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Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

2023-05-31

华北电力大学董雷等：基于两阶段算法的含SOP配电网多源协同随机恢复

摘要

大停电后，分布式能源(DERs)如本地可再生能源、可控分布式电源以及储能装置可以被用来恢复负载，提高系统弹性。本文提出一种含智能软开关(SOP)配电网多源协同的负荷恢复策略。所提出的负荷恢复策略充分利用了SOP的灵活调控能力，增加负荷恢复水平的同时能有效降低网络电压偏差。由于可再生能源和负荷具有不确定性，建立了混合整数非线性的随机负荷恢复模型。进而采用凸松弛和线性化方法，为模型开发了一种高效的两阶段求解方法。第一阶段通过求解松弛的二阶锥规划问题以确定储能装置的调度出力；第二阶段逐步求解小规模的混合整数二阶锥规划问题，得到SOP和DERs的功率输出。通过IEEE-33节点和IEEE-123节点算例系统仿真分析，结果验证了本文所提方法的有效性。

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

基于两阶段算法的含SOP配电网多源协同随机恢复

Xianxu Huo¹, Pan Zhang¹, Tao Zhang², Shiting Sun², Zhanyi Li³, Lei Dong²

1.Electric Power Research Institute of State Grid Tianjin Electric Power Company, Tianjin 300384, P. R. China

School of Electrical and Electronics Engineering, North China Electric Power University, Beijing 102206, P. R. China

State Grid Tianjin Baodi Electric Power Supply Company, Tianjin 301800, P. R. China

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Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

Abstract

After suffering from a grid blackout, distributed energy resources (DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points (SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed, and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Keywords

Load restoration; Soft open points; Distribution network; Stochastic optimization; Two-stage algorithm.

Fig.1 Service restoration and analysis framework

Fig.2 Probability distribution fitting based on GMM

Fig.3 Schematic diagram of the voltage deviation objective function

Fig.4 Installation schematic diagram and topological for an SOP

Fig.5 Diagram of a polygonal inner-approximation approach

Fig.6 Flowchart of the proposed two-stage algorithm

Fig.7 Topology diagram of the modified IEEE 33-bus test system

Fig.8 Power output prediction curves of WT,PV,and load

Fig.9 PDF curves and correlation matrix of WT,PV,and load

Fig.10 Results of GMM: (a) PDF curve based on GMM of WT; (c) scenarios reduction results of WT; (b) PDF curve based on GMM of PV; (d) scenarios reduction of PV; (e) PDF curve based on GMM of loads; (f) scenarios reduction of loads

Fig.11 Results of the load restoration level in three schemes

Fig.12 Optimized results by the proposed operation scheme:(a) power output of ESS; (b) power of DGs and loads

Fig.13 Voltage profiles: (a) reduced voltage deviation function is considered; (b) reduced voltage deviation function is not considered

Fig.14 Optimized results of SOPs: (a) active power flow;(b) reactive power compensation

Fig.15 Results of load restoration without and with SOP

Fig.16 Computational time of stage 2 in an IEEE 33-bus system

Fig.17 Topology diagram of the modified IEEE 123-bus test system

Fig.18 Comparison of optimized results in an IEEE 123-bus system: (a) restoration results; (b) results of the load restoration level in three schemes; (c) reduced voltage deviation function is considered; (d) reduced voltage deviation function is not considered

Fig.19 Computational time in IEEE 123-bus system at each step in the second stage

本文引文信息

Huo XX, Zhang P, Zhang T, et al (2023) Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm, 6(2): 141-153

霍现旭，张磐，张涛等 (2023) 基于两阶段算法的含SOP配电网多源协同随机恢复. 全球能源互联网（英文）, 6(2): 141-153

Biographies

Xianxu Huo

Xianxu Huo received the Ph.D. degree in electrical engineering from Institute of Electrical Engineering, Chinese Academy of Sciences in 2014. From July 2014 to November 2016, he was a Post-doctoral Fellow at Tianjin University. He is currently a Senior Engineer of State Grid Tianjin Electric Power Research Institute. His research interests include renewable energy and distribution network, and integrated energy system.

Pan Zhang

Pan Zhang received the M.S. degree in electric engineering and automation from North China Electric Power University (NCEPU), Beijing, China, in 2008. He is currently working as a senior engineer in of State Grid Tianjin Electric Power Research Institute. His research interests include smart distribution network and smart substation technology.

Tao Zhang

Tao Zhang received the B.S. degree in electrical engineering from the Hefei University of Technology (HFUT), Hefei, China, in 2016, and the M.S. degree in electric engineering and automation from North China Electric Power University (NCEPU), Beijing, China, in 2019. (Corresponding author). His research interests include power system analysis, distribution network optimization, and service restoration.

Shiting Sun

Shiting Sun was in Liaoning, China, in 1999. She received the B.S. degree in electrical engineering from Liaoning University in 2021. She is currently pursuing the M.S. degree in electrical engineering at North China Electric Power University. Her research interests include integrated energy system restoration and optimized operation of the power system.

Zhanyi Li

Zhanyi Li was in Hebei, China, in 1993. He received the master’s degree in electrical engineering from Yanshan University in 2020. Now he is an employee of Baodi Power Supply Branch of State Grid Tianjin Electric Power Company. His research interests mainly include new energy power generation technology and power conversion technology.

Lei Dong

Lei Dong received the M.S. degrees in Electrical Engineering and Automation in 1996 from Tianjin University, Tianjin, China. Since 2001, she has been an Associate Professor in the Electrical Engineering Department with North China Electric Power University. Her research interests include power systems analysis and control, power system optimal dispatch and operation control, application of artificial intelligence in power system.

编辑：刘通明

审核：王伟

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