Frequency Regulation in AC Microgrid with and without Electric Vehicle Using Multiverse-Optimized Fractional Order- PID controller

Singh, Amandeep; Suhag, Sathans

doi:10.12785/ijcds/080406

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…According to the results, the proposed controller has less frequency changes compared to other controllers presented in case studies. In reference 9, to design and implement a new control scheme for frequency regulation in an AC microgrid, under different operating conditions, using a fractional order integral derivative controller (FOPID) with its parameters optimized using a multi‐verse optimizer (MVO) is provided. Proportional integral derivative (PID) and proportional integral fraction (FOPI) controllers, both optimally tuned using MVO, have also been implemented for comparative analysis.…”

Section: Introductionmentioning

confidence: 99%

Compensation analysis of the active and reactive power of electric vehicles using optimized FOPID‐GA controller

Moghadam

Balochian

2023

Energy Storage

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Recently, the interest in using electric and environmentally friendly vehicles has risen increasingly. The most important challenge is charging these electric vehicles (EVs) at the right time based on the amount of charging demand, which if not paid attention to, can affect the network power profile. In this article, the effect of charging EVs in fast charging stations on the voltage profile and power factor in the IEEE Bus‐33 standard network is investigated. Therefore, a mathematical model is used to model the problem that the parameters of EV arrival time to charging stations, EV charging time, number of charging times, EV status of charging (SOC), distance traveled by the EV are considered as model variables. In this article, four charging stations are considered for each fast‐charging station. The power rate that each station in the fast‐charging station receives from the network is considered 200 kW. So, if all stations are charged simultaneously, the charging station will receive 800 kW of electricity from the grid. There are also 300 EVs in the network, each charging time in fast charging stations is less than 10 minutes. To control the charge and power of cars, the FOPID controller has been used to determine the optimal coefficients through the application of a genetic algorithm (GA). The simulation results showed that in the uncontrolled reactive power mode, the voltage range in the busbars to which the charging stations are connected is less than the standard value (0.95 p.u) in the interval, but using FOPID‐GA controller, we will be able to increase the voltage range and improve the power factor of the distribution system about 10.26% by controlling the reactive power of the vehicles.

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Section: Introductionmentioning

confidence: 99%

Compensation analysis of the active and reactive power of electric vehicles using optimized FOPID‐GA controller

Moghadam

Balochian

2023

Energy Storage

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A State-of-the-Art Review on Recent Load Frequency Control Architectures of Various Power System Configurations

Kumari

Pathak

2023

Electric Power Components and Systems

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Leveraging Big Data Analytics for Intelligent Transportation Systems: Optimize the Internet of Vehicles Data Structure and Modeling

Gebremeskel

2022

Preprint

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An intelligent transportation system is an efficient and modern system to deal with big data, which is pertinent for a future smart city. It is the inherent challenge of data processing in classic vehicular systems. Therefore, we proposed Big data to optimize location-based operability and safety performances using federated sensor data. The data preprocessing and feature extraction process includes vehicle mobility, multi-source data acquisition, distributed computation, and multi-path data transmission in an analytical model to enhance performance and safety. The proposed approach is capable and scalable to manage large-scale sensor data from its source and line of information flow. The findings revealed the imperfect, complex, and challenging data transformation into actionable, safe, and usable information for the intelligent transformation system modernization and healthy digital ecosystems. Big data analytics for sensor data is complete and informative for the learning process of the physical systems in various situations and localizations. The experiment showed that leveraging big data technology is a systematic approach to enhancing safe and optimal vehicular system applications. It provides access to shared resources through connected devices and reduces costs using the response time and data structuring to the system stability and to accommodate future modifications in sensor technology.

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Frequency Regulation in AC Microgrid with and without Electric Vehicle Using Multiverse-Optimized Fractional Order- PID controller

Cited by 7 publications

References 43 publications

Compensation analysis of the active and reactive power of electric vehicles using optimized FOPID‐GA controller

Compensation analysis of the active and reactive power of electric vehicles using optimized FOPID‐GA controller

A State-of-the-Art Review on Recent Load Frequency Control Architectures of Various Power System Configurations

Leveraging Big Data Analytics for Intelligent Transportation Systems: Optimize the Internet of Vehicles Data Structure and Modeling

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