Parameter Elicitation for Consumer Models in Demand Response Management

Abstract

Game theoretic models to demand response management typically assume that the electricity retailer has a perfect knowledge about the decision model that its consumers apply for scheduling their consumption, together with the exact parameter values. It is clearly impossible to satisfy this assumption in practice, which is a major barrier to the practical application of those approaches. At the same time, historic consumption data contains precious information about consumer behavior; in case of a variable, time-of-use electricity tariff, this also includes information about load flexibility at the consumer. This paper looks for ways to reconstruct the consumer's decision model from historic data accessible for the retailer. Assuming that consumer behavior can be captured by some formal mathematical model with a reasonable accuracy, we propose computational methods for eliciting parameter values for that consumer model by using inverse optimization and successive linear programming techniques. While the proposed approach is applicable to arbitrary consumer models that can be formulated as a linear programs, this paper investigates a special case with multiple types of controllable loads at the consumer, under a single smart metering device. Initial experimental results are presented and directions for future research are suggested.