Distribution network planning is a very complicated, non-linear, large scale multi-objective and multi-constraint combinatorial optimisation problem. The capacity, location and power supply range of the substation and the distribution network are optimised based on the load forecasting. In previous studies, this problem usually decomposes into two sub-problems, one is substation planning and the other is distribution network planning. The authors propose a method based on the self-adjustment weighted Voronoi diagram (WVD) using genetic algorithms and particle swarm optimisation for planning substations, which can optimise the location and power range of the substations when both the number and capacity of the substations are known. The weight is calculated according to the substation capacity and load distribution, and then the authors form the self-adjusted WVD whose weight can be adaptively adjusted. This method ensures the convergence of the algorithm and also makes the location and power supply range of the substations more reasonable. On this basis, the self-adjusted WVD based on the elitist selection genetic algorithm (ESGA-WVD) or the particle swarm optimisation algorithm (PSO-WVD) is achieved using the global search feature of the ESGA or the PSO. Numerical results show that ESGA-WVD and PSO-WVD are more reliable and reasonable than single ESGA, PSO or WVD; both in the determination of substation location and in the division of the substation power supply range. Compared with ESGA-WVD, PSO-WVD is better in terms of running time, convergence rate and investment costs.

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Substation planning method based on the weighted Voronoi diagram using an intelligent optimisation algorithm

References

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