Peak shaving and self-consumption maximization in home energy management systems: A combined integer programming and reinforcement learning approach

This paper proposes a novel framework for Home Energy Management System based on the
combination of integer programming and Reinforcement Learning (RL) for achieving efficient
home-based Demand Response (DR). In particular, RL is exploited to manage the charge and
discharge of Battery Energy Storage System (BESS), and Mixed Integer Linear Programming is
exploited for load scheduling. The idea is to focus the RL specifically on BESS management,
as its behavior is stochastic and is mainly affected by Photovoltaic (PV) production and user
behavior changes. The scheduling decisions of household appliances, Electric Vehicles (EVs),
and charging/discharging batteries can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR-induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV, PV panels, and electric storage. The test results demonstrate the effectiveness of the proposed home energy management framework under the application of different demand-side flexibility strategies.