With the increasing proportion of various unbalanced loads in the power grid, power quality is seriously challenged. It is of great significance to effectively detect, analyze, and evaluate the power quality problems. First, this paper introduces the current situation of power quality (PQ) disturbance detection methods. It summarizes that the current PQ disturbance detection methods include Wavelet Transform (WT), Hilbert–Huang Transform (HHT), and Ensemble Empirical Mode Decomposition (EEMD). EEMD has a better detection accuracy, but its running time is longer. Therefore, to reduce the running time of the EEMD algorithm, this paper proposed two improvements: increasing the screening threshold and selecting piecewise cubic Hermite interpolation polynomial fitting. At the same time, the mathematical models of transient power quality disturbance and harmonic were established for comparative verification. The experimental results showed that the improved Ensemble Empirical Mode Decomposition (IEEMD) algorithm greatly reduced the running time of the algorithm on the premise of ensuring the detection accuracy. Hence, the improvement of this paper is of great significance for the industrial application of the EEMD algorithm.

In the context of global energy transformation, it is particularly important to tap the potential of flexible load on the distribution network side to participate in grid operation. As an important flexible adjustable load in the distribution network, air conditioning loads have typical characteristics of thermal energy storage, rapid response, and flexible scheduling, which is an ideal load resource. Effective and reliable load aggregation technologies for air conditioning load participation are the basis for operation in a power grid. To better understand the current research status of air conditioning load aggregation technologies and make full use of the existing research results to carry out further research, this paper comprehensively reviews the various aggregation technologies being used in distribution networks, including the modeling strategy for air conditioning load aggregation, control strategy, control method, and application analysis. Moreover, future research directions are summarized as a guide to improving the technology of air conditioning load aggregation.

Accurate and effective particulate matter 2.5(PM2.5) concentration prediction can provide early warning information for decision-making departments, so as to take governance and preventive measures. A combined model based on double-layer decomposition(DLD) and feedback of model learning effect for PM 2.5 concentration prediction is proposed in this paper. Firstly, ensemble empirical mode decomposition(EEMD) and variational mode decomposition (VMD) were used for double-layer decomposition for the PM2.5 concentration series, to reduce the nonstationarity and nonlinearity of the concentration series and improve the predictability; Secondly, a wavelet neural network (WNN) prediction model based on the feedback of model learning effect is established for the subsequence obtained by double-layer decomposition. Finally, the prediction results of each subsequence are superimposed to obtain the final prediction results. The case study shows that the prediction model proposed in this paper is scientific.