Abstract:
It’s an indisputable fact that the Nigerian power system and that of other developing and under_developed countries has been in a very poor state technically in the sense that the supply capacity is always far less than the system’s load demand. Over the years, various efforts have been made to overcome this situation, which included basically, load-shedding and network expansion however, the system is still very unreliable even with the ever increasing load demand. This situation is evident in the frequent power outages that is been experience in almost all branches of the network. Therefore, in a bid to proffer a more efficient solution to this challenge, the Distribution Generation technology is currently being investigated by researchers round the world because of its ability to reinforce the distribution network by way of power loss reduction and voltage profile improvement. In this work, the optimal placement of DGs in radial distribution network for the minimization of voltage deviation, minimization of total real power loss and for maximization of Voltage Stability Index was investigated using the Improved Strength Pareto Evolutionary Algorithm (SPEA2). This proposed optimization method in combination with Newton-Raphson load flow method produces the approximate pareto optimal solution which is mapped into the objective space to form the Pareto Frontier from which the design Engineer would choose the best compromise solution based on preference and trade-offs amongst the objective functions. This methodology was tested on the IEEE 33-bus network; the result showed that the power loss on the test network was reduced by approximately 5%, 5% of its no DG value for single DG placement, 8%, 7% for two DGs placement and 15%, 14% for three DGs placement. The total bus voltage variation of the network was also improved by 5%, 10% and 15% of its no DG value. Therefore, it can be concluded that the objectives of integrating DGs in the electrical distribution network are achievable when they are optimally sited in the networks
