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Multi-microgrid (MMG) system is a new method that concurrently incorporates different types of distributed energy resources, energy storage systems and demand responses to provide reliable and independent electricity for the community. However, MMG system faces the problems of management, real-time economic operations and controls. Therefore, this study proposes an energy management system (EMS) that turns an infinite number of MMGs into a coherence and efficient system, where each MMG can achieve its goals and perspectives. The proposed EMS employs a cooperative game to achieve efficient coordination and operations of the MMG system and also ensures a fair energy cost allocation among members in the coalition. This study considers the energy cost allocation problem when the number of members in the coalition grows exponentially. The energy cost allocation problem is solved using a column generation algorithm. The proposed model includes energy storage systems, demand loads, real-time electricity prices and renewable energy. The estimate of the daily operating cost of the MMG using a proposed deep convolutional neural network (CNN) is analyzed in this study. An optimal scheduling policy to optimize the total daily operating cost of MMG is also proposed. Besides, other existing optimal scheduling policies, such as approximate dynamic programming (ADP), model prediction control (MPC), and greedy policy are considered for the comparison. To evaluate the effectiveness of the proposed model, the real-time electricity prices of the electric reliability council of Texas are used. Simulation results show that each MMG can achieve energy cost savings through a coalition of MMG. Moreover, the proposed optimal policy method achieves MG's daily operating cost reduction up to 87.86% as compared to 79.52% for the MPC method, 73.94% for the greedy policy method and 79.42% for ADP method.

We propose a new dimensionality reduction method called maximum margin projection (MMP), which aims to project data samples into the most discriminative subspace, where clusters are most well-separated. Specifically, MMP projects input patterns onto the normal of the maximum margin separating hyperplanes. As a result, MMP only depends on the geometry of the optimal decision boundary and not on the distribution of those data points lying further away from this boundary. Technically, MMP is formulated as an integer programming problem and we propose a column generation algorithm to solve it. Moreover, through a combination of theoretical results and empirical observations we show that the computation time needed for MMP can be treated as linear in the dataset size. Experimental results on both toy and real-world datasets demonstrate the effectiveness of MMP. [ABSTRACT FROM AUTHOR]