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论文类型：期刊论文

发表时间：2018-01-01

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

收录刊物：ESI高被引论文、SCIE、EI

卷号：94

页面范围：267-275

ISSN号：0142-0615

关键字：Hydropower reservoirs; Peak operation; Progressive optimality algorithm; Fork/Join framework; Parallel computing; Curse of dimensionality

摘要：With the rapid economic growth in recent years, the peak operation of hydropower system (POHS) is becoming one of the most important optimization problems in power system. However, the rapid expansion of system scale, refined management and operational constraints has greatly increased the optimization difficult of POHS. As a result, it is of great importance to develop effective methods that can ensure the computational efficiency of POHS. The progressive optimality algorithm (POA) is a commonly used technique for solving hydropower operation problem, but its execution time still grows sharply with the increasing number of hydropower plants, making it difficult to satisfy the efficiency requirement of POHS. To address this problem, a novel efficient method called parallel progressive optimality algorithm (PPOA) is presented in this paper. In PPOA, the complex problem is firstly divided into several two-stage optimization subproblems, and then the classical Fork/Join framework is used to realize parallel computation of subproblems, making a significant improvement on execution efficiency. The simulations in a real-world hydropower system demonstrate that as compared with the standard POA, PPOA can use abundant multi-core resources to reduce execution time while keeping the quality of solution, providing a new alternative to solve the complex hydropower peak operation problem. (C) 2017 Elsevier Ltd. All rights reserved.