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Deliverable D6.6 describes the international collaboration strategies for the ALGAESOL project. The aim of this work is to ensure that the project consortium undertake collaboration activities with various external stakeholders, such as relevant EU projects and their consortium partners, to enrich communication and dissemination activities, and widen the impact of the project. This report includes an overview of relevant international stakeholders, especially relevant EU projects, a first vision on possible future cooperation strategies, and a short overview of current activities regarding stakeholder engagement. In the follow up deliverable report, more detailed cooperation strategies with a long-term outlookthat will be developed together with the identified partners, such as joint events and consultation, position papers or even on-site technology demonstrations in the later phase of the project, will be described.

Abstract The Transpolar Drift (TPD) plays a crucial role in regulating Arctic climate and ecosystems by transporting fresh water and key substances, such as terrestrial nutrients and pollutants, from the Siberian Shelf across the Arctic Ocean to the North Atlantic. However, year-round observations of the TPD remain scarce, creating significant knowledge gaps regarding the influence of sea ice drift and ocean surface circulation on the transport pathways of Siberian fresh water and associated matter. Using geochemical provenance tracer data collected over a complete seasonal cycle, our study reveals substantial spatiotemporal variability in the dispersal pathways of Siberian matter along the TPD. This variability reflects dynamic shifts in contributions of individual Siberian rivers as they integrate into a large-scale current system, followed by their rapid and extensive redistribution through a combination of seasonal ice–ocean exchanges and divergent ice drift. These findings emphasize the complexity of Arctic ice–ocean transport pathways and highlight the challenges of forecasting their dynamics in light of anticipated changes in sea ice extent, river discharge, and surface circulation patterns.

[ES] Los autobuses urbanos realizan más de la mitad de los viajes en transporte público terrestre en la UE. La electrificación de estos autobuses podría reducir las emisiones del ciclo de vida en un 76% para 2030 en comparación con el diésel. En 2024, solo el 1,4% de la flota es eléctrica, pero para 2035 todos los nuevos autobuses urbanos deberán ser de cero emisiones, desempeñando un papel fundamental en la transición hacia una movilidad urbana sostenible. El sistema de calefacción, ventilación y aire acondicionado (HVAC) es esencial para el confort y la seguridad de los pasajeros, pero también es la principal carga auxiliar, representando el 1,5% del consumo mundial de petróleo. En los autobuses eléctricos urbanos, el HVAC puede reducir la autonomía hasta en un 50% en condiciones climáticas extremas, subrayando la necesidad de optimizar su eficiencia. La literatura actual revela importantes lagunas en el desarrollo de modelos detallados de sistemas HVAC para entornos urbanos reales. El desarrollo de dichas metodologías de evaluación del consumo energético podría facilitar la optimización de recursos y la reducción de costes e incertidumbres. En esta tesis, se han desarrollado e integrado seis modelos avanzados en un modelo global para simular y optimizar con precisión la operación y el consumo energético del sistema HVAC de autobuses eléctricos urbanos en condiciones reales de operación: 1.Modelo espacial: Crea una representación 3D de la ciudad con edificios, árboles y calles. 2.Modelo cinemático: Genera ciclos de conducción estocásticos anuales basados en rutas, límites de velocidad y tráfico. 3.Modelo climático: Calcula la temperatura, humedad, radiación y horizonte visible para todas las superficies del autobús según los datos climáticos, la posición y la orientación. 4.Modelo térmico: Determina las ganancias de calor, transferencias de humedad, temperaturas de los nodos y cargas térmicas según las propiedades ópticas y termofísicas del autobús, la ocupación, los sistemas auxiliares y las condiciones ambientales. 5.Modelo HVAC: Evalúa el modo de operación y los puntos de funcionamiento de cada componente, su consumo energético y eficiencia y las condiciones de salida del flujo de aire y de condensado. 