The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA) is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I) the awareness probability (AP) and (II) the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.

As a mean to provide reactive power compensation to radial distribution networks, capacitor banks are commonly installed within these electrical distribution systems. When properly allocated, capacitor banks provide several benefits, including an improvement on the feeder's voltage profile and significant reductions on power loss, which, in turn, leads to important amounts of energy savings and cost reductions. This formulation is known in the literature as optimal capacitor placement (OCP) problem. From an optimization perspective, OCP is considered remarkably complex due to its high multi-modality, discontinuity, and non-linearity. In this paper, the swarm optimization algorithm known as locust search (LS) is proposed for solving the OCP problem. The proposed approach has been tested by considering several IEEE's radial distribution test systems, and its performance has also been compared against that of other techniques currently reported on the literature to solve the OCP problem. Our experimental results suggest that the proposed LS-based method is able to competitively solve the OCP problem in terms of accuracy and robustness.

Efficient operation of power generation systems has become a significant issue in the research community, driven by the increasing demand for sustainable energy solutions and mitigating the environmental impact. Dynamic Economic Dispatch (DED) is a well-known optimization problem in power system operation. To accurately model DED, it is crucial to account for valve-point loading effects and ramp-rate limits. These factors make DED a non-linear, non-convex, and non-smooth problem, which is challenging to solve using traditional optimization techniques. In contrast, the Selfish Herd Optimizer (SHO) is a novel algorithm capable of finding optimal solutions to complex problems. In this work the SHO algorithm is used to optimize the DED problem, to evaluate the effectiveness of the proposed approach, the SHO algorithm was applied to three distinct power generation systems. The results obtained demonstrate the competitive performance of the SHO algorithm.