Abstract

The global energy scenario is changing, and clean generation is leading the way. As new ways of producing energy becomes more popular, microgeneration takes a greater share of the market, supplying both isolated and grid connected systems. In such context, hybrid systems that combine two or more different types of sources are a promising approach, although their valuable application depends on many intrinsic and ambient factors and should be thoroughly evaluated. To assess system performance in advance, not only to better predict its operation but also to well design it for specific applications without the need to install it on site, accurate computational models that reflect its real behavior should be developed. Aiming to contribute on this matter, this work presents a dynamic model for a solar-wind microgeneration system, which allows the evaluation of the system’s behavior under different demand scenarios, as well as to implement different energy management strategies. In addition to the base model comprised a photovoltaic panel, a wind turbine, an inverter, and an energy storage element, this work presents the implementation and performance comparison of two different maximum power point tracking algorithms based on soft computing techniques and on real data collected at a meteorological station. Results validate the hybrid microgeneration model and provides insight on the specification of maximum power point tracking algorithms for specific applications.

References

1.
Panwar
,
N.
,
Kaushik
,
S.
, and
Kothari
,
S.
,
2011
, “
Role of Renewable Energy Sources in Environmental Protection: A Review
,”
Renewable Sustainable Energy Rev.
,
15
(
3
), pp.
1513
1524
.
2.
Goldemberg
,
José
, and
Lucon
,
Oswaldo
,
2007
,
Revista USP
,
Universidade de São Paulo
,
São Paulo
, pp.
6
15
.
3.
Moreira
,
R. I. P.
,
2010
, “
Avaliação Do Potencial Eólico Em Regime De Microgeração
,” Master’s thesis,
Universidade do Porto
,
Porto, Portugal
.
4.
Masters
,
Gilbert M.
,
2013
,
Renewable and Efficient Electric Power Systems
, 2nd ed.,
John Wiley & Sons
,
Hoboken, NJ
.
5.
Marco Tina
,
G.
,
2016
, “
Simulation Model of Photovoltaic and Photovoltaic/Thermal Module/String Under Nonuniform Distribution of Irradiance and Temperature
,”
ASME J. Sol. Energy. Eng.
,
139
(
2
), p.
021013
.
6.
Sumathi
,
S.
,
Kumar
,
L. Ashok
, and
Surekha
,
P.
,
2015
,
Solar PV and Wind Energy Conversion Systems : An Introduction to Theory, Modeling With MATLAB/SIMULINK, and the Role of Soft Computing Techniques
,
Springer
,
Switzerland
.
7.
Khatib
,
T.
,
Direya
,
R.
, and
Said
,
A.
,
2021
, “
An Improved Method for Extracting Photovoltaic Module IV Characteristic Curve Using Hybrid Learning Machine System
,”
ASME J. Sol. Energy. Eng.
,
143
(
5
), p.
051006
.
8.
Bellia
,
H.
,
Youcef
,
R.
, and
Fatima
,
M.
,
2014
, “
A Detailed Modeling of Photovoltaic Module Using Matlab
,”
NRIAG J. Astron. Geophys.
,
3
(
1
), pp.
53
61
.
9.
Sumathi
,
V.
,
Jayapragash
,
R.
,
Bakshi
,
A.
, and
Akella
,
P. K.
,
2017
, “
Solar Tracking Methods to Maximize PV System Output—A Review of the Methods Adopted in Recent Decade
,”
Renewable Sustainable Energy Rev.
,
74
, pp.
130
138
.
10.
The Mathworks Inc.
,
2021
, “
Simulink User’s Guide
,” https://www.mathworks.com/help/pdf˙doc/simulink/simulink˙ug.pdfAccessed January 11, 2021.
11.
Rodrigues
,
Paulo Roberto
,
Andrade Guerra
,
J. B.
, and
Youssef
,
Y.
,
2011
,
Energia Eólica em Energias Renováveis
,
Unisul
,
Santa Catarina
.
12.
Yaramasu
,
Venkata
, and
Wu
,
Bin
,
2017
,
Model Predictive Control of Wind Energy Conversion Systems
,
IEEE Press John Wiley & Sons, Inc
,
Hoboken, NJ
.
13.
Astolfi
,
D.
,
Castellani
,
F.
,
Lombardi
,
A.
, and
Terzi
,
L.
,
2018
, “
About the Extension of Wind Turbine Power Curve in the High Wind Region
,”
ASME J. Sol. Energy. Eng.
,
141
(
1
), p.
014501
.
14.
Abouadane
,
H.
,
Fakkar
,
A.
, and
Oukarfi
,
B.
