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Power distribution network expansion planning (DNEP), based on innovative load flow analysis and optimization techniques, has drawn great attention of researchers around the world to cater for ever increasing demand of electrical power. In the present work, a new approach based on Multi-criteria Data Envelopment Analysis and Shannon Entropy analysis (MCDEA-SEA) is presented that strengthens the solution hunt process of DNEP problems. In the first stage of proposed methodology, various probable configurations are determined through load flow analysis considering different objective functions and constraints. In the next stage, large amount of complex data sets generated for various configurations from power flow perusal, then analyzed using pooled MCDEA-SEA. This multi-stage approach expedites search for best and impeccable solution, which leaves no space for sub-optimality. The efficacy of the proposed method has been verified by applying it on IEEE 33-bus test distribution system, considering impending load scenarios and the results show that the proposed methodology can effectively solve DNEP problem and provide optimal solution for DNEP problems, consuming lesser computation time compared to conventional approaches.

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This article presents a novel methodology for distribution network expansion planning (DNEP) considering the inclusion of electric vehicles (EV), especially, electric bus (EB) charging loads. The proposed methodology addresses network congestion through an optimum time of charging, cost optimization, new charging infrastructure, and minimization of losses under a set of technical and physical constraints, which represents practical uncertainties. Along with load flow analysis, selection of the number of ports and technology at the host charging station is obtained through the application of response surface methodology. The proposed methodology provides a coordinated planning for the development of EB charging station infrastructure that takes into account the effects of both the power dispersion framework and transportation framework. The effectiveness of the proposed methodology is investigated by applying it on 69-node IEEE modified distribution test system considering three charging technologies viz. fast charging, ultra-fast charging, and battery swapping. The results of the proposed model are compared with the direct statistical method and it revealed that the right selection of technology for EB charging and the right planning of the charging infrastructure can effectively optimize the cost of EV charging infrastructure and thereby catalyze the decarbonization of the transportation sector.

This article presents a novel methodology for distribution network expansion planning (DNEP) considering the inclusion of electric vehicles (EV), especially, electric bus (EB) charging loads. The proposed methodology addresses network congestion through an optimum time of charging, cost optimization, new charging infrastructure, and minimization of losses under a set of technical and physical constraints, which represents practical uncertainties. Along with load flow analysis, selection of the number of ports and technology at the host charging station is obtained through the application of response surface methodology. The proposed methodology provides a coordinated planning for the development of EB charging station infrastructure that takes into account the effects of both the power dispersion framework and transportation framework. The effectiveness of the proposed methodology is investigated by applying it on 69-node IEEE modified distribution test system considering three charging technologies viz. fast charging, ultra-fast charging, and battery swapping. The results of the proposed model are compared with the direct statistical method and it revealed that the right selection of technology for EB charging and the right planning of the charging infrastructure can effectively optimize the cost of EV charging infrastructure and thereby catalyze the decarbonization of the transportation sector.