article · Ain Shams Engineering Journal
Managing optimal reactive power dispatch is vital for electricity grids that integrate renewable energy resources such as wind power. This research introduces an enhanced coyote optimization algorithm designed to solve reactive power dispatch problems efficiently. The approach combines the principles of fuzzy logic with the conventional coyote optimization algorithm to achieve improved performance. A sensitivity analysis evaluates the effects of incorporating reactive power resources alongside wind units into the network. The capability and scalability of the method are tested on standard IEEE 30-bus, 118-bus, and 300-bus power systems under different levels of wind energy penetration. Statistical assessments demonstrate that the algorithm offers robust and competitive solutions across single-objective and multi-objective scenarios when compared with existing methods in the literature.
As power grids incorporate more fluctuating renewable energy like wind, maintaining stable voltage and efficient power flow becomes increasingly complex. Optimising reactive power dispatch helps lower operational costs and maintain network stability. Advanced computational methods that can handle large network sizes and variable renewable generation support the reliable transition toward cleaner energy systems.
The algorithm could be incorporated into grid management software and decision-support tools used by electrical utilities and transmission system operators managing wind power integration. Because the evaluation is limited to simulations on standard IEEE benchmark networks, the work currently sits at an early computational stage and requires validation on operational utility systems prior to commercialisation.
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This study is concerned with solving the optimal reactive power dispatch (ORPD) problem considering the existence of renewable energy resources (RERs). The sensitivity analysis aims at studying the impact of incorporating reactive power resources and wind units for solving the ORPD problem. An enhanced coyote optimization algorithm (ECOA) is developed for solving ORPD problem. The ECOA combines the merits of fuzzy logic principles and the conventional COA with the aim of obtaining the best performance of ORPD solution. The capability of ECOA are examined on IEEE 30-bus, 118-bus test systems while its scalability is tested for standard IEEE 300-bus test system. The impact of different penetration levels of wind energies on achieving the ORPD is assessed. Statistical analyses are carried out to prove the robustness of the proposed ECOA. Therefore, the ECOA leads to more competitive solutions for single and multi-objective cases compared with the reported methods in the literature.
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DOI: 10.1016/j.asej.2020.08.021
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