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基于模型预测控制含充换储一体站的配电网优化运行
作者:
作者单位:

1.上海电力大学电气工程学院,上海市 200090;2.上海外高桥第二发电有限责任公司,上海市 200137

作者简介:

袁洪涛(1995—),男,硕士研究生,主要研究方向:含电动汽车的主动配电网优化运行。E-mail: yuanhongtaoyht@sina.com
韦钢(1958—),男,通信作者,教授,主要研究方向:电力系统运行分析与计算、新能源与城市配电网。E-mail: weigang@shiep.edu.cn
张贺(1994—),女,学士,主要研究方向:计及不确定性的主动配电网优化运行。E-mail: 2960130345@qq.com

通讯作者:

基金项目:

上海绿色能源并网工程技术研究项目(13DZ2251900)。


Model Predictive Control Based Optimal Operation of Distribution Network with Charging-Swapping-Storage Integrated Station
Author:
Affiliation:

1.College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China;2.Shanghai Waigaoqiao No.2 Power Generation Co., Ltd., Shanghai 200137, China

Fund Project:

This work is supported by Shanghai Engineering Research Program of Green Energy Grid-connected Technology (No. 13DZ2251900).

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    摘要:

    提出一种综合考虑电动汽车充换储一体站与主动配电网的优化调度模型。基于快充用户行驶行为特点、城市道路速度-流量实用模型分别建立快充站和换电站模型,并结合梯级储能集成为一体站模型。在含有风机、光伏、微型燃气轮机和一体站接入的主动配电网中建立优化调度模型,并将模型转化为混合整数二阶锥模型求解。使用基于模型预测控制的多时间尺度优化调度策略实现对配电网日前调度、日内滚动调度和实时反馈校正,减少了分布式电源和负荷的预测误差对配电网运行的影响。以某市公交线路实际道路情况为例,验证了所提出的优化调度策略具有能够满足电动汽车充电负荷需求、抑制功率波动并降低配电网运行维护费用的优势。

    Abstract:

    An optimal scheduling model is proposed, which comprehensively considers electric vehicle (EV) charging-swapping-storage integrated station and active distribution network. Based on driving behavior characteristics of fast-charging users and speed-flow practical model of city roads, the fast charging station model and battery swapping station model are established respectively, which are combined with the cascaded energy storage system to form the integrated station model. The optimal scheduling model is established in the active distribution network, which includes wind turbines, photovoltaics, micro-turbines and the integrated station, and the model is transformed into a mixed integer second-order cone model to solve the problem. The multi-time scale optimal scheduling strategy based on model predictive control is used to realize day-ahead scheduling, intra-day rolling scheduling and real-time feedback correction of distribution network, which reduces the impact of distributed generator and load prediction errors on the distribution network operation. Taking the actual road conditions of bus lines in a city as an example, it is verified that the proposed optimal scheduling strategy has advantages of meeting the charging load demand of EVs, suppressing power fluctuation and reducing operation maintenance cost of distribution network.

    表 1 Table 1
    表 4 Table 4
    表 3 Table 3
    图1 CSSIS结构Fig.1 Structure of CSSIS
    图2 优化调度框架Fig.2 Framework of optimal scheduling
    图3 各时段之间的协调关系Fig.3 Coordination between different periods
    图4 优化调度模型求解步骤Fig.4 Solving steps for optimal scheduling model
    图5 CSSIS功率需求曲线Fig.5 Curves of power demand in CSSIS
    图6 日前优化调度结果Fig.6 Results of day-ahead optimal scheduling
    图7 日前优化支路有功潮流Fig.7 Active power flow of branch of day-ahead optimization
    图8 优化结果对比Fig.8 Comparison of optimization results
    图 CSSIS模型框架Fig. Framework of CSSIS
    图 BCS功率需求生成流程图Fig. Flow chart of BCS power demand generation
    图 BSS可调度潜力分析流程图Fig. Flow chart of schedulable potential for BSS
    图 某市公交线路网络示意图Fig. Schematic diagram of bus line network in a city
    图 改进PG&E69系统Fig. Specific PG&E69 System
    图 CSSIS电量曲线Fig. Curve of electric quantity in CSSIS
    图 长时间尺度预测Fig. Long time scale prediction
    图 短时间尺度预测Fig. Short time scale prediction
    表 2 Table 2
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引用本文

袁洪涛,韦钢,张贺,等.基于模型预测控制含充换储一体站的配电网优化运行[J].电力系统自动化,2020,44(5):187-197. DOI:10.7500/AEPS20190509008.
YUAN Hongtao,WEI Gang,ZHANG He,et al.Model Predictive Control Based Optimal Operation of Distribution Network with Charging-Swapping-Storage Integrated Station[J].Automation of Electric Power Systems,2020,44(5):187-197. DOI:10.7500/AEPS20190509008.

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  • 收稿日期:2019-05-09
  • 最后修改日期:2019-07-09
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  • 在线发布日期: 2020-03-08
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