Method and system for dynamic demand response of comprehensive energy system

Abstract

The invention discloses a method and a system for dynamic demand response of a comprehensive energy system. The method comprises the following steps: acquiring outdoor temperature and illumination data, and generating a distribution function; solving a joint distribution function considering temperature and illumination correlation; solving a joint distribution function considering the temperatureand illumination dynamic correlation; obtaining an edge distribution function of each variable, and generating an N * K high-dimensional sample; performing scene reduction and dimension reduction processing on the high-dimensional sample; calculating the active power output in the distributed photovoltaic power supply system and the thermal load power in the thermal load; establishing a node cold / heat / electricity / gas load physical model based on multi-task learning, and calculating the comprehensive trend of electricity_heat_ gas; establishing a random response surface regression model basedon statistical machine learning, and quickly calculating the digital characteristics of the probabilistic load flow; and establishing a comprehensive energy system dynamic demand response optimizationmodel based on probabilistic load flow, and formulating a user side dynamic demand response strategy matched with photovoltaic power generation characteristics according to a solving result.

Description

technical field

[0001] The invention relates to a method and system for dynamic demand response of an integrated energy system, belonging to the technical field of integrated energy system control. Background technique

[0002] The development of integrated energy system theory and technology helps to help solve energy security issues, improve social efficiency, and promote the development of new and renewable energy. Demand response is the main feature of the smart grid, which can improve the ability of the grid to absorb new energy and improve the utilization rate of grid equipment. With the development of smart grid, more and more loads will participate in the interaction. Therefore, it is urgent to develop from the traditional power generation optimization facing hundreds of generators to the integrated scheduling of power generation and loads facing hundreds of generators and thousands of loads at the same time, and the high-dimensional data processing is stochastic pr...

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