Leveraging IT Solutions for Optimizing Renewable Energy Systems

Authors

  • Isagani M. Tano, PhD-ELM, DIT Quezon City University Author

DOI:

https://doi.org/10.5281/zenodo.18197401

Keywords:

Information Technology, Renewable Energy, Smart Grid, Data Analytics, Predictive Maintenance, Energy Optimization, Sustainable Energy Systems

Abstract

The study "Leveraging IT Solutions for Optimizing Renewable Energy Systems" investigates the critical role of information technology in enhancing the efficiency and effectiveness of renewable energy systems. It explores how IT solutions, such as advanced data analytics, smart grid technologies, and predictive maintenance, can improve the integration, management, and performance of renewable energy sources. By analyzing current trends and observations, the study identifies the gaps in existing systems and proposes innovative IT-driven strategies to bridge these gaps and optimize energy production and distribution. Moreover, the research highlights the potential of IT to facilitate real-time monitoring and control of renewable energy assets, enabling more accurate forecasting and better decision-making. The integration of IT in renewable energy systems not only enhances operational efficiency but also contributes to sustainability by reducing waste and maximizing resource utilization. The findings of this study underscore the transformative impact of IT on the renewable energy sector and provide actionable insights for stakeholders aiming to achieve more resilient and sustainable energy systems.

Downloads

Download data is not yet available.

References

Books

Khang, A. (2024). Applications and Principles of Quantum Computing. Retrieved from Google

Books. https://books.google.com/books?hl=en&lr=&id=otzzEAAAQBAJ&oi=fnd&pg=PP1&dq=related:GaqMPggkfJ0J:scholar.google.com/&ots=zFFkwB7Omy&sig=2r2v_5Y0Mm1nAlmAT-VvNMWKSuM

Lawan, S. M., & Abidin, W. A. W. Z. (2020). Wind Solar Hybrid Renewable Energy System.

Retrieved from In Google Books. https://books.google.com/books?hl=en&lr=&id=iUP8DwAAQBAJ&oi=fnd&pg=PA95&dq=Status

Priyadarshi, N., Bhoi, A. K., Padmanaban, S., Balamurugan, S., & Holm-Nielsen, J. B. (2022).

Intelligent Renewable Energy Systems. Retrieved from In Google Books. https://books.google.com/books/about/Intelligent_Renewable_Energy_Systems.html?id=p9ZWEAAAQBAJ

Journals

Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022, May). Data-driven

probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Retrieved from Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2022.112128

Ahmad, T., & Zhang, D. (2021). Using the internet of things in smart energy systems and networks.

Retrieved from Sustainable Cities and Society, 68, 102783. https://doi.org/10.1016/j.scs.2021.102783

Alotaibi, I., Abido, M. A., Khalid, M., & Savkin, A. V. (2020). A Comprehensive Review of

Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources. Energies, 13(23), 6269. Retrieved from https://doi.org/10.3390/en13236269

Bibri, S. E., & Krogstie, J. (2020). Environmentally data-driven smart sustainable cities: applied

innovative solutions for energy efficiency, pollution reduction, and urban metabolism. Energy Informatics, 3(1). Retrieved from https://doi.org/10.1186/s42162-020-00130-8

Boza, P., & Evgeniou, T. (2021). Artificial intelligence to support the integration of variable

renewable energy sources to the power system. Retrieved from Applied Energy, 290, 116754.https://doi.org/10.1016/j.apenergy.2021.116754

Bragg‐Sitton, S. M., Boardman, R., Rabiti, C., & O’Brien, J. (2020). Reimagining future energy

systems: Overview of the US program to maximize energy utilization via integrated nuclear‐renewable energy systems. Retrieved from International Journal of Energy Research, 44(10), 8156–8169. https://doi.org/10.1002/er.5207

Dorninger, C., Abson, D. J., Apetrei, C. I., Derwort, P., Ives, C. D., Klaniecki, K., Lam, D. P. M.,

Langsenlehner, M., Riechers, M., Spittler, N., & von Wehrden, H. (2020). Leverage points for sustainability transformation: a review on interventions in food and energy systems. Retrieved from Ecological Economics, 171, 106570. https://doi.org/10.1016/j.ecolecon.2019.106570

