Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management

Vikas Ranveer Singh Mahala; Anshul Yadav; D. Saxena; Rajesh Kumar

doi:10.1109/RASSE60029.2023.10363518

Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management

Vikas Ranveer Singh Mahala
, Anshul Yadav
, D. Saxena
, Rajesh Kumar

Malaviya National Institute of Technology
Rajasthan Technical University
CSIR - Central Electronics Engineering Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

The global electricity landscape is undergoing a profound transformation, with an increasing demand for resilient and sustainable energy infrastructure. In this context, microgrids (MGs) have emerged as a promising solution, offering localized, decentralized energy generation and distribution. This research paper proposes a distributed energy management system for grid-connected hybrid AC-DC MGs, interconnected through a DC link. The work proposes a three-layer cloud fog-enabled energy management system of networked MGs which aims to minimize the energy cost by facilitating optimal energy utilization within each MG as well as among the connected MGs. The paper presents a fog-enabled comprehensive mathematical model of networked MGs to ensure fast data transmission and real-Time decision-making within the system. K-mean clustering is used to segregate the load into three categories residential, commercial, and industrial each of which is primarily supplied by an individual MG. Python 3.10.12 programming has been employed for simulating the model, ensuring a realistic and adaptable approach to assess the suggested energy management system's efficacy and performance within the context of networked MGs. Simulation results demonstrate that the proposed model of networked MGs integrating fog computing and MILP optimization, enhances optimal energy allocation and utilization within and among MGs along with minimizing the operating cost of networked MGs effectively.

Original language	English
Title of host publication	RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350341676
DOIs	https://doi.org/10.1109/RASSE60029.2023.10363518
Publication status	Published - 2023
Externally published	Yes
Event	3rd IEEE International Conference on Recent Advances in Systems Science and Engineering, RASSE 2023 - Kerala, India Duration: 8 Nov 2023 → 11 Nov 2023

Publication series

Name	RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings

Conference

Conference	3rd IEEE International Conference on Recent Advances in Systems Science and Engineering, RASSE 2023
Country/Territory	India
City	Kerala
Period	8/11/23 → 11/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 17 Partnerships for the Goals

Keywords

Energy Management
Fog Computing
MILP
Networked Hybrid Microgrid

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Energy Engineering and Power Technology
Control and Optimization
Modeling and Simulation

Access to Document

10.1109/RASSE60029.2023.10363518

Cite this

Singh Mahala, V. R., Yadav, A., Saxena, D., & Kumar, R. (2023). Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management. In RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings (RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RASSE60029.2023.10363518

Singh Mahala, Vikas Ranveer ; Yadav, Anshul ; Saxena, D. et al. / Networked Hybrid AC-DC Microgrids : Leveraging Fog Computing and Linear Solver for Efficient Energy Management. RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings).

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title = "Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management",

abstract = "The global electricity landscape is undergoing a profound transformation, with an increasing demand for resilient and sustainable energy infrastructure. In this context, microgrids (MGs) have emerged as a promising solution, offering localized, decentralized energy generation and distribution. This research paper proposes a distributed energy management system for grid-connected hybrid AC-DC MGs, interconnected through a DC link. The work proposes a three-layer cloud fog-enabled energy management system of networked MGs which aims to minimize the energy cost by facilitating optimal energy utilization within each MG as well as among the connected MGs. The paper presents a fog-enabled comprehensive mathematical model of networked MGs to ensure fast data transmission and real-Time decision-making within the system. K-mean clustering is used to segregate the load into three categories residential, commercial, and industrial each of which is primarily supplied by an individual MG. Python 3.10.12 programming has been employed for simulating the model, ensuring a realistic and adaptable approach to assess the suggested energy management system's efficacy and performance within the context of networked MGs. Simulation results demonstrate that the proposed model of networked MGs integrating fog computing and MILP optimization, enhances optimal energy allocation and utilization within and among MGs along with minimizing the operating cost of networked MGs effectively.",

keywords = "Energy Management, Fog Computing, MILP, Networked Hybrid Microgrid",

author = "\{Singh Mahala\}, \{Vikas Ranveer\} and Anshul Yadav and D. Saxena and Rajesh Kumar",

