Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt

Mohamed Hamdi; Hafez A. El Salmawy; Reda Ragab; Mohamed Hamdi; Hafez A. El Salmawy; Reda Ragab

doi:10.3934/energy.2023010

AIMS Energy

2023, Volume 11, Issue 1: 171-196. doi: 10.3934/energy.2023010

Previous Article Next Article

Research article Special Issues

Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt

Mechanical Power Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt

Received: 04 October 2022 Revised: 18 February 2023 Accepted: 20 February 2023 Published: 02 March 2023

The present paper examines the potential hybridization for a dispatchable hybrid renewable energy system (HRES). The plant has been examined for existence in the city of Ras Ghareb, Egypt and follows the load profile of Egypt. The proposed plant configuration contains a wind plant, a solar photovoltaic plant, vanadium redox flow batteries (VRFBs) and a hydrogen system consisting of an electrolyzer, hydrogen tanks and fuel cells (FCs), the latter of which are for both daily and seasonal storage. Professional software tools have been used to model the wind and solar resources. Simulations for both the battery and hydrogen generation and electrolyzer operation are also considered. The output of these simulations is used to configure the HRES using MATLAB. The optimization objective function of the HRES is based on the least levelized cost of energy (LCOE) with constraints for a zero loss of power supply probability (LPSP) and curtailed energy. The optimization has been achieved by using artificial neural networks and a MATLAB program. The results show that the optimal system can handle 91.2% of the load directly from the renewable energy sources (wind and solar), while the rest of the demand comes from the storage system (FCs and VRFBs). The LCOE of the optimal system configuration is (USD) 9.3 %/kWh, with both the LPSP and curtailed energy at zero values. This cost can be reduced by 14.5% if the constraint of zero curtailed energy is relaxed by 10%. Despite the load being maximum in summer, the energy storage requirement is predicted to be maximum in winter due to the low wind profile and solar radiation in winter months. Energy storage system size is dependent on both seasonal and daily variations in wind and solar profiles. In addition, energy storage size is the main factor that determines the LCOE of the system.
- hybrid renewable energy systems,
- dispatchable power plant,
- wind energy,
- solar energy,
- energy storage,
- ANN
Citation: Mohamed Hamdi, Hafez A. El Salmawy, Reda Ragab. Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt[J]. AIMS Energy, 2023, 11(1): 171-196. doi: 10.3934/energy.2023010

Related Papers:

Abstract

The present paper examines the potential hybridization for a dispatchable hybrid renewable energy system (HRES). The plant has been examined for existence in the city of Ras Ghareb, Egypt and follows the load profile of Egypt. The proposed plant configuration contains a wind plant, a solar photovoltaic plant, vanadium redox flow batteries (VRFBs) and a hydrogen system consisting of an electrolyzer, hydrogen tanks and fuel cells (FCs), the latter of which are for both daily and seasonal storage. Professional software tools have been used to model the wind and solar resources. Simulations for both the battery and hydrogen generation and electrolyzer operation are also considered. The output of these simulations is used to configure the HRES using MATLAB. The optimization objective function of the HRES is based on the least levelized cost of energy (LCOE) with constraints for a zero loss of power supply probability (LPSP) and curtailed energy. The optimization has been achieved by using artificial neural networks and a MATLAB program. The results show that the optimal system can handle 91.2% of the load directly from the renewable energy sources (wind and solar), while the rest of the demand comes from the storage system (FCs and VRFBs). The LCOE of the optimal system configuration is (USD) 9.3 %/kWh, with both the LPSP and curtailed energy at zero values. This cost can be reduced by 14.5% if the constraint of zero curtailed energy is relaxed by 10%. Despite the load being maximum in summer, the energy storage requirement is predicted to be maximum in winter due to the low wind profile and solar radiation in winter months. Energy storage system size is dependent on both seasonal and daily variations in wind and solar profiles. In addition, energy storage size is the main factor that determines the LCOE of the system.

