Research On Load Forecast And Pricing Of Smart Grid

As the growing scarcity of coal and the higher requirements of the reliability of power supply, the power grid in the future should be safer, more reliable, more economical and more efficient. It is a common choice for international power industry to build the smart grid that adapts to economy and social development so as to readily meet the future challenges. Based on what is mentioned above, the forecasting, optimization and interaction in the smart grid are studied as follows:On the basis of grey theory, NGBM model with general applicability is used for short-term load forecasting. Using minimum MAPE as the evaluation index, the optimization of background value and model order in the model is realized by PSO algorithm to yield a conclusion that the forecast curve is generally similar to the real curve. To further improve the accuracy of model, the residual error state is forecast by Markov process to correct the residual error.To realize the forecast of ultra short-term load, the sample consisting of 264 data is analyzed by time series method and an ultra short-term load forecasting model is built through stationarity analysis, pattern cognition and parameter test.A distributed generation system of Nantong is designed, in which the features of solar photovoltaic system, wind power generation and energy storage devices are analyzed comprehensively to build the mathematical model of these devices, ttes providing a scientific theory basis for the study of system simulation. Then HOMER is used to simulate the distributed energy system and the optimal system configuration is found after calculation combined with the forecast data.Based on these works, the correlation between generating capacity of distributed energy and electrical load are analyzed according to the features of real-time electricity price of smart grid and benefits of power supply bureau and users. The mathematical model of electricity consumption and electricity price is built through two stages:power abundance and power scarcity. And the practicability and reliability of this model are attested by a practical example.