Research on Load Forecasting and Scheduling Optimization Methods for Power Systems
To ensure the balance of supply and demand in the power grid,it is crucial to accurately predict the load of the power system and formulate a reasonable scheduling plan.The article analyzes the applications of statistical models,machine learning,and deep learning in load forecasting,as well as the applications of mathematical programming,simulated annealing,and genetic algorithms in load scheduling optimization.It deeply studies the correlation between load forecasting and scheduling optimization,and proposes a collaborative optimization strategy based on goal matching,constraint coordination,and algorithm integration.The case analysis results indicate that compared to independent prediction and scheduling methods,collaborative optimization can significantly reduce prediction errors and scheduling costs.Therefore,building cross level collaborative control is an effective way to achieve efficient and intelligent operation of the power grid.
power systemload forecastingscheduling optimizationcollaborative control
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