期刊
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
卷 129, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2020.106731
关键词
Hybrid renewable energy systems; Day-ahead energy management; Demand response; Probabilistic fuzzy inference systems; Particle swarm optimization
资金
- University of Manitoba
- Solar Solutions Inc., Canada
- NSERC Engage program
With the expected price reductions in renewable technologies, hybrid renewable energy systems have emerged as feasible retrofit solutions for primarily diesel-based remote off-grid power systems; effective energy management is crucial for stable and reliable power system operation; integrating demand response strategies with energy management framework can maximize benefits.
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.
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