Multi-objective optimization of power distribution system with distributed generation, capacitor bank and thyristor controlled series compensator using back-tracking search algorithm

The increase in the world population has to a rapid increase in the human being energy demand, causing a yearly rapid increase in the consumption of the energy. Due to already mentioned reasons, the conventional energy production method has affected the environment in a harmful way that opened the eyes of the mankind to innovate new sources for renewable energy generation. Recently, due to increase in the energy demand and consumption of the growing and expanding radial distribution systems (RDS), it has been more common to meet the energy demand through the use of solar energy systems (PV) as a renewable energy source. Such systems are called distributed generation (DG) systems. By the addition of the integrated equipment, such as shunt capacitors (CB) and thyristor-controlled series compensator (TCSC), it has gained the ability to solve the problem of re-planning in these RDS. By this planning, two main objective functions, power generation cost and power loss, have been considered. Considering the complexity, difficulty, linear and non-convex nature of the problem, it has been planned to use a heuristic method that has been proven in many different studies, superior to numerical methods. A heuristic algorithm has been preferred as it is more up-to-date, new and able to reach the optimal solution in shorter time than numerical methods. In this thesis study, a back-tracking search algorithm (BSA), which is a current and new heuristic algorithm, has been used for single and multi-objective optimization of power generation cost and power loss in IEEE 69-bit RDS with integrated DG, CB and TCSC. For the single and multi-objective optimization with the proposed algorithm; 12 and 3 different cases were planned. In this thesis, integration of the 3 different elements in RDS, and solution of single-objective and multi-objective optimization problems are performed with BSA for the first time. In multi-objective optimization, the Pareto optimal set was used with BSA. The optimum solution was obtained with the best compromise solution from the obtained results. By comparing the obtained results with the other results in the literature, it has been shown that the proposed algorithm is effective in solving the problem.