To address the problem of low-carbon, optimal operation of AC–DC hybrid microgrids, a carbon trading mechanism is introduced and the impact of multiple uncertainties on system optimization is considered. Firstly, a two-layer model with the comprehensive economy of the hybrid microgrid as the upper layer and the respective optimal operation of the AC and DC sub-microgrids as the lower layer is established and the demand-side response is introduced, based on which the uncertainty of the scenery load is simulated using the multiscenario analysis method. Then, the baseline method is used to allocate carbon emission allowances to the system without compensation, and the actual carbon emissions of diesel engines, microcombustion engines, and fuel cells are considered to construct a hybrid microgrid. Finally, the model is solved using the CPLEX solver in conjunction with the calculation example, and the simulation verifies the effectiveness and feasibility of the proposed strategy in coordinating and optimizing the economy and low carbon of the system. The results show that when the carbon trading mechanism is considered, the carbon emission of the hybrid microgrid is reduced by 4.95%, the output power of the diesel generator is reduced by 5.14%, the output power of the fuel cell is reduced by 18.22%, but the electricity purchase from the power grid is increased by 38.91%. In addition, the influence degrees of the model considering the uncertainty of renewable energy and load are simulated. Furthermore, the impact of different electricity price models on optimal operation is evaluated, and the results show that electricity price will affect electricity purchase from the power grid and further affect carbon emissions.

Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models.