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This paper presents the power supply on-line optimization in a Microgrid prototype. The power supply is formulated as a linear programming problem. With this formulation, a new recurrent neural network which solves linear programming is proposed and applied to the supply problem. The main features of the proposed system are the fixed convergence time to the programming solution and the fixed parameters...
This paper presents the development and implementation of a new recurrent neural network for optimization as applied to optimal operation of an electrical microgrid, which is interconnected to the utility grid; moreover, it incorporates batteries, for energy storing and supplying, and an electric car. The proposed neural network determines the optimal amount of power over a time horizon of one week...
This paper introduces a class of fixed-time stable dynamical systems with settling time as a explicit parameter, namely the inverse the gain. Those systems are defined as predefined-timed stable dynamical systems. Continuous and discontinuous are cases are presented. A detailed Lyapunov characterization of this class of systems is also shown. Finally, the application to the design of a class of first...
This paper presents an optimization and control scheme for power converters in a micro-grid, which is composed of a wind energy system, an energy storage element (supercapacitor), a load and the interconnection to the utility grid. Based on the results of a dynamic optimization model, which establish the energy flow in the micro-grid, an optimal control scheme uses these results of electrical power...
The aim of this paper is to introduce a new recurrent neural network to solve linear programming. The main characteristic of the proposed scheme is its design based on the predefined-time stability. The predefined-time stability is a stronger form of finite-time stability which allows the a priori definition of a convergence time that does not depend on the network initial state. The network structure...
The aim of this paper is to present a simple new class of recurrent neural networks, which solves linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with finite time stabilizing terms based on the unit control, instead of...
This paper presents the development of a dynamic optimization model to manage the generated energy in a micro-grid. The micro-grid consists of the following components: a wind energy system, an energy storage element, a load, and the interconnection to the utility grid. The optimization scheme considers the minimization of the associated cost due to the purchase of energy from the utility grid and...
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