Abstract：The “dual carbon” goals will promote the continuous application of wind power and other renewable energy. With the large-scale integration of wind power, there would be some risks when the power system operation because of the inherent uncertainty of wind power. While, the traditional deterministic method does not consider the wind power prediction error, and the unit reserves enough reserve capacity to deal with the uncertainty of wind power, so the system has great hidden Security Problems. In recent years, many scholars have constructed robust optimization models based on wind power prediction errors, but the results tend to be conservative. To address these issues, this paper proposes a bi-level optimization model which considering times series characteristic of wind power forecast error and wind power reliability. It effectively improves the economy of power system operation. Firstly, the adaptive bandwidth method is used to obtain the non-parametric kernel density estimation function of the prediction error, and the time series segment of wind power prediction error is optimized through correlation analysis, and the fluctuation domain of wind power is established according to the time series segment, and the intra-day wind power scenario is generated. Secondly, the bi-level optimization model is constructed. The upper model in the day-ahead phase the objective function is to maximize the utilization of wind power and minimize the generation cost and carbon transaction cost, to solve the planned output of each unit, wind power and allowable output area of wind power. The planned output of wind power is determined according to the reliability of intra-day wind power scenario. The allowable output area of wind power makes the control of wind power plant more flexible, and determines the output decision of Automatic Generation Control(AGC) units through participation factors to deal with wind power fluctuations. While the lower model in the intraday generates wind power scenarios take the system deviation correction cost and risk cost minimization as the objective function, the source-side considers Ns possible scenarios to get the reliability of wind power in stages and feedback to the upper model, the incentive demand response is introduced on the load side, and the lower model updated the allowable output area of wind power and adjusts the output of AGC units by tracking the planned output value obtained from the upper model. Finally, the proposed model is compared with other models based on the data of a certain region in Xinjiang, and the results are analyzed. A total of five scenario models are compared. The results show that in scenario 1, the cost is the lowest because the uncertainty of wind power is not considered; in scenario 2, the unit commitment result is conservative lead the cost highest; in scenario 4, the cost is higher than scenario 3 presented because does not distinguish AGC units, and all thermal power units track the command value of the plan and reserve enough spare capacity. In Scenario 5, the set of the same participation factor lead to same priority among AGC units, and distribute power equally to each unit during unit operation, so the cost increases compared with scenario 3. In order to explore the relationship between penetration of wind power permeability and wind power reliability, the optimization results of wind power reliability in the day-ahead operation stage are analyzed. It can be seen that when wind power permeability is high, wind power reliability is high, and on the contrary, wind power reliability is low. In addition, the spinning reserve cost and system operation risk cost are analyzed with different confidence levels, which prove that the appropriate confidence level is about 90% . The following conclusions can be drawn from the simulation analysis: (1) the fitting accuracy of the non-parametric kernel density estimation using the adaptive bandwidth method is better than other fitting methods; (2) The economy of the proposed bi-level model is better than other comparison models, and the wind power reliability considered by the model can be used as dispatching signals to guide wind power plants connected reasonably; (3) By analyzing the variation rules of spinning reserve cost and risk cost under different confidence levels, a reasonable confidence interval can be given to take into account the economy of system operation and the reliability of power supply.
徐询, 谢丽蓉, 梁武星, 叶家豪, 马兰. 考虑风电预测误差时序性及风电可信度的双层优化模型[J]. 电工技术学报, 2023, 38(6): 1620-1632.
Xu Xun, Xie Lirong, Liang Wuxing, Ye Jiahao, Ma Lan. Bi-Level Optimization Model Considering Time Series Characteristic of Wind Power Forecast Error and Wind Power Reliability. Transactions of China Electrotechnical Society, 2023, 38(6): 1620-1632.
