برنامه ریزی یکپارچه بهینه از سیستم های توزیع MV-LV با استفاده از DPSO
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27022||2011||10 صفحه PDF||سفارش دهید||8330 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Electric Power Systems Research, Volume 81, Issue 10, October 2011, Pages 1905–1914
A new technique for optimal planning of MV and LV segments of a distribution system is presented in this paper. The main goal is to find optimally distribution transformer and substation locations and ratings, as well as, the route and type of Medium Voltage (MV) and Low Voltage (LV) feeders. The proposed technique is applicable to both uniform and non-uniform load densities areas. In this method, the planning area is divided into regions with relatively uniform load density such as urban, semi-urban, sub-urban. Each of regions is divided into zones, called LV zone. Each LV zone is supplied by an MV/LV transformer. The dimensions of LV zones are found based on the average load of each region. The placement and rating of MV/LV transformers, the type and route of LV conductors in an LV zone all depend on its loads’ location and power. Regarding the placement and rating of MV/LV transformers in planning area and the space of regions, the dimensions of a zone which is supplied by a HV/MV transformer, called MV zone, is determined. Additionally, the location and rating of HV/MV transformers as well as the feeder's routes and types are calculated. Since the dimensions of an LV zone influence the associated length of MV feeder, the MV feeder cost needs to be included in the total cost associated with the LV zone. This requires the MV feeder type to be known to calculate the corresponding cost. However, the MV feeder type is determined as an output from MV zone planning. As a result, an iterative based method is proposed to consider this common element in computations to develop integrated planning of both LV and MV zones. It is observed that the iterative technique quickly converges to the same results as the exhaustive search method. Discrete particle swarm optimization (DPSO) method is employed for solving the planning problem. The results are compared with nonlinear programming, genetic algorithm and exhaustive search methods. It is observed that DPSO is as accurate as the exhaustive search method for integrated planning of MV–LV distribution systems while its computation time is significantly lower.
A distribution system consists of MV and LV networks. Although the LV network cost, to some extent, is comparable with the MV network cost, the majority of the published papers in this field are dedicated to the planning of MV networks , , , ,  and  rather than LV networks , ,  and . Furthermore, there are only a few papers that consider both MV and LV networks ,  and . The optimal planning of either of these networks separately will not lead to an accurate result. Since the dimensions of an LV network determine the associated length of MV feeder, this element should be included in the total cost associated with both LV and MV networks so both of these networks should be planned simultaneously as considered in this paper. Most of the papers referred above use a continuous cost function to model the cost of distribution system components, LV conductors, distribution transformers, MV feeders and substations. Only a few authors used the discrete cost function , ,  and . Due to the rounding process, the accuracy of the solution decreases with continuous cost functions. Therefore, a discrete cost function based on realistic discrete data, collected from the distribution system elements, is used in this paper. Selection of an appropriate optimization method for the optimal planning of distribution systems (OPDS) is crucial. The classical branch and bound techniques are employed for planning distribution networks in , , ,  and . Although these procedures can lead to minimum objective function value, they require excessive computation time owing to their combinatorial complexity. As a result, some other approaches have been presented to improve computational efficiency. Amongst these, the heuristic methods are extensively applied in the literature , , , , , , ,  and . The particle swarm optimization (PSO) is one of the heuristic methods  and . In this paper, PSO is employed to solve the OPDS problem. Due to the discreteness of the cost function, a Discrete PSO, called DPSO, is used. A comprehensive optimal planning of distribution systems for the urban/semi-urban areas is presented in this paper. Both MV and LV networks are optimized and the optimal location and size of transformers and substations, as well as, the route and type of MV and LV feeders are obtained. This work is aimed at Greenfield sites where the location of specific loads or substations is not pre-assigned. In this work, the cost of the distribution system elements is not assumed to be continuous, but discrete. The employed objective function consists of the capital cost, loss cost and reliability cost. The voltage drop and feeder current are incorporated as constraints in the optimization procedure.
نتیجه گیری انگلیسی
In this paper, a new methodology is introduced for integrated planning of MV and LV segments of a distribution system optimally considering feeder types and routes, as well as, transformer ratings and placements. The objective function associated with the LV segment planning is composed of the loss cost as well as the total capital cost for MV/LV transformers, LV conductors, and the part of the MV feeders located in an LV zone. The objective function associated with the MV segment planning consists of the reliability cost, the line loss cost and the total capital cost for HV/MV transformers and MV feeders. The voltage drop and the feeder current are considered as constraints in planning both LV and MV segments. Discrete particle swarm optimization is employed iteratively to solve the optimal integrated distribution planning problem. The results are compared with those obtained by NLP, GA and the exhaustive search method. NLP as an analytical method could not improve the initial values due to the high discreteness of the problem. The proposed algorithm illustrates higher accuracy in all cases compared with GA for similar expected computational effort. Also the results of the DPSO have been compared with the exhaustive search method and are found to be identical. However, the exhaustive search is more time consuming. A low computational effort iterative based technique is proposed for planning both LV and MV networks altogether. The results are found to be identical with those obtained by the exhaustive search. This illustrates the high accuracy of the proposed technique. It is shown that the proposed technique can be employed for planning of both uniform and non-uniform load densities. This paper can provide guidance for planning of practical MV and LV distribution systems.