Edem, K. (2023). HYBRID RENEWABLE ENERGY SYSTEMS MODELING. Retrieved from

Engineering Science & Technology Journal, 4(6), 571–588. https://doi.org/10.51594/estj.v4i6.1255

Guerrero, J., Gebbran, D., Mhanna, S., Chapman, A. C., & Verbič, G. (2020). Towards a

transactive energy system for integration of distributed energy resources: Home energy management, distributed optimal power flow, and peer-to-peer energy trading. Retrieved from Renewable and Sustainable Energy Reviews, 132, 110000. https://doi.org/10.1016/j.rser.2020.110000

Hoang, A. T., Pham, V. V., & Nguyen, X. P. (2021). Integrating renewable sources into energy

system for smart city as a sagacious strategy towards clean and sustainable process. Retrieved from Journal of Cleaner Production, 305, 127161. https://doi.org/10.1016/j.jclepro.2021.127161

Huang, P., Copertaro, B., Zhang, X., Shen, J., Löfgren, I., Rönnelid, M., Fahlen, J., Andersson,

D., & Svanfeldt, M. (2020). A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating. Retrieved from Applied Energy, 258, 114109. https://doi.org/10.1016/j.apenergy.2019.114109

Khalil, M. I., Jhanjhi, N. Z., Humayun, M., Sivanesan, S., Masud, M., & Hossain, M. S. (2021).

Hybrid smart grid with sustainable energy efficient resources for smart cities. Retrieved from Sustainable Energy Technologies and Assessments, 46, 101211. https://doi.org/10.1016/j.seta.2021.101211

Kington, L., Nwaimo, S., & Adegbola, D. (2024). Strategic financial decision-making in

sustainable energy investments: Leveraging big data for maximum impact. Retrieved from International Journal of Management & Entrepreneurship Research, 6(6), 1982–1996. https://doi.org/10.51594/ijmer.v6i6.1238

Li, Y., Wang, C., Li, G., & Chen, C. (2021). Optimal scheduling of integrated demand response-

enabled integrated energy systems with uncertain renewable generations: A Stackelberg game approach. Retrieved from Energy Conversion and Management, 235, 113996. https://doi.org/10.1016/j.enconman.2021.113996

McPherson, M., & Stoll, B. (2020). Demand response for variable renewable energy integration:

A proposed approach and its impacts. Retrieved from Energy, 197, 117205. https://doi.org/10.1016/j.energy.2020.117205

Mostafa, N., Ramadan, H. S. M., & Elfarouk, O. (2022). Renewable energy management in smart

grids by using big data analytics and machine learning. Retrieved from Machine Learning with Applications, 9, 100363. https://doi.org/10.1016/j.mlwa.2022.100363

Palys, M. J., Wang, H., Zhang, Q., & Daoutidis, P. (2021). Renewable ammonia for sustainable

energy and agriculture: vision and systems engineering opportunities. Retrieved from Current Opinion in Chemical Engineering, 31, 100667. https://doi.org/10.1016/j.coche.2020.100667

Perera, A. T. D., & Kamalaruban, P. (2021). Applications of reinforcement learning in energy

systems. Retrieved from Renewable and Sustainable Energy Reviews, 137, 110618. https://doi.org/10.1016/j.rser.2020.110618

Sorrenti, I., Harild Rasmussen, T. B., You, S., & Wu, Q. (2022). The role of power-to-X in hybrid

renewable energy systems: A comprehensive review. Renewable and Sustainable Energy Reviews, 112380. Retrieved from https://doi.org/10.1016/j.rser.2022.112380

Stephanie, F., & Karl, L. (2020). Incorporating Renewable Energy Systems for a New Era of Grid

Stability. Fusion of Multidisciplinary Research, an International Journal, 1(01), 37–49. Retrieved from https://fusionproceedings.com/fmr/1/article/view/13

Published Theses

Amjad, M. S., Rafique, M. Z., & Khan, M. A. (2021). Leveraging Optimized and Cleaner

Production through Industry 4.0. Retrieved from In Sustainable Production and Consumption (Vol. 26, pp. 859–871). https://doi.org/10.1016/j.spc.2021.01.001

