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

doi:10.1007/s11708-020-0665-4

Front. Energy

2020, Vol. 14

Issue (2) : 254-266 https://doi.org/10.1007/s11708-020-0665-4

RESEARCH ARTICLE

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

Jidong WANG, Boyu CHEN, Peng LI, Yanbo CHE(

)

Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China

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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

Corresponding Author(s): Yanbo CHE

Online First Date: 26 March 2020 Issue Date: 22 June 2020

Cite this article:

Jidong WANG,Boyu CHEN,Peng LI, et al. Distributionally robust optimization of home energy management system based on receding horizon optimization[J]. Front. Energy, 2020, 14(2): 254-266.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-020-0665-4
https://academic.hep.com.cn/fie/EN/Y2020/V14/I2/254

Fig.1 Structure of HEMS.

Fig.2 Interval number of outdoor temperature with 5 levels.

Fig.3 Time frame of RHO.

Fig.4 Flowchart of optimization process.

Fig.5 Forecast value.

Name	$b$	$e$	Duration/h	Power/kW
WM	20:30	23:00	1	1
DS	12:30	14:30	1	1.5
EV	00:30	9:30	3	3
PP	15:00	20:00	1	3

Tab.1 Data of ILs and NILs

C_p/(J·kg^?¹·°C^?¹)	M/L	γ_WH/°C	β_WH/(°C·kg^?¹)	P_WH,MAX/kW	$θ water min ?$ /°C	$θ water max$ /°C
4185	184	0.04	0.068	3	60	70

Tab.2 Data of EWH

R/(°C·kW^?¹)	C/(kWh·°C^?¹)	P_{AC, max}/kW	$θ room min$ /°C	$θ room max$ /°C
18	0.525	1.8 20		24

Tab.3 Data of AC

$L θ, t m$ /°C	$U θ, t m$ /°C	$L d, t m$ /L	$U d, t m$ /L	E_θ/°C	E_d/L	σ_θ/°C²	σ_d/L²
$θ out, ? t f − 3$	$θ out, ? t f +3$	$0.7 d WH, ? t f$	$1 .3 d WH, ? t f$	$θ out, ? t f$	$d WH, ? t f$	1	$0.1 d WH, ? t f$

Tab.4 Data of model of uncertain variables

Fig.6 Data. (a) Outdoor temperature; (b) hot water demand.

Fig.7 Scheduling results. (a) Scenario 1; (b) scenario 2; (c) scenario 3.

Fig.8 Indoor temperature and hot water temperature. (a) Scenario 1; (b) scenario 2; (c) scenario 3.

Tab.5 Data of electricity cost and violation in three scenarios

Fig.9 Electricity cost of RHO-DRO method and RHO-RO method in different optimization time domains.

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