15h30 - 15h55
Load Scheduling for Residential Demand Response on Smart Grids
We address a load scheduling problem for residential demand response on smart grids. A mixed integer linear programming formulation is presented, in which the loads are classified in different categories to capture their individual operational characteristics. The proposed model schedules the operation time and power consumption of each load, and controls the use of energy storage, to minimize the total energy cost and to manage peaks in power consumption. Results show that our model is able to achieve electricity costs savings and to reduce peaks in the power consumption, by shifting the demand and by efficiently using a battery.
15h55 - 16h20
The role of the capacity profiles in demand response for smart buildings
The capacity profiles allow to trade off end-users preferences and grid requirements. First, we explore the estimation problem from the user perspective, ensuring the desired quality of service for thermal and activity-based loads, while providing peak shaving. Then, we move to a higher level and study the effect of the grid-established capacity profiles on the operation of a smart building with shared storage and solar panels. In this case we achieve not only peak shaving, but also an active participation in demand response while ensuring consumers satisfaction.
16h20 - 16h45
Aggregation models for the grid integration of distributed energy resources
Distributed energy resources could prove highly valuable to the electric grid. However, because of their numbers and heterogeneity, their integration presents significant challenges. We formulate the aggregation problem and show that, while fully centralized control quickly becomes intractable, full decentralization can threaten the grid’s stability. We then propose an aggregation method where resources are incentivized to align their preferences with the grid’s needs, while operational control remains local. The proposed approach leverages the problem’s structure through decomposition and constraint aggregation, naturally addressing resources’ heterogeneity. Finally, we study the convergence of the algorithm, its computational efficiency, and present numerical results.