Stable Operation Of Smart Grid Research Articles

Hydrogen‐powered smart grid resilience

AbstractWith an increasing frequency of natural disasters and security attacks, the safe and stable operation of smart grid has been challenged unprecedently. To reduce the economic loss and social impact caused by power outage accidents, it is urgent to develop and improve the smart grid technology, and strengthen the disaster resistance and recovery capability of smart grids when faced with extreme events. As an efficient and flexible secondary energy source, hydrogen is crucial in improving the resilience of smart grid and supporting energy security. To further promote the deep integration of hydrogen systems and smart grid and improve the energy system resilience, the resilience of smart grids supported by hydrogen is assessed in this study. First, a technical framework of hydrogen‐powered smart grid resilience is established, and the value of hydrogen‐powered smart grid resilience is analysed considering different time frames (before, during and after an extreme event) of smart grids facing extreme events. Then, hydrogen‐powered smart grid resilience is investigated from perspectives of pre‐prevention regulation, in‐process correction regulation, and post‐recovery regulation. Finally, future direction for hydrogen‐powered smart grid resilience is investigated and related policy suggestions are provided.

Evaluation of Cross-Layer Network Vulnerability of Power Communication Network Based on Multi-Dimensional and Multi-Layer Node Importance Analysis

Power communication network (PCN) is the cornerstone of the stable operation of smart grid. The evaluation of the essential vulnerability of the PCN based on the node importance is advantageous to the protection of critical nodes and the improvement of the reliability and controllability of the system. So, firstly, this paper establishes a multi-index evaluation system for the analysis of important nodes. Based on the site operation level, site load capacity, network topology, service flow, service quality requirements and security operation requirements, the network characteristics of nodes are divided into four layers to describe. Meanwhile, the characteristics of node topology are subdivided into three dimensions to analyze. Then, fully considering the coupling between power network and communication network, a multi-dimensional and multi-layer node importance (MMNI) model based on entropy weight method is proposed. Combined the long-term and short-term vulnerability loss, the node cross-layer vulnerability loss (CVL) function and network cross-layer loss entropy (CVE) model are proposed. Finally, based on the actual power communication network, under different attacks and fault strategies, the changes of maximum network connectivity and network cross layer loss entropy are compared. It shows the effectiveness and accuracy of the MMNI model for the analysis and evaluation of important nodes and it provides a theoretical basis for key weak point identification and the network optimization.

Smart Grid Communication System based on OTN Technology

With the rapid development of society, the social demand for electricity is increasing day by day, which puts forward higher requirements for the power communication network, which makes the application effect of optical transmission network technology gradually significant. In view of the characteristics and functions of optical transmission network (OTN), continuous extension and expansion are carried out to realize the gradual integration of smart grid communication system. Based on this, this paper studies the smart grid communication system based on OTN technology. Based on the analysis of the characteristics and key technologies of OTN technology, combined with the communication requirements of smart grid communication system, the specific application of OTN technology in smart grid communication system is studied, and the application of OTN technology in power communication network is taken as the experimental group Finally, the application of OTN technology in power communication network is explored. The results show that the average score of the experimental group is 3.3 higher than that of the control group in terms of service adaptability, service demand satisfaction and intelligent level of communication network. Therefore, it is very necessary to establish the backbone optical transmission network of power communication by using OTN technology, so as to provide guarantee for the safe and stable operation of smart grid.

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy.

DGA and Weibull Distribution Model-based Transformer Fault Early Warning

As an important power transmission and transformation equipment, transformer fault has a great impact on the safe and stable operation of smart grid and probably incurs serious consequences. Therefore, how to detect and warn the fault of transformers as early as possible becomes particularly critical. In this paper, a fault early warning method based on DGA and Weibull distribution model is proposed for a large number of transformers in smart grid and the calculation formulas of the attention value and warning value of transformer fault are given. First, the defect rate and fault rate of transformers can be obtained by analysing the transformer maintenance data. Then, the attention value and warning value of transformer fault are calculated with the Weibull distribution model according to the gases volume distribution of oil chromatographic data, which provides an effective method for the transformer fault early warning. The actual case study shows that the proposed method can effectively achieve the transformer fault early warning.