Optimal Power Flow Solution Using Novel Optimization Technique: A Case Study

The Jaya algorithm is a novel and effective global optimization technique, yet it faces challenges such as slow convergence, limited exploitation capabilities, and suboptimal balance between exploration and exploitation. This research proposes a hybrid Jaya algorithm, designated HMJDE, that integrates a modified Jaya algorithm with Differential Evolution (DE) to address the Optimal Power Flow (OPF) problem. The modified Jaya algorithm facilitates global exploration of the search space, while DE enhances local search by refining solutions within the neighbourhoods identified by Jaya. The performance of HMJDE is first assessed using eight well-established benchmark functions. Subsequently, its effectiveness is validated through application to real-world OPF problems on the Algerian 59-bus and standard IEEE 57-bus systems. The HMJDE algorithm achieves minimum fuel costs of 1688.1210 $/h and 41656.0438 $/h, respectively, with standard deviations of 0.3295 and 0.2412, representing cost reductions of 13.1317% and 18.8753% compared to the base case. These translate to annual savings of approximately $2,235,922.55 and $84,903,463.50, respectively. Compared to other recent optimization techniques, HMJDE exhibits superior solution quality and computational efficiency for both benchmark functions and OPF applications. Statistical analysis, including best, worst, mean, and standard deviation metrics, further confirms that HMJDE is a robust and reliable optimization approach.