Research on Optimal Control Strategies on Distribution Network Power Transfer Under Extreme Weather Conditions

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Detalles Bibliográficos
Publicado en:Electronics vol. 14, no. 19 (2025), p. 3854-3876
Autor Principal: Su Biaolong
Outros autores: Xi Yanna, Li, Shuang, Yuan, Bo
Publicado:
MDPI AG
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024 7 |a 10.3390/electronics14193854  |2 doi 
035 |a 3261057077 
045 2 |b d20250101  |b d20251231 
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100 1 |a Su Biaolong  |u State Grid Electric Power Research Institute, Nanjing 211000, China; subiaolong@sgepri.sgcc.com.cn (B.S.); 
245 1 |a Research on Optimal Control Strategies on Distribution Network Power Transfer Under Extreme Weather Conditions 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Against the backdrop of global climate change, extreme weather events are increasingly challenging the safe and stable operation of power distribution networks. These events can cause sudden load fluctuations, equipment failures, and disruptions in power transfer. To address these, this paper proposes an optimal control strategy for distribution network power transfer, integrating Long Short-Term Memory (LSTM) networks and dynamic optimization models. By fusing meteorological data with grid characteristics, the LSTM model predicts load demand and fault probability, capturing complex system behaviors under extreme conditions. Combined with Mixed-Integer Linear Programming (MILP), a decision-making model is developed, and a deep-reinforcement-learning-based algorithm handles uncertainties in weather, load, and equipment faults, enabling accurate control. Validation on a 33-bus system shows the method enhances reliability under extreme weather, providing practical value. Furthermore, typhoons, as extreme weather events, can severely damage infrastructure, disrupt power lines, and affect grid stability. In the 33-bus system, typhoons can cause tower collapses and line failures, impacting power transfer. This paper explores the impact of typhoons on a bus model integrated with renewable energy, proposing optimal control strategies to ensure power supply to critical loads while minimizing equipment damage. 
653 |a Load 
653 |a Cold 
653 |a Linear programming 
653 |a Electrical loads 
653 |a Integer programming 
653 |a Forecasting 
653 |a Communication 
653 |a Typhoons 
653 |a Floods 
653 |a Optimization 
653 |a Real time 
653 |a Power transfer 
653 |a Weather 
653 |a Complex systems 
653 |a Damage 
653 |a Optimal control 
653 |a Machine learning 
653 |a Climate change 
653 |a Optimization models 
653 |a Mathematical programming 
653 |a Precipitation 
653 |a Wind power 
653 |a Solar energy 
653 |a Infrastructure 
653 |a Load fluctuation 
653 |a Decision making 
653 |a Power lines 
653 |a Mixed integer 
653 |a Storm damage 
653 |a Alternative energy sources 
653 |a Rain 
653 |a Meteorological data 
653 |a Electric power distribution 
700 1 |a Xi Yanna  |u State Grid Beijing Electric Power Company, Beijing 100031, China 
700 1 |a Li, Shuang  |u State Grid Electric Power Research Institute, Nanjing 211000, China; subiaolong@sgepri.sgcc.com.cn (B.S.); 
700 1 |a Yuan, Bo  |u State Grid Electric Power Research Institute, Nanjing 211000, China; subiaolong@sgepri.sgcc.com.cn (B.S.); 
773 0 |t Electronics  |g vol. 14, no. 19 (2025), p. 3854-3876 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3261057077/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3261057077/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3261057077/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch