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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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    Cited by:

    1. Zhang, Chao & Lasaulce, Samson & Hennebel, Martin & Saludjian, Lucas & Panciatici, Patrick & Poor, H. Vincent, 2021. "Decision-making oriented clustering: Application to pricing and power consumption scheduling," Applied Energy, Elsevier, vol. 297(C).
    2. 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).
    3. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang & Fang, Xi, 2021. "A clustering-based approach for “cross-scale” load prediction on building level in HVAC systems," Applied Energy, Elsevier, vol. 282(PB).

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