Hybrid renewable energy based smart grid system for reactive power management and voltage profile enhancement using artificial neural network

Chandrasekaran, Kumar; Selvaraj, Jaisiva; Amaladoss, Clement Raj; Veerapan, Logeshwari

doi:10.1080/15567036.2021.1902430

Cited by 41 publications

(19 citation statements)

References 34 publications

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“…A case study from Italy is provided as part of i-NEXT research project. In 2021, Chandrasekaran et al [102] have developed a "renewable energy-based smart grid system" that uses an ANN to establish a positive voltage profile and a regulated RP level across the grid network. The issue was addressed by the use of DSTATCOM to combine renewable energy sources with AI approaches for effective RP regulation.…”

Section: Hybrid (Wind and Solar) Systemmentioning

confidence: 99%

Reactive power control in renewable rich power grids: A literature review

Kumar¹,

Lakshmanan²,

Jaisiva³

et al. 2023

IET Renewable Power Gen

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Recently, there is vast progress in the integration of renewable energy generators (REGs) into the power grid. The reactive power, which is indeed a fundamental power system factor is being influenced highly by the renewable power penetration levels. As a consequence, this has paved the way toward the generation of steady-state voltage and dynamic/transient stability issues. Therefore, to provide a sustainable and dependable power grid, it is indeed important to maintain and control adequate reactive power reserves. This study introduces a comprehensive literature survey on "management of reactive power in renewable-rich power networks". A total of 75 research publications have been collected for this study, and a concise summary of each article is offered. The articles are categorized according to the types of REGs used, type of control, grid code consideration, controller device, implementation tools, grid type, load type as well as various reactive power support systems. In addition, this study examines the advantages and drawbacks of existing models. The performance recorded in each of the collected papers are discussed. Ultimately, the most critical challenges related to management of reactive power in renewable-rich power grids are identified, along with some essential methodological suggestions for the electricity sector, regulators, and research scientists.

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Section: Hybrid (Wind and Solar) Systemmentioning

confidence: 99%

Reactive power control in renewable rich power grids: A literature review

Kumar¹,

Lakshmanan²,

Jaisiva³

et al. 2023

IET Renewable Power Gen

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“…The paper [45] proposed a technique for regulating active and reactive power flow in a renewable generating system operating in the islanded mode for point of common connection (PCC). In [46], an artificial neural network based model is developed for maintaining a better voltage profile with balanced reactive power levels across the grid network. A two-stage random p-robust optimum energy mercantilism management model is developed in [47] for microgrids that include PVs, wind turbines, diesel engines, and microturbines.…”

Section: Energy Optimization Microgrid Centric Techniques For Islande...mentioning

confidence: 99%

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

et al. 2022

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Load scheduling, battery energy storage control, and improving user comfort are critical energy optimization problems in smart grid. However, system inputs like renewable energy generation process, conventional grid generation process, battery charging/discharging process, dynamic price signals, and load arrival process comprise controller performance to accurately optimize real-time battery energy storage scheduling, load scheduling, energy generation, and user comfort. Thus, in this work, the virtual queue stability based Lyapunov optimization technique (LOT) is adopted to investigate real-time energy optimization in a grid-connected sustainable smart home with a heating, ventilation, and air conditioning (HVAC) load considering unknown system inputs dynamics. The main goal is to minimize overall time average energy cost and thermal discomfort cost in a long time horizon for sustainable smart home accounting for changes in home occupancy state, the most comfortable temperature setting, electrical consumption, renewable generation output, outdoor temperature, and the electricity costs. The employed algorithm creates and regulates four queues for indoor temperature, electric vehicle (EV) charging, and energy storage system (ESS). Extensive simulations are conducted to validate the employed algorithm. Simulation results illustrate that the proposed algorithm performs real-time energy optimization and reduces the time average energy cost of 20.15% while meeting the user's energy and comfort requirement. INDEX TERMSReal-time, optimization, convex optimization, dynamic pricing, renewable energy generation, scheduling, energy storage, user comfort. NOMENCLATURE Abbreviations Explanation AI Artificial intelligence. ACO Ant colony optimization. ABC Artificial bee colony. AEP Annual energy production. BES Battery energy storage. DR Demand response.

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“…Based on it, ARMU determines and sets the operating points of active and reactive power sources connected in all the buses and all the controllable units contribute to online voltage and power control [11].Research work has also been carried out for reactive power sharing in an islanded microgrid using the concept of virtual impedance [12].An advanced renewable energy based smart grid model involving Artificial Neural Network (ANN) has been proposed for maintaining a balanced reactive power profile across the grid using DSTATCOM which injects/absorbs the reactive power on the grid network based on feedback from the ANN algorithm for maintaining the reactive power profile as per set operating points. This method utilizes all the controllable elements of the grid for attaining reactive power balance [13].…”

Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Centralized Reactive Power Controller for Grid Stability and Voltage Control

Baishya

Bhuyan

Das

2022

IJEER

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The integration of renewable energy sources like solar and wind energy into the transmission and distribution grid has increased gradually for quenching the increasing demand for alternative sources to fossil fuels. However, due to the intermittent nature of the renewable sources primarily solar and wind, the injection of the renewable power generation into the grid shall also be fluctuating which in turn will impact the voltage profile of the transmission and distribution grid. Also, in case of any major load disconnection or generator tripping in a weak grid, the voltage profile will be severely impacted in a weak grid. The aim is to control the sudden major voltage profile disturbance of a weak grid in case of variation of power injected into the weak grid from solar and wind energy and also due to sudden load tripping or generator tripping in the weak grid by controlling the reactive power in the weak grid. In this paper, a centralized reactive power controller has been proposed to control reactive power injection or absorption in the grid. By controlling the reactive power sources and sinks centrally via the centralized controller, any contingency can be met to prevent major disturbance of the voltage profile in the weak grid. This controller shall aim to control the connected reactive power sources & sinks based on the voltage profile of the transmission grid and it shall also engage the Line Commutated Converter (LCC) High Voltage Direct Current (HVDC) Reactive Power Controller. Various cases have been analyzed in this paper for implementation of the centralized reactive power controller for voltage control in the transmission grid.

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Hybrid renewable energy based smart grid system for reactive power management and voltage profile enhancement using artificial neural network

Cited by 41 publications

References 34 publications

Reactive power control in renewable rich power grids: A literature review

Reactive power control in renewable rich power grids: A literature review

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

Centralized Reactive Power Controller for Grid Stability and Voltage Control

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