Research On Very Short-term Load Forecasting And Thermal Power Plant Load Optimization

Electric energy can not be stored, this determines the load should be keep in balance in real-time. From this point of view, the ultra-short-term load forecasting and load dispatch optimization of thermal power plant which is a sub segment of very short term load dispatch of the system are studied.Load forecasting is the foundation of power system security and economic operating, through load forecasting, electricity demand can be estimated in advance. According to the outcomes of the prediction load, technical measure can be taken to improve the system economy and reliability. As for the power system, improving the operating security and economy, meliorating power quality, are mostly dependent on accuracy of load forecasting. So the key of the load forecasting is to improve its accuracy.The composition and characteristic of the load will be analyzed, the classification and characteristic and the influencing factors of the load forecasting also introduced in the beginning of the paper, all the work will be done to offering theoretical basis for the ultra-short-term load forecasting. Presently, the most commonly used ultra-short-term load forecasting models are neural network and linear extrapolation. As a kind of artificial intelligence, neural network method can get good prediction accuracy; the linear extrapolation is widely used due to its simply calculating and less time-consuming. But the linear extrapolation is difficult to reflect the nonlinear characteristics of the load, while the neural network have the shortcomings of easy to fall into local optimum exists, over-fitting and the generalization ability is not strong enough sexuality.Support Vector Machine is a kind of machine learning algorithms had arising much attention in recent years, and is applied in many aspects. The theory of support vector machines and least squares support vector machine are systematically described. Horizontal weighting will be introduced into the input vector of fuzzy weighted least squares support vector machine to form the bidirectional weighted least squares support vector machine model for ultra-short-term load forecasting, the model can reflect the characteristic that the nearer date has a greater impact on the predicting value. Update the training sample to determine the forecasting model before forecasting of each time. 15 minute-step is used to validating the proposal method, and the neural network and curve extrapolation prediction results will be compared with, the results show that the improved method of forecasting can get a better accuracy.As for the thermal power plant load distribution, this paper considered the economic load dispatch model, and the model of environmental, fast load changes requirements, that may be consideration during real-time scheduling of multi-objective load optimization of the plant. As for the multi-objective optimization algorithm, particle swarm optimization and multi-objective optimization theory will be systematically introduced. In order to improve the searching ability of particle swarm optimization algorithm, chaotic local search method will be applied into the multi-objective particle swarm optimization with crowding distance, to form a new multi-objective particle swarm optimization algorithm. Six-unit system will be used to verification of the algorithm. The result of the modified method is comparison with its original method and NSGA-â…¡multi-objective optimization method, the conclusion show that the modified method performance is well, and has a better searching ability of the solution space and distributional feature than its original method.