Long Term Multi-Stage Planning of Open Loop Distribution Networks Under Uncertainty

This paper presents a new and efficient method based on evolutionary algorithms to address the multi-stage (dynamic) planning of open loop structured distribution networks under uncertainty taking into account distributed generation connected to distribution system. The proposed model can cope with important features implicit in planning studies such as time-phased representation and uncertainty in loads, distributed generation and prices. As European distribution system in urban and suburban areas are operated as open loops, besides using modern reliability indexes as optimization attributes, the traditional approach of contingency coverage perspective in the horizon of study period is introduced as one of the constraints in the model of the problem. Besides optimal radial layout along several stages in time, the algorithm can determine the optimal locations of reserve feeders that achieve the best network reliability with the lowest expansion and operational costs. The uncertainty is modelled using fuzzy numbers. The model and evolutionary algorithms have been applied intensively to real life power distribution systems showing its potential applicability to large scale systems. Results have illustrated the significant influence of the uncertainties in the optimal distribution network planning mainly in terms of topology and supply capacity of the resulting optimal distribution system.