考虑需求侧灵活性资源的商业园区微网多目标优化调度

doi:10.12204/j.issn.1000-7229.2021.03.005

摘要/Abstract

摘要：

大规模可再生能源、电动汽车(electric vehicle,EV)和变频空调(inverter air conditioner,IAC)接入商业园区微网(business park microgrid,BPMG)产生的波动性和不确定性为系统灵活运行带来新的挑战。文章提出了一种考虑需求侧灵活性资源的BPMG优化调度方法。基于EV和IAC需求响应模型,量化BPMG需求侧灵活性资源提供的灵活性供给。结合源侧固有灵活性资源和灵活性需求,提出灵活性不足严重度指标,定量评估系统运行灵活性供需失衡的严重程度。在此基础上,建立兼顾运行经济性和灵活性的BPMG多目标优化调度模型,并使用CPLEX优化软件求解。仿真结果表明,所提模型能够协调调度源、荷两侧各类灵活性资源,有效提升BPMG的运行灵活性。

关键词: 商业园区微网, 需求侧灵活性资源, 灵活性不足严重度, 优化调度

Abstract:

Integration of renewable energy, electric vehicles (EV), and inverter air conditioners (IAC) in the business park microgrid (BPMG) result in fluctuation and uncertainty, which lead to new challenges in operational flexibility of the BPMG. Therefore, a BPMG optimal scheduling method considering demand-side flexible resources is presented. Moreover, the flexible supply provided by BPMG demand-side flexible resource is quantified according to the demand response models of EV and IAC. And then, combined with the flexible resource and flexible demand at the source side, flexibility shortage severity is proposed for quantifying the imbalance severity between the flexible supply and flexible demand. On this basis, a BPMG multi-objective optimal scheduling model considering economy and flexibility is constructed, which can be solved by CPLEX optimization software. Experimental results show that the proposed model can significantly improve the operational flexibility of BPMG by coordinating various flexible resources on both supply and demand sides.

Key words: business park microgrid, demand-side flexible resource, flexibility shortage severity, optimal scheduling

中图分类号:

TM73

王俊翔, 李华强, 邓靖微, 谢康胜, 安杨. 考虑需求侧灵活性资源的商业园区微网多目标优化调度[J]. 电力建设, 2021, 42(3): 35-44.

WANG Junxiang, LI Huaqiang, DENG Jingwei, XIE Kangsheng, AN Yang. Multi-objective Optimal Scheduling of Business Park Microgrid Considering Demand-Side Flexible Resources[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 35-44.

图/表 12

图1 BPMG系统结构图

Fig.1 Structure diagram of the BPMG

图2 考虑需求侧灵活性资源的商业园区微网优化调度框架图

Fig.2 Structure diagram of BPMG optimal scheduling considering demand-side flexible resources

图3 单辆EV能量状态与调度功率变化关系

Fig.3 The relationship between energy state and operating power of a single EV

表1 两种模式下调度结果对比

Table 1 Comparison of operation results in two modes

图4 两种模式下的功率平衡图

Fig.4 Power balance in two modes

图5 两种模式中BPMG灵活性不足严重度时序分布

Fig.5 Time series distribution of BPMG flexibility insufficient severity in two modes

图6 不同用户需求响应比例下的调度结果

Fig.6 Operation results under different demand-response ratios

图A1 风电光伏出力和基础负荷预测曲线

Fig.A1 Predicted power of wind,photovoltaic and basic load

表A1 BPMG内各项参数设置

Table A1 Parameters setting in the BPMG

表A2 成本系数设置

Table A2 The cost parameters setting

图A2 典型日室外温度

Fig.A2 Daily outdoor temperature

表B1 不同用户体验度约束下调度结果对比

Table B1 Comparison of operation results under different user experience constraints

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