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Advanced integrated supervisory and wind turbine control for optimal operation of large Wind Power Plants

Fact Sheet

Reporting

Results

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

Denmark

Objective

Cost of energy (COE) is the most important single factor in deployment of renewables in the energy system. Reduction of COE is, among other things, directly related to operational control of Wind Power Plants (WPP) as a whole and the individual wind turbines (WT) within them. In the Total Control project the COE reduction will be pursued by developing and validating advanced integrated WPP/WT control schemes, where all essential interactions between the WPP WT’s are accounted for including both production and load aspects.
Optimal WPP control is traditionally formulated as a one-parameter optimization problem focusing on the WPP production only. However, ultimately the optimal WPP performance should result from a multi objective optimization problem, where the optimal economic performance of a WPP is pursued over the WPP life time, conditioned on external grid demands. This is what Total Control is about.
The suggested integrated WPP/WT control approach seeks the optimal economical WPP revenue – i.e. the optimal economic balance between WPP power production and WPP operational costs. This is done by developing hierarchically coupled WPP and WT control schemes conditioned on a set of superior grid operator demands.
In the WPP control design phase information is only fed from the WPP controller to the individual WT controllers, whereas in on-line operational control available WT and WPP flow field information will be assimilated into the WPP control for optimal system performance. Furthermore, the WPP controller will also make use of current market information (e.g. energy price, demand for ancillary services etc.) as well as information about the state of individual turbines (e.g. current operational state, maintenance requirements and component lifetime consumption) to allow COE objectives to be optimised dynamically.

Field of Science

/social sciences/economics and business/business and management/commerce

/social sciences/economics and business/economics/sustainable economy

/social sciences/sociology/governance/public services

Programme(s)

H2020-EU.3.3.2. - Low-cost, low-carbon energy supply

Topic(s)

LCE-07-2016-2017 - Developing the next generation technologies of renewable electricity and heating/cooling

Call for proposal

H2020-LCE-2016-RES-CCS-RIA

See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Coordinator

DANMARKS TEKNISKE UNIVERSITET

Address

Anker Engelundsvej 1 Bygning 101 A
2800 Kgs Lyngby

Denmark

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 1 637 500

Website

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Participants (7)

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KATHOLIEKE UNIVERSITEIT LEUVEN

Belgium

EU Contribution

€ 626 875

SINTEF ENERGI AS

Norway

EU Contribution

€ 1 068 156,25

GARRAD HASSAN & PARTNERS LTD

United Kingdom

EU Contribution

€ 599 250

VATTENFALL AB

Sweden

EU Contribution

€ 202 500

OFFSHORE RENEWABLE ENERGY CATAPULT

United Kingdom

EU Contribution

€ 567 201,25

SIEMENS GAMESA RENEWABLE ENERGY AS

Denmark

EU Contribution

€ 95 000

EQUINOR ASA

Norway

EU Contribution

€ 80 000

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

Denmark

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

Denmark

Periodic Reporting for period 1 - TotalControl (Advanced integrated supervisory and wind turbine control for optimal operation of large Wind Power Plants)

Reporting period: 2018-01-01 to 2019-04-30

Summary of the context and overall objectives of the project

Cost of Energy (COE) is the most important single factor in deployment of renewables in the energy system. Reduction of COE is, among other things, directly related to operational control of Wind Power Plant (WPP) as a whole and the individual wind turbines (WT) within them. In the TotalControl project, the COE reduction is being pursued by developing and validating advanced integrated WPP/WT control schemes, where all essential interactions between the WPP WTs are accounted for including both production and load aspects.
Optimal WPP control is traditionally formulated as a one-parameter optimization problem focusing on the WPP production only. However, ultimately the optimal WPP performance should result from a multi-objective optimization problem, where the optimal economic performance of a WPP is pursued over the WPP life time, conditioned on external grid demands. This is what TotalControl is all about.
The suggested integrated WPP/WT control approach seeks the optimal economical WPP revenue - i.e. the optimal economic balance between WPP power production and WPP operational costs. This is done by developing hierarchically coupled WPP and WT control schemes conditioned on a set of superior grid operator demands. In the WPP control design phase, information is only fed from the WPP controller to the individual WT controllers, whereas in online operational control available WT and WPP flow field information will be assimilated into the WPP control for optimal system performance. Furthermore, the WPP controller will also make use of current market information (e.g. energy price, demand for ancillary services etc.) as well as information about the state of individual turbines (e.g. current operational state, maintenance requirements and component lifetime comsumption) to allow COE objectives to be optimised dynamically.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The following describes the work performed in the work packages of TotalControl in the first reporting period.

