Research on Day-Ahead Optimal Scheduling Considering Carbon Emission Allowance and Carbon Trading

In the context of the marketization of carbon trading in the power system, it is of great theoretical and practical significance to study a scientific and effective carbon emission quota allocation strategy. To solve this problem, under the current situation of large-scale access to new energy, considering the limitations of the carbon emissions from different emission subjects plus the construction of a carbon trading model among the emission subjects, a day-ahead optimal scheduling method that takes carbon emission quotas and carbon trading into account is proposed. Firstly, carbon transaction cost models of thermal power and wind power are constructed, respectively, and a carbon emission quota allocation strategy based on the entropy method is proposed to redistribute the weights of baseline emission factors for the regional power grid. Then, considering the additional carbon emissions of conventional thermal power units caused by wind power access, the carbon trading costs of different types of generation units are calculated on the basis of carbon trading price prediction. Thereafter, a day-ahead optimal scheduling model considering carbon emissions trading is constructed with the objective of minimizing the total cost of the system in the scheduling period. The model is solved as an MINLP problem based on MATLAB 2016a software utilizing CPLEX 12.4. Simulation results verify the correctness and effectiveness of the proposed method.

Automated summary

In the context of carbon trading marketization in the power system, it is important to study a scientific and effective carbon emission quota allocation strategy. This study proposes a day-ahead optimal scheduling method considering carbon emission quotas and carbon trading, by constructing carbon transaction cost models and redistributing baseline emission factor weights using the entropy method. The model aims to minimize the total cost of the system during the scheduling period and is solved as an MINLP problem using MATLAB and CPLEX. Simulation results validate the proposed method's accuracy and effectiveness.