1,721,011 research outputs found
Modelling and optimal control of wave energy systems: A data-driven approach
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Set-membership identification of an inertial wave energy system
No full textWithin the process of designing effective control strategies able to maximise the energy absorbed by wave energy systems, one of the crucial tasks to be achieved is the development of representative control-oriented models. Usually, these models are developed following a physics-based approach based on first principles. However, with this approach, the simplifications needed to derive models compatible with real-time control applications inherently add a certain degree of uncertainty, which could result in suboptimal energy-maximising performances. For this reason, the applications of system identification techniques are becoming popular in the field of wave energy. The consequent modelling process is data-based, and, in this way, the uncertainty associated with the device model depends on the data fidelity and the assumed model structure. However, the quantification of uncertainty is not trivial, and is done most of the time a posteriori, i.e after the system model has been identified. Within this paper, we propose a data-based modelling procedure based on the concept of set-membership, which provides dynamical models consistent with a given level of uncertainty. To assess the capabilities of this technique, we detail its application to the problem of identifying the dynamics of an inertial wave energy converter from data obtained through system-identification-oriented tests in simulation. The identified model is then validated on a dataset generated by different simulations, to assess the capabilities of this methodology
Assessing long-term metocean data variability for optimal energy system planning via static robust optimization approach
No full textA Model-Free Control Strategy Based on Artificial Neural Networks for PeWEC
No full textThis paper introduces a model-free control strategy aimed at maximizing the power absorbed by a Pendulum Wave Energy Converter (PeWEC). This control strategy is based on the development of a metamodel and on the optimization of the control action through it. The metamodel is built only from the collected data by linking the applied control action with an artificial neural network, which in this case is represented by a damping coefficient, and the faced sea-state parameters with the average absorbed power experienced with that configuration. To manage properly the choice between actions aimed at developing a sufficiently precise metamodel (exploration) and at maximizing the absorbed power (exploitation) a greediness function has been designed and adopted. The developed strategy has been tested by simulating the working conditions of PeWEC for 4 weeks, adopting 14 different sea states, each one with significant height, energy period and probability of occurrence typical of the Mediterranean Sea. Finally, the influence on the learning process of the time length adopted for the applied control action has been analyzed
Dynamic analysis and performance assessment of the Inertial Sea Wave Energy Converter (ISWEC) device via harmonic balance
Full text linkGiven the particular energy-maximising performance objective, wave energy converter (WEC) systems are prone to exhibit highly nonlinear behaviour. We present, in this paper,a detailed dynamic analysis and control synthesis for the Inertial Sea Wave Energy Converter (ISWEC) system, deriving and considering a comprehensive associated nonlinear model. In particular, we adopt a harmonic balance (HB) method to achieve this objective, producing the so-called amplitude-frequency curves (AFC) for the corresponding ISWEC nonlinear model,derived via a Lagrangian approach. We demonstrate that the system can present a variety of different behaviours which are completely neglected by its linear model counterpart. Leveraging both the efficiency, and convenient representation of the HB method, we synthesise so-called‘passive’ (i.e. proportional) energy-maximising controllers using a variety of input conditions.We provide a comparison of the obtained control parameters with those arising from standard linear modelling, showing a consistent improvement in performance by effectively considering the relevant nonlinear ISWEC dynamics
Extending Wave in situ Measurements Through Gaussian Process Regression: An Experimental Campaign
No full textWave energy is recognized as one of the most promising sources of clean and abundant energy. Nevertheless, as of today
this technology is still not commercially viable due to a number of reasons, such as the harshness of the sea environment,
the expenses needed for the deployment and maintenance of devices in open ocean, and the lack of information regarding
wave parameters worldwide. Indeed, a proper characterization of the resource in a site is of quintessential importance for
assessing the productivity of the site and dimensioning the supporting system of a device. This work aims to address the
problem of the lack of data by resorting to spatial prediction techniques, using data gathered through an experimental
campaign conducted at the wave basin facility at the Ocean and Coastal Engineering Laboratory in Aalborg University.
