This paper presents a new algorithm for distribution system reconstruction planning based on Mamdani type fuzzy inference and BellmanZadeh multi criteria decision making method. The proposed algorithm takes system attributes as inputs (number of customers served by renewed infrastructure, energy losses, power demand and cost of investment) and returns crisp output values which are used as planning criteria. The aim of this paper is to provide a logical decision making framework which can be used to model, evaluate, and rank projects according to required criteria. The proposed model is flexible and can be extended to include additional planning criteria. The proposed method is tested on a realistic distribution system to demonstrate its relevance. It is expected that this paper will make a contribution toward more effective management of power distribution network planning process and that it will be used by planning engineers in practical problems.

Uncertainty is one of the most important factors contributing to the complexity of the power system operation and management. This paper presents some of the most important uncertainty modelling techniques and compares their advantages and disadvantages. In particular, this paper focuses on identification, classification and comparison of uncertainty modelling approaches used in power systems, highlighting the Distributed Generation (DG) allocation problem. The main objective of this paper is to identify the sources of uncertainty in DG allocation problem, review the most important uncertainty modelling methods and propose the appropriate matching approach between the sources of uncertainty and modelling methods

Abstract This paper discusses the problem of finding the optimal network topological configuration by changing the feeder status. The reconfiguration problem is considered as a multiobjective problem aiming to minimize power losses and total interruptions costs subject to the system constraints: the network radiality voltage limits and feeder capability limits. Due to its complexity, the metaheuristic methods can be applied to solve the problem and often the choice is genetic algorithm. NSGA II is used to solve the multiobjective optimization problem in order to get Pareto optimal set with possible solutions. The proposed method has been tested on real 35 kV distribution network. The numerical results are presented to illustrate the feasibility of the proposed genetic algorithm. Keywords radial distribution network, multiobjective optimization, reconfiguration, genetic algorithms, NSGA II