配电网需求侧管理的预测监管策略

Abderrahman Benchekroun, A. Davigny, V. Courtecuisse, L. Coutard, Kahina Hassam-Ouari, B. Robyns
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引用次数: 1

摘要

在智能电网兴起的背景下,能源管理战略在应对其发展面临的各种挑战方面发挥着重要作用。本文提出了一种预测监管策略来管理配电网内电动汽车和电热水器的激活。为此,基于历史测量数据和气象数据,利用人工神经网络对能源生产和消费进行了预测。然后,使用这些预测来确定可控制的负荷分布,使能源传输成本最小化。该系统的性能最终通过模拟测试,使用从配电变电站收集的实时测量数据,显示出能源传输成本的显著降低,同时也增加了当地可再生能源的消耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Supervision Strategy for Demand-Side Management in Distribution Grids
In a context where smart grids are emerging, energy management strategies play a major role in tackling different challenges facing their development. This paper proposes a predictive supervision strategy to manage the activation of Electric Vehicles and Electric Water Heaters within a distribution grid. To do so, the energy production and consumption are forecasted using Artificial Neural Networks based on historical measurements and meteorological data. Then, these forecasts are used to determine controllable load profiles that minimize energy transmission costs. The system’s performance is finally tested through simulation using real-time measurements collected from a distribution substation, showing an important reduction in energy transmission costs and also an increase in local renewable energy consumption.
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