Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.

This paper aims to model interdependencies between power systems and dispatching data networks, and to analyze the intricate impacts on cascading failures. The functions of communication networks are embedded into dispatching data networks in China, thus we use dispatching data networks in the paper. The structures of dispatching data networks are generally categorized into two types: 1) double-star; and 2) mesh. The correlation of nodes in double-star networks and power systems is “degree to degree,” whereas “degree to betweenness” is the correlation for mesh networks. Furthermore, the interactive model between power grids and dispatching data networks is presented by a dynamic power flow model. Taking the IEEE 39-bus system and China's Guangdong 500-kV system as examples, in the case of random attacks on the interdependent system, simulation results show that the power grid coupled with double-star dispatching data networks has lower probability of catastrophic failures than with the mesh structure, because the double-star dispatching data network has outstanding capability of delivering information even though some communication nodes are out of order. In contrast, under intentional attacks, the decrement of the transmission performance of the double-star network is more serious than that in the mesh network. Therefore, the power system exhibits much higher vulnerability when coupled with the double-star network.

As the last link of an integrated future energy system, the smart home energy management system (HEMS) is critical for a prosumer to intelligently and conveniently manage the use of their domestic appliances, renewable energies (RES) generation, energy storage system (ESS), and electric vehicle (EV). In this paper, we propose a holistic model to center the preference of users when scheduling the involved physical equipment of different natures. Further, a dedicatedly designed charging and discharging strategy for both the ESS and EV considering their capital cost is proposed to integrate them into the HEMS for providing a better flexibility and economic advantages as well as to prolong the life of the batteries. Based on the mixed integer linear programming (MILP) and the proposed model, the energy schedule of the smart home can be derived to guarantee both the lowest cost and the comfort for the users. An illustrative case study is employed to demonstrate the effectiveness of the proposed method.

This letter proposes a novel cascading faults graph (CFG) based on fault chain theory to assess the transmission network vulnerability. The proposed CFG is able to effectively reveal the mechanism of fault propagation in a transmission network by transforming an electrical network with spatial information into a fault propagation graph with temporal information, thus providing a better way for transmission network vulnerability assessment. Numerical results on IEEE 39-and 118-bus systems demonstrate the effectiveness of the proposed method.