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A multi-objective approach for renewable distributed generator unit’s placement considering generation and load uncertainties

Abstract

Penetration of Renewable distributed generation (RDG) units has increased in recent years due to increased environmental concerns and depleting fossil fuels. Deployment of RDG units will offer technical benefits such as loss minimization, bus voltage profile improvement, line loading reduction. Optimal allocation of RDG units is a challenging task as the generation is time-varying and uncertain in nature. In this work, optimal RDG allocation problem is formulated by considering time-varying and uncertain nature of generation and load demand using a Point estimate method (PEM)-based load flow with an objective to simultaneously minimize losses, improve voltage profile and reduce line loading. An efficient pareto front-based Multi-objective Backtracking search algorithm (PMBSA) is proposed in this work to solve optimal renewable DG placement problem. Results obtained with PEM are compared with those obtained with Monte Carlo simulation method. Efficacy of formulated approach proposed in this paper is verified on a practical 67-bus distribution system and IEEE-118 bus test system. Results show that PMBSA is superior to the standard NSGA-II algorithm in obtaining near optimal solution for optimal RDG allocation problem. It is verified that proposed approach ensures very less voltage limit violations of bus voltages.

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The authors received no specific funding for this work.

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Contributions

RK conceived the concept, framed the mathematical modeling and drafted the manuscript. KJ has done MATLAB programming, carried out data analysis to find PDF parameters and helped to formulate mathematical modeling. KRKVP checked the modeling, analyzed and substantiated the results, and helped in drafting the manuscript. KVSRM revised the manuscript and provided technical support.

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Correspondence to Kollu Ravindra.

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Appendix

Appendix

See (Tables

Table 10 Shape and scale parameters of collected statistical wind data

10,

Table 11 Shape and scale parameters of collected statistical solar data

11,

Table 12 Mean and standard deviation of collected statistical domestic load data

12,

Table 13 Mean and standard deviation of collected statistical commercial load data

13,

Table 14 Mean and standard deviation of collected statistical mixed load data

14).

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Jayaram, K., Ravindra, K., Prasad, K.R.K.V. et al. A multi-objective approach for renewable distributed generator unit’s placement considering generation and load uncertainties. Int J Energy Environ Eng (2021). https://doi.org/10.1007/s40095-021-00438-5

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Keywords

  • Pareto Multi-objective Backtracking Search Algorithm (PMBSA)
  • DG allocation
  • NSGA-II
  • PEM load flow
  • Monte Carlo-based load flow