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电网弹性备用参与下的机组组合优化决策研究 被引量:1

Study on Optimal Decision of Unit Commitment with Flexible Reserve Participation in Power Grid
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摘要 电网结构的日益复杂使得电网应对各种事故的能力亟待提高,合理的增加电网备用容量有助于降低电网运行风险。首先考虑极端情况下电网中存在备用不足的风险,将新能源调频、直流调制以及可中断负荷归为电网弹性备用,对其备用潜力进行量化建模。其次,以系统运行总费用最小为目标函数建立计及弹性备用的机组组合优化模型,在Lingo环境下编写优化算法程序,对多种备用资源进行优化。最后,在IEEE-39节点系统中进行算例分析。结果表明,所建模型在保证系统安全的情况下,对日前机组计划进行了重新分配,可以实现资源最优分配,有效缓解常规备用压力,保证系统稳定运行。 The increasing complexity of the grid structure makes the grid′s ability to respond to various accidents urgently need to be improved,and a reasonable increase in grid reserve capacity can help reduce grid operation risks.Under extreme conditions of the power grid,the risk of insufficient reserve is considered in this article.New energy frequency modulation,DC modulation and interruptible load are classified as grid flexible reserve,and the reserve potential is quantified.Secondly,a unit combination model considering the flexible reserve is established with the objective function of minimizing the total operating cost of the system,and the optimization algorithm program is written in the Lingo environment to optimize a variety of reserve resources.Finally,an example is analyzed in IEEE 39-bus system.The results show that the built model redistributes the day-ahead unit plan under the condition of ensuring the safety of the system,which can realize the optimal allocation of resources,effectively alleviate the pressure of conventional reserve,and ensure the stable operation of the system.
作者 周毅 李一晨 傅幼书 秦康平 朱文 范栋琦 ZHOU Yi;LI Yichen;FU Youshu;QIN Kangping;ZHU Wen;FAN Dongqi(East China Branch of State Grid Corporation of China,Shanghai 200120,China;College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;NARI Technology Development Co.,Ltd.,Nanjing 211106,China)
出处 《哈尔滨理工大学学报》 CAS 北大核心 2023年第3期56-66,共11页 Journal of Harbin University of Science and Technology
基金 国家自然科学基金(62073173) 江苏省自然科学基金(BK20191376)。
关键词 弹性备用 备用量化 优化决策 机组组合 flexible reserve standby quantitative optimization decision unit commitment
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