During this campaign, two months of data from a real in situ measuring device were replicated in the basin. In the middle of
the basin, some concrete blocks were deployed underwater to replicate a sudden shift in the bathymetry, which should act as
a disturbance to the wave propagation and arise nonlinear phenomena. Nineteen wave gauges recorded the wave elevation
for the whole time. A scenario where only some of the measuring devices were working was replicated by considering only
the data from a subsample of wave gauges and inferring the parameters in the locations of the other devices from them,
through a Gaussian Process Regression (GPR) algorithm. The proposed algorithm was able to interpolate the parameters at
the other locations, at the expense of a relatively low error, indicating that this set up could be used to increase the spatial
coverage of the wave-measuring buoys deployed worldwide or to provide an estimate of the parameters at a buoy that is
not working, e.g., for maintenance operations
On the complementarity of wind, wave and solar energy in North Tyrrhenian Sea
Full text linkAchieving a fully renewable energy future can potentially benefit from the integration of offshore energy sources (whenever available), such as wave, wind and solar energy, into the current energy mix. These resources offer significant potential to enhance the reliability of renewable energy systems by complementing each across various temporal scales. The inclusion of offshore resources in the current energy portfolio may improve power output stability, reduce periods of energy generation shortfall, and lessen the dependency on energy storage solutions. This study employs innovative complementarity metrics—including total variation, variance, and standard deviation—to evaluate the interactions among wind, wave and solar resources in North Tyrrhenian Sea. Temporal complementarity analysis in the North Tyrrhenian Sea reveals significant differences when considering only two resources or multiple resources and when considering different resource pairings
Capacity Expansion and Unit Commitment with Reserve Requirements: A Comparative Analysis for an Italian Off-Grid Island
No full textAccurate modeling of operational aspects of fuelfired generators (FFGs) is critical for off-grid energy planning, yet traditional capacity expansion models often oversimplify unit commitment. This paper compares two approaches for representing generator commitment in Mixed-Integer Linear Programming (MILP): (i) a binary-variable formulation that offers detailed, per-unit fidelity; and (ii) a modular framework that aggregates identical units into integer variables. Both methods are applied to the off-grid island of Pantelleria, Italy, incorporating operational aspects such as standby costs and upward/downward reserve constraints, alongside capacity expansion. The results show that the modular approach reduces the computational time by up to two orders of magnitude, without compromising fidelity. This efficiency supports broader scenario exploration, contributing to more reliable energy planning models for offgrid systems
Input-Unknown Estimation for Arrays of Wave Energy Conversion Systems via LTI Synthesis
Full text linkThe incoming menace of global overheating and depletion of fossil fuels, highlight the need for alternative, renewable, energy sources. In this context, ocean wave energy has a massive potential to contribute towards global decarbonisation. In optimising wave energy converters (WEC) productivity, state-of-the-art, model-based optimal control techniques are fundamental to enhance energy absorption efficiency. However, the vast majority of these optimal approaches inherently require wave excitation force estimators. In particular, in array configurations, the interaction between WEC devices has to be taken into account to achieve a consistent excitation force estimation. In this paper, a linear time-invariant (LTI) estimation approach for a WEC farm is proposed. The technique proposed is based upon the so-called ‘simple and effective estimator’, recently presented in the WEC literature, which reformulates the wave excitation force estimation problem as a traditional tracking loop. The results show that the proposed approach provides accurate estimates of the exciting force for every device in the array, with almost no design effort, and mild computational requirements
Data-driven control of a Pendulum Wave Energy Converter: A Gaussian Process Regression approach
Full text linkThe energy coming from the motion of the waves of seas and oceans could be an important component in the solution of the energy problem related to the pursuit of alternatives to fossil fuels. However, wave energy is still technologically immature and it has not reached the economic feasibility required for economy of scale. One of the major technological challenges for the achievement of this goal is the development of control strategies capable of maximizing the extracted energy, adapting to the conditions of the seas and oceans that surround the Wave Energy Converter (WEC) devices. To perform this task, control systems often adopt explicitly control-oriented models, that are by nature affected by uncertainties. On the contrary, to address the problem a data-driven solution is proposed here. The presented strategy applies an optimization approach based on a Gaussian Process Regression (GPR) metamodel to learn the control strategy to be applied. In order to accelerate the learning process, we present a novel method that exploits in the initial phase a previous knowledge given by simulations with the system model and based on the co-kriging concept. To test this approach the Pendulum Wave Energy Converter has been adopted as a case study. To differentiate the previous knowledge and the real system behaviour, a simplified linear model is used to obtain the prior knowledge, while a complex nonlinear one acts as the environment in which simulate the behaviour of the real system. A month-long simulation is used to validate the effectiveness of the proposed strategy, showing the ability of adapting to a real system different from the simplified model on the basis only of data, and overcoming the model-based strategy in terms of performance
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