Electricity demand modeling techniques for hybrid solar PV system
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Ahmad Faiz Minai
,
Mohammed Aslam Husain
,
Mohammad Naseem
Abstract
In recent period, electricity need is increasing because of automatic control systems in developing modern societies. So it is necessary to estimate the consumption and needs of a particular sector to match the generation and demand for whole society. This task is performed by the modeling of electricity demand, in which many tools/plans and policies are involved. According to which, tariffs are made to benefit the society as well as energy suppliers. Electricity demand modeling is also needful when any of the generation plant is going to be installed, especially in the case of solar PV plant, in which number of panels, area, and balance of system completely depends upon the electricity demand. Hence in this work, modeling is proposed for electricity demand after analyzing various sectors. After the proper energy audit in initial stage, hybrid generation system (Thermal/Solar PV/DG/Batteries) will be modeled to match the demand in peak hours using metaheuristics optimization techniques. Low carbon emission and energy storage are the key features of power generation using solar PV system, which are very beneficial for any state of India.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Sadhu, M, Chakraborty, S, Das, N, Sadhu, PK. Role of solar power in sustainable development of India, Telkomnika Indones J Electr Eng 2015;14:34–41. https://doi.org/10.11591/telkomnika.v14i1.7668.Suche in Google Scholar
2. Chakraborty, S, Sadhu, PK. Technical mapping of solar photovoltaic for the Coal City of India. Renew Wind Water Solar 2015;2:11. https://doi.org/10.1186/s40807-015-0013-1.Suche in Google Scholar
3. Gajowniczek, K, Zabkowski, T. Two-stage electricity demand modeling using machine learning algorithms. Energies 2017;10:1547. https://doi.org/10.3390/en10101547.Suche in Google Scholar
4. Pilz, M, Naeini, FB, Grammont, K, Smagghe, C, Davis, M, Nebel, JC, et al.. Security attacks on smart grid scheduling and their defences: a game–theoretic approach; 2018. arXiv:1810.07670v1 [cs.GT] 17.Suche in Google Scholar
5. Sharma, H, Pal, N, Sadhu, PK. Modeling and simulation of off-grid power generation system using photovoltaic. Telkomnika Indones J Electr Eng 2015;13:418–24. https://doi.org/10.11591/telkomnika.v13i3.7061.Suche in Google Scholar
6. Faia, R, Faria, P, Vale, Z, Spinola, J. Demand response optimization using particle swarm algorithm considering optimum battery energy storage schedule in a residential house. Energies 2019;12:1645. https://doi.org/10.3390/en12091645.Suche in Google Scholar
7. Mohammed, OH, Amirat, Y, Bembouzid, M. Particle swarm optimization of a Hybrid Wind/Tidal/PV/Battery energy system, application to a remote area in Bretagne, France, special issue on emerging and renewable energy: generation and automation. Energy Procedia 2019;162:87–96. https://doi.org/10.1016/j.egypro.2019.04.010.Suche in Google Scholar
8. Ahangar, RA, Rosin, A, Niaki, AN, Palu, I, Korõtko, T. A review on real‐time simulation and analysis methods of microgrids. Int Trans Electr Energ Syst 2019;29:e12106. https://doi.org/10.1002/2050-7038.12106.Suche in Google Scholar
9. Mao, M, Cui, L, Zhang, Q, Guo, K, Zhou, L, Huang, H. Classification and summarization of solar photovoltaic MPPT techniques: a review based on traditional and intelligent control strategies. Energy Rep 2020;6:1312–27. https://doi.org/10.1016/j.egyr.2020.05.013.Suche in Google Scholar
10. Baba, AO, Liu, G, Chen, X. Classification and evaluation review of maximum power point tracking methods. Sustain Futures 2020;2:100020. https://doi.org/10.1016/j.sftr.2020.100020.Suche in Google Scholar
11. Werbos, PJ. Econometric techniques: theory versus practice, special issue of energy. Energy 1990;15:213–36. https://doi.org/10.1016/0360-5442(90)90085-g.Suche in Google Scholar
12. Lipinsky, A. Introduction, section 2, demand forecasting methodologies, special issue of energy. Energy 1990;15:207–11.Suche in Google Scholar
13. Bhattacharyya, SC, Timilsina, GR. Energy demand models for policy formulation: a comparative study of energy demand models. The World Bank Development Research Group Environment and Energy Team; 2009.10.1596/1813-9450-4866Suche in Google Scholar
