Wind Power with Energy Storage Arbitrage in Day-ahead Market by a Stochastic MILP Approach

Gomes, Isaías L. R.; Melício, Rui; Mendes, V.M.F.; Pousinho, Hugo Miguel Inácio

doi:10.1093/jigpal/jzz054

Cited by 22 publications

(6 citation statements)

References 32 publications

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“…They work with a 24-h time horizon, in a day ahead electricity market. In a similar way, the stochastic nature of the environment is taken into account in [23], where an information management system for a wind power producer is proposed. In this case, it also has an energy storage system and participates in a day-ahead electricity market.…”

Section: Related Workmentioning

confidence: 99%

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Sierra-García

Santos

2021

IEEE Access

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Data about wind are usually available from different databases, for different locations. In general, this information is the average of the wind speed over time. The wind reports are crucial for designing wind turbine controllers. But when working with floating offshore wind turbines (FOWT), two problems arise regarding the wind measurement. On the one hand, there are no buoys at deep sea, but near the coast where the wind is not so strong neither so stable; so the measurements do not fully correspond to reality. On the other hand, these floating devices are subjected to extreme environmental conditions (waves, currents, . . .) that produce disturbances and thus may distort wind measurements. To address this problem, this work presents a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system is composed of a proportional-integral-derivative (PID) controller, a lookup table, a neuro-estimator, and a virtual sensor. The neuro-estimator is used to estimate the effective wind in the FOWT and to forecast its future value. Both current and future wind signals are combined and power the controller. The virtual sensor also provides a measure of the effective wind based on other available signals related to the wind turbine, such as the pitch angle and the angular velocity of the generator. Neural networks are trained online to adapt to changes in the environment. Intensive simulations are carried out to validate the effectiveness of this neuro control approach. Controller performance is compared to a PID, obtaining better results. Indeed, an improvement of 16% for sinusoidal wind and an average improvement of 8% are observed.INDEX TERMS Floating offshore wind turbines (FOWTs), pitch control, neuro-estimator, neural network, virtual sensor, wind energy.

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Section: Related Workmentioning

confidence: 99%

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Sierra-García

Santos

2021

IEEE Access

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“…The world is now seeing a dramatic increase in energy consumption, which has led researchers to search for new sources of clean energy that leave no carbon footprint [1][2][3]. Wind power stands out among them as a viable and effective substitute.…”

Section: Introductionmentioning

confidence: 99%

Identification of Vibration Modes in Floating Offshore Wind Turbines

Serrano-Antoñanazas,

Sierra-Garcia,

Santos

et al. 2023

JMSE

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Compared to onshore turbines, floating offshore wind turbines (FOWTs) take advantage of the increased availability of offshore wind while causing less environmental impact. However, the strong winds, waves, and currents to which they are subjected trigger oscillations that can cause significant damage to the entire structural system and reduce its useful life. To reduce these loads, active tower damping techniques such as filter banks can be used. These filters must be carefully tuned to block specific vibration frequencies. Therefore, it is essential to analyze the nature of the oscillations in the FOWT and to understand how the frequencies vary in time. This topic is usually approached from a point of view very focused on a specific turbine. What is proposed here is a general method, which can be applied to any type of wind turbine, to automatically study the relationship between vibration frequencies and the degrees of freedom (DOF) of the turbine, which facilitates the design of structural control. Each frequency is associated with the DOF of the FOWT that produces it. This methodology has been successfully validated in simulation experiments with the NREL 5 MW ITI Barge FOWT. Under the wind conditions of the experiments, the main frequency found is 0.605 Hz. This frequency coincides with the 3P theoretical frequency of the FOWT. This proposal may help to design structural control systems able to damp these vibration frequencies with accuracy and efficiency.

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“…This concept, which is really general, can be applied over a wide range of fields, such as medicine diagnosis, fraud detection, cybersecurity and, of course, industry (Huang et al, 2012; Kou et al, 2004). This last field is the focus of this research, since the source of the abnormal situation can be diverse, from fault detection in actuators and instruments, to structural damage detection or wrong network performance (Gomes et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A hybrid one‐class approach for detecting anomalies in industrial systems

et al. 2022

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The significant advance of Internet of Things in industrial environments has provided the possibility of monitoring the different variables that come into play in an industrial process. This circumstance allows the supervision of the current state of an industrial plant and the consequent decision making possibilities. Then, the use of anomaly detection techniques are presented as a powerful tool to determine unexpected situations. The present research is based on the implementation of one‐class classifiers to detect anomalies in two industrial systems. The proposal is validated using two real datasets registered during different operating points of two industrial plants. To ensure a better performance, a clustering process is developed prior the classifier implementation. Then, local classifiers are trained over each cluster, leading to successful results when they are tested with both real and artificial anomalies. Validation results present in all cases, AUC values above 90%.

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Wind Power with Energy Storage Arbitrage in Day-ahead Market by a Stochastic MILP Approach

Cited by 22 publications

References 32 publications

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Identification of Vibration Modes in Floating Offshore Wind Turbines

A hybrid one‐class approach for detecting anomalies in industrial systems

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