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水力发电学报 ›› 2025, Vol. 44 ›› Issue (9): 1-14.doi: 10.11660/slfdxb.20250901

• •    下一篇

编辑部推荐论文:考虑源荷不确定性的日前市场出清价格区间预测模型

  

  • 出版日期:2025-09-25 发布日期:2025-09-25

Day-ahead market clearing price interval prediction model considering electrical source-load uncertainty

  • Online:2025-09-25 Published:2025-09-25

摘要: 为了在考虑源荷不确定性的条件下更为精确地估计水火风光等多电源参与电力市场的出清电价,从而达到在降低风险成本的同时提高发电主体参与现货市场效益和优化电力资源配置的目的,本文在日前市场出清价格预测过程中,以源荷不确定性作为主要变量要素,对发电端机组出力预测的多维参数随机分布特征和负荷端的需求响应作用进行不确定性量化,结合电力市场物理出清机制和IEEE30节点电力系统,以多电源参与日前市场发电总成本最小为目标,建立了考虑源荷不确定性的日前市场出清价格区间预测模型,可求解得到兼顾源荷不确定性的机组节点边际出清价格和中标出力区间预测结果。应用于IEEE30节点电力系统的算例分析结果表明,本文模型预测的出清电价和出力范围能够有效提升发电主体申报效率和中标效益,较传统单点出清预测方法整体效益提高了4.09%,发电期望效益增加0.47%,验证了模型的可行性和优越性,为发电主体参与日前现货市场的阶梯报价和机组出力申报提供了更为丰富的参考依据。

关键词: 源荷不确定性, 日前市场, 出清价格, 区间预测模型, IEEE30节点电力系统

Abstract: To improve the accuracy of clearing price estimation in electricity markets with multiple power sources under source-load uncertainty, this paper presents a day-ahead market clearing price interval prediction model. This model considers the uncertainty of generation-side outputs characterized by multidimensional stochastic distributions and the impact of load-side demand response, aimed at minimizing the total generation cost with the participation of hydropower, thermal, wind, and solar power, based on the physical clearing mechanism of electricity markets and the IEEE 30-bus power system. Its solutions give predictions of the interval of locational marginal price and generation commitments, taking source-load uncertainty into account. Case studies show its improvement on bidding efficiency and generation revenue; compared with traditional point forecasting methods, our new model achieves a 4.09% increase in overall economic efficiency and a 0.47% increase in expected revenue. The results verify its feasibility and effectiveness, and provide reliable decision-making support for generators in formulating stepwise bidding and output strategies in the day-ahead market.

Key words: source-load uncertainty, day-ahead market, clearing price, interval prediction model, IEEE 30-node power system

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