MV-LV network-secure bidding optimisation of an aggregator of prosumers in real-time energy and reserve markets
| dc.contributor.author | Barreira Iria, Jose Pedro | |
| dc.contributor.author | Scott, Paul | |
| dc.contributor.author | Attarha, Ahmad | |
| dc.contributor.author | Gordon, Daniel | |
| dc.contributor.author | Franklin, Evan | |
| dc.date.accessioned | 2024-03-07T00:59:41Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2022-10-16T07:27:09Z | |
| dc.description.abstract | The large-scale adoption of commercial and residential distributed energy resources (DER) is transforming passive consumers into active prosumers. This new paradigm opens a door for aggregators to transform DER flexibility into electricity market services. However, it also brings new challenges for the distribution system operator (DSO) since aggregator actions may cause voltage and congestion problems in medium voltage (MV) and low voltage (LV) distribution networks. This paper addresses these challenges by proposing a new bidding optimisation strategy for an aggregator of prosumers to make network-secure bidding decisions in real-time energy and reserve markets. The bidding strategy uses the alternating direction method of multipliers on a rolling horizon framework to negotiate MV-LV network-secure bids between the aggregator and DSO, without jeopardizing the data privacy of either agent. The experiments run on a real-world case study show that the proposed bidding strategy outperforms state-of-the-art strategies, by computing MV-LV network-secure bids in fast execution times. Furthermore, the experiments also show that most of the DER value can reach the market with the proposed bidding strategy, even in heavily network-constrained scenarios of DER (up to 87% of the potential value in the most extreme DER scenario). | en_AU |
| dc.description.sponsorship | The research leading to this work was supported by the Australian Renewable Energy Agency (ARENA), which provided $527k of the $1.18mtotal budget of the Optimal DER Scheduling for Frequency Stability project. | en_AU |
| dc.format.mimetype | application/pdf | en_AU |
| dc.identifier.issn | 0360-5442 | en_AU |
| dc.identifier.uri | http://hdl.handle.net/1885/315797 | |
| dc.language.iso | en_AU | en_AU |
| dc.publisher | Elsevier | en_AU |
| dc.rights | ©2021 Elsevier Ltd. | en_AU |
| dc.source | Energy | en_AU |
| dc.subject | Electricity markets | en_AU |
| dc.subject | Distribution networks | en_AU |
| dc.subject | Aggregators | en_AU |
| dc.subject | Coordination | en_AU |
| dc.subject | Distributed energy resources | en_AU |
| dc.subject | Alternating direction method of multipliers | en_AU |
| dc.title | MV-LV network-secure bidding optimisation of an aggregator of prosumers in real-time energy and reserve markets | en_AU |
| dc.type | Journal article | en_AU |
| local.bibliographicCitation.lastpage | 14 | en_AU |
| local.bibliographicCitation.startpage | 1 | en_AU |
| local.contributor.affiliation | Barreira Iria, Jose Pedro, College of Engineering and Computer Science, ANU | en_AU |
| local.contributor.affiliation | Scott, Paul, College of Engineering and Computer Science, ANU | en_AU |
| local.contributor.affiliation | Attarha, Ahmad, College of Health and Medicine, ANU | en_AU |
| local.contributor.affiliation | Gordon, Daniel, College of Engineering and Computer Science, ANU | en_AU |
| local.contributor.affiliation | Franklin, Evan, College of Engineering and Computer Science, ANU | en_AU |
| local.contributor.authoruid | Barreira Iria, Jose Pedro, u1091356 | en_AU |
| local.contributor.authoruid | Scott, Paul, u4216533 | en_AU |
| local.contributor.authoruid | Attarha, Ahmad, u6540773 | en_AU |
| local.contributor.authoruid | Gordon, Daniel, u3564616 | en_AU |
| local.contributor.authoruid | Franklin, Evan, u4038737 | en_AU |
| local.description.embargo | 2099-12-31 | |
| local.description.notes | Imported from ARIES | en_AU |
| local.identifier.absfor | 400805 - Electrical energy transmission, networks and systems | en_AU |
| local.identifier.absfor | 460209 - Planning and decision making | en_AU |
| local.identifier.ariespublication | a383154xPUB24634 | en_AU |
| local.identifier.citationvolume | 242 | en_AU |
| local.identifier.doi | 10.1016/j.energy.2021.122962 | en_AU |
| local.identifier.scopusID | 2-s2.0-85121816437 | |
| local.publisher.url | https://www.elsevier.com/ | en_AU |
| local.type.status | Published Version | en_AU |
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