ARTIFICIAL INTELLIGENCE-BASED DEMAND-SIDE RESPONSE MANAGEMENT OF RENEWABLE ENERGY

被引：0

作者：

Hanna, Bavly ^{[1
]}

Xu, Guandong ^{[1
]}

Wang, Xianzhi ^{[1
]}

Hossain, Jahangir ^{[1
]}

机构：

[1] Univ Technol Sydney, Sydney, NSW, Australia

来源：

ENERGY PRODUCTION AND MANAGEMENT IN THE 21ST CENTURY V: The Quest for Sustainable Energy | 2022年 / 255卷

关键词：

demand response; renewable energy; artificial intelligence; machine learning; OPTIMIZATION;

D O I：

10.2495/EPM220051

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Renewable energy (RE) sources will aid in the decarbonization of the energy sector, which would assist in alleviating the negative consequences of climate change. However, using RE resources for hybrid power generation has two technological challenges, uncertainty and variability owing to RE features, making estimating generated power availability difficult. Artificial intelligence techniques have been used in a variety of applications in power systems, but demand-side response (DR) is just lately receiving major research interest. The DR is highlighted as one of the most promising ways of providing the electricity system with demand flexibility; as a result, many system operators believe that growing the scale and breadth of the DR programme is critical. There are many different sorts of demand reduction programmes, and the most common classification is dependent on who begins the demand reduction. There are two types of DR schemes: (1) price-based programmes and (2) incentive-based programmes.

引用

页码：49 / 61

页数：13

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