Privacy-Preserving Cost-Efficient Smart Metering by Variational-Constraint Adversarial Reinforcement Learning

Smart metering of high-time-resolution energy data enables efficient power grid management. However, it also raises significant privacy concerns by revealing users’ consumption patterns. In this paper, a novel privacy-preserving idea is introduced by utilizing a rechargeable battery (RB) to reshape the smart meter readings to statistically align with random target readings, which are preset independently of the private user energy consumption data. For the long-term privacy-preserving and cost-efficient objectives, we formulate a sequential energy management unit (EMU) policy design as a constrained Markov decision process (CMDP), where the cost-efficient objective is optimized subject to the constraint on privacy preservation. We then develop a novel variational-constraint adversarial proximal policy optimization (VCA-PPO) algorithm to solve the CMDP without requiring prior knowledge of probabilistic models. Experimental results on a standard real-world dataset demonstrate the effectiveness of the proposed method and its superiority to the load-flatness benchmark method.