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Microgrids (MG) can be considered as a reduced power system network wherein the generation, transmission, and distribution of power take place within a limited geographical area. They are employed to utilize the maximum penetration of renewable energy sources (RES). Microgrid also has advantages such as reduction of transmission losses and costs pertaining to such losses. In this research, economic dispatch, emission dispatch, combined economic emission dispatch (CEED) based on fractional programming (FP), and environment-constrained economic dispatch (ECED) were assessed. An MG system for three distinct scenarios is studied. A novel, robust, and powerful swarm-intelligence hybrid algorithm, created by amalgamating the features of the conventional gray-wolf optimizer (GWO), sine-cosine algorithm (SCA), and crow search algorithm (CSA), is implemented as the optimization tool for the study. A significant reduction in generation costs of about 4.50-9.75% was achieved throughout the study when the time-of-usage (TOU) electricity market pricing strategy was used instead of the fixed pricing strategy. Furthermore, the paper also dealt with the Demand Side Management (DSM) to facilitate the generation cost by managing the controllable load demand. The generation cost decreased by a notable amount when ECED was evaluated with a 15% and 20% DSM-based revised load curve for the MG system. [ABSTRACT FROM AUTHOR]

In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned with the installation or removal of reactive power equipment in power system. This article proposes an effective way to find out the optimal parameter settings related to the system active power loss as well as operational cost. Genetic Algorithm (GA)-based strategy is applied to determine the optimal values of reactive power generation of generators, size of shunt capacitors, transformer tap settings prior to minimizing the system operating cost due to active power loss, installation cost of shunt capacitors at the weak nodes, and the cost of line-charging elements. The proposed approach is applied to IEEE 30 and IEEE 57 bus test system. Finally, a comparative analysis has been done to validate the efficacy of GA-based approach with some well-established meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Abstract—This article presents a novel approach for optimal flexible AC transmission systems devices planning in an interconnected power system under different loading conditions. The static VAR compensator and thyristor-controlled series capacitor are two types of flexible AC transmission systems devices considered for optimal power system operation. In the proposed approach, a fuzzy membership function is used to determine weak nodes in the power system for the placement of static VAR compensators as a flexible AC transmission systems device. The thyristor-controlled series capacitor is the other type of flexible AC transmission systems devices for which its positions are determined by the reactive power flow in lines. The genetic algorithm is used for the optimal setting of the power system variables, including flexible AC transmission systems devices. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes or point of VAR compensation is determined by eigenvalue analysis, and the amount of flexible AC transmission systems devices is determined by evolutionary techniques, such as the genetic algorithm, differential evolution, and particle swarm optimization. The superiority of the proposed fuzzy-based optimization approach is established by the results and comparative analysis with other methods. [ABSTRACT FROM PUBLISHER]