Local Consumption of PV Power Based on the Optimized Scheduling of Electric Vehicles Charging

2015 ◽  
Vol 751 ◽  
pp. 176-181
Author(s):  
Guo Zhao ◽  
Xue Liang Huang
Keyword(s):  
Power Generation ◽  
Electric Vehicles ◽  
Renewable Resources ◽  
Pareto Front ◽  
Nsga Ii ◽  
Charging Station ◽  
Scheduling Model ◽  
Utilization Ratio ◽  
The Cost ◽  
Pv Power Generation

According to the coordination and complementation of electric vehicles (EVs) and renewable resources, such as photovoltaic (PV) power generation, a micro gird system including EVs charging station and PV power generation was constructed firstly. Based on the target of maximizing the utilization ratio of PV power, considering the total cost of EVs charging, the time-of-use (TOU) price was introduced to establish the dual-objective optimization scheduling model of EVs charging. Furthermore, NSGA-II multi-objective optimization algorithm was applied to solve the model and the Pareto front of the non-dominated solutions was obtained. Finally, the optimized scheduling control strategy for EVs charging was proposed through normalized sorting of the non-dominated solutions. The optimal scheduling strategy could increase the utilization ratio of PV power on the basis of reducing the cost of EVs charging, promoting the local consumption of PV power.

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.

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.

Design of Fast Cycloconverter-Based Battery-Charging Circuit for High Penetration of Electric Vehicles

2020 ◽  
Vol 50 ◽  
pp. 64-69
Author(s):  
Funso Kehinde Ariyo ◽  
Oluwafemi Aworo ◽  
Michael Kuku
Keyword(s):  
Electric Vehicles ◽  
Battery Charging ◽  
Battery Modeling ◽  
The Road ◽  
Charging Station ◽  
High Penetration ◽  
Level Ii ◽  
On The Road ◽  
Time Required ◽  
The Cost

There have been growing concerns involving the penetration of Electric Vehicles (EVs) due to the time required by its battery to attain full charge. Interests in EVs had recently experienced a dramatic turn down due to mileage limitation on full battery charge amidst the cost of purchase, but most notably due to the absence of quick chargers that can keep the vehicle on the road within few minutes of arriving at the charging station. Researchers had proposed different charging schemes such as level II ac charging, dc charging, and in some cases, wireless charging schemes that later appear to be inefficient. The use of dynamic or simply road-way powered electric vehicles was also proposed in the literature. However, the proposed cycloconverter-based circuit was simulated in Simulink, and the results obtained proved that the rate of charge increased when compared to the conventional EV charging circuit. Also, the focus is on battery charging technology and battery modeling for application in an electric vehicle

scholarly journals Sensorless PV Power Forecasting in Grid-Connected Buildings through Deep Learning

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2529 ◽  
Author(s):  
Junseo Son ◽  
Yongtae Park ◽  
Junu Lee ◽  
Hyogon Kim
Keyword(s):  
Power Generation ◽  
Weather Forecast ◽  
Absolute Error ◽  
Coarse Grained ◽  
Forecast Errors ◽  
Forecast Horizon ◽  
Sensory Data ◽  
Alternative Approach ◽  
The Cost ◽  
Pv Power Generation

Existing works in photovoltaic (PV) power generation focus on accurately predicting the PV power output on a forecast horizon. As the solar power generation is heavily influenced by meteorological conditions such as solar radiation, the weather forecast is a critical input in the prediction performance. However, the weather forecast is traditionally considered to have coarse granularity, so many are compelled to use on-site meteorological sensors to complement it. However, the approach involving on-site sensors has several issues. First, it incurs the cost in the installation, operation, and management of the sensors. Second, the physical model of the sensor dynamics itself can be a source of forecast errors. Third, it requires an accumulation of sensory data that represent all seasonal variations, which takes time to collect. In this paper, we take an alternative approach to use a relatively large deep neural network (DNN) instead of the on-site sensors to cope with the coarse-grained weather forecast. With historical PV output power data from our grid-connected building with a rooftop PV power generation facility and the publicly available weather forecast history data, we demonstrate that we can train a six-layer feedforward DNN for the day-ahead forecast. It achieves the average mean absolute error (MAE) of 2.9%, comparable to that of the conventional model, but without involing the on-site sensors.

scholarly journals Design scheme for fast charging station for electric vehicles with distributed photovoltaic power generation

2019 ◽  
Vol 2 (2) ◽  
pp. 150-159 ◽  
Author(s):  
Jing Zhang ◽  
Chang Liu ◽  
Ruiming Yuan ◽  
Taoyong Li ◽  
Kang Li ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicles ◽  
Charging Station ◽  
Design Scheme ◽  
Photovoltaic Power ◽  
Fast Charging ◽  
Fast Charging Station

scholarly journals Grid Synchronisation of Photovoltaic Distributed Generation using Hybrid FUZZY-PI Controller

