Scope
Krasopoulos et al. explore Demand Response (DR) optimisation strategies to manage energy flexibility among residential consumers. Focus is given on how varying levels of incentives influence user participation in DR programs, taking into consideration uncertainties in users’ responses.
Summary
A probabilistic model is suggested to estimate the likelihood of user participation based on offered incentives, which is employed in an optimisation framework that either maximises total achievable flexibility within a fixed budget or minimises incentive expenditures under a flexibility constraint. The study includes multiple case analyses, demonstrating that targeting users based on specific responsiveness profiles can significantly enhance DR program efficiency. Practical aspects are also taken into account via a learning algorithm that identifies user parameters in practice and a scheme that estimates the confidence intervals of actual participation in the DR program.
Relevance for EXIGENCE
The ideas of such a probabilistic analysis can be employed in order for an aggregator to properly select the incentives’ budget for a particular slot, or apportion a given total budget per month among several slots. The considerations for the reselling of the flexibility in the market could influence the EXIGENCE scope and the learning algorithm could be employed to model the user reactions towards the provision of incentives.
T. Krasopoulos, T. G. Papaioannou and G. D. Stamoulis, “Flexibility Management for Residential Users Under Participation Uncertainty,” 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Singapore, Singapore, 2022, pp. 405-411.
