个人信息Personal Information
副教授
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
办公地点:大连理工大学 综合实验3#楼 518室 (主楼后面)
联系方式:shengliliao@dlut.edu.cn
电子邮箱:shengliliao@dlut.edu.cn
A multi-objective short term hydropower scheduling model for peak shaving
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论文类型:期刊论文
发表时间:2015-06-01
发表刊物:INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
收录刊物:SCIE、EI
卷号:68
页面范围:278-293
ISSN号:0142-0615
关键字:Hydropower systems; Short term; Scheduling; Multi-objective; Power grid
摘要:The short-term scheduling problem of a hydropower system in China Southern Power Grid (CSG) is studied. As one of the largest in China, the system consists of 92 hydro plants with total installed capacity of 41GW occupying 14.7% of the national hydropower capacity at the end of 2013. Abundant hydroelectricity of the system is transmitted from the western provinces to the eastern load centers in CSG. Obvious difficulties of the hydropower scheduling of CSG are large-scale system, complex constraints and multiple power receiving grids of single plants and cascaded systems due to huge capacity. A short-term hydropower scheduling model for peak shaving of multiple power grids is developed for the operations of the hydropower system of CSG. The model is composed of multi-objective optimal peak shaving (MOPS) model, inter grid power distribution (IGPD) model and load fluctuation balance (LFB) model. The MOPS model minimizes the maximum residual loads of each power grid in which the IGPD model is embedded to distribute power of a plant among several power grids. To solve the model, an aggregate function and a multi-objective fuzzy optimization model are combined to establish an alternative objective function, and a proposed constraint successively satisfying (CSS) algorithm is used to address the period coupling constraints in local search. A case study shows that the proposed approach is practicable, adaptable and robust to obtain near optimal results efficiently, and is applicable for large-scale hydropower systems with both multiple and single power receiving grids. (C) 2014 Elsevier Ltd. All rights reserved.