Abstract-- An efficient method to address the multi-stage planning of open loop structured mv distribution networks under uncertainty, taking into account distributed generation connected to distribution system, has been proposed. The fuzzy model can cope with important features implicit in planning studies such as time-phased representation, consideration of conflicting objectives and uncertainty in loads, distributed generation and economic data. Using two evolutionary algorithms simultaneous optimization of costs and the reliability is achieved. Thus, in addition to optimal radial layout along several stages in time, the algorithm can determine the optimal locations of reserve feeders that achieve the best network reliability with the lowest expansion and operational costs. The model and evolutionary algorithms have been applied intensively to real life power distribution systems showing its potential applicability to significantly larger systems than those frequently found in literature about dynamic distribution networks planning. Results have illustrated the significant influence of the uncertainties in the optimal distribution network planning mainly in terms of topology and supply capacity of the resulting optimal distribution system. Index Terms--primary distribution network, planning, open loop network, evolutionary algorithm, dynamic model, uncertainty, robustness, fuzzy sets I. INTRODUCTION ANY mathematical models have been proposed in the past for electrical distribution network planning. Most of those neglect that one is dealing with a dynamic multi- temporal problem under uncertainty. On the other hand, considering the evolution in power demand through time and consequent topological changes in the networks, dynamic planning have never been a definite success, when applied to real sized networks. Large network problems have been addressed for linearized objective functions. Branch and bound applications can be found in [1,2], mixed-integer programming in [3] together with Bender’s decomposition [4] and with branch exchange [5]. Dynamic programming approaches have been taken in [6,8]. More recently, evolutionary computation techniques have also been proposed [7,8,9,10]. In [7] evolutionary algorithm with integer codification has been proposed giving an optimal solution for a fixed set of data and a single time period only. [8,9,10] are string genotype approaches to small-size network problems. For large networks the combinatorial nature of decision M. Skok, D. Skrlec and S. Krajcar are with the Department of Power Systems, University of Zagreb, Faculty of Electrical Engineering and Computing, 10000 Zagreb, Croatia (e-mail: Minea.Skok@fer.hr). making turns such genetic algorithms (GAs) into computationally expensive approaches. Due to the use of standard (binary) solution encoding and standard genetic operators the following problems have been observed:  topological unfeasibility (connectivity, radiality) [8,9],  low heritability (a significant number of offsprings generated by the crossover operator hardly have substructures of their parents),  suitable building of additional lines (reserve feeders’ segments used in contingency conditions to improve reliability of supply) is not included, or if it is, then methods have been classified as single-stage (static). To our knowledge, only four researchers have devised methods suitable for the problem of open loop distribution system planning, and neither of these is multistage (dynamic) or considers the intrinsic uncertainties of data [11,12,13,14]. This paper presents a suitable fuzzy model to deal with the uncertainties from future demand, distributed generation and economic data in order to solve long term large scale multi- stage open loop mv distribution network planning problem under uncertainty. A method based on two interrelated evolutionary algorithms is used to generate sets of dynamic distribution network solutions. Furthermore, besides radial operational topology, optimal reserve cables that achieve the best network reliability with the lowest economical costs for multi-stage optimal designs are determined. The proposed method was tested with real size systems achieving optimal plans in reasonable CPU times compared with the dimensions of such systems. II. OPTIMIZATION TECNIQUE A. General outline (two evolutionary algorithms) Along several stages in time the proposed optimization technique searches for the set of decisions (associated with investment costs - new lines, new substations, reinforcement of an existing system, operation costs – maintenance, demand and energy costs - and reliability – expected energy not supplied) that “optimize” the global cost of system development. The development of the system is simulated by two interrelated evolutionary algorithms that are used to generate sets of dynamic distribution network solutions (Fig.1). The master (main) evolutionary algorithm is aimed at optimizing the open loop network layout in the last year of the study period (e.g. 5-20 years ahead) and the slave EA is used in each iteration of the master EA to produce an optimized plan for the final year, identifying schedule (as a set of yearly M. Skok, Member, IEEE, S. Krajcar, Member, IEEE , and D. Skrlec, Member, IEEE Dynamic Planning of Medium Voltage Open- Loop Distribution Networks under Uncertainty M 551 1-59975-028-7/05/$20.00 © 2005 ISAP.