Industrial load demand response scheduling method under high-proportion new energy

Abstract

The invention provides an industrial load demand response scheduling method under high-proportion new energy, and the method comprises the steps: determining the demand response modes of different types of industrial loads according to the demand response characteristics of different types of industrial loads; the overall scheduling is divided into three time scales of day-ahead scheduling, intra-day scheduling and real-time scheduling; determining different time scale optimization scheduling tasks; respectively training the improved C-DCGAN models of the three time scales to obtain source-load scene values under different time scales; constructing a day-ahead multi-scene stochastic programming model to obtain an optimal day-ahead scheduling instruction; constructing an intra-day multi-scene stochastic programming model according to the intra-day source-load scene value to obtain an optimal intra-day scheduling instruction; and constructing a real-time multi-scene stochastic programming model according to the real-time source-load scene value to obtain an optimal real-time scheduling instruction. According to the method, the uncertainty of the new energy output and the load power is described, uncertainty scene support is provided for scheduling, the scheduling cost is reduced, and the scheduling effect is enhanced.

Description

technical field [0001] The invention belongs to the technical field of regional power grid dispatching, and in particular relates to an industrial load demand response dispatching method under a high proportion of new energy. Background technique [0002] With the increasing depletion of fossil energy and the intensification of environmental problems, national energy conservation and emission reduction efforts have been continuously increased, and renewable energy sources such as wind energy and solar energy have developed rapidly. However, the output of new energy such as wind power and photovoltaic power generation has the characteristics of volatility and uncertainty. When the proportion of new energy in the power system is relatively small, the fluctuation of new energy can be regarded as a load forecast error; but when the proportion of new energy is large to a certain extent , the volatility and uncertainty of its output will bring many challenges to the power grid. ...

AI Technical Summary

Problems solved by technology

However, the output of new energy such as wind power and photovoltaic power generation has the characteristics of volatility and uncertainty. When the proportion of new energy in the power system is relatively small, the fluctuation of new energy can be regarded as a load forecast error; but when the proportion of new energy is large enough to a certain extent , the fluctuation and uncertainty of its output will bring many challenges to the power grid

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