Abstract
This study demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a one-hour dispatching period for an entire day utilizing battery and supercapacitor hybrid energy storage subsystem (HESS). A frequency management approach is deployed to extend the longevity of the batteries through extensively utilizing the high energy density property of batteries and the high power density property of supercapacitors in the HESS framework. A low-pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC). The cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations in MATLAB/SIMULINK platform. The curve fitting and Particle Swarm Optimization approaches are applied to seek the optimum value of the LPF time constant. Several control algorithms as a function of the battery state of charge are developed to achieve accurate estimation of the grid reference power for each one-hour dispatching period. This estimation helps to minimize the energy storage cost, in addition to ensuring that the HESS has sufficient capacity for next-day operation. The optimum value of depth of discharge for HESS considering both cycling and calendar expenses has also been investigated for the best competitive energy storage cost for hourly dispatching the power of the WSHPS. This research also presents an economic comparison to investigate the significance of using different types of energy storage for hourly dispatching the WSHPS. The simulation results show that the presented HESS is superior to battery or SC-only operation.
Original language | English |
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Article number | 9253513 |
Pages (from-to) | 210099-210115 |
Number of pages | 17 |
Journal | IEEE Access |
Volume | 8 |
DOIs | |
State | Published - 2020 |
Bibliographical note
Publisher Copyright:© 2013 IEEE.
Keywords
- Battery
- cost analysis
- hourly dispatching
- hybrid energy storage
- hybrid wind-solar power system
- particle swarm optimization
- supercapacitor
ASJC Scopus subject areas
- General Engineering
- General Computer Science
- General Materials Science