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

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Publicat a:Electronics vol. 14, no. 19 (2025), p. 3854-3876
Autor principal: Su Biaolong
Altres autors: Xi Yanna, Li, Shuang, Yuan, Bo
Publicat:
MDPI AG
Matèries:
Load
Cold
Linear programming
Electrical loads
Integer programming
Forecasting
Communication
Typhoons
Floods
Optimization
Real time
Power transfer
Weather
Complex systems
Damage
Optimal control
Machine learning
Climate change
Optimization models
Mathematical programming
Precipitation
Wind power
Solar energy
Infrastructure
Load fluctuation
Decision making
Power lines
Mixed integer
Storm damage
Alternative energy sources
Rain
Meteorological data
Electric power distribution
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Resum: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.
ISSN:2079-9292
DOI:10.3390/electronics14193854
Font:Advanced Technologies & Aerospace Database