Projekt

Optimized power grid operation with high shares of Renewable Energies

Udbyder

Vejleder

Sted

København og omegn

"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

I samarbejde med

FER-UNIZG Zagreb

Forudsætninger

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

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Kontakt

Virksomhed/organisation

DTU Compute

Navn

Frederik Banis

Stilling

Ph.d.-studerende

Mail

freba@dtu.dk

Vejleder-info

Kandidatuddannelsen i Byggeteknologi

Vejleder

Frederik Banis

Medvejledere

Niels Kjølstad Poulsen

ECTS-point

25 - 30

Type

Kandidatspeciale

Skal have taget

Stochastic Adaptive Control, Model Predictive Control

Kandidatuddannelsen i Informationsteknologi

Vejleder

Frederik Banis

Medvejledere

Niels Kjølstad Poulsen

ECTS-point

25 - 30

Type

Kandidatspeciale

Skal have taget

Stochastic Adaptive Control, Model Predictive Control

Kandidatuddannelsen i Elektroteknologi

Vejleder

Frederik Banis

Medvejledere

Niels Kjølstad Poulsen

ECTS-point

25 - 30

Type

Kandidatspeciale

Skal have taget

Stochastic Adaptive Control, Model Predictive Control

Kandidatuddannelsen i Matematisk Modellering og Computing

Vejleder

Frederik Banis

Medvejledere

Niels Kjølstad Poulsen

ECTS-point

25 - 30

Type

Kandidatspeciale

Skal have taget

Stochastic Adaptive Control, Model Predictive Control

Kandidatuddannelsen i Bæredygtig Energi

Vejleder

Frederik Banis

Medvejledere

Niels Kjølstad Poulsen

ECTS-point

25 - 30

Type

Kandidatspeciale

Skal have taget

Stochastic Adaptive Control, Model Predictive Control

Kandidatuddannelsen i Vindenergi

Vejleder

Frederik Banis

Medvejledere

Niels Kjølstad Poulsen

ECTS-point

25 - 30

Type

Kandidatspeciale

Skal have taget

Stochastic Adaptive Control, Model Predictive Control

OM DTU

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Anker Engelunds Vej 1
Bygning 101A
2800 Kgs. Lyngby


45 25 25 25

dtu@dtu.dk

CVR-nr. 30 06 09 46

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