PROJECT TITLE :
Demand Response Management in Smart Grids With Heterogeneous Consumer Preferences
Client demand profiles and fluctuating renewable power generation are 2 main sources of uncertainty in matching demand and offer. This paper proposes a model of the electricity market that captures the uncertainties on both the operator and user sides. The system operator (THUS) implements a temporal linear pricing strategy that depends on real-time demand and renewable generation within the considered amount combining real-time pricing with time-of-use pricing. The announced pricing strategy sets up a noncooperative game of incomplete data among the users with heterogeneous, but correlated consumption preferences. An explicit characterization of the optimal user behavior using the Bayesian Nash equilibrium resolution concept springs. This explicit characterization permits the THUS to derive pricing policies that influence demand to serve sensible objectives, such as minimizing peak-to-average ratio or attaining a desired rate of return. Numerical experiments show that the pricing policies yield close to optimal welfare values while improving these sensible objectives.
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