Centre for Marine and Renewable Energy (MaREI) - Journal Articles
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Item A framework for high resolution coupled global electricity & hydrogen models based on integrated assessment model scenarios(Elsevier B.V., 2024-12-02) Mathews, Duncan; Brinkerink, Maarten; Deane, Paul; Irish Research Council; Science Foundation IrelandTechnology-rich Integrated Assessment Models (IAMs) offer the possibility to endogenously resolve hydrogen demand as a function of competition between technologies to meet energy service demands while considering wider interactions with climate and the economy. Such models are typically configured with relatively low spatial and temporal resolution thereby limiting their utility in studying future global hydrogen trade scenarios. This work presents a framework for the soft-linking of IAM scenarios to a higher spatial and temporal resolution global model. This framework provides an “engine” with which to generate future-looking coupled hydrogen & electricity models with the co-optimization of production capacity, storage, and transmission infrastructure that allow the user to vary key technoeconomic input parameters. The modelling framework is applied to an IAM scenario and validated against the parent IAM. The utility of the model when examining electricity & hydrogen commodity trade in future scenarios is then demonstrated.Item Planning for citizen participation in the EU mission to restore our ocean and waters by 2030(Springer, 2024-09-26) Whyte, David; Debaveye, Line; Bjørkan, Maiken; Steiro, Vida Maria Daae; Maria Vittoria Marra; Seys, Jan; Deane, Aoife; Namisnik, Wendy; Pelegri, Josep L.; Simon, Carine; Falcieri, Francesco; Giuffredi, Rita; Laurenza, Lucia; Apazoglou, Eirini; Petersen, H. Cecilie; Carbajal, María Elena; Giannoukakou-Leontsini, Ifigeneia; Fuster, Noemí; Nys, Cécile; HORIZON EUROPE Framework ProgrammeThe European Commission’s Mission to “Restore our Ocean and Waters by 2030” (Mission Ocean & Waters) is, at the most superficial level, an overarching policy framework with the primary aim of improving the health of European ocean, sea, and freshwater ecosystems. However, its use of the Mission framing and emphasis on fostering social, political, and economic transformations through its activities makes it a much more holistic and ambitious undertaking. This article explores challenges and opportunities that arise with the emphasis placed on increasing citizen participation in Mission Ocean & Waters, in the context of “Post-Normal Science” (Funtowicz & Ravetz Funtowicz and Ravetz, Krimsky and Golding (eds), Social Theories of Risk, Greenwood Press, Westport, 1992). We begin with a description of Mission Ocean & Waters, discussing its citizen engagement ambitions through the lens of Post-Normal Science, before describing the research methods used by the Horizon Europe project Preparing the Research and Innovation Core for Mission Oceans, Seas, & Waters (PREP4BLUE). We then present our results, highlighting four citizen engagement-based challenges that the Mission faces, and how PREP4BLUE has engaged with them. Finally, we discuss the future activities or structural changes that will be required if the Mission’s citizen engagement targets are to be achieved and for citizens to become core actors in protecting European aquatic ecosystems and developing a sustainable blue economy. These insights should prove useful to those developing and delivering Mission projects and those researching citizen participation in ocean and freshwater related challenges.Item Mutation based improved dragonfly optimization algorithm for a neuro-fuzzy system in short term wind speed forecasting(Elsevier B.V., 2023-03-20) Parmaksiz, Huseyin; Yuzgec, Ugur; Dokur, Emrah; Erdogan, Nuh; Türkiye Bilimsel ve Teknolojik Araştırma KurumuThe Dragonfly algorithm (DA) is a heuristic optimization algorithm that is commonly used for complex optimization problems. Despite its widespread application, the abundance of social behaviors in its construct can lead to poor accuracy in solutions and an imbalance between exploration and exploitation phases. To overcome these issues, this paper proposes a mutation-based Dragonfly optimization algorithm (MIDA). In order to increase the solution accuracy of the original DA and reduce its handicaps, the proposed model includes three procedures, namely, mutation operation, boundary control, and greedy selection mechanisms. The mutation operator helps to find global optima by avoiding getting stuck at the local optimum point, while the boundary control and greedy selection mechanisms update the dragonflies at each iteration and thus use the better fitness value of the updated ones. The performance of the proposed MIDA is tested and shown to be superior to the original DA using several analyses such as convergence, search history, trajectory, average distance, computational complexity, diversity and balance. To validate the MIDA algorithm, it is tested on the 10, 30, 50 and 100 dimensions of the CEC2014 benchmark functions. Furthermore, a comparison against twelve state-of-the-art (SOTA) meta-heuristic optimization algorithms is performed. The performance of the proposed MIDA is also compared with the performances of some improved dragonfly algorithms taken from the literature. The statistical results obtained by the