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Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling

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  • Ioannou, Anastasia
  • Fuzuli, Gulistiani
  • Brennan, Feargal
  • Yudha, Satya Widya
  • Angus, Andrew

Abstract

In this paper, a multi-stage stochastic optimization (MSO) method is proposed for determining the medium to long term power generation mix under uncertain energy demand, fuel prices (coal, natural gas and oil) and, capital cost of renewable energy technologies. The uncertainty of future demand and capital cost reduction is modelled by means of a scenario tree configuration, whereas the uncertainty of fuel prices is approached through Monte Carlo simulation. Global environmental concerns have rendered essential not only the satisfaction of the energy demand at the least cost but also the mitigation of the environmental impact of the power generation system. As such, renewable energy penetration, CO2,eq mitigation targets, and fuel diversity are imposed through a set of constraints to align the power generation mix in accordance to the sustainability targets. The model is, then, applied to the Indonesian power generation system context and results are derived for three cases: Least Cost option, Policy Compliance option and Green Energy Policy option. The resulting optimum power generation mixes, discounted total cost, carbon emissions and renewable share are discussed for the planning horizon between 2016 and 2030.

Suggested Citation

  • Ioannou, Anastasia & Fuzuli, Gulistiani & Brennan, Feargal & Yudha, Satya Widya & Angus, Andrew, 2019. "Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling," Energy Economics, Elsevier, vol. 80(C), pages 760-776.
    • Handle: RePEc:eee:eneeco:v:80:y:2019:i:c:p:760-776
    DOI: 10.1016/j.eneco.2019.02.013
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    1. Brear, M.J. & Jeppesen, M. & Chattopadhyay, D. & Manzie, C. & Alpcan, T. & Dargaville, R., 2016. "Least cost, utility scale abatement from Australia's NEM (National Electricity Market). Part 2: Scenarios and policy implications," Energy, Elsevier, vol. 101(C), pages 621-628.
    2. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2017. "Risk-based methods for sustainable energy system planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 602-615.
    3. Asian Development Bank (ADB) & Asian Development Bank (ADB) & Asian Development Bank (ADB) & Asian Development Bank (ADB), 2016. "Viet Nam: Energy Sector Assessment, Strategy, and Road Map," ADB Reports RPT167828, Asian Development Bank (ADB).
    4. Qadrdan, Meysam & Cheng, Meng & Wu, Jianzhong & Jenkins, Nick, 2017. "Benefits of demand-side response in combined gas and electricity networks," Applied Energy, Elsevier, vol. 192(C), pages 360-369.
    5. Maria Vespucci & Marida Bertocchi & Mario Innorta & Stefano Zigrino, 2014. "A stochastic model for investments in different technologies for electricity production in the long period," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 407-426, June.
    6. Pisciella, P. & Vespucci, M.T. & Bertocchi, M. & Zigrino, S., 2016. "A time consistent risk averse three-stage stochastic mixed integer optimization model for power generation capacity expansion," Energy Economics, Elsevier, vol. 53(C), pages 203-211.
    7. Li, G.C. & Huang, G.H. & Sun, W. & Ding, X.W., 2014. "An inexact optimization model for energy-environment systems management in the mixed fuzzy, dual-interval and stochastic environment," Renewable Energy, Elsevier, vol. 64(C), pages 153-163.
    8. Min, Daiki & Chung, Jaewoo, 2013. "Evaluation of the long-term power generation mix: The case study of South Korea's energy policy," Energy Policy, Elsevier, vol. 62(C), pages 1544-1552.
    9. Thangavelu, Sundar Raj & Khambadkone, Ashwin M. & Karimi, Iftekhar A., 2015. "Long-term optimal energy mix planning towards high energy security and low GHG emission," Applied Energy, Elsevier, vol. 154(C), pages 959-969.
    10. Jeppesen, M. & Brear, M.J. & Chattopadhyay, D. & Manzie, C. & Dargaville, R. & Alpcan, T., 2016. "Least cost, utility scale abatement from Australia's NEM (National Electricity Market). Part 1: Problem formulation and modelling," Energy, Elsevier, vol. 101(C), pages 606-620.
    11. Ahn, Joongha & Woo, JongRoul & Lee, Jongsu, 2015. "Optimal allocation of energy sources for sustainable development in South Korea: Focus on the electric power generation industry," Energy Policy, Elsevier, vol. 78(C), pages 78-90.
    12. Prebeg, Pero & Gasparovic, Goran & Krajacic, Goran & Duic, Neven, 2016. "Long-term energy planning of Croatian power system using multi-objective optimization with focus on renewable energy and integration of electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 1493-1507.
    13. Krukanont, Pongsak & Tezuka, Tetsuo, 2007. "Implications of capacity expansion under uncertainty and value of information: The near-term energy planning of Japan," Energy, Elsevier, vol. 32(10), pages 1809-1824.
    14. Vithayasrichareon, Peerapat & MacGill, Iain F., 2012. "A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 41(C), pages 374-392.
    15. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, January.
    16. Tran, Thomas T.D. & Smith, Amanda D., 2018. "Incorporating performance-based global sensitivity and uncertainty analysis into LCOE calculations for emerging renewable energy technologies," Applied Energy, Elsevier, vol. 216(C), pages 157-171.
    17. Kim, Seunghyok & Koo, Jamin & Lee, Chang Jun & Yoon, En Sup, 2012. "Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors," Energy, Elsevier, vol. 44(1), pages 126-134.
    18. Costa, Oswaldo L.V. & de Oliveira Ribeiro, Celma & Rego, Erik Eduardo & Stern, Julio Michael & Parente, Virginia & Kileber, Solange, 2017. "Robust portfolio optimization for electricity planning: An application based on the Brazilian electricity mix," Energy Economics, Elsevier, vol. 64(C), pages 158-169.
    19. J. Cabello & M. Luque & F. Miguel & A. Ruiz & F. Ruiz, 2014. "A multiobjective interactive approach to determine the optimal electricity mix in Andalucía (Spain)," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 109-127, April.
    20. Li, Y.F. & Huang, G.H. & Li, Y.P. & Xu, Y. & Chen, W.T., 2010. "Regional-scale electric power system planning under uncertainty--A multistage interval-stochastic integer linear programming approach," Energy Policy, Elsevier, vol. 38(1), pages 475-490, January.
    21. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S. & Kopanos, Georgios M. & Pistikopoulos, Efstratios N. & Georgiadis, Michael C., 2014. "A spatial multi-period long-term energy planning model: A case study of the Greek power system," Applied Energy, Elsevier, vol. 115(C), pages 456-482.
    22. Cheng, Rui & Xu, Zhaofeng & Liu, Pei & Wang, Zhe & Li, Zheng & Jones, Ian, 2015. "A multi-region optimization planning model for China’s power sector," Applied Energy, Elsevier, vol. 137(C), pages 413-426.
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