10:30 AM - 10:55 AM
Optimization of phase unbalance on a distribution network with demand response
Phase unbalance is a problem for electricity grids that exists because of the transformation from three-phase to mono-phase current that leads to losses and to faster deterioration of devices. As electricity grids change, demand response via control of consumption, becomes possible. We propose a method based on black-box optimization that uses demand response to minimize phase unbalance.
Keywords: Smart grids; Demand response; phase unbalance; black-box optimization
10:55 AM - 11:20 AM
Optimal energy management in a multi-unit residential building integrating microgrid and demand response
This paper presents a high-level centralized optimal power control management mechanism based on model predictive control for grid-connected building integrated microgrid. The thermal load of a large residential building has been modeled according to resistance-capacitance networks model. Besides by participating in demand response program, for thermostatically controlled loads and electric vehicles, operation cost can be reduced.
keywords : Model predictive control, energy management, energy modelling, electric vehicles, demand response, residential buildings
11:20 AM - 11:45 AM
A framework for peak shaving through the coordination of smart homes
In demand--response programs, aggregators balance the needs of generation companies and end-users. This work proposes a two-phase framework that shaves the aggregated peak loads while maintaining the desired comfort level for users. In the first phase, the users determine their planned consumption. For the second phase, we develop a bilevel model with mixed-integer variables and reformulate it as a single-level model. We propose an exact centralized algorithm and a decentralized heuristic. Our computational results show that the heuristic gives small optimality gaps and is much faster than the centralized approach.
mots-clés: demand response, aggregator, bilevel optimization
11:45 AM - 12:10 PM
Demand response field tests on smart homes using reinforcement learning
This presentation discusses the control strategy for heating smart homes without prior knowledge of their designs and characteristics (model agnostic). Using reinforcement learning, intelligent agents are trained with data coming from those homes, which are equipped with multiple thermostats controlling electric baseboards. Demand response field tests on smart homes are currently in progress.