智能电网中电动汽车自适应联盟的形成

G. Ramos, J. C. Burguillo, A. Bazzan
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引用次数: 22

摘要

在过去的几年里,多种能源的使用需求使得智能电网的概念出现。智能电网是一个完全自动化的电力网络,它监测和控制所有能够以有效和可靠的方式供电的元素。在此背景下,电动汽车(ev)和车辆到电网(V2G)技术的使用被提倡为减少与可再生能源相关的间歇性供应的有效方法。然而,对电动汽车来说,以一种经济有效的方式在V2G会话上运行并不是一项简单的任务。为了解决这个问题,有人提议在电动汽车之间形成联盟,作为提高V2G会话盈利能力的一种手段。针对这些情况,本文引入了自适应联盟形成(SACF)方法,这是一种基于局部和动态启发式的联盟结构生成机制。在我们的方法中,观察电网对电动汽车施加的约束,形成联盟,电动汽车在局部协商形成可行的联盟。通过实验,我们看到SACF是一种高效的方法,以简单和低成本的方式提供了良好的解决方案。SACF比集中式方法更快,并提供接近最佳质量的解决方案。在动态场景中,SACF也显示出非常好的结果,即使在快速变化的环境中,也能够保持代理增益相对稳定。它的主要优点是计算量非常低,而经典的集中式方法仅限于在合理的时间内管理不超过十二个代理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Adapting Coalition Formation Among Electric Vehicles in Smart Grids
In the last years, the need for using multiple energy sources made the concept of smart grids emerge. A smart grid is a fully automated electricity network, which monitors and controls all its elements being able to supply energy in an efficient and reliable way. Within this context, the use of electric vehicles (EVs) and Vehicle-To-Grid (V2G) technologies have been advocated as an efficient way to reduce the intermittent supply associated with renewable energy sources. However, operating on V2G sessions in a cost effective way is not a trivial task for EVs. To address this problem, the formation of coalitions among EVs has been proposed as a mean to improve profitability on V2G sessions. Addressing these scenarios, in this paper we introduce the Self-Adapting Coalition Formation (SACF) method, which is a local and dynamic heuristic-based mechanism for coalition structure generation. In our approach, coalitions are formed observing constraints imposed by the grid to the EVs, which negotiate locally the formation of feasible coalitions among themselves. Based on experiments, we see that SACF is an efficient method, providing good solutions in a simple and low-cost way. SACF is faster than centralized methods and provides solutions with near optimal quality. In dynamic scenarios, SACF also shows very good results, being able to keep the agents gain relatively stable along time, even in quickly changing environments. Its main advantage is that the computational effort is very low, while classical centralized methods are limited to manage no more than a dozen agents in a reasonable amount of time.
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