Detection of Defaulting Participants of Demand Response Based on Sparse Reconstruction

Azuma, Shun‐ichi; Sato, Daisuke; Kobayashi, Koichi; Yamaguchi, Nobuyuki

doi:10.1109/tsg.2019.2922435

Cited by 6 publications

(1 citation statement)

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“…When examining the success rate of DR events, the overwhelming majority of research focuses on the incentives and the optimal selection of assets and resources for successfully delivering what has been requested (Parrish et al, 2020). On the contrary, there are very few findings on anticipating and identifying participants that are expected to default (Azuma et al, 2019), and none (to the authors knowledge) that tackle the failure during the active period of the DR event. Furthermore, interestingly enough, scientific community seems more keen to impose penalties (Ghorashi et al, 2020) towards increasing success rates than presenting with mitigation mechanisms that can salvage the risk imposed by a defaulting participant.…”

Section: Related Workmentioning

confidence: 99%

Optimal, dynamic and reliable demand-response via OpenADR-compliant multi-agent virtual nodes: Design, implementation & evaluation

Patsonakis

Bintoudi²,

Κωστόπουλος³

et al. 2021

Journal of Cleaner Production

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Extracting and exploiting the flexibility of electric demand has been shown to reduce the needs of network upgrades and generation capacity increases. Demand Response (DR) in considered as one of the few available solutions for accessing the untapped energy potential of small and medium customers. Over the past decade, rigorous research has produced significant results in optimally dispatching DR in an attempt to maximize flexibility extraction. However, the vast majority of works assumes a "happy path" scenario in which DR requests are always successfully completed. Hence, there is a large gap in the literature that fails to account for nondeterministic factors that manifest in practical deployments, e.g., the stochasticity of end-user behavior that can drastically influence the DR's outcomes. Investing on that notion, a novel, distributed, multi-agent system (MAS) that aggregates consumers and prosumers and handles automatically OpenADR-compliant DR requests is introduced, following virtual power plant (VPP) principles. Agents of the proposed MAS are able to service DR events originating from a higher level, e.g., Aggregators or Utilities, and optimally dispatch them to their assigned customers. The proposed framework ensures 100% DR success rate, compared to conventional methods, by not only optimally exploiting aggregated flexibility through a combination of clustering and optimisation engines, but also through a dynamic, bi-directional DR matchmaking process that can mitigate observed deviations both internally (intra), as well as, externally (inter) in real-time. Via experimentation, we demonstrate the framework's efficiency in ensuring technical DR fault-tolerance along with its ability to deliver savings of up to 3 orders of magnitude to Aggregators and the customers serving the DR requests.

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Section: Related Workmentioning

confidence: 99%