An Efficient Heuristic Algorithm for Stochastic Multi-Timescale Network Reconfiguration for Medium- and High-Voltage Distribution Networks with High Renewables

Wanjun Huang, Mingrui Xu, Xinran Zhang () and Le Zheng
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Wanjun Huang: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Mingrui Xu: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Xinran Zhang: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Le Zheng: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 21, 1-21

Abstract: To handle the uncertainties brought by the increasing penetration of renewable energy sources and random loads, we design a stochastic multi-timescale distribution network reconfiguration (SMTDNR) framework to coordinate diverse scheduling resources across different timescales and develop an efficient heuristic algorithm to solve this complex NP-hard combinatorial optimization problem with high efficiency for medium- and high-voltage distribution networks. First, the SMTDNR problem, incorporating distributed renewable generators, fuel generators, energy storage systems, and controllable loads, is simplified through circular constraint linearization, Jabr relaxation, and second-order cone (SOC) relaxation techniques. Then, a one-stage multi-timescale successive branch reduction (MTSBR) algorithm is developed for distribution networks with one redundant branch, which transforms the SMTDNR problem into a stochastic multi-timescale optimal power flow (SMTOPF) problem. This is extended to a two-stage MTSBR algorithm for general networks with multiple redundant branches, which iteratively runs the proposed one-stage MTSBR algorithm. Numerical results on modified IEEE 33-bus and 123-bus distribution networks validate the superior optimality, feasibility, and computational efficiency of the proposed algorithms, particularly in scenarios of high renewable penetration and increased uncertainty, offering robust and feasible solutions where traditional methods may fail.

Keywords: distribution network reconfiguration; multi-timescale; stochastic optimization; model predictive control; optimal power flow (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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