TOU pricing based energy management of public EV charging stations using energy storage system

2016 ◽  
Author(s):  
Kalpesh Chaudhari ◽  
Abhisek Ukil
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Storage System ◽  
Energy Storage System ◽  
Charging Stations ◽  
Ev Charging

scholarly journals Application of Machine Learning and Mathematical Programming in the Optimization of the Energy Management System for Hybrid-Electric Vessels Having Cyclic Operations

2018 ◽  
Author(s):  
N Mohammadzadeh ◽  
F Baldi ◽  
E Boonen
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Mathematical Programming ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Operational Profile ◽  
Charging Stations

Shipping contributes today to 2.1% of global anthropogenic greenhouse gas emissions and its share is expected to grow in the coming years. At the same time, fuel prices are increasing and companies of the related increase in operational costs. This demands for higher efficiency in ship operations. In these regards, batterypowered vessels are often regarded as a promising solution. The existence of an energy storage element in the system, however, introduces additional challenges in its efficient control. This paper presents the application of machine learning and mathematical programming to the optimization of the energy management system of Diesel-electric vessels with an energy storage system operating according to a cyclical operational profile. The proposed energy management system uses unsupervised exclusive machine learning algorithms,k-means or k-medoids, to learn from prior operations. Then mathematical programming based on mixed-integer linear programming is used to address the problem of the optimal unit commitment by means of optimizing the system’s operations for minimizing fuel consumption. The calculated optimal state of charge of the energy storage system is used as the reference value for a proportional-integral controller during the real-time operations. The proposed energy management system is evaluated through its application to a case study corresponding to a hybrid-electric ferry operating in a urban area having cyclic operations through several stations. The results show that the efficiency of the control action is high with an accuracy ranging between 87% and 99%, when compared to an ideal controller, even in presence of large variations in the operational profile and the charging stations. Between the two tested clustering algorithms, k-means showed higher efficiency in the reduction of fuel consumption in presence of charging stations, while in absence of these, k-medoids showed to provide a better performance. 

scholarly journals Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 230 ◽  
Author(s):  
Akhtar Hussain ◽  
Van-Hai Bui ◽  
Ju-Won Baek ◽  
Hak-Man Kim
Keyword(s):  
Performance Evaluation ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Performance Ratio ◽  
Cost Index ◽  
Stationary Energy ◽  
And Performance ◽  
Charging Stations ◽  
Ev Charging

In order to minimize the peak load of electric vehicles (EVs) and enhance the resilience of fast EV charging stations, several sizing methods for deployment of the stationary energy storage system (ESS) have been proposed. However, methods for assessing the optimality of the obtained results and performance of the determined sizes under different conditions are missing. In order to address these issues, a two-step approach is proposed in this study, which comprises of optimality analysis and performance evaluation steps. In the case of optimality analysis, random sizes of battery and converter (scenarios) are generated using Monte Carlo simulations and their results are compared with the results of sizes obtained from sizing methods. In order to carry out this analysis, two performance analysis indices are proposed in this study, which are named the cost index and the power index. These indices respectively determine the performance of the determined sizes in terms of total network cost and performance ratio of power bought during peak intervals and investment cost of the ESS. During performance evaluation, the performance of the determined sizes (battery and converter) are analyzed for different seasons of the year and typical public holidays. Typical working days and holidays have been analyzed for each season of the year and suitability of the determined sizes is analyzed. Simulation results have proved that the proposed method is suitable for determining the optimality of results obtained by different sizing methods.

Sign in / Sign up

Export Citation Format

Share Document