A review on virtual power plant for energy management

A Virtual Power Plant (VPP) is a practical concept that aggregates various Renewable Energy Sources (RESs) to improve energy management efficiency and facilitate energy trading. Operation scheduling for all energy components in VPPs plays a vital role from an energy management perspective. Technical...

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Vydáno v:Sustainable energy technologies and assessments Ročník 47; s. 101370
Hlavní autoři: Rouzbahani, Hossein Mohammadi, Karimipour, Hadis, Lei, Lei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2021
Témata:
Energy management
Optimum scheduling algorithm
Renewable energy sources
Uncertainties
Virtual power plant
Renewable energy sources
Optimum scheduling algorithm
Virtual power plant
Uncertainties
Energy management
ISSN:2213-1388
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Shrnutí:A Virtual Power Plant (VPP) is a practical concept that aggregates various Renewable Energy Sources (RESs) to improve energy management efficiency and facilitate energy trading. Operation scheduling for all energy components in VPPs plays a vital role from an energy management perspective. Technical and economic constraints and uncertainties that significantly affect the scheduling program must be considered simultaneously. This paper provides a comprehensive review of the scheduling problem in the VPP concept, following Kitchenham's guidelines, to address questions such as: What are the most frequent scheduling techniques in VPPs? How technical and economic aspects of scheduling have been considered to optimize the problem? Moreover, how to deal with different types of uncertainties? To that end, all previous studies on this topic have been extracted and analyzed, focusing on the scheduling algorithm's necessity. Several optimal scheduling methods are investigated that show learning-based approaches have not been well studied. Then, joint technical and economic limitations and dealing with various types of uncertainties are appraised. Contribute to better knowledge for future studies on VPPs, the research gaps regarding optimization techniques, joint techno-economic, and various kinds of uncertainties have been introduced. Finally, this paper also suggests utilizing a Deep Reinforcement Learning (DRL)-based technique to address the mentioned concerns due to generalization, scalability, and feature extraction, which are originated from a combination of reinforcement learning and deep learning.
ISSN:2213-1388
DOI:10.1016/j.seta.2021.101370