强化学习
电池(电)
启发式
马尔可夫决策过程
水准点(测量)
计算机科学
光伏系统
电
数学优化
马尔可夫过程
工程类
功率(物理)
人工智能
物理
量子力学
电气工程
统计
数学
大地测量学
地理
作者
Mostafa Rezaeimozafar,Maeve Duffy,Rory F.D. Monaghan,Enda Barrett
标识
DOI:10.1016/j.apenergy.2023.122244
摘要
The behind-the-meter (BTM) energy management problem has recently attracted a lot of attention due to the increase in the number of residential photovoltaic (PV)-battery energy storage systems (BESSs). In this work, the use of deep reinforcement learning (DRL) combined with a novel heuristic model for real-time control of home batteries is investigated. The control problem is formulated as a finite Markov Decision Process with discrete time steps, where a proximal policy optimization (PPO) algorithm is employed to train the DRL agent with discrete action space. The agent is trained using real-world measured data to learn the policy for sequential charge/discharge tasks, aiming to minimize daily electricity costs. The battery power is calculated using an innovative heuristic model considering the agent's decision and the battery's available capacity, ensuring demand-supply balance through PV self-consumption and load demand shifting. The performance of the model is evaluated by comparing it to four RL agents and two benchmark models based on rule-based and scenario-based stochastic optimization strategies. The results confirm that the presented model outperforms its counterparts, offering €80.38 savings on electricity bills over 46 days of the test data set. This figure exceeds the savings of the rule-based and stochastic models by €15.64 and €19.38, respectively.
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