,
2019
, “
Optimal Command for Photovoltaic Systems in Real Outdoor Weather Conditions
,”
ASME J. Sol. Energy. Eng.
,
142
(
1
), p.
011002
.
15.
Ram
,
J. P.
,
Babu
,
T. S.
, and
Rajasekar
,
N.
,
2017
, “
A Comprehensive Review on Solar PV Maximum Power Point Tracking Techniques
,”
Renewable Sustainable Energy Rev.
,
67
, pp.
826
847
.
16.
Verma
,
D.
,
Nema
,
S.
,
Shandilya
,
A.
, and
Dash
,
S. K.
,
2016
, “
Maximum Power Point Tracking (MPPT) Techniques: Recapitulation in Solar Photovoltaic Systems
,”
Renewable Sustainable Energy Energy. Rev.
,
54
, pp.
1018
1034
.
17.
Pathak
,
P. K.
,
Yadav
,
A. K.
, and
Alvi
,
P. A.
,
2020
, “
Advanced Solar MPPT Techniques Under Uniform and Non-Uniform Irradiance: A Comprehensive Review
,”
ASME J. Sol. Energy. Eng.
,
142
(
4
), p.
040801
.
18.
Thakran
,
S.
,
Singh
,
J.
,
Garg
,
R.
, and
Mahajan
,
P.
,
2018
, “
Implementation of P&O Algorithm for MPPT in SPV System
,”
2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC)
,
IEEE
,
Greater Noida, India
,
Apr. 13–14
, pp.
242
245
.
19.
Dhaouadi
,
G.
,
Djamel
,
O.
,
Youcef
,
S.
, and
Salah
,
C.
,
2019
, “
Implementation of Incremental Conductance Based MPPT Algorithm for Photovoltaic System
,”
2019 4th International Conference on Power Electronics and their Applications (ICPEA)
,
IEEE
,
Elazig, Turkey
,
Sep. 25–27
, pp.
1
5
.
20.
Anowar
,
M. H.
, and
Roy
,
P.
,
2019
, “
A Modified Incremental Conductance Based Photovoltaic MPPT Charge Controller
,”
2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)
,
IEEE
,
Cox’sBazar, Bangladesh
,
Feb. 7–9
, pp.
1
5
.
21.
Mokhlis
,
M.
,
Ferfra
,
M.
,
Vall
,
H. A.
, and
Taouni
,
A.
,
2020
, “
Comparative Study Between the Different MPPT Techniques
,”
2020 5th International Conference on Renewable Energies for Developing Countries (REDEC)
,
Marrakech, Morocco
,
Mar. 24–25
, pp.
1
6
.
22.
Zhang
,
X.
,
Zhang
,
H.
,
Zhang
,
H.
,
Zhang
,
P.
,
Wang
,
F.
,
Jia
,
H.
, and
Song
,
D.
,
2016
, “
A Variable Step-size P&O Method in the Application of MPPT Control for a PV System
,”
2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)
,
Xi’an, China
,
IEEE
,
Oct. 3–5
, pp.
1503
1506
.
23.
Jiandong
,
D.
,
Ma
,
X.
, and
Tuo
,
S.
,
2018
, “
A Variable Step Size P&O MPPT Algorithm for Three-Phase Grid-Connected PV Systems
,”
2018 China International Conference on Electricity Distribution (CICED)
,
Tianjin, China
,
IEEE
,
Sep. 17–19
, pp.
1997
2001
.
24.
Zhang
,
F.
,
Thanapalan
,
K.
,
Procter
,
A.
,
Carr
,
S.
, and
Maddy
,
J.
,
2013
, “
Adaptive Hybrid Maximum Power Point Tracking Method for a Photovoltaic System
,”
IEEE Trans. Energy Conversion
,
28
(
2
), pp.
353
360
.
25.
Wang
,
P.
,
Zhou
,
Z.
,
Cai
,
M. M.
, and
Zhang
,
J. B.
,
2013
, “
An Improved Multistage Variable-Step MPPT Algorithm for Photovoltaic System
,”
Applied Mechanics and Materials
,
347
, pp.
1833
1838
.
26.
Joshi
,
P.
, and
Arora
,
S.
,
2017
, “
Maximum Power Point Tracking Methodologies for Solar PV Systems: A Review
,”
Renewable Sustainable Energy Rev.
,
70
, pp.
1154
1177
.
27.
Kumar
,
D.
, and
Chatterjee
,
K.
,
2016
, “
A Review of Conventional and Advanced MPPT Algorithms for Wind Energy Systems
,”
Renewable Sustainable Energy Rev.
