BACKGROUNDA key socio-economic challenge for Europe is: how to deal with a climate change, while meeting increasing demand for energy? Wind energy is part of the answer and the new frontier of the wind industry is large-scale offshore wind farms.

To address objectives related to cost, and mechanical loads, the research focus on:

TECHNICAL APPROACHThe basis for increased energy performance of large-scale wind farms is knowledge of the wind field. To this end we investigate models that describe the flow within the farm and the associated expected electrical power output and mechanical loads.

Model predictive and reconfigurable paradigms are devel-oped that combine local controllers at each turbine with centralized supervision on a slower time scale. As a more flexible approach, decentralized strategies are developed.

To support development, validation and dissemination of the proposed research a simulated benchmark is created and the results are also physically validated on a scaled wind farm available at a partner site (ECN).

Partners: Aalborg University, DenmarkIndustrial Systems and Control Ltd., UKLund University, SwedenUniversity of Zagreb, CroatiaEnergy Research Centre of the Netherlands, The NetherlandsVestas Wind Systems A/S, Denmark

Duration: 36 monthsStart: 2008.05.01Total Cost: € 3.4 MillionEC Contribution: €2.5 Million

Contract Number: INFSO-ICT-224548

Contact: Thomas Bak Aalborg University tba@es.aau.dk

aeolus ict-aeolus.euDistributed Control of Large ScaleOffshore Wind Farms

EXPECTED IMPACTComplexity. Aeolus will support a paradigm shift from sin-

gle turbine control to farm level control, by investigat-ing a centralized and a decentralized control principle.

New markets. Aeolus help develop the European leader-ship in the new market for large scale wind farms.

Efficiency and flexibility. Aeolus provides optimal solu-tions to maximize power production while minimizing structural loads. Flexibility is achieved by a distributed control paradigm.

Low-cost monitoring. Aeolus will provide new knowl-edge on how to generally predict dynamic flows based on monitoring of the speed and direction of the flow through a network of distributed sensors.

KEY ISSUESThe key issues addressed in Aeolus are:Models that allow predictions of the flow at all turbine lo-

cations and support online flow measurements. Sep-erated into quasi-static and dynamic models.

Control principles that incorporate knowledge of the wind flow variations, provide robustness by address-ing reconfiguration and at the same time minimize the (extreme and fatigue) loads experienced by tur-bines.

Principles for decentralized control of the wind power and fatigue load relations. The basic approach for de-centralization is to split the global design objective for the wind farm into separate utility functions for each turbine, then let the turbines cooperate by buying and selling support from each other in an on-line vir-tual market system.

Benchmark. Models and simulation software to support coherence between the activities in Aeolus (both at the methodological and at the validation level), their combined application on a case study, their realization as components for farm level and turbine level con-trol.

Validation. Experimental validation of the results.

Aeolus focus on gener-ating new knowledge on how to dynamical-ly model flows based on distributed sensors and use this informa-tion as the basis for control of the flow re-source.

- Models that allow real-time predictions of flows and incorporate data from a network of sensors.

- Control paradigms that dynamically manages the flow resource in order to optimise specific control objectives.

- Foundations for a systematic approach to wind farm control

reference: total farm power

control object =

wind farm

model based control =

farm control

System Controller

power & speed references disturbances

to wind field

power produced fatigue measures

Paradigm shift from single turbine control to wind farm control (ECN).

Turbine mechanical loads (EWEA).