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河海大学能源与电气学院,江苏省南京市 211100
谢俊(1979—),男,博士,教授,博士生导师,主要研究方向:电力市场与电力系统经济运行。E-mail:jxie@hhu.edu.cn
段佳南(1999—),女,通信作者,硕士研究生,主要研究方向:电力市场与电力系统经济运行。E-mail:djn9927@hhu.edu.cn
收稿:2023-05-25,
修回:2023-08-18,
网络出版:2023-10-08,
纸质出版:2023-11-10
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谢俊,段佳南.基于合作博弈的风-光-水互补发电系统保证容量分配策略[J].电力系统自动化,2023,47(21):79-88.
XIE Jun,DUAN Jianan.Firm Generation Capacity Allocation Strategy of Wind-Solar-Hydro Complementary Generation System Based on Cooperative Game[J].Automation of Electric Power Systems,2023,47(21):79-88.
谢俊,段佳南.基于合作博弈的风-光-水互补发电系统保证容量分配策略[J].电力系统自动化,2023,47(21):79-88. DOI: 10.7500/AEPS20230525003.
XIE Jun,DUAN Jianan.Firm Generation Capacity Allocation Strategy of Wind-Solar-Hydro Complementary Generation System Based on Cooperative Game[J].Automation of Electric Power Systems,2023,47(21):79-88. DOI: 10.7500/AEPS20230525003.
风电、光伏出力具有间歇性、随机性,而梯级水电出力受限于天然来水,因此,保证容量是衡量风、光、水等清洁能源发电出力稳定性的一个重要指标。采用基于Copula函数的一阶季节性模型对风-光-水发电资源进行多年随机模拟,构建风-光-水互补发电系统的保证容量计算模型。为实现风-光-水互补发电系统保证容量在各利益主体间的高效、公平分配,提出一种基于混合整数规划的合作博弈分配策略:在计算模型中引入二进制变量,将单一场景求解问题转变为多场景的混合整数规划问题;构建“超量”指标量化各组合场景分配结果的联盟理性不满意度;构建基于核的分配模型,依次将不满意度最高的场景约束添加至模型中进行迭代优化并获取新的分配方案。最后,采用中国雅砻江下游流域12利益主体算例进行验证,结果表明所提分配策略通过寻求有效约束,可降低保证容量分配模型求解规模;将最大最小成本法分配结果作为迭代初值可加速算法收敛,在核内求得满足公平性要求的分配方案。
The output of wind power and photovoltaic (PV) are intermittent and random
while the output of cascade hydropower is limited by natural inflow. So
firm generation capacity (FGC) is an important indicator for measuring the stability of power generation of clean energy such as wind power
PV and hydropower. A first-order seasonal model based on Copula function is used to perform stochastic simulation of power generation resources for years
and a calculation model for FGC of wind-solar-hydro complementary generation system is constructed. In order to allocate FGC of wind-solar-hydro complementary generation system efficiently and fairly among various stakeholders
a cooperative game allocation strategy based on mixed-integer programming (MIP) is proposed: binary variables are employed in the calculation model
and the problem of solving a single scenario is transformed into a multi-scenario MIP problem; the “excess” indicator is constructed to quantify the cooperative rational dissatisfaction to the allocation results of each combination scenario; and the core allocation model is constructed and the scenario constraints with the highest dissatisfaction are added into the model in proper order for seeking a new allocation strategy through iterative optimization. Finally
a case study of 12 stakeholders in the lower reaches of the Yalong River in China is used for validation
the results show that the proposed allocation strategy can reduce the solving scale of the allocation model by seeking effective constraints. Taking the allocation results of the minimum cost remaining saving method as the initial value of iteration can accelerate the convergence of the algorithm and obtain the fair allocation strategy in the core.
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