A New Wind Farm Active Power Control Strategy to Boost Tracking Margins in High-demand Scenarios

Tamaro, Simone; Bottasso, Carlo L.

doi:10.23919/acc55779.2023.10156275

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…As HPP power tracking is of key importance to maximize performance, the implementation of WPP power tracking is based here on a margin-boosting wind farm active power controller (APC), which reduces the occurrence of saturation events of individual turbines [20]. The method combines wake control with induction control, where improved tracking performance is achieved by explicitly maximizing the power margin to hedge against wind lulls.…”

Section: Hybrid Power Plant: Controllermentioning

confidence: 99%

Profit-optimal data-driven operation of a hybrid power plant participating in energy markets

Anand,

Petzschmann,

Strecker

et al. 2024

J. Phys.: Conf. Ser.

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An energy management system (EMS) is formulated for a hybrid power plant (HPP), consisting of a wind power plant and battery storage plant, participating in bidding stages in the German energy market. The EMS utilizes supervisory control and data acquisition (SCADA) measurements from the site to improve power forecast from the wind power plant. First, the measurement data are used together with numerical weather prediction data to accurately forecast local wind conditions. Second, the measurement data are used to adapt a baseline engineering wake model that gives the total wind power generation for a given input wind condition. The EMS also uses an online cyclic damage minimization approach to accurately balance the battery damage cost against the revenue obtained by market bidding. An HPP controller is formulated to ensure proper tracking of optimal set-points. When compared with standard formulations, the proposed approach shows an accurate estimation and balancing of revenue and costs and a significant reduction in the power deviation penalty, which leads to significantly higher overall profit.

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Section: Hybrid Power Plant: Controllermentioning

confidence: 99%

Profit-optimal data-driven operation of a hybrid power plant participating in energy markets

Anand,

Petzschmann,

Strecker

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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“…In addition, grid stability is improved with the incorporation of a, especially in the face of disturbances [21], [22]. As the PIAT grid increasingly incorporates renewable energy sources such as wind and solar, frequency regulation becomes even more crucial.…”

Section: Introductionmentioning

confidence: 99%

Robust hybrid control strategy for active power management in Kabertene wind farm within Algeria’s PIAT grid

Abderrazak,

Iliace,

Hichem

et al. 2024

Bulletin EEI

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The paper introduces a hybrid control strategy for optimised active power management in Algeria's Kabertene wind farm, crucial for the pole insalah-adrar-timimoune (PIAT) grid's stability. This strategy merges simultaneous interconnection and damping assignment (SIDA) passivity theory, passivity-based control (PBC), and multivariable proportional-integral-derivative (PID) controllers. This combined approach ensures frequency and voltage stability within the PIAT grid, which encompasses various elements like wind farms, solar plants, gas turbines, and dynamic impedance (Z), current (I), and active power (P) (D-ZIP), load model. By tailoring controllers for doubly fed induction generators (DFIGs) using SIDA-PBC principles and optimising internal parameters, the strategy achieves precise control of active power output. Additionally, particle swarm optimisation (PSO) refines power scheduling, which is especially beneficial for intermittent renewable sources like DFIGs. This comprehensive strategy offers numerous advantages: improved network stability, minimized voltage deviations, reduced frequency fluctuations, and enhanced integration of renewable energy sources. The paper emphasises practical implementation considerations, providing valuable guidance for efficient Kabertene wind farm operation and integration. This research contributes significantly to fostering cleaner and more reliable energy systems, facilitating the PIAT grid's transition towards sustainable energy generation.

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Site-specific production forecast through data-driven and engineering models

Braunbehrens,

Strecker,

Anand

et al. 2024

J. Phys.: Conf. Ser.

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The present study investigates the predictive capabilities of two site-specific wind power forecasting models. The two approaches make use of a machine learning ensemble model that combines forecasts from several numerical weather predictions (NWP) to match local observations. In the first approach, the training data consists of historical site power production recordings. This yields a black-box model that includes the wind farm behaviour. The second approach is a combination of data-driven and explicit modelling. In the first step, the machine learning ensemble model forecasts the site wind conditions. Then, this input is fed to a site-specific engineering wake model. This hybrid ”grey-box” approach explicitly resolves some farm flow effects. The study shows that the performance of both methods differs over the wind speed range. The purely data-driven model performs better at medium wind speeds, which also occur more often. The hybrid method predictions agree better during periods of higher wind speeds. In the last part, the study showcases the capabilities of the forecasting methods when participating in the energy market. Additionally, the engineering wake model allows for power maximisation through wind farm control.

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A New Wind Farm Active Power Control Strategy to Boost Tracking Margins in High-demand Scenarios

Cited by 4 publications

References 28 publications

Profit-optimal data-driven operation of a hybrid power plant participating in energy markets

Profit-optimal data-driven operation of a hybrid power plant participating in energy markets

Robust hybrid control strategy for active power management in Kabertene wind farm within Algeria’s PIAT grid

Site-specific production forecast through data-driven and engineering models

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