Predictive Supervision Strategy for Demand-Side Management in Distribution Grids

被引:1
|
作者
Benchekroun, Abderrahman [1 ]
Davigny, Arnaud [1 ]
Courtecuisse, Vincent [2 ]
Coutard, Leo [2 ]
Hassam-Ouari, Kahina [3 ]
Robyns, Benoit [1 ]
机构
[1] Univ Lille, Inst Technol Arts & Metiers, L2EP, Yncrea Hauts France,Cent Lille,ULR 2697, F-59000 Lille, France
[2] GEREDIS Deux Sevres, F-79028 Niort, France
[3] Yncrea Hauts France, F-59000 Lille, France
来源
2020 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST) | 2020年
关键词
Demand-Side Management; Artificial Neural Networks; Electric Vehicles; Electric Water Heaters; Distribution Grids;
D O I
10.1109/sest48500.2020.9203277
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页数:6
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