Microgrid Load Forecasting System and Method Based on Online Power Purchase and Dynamic Correlation Factors

Abstract

The invention relates to a microgrid load prediction system and method based on online electricity purchase and dynamic correlation factors. The system includes an online electricity purchase module, a load characteristic analysis module, a short-term load prediction module and a prediction result output module; the microgrid combines preliminary power orders and reference The electricity price is pushed to the electric energy users, and the user corrects the initial electricity order, and feeds the electricity correction order back to the microgrid. The statistics of online electricity purchase statistics and historical load data of various load users are collected, and the load type of the microgrid and its associated factors are determined. , establish a mathematical model of the RBF neural network, use the clustering reduction K-means optimization algorithm based on input data and output data to obtain the initial network parameters of the RBF neural network model, use the quantum particle swarm optimization algorithm to optimize the initial network parameters, and obtain the micro The final predicted value of each type of load in the power grid and the final predicted value of the total load are output, and the final predicted value of each type of load and the final predicted value of the total load of the microgrid are output.

Description

technical field [0001] The invention belongs to the technical field of micro-grid load forecasting, in particular to a micro-grid load forecasting system and method based on online power purchase and dynamic correlation factors. Background technique [0002] With the increasing penetration of distributed power in microgrids, the popularity of electric vehicles, and the application of various operation scheduling strategies (such as using real-time electricity prices to achieve the purpose of "shaving peaks and filling valleys"; Good power quality, etc.), the load fluctuation range is enlarged, the load is more sensitive to meteorological factors, and the randomness and uncertainty of the load are more prominent. The complexity of microgrid load performance in the entire time series makes short-term load forecasting more difficult, so further research on short-term load forecasting of microgrid is needed. [0003] As an important part of the smart grid, the microgrid's short...

AI Technical Summary

Problems solved by technology

In recent years, many people have proposed short-term load forecasting methods under the condition of real-time electricity prices. Although the forecasting accuracy has been improved, it is still not possible to make good forecasts for short-term sharply changing loads or holiday loads with less historical data.

[0004] At present, the shortcomings of short-term load forecasting methods for microgrids are: first, they fail to make full use of the interaction between microgrids and power users, that is, microgrids obtain historical load data from users unidirectionally, without considering that users may Feedback its own future power consumption information to the microgrid; second, the factors that affect the load characteristics are often fixed, that is, with the passage of time and the internal environment of the microgrid, the factors that affect the load characteristics will often change. For different types of loads, their influencing factors are often different. If the forecasting model cannot accurately extract the influencing factors, the forecasting accuracy will be reduced; the third is for the prediction method based on the QPSO-RBF neural network. It is often impossible to accurately determine the number of hidden layer nodes, and the random initialization of individual particles will lead to slower convergence

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