Fitness distance balance based triangulation topology aggregation optimizer for optimal power flow including renewable energy sources

With the substantial increase in the amount of energy demanded and consumed today, there is also an increase in energy generation from renewable energy sources. Including renewable energy sources in an electrical grid and using them introduces the problem of planning the network in the most economical and efficient way. This thesis addresses the optimal power flow problem, which is one of the power system problems integrated with wind and solar power, both renewable energy sources. The optimal power flow problem is an optimization problem with a nonlinear structure and various constraints, where the best values of control parameters are determined. Additionally, combining the nature of solar and wind energy increases the complexity of the problem. Heuristic search algorithms, which are a type of artificial intelligence technique, are preferred in solving such problems. In this thesis, the Triangulation Topology Aggregation Optimizer (TTAO) algorithm was first developed based on distance adequacy balance for the solution of the optimal power flow problem. The developed algorithm was applied to the optimal power flow problem including wind and solar energy sources and compared with the results of different algorithms in the literature. The obtained results clearly show that the proposed algorithm is effective in this power system problem.