Variable renewable energy (VRE) resources increasingly add fluctuations to power systems. The required types and capacities of balancing measures, amounts of curtailment, and costs associated with system integration need to be assessed for advising policy makers and economic actors. Previous studies mostly exclude storage from model-endogenous capacity expansion and omit concentrated solar power (CSP) completely. In this study, we stress the need for grid and backup capacity by investigating an integrated market in Europe, allowing for additional short-term as well as long-term storage and considering CSP as a dispatchable backup option. The Renewable Energy Mix (REMix) energy system model is introduced and applied to assess the capacity expansion and hourly dispatch at various levels of photovoltaic and wind power penetration. The model results demonstrate combinations of spatial and temporal balancing measures that enable net photovoltaic and wind supply shares of 60% and 70% of the annual demand, respectively. The usage of storage and grid can keep curtailments below 20% of the demand for theoretical VRE shares of up to 100%. Furthermore, we determine that the VRE supply structure has a strong impact on the least-cost allocation of power plants across Europe but only a limited effect on supply costs

In this study the sensitivity of the model performance of the chemistry transport model (CTM) LOTOS-EUROS to the description of the temporal variability of emissions was investigated. Currently the temporal release of anthropogenic emissions is described by European average diurnal, weekly and seasonal time profiles per sector. These default time profiles largely neglect the variation of emission strength with activity patterns, region, species, emission process and meteorology. The three sources dealt with in this study are combustion in energy and transformation industries (SNAP1), nonindustrial combustion (SNAP2) and road transport (SNAP7). First of all, the impact of neglecting the temporal emission profiles for these SNAP categories on simulated concentrations was explored. In a second step, we constructed more detailed emission time profiles for the three categories and quantified their impact on the model performance both separately as well as combined. The performance in comparison to observations for Germany was quantified for the pollutants NO2, SO2 and PM10 and compared to a simulation using the default LOTOS-EUROS emission time profiles. The LOTOS-EUROS simulations were performed for the year 2006 with a temporal resolution of 1 h and a horizontal resolution of approximately 25 × 25km2. In general the largest impact on the model performance was found when neglecting the default time profiles for the three categories. The daily average correlation coefficient for instance decreased by 0.04 (NO2), 0.11 (SO2) and 0.01 (PM10) at German urban background stations compared to the default simulation. A systematic increase in the correlation coefficient is found when using the new time profiles. The size of the increase depends on the source category, component and station. Using national profiles for road transport showed important improvements in the explained variability over the weekdays as well as the diurnal cycle for NO2. The largest impact of the SNAP1 and 2 profiles were found for SO2. When using all new time profiles simultaneously in one simulation, the daily average correlation coefficient increased by 0.05 (NO2), 0.07 (SO2) and 0.03 (PM10) at urban background stations in Germany. This exercise showed that to improve the performance of a CTM, a better representation of the distribution of anthropogenic emission in time is recommendable. This can be done by developing a dynamical emission model that takes into account regional specific factors and meteorology.

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.

REMix is a framework for modeling energy systems of almost any temporal, spatial, and technological scale and detail. It can be used to model processes of extraction, storage, conversion, transfer and demand for any commodity, usually representing energy carriers such as electricity, gases, or heat, and to flexibly link them with each other. All processes can be characterized by indicators such as costs or CO 2 emissions. The mathematical core of the framework allows integer and continuous decision variables and the objective function includes one or more indicators. Energy system infrastructures can be considered for both economic dispatch and capacity expansion planning along either single target years or full infrastructure pathways. Research subjects are usually regional to continental systems, and their transformation to a climate-neutral energy supply.

The utilization of cropland and rooftops for solar photovoltaics (PVs) installation holds significant potential for enhancing global renewable energy capacity with the advantage of dual land-use. This study focuses on estimating the global area suitable for agrivoltaics (PV over crops) and rooftop PVs by employing open-access data, existing literature and simple numerical methods in a high spatial resolution of 10 km × 10 km. For agrivoltaics, the suitability is assessed with a systematic literature review on crop-dependent feasibility and profitability, especially for 18 major crops of the world. For rooftop PV, a non-linear curve-fitting method is developed, using the urban land cover to calculate the PV-suitable built-up areas. This method is then verified by comparing the results with open-access building footprints. The spatially resolved suitability assessment unveils 4.64 million km² of global PV-usable cropland corresponding to a geographic potential of about 217 Terawatts (TW) in an optimistic scenario and 0.21 million km² of rooftop-PV suitable area accounting for about 30.5 TW maximum installable power capacity. The estimated suitable area offers a vast playground for energy system analysts to undertake techno-economic assessments, and for technology modellers and policy makers to promote PV implementation globally with the vision of net-zero emissions in the future.

The accelerated urbanization and industrialization in China is leading to major challenges due to rising energy demand and emissions. Cities in particular play an important role in the decision-making and implementation processes for the energy transition. However, they often have only limited local energy potential and are heavily dependent on supply regions. We therefore assess how a predominantly renewable power supply can be implemented based on the availability of local or imported renewable resources. We present a case study in which an advanced energy system model is parametrized and applied to address questions which are relevant to the transformation of the energy system in China. The model is capable of simultaneously optimizing investment decisions and hourly power balances of a scenario year, taking into account different storage technologies, regional power exchange and policy constraints such as carbon cap, carbon price and renewable portfolio standards. The study takes the Beijing-Tianjin-Hebei metropolitan region with Inner Mongolia as a supply region—considered as exemplary regions characterized by heterogeneous infrastructures, resources and consumption—as its model. Starting from a context-related normative energy scenario, we analyze a possible future electricity system under various assumptions using the Renewable Energy Mix (REMix) energy system model developed at the DLR (German Aerospace Center). Depending on the estimated potentials of renewable energies, technology costs and the projected electricity demand, the metropolitan region is mainly supplied with imported wind and solar power. A sensitivity analysis considers installed capacities, annual generation, CO₂ emissions and costs. The results indicate that the assumption of storage costs is of great importance for the future total costs of an electricity system. Variations in other parameters led to different generation portfolios with similar system costs. Our results provide insights into future regional infrastructure needs, and underline the importance of regional coordination and governance for the energy transition in China.