ARTIFICIAL INTELLIGENCE FOR POWER QUALITY OPTIMIZATION IN RENEWABLE ENERGY CIRCULATION RELATIONS
DOI:
https://doi.org/10.51699/y2xfck12Keywords:
Distribution Network, Power Quality, Renewable Energy, Artificial IntelligenceAbstract
Background: The rising adoption of renewable power systems throughout present-day distribution systems has produced various obstacles which affect the delivery of stable power with high quality. The irregular operation of solar and wind power systems creates multiple problems which include voltage changes and harmonic interference and unstable power grid frequency. Methods: The study used a quantitative cross-sectional survey framework to assess AI systems optimize power quality for renewable energy networks which serve American power distribution systems. A total of 185 respondents, including engineers, utility operators, and energy researchers, participated in a structured questionnaire. Pearson correlation along with descriptive statistics based on percentage analysis to study the connection between AI adoption rates and power system performance indicators. Results: AI technology produces a 26% rise in fault detection precision and creates a 20% improvement in load distribution performance and voltage network stability. Machine Learning stands as the most popular AI method which receives 28% of usage while Neural Networks follow with 22% usage. AI adoption creates strong positive connections with power quality and system reliability and fault detection efficiency according to correlation analysis results r = 0.78, 0.74, and 0.81 respectively. The deployment of this technology faces three main obstacles which include expensive installation costs that affect 25% of users and 22% of users struggle to find qualified personnel and 15% of users need to handle security threats. Conclusion: Artificial Intelligence plays a crucial role in enhancing power quality and reliability in renewable energy distribution networks.
References
[1] C. J. Khare, H. Verma, and V. Khare, “Optimal Power Generation and Power Flow Control Using Artificial Intelligence Techniques,” in Elsevier eBooks, Elsevier, 2021, pp. 607–631. doi: 10.1016/b978-0-12-820004-9.00028-0.
[2] P. W. Khan, Y. Byun, S. Lee, D. Kang, J. Kang, and H. Park, “Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources,” Energies (Basel)., vol. 13, no. 18, p. 4870, 2020, doi: 10.3390/en13184870.
[3] K. W. Kow, Y. W. Wong, R. K. Rajkumar, and R. K. Rajkumar, “A Review on Performance of Artificial Intelligence and Conventional Method in Mitigating PV Grid-Tied Related Power Quality Events,” Renewable and Sustainable Energy Reviews, vol. 56, pp. 334–346, 2015, doi: 10.1016/j.rser.2015.11.064.
[4] Y. S. Afridi, K. Ahmad, and L. Hassan, “Artificial Intelligence Based Prognostic Maintenance of Renewable Energy Systems: A Review of Techniques, Challenges, and Future Research Directions,” Int. J. Energy Res., vol. 46, no. 15, pp. 21619–21642, 2021, doi: 10.1002/er.7100.
[5] L. Cheng and T. Yu, “A New Generation of AI: A Review and Perspective on Machine Learning Technologies Applied to Smart Energy and Electric Power Systems,” Int. J. Energy Res., vol. 43, no. 6, pp. 1928–1973, 2019, doi: 10.1002/er.4333.
[6] K. T. Chui, M. D. Lytras, and A. Visvizi, “Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption,” Energies (Basel)., vol. 11, no. 11, p. 2869, 2018, doi: 10.3390/en11112869.
[7] S. S. Ali and B. J. Choi, “State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review,” Electronics (Basel)., vol. 9, no. 6, p. 1030, 2020, doi: 10.3390/electronics9061030.
[8] L. Yavuz, A. Önen, S. Muyeen, and I. Kamwa, “Transformation of Microgrid to Virtual Power Plant: A Comprehensive Review,” IET Generation, Transmission & Distribution, vol. 13, no. 11, pp. 1994–2005, 2019, doi: 10.1049/iet-gtd.2018.5649.
[9] W. Zhou, C. Lou, Z. Li, L. Lu, and H. Yang, “Current Status of Research on Optimum Sizing of Stand-Alone Hybrid Solar–Wind Power Generation Systems,” Appl. Energy, vol. 87, no. 2, pp. 380–389, 2009, doi: 10.1016/j.apenergy.2009.08.012.
[10] Y. Yoldaş, A. Önen, S. Muyeen, A. V Vasilakos, and .{I}. Alan, “Enhancing Smart Grid with Microgrids: Challenges and Opportunities,” Renewable and Sustainable Energy Reviews, vol. 72, pp. 205–214, 2017, doi: 10.1016/j.rser.2017.01.064.
[11] M. Liao and Y. Yao, “Applications of Artificial Intelligence-Based Modeling for Bioenergy Systems: A Review,” GCB Bioenergy, vol. 13, no. 5, pp. 774–802, 2021, doi: 10.1111/gcbb.12816.
[12] Y. Wu et al., “Towards Collective Energy Community: Potential Roles of Microgrid and Blockchain to Go Beyond P2P Energy Trading,” Appl. Energy, vol. 314, p. 119003, 2022, doi: 10.1016/j.apenergy.2022.119003.
[13] E. Grover-Silva, R. Girard, and G. Kariniotakis, “Optimal Sizing and Placement of Distribution Grid Connected Battery Systems through an SOCP Optimal Power Flow Algorithm,” Appl. Energy, vol. 219, pp. 385–393, 2017, doi: 10.1016/j.apenergy.2017.09.008.
