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...

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Vydáno v:	IEEE transactions on cloud computing Ročník 10; číslo 1; s. 17 - 30
Hlavní autoři:	Chung, Hwei-Ming, Maharjan, Sabita, Zhang, Yan, Eliassen, Frank, Strunz, Kai
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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 [Formula Omitted] higher revenue on average by participating in the proposed internal market. Moreover, users with only EVs can reduce their charging costs by nearly [Formula Omitted] in average. Users with both EVs and storage devices can reduce the charging costs even further by approximately [Formula Omitted] where the users get profit by utilizing the internal market. 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.
Author	Zhang, Yan Eliassen, Frank Maharjan, Sabita Chung, Hwei-Ming Strunz, Kai
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SubjectTerms	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
Title	Optimal Energy Trading With Demand Responses in Cloud Computing Enabled Virtual Power Plant in Smart Grids
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