Key Technologies for Online Joint Optimization of Transmission-Distribution Coordination for Reactive Power Reserve Requirements in New Power Systems

Bing Chai; Xiangyu Gong; Kun Qian

doi:10.6919/ICJE.202606_12(6).0014

Authors

Bing Chai
Xiangyu Gong
Kun Qian

DOI:

https://doi.org/10.6919/ICJE.202606_12(6).0014

Keywords:

New-type Power System; Reactive Power Reserve; Transmission-distribution Collaboration; Online Optimization; Alternating Direction Method of Multipliers.

Abstract

With the rapid development of high-proportion renewable energy power systems, reactive power and voltage regulation faces multiple challenges including intensified source-load bilateral uncertainty and deepened coupling between transmission and distribution networks. The traditional independent optimization mode for transmission and distribution networks can hardly satisfy the requirements of global voltage stability and economic operation. This paper proposes a transmission-distribution collaborative online joint optimization method for reactive power reserve demand in new-type power systems. First, the spatio-temporal distribution characteristics of reactive power reserve demand under high renewable energy penetration are analyzed, and a reactive power reserve demand assessment model considering voltage stability margin (VSM) constraints is established. Second, a transmission-distribution collaborative multi-objective joint optimization model is constructed, taking system active power loss, reactive power reserve cost, voltage deviation, and voltage stability margin penalty as optimization objectives. Then, the alternating direction method of multipliers (ADMM) is adopted to achieve decomposition-coordination solving between transmission and distribution networks, meeting the computational efficiency requirements for online applications. Finally, case studies are carried out based on a coupled test system consisting of a modified IEEE 30-bus transmission network and an IEEE 33-bus distribution network. The results demonstrate that compared with independent optimization strategies, the proposed method can reduce network losses by 21.6%, improve voltage stability margin by 83.3%, and the computing time is only 20.8% of that of centralized optimization, verifying the effectiveness and practicality of the method.

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