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Clustering based assessment of cost, security and environmental tradeoffs with possible future electricity generation portfolios

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  • Tanoto, Yusak
  • Haghdadi, Navid
  • Bruce, Anna
  • MacGill, Iain

Abstract

The electricity sector has a key role to play in the sustainable energy transition. The falling costs of wind and solar PV have added to both the opportunities yet also challenges of balancing sometimes competing industry objectives of costs, security, and environmental impacts. This paper presents novel techniques for assessing possible future industry generation portfolios in three ways: (1) incorporating explicit metrics for energy trilemma objectives into modelling, (2) using the optimization process of evolutionary programming to map the solution space of ‘high performing’, near least-cost, portfolio solutions, and (3) applying boundary min–max cases and clustering to categorize these varied portfolios to better facilitate planning and policy making. We use an open-source evolutionary programming tool, National Electricity Market Optimiser, to assess possible future generation portfolios for Indonesia’s Java-Bali interconnected power system. Our findings highlight the wide range of possible portfolios that might potentially deliver similar total industry costs, and their different security and environmental implications. In particular, additional solar photovoltaic deployment appears a low-risk opportunity to reduce costs and emissions compared to more fossil-fuel oriented mixes. Our novel techniques may be useful for the energy modelling community seeking to better understand and communicate complex, uncertain, and multi-dimensional choices for electricity industry planning.

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  • Tanoto, Yusak & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2020. "Clustering based assessment of cost, security and environmental tradeoffs with possible future electricity generation portfolios," Applied Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:appene:v:270:y:2020:i:c:s0306261920307315
    DOI: 10.1016/j.apenergy.2020.115219
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    as
    1. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    2. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
    3. Scott, Ian J. & Carvalho, Pedro M.S. & Botterud, Audun & Silva, Carlos A., 2019. "Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
    5. Kreith, Frank & Norton, Paul & Brown, Daryl, 1990. "A comparison of CO2 emissions from fossil and solar power plants in the United States," Energy, Elsevier, vol. 15(12), pages 1181-1198.
    6. Burke, Paul J. & Widnyana, Jinnie & Anjum, Zeba & Aisbett, Emma & Resosudarmo, Budy & Baldwin, Kenneth G.H., 2019. "Overcoming barriers to solar and wind energy adoption in two Asian giants: India and Indonesia," Energy Policy, Elsevier, vol. 132(C), pages 1216-1228.
    7. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    8. Meschede, Henning & Esparcia, Eugene A. & Holzapfel, Peter & Bertheau, Paul & Ang, Rosario C. & Blanco, Ariel C. & Ocon, Joey D., 2019. "On the transferability of smart energy systems on off-grid islands using cluster analysis – A case study for the Philippine archipelago," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    10. Veldhuis, A.J. & Reinders, A.H.M.E., 2013. "Reviewing the potential and cost-effectiveness of grid-connected solar PV in Indonesia on a provincial level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 315-324.
    11. Gunningham, Neil, 2013. "Managing the energy trilemma: The case of Indonesia," Energy Policy, Elsevier, vol. 54(C), pages 184-193.
    12. 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.
    13. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    14. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
    16. Clark, Richard & Zucker, Noah & Urpelainen, Johannes, 2020. "The future of coal-fired power generation in Southeast Asia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    17. Barrington-Leigh, Christopher & Ouliaris, Mark, 2017. "The renewable energy landscape in Canada: A spatial analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 809-819.
    18. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    19. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    20. Elliston, Ben & MacGill, Iain & Diesendorf, Mark, 2014. "Comparing least cost scenarios for 100% renewable electricity with low emission fossil fuel scenarios in the Australian National Electricity Market," Renewable Energy, Elsevier, vol. 66(C), pages 196-204.
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    Cited by:

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    3. Tanoto, Yusak & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2021. "Reliability-cost trade-offs for electricity industry planning with high variable renewable energy penetrations in emerging economies: A case study of Indonesia’s Java-Bali grid," Energy, Elsevier, vol. 227(C).

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