References

[1]	Akuru UB, Onukwube IE, Okoro OI, et al. (2017) Towards 100% renewable energy in Nigeria. Renewable Sustainable Energy Rev 71: 943–953. https://doi.org/10.1016/j.rser.2016.12.123 doi: 10.1016/j.rser.2016.12.123
[2]	Kiwan S, Al-Gharibeh E (2020) Jordan toward a 100% renewable electricity system. Renewable Energy 147: 423–436. https://doi.org/10.1016/j.renene.2019.09.004 doi: 10.1016/j.renene.2019.09.004
[3]	Hansen K, Mathiesen BV, Skov IR (2019) Full energy system transition towards 100% renewable energy in Germany in 2050. Renewable Sustainable Energy Rev 102: 1–13. https://doi.org/10.1016/j.rser.2018.11.038 doi: 10.1016/j.rser.2018.11.038
[4]	Zappa W, Junginger M, van den Broek M (2019) Is a 100% renewable European power system feasible by 2050? Appl Energy 233–234: 1027–1050. https://doi.org/10.1016/j.apenergy.2018.08.109 doi: 10.1016/j.apenergy.2018.08.109
[5]	International Energy Agency (2022) Renewable Energy Market Update. https://doi.org/10.1787/faf30e5a-en
[6]	IEA (2019) Data tables—Data & Statistics. Available from: https://www.iea.org/data-and-statistics/data-tables/?country = WORLD & energy = Electricity & year = 2019.
[7]	Welsch M, Deane P, Howells M, et al. (2014) Incorporating flexibility requirements into long-term energy system models—A case study on high levels of renewable electricity penetration in Ireland. Appl Energy 135: 600–615. https://doi.org/10.1016/j.apenergy.2014.08.072 doi: 10.1016/j.apenergy.2014.08.072
[8]	Panagiotakopoulou P (2012) Analysing the effects of future generation and grid investments on the Spanish power market, with large scale wind integration, using PLEXOS Ⓡ for Power. 11th Wind Integr Work.
[9]	Steurer M, Fahl U, Voß A, et al. (2017) Curtailment: An option for cost-efficient integration of variable renewable generation? Eur Energy Transit, 97–104. https://doi.org/10.1016/B978-0-12-809806-6.00015-8 doi: 10.1016/B978-0-12-809806-6.00015-8
[10]	Van Den Bergh K, Delarue E (2015) Cycling of conventional power plants: Technical limits and actual costs. Energy Convers Manage 97: 70–77. https://doi.org/10.1016/j.enconman.2015.03.026 doi: 10.1016/j.enconman.2015.03.026
[11]	Bizon N, Oproescu M, Raceanu M (2015) Efficient energy control strategies for a standalone renewable/fuel cell hybrid power source. Energy Convers Manage 90. https://doi.org/10.1016/j.enconman.2014.11.002 doi: 10.1016/j.enconman.2014.11.002
[12]	Aziz AS, Tajuddin MFN, Adzman MR, et al. (2019) Energy management and optimization of a PV/diesel/battery hybrid energy system using a combined dispatch strategy. Sustainability 11: 683. https://doi.org/10.3390/su11030683 doi: 10.3390/su11030683
[13]	Pérez-Navarro A, Alfonso D, Ariza HE, et al. (2016) Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy 86: 384–391. https://doi.org/10.1016/j.renene.2015.08.030 doi: 10.1016/j.renene.2015.08.030
[14]	Al-Ghussain L, Ahmed H, Haneef F (2018) Optimization of hybrid PV-wind system: Case study Al-Tafilah cement factory, Jordan. Sustainable Energy Technol Assess 30: 24–36. https://doi.org/10.1016/j.seta.2018.08.008 doi: 10.1016/j.seta.2018.08.008
[15]	Samy MM, Emam A, Tag-Eldin E, et al. (2022) Exploring energy storage methods for grid-connected clean power plants in case of repetitive outages. J Energy Storage 54: 105307. https://doi.org/10.1016/j.est.2022.105307 doi: 10.1016/j.est.2022.105307
[16]	Yang Y, Menictas C, Bremner S, et al. (2018) A Comparison study of dispatching various battery technologies in a hybrid PV and wind power plant. 2018 IEEE Power & Energy Society General Meeting (PESGM), 1–5. https://doi.org/10.1109/PESGM.2018.8585803 doi: 10.1109/PESGM.2018.8585803
[17]	Fathima H, Palanisamy K (2015) Optimized sizing, selection, and economic analysis of battery energy storage for grid-connected wind-PV hybrid system. Model Simul Eng 2015: 1–16. https://doi.org/10.1155/2015/713530 doi: 10.1155/2015/713530
[18]	Ghorbani N, Kasaeian A, Toopshekan A, et al. (2017) Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability. Energy 154: 581–591. https://doi.org/10.1016/j.energy.2017.12.057 doi: 10.1016/j.energy.2017.12.057
[19]	Cano A, Jurado F, Sánchez H, et al. (2014) Optimal sizing of stand-alone hybrid systems based on PV/WT/FC by using several methodologies. J Energy Inst 87: 330–340. https://doi.org/10.1016/j.joei.2014.03.028 doi: 10.1016/j.joei.2014.03.028
