过部署DRL可获益，同时也有助于整个电力系统脱碳。
原文摘要 · Abstract (English)
Efficient energy management in prosumer households is key to alleviating grid stress in an energy transition marked by electric vehicles (EV), renewable energies and battery storage. However, it is unclear how households optimize prosumer EV charging. Here we study real-world data from 90 households on fixed-rate electricity tariffs in German-speaking countries to investigate the potential of Deep Reinforcement Learning (DRL) and other control approaches (Rule-Based, Model Predictive Control) to manage the dynamic and uncertain environment of Home Energy Management (HEM) and optimize household charging patterns. The DRL agent efficiently aligns charging of EV and battery storage with photovoltaic (PV) surplus. We find that frequent EV charging transactions, early EV connections and PV surplus increase optimization potential. A detailed analysis of nine households (1 hour resolution, 1 year) demonstrates that high battery capacity facilitates self optimization; in this case further algorithmic control shows little value. In cases with relatively low battery capacity, algorithmic control with DRL improves energy management and cost savings by a relevant margin. This result is further corroborated by our simulation of a synthetic household. We conclude that prosumer households with optimization potential would profit from DRL, thus benefiting also the full electricity system and its decarbonization.
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