With the integration of a high proportion of new energy into the power grid, the current low-carbon economy scheduling method based on deterministic model is unable to accurately describe the impact of uncertainties on carbon emissions.With the consideration of the uncertainties on both sides of the source-load from the system level, a dynamic optimal scheduling model for low-carbon economy is built.First , the uncertainty of wind and light prediction errors is described by mixed Gaussian probability density estimation, and a probabilistic constraint method of positive and negative rotating reserve capacity considering net load forecasting errors is proposed.Second, to reduce the carbon emissions of the system:on the power generation side, the carbon trading mechanism is introduced to build the scheduling objective function with the lowest total operating cost of the system with stepped carbon transaction cost; on the users' side, the demand response load and the actual energy storage equipment are regarded as generalized energy storage to participate in the day-to-day rolling optimal dispatching.Finally, different scenarios are analyzed in the IEEE39 node system to verify the effectiveness of the proposed scheduling model to achieve low-carbon and efficient operation goals, tapping the carbon reduction potential of the demand response load.
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