{"id":3495,"date":"2026-04-11T16:58:29","date_gmt":"2026-04-11T08:58:29","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3495"},"modified":"2026-06-05T10:00:45","modified_gmt":"2026-06-05T02:00:45","slug":"virtual-inertia-optimization-allocation-based-on-grey-wolf-algorithm","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=virtual-inertia-optimization-allocation-based-on-grey-wolf-algorithm","title":{"rendered":"Virtual Inertia Optimization Allocation Based on Grey Wolf Algorithm"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 11<\/a><\/p>\n<\/div>\n\n\n\n

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Genzhu Wu1<\/sup><\/i><\/a>, Dongliang Nan1,2<\/sup>, Yu Duan2<\/sup>, Lu Zhang2<\/sup>, Ziming Zhu2<\/sup>, and Xiqiang Chang1<\/sup><\/p>\n\n\n\n

1School of Electrical Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China<\/p>\n\n\n\n

2State Grid Xinjiang Electric Power Co., Ltd. Electric Power Science Research Institute, Urumqi 830000, Xinjiang, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0October 17, 2023
Accepted:\u00a0January 01, 2024
Publication Date:\u00a0April 11, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Modified WSCC 9-Bus System.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202411_27(11).0009<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

Keywords:\u00a0virtual inertia; critical inertia; frequency stability; investment cost; grey wolf algorithm<\/em><\/p>\n\n\n\n

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