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Title: Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms Using Surrogate Modeling
Wave energy converters (WECs) are a promising candidate for meeting the increasing energy demands of today’s society. It is known that the sizing and power take-off (PTO) control of WEC devices have a major impact on their performance. In addition, to improve power generation, WECs must be optimally deployed within a farm. While such individual aspects have been investigated for various WECs, potential improvements may be attained by leveraging an integrated, system-level design approach that considers all of these aspects. However, the computational complexity of estimating the hydrodynamic interaction effects significantly increases for large numbers of WECs. In this article, we undertake this challenge by developing data-driven surrogate models using artificial neural networks and the principles of many-body expansion. The effectiveness of this approach is demonstrated by solving a concurrent plant (i.e., sizing), control (i.e., PTO parameters), and layout optimization of heaving cylinder WEC devices. WEC dynamics were modeled in the frequency domain, subject to probabilistic incident waves with farms of 3, 5, 7, and 10 WECs. The results indicate promising directions toward a practical framework for array design investigations with more tractable computational demands. more »« less
Gaebele, Daniel T; Magana, Mario E(
, Proceedings of the European Wave and Tidal Energy Conference)
null
(Ed.)
—Ocean wave energy is a renewable energy which remains costly for large-scale electricity generation. Although the oscillating water column (OWC) wave energy converter (WEC) is a promising device type with a rectifying air turbine and generator which convert alternating airflow induced by the water motion into kinetic energy then to electric energy, there are still several challenges to overcome to achieve commercial energy production. A first step is deploying multiple devices close to each other in WEC parks, to save cost associated with mooring lines and power transmission cables and a second step is applying control at each stage of energy conversion to increase the electric energy output of the devices and ensure a safe operation. Herein, we first present a state-space model of a park of seven hydrodynamically interacting floating OWC WECs in all degrees of freedom with nonlinear PTO dynamics and a shared, quasi-static mooring model. The electric power flow is modeled by considering the conversion losses from the AC generators over a DC link, including a storage unit to the grid connection. Secondly, the OWC WEC park is expressed from a higher hierarchical level as an automaton driven by discrete events. Finally, we use a standard supervisory control approach to enable different local control schemes to ensure a save operation of the individual WEC and the park. The supervisor has good adaptability potential for different WECs and the incorporation of safety mechanisms.
Shabara, Mohamed; Zou, Shangyan; Abdelkhalik, Ossama(
, The 40th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2021)
null
(Ed.)
A novel Variable-Shape Buoy Wave Energy Converter (VSB WEC) that aims at eliminating the requirement of reactive power is analyzed in this paper. Unlike conventional Fixed Shape Buoy Wave Energy Converters (FSB WECs), the VSB WEC allows continuous shape-changing (flexible) responses to ocean waves. The non-linear interaction between the device and waves is demonstrated to result in more power when using simple, low-cost damping control system.
High fidelity numerical simulations are conducted to compare the performance of a VSB WEC to a conventional FSB WEC, of the same volume and mass, in terms of power conversion, maximum displacements, and velocities. A Computational Fluid Dynamics (CFD) based Numerical Wave Tank (CNWT), developed using ANSYS 2-way fluid-structure interaction (FSI) is used for simulations. The results show that the average power conversion is significantly increased when using the VSB WEC.
Abstract—This paper presents a control co-design method for
designing the mechanical power takeoff (PTO) system of a dual-
flap oscillating surge wave energy converter. Unlike most existing
work’s simplified representation of harvested power, this paper
derives a more realistic electrical power representation based
on a concise PTO modelling. This electrical power is used as
the objective for PTO design optimization with energy maxi-
mization control also taken into consideration to enable a more
comprehensive design evaluation. A simple PI control structure
speeds up the simultaneous co-optimization of control and PTO
parameters, and an equivalent circuit model of the WEC not
only streamlines power representation but also facilitates more
insightful evaluation of the optimization results. The optimized
PTO shows a large improvement in terms of power potential and
actual power performance. It’s found the generator’s
Capper, Joseph; Mi, Jia; Li, Qiaofeng; Zuo, Lei(
, 2021 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)
Abstract
Easily portable, small-sized ocean wave energy converters (WECs) may be used in many situations where large-sized WEC devices are not necessary or practical. Power maximization for small-sized WECs amplifies challenges that are not as difficult with large-sized devices, especially tuning the device’s natural frequency to match the wave frequency and achieve resonance. In this study, power maximization is performed for a small-sized, two-body attenuator WEC with a footprint constraint of about 1m. A thin, submerged tuning plate is added to each body to increase added mass without significantly increasing hydrostatic stiffness in order to reach resonance. Three different body cross-section geometries are analyzed. Device power absorption is determined through time domain simulations using WEC-Sim with a simplified two-degree-of-freedom (2DOF) model and a more realistic three-degree-of-freedom (3DOF) model. Different drag coefficients are used for each geometry to explore the effect of drag. A mooring stiffness study is performed with the 3DOF model to investigate the mooring impact. Based on the 2DOF and 3DOF power results, there is not a significant difference in power between the shapes if the same drag coefficient is used, but the elliptical shape has the highest power after assigning a different approximate drag coefficient to each shape. The mooring stiffness study shows that mooring stiffness can be increased in order to increase relative motion between the two bodies and consequently increase the power.