6.Modelo de batería: Estima el consumo energético total del autobús, incluyendo el motor, el frenado regenerativo, el HVAC y otros sistemas. El modelo global se implementa en una herramienta de simulación aplicada a rutas reales, permitiendo analizar estrategias para reducir la demanda energética, optimizar los componentes del sistema HVAC y evaluar su impacto en el consumo total de energía. Los resultados muestran que una simplificación excesiva del modelo puede generar errores significativos, con imprecisiones superiores al 50 % en la irradiación solar directa si no se consideran los efectos del sombreado. Los resultados destacan el papel crítico de los modelos térmicos transitorios y el impacto de la estocasticidad en la alta ocupación sobre la carga térmica. Las simulaciones revelan que la demanda media de aire acondicionado en días cálidos de verano es de 12,1 kW, debido especialmente a las cargas solares y de ocupación, mientras que la demanda de calefacción en días fríos de invierno es de 3,3 kW, principalmente debido al aire fresco no recirculado. El modo de aire acondicionado es predominante (44,6% del tiempo), seguido por la ventilación (31,4%). El compresor consume el 69-75% de la energía en verano y el 58-65% en meses templados y en invierno. El consumo del HVAC representa el 5-12% del uso total de energía del autobús. Esta proporción es mayor en paradas más frecuentes y a velocidades más bajas, reduciendo la autonomía en un 15-20% en días cálidos y hasta un 165 % en condiciones extremas. La mejora del aislamiento y los recubrimientos pueden reducir la demanda de calefacción y aire acondicionado en un 20-31%, mientras que reducir la capacidad el compresor en un 25% ofrece ahorros de costes sin pérdida de eficiencia. [CA] Els autobusos urbans realitzen més de la meitat dels viatges en transport públic terrestre a la UE. L'electrificació d'aquests autobusos podria reduir les emissions del cicle de vida en un 76% per a 2030 en comparació amb el dièsel. En 2024, només el 1,4% de la flota és elèctrica, però per a 2035 tots els nous autobusos urbans hauran de ser de zero emissions, exercint un paper fonamental en la transició cap a una mobilitat urbana sostenible. El sistema de calefacció, ventilació i aire condicionat (HVAC) és essencial per al confort i la seguretat dels passatgers, però també és la principal càrrega auxiliar, representant el 1,5% del consum mundial de petroli. En els autobusos elèctrics urbans, l'HVAC pot reduir l'autonomia fins a un 50% en condicions climàtiques extremes, subratllant la necessitat d'optimitzar la seua eficiència. La literatura actual revela importants llacunes en el desenvolupament de models detallats de sistemes HVAC per a entorns urbans reals. El desenvolupament d'aquestes metodologies d'avaluació del consum energètic podria facilitar l'optimització de recursos i la reducció de costos i incerteses. En aquesta tesi, s'han desenvolupat i integrat sis models avançats en un model global per a simular i optimitzar amb precisió l'operació i el consum energètic del sistema HVAC d'autobusos elèctrics urbans en condicions reals d'operació: 1.Model espacial: Crea una representació 3D de la ciutat amb edificis, arbres i carrers. 2.Model cinemàtic: Genera cicles de conducció estocàstics anuals basats en rutes, límits de velocitat i trànsit. 3.Model climàtic: Calcula la temperatura, humitat, radiació i horitzó visible per a totes les superfícies de l'autobús segons les dades climàtiques, la posició i l'orientació. 4.Model tèrmic: Determina els guanys de calor, transferències d'humitat, temperatures dels nodes i càrregues tèrmiques segons les propietats òptiques i termofísiques de l'autobús, l'ocupació, els sistemes auxiliars i les condicions ambientals. 5.Model HVAC: Avalua el mode d'operació i els punts de funcionament de cada component, el seu consum energètic i eficiència i les condicions d'eixida del flux d'aire i de condensat. 6.Model de bateria: Estima el consum energètic total de l'autobús, incloent-hi el motor, la frenada regenerativa, el HVAC i altres sistemes. El model global s'implementa en una eina de simulació aplicada a rutes reals, permetent analitzar estratègies per a reduir la demanda energètica, optimitzar els components del sistema HVAC i avaluar el seu impacte en el consum total d'energia. Els resultats mostren que una simplificació excessiva del model pot generar errors significatius, amb imprecisions superiors al 50% en la irradiació solar directa si no es consideren els efectes de l'ombreig. Els resultats destaquen el paper crític dels models tèrmics transitoris i l'impacte de la estocasticitat