14. Ghanadan, R, Koomey, JG. Using energy scenarios to explore alternative energy pathways in California. Energy Pol 2005;33:1117–42. https://doi.org/10.1016/j.enpol.2003.11.011.Suche in Google Scholar
15. Maleki, A, Alhuyi Nazari, M, Pourfayaz, F. Harmony search optimization for optimum sizing of hybrid solar schemes based on battery storage unit. Energy Rep 2020;6:102–11. https://doi.org/10.1016/j.egyr.2020.03.014.Suche in Google Scholar
16. Schellong, W. Energy demand analysis and forecast. No. 101-120. Germany: Cologne University of Applied Sciences; 2011.10.5772/21022Suche in Google Scholar
17. Pukšec, TP, Vad, MB, Tomislav, N, Neven, D. Energy demand modelling and GHG emission reduction: case study Croatia. In: Book of abstracts: 8th conference on sustainable development of energy, water and environment systems; 2013.Suche in Google Scholar
18. Pilz, M, Fagih, LA. Recent advances in local energy trading in the smart grid based on game–theoretic approaches; 2017. arXiv:1702.02915v2 [cs.GT].Suche in Google Scholar
19. Paliwal, NK, Singh, AK, Singh, NK, Kumar, P. Optimal sizing and operation of battery storage for economic operation of hybrid power system using artificial bee colony algorithm. Int Trans Electr Energ Syst 2019;29:e2685. https://doi.org/10.1002/etep.2685.Suche in Google Scholar
20. Mosaad, MI, El-Raouf, MOA, Al-Ahmar, MA, Banakher, FA. Maximum power point tracking of PV system based Cuckoo search algorithm; review and comparison. Special issue on emerging and renewable energy: generation and automation. Energy Procedia 2019;162:117–26. https://doi.org/10.1016/j.egypro.2019.04.013.Suche in Google Scholar
21. Bargur, J, Mandel, A. Energy consumption and economic growth in Israel: trend analysis (1960–1979). In: Proceedings of the third international conference on energy use management; 1981.Suche in Google Scholar
22. Farahbalhsh, H, Ugursal, VI, Fung, AS. A residential enduse energy consumption model for Canada. Energy Res 1998;22:1133–43.10.1002/(SICI)1099-114X(19981025)22:13<1133::AID-ER434>3.0.CO;2-ESuche in Google Scholar
23. Suganthi, L, Jagadeesan, TR. A modified model for prediction of India’s future energy requirement. Energy Environ 1992;3:371–86. https://doi.org/10.1177/0958305x9200300403.Suche in Google Scholar
24. Ang, BW. Decomposition methodology in industrial energy demand analysis. Energy 1995;20:1081–95. https://doi.org/10.1016/0360-5442(95)00068-r.Suche in Google Scholar
25. Dincer, I, Dost, S. Energy and GDP. Energy Res 1997;21:153–67. https://doi.org/10.1002/(sici)1099-114x(199702)21:2<153::aid-er227>3.0.co;2-z.10.1002/(SICI)1099-114X(199702)21:2<153::AID-ER227>3.0.CO;2-ZSuche in Google Scholar
26. Cho, MY, Hwang, JC, Chen, CS. Customer short-term load forecasting by using ARIMA transfer function model. In: Proceedings of the international conference on energy manage power delivery; 1995, vol 1:317–22 pp.10.1109/EMPD.1995.500746Suche in Google Scholar
27. Aydinalp, M, Ismet Ugursal, V, Fung, AS. Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks. Appl Energy 2002;71:87–110. https://doi.org/10.1016/s0306-2619(01)00049-6.Suche in Google Scholar
28. Lu, IJ, Lin, SJ, Lewis, C. Grey relation analysis of motor vehicular energy consumption in Taiwan. Energy Pol 2008;36:2556–61. https://doi.org/10.1016/j.enpol.2008.03.015.Suche in Google Scholar
29. Wei, YM, Liang, QM, Fan, Y, Okada, N, Tsai, HT. A scenario analysis of energy requirements and energy intensity for China’s rapidly developing society in the year 2020. Technol Forecast Soc Change 2006;73:405–21. https://doi.org/10.1016/j.techfore.2004.12.003.Suche in Google Scholar
30. Kiartzis, SJ, Bakirtzis, AG. A fuzzy expert system for peak load forecasting: application to the Greek power system. In: Proceedings of the 10th mediterranean electro technical conference; 2000, vol 3:1097–100 pp.10.1109/MELCON.2000.879726Suche in Google Scholar
31. Canyurt, OE, Ozturk, HK. Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey. Energy Pol 2008;36:2562–9. https://doi.org/10.1016/j.enpol.2008.03.010.Suche in Google Scholar
32. Suganthia, L, Samuel, AA. Energy models for demand forecasting—a review. Renew Sustain Energy Rev 2012;16:1223–40. https://doi.org/10.1016/j.rser.2011.08.014.Suche in Google Scholar
33. Fishbone, LG, Abilock, H. MARKAL, a linear-programming model for energy systems analysis: technical description of the BNL version. Energy Res 1981;5:353–75. https://doi.org/10.1002/er.4440050406.Suche in Google Scholar
