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.

This paper proposes a novel nonlinear partial least square (PLS) approach for dealing with the modeling problem of industrial processes with input variables in collinearity. The new method combines the external linear PLS framework with the internal extreme learning machine (ELM) function. First, PLS is used as the outer framework to extract the input and output latent variables as well as eliminating the collinearity of the original variables, and then ELM is employed to describe the nonlinear relation between pairs of latent variables. Besides, the weight updating strategy based on errors minimization is also involved to improve the prediction accuracy. Then, the pH-neutralization process is taken as a benchmark to verify the validity of the new model. Finally, this method is applied to model the NO x emission of a 1000-MW coal-fired boiler, and root-mean-square error (RMSE) values are 5.9541 for the training dataset and 6.8323 for the testing dataset. Compared with linear PLS and another two nonlinear PLS methods, smaller prediction errors is obtained. The results indicate new nonlinear PLS model can be a better choice for establishing the model of NO x emission for coal-fired boilers. INDEX TERMS Nonlinear partial least square, extreme learn machine, weight update, NO x emission, coal-fired boilers.

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The increase in the number of new energy sources connected to the grid has made it difficult for power systems to regulate frequencies. Although battery energy storage can alleviate this problem, battery cycle lives are short, so hybrid energy storage is introduced to assist grid frequency modulation. In this paper, a hybrid energy storage system composed of battery energy storage and super-capacitor energy storage systems was studied, and a comprehensive control strategy was proposed. Firstly, by setting the frequency dead zone of the energy storage to be smaller than that of the thermal power unit, the frequent action of the thermal power unit was avoided. Secondly, virtual inertial control and virtual droop control were effectively combined. Then, the state of charge of battery energy storage and super-capacitor energy storage was considered so that they could operate in harmony. Finally, a simulation model was built in MATLAB/SIMULINK, and case studies were conducted to verify the proposed control strategy. Results showed that the proposed control strategy could effectively reduce the frequency deviation of the power grid, and maintain the state of charge, reduce the number of operated batteries, and improve cycle life.