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Multi-year stochastic generation capacity expansion planning under environmental energy policy

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  • Park, Heejung
  • Baldick, Ross

Abstract

We present a multi-year stochastic generation capacity expansion planning model to investigate changes in generation building decisions and carbon dioxide (CO2) emissions under environmental energy policies, including carbon tax and a renewable portfolio standard (RPS). A multi-stage stochastic mixed-integer program is formulated to solve the generation expansion problem. The uncertain parameters of load and wind availability are modeled as random variables and their independent and identically distributed (i.i.d.) random samples are generated using the Gaussian copula method, which represents the correlation between random variables explicitly. A multi-stage scenario tree is formed with the generated random samples, and the scenario tree is reduced for improved computation performance. A rolling-horizon method is applied to obtain one generation plan at each stage.

Suggested Citation

  • Park, Heejung & Baldick, Ross, 2016. "Multi-year stochastic generation capacity expansion planning under environmental energy policy," Applied Energy, Elsevier, vol. 183(C), pages 737-745.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:737-745
    DOI: 10.1016/j.apenergy.2016.08.164
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    Cited by:

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    13. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2017. "Generation expansion planning with high share of renewables of variable output," Applied Energy, Elsevier, vol. 190(C), pages 1275-1288.
    14. Lei, Yang & Wang, Dan & Jia, Hongjie & Chen, Jingcheng & Li, Jingru & Song, Yi & Li, Jiaxi, 2020. "Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
    15. Wang, Delu & Li, Chunxiao & Mao, Jinqi & Yang, Qing, 2023. "What affects the implementation of the renewable portfolio standard? An analysis of the four-party evolutionary game," Renewable Energy, Elsevier, vol. 204(C), pages 250-261.
    16. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Yin, S., 2018. "Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China," Applied Energy, Elsevier, vol. 212(C), pages 834-849.
    17. Backe, Stian & Ahang, Mohammadreza & Tomasgard, Asgeir, 2021. "Stable stochastic capacity expansion with variable renewables: Comparing moment matching and stratified scenario generation sampling," Applied Energy, Elsevier, vol. 302(C).
    18. Pilpola, Sannamari & Lund, Peter D., 2020. "Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways," Energy, Elsevier, vol. 201(C).
    19. Quiroga, Daniela & Sauma, Enzo & Pozo, David, 2019. "Power system expansion planning under global and local emission mitigation policies," Applied Energy, Elsevier, vol. 239(C), pages 1250-1264.
    20. Zeinolabedin Sadeghi & Yahya Hatami & Seyed Abdolmajid Jalaei & Amir Abdollahi, 2020. "Generation capacity expansion economic-environmental planning under uncertainty of demand and supply," Environment Systems and Decisions, Springer, vol. 40(4), pages 544-559, December.
    21. Chen, Yixuan & Qu, Kaiping & Pan, Zhenning & Yu, Tao, 2020. "Multi-objective electricity-gas flow with stochastic dispersion control for air pollutants using two-stage Pareto optimization," Applied Energy, Elsevier, vol. 279(C).
    22. Chi Kong Chyong & Carmen Li & David Reiner & Fabien Roques, 2020. "A Portfolio approach to wind and solar deployment in Australia," Working Papers EPRG2022, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    23. 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.
    24. Fitiwi, Desta Z. & Lynch, Muireann & Bertsch, Valentin, 2020. "Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    25. 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).

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