Solving power system problems integrated wind power using improved coyote optimization algorithm

Nowadays, considering the developments in industry and technology, it can be seen a serious increase in the energy demanded and consumed. This increase has led to various plans in the energy sector. Economic Load Distribution (ELD) and Optimal Power Flow OPF problems are the most important optimization problems in power systems planning. ELD can described as allocation of the demand among the generating units with minimum costs while meeting the various system constraints. OPF is a nonlinear and non-convex optimization problem which is aim to find the best control parameters while providing the equality and inequality constraints. Today, the fossil fuels used in order to meet the required load demand are in the depletion phase and cause greenhouse effect in the atmosphere due to gas emissions such as COx, NOx and SOx. Recently, renewable energy and especially wind power have become more widely used in power systems due to increasing environmental awareness and economic factors. In this thesis, wind power integrated various power systems problems are handled. Weibull Probability Distribution Function (PDF) and Incomplete Gamma Function Function (IGF) were used because of the uncertainty of wind speed in nature. The classical mathematical methods used in the past to solve EYD and OPF problems have been replaced by heuristic algorithms today. In this thesis, it is provided to improved Coyote Optimization Algorithm (COA) which is a heuristic and swarm intelligence based method to solve problems by using Levy flight method. Improved Coyote Optimization Algorithm (ICOA) has been tested in CEC-2005 problems and it has been seen that the improvement has significantly increased the performance of the algorithm. ICOA is an optimization algorithm that can give effective results and can be applied to the problem easily. According to the results obtained, it is clearly seen that ICOA provides more effective results in the solution of power systems problems compared to effective algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Moth Swarm Algorithm (MSA) Chaotic Moth Swarm Algorithm (CMSA) and Coyote Optimization Algorithm (COA).