Optimization and Stability Research of Control Strategies for Multienergy Complementary AC–DC Hybrid Power Grids

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Publicado en:Journal of Electrical and Computer Engineering vol. 2025, no. 1 (2025)
Autor principal: Junfang, Hao
Otros Autores: Qingfan, Wu, Chen, Cui, Chengfei, Yang, Jinbo, Wu, Jie, Yang, Xiaohe, Zhao
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John Wiley & Sons, Inc.
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Acceso en línea:Citation/Abstract
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022 |a 2090-0147 
022 |a 2090-0155 
024 7 |a 10.1155/jece/5569959  |2 doi 
035 |a 3284398089 
045 2 |b d20250101  |b d20251231 
084 |a 131428  |2 nlm 
100 1 |a Junfang, Hao  |u XJ Group Corporation Co., Ltd, , Xuchang, , , China 
245 1 |a Optimization and Stability Research of Control Strategies for Multienergy Complementary AC–DC Hybrid Power Grids 
260 |b John Wiley & Sons, Inc.  |c 2025 
513 |a Journal Article 
520 3 |a The randomness of power grid has been greatly increased as the new energy power proportion increases due to the uncertainty of wind turbine (WT) and photovoltaic (PV) power, posing significant challenges to grid security and economic efficiency. In this paper, the typical‐day WT and PV power outputs were obtained by the Latin hypercube sampling method. A multiobjective dual‐layer optimization model has the goal of reducing network loss and voltage deviation. The Whale Optimization Algorithm (WOA) was employed to solve the model. Based on the optimization results, the dispatch schemes for reactive power compensation devices, energy storage systems, and on‐load voltage regulation devices are formulated to improve system stability and smooth the output fluctuations of new energy sources. Finally, the proposed method is verified in the improved AC/DC hybrid grid based on IEEE 39‐notes system. The results indicated that the method can effectively reduce the network loss and smooth voltage fluctuations. It provides a theoretical basis for the stable and economical of grids with a high proportion of new energy power. 
653 |a Reactive power 
653 |a Electric potential 
653 |a Voltage 
653 |a Probability 
653 |a Wind turbines 
653 |a Hypercubes 
653 |a Energy 
653 |a Systems stability 
653 |a Multiple objective analysis 
653 |a Optimization algorithms 
653 |a Photovoltaic cells 
653 |a Efficiency 
653 |a Optimization models 
653 |a Latin hypercube sampling 
700 1 |a Qingfan, Wu  |u XJ Group Corporation Co., Ltd, , Xuchang, , , China 
700 1 |a Chen, Cui  |u XJ Group Corporation Co., Ltd, , Xuchang, , , China 
700 1 |a Chengfei, Yang  |u XJ Group Corporation Co., Ltd, , Xuchang, , , China 
700 1 |a Jinbo, Wu  |u XJ Group Corporation Co., Ltd, , Xuchang, , , China 
700 1 |a Jie, Yang  |u Henan Institute of Technology, , Xinxiang, , , China, <url href="http://haust.edu.cn">haust.edu.cn</url> 
700 1 |a Xiaohe, Zhao  |u Henan Institute of Technology, , Xinxiang, , , China, <url href="http://haust.edu.cn">haust.edu.cn</url> 
773 0 |t Journal of Electrical and Computer Engineering  |g vol. 2025, no. 1 (2025) 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3284398089/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3284398089/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3284398089/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch