Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

Shabbar, Rawan; Kasasbeh, Anemone; Ahmed, Mohamed M.

doi:10.3390/su13063314

Cited by 25 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Metaheuristic algorithms are designed to generate high-quality solutions from a random population. The generation takes inspiration from natural system behaviours and continues until a specific termination condition is fulfilled 76 . GWO is based on three key steps i.e., surrounding prey, hunting, and sand attacking prey.…”

Section: Methodsmentioning

confidence: 99%

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Jalal,

Iqbal,

Khan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

show abstract

Section: Methodsmentioning

confidence: 99%

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Jalal,

Iqbal,

Khan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Furthermore, there is a substantial body of literature on the simultaneous installation of EVCS with DGs and DSTAT-COMs/capacitors. Table-1 [2], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36] provides a complete review of current research on EVCS planning using Distribution Generator (DG), Capacitor, Network Reconfiguration (NR), Battery Energy Storage System (BESS), and DSTATCOM for various types of distribution systems, optimization methodologies, and objective functions.…”

Section: Related Workmentioning

confidence: 99%

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

et al. 2023

View full text Add to dashboard Cite

Because of the increasing growth of Electric Vehicle (EV) in India, more electricity is required to power such vehicles. It is also gaining popularity because of its low maintenance, improved performance, and zero carbon impact. As the usage of electric vehicles grows, the distribution system's performance is impacted. As an outcome, the reliability of the distribution system (DS) is dependent on the position of the electric vehicle charging station (EVCS). The fundamental difficulty is the deterioration of the DS due to an incorrect EVCS location. The DS is linked to the charging station and works with the distribution static compensator (DSTATCOM) to minimize the impact of the EVCS. A new nature-inspired Bald Eagle Search Algorithm (BESA) based optimization technique was utilized to find the optimal allocation of DSTATCOM and EVCS in the DS. The proposed strategy for mitigating the real power loss has been tested on practical Indian 28-bus and 108-bus distribution networks. Power loss reduction optimizes the system's net savings, voltage stability, and bus voltage. The test case findings show that the BESA-based optimization is more accurate regarding power loss mitigation, bus voltage enhancement, and annual net saving improvement than the BA-based optimization in the DS. INDEX TERMS Electrical vehicle (EV), bald eagle search algorithm (BESA), bat algorithm (BA), distribution STATicCOMpensator (DSTATCOM), voltage stability index (VSI), distribution system (DS), electric vehicle charging station (EVCS). I. INTRODUCTIONLong-term development and poverty eradication depend heavily on energy. In India, 97.2% of the population has access to electricity. Natural gas and coal are the most frequent fuels utilized in the power sector. India has a total capacity for electricity generation of 388.134GW, with offgrid renewable energy (RE) accounting for 21.26% of that capacity. The lack of coal and natural gas might threaten the production of power. To address the nation's expandingThe associate editor coordinating the review of this manuscript and approving it for publication was Junho Hong .

show abstract

“…Shabbar et al estimated the demand for a charging station and investigated the desired number of electric sockets in each charging station using a Markov-chain network model. They also proposed using the Grey Wolf Optimization (GWO) algorithm to select the best charging station locations with the objective of maximizing the net profit under both budget and routing constraints [17]. He et al proposed a contextualized EV charger optimization model that incorporates supply-and-demand constraints to plan public EV charging infrastructure in a high-density city.…”

Section: An Analysis Of Charging Stations Locationmentioning

confidence: 99%

Location Selection of Charging Stations for Electric Taxis: A Bangkok Case

2022

View full text Add to dashboard Cite

The transition from ICE to BEV taxis is one of the most important methods for reducing fossil fuel consumption and air pollution in cities such as Bangkok. To support this transition, an adequate number of charging stations to cover each area of charging demand must be established. This paper presents a data-driven process for determining suitable charging locations for BEV taxis based on their characteristic driving patterns. The location selection process employs GPS trajectory data collected from taxis and the locations of candidate sites. Suitable locations are determined based on estimated travel times and charging demands. A queueing model is used to simulate charging activities and identify an appropriate number of chargers at each station. The location selection results are validated using data from existing charging services. The validation results show that the proposed process can recommend better locations for charging stations than current practices. By using the traveling time data that take the current traffic condition into account, e.g., via Google Maps API, we can minimize the overall travel time to charging stations of the taxi fleet better than using the distance data. This process can also be applied to other cities.

show abstract

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

Cited by 25 publications

References 32 publications

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

Location Selection of Charging Stations for Electric Taxis: A Bangkok Case

Contact Info

Product

Resources

About