储能
电池(电)
计算机科学
蓄电池储能
环境科学
物理
量子力学
功率(物理)
作者
Hui Song,Chen Liu,Ali Moradi Amani,Mingchen Gu,Mahdi Jalili,Lasantha Meegahapola,Xinghuo Yu,George Dickeson
出处
期刊:Energy and AI
[Elsevier BV]
日期:2024-05-22
卷期号:17: 100378-100378
被引量:12
标识
DOI:10.1016/j.egyai.2024.100378
摘要
The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.
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