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A posteriori multiobjective techno-economic accommodation of DGs in distribution network using Pareto optimality and TOPSIS approach

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Abstract

Distributed generation (DG) integration with distribution networks has technical and economic implications. Solution for optimal DG accommodation problem catering only to technical objectives may not be economically feasible. On the other hand, satisfactory enhancement in technical performance of distribution networks may not be attained while economic aspects only are considered. This paper tackles this conflict by framing a multiobjective problem embedding technical and economic objectives. Multiobjective grey wolf optimizer (MOGWO) and multiobjective grasshopper optimizer algorithm (MOGOA) are used for solving the multiobjective optimization problem. A posteriori multiobjective optimization approach is adopted, and the technique for order of preference by similarity to ideal solution (TOPSIS) is used to find the best feasible solution from non-dominated Pareto optimal solutions. The approach is tested on 33-bus, and 69-bus systems and multiple optimal solutions are presented as per the decision-makers preference for the objectives. The maximum reduction in power loss on the 33-bus system is noted to be 63.51%, whereas on 69-bus system, it is observed as 68.65%.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, Jharkhand, India

    Matta Mani Sankar & Kalyan Chatterjee

  2. Department of Electrical Engineering, BIT Sindri, Dhanbad, 828123, Jharkhand, India

    Matta Mani Sankar

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  1. Matta Mani Sankar
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  2. Kalyan Chatterjee
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Correspondence to Matta Mani Sankar.

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Sankar, M.M., Chatterjee, K. A posteriori multiobjective techno-economic accommodation of DGs in distribution network using Pareto optimality and TOPSIS approach. J Ambient Intell Human Comput 14, 4099–4114 (2023). https://doi.org/10.1007/s12652-022-04473-w

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