Energy management for a grid-connected wave energy park through a hybrid energy storage system

Parwal, Arvind; Fregelius, Martin; Temiz, Irina; Göteman, Malin; Oliveira, Janaína Gonçalves de; Boström, Cecilia; Leijon, Mats

doi:10.1016/j.apenergy.2018.09.146

Cited by 77 publications

(33 citation statements)

References 54 publications

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“…Blavette et al (2016) estimated the flicker level as a function of grid impedance angle. Hydrodynamics and electro-mechanic aspects were included in the works by Tedeschi and Santos-Mugica (2013) and Parwal et al (2018). A 20-MW park at the Bimet facility was chosen as a test case by Tedeschi and Santos-Mugica (2013), and a control strategy and simplified shortterm energy storage system were proposed to reduce the power variability.…”

Section: Delivering Grid-compatible Electricitymentioning

confidence: 99%

“…A 20-MW park at the Bimet facility was chosen as a test case by Tedeschi and Santos-Mugica (2013), and a control strategy and simplified shortterm energy storage system were proposed to reduce the power variability. Parwal et al (2018) proposed an energy management system in terms of a combined battery and supercapacitor to minimize the power fluctuations from a wave energy park to the grid. Other dedicated power electronics and filtering equipment can also be used to reduce flicker and obtain acceptably smooth electricity output, but these methods fall outside the scope of this paper.…”

Section: Delivering Grid-compatible Electricitymentioning

confidence: 99%

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Advances and Challenges in Wave Energy Park Optimization—A Review

et al. 2020

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A commercial wave energy system will typically consist of many interacting wave energy converters installed in a park. The performance of the park depends on many parameters such as array layout and number of devices, and may be evaluated based on different measures such as energy absorption, electricity quality, or cost of the produced electricity. As wave energy is currently at the stage where several large-scale installations are being planned, optimizing the park performance is an active research area, with many important contributions in the past few years. Here, this research is reviewed, with a focus on identifying the current state of the art, analyzing how realistic, reliable, and relevant the methods and the results are, and outlining directions for future research.

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Section: Delivering Grid-compatible Electricitymentioning

confidence: 99%

Section: Delivering Grid-compatible Electricitymentioning

confidence: 99%

Advances and Challenges in Wave Energy Park Optimization—A Review

et al. 2020

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“…As discussed before, the output power characteristic of a fuel cell makes it difficult to meet this prompt high-power demand. To solve this problem, a frequency-decoupling method that can separate low-frequency components from demanded power by filtering high-frequency components is essential for the efficient operations of a hybrid-source-powered FCEV [42][43][44]. The low-frequency components of the demanded power are fulfilled by the fuel cell, while high-frequency power requirements are satisfied by the battery.…”

Section: Hull-moving-average-based Frequency Decouplingmentioning

confidence: 99%

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Yao

et al. 2019

Energies

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In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles' hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle's running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV's hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

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“…Specifically, DG brings the power of its utilization; this likewise incorporates low warmth misfortunes and maintaining a strategic distance from points of confinement forced by a packed transmitter network . Besides, the expanding inclination towards the high entrance of RES originates from their condition neighborly and cost upper hands over ordinary generation …”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] Besides, the expanding inclination towards the high entrance of RES originates from their condition neighborly and cost upper hands over ordinary generation. [4][5][6] MG works on grid coupled or island mode, organizing systems with private or business clients in rustic or urban territories. The regular designs of the MG system appeared in Figure 1 and seen additionally in previous works.…”

Section: Introductionmentioning

confidence: 99%

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

et al. 2019

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This paper proposes an efficient hybrid approach-based energy management strategy (EMS) for grid-connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasioppositional-chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS-RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid-connected MG system is continuously tracked by the RF technique.The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two-strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques. KEYWORDS energy management strategy, grid-connected MG, power flow constraints, random forest, quasioppositional-chaotic symbiotic organisms search algorithm How to cite this article: Chen J, Zhou Z, Karunakaran V, Zhao S. An efficient technique-based distributed energy management for hybrid MG system: A hybrid QOCSOS-RF technique. Wind Energy. 2020;23:575-592. https://doi.

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Energy management for a grid-connected wave energy park through a hybrid energy storage system

Cited by 77 publications

References 54 publications

Advances and Challenges in Wave Energy Park Optimization—A Review

Advances and Challenges in Wave Energy Park Optimization—A Review

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

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