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Frontiers in Energy

Front. Energy    2020, Vol. 14 Issue (1) : 139-151
A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm
Aeidapu MAHESH(), Kanwarjit Singh SANDHU()
Department of Electrical Engineering, National Institute of Technology, Kurukshetra 136119, India
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In this paper, the genetic algorithm (GA) is applied to optimize a grid connected solar photovoltaic (PV)-wind-battery hybrid system using a novel energy filter algorithm. The main objective of this paper is to minimize the total cost of the hybrid system, while maintaining its reliability. Along with the reliability constraint, some of the important parameters, such as full utilization of complementary nature of PV and wind systems, fluctuations of power injected into the grid and the battery’s state of charge (SOC), have also been considered for the effective sizing of the hybrid system. A novel energy filter algorithm for smoothing the power injected into the grid has been proposed. To validate the proposed method, a detailed case study has been conducted. The results of the case study for different cases, with and without employing the energy filter algorithm, have been presented to demonstrate the effectiveness of the proposed sizing strategy.

Keywords PV-wind-battery hybrid system      size optimization      genetic algorithm     
Corresponding Authors: Aeidapu MAHESH,Kanwarjit Singh SANDHU   
Online First Date: 25 July 2017    Issue Date: 16 March 2020
 Cite this article:   
Aeidapu MAHESH,Kanwarjit Singh SANDHU. A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm[J]. Front. Energy, 2020, 14(1): 139-151.
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Aeidapu MAHESH
Kanwarjit Singh SANDHU
Fig.1  Block diagram representation of hybrid PV-wind-battery system
Fig.2  Wind turbine characteristics
Fig.3  Proposed optimal sizing strategy
ParameterPopulation sizeMaximum
Tab.1  Parameters used for GA
Fig.4  Proposed energy filter algorithm
Fig.5  Meteorological data and load patterns
PV panel: SANYO HIT Power 200Maximum power/W200
OC voltage /V68.7
SC current/A3.83
Voltage at MPP/V55.8
Current at MPP/A3.59
Efficiency at STC17.2
Slope (fixed slope)/(°)40.98
Cost per panel (Cpv)/$420
O & M cost (Cpvo&m)/($·kW−1)15
Wind turbine: PGE 35 kWRated power/kW35
Cut-in wind speed/(m·s−1)3
Cut-out wind speed/(m·s−1)25
Hub height/m24
Rated wind speed/(m·s−1)11
Rotor diameter/m19.2
Blade length/m9
Cost per turbine (Cwt)/$25000
O & M cost (Cwto&m)/($ ·kW−1)30
Battery: Hoppecke 6OPzS 600Rated capacity/Ah600
Rated voltage/V2
Round trip efficiency/%85
Max. Ch./disch. rate/(A·Ah–1)0.5
Max. ch/dis. current/A100, 75
A self-discharge rate/%1
Cost per battery (Cbs)/$150
O & M cost (Cbso&m)/( $·kAh−1)20
Tab.2  Parameters of system components
Tab.3  Results of optimal sizing without energy filter
Fig.6  Power generated by the hybrid system
Fig.7  Power supplied to the grid without filter
Tab.4  Results of optimal sizing with energy filter
Fig.8  Comparison of power injected into the grid
Fig.9  Battery power with energy filter
Tab.5  Results of optimal sizing with proposed energy filter
Fig.10  Power injected into the grid with proposed energy filter
Fig.11  No. of iterations versus cost function plot
Fig.12  Battery state of charge
Fig.13  Probability of fluctuation rate
ParameterWithout filterWith energy filterWith proposed filter
Cpc(×106) ($)5.671700
Tab.6  Comparison of three strategies
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