Virtual Inertia Optimization Allocation Based on Grey Wolf Algorithm

Genzhu  Wu; Dongliang  Nan; Yu  Duan; Lu  Zhang; Ziming  Zhu; Xiqiang  Chang

doi:10.6180/jase.202411_27(11).0009

Virtual Inertia Optimization Allocation Based on Grey Wolf Algorithm

Genzhu Wu¹This email address is being protected from spambots. You need JavaScript enabled to view it., Dongliang Nan^1,2, Yu Duan², Lu Zhang², Ziming Zhu², and Xiqiang Chang¹

¹School of Electrical Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China

²State Grid Xinjiang Electric Power Co., Ltd. Electric Power Science Research Institute, Urumqi 830000, Xinjiang, China

Received: October 17, 2023
Accepted: January 1, 2024
Publication Date: February 6, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202411_27(11).0009

To address the optimal allocation of virtual inertia as a replacement for rotating inertia in power systems, this paper proposes a virtual inertia optimal allocation method. First, the calculation method for determining the minimum inertia requirement of power systems with high penetration of renewable energy is clarified. Next, considering factors like frequency stability and virtual inertia investment costs, a virtual inertia optimization allocation model is constructed with the goals of minimizing the frequency security index and investment costs, subject to constraints such as critical inertia, rate of change of frequency, and maximum frequency deviation. And the grey wolf algorithm is utilized to solve this model. Finally, the modified WSCC 9-bus system and IEEE 39-bus system are simulated to validate the effectiveness and universality of the proposed model in optimally allocating virtual inertia while balancing frequency stability and investment costs.

Keywords: virtual inertia; critical inertia; frequency stability; investment cost; grey wolf algorithm

[1] K. B. Kiran, M. Indira, R. Nagaraja, et al., (2021) “Mathematical modeling and evaluation of performance characteristics of a hybrid solar PV and wind energy system" Journal of Applied Science and Engineering 25(4): 785–797. DOI: 10.6180/jase.202208_25(4).0014.
[2] K. Prabhakar, S. K. Jain, and P. K. Padhy, (2022) “Inertia estimation in modern power system: A comprehensive review" Electric Power Systems Research 211: 108222. DOI: 10.1016/j.epsr.2022.108222.
[3] D. Sun, H. Liu, S. Gao, L. Wu, P. Song, and X. Wang, (2020) “Comparison of Different Virtual Inertia Control Methods for Inverter-based Generators" Journal of Modern Power Systems and Clean Energy 8(4): 768–777. DOI: 10.35833/MPCE.2019.000330.
[4] M. Chen, D. Zhou, and F. Blaabjerg, (2020) “Modelling, Implementation, and Assessment of Virtual Synchronous Generator in Power Systems" Journal of Modern Power Systems and Clean Energy 8(3): 399–411. DOI: 10.35833/MPCE.2019.000592.
[5] D. Li, Q. Wang, X. Zhang, T. Wang, and W. Hou, (2023) “Inertia Configuration Method to Enhance Frequency Stability of Power Grids Considering the Spatial Distribution Characteristics" IET Conference Proceedings 2023(15): 1270–1275. DOI: 10.1049/icp.2023.2485.
[6] B. K. Poolla, S. Bolognani, and F. Dörfler. “Placing Rotational Inertia in Power Grids”. In: 2016 American Control Conference (ACC). 2016, 2314–2320. DOI: 10.1109/ACC.2016.7525263.
[7] B. K. Poolla, S. Bolognani, and F. Dörfler, (2017) “Optimal Placement of Virtual Inertia in Power Grids" IEEE Transactions on Automatic Control 62(12): 6209– 6220. DOI: 10.1109/TAC.2017.2703302.
[8] H. B. Liu, H. Ma, Z. Z. Xiong, and X. X. Tian, (2019) “Virtual Inertia Configuration Strategy Based on Improved Fireworks Algorithm" Science Technology and Engineering 19(154-158):
[9] J. K. Huang, Z. F. Yang, J. L. Liu, J. Yu, and J. Ren, (2020) “Optimal Allocation of Virtual Inertia for Improving Small-signal Stability" Proceedings of the CSEE 40(713-723): DOI: 10.13334/j.0258-8013.pcsee.190887.
[10] H. B. Liu. “Virtual inertia configuration based on multi-objective optimization". (mathesis). China Three Gorges University, 2021.
[11] T. Han and D. J. Hill, (2021) “Dispatch of virtual inertia and damping: Numerical method with SDP and ADMM" International Journal of Electrical Power & Energy Systems 133: 107259.
[12] F. Zeng, J. Zhang, G. Chen, Z. Wu, S. Huang, and Y. Liang, (2020) “Online Estimation of Power System Inertia Constant Under Normal Operating Conditions" IEEE Access 8: 101426–101436. DOI: 10.1109/ACCESS.2020.2997728.
[13] M. Liu, J. Chen, and F. Milano, (2021) “On-Line Inertia Estimation for Synchronous and Non-Synchronous Devices" IEEE Transactions on Power Systems 36(3): 2693–2701. DOI: 10.1109/TPWRS.2020.3037265.
[14] X. Deng, R. Mo, P. Wang, J. Chen, D. Nan, and M. Liu, (2023) “Review of RoCoF Estimation Techniques for Low-Inertia Power Systems" Energies 16(9): DOI: 10.3390/en16093708.
[15] H. Delkhosh and H. Seifi, (2021) “Power System Frequency Security Index Considering All Aspects of Frequency Profile" IEEE Transactions on Power Systems 36(2): 1656–1659. DOI: 10.1109/TPWRS.2020.3047510.
[16] Y. Z. Xie. “Power system frequency stability mechanism and coordination control with large frequency deviation scenario". (phdthesis). Shandong University, 2021.
[17] A. Pepiciello and A. Vaccaro. “An Optimizationbased Method for Estimating Critical Inertia in Smart Grids”. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). 2019, 2237–2241. DOI: 10.1109/SMC.2019.8914156.
[18] Y. Liu, Q. Zhao, and D. Liang. “Inertia Characteristic Analysis and Inertia Estimation of New Energy Power System”. In: 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). 2022, 1350– 1355. DOI: 10.1109/ICPSAsia55496.2022.9949746.
[19] S. Mirjalili, S. Saremi, S. M. Mirjalili, and L. dos S. Coelho, (2016) “Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization" Expert Systems with Applications 47: 106–119. DOI: 10.1016/j.eswa.2015.10.039.
[20] F. Wang, C. Chen, H. Zhang, Y. Ma, et al., (2022) “Short-term Load Forecasting Based On Variational Mode Decomposition And Chaotic Grey Wolf Optimization Improved Random Forest Algorithm" Journal of Applied Science and Engineering 26(1): 69–78.
[21] F. Milano. “A python-based software tool for power system analysis”. In: 2013 IEEE Power and Energy Society General Meeting. 2013, 1–5. DOI: 10.1109/PESMG.2013.6672387.
[22] F. Milano. Power system modelling and scripting. Springer Science & Business Media, 2010.