Data-driven energy management of virtual power plants: a review
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Published version
Author(s)
Ruan, Guangchun
Qiu, Dawei
Sivaranjani, S
Awad, Ahmed SA
Strbac, Goran
Type
Journal Article
Abstract
A virtual power plant (VPP) refers to an active aggregator of heterogeneous distributed energy resources (DERs), which creates a promising pathway to expand renewable energy and demand-side electrification for deep decarbonization. The VPP market is projected to have a significant growth potential, with the global investment surging from $6.47 billion in 2022 to $16.90 billion by 2030. Up to now, VPPs still face technical challenges in dealing with the inherent uncertainty of DERs, and data emerge as a promising and essential resource to handle this issue. This paper makes the first effort to review the development of VPP technologies from a data-centric perspective, and then analyze the major role of data within every decision phase of VPPs. We examine the VPP energy management through a data lifecycle lens, and extensively survey the progress in data creation, data communication, data-driven decision support, data sharing and privacy, as well as technical solutions stemming from reinforcement learning, peer-to-peer sharing, blockchain, and market participation. In addition, we offer a unique overview of open data and recent real-world projects around the world to showcase the latest VPP practices. We finally discuss the major challenges and future opportunities in detail, with a focus on topics such as public benchmarks, internet of things, 5G, explainable artificial intelligence, and federated learning. We highlight the need for technical advances in data management and support systems for the growing scale of future VPP systems, and suggest VPPs delivering more ancillary grid services in the future.
Date Issued
2024-07
Date Acceptance
2024-02-08
Citation
Advances in Applied Energy, 2024, 14
ISSN
2666-7924
Publisher
Elsevier
Journal / Book Title
Advances in Applied Energy
Volume
14
Copyright Statement
© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Identifier
https://www.sciencedirect.com/science/article/pii/S2666792424000088
Subjects
ARCHITECTURE
Battery
Big data
Blockchain
Communication
DEMAND RESPONSE
Distributed energy resource
ECONOMIC-DISPATCH
Electricity market
Energy & Fuels
Game theory
GENERATION
Machine learning
MARKET
Microgrid
MICROGRIDS
MODEL
Peer-to-peer
Privacy
Real-world projects
Reinforcement learning
Renewable energy
RESOURCES
Risk management
SCHEME
Science & Technology
STRATEGY
Technology
Uncertainty
Publication Status
Published
Article Number
100170
Date Publish Online
2024-03-05