TY  - JOUR
VL  - 213
EP  - 73
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84871607999&doi=10.1016%2fj.fss.2012.07.005&partnerID=40&md5=6619f57ac2d6a205bfe6f34fa4a98f4a
JF  - Fuzzy Sets and Systems
A1  - Ganguly, S.
A1  - Sahoo, N.C.
A1  - Das, D.
SN  - 01650114
Y1  - 2013///
KW  - Electrical distribution system; Fuzzy numbers; Load models; Multi objective optimizations (MOO); Pareto dominance
KW  -  Distributed power generation; Fuzzy sets; Multiobjective optimization; Particle swarm optimization (PSO); Planning
KW  -  Local area networks
TI  - Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation
ID  - scholars3729
SP  - 47
N1  - cited By 95
N2  - This paper presents a multi-objective planning approach for electrical distribution systems under uncertainty in load demand incorporating distributed generation (DG). Both radial and meshed systems are considered. The overall influence of load demand uncertainty on planned networks is investigated in detail. Uncertainty in load demand is possibilistically modeled using a fuzzy triangular number. The two objectives in system planning are: (i) minimization of total installation and operational costs, and (ii) minimization of the risk factor. The risk factor is a function of the contingency load-loss index (CLLI), which measures load loss under contingencies, and the degree of network constraints violations. CLLI minimization improves network reliability. The network variables optimized are: (i) the network structure type (radial or meshed), (ii) the number of feeders and their routes, and (iii) the number and location of sectionalizing switches. The optimization tool is a multi-objective particle swarm optimization (MOPSO) variant that uses heuristic selection and assignment of leaders or guides for efficient identification of non-dominated solutions. The optimal number, location, and size of the DG units are determined in another planning stage. Performance comparisons between the planning approaches with possibilistic and deterministic load models highlight the relative merits and demerits. The advantages of networks obtained using the proposed planning approach in the context of DG integration are described. The proposed planning approach is validated using three typical distribution systems. © 2012 Elsevier B.V.
AV  - none
ER  -