The growing share of intermittent and partly predictable renewable energy
sources (RES) requires a more flexible operation of the power system.
Flexibility is a key to maximize the utilization of RES, while minimizing the
negative impact of their associated variability and uncertainty. An effective
way of increasing system flexibility is the integration of price-responsive
Price-responsiveness can be incorporated by means of a market entity managing the portfolio of e.g. energy producing units and participating in electricity markets utilizing the portfolio. This market entity is denoted as Aggregator, as this entity places lumped bids for the whole portfolio. As such the microgrid can be perceived as Virtual Power Plant (VPP).
Flexibility in operational means of a power system can be described as degrees of freedom. Given high shares of RES, we deal with systems that are to great extent driven by stochastic processes. We aim to drive the system within operational boundaries whilst optimizing its economical performance (operational costs, arbitrage, ...). Advanced control Strategies such as Model Predictive Control and a control architecture designed for the rejection of disturbances at various levels are employed for achieving this goal.
This approach involves among others forecasts, stochastic programming techniques, activation of the demand side and optimal bidding. Furthermore, good knowledge of the current state of the system and its boundaries is needed in order to maximize the available flexibility within the system.
The scope of the project may be in the areas of:
- Stochastic Programming for long-term optimal power grid operation (Real-Time Optimization Layer)
- Model Predictive Control (Dynamic Control Layer) with a focus on power system control
- Due to that this project naturally incorporates a broad range of aspects, other topics may be interesting as well.
- FER-UNIZG Zagreb and other uGrip project member organizations
 ERA-NET SmartGrids Plus initiative
In collaboration withFER-UNIZG Zagreb
Mathematical Modeling and Computing, Optimal Control, Stochastic Programming & Dynamic Program- ming, Applied and Engineering Mathematics