Data Enabled Predictive Energy Management of a PV-Battery Smart Home Nanogrid

被引：0
|
作者：
Sun, Chao [1 ]
Sun, Fengchun [1 ]
Moura, Scott J. [2 ]
机构：
[1] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
[2] Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA
来源：
2015 AMERICAN CONTROL CONFERENCE (ACC) | 2015年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a data-enabled predictive energy management strategy for a smart home nanogrid (NG) that includes a photovoltaic system and second-life battery energy storage. The key novelty is utilizing data-based forecasts of future load demand, weather conditions, electricity price, and power plant CO2 emissions to improve the NG system efficiency. Specifically, a load demand forecast model is developed using an artificial neural network (ANN). The forecast model predicts load demand signals for a model predictive controller (MPC). Simulation results show that the data-enabled predictive energy management strategy achieves 96%-98% of the optimal NG performance derived via dynamic programming (DP). Its sensitivity to the control horizon length and load demand forecast accuracy are also investigated.
引用
收藏
页码：1023 / 1028
页数：6
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