Review on green resources and AI for biogenic solar power

Jyoti Bhattacharjee; Subhasis Roy

doi:10.59400/esc.v2i1.457

Open Journal Systems

Review on green resources and AI for biogenic solar power

Jyoti Bhattacharjee, Subhasis Roy

Abstract

The need for clean and renewable energy has grown dramatically during the past few years. As potential candidates for producing green energy in this region, photovoltaic and bio-solar energy technologies have arisen. This review presents a novel approach for designing and developing photovoltaics and bio-solar cells using eco-friendly materials and artificial intelligence (AI) techniques. An intriguing architecture is outlined for a bio-solar cell that fuses photovoltaic electronics with photosynthetic organisms. A recyclable thin-film solar cell serves as the basis of our photovoltaic system. To further maximize the effectiveness of the device, we use AI algorithms. According to statistical calculations, the proposed bio-solar cell can produce a sizable amount of electricity while being ecologically sound. This paper outlines significant advances in developing solar cells and photovoltaics using green nanomaterials and AI, which provide exciting potential for improving energy harvesting capacity. This review also presents an overview of the effects of the potential commercialization of our strategy, its social and environmental benefits, and its pitfalls.

Keywords

bio-solar cell; energy efficiency; green nanomaterials; machine learning; photovoltaics; sustainability

Full Text:

PDF

References

1. Dubey S, Jadhav NY, Zakirova B. Socio-Economic and Environmental Impacts of Silicon Based Photovoltaic (PV) Technologies. Energy Procedia. 2013; 33: 322-334. doi: 10.1016/j.egypro.2013.05.073

2. Fraas LM. History of Solar Cell Development. In Low-Cost Solar Electric Power. Springer International Publishing; 2013. pp. 1–12.

3. Goetzberger A, Luther J, Willeke G. Solar cells: past, present, future. In: Solar Energy Materials and Solar Cells. Elsevier BV; 2002. pp. 1–11.

4. Lyu S, Bertrand C, Hamamura T, et al. Molecular engineering of ruthenium-diacetylide organometallic complexes towards efficient green dye for DSSC. Dyes and Pigments. 2018; 158: 326-333. doi: 10.1016/j.dyepig.2018.05.060

5. Bera S, Sengupta D, Roy S, et al. Research into dye-sensitized solar cells: a review highlighting progress in India. Journal of Physics: Energy. 2021; 3(3): 032013. doi: 10.1088/2515-7655/abff6c

6. De A, Bhattacharjee J, Chowdhury SR, et al. A Comprehensive Review on Third-Generation Photovoltaic Technologies. Journal of Chemical Engineering Research Updates. 2023; 10: 1-17. doi: 10.15377/2409-983x.2023.10.1

7. Palacios A, Barreneche C, Navarro ME, et al. Thermal energy storage technologies for concentrated solar power – A review from a materials perspective. Renewable Energy. 2020; 156: 1244-1265. doi: 10.1016/j.renene.2019.10.127

8. Kim SG, Jung JY, Sim M. A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning. Sustainability. 2019; 11(5): 1501. doi: 10.3390/su11051501

9. Samuel MS, Ravikumar M, John J. A, et al. A Review on Green Synthesis of Nanoparticles and Their Diverse Biomedical and Environmental Applications. Catalysts. 2022; 12(5): 459. doi: 10.3390/catal12050459

10. Parthiban R, Ponnambalam P. An Enhancement of the Solar Panel Efficiency: A Comprehensive Review. Frontiers in Energy Research. 2022, 10. doi: 10.3389/fenrg.2022.937155

11. Freire-Gormaly M, Bilton AM. Design of photovoltaic powered reverse osmosis desalination systems considering membrane fouling caused by intermittent operation. Renewable Energy. 2019; 135: 108-121. doi: 10.1016/j.renene.2018.11.065

12. Paletta Q, Terrén-Serrano G, Nie Y, et al. Advances in solar forecasting: Computer vision with deep learning. Advances in Applied Energy. 2023; 11: 100150. doi: 10.1016/j.adapen.2023.100150

13. Sharma N, Puri V, Mahajan S, et al. Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks. Scientific Reports. 2023; 13(1). doi: 10.1038/s41598-023-35457-1

14. Nijsse FJMM, Mercure JF, Ameli N, et al. The momentum of the solar energy transition. Nature Communications. 2023; 14(1). doi: 10.1038/s41467-023-41971-7

15. Ye L, Zhang S, Ma W, et al. From Binary to Ternary Solvent: Morphology Fine‐tuning of D/A Blends in PDPP3T‐based Polymer Solar Cells. Advanced Materials. 2012; 24(47): 6335-6341. doi: 10.1002/adma.201202855

16. Roy S, Botte GG. Perovskite solar cell for photocatalytic water splitting with a TiO2/Co-doped hematite electron transport bilayer. RSC Advances. 2018; 8(10): 5388-5394. doi: 10.1039/c7ra11996h

