In view of the fact that the potential high-dimensional features in the historical sequence are difficult to be effectively extracted by traditional power load forecasting methods and the coupling factors of electricity, heat, and gas have not been considered, the correlation of electric heating and gas load is considered in this paper, and a short-term power load forecasting method for integrated energy systems based on Attention-CNN- (Convolutional Neural Network-) DBILSTM (Deep Bidirectional Long-Short-Term Memory) is proposed. First, the correlation between the multiple load influencing factors is considered, and the Pearson coefficient is used to quantitatively calculate the correlation between the multiple loads. Second, a CNN network consisting of a one-dimensional convolutional layer and a pooling layer is established. High-dimensional features reflecting the dynamic changes of the load are extracted, and the proposed feature vector is constructed in the form of time series as the input of the DBILSTM network; the dynamic change law of time series data is modeled and learned. Then, the Attention mechanism is introduced to assign different weights to the hidden state of DBILSTM through the mapping weight and the learning parameter matrix, to reduce the loss of historical information and strengthen the impact of key information, and the Dense layer is used to output the load prediction results. Finally, the influence of the correlation of multiple loads and its influencing factors on the power load forecasting results is analyzed, based on the historical load data of the integrated energy system in a certain area of Northeast China. The simulation results of the calculation example show that the prediction accuracy of the method reaches 97.99%, and the integrated energy system electric, heat, and gas load correlation coefficients as the input parameters of the Attention-CNN-DBILSTM network can reduce the average prediction error by 0.37%∼1.93%. The proposed method has been verified to effectively improve the prediction accuracy by comparison with the prediction model results of CNN-LSTM network, CNN-BILSTM network, and CNN-DBILSTM network.

Distributed power supplies have gradually become a new trend in power supply development, but access to a large number of distributed energy sources has a certain impact on the stable operation of the power grid. A virtual power plant (VPP) can integrate a variety of distributed power sources for coordination and optimization; thus, it can effectively solve the difficulties faced by a distributed energy grid connection and promote the complementarity of energy sources. However, renewable energy often has a degree of volatility and randomness when distributed, which can bring certain risks to the operation of the VPP. In order to consider the risks brought by renewable energy, an optimal scheduling model of the VPP, based on an improved generative adversarial network (GAN) and the conditional value at risk (CVaR), was proposed to measure the relationship between the benefits and risks. Firstly, the uncertainty of new energy is analyzed, and wind power and photovoltaic scenarios are generated by the improved GAN; then, typical scenarios are generated by the k-medoids method. Finally, based on the CVaR, the optimal scheduling model of the VPP is established to study the effect of risk weight on VPP revenue. The results show that the model can effectively measure the relationship between the benefits and risks and can provide some references for the VPP to make reasonable operational decisions with different risk preferences.

To further improve the economic benefits of operators and the low-carbon performance within the system, this paper proposes a hierarchical distributed low-carbon economic dispatch strategy for regional integrated energy systems (RIESs) based on the Alternating Direction Method of Multipliers (ADMM). First, the energy coupling relationships among conversion devices in RIESs are analyzed, and a structural model of RIES incorporating an energy generation operator (EGO) and multiple load aggregators (LAs) is established. Second, considering the stepwise carbon trading mechanism (SCTM) and the average thermal comfort of residents, economic optimization models for operators are developed. To ensure optimal energy trading strategies between conflicting stakeholders, the EGO and LAs are embedded into a master–slave game trading framework, and the existence of the game equilibrium solution is rigorously proven. Furthermore, considering the processing speed of the optimization problem by the operators and the operators’ data privacy requirement, the optimization problem is solved in a hierarchical distributed manner using ADMM. To ensure the convergence of the algorithm, the non-convex feasible domain of the subproblem bilinear term is transformed into a convex polyhedron defined by its convex envelope so that the problem can be solved by a convex optimization algorithm. Finally, an example analysis shows that the scheduling strategy proposed in this paper improves the economic efficiency of energy trading participants by 3% and 3.26%, respectively, and reduces the system carbon emissions by 10.5%.

This paper introduces a novel control strategy that merges integral sliding mode control with fuzzy adaptive PI control. This hybrid approach maximizes the benefits of both techniques to ensure voltage stability in DC microgrid. Firstly, a mathematical model characterizes the DC–DC boost converter. Subsequently, a sliding surface, incorporating an integral term, is employed to regulate the converter’s output voltage and current errors. To address uncertainties stemming from factors like input inductance and output capacitance, a dynamic sliding mode controller is formulated. The proposed sliding mode control scheme significantly reduces the time required for voltage stability, curbs system oscillations, and showcases robustness. Furthermore, the inclusion of fuzzy adaptive PI control aids in refining the voltage deviation signal and droop resistance. This enhancement improves the precision of the error tracking system. Finally, the effectiveness of this strategy is demonstrated through MATLAB simulations, supported by experimental validation and analysis. The findings reveal that this control strategy efficiently accelerates the convergence of DC microgrid voltage to a stable state.