Method for Wind Power Balancing Cost Allocation Considering Spatiotemporal Characteristics of Wind Power and Relative Volatility

HE Yiqiong, LI Xin, JIA Haiqing, LIU Changxi, LIU Ru, GU Xudong, LEI Xia

Electric Power Construction ›› 2024, Vol. 45 ›› Issue (1) : 157-166.

PDF(3168 KB)
PDF(3168 KB)
Electric Power Construction ›› 2024, Vol. 45 ›› Issue (1) : 157-166. DOI: 10.12204/j.issn.1000-7229.2024.01.015
Power Economic Research

Method for Wind Power Balancing Cost Allocation Considering Spatiotemporal Characteristics of Wind Power and Relative Volatility

Author information +
History +

Abstract

A wind-power balance cost allocation method was developed in response to China’s goal of increasing wind-power consumption capacity and achieving sustainable development, combined with the need for electricity spot market construction. The proposed method considers the spatiotemporal characteristics of wind power and the impact of relative load power fluctuations on system operation. The “equal electricity quantity following load” method was used to construct a zero-wind power balance cost equivalent scenario to optimize and dispatch system operation in advance. By comparing the predicted scenario with the equivalent scenario of the optimized dispatch, the total operating cost of wind power balance was solved for the dispatch cycle. Based on the spatial distribution and capacity of each wind power plant, a balance cost allocation model between wind power plants was established. An indicator, the relative fluctuation rate of each time period, was proposed to quantitatively express the temporal evolution characteristics of wind-power relative load fluctuations, and the corresponding model was established. Based on the relative fluctuation rate of each time period, the local fluctuation impact factor was obtained to allocate the wind-power balance cost of the dispatch cycle by time period, resulting in wind power balance costs for each time period. Using the grid structure of a region with a high proportion of wind power as an example, the balance costs of different wind-power grid capacities and fluctuation levels were calculated to verify the effectiveness of the proposed calculation method and model.

Key words

balanced cost sharing among wind farms / time-segment balancing cost / wind power generation / optimal scheduling / time-of-day relative volatility

Cite this article

Download Citations
Yiqiong HE , Xin LI , Haiqing JIA , et al . Method for Wind Power Balancing Cost Allocation Considering Spatiotemporal Characteristics of Wind Power and Relative Volatility[J]. Electric Power Construction. 2024, 45(1): 157-166 https://doi.org/10.12204/j.issn.1000-7229.2024.01.015