Cox, J. L., Hamilton, W. T., Newman, A. M., Wagner, M. J., & Zolan, A. J. (2022, June 25). Real-

time dispatch optimization for concentrating solar power with thermal energy storage. Retrieved from Optimization and Engineering. https://doi.org/10.1007/s11081-022-09711-w

Emmanuel, M., Doubleday, K., Cakir, B., Marković, M., & Hodge, B.-M. (2020). A review of

power system planning and operational models for flexibility assessment in high solar energy penetration scenarios. Retrieved from In Solar Energy (Vol. 210, pp. 169–180). https://doi.org/10.1016/j.solener.2020.07.017

Hassan, Q., Algburi, S., Sameen, A. Z., Salman, H. M., & Jaszczur, M. (2023). A review of hybrid

renewable energy systems: Solar and wind-powered solutions: Challenges, opportunities, and policy implications. Retrieved from In Results in Engineering (Vol. 20, p. 101621). https://doi.org/10.1016/j.rineng.2023.101621

Hoang, A. T., Pham, V. V., & Nguyen, X. P. (2021). Integrating renewable sources into energy

system for smart city as a sagacious strategy towards clean and sustainable process. Retrieved from In Journal of Cleaner Production (Vol. 305, p. 127161). https://doi.org/10.1016/j.jclepro.2021.127161

Rangu, S. K., Lolla, P. R., Dhenuvakonda, K. R., & Singh, A. R. (2020). Recent trends in power

management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review. Retrieved from In International Journal of Energy Research (Vol. 44, Issue 13, pp. 9889–9911). https://doi.org/10.1002/er.5649

Vieira, G., & Zhang, J. (2021). Peer-to-peer energy trading in a microgrid leveraged by smart

contracts. Retrieved from In Renewable and Sustainable Energy Reviews (Vol. 143, p. 110900). https://doi.org/10.1016/j.rser.2021.110900

Dissertations

Copp, D. A., Nguyen, T. A., Byrne, R. H., & Chalamala, B. R. (2021). Optimal sizing of distributed

energy resources for planning 100% renewable electric power systems. Retrieved from In Energy (p. 122436). https://doi.org/10.1016/j.energy.2021.122436

Dey, B., Basak, S., & Pal, A. (2022). Demand‐side management based optimal scheduling of

distributed generators for clean and economic operation of a microgrid system. Retrieved from In International Journal of Energy Research (Vol. 46, Issue 7, pp. 8817–8837). https://doi.org/10.1002/er.7758

Hamilton, W. T., Husted, M. A., Newman, A. M., Braun, R. J., & Wagner, M. J. (2019). Dispatch

optimization of concentrating solar power with utility-scale photovoltaics [Springer Science+Business Media]. Retrieved from In Optimization and Engineering (Vol. 21, Issue 1, pp. 335–369). https://doi.org/10.1007/s11081-019-09449-y

Hirwa, J., Ogunmodede, O., Zolan, A., & Newman, A. M. (2022). Optimizing design and dispatch

of a renewable energy system with combined heat and power [Springer Science+Business Media]. Retrieved from In Optimization and Engineering (Vol. 23, Issue 3, pp. 1–31). https://doi.org/10.1007/s11081-021-09674-4

Islam, M., Yang, F., & Amin, M. (2021). Control and optimisation of networked microgrids: A

review. Retrieved from In IET Renewable Power Generation (Vol. 15, Issue 6, pp. 1133–1148). https://doi.org/10.1049/rpg2.12111

Scioletti, M., Newman, A. M., Goodman, J. K., Zolan, A., & Leyffer, S. (2019). Optimal design

and dispatch of a system of diesel generators, photovoltaics and batteries for remote locations [Springer Science+Business Media]. Retrieved from In Optimization and Engineering (Vol. 18, Issue 3, pp. 755–792). https://doi.org/10.1007/s11081-017-9355-4

Websites

Ajagekar, A., & You, F. (2022, September). Quantum computing and quantum artificial

intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality. Retrieved from Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2022.112493

Aleem, S. A., Hussain, S. M. S., & Ustun, T. S. (2020, February 3). A Review of Strategies to

Increase PV Penetration Level in Smart Grids. Retrieved from Energies. https://doi.org/10.3390/en13030636

