Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm

作     者：Wang, Wenxiao Li, Chaoshun Liao, Xiang Qin, Hui 

作者机构：Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China 

出 版 物：《APPLIED ENERGY》 (实用能源)

年 卷 期：2017年第187卷

页      面：612-626页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：National Natural Science Foundation of China [51679095, 51479076] Fundamental Research Funds for the Central Universities, HUST [2016YXZD047] 

主　　题：Unit commitment Wind power Photovoltaic power Binary artificial sheep algorithm Scenario analysis Pumped hydro-energy system 

摘      要：Wind power and photovoltaic power, two types of renewable energy (RE), have made large inroads into the power system. In this paper, we study a unit commitment (UC) problem that considers the uncertainty in RE and pumped hydro-energy storage (PHES). To improve the optimisation performance for this problem, we propose a novel heuristic algorithm called the Binary Artificial Sheep Algorithm (BASA) that is based on the social behaviour of sheep flock. To evaluate the effect of the uncertainty of RE, a scenario evaluation method is defined to assess quantitatively the stability and economy of the UC results with respect to different levels of RE forecasting errors. In addition, we investigate and analyse the effect of PHES on the UC problem. Three UC test systems with different RE and PHES combinations are used to verify the feasibility and effectiveness of the proposed BASA as well as its performance. The proposed BASA performed better than traditional fundamental metaheuristics in solving UC problems. Our results also demonstrated that the equivalent load fluctuation and operating costs of the thermal units will increase significantly with an increase in RE power forecast error, but the PHES can effectively counterbalance this adverse effect. (C) 2016 Elsevier Ltd. All rights reserved.