[20]	Ceran B (2019) The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer. Energy 167: 853–865. https://doi.org/10.1016/j.energy.2018.11.028 doi: 10.1016/j.energy.2018.11.028
[21]	Nasiraghdam H, Jadid S (2012) Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm. Sol Energy 86: 3057–3071. https://doi.org/10.1016/j.solener.2012.07.014 doi: 10.1016/j.solener.2012.07.014
[22]	Das HS, Tan CW, Yatim AHM, et al. (2017) Feasibility analysis of hybrid photovoltaic/battery/fuel cell energy system for an indigenous residence in East Malaysia. Renewable Sustainable Energy Rev 76: 1332–1347. https://doi.org/10.1016/j.rser.2017.01.174 doi: 10.1016/j.rser.2017.01.174
[23]	Zurita A, Mata-Torres C, Valenzuela C, et al. (2018) Techno-economic evaluation of a hybrid CSP + PV plant integrated with thermal energy storage and a large-scale battery energy storage system for base generation. Sol Energy 173: 1262–1277. https://doi.org/10.1016/j.solener.2018.08.061 doi: 10.1016/j.solener.2018.08.061
[24]	Hosseinalizadeh R, Shakouri G H, Amalnick MS, et al. (2016) Economic sizing of a hybrid (PV-WT-FC) renewable energy system (HRES) for stand-alone usages by an optimization-simulation model: Case study of Iran. Renewable Sustainable Energy Rev 54: 139–150. https://doi.org/10.1016/j.rser.2015.09.046 doi: 10.1016/j.rser.2015.09.046
[25]	Jing W, Lai CH, Wong WSH, et al. (2018) A comprehensive study of battery-supercapacitor hybrid energy storage system for standalone PV power system in rural electrification. Appl Energy 224: 340–356. https://doi.org/10.1016/j.apenergy.2018.04.106 doi: 10.1016/j.apenergy.2018.04.106
[26]	Luta DN, Raji AK (2019) Optimal sizing of hybrid fuel cell-supercapacitor storage system for off-grid renewable applications. Energy 166: 530–540. https://doi.org/10.1016/j.energy.2018.10.070 doi: 10.1016/j.energy.2018.10.070
[27]	Wu T, Zhang H, Shang L (2020) Optimal sizing of a grid-connected hybrid renewable energy systems considering hydroelectric storage. Energy Sources, Part A: Recover Util Environ Eff. https://doi.org/10.1080/15567036.2020.1731018 doi: 10.1080/15567036.2020.1731018
[28]	Heydari A, Askarzadeh A (2016) Techno-economic analysis of a PV/biomass/fuel cell energy system considering different fuel cell system initial capital costs. Sol Energy 133: 409–420. https://doi.org/10.1016/j.solener.2016.04.018 doi: 10.1016/j.solener.2016.04.018
[29]	Halabi LM, Mekhilef S, Olatomiwa L, et al. (2017) Performance analysis of hybrid PV/diesel/battery system using HOMER: A case study Sabah, Malaysia. Energy Convers Manage 144: 322–339. https://doi.org/10.1016/j.enconman.2017.04.070 doi: 10.1016/j.enconman.2017.04.070
[30]	Lau KY, Yousof MFM, Arshad SNM, et al. (2010) Performance analysis of hybrid photovoltaic/diesel energy system under Malaysian conditions. Energy 35: 3245–3255. https://doi.org/10.1016/j.energy.2010.04.008 doi: 10.1016/j.energy.2010.04.008
[31]	Javed MS, Song A, Ma T (2019) Techno-economic assessment of a stand-alone hybrid solar-wind-battery system for a remote island using genetic algorithm. Energy 176: 704–717. https://doi.org/10.1016/j.energy.2019.03.131 doi: 10.1016/j.energy.2019.03.131
[32]	Nagapurkar P, Smith JD (2019) Techno-economic optimization and environmental life cycle assessment (LCA) of microgrids located in the US using genetic algorithm. Energy Convers Manage 181: 272–291. https://doi.org/10.1016/j.enconman.2018.11.072 doi: 10.1016/j.enconman.2018.11.072
[33]	Tabanjat A, Becherif M, Hissel D, et al. (2018) Energy management hypothesis for hybrid power system of H2/WT/PV/GMT via AI techniques. Int J Hydrogen Energy 43: 3527–3541. https://doi.org/10.1016/j.ijhydene.2017.06.085 doi: 10.1016/j.ijhydene.2017.06.085
[34]	Patel AM, Singal SK (2019) Optimal component selection of integrated renewable energy system for power generation in stand-alone applications. Energy 175: 481–504. https://doi.org/10.1016/j.energy.2019.03.055 doi: 10.1016/j.energy.2019.03.055
[35]	Dhundhara S, Verma YP, Williams A (2018) Techno-economic analysis of the lithium-ion and lead-acid battery in microgrid systems. Energy Convers Manage 177: 122–142. https://doi.org/10.1016/j.enconman.2018.09.030 doi: 10.1016/j.enconman.2018.09.030
