A Multiple-objective Neurodynamic Optimization to Electric Load Management Under Demand-Response Program

被引：2

作者：

Le, Xinyi ^{[1
]}

Chen, Sijie ^{[1
]}

Zheng, Yu ^{[1
]}

Xi, Juntong ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS, PT II | 2017年 / 10262卷

基金：

中国国家自然科学基金;

关键词：

Demand response; Power system economics; Multi-objective optimization; Two-time-scales; Neurodynamics; RECURRENT NEURAL-NETWORK; SYSTEMS;

D O I：

10.1007/978-3-319-59081-3_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In a power system, a price-based demand-response program offers end electricity users time-varying prices, incentivizing them to shift demand from high-price hours to low-price hours during a day. Heating/cooling (H/C) loads are typical flexible loads to be shifted. Specifically, end users optimize the hourly H/C load to balance electricity costs and comfort. In this paper, a two-time-scale neurodynamic optimization approach is applied for this multi-objective optimization problem. As a result, optimal use of H/C loads is derived that yields significant savings and acceptable comfort. A case study of the Houston City is presented to show the effectiveness of the proposed neurodynamic approach.

引用

页码：169 / 177

页数：9

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