含电池储能系统的智能楼宇多阶段能量管理策略

doi:10.3969/j.issn.2097-0706.2022.03.005

摘要/Abstract

摘要：

智能楼宇是新型电力系统中不可或缺的组成部分。面对分布式电源出力与负荷需求的不确定性的问题,智能楼宇系统的可靠与经济运行更加依赖于内部能量的优化调度。为协调优化智能楼宇的能量管理,为含电池储能系统以及充电场站的智能楼宇提出了一种多阶段能量管理方法：在日前阶段,设计采用能够应对可再生电源出力和负荷需求不确定性的鲁棒优化策略,同时考虑了储能系统的时间耦合约束;在日内阶段,基于加权模型预测控制的方法,以日前优化策略为参考,采用滚动优化和反馈校正的方式,动态调整各可控机组的有功出力、储能系统的充放电功率、电动汽车的充电策略以及与主网的能量交易,以适应源荷实时波动性。对某智能楼宇利用风电和光伏能源、采用传统的和本文提出的能源管理策略时的数据进行仿真分析。结果表明,本文所提出多阶段能量管理方法可有效控制含电池储能系统的智能楼宇的运行成本,同时可降低与主网的联络线功率,减小可再生能源和负荷的随机性和波动性对系统运行的影响。

关键词: 智能楼宇, 能量管理, 电池储能系统, 电动汽车, 多阶段模型预测控制, 新型电网, 碳中和, 新型电力系统

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

中图分类号:

TK02

刘静, 史梦鸽, 胡永锋. 含电池储能系统的智能楼宇多阶段能量管理策略[J]. 综合智慧能源, 2022, 44(3): 29-37.

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.

图/表 14

图1

表1

柴油发电机组参数

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)

表1

表2

HVAC机组参数

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 ℃

表2

表3

电池储能系统参数

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

表3

图2

图3

图4

图5

图6

图7

图8

表4

表5

表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