Learning Dynamical Demand Response Model in Real-Time Pricing Program

被引:3
|
作者
Xu, Hanchen [1 ,2 ]
Sun, Hongbo [2 ]
Nikovski, Daniel [2 ]
Kitamura, Shoichi [3 ]
Mori, Kazuyuki [3 ]
机构
[1] Univ Illinois, Dept Elect & Comp Engn, 1406 W Green St, Urbana, IL 61801 USA
[2] Mitsubishi Elect Res Labs, 201 Broadway, Cambridge, MA 02139 USA
[3] Mitsubishi Electr Corp, Adv Technol R&D Ctr, Kobe, Hyogo 6618661, Japan
来源
2019 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT) | 2019年
关键词
OPERATIONS;
D O I
10.1109/isgt.2019.8791624
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.
引用
收藏
页数:5
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