The increasing penetration of various distributed and renewable energy resources at the consumption premises, along with the advanced metering, control and communication technologies, promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids (MMGs). The networked MMG system is an interconnected cluster of distributed generators, energy storage as well as controllable loads in a distribution system. And its operation complexity can be decomposed to decrease the burdens of communication and control with a decentralized framework. Consequently, the multi-microgrid energy management system (MMGEMS) plays a significant role in improving energy efficiency, power quality and reliability of distribution systems, especially in enhancing system resiliency during contingencies. A comprehensive overview on typical functionalities and architectures of MMGEMS is illustrated. Then, the emerging communication technologies for information monitoring and interaction among MMG clusters are surveyed. Furthermore, various energy scheduling and control strategies of MMGs for interactive energy trading, multi-energy management, and resilient operations are thoroughly analyzed and investigated. Lastly, some challenges with great importance in the future research are presented.
As the integration of wind power into electrical energy network increasing, accurate forecast of wind speed becomes highly important in the case of large-scale wind power connected into the grid. In order to improve the accuracy of wind speed forecast and the generalization ability of the model, Extreme Gradient Boosting (XGBoost) as an improvement from gradient boosting decision tree (GBDT) is trained and deployed in the cheaper central processing unit (CPU) devices instead of graphics processing unit (GPU) devices, thus, a wind speed forecast model based on Extreme Gradient Boosting is proposed in this paper. Firstly, the historical data is taken as a part of the input vectors for the model. Moreover, considering the monthly change of wind speed characteristics, the dataset of wind power is divided into four parts by month so that the models are constructed in different complexity by month. Finally, compared with back propagation neural networks (BPNN) and linear regression (LR) models, the experimental results show that the improved XGBoost model can promote the forecast accuracy effectively. INDEX TERMS Short-term wind speed forecasting, XGBoost, time series, historical characteristics, power grid.
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