Contact: Dan-Cristian Tomozei
The electricity distribution grid is undergoing a transformation process. The increased renewable production leads to a decrease of the production’s predictability. “Smart” energy storage systems are viewed as a way to mitigate this stochasticity and steer the grid towards stable operation.
Model predictive control (MPC) is a very popular method for scheduling energy production and storage. A decision policy based on MPC considers that the future production is known and updates the decisions frequently as soon as new forecasts are made available. The goal of this project is to study an improved control policy that uses trajectorial stochastic forecasts.
The goal of the project is to write a simulator for comparing the various heuristics on traces of real data. In a first stage, we will consider small a data set that can be computed on a single machine. Next, you will be asked to develop a parallelized version that can run in real-time.
This semester project is aimed for one bachelor or master student. The project will be supervised by Nicolas Gast and Dan-Cristian Tomozei (LCA 2).
At the end of the project you will submit a final report that may lead to a publication in a conference, or journal.
- Knowledge of performance evaluation: statistical tests, confidence intervals, etc.
- Knowledge of a programming language (e.g. C/C++, Matlab)
- Basic knowledge of simulation
- Wind Power Forecasting
- A Control Theorist’s Perspective on Dynamic Competitive Equilibria in Electricity Markets, G. Wang, A. Kowli, M. Negrete-Pincetic, E. Shafieepoorfard, and S. Meyn
- Optimal Generation and Storage Scheduling in the Presence of Renewable Forecast Uncertainties, N. Gast, D.-C. Tomozei, J.-Y. Le Boudec
- Reducing Peak Electricity Demand in Building Climate Control using Real-Time Pricing and Model Predictive Control, F. Oldewurtel, A. Ulbig, A. Parisio, G. Andersson, M. Morari