References

[1]
黄碧斌, 张运洲, 王彩霞. 中国“十四五”新能源发展研判及需要关注的问题[J]. 中国电力, 2020, 53(1): 1-9.
HUANG Bibin, ZHANG Yunzhou, WANG Caixia. China’s new energy development in the 14th five-year plan and the problems needing attention[J]. Electric Power, 2020, 53(1): 1-9.
[2]
郝倛晗, 裘智峰, 曹胡辉, 等. 基于多级市场驱动的风电协同消纳策略[J]. 电网技术, 2020, 44(7): 2590-2600.
HAO Qihan, QIU Zhifeng, CAO Huhui, et al. Collaborative consumption strategy of wind power based on multi-level market drive[J]. Power System Technology, 2020, 44(7): 2590-2600.
[3]
王蓓蓓, 仇知, 丛小涵, 等. 基于两阶段随机优化建模的新能源电网灵活性资源边际成本构成的机理分析[J]. 中国电机工程学报, 2021, 41(4): 1348-1359, 1541.
WANG Beibei, QIU Zhi, CONG Xiaohan, et al. Mechanism analysis of marginal cost of flexible resources in new energy grid based on two-stage stochastic optimization modeling[J]. Proceedings of the CSEE, 2021, 41(4): 1348-1359, 1541.
[4]
HIRTH L, UECKERDT F, EDENHOFER O. Integration costs revisited - an economic framework for wind and solar variability[J]. Renewable Energy, 2015, 74: 925-939.
[5]
DURRWACHTER H L, LOONEY S K. Integration of wind generation into the ERCOT market[J]. IEEE Transactions on Sustainable Energy, 2012, 3(4): 862-867.
[6]
SWINAND G P, GODEL M. Estimating the impact of wind generation on balancing costs in the GB electricity markets[C]// 2012 9th International Conference on the European Energy Market. IEEE, 2012: 1-8.
[7]
ISSAEVA N. Quantifying the system balancing cost when wind energy is incorporated into electricity generation system[D]. Edinburgh, UK: University of Edinburgh, 2009.
[8]
MILLIGAN M, ELA E, HODGE B M, et al. Integration of variable generation, cost-causation, and integration costs[J]. The Electricity Journal, 2011, 24(9): 51-63.
[9]
耿建, 程海花, 张凯锋, 等. 风电调度接纳成本的等电量顺负荷计算方法及分析[J]. 电力系统自动化, 2017, 41(20): 32-37, 132.
GENG Jian, CHENG Haihua, ZHANG Kaifeng, et al. Calculation method and analysis of equal power load following the acceptance cost of wind power dispatching[J]. Automation of Electric Power Systems, 2017, 41(20): 32-37, 132.
[10]
陈晓榕, 江岳文. 风电波动成本分摊方法[J]. 电力自动化设备, 2020, 40(11): 99-106.
CHEN Xiaorong, JIANG Yuewen. Wind power variability cost allocation method[J]. Electric Power Automation Equipment, 2020, 40(11): 99-106.
[11]
吴迪, 程海花, 赵晋泉, 等. 基于平衡成本的风电分段及火电调峰补偿方法[J]. 电力系统自动化, 2019, 43(3): 116-122.
WU Di, CHENG Haihua, ZHAO Jinquan, et al. Balancing cost based wind power segmentation and compensation method of peak regulation for thermal power[J]. Automation of Electric Power Systems, 2019, 43(3): 116-122.
[12]
饶志, 杨再敏, 蒙文川, 等. 基于改进型非参数核密度估计法的南方区域风电出力特性分析[J]. 电网与清洁能源, 2022, 38(6): 81-88, 97.
RAO Zhi, YANG Zaimin, MENG Wenchuan, et al. An analysis of wind power output characteristics in Southern China region based on improved non-parametric kernel density estimation[J]. Power System and Clean Energy, 2022, 38(6): 81-88, 97.
[13]
万书亭, 万杰. 基于量化指标和概率密度分布的风电功率波动特性研究[J]. 太阳能学报, 2015, 36(2): 362-368.
WAN Shuting, WAN Jie. Research on wind power fluctuation characteristics based on quantitative index and probability density distribution[J]. Acta Energiae Solaris Sinica, 2015, 36(2): 362-368.
[14]
范磊, 卫志农, 李慧杰, 等. 基于变分模态分解和蝙蝠算法-相关向量机的短期风速区间预测[J]. 电力自动化设备, 2017, 37(1): 93-100.
FAN Lei, WEI Zhinong, LI Huijie, et al. Short-term wind speed interval prediction based on VMD and BA-RVM algorithm[J]. Electric Power Automation Equipment, 2017, 37(1): 93-100.
[15]
林卫星, 文劲宇, 艾小猛, 等. 风电功率波动特性的概率分布研究[J]. 中国电机工程学报, 2012, 32(1): 38-46.
LIN Weixing, WEN Jinyu, AI Xiaomeng, et al. Probability density function of wind power variations[J]. Proceedings of the CSEE, 2012, 32(1): 38-46.
[16]
杨茂, 齐玥. 基于相空间重构的风电功率波动特性分析及其对预测误差影响[J]. 中国电机工程学报, 2015, 35(24): 6304-6314.
