期刊
APPLIED ENERGY
卷 99, 期 -, 页码 13-22出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2012.04.022
关键词
Agent-based modeling; Wind power; Forecasting; Bidding strategy; Day-ahead electricity markets; Electricity markets
This paper, for the first time, adopts agent-based simulation approach to investigate the bidding optimization of a wind generation company in the deregulated day-ahead electricity wholesale markets, by considering the effect of short-term forecasting accuracy of wind power generation. Two different wind penetration levels (12% and 24%) are investigated and compared. Based on MATPOWER 4.0 software package and the 9-bus 3-generator power system defined by Western System Coordinating Council, the agent-based models are built and run under the uniform price auction rule and locational marginal pricing mechanism. Each generation company could learn from its past experience and improves its day-ahead strategic offers by using Variant Roth-Erev reinforcement learning algorithm. The results clearly demonstrate that improving wind forecasting accuracy helps increase the net earnings of the wind generation company. Also, the wind generation company can further increase its net earnings with the adoption of learning algorithm. Besides, it is verified that increasing wind penetration level within the investigation range can help reduce the market clearing price. Furthermore, it is also demonstrated that agent-based simulation is a viable modeling tool which can provide realistic insights for the complex interactions among different market participants and various market factors. (C) 2012 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据