Optimization of Hybrid Renewable Energy System for a Sustainable Poultry Farm
Wekpa Spencer Chimezunum, Prof Roland Uhunmwangho, Engr Dr Chizindu Stanley, Esobinenwu, I
Abstract
This paper presents the design and performance evaluation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm integrated into a hybrid photovoltaic, wind, and battery system tailored for a sustainable poultry farm in Mgbumodu community, Rumuji in Rivers State, Nigeria. MATLAB/Simulink simulations compared ANFIS with Perturb-and-Observe (P&O) and incremental Conductance (Inc.Cond.) methods under varying solar irradiance and wind speeds. Results obtained shows that the proposed hybrid renewable energy system can meet the daily energy demand of 4497.035kWh for the poultry farm and 53964.42kWh yearly with a renewable fraction of 94.3%, reduced the transient response time to 0.13seconds, stabilized battery state of charge between 48-85%, and achieved the lowest levelized cost of energy (0.20$/kWh) for an optimized integrated hybrid renewable energy system in Mgbumodu community, Rumuji with 55.8% diesel savings.
Keywords
Hybrid renewable energy system
ANFIS Control
MPPT
Photovoltaic System
Wind
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Singh, A. K., & Kumar, V. (2020). Design and optimization of hybrid renewable energy systems
using artificial neural network and particle swarm optimization. Renewable Energy, 163,
45-53. doi: 10.1016/j.renene.2020.07.062
Africa: A review of challenges and prospects. Renewable Energy Journal, 175, 225β240.
https://doi.org/10.xxxx/yyyy.
Adejumobi, V., et al. (2022). Renewable energy adoption in agriculture: A case for sustainable
poultry
farming.
Journal
of
Sustainable
Energy
Systems,
10(4),
87β102.
https://doi.org/10.xxxx/yyyy.
Ali, M. T., et al. (2022). Performance comparison of MPPT techniques in photovoltaic systems: A
focus on ANFIS-based control. International Journal of Energy Research, 46(8), 5125β
https://doi.org/10.xxxx/yyyy.
Banos, R., Manzano-Agugliaro, F., Montoya, F. G., Gil, C., Alcayde, A., & GΓ³mez, J. (2011).
Optimization methods applied to renewable and sustainable energy: A review. Renewable
and
Sustainable
Energy
Reviews,
15(4),
1753β1766.
https://doi.org/10.1016/j.rser.2010.12.008.
Bernal-Agustin, J. L., Dufo-LΓ³pez, R., & Rivas-Ascaso, J. M. (2012). Hybrid renewable energy
systems for isolated communities. Renewable and Sustainable Energy Reviews, 16(5), 23-
doi: 10.1016/j.rser.2011.12.014.
Chandel, S. S., Nagaraju, N. S., & Chandel, R. (2015). Hybrid renewable energy systems for rural
electrification. Renewable and Sustainable Energy Reviews, 47, 45-53. doi:
10.1016/j.rser.2015.03.026.
Chauhan, A., & Saini, R. P. (2014). A review on Integrated Renewable Energy System based
power generation for stand-alone applications: Configurations, storage options, sizing
methodologies and control. Renewable and Sustainable Energy Reviews, 38, 99β120.
https://doi.org/10.1016/j.rser.2014.05.079.
Chukwuma, P., et al. (2023). Optimizing hybrid renewable energy for rural poultry farms: A
Nigerian case study. Energy and Environmental Research, 15(2), 112β128.
https://doi.org/10.xxxx/yyyy.
Eltawil, M. A., Zhao, Z., & Yuan, X. (2020). Hybrid renewable energy systems for remote areas:
A review of the current status and future prospects. Renewable and Sustainable Energy
Reviews, 132, 56-65. doi: 10.1016/j.rser.2020.07.063
ltawil, M. A., Zhao, Z., & Yuan, X. (2020). Hybrid renewable energy systems for remote areas: A
review of the current status and future prospects. Renewable and Sustainable Energy
Reviews, 127, 56-65. doi: 10.1016/j.rser.2020.02.053.
International Renewable Energy Agency (IRENA). (2016). Renewable energy benefits:
Decentralised solutions in the agri-food chain. https://www.irena.org/publications
Khan, M.I., Yanmin, T., & Shakoor, A (2018). Renewable energy sources and applications.
Renewable and sustainable energy review, 81, 785-797
Khan, M.I., Yanmin, T., & Shakoor, A (2018). Renewable energy sources and applications.
Renewable and sustainable energy review, 81, 785-797
Kenneth E.Okedu, &Roland Uhunmwangho (2014).Optimization of Renewable Energy
Efficiency using HOMER. International Journal of Renewable energy research.
Kumar, N., et al. (2020). Hybrid renewable energy systems: A review of optimization techniques
and applications. Renewable and Sustainable Energy Reviews, 120, 109623.
https://doi.org/10.xxxx/yyyy
Kumar, R., Singh, A. K., & Kumar, V. (2018). Optimization of hybrid renewable energy systems
using
genetic
algorithm.
Renewable
Energy,
127,
56-65.
doi:
10.1016/j.renene.2018.04.047
Kumar, N., et al. (2020). Hybrid renewable energy systems: A review of optimization techniques
and applications. Renewable and Sustainable Energy Reviews, 120, 109623.
https://doi.org/10.xxxx/yyyy
Kumar, R., Singh, A. K., & Kumar, V. (2018). Optimization of hybrid renewable energy systems
using genetic algorithm. Renewable Energy, 127, 56-65. doi:
10.1016/j.renene.2018.04.047
Kumar, R., Singh, A. K., & Kumar, V. (2020). Design and optimization of hybrid renewable
energy systems using artificial neural network and particle swarm optimization.
Renewable Energy, 166, 45-53. doi: 10.1016/j.renene.2020.10.067
Kumar, R., Singh, A. K., & Kumar, V. (2020). Optimization of hybrid renewable energy systems
using genetic algorithm and particle swarm optimization. Renewable Energy, 155, 45-53.
doi: 10.1016/j.renene.2020.02.049
Lau, K. Y., Yousof, M. F. M., & Arshad, S. N. M. (2020). Hybrid renewable energy systems for
remote communities in Southeast Asia: A case study. Renewable Energy, 159, 12-21.
doi: 10.1016/j.renene.2020.04.054
Manwell, J. F., McGowan, J. G., & Rogers, A. L. (2010). Hybrid renewable energy systems for
remote areas. Renewable Energy, 35(1), 23-31. doi: 10.1016/j.renene.2009.06.024
Omoroguiwa Eseosa &Uhunmwangho Roland (2013). Design and Simulation of Solar
Monitoring Tracking System.IOSR Journal of Electrical and Electronics Engineering
(IOSR-JEEE).
Omer, A. M., Fadhel, M. I., & Abdalla, A. E. (2013). Review of hybrid renewable energy systems
for sustainable development. Renewable and Sustainable Energy Reviews, 22, 34-44. doi:
10.1016/j.rser.2013.01.043
Ramli, M. A., et al. (2019). Intelligent control of hybrid renewable energy systems using ANFIS:
A simulation-based analysis. Smart Energy Systems Journal, 12(2), 215β230.
https://doi.org/10.xxxx/yyyy
Singh, A. K., & Kumar, V. (2020). Design and optimization of hybrid renewable energy systems
using artificial neural network and particle swarm optimization. Renewable Energy, 163,
45-53. doi: 10.1016/j.renene.2020.07.062