34. Farzaneh, H. Energy systems modeling; 2019:45–80 pp. Chapter 3.10.1007/978-981-13-6221-7_3Suche in Google Scholar
35. Ahmad, F, Usmani, T, Mallick, MA. Optimum sizing and estimation of a 30 kWp hybrid solar photovoltaic system with multilevel inverter. Int J Res Sci Innov IJRSI Res Sci Innov Soc 2015;2:31–6.Suche in Google Scholar
36. Kumar, M, Minai, AF, Khan, AA, Kumar, S. IoT based energy management system for smart grid. In: International conference on advances in computing. Dehradun, India: Communication & Materials (ICACCM); 2020:121–5 pp. https://doi.org/10.1109/ICACCM50413.2020.9213061.Suche in Google Scholar
37. Naseem, M, Husain, MA, Minai, AF, et al.. Assessment of meta-heuristic and classical methods for GMPPT of PV system. Trans. Electr. Electron. Mater. 2021. https://doi.org/10.1007/s42341-021-00306-3.Suche in Google Scholar
38. Husain, MA, Jain, A, Tariq, A. A novel fast mutable duty (FMD) MPPT technique for solar PV system with reduced searching area. J Renew Sustain Energy 2016;8:054703. https://doi.org/10.1063/1.4963314.Suche in Google Scholar
39. Husain, MA, Tariq, A, Hameed, S, Arif, MSB, Jain, A. Comparative assessment of maximum power point tracking procedures for photovoltaic systems. Green Energy Environ 2017;2:5–17. https://doi.org/10.1016/j.gee.2016.11.001.Suche in Google Scholar
40. Husain, MA, Jain, A, Tariq, A, Iqbal, A. Fast and precise global maximum power point tracking techniques for photovoltaic system. IET Renew Power Gener 2019;13:2569–79. https://doi.org/10.1049/iet-rpg.2019.0244.Suche in Google Scholar
41. Minai, AF, Malik, H. Metaheuristics Paradigms for renewable energy systems: advances in optimization algorithms. In: Malik, H, Iqbal, A, Joshi, P, Agrawal, S, Bakhsh, FI, editors. Metaheuristic and evolutionary computation: algorithms and applications. Studies in computational intelligence, Singapore: Springer; 2021, vol 916.https://doi.org/10.1007/978-981-15-7571-6_2.Suche in Google Scholar
42. Campillo, J, Wallin, F, Torstensson, D, Vassileva, I. Energy demand model design for forecasting electricity consumption and simulating demand response scenarios in Sweden. In: International conference on applied energy ICAE 2012, Suzhou, China, Jul 5–8; 2012.Suche in Google Scholar
43. Hardt, L, Brockway, P, Taylor, P, Barrett, J, Gross, R, Heptonstall, P. UKERC Technology and Policy Assessment modelling demand-side energy policies for climate change mitigation in the UK: a rapid evidence assessment. Working Paper, Version 2.0; 2019.Suche in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Differential positive sequence power angle-based microgrid feeder protection
- Real-time hardware emulation of wind turbine model with asynchronous generator under hardware-in-the-loop platform
- Frequency stability analysis with fuzzy adaptive selfish herd optimization based optimal sliding mode controller for microgrids
- Seamless control of grid-tied PV-Hybrid Energy Storage System
- Improved higher order adaptive sliding mode control for increased efficiency of grid connected hybrid systems
- Optimal siting of solar based distributed generation (DG) in distribution system for constant power load model
- Electricity demand modeling techniques for hybrid solar PV system
- Robust decentralized model predictive load-frequency control design for time-delay renewable power systems
- A techno-economic analysis of the roof top off-grid solar PV system for Jamshedpur, Jharkhand, India
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Differential positive sequence power angle-based microgrid feeder protection
- Real-time hardware emulation of wind turbine model with asynchronous generator under hardware-in-the-loop platform
- Frequency stability analysis with fuzzy adaptive selfish herd optimization based optimal sliding mode controller for microgrids
- Seamless control of grid-tied PV-Hybrid Energy Storage System
- Improved higher order adaptive sliding mode control for increased efficiency of grid connected hybrid systems
- Optimal siting of solar based distributed generation (DG) in distribution system for constant power load model
- Electricity demand modeling techniques for hybrid solar PV system
- Robust decentralized model predictive load-frequency control design for time-delay renewable power systems
- A techno-economic analysis of the roof top off-grid solar PV system for Jamshedpur, Jharkhand, India
