Demand response integrated day-ahead energy management strategy for remote off-grid hybrid renewable energy systems

被引:46
|
作者
Kaluthanthrige, Roshani [1 ]
Rajapakse, Athula D. [1 ]
机构
[1] Univ Manitoba, Elect & Comp Engn, Winnipeg, MB, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Hybrid renewable energy systems; Day-ahead energy management; Demand response; Probabilistic fuzzy inference systems; Particle swarm optimization; OPTIMIZATION; MODELS; WATER;
D O I
10.1016/j.ijepes.2020.106731
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the anticipated future price reductions in renewable technologies, hybrid renewable energy systems have emerged as a feasible retrofit to the primarily diesel-based remote off-grid power systems. However, in the presence of many supply/storage systems along with high penetration of intermittent renewable energy, energy management becomes a decisive step to achieve a stable and reliable power system operation. The intended benefits can be further maximized when the energy management framework is unified with demand response strategies. This paper presents a novel demand response integrated day-ahead energy management framework subjecting remote off-grid power systems. Several measures are taken to enhance the consumer acceptance and practical implementation of the demand response platform. Responsiveness of the customers to price-based incentives is estimated using a probabilistic fuzzy inference system to accurately model the stochastic human nature. Results are demonstrated for an isolated remote community in Northern Canada for both summer and winter seasons. The results confirm the applicability of the proposed method in achieving the intended objectives of day-ahead energy management.
引用
收藏
页数:18
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