scholarly journals Sizing Procedure for System Hybridization Based on Experimental Source Modeling in Grid Application

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4685
Author(s):  
Nissim Amar ◽  
Arron Shmaryahu ◽  
Michael Kolleti ◽  
Ilan Aharon
Keyword(s):  
Energy Source ◽  
Real Time ◽  
Energy Management ◽  
Management System ◽  
Target Function ◽  
Energy Management System ◽  
Source Modeling ◽  
System Controller ◽  
And Storage ◽  
Mission Profile

Hybridization of sources is spreading worldwide by utilizing renewable sources and storage units as standard parts of every grid. The conjunction of energy source and storage type open the door to reshaping the sustainability and robustness of the mains while improving system parameters such as efficiency and fuel consumption. The solution fits existing networks as well as new ones. The study proposes the creation of an accurate optimal sizing procedure for setting the required rating of each type of source. The first step is to model the storage and energy sources by using real experimental results for creating the generic database. Then, data on the mission profile, system constraints, and the minimization target function are inserted. The mission profile is then analyzed to determine the minimum and maximum energy source rating. Next, the real time energy management system controller is used to find the set of solutions for each available energy source and the optimal compatible storage in the revealed band to fulfil the mission task. A Pareto-curve is then plotted to present the optimal findings of the sizing procedure. Ultimately, the main research contribution is the far more accurate sizing results. A case study shows that relying on the standard method leads to noncompliance of sizing constraints, while the proposed procedure leads to fulfilling the mission successfully. First, by utilizing experimentally based energy and a storage unit. Second, by using the same real time energy management system controller in the sizing procedure.

scholarly journals Sizing Procedure for System Hybridization Based on Experimental Source Modeling for Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5275
Author(s):  
Aaron Shmaryahu ◽  
Nissim Amar ◽  
Alexander Ivanov ◽  
Ilan Aharon
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Energy Management ◽  
Management System ◽  
Target Function ◽  
Proton Exchange ◽  
Energy Management System ◽  
Hydrogen Fuel Cell ◽  
Source Modeling ◽  
Mission Profile

Hybrid vehicles are now more common in response to increasing global warming. The hybridization of energy sources and energy storage units enables improving the sustainability, reliability, and robustness of power systems. To reach the objective of zero emissions, a proton exchange membrane hydrogen fuel-cell was utilized as an energy source. The aim of this research was to create an accurate optimal sizing procedure for determining the nominal rating of the necessary sources. We modeled the fuel cell and the battery pack using data from real experimental results to create the generic database. Then, we added data on the mission profile, system constraints, and the minimization target function. The mission profile was then analyzed by the sizing algorithm to determine optional minimum and maximum fuel cell ratings. Analyzing the optional solutions using the vehicle real time energy management system controller resulted in a set of solutions for each available rated fuel cell, and the optimal compatible battery in the revealed band successfully accomplished the route of the driving cycle within the system limitations. Finally, the Pareto curve represented the optimal finding of the sizing procedure. Ultimately, in contrast to previous works that utilize gross manufacturer data in the sizing procedure, the main research contribution and novelty of this research is the very accurate sizing results, which draw on real experimental-based fuel-cell and battery sizing models. Moreover, the actual vehicle real time energy management system controllers were used in the sizing procedure.

scholarly journals Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power

2016 ◽  
Vol 6 (10) ◽  
pp. 285 ◽  
Author(s):  
Yuefei Wang ◽  
Hao Hu ◽  
Li Zhang ◽  
Nan Zhang ◽  
Xuhui Sun
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Electrical Load ◽  
Load Power ◽  
Optimized Distribution

Operation Scheduling of Household Load, EV and BESS Using Real Time Pricing, Incentive Based DR and Peak Power Limiting Strategy

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Sandeep Kakran ◽  
Saurabh Chanana
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Cost Reduction ◽  
Management System ◽  
Peak Power ◽  
Base Case ◽  
Energy Management System ◽  
Electricity Cost ◽  
Home Energy Management ◽  
Home Energy Management System

Abstract Demand response (DR) programs have become powerful tools of the smart grids, which provide opportunities for the end-use consumers to participate actively in the energy management programs. This paper investigates impact of different DR strategies in a home-energy management system having consumer with regular load, electric vehicle (EV) and battery-energy storage system (BESS) in the home. The EV is considered as a special type of load, which can also work as an electricity generation source by discharging the power in vehicle-to-home mode during high price time. BESS and a small renewable energy source in form of rooftop photovoltaic panels give a significant contribution in the energy management of the system. As the main contribution to the literature, a mixed integer linear programming based model of home energy management system is formulated to minimize the daily cost of electricity consumption under the effect of different DR programs; such as real time price based DR program, incentive based DR program and peak power limiting DR program. Finally, total electricity prices are analysed in the case studies by including different preferences of the household consumer under mentioned DR programs. A total of 26.93 % electricity cost reduction is noticed with respect to base case, without peak limiting DR and 19.93 % electricity cost reduction is noticed with respect to base case, with peak limiting DR.

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