Abstract

The fastest growing renewable energy technology in Europe is solar photovoltaic (PV). Despite no burning fossil fuels during its operation, the production of PV modules and other components cause the emission of greenhouse gases. The global warming potential (GWP) associated with the generation of PV energy can be higher than that from the grid in some locations. This study explores the fraction of the year, and its related energy generation, during which PV energy has a lower global warming potential than the local grid. This analysis is done in seven different locations within Europe, for three PV systems with different GWP. The results show that in most cases the PV systems have the capacity of reducing the GWP from the grid 100% of the time. However, there are a few exceptions in countries with a low-carbon grid such as France, Sweden and Norway with a GWP reduction related to only 46%, 16% and 17% of the energy generation respectively.

Abstract

This study conducts a techno-environmental assessment of a fully electrified rural single-family house in Västerås, Sweden, functioning as a resilient, sustainable prosumer. It evaluates: (i) grid independence under normal conditions; (ii) thermal resilience during power outages with various enhancement strategies; and (iii) operational COâ‚‚ emission reductions versus combustion-based heating. Using IDA ICE simulations, the building’s energy performance is analyzed with integrated renewable energy (RE) systems: PV, solar thermal, batteries, thermal storage, electric heater, and heat pump. Key performance indicators include self-sufficiency rate (SSR), operational COâ‚‚ emissions equivalent, and thermal resilience metrics. Results show that combining RE technologies significantly improves year-round reliability and emissions reduction. PV-only systems perform well during long outages, while battery-only setups suffice for short ones. Even basic electric heaters maintain habitability during 3-day outages. The findings offer practical insights into enhancing resilience and sustainability in rural prosumer buildings through distributed RE systems.

Abstract

Polymer-based absorbers offer a lightweight, corrosion-resistant alternative to metals in Photovoltaic-thermal (PVT) and solar thermal collectors. However, their production method is critical to cost, scalability, and performance. This study compares two polymer processing methods, namely vacuum forming and extrusion, by analysing data from systematic qualitative interviews with industry experts and researchers, as well as quantitative production data collected from two European manufacturers. The results indicate that extrusion would be the superior option for commercial- scale deployment due to its higher throughput and reduced environmental impact per unit. In contrast, vacuum forming is more suitable for prototypes and small production runs, particularly when more complex designs are needed. The findings of this study contribute to the strategic decision-making framework concerning the development of cost-effective and efficient photovoltaic thermal (PVT) absorbers.

Abstract

The global transition to sustainable energy has renewed interest in hydrogen as a clean and versatile energy carrier.Its versatility lies in its ability to serve multiple roles, from electricity generation and industrial heating to long-duration energy storage and mobility applications.This paper evaluates the techno-economic feasibility of a hydrogen-based energy system designed to provide backup power for a newly developing industrial area. Two system configurations, i.e., compressed and liquid hydrogen, are assessed in terms of their performance and cost-effectiveness. The model captures the dynamic operation of key components, including electrolyzers, storage systems, compressors, liquefiers, gasifiers, fuel cells, batteries, and heat exchangers, using high-resolution, yearlong data on solar irradiance, electricity prices, and industrial load patterns in Eskilstuna, Sweden. A structured sensitivity analysis examines how design decisions affect economic outcomes such as net profit and the Levelized Cost of Hydrogen (LCOH).Results show that compressed hydrogen achieves an LCOH of 81 SEK/kg and a net loss of –1.985MSEK, compared to liquid hydrogen with 90S EK/kg and –2.817 MSEK. However, under current market conditions and cost structures, neither system achieves profitability.The findings provide actionable guidance for industrial stakeholders and suggest avenues for future cost reduction, value stacking, and policy support.

Abstract

This study presents the design, implementation, and performance evaluation of a solar-assisted heat pump system integrating photovoltaic (PV) and photovoltaic–thermal (PV/T) technologies to meet the thermal and electrical energy demands of an administrative buildingwith an area of 1637 m². The proposed system combines PV and PV/T collectors with an air-source heat pump to provide space heating, cooling, and electricity generation, aiming to improve energy efficiency and reduce carbon emissions. Stable year-round operation is achieved through seasonal control strategies, including hot water circulation for snow removal on PV/T collectors in winter and active collector cooling when surface temperatures exceed 40 °C in summer. System operation is constantly monitored by a SCADAsystem. Long-term operationaldata show that the integrated system increases overall energy efficiency by approximately 66% and reduces CO₂ emissions by about 65% compared to conventional fossil fuel–based systems.The results confirm that PV and PV/T-assisted heat pump system, supported by smart control strategies, provide an effective solution for increasing renewable energy use in buildings, reducing dependence on energy imports, and supporting the transition toward low-carbon and positive-energy building concepts.

