Multi-stage energy management strategy for smart buildings with BESS

doi:10.3969/j.issn.2097-0706.2022.03.005

Abstract

Abstract:

Smart building is an indispensable part of new power system. Dealing with the uncertain renewable energy output and load demand, a smart building system has to reasonably schedule the internal energy to realize the reliable and economical operation. To manage the energy optimal scheduling in smart buildings, a multi-stage energy management strategy for smart buildings including battery energy storage system（BESS） and electric vehicle charging stations is proposed. In day-ahead stage, a robust optimization strategy is designed to deal with the uncertainty of renewable power output and load demand, considering the time coupling constraints of the BESS. In intra-day stage, weighted model predictive control method is taken to adjust the optimization strategy in day-ahead stage, and rolling optimization and feedback correction methods are employed to dynamically control the active output of each controllable power supplier, the power charged and discharged from BESS, electric vehicle charging strategy and power transaction with the main power grid in order to adapt to the real-time fluctuation of energy output and load demand. The simulation results show that the proposed multi-stage energy management structure of the smart building embedded BESS can effectively reduce the operation cost of the smart building,lower the tie-line power and reduce the influence of the randomness and volatility brought by renewable energy and load demand on the system operation.

Key words: smart building, energy management, BESS, electric vehicle, multi-stage model prediction control, new power grid, carbon neutrality, new power system

CLC Number:

TK02

LIU Jing, SHI Mengge, HU Yongfeng. Multi-stage energy management strategy for smart buildings with BESS[J]. Integrated Intelligent Energy, 2022, 44(3): 29-37.

Figures/Tables 14

Fig.1

Table 1

Parameters of the generator

项目	参数	项目	参数
$P G max$	100 kW	$P G up$	40 kW/min
$P G min$	20 kW	$P G down$	30 kW/min
$a$	0.15 美元/h	$t G on$	2 h
$b$	0.06 美元/(kW·h)	$t G off$	2 h
$c$	0.000 2 美元/(kW²·h)

Table 1

Table 2

Parameters of the HVAC unit

项目	参数	项目	参数
$G$	1.5 kW·h/℃	室温设定值	24 ℃
$R$	0.95 ℃/（kW·h）	室温最小值	22 ℃
$η$	0.15	室温最大值	27 ℃

Table 2

Table 3

Parameters of the BESS

项目	参数	项目	参数
$η ch$	0.95	$S OC, min$	0.2
$η dis$	0.95	$S OC, max$	0.95
$P ch max$	50 kW	$S OC, 0$	0.5
$P dis max$	50 kW	$K s$	0.061 3 美元/(kW·h)
$E bat$	100 kW

Table 3

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 4

Table 6

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场景	项目	运行成本/美元	交互功率/kW
1	含BESS	556.04	3 815.55
1	不含BESS	568.72	4 059.26
2	含BESS	1 254.92	5 165.86
2	不含BESS	1 295.61	5 370.91

策略	成本/美元		偏差/%
策略	场景1	场景2	场景1	场景2
本文所提策略	556.04	1 254.92	13.77	8.65
传统鲁棒优化策略	623.43	1 340.73	27.56	16.08
理想调度	488.73	1 155.04	—	—

场景	能量管理策略	运行成本/美元	交互功率/kW
1	单一阶段	652.36	4 673.23
1	多阶段	556.04	3 815.55
2	单一阶段	1 402.75	5 837.49
2	多阶段	1 254.92	5 165.86