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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (4): 736-744    DOI: 10.3785/j.issn.1008-973X.2022.04.013
计算机技术、信息工程     
考虑风电不确定性的风电场参与调频市场投标策略
杨锡运1(),刘雅欣1,马文兵1,邢国通2,高峰1
1. 华北电力大学 控制与计算机工程学院，北京 102206
2. 中国核电工程有限公司郑州分部，河南 郑州 450052
Bidding strategy of wind farm participation in frequency regulation market considering wind power uncertainty
Xi-yun YANG1(),Ya-xin LIU1,Wen-bing MA1,Guo-tong XING2,Feng GAO1
1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
2. Zhengzhou Branch, China Nuclear Power Engineering Limited Company, Zhengzhou 450052, China
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摘要：

针对风电大规模并网将增加电网调频需求的问题，提出考虑风电不确定性的风电机组同时参与能量市场与调频市场的日前投标方法.分析风电场在2种市场中的收益机制，在调频市场收益中考虑调频性能指标（FRPI），提出调频性能指标的估值方法. 分析风电场参与2种市场的投标策略. 利用核极限学习机（KELM）和核密度估计（KDE），建立风电功率概率预测模型KELM-PSO-KDE.基于功率概率密度的预测结果，以风电场收益最大为目标函数建立优化模型，利用蚁狮优化（ALO）算法求解该模型，得到风电场同时参与2种市场的日前最优投标功率. 风电场真实数据的仿真表明，提出的风电场同时参与2种市场的投标策略，可以使风场侧获得更大收益，有助于缓解电网的调频压力，具有优越性和普适性.
关键词： 风电;  能量市场;  调频市场;  风功率预测;  投标策略;  蚁狮优化算法;  调频性能指标(FRPI)    
Abstract:

The day-ahead bidding method of wind farms participation in the energy market and frequency regulation (FR) market was proposed in order to solve the problem that large-scale wind power integration on power system can increase the FR demand of the power system. The revenue mechanisms of wind farms in the energy market and FR market were analyzed. The frequency regulation performance index (FRPI) was considered in the FR market revenue, and the evaluation method of FRPI was proposed. The bidding strategy of wind farms participating in the energy and FR (E&FR) markets was analyzed. The wind power probability density prediction model KELM-PSO-KDE was established by using the kernel extreme learning machine (KELM) and the kernel density estimation (KDE). An optimization model for the wind farm participating in the E&FR markets was established with the goal of maximizing the wind farm revenue based on the probability density prediction results of wind power. The ant lion optimizer (ALO) algorithm was used to solve the optimization model in order to obtain the day-ahead optimal bidding power for the wind farm participating in the E&FR markets. The simulation results based on the actual wind farm data show that the bidding strategy for wind farms in the E&FR markets can help wind farms to obtain more revenue, and help the power system to relieve the FR pressure. The bidding strategy has more advantages and universality.
Key words: wind power    energy market    frequency regulation market    wind power prediction    bidding strategy    ant lion optimizer algorithm    frequency regulation performance index (FRPI)
收稿日期: 2021-05-08 出版日期: 2022-04-24
CLC:  TK 89  
基金资助: 国家自然科学基金资助项目(51677067)
作者简介: 杨锡运（1973—），女，教授，博导，从事新能源发电控制的研究. orcid.org/0000-0003-0192-1437. E-mail： yangxiyun916@sohu.com
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杨锡运
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引用本文:

杨锡运,刘雅欣,马文兵,邢国通,高峰. 考虑风电不确定性的风电场参与调频市场投标策略[J]. 浙江大学学报(工学版), 2022, 56(4): 736-744.

Xi-yun YANG,Ya-xin LIU,Wen-bing MA,Guo-tong XING,Feng GAO. Bidding strategy of wind farm participation in frequency regulation market considering wind power uncertainty. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 736-744.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.04.013    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I4/736

图 1  风电场在能量市场的收益情况
图 2  风电场在调频市场的收益情况
图 3  风电场同时参与2种市场的投标策略及收益情况
图 4  核极限学习机-粒子群-核密度估计预测模型
图 5  某些时刻的风电功率概率密度预测曲线
蚁狮数目 迭代次数 $ {P_{\rm{e}}} $/MW $ {P_{\rm{r}}} $/MW
50 100 [0,50] [0,50]
表 1  ALO算法的初始参数
图 6  在2种市场同时投标与单独在能量市场投标的功率对比
时刻 同时参与能量市场与调频市场 单独参与能量市场
$ {P_{\rm{e}}} $/MW $ {P_{\rm{r}}} $/MW R/元 $ {P_{{\rm{only E}}}} $/MW Re
/元
$ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $
0:30 6.274 8 0.725 5 478.836 1 6.864 1 439.943 7
6:30 11.853 4 0.934 9 889.246 7 12.538 5 844.201 9
21:00 8.505 3 0.821 7 635.723 0 9.198 8 589.714 7
$ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $
一天 ? ? 56803 ? 53514
表 2  风电场部分时刻的收益及一天总收益的对比
图 7  能量市场投标功率与第2天在能量市场提供功率的对比图
时刻 EE-FR/MW EonlyE/MW
$ \vdots $ $ \vdots $ $ \vdots $
0:30 0.110 8 0.700 1
6:30 0.325 3 1.010 3
16:00 0.682 8 1.132 8
$ \vdots $ $ \vdots $ $ \vdots $
一天平均值 0.616 4 1.088 1
表 3  能量市场投标功率与实际提供功率的误差对比
投标策略 R/元 Eb/MW
常量投标策略 50626 0.962 0
本文投标策略 56803 0.616 4
表 4  不同投标策略下的结果对比
图 8  常量投标策略下风电场的投标功率
图 9  3种风电功率概率密度预测模型下的风电场投标功率
风电功率概率密度预测模型 R/元 E/MW
BP-Beta 56287 0.671 6
KELM-PSO-Beta 56305 0.637 3
KELM-PSO-KDE 56803 0.616 4
表 5  不同风电功率概率密度预测模型下的结果对比
求解方法 R/元 E/MW
PSO 56634 0.624 9
ALO 56803 0.616 4
表 6  不同求解方法下的结果对比
投标策略 R/元 E/MW
单独参与能量市场 158720 3.274 3
常量投标策略 153920 2.524 3
本文投标策略 161410 2.066 7
表 7  另外一天的风电场收益及投标误差
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