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
N1 - Publisher Copyright:
© 2023 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85182524196&partnerID=8YFLogxK
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 -
