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Microgrid systems are becoming a very promising solution to meet the power demand growth especially in remote areas where diesel generators (DG) are commonly used as a main energy source. Photovoltaic (PV) systems are commonly used as a sustainable energy source to economize DG fuel. Due to the intermittent and fluctuating behavior of PV generators, energy storage systems (ESS) such as electrochemical battery are suggested. PV and ESS are usually connected using one inverter/charger called hybrid inverter. The power management is crucial to optimize the fuel consumption and operate efficiently ESS. Additionally, in an off-grid operation, the microgrid frequency becomes sensible due to the slow dynamic of DG which requires an additional control tool to improve the frequency regulation. This paper proposes a new power management based on Mamdani fuzzy logic. The proposed controller considers the targets mentioned above by only controlling the hybrid inverter. Simulation results prove that fuzzy-based controller reduces the DG fuel consumption by more than 12% compared to classical hysteresis management control. Moreover, the proposed controller performs efficiently regarding the conventional frequency regulation, which is widely used in microgrid control. [ABSTRACT FROM AUTHOR]

This paper analyzes the mode provisioning and scheduling, in light of the aggregation over distributed energy storage system for improving the interactions and energy trading decisions under the smart grid networks. Further a new smart power system equipped with energy storage devices yields efficiency and robustness in a novel structure, which can identify and react on the energy market equilibrium in a timely manner. An energy consumption and stochastic linear programming game in the distributed structure is proposed for the energy payments, so that scheduling for appliances and storage devices can be used here as well. Furthermore, it is easy to implement a proposed two-phase DSLPM (distributed stochastic linear programming management) algorithm to bring about optimality with both energy provider and users to approach payoff sharing under uncertainty. With the incomplete information, a price equilibrium scheme is proposed. Experimental results are shown to verify the consumed energy, payment, and convergence properties of the proposed models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]