[36]	Zhang W, Maleki A, Rosen MA, et al. (2018) Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy 163: 191–207. https://doi.org/10.1016/j.energy.2018.08.112 doi: 10.1016/j.energy.2018.08.112
[37]	Brekken TKA, Yokochi A, Von Jouanne A, et al. (2011) Optimal energy storage sizing and control for wind power applications. IEEE Trans Sustainable Energy 2: 69–77. https://doi.org/10.1109/TSTE.2010.2066294 doi: 10.1109/TSTE.2010.2066294
[38]	Zhang W, Maleki A, Rosen MA, et al. (2019) Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm. Energy Convers Manage 180: 609–621. https://doi.org/10.1016/j.enconman.2018.08.102 doi: 10.1016/j.enconman.2018.08.102
[39]	Hamdi M, Ragab R, El Salmawy HA (2023) The value of diurnal and seasonal energy storage in baseload renewable energy systems: A case study of Ras Ghareb-Egypt. J Energy Storage 61: 106764. https://doi.org/10.1016/j.est.2023.106764 doi: 10.1016/j.est.2023.106764
[40]	Rahman MM, Shakeri M, Tiong SK, et al. (2021) Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks. Sustainability 13: 1–28. https://doi.org/10.3390/su13042393 doi: 10.3390/su13042393
[41]	Ragab R, Hamdi M, Al ST, et al. (2021) Optimized hybrid renewable energy system for a baseload plant. Appl Energy Symp.
[42]	Mortensen, NG, Hansen JC, Badger J, et al. (2005) Wind Atlas for Egypt, measurements and modelling. 1991–2005.
[43]	EMD International, windPRO. Available from: https://www.emd-international.com/windpro/.
[44]	Renewable power generation costs in 2020. Available from: https://www.irena.org/publications/2021/Jun/Renewable-Power-Costs-in-2020.
[45]	Egyptian electricity holding company (2020) Annual report of the Egyptian electricity holding company2019/2020.
[46]	Goyena R, Fallis A (2019) Solar atlas of Egypt.
[47]	EU Science Hub. Photovoltaic Geographical Information System (PVGIS). Available from: https://ec.europa.eu/jrc/en/pvgis.
[48]	Home-System Advisor Model (SAM). Available from: https://sam.nrel.gov/.
[49]	Home Page-New and Renewable Energy Authoroty. Available from: http://www.nrea.gov.eg/.
[50]	Guarnieri M, Mattavelli P, Petrone G, et al. (2016) Vanadium redox flow batteries: Potentials and challenges of an emerging storage technology. IEEE Ind Electron Mag 10: 20–31. https://doi.org/10.1109/MIE.2016.2611760 doi: 10.1109/MIE.2016.2611760
[51]	Castillo A, Gayme DF (2014) Grid-scale energy storage applications in renewable energy integration: A survey. Energy Convers Manage 87: 885–894. https://doi.org/10.1016/j.enconman.2014.07.063 doi: 10.1016/j.enconman.2014.07.063
[52]	IRENA (2017) Electricity storage and renewables: Costs and markets to 2030.
[53]	Abdalla AM, Hossain S, Nisfindy OB, et al. (2018) Hydrogen production, storage, transportation and key challenges with applications: A review. Energy Convers Manage 165: 602–627. https://doi.org/10.1016/j.enconman.2018.03.088 doi: 10.1016/j.enconman.2018.03.088
[54]	Abe JO, Popoola API, Ajenifuja E, et al. (2019) Hydrogen energy, economy and storage: Review and recommendation. Int J Hydrogen Energy 44: 15072–15086. https://doi.org/10.1016/j.ijhydene.2019.04.068 doi: 10.1016/j.ijhydene.2019.04.068
[55]	Moradi R, Groth KM (2019) Hydrogen storage and delivery: Review of the state of the art technologies and risk and reliability analysis. Int J Hydrogen Energy 44: 12254–12269. https://doi.org/10.1016/j.ijhydene.2019.03.041 doi: 10.1016/j.ijhydene.2019.03.041
[56]	Gahleitner G (2012) Hydrogen from renewable electricity: An international review of power-to-gas pilot plants for stationary applications. Int J Hydrogen Energy 38: 2039–2061. https://doi.org/10.1016/j.ijhydene.2012.12.010 doi: 10.1016/j.ijhydene.2012.12.010
[57]	El-Emam RS, Özcan H (2019) Comprehensive review on the techno-economics of sustainable large-scale clean hydrogen production. J Clean Prod 220: 593–609. https://doi.org/10.1016/j.jclepro.2019.01.309 doi: 10.1016/j.jclepro.2019.01.309
[58]	Siemens Energy—Benefits of green hydrogen. Available from: https://www.siemens-energy.com/global/en/offerings/renewable-energy/hydrogen-solutions.html.
[59]	IRENA (2020) Green hydrogen cost reduction.

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)