Virtual Power Plant for Optimizing Power Flow in Large Scale Distributed Networks

References

• Z. Tao et al., “A master–slave game optimization model for electric power companies considering virtual power plant,” IEEE Access, vol. 10, pp. 21812–21820, 2022. DOI: 10.1109/ACCESS.2022.3152805.
   Web of Science ®Google Scholar
• Y. Wu, J. Wu, and G. De, “Research on trading optimization model of virtual power plant in medium- and long-term market,” Energies, vol. 15, no. 3, pp. 759, 2022. DOI: 10.3390/en15030759.
   Web of Science ®Google Scholar
• J. Aguilar, C. Bordons, A. Arce, and R. M. Galan, “Intent profile strategy for virtual power plant participation in simultaneous energy markets with dynamic storage management,” IEEE Access, vol. 10, pp. 22599–22609, 2022. DOI: 10.1109/ACCESS.2022.3155170.
   Web of Science ®Google Scholar
• H. Abubakr et al., “Adaptive LFC incorporating modified virtual rotor to regulate frequency and tie-line power flow in multi-area microgrids,” IEEE Access, vol. 10, pp. 33248–33268, 2022. DOI: 10.1109/ACCESS.2022.3161505.
   Web of Science ®Google Scholar
• X. Wu, H. Xiong, S. Li, S. Gan, C. H. Hou, and Z. Ding, “Improved light robust optimization strategy for virtual power plant operations with fluctuating demand,” IEEE Access, vol. 11, pp. 1–1, 2023. DOI: 10.1109/ACCESS.2023.3280057.
   Web of Science ®Google Scholar
• Y. Wu, J. Wu, G. De, and W. Fan, “Research on optimal operation model of virtual electric power plant considering net-zero carbon emission,” Sustain, vol. 14, no. 6, pp. 3276, 2022. DOI: 10.3390/su14063276.
   Web of Science ®Google Scholar
• Q. Wang et al., “Asynchronous decomposition method for the coordinated operation of virtual power plants,” IEEE Trans. Power Syst., vol. 38, no. 1, pp. 767–782, 2023. DOI: 10.1109/TPWRS.2022.3162329.
   Web of Science ®Google Scholar
• C. Feng, Q. Chen, Y. Wang, J. Ma, and X. Wang, “Frequency regulation service provision for virtual power plants through 5G RAN slicing,” IEEE Trans. Smart Grid, vol. 13, no. 6, pp. 4943–4956, 2022. DOI: 10.1109/TSG.2022.3177765.
   Web of Science ®Google Scholar
• W. Nafkha-Tayari, S. Ben Elghali, E. Heydarian‐Forushani, and M. E. Benbouzid, “Virtual power plants optimization issue: a comprehensive review on methods, solutions, and prospects,” Energies, vol. 15, no. 10, pp. 3607, 2022. DOI: 10.3390/en15103607.
   Web of Science ®Google Scholar
• J. Liu, S. S. Yu, H. Hu, J. Zhao, and H. M. Trinh, “Demand-side regulation provision of virtual power plants consisting of interconnected microgrids through double-stage double-layer optimization,” IEEE Trans. Smart Grid, vol. 14, no. 3, pp. 1946–1957, 2023. DOI: 10.1109/TSG.2022.3203466.
   Web of Science ®Google Scholar
• L. Ren, D. Peng, D. Wang, J. Li, and H. Zhao, “Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode,” Front. Energy Res., vol. 10, pp. 983743, 2023. DOI: 10.3389/fenrg.2022.983743.
   Web of Science ®Google Scholar
• L. Ju et al., “Robust optimal dispatching model and a benefit allocation strategy for rural novel virtual power plants incorporating biomass waste energy conversion and carbon cycle utilization,” Front. Energy Res., vol. 11, pp. 1181310, 2023. DOI: 10.3389/fenrg.2023.1181310.
   Web of Science ®Google Scholar
• S. Li, W. Wu, and Y. Lin, “Robust data-driven and fully distributed volt/VAR control for active distribution networks with multiple virtual power plants,” IEEE Trans. Smart Grid, vol. 13, no. 4, pp. 2627–2638, 2022. DOI: 10.1109/TSG.2022.3166274.