WP1 - development and validation of wind power plant (WPP) design and control models
A modern-sized reference WPP has been designed to be used as a reference throughout the project in all possible simulation tools and associated high-fidelity CFD flow fields produced and stored. Besides specification of topology, the reference WPP also include an electro-mechanical model.
Totalcontrol uses a suite of flow models ranging high-fidelity CFD models over medium-fidelity models to simple and fast engineering models. Two of the medium fidelity models - the linear CFD RANS solver Fuga and the Dynamic Wake Meandering (DWM) model - have been updated to include non-neutral stratification and turbine yaw. Further, a simple and fast dynamic engineering WPP model has been developed and validated. Finally, machine learning approaches has been investigated as an alternative to first-principles modelling.
As for the full-scale validation part, 3 long-range lidars have been installed at the Lillgrund WPP - one providing information of the (undisturbed) inflow field, and the two others resolving the wake affected flow field inside the WPP. The recorded data will be used for validation of high- and low-fidelity models.

WP2 - open-loop quasi-static control of offshore wind farms
An open-loop wind farm control optimization platform has been developed which, using the linearized CFD flow solver Fuga, derive optimized wind farm control schedules conditioned on mean wind speed and mean wind direction. This platform uses wind farm power production as the objective function and optimize individual wind turbine de-rate settings formulated in terms of rotor rotational speed and collective pitch setting.
Moving towards quantification of (optimal) WPP control on OPEX, energy production and lifetime cost models and load surrogate models are needed. A cost model has been developed and to complemented with a surrogate model that quickly reproduce the energy capture and loads on the individual WPP turbines. The surrogate model are based on a huge number of aeroelastic simulations using the state-of-the-art aeroelastic code HAWC2. Once verified, the wind farm control set-points can be optimized to maximize energy production given life consumption targets on the wind turbine structures.

WP3 - development and full-scale implementation of new turbine controller features of relevance in the context of wind farm control
The 7MW Levenmouth WT (LWT) is used at the demonstration case. Detailed aeroelastic simulations are used for development of new WT controller functionalities needed for WPP control. For this purpose the LWT have been modelled in state-of-the-art aeroelastic platforms (i.e. HAWC2 and Bladed). After validation, a set of reference loads has been simulated, and controller design is ongoing. Related to ancillary active power control, operation of an inverter as a Virtual Synchronous Machine to contribute to grid frequency response has been analysed. With respect to load alleviation, a specification document for tower-top sensor requirements for Individual Pitch Control (IPC) has been completed, and lidar characteristics and controller interface have been designed to facilitate lidar-assisted WT control. Finally, a Model Predictive Control scheme has been developed.
On the experimental part, hardware needed for the lidar installation on the LWT has been designed and produced.

WP4 - to unify the results obtained in WP’s 1-3 into a suite of applicable WPP controllers
Work on grid modelling have been initiated. Regarding modelling work, implementation of the LongSim code has been completed, and the LongSim-Kermit interface has been specified. On the experimental side, the VSM scheme almost ready in the lab at SINTEF.

WP5 - communication and dissemination
The goal of the WP is to communicate and raise awareness of the project and the project

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

In TotalControl advanced WPP controllers that increase WPP profitability, and enlarge operational versatility is developed. This is achieved by moving WPP controller design philosophy from individual optimization of WT’s operation to a coordinated, cooperative optimization of the overall WPP performance. In the reporting period a platform for design of optimized WPP control schedules with power production as the objective function has been developed based on a consistent and very fast linear CFD RANS solver, and work with including load aspects, using surrogate models, initiated.
TotalControl aims at developing advanced WPP control optimization covering power production, fatigue loading, O&M as well as grid integration aspects. The multi-objective optimization of wind-farm energy extraction, WT loading, taking into account estimated O&M costs and timing, WPP grid losses, and electricity market prices will lead to an increase in WPP profits. Moreover, extending the WPP’s capability to provide ancillary services (primary, secondary and tertiary reserves) and actively participate in balancing, opens up new operational and contracting opportunities for WPP owners, which may significantly increase future revenues in electricity markets.

