Mixed Multi-objective Optimization using Hybrid Algorithms: Application to smart grids
El-GHazali Talbi & Rachid Ellaia
Principal investigators
Rachid Ellaia – EMI, Rabat, Maroc
El-Ghazali Talbi, research team DOLPHIN, Inria
Abstract
This associate-team entered in LIRIMA in January 2016. With the smart grid revolution, house energy consumption will play a significant role in the energy system. Home users are indeed responsible for a significant portion of the world’s energy needs portion, but are totally inelastic with respect to the market (i.e. the energy demand does not follow the price of the energy itself). Thus, the whole energy generation and distribution system performance can be improved by optimizing the house energy management. Those problems are concerned by multiple objectives such as cost and users’ comfort, and multiple decision makers such as end-users and energy operators. We propose a home automation system that can monitor appliance scheduling in order to simultaneously optimize the total energy cost and the customer satisfaction.
The key challenge of this project is to propose new optimization models and new hybrid algorithms to the demand side management of smart grids in a context of uncertainty and in the presence of several conflicting objectives. Those complex optimization problems are also characterized by the presence of both continuous and discrete variables.
The challenge of the mixed multi-objective household energy management problem is to control electrical appliances based on a user’s operational information in order to minimize the electrical energy cost for the consumer and to maximize its satisfaction. The home automation system would be able to monitor the equipment in housing by determining the starting time of some services and also by controlling the temperature set point of heater and cooling systems. The system would also generate the optimal charging schedule of domestic batteries (including electric cars) by suggesting charging and discharging time decisions.
Moreover, the model will integrate some uncertainties in the data. Indeed, many input data of the model are uncertain in real-life; e.g. stochastic production and consumption caused for instance by the atmospheric conditions (wind, temperature …). Then, robust solutions, that are not sensitive to those uncertainties, must be found.