[14] J. F. Bermejo, J. F. G. Fernández, F. O. Polo, and A. C. Márquez, “A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction: A Study of the Solar PV, Hydraulic and Wind Energy Sources,” Applied Sciences, vol. 9, no. 9, p. 1844, 2019, doi: 10.3390/app9091844.
[15] M. Pérez-Ortiz, S. Jiménez-Fernández, P. Gutiérrez, E. Alexandre, C. Hervás-Martínez, and S. Salcedo-Sanz, “A Review of Classification Problems and Algorithms in Renewable Energy Applications,” Energies (Basel)., vol. 9, no. 8, p. 607, 2016, doi: 10.3390/en9080607.
[16] N. Ghadami et al., “Implementation of Solar Energy in Smart Cities Using an Integration of Artificial Neural Network, Photovoltaic System and Classical Delphi Methods,” Sustain. Cities Soc., vol. 74, p. 103149, 2021, doi: 10.1016/j.scs.2021.103149.
[17] S. Agostinelli, F. Cumo, G. Guidi, and C. Tomazzoli, “Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence,” Energies (Basel)., vol. 14, no. 8, p. 2338, 2021, doi: 10.3390/en14082338.
[18] L. Abualigah et al., “Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques,” Energies (Basel)., vol. 15, no. 2, p. 578, 2022, doi: 10.3390/en15020578.
[19] N. Mlilo, J. Brown, and T. Ahfock, “Impact of Intermittent Renewable Energy Generation Penetration on the Power System Networks: A Review,” Technology and Economics of Smart Grids and Sustainable Energy, vol. 6, no. 1, 2021, doi: 10.1007/s40866-021-00123-w.
[20] K. Reddy, M. Kumar, T. Mallick, H. Sharon, and S. Lokeswaran, “A Review of Integration, Control, Communication and Metering (ICCM) of Renewable Energy Based Smart Grid,” Renewable and Sustainable Energy Reviews, vol. 38, pp. 180–192, 2014, doi: 10.1016/j.rser.2014.05.049.
[21] G. Kear, A. A. Shah, and F. C. Walsh, “Development of the All-Vanadium Redox Flow Battery for Energy Storage: A Review of Technological, Financial and Policy Aspects,” Int. J. Energy Res., vol. 36, no. 11, pp. 1105–1120, 2011, doi: 10.1002/er.1863.
[22] S. K. Rathor and D. Saxena, “Energy Management System for Smart Grid: An Overview and Key Issues,” Int. J. Energy Res., vol. 44, no. 6, pp. 4067–4109, 2020, doi: 10.1002/er.4883.
[23] F. Al-Turjman and M. Abujubbeh, “IoT-Enabled Smart Grid via SM: An Overview,” Future Generation Computer Systems, vol. 96, pp. 579–590, 2019, doi: 10.1016/j.future.2019.02.012.
[24] T. Lan, K. Jermsittiparsert, S. T. Alrashood, M. Rezaei, L. Al-Ghussain, and M. A. Mohamed, “An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand,” Energies (Basel)., vol. 14, no. 3, p. 569, 2021, doi: 10.3390/en14030569.
[25] A. T. Hammid, M. H. B. Sulaiman, and A. N. Abdalla, “Prediction of Small Hydropower Plant Power Production in Himreen Lake Dam (HLD) Using Artificial Neural Network,” Alexandria Engineering Journal, vol. 57, no. 1, pp. 211–221, 2017, doi: 10.1016/j.aej.2016.12.011.
[26] S. Shivashankar, S. Mekhilef, H. Mokhlis, and M. Karimi, “Mitigating Methods of Power Fluctuation of Photovoltaic (PV) Sources: A Review,” Renewable and Sustainable Energy Reviews, vol. 59, pp. 1170–1184, 2016, doi: 10.1016/j.rser.2016.01.059.
[27] Z. Jun, L. Junfeng, W. Jie, and H. Ngan, “A Multi-Agent Solution to Energy Management in Hybrid Renewable Energy Generation System,” Renew. Energy, vol. 36, no. 5, pp. 1352–1363, 2010, doi: 10.1016/j.renene.2010.11.032.
[28] D. P. Tabor et al., “Accelerating the Discovery of Materials for Clean Energy in the Era of Smart Automation,” Nat. Rev. Mater., vol. 3, no. 5, pp. 5–20, 2018, doi: 10.1038/s41578-018-0005-z.
[29] F. Pelletier, C. Masson, and A. Tahan, “Wind Turbine Power Curve Modelling Using Artificial Neural Network,” Renew. Energy, vol. 89, pp. 207–214, 2015, doi: 10.1016/j.renene.2015.11.065.
[30] M. D. S. Rahman, “Does Openness to Trade Have a Positive Impact on Economic Growth? Empirical Evidence from Bangladesh,” SSRN Electronic Journal, 2021, doi: 10.2139/ssrn.4209667.
[31] H. Sun, C. Qiu, L. Lu, X. Gao, J. Chen, and H. Yang, “Wind Turbine Power Modelling and Optimization Using Artificial Neural Network with Wind Field Experimental Data,” Appl. Energy, vol. 280, p. 115880, 2020, doi: 10.1016/j.apenergy.2020.115880.
