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.
The selective catalytic reduction (SCR) decomposition of nitrogen oxide (de-NO x ) process in coal-fired power plants not only displays nonlinearity, large inertia and time variation but also a lag in NO x analysis; hence, it is difficult to obtain an accurate model that can be used to control NH 3 injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NO x emission prediction. First, k -nearest neighbour mutual information was used to estimate the time delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay time difference method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NO x process. Through the analysis of the experimental field data in the steady state, the variable state and the NO x analyser blowback process, the results proved that this dynamic model has high prediction accuracy during state changes and can realize advance prediction of the NO x emission.
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.
In order to control NH3 injection for the selective catalytic reduction of nitrogen oxide (NOx) denitration (SCR de-NOx) process, a model that can accurately and quickly predict outlet NOx emissions is required. This paper presents a dynamic kernel partial least squares (KPLS) model incorporated with delay estimation and variable selection for outlet NOx emission and investigated control strategy for NH3 injection. First, k-nearest neighbor mutual information (KNN_MI) was used for delay estimation, and the effect of historical data lengths on KNN_MI was taken into account. Bidirectional search based on the change rate of KNN_MI (KNN_MI_CR) was used for variable selection. Delay–time difference update algorithm and feedback correction strategy were proposed. Second, the NH3 injection compensator (NIC) and the outlet NOx emission model constituted a correction controller. Then, its output and the output of the existing controller are added up to suitable NH3 injection. Finally, the KNN_MI_CR method was compared with different algorithms by benchmark dataset. The field data results showed that the KNN_MI_CR method could improve model accuracy for reconstructed samples. The final model can predict outlet NOx emissions in different operating states accurately. The control result not only meets the NOx emissions standard (50 mg/m3) but also keeps high de-NOx efficiency (80%). NH3 injection and NH3 escape are reduced by 11% and 39%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.