[1] 黄雨涵, 丁涛, 李雨婷, 等. 碳中和背景下能源低碳化技术综述及对新型电力系统发展的启示[J]. 中国电机工程学报, 2021, 41(增刊1): 28-51. Huang Yuhan, Ding Tao, Li Yuting, et al.Decarbonization technologies and inspirations for the development of novel power systems in the context of carbon neutrality[J]. Proceedings of the CSEE, 2021, 41(S1): 28-51. [2] 叶林, 路朋, 赵永宁, 等. 含风电电力系统有功功率模型预测控制方法综述[J]. 中国电机工程学报, 2021, 41(18): 6181-6197. Ye Lin, Lu Peng, Zhao Yongning, et al.Review of model predictive control for power system with large-scale wind power grid-connected[J]. Proceedings of the CSEE, 2021, 41(18): 6181-6197. [3] 路朋, 叶林, 裴铭, 等. 风电集群有功功率模型预测协调控制策略[J]. 中国电机工程学报, 2021, 41(17): 5887-5899. Lu Peng, Ye Lin, Pei Ming, et al.Coordinated control strategy for active power of wind power cluster based on model predictive control[J]. Proceedings of the CSEE, 2021, 41(17): 5887-5899. [4] Khorramdel B, Zare A, Chung C Y, et al.A generic convex model for a chance-constrained look-ahead economic dispatch problem incorporating an efficient wind power distribution modeling[J]. IEEE Transactions on Power Systems, 2020, 35(2): 873-886. [5] Yan Jing, Ouyang Tinghui.Advanced wind power prediction based on data-driven error correction[J]. Energy Conversion and Management, 2019, 180: 302-311. [6] 张沛, 田佳鑫, 谢桦. 计及多个风场预测误差的电力系统风险快速计算方法[J]. 电工技术学报, 2021, 36(9): 1876-1887. Zhang Pei, Tian Jiaxin, Xie Hua.A fast risk assessment method with consideration of forecasting errors of multiple wind farms[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1876-1887. [7] 毕平平, 许晓艳, 梅文明, 等. 风电基地连锁脱网风险评估方法及送出能力研究[J]. 电网技术, 2019, 43(3): 903-909. Bi Pingping, Xu Xiaoyan, Mei Wenming, et al.Study on cascaded tripping-off risk assessment method and delivery capacity of wind power base[J]. Power System Technology, 2019, 43(3): 903-909. [8] 孙亚南, 黄越辉, 孙谊媊, 等. 基于运行数据的短期风电功率预测误差互补特性探析[J]. 电力系统自动化, 2021, 45(21): 215-223. Sun Yanan, Huang Yuehui, Sun Yiqian, et al.Operation data based analysis on complementary characteristics of short-term power prediction error for wind power[J]. Automation of Electric Power Systems, 2021, 45(21): 215-223. [9] 余沣, 董存, 王铮, 等. 考虑山东近海不同风能天气特征的风电功率区间预测模型[J]. 电网技术, 2020, 44(4): 1238-1246. Yu Feng, Dong Cun, Wang Zheng, et al.Wind power interval forecasting model considering different wind energy weather characteristics in Shandong offshore areas[J]. Power System Technology, 2020, 44(4): 1238-1246. [10] Gu Bo, Shen Huiqiang, Lei Xiaohui, et al.Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method[J]. Applied Energy, 2021, 299: 117291. [11] Yang Jun, Su Changqi.Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty[J]. Energy, 2021, 223(6): 120043. [12] 罗毅, 邵周策, 张磊, 等. 考虑风电不确定性和气网运行约束的鲁棒经济调度和备用配置[J]. 电工技术学报, 2018, 33(11): 2456-2467. Luo Yi, Shao Zhouce, Zhang Lei, et al.Robust economic dispatch and reserve configuration considering wind uncertainty and gas network constraints[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2456-2467. [13] 周博, 艾小猛, 方家琨, 等. 计及超分辨率风电出力不确定性的连续时间鲁棒机组组合[J]. 电工技术学报, 2021, 36(7): 1456-1467. Zhou Bo, Ai Xiaomeng, Fang Jiakun, et al.Continuous-time modeling based robust unit commitment considering beyond-the-resolution wind power uncertainty[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1456-1467. [14] 朱兰, 李孝均, 唐陇军, 等. 