As an effective approach of implementing power load shifting, fostering the accommodation of renewable energy, such as the wind and solar generation, energy storage technique is playing an important role in the smart grid and energy internet. Compressed air energy storage (CAES) is a promising energy storage technology due to its cleanness, high efficiency, low cost, and long service life. This paper surveys state-of-the-art technologies of CAES, and makes endeavors to demonstrate the fundamental principles, classifications and operation modes of CAES. Critical subsystems of CAES are elaborated exhaustively. The application prospects and further research directions are summarized to promote the popularization of CAES in smart grid and energy internet.

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The energy internet is one of the most promising future energy infrastructures that could both enhance energy efficiency and improve its operating flexibility. Analogous to the micro-grid, the micro energy internet emphasizes the distribution level and demand side. This paper proposes concepts and design principles of a smart micro energy internet for accommodating micro-grids, distributed poly-generation systems, energy storage facilities, and associated energy distribution infrastructures. Since the dispatch and control system of the smart micro energy internet is responsible for external disturbances, it should be able to approach a satisfactory operating point while supporting multiple criteria, such as safety, economy, and environmental protection. To realize the vision of a smart micro energy internet, an engineering game theory based energy management system with self-approaching-optimum capability is investigated. Based on the proposed concepts, design principles, and energy management system, this paper presents a prototype of China's first conceptual solar-based smart micro energy internet, established in Qinghai University.Index Terms-Smart micro energy internet, self-approachingoptimum, energy management, engineering game theory, solarbased conceptual prototype.

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A timely and accurate crop type mapping is very significant, and a prerequisite for agricultural regions and ensuring global food security. The combination of remotely sensed optical and radar datasets presents an opportunity for acquiring crop information at relative spatial resolution and temporal resolution adequately to capture the growth profiles of various crop species. In this paper, we employed Sentinel-1A (S-1) and Sentinel-2A (S-2) data acquired between the end of June and early September 2016, on a semi-arid area in northern Nigeria. A different set of (VV and VH) SAR and optical (SI and SB) images, illustrating crop phenological development stage, were employed as inputs to the two machines learning Random Forest (RF) and Support Vector Machine (SVM) algorithms to automatically map maize fields. Significant increases in overall classification were shown when the multi-temporal spectral indices (SI) and spectral band (SB) datasets were added with the different integration of SAR datasets (i.e., VV and VH). The best overall accuracy (OA) for maize (96.93%) was derived by using RF classification algorithms with SI-SB-SAR datasets, although the SI datasets for RF and SB datasets for SVM also produced high overall maize classification accuracies, of 97.04% and 97.44%. The outcomes indicate the robustness of the RF or SVM methods to produce high-resolution maps of maize for subsequent application from agronomists, policy planners, and the government, because such information is lacking in our study area.