Low-carbon economic scheduling strategy for active distribution network considering carbon emissions trading and source-load side uncertainty

The double uncertainty of the source-load side poses a new challenge to the reliability of the low carbon scheduling in the active distribution network (ADN) system. In this study, a low-carbon economic scheduling model of the ADN system is proposed, which considers step-type carbon emissions trading (CET) mechanism and source-load side uncertainty. The multi-dimensional discrete probability sequences of source-load side are converted into the global equivalent load (EL) by the proposed discretized step transformation (DST) and the convolutional sequence operation (CSO) method, and the predicted value of EL is expressed by the expected value of the EL probability sequence. By setting the probability constraint of the spinning reserve capacity, the influence of power prediction error on the ADN system is fully considered. On this basis, the chance constraint problem (CCP) is further converted to a deterministic mixed-integer linear programming (MILP) problem by linearizing power flow constraints. The proposed strategy is verified based on the IEEE 33-bus ADN system. The experimental results indicate that the system can further realize the synergy of economic benefits and environmental benefits. In addition, compared with other intelligent algorithms, the solution time of the proposed strategy is significantly reduced, and the optimization effect is greatly improved.

Automated summary

This study proposes a low-carbon economic scheduling model for the ADN system, taking into account the step-type carbon emissions trading mechanism and source-load side uncertainty. By using the discretized step transformation and convolutional sequence operation, the multi-dimensional discrete probability sequences of the source-load side are converted into the global equivalent load (EL), with the predicted value of EL expressed as the expected value of the EL probability sequence. The proposed strategy considers the influence of power prediction error on the ADN system by setting the probability constraint of the spinning reserve capacity, and converts the chance constraint problem to a deterministic mixed-integer linear programming problem by linearizing power flow constraints. Experimental results show that the strategy achieves synergy between economic and environmental benefits, with significantly reduced solution time compared to other intelligent algorithms and improved optimization effect.