Load optimization scheduling method and system for residential micro-grid, and storage medium

Abstract

The invention provides a load optimization scheduling method and system for a residential micro-grid, and a storage medium. The method comprises the following steps of acquiring environmental data andtime data of the residential micro-grid in a preset future time period; inputting the environmental data and the time data into a pre-trained load prediction model in order to obtain electrical loaddata of the residential micro-grid in the future time period; inputting the environmental data and the time data into a pre-trained photovoltaic output power prediction model in order to obtain photovoltaic output power data of the residential micro-grid in the future time period; determining an objective function of the residential micro-grid in the future time period and a corresponding constraint condition, wherein the optimization objective of the objective function is that the total cost of the residential micro-grid is minimum; and solving the objective function through a particle swarmalgorithm in order to obtain a load scheduling scheme of the residential micro-grid in the future time period. The invention provides the load scheduling scheme suitable for the current micro-grid, and the operating cost of the residential micro-grid is reduced.

Description

technical field [0001] The invention relates to the technical field of residential electricity dispatching, in particular to a load optimization dispatching method, system and storage medium of a residential microgrid. Background technique [0002] With the rapid growth of population and rapid economic development, residential electricity consumption has increased significantly. In order to balance electricity supply and demand, reduce carbon emissions and costs, the development of residential microgrids and smart grids has received more and more attention. Residential microgrids are generally composed of residential electricity, solar energy, energy storage units, and electric vehicles. The intermittency and volatility of solar energy have a certain impact on the operation of microgrids. At the same time, the number of smart electricity terminals in residential buildings is increasing, and residential electricity consumption is highly volatile and random, which will affect...

AI Technical Summary

Problems solved by technology

However, due to the randomness and volatility of residential electricity consumption, the accuracy of existing short-term load forecasting methods is not high.

The photovoltaic output power prediction method is roughly similar to the load prediction method, but there are also problems such as low prediction accuracy

[0004] Due to the low accuracy of electricity load forecasting and photovoltaic output power forecasting, the load dispatching scheme of residential microgrid is not suitable for the current residential microgrid, which makes the operating cost of residential microgrid relatively high.

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