A framework for optimal planning in large distribution networks

Large scale distribution system planning is a relatively complex and reasonably difficult problem. This paper proposes the application of improved genetic algorithm (GA) for the optimal design of large scale distribution systems in order to provide optimal sizing and locating of the high and medium voltage (HV and MV) substations, as well as medium voltage (MV) feeders routing, using their corresponding fixed and variable costs associated with operational and optimization constraints. The novel approach presented in the paper solves hard satisfactory optimization problems with different constraints in large scale distribution networks. This paper presents a new concept based on loss characteristic matrix introduced for optimal locating of MV substations, followed by new methodology based on graph theory and GA for optimal locating of the HV substations and MV feeders routing in a real size distribution network. Minimum spanning tree algorithm is employed to generate set of feasible initial population. In the present article to reduce computational burden and avoid huge search space leading to infeasible solutions, special coding methods are generated for GA operators to solve optimal feeders routing. The proposed coding methods guarantee the validity of the solution during the progress of the genetic algorithm toward the global optimal solution. The developed GA-based software is tested in a real size large scale distribution system and the well satisfactory results are presented. Index Terms--Distribution system planning (DSP), genetic algorithm (GA), graph theory, long-term load forecasting.