Bidding strategy of wind farm participation in frequency regulation market considering wind power uncertainty

doi:10.3785/j.issn.1008-973X.2022.04.013

Journal of ZheJiang University (Engineering Science)

2022, Vol. 56

Issue (4): 736-744 DOI: 10.3785/j.issn.1008-973X.2022.04.013

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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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)

Received: 08 May 2021 Published: 24 April 2022

CLC:

TK 89

Fund: 国家自然科学基金资助项目(51677067)

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	Xi-yun YANG
	Ya-xin LIU
	Wen-bing MA
	Guo-tong XING
	Feng GAO

Cite this article:

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.

URL:

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

考虑风电不确定性的风电场参与调频市场投标策略

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)

Fig.1 Wind farm revenue in energy market

Fig.2 Wind farm revenue in frequency regulation market

Fig.3 Bidding strategy and revenue of wind farm participating in both markets

Fig.4 Wind power probability prediction model of KELM-PSO-KDE

Fig.5 Probability density prediction curves of wind power at some time

Tab.1 Initial parameters of ALO algorithm

Fig.6 Comparison of bidding power in both two markets versus only in energy market

Tab.2 Comparison of wind farm revenues at different time and total daily revenue

Fig.7 Comparison of bidding power and actual power of next day in energy market

Tab.3 Comparison of errors between bidding power and actual power in energy market

Tab.4 Comparison of results under different bidding strategies

Fig.8 Bidding power under constant bidding strategy

Fig.9 Bidding power in E&FR markets obtained from three probability density prediction models of wind power

Tab.5 Comparison of results under different probability density prediction models of wind power

Tab.6 Comparison of results under different bidding strategies

Tab.7 Wind farm revenues and bidding errors of other day


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