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This paper proposes a fuzzy logic based energy management system (FEMS) for a grid-connected microgrid with renewable energy sources (RES) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state-of-charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions. The fuzzy membership functions of the FEMS are optimized offline using a Pareto based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy logic controller. A comparison with other control strategies with similar objectives are carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at

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This paper presents a new control method to regulate various parameters such as voltage, current and power of the inverter interfaced with Distributed Generation (DG) in a microgrid. Model Predictive Control (MPC) is used to control the inverter of the DG using a state-space model of the inverter based microgrid. The operation of the microgrid is tested under grid-connected and stand-alone conditions. MATLAB/Simulink is used to simulate the proposed microgrid under these two operating conditions. Simulation results show that the DG inverter can operate effectively with MPC in the microgrid to provide the desired voltage, current and power under gridconnected and stand-alone conditions.

In this paper, a new non-local active compensation method is developed for a multi-microgrid (MMG) system. The current industry practice is to utilize local harmonic current and reactive power compensation methods, however local compensation methods are not practical for large MMG system with widely dispersed non-linear loads, because each nonlinear load would require its own compensator. To overcome this problem, a novel compensating technology called a seriesshunt network device (SSND) is installed between a pair of microgrids in the proposed MMG system. The SSND reduces the number of local compensators required while also performing additional functions in comparison with conventional devices. For effective control of the SSND, an improved model predictive control (MPC) algorithm, which gives better tracking accuracy and faster set-point change than that of a conventional proportional-integral (PI) controller, is also presented. Analysis and simulations verify the capability of SSND in performing both local and non-local active compensation of harmonic current and reactive power in the proposed MMG system under various test scenarios. Simulation results show that the MPCcontrolled SSND can achieve effective compensation, thereby resulting in a very low current total harmonic distortion (THD) value of 2.4% and a unity power factor at the distribution grid side.