Enhancing Resilience and Efficiency in Power System: A Five‐Level Dynamic Planning Model

• Published in: Journal of electrical and computer engineering Vol. 2024; no. 1
• Main Authors: Rezaei, Mahnaz, Tolou Askari, Mohammad, Amirahmadi, Meysam, Ghods, Vahid
• Format: Journal Article
• Language: English
• Published: New York Hindawi Limited 01.01.2024; Wiley
• Subjects: Algorithms; Capital costs; Consumers; Costs; Dynamic models; Earthquakes; Electric power systems; Electricity; Energy costs; Energy management; Evolutionary algorithms; Failure; Infrastructure; Natural disasters; Operating costs; Optimization; Orchestras; Planning; Profitability; Reconfiguration; Resilience; Search algorithms; Seismic response; Thermal energy; Thermal utilization; Wind turbines; Iran
• ISSN: 2090-0147; 2090-0155
• DOI: 10.1155/2024/5914278

Researchers consider the planning of power systems to be of utmost importance, and due to its significant budget, further studies on the resilience of energy systems are necessary. Therefore, a five‐level dynamic model is developed to improve resilience to natural disaster events. The initial level focuses on expansion planning and system uncertainties, specifically examining the utilization of wind turbines in the proposed energy hub to incorporate the thermal energy market together with demand response program. Its purposes are to improve resilience, minimize input transmission costs, and minimize operating costs. The second level is related to the corrective actions that the system operator should take after the accident for network reconfiguration. The earthquake‐induced and attack events, considering unknown events, are modeled and assessed in the third level. Generation expansion planning is performed at the fourth level. As with the whole model, this level tries to improve resilience. The fifth level deals with transmission expansion planning to boost resilience, and the capability of transmission switching is regarded as a low‐cost correction factor. The first level optimizes energy hub according to the differential evolutionary algorithm, but the next four levels employ a robust symphony orchestra search algorithm. Finally, the dynamic planning model proposed here is employed on a Garvers 6‐bus system and then on a 400 kV 52‐bus Iranian power grid. According to the findings, the proposed planning model demonstrates a reduction in capital costs of transmission switches by up to 26%. Also, the expected profit of the generation units increases by as much as 3.75%. Accordingly, the results show that the model is both feasible and effective.