The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Multiclass Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer Preferences
Abstract
This paper proposes a peer-to-peer energy market platform based on the new concept of multiclass energy management, to coordinate trading between prosumers with heterogeneous (i.e., beyond purely financial) preferences. Power networks are undergoing a fundamental transition, with traditionally passive distribution network consumers becoming “prosumers”; proactive consumers that actively manage their production and consumption of energy. The paper introduces the new concept of energy classes, allowing energy to be treated as a heterogeneous product, based on attributes of its source, which are perceived by prosumers to have value. Examples include generation technology, location in the network and owner's reputation. The proposed peer-to-peer energy market platform coordinates trading between subscribed prosumers and the wholesale electricity market, to minimize costs associated with losses and battery depreciation, while providing added value by accounting for the prosumers' individual preferences for the source/destination of the energy they consume/produce. The decomposable structure of the multiclass energy management problem is exploited to devise a distributed price-directed optimization mechanism, providing scalability and prosumer data privacy. Receding horizon model predictive control allows the prosumers to adjust their planned power flows based on the wholesale energy price, and up-to-date renewable generation and load predictions.
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,677
Aitzhan, N. Z., & Svetinovic, D. (2018) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2616861 · Cited by 1,297
Electricity market design for the prosumer era
Parag, Y., & Sovacool, B. K. (2016) · Nature Energy · doi:10.1038/nenergy.2016.32 · Cited by 1,245
Hagiu, A., & Wright, J. (2015) · International Journal of Industrial Organization · doi:10.1016/j.ijindorg.2015.03.003 · Cited by 995
Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants
Morstyn, T., Farrell, N., Darby, S. J., & McCulloch, M. D. (2018) · Nature Energy · doi:10.1038/s41560-017-0075-y · Cited by 831
Self-citationA Microgrid Energy Management System Based on the Rolling Horizon Strategy
Palma-Behnke, R., Benavides, C., Lanas, F., Severino, B., Reyes, L., Llanos, J., & Sáez, D. (2013) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2231440 · Cited by 771
Smart Grids: A Cyber–Physical Systems Perspective
Yu, X., & Xue, Y. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2503119 · Cited by 717
Schollmeier, R. (2002) · Proceedings First International Conference on Peer-to-Peer Computing · doi:10.1109/p2p.2001.990434 · Cited by 707
Cited by
Tushar, W., Yuen, C., Saha, T. K., Morstyn, T., Chapman, A. C., Alam, M. J. E., Hanif, S., & Poor, H. V. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116131 · Cited by 523
Self-citationCharbonnier, F., Morstyn, T., & McCulloch, M. D. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118825 · Cited by 78
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Metadata
- Authors
- Morstyn, T., & McCulloch, M. D.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2019-09
- Volume
- 34
- Issue
- 5
- Pages
- 4005-4014
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2801389981
- Cited by
- 533
- References
- 37
- FWCI
- 19.10
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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