程春田

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:水利工程系

学科:水文学及水资源. 水利水电工程. 电力系统及其自动化. 计算机应用技术

联系方式:ctcheng@dlut.edu.cn

电子邮箱:ctcheng@dlut.edu.cn

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Short-term peak shaving operation for multiple power grids with pumped storage power plants

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论文类型:期刊论文

发表时间:2015-05-01

发表刊物:INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

收录刊物:SCIE、EI

卷号:67

页面范围:570-581

ISSN号:0142-0615

关键字:Pumped storage; Peak shaving; Multiple power grids; Fuzzy sets

摘要:The East China Power Grid (ECPG) is the biggest regional power grid in China. It has the biggest installed capacity of pumped storage power plants (PSPPs) and is responsible to coordinate the operation of its five provincial power grids. A recent challenge of coordinating operations is using PSPPs to absorb surplus energy during off-peak periods and generate power during peak periods. Differing from the traditional operations of single power grids, however, the PSPPs are required to respond to load demands from multiple provincial power grids simultaneously. This paper develops a three-step hybrid algorithm for the day-ahead quarter-hourly schedules of PSPPs to meet load demands of multiple provincial power grids. A normalization method is first proposed to reconstruct a total load curve to deal with the load differences of multiple provincial power grids, and to reflect the effect of specified electricity contract ratio on multiple provincial power grids. Secondly, a heuristic search method is presented to determine the generating and pumping powers of PSPP. Thirdly, a combination optimization method is used to allocate the determined generating and pumping powers among multiple provincial power grids to smooth the individual remaining load curve for their thermal systems. Two case studies with greatly different load demands are used to test the proposed algorithm. The simulation results show that the presented method can effectively achieve the goal of shaving the peak load and filling the off-peak load for multiple provincial power grids. (C) 2014 Elsevier Ltd. All rights reserved.