Energy Trading and Optimum Scheduling for Microgrids Using Multiple Agents Based DL Approach

Anita, S.; Rodrigues, Paul; Nagabhooshanam, N.; Londhe, Gaurav Vishnu; Salunkhe, Satish S.; Kumar, P. Dharmendra; L, Natrayan; Bhima Raju, P. S. D.

doi:10.1080/15325008.2023.2300329

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Cited by 3 publications

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Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Liu,

Mao

2025

Electric Power Systems Research

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Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Liu,

Mao

2025

Electric Power Systems Research

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Enhancing Wearable Device Transmission on Reactive Power Management Using Linearized AC Models

Krishnakumari,

Saravanan,

Socrates

et al. 2024

Advances in Chemical and Materials Engineering

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This study introduces an innovative approach to enhance the transmission capabilities of wearable devices through reactive power management using linearized AC models. The research presents an estimated AC framework tailored for optimizing power scheduling in wearable devices, with a particular emphasis on integrating renewable energy sources such as solar and wind power. The complexity of the AC Transmission Expansion Planning (AC-TEP) issue is addressed by simplifying the simulation model, focusing on the interplay between circuit layout, bus voltage capabilities, and the incorporation of renewable energy sources. The operational costs, including primary energy expenditure and the management of voltage and frequency stability, are key considerations in the model. The research employs scenario analysis to address uncertainties in wind availability and power demand, utilizing thresholding techniques to minimize discrepancies.

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Enhancing Operational Cost Savings in Electric Utilities on Global Optimization in Power System Planning and Operation

Rajkamal,

Mohammed Ali,

Krishnakumari

et al. 2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Operational cost savings in electric utilities using the application of genetic algorithms in power system planning and operation characterize an innovative approach that involves computational intelligence to optimize complex decision-making processes in power grid functioning. Electric utilities involve various challenges which involve managing power generation, transmission and distribution that are necessary to meet the ever-growing demand for electricity with the reduction in operational costs. These challenges are overcome using the aid of a genetic algorithm. In the field of power system planning, the genetic algorithms are engaged to optimize the configuration and expansion of generation, transmission and distribution.

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Energy Trading and Optimum Scheduling for Microgrids Using Multiple Agents Based DL Approach

Cited by 3 publications

References 35 publications

Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods

Enhancing Wearable Device Transmission on Reactive Power Management Using Linearized AC Models

Enhancing Operational Cost Savings in Electric Utilities on Global Optimization in Power System Planning and Operation

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