Design of Green Power Clouds for Intelligent Virtual Power Plants

Ting-Chia Ou; Hao Tieng; Tsung Han Tsai; Yu Yong Li; Min Hsiung Hung; Fan Tien Cheng

doi:10.1109/TASE.2024.3406412

Design of Green Power Clouds for Intelligent Virtual Power Plants

Ting-Chia Ou, Hao Tieng, Tsung Han Tsai, Yu Yong Li, Min Hsiung Hung, Fan Tien Cheng

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Traditional virtual power plants (VPPs) combine power from distributed energy resources (DER) to supply energy to users. However, they fall short of net-zero goals because of neglecting carbon footprints during power aggregation. This paper proposes a novel intelligent virtual power plant framework (<inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPF) to address this gap. <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPF comprises a central <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP (<inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{C}})$</tex-math> </inline-formula> and several regional <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPs (<inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{n}})$</tex-math> </inline-formula>, n <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> E, S, M, and N. These <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{n}}$</tex-math> </inline-formula> are geographically distributed systems for intelligently managing <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPs in four regions: east, south, middle, and north, respectively, while the <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{C}}$</tex-math> </inline-formula> is responsible for dispatching power across <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{n}}$</tex-math> </inline-formula>. We built <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{C}}$</tex-math> </inline-formula> and <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{n}}$</tex-math> </inline-formula> on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{n}}$</tex-math> </inline-formula>. <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPP<inline-formula> <tex-math notation="LaTeX">$_{\mathrm{n}}$</tex-math> </inline-formula> can intelligently control DERs based on the collected data. Also, <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed <inline-formula> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula>VPPF promises to contribute economic benefits for businesses and the pursuit of net-zero emissions. <italic>Note to Practitioners</italic>—This paper proposes an intelligent virtual power plant framework <inline-formula> <tex-math notation="LaTeX">$({\it i} \text{VPPF})$</tex-math> </inline-formula> consisting of a central coordinator <inline-formula> <tex-math notation="LaTeX">$({\it i}\text{VPP}_{\text{C}})$</tex-math> </inline-formula> and distributed regional managers <inline-formula> <tex-math notation="LaTeX">$({\it i}\text{VPP}_{\text{n}}$</tex-math> </inline-formula> for East, South, Middle, and North). Leveraging green power clouds, both <inline-formula> <tex-math notation="LaTeX">${\it i} \text{VPP}_{\text{C}}$</tex-math> </inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\it i}\text{VPP}_{\text{n}}$</tex-math> </inline-formula> harness AI for intelligent management and power dispatch across regions. We detail the system architecture and showcase practical applications, including scenarios like dispatching and aggregating for demand response, using the IEEE 13-node test feeder. Additionally, we explore the design of green power clouds and cyber-physical agents (CPAs).

Original language	English
Pages (from-to)	1-12
Number of pages	12
Journal	IEEE Transactions on Automation Science and Engineering
DOIs	https://doi.org/10.1109/TASE.2024.3406412
Publication status	Accepted/In press - 2024

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/TASE.2024.3406412

Cite this

@article{d5f93447880d4f639fd666526f5aced2,

title = "Design of Green Power Clouds for Intelligent Virtual Power Plants",

abstract = "Traditional virtual power plants (VPPs) combine power from distributed energy resources (DER) to supply energy to users. However, they fall short of net-zero goals because of neglecting carbon footprints during power aggregation. This paper proposes a novel intelligent virtual power plant framework ( $i$ VPPF) to address this gap. $i$ VPPF comprises a central $i$ VPP ( $i$ VPP $_{\mathrm{C}})$ and several regional $i$ VPPs ( $i$ VPP $_{\mathrm{n}})$ , n $=$ E, S, M, and N. These $i$ VPP $_{\mathrm{n}}$ are geographically distributed systems for intelligently managing $i$ VPPs in four regions: east, south, middle, and north, respectively, while the $i$ VPP $_{\mathrm{C}}$ is responsible for dispatching power across $i$ VPP $_{\mathrm{n}}$ . We built $i$ VPP $_{\mathrm{C}}$ and $i$ VPP $_{\mathrm{n}}$ on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for $i$ VPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for $i$ VPP $_{\mathrm{n}}$ . $i$ VPP $_{\mathrm{n}}$ can intelligently control DERs based on the collected data. Also, $i$ VPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate $i$ VPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that $i$ VPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed $i$ VPPF promises to contribute economic benefits for businesses and the pursuit of net-zero emissions. Note to Practitioners—This paper proposes an intelligent virtual power plant framework $({\it i} \text{VPPF})$ consisting of a central coordinator $({\it i}\text{VPP}_{\text{C}})$ and distributed regional managers $({\it i}\text{VPP}_{\text{n}}$ for East, South, Middle, and North). Leveraging green power clouds, both ${\it i} \text{VPP}_{\text{C}}$ and ${\it i}\text{VPP}_{\text{n}}$ harness AI for intelligent management and power dispatch across regions. We detail the system architecture and showcase practical applications, including scenarios like dispatching and aggregating for demand response, using the IEEE 13-node test feeder. Additionally, we explore the design of green power clouds and cyber-physical agents (CPAs).",

author = "Ting-Chia Ou and Hao Tieng and Tsai, {Tsung Han} and Li, {Yu Yong} and Hung, {Min Hsiung} and Cheng, {Fan Tien}",

note = "Publisher Copyright: IEEE",

year = "2024",

doi = "10.1109/TASE.2024.3406412",

language = "English",

pages = "1--12",

journal = "IEEE Transactions on Automation Science and Engineering",

issn = "1545-5955",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Design of Green Power Clouds for Intelligent Virtual Power Plants

