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Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk, fuel cost and CO2 emission

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Abstract

Power system planning is a capital intensive investment-decision problem. The majority of the conventional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO2 emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design practices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies.

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References

  1. Wang X, McDonald J R. Modern Power System Planning. McGraw Hill, 1994

    Google Scholar 

  2. Mishra S K, Panda G, Majhi R. Constrained portfolio asset selection using multiobjective bacteria foraging optimization. Operations Research, 2014, 14(1): 113–145

    Article  Google Scholar 

  3. Liu S T. A fuzzy modeling for fuzzy portfolio optimization. Expert Systems with Applications, 2011, 38(11): 13803–13809

    Google Scholar 

  4. Bazilian M, Roques F. Analytical Methods for Energy Diversity and Security. Elsevier Science, 2008

    Google Scholar 

  5. Awerbuch, S. Getting it right: the real cost impacts of a renewables portfolio standard. Public Utilities Fortnightly, February 15, 2000.

    Google Scholar 

  6. Awerbuch S. New economic cost perspectives for valuing solar technologies. In: Böer K W ed. Advances in Solar Energy: an Annual Review of Research and Development. Boulder: ASES. 1995

    Google Scholar 

  7. Awerbuch S. Market-Based IRP: It’s Easy! Electricity Journal, 1995, 8(3): 50–67

    Google Scholar 

  8. Awerbuch S, Berger M. Applying portfolio theory to EU electricity planning and policy-making. International Energy Agency Report number EET/2003/03. 2003

    Google Scholar 

  9. Awerbuch S, Dillard J, Mouck T, Preston A. Capital budgeting, technological innovation and the emerging competitive environment of the electric power industry. Energy Policy, 1996, 24(2): 195–202

    Article  Google Scholar 

  10. Stirling A. On the economics and analysis of diversity. SPRU Electronic Working Paper No. 28. 1998

    Google Scholar 

  11. Krey B, Zweifel P. Efficient electricity portfolios for the United States and Switzerland: an investor view. 2008-10, http://www.econ.uzh.ch/faculty/groupzweifel/research/wp0812.pdf

    Google Scholar 

  12. Awerbuch S. The Cost of geothermal energy in the western us regions: a portfolio based approach. Sandia Report SAND 2005-5173, Sandia National Laboratories, TN, USA, 2005

    Google Scholar 

  13. Anagnostopoulos K P, Mamanis G. The mean-variance cardinality constrained portfolio optimization problem: an experimental evaluation of five multiobjective evolutionary algorithms. Expert Systems with Applications, 2011, 38(11): 14208–14217

    Google Scholar 

  14. Gent M R, Lamont J W. Minimum emission dispatch. IEEE Transactions on Power Apparatus and Systems, 2007, PAS-90(60): 2650–2660

    Google Scholar 

  15. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197

    Article  Google Scholar 

  16. Carrano E G, Soares L A E, Takahashi R H C, Saldanha R R, Neto O M. Electric distribution network multiobjective design using a problem-specific genetic algorithm. IEEE Transactions on Power Delivery, 2006, 21(2): 995–1005

    Article  Google Scholar 

  17. Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 1994, 2(3): 221–248

    Article  Google Scholar 

  18. Kumar D, Samantaray S R, Kamwa I, Sahoo N C. Reliability-constrained optimal placement of multiple distributed generators in power distribution network using cat-swarm-optimization. Electric Power Components and Systems, 2014, 42(2): 149–164

    Article  Google Scholar 

  19. California Energy Commission. Comparative costs of California central station electricity generation technologies (Draft Staff Report). 2007, www.energy.ca.gov/2007publications/CEC-200-2007-011/CEC-200-2007-011-SD.PDF

  20. Planning Commission of India. Mid-term appraisal for eleventh five year plan (2007–2012). 2010, http://planningcommission.nic.in/plans/mta/11th_mta/MTA.html

  21. Planning Commission of India. Approach paper to the twelfth five year plan (2012-2017). 2011-10, http://planningcommission.nic.in/plans/planrel/12appdrft/appraoch_12plan.pdf

  22. Corporate Catalyst India. A report on Indian power and energy industry. 2007, www.cci.in/pdfs/surveys-reports/power-and-energyindustry-in-india.pdf

  23. Ministry of New and Renewable Energy. India. Annual Report 2010–2011. http://www.mnre.gov.in/file-manager/annual-report/2010-2011/EN/index.htm.

  24. Lazard. Levelized cost of energy analysis. 2009, http://solarthermalworld.org/content/levelized-cost-energy-analysis-2009

  25. Central Electricity Authority. India. Cost of generation for the year 2008-2009. 2010, http://www.cea.nic.in/report.html

  26. Central Electricity Authority. India. Growth of electricity sectors in India from 1947–2013. 2013-07, http://www.cea.nic.in/reports/planning/dmlf/growth.pdf

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Authors and Affiliations

  1. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Odisha, 751013, India

    Deepak Kumar

  2. Electrical Engineering Department, Birla Institute of Technology, Mesra, Ranchi, 835215, India

    D. K. Mohanta

  3. Electrical Engineering Department, National Institute of Technology, Trichy, 620015, India

    M. Jaya Bharata Reddy

Authors
  1. Deepak Kumar
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  2. D. K. Mohanta
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  3. M. Jaya Bharata Reddy
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Correspondence to Deepak Kumar.

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Kumar, D., Mohanta, D.K. & Reddy, M.J.B. Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk, fuel cost and CO2 emission. Front. Energy 9, 91–105 (2015). https://doi.org/10.1007/s11708-015-0345-y

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