Considering wide deployment of advanced IoT infrastructure in the future power system as well as the development of 5G technology, a fully distributed three-stage phase independently voltage regulation strategy is proposed in this paper. Voltage issue can be regulated collectively by utilizing the available real and reactive power of distributed energy resource (DER). In the first planning stage, a perturbation approach is proposed as the substitute of conventional Jacobian analysis to quantify the local voltage sensitivity in the three-phase unbalanced nonlinear distribution network under different R/X of the line. In the second stage, based on the consensus algorithm, an average voltage deviation can be achieved without the need of central controllers via an undirected communication network. In the third stage, independent droop controller of each DER is initiated to proportionally allocate the available P/Q of DER to support the voltage. Results of case study verify that the proposed regulation strategy can effectively deal with the unbalanced voltage problems in the network.

In recent years, wind power curtailment has been a big concern with the rapid growth of wind power installed capacity in China. It is essential to investigate influential factors of wind power curtailment and how much these factors affect wind power curtailment. A novel method to quantify influential factors of wind power curtailment caused by lack of load-following capability is proposed. First, this paper applies back propagation(BP) neural network to model the nonlinear relationship between influential factors and wind power curtailment caused by lack of load-following capability. Then, this paper utilizes mean impact value(MIV) to compute contribution of influential factors. Finally, case study on a provincial power grid in northwest region of China is carried out to validate the proposed method. The study result indicates that the proposed method can quantify the importance of each influential factor.

Distributed energy systems that combined with cloud computing, big data and artificial intelligence call for the support of new requirements by information and communication services. A network slice is intended as a collection of logical network functions and parameter configurations tailored to support the requirements of a particular service. In this article, we present our vision on the necessity of applying the network slicing technologies in distributed energy systems. And then, based on the analysis of 5G network slicing technology, we give our suggestions on slicing solutions, involving network slice deployment and network slice management strategy.

In recent years, due to the increasing of distributed energy resources (DERs) in distribution system, power companies experience new challenges in grid operation, i. e. reverse power flow, protection coordination, volt/var coordination and so on. Conventional grid operation softwares are not designed for these operation scenarios. It is key for the next-generation tools to provide better situational awareness and effective way to manage the impacts of DERs on grid reliability. Conventional DER management systems are often developed in a vertically integrated way, that data communication structure, user interface and analytical functions are deeply coupled together. It typically requires significant efforts to add new functions or integrate with third-party tools. In this paper, an innovative internet of thing (IoT) data analytical framework is proposed featuring an open end-to-end architecture. The proposed data framework enables efficient monitoring, analyzing and coordinating of various distributed grid resources in real time. Its hierarchical layered design consists of edge computing layer, IoT data management layer, and modular application layer. A Proof of Concept PV management system is developed based on this proposed IoT framework for a commercial PV fleet owner. This online PV management system provides real-time monitoring of PV fleet across multiple sites. Different analytical functions are integrated onto this framework, e. g. performance analysis, PV power forecasting, etc.