en l'alta ocupació sobre la càrrega tèrmica. Les simulacions revelen que la demanda mitjana d'aire condicionat en dies càlids d'estiu és de 12,1 kW, degut especialment a les càrregues solars i d'ocupació, mentre que la demanda de calefacció en dies freds d'hivern és de 3,3 kW, principalment a causa de l'aire fresc no recirculat. El mode d'aire condicionat és predominant (44,6% del temps), seguit per la ventilació (31,4%). El compressor consumeix el 69-75% de l'energia a l'estiu i el 58-65% en mesos temperats i a l'hivern. El consum del HVAC representa el 5-12% de l'ús total d'energia de l'autobús. Aquesta proporció és major en parades més freqüents i a velocitats més baixes, reduint l'autonomia en un 15-20% en dies càlids i fins a un 165% en condicions extremes. La millora de l'aïllament i els recobriments poden reduir la demanda de calefacció i aire condicionat en un 20-31%, mentre que reduir la capacitat el compressor en un 25% ofereix estalvis de costos sense pèrdua d'eficiència. [EN] Urban buses account for more than half of all inland public transport journeys in the EU. Electrifying these buses could reduce life-cycle emissions by 76% by 2030 compared to diesel. In 2024, only 1.4% of the fleet is electric, but by 2035, all new urban buses must be zero-emission, highlighting their pivotal role in the transition to sustainable urban mobility. The Heating, Ventilation, and Air Conditioning (HVAC) system is crucial for passenger comfort and safety, yet it is the primary auxiliary load, accounting for 1.5% of global oil consumption. In urban electric buses, it can reduce the driving range by up to 50% under extreme weather, emphasizing the need to optimize its efficiency. Current literature reveals significant gaps in the development of detailed HVAC system models for real urban environments. The development of these energy consumption evaluation methodologies could enable resource optimization and the reduction of costs and uncertainties. In this thesis, a set of six advanced models has been developed and integrated into a comprehensive global model to accurately simulate and optimize the HVAC system operation and energy consumption of urban electric buses under real operating conditions: 1.Spatial model: Creates a 3D city representation with buildings, trees, and streets. 2.Kinematic model: Generates annual stochastic driving cycles based on routes, speed limits, and traffic. 3.Climate model: Calculates the temperature, humidity, radiation, and visible horizon for all bus surfaces based on climate data, the position, and orientation. 4.Thermal model: Determines the heat gains, moisture transfers, node temperatures, and thermal loads based on the optical and thermophysical properties of the bus, the occupancy, auxiliary systems, and environmental conditions. 5.HVAC model: Assesses the operating mode and the operation points of each component, their energy consumption, and system efficiency, and the outlet conditions of the air and condensate flowrate. 6.Battery model: Estimates overall bus energy consumption, including the motor, regenerative braking, HVAC, and other systems. The global model is implemented in a simulation tool applied to real routes, enabling the analysis of strategies to reduce the overall energy demand, optimize HVAC system components, and evaluate their impact on the total energy consumption. The findings reveal that a model oversimplification can lead to significant errors, with inaccuracies exceeding 50% in direct solar irradiation due to unaccounted shading effects. The results highlight the critical role of transient thermal models and the impact of high occupancy stochasticity on the thermal load. The simulations show that the mean cooling demand on warm summer days is 12.1 kW, driven by solar and occupancy loads, while the heating demand on cold winter days averages 3.3 kW, mainly due to non-recirculated fresh air. The cooling mode is predominant (44.6% of the time), followed by ventilation (31.4%). The compressor consumes 69-75% of energy in summer and 58-65% in mild months and winter. The HVAC consumption accounts for 5-12% of the total bus energy use. This share is higher on higher frequency stops and lower speeds, reducing the driving range by 15-20% on warm days and up to 165% under extreme conditions. The improvement of insulation and coatings can reduce heating and cooling demand by 20-31%, while resizing the compressor by 25% offers cost savings without any efficiency loss. This work has been supported by the Generalitat Valenciana under the program “Subvencions per a la contractació de personal investigador de caràcter predoctoral (ACIF/2019/239)”

