Optimal placement of shunt capacitor banks on a sub-transmission network.
MetadataShow full item record
The optimal capacitor placement problem is the determination of the optimal location of the shunt capacitors on the sub-transmission networks such that energy losses are minimised, the power factor and the network voltage profile are improved. During this period when Eskom is experiencing an unacceptably low generation reserve margin, it’s quite critical that the electrical Transmission and Distribution network losses be kept to a minimum to optimise on the scarce generation that is available to supply South Africa’s current and future power demand. One of the ways of minimising technical losses is through the optimal placement or installation of capacitor banks on the network. The placement of shunt capacitors on a bulk Transmission network is essentially to improve the voltage profile on the network, increase system security and reduce transmission losses. The optimal placement of shunt capacitors with the above objectives would assist in minimising the cost of the investment whilst maximising the return on investment to the utility. This research subject is treated as an optimization problem and hence optimization solutions were considered to address the “Optimal capacitor placement problem”. This optimisation problem is solved for all loading levels i.e. peak, standard and off peak periods and for different seasons in a given typical year. This thesis investigates the capability of Genetic Algorithms technique in solving this optimisation problem. Genetic algorithms utilize a guided search principle to develop a robust solution to this research problem. Given their capability to traverse the complicated search space with a multivariate objective function, Genetic Algorithm are versatile and robust to locate the global optimum of the objective function. These Genetic Algorithms (GA’s) were implemented on real sub transmission networks modelled on DigSilent/ Powerfactory. The modelled GA’s on DigSilent were then tested on different network types i.e. commercial, mining, residential and industrial load mixes. The solutions determined by the different GA’s are then compared in terms of time taken to locate the solution, reliability and robustness. The most reliable GA is then identified and recommended as the preferred optimisation approach. A methodology of using GA’s to solve the above mentioned problem is therefore proposed