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Triple Bottom Line Analysis and Optimum Sizing of Renewable Energy Using Improved Hybrid Optimization Employing the Genetic Algorithm: A Case Study from India

Author

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  • Nithya Saiprasad

    (College of Engineering and Science, Victoria University, Footscray, Melbourne 3011, Australia)

  • Akhtar Kalam

    (College of Engineering and Science, Victoria University, Footscray, Melbourne 3011, Australia)

  • Aladin Zayegh

    (College of Engineering and Science, Victoria University, Footscray, Melbourne 3011, Australia)

Abstract

Techno-economic, social, and environmental factors influence a large part of society, predominantly in developing countries. Due to energy poverty and bloating populations, developing countries like India are striving to meet the energy balance. One initiative of India to achieve the country’s Renewable Energy Target (RET) is the setting up of the National Solar Mission (NSM) to meet a target of 175 GW (non-hydro) by the year 2022. Prioritizing Renewable Energy (RE) utilization to achieve techno-economic balance is India’s primary objective and creating a positive environmental impact is a bonus. In this study, various scenarios are explored by investigating the techno-economic and environmental impact on RE adoption for a small community in India by optimally sizing the Hybrid Renewable Energy System (HRES). This study is an exemplar in understanding and exploring RE utilization, whilst examining the recent RE market in depth and exploring the advantages and disadvantages of the current RE situation by initiating it in a smaller community. Improved Hybrid Optimization using Genetic Algorithm (iHOGA) PRO+ software, (Version 2.4 -Pro+, Created by Dr Rodolfo Dufo López, University Zaragoza (Spain)) is used to size the RE systems. The results are categorized using triple bottom line analysis (TBL analysis) and for different scenarios, the techno-economic, environmental, and social merits are weighed upon. The probable hurdles that India has to surpass to achieve easy RE adoption are also discussed in this work. The influential merits for analyzing the TBL for a real-time scenario are Net Present Cost (NPC), Carbon-di-oxide (CO 2 ) emissions, and job criteria. Compared to Hybrid Optimization of Multiple Energy Resources (HOMER) software, iHOGA remains less explored in the literature, specifically for the grid-connected systems. The current study provides a feasibility analysis of grid-connected RE systems for the desired location. iHOGA software simulated 15 sets of results for different values of loads considered and various acquisition costs of HRES. At least 70% of RE can be penetrated for the Aralvaimozhi community with the lowest value of NPC of the HRES. From the TBL analysis conducted, integrating HRES into a micro-grid for the community would result in mitigating CO 2 emissions and provide job opportunities to the local community; although, the economic impact should be minimized if the acquisition costs of the HRES are reduced, as has been established through this study.

Suggested Citation

  • Nithya Saiprasad & Akhtar Kalam & Aladin Zayegh, 2019. "Triple Bottom Line Analysis and Optimum Sizing of Renewable Energy Using Improved Hybrid Optimization Employing the Genetic Algorithm: A Case Study from India," Energies, MDPI, vol. 12(3), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:349-:d:200159
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