Abstract
Utilities are employing various price-based demand response schemes to curtail the maximum demand on the system. For price-based demand response to succeed, better algorithms, dataset and critical studies analyzing the effect of different pricing schemes need to be done. This article models the load-scheduling problem from the consumers' perspective as a decision-making problem and proposes algorithms to learn the optimal schedule. This article provides a simulation framework with a generalized load pattern and a generalized tariff profile so that different tariffs and load profiles can be tested with different load-scheduling algorithms to design and evaluate different demand response policies. The framework is also generalized in terms of temporal granularity. Moreover, a parameter udc to account for user discomfort due to the delay in scheduling the load is also incorporated. The effect of this parameter on load scheduling is studied and guidelines to choose the same are presented. The performance of the proposed algorithms is tested using this framework. The effect of penetration (defined as the percentage of consumers participating in the demand response program) of scheduling algorithm on the maximum demand is also studied.