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Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System

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Abstract

This paper shows an application of hybrid PV/wind energy and battery storage in the islanded area. This work’s main target allows the distributed energy resources to contribute efficiently in the economic feasibility and enhance the environmental impact of the hybrid renewable energy source. Several factors such as levelized cost of energy (COE), greenhouse gas (GHG) emissions, and loss of power supply probability are studied. A combined solution is to compromise the economic and environmental aspects via the Utopia point approach is investigated. The optimal configuration of the hybrid PV/wind along with battery-storage and diesel engine as secondary source is obtained via meta-heuristic optimizers: Genetic Algorithm (GA) and Particle-Swarm Optimization (PSO) and impartial comparison of the results with HOMER software. The results of Utopia point solution show that the PV (about 46%) and wind turbine (about 13%) are shared significantly as primary renewable sources and battery storage (about 39%) as storage options. Meanwhile, the diesel engine (about 3%) has insignificant sharing in feeding the demand load. The optimal COE and GHG, which are achieved via GA and PSO optimization techniques are 0.182$/kWh and 12076 kg/year, agansit 0.343$/kWh and 17618 kg/year that are obtained via HOMER software, respectively. This corssponing to 47% and 31% reduction in COE and GHG, respectively. Sensitivity studies demonstrate that the variation of the wind energy sharing from 50 to 150% shows that the wind energy has a slight effect considering the GHG emissions. Contrarily, lower PV sharing ratios undesirably raises the levelized COE, however, reduces the GHG emissions.

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Abbreviations

AD:

Autonomy days

COE:

Cost of energy ($/kWh)

DOD:

Depth of discharge (%)

GHG:

Green-house gas

HRGs:

Hybrid renewable energy system

GA:

Genetic algorithm

PSO:

Particle swarm optimization

NOCT:

Normal operating cell temperature (°C)

NPC:

Net present cost

PV:

Photovoltaic cell

TNPC:

Total net present cost

LPSP:

Loss of power supply probability

O&M:

Operational & maintenance cost

REGU:

Renewable energy generating units

RF:

Replacement factors

CRF:

The capital recovery

PWF:

Present worth of maintenance

i:

Interested rate

if :

Inflation rate

N:

Total system lifetime in year

References

  1. Askari IB, Ameri M (2012) Techno-economic feasibility analysis of stand-alone renewable energy systems (PV/bat, wind/bat and hybrid PV/wind/bat) in Kerman, Iran. Energy Sources Part B Econ Plan Policy 7(1):45–60

    Article  Google Scholar 

  2. Singh S, Singh M, Kaushik SC (2016) Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system. Energy Convers Manag 128:178–190

    Article  Google Scholar 

  3. Isa NM, Das HS, Tan CW, Yatim AHM, Lau KY (2016) A techno-economic assessment of a combined and power photovoltaic/fuel cell/battery energy system in Malaysia hospital. Energy 12:75–90

    Article  Google Scholar 

  4. Bahramara S, Parsa Moghaddam M, Haghifam MR (2016) Optimal planning of hybrid renewable energy systems using HOMER: a review. Renew Sustain Energy Rev 62:609–620

    Article  Google Scholar 

  5. Erdinc O, Uzunglu M (2012) Optimum design of hybrid renewable energy systems: overview of different approaches. Renew Sustain Energy Rev 16:1412–1425

    Article  Google Scholar 

  6. Borhanazad H, Mekhilef S, Ganapathy VG, Modiri-Delshad M, Mirtaheri A (2014) Optimization of micro-grid system using MOPSO. Renew Energy 71:295–306

    Article  Google Scholar 

  7. Luna-Rubio R, Trejo-Perea M, Vargas-Vázquez D, Ríos-Moreno GJ (2012) Optimal sizing of renewable hybrids energy systems: a review of methodologies. Sol Energy 86(4):1077–1088

    Article  Google Scholar 

  8. Ellabban H. Abu-Rub, Blaabjerg F (2014) Renewable energy resources: current status, future prospects and their enabling technology. Renew Sustain Energy Rev 39:748–764

    Article  Google Scholar 

  9. Khani H, Farag HEZ (2018) Optimal day-ahead scheduling of power-to-gas energy storage and gas load management in wholesale electricity and gas markets. IEEE Trans Sustain Energy 9(2):940–951