Green wireless networks Wake-up radio
Energy harvesting Routing
Markov decision process Reinforcement learning
1. Introduction
With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite.
Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailing radio technology, especially, consumes relevant amount of energy even when in idle state, i.e., even when no transmissions or receptions occur. Even duty cycling, namely, operating with the radio in low energy consumption
∗ Corresponding author.
E-mail address: koutsandria@di.uniroma1.it (G. Koutsandria).
https://doi.org/10.1016/j.comcom.2020.05.046
(sleep) mode for pre-set amounts of time, has been shown to only mildly alleviate the problem of making IoT devices durable [4]. An effective answer to eliminate all possible forms of energy consumption that are not directly related to communication (e.g., idle listening) is provided by ultra low power radio triggering techniques, also known as wake-up radios [5,6]. Wake-up radio-based networks allow devices to remain in sleep mode by turning off their main radio when no communication is taking place. Devices continuously listen for a trigger on their wake-up radio, namely, for a wake-up sequence, to activate their main radio and participate to communication tasks. Therefore, devices wake up and turn their main radio on only when data communication is requested by a neighboring device. Further energy savings can be obtained by restricting the number of neighboring devices that wake up when triggered. This is obtained by allowing devices to wake up only when they receive specific wake-up sequences, which correspond to particular protocol requirements, including distance from the destina- tion, current energy status, residual energy, etc. This form of selective awakenings is called semantic addressing [7]. Use of low-power wake-up radio with semantic addressing has been shown to remarkably reduce the dominating energy costs of communication and idle listening of traditional radio networking [7–12].
This paper contributes to the research on enabling green wireless networks for long lasting IoT applications. Specifically, we introduce a
ABSTRACT
This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively.
Azad, Saeed, and Herber, Daniel R. Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms Using Surrogate Modeling. Retrieved from https://par.nsf.gov/biblio/10479668. ASME 2023 International Design Engineering Technical Conferences . Web. doi:10.1115/DETC2023-116896.
Azad, Saeed, & Herber, Daniel R. Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms Using Surrogate Modeling. ASME 2023 International Design Engineering Technical Conferences, (). Retrieved from https://par.nsf.gov/biblio/10479668. https://doi.org/10.1115/DETC2023-116896
Azad, Saeed, and Herber, Daniel R.
"Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms Using Surrogate Modeling". ASME 2023 International Design Engineering Technical Conferences (). Country unknown/Code not available: American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2023-116896.https://par.nsf.gov/biblio/10479668.
@article{osti_10479668,
place = {Country unknown/Code not available},
title = {Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms Using Surrogate Modeling},
url = {https://par.nsf.gov/biblio/10479668},
DOI = {10.1115/DETC2023-116896},
abstractNote = {Wave energy converters (WECs) are a promising candidate for meeting the increasing energy demands of today’s society. It is known that the sizing and power take-off (PTO) control of WEC devices have a major impact on their performance. In addition, to improve power generation, WECs must be optimally deployed within a farm. While such individual aspects have been investigated for various WECs, potential improvements may be attained by leveraging an integrated, system-level design approach that considers all of these aspects. However, the computational complexity of estimating the hydrodynamic interaction effects significantly increases for large numbers of WECs. In this article, we undertake this challenge by developing data-driven surrogate models using artificial neural networks and the principles of many-body expansion. The effectiveness of this approach is demonstrated by solving a concurrent plant (i.e., sizing), control (i.e., PTO parameters), and layout optimization of heaving cylinder WEC devices. WEC dynamics were modeled in the frequency domain, subject to probabilistic incident waves with farms of 3, 5, 7, and 10 WECs. The results indicate promising directions toward a practical framework for array design investigations with more tractable computational demands.},
journal = {ASME 2023 International Design Engineering Technical Conferences},
publisher = {American Society of Mechanical Engineers},
author = {Azad, Saeed and Herber, Daniel R.},
}
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