TotalControl project logo

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

Denmark

Deliverables

Other (6)

Simple dynamic wind farm model

Report describing dynamic model with testing and validation results. the deliverable is referring to task 1.3.3. Measure of success: Simulations run rapidly enough for iterative tuning of relevant control parameters; consistency of 10-minute statistics compared to SCADA data. Participants actions: DNV is solely responsible.

First project video

Each year a short video (24 min) will be produced presenting core aspects or achievements of the project. This is the first video. Thedeliverable is refering to task 5.1 and 5.2. Measure of success: 1 of 4 videos available to the public at the TotalControl website. Participants actions: DTU will work with professional videomakers to produce the video. Project partners will contribute to the content and take part in the videos as relevant.

Dissemination and communication plan and annual reports on the dissemination and communication activities, 1

The dissemination plan will be monitored and updated. The dissemination and communication activities conducted in M1-12 will be summarized in an annual report to record and assess the progress in the project promotion. The annual internal workshop held within the consortium within the first 12 month of the project will be reported. The deliverable is referring to task 5.1 and 5.3. Measure of success: a document with the dissemination plan and workshops summarizing the dissemination activities including workshops and training activities. Participant actions: DTU (with the support of all partners) will prepare a detail document with the dissemination plan and the annual report.

Upgrade of DWM

Upgrade of the DMW for non-neutral ABL and turbine yaw control, and report with testing and validation results. The deliverable is referring to task 1.2.5. Measure of success: Level of accuracy reached for the new conditions compared to the current DMW accuracy for neutral non-yawed conditions. Participants actions: DTU is solely responsible.

Electro-mechanical model of reference wind power plant

Completed models of the Reference WPP in STAS and PSCAD/EMTP-RV. The deliverable is referring to task 1.2.4. Measure of success: STAS: calculation of reference plant modal frequencies and damping ratios, PSCAD/EMTP-RV: Compute voltage and current waveforms at the PCC. Participants actions: SINTEF is solely responsible.

Flow database for reference wind farm

Collection of all flow simulation results for the reference WPP. The deliverable is referring to task 1.2.3. Measure of success: Complete data base covering detailed wind field and WPP operation parameters for different atmospheric conditions (e.g. stability classes) and transients, publically avialable. Participants actions: KUL, DTU, ORE will assemble the results data base. Hosted on the project website.

Documents, reports (4)

Reference Wind Power Plant

Document containing the technical specifications of the Reference WPP. The deliverable is referring to task 1.2.2. Measure of success: Complete Reference WPP description for the subsequent simulation and testing tasks. Participants actions: SINTEF main editor, contributions from all task participants.

Machine learning approaches to wind farm control

Report on the feasibility and applicability of machine learning approaches. the deliverable is referring to task 1.3.4. Measure of success: Detailed recommendations for applying the approach in practice. Participants actions: DNV is solely reesponsible.

Project master plan including full transparency of resources, schedule and cost/performance

The purpose is to give a brief description of the appropriate procedures, the templates and the reporting tools developed for TotalControl. The deliverable is referring to task 6.2. Measure of success: The report contains all relevant information. Participant actions: DTU will develop the procedures and the tools.

Cost model for fatigue degradation and O&M

Development of cost models quantifying the cost of O&M and fatigue degradation of mechanical and electrical components. The deliverable is referring to task 2.1.1, 2.1.2, and 2.2.3. Measure of Success: Operational models that can be used to account for load effects in the development of cost optimized control schemes. Participant actions: DVN GL is responsible for the analysis of correlation between fatigue loading and O&M), DTU is responsible for the cost model for mechanical components; and SINTEF is responsible for the cost model for electrical components.

