考虑电动汽车及负荷聚合商参与的综合能源系统优化调度

doi:10.16183/j.cnki.jsjtu.2022.029

摘要/Abstract

摘要：

充分发挥用户侧调节作用可以降低综合能源系统(IES)能源购买成本.需求响应(DR)和电动汽车(EV)作为用户侧的可调度资源,是IES优化调度的重要调节手段.但实际运行过程中,受负荷聚合商(LA)经济激励和EV出行的影响,用户侧DR的不确定性给IES带来的经济影响不容忽略.基于此,提出考虑EV鲁棒随机优化及LA参与的IES优化运行模型,该模型考虑IES从上级网络的购能成本和LA的经济损失成本等.首先构建基于经济激励的响应率模型和EV不确定性模型;然后建立EV鲁棒优化模型,并分析EV出行不确定性的负荷需求.最后利用仿真算例分析用户DR不确定性和EV不确定性对IES运行经济性以及对功率平衡的影响.仿真结果表明:考虑DR和EV的不确定性可优化IES经济运行、减小LA经济损失、降低系统总成本,验证了所提模型的有效性和经济性.

关键词: 综合能源系统, 负荷聚合商, 需求响应, 电动汽车, 不确定性

Abstract:

Fully tapping into the role of user side regulation helps reduce the energy cost of integrated energy system (IES). Demand response (DR) and electric vehicle (EV) as schedulable resources on the user side are important regulation means for optimal scheduling of IES. However, in the actual operation process, due to the influence of load aggregator (LA) economic incentives and EV travel, the economic impact of the uncertainty of user side DR on IES cannot be ignored. Based on this, this paper proposes an IES optimal operation model considering the robust stochastic optimization of EV and the participation of LA which considers the energy purchase cost of IES from the superior network and the economic loss cost of LA. First, the response rate model and EV uncertainty model based on economic incentive are constructed. Then, the robust optimization model of EV is built, and the load demand of EV travel uncertainty is analyzed. Finally, a simulation example is given to analyze the impact of user DR uncertainty and EV uncertainty on IES operation economy and power balance. The simulation results show that considering the uncertainty of DR and EV can optimize the economic operation of IES and reduce the economic loss of LA and the total cost, which verifies the effectiveness and economy of the proposed models.

Key words: integrated energy system (IES), load aggregator (LA), demand response (DR), electric vehicle (EV), uncertainty

中图分类号:

TM732

王精, 邢海军, 王华昕, 彭思佳. 考虑电动汽车及负荷聚合商参与的综合能源系统优化调度[J]. 上海交通大学学报, 2023, 57(7): 814-823.

WANG Jing, XING Haijun, WANG Huaxin, PENG Sijia. Optimal Scheduling of Integrated Energy System Considering Integration of Electric Vehicles and Load Aggregators[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 814-823.

图/表 15

图1

图2

图3

图4

图5

表1

表2

各设备数据

装置	参数
CHP	$P C H P m a x$ =2 000 kW, $η e C H P$ =0.35, $η h C H P$ =0.4
GB	$P G B m a x$ =800 kW, $η h G B$ =0.85
EC	$P E C m a x$ =400 kW, COP_EC=4.0
AR	$P A R m a x$ =1 000 kW, COP_AR=0.8
HST	$E h m a x$ =500 kW·h, $E h m i n$ =50 kW·h
	$P H S T m a x$ =125 kW, η_h,ch=η_h,dis=0.96

表2

表3

表4

图6

图7

图8

图9

图10

图11

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用电负荷	时段	价格/[元·(kW·h)^-1]
峰	8:00—11:00	1.2
	18:00—21:00	1.2
平	6:00—8:00	0.75
	12:00—14:00	0.75
谷	0:00—5:00	0.35
	15:00—17:00	0.35
	22:00—24:00	0.35

场景	DR不确定性	EV不确定性
1	√	×
2	×	√
3	×	×
4	√	√

场景	成本
场景	购能成本× 10^-4/元	LA经济损失成本×10^-4/元	EV充放电成本/元	总成本× 10^-4/元
1	117.14	5.41	1 169.58	122.66
2	116.55	5.85	1 115.60	122.52
3	119.04	4.84	1 210.76	124.01
4	116.25	4.13	1 097.32	121.38