  In response to the growing uptake of distributed energy resources (DERs),
community batteries have emerged as a promising solution to support renewable
energy integration, reduce peak load, and enhance grid reliability. This paper
presents a deep reinforcement learning (RL) strategy, centered around the soft
actor-critic (SAC) algorithm, to schedule a community battery system in the
presence of uncertainties, such as solar photovoltaic (PV) generation, local
demand, and real-time energy prices. We position the community battery to play
a versatile role, in integrating local PV energy, reducing peak load, and
exploiting energy price fluctuations for arbitrage, thereby minimizing the
system cost. To improve exploration and convergence during RL training, we
utilize the noisy network technique. This paper conducts a comparative study of
different RL algorithms, including proximal policy optimization (PPO) and deep
deterministic policy gradient (DDPG) algorithms, to evaluate their
effectiveness in the community battery scheduling problem. The results
demonstrate the potential of RL in addressing community battery scheduling
challenges and show that the SAC algorithm achieves the best performance
compared to RL and optimization benchmarks.
