Trade-Offs between Geographic Scale, Cost, and Infrastructure Requirements for Fully Renewable Electricity in Europe

Tröndle, Tim; Lilliestam, Johan; Marelli, Stefano; Pfenninger, Stefan

doi:10.1016/j.joule.2020.07.018

Cited by 159 publications

(99 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Energy models with high spatial resolutions (dozens of nodes per country) have also predicted a prominent role of solar PV. [121][122][123][124] Although the large seasonality in heating demand in Europe, opposed to solar seasonal variation, limits the solar PV penetration in that region, it is important to acknowledge that most of the global population, expected population growth, and energy demand growth are located at lower latitudes where the solar resource and energy demand show low seasonal variations (Figure 4). Bogdanov et al 28 and Ram et al 125 found that the optimal solar share increases to 68% for a global analysis not only for the power sector but also the entire energy system (Table S1).…”

Section: Llmentioning

confidence: 99%

Solar photovoltaics is ready to power a sustainable future

Victoria

Haegel²,

Peters

et al. 2021

Joule

394

165

View full text Add to dashboard Cite

Thanks to fast learning and sustained growth, solar photovoltaics (PV) is today a highly cost-competitive technology, ready to contribute substantially to CO 2 emissions mitigation. However, many scenarios assessing global decarbonization pathways, either based on integrated assessment models or partial-equilibrium models, fail to identify the key role that this technology could play, including far lower future PV capacity than that projected by the PV community. In this perspective, we review the factors that lie behind the historical cost reductions of solar PV and identify innovations in the pipeline that could contribute to maintaining a high learning rate. We also aim at opening a constructive discussion among PV experts, modelers, and policymakers regarding how to improve the representation of this technology in the models and how to ensure that manufacturing and installation of solar PV-can ramp up on time, which will be crucial to remain in a decarbonization path compatible with the Paris Agreement.

show abstract

Section: Llmentioning

confidence: 99%

Solar photovoltaics is ready to power a sustainable future

Victoria

Haegel²,

Peters

et al. 2021

Joule

394

165

View full text Add to dashboard Cite

show abstract

“…The sector-coupled extension PyPSA-Eur-sec (Brown et al, 2018) calculates heat-demand profiles as well as heat pump coefficients with atlite. The Euro Calliope studied in Tröndle et al (2020) uses atlite to generate hydroelectricity time series from reservoirs. The interactive tool model.energy also employs the atlite libary.…”

Section: Related Researchmentioning

confidence: 99%

atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series

Hofmann¹,

Hampp²,

Neumann³

et al. 2021

JOSS

View full text Add to dashboard Cite

Renewable energy sources are likely to build the backbone of the future global energy system. One important key to a successful energy transition is to analyse the weather-dependent energy outputs of existing and eligible renewable resources. atlite is an open Python software package for retrieving global historical weather data and converting it to power generation potentials and time series for renewable energy technologies like wind turbines or solar photovoltaic panels based on detailed mathematical models. It further provides weather-dependent output on the demand side like building heating demand and heat pump performance. The software is optimized to aggregate data over multiple large regions with user-defined weightings based on land use or energy yield. Statement of needDeriving weather-based time series and maximum capacity potentials for renewables over large regions is a common problem in energy system modelling. Websites with exposed open APIs such as renewables.ninja ) exist for such purpose but are difficult to use for local execution, e.g. in cluster environments, and restricted to non-commercial use. Further, by design, they neither expose the underlying datasets nor methods for deriving time series, here referred to as conversion functions/methods. This makes them unsuited for utilizing different weather datasets or exploring alternative conversion functions. The pvlib (Holmgren et al., 2018) is suited for local execution and allows interchangeable input data but is specialized to PV systems only and intended for single location modelling. Other packages like the Danish REatlas (Andresen et al., 2015) face obstacles with accessibility, are based on proprietary code, miss documentation and are restricted in flexibility regarding their inputs.

show abstract

“…It allows users to consider the behavior of energy systems both in time and in space (i.e., energy fluxes between nodes) and is quite flexible as an energy modeling tool. There are several studies exploring this framework, with different spatial scales and end goals: evaluating concentrated solar power as a possible replacement for nuclear power plants in South Africa [45]; assessing if the phasing out of nuclear power in Switzerland is feasible [46]; explore the potential of gas power as a bridging fuel in the transition to wind and solar power [47]; or evaluating the trade-offs between geographic scale, cost, and infrastructure requirements in a 100% renewable Europe [48]. As pointed out before, Calliope has also been applied for smaller spatial scales, such as neighborhoods or small urban districts [29,30].…”

Section: Calliope-an Overviewmentioning

confidence: 99%

Modeling Energy Communities with Collective Photovoltaic Self-Consumption: Synergies between a Small City and a Winery in Portugal

Luz

Silva

2021

Energies

View full text Add to dashboard Cite

The recently approved regulation on Energy Communities in Europe is paving the way for new collective forms of energy consumption and production, mainly based on photovoltaics. However, energy modeling approaches that can adequately evaluate the impact of these new regulations on energy community configurations are still lacking, particularly with regards to the grid tariffs imposed on collective systems. Thus, the present work models three different energy community configurations sustained on collective photovoltaics self-consumption for a small city in southern Portugal. This energy community, which integrates the city consumers and a local winery, was modeled using the Python-based Calliope framework. Using real electricity demand data from power transformers and an actual winery, the techno-economic feasibility of each configuration was assessed. Results show that all collective arrangements can promote a higher penetration of photovoltaic capacity (up to 23%) and a modest reduction in the overall cost of electricity (up to 8%). However, there are clear trade-offs between the different pathways: more centralized configurations have 53% lower installation costs but are more sensitive to grid use costs (which can represent up to 74% of the total system costs). Moreover, key actor’s individual self-consumption rate may decrease by 10% in order to benefit the energy community as a whole.

show abstract

Trade-Offs between Geographic Scale, Cost, and Infrastructure Requirements for Fully Renewable Electricity in Europe

Cited by 159 publications

References 52 publications

Solar photovoltaics is ready to power a sustainable future

Solar photovoltaics is ready to power a sustainable future

atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series

Modeling Energy Communities with Collective Photovoltaic Self-Consumption: Synergies between a Small City and a Winery in Portugal

Contact Info

Product

Resources

About