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Environmental concerns and energy security are pressing issues of the 21st century, with a heavy reliance on fossil fuels causing significant environmental pollution and resource depletion. To mitigate these problems, it is crucial to explore and implement alternative clean energy sources. This manuscript proposes a novel crayfish optimization algorithm (COA) for optimal scheduling in a hybrid power system that incorporates various renewable energy sources, like battery energy storage systems (BESS), fuel cells (FC), wind turbines (WT), micro turbines (MT) and photovoltaic (PV) panels. The importance of the work lies in its ability to optimize the entire operating costs of a grid‐connected microgrid while improving the accuracy and efficiency of energy management. The COA method addresses economic dispatch problems and manages energy within the grid‐connected microgrid, accounting for high levels of uncertainty. The proposed approach, tested using MATLAB Simulink, achieved a cost value of 252, outperforming existing methods such as GTO, PSO, SSA, and ALO. This illustrates the potential of the proposed technique to provide more cost‐effective and efficient energy management solutions in hybrid power systems. [ABSTRACT FROM AUTHOR]

Summary: The potential for distributed generation (DG) to minimize power loss, increase productivity, lower investment costs, and, most significantly, the ability to leverage renewable energy resources such as wind, photovoltaic (PV), and microturbines, which generate power with low greenhouse‐gas emissions, has piqued interest in the power sector. Deploying DG in an unfavorable location can result in a slew of problems, including increased system failures and costs, voltage spikes and swings, and stability and resilience issues. As a result, an optimization or perceptual technique‐based paradigm is needed to identify the optimal configuration of distributed renewable energy generation for a given system that can provide fiscal, environmental, and technological benefits. Several researchers looked at the best possible location for distributed renewable generation based on their needs and goals. The formal theory for this issue, however, remains unsolved. Individual studies on solar PV systems, types of solar PV systems, wind power generation, and pumped hydro storage systems are all covered in detail in this paper. In addition, the study focuses on a variety of optimization techniques and algorithms for providing a better perspective on resolving issues related to the integration and functionality of many renewable resources in an islanded as well as grid‐connected environment. The heuristic algorithms are explained with respective flow charts individually. Finally, a comparative analysis has been carried out in order to understand and validate the usefulness of individual optimization techniques with itself and others. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the solution of optimal power generation for economic emission load dispatch (EELD) using space search algorithm to a hybrid power system. The proposed space search algorithm (SSA) has been applied on 23 benchmark test functions and the results were compared with Gravitational search algorithm (GSA), Particle swarm optimization (PSO), Differential evolution (DE) and Grey wolf optimizer (GWO). The results show that SSA method is able to provide very competitive results compared to other established meta-heuristics. After that, the economy objective function is minimized, followed by minimization of emission level objective function. Then, both the objectives are combined through a fuzzy coordination method to form a fuzzy decision making (FDM) function. Maximizing the FDM function then solves the original two-objective problem. The minimization and maximization tasks of this optimization problem are solved by the space search algorithm. The optimisation technique was applied to the hybrid system and a conventional power system with only three conventional thermal generators. The results by hybrid system provide cheaper generation schedule in comparison to individual system of thermal generators. [ABSTRACT FROM AUTHOR]