YANG Mao, QI Yue. Volatility of wind power sequence and its influence on prediction error based on phase space reconstruction[J]. Proceedings of the CSEE, 2015, 35(24): 6304-6314.
[17]
杨茂, 董骏城. 基于混合分布模型的风电功率波动特性研究[J]. 中国电机工程学报, 2016, 36(S1): 69-78.
YANG Mao, DONG Juncheng. Study on characteristics of wind power fluctuation based on mixed distribution model[J]. Proceedings of the CSEE, 2016, 36(S1): 69-78.
[18]
吴耀武, 汪昌霜, 娄素华, 等. 计及风电—负荷耦合关系的含大规模风电系统调峰运行优化[J]. 电力系统自动化, 2017, 41(21): 163-169.
WU Yaowu, WANG Changshuang, LOU Suhua, et al. Peak load regulating operation and optimization in power systems with large-scale wind power and considering coupling relation between wind power and load[J]. Automation of Electric Power Systems, 2017, 41(21): 163-169.
[19]
何方波, 赵明, 王楷, 等. 考虑需求响应的源荷协调多目标优化方法[J]. 电网与清洁能源, 2021, 37(10): 51-58.
HE Fangbo, ZHAO Ming, WANG Kai, et al. A multi objective optimization method of source load coordination considering demand response[J]. Power System and Clean Energy, 2021, 37(10): 51-58.
[20]
李东东, 董楠, 姚寅, 等. 考虑频率响应分散性及系统分区的含风电系统等效惯量估计[J]. 电力系统保护与控制, 2023, 51(3): 36-45.
LI Dongdong, DONG Nan, YAO Yin, et al. Equivalent inertia estimation of wind power system considering frequency response dispersion and system partition[J]. Power System Protection and Control, 2023, 51(3): 36-45.
[21]
刘海南, 蔺红, 樊国旗, 等. 基于风荷耦合特性的源荷储的优化调度[J]. 智慧电力, 2021, 49(1): 42-47.
LIU Hainan, LIN Hong, FAN Guoqi, et al. Optimal scheduling of source-load-storage based on wind-load coupling characteristics[J]. Smart Power, 2021, 49(1): 42-47.
[22]
赵伟哲, 崔成, 严干贵, 等. 用于次同步振荡分析的直驱风电场等值模型[J]. 智慧电力, 2022, 50(2): 22-28, 68.
ZHAO Weizhe, CUI Cheng, YAN Gangui, et al. Equivalent model of D-PMSG-based wind farm for subsynchronous oscillation analysis[J]. Smart Power, 2022, 50(2): 22-28, 68.
[23]
刘侃, 贾祺, 翟文超, 等. 面向次同步振荡的直驱风电机组阻抗频率响应特性辨识[J]. 智慧电力, 2021, 49(9): 39-46.
LIU Kan, JIA Qi, ZHAI Wenchao, et al. Identification of impedance frequency response characteristics of direct-driven wind turbine for subsynchronous oscillation[J]. Smart Power, 2021, 49(9): 39-46.
[24]
李剑楠, 乔颖, 鲁宗相, 等. 大规模风电多尺度出力波动性的统计建模研究[J]. 电力系统保护与控制, 2012, 40(19): 7-13.
LI Jiannan, QIAO Ying, LU Zongxiang, et al. Research on statistical modeling of large-scale wind farms output fluctuations in different spacial and temporal scales[J]. Power System Protection and Control, 2012, 40(19): 7-13.
[25]
李剑楠, 乔颖, 鲁宗相, 等. 多时空尺度风电统计特性评价指标体系及其应用[J]. 中国电机工程学报, 2013, 33(13): 53-61.
LI Jiannan, QIAO Ying, LU Zongxiang, et al. An evaluation index system for wind power statistical characteristics in multiple spatial and temporal scales and its application[J]. Proceedings of the CSEE, 2013, 33(13): 53-61.
[26]
罗毅, 周创立, 刘向杰. 多层次灰色关联分析法在火电机组运行评价中的应用[J]. 中国电机工程学报, 2012, 32(17): 97-103, 150.
LUO Yi, ZHOU Chuangli, LIU Xiangjie. Application of the multi-level grey relational analysis method in operation assessment of thermal power units[J]. Proceedings of the CSEE, 2012, 32(17): 97-103, 150.
[27]
孔令达, 李蓓, 靳文涛, 等. 基于灰色关联决策的间歇式电源数据采集粒度标定[J]. 中国电机工程学报, 2016, 36(9): 2342-2349.
KONG Lingda, LI Bei, JIN Wentao, et al. Data collection granularity selecting in intermittent energy power generation based on grey incidence decision[J]. Proceedings of the CSEE, 2016, 36(9): 2342-2349.
[28]
杨茂, 杨春霖, 李大勇, 等. 基于局部极差变化率的风电功率波动定量刻画[J]. 电力自动化设备, 2018, 38(7): 82-88.
YANG Mao, YANG Chunlin, LI Dayong, et al. Quantitative description of wind power fluctuation based on local range change rate[J]. Electric Power Automation Equipment, 2018, 38(7): 82-88.

Funding

National Natural Science Foundation of China(51877181)
Science and Technology Project of State Grid Inner Mongolia Eastern Power Co., Ltd.(526601220045)
PDF(3168 KB)

Accesses

Citation

Detail

Sections
Recommended

/