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arXiv · 2203.08639

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

Julian Lemos-Vinasco·Amos Schledorn·S. Ali Pourmousavi·Daniela Guericke
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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.

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Julian Lemos-Vinasco, Amos Schledorn, S. Ali Pourmousavi, Daniela Guericke. 2022-03-16. Economic evaluation of stochastic home energy management systems in a realistic rolling horizon setting. https://arxiv.org/abs/2203.08639

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