  10. Shezan S, Farzana M, Hossain A, Ishrak A (2015) Techno-economic and feasibility analysis of a micro-grid winddg-battery hybrid energy system for remote and decentralized areas. Int J Adv Eng Technol IJAET 8:874–888

    Google Scholar 

  11. Kim D, Resasco J, Yu Y, Asiri AM, Yang P (2014) Synergistic geometric and electronic effects for electrochemical reduction of carbon dioxide using gold copper bimetallic nanoparticles. Nat Commun 5:4948

    Article  Google Scholar 

  12. Abo-Elyousr FK, Elnozahy A (2018) Bi-objective economic feasibility of hybrid micro-grid systems with multiple fuel options for islanded areas in Egypt. Renew Energy 128:37–56

    Article  Google Scholar 

  13. Nsafon BEK, Owolabi AB, Butu HM, Roh JW, Suh D, Huh J-S (2020) Optimization and sustainability analysis of PV/wind/diesel hybrid energy system for decentralized energy generation. Energy Strategy Rev 32:100570

    Article  Google Scholar 

  14. Jayachandran M, Ravi G (2017) Design and optimization of hybrid micro-grid system. In: 1st International conference on power engineering, computing and control, PECCON-2017, 2–4 March 2017, VIT University, Chennai Campus. Energy Procedia 117:95–103

  15. Namaane MAA, M’Sirdi NK (2013) Optimization of hybrid renewable energy systems (HRES) using PSO for cost reduction. The Mediterranean Green Energy Forum 2013, Energy Procedia 42:318–327

  16. Awan AB (2019) Performance analysis and optimization of a hybrid renewable energy system for sustainable NEOM city in Saudi Arabia. J Renew Sustain Energy 11:11–17

    Google Scholar 

  17. Dawoud SM, Lin X, Okba MI (2018) Hybrid renewable microgrid optimization techniques: a review. Renew Sustain Energy Rev 82:2039–2052

    Article  Google Scholar 

  18. Diab AAZ, Sultan HM, Mohamed IS, Oleg K, Do TD (2019) Application of different optimization algorithms for optimal sizing of PV/Wind/Diesel/Battery storage stand-alone hybrid microgrid. IEEE Access 7:119223–119245

    Article  Google Scholar 

  19. Borhanazad H, Mekhilef S (2014) Velappa gounder ganapathy, mostafa modiri-delshad and ali mirtaheri, optimization of micro-grid system using MOPSO. Direct Renew Energy 71:295–306

    Article  Google Scholar 

  20. Emad D, El Hameed EMA, Yousef EMT, El Fergany EAA (2020) Computational methods for optimal planning of hybrid renewable microgrids: a comprehensive review and challenges. Archiv Comput Methods Eng 27:1297–1319

    Article  MathSciNet  Google Scholar 

  21. Yimen N, Tchotang T, Kanmogne A, Idriss IA, Musa B, Aliyu A, Okonkwo EC, Abba SI, Tata D, Meva L, Hamandjoda O, Dagbasi M (2020) Optimal sizing and techno-economic analysis of hybrid renewable energy systems—a case study of a photovoltaic/wind/battery/diesel system in Fanisau, Northern Nigeria. MDPI Process 1381(8):1–25

    Google Scholar 

  22. Aziz AS, Tajuddin MFN, Adzman MR, Ramli MAM, Mekhilef S (2019) Energy management and optimization of a PV/diesel/battery hybrid energy system using a combined dispatch strategy. MDPI Sustain 683(11):1–26

    Google Scholar 

  23. Rousis AO, Tzelepis D, Konstantelos I, Booth C, Strbac G (2018) Design of a hybrid AC/DC microgrid using HOMER pro: case study on an Islanded residential application. MDPI Invent 55(3):1–14

    Google Scholar 

  24. Tazay AF, Samy MM, Barakat S (2020) A techno-economic feasibility analysis of an autonomous hybrid renewable energy sources for university building at Saudi Arabia. J Electr Eng Technol 15:2519–2527

    Article  Google Scholar 

  25. Elbaz A, Guneser MT (2021) Multi-objective optimization method for proper configuration of grid-connected PV-wind hybrid system in terms of ecological effects, outlay, and reliability. J Electr Eng Technol 16:771–782

    Article  Google Scholar 

  26. Elnozahy A, Yousef AM, Abo‑Elyousr FK, Mohamed M, Abdelwahab SAM (2021) Performance improvement of hybrid renewable energy sources connected to the grid using artificial neural network and sliding mode control. J Power Electron. https://doi.org/10.1007/s43236-021-00242-8

