Multi-stage scene generation electric heating system optimal scheduling method based on wind power consumption

Abstract

The invention discloses a multi-stage scene generation electric heating system optimization scheduling method based on wind power consumption, and belongs to the technical field of economic dispatch of an electric heating combined system. The multi-stage scene generation electric heating system optimization scheduling method comprises the steps of generating day-ahead wind power prediction and intra-day and real-time wind power prediction containing prediction errors through a Monte Carlo and roulette selection mechanism on the basis of considering uncertainty of wind power output, determiningreal-time electricity price according to an electric quantity proportion, and constructing an intra-day demand response model. In order to effectively promote the system to consume wind power, a windcurtailment penalty term with the cost dynamically increased along with the wind curtailment amount is constructed. The minimum system cost is used as a target function; and scheduling is carried outby adjusting a conventional unit and a CHP unit before the day; and intra-day scheduling is implemented through a heat accumulating type electric boiler and an intra-day demand response model set based on electric quantity, and unbalanced electric quantity of the real-time market is adjusted through an electric storage device and a unit, and the model is solved through a particle swarm algorithmof dynamic weight, and a reasonable electric-heat joint optimization scheme is obtained.

Description

technical field [0001] The present invention relates to the technical field of electric-heat combined system dispatching, in particular to a multi-stage scenario generation electric-heat system optimization dispatching method for wind power consumption. Background technique [0002] Wind energy is an important renewable energy source. The large-scale development and utilization of wind power generation has effectively alleviated the difficulties faced by sustainable energy development and brought huge economic benefits. But at the same time, the randomness, uncertainty and intermittency of its output lead to further expansion of the power fluctuation of the system, and the adjustment ability of conventional power sources is difficult to effectively deal with, which brings new challenges to the traditional dispatching operation mode of the power grid. In winter, due to the demand for heat supply, the thermal power unit (CHP) maintains a high heat-to-electricity ratio, that is...

AI Technical Summary

Problems solved by technology

Currently widely used demand response is mainly based on day-ahead scheduling of time-of-use electricity prices and incentives. The correlation between the intraday market and scheduling time is poor, the enthusiasm for load response cannot be mobilized, and the participation of users is not high, so it cannot well realize load shifting.

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