Incentive-based load shifting dynamics and aggregators response predictability

•Demand flexibility exhibits important dynamics that cannot be modelled by price elasticity alone.•Demand response to price is non-linear and hysteretic, and hysteresis depends on load use density.•To understand hysteresis, aggregate response dynamic models are derived from particle-hopping models o...

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Bibliographic Details
Published in:Electric power systems research 2020-12, Vol.189, p.106744, Article 106744
Main Authors: Carvalho, Pedro M.S., Peres, José D.S., Ferreira, Luís A.F.M., Ilic, Marija D., Lauer, Michelle, Jaddivada, Rupamathi
Format: Article
Language:English
Subjects:
Ancillary services
Demand
Demand response
Dynamic systems
Dynamics
Flexibility
Fluid dynamics
Load shifting
Machine learning
Price regulations
Supply & demand
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Summary:•Demand flexibility exhibits important dynamics that cannot be modelled by price elasticity alone.•Demand response to price is non-linear and hysteretic, and hysteresis depends on load use density.•To understand hysteresis, aggregate response dynamic models are derived from particle-hopping models of load shifting.•Challenges in parameterizing dynamic models of aggregate response are discussed.•Machine learning inputs necessary to overcome predictability are presented. Demand flexibility and its responsiveness under price-based control is a major research field. Much attention has been paid to model demand elasticity as synonymous with demand flexibility. But demand flexibility comes mostly as load shifting, and load shifting dynamics have been neglected when modelling demand response. In this paper, we model load shifting dynamics to simulate aggregate responses and analyse their predictability under time-varying prices. Our experience with simulation is then used to discuss possible enhancements in machine learning capable of predicting aggregate load dynamics.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106744