   Web of Science ®Google Scholar
• S. Seven et al., “Energy trading on a peer-to-peer basis between virtual power plants using decentralized finance instruments,” Sustain, vol. 14, no. 20, pp. 13286, 2022. DOI: 10.3390/su142013286.
   Web of Science ®Google Scholar
• J. F. Venegas-Zarama, J. I. Muñoz-Hernandez, L. Baringo, P. Diaz-Cachinero, and I. D. Domingo-Mondejar, “A review of the evolution and main roles of virtual power plants as key stakeholders in power systems,” IEEE Access, vol. 10, pp. 47937–47964, 2022. DOI: 10.1109/ACCESS.2022.3171823.
   Web of Science ®Google Scholar
• C. Feng, Y. Wang, X. Wang, and Q. Chen, “Device access optimization for virtual power plants in heterogeneous networks,” IEEE Trans. Smart Grid, vol. 13, no. 2, pp. 1478–1489, 2022. DOI: 10.1109/TSG.2021.3125042.
   Web of Science ®Google Scholar
• Z. Xu, Y. Guo, and H. Sun, “Competitive pricing game of virtual power plants: models, strategies, and equilibria,” IEEE Trans. Smart Grid, vol. 13, no. 6, pp. 4583–4595, 2022. DOI: 10.1109/TSG.2022.3184171.
   Web of Science ®Google Scholar
• M. Zhelonkin, A. Kurkin, A. Loskutov, A. Plekhov, D. Malyarov, and E. Kryukov, “Developing a simulation model to numerically estimate energy parameters and wave energy converter efficiency of a floating wave power plant,” Energies, vol. 16, no. 10, pp. 4150, 2023. DOI: 10.3390/en16104150.
   Web of Science ®Google Scholar
• F. Daqaq, M. Ouassaid, S. Kamel, R. Ellaia, and M. F. El-Naggar, “A novel chaotic flower pollination algorithm for function optimization and constrained optimal power flow considering renewable energy sources,” Front. Energy Res., vol. 10, pp. 941705, 2022. DOI: 10.3389/fenrg.2022.941705.
   Web of Science ®Google Scholar
• A. Saha, P. Dash, N. R. Babu, T. Chiranjeevi, M. Dhananjaya, and L. Knypiński, “Dynamic stability evaluation of an integrated biodiesel-geothermal power plant-based power system with spotted hyena optimized cascade controller,” Sustainability, vol. 14, no. 22, pp. 14842, 2022. DOI: 10.3390/su142214842.
   Web of Science ®Google Scholar
• T. Chu, X. An, W. Y. Zhang, Y. Lu, and J. Tian, “Multiple virtual power plants transaction matching strategy based on alliance blockchain,” Sustainability, vol. 15, no. 8, pp. 6939, 2023. DOI: 10.3390/su15086939.
   Web of Science ®Google Scholar
• G. Weishang, W. Qiang, L. Haiying, and W. Jing, “A trading optimization model for virtual power plants in day-ahead power market considering uncertainties,” Front. Energy Res., vol. 11, pp. 1152717, 2023. DOI: 10.3389/fenrg.2023.1152717.
   Web of Science ®Google Scholar
• H. Hajian, R. Simson, and J. Kurnitski, “Heating sizing power reduction in buildings connected to district heating with dynamically controlled DHW setback and flow limiters,” Energies, vol. 15, no. 14, pp. 5278, 2022. DOI: 10.3390/en15145278.
   Web of Science ®Google Scholar
• Zahid Ullah, Hany Hassanin, and   Arshad, “Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources,” Energies, vol. 15, no. 14, pp. 5296, 2022. DOI: 10.3390/en15145296.
   Web of Science ®Google Scholar
• Y. Wang, Y. Zhang, X. Qi, M. Wang, and X. Wang, “A bi-level optimization model for virtual power plant membership selection considering load time series,” Sustainability, vol. 15, no. 3, pp. 2138, 2023. DOI: 10.3390/su15032138.
   Web of Science ®Google Scholar
• H. Sharma et al., “Feasibility of solar grid-based industrial virtual power plant for optimal energy scheduling: a case of indian power sector,” Energies, vol. 15, no. 3, pp. 752, 2022. DOI: 10.3390/en15030752.