Abstract Expanding electric vehicle (EV) charging infrastructure is essential for transitioning to an electrified mobility system. With rising EV adoption rates, firms face increasing regulatory pressure to build up workplace charging facilities for their employees. However, the impact of EV charging loads on businesses’ specific electricity consumption profiles remains largely unknown. Our study addresses this challenge by presenting a mathematical optimisation model, available via an open-source web application, that empowers business executives to manage energy consumption effectively, enabling them to assess peak loads, charging costs and carbon emissions specific to their power profiles and employee needs. Using real-world data from a global car manufacturer in South East England, UK, we demonstrate that smart charging strategies can reduce peak loads by 28% and decrease charging costs and emissions by 9% compared to convenience charging. Our methodology is widely applicable across industries and geographies, offering data-driven insights for planning EV workplace charging infrastructure.

Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) emerge as a potential solution to the Electric Vehicles' (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced prospective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and G2V strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, a simulation module for researching in both V2G and G2V energy management strategies, that models EVs, their charging infrastructure and associated energy flexibility dynamics; second, this paper integrates EVLearn with the existing CityLearn framework, providing V2G and G2V simulation capabilities into the study of broader energy management strategies. Results validated EVLearn and its integration into CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario. 10 pages, 7 figures, 3 tables, 11 equations

Spain’s National Integrated Energy and Climate Plan (PNIEC) addresses the policies and measures needed to contribute to the European target of a 23% reduction in greenhouse gas emissions by 2030 compared to 1990 levels. To this end, the decarbonization of the transport sector is very important in order to increase electric mobility. Electric mobility depends on the conditions of the electrical infrastructure. This research focuses on the electrical distribution network in terms of its current capacity for recharging electric vehicles, which are estimated to account for 20.7% of vehicles, which is about 4 million vehicles. This, therefore, illustrates the need to legislate to improve the electrical infrastructure for recharging electric vehicles, with the aim of deploying electric vehicles on a larger scale and, ultimately, allowing society to benefit from the advantages of this technology.

Weather routing has been extensively used as a decision support system in merchant ship operations and traffic management. A critical component of such a system is the optimisation method. Over recent years, substantial research efforts have been devoted to developing voyage optimisation algorithms, either to support decisionmaking of weather routing in merchant shipping or to assist autonomous ships in academic research. The requirements for optimisation methods for merchant shipping differ significantly from those in academic autonomous ship applications. However, many optimisation-related terminologies and algorithms are often used arbitrarily across these two fields, easily leading to confusion. In addition, the emergence of machine learning after 2020 has shown a significant impact on the development of those optimisation algorithms. Still, we see a lack of a systematic review and in-depth summary of recent developments in the optimisation methods focused on weather routing. This paper presents an overview of recent scientific publications to show state-of-the-art research and development status and trends. Focusing on the optimisation methods used in weather routing, we clarify optimisation terminologies. In addition, we propose a general framework to develop voyage optimisation methods to summarise and categorise various developed algorithms. Then, we review scientific papers published in recent years for weather routing developments and applications. Finally, future research and outlooks are discussed for further development of weather routing algorithms.