M4SE Math for Smart Energy
Optimization of energy storage within house holds.
Centre for modeling and simulation in Strasbourg
Energy and Environment
New problems emerged with the rise of photovoltaic panels, wind turbines and the ability to own its energy production: Network disturbances, regulated consumption in function of spot prices and waste of non- consumed or non-selled energy.
Challenges and goals
Control energy storage via energy buffers (batteries) and rejection in the electrical network with respect to distributors consigns (eroadmap).
Minimizing energy costs, maximizing energy resale during free schedule.
Mathematical and computational methods
This project involves an optimization problem with constraints as wel as knowledges (predictive data):
- Minimize a cost function to fit the eroadmap in function of real energy production.
- Introduce constraints in the problem (Battery types, household production law imposed by governments).
- Add predictive data (weather prediction, house energy knowledges,...) in the model to handle free schedule.
Results and Benefits
A prototype algorithm has been proposed within a funding from AMIES (6 months) and a master internship.
Solving a blocking problem,it proposes a strategy to fit the eroadmap including predictive data.
The company implemented the patented algorithminits EMG controller for production.