   Web of Science ®Google Scholar
• T. Kesavan and K. Lakshmi, “Optimization of a renewable energy source-based virtual power plant for electrical energy management in an unbalanced distribution network,” Sustainability, vol. 14, no. 18, pp. 11129, 2022. DOI: 10.3390/su141811129.
   Web of Science ®Google Scholar
• K. Borisoot, R. Liemthong, C. Srithapon, and R. Chatthaworn, “Optimal energy management for virtual power plant considering operation and degradation costs of energy storage system and generators,” Energies, vol. 16, no. 6, pp. 2862, 2023. DOI: 10.3390/en16062862.
   Web of Science ®Google Scholar
• A. K. Pandey, V. K. Jadoun, and J. N. Sabhahit, “Real-time peak valley pricing based multi-objective optimal scheduling of a virtual power plant considering renewable resources,” Energies, vol. 15, no. 16, pp. 5970, 2022. DOI: 10.3390/en15165970.
   Web of Science ®Google Scholar
• Zahid Ullah, Hany Hassanin, James Cugley, Mohammed Al Alawi, and   Arshad, “Planning, operation, and design of market-based virtual power plant considering uncertainty,” Energies, vol. 15, no. 19, pp. 7290, 2022. DOI: 10.3390/en15197290.
   Web of Science ®Google Scholar
• J. U. Ali, S. A. Kazmi, A. Altamimi, Z. A. Khan, O. Alrumayh, and M. M. Malik, “Smart energy management in virtual power plant paradigm with a new improved multi-level optimization based approach,” IEEE Access, vol. 10, pp. 50062–50077, 2022. DOI: 10.1109/ACCESS.2022.3169707.
   Web of Science ®Google Scholar
• Y. Wang, M. Zhang, J. Ao, Z. Wang, H. Dong, and M. Zeng, “Profit allocation strategy of virtual power plant based on multi-objective optimization in electricity market,” Sustainability, vol. 14, no. 10, pp. 6229, 2022. DOI: 10.3390/su14106229.
   Web of Science ®Google Scholar
• M. Vahedipour-Dahraie, H. Rashidizadeh-Kermani, M. Parente, M. Shafie‐khah, and P. Siano, “Investigating the impact of external demand response flexibility on the market power of strategic virtual power plant,” IEEE Access, vol. 10, pp. 84960–84969, 2022. DOI: 10.1109/ACCESS.2022.3197908.
   Web of Science ®Google Scholar
• D. Yu, X. Zhao, Y. Wang, L. Jiang, and H. Liu, “Research on energy management of a virtual power plant based on the improved cooperative particle swarm optimization algorithm,” Front. Energy Res., vol. 10, pp. 785569, 2022. DOI: 10.3389/fenrg.2022.785569.
   Web of Science ®Google Scholar
• A. Rajaram and K. Sathiyaraj, “An improved optimization technique for energy harvesting system with grid connected power for green house management,” J. Electr. Eng. Technol., vol. 17, no. 5, pp. 2937–2949, 2022. DOI: 10.1007/s42835-022-01033-2.
   Web of Science ®Google Scholar
• S. Wang et al., “Research on capacity allocation optimization of commercial virtual power plant (CVPP),” Energies, vol. 15, no. 4, pp. 1303, 2022. DOI: 10.1080/15325008.2023.2217175.
   Web of Science ®Google Scholar
• P. Ashok Babu, J. L. Mazher Iqbal, S. Siva Priyanka, M. Jithender Reddy, G. Sunil Kumar, and A. Rajaram, “Power control and optimization for power loss reduction using deep learning in microgrid systems,” Electr. Power Comput. Syst., pp. 1–14, 2023. DOI: 10.1080/15325008.2023.2217175.
   Web of Science ®Google Scholar
• H. Shekhar et al., “Demand side control for energy saving in renewable energy resources using deep learning optimization,” Electr. Power Comput. Syst., vol. 51, no. 19, pp. 2397–2413, 2023. DOI: 10.1080/15325008.2023.2246463.
   Web of Science ®Google Scholar