2019 ◽  
Vol 9 (1) ◽  
pp. 5385-5390
Keyword(s):  
Power Generation ◽  
Distributed Generation ◽  
High Power ◽  
Fuzzy Logic Control ◽  
Power Production ◽  
Pi Controller ◽  
Logic Control ◽  
Fuzzy Pi Controller ◽  
The Cost ◽  
Pv Power Generation

The power generation of the world has to be increased every year as the demand increases rapidly. The high power generations are done by hydro, thermal and atomic. The power production cant is increased every year as the cost of power production increases due to this constraint and increase in high power generation increases pollutions. So the distribution side generation are used, and the PV based power generations are used mostly as the maintenance is less comparatively. The power control in PV power generation is done by DQ technique which is most widely used. It has a PI controller to regulate the real power. In this paper, the fuzzy logic control which is combined with the PI controller is used to increase the controllability of the power system. The MATLAB 2017b is used to do the simulation of the applied technique and the results are discussed with improvements.

scholarly journals Smart Charging of Electric Vehicles: An Innovative Business Model for Utility Firms

2021 ◽  
Author(s):  
Owen Q. Wu ◽  
Şafak Yücel ◽  
Yangfang (Helen) Zhou
Keyword(s):  
Business Model ◽  
Electric Vehicles ◽  
Completion Time ◽  
The United States ◽  
Problem Definition ◽  
Charging Station ◽  
Smart Charging ◽  
Procurement Cost ◽  
The Cost ◽  
Completion Times

Problem definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, after an electric vehicle is plugged in, the charging station completes charging as soon as possible. Given that the procurement cost of electricity and associated emissions vary significantly during a day, substantial savings can be achieved by smart charging—delaying charging until the cost is lower. In this paper, we study smart charging as an innovative business model for utility firms. Academic/practical relevance: Utility firms are already investing in charging stations, and they can achieve significant cost savings through smart charging. Methodology: We consider a mechanism design problem in which a utility firm first announces pairs of charging price and completion time. Then, each customer selects the pair that maximizes their utility. Given the selected completion times, the utility firm solves the optimal control problem of determining the charging schedule that minimizes the cost of charging under endogenous, time-varying electricity procurement cost. We assume that there are ample parking spots with chargers at the charging station. Results: We devise an intuitive and practically implementable policy for scheduling charging of electric vehicles under given completion times. We prove that this policy is optimal if all customers arrive at the station simultaneously. We also characterize the optimal pairs of charging price and completion time. By using real electricity demand and generation data from the largest electricity market in the United States, we find that cost and emissions savings from smart charging are approximately 20% and 15%, respectively, during a typical summer month. Managerial implications: In contrast to the current practice of charging vehicles without delay, we show that it is economically and environmentally beneficial to delay charging some vehicles and to set charging prices based on customers’ inconvenience cost of delays. We also find that most of the savings from implementing smart charging can be achieved during peak-demand days, highlighting the effectiveness of smart charging.

scholarly journals Analysing the Cost-Effectiveness of Charging Stations for Electric Vehicles in the U.K.’s Rural Areas

2021 ◽  
Vol 12 (4) ◽  
pp. 232
Author(s):  
Kai Sheng ◽  
Mahdieh Dibaj ◽  
Mohammad Akrami
Keyword(s):  
Cost Effectiveness ◽  
Electric Vehicles ◽  
Rural Areas ◽  
Capital Expenditure ◽  
Business Strategies ◽  
Private Companies ◽  
Charging Station ◽  
Geographic Coverage ◽  
The Cost ◽  
Charging Stations

While U.K. authorities have attempted to tailor measures to boost sales of electric vehicles (EVs) and support citizens through different schemes, the size and geographic coverage of the existing charging network are insufficient, which undermines electromobility promotion. There are 15,853 public charging points installed in the U.K. as of 3 August 2021, and the demands for public EV charging are rising. For rural areas, there is little support from local authorities or private companies. To identify how a charging station can be installed and work, this study researches existing charging stations nationwide. Generally, most Public Charging Stations (PCS) in rural areas have unsatisfactory cost-effectiveness due to their long payback period. This paper presents how many rural PCS are able to afford the cost in the first eight years. Based on the ever-increasing demands of the market, EV producers are switching their business strategies. Meanwhile, the rural areas may become urban with the same definition. When it comes to the analysis of cost-effectiveness, it is possible for the PCS to bring more elements into the calculation. For Capital Expenditure (CAPEX) and Operation Expenditure (OPEX), the unnecessary cost leaves more profit space, like the possibility of unplanned maintenance costs.

scholarly journals Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

2020 ◽  
Vol 155 ◽  
pp. 1191-1210
Author(s):  
Ying Hao ◽  
Lei Dong ◽  
Jun Liang ◽  
Xiaozhong Liao ◽  
Lijie Wang ◽  
...  
Keyword(s):  
Power Generation ◽  
Electric Vehicles ◽  
Pv Power Generation ◽  
Power Forecasting

A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

2012 ◽  
pp. 143-164
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Turnaround Time ◽  
Computational Grid ◽  
Computational Grids ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Scheduling Model ◽  
Comparison Of The Results ◽  
The Cost

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.

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