Multi-objective confidence gap decision-making robust optimization scheduling method for integrated energy system

Abstract

The invention relates to the technical field of comprehensive energy system optimal scheduling and relates to a multi-objective confidence gap decision-making robust optimization scheduling method foran integrated energy system. The method comprises the following steps of: combining the confidence interval of a Gaussian mixture probability model and the robust thought of an information gap decision theory to establish a confidence gap decision model based on robust driving; based on the confidence gap decision model, combining a plurality of optimization objectives of the integrated energy system, and constructing a multi-objective confidence gap decision robust optimization scheduling model of the integrated energy system; and designing an adaptive harmonic aliasing multi-objective composite differential evolution algorithm to solve the multi-objective confidence gap decision robust optimization scheduling model of the integrated energy system. The uncertainty modeling and optimization thought based on confidence gap decision provided by the invention provides a new thought for the scheduling of the integrated energy system, and can be further expanded to other research fields such as the planning of integrated energy systems, the coordinated operation of multi-region integrated energy systems and the like.

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. An 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 th...

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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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