Dynamic demand response solving method considering time-of-use pricing

Abstract

The invention discloses a dynamic demand response solving method considering time-of-use pricing. The method comprises steps of adopting a fuzzy clustering algorithm to divide a load prediction curvein a scheduling period into a peak time period, a flat time period and a valley time period; determining corresponding points (p, q) on the basis of historical data fitting demand and price functionsaccording to the load mean value of each time period, and linearizing the functions near the points (p, q); according to the coefficients of the linear function in each time period, respectively calculating the elastic coefficients of the peak time period, the flat time period and the valley time period so as to establish a dynamic elastic coefficient matrix; establishing a dynamic demand responsemodel according to the dynamic elastic coefficient matrix; solving the dynamic demand response model so as to obtain the prices of the peak, flat and valley time periods in the scheduling period anda new load prediction curve formed after implementing the dynamic demand response. According to the invention, the technical problem that the optimal effect cannot be achieved when the demand side response is stimulated through the price change due to the fact that the fixed price elastic coefficient cannot dynamically reflect the relationship between the load characteristics and the time-of-use price in different scheduling periods is solved.

Description

technical field [0001] The invention belongs to the field of customer demand response decision-making in the electric power or heat market, and in particular relates to a dynamic demand response solution method considering time-of-use pricing. Background technique [0002] The energy problem in today's society is getting more and more serious, and it is very important to make full use of the energy in the system now. On the one hand, start from the energy side, change the energy structure of the system, increase the access ratio of renewable energy, and reduce the system's demand for fossil fuels (coal, natural gas, etc.); on the other hand, start from the load side, change the user's The habit makes the user load curve more gentle, which is conducive to the better use of renewable energy by the system, thereby reducing the burning of fossil fuels, and the demand response helps the stable and safe operation of the power system by transferring load. Time-of-use pricing is a ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the above-mentioned prior art, the present invention provides a dynamic demand response solution method that takes into account time-of-use pricing, which solves the problem that the prior art cannot reflect the relationship between time-of-use pricing and load characteristics within the scheduling cycle due to the fixed price elasticity coefficient , which leads to the technical problem that the optimal effect cannot be ach

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