The Optimal and Economic Planning of a Power System Based on the Microgrid Concept with a Modified Seagull Optimization Algorithm Integrating Renewable Resources

Abstract

In the past, planning to develop an electricity generation capacity supply of consumable load, an acceptable level of reliability, and minimum cost has played significant roles. Due to technological development in energy and the support of energy policymakers to make the most of these clean and cheap resources, a significant amount of research has been conducted to make the most of such energy. Constraints such as low capacity, output power uncertainty, and sustainability problems have made using distributed energy sources costly and complex. Theoretically, capacity development planning in a power system is part of macro-energy planning. It is generally based on specific development policies in each country’s national interest. In addition to being economical, the purpose of this planning was to find the best capacity development plan commensurate with the amount of consumption so that the development plan does not go beyond the permissible limits of reliability, environmental issues, and other constraints. On the other hand, due to the considerable growth of divided production, especially energy sources, it is essential to use microgrids. Accordingly, in this research study, in the process of solving the problem of planning and providing load growth by the distributed generation units to maximize reliability and minimize investment costs, the creation of smaller networks was investigated. To optimize zoning, the weighted graph theory method, in which the weight of the edges is the apparent power passing through the lines, was adopted. In addition, reactive power reliability was included in the calculations to improve the economic aspects. Probabilistic modeling for the presence of renewable resources was employed to bring the model to reality. Since the above problem is very complex, a Seagull-based algorithm and chaos theory were utilized to solve this matter. Finally, the suggested method for the sample system is discussed in different scenarios, indicating an improvement in the system’s performance. According to the numerical results, the NSGA, SPEA, and MOPSO have mean values of 68.3%, 50.2%, and 48.3%, which are covered by the proposed optimization algorithm.