Multi-port coordination: Unlocking flexibility and hydrogen opportunities in green energy networks

\ua9 2024Seaports are responsible for consuming a large amount of energy and producing a sizeable amount of environmental emissions. However, optimal coordination and cooperation present an opportunity to transform this challenge into an opportunity by enabling flexibility in their generation and load units. This paper introduces a coordination framework for exploiting flexibility across multiple ports. The proposed method fosters cooperation between ports in achieving lower environmental emissions while leveraging flexibility to increase their revenue. This platform allows ports to participate in providing flexibility for the energy grid through the introduction of a green port-to-grid concept while optimising their cooperation. Furthermore, the proximity to offshore wind farms is considered an opportunity for the ports to investigate their role in harnessing green hydrogen. The proposed method explores the hydrogen storage capability of ports as an opportunity for increasing the techno-economic benefits, particularly through coupling them with offshore wind farms. Compared to existing literature, the proposed method enjoys a comprehensive logistics-electric model for the ports, a novel coordination framework for multi-port flexibility, and the potentials of hydrogen storage for the ports. These unique features position this paper a valuable reference for research and industry by demonstrating realistic cooperation among ports in the energy network. The simulation results confirm the effectiveness of the proposed port flexibility coordination from both environmental and economic perspectives

A stochastic multi-range robust approach for low carbon technology participation in electricity markets

\ua9 2024Ambitious emission reduction targets require fostering more low-carbon technologies (LCTs) in distribution networks. Projections for future energy use predict a significant implementation of these technologies in residential areas. Despite this, individually they cannot effectively participate in electricity markets. This study examines the potential participation of residential LCTs (RLCTs) in multiple electricity markets, including wholesale day-ahead, real-time, and local energy markets (LEM), through the aggregators. We propose a stochastic weighted multi-range robust model to provide a strategy for RLCT aggregators to function as both sellers and buyers in these markets, as price-makers in LEM and price-takers in wholesale markets. The proposed model accounts for the uncertainty associated with the effect of offers/bids on the market clearing price of LEM and the availability patterns of aggregated LCTs. Results of a case study using realistic data reveal that the proposed approach results in higher overall profits compared to both risk-neutral and risk-averse robust methods. Furthermore, the introduced model is resilient to forecast errors, as evidenced by a 12% decrease in profits with the proposed approach compared to a 26% decrease with a risk-neutral strategy when the forecast error was increased by 20%

Designing Tuneable Narrowband Bandpass Filter Utilizing Neural Network

Abstract: In this study we aim at adjusting the singleband and dualband bandpass filter designed in a ED02AH technology. The quality factor and center frequency of the filter will change by varactor diodes. Here, we use a neural network to acquire the proper biasing voltages of varactor diodes in order to obtain specific gain and quality factor

A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time

\ua9 2024Maritime transport is responsible for producing a considerable amount of environmental pollution due to the reliance of ports and ships on the carbon-based energy sources. With the increasing trend towards port electrification to reduce carbon emissions, the operation of ports will be increasingly relying on the electricity network. This interconnection creates multiple challenges due to the complexity of power flow in the port network, uncertainty of vessel arrival time and fluctuation of power generation of renewable energy sources. These uncertainties can lead to an overload in electricity networks and delays in cargo-handling activities, resulting in increased vessel handling times and environmental emissions. This paper presents a joint logistics-electric framework for optimal operation and power management of electrified ports, considering multiple uncertainties in the arrival time of vessels, network demand, and renewable power generation. An optimal power flow method is developed for a real-life port, with consideration for multiple port logistic assets such as cargo handling equipment, reefers, and renewable energy sources. The proposed model ensures feasible port operation for all uncertainty realisations defined by robust optimisation, while minimising operational costs. Simulation results demonstrate that the probability of a network constraint violation can be as high as 70% for an electrified major UK port if the uncertainty in the port operation is neglected, presenting an unacceptable risk of disruption to port activities. Furthermore, such uncertainty can cause 150% increase in emissions if the ships use their auxiliary engine instead of using shore power. The numerical study shows that such challenges can be handled by a 0.3% increase in the robustness in face of uncertainty, while the cost increase in the worst case does not exceed 4.7%. This shows the effectiveness of the proposed method enhancing robustness against uncertainty at the minimum cost

Agile in the Era of Digitalization : A Finnish Survey Study

This paper was a Candidate for the Best Full Paper.Peer reviewe

Theory and Validation of Magnetic Resonance Fluid Motion Estimation Using Intensity Flow Data

15 p.Background Motion tracking based on spatial-temporal radio-frequency signals from the pixel representation of magnetic resonance (MR) imaging of a non-stationary fluid is able to provide two dimensional vector field maps. This supports the underlying fundamentals of magnetic resonance fluid motion estimation and generates a new methodology for flow measurement that is based on registration of nuclear signals from moving hydrogen nuclei in fluid. However, there is a need to validate the computational aspect of the approach by using velocity flow field data that we will assume as the true reference information or ground truth. Methodology/Principal Findings In this study, we create flow vectors based on an ideal analytical vortex, and generate artificial signal-motion image data to verify our computational approach. The analytical and computed flow fields are compared to provide an error estimate of our methodology. The comparison shows that the fluid motion estimation approach using simulated MR data is accurate and robust enough for flow field mapping. To verify our methodology, we have tested the computational configuration on magnetic resonance images of cardiac blood and proved that the theory of magnetic resonance fluid motion estimation can be applicable practically. Conclusions/Significance The results of this work will allow us to progress further in the investigation of fluid motion prediction based on imaging modalities that do not require velocity encoding. This article describes a novel theory of motion estimation based on magnetic resonating blood, which may be directly applied to cardiac flow imaging.Kelvin Kian Loong Wong, Richard Malcolm Kelso, Stephen Grant Worthley, Prashanthan Sanders, Jagannath Mazumdar, Derek Abbot

Mixed Climatology, Non-synoptic Phenomena and Downburst Wind Loading of Structures

Modern wind engineering was born in 1961, when Davenport published a paper in which meteorology, micrometeorology, climatology, bluff-body aerodynamics and structural dynamics were embedded within a homogeneous framework of the wind loading of structures called today \u201cDavenport chain\u201d. Idealizing the wind with a synoptic extra-tropical cyclone, this model was so simple and elegant as to become a sort of axiom. Between 1976 and 1977 Gomes and Vickery separated thunderstorm from non-thunderstorm winds, determined their disjoint extreme distributions and derived a mixed model later extended to other Aeolian phenomena; this study, which represents a milestone in mixed climatology, proved the impossibility of labelling a heterogeneous range of events by the generic term \u201cwind\u201d. This paper provides an overview of this matter, with particular regard to the studies conducted at the University of Genova on thunderstorm downbursts