Distributed Generation Expansion Planning Considering Load Growth Uncertainty: A Novel Multi-Period Stochastic Model

Distributed generation (dg) technology is known as an efficient solution for applying in distribution system planning (dsp) problems. load growth uncertainty associated with distribution network is a significant source of uncertainty which highly affects optimal operation of dgs. in order to handle this problem, a novel model is proposed in this paper based on dg solution, considering load uncertainty. the proposed model is designed to minimize network costs including operation and losses. genetic algorithm (ga) is used with the purpose of finding the optimal places, sizes as well as times for dgs. load uncertainty is also modeled through markov tree. to illustrate the effectiveness of the proposed model, it is tested in different scenarios considering the effects of the purchased electricity price, dg penetration factor (pf) and dg operation intervals. these scenarios are conducted in two different phases, with and without uncertainty, and the results are then compared and discussed. moreover, by considering load uncertainty, planning models would be robust against network future load variations.