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Intelligent Modelling of Renewable Energy Resources-Based Hybrid Energy System for Sustainable Power Generation and Monitoring

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AI and Machine Learning Paradigms for Health Monitoring System

Part of the book series: Studies in Big Data ((SBD,volume 86))

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Abstract

The electrical energy is playing a vital role in the development and sustainability of any country. The demand of electrical energy is rising due to the increased comfort levels, urbanization and technological advancements. Therefore, it is utmost important to invigorate the use of renewable energy resources for bridging the gap and accepting the challenges of increasing electrical energy demands and greenhouse gas emissions. In view of the above, the present research focuses on the optimal design and sizing of hybrid energy system (HES) based on renewable energy resources, including solar photovoltaic (SPV), wind energy system, biomass and biogas with battery to electrify the rural areas of India’s Haryana state. Different models of hybrid energy systems have been chosen and optimized using different intelligent approaches such as grey wolf optimization (GWO), harmony search (HS) and particle swarm optimization (PSO) on the MATLAB platform. Finally, the results are compared in view of minimizing net present cost (NPC) and cost of energy (COE) and found the most optimal solution.

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References

  1. Akinyele, D.: Techno-economic design and performance analysis of nanogrid systems for households in energy-poor villages. Sustain. Cities Soc. 34, 335–357 (2017). https://doi.org/10.1016/j.scs.2017.07.004

    Article  Google Scholar 

  2. Anand, P., Rizwan, M., Bath, S.K.: Sizing of renewable energy based hybrid system for rural electrification using grey wolf optimization approach. IET Energy Syst. Integr. 1:158–172 (2019). https://doi.org/10.1049/iet-esi.2018.0053

  3. Bagen, B.R.: Evaluation of different operating strategies in small stand-alone power systems. IEEE Trans Energy Convers. 20, 654–60 (2005). https://doi.org/10.1109/tec.2005.847996

  4. Himri, Y., BoudgheneStambouli, A., Draoui, B., Himri, S.: Techno-economical study of hybrid power system for a remote village in Algeria. Energy 33, 1128–1136 (2008). https://doi.org/10.1016/j.energy.2008.01.016

    Article  Google Scholar 

  5. Razmjoo, A.D.: Developing various hybrid energy systems for residential application as an appropriate and reliable way to achieve energy sustainability. Energy Sources, Part A: Recovery, Utilization, Environ. Eff. 41(10), 1180–1193 (2019). https://doi.org/10.1080/15567036.2018.1544996

  6. Murugaperumala, K., Ajay, P., Vimal Ra, D.: Feasibility design and techno-economic analysis of hybrid renewable energy system for rural electrification. Sol. Energy 188, 1068–1083 (2019). https://doi.org/10.1016/j.solener.2019.07.008

  7. Anayochukwu, V., Nnene, E.A.: Simulation and optimization of photovoltaic/diesel hybrid power generation systems for health service facilities in rural environments. Electron J Energy Environ 1(1), 5770–5775 (2013). https://doi.org/10.7770/ejee-v1n1-art485

    Article  Google Scholar 

  8. Anand, P., Bath, S.K., Rizwan, M.: Design and development of stand-alone renewable energy based hybrid power system for remote base transceiver station. Int. J. Comput. Appl. 169, 34–41 (2017). https://doi.org/10.5120/ijca2017914776

  9. Borowy, B.S., Salameh, Z.M.: Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Trans. Energy Convers. 11(2), 367–375 (1996). https://doi.org/10.1109/60.507648

  10. Markvart, T., Fragaki, A., Ross, J.: PV system sizing using observed time series of solar radiation. Sol. Energy 80(1), 46–50 (2006). https://doi.org/10.1016/j.solener.2005.08.011

    Article  Google Scholar 

  11. Karaki, S., Chedid, R., Ramadan, R.: Probabilistic performance assessment of autonomous solar-wind energy conversion systems. IEEE Trans. Energy Convers. 14(3), 766–772 (1999). https://doi.org/10.1109/60.790949

  12. Tina, G., Gagliano, S., Raiti, S.: Hybrid solar/wind power system probabilistic modelling for long-term performance assessment. Sol. Energy 80(5), 578–588 (2006). https://doi.org/10.1016/j.solener.2005.03.013

    Article  Google Scholar 

  13. Lujano-Rojas, J.M., Dufo-López, R., Bernal-Agustín, J.L.: Probabilistic modelling and analysis of stand-alone hybrid power systems. Energy 63, 19–27 (2013). https://doi.org/10.1016/j.energy.2013.10.003

