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Optimization of India's power sector strategies using weight-restricted stochastic data envelopment analysis

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  • Vazhayil, Joy P.
  • Balasubramanian, R.

Abstract

India's power sector has a significant impact on the country's development and climate change mitigation efforts. Optimization of energy planning is therefore, key to achieving the overall planning goals. The hierarchical multi-objective policy optimization is a policy-centric multi-level bottom-up iterative approach, designed from a developing country perspective, utilizing the optimality principle of dynamic programming. It is applied to the Indian power sector by grouping the strategies into three portfolios, namely, power generation mix, demand side efficiency group and supply side efficiency group. Each portfolio is optimized taking into account the objectives of cost minimization and comprehensive risk and barrier reduction. The portfolios are further combined and optimized at a higher level with respect to higher level objectives, namely, economic growth, energy equity, energy security and climate sustainability. This paper focuses on the second level optimization utilizing data envelopment analysis (DEA). Both the deterministic and stochastic variations have been analysed and the results compared in respect of unrestricted as well as restricted weight models. The analysis shows that weight-restricted stochastic DEA model is most appropriate for efficiency optimization of power sector strategies. The methodology can be effectively used for energy planning in developing countries.

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  • Vazhayil, Joy P. & Balasubramanian, R., 2013. "Optimization of India's power sector strategies using weight-restricted stochastic data envelopment analysis," Energy Policy, Elsevier, vol. 56(C), pages 456-465.
    • Handle: RePEc:eee:enepol:v:56:y:2013:i:c:p:456-465
    DOI: 10.1016/j.enpol.2013.01.003
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    6. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    7. Chen, Hao & Tang, Bao-Jun & Liao, Hua & Wei, Yi-Ming, 2016. "A multi-period power generation planning model incorporating the non-carbon external costs: A case study of China," Applied Energy, Elsevier, vol. 183(C), pages 1333-1345.
    8. Ji, L. & Niu, D.X. & Huang, G.H., 2014. "An inexact two-stage stochastic robust programming for residential micro-grid management-based on random demand," Energy, Elsevier, vol. 67(C), pages 186-199.

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