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A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration

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  • Sun, Mingyang
  • Cremer, Jochen
  • Strbac, Goran

Abstract

Transmission expansion planning (TEP) is facing unprecedented challenges with the rise of integrated renewable energy resources (RES), flexible load elements, and the potential electrification of transport and heat sectors. Under this reality, the inadequate information of the stochastic parameters’ behavior may lead to inefficient expansion decisions, especially in the context of very high renewable penetration. This paper proposes a novel data-driven scenario generation framework for the TEP problem to generate unseen but important load and wind power scenarios while capturing inter-spatial dependencies between loads and wind generation units’ output in various locations, using a vine-copula based high-dimensional stochastic variable modeling approach. The superior performance of the proposed model is demonstrated through a case study on a modified IEEE 118-bus system. The expected result of using the expected value problem solution (EEV) and the net benefits of transmission expansion (NBTE) are used as the evaluation metrics to quantitatively illustrate the advantages of the proposed approach. In addition, the case of very high wind penetration is carried out to further highlight the importance of the multivariate stochastic dependence of load and wind power generation. The results demonstrate that the proposed scenario generation method can result in near-optimal investment decisions for the TEP problem that make more net benefits than using limited number of historical data.

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  • Sun, Mingyang & Cremer, Jochen & Strbac, Goran, 2018. "A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration," Applied Energy, Elsevier, vol. 228(C), pages 546-555.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:546-555
    DOI: 10.1016/j.apenergy.2018.06.095
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    10. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.
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    14. Nikita Belyak & Steven A. Gabriel & Nikolay Khabarov & Fabricio Oliveira, 2023. "Renewable Energy Expansion under Taxes and Subsidies: A Transmission Operator's Perspective," Papers 2302.10562, arXiv.org, revised Apr 2024.
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