Day-ahead and hour-ahead two-stage demand response method based on distributed power generation consumption

Abstract

The invention provides a day-ahead and hour-ahead two-stage demand response method based on distributed power generation consumption, and belongs to the technical field of power systems. The method comprises the steps that a cloud computing node initiates a day-ahead demand response to an edge node; the edge node issues a response task matched with the historical response capability of an aggregator to the aggregator according to the total day-ahead load response amount; the edge node receives day-ahead load response decision information uploaded by the aggregator; the edge node performs first-stage day-ahead power load prediction updating according to the day-ahead load response decision information; the edge node carries out second-stage hour-ahead power demand prediction updating according to hour-ahead power consumption overhead data reported by the aggregator on the day; the edge node analyzes the current consumption condition of the distributed power supply according to the hour-ahead distributed generation power reported by the aggregator on that day; when the consumption demand of the distributed generation is not met, the aggregator initiates a real-time demand response; and when the consumption demand is met, the cloud computing node corrects the time-of-use electricity price according to the time-ahead power consumption overhead data.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a two-stage demand response method based on distributed generation consumption, which is a day-ahead and a time-ahead. Background technique [0002] With the development of the economy and the increasing power load, there is a big gap between the power consumption during the peak period and the trough period. The traditional power generation scheme needs to use high-cost power generation devices to meet the power supply demand when it consumes excess during peak periods, and needs to use power storage devices and heat storage devices to store electric energy and thermal energy when consumption is insufficient during low-peak periods. Participating in energy dispatch will cause waste of resources and economic losses due to limited energy storage space. [0003] In the traditional transaction mode, retailers purchase electricity from the wholesale market at fluctuating price...

AI Technical Summary

Problems solved by technology

The indifference of prices makes users unintentionally plan their own electricity consumption

Moreover, retailers face double economic risks in this case. First, users do not participate in demand uploads, so retailers can only purchase energy based on experience, and may purchase excessive or insufficient energy, resulting in economic losses; second, purchase at variable prices and sell at stable prices , there may be periods when the selling price is lower than the buying price, resulting in an overall loss

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