Pseudodynamic Planning for Expansion of Power Distribution Systems

Dept. de Ingenieria Electr. e Inf., Zaragoza Univ.
IEEE Transactions on Power Systems (Impact Factor: 2.81). 03/1991; 6(1):245 - 254. DOI: 10.1109/59.131069
Source: IEEE Xplore


Basic and extended planning models, based on a pseudodynamic
methodology, for solving the global expansion problem (sizing, locating,
and timing) of distribution substations and feeders throughout the
planning time period are presented. The objective functions that
represent the expansion costs are minimized by successive concatenated
optimizations subject to the Kirchhoff's current law, power capacity
limits, and logical constraints in the basic model. Also presented is an
extended model that is obtained by including the voltage drop
constraints in the basic model

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    • "No Yes Yes Yes No [11] Yes Yes Yes Yes No [3] No No No Yes No [12] Yes Yes Yes No No [4] No Yes Yes Yes Yes [13] Yes Yes Yes Yes No [5] No Yes Yes Yes No [14] Yes Yes Yes Yes No [6] No Yes Yes Yes No [15] Yes Yes Yes Yes No [7] No Yes Yes Yes No [16] No Yes Yes Yes No [8] No Yes Yes Yes No [17] No No Yes No No [9] No Yes Yes Yes No [18] No Yes No No No [10] Yes Yes Yes Yes No [19] "
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    ABSTRACT: In this paper, a new methodology is presented for dynamic expansion planning of sub-transmission substations (DEPSS). The proposed method deals with the expansion schemes of the facilities which should be installed and/or reinforced in order to make the sub-transmission system capable of supplying the forecasted demand at the lowest cost while all technical constraints are satisfied. DEPSS is inherently a mixed integer nonlinear programming (MINLP) problem due to the prevalent electrical and expansion constraints, cost indices in objective function and decision variables. This nonlinear problem is simplified to a linear problem without any neglecting by the proposed method. The location, capacity and construction time of substations and MV feeders, as well as the optimal service area of substations are determined through a dynamic approach for the planning years. Meanwhile, the optimal operation capacities of substations are determined at each load level in every planning year. The effectiveness of the proposed optimization method is discussed in the first case study which is related to just placement and defining the associated service area. Also, the proposed dynamic method is tested on a realistic case study and compared with the static and multistage approaches.
    Full-text · Article · Nov 2014 · Electric Power Systems Research
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    • "Moreover, some candidate locations exist for the construction of new substations, and there is the possibility of expanding some existing ones. The aim of SEP is to determine substation locations, capacity, associated service areas, and equipment installation timing within the planning horizon, whereas the technical constraints are respected with minimum cost [3]. Technical constraints that have to be satisfied are the following: supplying all loads, maximum allowable voltage drops at load points, maximum permissible loading of substations, network radiality, and the feeders' maximum current limits. "
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    ABSTRACT: This paper presents an enhanced evolutionary algorithm to solve the static distribution substation planning problem within large distribution networks. It is based on a deterministic heuristic algorithm to find the approximate substation service areas for each substation and an expert selection strategy that increases the convergence chance to a global optimal solution. The introduced algorithm takes different electrical constraints such as voltage drops, power flow, radial flow constraints, and all prevalent cost indices into consideration. In addition, effects of unreliability within network feeders and substations are investigated on the obtained layouts. The developed method is applied to four benchmark test systems and an actual large-scale distribution system with about 140 000 customers, followed by a discussion on results.
    Full-text · Article · Jan 2014 · IEEE Systems Journal
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    • ") ( r ) ( r , 2 1 GB k i ij,k LB i,k ij,k ij,k ij,k x x c x x c v w v − × × + − × × + × = (3) where 1 2 c c < , g N i 1,2,..., = , pg N j 1,2,..., = , n k 1,2,..., = and the following notation is used: w Inertia weight 2 1 ,c c Positive constants as learning factors r Random function generating random numbers uniformly distributed within the range [0] [1] g N number of all groups pg N number of particles in each group The second term in right part of Eq. (3) prevents the particles moving to ineffective regions; however the third part leads the particles to move into the global optimum. In the proposed improved PSO, the particles are manipulated regarding not only to the velocity vector, but also to the local best particles, the global best particles and a new controller parameter (q) as follows: "
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    ABSTRACT: Optimal multistage expansion of medium-voltage power network because of load growth is a common issue in electrical distribution planning. Minimizing total cost of the objective function with technical constraints, make it a combinatorial problem which should be solved by optimization algorithms. In this paper, a new improved hybrid Tabu Search/ Particle Swarm Optimization algorithm is proposed for multistage distribution network expansion planning. The proposed algorithm is executed in two phases but it is not limited by some optimal solutions of each stage of the planning. It contains adequate adjustment between intensification and diversification. To show the ability of the proposed algorithm, it is applied to a 71-bus 20 kV distribution network as a test case. The numerical results and comparisons show the proposed algorithm can efficiently improve the total cost of distribution network.
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