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Due to the aging power-grid infrastructure and increased usage of renewable energies, microgrids (mGrids) have emerged as a promising paradigm. It is reasonable to expect that they will become one of the fundamental building blocks of a smart grid, since effective energy transfer and coordination of mGrids could help maintain the stability and reliability of the regional large-scale power-grid. From the control perspective, one of the key objectives of mGrids is load management using local generation and storage for optimized performance. Accomplishing this task can be challenging, however, particularly in situations where local generation is unpredictable both in quality and in availability. This paper proposes to address that problem by developing a new optimal energy management scheme, which meets the requirements of supply and demand. The method that will be described in the following models mGrids as a stochastic hybrid dynamic system. Jump linear theory is used to maximize storage and renewable energy usage, and Markov chain theory is applied to model the intermittent generation of renewable energy based on real data. Although the model itself is quite general, we will focus exclusively on solar energy, and will define the performance measure accordingly. We will demonstrate that the optimal solution in this case is a state feedback law with a piecewise constant gain. Simulation results are provided to illustrate the effectiveness of such an approach.

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In recent years, voltage and frequency regulation issues have been extensively discussed for the microgrid clusters (MGCs), as the high penetration of renewable energy resources (RES) might affect the continuous operation of the microgrid (MG). Furthermore, to enhance the MGC's operation reliability, stability concerns need to be addressed. In this study, a residential MGC connected to a commercial MGC has been considered. A novel control scheme that combines both droop control and virtual inertia control is proposed. This control strategy relies on online measurements, and it can be adapted to different situations. At each iteration, the damping coefficient and droop coefficient are calculated this allows the system to switch as needed between the droop and the virtual inertia controller. This dynamic coefficient calculation allows plug and play capability, which provides the MGC with major flexibility in terms of the MGs operation and flexibility.

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This paper proposes a new method for permuting sparse matrices into an upper block triangular from. The algorithm is highly parallelizable, which makes it suitable for large-scale systems with uncertain interconnection patterns. In such cases, the proposed decomposition can be used to develop flexible decentralized control strategies that produce a different gain matrix whenever the configuration changes. Applications to interconnected microgrids and supply and demand networks are provided to illustrate the versatility of the proposed approach.INDEX TERMS Large-scale systems, reconfigurable interconnections, uncertainty, decentralized control, sparse matrices, block triangular structure, parallel graph theoretic decompositions.