Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

Álvarez, Jorge García; González, Miguel A.; Vela, Camino R.; Varela, Ramiro

doi:10.3390/en11102752

Cited by 36 publications

(13 citation statements)

References 43 publications

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“…Ramos Muñoz et al [47] evaluated six algorithms, including Grid Valley filling by Zhang et al [19] and Time-Of-Use (TOU) Charging from Southern California Edison (SCE) [54]. This study determined that the Grid Valley Filling with Modified Timeslot Rejection strategy produces the best results, preventing all local transformers from experiencing significant overloading.…”

Section: Resultsmentioning

confidence: 99%

Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates

2021

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This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-world applications. In addition, this study proves that when the forecasted and actual baseload vary by more than 20%, its real-time capability is more advantageous than algorithms that use optimization techniques on predicted baseload data.

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Section: Resultsmentioning

confidence: 99%

Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates

2021

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“…For future work, we plan to extend the model considered in this work with additional realistic constraints (e.g., variable charging rates) and integrate a local search operator to further improve the performance of ACO. In fact, the performance of other metaheuristics has been significantly enhanced with a local search in [29], [30] on the described scheduling problem. Recall that in Table I the metaheuristic approaches are not utilizing any local search operator.…”

Section: Discussionmentioning

confidence: 99%

Electric Vehicle Charging Scheduling Using Ant Colony System

Mavrovouniotis

Ellinas

Polycarpou

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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In this work we consider the scheduling problem for charging a fleet of electric vehicles (EVs) within a station such that the total tardiness of the problem is minimized. The generation of a feasible and efficient schedule is a difficult task due to the physical and power constraints of the charging station, i.e., the maximum contracted power and the maximum power imbalance between the lines of the electric feeder. The ant colony optimization (ACO) metaheuristic is applied to coordinate the charging process of the EVs within the charging station by generating efficient schedules. The behaviour and performance of ACO is analyzed and compared against state-of-the-art approaches on a benchmark set inspired by real-world scenarios. The experimental results show that the application of ACO is highly effective and outperforms other approaches.

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“…Bahman Najafi et al [15] used the intelligent artificial neural network and RSM to get the optimal fuel blend of the diesel engine, the calculation and experiment results show that optimum values of exergy and energy efficiencies are in the range of 25-30% of full load. There are also some other algorithms applied to this field, e.g., support vector machine (SVM) [16], artificial bee colony (ABC) [17], energy blockchain network (EBN) [18], Multiple Utility Problem Table (MUPT) [19], quasi-optimal (QO) [20] algorithm and so on.…”

Section: Introductionmentioning

confidence: 99%

A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment

Dong¹,

Liu²,

Liu³

et al. 2020

Energies

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In order to solve issues concerning performance induction and in-cylinder heat accumulation of a certain heavy-duty diesel engine in a plateau environment, working state parameters and performance indexes of diesel engine are calculated and optimized using the method of artificial neural network and genetic algorithm cycle multi-objective optimization. First, with an established diesel engine simulation model and an orthogonal experimental method, the influence rule of five performance indexes affected by five working state parameters are calculated and analyzed. Results indicate the first four of five working state parameters have a more prominent influence on those five performance indexes. Subsequently, further calculation generates correspondences among four working state parameters and five performance indexes with the method of radial basis function neural network. The predicted value of the trained neural network matches well with the original one. The approach can fulfill serialization of discrete working state parameters and performance indexes to facilitate subsequent analysis and optimization. Next, we came up with a new algorithm named RBFNN & GACMOO, which can calculate the optimal working state parameters and the corresponding performance indexes of the diesel engine working at 3700 m altitude. At last, the bench test of the diesel engine in a plateau environment is employed to verify accuracy of the optimized results and the effectiveness of the algorithm. The research first combined the method of artificial neural network and genetic algorithm to specify the optimal working state parameters of the diesel engine at high altitudes by focusing on engine power, torque and heat dissipation, which is of great significance for improving both performance and working reliability of heavy-duty diesel engine working in plateau environment.

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Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

Cited by 36 publications

References 43 publications

Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates

Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates

Electric Vehicle Charging Scheduling Using Ant Colony System

A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment

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