This paper proposes measures to improve the protection of MV distribution networks operating with feeders in a closed-loop arrangement. Bi-directional overcurrent relays (OCRs) are discussed, the selectivity of which is achieved through the timing coordination of their operation. The classic approach is formulated as a minimization of the operating times of all the OCRs. The proposed approach enhances the selectivity by considering the maximum operating time of substation OCRs and the unwanted trips of inloop OCRs. Moreover, the sensitivity is also increased by introducing an objective function that minimizes the pickup-current settings of all the OCRs together with their operating times. Furthermore, to fulfill the demanding requirements for operating times, variable penalties are introduced. Thus, the optimization procedure is forced towards the region with viable solutions for the optimization problem. Two variants of self-adaptive differential evolution have been used that both show better convergence when compared to the classic differential evolution. Moreover, ten mutation strategies were tested, where “rand/1/bin” showed the best results. A comparison with other methods for timing coordination shows that the proposed optimization results in a comparable value for the OCRs’ operating times. In order to further reduce the operating times, GOOSE communications between the OCRs are adopted. The proposed measures for improved protection operation are fully confirmed through dynamic simulations of the faults in the discussed 20-kV network. Moreover, the proposed protection design is already implemented and permanently operates in a 20-kV network with more than 5000 customers, whereas the field results show selective and reliable protection operation.

Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces $MsMA$, a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, $MsMA$ divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques.

Diesel generators are indispensable in areas lacking electricity infrastructure or requiring alternative power sources. However, while they offer significant advantages, they also suffer notable drawbacks, particularly in fuel efficiency during lower loads. Typically, generators operate in tandem with load profiles, resulting in inefficient operation during reduced loads. This article proposes a solution by integrating generator operation with a battery system. By combining the generator with batteries during periods of lower efficiency, we aim to enhance overall system efficiency.