سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات158
تعداد شماره‌ها6,225
تعداد مقالات67,685
تعداد مشاهده مقاله114,996,108
تعداد دریافت فایل اصل مقاله89,668,135
Zarei, Alireza, Ghaffarzadeh, Navid. (1402). Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM. , 8(2), 1367-1379. doi: 10.22059/jser.2023.352567.1271
Alireza Zarei; Navid Ghaffarzadeh. "Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM". , 8, 2, 1402, 1367-1379. doi: 10.22059/jser.2023.352567.1271
Zarei, Alireza, Ghaffarzadeh, Navid. (1402). 'Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM', , 8(2), pp. 1367-1379. doi: 10.22059/jser.2023.352567.1271
Zarei, Alireza, Ghaffarzadeh, Navid. Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM. , 1402; 8(2): 1367-1379. doi: 10.22059/jser.2023.352567.1271

Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM

Journal of Solar Energy Research
دوره 8، شماره 2، تیر 2023، صفحه 1367-1379    اصل مقاله (784.33 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22059/jser.2023.352567.1271
نویسندگان
Alireza Zarei؛ Navid Ghaffarzadeh*
Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran
چکیده
By adding renewable energy sources, such as solar and wind, advanced metering infrastructure, and energy storage systems, the traditional power grid is becoming a smart grid. To prevent the uneconomic operation of a smart grid and increase the penetration of renewable resources, the Demand Response (DR) method is crucial for reducing the peak load and passing critical conditions. In this context, this study presents a multi-objective optimization of the AC optimal power flow (AC-OPF) problem with respect to DR. The novelty of the proposed demand-response-based OPF approach consists of decreasing the system cost through the simultaneous participation of active and reactive power in DR, considering the physical constraints of the AC network and various renewable energy sources in the smart grid, and increasing the calculation accuracy by demand prediction based on previous data using deep learning methods. Finally, using the TOPSIS method, the best DR value was determined according to multi-objective optimization. The effectiveness and resiliency of the proposed method were validated using a modified IEEE 24-bus testing system. The results illustrate that the optimal demand response (20%) achieved not only peak reduction and valley filling in active and reactive power but also minimized the total voltage deviation and system cost.  
کلیدواژه‌ها
Solar power plant؛ Demand response؛ Optimal power flow؛ Renewable energy؛ Load forecasting؛ Smart grid
مراجع
  1. Hussain, S., et al., Multi-level energy management systems toward a smarter grid: A review. IEEE Access, 2021. 9: p. 71994-72016.
  2. Nasir, T., et al., Recent challenges and methodologies in smart grid demand side management: State-of-the-art literature review. Mathematical Problems in Engineering, 2021. 2021.
  3. Kabir, E., et al., Solar energy: Potential and future prospects. Renewable and Sustainable Energy Reviews, 2018. 82: p. 894-900.
  4. Holm-Nielsen, J.B. and E.A. Ehimen, Biomass supply chains for bioenergy and biorefining. 2016: Woodhead Publishing.
  5. Nazir, M.S., et al., Environmental impact and pollution-related challenges of renewable wind energy paradigm – A review. Science of The Total Environment, 2019. 683: p. 436-444.
  6. Zhao, L.-C., et al., Magnetic coupling and flextensional amplification mechanisms for high-robustness ambient wind energy harvesting. Energy Conversion and Management, 2019. 201: p. 112166.
  7. Olabi, A. and M. Abdelkareem, Energy storage systems towards 2050. 2021, Elsevier. p. 119634.
  8. Mbachu, V.M., et al., An Economic Based Analysis of Fossil Fuel Powered Generator and Solar Photovoltaic System as Complementary Electricity Source for a University Student’s Room. Journal of Solar Energy Research, 2022. 7(4): p. 1159-1173.
  9. Jalili Jamshidian, F., S. Gorjian, and M. Shafiee Far, An Overview of Solar Thermal Power Generation Systems. Journal of Solar Energy Research, 2018. 3(4): p. 301-312.
  10. Logenthiran, T., D. Srinivasan, and T.Z. Shun, Demand side management in smart grid using heuristic optimization. IEEE transactions on smart grid, 2012. 3(3): p. 1244-1252.
  11. Setlhaolo, D. and X. Xia, Combined residential demand side management strategies with coordination and economic analysis. International Journal of Electrical Power & Energy Systems, 2016. 79: p. 150-160.
  12. Vardakas, J.S., N. Zorba, and C.V. Verikoukis, A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Communications Surveys & Tutorials, 2014. 17(1): p. 152-178.
  13. Bhattacharya, S., K. Kar, and J.H. Chow. Optimal precooling of thermostatic loads under time-varying electricity prices. in 2017 American Control Conference (ACC). 2017. IEEE.
  14. Bari, A., et al., Challenges in the smart grid applications: an overview. International Journal of Distributed Sensor Networks, 2014. 10(2): p. 974682.
  15. Xia, X., D. Setlhaolo, and J. Zhang. Residential demand response strategies for South Africa. in IEEE Power and Energy Society Conference and Exposition in Africa: Intelligent Grid Integration of Renewable Energy Resources (PowerAfrica). 2012. IEEE.
