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Voltage Control and Reactive Power Support Optimization Using (SA-STO) Algorithm in Virtual Power Plants (VPPs)

International Journal of Electrical and Electronics Engineering
© 2025 by SSRG - IJEEE Journal
Volume 12 Issue 8
Year of Publication : 2025
Authors : Srinivasa Rao.Sureddy, Nageswara Rao.Pulivarthi
10.14445/23488379/IJEEE-V12I8P110
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How to Cite?

Srinivasa Rao.Sureddy, Nageswara Rao.Pulivarthi, "Voltage Control and Reactive Power Support Optimization Using (SA-STO) Algorithm in Virtual Power Plants (VPPs)," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 8, pp. 99-111, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I8P110

Abstract:

The complexity of voltage control and reactive power support has risen due to the increasing integration of renewable energy sources in Virtual Power Plants (VPPs). Therefore, effective management is vital for grid stability and energy optimization. To improve voltage control and reactive power management in VPPs, this research introduces a new method that uses the SA-STO algorithm. The proposed Self-Adaptive Siberian Tiger Optimization (SA-STO) algorithm continuously modifies its search parameters according to the power grid's operating circumstances to achieve adaptive and efficient optimization. The approach increases the stability of voltage profiles, speeds up convergence, and prevents local optima by using the self-adaptive mechanism. Many constraints are considered to optimize for both efficient energy use and grid dependability, such as reactive power compensation, voltage deviation reduction, and power flow balancing. Key results include a reduction in average voltage deviation by ~35%, a decrease in power loss from 12 kW to 1–2 kW, and convergence within ~100 iterations—outperforming PI, PID, and static FOPID controllers. Compared to traditional optimization approaches, the SA-STO algorithm achieves better simulation results for voltage regulation accuracy, computational resilience, and reactive power support efficiency. The results show that it might be an effective optimization tool for making current VPPs more efficient, leading to a more stable and renewable energy grid.

Keywords:

Voltage control, VPPs, Reactive power, Optimization, Renewable energy.

References:

[1] Daiva Stanelyte, and Virginijus Radziukynas, “Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks,” Energies, vol. 13, no. 1, pp. 1-26, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Tomoya Enokido, Ailixier Aikebaier, and Makoto Takizawa, “Computation and Transmission Rate Based Algorithm for Reducing the Total Power Consumption,” 2011 International Conference on Complex, Intelligent, and Software Intensive Systems, Seoul, Korea (South), pp. 233-240, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Bianca Goia, Tudor Cioara, and Ionut Anghel, “Virtual Power Plant Optimization in Smart Grids: A Narrative Review,” Future Internet, vol. 14, no. 5, pp. 1-22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Taher Niknam et al., “Multiobjective Optimal Reactive Power Dispatch and Voltage Control: A New Opposition-Based Self-Adaptive Modified Gravitational Search Algorithm,” IEEE Systems Journal, vol. 7, no. 4, pp. 742-753, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[5] G. Suresh, and D. Lenine, “Gaps of Indian Electrical Energy Sector and Its Optimal Mitigation by Using Optimal Utilization of Indian Renewable Energy Policy with the Help of the P&O MPPT Technique,” Archives for Technical Sciences, vol. 31, no. 2, pp. 94-115, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Giuseppe Fusco, Mario Russo, and MicheleDeSantis, “Decentralized Voltage Control in Active Distribution Systems: Features and Open Issues,” Energies, vol. 14, no. 9, pp. 1-31, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Kun Wang et al., “Flexible Resource Dynamic Aggregation Regulation Method of Virtual Power Plant to Ensure More Renewable Energy Generation,” Process Safety and Environmental Protection, vol. 180, pp. 339-350, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Abeer A. Kadhim, Samir J. Mohammed, and Qais Al-Gayem, “DVB-T2 Energy and Spectral Efficiency Trade-off Optimization based on Genetic Algorithm,” Journal of Internet Services and Information Security, vol. 14, no. 3, pp. 213-225, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Subhojit Dawn et al., “Integration of Renewable Energy in Microgrids and Smart Grids in Deregulated Power Systems: A Comparative Exploration,” Advanced Energy and Sustainability Research, vol. 5, no. 10, pp. 1-23, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Tomasz Sikorski et al., “A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects,” Energies, vol. 13, no. 12, pp. 1-30, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Anju Rajput et al., “Design of Novel High-Speed Energy-Efficient Robust 4:2 Compressor,” Journal of VLSI Circuits and Systems, vol. 6, no. 2, pp. 53-64, 2024.
[Google Scholar] [Publisher Link]
[12] Junhui Huang, Hui Li, and Zhaoyun Zhang, “Review of Virtual Power Plant Response Capability Assessment and Optimization Dispatch,” Technologies, vol. 13, no. 6, pp. 1-31, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Bogdan Marinescu et al., “Dynamic Virtual Power Plant: A New Concept for Grid Integration of Renewable Energy Sources,” IEEE Access, vol. 10, pp. 104980-104995, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] G.A. Bakare et al., “Comparative Application of Differential Evolution and Particle Swarm Techniques to Reactive Power and Voltage Control,” 2007 International Conference on Intelligent Systems Applications to Power Systems, Kaohsiung, Taiwan, pp. 1-6, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Dimitris Mourtzis, and John Angelopoulos, “Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm,” Machines, vol. 11, no. 7, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Hossein Nezamabadi, and Mehrdad Setayesh Nazar, “Arbitrage Strategy of Virtual Power Plants in Energy, Spinning Reserve and Reactive Power Markets,” IET Generation, Transmission & Distribution, vol. 10, no. 3, pp. 750-763, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Md. Shahid Iqbal et al., “A Hybrid Optimization Algorithm for Improving Load Frequency Control in Interconnected Power Systems,” Expert Systems with Applications, vol. 249, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Neethu Elizabeth Michael et al., “Reactive Power Compensation Using Electric Vehicle and Data Center by Integrating Virtual Power Plan,” Electric Power Components and Systems, vol. 50, no. 4-5, pp. 245-255, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Duncan S. Callaway, and Ian A. Hiskens, “Achieving Controllability of Electric Loads,” Proceedings of the IEEE, vol. 99, no. 1, pp. 184-199, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mohabbat Vafa, Mohammad Hossain Ershadi, and Behdad Arandian, “Robust Operation of Renewable Virtual Power Plant in Intelligent Distribution System Considering Active and Reactive Ancillary Services Markets,” Scientific Reports, vol. 15, no. 1, pp. 1-24, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Abdallah Mohammed et al., “A Comprehensive Review of Advancements and Challenges in Reactive Power Planning for Microgrids,” Energy Informatics, vol. 7, no. 1, pp. 1-27, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Mohammad Almomani, Ahmed Alkhonain, and Venkataramana Ajjarapu, “Extended Sensitivity‑Aware Reactive Power Dispatch Algorithm for Smart Inverters with Multiple Control Modes,” arXiv Preprint, pp. 1-5, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Moein Esfahani et al., “A Distributed VPP-Integrated Co-Optimization Framework for Energy Scheduling, Frequency Regulation, and Voltage Support Using Data-Driven Distributionally Robust Optimization with Wasserstein Metric,” Applied Energy, vol. 361, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]