Abstract

This study investigates how digital tools contribute to building resilience, structured around the widely recognized 4Rs framework:Robustness, Redundancy, Resourcefulness, and Rapidity. Given the range of technologies, we beganwith semi-structured interviews with seven building expertsin the Swedish constructionto identify which digital tools are most relevant and commonly used in practice. Four core technologies; Building Information Modeling (BIM), Internet of Things (IoT), Artificial Intelligence (AI), and Smart Sensorsemerged as central to enhancing resilience. In parallel, a Scopus-based literature review was conducted to identify existing building resilience frameworks and assess the integration of digital technologies within them. A 4×4 scoring heatmap matrix was developed to quantify each tool’s contribution across the 4Rs. Results show that BIM supports robustness in the design phase; IoT and Smart Sensors improve redundancy and rapidity through real-time monitoring and automated responses; AI enhances resourcefulness and rapidity via predictive analytics and decision-making. The study highlights a critical gap in current resilience indices and proposes the integration of digital tools to support more adaptive and data-informed approaches to resilient building designand operation.

Abstract

he rise of electric aviation presents new challenges for airport infrastructure, particularly regarding the high power demands of electric aircraft charging. This study presents a microgrid design for Norrköping airport in Sweden, featuring a photovoltaic power plant, battery energy storage system, a main grid connection and a 2 MW DC fast charger for electric aircraft. The selection of a photovoltaic system and batteries for energy storage is discussed, motivated by their potential to improve the resilience and sustainability of aviation operations. Simulations were carried out to assess the ability of the microgrid to maintain reliable performance throughout the year, demonstrating that the system can reduce grid dependency and support environmental and operational goals.

Abstract

This study explores how electric vehicles, and a stationary battery energy storage system associated with a PV park can help manage capacity constraints and enhance resilience in a local power grid. Using real grid data and hourly power usage, simulations were carried out for three representative periods with different penetration rates for PV and electric vehicles. Results show that a virtual power line activates to different extents during different seasons. In low and medium-load periods, it primarily transfers PV produced electricity to electric vehicles, enabling up to 32% more PV utilization. In high load period the concept can supply all peak demand via the electric vehicles. Aggregators and electric vehicles are likely to become a natural part of the energy ecosystem. With suitable business models, those actors can increase network resilience without CAPEX or OPEX for the grid company.

Abstract

This work presents a methodology for a condensed laboratory test sequence of residential PV and battery systems to represent annual performance. Test sequences for four case studies—representing a broad spectrumin electricity demand in a Swedish context—arepresented as a 4-day Frankenstein dataset, comprisingdifferent days throughout a typical climate year. The sequences are compared to annual data,and the system’s performance is validated for varying PV and battery system sizes. Laboratory tests are performed using the Frankenstein dataset and evaluated on two commercial PV and battery systems. The evaluation is done for system losses and efficiency, self-consumption, self-sufficiency, and the impact on peak power grid import and exportfrom adding a battery storage system.

Abstract

Accurate dynamic modelling of CO2 capture is essential for real time process integration, control and optimization. This work aims to investigate the feasibility of using machine learning (ML) methods for different purposes of dynamic modelling of CO2 capture. Since the development of ML models relies on the selection of input features, the key input parameters are first reviewed and determined based on requirements of various dynamic model applications. Four cases are covered in this work: system identification for control development, system monitoring and diagnosis, operation optimization and system performance assessment. Three ML methods, Informer, Long Short-Term Memory (LSTM) and Backpropagation Neural Network (BPNN), are used. The data needed for ML model development are generated by using a validated physical dynamic model developed in Aspen HYSYS Dynamics, based on real data of flue gas obtained from a waste fired combined heat and power plant. It was found that with selected input parameters, ML models can achieve high accuracy for all cases, with mean absolute percentage errors (MAPEs) less than 5%. No single model outperforms the others across all cases.