考虑相变储能与建筑蓄能特性的微网分布鲁棒优化调度[J]. 电网技术, 2021, 45(6): 2308-2318. Zhu Lan, Li Xiaojun, Tang Longjun, et al.Distributionally robust optimal operation for microgrid considering phase change storage and building storage[J]. Power System Technology, 2021, 45(6): 2308-2318. [15] 郑义, 白晓清, 苏向阳. 考虑风电不确定性的φ-散度下基于条件风险价值的鲁棒动态经济调度[J]. 电力自动化设备, 2021, 41(2): 63-70. Zheng Yi, Bai Xiaoqing, Su Xiangyang.Robust dynamic economic dispatch considering uncertainty of wind power based on conditional value-at-risk under φ-divergence[J]. Electric Power Automation Equipment, 2021, 41(2): 63-70. [16] 张智, 陈艳波, 刘芳, 等. 计及运行风险和需求响应的两阶段鲁棒机组组合模型[J]. 中国电机工程学报, 2021, 41(3): 961-972. Zhang Zhi, Chen Yanbo, Liu Fang, et al.Two-stage robust unit commitment model considering operation risk and demand response[J]. Proceedings of the CSEE, 2021, 41(3): 961-972. [17] 刘文颖, 徐鹏, 赵子兰, 等. 基于区间估计的风电出力多场景下静态电压安全域研究[J]. 电工技术学报, 2015, 30(3): 172-178. Liu Wenying, Xu Peng, Zhao Zilan, et al.A research of static voltage stability region in wind power scenario based on interval estimation[J]. Transactions of China Electrotechnical Society, 2015, 30(3): 172-178. [18] 赵冬梅, 殷加玞. 考虑源荷双侧不确定性的模糊随机机会约束优先目标规划调度模型[J]. 电工技术学报, 2018, 33(5): 1076-1085. Zhao Dongmei, Yin Jiafu.Fuzzy random chance constrained preemptive goal programming scheduling model considering source-side and load-side uncertainty[J]. Transactions of China Electrotechnical Society, 2018, 33(5): 1076-1085. [19] 马燕峰, 陈磊, 李鑫, 等. 基于机会约束混合整数规划的风火协调滚动调度[J]. 电力系统自动化, 2018, 42(5): 127-132, 175. Ma Yanfeng, Chen Lei, Li Xin, et al.Rolling dispatch of wind-coal coordinated system based on chance-constrained mixed integer programming[J]. Automation of Electric Power Systems, 2018, 42(5): 127-132, 175. [20] 徐野驰, 颜云松, 张俊芳, 等. 考虑预测误差与频率响应的随机优化调度[J]. 电网技术, 2020, 44(10): 3663-3670. Xu Yechi, Yan Yunsong, Zhang Junfang, et al.Stochastic optimal dispatching considering prediction error and frequency response[J]. Power System Technology, 2020, 44(10): 3663-3670. [21] 李春燕, 陈骁, 张鹏, 等. 计及风电功率预测误差的需求响应多时间尺度优化调度[J]. 电网技术, 2018, 42(2): 487-494. Li Chunyan, Chen Xiao, Zhang Peng, et al.Multi-time-scale demand response dispatch considering wind power forecast error[J]. Power System Technology, 2018, 42(2): 487-494. [22] 杨正清, 汪震, 展肖娜, 等. 考虑风电有功主动控制的两阶段系统备用双层优化模型[J]. 电力系统自动化, 2016, 40(10): 31-37. Yang Zhengqing, Wang Zhen, Zhan Xiaona, et al.Bi-level optimization model of two-stage reserve scheduling with proactive wind power control[J]. Automation of Electric Power Systems, 2016, 40(10): 31-37. [23] Zougab N, Adjabi S, Kokonendji C C.Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation[J]. Computational Statistics & Data Analysis, 2014, 75: 28-38. [24] 李志伟, 赵书强, 董凌. 考虑预测误差的风火协调滚动调度[J]. 电力自动化设备, 2020, 40(12): 88-95. Li Zhiwei, Zhao Shuqiang, Dong Ling.Coordinated rolling dispatch of wind and thermal power considering forecasting error[J]. Electric Power Automation Equipment, 2020, 40(12): 88-95. [25] 杨茂, 董昊. 基于数值天气预报风速和蒙特卡洛法的短期风电功率区间预测[J]. 电力系统自动化, 2021, 45(5): 79-85. Yang Mao, Dong Hao.Short-term wind power interval prediction based on wind speed of numerical weather prediction and Monte Carlo method[J]. Automation of Electric Power Systems, 2021, 45(5): 79-85.
2023, Vol. 38 
