面向输电网弹性提升的灵活控制装置配置规划

doi:10.11930/j.issn.1004-9649.202310024

中国电力 ›› 2023, Vol. 56 ›› Issue (12): 113-126.DOI: 10.11930/j.issn.1004-9649.202310024

• 电力系统弹性提升关键技术 • 上一篇下一篇

面向输电网弹性提升的灵活控制装置配置规划

李力¹(), 黄欣²(), 徐天元²(), 陈玥¹, 陆秋瑜¹, 杨银国¹, 刘洋¹, 朱誉¹, 李更丰², 邵成成²()

1. 广东电网有限责任公司电力调度控制中心，广东广州　610041
2. 西安交通大学电气工程系，陕西西安　710049

收稿日期:2023-10-09 接受日期:2023-12-01 出版日期:2023-12-28 发布日期:2023-12-28
作者简介:李力（1971—），女，硕士，高级工程师，从事电力系统调度控制研究，E-mail： lili@gddd.csg.cn
黄欣（1998—），男，硕士研究生，从事电力系统需求响应优化调度研究，E-mail： hx790025301@stu.xjtu.edu.cn
徐天元（1999—），男，通信作者，博士研究生，从事综合能源系统规划研究，E-mail： xty886@stu.xjtu.edu.cn
邵成成（1989—），男，博士，副教授，从事电力能源系统规划与运行研究，E-mail： c.c.shao22@msn.cn
基金资助:
中国南方电网有限公司科技项目（036000KK52200061）。

Flexible Control Device Configuration Planning for Transmission Network Resilience Enhancement

Li LI¹(), Xin HUANG²(), Tianyuan XU²(), Yue CHEN¹, Qiuyu LU¹, Yinguo YANG¹, Yang LIU¹, Yu ZHU¹, Gengfeng LI², Chengcheng SHAO²()

1. Power Dispatching Control Center, Guangdong Electric Power Co., Ltd., Guangzhou 610041, China
2. Department of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

Received:2023-10-09 Accepted:2023-12-01 Online:2023-12-28 Published:2023-12-28
Supported by:
This work is supported by Science and Technology Project of China Southern Power Grid Corporation (No.036000KK52200061).

摘要/Abstract

摘要：

为提升电力系统弹性，确保主网应对小概率-高影响事件的能力，基于鲁棒优化理论提出主网灵活控制装置配置的规划框架，充分考虑远动开关配置下的最优线路开断（optimal transmission switch，OTS）、线路强化加固装置以及储能设施之间的最优协同作用，最大限度降低投资运行成本的同时减少由潜在极端事件造成的经济损失。首先，基于持续负荷曲线构建考虑典型日线路中断与新能源出力波动的两阶段鲁棒规划模型；然后，采用修改后的嵌套列和约束生成算法（nested column-and-constraints generation，NCCG）对模型进行求解，使用相关加速策略提升模型求解效率；最后，通过IEEE-24节点系统进行仿真分析，验证异质灵活控制装置之间的规划协同作用以及模型的有效性。

关键词: 电力系统弹性, 鲁棒优化, 灵活控制装置配置, 最优线路开断, 线路强化加固, 储能规划, 嵌套列和约束生成算法

Abstract:

To enhance power system resilience and ensure the capability of transmission grid to handle the events of low-probability but high impacts, a flexible control device configuration planning framework for transmission network is proposed based on robust optimization theory. This framework fully considers the optimal synergy among different categories of control devices, including optimal transmission switch (OTS) under remote switch configuration, transmission line reinforcement devices and energy storage systems (ESS), aiming to minimize the operational costs while reducing the economic losses caused by extreme events. Firstly, a two-stage robust planning model is constructed based on the load duration curves (LDC), considering typical daily interruptions of transmission lines and renewable energy output fluctuations. And then, the modified Nested Column-and-Constraints Generation (NCCG) algorithm is employed to solve the proposed model, and relevant acceleration strategies are utilized to enhance the computational efficiency. Finally, a simulation analysis is conducted using the IEEE 24-bus system to validate the planning synergy among different flexible control devices and to assess the effectiveness of the proposed model.

Key words: power system resilience, robust optimization, flexible control device configuration, optimal transmission switch, transmission line reinforcement, energy storage system planning, nested column-and-constraints generation

李力, 黄欣, 徐天元, 陈玥, 陆秋瑜, 杨银国, 刘洋, 朱誉, 李更丰, 邵成成. 面向输电网弹性提升的灵活控制装置配置规划[J]. 中国电力, 2023, 56(12): 113-126.

Li LI, Xin HUANG, Tianyuan XU, Yue CHEN, Qiuyu LU, Yinguo YANG, Yang LIU, Yu ZHU, Gengfeng LI, Chengcheng SHAO. Flexible Control Device Configuration Planning for Transmission Network Resilience Enhancement[J]. Electric Power, 2023, 56(12): 113-126.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310024

https://www.electricpower.com.cn/CN/Y2023/V56/I12/113

图/表 13

图 1 基于电力电量平衡的典型日多场景规划框架

Fig.1 The typical daily multi-scenario planning framework based on balance of electric power and energy

图 2 考虑新能源出力偏移概率的multi-band模型

Fig.2 A multi-band model considering offset probability of new energy output

图 3 两阶段三层规划模型结构

Fig.3 The two-stage three-level planning model structure

图 4 虚拟潮流示意

Fig.4 Visual power flow diagram

图 5 N-CCG算法流程

Fig.5 The N-CCG algorithm flow chart

图 6 改进的IEEE RTS-79 24节点系统

Fig.6 The modified IEEE RTS-79 24 bus system

图 7 负荷水平与风电出力期望值

Fig.7 Forecasts of system load and wind power output

图 8 G0组案例与Case1-1投资运行成本对比

Fig.8 Comparison of investment and operation costs between G0 group case and Case 1-1

表 1 G0组案例与Case1-1投资运行成本

Table 1 Investment and operation costs of G0 case and Case 1-1 单位：亿元

案例	总成本	运行成本	弃电	切负荷
Case 0-1	5.9291683	5.9291683	0	0
Case 0-2	7.0590519	6.0826727	0.795336	0.181043
Case 0-3	7.4067312	6.1049930	1.1693017	0.132437
Case 1-1	7.0990196	6.0475391	0.466506	0.569706

图 9 案例总体投资运行成本对比

Fig.9 Comparison of investment and operation costs between all case

图 10 G1组对比案例投资运行成本对比

Fig.10 Comparison of investment and operation costs between G1 group case

图 11 BRM=0对应G1组部分案例投资运行成本对比

Fig.11 Comparison of investment and operation costs between G1 group case corresponding to BRM=0

图 12 G2组案例投资运行成本对比

Fig.12 Comparison of investment and operation costs between G2 group case

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面向输电网弹性提升的灵活控制装置配置规划

Flexible Control Device Configuration Planning for Transmission Network Resilience Enhancement

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面向输电网弹性提升的灵活控制装置配置规划

Flexible Control Device Configuration Planning for Transmission Network Resilience Enhancement

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