Distributionally robust optimization of home energy management system based on receding horizon optimization
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RESEARCH ARTICLE
Jidong WANG, Boyu CHEN, Peng LI, Yanbo CHE
Distributionally robust optimization of home energy management system based on receding horizon optimization
© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords distributionally robust optimization (DRO), home energy management system (HEMS), receding horizon optimization (RHO), uncertainties
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Introduction
Home energy management system (HEMS) has attracted extensive attention in recent years, with the development of advanced metering infrastructure, smart sensor technologies, smart home appliances, and home area network, etc Received Jun. 6, 2019; accepted Nov. 20, 2019; online Mar. 30, 2020
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Jidong WANG, Boyu CHEN, Peng LI, Yanbo CHE ( ) Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China E-mail: [email protected]
[1]. By using HEMS, users can have two-way communication with smart grids and actively participate in demand response based on incentives from the grid, e.g., real-time price (RTP). Since the static stability problem of the grid can result from the high penetration of renewable energy resources contained in the HEMS, a novel small signal modeling approach based on characteristic equation for converter-dominated as microgrids is proposed to assess the low-frequency stability of the system [2]. It is crucial to design an efficient scheduling strategy for HEMS [3] and research on the scheduling strategy has received great interest. Hu et al. presented a hardware design of HEMS [4] and used the machine learning algorithm to achieve a control strategy, which contains three modes, based on RTP. A combined pricing model of RTP and inclining block rate was proposed [5] to reduce both the electricity cost and peak-to-average ratio. Reference [6] optimized the operation of heating, ventilation, and air conditioning (HVAC) based on RTP and significantly reduced peak loads and electricity cost with a modest vari
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