Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station

Abraham, Dominic Savio; Chandrasekar, Balaji; Narayanamoorthi, R.; Vishnuram, Pradeep; Ramakrishnan, V.; Bajaj, Mohit; Piecha, Marian; Blažek, Vojtěch; Prokop, Lukáš

doi:10.3390/en16062753

Cited by 37 publications

(13 citation statements)

References 41 publications

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“…A multi-objective optimization issue, in comparison, adds a layer of complexity because there are numerous interconnected objectives 57 , 58 . Finding a single "best" option becomes challenging because these objectives frequently have varying degrees of importance or priority.…”

Section: Optimization Methodologiesmentioning

confidence: 99%

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Kumar,

Jyothi,

Singh

et al. 2024

Sci Rep

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Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

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Section: Optimization Methodologiesmentioning

confidence: 99%

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Kumar,

Jyothi,

Singh

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…Among these, the fuzzy logic controller (FLC) emerges as a promising choice, particularly for handling nonlinear mappings between input and output data, ultimately improving system efficiency. K-additive FLC consists of three main components: fuzzification, fuzzy inference engine, and defuzzification [3][4][5][6]. Fuzzification involves converting input and output variables into fuzzy linguistic sets using membership functions.…”

Section: Delay Time At the Charging Stationmentioning

confidence: 99%

“…To counteract the surge in peak load resulting from uncontrolled charging, it is crucial to avoid the simultaneous charging of electric vehicles [4]. Several research papers have explored the application of fuzzy logic-based systems [5][6][7] for managing electric vehicle (EV) charging. Faddel et al [8] presented a fuzzy algorithm to operate in conjunction with a virtual synchronous generator.…”

Section: Introductionmentioning

confidence: 99%

A K-Additive Fuzzy Logic Approach for Optimizing FCS Sizing and Enhanced User Satisfaction

Guler,

Ben Hazem

2024

WEVJ

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Traditional electric vehicle (EV) charging methods can lead to extended waiting times for users, resulting in decreased travel efficiency and user satisfaction, therefore impacting overall convenience. Moreover, a limited number of charging stations can lead to congestion, exacerbating waiting times, while an excessive number of stations incurs inefficient costs and reduces utilization. While prior research has primarily focused on sizing and allocating charging stations to enhance user performance, there has been comparatively less emphasis on optimizing waiting times and determining the optimal number of charging stations, which is crucial from the EV user’s perspective. This study introduces a K-additive fuzzy logic algorithm to predict the average waiting time and the optimal number of charging stations. The K-additive fuzzy inference system (K-FIS) defines membership functions, expert rules, and a formulation for achieving the optimal solution. The proposed approach integrates uncertain and independent input parameters into weighted control variables, addressing the objective function to optimize EV waiting times and costs represented by the number of charging stations. The scheme utilizes both Type 1 and Type 2 membership functions, offering a detailed comparison. To validate its efficiency, the proposed scheme undergoes a comparative study against related state-of-the-art approaches.

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“…The controller takes input from sensors measuring the temperature of the heated fluid, or the temperature of the room, and possibly the outdoor temperature, as well as the desired room temperature set by the user. The controller then uses fuzzy logic algorithms to determine the appropriate valve positions to maintain the desired room temperature while minimizing energy consumption (Abraham et al, 2023). The controller also has a user interface implemented in LabVIEW that allows the user to adjust the controller settings and view the status of the system.…”

Section: Figure 9 Fuzzy Logic Smart Energy Control -Scadamentioning

confidence: 99%

Energy efficient smart home heating system using renewable energy source with fuzzy control design

Simon,

Róbert Sánta

2023

Decis. Mak. Appl. Manag. Eng.

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This research article presents an energy- efficient smart home heating system that uses a renew-able energy source and incorporates fuzzy control design. The objective of the study is to design a heating system that optimizes energy consumption while providing a comfortable indoor temperature. The main methods used in the study include the integration of a renewable energy source, such as solar energy or geothermal energy, with a fuzzy control system that adjusts the heating power based on indoor and outdoor temperature, humidity, and occupancy. The main results of the study show that the proposed heating system can reduce energy consumption by up to 40% compared to conventional heating systems, while maintaining a comfortable indoor temperature. The fuzzy control system provides precise control of the heating power and ensures efficient use of the renewable energy source. The main conclusion of the article is that the proposed smart home heating system is a viable solution for reducing energy consumption and promoting a sustainable lifestyle, especially in areas with abundant renewable energy sources.

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Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station

Cited by 37 publications

References 41 publications

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

A K-Additive Fuzzy Logic Approach for Optimizing FCS Sizing and Enhanced User Satisfaction

Energy efficient smart home heating system using renewable energy source with fuzzy control design

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