Improving energy system planning solutions by accounting for inherent uncertainties through robust optimization

More and more distributed energy resources (smart loads, self-generation, electric vehicles, etc.) are installed directly at the customers.  This causes fluctuations in the distribution network that can reverse the power flow or increase the cold pick-up effect. The infrastructures in place have not been designed for this new reality and they must be adapted accordingly, and ideally, at minimum costs.  Herein, we will develop a new methodology to optimize such networks in presence of local renewable energy producers (new control device location, generation type limitations, nominal power, battery storage eventually). To accomplish this, we will extend the mesh-adaptive direct search algorithm (MADS) to perform robust optimization on high-dimensional and time-consuming models with inherent uncertainties and apply it to the present problem.

Intern: 
Miguel Diago Martinez
Faculty Supervisor: 
Sébastien Le Digabel
Project Year: 
2019
Province: 
Quebec
Sector: 
Program: