Pareto Based Multi Stage And Multi Objective Planning Of Dg Enhanced Distribution System

767 words - 4 pages

Index Terms—Distributed generation, evolutionary algorithm, optimum reclosers placement, protection system planning, theory of the firm optimization.
I. INTRODUCTION
HE main purpose of the planning stage and operation of a power distribution feeder is to satisfy the energy demand as economically as possible, ensuring acceptable indices of quality and continuity of the service [1]. Nevertheless, previous objectives are in conflict whereas the enhancement of reliability indices requires large investments. Therefore, most of the time, the network operator (NOp) designs a distribution system (DS) attaining the basic requirements of reliability and quality established in the regulation.
Currently, given the smart grid concept, automated operation improves the reliability indices on distribution feeders. That is, there is a reduction in indices such as SAIFI, SAIDI, CAIDI, ASAI, among others. The automation allows the NOp to manage, control, and operate efficiently available resources on the system, such that suitable operation is given automatically (i.e., without manual intervention). In addition, it is essential to optimize the feeder operation taking into account the benefits obtained by the islanded operation of distributed generation (DG) when a fault event arises in the system.
From a technical perspective, the reliability indices can be enhanced with the use of protective devices (e.g., fuses and reclosers) and disconnectors, which allow automation in the system. Considering that the acquisition of these devices involves an economic cost, it is necessary to size such schemes by minimizing investment and maximizing the improvement of the reliability indices. In this sense, the number and location of devices in the system are critical variables to accomplish preceding objectives.
According to the literature, the optimal placement of devices in a DS is a complex problem. A Pareto multiobjective optimization is presented in [1], based on ant colony system (ACS), which minimizes both total costs and reliability indices such as SAIFI and SAIDI. A non-linear programming approach, along with a genetic algorithm (GA), is proposed in [2]. This approach identifies the position of reclosers on a radial feeder considering the minimization of SAIFI. Authors in [3] and [4] consider DG penetration on a DS while planning the optimal placement of reclosers. They minimize SAIFI and SAIDI applying ACS and GA.
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