纸质出版:2026
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安 娟,米金梁,谭忠富.考虑补偿激励的新能源基地多类型机组协同规划模型[J].智慧电力,2026,54(2):46-53.
doi:10.20204/j.sp.2026.02006
安 娟,米金梁,谭忠富.考虑补偿激励的新能源基地多类型机组协同规划模型[J].智慧电力,2026,54(2):46-53. DOI: 10.20204/j.sp.2026.02006.
doi:10.20204/j.sp.2026.02006 DOI:
在新能源大规模接入背景下,系统容量保障能力与调节资源配置效率面临同步提升的现实需求,传统规划方法难以刻画多类型机组在不同运行条件下的动态调节能力与容量支撑价值。针对新能源基地多类型机组多形态贡献差异难以量化、调节资源激励不足的问题,提出一种考虑补偿激励的新能源基地多类型机组协同规划模型,以实现容量与电量的协同优化配置。该模型引入核密度估计方法对容量需求裕度进行概率刻画,并构建基于边际成本回收的补偿价格机制,引导不同类型机组按其调节能力参与容量配置决策。算例分析表明,所提模型能够有效提升调节资源出力意愿和系统运行灵活性,显著提高新能源消纳水平,并为调节资源分类配置及容量机制完善提供量化依据。
Against the backdrop of large-scale integration of renewable energy sources
there is a practical need to simultaneously enhance the system’s capacity support capability and the allocation efficiency of regulation resources. Traditional planning methods struggle to capture the dynamic regulation capabilities and capacity support value of multiple types of units under varying operating conditions. To address the challenges of quantifying the diverse contributions of different types of units in renewable energy bases and the insufficient incentives for regulation resources
this paper proposes a coordinated planning model for multiple types of units in renewable energy bases that incorporates compensation incentives
aiming to achieve the coordinated optimization of capacity and energy allocation. The model introduces a kernel density estimation method to probabilistically characterize capacity demand margins and establishes a compensation pricing mechanism based on marginal capacity contribution. This mechanism guides different types of units to participate in capacity allocation decisions according to their regulation capabilities. Case studies demonstrate that the proposed model can effectively enhance the willingness of regulation resources to provide support and improve system operational flexibility
significantly increasing the level of renewable energy integration. It also provides a quantitative basis for the classified allocation of regulation resources and the refinement of capacity mechanisms.
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