Flexible Control Device Configuration Planning for Transmission Network Resilience Enhancement

doi:10.11930/j.issn.1004-9649.202310024

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202310024

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

Figures/Tables 13

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

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

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

Fig.4 Visual power flow diagram

Fig.5 The N-CCG algorithm flow chart

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

Fig.7 Forecasts of system load and wind power output

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

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

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

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

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

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

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