    Article  Google Scholar 

  14. Kellogg, W., Nehrir, M., Venkataramanan, G., Gerez, V.: Generation unit sizing and cost analysis for stand-alone wind, photovoltaic, and hybrid wind/PV systems. IEEE Trans. Energy Convers. 13(1), 70–75 (1998). https://doi.org/10.1109/60.658206

  15. Ekren, B.Y., Ekren, O.: Simulation based size optimization of a PV/wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions. Appl. Energy 86(9), 1387–1394 (2009). https://doi.org/10.1016/j.apenergy.2008.12.015

    Article  Google Scholar 

  16. Zhang, X., Tan, S.C., Li, G., Li, J., Fang, Z.: Components sizing of hybrid energy systems via the optimization of power dispatch simulations. Energy 52, 165–172 (2013). https://doi.org/10.1016/j.energy.2013.01.013

    Article  Google Scholar 

  17. Tong, W., Zhang, H., Shang, L.: Optimal sizing of a grid-connected hybrid renewable energy systems considering hydroelectric storage. Energy Sources, Part A: Recov. Utilization, and Environ. Eff. 1–17 2020. https://doi.org/10.1080/15567036.2020.1731018

  18. Sadeghi, D., Naghshbandy, A.H., Bahramara, S.: Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization. Energy 209(118471), 1–17 (2020). https://doi.org/10.1016/j.energy.2020.118471

    Article  Google Scholar 

  19. Lu, X., Wang, H.: Optimal sizing and energy management for cost-effective pev hybrid energy storage systems. IEEE Trans. Ind Inf. 16(5), 3407–3416 (2020). https://doi.org/10.1109/tii.2019.2957297

  20. Koutroulis, E., Kolokotsa, D., Potirakis, A., Kalaitzakis, K.: Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Sol. Energy 80(9), 1072–1088 (2006). https://doi.org/10.1016/j.solener.2005.11.002

    Article  Google Scholar 

  21. Askarzadeh, A.: A discrete chaotic harmony search-based simulated annealing algorithm for optimum design of pv/wind hybrid system. Sol. Energy, 97, 93–101 (2013). https://doi.org/10.1016/j.solener.2013.08.014

  22. Kumar, R., Gupta, R.A., Bansal, A.K.: Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm. Swarm Evol. Comput. 8, 33–43 (2013). https://doi.org/10.1016/j.swevo.2012.08.002

    Article  Google Scholar 

  23. Gupta, R.A., Kumar, R., Bansal, A.K.: Economic nalysis and design of stand-alone wind/photovoltaic hybrid energy system using genetic algorithm. In: International Conference on Computing, Communication and Applications (ICCCA), Dindigul, Tamilnadu, India, 1–6 (2012). https://doi.org/10.1109/iccca.2012.6179189

  24. Mostofi, F., Shayeghi, H.: Feasibility and optimal reliable design of renewable hybrid energy system for rural electrification in Iran. Int. J. Renew. Energy Res. 2(4), 574–582 (2012). https://doi.org/10.1.1.462.8025

    Google Scholar 

  25. Askarzadeh, S.C., dos, L.: A novel framework for optimization of a grid independent hybrid renewable energy system: a case study of Iran. Sol. Energy 112(1), 383–396 (2015). https://doi.org/10.1016/j.solener.2014.12.013

    Article  Google Scholar 

  26. Mohamed, M.A., Eltamaly, A.M., Alolah, A.I.: Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems. Renew. Sustain. Energy Rev. 77, 515–524 (2017). https://doi.org/10.1016/j.rser.2017.04.048

    Article  Google Scholar 

  27. Singh, S., Kaushik, S.C.: Optimal sizing of grid integrated hybrid pv-biomass energy system using artificial bee colony algorithm. IET Renew. Power Gener. 10(5), 642–650 (2016). https://doi.org/10.1049/iet-rpg.2015.0298

  28. Ogunjuyigbe, S.O., Ayodele, T.R., Akinola, O.A.: Optimal allocation and sizing of pv/wind/split-diesel/battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building. Appl. Energy 171, 153–171 (2016). https://doi.org/10.1016/j.apenergy.2016.03.051

    Article  Google Scholar 

  29. Bartolucci, L., Cordiner, S., Mulone, V., Rocco, V., Rossi, J.L.: Hybrid renewable energy systems for renewable integration in microgrids: influence of sizing on performance. Energy 152, 744–758 (2018). https://doi.org/10.1016/j.energy.2018.03.165

