To combat energy shortage, the multi-energy system has gained increasing interest in contemporary society. In order to fully utilize adjustable multi-energy resources on the demand side and reduce interactive compensation, this paper presents an integrated demand response (IDR) model in consideration of conventional load-shedding and novel resource-shifting, due to the fact that participants in IDR can use more abundant resources to reduce the consumption of energy. In the proposed IDR, cooling, heating, electricity, gas and so forth are considered, which takes the connection between compensation and load reductions into consideration. Furthermore, a bilevel optimal dispatch strategy is proposed to decrease the difficulty in coordinated control and interaction between lower-level factories and upper-level multi-energy operators in industrial parks. In this strategy, resources in both multi-energy operator and user sides are optimally controlled and scheduled to maximize the benefits under peak shifting constraint. In the normal operation mode, this strategy can maximize the benefits to users and multi-energy operators. Particularly in heavy load conditions, compared to the conventional electricity demand response, there are more types of adjustable resources, more flexibility, and lower interactive compensations in IDR. The results indicate that optimal operation for factories and multi-energy operators can be achieved under peak shifting constraint and the overall peak power value in industrial park is reduced.

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With the increase of various loads connected to the low-voltage distribution system, the difficulty of identifying low-voltage series fault arcs has greatly increased, which seriously threatens the electricity safety. Aiming at such problems, a neural network algorithm based on multi-feature fusion is proposed. The fault current has the characteristics of randomness, high frequency noise, and singularity. A GA-BP neural network model is built, and the wavelet analysis method (based on singularity), Fourier transform method (based on high frequency noise), current cycle difference method (based on randomness), and current cycle similarity derivation method (based on randomness) are used for feature extraction and can more comprehensively reflect the characteristics of arc faults. Simulation results show that the multi-feature fusion algorithm has a higher recognition rate than other algorithms. Moreover, compared with the support vector machine model, logistic regression model, and AlexNet model, the GA-BP neural network model has a higher recognition accuracy than the other three models, which can reach 99%.

Neutral non-effectively grounded mode is widely used in medium-voltage distribution networks in China. When a single-phase grounding fault occurs in distribution networks, abundant transient signals will be generated. Here, a fault location method based on improved Hausdorff distance is proposed with transient zero-sequence current and transient zero-sequence voltage of bus. First, according to the impedance characteristic analysis of the zero-sequence network, the selected frequency band (SFB) is determined, and the transient signals in the SFB are extracted by the digital filter. Second, the projection components of transient zero-sequence currents at each monitoring point of faulty feeder are obtained by the principle of orthogonalization. Finally, the projection component values of each section are calculated by the improved Hausdorff distance, and the maximum value is selected. If the maximum value is much larger than the sum of the values of other sections excluding the maximum value, the section where the maximum value is located is judged as a faulty section, otherwise, the downstream section of the last monitoring point is determined as the faulty section. Matlab/Simulink simulation shows that the proposed method can locate accurately under different fault conditions.