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 gro...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cloud computing Vol. 10; no. 1; pp. 17 - 30
Main Authors: Chung, Hwei-Ming, Maharjan, Sabita, Zhang, Yan, Eliassen, Frank, Strunz, Kai
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Charging
Cloud computing
cloud energy trading
Demand
Demand response
Electric power grids
Electric vehicles
Electrical loads
Energy
Energy storage
Game theory
Games
Peak load
renewable energy
Renewable energy sources
Revenue
Smart grid
Smart grids
Uncertainty
Virtual power plant
Virtual power plants
ISSN:2168-7161, 2372-0018
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 <inline-formula><tex-math notation="LaTeX">200\%</tex-math> <mml:math><mml:mrow><mml:mn>200</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chung-ieq1-3118563.gif"/> </inline-formula> higher revenue on average by participating in the proposed internal market. Moreover, users with only EVs can reduce their charging costs by nearly <inline-formula><tex-math notation="LaTeX">50\%</tex-math> <mml:math><mml:mrow><mml:mn>50</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chung-ieq2-3118563.gif"/> </inline-formula> in average. Users with both EVs and storage devices can reduce the charging costs even further by approximately <inline-formula><tex-math notation="LaTeX">120\%</tex-math> <mml:math><mml:mrow><mml:mn>120</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chung-ieq3-3118563.gif"/> </inline-formula> where the users get profit by utilizing the internal market.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2021.3118563