Project

Optimized power grid operation with high shares of Renewable Energies

Publisher

Supervisor

Location

Greater Copenhagen area

"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
microgrids"[1]

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.

Collaborations:
- FER-UNIZG Zagreb and other uGrip[2] project member organizations

[1] ERA-NET SmartGrids Plus initiative
[2] http://www.ugrip.eu/uGRIPProject.html

In collaboration with

FER-UNIZG Zagreb

Requirements

Mathematical Modeling and Computing, Optimal Control, Stochastic Programming & Dynamic Program- ming, Applied and Engineering Mathematics

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Contact

Company / Organization

DTU Compute

Name

Frederik Banis

Position

Ph.d.-studerende

Mail

freba@dtu.dk

Supervisor info

MSc in Civil Engineering

Supervisor

Frederik Banis

Co-supervisors

Niels Kjølstad Poulsen

ECTS credits

25 - 30

Type

MSc thesis

Must be completed

Stochastic Adaptive Control, Model Predictive Control

MSc in Computer Science and Engineering

Supervisor

Frederik Banis

Co-supervisors

Niels Kjølstad Poulsen

ECTS credits

25 - 30

Type

MSc thesis

Must be completed

Stochastic Adaptive Control, Model Predictive Control

MSc in Electrical Engineering

Supervisor

Frederik Banis

Co-supervisors

Niels Kjølstad Poulsen

ECTS credits

25 - 30

Type

MSc thesis

Must be completed

Stochastic Adaptive Control, Model Predictive Control

MSc in Mathematical Modelling and Computation

Supervisor

Frederik Banis

Co-supervisors

Niels Kjølstad Poulsen

ECTS credits

25 - 30

Type

MSc thesis

Must be completed

Stochastic Adaptive Control, Model Predictive Control

MSc in Sustainable Energy

Supervisor

Frederik Banis

Co-supervisors

Niels Kjølstad Poulsen

ECTS credits

25 - 30

Type

MSc thesis

Must be completed

Stochastic Adaptive Control, Model Predictive Control

MSc in Wind Energy

Supervisor

Frederik Banis

Co-supervisors

Niels Kjølstad Poulsen

ECTS credits

25 - 30

Type

MSc thesis

Must be completed

Stochastic Adaptive Control, Model Predictive Control

Technical University of Denmark

For almost two centuries DTU, Technical University of Denmark, has been dedicated to fulfilling the vision of H.C. Ørsted – the father of electromagnetism – who founded the university in 1829 to develop and create value using the natural sciences and the technical sciences to benefit society.


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