  16. Rahmani-andebili, M., Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets. Electric Power Systems Research, 2016. 132: p. 115-124.
  17. Ahmadipour, M., et al., Optimal load shedding scheme using grasshopper optimization algorithm for islanded power system with distributed energy resources. Ain Shams Engineering Journal, 2023. 14(1): p. 101835.
  18. Louca, R. and E. Bitar, Robust AC optimal power flow. IEEE Transactions on Power Systems, 2018. 34(3): p. 1669-1681.
  19. Taylor, J.A., Convex optimization of power systems. 2015: Cambridge University Press.
  20. Chatzivasileiadis, S., Lecture notes on optimal power flow (OPF). arXiv preprint arXiv:1811.00943, 2018.
  21. Li, B., et al., Generalized linear‐constrained optimal power flow for distribution networks. IET Generation, Transmission & Distribution, 2023.
  22. Abdi, H., S.D. Beigvand, and M. La Scala, A review of optimal power flow studies applied to smart grids and microgrids. Renewable and Sustainable Energy Reviews, 2017. 71: p. 742-766.
  23. Jabari, F., M. Mohammadpourfard, and B. Mohammadi-Ivatloo, AC Optimal Power Flow Incorporating Demand-Side Management Strategy, in Demand Response Application in Smart Grids. 2020, Springer. p. 147-165.
  24. Haggi, H., et al., Risk-averse cooperative operation of PV and hydrogen systems in active distribution networks. IEEE Systems Journal, 2021. 16(3): p. 3972-3981.
  25. Shi, Y., et al., Distributed model predictive control for joint coordination of demand response and optimal power flow with renewables in smart grid. Applied Energy, 2021. 290: p. 116701.
  26. Yao, M., D.K. Molzahn, and J.L. Mathieu, An optimal power-flow approach to improve power system voltage stability using demand response. IEEE Transactions on Control of Network Systems, 2019. 6(3): p. 1015-1025.
  27. Heidari Yazdi, S.S., et al., Over-voltage regulation of distribution networks by coordinated operation of PV inverters and demand side management program. 2022.
  28. Merrad, Y., et al., Fully Decentralized, Cost-Effective Energy Demand Response Management System with a Smart Contracts-Based Optimal Power Flow Solution for Smart Grids. Energies, 2022. 15(12): p. 4461.
  29. Ghalehkhondabi, I., et al., An overview of energy demand forecasting methods published in 2005–2015. Energy Systems, 2017. 8(2): p. 411-447.
  30. Nti, I.K., et al., Electricity load forecasting: a systematic review. Journal of Electrical Systems and Information Technology, 2020. 7(1): p. 13.
  31. Bouktif, S., et al., Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 2018. 11(7): p. 1636.
  32. Dargan, S., et al., A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering, 2020. 27(4): p. 1071-1092.
  33. Alzubi, J., A. Nayyar, and A. Kumar. Machine learning from theory to algorithms: an overview. in Journal of physics: conference series. 2018. IOP Publishing.
  34. https://www.transpower.co.nz/system-operator/operational-information/load-graphs#download. Available from: “https://www.transpower.co.nz/system-operator/operational-information/load-graphs#download.
  35. Soroudi, A., Energy Storage Systems: Power system optimization modeling in GAMS. Vol. 78. 2017: Springer.
  36. Wolgast, T., S. Ferenz, and A. Nieße, Reactive power markets: A review. IEEE Access, 2022.
  37. Halbhavi, S., S. Karki, and S. Kulkarni, Reactive Power Pricing Framework Problems and a proposal for a competitive market. International Journal of Innovations in Engineering and Technology, 2012. 1(2): p. 22-27.
  38. Deng, Z., M.D. Rotaru, and J.K. Sykulski. A study of evolutionary based optimal power flow techniques. in 2016 51st International Universities Power Engineering Conference (UPEC). 2016. IEEE.
  39. Aalami, H., M.P. Moghaddam, and G. Yousefi, Modeling and prioritizing demand response programs in power markets. Electric Power Systems Research, 2010. 80(4): p. 426-435.
  40. Grigsby, L.L., Electric power engineering handbook. 2006: CRC Press LLC, London.
  41. Ghaffarzadeh, N. and H. Faramarzi, Optimal Solar plant placement using holomorphic embedded power Flow Considering the clustering technique in uncertainty analysis. Journal of Solar Energy Research, 2022. 7(1): p. 997-1007.
  42. Bozorgavari, S.A., et al., Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study. Renewable and Sustainable Energy Reviews, 2020. 123: p. 109739.
  43. Jabari, F., M. Mohammadpourfard, and B. Mohammadi-Ivatloo, Implementation of Demand Response Programs on Unit Commitment Problem, in Demand Response Application in Smart Grids. 2020, Springer. p. 37-54.
  44. Siddique, M.B. and J. Thakur, Assessment of curtailed wind energy potential for off-grid applications through mobile battery storage. Energy, 2020. 201: p. 117601.
  45. Bolinger, M., J. Seel, and D. Robson, Utility-scale solar: Empirical trends in project technology, cost, performance, and PPA pricing in the United States–2019 Edition. 2019.
  46. Elattar, E.E., et al., Optimal power flow with emerged technologies of voltage source converter stations in meshed power systems. IEEE Access, 2020. 8: p. 166963-166979.
آمار
تعداد مشاهده مقاله: 378
تعداد دریافت فایل اصل مقاله: 316


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب