engleski

A Comprehensive Methodology For Long-Term Planning Of Distribution Networks With Intrinsic Contingency Support

Long-term distribution planning is crucial for any electric utility striving for an economically viable development with permanent profit. Since long-term distribution system planning is recognized as a complex combinatorial problem due to the large number of variables and parameters involved, the exploration of planning methods has been a topic of interest for over four decades. Fortunately, advances in applied mathematics and computer technology gave way for the creation of numerous methods. On the other hand, deregulation processes affecting the power industry diminished the usability of existing (usually least-cost) planning methods and produced a need for more flexible methodologies. All in all, despite the fact that various planning methods exist and new ones are innovated, accompanying long-term planning tools are scarcely used. Implementation difficulties, method inadequacies and usability shortcomings are regularly indicated as reasons for this poor usage, although these reasons are normal side-effects of converting research into practice. In order to promote the potential of heuristic optimization methods for optimal long-term distribution planning, this paper presents a complete methodology for planning open-loop (link) medium-voltage networks. The emphasis of this paper is on the whole planning process surrounding the actual implementation of the optimization method. The procedures preceding and succeeding the optimization are decisive for usability which explains why many methods and methodologies are inadequate. In this paper, the complete planning process is presented with special attention to details that usually render other optimization methods ineffective (decomposition, special loads, spatial constrains, etc.). In addition, some explanations related to the underlying evolutionary algorithm are given in order to clarify planning goals and constraints.

Electric distribution planning; genetic algorithms; optimization

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