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Risk-based optimal power portfolio methodology for generation companies considering cross-region generation right trade

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  • Shang, Nan
  • Ye, Chengjin
  • Ding, Yi
  • Tu, Teng
  • Huo, Baofeng

Abstract

The generation right trades (GRTs) are of great significance for the improvement of unit utilization hours and reduction of carbon emission, which plays an important role in the cross-regional electricity transactions. In order to hedge the risk in spot markets and ensure the incomes, it is crucial for generation companies (Gencos) to determine the optimal GRT scheme at their risk preferences. In this paper, a power portfolio optimization methodology is proposed considering the bidding behaviours in spot markets, independent system operator (ISO) centralized dispatching, and the cross-region GRTs. Specifically, various risks for Gencos are modelled including spot price fluctuation and system component failures. Then, a bi-level optimal portfolio (BLOP) model is established where the Gencos maximize their total incomes with subject to the ISOs which minimize the local dispatching costs simultaneously. The BLOP model is transformed into a solvable single-level mathematical program with equilibrium constraints (MPCE) through the Karush-Kuhn-Tucker (KKT) conditions. The numerical results on a realistic Chinese testing system illustrate the effect of GRTs for income assurance of Gencos and the necessity to consider the stochastic contingencies in portfolio decisions. Specifically, it is a preferred option for Gencos to exploit approximately 77% of spot power to GRT at the normal state, which can increase the total income by 266.21%. At contingency states, the optimal GRT ratios are 70–80%, the corresponding income grows nearly twice more than pure spot trades.

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  • Shang, Nan & Ye, Chengjin & Ding, Yi & Tu, Teng & Huo, Baofeng, 2019. "Risk-based optimal power portfolio methodology for generation companies considering cross-region generation right trade," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919311857
    DOI: 10.1016/j.apenergy.2019.113511
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    1. Chai, Shanglei & Zhang, Xichun & Abedin, Mohammad Zoynul & Chen, Huizheng & Lucey, Brian & Hajek, Petr, 2023. "An optimized GRT model with blockchain digital smart contracts for power generation enterprises," Energy Economics, Elsevier, vol. 128(C).
    2. Sun, Xiaocong & Bao, Minglei & Guo, Chao & Ding, Yi & Zheng, Chenghang & Gao, Xiang, 2024. "An equilibrium capacity expansion model for power systems considering Gencos' coupled decisions between carbon and electricity markets," Applied Energy, Elsevier, vol. 359(C).
    3. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    4. Meina Shen & Runkun Cheng & Da Liu, 2024. "Optimal Bidding Strategies for Wind-Thermal Power Generation Rights Trading: A Game-Theoretic Approach Integrating Carbon Trading and Green Certificate Trading," Sustainability, MDPI, vol. 16(16), pages 1-15, August.
    5. Zeng, Lijun & Du, Wenjing & Zhao, Laijun & Zhan, Yanhong, 2023. "An inter-provincial transfer fee model under renewable portfolio standard policy," Energy, Elsevier, vol. 277(C).

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