Berger, C., Di Paolo, A., Forrest, T., Hadfield, S., Sawaya, N., Stęchły, M., & Thibault, K. (2021,

June 23). Quantum technologies for climate change: Preliminary assessment. Retrieved from ArXiv:2107.05362 [Quant-Ph]. https://arxiv.org/abs/2107.05362

Bibri, S. E. (2020, June 15). The eco-city and its core environmental dimension of sustainability:

green energy technologies and their integration with data-driven smart solutions. Retrieved from Energy Informatics. https://doi.org/10.1186/s42162-020-00107-7

Dhameliya, N. (2022, November 25). Power Electronics Innovations: Improving Efficiency and

Sustainability in Energy Systems. Retrieved from Asia Pacific Journal of Energy and Environment. https://doi.org/10.18034/apjee.v9i2.752

Hannan, M. A., Faisal, M., Jern Ker, P., Begum, R. A., Dong, Z. Y., & Zhang, C. (2020, October).

Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications. Retrieved from Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2020.110022

He, W., King, M., Luo, X., Dooner, M., Li, D., & Wang, J. (2021, November 19). Technologies

and economics of electric energy storages in power systems: Review and perspective. Retrieved from Advances in Applied Energy. https://doi.org/10.1016/j.adapen.2021.100060

Muhammad, Y., Khan, R., Raja, M. A. Z., Ullah, F., Chaudhary, N. I., & He, Y. (2020, November).

Solution of optimal reactive power dispatch with FACTS devices: A survey. Retrieved from Energy Reports. https://doi.org/10.1016/j.egyr.2020.07.030

O’Dwyer, E., Pan, I., Charlesworth, R., Butler, S., & Shah, N. (2020, November). Integration of

an energy management tool and digital twin for coordination and control of multi-vector smart energy systems. Retrieved from Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2020.102412

Osman, O., Sgouridis, S., & Sleptchenko, A. (2020, October). Scaling the production of renewable

ammonia: A techno-economic optimization applied in regions with high insolation. Retrieved from Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2020.121627

Shaheen, A. M., Alassaf, A., Alsaleh, I., & Elsayed, A. M. (2024, May 1). Enhanced Kepler

optimization for efficient penetration of PV sources integrated with STATCOM devices in power distribution systems. Retrieved from Expert Systems with Applications; Elsevier BV. https://doi.org/10.1016/j.eswa.2024.124333

Shen, F., Zhao, L., Du, W., Zhong, W., & Qian, F. (2020, February 1). Large-scale industrial

energy systems optimization under uncertainty: A data-driven robust optimization approach. Retrieved from Applied Energy; Elsevier BV. https://doi.org/10.1016/j.apenergy.2019.114199

Silva, B. N., Khan, M., & Han, K. (2020, July 10). Futuristic Sustainable Energy Management in

Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities. Retrieved from Sustainability. https://doi.org/10.3390/su12145561

Sweeney, C., Bessa, R. J., Browell, J., & Pinson, P. (2019, September 9). The future of forecasting

for renewable energy. Retrieved from WIREs Energy and Environment. https://doi.org/10.1002/wene.365

Ullah, M. H., Eskandarpour, R., Zheng, H., & Khodaei, A. (2022, September 7). Quantum

Computing for Smart Grid Applications. Retrieved from IET Generation, Transmission & Distribution. https://doi.org/10.1049/gtd2.12602

Yang, T., Zhao, L., Li, W., & Zomaya, A. Y. (2020). Reinforcement learning in sustainable energy

and electric systems: a survey. Retrieved from Annual Reviews in Control. https://doi.org/10.1016/j.arcontrol.2020.03.001

Zakaria, A., Ismail, F. B., Lipu, M. S. H., & Hannan, M. A. (2020, January). Uncertainty models

for stochastic optimization in renewable energy applications. Retrieved from Renewable Energy. https://doi.org/10.1016/j.renene.2019.07.081

Downloads

Published

2026-01-09

How to Cite

Tano, I. (2026). Leveraging IT Solutions for Optimizing Renewable Energy Systems. International Journal of Education, Research, and Innovation Perspectives, 2(1), 448-538. https://doi.org/10.5281/zenodo.18197401

Similar Articles

21-30 of 102

You may also start an advanced similarity search for this article.