,
55
, pp.
957
970
.
28.
Seyedmahmoudian
,
M.
,
Horan
,
B.
,
Soon
,
T. K.
,
Rahmani
,
R.
,
Than Oo
,
A. M.
,
Mekhilef
,
S.
, and
Stojcevski
,
A.
,
2016
, “
State of the Art Artificial Intelligence-Based MPPT Techniques for Mitigating Partial Shading Effects on PV Systems: A Review
,”
Renewable Sustainable Energy Rev.
,
64
, pp.
435
455
.
29.
Shams
,
I.
,
Mekhilef
,
S.
, and
Tey
,
K. S.
,
2020
, “
Maximum Power Point Tracking Using Modified Butterfly Optimization Algorithm for Partial Shading, Uniform Shading, and Fast Varying Load Conditions
,”
IEEE Trans. Power Electron.
,
36
(
5
), pp.
5569
5581
.
30.
Boukenoui
,
R.
, and
Mellit
,
A.
,
2019
,
Applications of Improved Versions of Fuzzy Logic Based Maximum Power Point Tracking for Controlling Photovoltaic Systems
,
Springer Singapore
,
Singapore
, pp.
143
164
.
31.
Sachan
,
A.
,
Gupta
,
A. K.
, and
Samuel
,
P.
,
2016
, “
A Review of MPPT Algorithms Employed in Wind Energy Conversion Systems
,”
J. Green Eng.
,
6
(
4
), pp.
385
402
.
32.
Oliveira
,
P. H. T. M.
,
2020
, “
Modelagem De Um Sistema De Microgeração De Energia Solar-eólico Com Busca De Ponto De Máxima Potência De Operação
,”
Master’s thesis
,
Universidade do Estado do Rio de Janeiro
,
Rio de Janeiro, Brazil
.
33.
Chakraverty
,
S.
,
Sahoo
,
D. M.
, and
Mahato
,
N. R.
,
2019
,
Concepts of Soft Computing: Fuzzy and ANN With Programming
,
Springer
,
Switzerland
.
34.
Reddy
,
D.
, and
Ramasamy
,
S.
,
2017
, “
A Fuzzy Logic MPPT Controller Based Three Phase Grid-Tied Solar PV System With Improved CPI Voltage
,”
2017 Innovations in Power and Advanced Computing Technologies (i-PACT)
,
IEEE
,
Vellore, India
,
Apr. 21–22
, pp.
1
6
.
35.
Ram
,
J. P.
,
Rajasekar
,
N.
, and
Miyatake
,
M.
,
2017
, “
Design and Overview of Maximum Power Point Tracking Techniques in Wind and Solar Photovoltaic Systems: A Review
,”
Renewable Sustainable Energy Rev.
,
73
, pp.
1138
1159
.
36.
Instituto Nacional de Meteorologia
,
2020
, “
Dados Histricos Anuais
,” https://portal.inmet.gov.br/dadoshistoricos, Accessed August 13, 2020.
37.
Lima
,
B. G. d.
,
Hack
,
R. R.
, and
Avença
,
R. B.
,
2015
, “
Comparação Dos Níveis De Irradiação Apresentados Por Diferentes Fontes De Dados No Estado Do Paraná E Determinação Do Potencial De Geração De Energia Elétrica Por Fonte Fotovoltaica Em Curitiba
,”
B.S. thesis
,
Universidade Tecnológica Federal do Paraná
,
Paraná, Brazil
.
38.
PS
,
A. S.
,
Suresh
,
K.
, and
Vinayaka
,
K.
,
2015
, “
Hybrid Wind-Solar Systems Using CUK-SEPIC Fused Converter With Quasi-Z-source Inverter
,”
2015 IEEE Power, Communication and Information Technology Conference (PCITC)
,
Bhubaneswar, India
,
IEEE
,
Oct. 15–17
, pp.
856
861
.
39.
Dali
,
M.
,
Belhadj
,
J.
, and
Roboam
,
X.
,
2010
, “
Hybrid Solar-Wwind System With Battery Storage Operating in Grid-Connected and Standalone Mode: Control and Energy Management Experimental Investigation
,”
Energy
,
35
(
6
), pp.
2587
2595
. 7th International Conference on Sustainable Energy Technologies.
40.
Lagorse
,
J.
,
Simões
,
M. G.
, and
Miraoui
,
A.
,
2009
, “
A Multiagent Fuzzy-Logic-Based Energy Management of Hybrid Systems
,”
IEEE. Trans. Ind. Appl.
,
45
(
6
), pp.
2123
2129
.
You do not currently have access to this content.