Peak-to-Average Ratio Analysis of A Load Aggregator for Incentive-based Demand Response

In smart grid, demand response programs have proven to have significant potential in terms of managing distributed systems. Demand response is a strategy to flatten the load profile of the grid by motivating the users based on utility incentives or price signals. As a result, users are inclined to shift their consumption by adjusting their flexible loads to reduce the peak hours and thus the peak-to-average ratio of the grid load profile. The emergence of energy aggregators facilitates the management of financial interactions between the power market and customers. These new players extract the demand flexibility potentials from the grid by employing optimization techniques. This paper studies a multi-agent system that simulate a set of houses under an incentive-based demand response program. Residential agents are capable of performing a model predictive control and forecasting the outside temperature in order to control thermal loads. The peak to average ratio is used to develop the optimization problem of the residential agents and propose a cost function for the aggregator. The results of the simulations allow to analyze the agents response under an incentive-based demand response program and their impact over proposed cost function for the aggregator. The simulated framework enables aggregators to examine the effectiveness of optimization techniques that are aimed for actual implementation.