• Energy Research
• 2025-2025close
• engineering and technology close
• EU close
• PL close

Outdoor monitoring of Roll-to-Roll (R2R) printed flexible organic photovoltaic (OPV) modules for building integrated/attached photovoltaics (BIPV/BAPV) applications.

Throughout Europe, large housing estates dating from the socialist era are an important part of the housing stock and cultural life, and in some cities the apartment buildings within them house more than half of the residents. There have been many attempts to regenerate large housing estates, since they were built with cheap materials, using contemporaneous (and outdated) planning principles, and were projected to be used for only one generation as ‘temporary housing’ until more permanent solutions could be established. Consequently, many large housing estates have survived beyond their useful life and are in need of renovation. This article moves beyond individual and disconnected rehabilitation projects and renewal strategies and conceptualises a larger transformation of large housing estates in Central and Eastern Europe based on sustainability principles. We envision a comprehensive ‘green transformation’ that would transform large housing estates into ‘eco-villages’ that not only address current challenges but also propel large housing estates into their next lives as desirable residential space. The image of housing estates can be vastly improved if the built environments and landscapes are transformed so that housing estates possess green infrastructure and other features of sustainable design. This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licences/by-nc-nd/4.0/ .

The implementation of battery electric vehicles (BEVs) in underground mining is relatively recent. BEVs offer several advantages over diesel machines, including enhanced working conditions through reduced noise and heat and the lack of toxic exhaust gases or diesel particulate matter. In addition to reducing greenhouse gases, they have the potential to reduce ventilation and air conditioning costs. Nevertheless, there are certain concerns about BEVs, in areas of productivity, fire safety, economic viability, user-friendliness, and potential electrical-related issues. To address these, two surveys were conducted, one among underground mine management and the other among mine personnel to ascertain their opinions and experiences of BEVs. The results indicated the primary motivators for mines to adopt BEVs were to create a healthier working environment and reduce carbon emissions. The factors hindering the implementation were high costs and the lack of proven reliability. Mine personnel appreciated BEVs for their quietness and reduced fluids and components; however, they had concerns about fire safety and limited battery duration. This study presents the extent of BEV use in underground mining and associated fire incidents and a summary of the survey results. Validerad;2024;Nivå 2;2024-10-30 (joosat);Full text: CC BY license

The recently completed Horizon-2020 project “Management and Uncertainties of Severe Accidents (MUSA)” has reviewed uncertainty sources and Uncertainty Quantification methodology for assessing Severe Accidents (SA), and has made a substantial effort at stimulating uncertainty applications in predicting the radiological Source Term of reactor and Spent Fuel Pool accident scenarios. The key motivation of the project has been to bring the advantages of the Best Estimate Plus Uncertainty approach to the field of Severe Accident modelling. With respect to deterministic analyses, expected gains are avoiding adopting conservative assumptions, identifying uncertainty bands of estimates, and gaining insights into dominating uncertain parameters. Also, the benefits for understanding and improving Accident Management were to be explored. The reactor applications brought together a large group of participants that set out to apply uncertainty analysis (UA) within their field of SA modelling expertise – in particular reactor types, but also SA code used (ASTEC, MELCOR, MAAP, RELAP/SCDAPSIM), uncertainty quantification tools used (DAKOTA, SUSA, URANIE, self-developed tools based on Python code), detailed accident scenarios, and in some cases SAM actions. The setting up of the analyses, challenges faced during that phase, and solutions explored, are described in Brumm et al. ANE 191 (2023). This paper synthesizes the reactor-application work at the end of the project. Analyses of 23 partners are presented in different categories, depending on whether their main goal is/are (i) uncertainty bands of simulation results; (ii) the understanding of dominating uncertainties in specific sub-models of the SA code; (iii) improving the understanding of specific accident scenarios, with or without the application of SAM actions; or, (iv) a demonstration of the tools used and developed, and of the capability to carry out an uncertainty analysis in the presence of the challenges faced. A cross-section of the partners’ results is presented and briefly discussed, to provide an overview of the work done, and to encourage accessing and studying the project deliverables that are open to the public. Furthermore, the partners’ experiences made during the project have been evaluated and are presented as good practice recommendations. The paper ends with conclusions on the level of readiness of UA in SA modelling, on the determination of governing uncertainties, and on the analysis of SAM actions.

The current paper presents the design and energy performance analysis of a propane-based reversible Dual Source/Sink Heat Pump (DSHP). DSHPs offer an alternative to conventional water to water and air to water heat pumps, leveraging the strengths of both technologies in an efficient manner. The developed prototype incorporates an innovative Dual Source/Sink Heat eXchanger (DSHX), enabling the unit operating in various modes, including space heating, space cooling, and domestic hot water production using brine, air or both simultaneously as a source/sink. The DSHX serves as as both a condenser or an evaporator, directly rejecting or absorbing heat from air and/or brine. By eliminating secondary loops and defrost cycles, the DSHX minimizes energy losses. The main novelty of this work lies in the DSHX that integrates external units typically duplicated in DSHPs into a single component, eliminating the need for split refrigerant flow rates, thus avoiding maldistribution, refrigerant charge increase and draining valves. A steady state experimental campaign was conducted in a climatic chamber to characterize the DSHP prototype and validate the DSHX performance models. Heating capacity up to 11.2 kW and COP values up to 4.7 were achieved at nominal compressor speed by supplying hot water at 35 °C with an ambient temperature of 7 °C. Similarly, when producing cold water at 7 °C, cooling capacity and EER reached 9.8 kW and 3.6, respectively, at nominal compressor speed using air as heat sink at 35 °C. The effects of various operating parameters on the overall coefficient of performance and heat duty in both heating and cooling modes, considering air or brine as heat source/sink are analyzed in detail. Results demonstrate enhancements of approximately 15 % in capacity and efficiency compared to earlier work. Moreover, four deterministic models were created in order to predict the behaviour of the DSHX and validated against experimental results, reaching deviation values below 15 %. The authors would like to thank the support of the TRI-HP project (https://www.tri-hp.eu/project) funded by the European Union's Horizon 2020 research and innovation programme, Project No. 814888.

A network of spatially distributed data centers can provide operational flexibility to power systems by shifting computing tasks among electrically remote locations. However, harnessing this flexibility in real-time through the standard optimization techniques is challenged by the need for sensitive operational datasets and substantial computational resources. To alleviate the data and computational requirements, this paper introduces a coordination mechanism based on contextual regression. This mechanism, abbreviated as AgentCONCUR, associates cost-optimal task shifts with public and trusted contextual data (e.g., real-time prices) and uses regression on this data as a coordination policy. Notably, regression-based coordination does not learn the optimal coordination actions from a labeled dataset. Instead, it exploits the optimization structure of the coordination problem to ensure feasible and cost-effective actions. A NYISO-based study reveals large coordination gains and the optimal features for the successful regression-based coordination.

We are considering fundamental questions of how redox reactions take place and charge is conducted in redox solids. Redox properties of electrode materials can be explained by considering the Nernst equations for homogeneous or segregated materials.

Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that explicitly distinguishes between "normal operation", in which automatic generation control (AGC) is sufficient to guarantee system security, and "adverse operation", in which the system operator is required to take additional actions, e.g., manual reserve deployment. The new formulation has been compared with the classical ones in a case study on the IEEE-118 and IEEE-300 bus systems. We observe that our consideration of extreme scenarios enables solutions that are more secure than typical chance-constrained formulations, yet less costly than solutions that guarantee robust feasibility with only AGC. 10 pages