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year = "2023",

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language = "English",

series = "RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings",

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Singh Mahala, VR, Yadav, A, Saxena, D & Kumar, R 2023, Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management. in RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings. RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings, Institute of Electrical and Electronics Engineers Inc., 3rd IEEE International Conference on Recent Advances in Systems Science and Engineering, RASSE 2023, Kerala, India, 8/11/23. https://doi.org/10.1109/RASSE60029.2023.10363518

Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management. / Singh Mahala, Vikas Ranveer; Yadav, Anshul; Saxena, D. et al.
RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Networked Hybrid AC-DC Microgrids

T2 - 3rd IEEE International Conference on Recent Advances in Systems Science and Engineering, RASSE 2023

AU - Singh Mahala, Vikas Ranveer

AU - Yadav, Anshul

AU - Saxena, D.

AU - Kumar, Rajesh

PY - 2023

Y1 - 2023

N2 - The global electricity landscape is undergoing a profound transformation, with an increasing demand for resilient and sustainable energy infrastructure. In this context, microgrids (MGs) have emerged as a promising solution, offering localized, decentralized energy generation and distribution. This research paper proposes a distributed energy management system for grid-connected hybrid AC-DC MGs, interconnected through a DC link. The work proposes a three-layer cloud fog-enabled energy management system of networked MGs which aims to minimize the energy cost by facilitating optimal energy utilization within each MG as well as among the connected MGs. The paper presents a fog-enabled comprehensive mathematical model of networked MGs to ensure fast data transmission and real-Time decision-making within the system. K-mean clustering is used to segregate the load into three categories residential, commercial, and industrial each of which is primarily supplied by an individual MG. Python 3.10.12 programming has been employed for simulating the model, ensuring a realistic and adaptable approach to assess the suggested energy management system's efficacy and performance within the context of networked MGs. Simulation results demonstrate that the proposed model of networked MGs integrating fog computing and MILP optimization, enhances optimal energy allocation and utilization within and among MGs along with minimizing the operating cost of networked MGs effectively.

AB - The global electricity landscape is undergoing a profound transformation, with an increasing demand for resilient and sustainable energy infrastructure. In this context, microgrids (MGs) have emerged as a promising solution, offering localized, decentralized energy generation and distribution. This research paper proposes a distributed energy management system for grid-connected hybrid AC-DC MGs, interconnected through a DC link. The work proposes a three-layer cloud fog-enabled energy management system of networked MGs which aims to minimize the energy cost by facilitating optimal energy utilization within each MG as well as among the connected MGs. The paper presents a fog-enabled comprehensive mathematical model of networked MGs to ensure fast data transmission and real-Time decision-making within the system. K-mean clustering is used to segregate the load into three categories residential, commercial, and industrial each of which is primarily supplied by an individual MG. Python 3.10.12 programming has been employed for simulating the model, ensuring a realistic and adaptable approach to assess the suggested energy management system's efficacy and performance within the context of networked MGs. Simulation results demonstrate that the proposed model of networked MGs integrating fog computing and MILP optimization, enhances optimal energy allocation and utilization within and among MGs along with minimizing the operating cost of networked MGs effectively.

KW - Energy Management

KW - Fog Computing

KW - MILP

KW - Networked Hybrid Microgrid

UR - https://www.scopus.com/pages/publications/85182524196

U2 - 10.1109/RASSE60029.2023.10363518

DO - 10.1109/RASSE60029.2023.10363518

M3 - Conference contribution

AN - SCOPUS:85182524196

T3 - RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings

BT - RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 8 November 2023 through 11 November 2023

ER -

Singh Mahala VR, Yadav A, Saxena D, Kumar R. Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management. In RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (RASSE 2023 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Proceedings). doi: 10.1109/RASSE60029.2023.10363518

Networked Hybrid AC-DC Microgrids: Leveraging Fog Computing and Linear Solver for Efficient Energy Management

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this