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A methodology for analysis of impacts of grid integration of renewable energy

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  • George, Mel
  • Banerjee, Rangan

Abstract

Present electricity grids are predominantly thermal (coal, gas) and hydro based. Conventional power planning involves hydro-thermal scheduling and merit order dispatch. In the future, modern renewables (hydro, solar and biomass) are likely to have a significant share in the power sector. This paper presents a method to analyse the impacts of renewables in the electricity grid. A load duration curve based approach has been developed. Renewable energy sources have been treated as negative loads to obtain a modified load duration curve from which capacity savings in terms of base and peak load generation can be computed. The methodology is illustrated for solar, wind and biomass power for Tamil Nadu (a state in India). The trade-offs and interaction between renewable sources are analysed. The impacts on capacity savings by varying the wind regime have also been shown. Scenarios for 2021-22 have been constructed to illustrate the methodology proposed. This technique can be useful for power planners for an analysis of renewables in future electricity grids.

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  • George, Mel & Banerjee, Rangan, 2011. "A methodology for analysis of impacts of grid integration of renewable energy," Energy Policy, Elsevier, vol. 39(3), pages 1265-1276, March.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:3:p:1265-1276
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    References listed on IDEAS

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    1. George, Mel & Banerjee, Rangan, 2009. "Analysis of impacts of wind integration in the Tamil Nadu grid," Energy Policy, Elsevier, vol. 37(9), pages 3693-3700, September.
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    1. Chaiamarit, Kunjana & Nuchprayoon, Somboon, 2014. "Impact assessment of renewable generation on electricity demand characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 995-1004.
    2. Schroeder, Andreas & Oei, Pao-Yu & Sander, Aram & Hankel, Lisa & Laurisch, Lilian Charlotte, 2013. "The integration of renewable energies into the German transmission grid—A scenario comparison," Energy Policy, Elsevier, vol. 61(C), pages 140-150.
    3. Harmsen, Robert & Graus, Wina, 2013. "How much CO2 emissions do we reduce by saving electricity? A focus on methods," Energy Policy, Elsevier, vol. 60(C), pages 803-812.
    4. Calvert, K. & Pearce, J.M. & Mabee, W.E., 2013. "Toward renewable energy geo-information infrastructures: Applications of GIScience and remote sensing that build institutional capacity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 416-429.
    5. Salci, Sener & Jenkins, Glenn, 2016. "An Economic and Stakeholder Analysis for the Design of IPP Contracts for Wind Farms," MPRA Paper 70578, University Library of Munich, Germany.
    6. Salci, Sener & Jenkins, Glenn P., 2018. "An economic analysis for the design of ipp contracts for grid-connected renewable energy projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2410-2420.
    7. Thiam, Djiby-Racine & Benders, René M.J. & Moll, Henri C., 2012. "Modeling the transition towards a sustainable energy production in developing nations," Applied Energy, Elsevier, vol. 94(C), pages 98-108.
    8. Chaudhry, Nikhil & Hughes, Larry, 2012. "Forecasting the reliability of wind-energy systems: A new approach using the RL technique," Applied Energy, Elsevier, vol. 96(C), pages 422-430.
    9. Verbruggen, Aviel & Lauber, Volkmar, 2012. "Assessing the performance of renewable electricity support instruments," Energy Policy, Elsevier, vol. 45(C), pages 635-644.
    10. Chen, Tao & Pipattanasomporn, Manisa & Rahman, Imran & Jing, Zejia & Rahman, Saifur, 2020. "MATPLAN: A probability-based planning tool for cost-effective grid integration of renewable energy," Renewable Energy, Elsevier, vol. 156(C), pages 1089-1099.

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