POWERING UP RESILIENCE: ACOQPCI'S IMPACT ON ENHANCING ELECTRIC GRID STABILITY
Abstract
The integration of large-scale renewable energy sources into the power grid has become increasingly important in recent years, driven by concerns over energy sustainability and environmental impact. However, the intermittent and unpredictable nature of renewable energy generation has led to power supply and demand imbalances, resulting in voltage and frequency fluctuations and harmonic pollution. To tackle these challenges, the first-generation Electric Spring (ES-1) was introduced, providing reactive power compensation to stabilize critical load voltage. Compared to traditional demand-side management and energy storage solutions, Electric Spring technology offers real-time and seamless control of load voltage during grid voltage fluctuations.
This abstract provides an overview of the advancements in Electric Spring technology, emphasizing its role in addressing power grid challenges related to renewable energy integration. It explores the evolution of ES topology structures and control strategies, focusing on ES-2. Current research efforts are directed towards enhancing the performance of Electric Springs through innovative control schemes and optimization algorithms, including phase control, Proportional Resonant controller with grid voltage feedforward, fuzzy PI algorithms, and variable universe PI control. These control strategies are continually evolving to exhibit greater robustness, adaptability to nonlinearity, and intelligence.
Keywords:
Electric Spring (ES), Renewable Energy Integration, Power Grid Stability, Control Strategies, Voltage FluctuationsDownloads
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Copyright (c) 2023 Li Xin Ming
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Hossein S, Hesam M, Hossein R, et al. Utilization of in-pipe hydropower renewable energy technology and energy storage systems in mountainous distribution networks[J]. Renewable Energy, 2021, 172.
S. Geethanjali, S. Shanmugapriya, Niket, et al. Electric Springs with Management of Battery in Order to Reduce Voltage Fluctuations [J]. International Journal of Recent Technology and Engineering (IJRTE), 2019, 8(2s11).
Mohamed F, Ahmed I. O, Israa M. M, et al. Strategies to save energy in the context of the energy crisis: a review [J]. Environmental Chemistry Letters, 2023, 21(4).
Hui S Y R, Lee C K, Wu F. Electric springs: a new smart grid technology[J]. IEEE Transactions on Smart Grid, 2012, 3 (3): 1552-1561.
A. J. Roscoe and G. Ault, Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response, IET Renewable Power Generation, vol. 4, no. 4, pp. 369–382, Jul. 2010
A. Mohd, E. Ortjohann, and A. Schmelter, Challenges in integrating distributed energy storage systems into future smart grid, IEEE Symposium on Industrial Electronics, pp. 1627-1632, 2008.
Yin F G, Wang C. Analysis of the effective operating range of electric spring based on reactive power compensation [J]. Power System Protection and Control, 2019, 43(01): 174-184.
Wang Q S, Cheng M, Chen Z, Wang Z. Steady-State Analysis of Electric Springs With a Novel delta Control [J]. IEEE Transactions on Power Electronics, 2015, 30(12).
Cheng M, Wang Q S, Zhang J Z. Theoretical Analysis and Controller Design of Electric Springs [J]. Proceedings of the CSEE, 2015, 35(10): 2436-2444.
Wu J, Wang B H. Design of an electric spring controller based on optimal PI [J]. Electrical Automation, 2017, 39(04):39-43.
Xuan M H, Zhang J H, Wang S J, et al. Electric spring control strategy based on variable universe fuzzy PI control [J]. Science Technology and Engineering, 2020, 20(18):6.
Meng X, Zhu X, Zhao J. Obstacle avoidance path planning using the elite ant colony algorithm for parameter optimization of unmanned aerial vehicles [J]. Arabian Journal for Science and Engineering, 2023, 48(2): 2261-2275.
Lina H. The global path planning for vehicular communication using ant colony algorithm in emerging wireless cloud computing [J]. Wireless Networks, 2022, 29(2).
Kang Y F, Li Z B, Wang T. Application of PaID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot [J]. Mathematical Problems in Engineering, 2022, 2022.
Riabko A V , Zaika O V , Kukharchuk R P, et al. Algorithm of ant colony optimization (ACO) for 3D variation traveling salesman problem[J]. Journal of Physics: Conference Series, 2022, 2288(1).
