• Energy Research
• 2021-2025close
• NL close

We introduce a novel simulation tool capable of calculating the energy yield of a PV system based on its fundamental material properties and using self-consistent models. Thus, our simulation model can operate without measurements of a PV device. It combines wave and ray optics and a dedicated semiconductor simulation to model the optoelectronic PV device properties resulting in the IV-curve. The system surroundings are described via spectrally resolved ray tracing resulting in a cell resolved irradiance distribution, and via the fluid dynamics-based thermal model, in the individual cell temperatures. A lumped-element model is used to calculate the IV-curves of each solar cell for every hour of the year. These are combined factoring in the interconnection to obtain the PV module IV-curves, which connect to the inverter for calculating the AC energy yield. In our case study, we compare two types of 2 terminal perovskite/silicon tandem modules with STC PV module efficiencies of 27.7% and 28.6% with a reference c-Si module with STC PV module efficiency of 20.9%. In four different climates, we show that tandem PV modules operate at 1–1.9 °C lower yearly irradiance weighted average temperatures compared to c-Si. We find that the effect of current mismatch is significantly overestimated in pure optical studies, as they do not account for fill factor gains. The specific yields in kWh/kWp of the tandem PV systems are between −2.7% and +0.4% compared to the reference c-Si system in all four simulated climates. Thus, we find that the lab performance of the simulated tandem PV system translates from the laboratory to outdoors comparable to c-Si systems. Photovoltaic Materials and Devices Electrical Sustainable Energy Energie and Industrie

We introduce a novel simulation tool capable of calculating the energy yield of a PV system based on its fundamental material properties and using self-consistent models. Thus, our simulation model can operate without measurements of a PV device. It combines wave and ray optics and a dedicated semiconductor simulation to model the optoelectronic PV device properties resulting in the IV-curve. The system surroundings are described via spectrally resolved ray tracing resulting in a cell resolved irradiance distribution, and via the fluid dynamics-based thermal model, in the individual cell temperatures. A lumped-element model is used to calculate the IV-curves of each solar cell for every hour of the year. These are combined factoring in the interconnection to obtain the PV module IV-curves, which connect to the inverter for calculating the AC energy yield. In our case study, we compare two types of 2 terminal perovskite/silicon tandem modules with STC PV module efficiencies of 27.7% and 28.6% with a reference c-Si module with STC PV module efficiency of 20.9%. In four different climates, we show that tandem PV modules operate at 1–1.9 °C lower yearly irradiance weighted average temperatures compared to c-Si. We find that the effect of current mismatch is significantly overestimated in pure optical studies, as they do not account for fill factor gains. The specific yields in kWh/kWp of the tandem PV systems are between −2.7% and +0.4% compared to the reference c-Si system in all four simulated climates. Thus, we find that the lab performance of the simulated tandem PV system translates from the laboratory to outdoors comparable to c-Si systems. Photovoltaic Materials and Devices Electrical Sustainable Energy Energie and Industrie

This report describes the modelling approach, input data, scenarios of biobased fertiliser application, and the results and conclusions in terms of environmental impacts. For all demonstration plants scenarios were worked out in terms of application rates of digestate and/or biobased fertilisers, and the associated applied nutrients and heavy metals to the soil. Thereafter, the model simulations were carried out which were discussed during a SYSTEMIC internal webinar. Finally, the outcome of the environmental impact assessments were reviewed by the demoplants and other partners of the SYSTEMIC project consortium.

This report describes the modelling approach, input data, scenarios of biobased fertiliser application, and the results and conclusions in terms of environmental impacts. For all demonstration plants scenarios were worked out in terms of application rates of digestate and/or biobased fertilisers, and the associated applied nutrients and heavy metals to the soil. Thereafter, the model simulations were carried out which were discussed during a SYSTEMIC internal webinar. Finally, the outcome of the environmental impact assessments were reviewed by the demoplants and other partners of the SYSTEMIC project consortium.

District heating is a network of pipes through which heat is delivered from a centralised source. It is expected to play an important role in the decarbonisation of the energy sector in the coming years. In district heating, heat is traditionally generated through fossil fuels, often with combined heat and power (CHP) units. However, increasingly, waste heat is being used as a low carbon alternative, either directly or, for low temperature sources, via a heat pump. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance). Contrary to traditional uncertainty metrics that provide statistical summaries and impose total ordering, stochastic ordering is a partial ordering and operates with full probability distributions. In our analysis, we apply the orderings in the mean and dispersion to the waste heat recovery problem in Brunswick, Germany.

District heating is a network of pipes through which heat is delivered from a centralised source. It is expected to play an important role in the decarbonisation of the energy sector in the coming years. In district heating, heat is traditionally generated through fossil fuels, often with combined heat and power (CHP) units. However, increasingly, waste heat is being used as a low carbon alternative, either directly or, for low temperature sources, via a heat pump. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance). Contrary to traditional uncertainty metrics that provide statistical summaries and impose total ordering, stochastic ordering is a partial ordering and operates with full probability distributions. In our analysis, we apply the orderings in the mean and dispersion to the waste heat recovery problem in Brunswick, Germany.