Energy router modeling and optimization control method based on data driving

Abstract

The invention provides an energy router modeling and optimization control method based on data driving, and the method comprises the steps: dividing the power data corresponding to the ports of an energy router into a load demand set and a scheduling set; obtaining enough data samples by collecting the electrical data of each port during the operation of the energy router, and then establishing a mathematical mapping model between two types of ports online by using a data mining technology as a power relationship constraint between the ports; meanwhile, establishing mathematical constraint forms corresponding to different optimization modes, converting the optimization scheduling mode of the energy router into an index optimization problem with constraints, and calling a group optimization algorithm to obtain optimization scheduling parameters; then establishing a model based on data driving between the optimal optimization scheduling parameters and the demand load, thereby realizing real-time optimization control of the energy router under different operation loads. The method realizes high transportability and high real-time optimization capability, and can be used for improving operation performance indexes of various energy routers.

Description

technical field [0001] The invention belongs to the technical field of micro-grids, and in particular relates to a data-driven energy router modeling and optimization control method. Background technique [0002] Due to the decreasing reserves of traditional non-renewable energy and the negative impact on the global environment when non-renewable energy is used, in recent years, low-carbon, environmentally friendly and sustainable new energy power generation (such as wind power, photovoltaic power generation) has gradually become one of the important forms of power generation. one. However, compared with traditional power generation methods, new energy power generation methods have the characteristics of intermittency, volatility, uncertainty, and distribution, and their massive access poses new challenges to the operation and control of distribution networks. The centralized power generation mode is transformed into a distributed and centralized power generation mode. In ...

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Problems solved by technology

Among them, the optimal scheduling method is mainly divided into two categories: the first category is to obtain the physical model between each node with a clear mechanism through the analysis of the internal power electronics topology of the energy router, and combine the physical model to refine it to the internal nodes. However, this method relies on a complete understanding of the internal structure of the energy router, has poor portability, and is greatly affected by changes in system parameters; the second type is to directly collect the data of each port, according to a certain The optimization index or mode of the energy router is used to schedule each port without the internal parameters and mechanism model of the energy router. Adjustment speed is slow

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