This paper centers local processes for co-creating transitions towards more sustainable, inclusive, and resilient coastal community development. We have conceptualized a path for change processes with phases of transition including 1. Reasons to convene; 2. Governance and participation "rules"; 3. Building knowledge together; 4. Implementation and experimentation; 5. Post-hoc reflections and assessment; 6. Transfer/reproduction of practices. Here, we focus on the first three stepping stones, which form the foundation of the collaborative process, focusing on the challenges and opportunities encountered as a pilot intervention is planned. We use a framework informed by partnership-, co-creation-, transition-, and justice literatures, to analyze data focused on establishing partnerships for ongoing co-creation of knowledge, empowering actors in the local communities, and selecting options for an intervention pilot. Conclusions relate to (a) trust and preexisting relationships, (b) what inclusion means, (c) internal power differentials, (d) preexisting tensions in the community, (e) challenges to co-creation.
This paper examines the stability of a weak island namely Sumbawa-Lombok of Indonesian grid, interconnected with two infeed HVDC links facilitating 2 x 120 MW power transfer from Sumba and Flores Island. Through power flow, short circuit, small signal stability, resonance stability, and transient stability analyses, it is demonstrated that the existing infrastructure fails to support such transfer due to voltage drops, overloading, and stability limitations. Upgrading to 150 kV and its subsequent component resolves the small-signal and transient stability constraint as its grid strength is increasing. The current findings underscore that the primary limitation lies in the grid's infrastructure, not in dynamic or control constraints. The current result establishes the need for strategic grid reinforcements to support HVDC integration in weak systems and sets the stage for future research on optimizing the extent of such reinforcements.
The expansion of Carbon Capture, Utilization, and Storage (CCUS) highlights the growing need for carbon dioxide (CO2) pipeline transportation. While pure CO2 is non-corrosive, impurities such as H2O and NO2 create a corrosive environment that risks pipeline integrity. This study investigates how H2O and NO2 concentrations, along with temperature, influence corrosion under CO2 pipeline conditions. The investigation was performed in an autoclave setup emulating a linear velocity of 0.96 m/s at 100 bar and temperatures of 5 °C and 25 °C, testing X52 and GR70, and a more corrosion-resistant 9Cr alloy. The results indicated that the presence of NO2 elevated the corrosion rate compared to scenarios without. Low H2O concentration led to a corrosion rate of up to five times higher at 5 °C, compared to at 25 °C, in the presence of NO2. Low to moderate corrosion was observed for the carbon steels without NO2 and with 70 ppmv H2O at both temperatures. Reducing the H2O concentration below 70 ppmv and removing NO2, while SO2 and O2 are present, will only result in low to moderate corrosion in the carbon steel CO2 pipeline. The corrosion rate for X52 and GR70 was 0.065 mm/y and 0.016 mm/y higher or 5 and 3 times greater, respectively, at 5 °C compared to 25 °C. The study concludes that H2O should be maintained below 70 ppmv and NO2 should be eliminated to prevent severe corrosion. Emphasizing the importance of CO2 specification compliance and the need for further research into CO2 compositions that align with the specifications.
Coastal communities have ideas and plans on how to redirect the blue economy to
support thriving societies, but how can EU Member States better support bottom-up
transitions?
This is a policy brief included in D5.3 of EmpowerUs.
The EU Green Deal calls for a rapid and efficient green transition. On-going climate change and an increasing need for secure and sustainable energy means ambitious projects and goals are accelerated. To expand and exchange offshore wind energy across North Sea neighbouring countries, the Danish government presented in 2020 the Danish North Sea Energy Island (NSEI) project. This pilot project illustrates the shift from ‘nationally individualistic’ modes of connecting offshore wind energy projects, to supplying a multi-lateral renewable offshore energy grid. The Energy Island project builds on the Hub-and-Spoke (H&S) approach, which introduces a new level of complexity to governing the next generation of offshore wind energy projects. This paper analyses the political motivations for the Danish project and the planning and implementation of the Energy Islands, integrating a combination of collaborative and transboundary governance perspectives. The qualitative analysis is based on a document analysis and a literature review. Findings show how planning for the Danish Energy Island has faced delays and challenges, causing uncertainties about the Island’s capability to support Green Deal goals, as well as a mismatch between political ambitions and practical implementation. The artificial offshore island is currently under reconsideration due to costs and is, as of March 2024, still in its planning phase. This case study on the Danish NSEI serves as an introduction to the general functionalities and development of the Island and defines a Danish Energy Island. Results indicate that the combination of transboundary and collaborative governance structures are necessary as part of a successful implementation of Energy Islands.
