面向两个细则考核的风-储场站日前-日内两阶段功率优化上报策略

doi:10.19595/j.cnki.1000-6753.tces.221837

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1482 KB)
输出: BibTeX | EndNote (RIS)

摘要针对两个细则考核风功率上报准确率的问题,本文提出一种可有效降低风-储场站风险考核电量的日前-日内两阶段功率优化上报策略。首先,提出面向两个细则的风-储场站日前-日内功率协同上报框架。同时,以期望考核电量最小化为目标,建立计及储能调节的日前-日内两阶段风-储功率优化上报模型。模型中,日前、日内风功率场景集通过基于预测误差分箱的Cornish-Fisher级数与Cholesky分解法生成。针对模型的多重非线性特征与不同时间尺度的求解效率要求,制定了基于小步长线性化迭代的求解算法。算例仿真结果表明,相较其余上报策略与求解算法,本文提出的日前-日内协同上报策略及相应算法有效提升了上报准确率以及求解性能。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	吴浩天
	柯德平
	刘念璋
	方珂
	郑景文

关键词 ：两个细则, 风功率上报, 日前-日内协同优化, 时间相关性, 储能调节。

Abstract：The rational optimization of the power reported by wind farms to the grid is a critical routine to improve the benefits of wind farms under the assessment of the ‘two detailed rules’. However, most of the traditional wind power reporting studies report the predicted power directly, or report the expected wind power obtained by superimposing the forecast error distributions with the predicted power. In addition, the sales revenue, the compensation for ancillary services are employed as the main optimization goals in these studies. Hence, it is difficult to apply the traditional reporting strategies directly under the background of the ‘two detailed rules’ assessment. To address these issues, this paper proposes a two-stage day-ahead and intra-day optimized power reporting strategy of wind-storage stations that fits the mechanism of the ‘two detailed rules’ assessment.
Firstly, the framework of the proposed strategy is constructed, where the day-ahead reporting results are embedded in the intra-day reporting model as the key to synergy, in order to achieve the optimal power reporting on both two time scales for the ‘two detailed rules’ assessment. Secondly, the specific assessment mechanism of the ‘two detailed rules’ in central China is used as an example to formulate the day-ahead and intra-day optimized wind power reporting model respectively. Particularly, the expected assessment power of day-ahead wind power scenarios is regarded as the objective function of the day-ahead reporting model. Besides, the reported wind power series obtained by the day-ahead model is utilized in the intra-day reporting model. With the energy storage as the medium, the total assessment power of all intra-day wind power scenarios on the two time scales can be minimized in the intra-day model. As for the scenario generation, the day-ahead and intra-day historical errors are binned according to meteorological conditions and different forecast time steps respectively. The Cornish-Fisher series and Cholesky decomposition are applied to generate and reorder the error scenarios considering temporal correlations. By superimposing the reordered error scenarios with the predicted power, the day-ahead and intra-day wind power scenarios can be obtained to refine the wind power reporting model. Owing to the requirements of different time scales for the model solving, a two-stage algorithm is proposed. In the day-ahead stage, due to the small scale of the model, the improved bald eagle search (IBES) algorithm with strong global search ability is adopted. In the intra-day stage, in order to meet the requirements of 15-minutes rolling reporting for solving efficiency, a small step linearized iteration-based solving algorithm is developed to transform the intra-day model with multiple nonlinear constraints into linear models. Simulation results on an actual wind farm in central China show that, the difference between the correlation coefficients of reordered error scenarios and historical error samples are no more than 0.0228 on average. Furthermore, compared with reporting the forecast power and reporting the optimized power based on independently solving the day-ahead and intra-day model, the two-stage collaborative power reporting strategy can reduce the assessment power by more than 50% in both two time scales. In addition, the small step linearized iteration-based solving algorithm can efficiently solve the intra-day model on average 30s, which is less than other types of intelligent algorithms. Meanwhile, compared with the regulation of the energy storage based on the predicted power, the proposed reporting strategy saves about half of the discharge electricity.
The following conclusions can be drawn from the simulations: (1) The reordered wind power scenarios is more in line with the correlation characteristics of historical samples, which lays a foundation for the modeling of reporting. (2) Compared with other reporting strategies, the two-stage collaborative reporting strategy can better fit the trend of actual wind power and reduce the assessment power. (3) The small step linearized iteration-based solving algorithm balances the optimization effect and solution efficiency, which is worthy of practical application. (4) The proposed energy storage optimization scheme is more energy-efficient than the scheme based on the predicted power, and it helps to extend the life of energy storage.

