Electrical Microgrid Optimization via a New Recurrent Neural Network
Electrical Microgrid Optimization via a New Recurrent Neural Network
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DOI：
10.1109/jsyst.2014.2305494
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发表时间：
2015-09
影响因子：
4.4
通讯作者：
Manuel E. Gamez Urias;E. Sánchez;L. J. Ricalde
中科院分区：
文献类型：
--
作者：
Manuel E. Gamez Urias;E. Sánchez;L. J. Ricalde
本文介绍了一种新的递归神经网络的开发和实施，用于优化应用于微电网的优化操作，微电网与公用电网互连;此外，它还包括电池，用于能量存储和供应，以及电动汽车。所提出的神经网络确定在一周的时间范围内，风能，太阳能和电池系统，包括电动汽车的最佳功率量，以最大限度地减少从公用电网获得的功率，并最大限度地提高可再生能源提供的功率。仿真结果表明，在一个时间范围内，每个能源的发电水平可以达到最佳形式。
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 for wind, solar, and battery systems, including that of the electric car, in order to minimize the power acquired from the utility grid and to maximize the power supplied by the renewable energy sources. Simulation results illustrate that generation levels for each energy source over a time horizon can be reached in an optimal form.