Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

Rehman, Anis Ur; Ullah, Zia; Qazi, Hasan Saeed; Hasanien, Hany M.; Khalid, Haris M.

doi:10.1016/j.renene.2024.120590

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…All the prototypes collectively enhance reliability, prevent overloads, and promote cost savings. Real-time monitoring feature in smart grids provides information about energy consumption and demand analysis (Rehman et al, 2024;Hakimi and Hasankhani, 2020). This leads to efficient load management and aligns energy consumption with renewables.…”

Section: Integration Of Wireless Sensor Network (Wsns)-focus Of the R...mentioning

confidence: 99%

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

Alharbey,

Shafiq,

Daud

et al. 2024

Front. Energy Res.

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This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.

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Section: Integration Of Wireless Sensor Network (Wsns)-focus Of the R...mentioning

confidence: 99%

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

Alharbey,

Shafiq,

Daud

et al. 2024

Front. Energy Res.

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Economic and strategic challenges in microgrid integration: Insights from operational dynamics and renewable energy potential

Rehman

2025

Green Technologies and Sustainability

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Load frequency optimal control of the hydropower-photovoltaic hybrid microgrid system based on the off-policy integral reinforcement learning algorithm

Wang,

Yuan,

Zeng

et al. 2024

Front. Energy Res.

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With the promotion and development of clean energy, it is challenging to ensure the optimization of control performance in frequency control of the hydropower-photovoltaic hybrid microgrid system caused by the output power fluctuation of photovoltaic power generation. In this study, an optimal load frequency controller (LFC) for a hydropower-photovoltaic hybrid microgrid system was designed to improve the dynamic response when the load and photovoltaic output power are perturbed based on the off-policy integral reinforcement learning algorithm. First, a mechanism model of the hydropower-photovoltaic hybrid microgrid system was established. Next, the LFC problem was transformed into a zero-sum game control problem based on the characteristics of the power system. Subsequently, three neural networks were employed to approximate the Nash equilibrium solution of the zero-sum game with historical input and output data when the system dynamics are completely unknown. Finally, simulation experiments were conducted to verify the effectiveness and optimality of the proposed method. The introduction of this method provides a new perspective for frequency control for the hydropower-photovoltaic hybrid microgrid system.

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Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

Cited by 4 publications

References 51 publications

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

Economic and strategic challenges in microgrid integration: Insights from operational dynamics and renewable energy potential

Load frequency optimal control of the hydropower-photovoltaic hybrid microgrid system based on the off-policy integral reinforcement learning algorithm

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