Centre for Marine and Renewable Energy (MaREI) - Journal Articles
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Item A low-carbon electricity transition for small island developing states: The case of Mauritius(Elsevier Ltd., 2024-12-20) Jaggeshar, Doorgeshwaree; Mao, Xianqiang; Guo, Zhi; Zusman, Eric; Tu, Kevin; Chen, Xing; Ma, Zhiyuan; Global Energy Interconnection GroupA clean energy transition can not only help rebuild the energy landscape of small island developing states (SIDS) but also boost their resilience and long-term development prospects. This study employs the Open-Source Energy Modeling System (OSeMOSYS) model to analyze low-carbon transition pathways for Mauritius, which are aligned with its nationally determined contribution (NDC) objectives to increase renewable energy to 60 % and phase out coal by 2030. The study applied key performance indicators to assess this pathway against energy self-sufficiency, economic, environmental and social criteria. The study showed that renewable energy sources, namely, solar, biomass, wind and waste-to-energy, can be game-changers for the island. The optimal transition pathway would achieve a renewable target of 76.8 % and reduce CO2 emissions by more than 67 % across the modeling period at an estimated cost of 1.94 billion USD by 2040. Finally, the study evaluated the implications of Mauritius' clean transition in terms of its effects on green jobs as well as whether there is sufficient infrastructure, administrative and financial capacity and energy pricing policies to support the optimal pathway. The study concludes that the experience modeling a low-carbon transition pathway for Mauritius could also offer useful lessons for other SIDS contemplating similar transitions.Item A tri-level distribution locational marginal price-based demand response framework(Elsevier B.V., 2025-01-01) Pandey, Vipin Chandra; Rawat, Tanuj; Ospina, Juan; Dvorkin, Yury; Konstantinou, Charalambos; Keane, James; Science Foundation Ireland; Popov, Jelena; Stanton, CatherineIn this paper, we propose a tri-level, nested, two-stage price-based demand response (PBDR) framework that considers distribution locational marginal price (DLMP) as DR enabler between load-serving entities (LSE), demand response providers (DRPs), and customers in the day-ahead distribution market. It enables LSE and customer interactions by using multiple DRPs, positioned in-between, and independently optimizes their objectives. The problem is formulated using linear power flow with approximated power losses and its application in DLMP as DR pricing. The tri-level problem is solved using a nested reformulation & decomposition (R&D) method and tested on the real Indian-108 bus distribution system under various dynamic pricings. Further, the temporal–spatial variations in DLMPs are assessed using fairness criteria. Numerical analyses demonstrate that DLMP applications can effectively improve economic efficiency, and transparency in DR programs valuation with a favorable fairness margin. The results show that DLMP as DR pricing signal induces (0-2) % variation in DLMP for DR participation up to 10 %. Further, it gives over 90 % fairness over temporal–spatial variation for all the customers.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.