A Robust Optimal Scheduling Method for Multi-objective Confidence Gap Decision Making in Integrated Energy System

Abstract

The present invention relates to the technical field of optimal scheduling of integrated energy systems, in particular to a robust optimal scheduling method for multi-objective confidence gap decision-making in integrated energy systems, comprising the following steps: combining the confidence interval of the Gaussian mixture probability model with the robust Combining with the idea of ​​sticks, a confidence gap decision-making model based on robust drive is established; based on the confidence gap decision-making model, combined with multiple optimization objectives of the integrated energy system, a multi-objective confidence gap decision-making robust optimal scheduling model for the integrated energy system is constructed; an adaptive optimization scheduling model is designed. Harmonic aliasing multi-objective compound differential evolution algorithm is used to solve the multi-objective confidence gap decision-making robust optimization scheduling model of integrated energy system. The uncertainty modeling and optimization idea based on confidence gap decision-making proposed by the present invention provides a new idea for integrated energy system scheduling, and can be further expanded to other research fields such as integrated energy system planning and coordinated operation of multi-regional integrated energy systems.

Description

technical field [0001] The invention relates to the technical field of integrated energy system optimization scheduling, in particular to a robust optimal scheduling method for multi-objective confidence gap decision-making in an integrated energy system. Background technique [0002] With the rapid development of industry and the increasing demand of users for various types of energy, the rational utilization and distribution of energy has become a hot topic widely discussed by all walks of life. The integrated energy system (Integrated Energy System, IES) is mainly composed of functions such as energy conversion, distribution and storage, and various end users. It is a sustainable integrated energy system. IES optimal scheduling is an important prerequisite to ensure system economy and energy efficiency. However, the uncertainty of wind, light and other renewable energy output and load demand in the system often makes it difficult for IES optimal scheduling to achieve the ...

AI Technical Summary

Problems solved by technology

On the whole, the processing methods mainly include stochastic programming and robust optimization, but at present, these two methods are insufficient: stochastic programming simplifies the uncertainty from the original interval domain to several representative scenarios through scenario reduction for multi-scenario Deterministic optimization, which not only loses the ergodicity of the uncertain interval, but makes it difficult to guarantee robustness, and if the clustering algorithm is not accurate enough, the "typical" scene after scene reduction will not be representative enough; while the existing robust In rod optimization research, robustness analysis and optimization calculations are generally based on pre-set robust coefficients, and the robustness settings are often too subjective or conservative.

This type of uncertain set fails to fully reflect the actual polymorphism of various random factors in the system (for example, the random distribution characteristics of wind power / photovoltaics usually have asymmetry and polymorphism, etc.), as well as the robustness and uncertainty. The actual possible non-linear relationship between the upper and lower limits of the interval

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