Economic evaluation of stochastic home energy management systems in a realistic rolling horizon setting

TL;DR

This paper presents a stochastic, optimization-based home energy management system that considers load uncertainty and real market conditions, demonstrating significant economic benefits through comprehensive simulations with real data.

Contribution

It introduces a new HEMS controller integrating probabilistic forecasting and stochastic optimization in a rolling horizon, evaluated with real data from Danish homes.

Findings

Seasonality significantly affects control strategy performance.

Optimization-based strategies outperform naive control in certain conditions.

Combined control strategies yield the best economic outcomes.

Abstract

Home energy management systems (HEMSs) are expected to become a crucial part of future smart grids. However, there is a limited number of studies that comprehensively assess the potential economic benefits of HEMS for consumers under real market conditions and which take account of consumers' capabilities. In this study, a new optimization-based HEMS controller is presented to operate a photovoltaic and battery system. The HEMS controller considers the consumers' electrical load uncertainty by integrating multivariate probabilistic forecasting methods and a stochastic optimization in a rolling horizon. As a case study, a comprehensive simulation study is designed to emulate the operation of a real HEMS using real data from nine Danish homes over different seasons under real-time retail prices. The optimization-based control strategies are compared with a default (naive) control strategy that encourages self consumption. Simulation results show that seasonality in the consumers' load and electricity prices have a significant impact on the performance of the control strategies. A combination of optimization-based and naive control strategy presents the best overall results.