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we present a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimates.
The liner shipping industry is undergoing an extensive decarbonization process to reduce its 275 million tons of CO2 emissions as of 2018. In this process, the long-term solution is the introduction of new alternative maritime fuels. The introduction of alternative fuels presents a great set of unknowns. Among these are the strategic concerns regarding sourcing of alternative fuels and, operationally, how the new fuels might affect the network of shipping routes. We propose a problem formulation that integrates fuel supply/demand into the liner shipping network design problem. Here, we present a model to determine the production sites and distribution of new alternative fuels-we consider methanol and ammonia. For the network design problem, we apply an adaptive large neighborhood search combined with a delayed column generation process. In addition, we wish to test the effect of designing a robust network under uncertain demand conditions because of the problem's strategic nature and importance. Therefore, our proposed solution method will have a deterministic and stochastic setup when we apply it to the second-largest multihub instance, WorldSmall, known from LINER-LIB. In the deterministic setting, our proposed solution method finds a new best solution to three instances from LINER-LIB. For the main considered WorldSmall instance, we even noticed a new best solution in all our tested fuel settings. In addition, we note a profit drop of 7.2% between a bunker-powered and pure alternative fuel-powered network. The selected alternative fuel production sites favor a proximity to European ports and have a heavy reliance on wind turbines. The stochastic results clearly showed that the found networks were much more resilient to the demand changes. Neglecting the perspective of uncertain demand leads to highly fluctuating profits.
This study introduces WindWise, a cost–benefit analysis and design optimization tool for Wind Propulsion Systems (WPS) in sustainable shipping. By integrating route simulations, ship constraints, and fuel pricing scenarios, WindWise determines the optimal WPS configuration to maximize fuel savings and minimize payback periods. A retrofit case study of an oil tanker evaluates two WPS classes—DynaRigs and Rotor Sails—across multiple operational and economic conditions. Results reveal that optimal configurations vary based on constraints: in an unconstrained scenario, larger, well-spaced installations minimize aerodynamic losses, whereas realistic constraints shift the preference towards smaller, distributed setups to mitigate cargo loss and air draft penalties. Rotor Sails offer lower upfront costs and shorter payback periods for modest savings targets and for side-wind routes, while DynaRigs emerge as the more viable solution for higher emissions reductions and long-term profitability. Optimization of WPS configurations proves crucial, with non-optimized configurations exhibiting payback periods over 150% higher than optimized ones. Although payback period remains an important metric, considering both payback and net present value provides a more comprehensive assessment of WPS financial viability, with Rotor Sails generally offering faster payback but DynaRigs delivering higher long-term profitability across most scenarios.
This article explores how adopting a combined ecosystem and justice approach to deep-sea mining (DSM)-particularly in vulnerable regions like the Arctic-would constitute a paradigm shift in ocean environmental law and governance. Such a shift would move ocean governance beyond fragmented, technocratic, and resource-driven frameworks toward an integrated, equitable, and sustainability-centered regime grounded in ecological integrity, social justice, and respect for human rights and local traditions.
Automated fish documentation processes are in the near future expected to play an essential role in sustainable fisheries management and for addressing challenges of overfishing. In this paper, we present a novel and publicly available dataset named AutoFish designed for fine-grained fish analysis. The dataset comprises 1,500 images of 454 specimens of visually similar fish placed in various constellations on a white conveyor belt and annotated with instance segmentation masks, IDs, and length measurements. The data was collected in a controlled environment using an RGB camera. The annotation procedure involved manual point annotations, initial segmentation masks proposed by the Segment Anything Model (SAM), and subsequent manual correction of the masks. We establish baseline instance segmentation results using two variations of the Mask2Former architecture, with the best performing model reaching an mAP of 89.15%. Additionally, we present two baseline length estimation methods, the best performing being a custom MobileNetV2-based regression model reaching an MAE of 0.62cm in images with no occlusion and 1.38cm in images with occlusion. Link to project page: https://vap.aau.dk/autofish/.