The paper presents the Computer Aided Design of Distribution
Network (CADDiN) application based on geographic information system
(GIS) and genetic algorithms designed to improve the planning of major
investment in open-loop distribution network over the coming years. The
system allows the planner to identify, from load forecast, data on the
existing distribution system and all future projects, the projects to be
undertaken together with the commissioning year, in such a way that the
investment and maintenance costs, as well as cost of losses are
minimized over a planning horizon. The application has been tested with
real size distribution systems achieving optimal designs in reasonable
CPU times compared with respect to the dimensions of such distribution
"Traditional methods were based on heuristic approaches (Glamocanin and Filipovic, 1993; Cannas, et al., 1999.) and different search techniques (Glamocanin, 1990). Recent papers apply new technologies, like fuzzy logic (Skok, et al., 2006), tabu search (Nara, et al., 2002) or genetic algorithm (GA) (Filipec, et al., 1999). The fuzzy logic approach is well fitted especially when dealing with uncertain data, such as load data. "
[Show abstract][Hide abstract] ABSTRACT: A dual particle swarm optimization - immune algorithm solution is presented in this paper to deal with the problem of optimum radial reconfiguration and reactive power compensation in distribution systems. The optimization problem uses as minimization function power losses in the distribution system – lines and transformers – and addresses constraints referring lower and upper voltage limits, nodal reactive power limits, topology supply constraints and the maximum number of capacitor banks. The analysis conducted for a pilot and a complex test system has proven the feasibility of the proposed method.
[Show abstract][Hide abstract] ABSTRACT: In this paper, an advanced method for the planning of open-loop medium voltage (MV) distribution network is proposed. The multiobjective planning model is constructed, and a special multiobjective genetic algorithm (MOGA) is designed based on the network dataset built by component geographical information systems (ComGIS). The network analysis function of the ComGIS is embedded in the overall optimization process to find single-loop optimal paths. Crossover and mutation operator are designed according to characteristics of the coding. The evolutionary orientation is directed by the fitness function based on Pareto order of individuals. The Pareto optimal set (POS) including several candidate planning schemes is gotten through MOGA, from which the recommended scheme is selected. The method has been applied to real life power distribution networks, showing its potential in practical applications.
Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on; 01/2008
[Show abstract][Hide abstract] ABSTRACT: An advanced method for optimal planning of open-loop medium voltage (MV) distribution network is proposed in this paper. The multiobjective planning model of open-loop MV distribution network is formulated, and an outage cost calculating method applicable for open-loop MV distribution networks is proposed. To solve the optimization problem, a special Multiobjective Genetic Algorithm (MOGA) is designed based on the network dataset built by Component Geographical Information Systems (ComGIS). The network analysis function of the ComGIS is embedded in the overall optimization process of the MOGA to find single-loop optimal paths. Crossover and mutation operator are designed according to characteristics of the coding. The evolutionary orientation is directed by the fitness function based on the Pareto order of individuals. The Pareto Optimal Set (POS) including several candidate planning schemes is obtained through the MOGA, from which the recommended scheme is selected. The practicability of the method is tested by its application to a real distribution system.
Electric Power Systems Research 02/2009; 79(2-79):390-398. DOI:10.1016/j.epsr.2008.08.004 · 1.75 Impact Factor
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