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Corresponding Author

Mohammed Sonbol Ebrahim

Authors ORCID

https://orcid.org/0000-0001-5160-0100

Document Type

Original Study

Keywords

Cost optimization reactive power planning, Monte Carlo simulation and cuckoo catfish optimizer, point estimation method, probabilistic reactive power planning, renewable energy uncertainty, and static synchronous compensators

Abstract

This paper presents a cost-optimized reactive power planning framework to determine the optimal location and ratings of Static Synchronous Compensators in renewable-rich power systems. The proposed methodology formulates a comprehensive cost-objective function that incorporates both investment and operational costs while considering normal and contingency (N−1) operating condition. Load variability and uncertainties associated with solar irradiance and wind speed are incorporated into the cost fitness function. These uncertainties are modeled using the point estimation method, generating 144 probabilistic scenarios that represent both daytime and nighttime operating conditions. The proposed approach ensures the minimum required number of Static Synchronous Compensators while maintaining all bus voltage magnitudes within permissible limits under both normal and contingency conditions across all scenarios. To validate the robustness of the framework, Monte Carlo simulation is performed using 1000 probabilistic scenarios. The results confirm that all bus voltages remain within acceptable limits under both normal and (N−1) contingency conditions. The optimization of the cost fitness function is carried out using the Cuckoo Catfish Optimizer, and its performance is benchmarked against several well-known metaheuristic algorithms, including Painting Training Based Optimization, Honey Badger Algorithm, Archimedes Optimization Algorithm, and Grey Wolf Optimizer. Comparative results demonstrate that the proposed probabilistic reactive power planning approach based on Cuckoo Catfish Optimizer achieves superior performance due to its balanced exploration–exploitation capability, fast convergence. The proposed methodology is validated on modified IEEE 14-bus and modified IEEE 30-bus test systems integrated with renewable energy sources, including wind and photovoltaic generation. All simulations and analyses are implemented using MATLAB.

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