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中国管理科学 ›› 2024, Vol. 32 ›› Issue (3): 48-59.doi: 10.16381/j.cnki.issn1003-207x.2021.1778

• • 上一篇    下一篇

USP电价机制下引入金融输电权的发电商报价模型

吴忠群1,2,3(),郑瑞锦1,2   

  1. 1.华北电力大学经济与管理学院, 北京 102206
    2.华北电力大学金融研究所, 北京 102206
    3.新能源电力与低碳发展研究北京市重点实验室, 北京 102206
  • 收稿日期:2021-09-02 修回日期:2023-06-07 出版日期:2024-03-25 发布日期:2024-03-25
  • 通讯作者: 吴忠群 E-mail:wuzhongqun@ncepu.edu.cn
  • 基金资助:
    国家社会科学基金面上项目(19BJY074)

The Generators Bidding Model Introducing Financial Transmission Rights under the USP Electricity Price Mechanism

Zhongqun Wu1,2,3(),Ruijin Zheng1,2   

  1. 1.School of Economics and Management, North China Electric Power University, Beijing 102206, China
    2.Institute of Banking and Finance, North China Electric Power University, Beijing 102206, China
    3.Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing, 102206, China
  • Received:2021-09-02 Revised:2023-06-07 Online:2024-03-25 Published:2024-03-25
  • Contact: Zhongqun Wu E-mail:wuzhongqun@ncepu.edu.cn

摘要:

加快电力市场建设对于实现“双碳”目标具有重要意义。用户侧统一结算点电价机制(unified settlement point, USP)被认为是平稳推进电力市场化的有效措施,我国多地(如广东、浙江等)正在进行试点,金融输电权(financial transmission rights,FTRs)是电力市场中较为重要的电价风险规避工具,因此需要在理论上对新电价机制的影响做出全面分析。但是迄今为止,在该电价机制对发电商报价以及FTRs避险作用的影响等关乎其实际运行的问题上仍缺少深入细致的研究。鉴于此,本文分析了用户侧USP电价机制下引入FTRs对发电商报价行为的影响,综合考虑FTRs、远期合约以及现货市场,构建了发电商报价模型。模型中还考虑了电力系统安全运行约束以及需求响应因素,使模型更贴合实际。本文的基本结论是,USP电价机制通过利润和风险两个途径对发电商报价产生影响,引入FTRs有助于发电商规避价差风险,并使发电商利润提高约1%。基于此,本文提出了引入输电权、公开历史报价信息、加强阻塞线路的电网建设以及提高参与市场的发电商比例等政策建议。本文所建报价模型可为电力市场分析与预测以及发电商报价策略选择提供依据。

关键词: 报价模型, 电价机制, 金融输电权, 电力市场, 市场效率

Abstract:

Speeding up the construction of the electricity market is of great significance to the realization of the “dual carbon” goal. The Unified Settlement Point (USP) mechanism on the user side is considered an effective measure to smoothly promote electricity marketisation and is being piloted in a number of provinces in China(e.g. Guangdong, Zhejiang, etc.). Financial Transmission Rights (FTRs) are important electricity price risk aversion tools in the electricity market. Therefore, a comprehensive theoretical analysis of the impact of the new electricity price mechanism is required. However, up to now, there has been a lack of in-depth research on issues relating to the practical operation of the new electricity price mechanism, such as its impact on generators' bids and on the hedging effect of FTRs. In view of this, the impact of the introduction of FTRs on generators' bidding behavior under the user-side USP price mechanism is analyzed, and a model for generators’ bids is constructed, taking into account FTRs, forward contracts and the spot market. The model also takes into account power system security constraints and demand response factors to make the model more realistic. The basic conclusion of this paper is that the USP price mechanism affects the bids of power generators through two ways: profit and risk, and the introduction of FTRs can help power generators avoid the risk of price difference. The simulation results show that under the scenario that the bid factor of the other generator set in this paper is 1.2, the bid factor of the own generator in the forward market is 2 and the bid factor in the spot market is 1.55, the optimal FTRs market strategy is to purchase, with a purchase quantity of 448.70MW. Under the scenario of uncertainty about other generator’s bid factor, 500 simulations show that the optimal bid factor in the spot market ranges from 1 to 1.75, the optimal bid factor in the contract market ranges from 1.45 to 2. The purchase of FTRs increases generators' profits by approximately 1%, indicating that FTRs effectively assist generators in reducing losses under the user-side USP price mechanism. Based on the results, policy recommendations are put forward such as the introduction of transmission rights, the disclosure of historical bid information, the strengthening of grid construction of congestion lines, and the increase of the proportion of power generation companies participating in the market. The bidding model developed in this paper can provide a theoretical basis for electricity market analysis and forecasting, as well as for the choice of bidding strategies by power generators.

Key words: bidding model, electricity price mechanism, financial transmission rights, electricity market, market efficiency

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