• Energy Research

This data included the detailed mathematical description of the model and all the data adopted in the model

This data included the detailed mathematical description of the model and all the data adopted in the model

One commonly-used argument against fluctuating renewables is their unpredictability. In contrast, thermal power generation and hydropower are regularly presented as reliable and dispatchable. However, droughts and floods can render useless the share of the power generation infrastructure that directly depends on freshwater. In this work, the global power sector is analysed from an energy-water nexus perspective to evaluate its reliability in case of severe water scarcity on a per-power plant basis, proposing a new method for combining it with water stress scores. At a country level, known individual thermal and hydropower plants are paired with regional water stress projections from 2020 to 2030 and their water source as a bottom-up approach to account for the capacities at risk and identify the points where water dependence could render a power system unreliable. The results show that, globally, about 65 % of generating capacities are directly freshwater-dependent. Moreover, the share of capacities placed in the low-resiliency group increases from 9 % of the total installed in 2020 to over 24 % in 2030 in all scenarios. The findings could help guide the development of the global power sector towards a less water-dependent system and accelerate the deployment of low water-demand power generation technologies. Smart Energy, 14 ISSN:2666-9552

One commonly-used argument against fluctuating renewables is their unpredictability. In contrast, thermal power generation and hydropower are regularly presented as reliable and dispatchable. However, droughts and floods can render useless the share of the power generation infrastructure that directly depends on freshwater. In this work, the global power sector is analysed from an energy-water nexus perspective to evaluate its reliability in case of severe water scarcity on a per-power plant basis, proposing a new method for combining it with water stress scores. At a country level, known individual thermal and hydropower plants are paired with regional water stress projections from 2020 to 2030 and their water source as a bottom-up approach to account for the capacities at risk and identify the points where water dependence could render a power system unreliable. The results show that, globally, about 65 % of generating capacities are directly freshwater-dependent. Moreover, the share of capacities placed in the low-resiliency group increases from 9 % of the total installed in 2020 to over 24 % in 2030 in all scenarios. The findings could help guide the development of the global power sector towards a less water-dependent system and accelerate the deployment of low water-demand power generation technologies. Smart Energy, 14 ISSN:2666-9552

This package provides material related to the paper`B. Hadengue, A. Scheidegger, E. Morgenroth, T.A. Larsen, Modeling the Water-Energy Nexus in Households, Energy & Buildings (2020), doi: https://doi.org/10.1016/j.enbuild.2020.110262`Various scripts and code, as well as raw results, are included.### Paper AbstractOne third of the global carbon emissions are emitted by the building sector. Over the last decades, space heating loads have decreased in modern buildings, and domestic hot water (DHW) is now oftentimes the largest energy consumer in the household. We developed the WaterHub modeling framework to assess the potential of technologies or measures targeting DHW energy demand. The framework combines process-based technological models and stochastic water demand modeling in a modular way to allow for holistic simulations of complex DHW systems. In two rigorous tests of the modeling framework, we demonstrated the importance of water consumption dynamics in the modeling of DHW systems, showing that static modeling leads to underestimated heat losses and wrong energy consumption predictions. In an exemplary case study, we identified and quantified the synergistic interactions between water boiler temperatures and a drain water heat recovery device, demonstrating the strength of this methodology for optimizing strategies targeting DHW systems. With its modular structure, this open-source modeling framework can be extended to include any DHW-related technology, providing a useful common platform for collaboration between technology developers and water experts.

This package provides material related to the paper`B. Hadengue, A. Scheidegger, E. Morgenroth, T.A. Larsen, Modeling the Water-Energy Nexus in Households, Energy & Buildings (2020), doi: https://doi.org/10.1016/j.enbuild.2020.110262`Various scripts and code, as well as raw results, are included.### Paper AbstractOne third of the global carbon emissions are emitted by the building sector. Over the last decades, space heating loads have decreased in modern buildings, and domestic hot water (DHW) is now oftentimes the largest energy consumer in the household. We developed the WaterHub modeling framework to assess the potential of technologies or measures targeting DHW energy demand. The framework combines process-based technological models and stochastic water demand modeling in a modular way to allow for holistic simulations of complex DHW systems. In two rigorous tests of the modeling framework, we demonstrated the importance of water consumption dynamics in the modeling of DHW systems, showing that static modeling leads to underestimated heat losses and wrong energy consumption predictions. In an exemplary case study, we identified and quantified the synergistic interactions between water boiler temperatures and a drain water heat recovery device, demonstrating the strength of this methodology for optimizing strategies targeting DHW systems. With its modular structure, this open-source modeling framework can be extended to include any DHW-related technology, providing a useful common platform for collaboration between technology developers and water experts.

The dataset is composed by 12 files reporting the water availability and temperature scenarios for 167 water-dependent power plants in the Danube river basin and the Iberian Peninsula. The dataset is split into multiple files by region (Danube river basin (Danube) or Iberian Peninsula (IP)), variable (discharge or river temperature) and scenario (baseline, RCP26 or RCP85) considered. The title of each file is composed by the variable reported (discharge or river temperature) and the scenario considered (baseline: 1951-2004, RCP26: 2006-2100, RCP85: 2006-2100). The first row is used to report the fields considered: the first three columns report the day, the month and the year. The remaining columns report the name of the power plant considered in each region (57 for the Daube river basin and 110 for the Iberian Peninsula). In each row day, month, year and streamflow or river temperature values are reported for every water-dependent power plant examined in the study. Temperature is reported as daily average temperature in degrees Celsius (°C) while water availability is reported as daily average streamflow in cubic meters per second (m^3/s). For a description on how these files were obtained, please refer to https://doi.org/10.2777/135510. This dataset was produced in the context of the project "US-EU integrated power and water systems modelling" funded by the European Commission Directorate General for Research and Innovation, Directorate G. - Energy, Unit G3 - Renewable Energy Sources, PP-06161-2017.

The dataset is composed by 12 files reporting the water availability and temperature scenarios for 167 water-dependent power plants in the Danube river basin and the Iberian Peninsula. The dataset is split into multiple files by region (Danube river basin (Danube) or Iberian Peninsula (IP)), variable (discharge or river temperature) and scenario (baseline, RCP26 or RCP85) considered. The title of each file is composed by the variable reported (discharge or river temperature) and the scenario considered (baseline: 1951-2004, RCP26: 2006-2100, RCP85: 2006-2100). The first row is used to report the fields considered: the first three columns report the day, the month and the year. The remaining columns report the name of the power plant considered in each region (57 for the Daube river basin and 110 for the Iberian Peninsula). In each row day, month, year and streamflow or river temperature values are reported for every water-dependent power plant examined in the study. Temperature is reported as daily average temperature in degrees Celsius (°C) while water availability is reported as daily average streamflow in cubic meters per second (m^3/s). For a description on how these files were obtained, please refer to https://doi.org/10.2777/135510. This dataset was produced in the context of the project "US-EU integrated power and water systems modelling" funded by the European Commission Directorate General for Research and Innovation, Directorate G. - Energy, Unit G3 - Renewable Energy Sources, PP-06161-2017.