Abstract:

In order to enhance the consumption capacity of wind power and reduce carbon emissions, this study proposes a two-stage distributionally robust low-carbon economic dispatch model that takes into account carbon trading and electric vehicles. Carbon trading costs are introduced in the proposed study, and the cooperation between electric vehicle energy storage and wind power generation is utilized to reduce the system's carbon emissions and to increase the wind power consumption capacity. Considering the uncertainty of wind power, a distributionally robust optimization method based on the general moment uncertainty is established. The fuzzy set of distributionally robust is established using the moment information of the historical wind power output data that can be obtained to characterize the uncertain wind power output characteristics. The distributionally robust model is transformed into a quadratic programming model using duality and linear decision rules, and the model is solved through CPLEX. Experimental results show that the wind power consumption capacity increases by 11.35% and carbon emissions decrease by 1 579 t under the proposed method, which verifies the effectiveness and superiority of the two-stage distributionally robust model.

Key words: distributionally robust optimization, uncertainty, clean energy, electric vehicle, carbon trading, linear decision rule, quadratic programming, low-carbon economic dispatching