• Energy Research
• 2016-2025close
• Open Access close
• Closed Access close
• Open Source close
• DE close

Abstract The objective of this work is to evaluate the surge characteristics of an axial compressor operating under surge conditions, by using a hybrid BDF/harmonic balance method. During a surge event, a large amount of backflow occurs in the compressor. Consequently, the airflow oscillates along the axial direction at a low frequency, which can considerably damage the compressor. In this paper, a numerical investigation is performed to examine the effects of the system volume and average mass flow on the surge characteristics of a transonic high speed single stage axial compressor (stage 35) designed and evaluated at the NASA Glenn Center. The results show that the oscillation frequency of the airflow in the compressor generally ranges from 14.32–26.04 Hz during surge conditions, and both the system volume and average mass flow considerably influence the surge characteristics. For a constant average mass flow, as the system volume decreases, the oscillation frequency of the airflow in the compressor increases slightly, while the oscillation amplitude of the exit static pressure decreases significantly, and the maximum static pressure at the outlet remains nearly constant. In the case of a constant system volume, with the decrease in the average mass flow, the oscillation frequency of the airflow in the compressor gradually decreases, and the oscillation amplitude of the exit pressure increases significantly. With a further decrease in the average mass flow, the oscillation frequency stabilizes at a certain point and does not change thenceforth. Furthermore, the surge frequency obtained using the numerical method shows a same tendency with the Helmholtz frequency, but there is a considerable difference between them.

Abstract The objective of this work is to evaluate the surge characteristics of an axial compressor operating under surge conditions, by using a hybrid BDF/harmonic balance method. During a surge event, a large amount of backflow occurs in the compressor. Consequently, the airflow oscillates along the axial direction at a low frequency, which can considerably damage the compressor. In this paper, a numerical investigation is performed to examine the effects of the system volume and average mass flow on the surge characteristics of a transonic high speed single stage axial compressor (stage 35) designed and evaluated at the NASA Glenn Center. The results show that the oscillation frequency of the airflow in the compressor generally ranges from 14.32–26.04 Hz during surge conditions, and both the system volume and average mass flow considerably influence the surge characteristics. For a constant average mass flow, as the system volume decreases, the oscillation frequency of the airflow in the compressor increases slightly, while the oscillation amplitude of the exit static pressure decreases significantly, and the maximum static pressure at the outlet remains nearly constant. In the case of a constant system volume, with the decrease in the average mass flow, the oscillation frequency of the airflow in the compressor gradually decreases, and the oscillation amplitude of the exit pressure increases significantly. With a further decrease in the average mass flow, the oscillation frequency stabilizes at a certain point and does not change thenceforth. Furthermore, the surge frequency obtained using the numerical method shows a same tendency with the Helmholtz frequency, but there is a considerable difference between them.

This deliverable includes the methodology in EnergyPROSPECTS for an in-depth study of energy citizenship. It features the criteria used for selecting the cases for indepth study, the list of cases selected for in-depth study as well as key research foci and empirical research questions.

This deliverable includes the methodology in EnergyPROSPECTS for an in-depth study of energy citizenship. It features the criteria used for selecting the cases for indepth study, the list of cases selected for in-depth study as well as key research foci and empirical research questions.

Abstract The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect.

Abstract The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect.

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.