Degradation-Aware Learning-Based Control for Residential PV–Battery Systems

Residential photovoltaic (PV)–battery systems are increasingly deployed to reduce electricity costs under time-of-use and demand-charge tariffs, yet their economic value depends critically on how storage is operated over time. Effective control must simultaneously address short-term energy costs, peak-demand exposure, and long-term battery degradation, all under substantial uncertainty in load and PV generation. While optimization-based approaches can achieve strong performance with accurate forecasts, they are sensitive to forecast errors, whereas learning-based methods often neglect degradation effects or deplete the battery prematurely, leading to suboptimal peak-shaving behavior. This paper proposes a forecast-free, degradation-aware reinforcement learning (RL) framework for residential PV–battery energy management that jointly addresses demand-charge mitigation and battery aging. The proposed controller internalizes both calendar aging and rainflow-based cycling degradation within its objective and incorporates demand-aware reward shaping with time-varying penalties on on-peak grid imports. In addition, a complementary state-of-charge reserve mechanism discourages premature battery depletion and improves responsiveness to late on-peak demand surges, despite the absence of explicit load or PV forecasts. Physical feasibility is guaranteed through an execution-time safety layer that enforces all device and operational constraints by construction. The proposed framework is evaluated on high-resolution residential datasets and compared against optimization-based baselines, including a day-ahead scheduler with perfect foresight and a receding-horizon MPC controller using short-horizon forecasts. Overall, the results show that the proposed RL controller substantially reduces demand charges and total electricity costs relative to forecast-based MPC while maintaining degradation-aware operation, demonstrating the potential of forecast-free reinforcement learning as a practical control strategy for residential PV–battery systems under demand-charge tariffs.