17. Zhao W, Dall’Agnese C, Duan S, et al. Trilayer Chlorophyll-Based Cascade Biosolar Cells. ACS Energy Letters. 2019; 4(2): 384-389. doi: 10.1021/acsenergylett.8b02279

18. Grott S, Kotobi A, Reb LK, et al. Solvent Tuning of the Active Layer Morphology of Non‐Fullerene Based Organic Solar Cells. Solar RRL. 2022; 6(6). doi: 10.1002/solr.202101084

19. Attoye D, Adekunle T, Tabet Aoul K, et al. A Conceptual Framework for a Building Integrated Photovoltaics (BIPV) Educative-Communication Approach. Sustainability. 2018; 10(10): 3781. doi: 10.3390/su10103781

20. Li S, Liu L, Peng C. A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges. Sustainability. 2020; 12(4): 1427. doi: 10.3390/su12041427

21. Freire-Gormaly M, Bilton AM. Impact of intermittent operation on reverse osmosis membrane fouling for brackish groundwater desalination systems. Journal of Membrane Science. 2019; 583: 220-230. doi: 10.1016/j.memsci.2019.04.010

22. Zhang W, Saliba M, Stranks SD, et al. Enhancement of Perovskite-Based Solar Cells Employing Core–Shell Metal Nanoparticles. Nano Letters. 2013; 13(9): 4505-4510. doi: 10.1021/nl4024287

23. Srivastava SK, Piwek P, Ayakar SR, et al. A Biogenic Photovoltaic Material. Small. 2018; 14(26). doi: 10.1002/smll.201800729

24. Lee J, Choi J, Park W, et al. A Dual-Stage Solar Power Prediction Model That Reflects Uncertainties in Weather Forecasts. Energies. 2023; 16(21): 7321. doi: 10.3390/en16217321

25. Mehdinia A, Mehrabi H. Application of nanomaterials for removal of environmental pollution. Industrial Applications of Nanomaterials. Published online 2019: 365-402. doi: 10.1016/b978-0-12-815749-7.00013-x

26. Abdelkareem MA, El Haj Assad M, Sayed ET, et al. Recent progress in the use of renewable energy sources to power water desalination plants. Desalination. 2018; 435: 97-113. doi: 10.1016/j.desal.2017.11.018

27. Bera S, Saha A, Mondal S, et al. Review of defect engineering in perovskites for photovoltaic application. Materials Advances. 2022; 3(13): 5234-5247. doi: 10.1039/d2ma00194b

28. Richards BS, Shen J, Schäfer AI. Water–Energy Nexus Perspectives in the Context of Photovoltaic‐Powered Decentralized Water Treatment Systems: A Tanzanian Case Study. Energy Technology. 2017; 5(7): 1112-1123. doi: 10.1002/ente.201600728

29. Ben Ali I, Turki M, Belhadj J, et al. Using quasi-static model for water/power management of a stand-alone wind/photovoltaic/BWRO desalination system without batteries. 2016 7th International Renewable Energy Congress (IREC). Published online March 2016. doi: 10.1109/irec.2016.7478871

30. Kharraz JA, Richards BS, Schäfer AI. Autonomous Solar-Powered Desalination Systems for Remote Communities. Desalination Sustainability. Published online 2017: 75-125. doi: 10.1016/b978-0-12-809791-5.00003-1

31. Wang D, Zhou G, Li Y, et al. High‐Performance Organic Solar Cells from Non‐Halogenated Solvents. Advanced Functional Materials. 2021; 32(4). doi: 10.1002/adfm.202107827

32. Richards BS, Capão DPS, Früh WG, et al. Renewable energy powered membrane technology: Impact of solar irradiance fluctuations on performance of a brackish water reverse osmosis system. Separation and Purification Technology. 2015; 156: 379-390. doi: 10.1016/j.seppur.2015.10.025

33. Danladi E, Ichoja A, Onoja ED, et al. Broad-band-enhanced and minimal hysteresis perovskite solar cells with interfacial coating of biogenic plasmonic light trapping silver nanoparticles. Materials Research Innovations. 2023; 27(7): 521-536. doi: 10.1080/14328917.2023.2204585

34. Meena RS, Singh A, Urhekar S, et al. Artificial Intelligence-Based Deep Learning Model for the Performance Enhancement of Photovoltaic Panels in Solar Energy Systems. Ramesh Bapu BR, ed. International Journal of Photoenergy. 2022; 2022: 1-8. doi: 10.1155/2022/3437364

35. Mateo Romero HF, González Rebollo MÁ, Cardeñoso-Payo V, et al. Applications of Artificial Intelligence to Photovoltaic Systems: A Review. Applied Sciences. 2022; 12(19): 10056. doi: 10.3390/app121910056

DOI: https://doi.org/10.59400/esc.v2i1.457
(66 Abstract Views, 67 PDF Downloads)