• Energy Research
• 2021-2025close
• 7. Clean energy close
• 13. Climate action close
• DE close
• JP close

Comfort temperature and sleep quality involving 20 participants were determined in two cases: Case A (arbitrary, controlled air-conditioner setting) and Case B (adjustment of 3 °C higher than the setting of Case A with cool bed linen). Data of indoor thermal comfort and electricity consumption were collected every night throughout the measurement period. Questionnaires on thermal comfort and sleep quality were distributed twice a night for a duration of three nights for each case; the first night was for respondents’ adaptation and the following two nights were for measurement. The sleep quality of the respondents was objectively measured using a commercially available activity tracker. Results found that most respondents were thermally comfortable in both cases, with 39% lower energy consumption reported for Case B compared to Case A. The thermal conditions of Case B were found to be more tolerable than those of Case A. Most respondents reported to have a calm and satisfied sleep for both cases. Comfort temperature and Sleep Efficiency Index (SEI) were found to be maintained in both cases.

Comfort temperature and sleep quality involving 20 participants were determined in two cases: Case A (arbitrary, controlled air-conditioner setting) and Case B (adjustment of 3 °C higher than the setting of Case A with cool bed linen). Data of indoor thermal comfort and electricity consumption were collected every night throughout the measurement period. Questionnaires on thermal comfort and sleep quality were distributed twice a night for a duration of three nights for each case; the first night was for respondents’ adaptation and the following two nights were for measurement. The sleep quality of the respondents was objectively measured using a commercially available activity tracker. Results found that most respondents were thermally comfortable in both cases, with 39% lower energy consumption reported for Case B compared to Case A. The thermal conditions of Case B were found to be more tolerable than those of Case A. Most respondents reported to have a calm and satisfied sleep for both cases. Comfort temperature and Sleep Efficiency Index (SEI) were found to be maintained in both cases.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Real-time pricing demand response programs (RTP-DRPs) are practical measures that ensure the end user's profitability from using electricity by adjusting the supply and demand equilibrium without activating costly solutions. This study explores the potential of RTP-DRPs by developing and applying a region-wise modeling approach based on maximizing the end user's social welfare in the wholesale electricity market in Japan. The regions of the wholesale market are classified based on their response into regions with excess supply, regions with high demand burden, and regular suppliers of inter-regional connections. The results revealed that the RTP-DRPs could potentially reduce the peak demand of the residential sector in Chubu, Chugoku, Kansai, Kyushu, Tokyo, and Tohoku by 1.91%-7.81%. Meanwhile, in Hokkaido, Hokuriku, and Shikoku, by 16.13%-22.9%. The avoided greenhouse emission (GHG) in Tokyo is estimated to be 82.6 and 192.2 tons in summer and winter, respectively.

Real-time pricing demand response programs (RTP-DRPs) are practical measures that ensure the end user's profitability from using electricity by adjusting the supply and demand equilibrium without activating costly solutions. This study explores the potential of RTP-DRPs by developing and applying a region-wise modeling approach based on maximizing the end user's social welfare in the wholesale electricity market in Japan. The regions of the wholesale market are classified based on their response into regions with excess supply, regions with high demand burden, and regular suppliers of inter-regional connections. The results revealed that the RTP-DRPs could potentially reduce the peak demand of the residential sector in Chubu, Chugoku, Kansai, Kyushu, Tokyo, and Tohoku by 1.91%-7.81%. Meanwhile, in Hokkaido, Hokuriku, and Shikoku, by 16.13%-22.9%. The avoided greenhouse emission (GHG) in Tokyo is estimated to be 82.6 and 192.2 tons in summer and winter, respectively.

AbstractLightning is the main precursor of natural wildfires and Long-Continuing-Current (LCC) lightning flashes are proposed to be the main igniters of lightning-ignited wildfires (LIW). Previous studies predict a change of the global occurrence rate and spatial pattern of total lightning. Nevertheless, the sensitivity of lightning-ignited wildfire occurrence to climate change is uncertain. Here, we investigate space-based measurements of LCC lightning associated with lightning ignitions and present LCC lightning projections under the Representative Concentration Pathway RCP6.0 for the 2090s by applying a recent LCC lightning parameterization based on the updraft strength in thunderstorms. We find a 41% global increase of the LCC lightning flash rate. Increases are largest in South America, the western coast of North America, Central America, Australia, Southern and Eastern Asia, and Europe, while only regional variations are found in northern polar forests, where fire risk can affect permafrost soil carbon release. These results show that lightning schemes including LCC lightning are needed to project the occurrence of lightning-ignited wildfires under climate change.