Stochastic power system optimisation algorithm with applications to distributed generation integration

Abstract

The ever increasing level of penetration of Distributed Generation (DG) in power distribution networks is not without its challenges for network planners and operators. Some of these challenges are in the areas of voltage regulation, increase of network fault levels and the disturbance to the network protection settings. Distributed generation can be beneficial to both electricity consumers and if the integration is properly engineered the energy utility. Thus, the need for tools considering these challenges for the optimal placement and sizing of DG units cannot be over emphasized. This dissertation focuses on the application of a soft computing technique based on a stochastic optimisation algorithm (Particle Swarm Optimisation or PSO) for the integration of DG in a power distribution network. The proposed algorithm takes into consideration the inherent nature of the control variables that comprise the search space in the optimal DG sizing/location optimisation problem, without compromising the network operational constraints. The developments of the proposed Multi-Search PSO algorithm (MSPSO) is described, and the algorithm is tested using a standard, benchmarking 69-bus radial distribution network. MSPSO results and performance are compared with that of a conventional PSO algorithm (and other analytical and stochastic methods). Both single-objective (minimising network power loss) and multi-objective (considering nodal voltages as part of the cost function) optimisation studies were conducted. When compared with previously published studies, the proposed MSPSO algorithm produces more realistic results since it accounts for the discrete sizes of commercially available DG units. The new MSPSO algorithm was also found to be the most computationally efficient, substantially reducing the search space and hence the computational cost of the algorithm compared with other methods, without loss of quality in the obtained solutions. As well as the size and location of DG units, these studies considered the operation of the generators to provide ancillary voltage support to the network (i.e. with the generators operating over a realistic range of lagging power factors, injecting reactive power into the network). The algorithm was also employed to optimise the integration of induction generation based DG into the network, considering network short-circuit current ratings and line loading constraints. A new method for computing the reactive power requirement of the Abstract V induction generator (based on the machine equivalent circuit) was developed and interfaced with the MSPSO to solve the optimization problem, including the generator shunt compensation capacitors. Finally, the MSPSO was implemented to carry out a DG integration problem for a real distribution network and the results validated using a commercial power system analysis tool (ERACS).