Key words： Two detailed rules wind power reporting day-ahead and intra-day coordinative optimization temporal correlation regulation of energy storage

收稿日期: 2022-09-30

PACS:

TM76

基金资助:国家电网公司总部科技项目资助（5100-202199286A-0-0-00）

通讯作者: 刘念璋男,1997年生,硕士研究生,研究方向为新能源预测与误差建模等。E-mail: nianzhangliu@whu.edu.cn

作者简介: 吴浩天男,1998年生,博士研究生,研究方向为新能源概率预测与优化上报,随机经济调度等。E-mail: gswcurry30ww@qq.com

引用本文:

吴浩天, 柯德平, 刘念璋, 方珂, 郑景文. 面向两个细则考核的风-储场站日前-日内两阶段功率优化上报策略[J]. 电工技术学报, 0, (): 98-98. Wu Haotian, Ke Deping, Liu Nianzhang, Fang Ke, Zheng Jingwen. Two-Stage Day-Ahead And Intra-Day Optimized Power Reporting Strategy of Wind-Storage Stations for the ‘Two Detailed Rules’ Assessment. Transactions of China Electrotechnical Society, 0, (): 98-98.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.221837 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/98

[1] 国家能源局. 国家能源局2022年一季度网上新闻发布会文字实录[EB/OL].[2022-01-28]. http://www.nea.gov.cn/2022-01/28/c1310445390.htm.
[2] 国家能源局华中监管局. 关于印发《华中区域并网发电厂辅助服务管理实施细则》和《华中区域发电厂并网运行管理实施细则》的通知[EB/OL].[2020-09-07],http://hzj.nea.gov.cn/adminContent/initViewContent.do?pk=AEB05CC013339FBDE050A8C0C1C8659B.
[3] 国家能源局南方监管局. 关于印发《南方区域发电厂并网运行管理实施细则》及《南方区域并网发电厂辅助服务管理实施细则》(2020年版)的通知[EB/OL].[2020-09-07],http://nfj.nea.gov.cn/adminContent/initViewContent.do?pk=4028811c752520120176b6b64cbf0147.
[4] 国家能源局华东监管局. 关于修订印发《华东区域并网发电厂辅助服务管理实施细则》和《华东区域发电厂并网运行管理实施细则》的通知[EB/OL].[2020-11-30],http://hdj.nea.gov.cn/load.loadPage.d?newsid=1482854616&page=detail_index.xml&siteCode=hddjwucm&urlChannelId=1481001274&urlMenuId=1481001274.
[5] Tang Chenghui, Xu Jian, Sun Yuanzhang, et al.A versatile mixture distribution and its application in economic dispatch with multiple wind farms[J]. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1747-1762.
[6] Botterud A, Wang J, Bessa R J, et al.Risk management and optimal bidding for a wind power producer[C]//IEEE PES General Meeting, Minneapolis, MN, USA, 2010: 1-8.
[7] Bitar E Y, Rajagopal R, Khargonekar P P, et al.Bringing wind energy to market[J]. IEEE Transactions on Power Systems, 2012, 27(3): 1225-1235.
[8] 吴政球, 王韬. 风电功率预测偏差管理与申报出力决策[J]. 电网技术, 2011, 35(12): 160-164.
Wu Zhengqiu, Wang Tao.Deviation management of wind power prediction and decision-making of wind power bidding[J]. Power System Technology, 2011, 35(12): 160-164.
[9] 谢春雨. 风电场发电计划上报策略研究[D]. 北京: 华北电力大学, 2014.
[10] 赵会茹, 高婧瑶, 王玉玮, 等. 基于鲁棒优化的风电企业日前申报策略[J]. 电网技术, 2018, 42(4): 1177-1182.
Zhao Huiru, Gao Jingyao, Wang Yuwei, et al.Day-ahead offering strategy for a wind power producer based on robust optimization[J]. Power System Technology, 2018, 42(4): 1177-1182.
[11] 周玮, 蓝嘉豪, 麦瑞坤, 等. 无线充电电动汽车V2G模式下光储直流微电网能量管理策略[J]. 电工技术学报, 2022, 37(1): 82-91.
Zhou Wei, Lan Jiahao, Mai Ruikun, et al.Research on power management strategy of DC microgrid with photovoltaic, energy storage and EV-wireless power transfer in V2G mode[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 82-91.
[12] 曹明浩, 于继来. 面向风电场发电曲线偏差校正的电化学储能系统容量规划方法[J]. 电力系统自动化, 2022, 46(11): 27-36.
Cao Minghao, Yu Jilai.Capacity planning method of electrochemical energy storage system for generation curve deviation correction of wind power farm[J]. Automation of Electric Power Systems, 2022, 46(11): 27-36.
[13] 郭立东, 雷鸣宇, 杨子龙, 等. 光储微网系统多目标协调控制策略[J]. 电工技术学报, 2021, 36(19): 4121-4131.
Guo Lidong, Lei Mingyu, Yang Zilong, et al.Multi-objective coordinated control strategy for photovoltaic and energy-storage microgrid system[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4121-4131.
[14] 张靖, 张志文, 胡斯佳, 等. 独立微电网风储协同调频的功率柔性分配策略[J]. 电工技术学报, 2022, 37(15): 3767-3780.
