• Energy Research

Increasing energy autonomy is one of the main reasons for municipalities to invest in renewable energy technologies. In this study, the potential of weather-robust autonomous energy systems is evaluated for 11 003 German municipalities in over one million parallelized techno-economic optimizations utilizing high-performance computing clusters. For this purpose, a holistic municipal-level energy system model (ETHOS.FineRegions) was developed that minimizes annualized system costs in 2045. The completely energy autonomous supply can be established in around 90% of German municipalities corresponding to 50% of the country‘s population. Especially highly populated municipalities often do not have the capacity to meet their own energy demands due to low wind and open-field PV potentials. Large rooftop PV capacities account for 40% of installed capacity in the autonomous municipalities. Seasonal storage needs are met by large underground thermal storage tanks and batteries provide intraday storage. Furthermore, huge capacity increases are often required for the final 20% of energy demand to be met in order to achieve a degree of autonomy of 100%. The large storage and rooftop PV capacities lead to high specific system costs in the autonomous municipalities with between 144 €/MWh and 174 €/MWh on average, depending on legislation and opposition towards onshore wind installations. By paying a premium of up to 50% compared to the grid-dependent system, 3945 municipalities with 17.2 million inhabitants could become completely autonomous by 2045. For regions that could achieve an autonomous energy supply at moderate costs, however, lost revenues through energy exports could be a decisive argument against autonomy efforts.

Advances in applied energy 16, 100190 - (2024). doi:10.1016/j.adapen.2024.100190 Published by Elsevier ScienceDirect, [Amsterdam]

The complexity of Mixed-Integer Linear Programs (MILPs) increases with the number of nodes in energy system models. An increasing complexity constitutes a high computational load that can limit the scale of the energy system model. Hence, methods are sought to reduce this complexity. In this paper, we present a new 2-Level Approach to MILP energy system models that determines the system design through a combination of continuous and discrete decisions. On the first level, data reduction methods are used to determine the discrete design decisions in a simplified solution space. Those decisions are then fixed, and on the second level the full dataset is used to ex-tract the exact scaling of the chosen technologies. The performance of the new 2-Level Approach is evaluated for a case study of an urban energy system with six buildings and an island system based on a high share of renewable energy technologies. The results of the studies show a high accuracy with respect to the total annual costs, chosen system structure, installed capacities and peak load with the 2-Level Approach compared to the results of a single level optimization. The computational load is thereby reduced by more than one order of magnitude.

Mitigating anthropogenic climate change and achieving the Paris climate goals is one of the greatest challenges of the twenty-first century. To meet the Paris climate goals, sector-specific transformation pathways need to be defined. The different transformation pathways are used to hypothetically quantify whether a defined climate target is achievable or not. For this reason, a bottom-up model was developed to assess the extent of selected industrial decarbonization options compared to conventionally used technologies from an emissions perspective. Thereby, the bottom-up model is used to analyze the German container and flat glass industries as an example. The results show that no transformation pathway can be compatible with the 1.5 °C based strict carbon dioxide budget target. Even the best case scenario exceeds the 1.5 °C based target by approximately + 200 %. The 2 °C based loose carbon dioxide budget target is only achievable via fuel switching, the complete phase-out from natural gas to renewable energy carriers. Furthermore, the results of hydrogen for flat glass production demonstrate that missing investments in renewable energy carriers may lead to the non-compliance with actually achievable 2 °C based carbon dioxide budget targets. In conclusion, the phase-out from natural gas to renewable energies should be executed at the end of the life of any existing furnace, and process emissions should be avoided in the long term to contribute to 1.5 °C based strict carbon dioxide budget target. Energy conversion and management: X 17, 100336 - (2023). doi:10.1016/j.ecmx.2022.100336 Published by Elsevier, Amsterdam

Modelling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable. To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design. It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself, e.g. k means clustering, plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable.

Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

The optimization-based design of renewable energy systems is a computationally demanding task because of the high temporal fluctuation of supply and demand time series. In order to reduce these time series, the aggregation of typical operation periods has become common. The problem with this method is that these aggregated typical periods are modeled independently and cannot exchange energy. Therefore, seasonal storage cannot be adequately taken into account, although this will be necessary for energy systems with a high share of renewable generation. To address this issue, this paper proposes a novel mathematical description for storage inventories based on the superposition of inter-period and intra-period states. Inter-period states connect the typical periods and are able to account their sequence. The approach has been adopted for different energy system configurations. The results show that a significant reduction in the computational load can be achieved also for long term storage-based energy system models in comparison to optimization models based on the full annual time series. Submitted to Applied Energy