Deep Reinforcement Learning-based Capacity Scheduling for PV-Battery Storage System
Published in IEEE Transactions on Smart Grid, 2020
Recommended citation: Huang, Bin, et al. (2020). "Deep Reinforcement Learning-based Capacity Scheduling for PV-Battery Storage System." IEEE TRANSACTIONS ON SMART GRID. http://binhuangscut.github.io/files/J3.pdf
Investor-owned photovoltaic-battery storage systems (PV-BSS) can gain revenue by providing stacked services, including PV charging and frequency regulation, and by performing energy arbitrage. Capacity scheduling (CS) is a crucial component of PV-BSS energy management, aiming to ensure the secure and economic operation of the PV-BSS. This paper proposes a Proximal Policy Optimization (PPO)-based deep reinforcement learning (DRL) agent to perform the CS of PV-BSS.
Recommended citation: Huang, Bin, et al. (2020). “Deep Reinforcement Learning-based Capacity Scheduling for PV-Battery Storage System.” IEEE TRANSACTIONS ON SMART GRID.