A two-stage demand response method based on distributed generation and consumption

Abstract

The invention provides a demand response method in two stages of day-ahead and time-ahead based on distributed generation consumption, and belongs to the technical field of power systems. Including: the cloud computing node initiates a day-ahead demand response to the edge node; the edge node sends a response task matching the historical response capability of the aggregator to the aggregator according to the total amount of the day-ahead load response; the edge node receives the day-ahead load response decision uploaded by the aggregator information; edge nodes update the first-stage power load forecast according to the day-ahead load response decision information; edge nodes update the second-stage electricity demand forecast based on the current-day electricity consumption data reported by the aggregator; Analyze the current consumption situation of distributed power generation according to the generation power of distributed power generation at the current time; when the consumption demand of distributed power is not met, the aggregator initiates a real-time demand response; when the consumption demand is met, the cloud computing node The time-of-use electricity price is corrected by electricity consumption data.

Description

technical field [0001] The invention relates to the technical field of electric power systems, and in particular to a demand response method based on distributed generation and consumption in two stages: day-ahead and time-ahead. Background technique [0002] With the development of the economy, the electricity load is increasing day by day, and there is a big gap between the electricity 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 the consumption is excessive during the peak period. When the consumption is insufficient during the trough period, it is necessary to store electric energy and heat energy through the power storage device and the heat storage device. Participating in energy scheduling will cause resource waste and economic losses due to the limited energy storage space. [0003] In the traditional trading model, retailers buy ...

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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