Auto-optimization algorithms used in smart grids and application to an actual system

Today's power systems were established according to the design principles of Tesla, developed in the 1880s, and developed over time and took their current form. Despite the rapid development of communication technology, the development of power systems has not kept up. With the rapid development of today's technology, it has become possible to make the electricity network better by using a computer and network technologies in electricity networks. Thus, electricity grids will be able to provide sustainable, safe, and uninterrupted energy to consumers by allowing a two-way flow of information and electricity. Networks that can do this are called smart grids. Smart grids are one of the most important developments in energy management systems, as they enable integrated systems, including decentralized energy systems, large-scale renewable energy use, significant improvements in demand-side management, and sustainable energy. With the development of smart grids; The optimal load distribution problem has become more complex due to characteristics such as rapid response to malfunctions, the presence of distributed energy sources and electric vehicles in the network, and the ability to enable customers to participate in the network. Various mathematical and optimization methods have been developed to solve the load distribution problem. Optimization; mathematically it is defined as minimizing or maximizing a function. First of all, in this thesis, a wound healing algorithm based on the clonal selection principle has been developed in order to obtain optimum load distribution in smart grids. A package program was developed in Matlab GUI environment using the developed algorithm and clonal selection, artificial bee, firefly, and cuckoo algorithms, and the program was named oyamatlab. Algorithms have been applied to optimum fuel cost, optimum power loss, and voltage adjustment functions. The proposed method and other algorithms were applied to IEEE 9, IEEE 30, and IEEE 118-bus test systems, which are frequently used in the literature, and the results obtained were compared with other meta-heuristic algorithms. It has been determined that the proposed algorithm gives more optimum results than other algorithms and has shown that it can eliminate the wound healing algorithm gap in the literature. Key Words: Smart grids, optimum load flow, optimization algorithms, clonal selection algorithm, wound healing algorithm