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Explicit cost-risk tradeoff for renewable portfolio standard constrained regional power system expansion: A case study of Guangdong Province, China

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  • Ji, Ling
  • Huang, Guo-He
  • Xie, Yu-Lei
  • Niu, Dong-Xiao
  • Song, Yi-Hang

Abstract

In this paper, a risk explicit interval two-stage programming (REITSP) model was proposed for supporting the regional electricity generation expansion with renewable portfolio standard (RPS) constraint. It could effectively tackle multiple uncertainties expressed as interval numbers. But unlike the traditional interval two-stage programming model, the proposed REITSP model could provide an explicit trade-off information between system cost and risk for decision makers with different risk preferences. It could minimize the total system cost, as well as the decision risk according to the aspiration risk level of decision maker. The developed REITSP model was applied to the case study in Guangdong Province, China for its long-term electricity system planning. Crisp solutions under different aspiration risk levels for varying RPS targets were obtained and analyzed. The results showed that according to the current available renewable energy and affordable construction speed, the maximum RPS target for Guangzhou Province during 2016–2025 should be 17%. Higher RPS level would promote the renewable energy generation, especially solar power; meanwhile, it would reduce the CO2 emission and the imported electricity, but with greater investment cost. The obtained results and trade-off information would be valuable for the optimal long-term electricity system expansion planning when facing future uncertain situation.

Suggested Citation

  • Ji, Ling & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao & Song, Yi-Hang, 2017. "Explicit cost-risk tradeoff for renewable portfolio standard constrained regional power system expansion: A case study of Guangdong Province, China," Energy, Elsevier, vol. 131(C), pages 125-136.
    • Handle: RePEc:eee:energy:v:131:y:2017:i:c:p:125-136
    DOI: 10.1016/j.energy.2017.05.017
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    Cited by:

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    2. Rodgers, Mark D. & Coit, David W. & Felder, Frank A. & Carlton, Annmarie, 2018. "Generation expansion planning considering health and societal damages – A simulation-based optimization approach," Energy, Elsevier, vol. 164(C), pages 951-963.
    3. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Li, Na & Liu, Feng, 2022. "Spatiotemporal characteristics and influencing factors of renewable energy production in China: A spatial econometric analysis," Energy Economics, Elsevier, vol. 116(C).
    4. 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.
    5. Li, Wei & Lu, Can & Zhang, Yan-Wu, 2019. "Prospective exploration of future renewable portfolio standard schemes in China via a multi-sector CGE model," Energy Policy, Elsevier, vol. 128(C), pages 45-56.
    6. Yin, J.N. & Huang, G.H. & Xie, Y.L. & An, Y.K., 2021. "Carbon-subsidized inter-regional electric power system planning under cost-risk tradeoff and uncertainty: A case study of Inner Mongolia, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    7. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    8. Zhou, Xianyang & Zhou, Dequn & Ding, Hao & Zhao, Siqi & Wang, Qunwei, 2023. "Low-carbon transition of China's provincial power sector under renewable portfolio standards and carbon cap," Energy, Elsevier, vol. 283(C).
    9. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Liu, Feng, 2021. "Provincial allocation of renewable portfolio standard in China based on efficiency and fairness principles," Renewable Energy, Elsevier, vol. 179(C), pages 1233-1245.
    10. Ji, Ling & Zhang, Bei-Bei & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao, 2018. "Explicit cost-risk tradeoff for optimal energy management in CCHP microgrid system under fuzzy-risk preferences," Energy Economics, Elsevier, vol. 70(C), pages 525-535.

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