Optimal Energy Trading With Demand Responses in Cloud Computing Enabled Virtual Power Plant in Smart Grids

The increasing penetration of renewable energy sources and electric vehicles (EVs) poses a significant challenge for the power grid operator in terms of increasing peak load and power quality reduction. Moreover, there is a growing demand for fast charging services in smart grids. Addressing the growing demand from fast charging services is challenging. To overcome this challenge, in this article, we propose a new computational architecture combining energy trading and demand responses based on cloud computing for managing virtual power plants (VPPs) in smart grids. In the proposed system, EVs can be charged at high charging rates without affecting the operation of the power grid by purchasing energy through the energy trading platform in the cloud. In addition, users with storage devices can sell energy surplus to the market. On the one hand, the energy trading platform can be regarded as an internal market of the VPP that aims to maximize its revenue. The interest of the EV owners, on the other hand, is to minimize the cost for charging. Therefore, we model the interactions between the EV owners and the VPP as a non-cooperative game. To search for the Nash equilibrium (NE) of the game, we design an algorithm and then analyze its computational complexity and communication overhead. We utilize real data from the California Independent System Operator (CAISO) to evaluate the performance of the proposed algorithm. Our results illustrate that the users with only storage devices can obtain nearly
$200\%$
higher revenue on average by participating in the proposed internal market. Moreover, users with only EVs can reduce their charging costs by nearly
$50\%$
in average. Users with both EVs and storage devices can reduce the charging costs even further by approximately
$120\%$
where the users get profit by utilizing the internal market.