Peak shifting scheduling method based on power supply enterprise big data model base

Abstract

The invention relates to a peak shifting scheduling method based on a power supply enterprise big data model base. The peak shifting scheduling method comprises the following steps: S1, calling an off-peak scheduling model in an off-peak scheduling model library; S2, training data are acquired to train the peak shifting scheduling model, and updating of a peak shifting scheduling model library isachieved; S3, performing electric power peak shifting scheduling by using the trained peak shifting scheduling model, wherein the peak shifting scheduling model library comprises a hierarchical grouping scheduling model and an economic optimal scheduling model; performing hierarchical grouping on the electricity users according to the peak prevention sequence table and the total peak prevention load amount via the hierarchical grouping scheduling model. Compared with the prior art, the economic optimal scheduling model takes the composite optimization of the economy of the power company and the user as the target, the target functions during the training of the economic optimal scheduling model comprise the economy target function and the reliability target function and therefore the method has the advantages of improving the scheduling effectiveness and practicability and the like.

Description

Technical field [0001] The present invention relates to power information technology, and more particularly to a peak scheduling method based on power supply enterprise large data model library. Background technique [0002] Large-scale power supply has a large amount of business data, and has gradually recognized the importance of data in recent years, and has carried out the construction of many major data related projects. However, using large data technology in the power system, data analysis and modeling is a difficult point, the technical threshold is high, although a large number of users and power grid operations are integrated, but the data application and modeling capacity are serious. [0003] With the arrival of big data era, large-scale power supply enterprises have more significant demand, with the business development, various general and dedicated models are huge, need to continuously iterate, design, and training. The model requires huge manpower, play the value ...

AI Technical Summary

Problems solved by technology

[0004] At present, the imbalance between power supply and demand in my country is mainly manifested in structural and periodical power shortages, and the proportion of peak load time is relatively small. Traditional measures such as increasing installed capacity can easily lead to increased grid investment and waste of installed capacity during low-peak load periods. To solve the shortage of peak load supply and periodical power shortage, power companies must do a good job in peak shift scheduling to maximize the effectiveness of limited energy resources

Peak shift dispatching is still a normalized management work to accommodate the gap between supply and demand, control electricity demand, and maintain a stable order of electricity supply and consumption. Traditional data analysis methods are not enough to meet the needs of massive data mining. A single optimization model achieves effective and practical error peak scheduling

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