scholarly journals Tertiary Control for Energy Management of EV Charging Station Integrated With PV and Energy Storage

2022 ◽  
Vol 9 ◽  
Author(s):  
Yangqing Dan ◽  
Shuran Liu ◽  
Yanwei Zhu ◽  
Hailian Xie
Keyword(s):  
Sensitivity Analysis ◽  
Energy Storage ◽  
Energy Management ◽  
Prediction Error ◽  
Dynamic State ◽  
Operational Cost ◽  
Control Logic ◽  
Charging Station ◽  
Battery Capacity ◽  
Ev Charging

Along with the rapid increase in the number of electric vehicles, more and more EV charging stations tend to have charging infrastructure, rooftop photovoltaic and energy storage all together for energy saving and emission reduction. Compared with individual design for each of the components in such kind of systems, an integrated design can result in higher efficiency, increased reliability, and lower total capital cost. This paper mainly focuses on the tertiary control strategy for dynamic state operation, such as PV generation fluctuation and random arrival/leave of EVs. The tertiary control aims to achieve stable operation under dynamic states, as well as to optimize the energy flow in the station to realize maximal operational benefits with constraints such as peak/valley price of electricity, state of discharge limitation of battery, etc. In this paper, four energy management functions in tertiary control level are proposed, and their performance is verified by simulations. By using prediction of PV power and EV load in the following 72 h, a novel tertiary control logic is proposed to optimize PVC and ESC power flow by changing their droop characteristics, so that minimum operational cost for the station can be achieved. Furthermore, a sensitivity analysis is conducted for three parameters, including ES battery capacity, weather influence, and PV and EV load prediction error. The results from sensitivity analysis indicate that ES battery capacity and weather condition lead to a great impact on the operational cost of the integrated charging station, while a typical prediction error of PV power and EV load will not influence the optimization result significantly.

scholarly journals Optimum Resilient Operation and Control DC Microgrid Based Electric Vehicles Charging Station Powered by Renewable Energy Sources

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4240 ◽  
Author(s):  
Khairy Sayed ◽  
Ahmed G. Abo-Khalil ◽  
Ali S. Alghamdi
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Solar Pv ◽  
Dc Microgrid ◽  
Charging Station ◽  
And Control ◽  
Ev Charging

This paper introduces an energy management and control method for DC microgrid supplying electric vehicles (EV) charging station. An Energy Management System (EMS) is developed to manage and control power flow from renewable energy sources to EVs through DC microgrid. An integrated approach for controlling DC microgrid based charging station powered by intermittent renewable energies. A wind turbine (WT) and solar photovoltaic (PV) arrays are integrated into the studied DC microgrid to replace energy from fossil fuel and decrease pollution from carbon emissions. Due to the intermittency of solar and wind generation, the output powers of PV and WT are not guaranteed. For this reason, the capacities of WT, solar PV panels, and the battery system are considered decision parameters to be optimized. The optimized design of the renewable energy system is done to ensure sufficient electricity supply to the EV charging station. Moreover, various renewable energy technologies for supplying EV charging stations to improve their performance are investigated. To evaluate the performance of the used control strategies, simulation is carried out in MATLAB/SIMULINK.

scholarly journals Optimal Energy Management of V2B with RES and ESS for Peak Load Minimization

2018 ◽  
Vol 8 (11) ◽  
pp. 2125 ◽  
Author(s):  
Nandinkhuu Odkhuu ◽  
Ki-Beom Lee ◽  
Mohamed A. Ahmed ◽  
Young-Chon Kim
Keyword(s):  
Energy Management ◽  
Communication Networks ◽  
Time Allocation ◽  
Power Grid ◽  
Peak Load ◽  
The Real ◽  
Optimal Energy ◽  
Battery Capacity ◽  
Optimal Energy Management ◽  
Ev Charging

In order to decrease fuel consumption and greenhouse gas emissions, electric vehicles (EVs) are being widely adopted as a future transportation system. Accordingly, increasing the number of EVs will mean battery charging will have a significant impact on the power grid. In order to manage EV charging, an intelligent charging strategy is required to prevent the power grid from overloading. Therefore, we propose an optimal energy management algorithm (OEMA) to minimize peak load on a university campus consisting of an educational building with laboratories, a smart parking lot, EVs, photovoltaic (PV) panels and an energy storage system (ESS). Communication networks are used to connect all the system components to a university energy management system (UEMS). The proposed OEMA algorithm coordinates EV charging/discharging so as to reduce the peak load of the building’s power consumption by considering the real-time price (RTP). We also develop a priority determination method for the time allocation of the optimal charging algorithm. Priority is determined by arrival time, departure time, state-of-charge (SOC), battery capacity and trip distance. The performance of the proposed algorithm is evaluated in terms of charging cost and peak load under the real environment of the university engineering building.

scholarly journals Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 736
Author(s):  
Hedayat Saboori ◽  
Shahram Jadid ◽  
Mehdi Savaghebi
Keyword(s):  
Energy Storage ◽  
Optimal Management ◽  
Battery Energy Storage ◽  
Charging Station ◽  
Demand Pattern ◽  
Self Powered ◽  
Battery Energy ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

Sign in / Sign up

Export Citation Format

Share Document