  27. Mohamed MA, Eltamaly AM, Alolah AI (2016) PSO-based smart grid application for sizing and optimization of hybrid renewable energy systems PSO-based smart grid application for sizing and optimization of hybrid renewable energy systems. PLOS ONE 1–22:0159702

    Google Scholar 

  28. Dufo-López R, Bernal-Agustín JL (2005) Design and control strategies of PV-Diesel systems using genetic algorithms. Solar Energy 79(1):33–46

    Article  Google Scholar 

  29. Goya S, Mishra S, Bhatia A (2018) Optimization strategies for hybrid energy system: a review. Int J Appl Eng Res 13(9):7010–7018

    Google Scholar 

  30. Bhandari B, Lee K-T, Lee G-Y, Cho Y-M, Ahn S-H (2015) Optimization of hybrid renewable energy power systems: a review. Int J Precis Eng Manuf Green Technol 2(1):99–112

    Article  Google Scholar 

  31. Yang H, Zhou W, Lu L, Fang Z (2008) Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using genetic algorithm. Sol Energy 82(4):354–367

    Article  Google Scholar 

  32. Elbaset AA, Abdelwahab SAM, Ibrahim HA, Elsayed Eid MA (2019) Performance analysis of photovoltaic systems with energy storage systems. vol 1, pp 11–26, Renew Green Energy. Springer

  33. Zeng J, Li M, Liu JF, Wu J, Ngan HW (2010) Operational optimization of a stand-alone hybrid renewable energy generation system based on an improved genetic algorithm. In: Proceedings of IEEE conference, Minneapolis, MN, USA

  34. Ghofrani M, Hosseini NN (2016) Optimizing hybrid renewable energy systems: a review. Sustain Energy Technol Issues Appl Case Stud 1–17

  35. Abdelwahab SAM, Hamada AM, Abdellatif WSE (2020) Comparative analysis of the modified perturb and observe with different MPPT techniques for PV grid connected systems. Int J Renew Energy Res IJRER 10(1):156–164

    Google Scholar 

  36. Yousef AM, Abo-Elyousr FK, Elnozohy A, Mohamed M, Saad Abdelwahab AM (2020) Fractional order PI control in hybrid renewable power generation system to three phase grid connection. Int J Electr Eng Inf 12(3):470–493

    Google Scholar 

  37. Rey L, Santiago RVM, Pacis MC (2017) Modeling of a hybrid renewable power system for Calayan Island, Cagayan using the HOMER software. In: 2017 IEEE 9th international conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM), Manila, pp 1–6

  38. Amor WO, Ben Amar H, Ghariani M (2015) Energetic and cost analysis of two conversion systems connected to the grid by using homer pro. Int J Renew Energy Res 5(3):926–936

    Google Scholar 

  39. Yousef AM, Ebeed M, Abo-Elyousr FK, Elnozohy A, Mohamed M, Abdelwahab SAM (2020) Optimization of PID controller for hybrid renewable energy system using adaptive sine cosine algorithm. Int J Renew Energy Res IJRER 10(2):670–677

    Google Scholar 

  40. Elnozahy A, Ramadan HS, Abo-Elyousr FK (2021) Efficient metaheuristic Utopia-based multi objective solutions of optimal battery-mix storage for microgrids. J Clean Prod 303:127038

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the financial support received from Taif University Researchers Supporting Project Number (TURSP-2020/34), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Assiut University, Assiut, Egypt

    Ahmed Elnozahy, Ali M. Yousef & Farag K. Abo-Elyousr

  2. Department of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia

    Sherif S. M. Ghoneim

  3. Electrical Department, Faculty of Technology and Education, Suez University, Suez, Egypt

    Saad A. Mohamed Abdelwahab

  4. High Institute of Electronic Engineering, Ministry of Higher Education, Bilbis, Sharqiya, Egypt

    Saad A. Mohamed Abdelwahab

  5. Electrical Department, Faculty of Technology and Education, Sohag University, Sohag, Egypt

    Moayed Mohamed

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  1. Ahmed Elnozahy
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Correspondence to Saad A. Mohamed Abdelwahab.

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Elnozahy, A., Yousef, A.M., Ghoneim, S.S.M. et al. Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System. J. Electr. Eng. Technol. 16, 2847–2862 (2021). https://doi.org/10.1007/s42835-021-00810-9

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