Research Specifications

Regards to widespread impacts of uncertainties in power system planning and operation, some strategies must be devised in order to well incorporate the uncertainties in power system modeling and hence achieve the best possible strategy to be adopted. The most important uncertainties in long-term distribution network planning are due to errors in forecasting of load demand and market price. This paper presents a stochastic multistage expansion planning method to consider the forecasting errors as well as pseudo dynamic behavior of network parameters and geographical constraints. In this paper, the optimal routes of MV feeders as the backbone of distribution networks are obtained for both mid and long-term cases with probabilistic modeling. To enhance the accountability of the power system and to improve the system performance parameters simultaneously to the best possible condition multi-objective functions are proposed and are solved using NSGA II (Non-Dominated Sorting Genetic Algorithm). The employed objectives contains all economical, environmental and technical aspects of distribution network e.g. cost of Feeders installations, active and reactive power losses cost, cost of purchased power from power market, Reliability cost, Voltage Stability enhancements, Minimization of Voltage Deviation and Emission reduction. One of the most important advantages of the proposed multi-objective formulation is that, it obtains non- dominated solutions allowing the system operator (decision maker) to exercise his/her personal preference in selecting each of those solutions based on the operating conditions of the system and the costs. To validate the effectiveness of the proposed scheme, the simulations are carried out on a relatively large-scale distribution network.