AU - Ou, Ting-Chia

AU - Tieng, Hao

AU - Tsai, Tsung Han

AU - Li, Yu Yong

AU - Hung, Min Hsiung

AU - Cheng, Fan Tien

N1 - Publisher Copyright: IEEE

PY - 2024

Y1 - 2024

N2 - Traditional virtual power plants (VPPs) combine power from distributed energy resources (DER) to supply energy to users. However, they fall short of net-zero goals because of neglecting carbon footprints during power aggregation. This paper proposes a novel intelligent virtual power plant framework ( $i$ VPPF) to address this gap. $i$ VPPF comprises a central $i$ VPP ( $i$ VPP $_{\mathrm{C}})$ and several regional $i$ VPPs ( $i$ VPP $_{\mathrm{n}})$ , n $=$ E, S, M, and N. These $i$ VPP $_{\mathrm{n}}$ are geographically distributed systems for intelligently managing $i$ VPPs in four regions: east, south, middle, and north, respectively, while the $i$ VPP $_{\mathrm{C}}$ is responsible for dispatching power across $i$ VPP $_{\mathrm{n}}$ . We built $i$ VPP $_{\mathrm{C}}$ and $i$ VPP $_{\mathrm{n}}$ on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for $i$ VPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for $i$ VPP $_{\mathrm{n}}$ . $i$ VPP $_{\mathrm{n}}$ can intelligently control DERs based on the collected data. Also, $i$ VPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate $i$ VPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that $i$ VPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed $i$ VPPF promises to contribute economic benefits for businesses and the pursuit of net-zero emissions. Note to Practitioners—This paper proposes an intelligent virtual power plant framework $({\it i} \text{VPPF})$ consisting of a central coordinator $({\it i}\text{VPP}_{\text{C}})$ and distributed regional managers $({\it i}\text{VPP}_{\text{n}}$ for East, South, Middle, and North). Leveraging green power clouds, both ${\it i} \text{VPP}_{\text{C}}$ and ${\it i}\text{VPP}_{\text{n}}$ harness AI for intelligent management and power dispatch across regions. We detail the system architecture and showcase practical applications, including scenarios like dispatching and aggregating for demand response, using the IEEE 13-node test feeder. Additionally, we explore the design of green power clouds and cyber-physical agents (CPAs).

AB - Traditional virtual power plants (VPPs) combine power from distributed energy resources (DER) to supply energy to users. However, they fall short of net-zero goals because of neglecting carbon footprints during power aggregation. This paper proposes a novel intelligent virtual power plant framework ( $i$ VPPF) to address this gap. $i$ VPPF comprises a central $i$ VPP ( $i$ VPP $_{\mathrm{C}})$ and several regional $i$ VPPs ( $i$ VPP $_{\mathrm{n}})$ , n $=$ E, S, M, and N. These $i$ VPP $_{\mathrm{n}}$ are geographically distributed systems for intelligently managing $i$ VPPs in four regions: east, south, middle, and north, respectively, while the $i$ VPP $_{\mathrm{C}}$ is responsible for dispatching power across $i$ VPP $_{\mathrm{n}}$ . We built $i$ VPP $_{\mathrm{C}}$ and $i$ VPP $_{\mathrm{n}}$ on individual green power clouds, which can provide abundant computing resources and realize intelligence through AI technologies for $i$ VPPF. We also design universal computing devices called cyber-physical agents (CPAs) to collect essential data on manufacturing, carbon footprint, and energy usage for $i$ VPP $_{\mathrm{n}}$ . $i$ VPP $_{\mathrm{n}}$ can intelligently control DERs based on the collected data. Also, $i$ VPPF can empower enterprises to participate in power balancing services offered by Taipower, thereby enhancing the flexibility of the overall power grid. Furthermore, we integrate $i$ VPPF with the I4.2-GiM framework, offering intelligent carbon and energy management capabilities to achieve the net-zero goal. The testing results show that $i$ VPPF can significantly reduce energy usage (up to 25.6%) and carbon emissions (up to 509 kg) through power dispatch. Thus, the proposed $i$ VPPF promises to contribute economic benefits for businesses and the pursuit of net-zero emissions. Note to Practitioners—This paper proposes an intelligent virtual power plant framework $({\it i} \text{VPPF})$ consisting of a central coordinator $({\it i}\text{VPP}_{\text{C}})$ and distributed regional managers $({\it i}\text{VPP}_{\text{n}}$ for East, South, Middle, and North). Leveraging green power clouds, both ${\it i} \text{VPP}_{\text{C}}$ and ${\it i}\text{VPP}_{\text{n}}$ harness AI for intelligent management and power dispatch across regions. We detail the system architecture and showcase practical applications, including scenarios like dispatching and aggregating for demand response, using the IEEE 13-node test feeder. Additionally, we explore the design of green power clouds and cyber-physical agents (CPAs).

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JO - IEEE Transactions on Automation Science and Engineering

JF - IEEE Transactions on Automation Science and Engineering

ER -

Design of Green Power Clouds for Intelligent Virtual Power Plants

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