基于时空协方差函数的风能场景生成方法与应用

doi:10.16183/j.cnki.jsjtu.2022.180

摘要/Abstract

摘要：

与传统发电不同,风力发电具有较大的随机性与时空相关性.在风力发电并网的电力系统优化调度问题中,保障电力调度在不同风力发电功率场景中的最优执行是决策问题的关键点,因此高质量的风能场景生成非常重要.基于高斯随机过程和时空协方差函数表征风力发电站输出功率的时空相关性,由Pair Copula模型建立联合概率分布,通过经验概率逆变换方法实现具体场景.评估生成场景的多种指标,验证生成场景的优越性.基于修改的IEEE 6总线系统建立电力系统机组组合的混合整数规划模型,求解不同场景下的问题,验证场景生成方法在风力发电并网调度问题中所具有的经济性和可行性.

关键词: 风力发电, 场景生成, 时空协方差函数, Copula函数, 经济调度

Abstract:

Wind power generation is different from traditional power generation in which wind power output is highly stochastic and spatio-temporally dependent. In the optimal scheduling problem of wind power grid-connected power system, ensuring the optimal execution of power scheduling in different wind power scenarios is the key of the decision-making problem. Therefore, high quality wind power scenario generation is of great importance. The spatiotemporal correlation of the output power of wind power plants are characterized based on Gaussian stochastic process and spatiotemporal covariance function, and the joint probability distribution is established by the Pair Copula model, and specific scenarios are implemented by the method of empirical probability inverse transformation. A variety of scene metrics of the generated scene are generated, which verifies the superiority of the generated scene. Finally, based on the modified IEEE 6-bus system, a mixed integer programming model for the unit output of the power system is established to solve the problems in different scenarios and verify the economic advantages of the scenario generation method in the dispatching problem of wind power grid connection.

Key words: wind power generation, scenario generation, spatiotemporal covariance function, Copula function, economic dispatch

中图分类号:

TM715

彭星皓, 李艳婷. 基于时空协方差函数的风能场景生成方法与应用[J]. 上海交通大学学报, 2023, 57(12): 1531-1542.

PENG Xinghao, LI Yanting. Wind Power Scenario Generation Method and Application Based on Spatiotemporal Covariance Function[J]. Journal of Shanghai Jiao Tong University, 2023, 57(12): 1531-1542.

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Copula类型	d₂
Frank	6.8323
Clayton	7.4544
Gumbel	10.2047

方法	S_E	S_V	P_c	b_AI	b_SI
M1	25.480	686.613	0.976	41.621	41.659
M2	27.091	722.756	0.983	50.279	50.303
M3	26.922	733.940	0.991	63.185	63.193
M4	28.302	751.359	0.975	63.971	62.979

方法	C_fuel	C_setup	C_RUD	C_WC	C_LS	C_AF	C_total
M1	103169	4275	5881	2046	3966	622	119959
M2	103150	4308	7526	2093	5688	1716	124459
M3	103160	4294	6523	2259	6629	888	123752
M4	103156	4309	8344	2837	6315	1845	126807