scholarly journals Discrete event optimization of a vehicle charging station with multiple sockets

2020 ◽  
Author(s):  
Giulio Ferro ◽  
Riccardo Minciardi ◽  
Luca Parodi ◽  
Michela Robba
Keyword(s):  
Urban Areas ◽  
Optimization Problems ◽  
Discrete Event ◽  
Time Discretization ◽  
Real Case ◽  
Storage Element ◽  
Charging Station ◽  
Discretization Step ◽  
Fundamental Reason ◽  
Vehicle Charging

AbstractThe relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

Optimal Charging Management of Microgrid-Integrated Electric Vehicles

2022 ◽  
pp. 133-155
Author(s):  
Giulio Ferro ◽  
Riccardo Minciardi ◽  
Luca Parodi ◽  
Michela Robba
Keyword(s):  
Electric Vehicles ◽  
Discrete Event ◽  
Time Discretization ◽  
Small Time ◽  
Storage Element ◽  
Charging Station ◽  
Decision Variables ◽  
Discretization Step ◽  
Definition Of ◽  
The Cost

The relevance of electric vehicles (EVs) is increasing along with the relative issues. The definition of smart policies for scheduling the EVs charging process represents one of the most important problems. A discrete-event approach is proposed for the optimal scheduling of EVs in microgrids. This choice is due to the necessity of limiting the number of the decision variables, which rapidly grows when a small-time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles in a microgrid characterized by renewable energy source, a storage element, the connection to the main grid, and a charging station. The objective function to be minimized results from the weighted sum of the cost for purchasing energy from the external grid, the weighted tardiness of the services provided, and a cost related to the occupancy of the socket. The approach is tested on a real case study.

scholarly journals Evaluation of Electric Vehicle Charging Station Network Planning via a Co-Evolution Approach

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 25 ◽  
Author(s):  
Hassan S. Hayajneh ◽  
Xuewei Zhang
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Optimal Planning ◽  
Modeling Framework ◽  
Charging Infrastructure ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

The optimal planning of electric vehicle charging infrastructure has attracted extensive research interest in recent years. Most of the optimization problems were formulated by assuming that the configurations will be fixed at the optimal solution while overlooking the fact that the charging stations and the electric vehicles are “evolving” over time and have mutual impacts. On the other hand, little attention has been paid to evaluate the performance of the solutions in such a dynamic environment. Motivated by these gaps, this work develops a simulation model that captures the interactions between charging station configurations and electric vehicle population (and the preference of electric vehicles when choosing charging station). This modeling framework is then implemented to evaluate the performance of planned charging infrastructure in providing services to electric vehicles. Two indicators are calculated, i.e., usage rate and rejection rate. The former measures the “waste” due to abundant facilities installed; the latter measures the inadequacy of planned facilities, especially when the electric vehicle population is larger. The simulation results presented in this work validate the model and show the potential of the model not only to evaluate designs but also to be used for optimal planning in subsequent works.

Joint optimization of reliability design and spares inventory for a use-oriented product-service system based on the electric vehicle charging station

2021 ◽  
pp. 1748006X2098397
Author(s):  
Jing Zhang ◽  
Xian Zhao ◽  
Yanbo Song
Keyword(s):  
Optimization Model ◽  
Service System ◽  
Optimization Problems ◽  
Joint Optimization ◽  
Model Parameters ◽  
Product Service System ◽  
Reliability Design ◽  
Charging Station ◽  
Product Service ◽  
Vehicle Charging

With the shift of business pattern from the traditional manufacturer orientation to customer orientation, the product-service system has become more and more popular. This paper takes an electrical vehicle charging station as the research object and builds a joint optimization model of reliability design and spares inventory with the objective of maximizing net profit under the constraints of availability and customer’s loss rate. Particularly, this model considers not only two types of services, namely fast charging and slow charging but also the transfer of customers between different types of service. This new model is more accordant with the practical circumstances by involving a series of elements of the product-service system simultaneously, such as the customer’s arrival, service, replacement maintenance and spares replenishment process. Then, the probabilistic indices related to reliability are derived by applying the continuous time Markov Process. A balance between the number of charging piles and spares inventory policy can be achieved by solving the joint optimization model. The influence of model parameters on these indices and the superiority of the proposed joint optimization model are analyzed in detail by numerical examples. Finally, some critical conclusions are obtained with the intent of promoting the system reliability and profit. Furthermore, this joint optimization model can be extended to more circumstances and provides guidances to solve the reliability optimization problems regarding a similar system.

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

scholarly journals Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation

2021 ◽  
Vol 13 (11) ◽  
pp. 6163
Author(s):  
Yongyi Huang ◽  
Atsushi Yona ◽  
Hiroshi Takahashi ◽  
Ashraf Mohamed Hemeida ◽  
Paras Mandal ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
System Optimization ◽  
Mixed Integer ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Photovoltaic Power ◽  
Vehicle Charging ◽  
Charging Stations

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

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