Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

Fernandez, George S.; Vijayakumar, K.; Kuppusamy, Selvakumar; Mohd.Ali, Jagabar Sathik; Ali, Ziad M.; El-Shahat, Adel; Aleem, Shady H. E. Abdel

doi:10.3390/en13236384

Cited by 26 publications

(7 citation statements)

References 62 publications

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“…The cost analysis of the proposed technique has been examined over the existing techniques 33 such as FCFS, PSO, and shuffled frog leaping algorithm (SFLA). The FCFS algorithm scheduled the charging without priority, whereas PSO scheduled the charging in a random population manner.…”

Section: Resultsmentioning

confidence: 99%

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Bhaskar¹,

Prasanth

Saranya³

2022

Int J Communication

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In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain-based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state-of-the-art techniques.

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Section: Resultsmentioning

confidence: 99%

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Bhaskar¹,

Prasanth

Saranya³

2022

Int J Communication

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“…In each case, a basic scenario is considered from which the figures in the second column was obtained. For instance, in [ 1 , 58 ], the deterministic optimizers were utilized with different scenarios. The advanced techniques such as those investigated in [ 12 , 40 ], respectively, demonstrated better results compared to the deterministic optimizers.…”

Section: Resultsmentioning

confidence: 99%

“…By optimizing the charge/discharge profiles of the vehicle to grid (V2G) at a parking lot, microgrids can effectively handle and monitor V2G while maximizing the economic revenue and the satisfaction of vehicles stakeholders [ 1 , 2 ]. Due to the recent technological advances, governmental incentives, and modern battery development, EVs have been resurged.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators

Abdel-Hakim

Abo‐Elyousr

2022

Sensors

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In on-grid microgrids, electric vehicles (EVs) have to be efficiently scheduled for cost-effective electricity consumption and network operation. The stochastic nature of the involved parameters along with their large number and correlations make such scheduling a challenging task. This paper aims at identifying pertinent innovative solutions for reducing the relevant total costs of the on-grid EVs within hybrid microgrids. To optimally scale the EVs, a heuristic greedy approach is considered. Unlike most existing scheduling methodologies in the literature, the proposed greedy scheduler is model-free, training-free, and yet efficient. The proposed approach considers different factors such as the electricity price, on-grid EVs state of arrival and departure, and the total revenue to meet the load demands. The greedy-based approach behaves satisfactorily in terms of fulfilling its objective for the hybrid microgrid system, which is established of photovoltaic, wind turbine, and a local utility grid. Meanwhile, the on-grid EVs are being utilized as an energy storage exchange location. A real time hardware-in-the-loop experimentation is comprehensively conducted to maximize the earned profit. Through different uncertainty scenarios, the ability of the proposed greedy approach to obtain a global optimal solution is assessed. A data simulator was developed for the purposes of generating evaluation datasets, which captures uncertainties in the behaviors of the system’s parameters. The greedy-based strategy is considered applicable, scalable, and efficient in terms of total operating expenditures. Furthermore, as EVs penetration became more versatile, total expenses decreased significantly. Using simulated data of an effective operational duration of 500 years, the proposed approach succeeded in cutting down the energy consumption costs by about 50–85%, beating existing state-of-the-arts results. The proposed approach is proved to be tolerant to the large amounts of uncertainties that are involved in the system’s operational data.

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“…A Vehicle-to-Grid (V2G) architecture that would let utilities or aggregators control Electric Vehicles (EV) through grid-to-car communication was put forward in [3]. Peak shaving, power quality improvement, frequency regulation, spinning reserve, valley filling and support for renewable power are some of the auxiliary services offered by EVs [4]. Most research papers were written to address the difficulties in planning the use of EVs in parking areas (PLs), including fast charging capacity, varying electricity prices, the number of charging points, and charging limits [5].…”

Section: Introductionmentioning

confidence: 99%

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Shanmugam,

Thomas

2024

Gazi University Journal of Science

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Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

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Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

Cited by 26 publications

References 62 publications

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Heuristic Greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

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