Model-Free Reinforcement Learning Economic Dispatch Algorithms for Price-Based Residential Demand Response Management System

It is projected that plug-in electric vehicles (PEVs) would steadily increase as household appliances. However, PEVs' high power consumption, stochastic usage patterns, and storage capacity will surely result in a rise in the elasticity of demand response and pose significant difficulties for price-based residential demand response management (PRDRM). This artcle aims to optimize a two-tier globally shared nonconvex PRDRM problem with local constraints and PEVs, known as social welfare: maximizing retailer profits and minimizing the combined residential costs. This is done by balancing residential electricity use with retail electricity prices in an unknown market environment. The proposed online/offline model-free reinforcement learning-based economic dispatch (MFRL-ED) methods can adaptively decide on the ideal retail price sequence by integrating the daily residential-retailer behavior model with the agent-environment interaction method, providing a basic MFRL-ED solution for PRDRM without a system identification step and an accurate load-retail model. Experiments show that MFRL-ED methods provide an effective class of PRDRM solutions.