    Article  Google Scholar 

  30. Ghaffaria, A., Askarzadeh, A.: Design optimization of a hybrid system subject to reliability level and renewable energy penetration. Energy 193, 116754 (2020). https://doi.org/10.1016/j.energy.2019.116754

    Article  Google Scholar 

  31. Anand, P., Bath, S.K., Rizwan, M.: Renewable energy based hybrid model for rural electrification. Int. J. Energy Technol. Policy 15(1), 86–113, (2019). https://doi.org/10.1504/ijetp.2019.096633

  32. Anand, P., Bath, S.K., Rizwan, M.: Size optimization of res based grid connected hybrid power system using harmony search algorithm. Int. J. Energy Technol. Policy 16(3), 238–276 (2020). https://doi.org/10.1504/ijetp.2020.107035

  33. Solar irradiance data and ambient temperature of study area: NASA. Surface meteorology and solar energy: a renewable energy resource website: Accessed on 04 June 2020 at https://eosweb.larc.nasa.gov/sse/

  34. Wind energy data of study area: National Institute of Wind Energy. Ministry of New and Renewable Energy, Government of India; 2020, Accessed on 04 June 2020 at http://niwe.res.in/department_wra_est.php

  35. Chauhan, A., Saini, R.P.: Discrete harmony search based size optimization of integrated renewable energy system for remote rural areas of uttarakhand state in India. Renew. Energy 94, 587–604 (2016). https://doi.org/10.1016/j.renene.2016.03.079

    Article  Google Scholar 

  36. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  37. Kamboj, V.K., Bath, S.K., Dhillon, J.S.: Solution of non-convex economic load dispatch problem using grey wolf optimizer. Neural Comput. Appl. 27(5), 1301–1316 (2016). https://doi.org/10.1007/s00521-015-1934-8

    Article  Google Scholar 

  38. Askarzadeh, A.: Developing a discrete harmony search algorithm for size optimization of wind-photovoltaic hybrid energy system. Solar Energy, 98, 190–195 (2013). https://doi.org/10.1016/j.solener.2013.10.008

  39. Kamboj, V., Bath, S.K., Dhillon, J.S.: Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem. Electr. Power Energy Syst. 77, 228–249 (2016). https://doi.org/10.1016/j.ijepes.2015.11.045

    Article  Google Scholar 

  40. Mahesh, K.S.S.: Optimal sizing of a grid-connected pv/wind/battery system using particle swarm optimization. Iran. J. Sci. Technol. Trans. Electr. Eng. 43, 107–121 (2019). https://doi.org/10.1007/s40998-018-0083-3

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, B.P.S. Mahila Vishwavidyalaya, Sonipat, Haryana, 131305, India

    Priyanka Anand

  2. Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah, 52571, Qassim, Saudi Arabia

    Mohammad Rizwan

  3. Department of Electrical Engineering, Delhi Technological University, Delhi, 110042, India

    Mohammad Rizwan

  4. Department of Electrical Engineering, Giani Zail Singh Campus College of Engineering and Technology, Bathinda, Punjab, 151001, India

    Sarabjeet Kaur Bath

  5. Defence Research and Development Organisation, Metcalfe House Annexe, Delhi, India

    Gulnar Perveen

Authors
  1. Priyanka Anand
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  2. Mohammad Rizwan
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  3. Sarabjeet Kaur Bath
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  4. Gulnar Perveen
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Corresponding author

Correspondence to Priyanka Anand .

Editor information

Editors and Affiliations

  1. National University of Singapore (NUS), BEARS, Singapore, Singapore

    Hasmat Malik

  2. Universiti Sultan Zainal Abidin (UniSZA), Kampung Gong Badak, Malaysia

    Nuzhat Fatema

  3. Wake Forest University, Winston Salem, NC, USA

    Jafar A. Alzubi

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Anand, P., Rizwan, M., Bath, S.K., Perveen, G. (2021). Intelligent Modelling of Renewable Energy Resources-Based Hybrid Energy System for Sustainable Power Generation and Monitoring. In: Malik, H., Fatema, N., Alzubi, J.A. (eds) AI and Machine Learning Paradigms for Health Monitoring System. Studies in Big Data, vol 86. Springer, Singapore. https://doi.org/10.1007/978-981-33-4412-9_14

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