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

2021 ◽  
Vol 13 (6) ◽  
pp. 3314
Author(s):  
Rawan Shabbar ◽  
Anemone Kasasbeh ◽  
Mohamed M. Ahmed
Keyword(s):  
Markov Chain ◽  
Electric Vehicles ◽  
Process Model ◽  
Transportation Network ◽  
Optimal Placement ◽  
Death Process ◽  
Chain Model ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Net Profit

Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

scholarly journals Multi-Objective Grey Wolf Optimization for Optimal Allocation of Distributed Generators in Distribution Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 664-670
Keyword(s):  
Radial Distribution ◽  
Power Loss ◽  
Distribution Network ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Real Power ◽  
Multi Objective ◽  
Distributed Generators

The power loss in the radial distribution network is appreciable as compared to transmission network. To reduce the power loss in distribution network which is radial in nature, the solution methodology adopted in this paper is optimal placement of distributed generators (DG). The optimization incorporated is Multi-objective Grey Wolf Optimization (MOGWO). The optimization is accomplished for three different cases. In each case two objective functions are simultaneously optimized to obtain non-dominated solutions using Multi-objective Grey Wolf Optimization. Case (1): To minimize the real power loss and maximize the savings obtained due to DG installation. Case (2): To minimize real power loss and maximum voltage deviation in the network. Case (3): To minimize real power loss and rating of DG installed. MOGWO method maintains an archive which contains pareto-optimal solutions. The archive mimics the behaviour of grey wolves. MOGWO method is verified on radial distribution networks. The effectiveness of the optimization method is proven by comparing the results with other optimization methods available in the literature.

scholarly journals Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82844-82854 ◽  
Author(s):  
Zhaoyang Qu ◽  
Qianhui Xie ◽  
Yuqing Liu ◽  
Yang Li ◽  
Lei Wang ◽  
...  
Keyword(s):  
Markov Chain ◽  
Physical System ◽  
Cyber Physical System ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Risk Area ◽  
System Risk

scholarly journals Deterioration Prediction of Urban Bridges on Network Level Using Markov-Chain Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Li Li ◽  
Lijun Sun ◽  
Guobao Ning
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Transportation Network ◽  
Urban Transportation ◽  
Chain Model ◽  
Maintenance And Repair ◽  
Condition Rating ◽  
Maintenance Factors ◽  
Deterioration Process ◽  
Natural Decay

Bridges play an important role in urban transportation network. However, it is hard to predict the bridge deterioration precisely in Shanghai, because records of various bridge types with different maintenance status coexist in the same database and the bridge age span is also large. Therefore a Markov-chain model capable of considering maintenance factors is proposed in this study. Three deterioration circumstances are modeled including natural decay, conventional recoverable decay, and enhanced recoverable decay. Three components as well as the whole bridge are predicted including bridge deck system, superstructure, and substructure. The Markov-chain model proposed can predict not only the distribution of the percentage of different condition rating (CR) grades on network level in any year but also the deterioration tendency of single bridge with any state. Bridge data records of ten years were used to verify the model and also to find the deterioration tendency of urban bridges in Shanghai. The results show that the bridge conditions would drop rapidly if no recoverable repair treatments were conducted. Proper repair could slow down the deterioration speed. The enhanced recoverable repair could significantly mitigate the deterioration process and even raise the CR grades after several years of maintenance and repair.

scholarly journals GREY WOLF OPTIMIZER BASED OPTIMAL PLACEMENT OF MULTIPLE FACTS DEVICES IN THE TRANSMISSION SYSTEM UNDER DYNAMIC LOADING SYSTEM

2021 ◽  
Vol 27 (1) ◽  
pp. 132-143
Author(s):  
YUSUF SAMUEL SUNDAY ◽  
OKORIE PATRICK UBEH ◽  
ABUBAKAR ADAMU SAIDU ◽  
ALHASSAN FAHAD
Keyword(s):  
Optimization Technique ◽  
Transmission System ◽  
Optimal Number ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Total System ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Facts Devices ◽  
Voltage Deviation

The application of grey wolf optimization technique for multiple FACTS placement is presented in this paper for the reduction of total system losses and minimization of voltage deviation via optimal placement of Flexible AC Transmission System (FACTS) device. Grey wolf optimization (GWO) technique is inspired by social hierarchy and hunting behaviour of wolves and offers a right balance between exploration and exploitation during the search for global optimal. Series-shunt FACTS device; unified power flow controller (UPFC) is considered as a formidable device that can provides an alternative option for the flexible controllability and improvement of power transfer capability of a transmission lines. The analyses were conducted by increasing the number of UPFC in the network in order to evaluate the optimal number of FACTS devices that would give the least loss under maximum loading and contingency conditions. The efficacy of this proposed technique is demonstrated on 31-bus, 330 kV Nigeria National Grid (NNG) using MATLAB environment. The results show that optimal placement of FACTS device along with optimization technique provides a promising solution to the high power loss and voltage deviation bedevilling Nigeria National Grid.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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