Zhang Jing, Zhang Zhiwen, Hu Sijia, et al.A flexible power distribution strategy with wind turbine generator and energy storage for frequency regulation in isolated microgrid[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3767-3780.
[15] 张峰, 张鹏, 梁军. 考虑风电功率不确定性的风电场出力计划上报策略[J]. 电力自动化设备, 2019, 39(11): 34-40.
Zhang Feng, Zhang Peng, Liang Jun.Wind farm generation schedule strategy considering wind power uncertainty[J]. Electric Power Automation Equipment, 2019, 39(11): 34-40.
[16] 德格吉日夫, 谭忠富, 李梦露, 等. 考虑不确定性的风储电站参与电力现货市场竞价策略[J]. 电网技术, 2019, 43(8): 2799-2807.
De Gejirifu, Tan Zhongfu, Li Menglu, et al.Bidding strategy of wind-storage power plant participation in electricity spot market considering uncertainty[J]. Power System Technology, 2019, 43(8): 2799-2807.
[17] 李军徽, 侯涛, 穆钢, 等. 电力市场环境下考虑风电调度和调频极限的储能优化控制[J]. 电工技术学报, 2021, 36(9): 1791-1804.
Li Junhui, Hou Tao, Mu Gang, et al.Optimal control strategy for energy storage considering wind farm scheduling plan and modulation frequency limitation under electricity market environment[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1791-1804.
[18] Bao Zhejing, Zhou Qin, Yang Zhihui, et al.A multi time-scale and multi energy-type coordinated microgrid scheduling solution—part I: model and methodology[J]. IEEE Transactions on Power Systems, 2014, 30(5): 2257-2266.
[19] 何翔路, 娄素华, 吴耀武, 等. 双结算模式下风储一体化电站两阶段市场投标调度策略[J]. 电力系统自动化, 2022, 46(4): 47-55.
He Xianglu, Lou Suhua, Wu Yaowu, et al.Two-stage market bidding and scheduling strategy of integrated wind power and energy storage station in dual-settlement mode[J]. Automation of Electric Power Systems, 2022, 46(4): 47-55.
[20] Ding Tao, Zhang Xiaosheng, Lu Runzhao, et al.Multi-stage distributionally robust stochastic dual dynamic programming to multi-period economic dispatch with virtual energy storage[J]. IEEE Transactions on Sustainable Energy, 2022, 13(1): 146-158.
[21] 颜宁, 潘霄, 张明理, 等. 基于复合储能的多互联微电网日内调度研究[J]. 电工技术学报, 2018, 33(增刊2): 577-585.
Yan Ning, Pan Xiao, Zhang Mingli, et al.Research on intra-day dispatch of multi-connected microgrids based on hybrid energy storage[J]. Transactions of China Electrotechnical Society, 2018, 33(S2): 577-585.
[22] 刘念璋, 杨健, 柳玉, 等. 分布函数差异化导向的风电功率预测误差气象条件概率建模方法[J]. 电力自动化设备, 2022, 42(12): 58-65.
Liu Nianzhang, Yang Jian, Liu Yu, et al.Probabilistic modeling method of weather condition for wind power forecasting error based on differentiation orientation of distribution function[J]. Electric Power Automation Equipment, 2022, 42(12): 58-65.
[23] 吴浩天, 孙荣富, 廖思阳, 等. 基于改进气象聚类分型的短期风电功率概率预测方法[J]. 电力系统自动化, 2022, 46(15): 56-65.
Wu Haotian, Sun Rongfu, Liao Siyang, et al.Short-term wind power probability forecasting method based on improved meteorological clustering and classification[J]. Automation of Electric Power Systems, 2022, 46(15): 56-65.
[24] Yu H, Chung C Y, Wong K P, et al.Probabilistic load flow evaluation with hybrid Latin hypercube sampling and cholesky decomposition[J]. IEEE Transactions on Power Systems, 2009, 24(2): 661-667.
[25] 方珂, 柯德平, 孙元章, 等. 考虑大规模直流馈入稳定约束的电网优化调度模型[J]. 南方电网技术, 2022, 16(7): 1-9.
Fang Ke, Ke Deping, Sun Yuanzhang, et al.Grid optimal dispatching model considering stability constraints of large-scale HVDC infeed[J]. Southern Power System Technology, 2022, 16(7): 1-9.
[26] 赵洋, 王瀚墨, 康丽, 等. 基于时间卷积网络的短期电力负荷预测[J]. 电工技术学报, 2022, 37(5): 1242-1251.
Zhao Yang, Wang Hanmo, Kang Li, et al.Temporal convolution network-based short-term electrical load forecasting[J]. Transactions of China Electrotechnical Society, 2022, 37(5): 1242-1251.
[27] 宫宇. 基于粒子群算法的配电网故障区段定位与恢复重构研究[D]. 重庆: 重庆理工大学, 2022.