Real-Time Residential Energy Optimization in Smart Grids: A Deep Reinforcement Learning Framework for Demand-Side Management
TL;DR
A Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management that learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions is proposed.
Abstract
The integration of photovoltaic generation, battery storage, electric vehicles, smart appliances, and dynamic electricity pricing has made residential energy management a challenging real-time optimization problem. Conventional demand-side management methods often depend on fixed rules and are less effective under uncertain solar generation, changing tariffs, and variable user demand. To address this issue, this paper proposes a Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management. The proposed PPO controller learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions. The controller coordinates shiftable, controllable, and non-shiftable loads while reducing electricity cost and maintaining user comfort. The proposed method is compared with DDPG and TRPO. Simulation results show that PPO reduces the average daily energy cost by 4.7% compared with TRPO and 8.3% compared with DDPG. The results confirm that PPO is an effective and stable approach for real-time residential demand-side management.