original DA and MIDA for ten minimization problems with 5, 10, 15 and 20 dimensions from the CEC2020 test suite are presented. Finally, the proposed algorithm is applied to optimize the ANFIS model parameters in order to be used for short-term wind forecasting as a real-world problem. The results obtained for the different dimensions of CEC2014 and CEC2020 test problems in the study show that the search performance of proposed MIDA is better than that of the original DA. MIDA outperformed the original DA by 91.33% for CEC2014 benchmarks and 94.25% for CEC2020 benchmarks. In addition, MIDA came first in comparison with twelve different SOTAs from the literature. In comparisons with different DA versions, MIDA took the second place in terms of statistical performance. Computational complexity was examined in the CEC2020 benchmarks and it was seen that MIDA has a more effective run time than DA. Finally, the short-term wind speed forecasting results of the ANFIS-DA hybrid model according to four different error metrics are more successful than those of the ANFIS-DA hybrid model.Item Modelling the installation of next generation floating offshore wind farms(Elsevier Ltd., 2024-07-26) Devoy McAuliffe, Fiona; Judge, Frances M.; Murphy, JimmyThe offshore wind industry is advancing with larger turbines (10 MW+) into sites in deeper waters (>60 m), necessitating innovative floating substructures. However, Floating Offshore Wind (FOW) must still be proven to be cost-efficient, with special attention paid to developing installation, Operations and Maintenance (O&M) and decommissioning strategies that will ensure FOW is competitive. Simulation is an efficient way to assess the viability of new technologies and the innovative operations required to install and maintain them. This paper presents a novel tool that simulates the installation of fixed/floating offshore wind farms across an hourly time-series of Metocean data, producing a total estimation of costs as part of the total capital expenditure (CAPEX). The paper validates the model by simulating installation of Hywind Scotland, the FOW farm, and comparing results with published data. Given the lack of real-world cost data and experience, this paper then seeks to provide a well-defined case-study for future researchers to develop further in other regions and with different technologies. The tool is applied to a theoretical large commercial 1GW FOW farm commissioned in 2035 at a representative site in the Celtic Sea. This considers a promising location for FOW development outside the existing demonstration/early commercial FOW projects, which are primarily focused on the North Sea. Results indicate a CAPEX (including installation) of €3492/kW, which is in line with industry expectations. Sensitivity analysis applies a learning rate to reduce platform costs, and varies farm capacity, demonstrating the potential economies of scale when increasing farm size. Simulation identifies weather operational limits and cable installation duration as key contributors to installation time and costs. This quantifies where cost-savings can be found and optimisation should focus. Further modelling considers the scale of their impact on results. A final Levelised Cost of Energy assessment estimating an LCoE range of €52.3–64.59/MWh, placing the FOW farm in the context of global economic targets.Item Integrating short term variations of the power system into integrated energy system models: A methodological review(Elsevier Ltd., 2017-03-27) Collins, Seán; Deane, John Paul; Poncelet, Kris; Panos, Evangelos; Pietzcker, Robert C.; Delarue, Erik; Ó Gallachóir, Brian Pádraig; SFI Research Centre for Energy, Climate and Marine; Vlaamse Instelling voor Technologisch OnderzoekIt is anticipated that the decarbonisation of the entire energy system will require the introduction of large shares of variable renewable electricity generation into the power system. Long term integrated energy systems models are useful in improving our understanding of decarbonisation but they struggle to take account of short term variations in the power system associated with increased variable renewable energy penetration. This can oversimplify the ability of power systems to accommodate variable renewables and result in mistaken signals regarding the levels of flexibility required in power systems. Capturing power system impacts of variability within integrated energy system models is challenging due to temporal and technical simplifying assumptions needed to make such models computationally manageable. This paper addresses a gap in the literature by reviewing prominent methodologies that have been applied to address this challenge and the advantages & limitations of each. The methods include soft linking between integrated energy systems models and power systems models and improving the temporal and technical representation of power systems within integrated energy systems models. Each methodology covered approaches the integration of short term variations and assesses the flexibility of the system differently. The strengths, limitations, and applicability of these different methodologies are analysed. This review allows users of integrated energy systems models to select a methodology (or combination of methodologies) to suit their needs. In addition, the analysis identifies remaining gaps and shortcomings.