scholarly journals Energy Management in a Prosumer Installation Using Hybrid Systems Combining EV and Stationary Storages and Renewable Power Sources

2021 ◽  
Vol 11 (11) ◽  
pp. 5003
Author(s):  
Paweł Kelm ◽  
Rozmysław Mieński ◽  
Irena Wasiak
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Electric Vehicles ◽  
State Of Charge ◽  
Synergy Effect ◽  
Power Sources ◽  
Distant Future ◽  
Efficient Energy ◽  
Renewable Power ◽  
Stationary Energy

Modern consumer installations can be equipped with renewable power sources (RESs) and stationary energy storage systems (ESSs). In addition, electric vehicles (EVs) are expected to become part of such installations in the not-too-distant future. The paper presents the control strategy that allows for efficient energy management and the option of EV “fast-home” charging. The novelty of this approach includes the use of the “time-dependent energy storage” (EV battery) together with ESS and PV sources with the focus on prosumer benefits. All goals can be achieved without the need for extensive expenses in the home electric infrastructure. To enable the synergy effect, it was necessary to develop a controller algorithm that uses the operating status of the prosumer infrastructure (current power generation and consumption), the state of charge of both the stationary storage and the EV battery, and the possibility to control the EV drive inverter during the parking state. The paper presents a developed simulator built in the PSCAD environment and the simulation results.

scholarly journals Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids

2021 ◽  
Vol 13 (20) ◽  
pp. 11429
Author(s):  
Fahad R. Albogamy ◽  
Ghulam Hafeez ◽  
Imran Khan ◽  
Sheraz Khan ◽  
Hend I. Alkhammash ◽  
...  
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Electric Vehicles ◽  
Optimization Algorithm ◽  
Energy Cost ◽  
Carbon Emission ◽  
Management Model ◽  
Ant Colony Optimization Algorithm ◽  
Efficient Energy ◽  
Average Ratio

In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bidirectional communication and power transfer technology enables electric vehicles (EVs) charging/discharging scheduling, load shifting/scheduling, and optimal energy sharing, making the power grid smart. With this motivation, efficient energy management model for a microgrid with ant colony optimization algorithm to systematically schedule load and EVs charging/discharging of is introduced. The smart microgrid is equipped with controllable appliances, photovoltaic panels, wind turbines, electrolyzer, hydrogen tank, and energy storage system. Peak load, peak to average ratio, cost, energy cost, and carbon emission operation of appliances are reduced by the charging/discharging of electric vehicles, and energy storage systems are scheduled using real time pricing tariffs. This work also predicts wind speed and solar irradiation to ensure efficient energy optimization. Simulations are carried out to validate our developed ant colony optimization algorithm-based energy management scheme. The obtained results demonstrate that the developed efficient energy management model can reduce energy cost, alleviate peak to average ratio, and carbon emission.

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

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