Algorithm for distribution network reconfiguration and reactive power compensation with battery energy storage systems
•The optimal reconfiguration and compensation problem with presence of distributed generators.•Algorithm is combination of simulating annealing and minimum spanning tree algorithm.•Loads in nodes and generators’ production change in hourly resolution.•Taking into account multi objective functions an...
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| Vydané v: | Electric power systems research Ročník 244; s. 111547 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.07.2025
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| Predmet: | |
| ISSN: | 0378-7796 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | •The optimal reconfiguration and compensation problem with presence of distributed generators.•Algorithm is combination of simulating annealing and minimum spanning tree algorithm.•Loads in nodes and generators’ production change in hourly resolution.•Taking into account multi objective functions and many real constraints.•Special contribution is considering battery storage systems and economic analyses.
The paper deals with distribution network reconfiguration and reactive power compensation, taking into account the existence of distributed energy sources, Distributed Generation and Battery Energy Storage Systems. A large number of constraints are included in order to present the problem as realistically as possible. The limitations are the satisfaction of the input power factor, non-overcompensation with reactive power, the minimum and maximum value of the voltage in the nodes, the maximum allowed effective value of the network branch currents and the number of commutations per switching element within 24 h. Contributions are realistic representation of hourly changes in consumption and production from renewable energy sources and BESS, alongside reconfiguration (with commutaton constraint), what is rarity in modern works. The idea of the paper is to present the method that combines the Kruskal algorithm for reconfiguration and the Simulated Annealing (SA) for compensation. The optimization method determines the position of the capacitor banks and the network configuration that minimizes active power losses and cost on an hourly basis. Detailed analyses show how BESS affect technically optimal solution and what is the final cost. The addressed problem is of Mixed Intiger Nonlinear Programming optimization class. |
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| ISSN: | 0378-7796 |
| DOI: | 10.1016/j.epsr.2025.111547 |