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In this research endeavour, we delve into the realm of microgrid management, focusing specifically on enhancing its performance in the aftermath of a fault event. Microgrids, characterized by their incorporation of diverse replenishable energy sources like the sun and wind, alongside storage options like batteries and conventional methods of backup, like diesel generators, face significant challenges when confronted with sudden spikes in demand due to faults or disruptions. To address these challenges, we explore the application of three distinct optimization methodologies: Genetic Algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). These techniques are employed to dynamically adjust load demand within the microgrid, aiming to mitigate the impacts of the fault and restore stability and efficiency to the system. Through a comprehensive comparative analysis, we assess the efficacy of every optimization approach regarding its capacity for optimize load demand effectively, maintain system reliability, and maximize resource utilization. By examining key performance metrics such as cost reduction, load balancing, and energy efficiency improvement, Our goal is to provide insightful information about the strengths additionally limitations of each optimization technique. Ultimately, our study contributes to the body of knowledge surrounding microgrid management strategies, offering practical guidance for decisionmakers and engineers tasked with optimizing microgrid performance in real-world scenarios. [ABSTRACT FROM AUTHOR]

The multi-objective economic load dispatch problem (ELDP) with non-smooth cost functions and ramprate limits presents a challenging optimization task in power systems. This paper proposes the use of a modified cheetah optimizer (MCO) that incorporates opposition-based learning (OBL) and a dynamic adaptive weighting factor to efficiently solve this problem. The simulations are conducted on standard test systems using MATLAB programming. A comparative study is performed, evaluating the performance of the MCO against basic CO and other similar heuristics. The results demonstrate the effectiveness of the MCO in achieving optimal solutions for the multi-objective ELDP with non-smooth cost functions and ramp-rate limits. The proposed approach offers a promising solution for addressing the complex optimization requirements in power system operation and planning. The optimal cost is determined using multi-criterion optimization (MCO) in 3-bus system as $6,838.6434/h, $7,738.789/h, and $8,252.033/h for 700 MW, 800 MW and 850 MW demand levels respectively. The optimal cost is evaluated as $17,988.96/h for the 13-bus test system considering a total demand of 1800 MW. The optimal cost is evaluated as 121960.30 $/hr, for the 40-bus test system considering a total demand of 10500 MW. These outcomes demonstrate the efficacy of MCO in resolving the ELDP with generator and valve controls. [ABSTRACT FROM AUTHOR]

Many countries have benefited from industrialisation and there is a growing global demand for power. For a power system to operate steadily, securely, and dependably, infrastructure must be upgraded and supply and demand must be balanced. In particular, reactive power (VAr) compensation is crucial for balancing the demand for reactive power from industries and, as a result, to ensure a sufficient voltage profile and voltage stability in low-voltage distribution lines. This study suggests the use of switched and fixed capacitors for dynamic volt-var controllers to handle heavy industrial loads. The voltage stability improvement, cost reduction, and loss reduction are the three goals of the multi-objective function. a new, straightforward meta-heuristic for war strategy optimisation (WSO) that combines the power loss index (PLI) to narrow the search space and boost the computing effectiveness. For various industrial load growth scenarios, simulations were performed on IEEE 33-bus low-voltage distribution feeder. A comparative study was also conducted using and compared with WOS (i.e., without reduced search space with PLIs) and whale optimization algorithm (WOA). In terms of global optima, the PLI-WSO findings are superior. In basic 33-bus feeder, having 84.78% VAr compensation results in a 34.39 % loss reduction and 33.23 % cost reduction in a 33-bus feeder, whereas having maximum 16% of industrial load growth in 10 years, the losses and costs are increased by 24.41 times to the base case, respectively. However, by having optimal VAr compensation, the losses and cost savings are resulted for 8.627% and 8.404%, respectively. Different load increase scenarios showed a similar type of overall benefit, demonstrating the scalability of the suggested methodology for real-time larger systems. [ABSTRACT FROM AUTHOR]

Power systems around the world are transforming into automated networks for more efficient, secure, reliable, and resilient operations, while satisfying technological, economic, and environmental goals. This study addresses the optimal network reconfiguration (ONR) problem by developing a multi-objective function that targets loss reduction, voltage profile improvement, and reliability enhancement. It recommends merging renewable energy sources (RES) with energy storage systems (ESS) to improve resilience. The complex nonlinear multi-objective, multi-variable, and multi-constraint problem is simplified by an efficient single candidate optimizer (SCO). Further, a novel severity index of interruption (SII) is introduced for reducing the search space and thus, improving the computational efficiency of SCO in the proposed hybridization algorithm. In terms of statistical metrics and computing time, simulations on IEEE test systems show that the SCO-SII approach outperforms the basic SCO, grasshopper optimization algorithm (GOA), cuckoo search algorithm (CSA) and Pathfinder algorithm (PFA) in reconfiguration, RES integration, and robust mode augmentation. The capacity of the methodology to handle uncertainty in current electrical networks emphasises its importance. [ABSTRACT FROM AUTHOR]