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文章摘要
分布式电采暖负荷群建模及备用优化
Distributed electric heating load group modeling and standby optimization
Received:October 01, 2018  Revised:November 20, 2018
DOI:10.19753/j.issn1001-1390.2020.02.013
中文关键词: 分布式电采暖负荷  聚合模型  备用模型  风电功率预测误差区间  经济性分析。
英文关键词: Distributed electric heating load  polymerization model  standby model  Wind power prediction error interval  economic analysis.
基金项目:国家重点研发计划基金资助项目(2017YFB0902200),2017国家电网公司总部科技项目(2017GW-20)。
Author NameAffiliationE-mail
YANG Yulong* School of Electrical Engineering,Northeast Electric Power University yougyokuryuu@hotmail.com 
WANG Tong School of Electrical Engineering,Northeast Electric Power University 1226717516@qq.com 
ZHAO Leiyang School of Electrical Engineering,Northeast Electric Power University
Jilin Province,China 
2931803175@qq.com 
LIUJINSONG Electric Power Research Institute,State Grid Liaoning Electric Power Co., Ltd. 13555788910@163.com 
HANYUE .Electric Power Research Institute,State Grid Liaoning Electric Power Co., Ltd. hanyuehaha1@126.com 
LIURUITONG Electric Power Research Institute,State Grid Liaoning Electric Power Co., Ltd. 41300743@qq.com 
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中文摘要:
      能源低碳化革命推动能源结构转型,可再生能源占比不断提升,电力运行调控机制面临严峻挑战,而负荷侧有大量的电采暖负荷可加入调节资源。本文首先建立单元电采暖负荷并分析模型参数,利用聚类分组控制的方法实现异质电采暖负荷聚合,从而建立电采暖负荷群模型。其次分析电采暖负荷群提供备用的原理,建立基于风电功率预测误差区间的备用优化模型。最后基于实测数据仿真验证该方法的有效性与经济性。
英文摘要:
      The energy low-carbon revolution has promoted the transformation of energy structure, and the proportion of renewable energy has been continuously improved. The power operation regulation mechanism is facing severe challenges, and a large amount of electric heating load on the load side can be added to the regulation resources. In this paper, the unit electric heating load is firstly established and the model parameters are analyzed. The clustering group control method is used to realize the heterogeneous electric heating load aggregation, and the electric heating load group model is established. Secondly, it analyzes the principle that the electric heating load group provides standby, and establishes a standby optimization model based on the wind power prediction error interval. Finally, the effectiveness and economy of the method are simulated based on the measured data.
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