考虑纯电动汽车不确定性的居民电力市场博弈建模
Game Theory Approach on Modeling of Residential Electricity Market by Considering the Uncertainty due to the Battery Electric Vehicles (BEVs)
摘要：
先进的高级计量基础设施（AMI）的快速发展使我们能够更好地了解需求响应（DR）解决方案的数据。物联网及其在智能电网上的应用已经过大量的研究，以找到用户最优化的账单;然而，我们提出了一种新的家庭负载方法，结合拥有配备有BEV通信控制器和车辆到电网（V2 G）技术的电池电动汽车（BEV）。在本文中，我们使用的Stackelberg游戏的方法来实现一个高效和有效的优化算法的用户（追随者）的时间相关定价的基础上。我们还假设了一个电力零售商公司（领导者）和一个双向的双边通信程序。基于使用的一方的游戏已经研究了需求侧管理（DSM）。使用时间（TOU）公司的实时定价（RTP）已用于获得更好的结果，蒙特卡洛模拟（MCS）处理BEV驾驶员的不确定行为。数值结果与仿真结果的比较表明，该方法可以有效地重塑客户的需求，以获得最佳的效率。
英文摘要：
The rapid progression of sophisticated advance metering infrastructure (AMI), allows us to have a better understanding and data from demand-response (DR) solutions. There are vast amounts of research on the internet of things and its application on the smart grids has been examined to find the most optimized bill for the user; however, we propose a novel approach of house loads, combined with owning a battery electric vehicle (BEV) equipped with the BEV communication controllers and vehicle-to-grid (V2G) technology. In this paper we use the Stackelberg game approach to achieve an efficient and effective optimized algorithm for the users (followers) based on time dependent pricing. We also assumed an electricity retailer company (leader) and a two-way bilateral communication procedure. The usage-based side of the game has been studied together with demand side management (DSM). Real-time pricing (RTP) from time-of-use (TOU) companies has been used for better results, and Monte Carlo simulation (MCS) handles the uncertain behavior of BEV drivers. Numerical results compared to those from the simulation show that with this method we can reshape the customer's demand for the best efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
