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Distributionally robust optimization of home energy management system based on receding horizon optimization

Distributionally robust optimization of home energy management system based on receding horizon... This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Frontiers in Energy" Springer Journals

Distributionally robust optimization of home energy management system based on receding horizon optimization

Wang, Jidong; Chen, Boyu; Li, Peng; Che, Yanbo
"Frontiers in Energy" , Volume 14 (2) – Jun 30, 2020
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References (35)

Publisher
Springer Journals
Copyright
Copyright © Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
ISSN
2095-1701
eISSN
2095-1698
DOI
10.1007/s11708-020-0665-4
Publisher site
See Article on Publisher Site

Abstract

This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.

Journal

"Frontiers in Energy" – Springer Journals

Published: Jun 30, 2020

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