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Optimal allocation of hydropower and hybrid electricity injected from inter-regional transmission lines among multiple receiving-end power grids in China

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Indexed by:期刊论文

Date of Publication:2018-11-01

Journal:ENERGY

Included Journals:SCIE

Volume:162

Page Number:444-452

ISSN No.:0360-5442

Key Words:Peak shaving operation; Inter-regional transmission electricity; Mixed integer linear programming; Multiple receiving-end power grids; Optimal generation allocation; Hydropower and hybrid electricity

Abstract:In China, due to the unbalanced distribution of energy center and load center, numerous transmission projects are built to improve the national energy allocation efficiency and the external electricity is playing an increasing important role in the daily operation of receiving-end grids. The current fixed proportion method for energy allocation is relatively simple and easy to implement, but may produce unreasonable energy injection with the features of a straight line or "anti-peak regulation" in some cases, increasing the peak operation pressure of power systems. Thus, it is of great necessity to further improve the allocation scheme of inter-regional transmitted electricity among receiving-end power grids. As a new contribution to the research field, this paper develops a mixed integer linear programming model with the goal of minimizing the weighted peak-valley difference of multiple remaining load curves to address this problem. The presented model is applied to the East China Power Grid, and the results show that the presented model can obtain satisfying results in reducing the peak loads of multiple power grids. For instance, in the long-term simulations, the presented model can make about 23.7%, 5.9%, 5.7% and 8.3% reductions in the maximum loads of Shanghai, Jiangsu, Zhejiang and Anhui, respectively. Then, it can be concluded that optimizing the allocation of inter-regional transmitted electricity will be a viable way to reduce the peak operation pressure of multiple receiving-end power grids in China. (C) 2018 Elsevier Ltd. All rights reserved.

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