optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

Author:

Caminiti Corrado Maria1ORCID,Merlo Marco1ORCID,Fotouhi Ghazvini Mohammad Ali2ORCID,Edvinsson Jacob2

Affiliation:

1. Department of Energy, Politecnico di Milano, 20156 Milano, Italy

2. Volvo Car Corporation, 405 31 Göteborg, Sweden

Abstract

This study aims to develop an adaptable home energy management system capable of integrating the bidirectional smart charging of electric vehicles. The final goal is to achieve a user-defined objectives such as cost minimization or maximizing renewable self-consumption. Industrialwise, the present work yields valuable outcomes in identifying operational frameworks and boundary conditions. Optimal scheduling benefits both users and the electric network, thus enhancing grid utilization and increasing renewable energy integration. By coordinating power interactions with dynamic time-of-use tariffs, the energy management system minimizes user costs and aids the grid by cutting peak hour energy consumption. Charging and discharging operations in electric vehicles comply with energy level constraints outlined by bidirectional charging protocols. The proposed approach ensures the scheduling of cycles that minimize detrimental effects on battery health when evaluating an economically ageing mechanism. Compared to uncontrolled charging, optimal scheduling resulted in a significant reduction in the total operational cost of the dwelling. Trade-off conditions between renewable integration and potential savings are identified and numerically evaluated by means of multiobjective optimization. In contrast to scheduling-based models, the proposed architecture possesses the ability to iteratively adapt decision variables in response to system changes, thus responding effectively to external stochastic uncertainty.

Publisher

MDPI AG

Reference41 articles.

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3. Weiss, X., Xu, Q., and Nordström, L. (2022, January 5–9). Energy Management of Smart Homes with Electric Vehicles Using Deep Reinforcement Learning. Proceedings of the 2022 24th European Conference on Power Electronics and Applications (EPE’22 ECCE Europe), Hannover, Germany.

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