Demonstrators, pilots, prototypes (1)

Setup of the website

Setup of the project website, including collaboration and communication tools among partners, repository for dissemination material, newsletters and social networks links. The deliverable is referring to task 5.2. Measure of success: a full operative website. Participant actions: DTU will design and maintain the website as well as newsletters and social media platforms to ensure that fully updated project information will be available online.

Publications

Peer reviewed articles (3)

Combining induction control and wake steering for wind farm energy and fatigue loads optimisation

Author(s): Ervin Bossanyi

Published in: Journal of Physics: Conference Series, Issue 1037, 2018, Page(s) 032011, ISSN 1742-6588

DOI: 10.1088/1742-6596/1037/3/032011

Improved modelling of fatigue loads in wind farms under non-neutral ABL stability conditions

Author(s): G.C. Larsen, S. Ott, T.J. Larsen, K.S. Hansen, A. Chougule

Published in: Journal of Physics: Conference Series, Issue 1037, 2018, Page(s) 072013, ISSN 1742-6588

DOI: 10.1088/1742-6596/1037/7/072013

Optimal dynamic induction and yaw control of wind farms: effects of turbine spacing and layout

Author(s): Wim Munters, Johan Meyers

Published in: Journal of Physics: Conference Series, Issue 1037, 2018, Page(s) 032015, ISSN 1742-6588

DOI: 10.1088/1742-6596/1037/3/032015

Other (3)

Virtual Synchronous Machine Control for Wind Turbines: A Revies

Author(s): L. Lu and N. A. Cutululis

Published in: 2019

A data-driven flow model for wind-farm control based on Koopman mode decomposition of large-eddy simulations

Author(s): Munters, W. and J. Meyers

Published in: 2018

Enhanced Frequency Control Capability from Wind Turbine Generators and Wind Power Plants

Author(s): Liang Lu

Published in: 2018

Conference proceedings (2)

TotalControl - Advanced integrated control of large-scale wind power plants and wind turbines

Author(s): Giebel, G; G. Larsen; A. Natarajan; J. Meyers; E. Bossanyi and K. Merz

Published in: WindEurope 2019 Conference Proceedings, 2019

TotalControl - Advanced integrated control of large-scale wind power plants and wind turbines

Author(s): Giebel G.; G. Larsen; A. Natarajan; J. Meyers; E. Bossanyi and K. Merz

Published in: WindEurope 2018 Conference Proceedings, 2018

Datasets

Datasets via OpenAire

Reference Windfarm database CNk4 90

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Precursor dataset PDkhi

Author(s): Munters, Wim,Meyers, Johan,Sood, Ishaan

Published in: Figshare

Precursor dataset PDk

Author(s): Munters, Wim,Meyers, Johan,Sood, Ishaan

Published in: Figshare

Reference Windfarm database CNk2 30

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Reference Windfarm database PDk 30

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Reference Windfarm database CNk8 90

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Precursor dataset CNk2

Author(s): Munters, Wim,Meyers, Johan,Sood, Ishaan

Published in: Zenodo

Precursor dataset CNk4

Author(s): Munters, Wim,Sood, Ishaan,Meyers, Johan

Published in: Zenodo

Reference Windfarm database PDkhi 0

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Reference Windfarm database PDk 0

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Reference Windfarm database CNk4 30

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Reference Windfarm database CNk2 60

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Reference Windfarm database CNk8 0

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Reference Windfarm database PDk 90

Author(s): Ishaan Sood,Johan Meyers

Published in: Zenodo

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

Denmark

Project information

TotalControl

Grant agreement ID: 727680

Project website

Status

Ongoing project

Start date

1 January 2018
End date

31 December 2021

Funded under:

H2020-EU.3.3.2.

Overall budget:

€ 4 876 482,50
EU contribution

€ 4 876 482,50

Coordinated by:

DANMARKS TEKNISKE UNIVERSITET

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Last update: 23 July 2019

Record number: 212578

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Periodic Reporting for period 1 - TotalControl (Advanced integrated supervisory and wind turbine control for optimal operation of large Wind Power Plants)

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