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Citation:Jun Xiao,Member,IEEE,et al.A general simplification and acceleration method for distribution system optimization problems[J].Protection and Control of Modern Power Systems,2025,V10(1):148-167[Copy]
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A general simplification and acceleration method for distribution system optimization problems
Jun Xiao, Member, IEEE,Yupeng Zhou, Student Member, IEEE,Buxin She, Member, IEEE,Zhenyu Bao
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Abstract:
Solving optimization problems plays a vital role in ensuring the secure and economic operation of distribution systems. To enhance computational efficiency, this paper proposes a general simplification and acceler ation method for distribution system optimization prob lems. Firstly, the capacity boundary and voltage bound ary model of distribution systems are established. The relative position between the two boundaries reflects the strength of capacity and voltage constraints, leading to the definition of two critical feeder lengths (CFLs) to quantify these strengths. Secondly, simplification criteria and an acceleration method are proposed. Given a distribution system, if the distance from the end load/DG node to the slack bus is less than the corresponding CFL, we can conclude that the capacity constraints are stricter than the voltage constraints. Then, the distribution system can be simplified by adopting DC power flow model or disre garding the voltage constraints. After that, the reference value tables of CFL are presented. Finally, the effective ness of the proposed method is verified by exemplifying the method in network reconfiguration and reactive power optimization problems. By implementing the pro posed acceleration method, a significant reduction in computation time is achieved while ensuring accuracy. This method applies to most urban distribution systems in optimization problems involving power flow equations or voltage constraints.
Key words:  Distribution system, optimization problem, power flow equation, security region, simplification criteria, acceleration method, network reconfiguration, reactive power optimization.
DOI:10.23919/PCMP.2023.000210
Fund:This work is supported by the National Natural Science Foundation of China (No. 52177105).
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