A novel approach to distributed energy resource planning using NSGA-II

Increasing share of distribution generation in electrical energy supply has made the distributed energy resource planning (DER) more significant as improper placement, capacity or number of distributed generation (DG) units in power systems would increase power loss as well as jeopardizing the system operation. Diversities and conflicting nature of objectives, uncertainties about benefits or drawbacks of the existing optimization techniques, and suitability for discreet domain problems project more appeal in the invention of efficient and robust techniques in resource planning. In this paper, a new application of multi-objective optimization techniques for sitting and sizing of potential resources (DGs) has been investigated. The system is modeled in Matlab using a fast non-dominated sorting genetic algorithm (NSGA-II) and Matpower within a planning horizon. The capital and running cost of DGs have been considered in the planning period. The results show that the proposed multi-objective framework permits diversity of solutions, requiring minimum number of objective functions to yield a better voltage profile and more independency from the main grid.