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
联系方式:wuxinyu@dlut.edu.cn
电子邮箱:wuxinyu@dlut.edu.cn
Optimization of peak loads among multiple provincial power grids under a central dispatching authority
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论文类型:期刊论文
发表时间:2014-09-01
发表刊物:26th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS)
收录刊物:SCIE、EI、CPCI-S
卷号:74
期号:C
页面范围:494-505
ISSN号:0360-5442
关键字:Peak loads; Provincial power grids; Operation; Central dispatching authority; Quadratic programming
摘要:There is a lack of the capacity to respond to peak loads in most provincial power grids of eastern China and coastal regions. A CDA (central dispatching authority), which is usually a regional power grid, is responsible to dispatch its own plants and allocate power generation to multiple subordinate provincial power grids for responding to their peak loads simultaneously. Hence, this paper develops an optimization model to determine the quarter-hourly generation schedules allocated for provincial power grids under a CDA. To meet the need for peak shaving, the selected objective function in the model requires minimization of the variance of remaining load that is obtained by subtracting power generation from the original load of each provincial power grid. The objectives of multiple power grids are first reduced to an equivalent scalar objective through the weighted sum method. The general quadratic and linear formulations applying to the conventional plants and pumped-storage plants are developed to respectively deal with complex composite objective and spatial-temporal coupling constraints such as multilateral electricity contracts constraints and load balance constraints. The resulting problem is finally solved via the convex quadratic optimization technique. The proposed method is applied to scheduling plants owned by the East China Grid which covers five provinces. The simulations show that the method can rationally coordinate electric power among provinces to meet different peak demands. The comparison with the conventional method further demonstrates the advantages of our method. (C) 2014 Elsevier Ltd. All rights reserved.