Stochastic optimization of home energy management system using clustered quantile scenario reduction

Abstract

Recent proliferation of renewable energy has increased the installation of residential energy sources (e.g., roof-top photovoltaic (PV) panel and residential wind turbine) in households. To manage electricity usage incurred by renewable energy and residential load, home energy management systems (HEMSs) provide intelligence to home by real-time monitoring and controlling appliances. In this paper, we propose a novel HEMS framework considering multiple uncertainties from renewable generation and load profiles. First, we generate scenarios of each uncertainty through deep learning. Then, we propose an algorithm called clustered quantile scenario reduction (CQSR) to reduce computation time while preserving the stochastic properties of generated scenarios based on the Wasserstein-1 distance. We prove that solution of CQSR is determined by the number of clustered scenarios. Also, we show provable upper bound of performance degradation incurred by the scenario reduction. Simulation results show that the optimality gap and computation time of the proposed framework is substantially reduced compared to other HEMS algorithms, e.g., by up to 81.4% and 93.7%, respectively. Furthermore, although the original scenarios are generated through different scenario generation algorithms, HEMS using CQSR is less vulnerable to performance degradation incurred by scenario reduction.