The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Bilateral Contract Networks for Peer-to-Peer Energy Trading
Open accessAbstract
This paper proposes bilateral contract networks as a new scalable market design for peer-to-peer energy trading. Coordinating small-scale distributed energy resources to shape overall demand could offer significant value to power systems, by alleviating the need for investments in upstream generation and transmission infrastructure, increasing network efficiency and increasing energy security. However, incentivising coordination between the owners of large-scale and small-scale energy resources at different levels of the power system remains an unsolved challenge. This paper introduces real-time and forward markets, consisting of energy contracts offered between generators with fuel-based sources, suppliers acting as intermediaries and consumers with inflexible loads, time-coupled flexible loads and/or renewable sources. For each type of agent, utility-maximising preferences for real-time contracts and forward contracts are derived. It is shown that these preferences satisfy full substitutability conditions essential for establishing the existence of a stable outcome-an agreed network of contracts specifying energy trades and prices, which agents do not wish to mutually deviate from. Important characteristics of energy trading are incorporated, including upstream-downstream energy balance and forward market uncertainty. Full substitutability ensures a distributed price-adjustment process can be used, which only requires local agent decisions and agent-to-agent communication between trading partners.
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Cites
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Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers
Liu, N., Yu, X., Wang, C., Li, C., Ma, L., & Lei, J. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2649558 · Cited by 855
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“Sectors amenable to P2P trading are characterised by a lack of economies of scale, and diversity of demand [17].”
BackgroundDistributed Generation: Toward a New Energy Paradigm
Guerrero, J., Blaabjerg, F., Zhelev, T., Hemmes, K., Monmasson, E., Jemei, S., Comech, M., Granadino, R., & Frau, J. (2010) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2010.935862 · Cited by 483
Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets
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Cited by
Peer-to-peer and community-based markets: A comprehensive review
Sousa, T., Soares, T., Pinson, P., Moret, F., Baroche, T., & Sorin, E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.01.036 · Cited by 936
Multiclass Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer Preferences
Morstyn, T., & McCulloch, M. D. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2834472 · Cited by 533
Self-citationTushar, 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-citationA motivational game-theoretic approach for peer-to-peer energy trading in the smart grid
Tushar, W., Saha, T. K., Yuen, C., Morstyn, T., McCulloch, M. D., Poor, H. V., & Wood, K. L. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.03.111 · Cited by 384
Self-citationLocal flexibility markets: Literature review on concepts, models and clearing methods
Jin, X., Wu, Q., & Jia, H. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114387 · Cited by 373
Metadata
- Authors
- Morstyn, T., Teytelboym, A., & Mcculloch, M. D.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2019-03
- Volume
- 10
- Issue
- 2
- Pages
- 2026-2035
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2786215208
- Cited by
- 543
- References
- 27
- FWCI
- 23.05
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
