Optimal placement and sizing of distributed generation in radial distribution networks using particle swarm optimization and forward backward sweep method

Among major concerns of the conventional electrical power generation systems, are the issues related to emission and the environmental hazards, as well as the economic viability of building new ones. Distributed Generation (DG) has become one of the options in electrical power provision, in order to curtail or reduce the problems posed by the conventional power systems. As DG is becoming increasingly popular with high level of acceptability, the problem of optimum placement of DG with the correct capacity are the main challenges for power utilities. To address these issues, this thesis focuses mainly on the optimal placement and sizing of DG in the distribution networks. Electrical distribution network systems normally include distribution feeders,which are arranged or configured either in mesh or radial pattern and they are mainly fed by a utility substation. However, distribution networks have been found to be exhibiting significant voltage drop, due to their high R/X ratio that could cause substantial power losses along the feeders. In light of this aforementioned problem, installation of DG within the distribution level, will have an overall positive impact towards reducing the power loss and voltage deviation as well as improving the networks voltage profiles. Voltage deviation is an important factor that needs an immediate attention in the power system,which is affecting the operating conditions of the present day power systems. The evaluation and minimizing voltage deviation will reduce the problems power quality and bring about stability in the nominal voltage. The minimization of this fluctuation that leads to derailing from the nominal voltage need to be emphasized. To achieve the set target, particle swarm optimization (PSO) is used as an optimization technique. PSO is among the meta-heuristics search methods like Genetic Algorithm (GA) but has been found to be computationally efficient, because it uses less number of functions for evaluation compared to GA that has genetic operators (Selection, crossover and mutation) and also the computational effort (time) required by PSO to arrive at high quality solutions is less than the effort required to the same high quality solutions by other heuristic search methods. The output indicates that, PSO algorithm technique shows an edge over other types of meta-heuristics search methods due to its effectiveness and computational efficiency. The proposed PSO algorithm is used to determine optimal placement and size of DG in radial distribution networks, where Forward Backward Sweep Method (FBSM) of distribution load flow analysis was used, to determine the actual power loss in the system. FBSM is adopted in this work due to its advantages over other conventional load flow studies such as Newton raphson, Gauss-siedal and fast decoupled load flow methods, these conventional methods are found not to be suitable for distribution load flow analysis due to high R/X ratio. FBSM offers better solutions, faster and high level of accuracy. The computational time of building the Jacobian matrix, LU factorization, and backward/forward substitution needed for Newton's method are no longer required in FBSM. The FBSM is proven to be robust and to have the lowest CPU execution time when compared with other conventional methods. The proposed method is tested on the standard IEEE 34-bus test systems. Results indicate that the sizing and location of DG are system dependent and should be optimally selected before installing the distributed generators in the system. Improvements in the voltage profile, power loss and voltage deviation reduction have been achived