Two‐stage stochastic‐robust planning of distributed energy storage systems with Archimedes optimisation algorithm

With the advancement of energy storage technologies, energy storage systems (ESSs) have emerged as a promising solution for distribution networks to mitigate the impact of intermittent and violate renewable energy sources. The optimal planning of distributed ESS is studied to minimise the investment...

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Veröffentlicht in:IET smart grid Jg. 7; H. 5; S. 643 - 652
Hauptverfasser: Yuan, Tianmeng, Chen, Zhuoxu, Hu, Zechun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Durham John Wiley & Sons, Inc 01.10.2024
Wiley
Schlagworte:
Algorithms
Costs
Decomposition
Discount rates
Distributed generation
Electric vehicles
Energy storage
Genetic algorithms
Global optimization
Mathematical programming
Mixed integer
power system planning
Renewable energy sources
Robustness (mathematics)
Uncertainty
ISSN:2515-2947, 2515-2947
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Zusammenfassung:With the advancement of energy storage technologies, energy storage systems (ESSs) have emerged as a promising solution for distribution networks to mitigate the impact of intermittent and violate renewable energy sources. The optimal planning of distributed ESS is studied to minimise the investment and operational costs for the distribution system operator. To address the various uncertainties associated with load demand and distributed generation, the authors formulate the problem as a two‐stage stochastic‐robust optimisation problem. The proposed formulation implements various representative scenarios of actual operating conditions and constructs the robust uncertainty set to ensure feasibility under worst‐case scenarios. In view of the computational complexity of the proposed model, a solution approach combining the Archimedes optimisation algorithm and the global optimisation method is presented. By decomposing the investment and operation stages, the subproblems are relaxed into mixed integer second‐order cone programming models, which can be solved in parallel based on scenarios. Numerical studies are carried out on a 17‐node test system to demonstrate the validity of the proposed model and algorithm. In addition, a comparison between the proposed method and the genetic algorithm is performed, to illustrate its superiority in solving speed and solution optimality. To address the uncertainties associated with load demand and distribution generation, the authors formulate a two‐stage stochastic‐robust optimisation problem, including various representative scenarios of actual operating conditions and a robust uncertainty set to ensure feasibility under worst‐case scenarios. In view of the computational complexity of the proposed model, a solution approach combining the Archimedes optimisation algorithm and the global optimisation method is presented.
Bibliographie:ObjectType-Article-1
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ISSN:2515-2947
2515-2947
DOI:10.1049/stg2.12171