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Recent global events emphasize the importance of a reliable energy supply. One way to increase energy supply security is through decentralized off-grid renewable energy systems, for which a growing number of case studies are researched. This review gives a global overview of the levelized cost of electricity (LCOE) for these autonomous energy systems, which range from 0.03 \$_{2021}/kWh to over 1.00 \$_{2021}/kWh worldwide. The average LCOEs for 100% renewable energy systems have decreased by 9% annually between 2016 and 2021 from 0.54 \$_{2021}/kWh to 0.29 \$_{2021}/kWh, presumably due to cost reductions in renewable energy and storage technologies. Furthermore, we identify and discuss seven key reasons why LCOEs are frequently overestimated or underestimated in literature, and how this can be prevented in the future. Our overview can be employed to verify findings on off-grid systems, to assess where these systems might be deployed and how costs evolve.

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making., Comment: MTC, JW and LPS contributed equally to this manuscript. 25 pages, 2 figures. Fixed typos in manuscript title. This perspective is based on the discussion held at the 2021 Next Generations Challenges in Energy-Climate Modelling (NextGenEC'21) workshop

Potential analyses identify possible locations for renewable energy installations, such as as wind turbines and photovoltaic arrays. The results of previous potential studies, however, are not consistent due to different assumptions, methods, and datasets. In this study, we compare commonly used land use data sources with regard to area and position. Using Corine Land Cover leads to an overestimation of the potential areas in a typical wind potential analysis by a factor of 4.6 and 5.2 in comparison to Basis-DLM and Open Street Map, respectively. Furthermore, we develop scenarios for onshore wind, offshore wind, and open-field photovoltaic potential estimations based on land eligibility analyses and calculate rooftop photovoltaic potential using 3D building data. The potential capacities and possible locations are published for all administrative levels in Germany in the freely accessible database trep-db, for example, to be incorporated into energy system models. The investigations are validated using high-resolution regional potential analyses and benchmarked against other studies in the literature. Findings from the literature, which can be used by legislators to design regulation, are rarely comparable and consistent due to differences in the datasets used., Comment: Submitted to Applied Energy

To achieve climate neutrality, synergies between circular economy and reduction of greenhouse gas emissions must be strengthened. Previously idle emission reduction potentials of resource efficiency are to be exploited. Since all potentially possible emission reduction measures are linked by interactions, the evaluation of a single measure in terms of cost efficiency, effectiveness, and compliance with climate protection targets is very complex and requires a model-based analysis that takes the entire energy system into account. This work advances an energy system model for Germany so that through comprehensive modeling of industrial processes and implementation of recycling options, the impact of recycling measures in the context of national greenhouse gas mitigation strategies can be analyzed. The scenario evaluation shows that different recycling strategies have large effects on the German energy system. Without recycling energy demand in 2050 will increase by more than 300 TWh and cost of transformation will rise by 85% compared to a reference scenario. On the other hand, if maximum recycling rates can be achieved, costs of transformation can be reduced by 26% until 2050. Recycling is an essential and cost-efficient greenhouse gas reduction strategy for future low-carbon energy system designs., Comment: Article and supplementary material submitted to Energy

The growing share of intermittent renewable energy sources, storage technologies, and the increasing degree of so-called sector coupling necessitates optimization-based energy system models with high temporal and spatial resolutions, which significantly increases their runtimes and limits their maximum sizes. In order to maintain the computational viability of these models for large-scale application cases, temporal aggregation has emerged as a technique for reducing the number of considered time steps by reducing the original time horizon down to fewer, more representative ones. This study presents advanced but generally applicable clustering techniques that allow for ad-hoc improvements of state-of-the-art approaches without requiring profound knowledge of the individual energy system model. These improvements comprise the optimal tradeoff between the number of typical days and inner-daily temporal resolutions, as well as constituting a representation method that can reproduce the value distribution of the original time series. We prove the superiority of these approaches by applying them to two fundamentally different model types, namely a single-node building energy system and a European carbon-neutral energy scenario, and benchmark these against state-of-the-art approaches. This is performed for a variety of temporal resolutions, which leads to many hundreds of model runs. The results show that the proposed improvements on current methods strictly dominate the status quo with respect to Pareto-optimality in terms of runtime and accuracy. Although a speeding up factor of one magnitude could be achieved using traditional aggregation methods within a cost deviation range of two percent, the algorithms proposed herein achieve this accuracy with a runtime speedup by a factor of two orders of magnitude.

Designing an optimal energy system with large shares of renewable energy sources is computationally challenging. Considering greater spatial horizon and level of detail, during the design, exacerbates this challenge. This paper investigates spatial and technology aggregation of energy system model, as a complexity-reduction technique. To that end, a novel two-step aggregation scheme based on model parameters such as Variable Renewable Energy Sources (VRES) time series and capacities, transmission capacities and distances, etc, is introduced. First, model regions are aggregated to obtain reduced region set. The aggregation is based on a holistic approach that considers all model parameters and spatial contiguity of regions. Next, technology aggregation is performed on each VRES, present in each newly-defined region. Each VRES is aggregated based on the temporal profiles to obtain a representative set. The impact of these aggregations on accuracy and computational complexity of a cost-optimal energy system design is analyzed for a European energy system scenario.The aggregations are performed to obtain different combinations of number of regions and VRES types, and the results are benchmarked against initial spatial resolution of 96 regions and 68 VRES types in each region. The results show that the system costs deviate significantly when lower number of regions and/or VRES types are considered. As the spatial resolution is increased in terms of both number of regions and VRES types, the system cost fluctuates at first and stabilizes at some point, approaching the benchmark value. Optimal combination can be determined based on an acceptable cost deviation and the point of stabilization. For instance, if <5% deviation is acceptable, 33 regions and 38 VRES types in each region is optimal. With this setting, the system cost is under-estimated by 4.42% but the run time is reduced by 92.95%., Comment: 18 pages, 14 figures

Energy system models are challenged by the need for high temporal and spatial resolutions in or-der to appropriately depict the increasing share of intermittent renewable energy sources, storage technologies, and the growing interconnectivity across energy sectors. This study evaluates methods for maintaining the computational viability of these models by ana-lyzing different temporal aggregation techniques that reduce the number of time steps in their in-put time series. Two commonly-employed approaches are the representation of time series by a subset of single (typical) time steps, or by groups of consecutive time steps (typical periods). We test these techniques for two different energy system models that are implemented using the Frame-work for Integrated Energy System Assessment (FINE) by benchmarking the optimization results based on aggregation to those of the fully resolved models and investigating whether the optimal aggregation method can, a priori, be determined based on the clustering indicators. The results reveal that typical time steps consistenly outperform typical days with respect to cluster-ing indicators, but do not lead to more accurate optimization results when applied to a model that takes numerous storage technologies into account. Although both aggregation techniques are ca-pable of coupling the aggregated time steps, typical days offer more options to depict storage oper-ations, whereas typical time steps are more effective for models that neglect time-linking con-straints. In summary, the adequate choice of aggregation methods strongly depends on the mathematical structure of the considered energy system optimization model, and a priori decisions of a sufficient temporal aggregation are only possible with good knowledge of this mathematical structure.

The determination of environmentally- and economically-optimal energy system designs and operations is complex. In particular, the integration of weather-dependent renewable energy technologies into energy system optimization models presents new challenges to computational tractability that cannot only be solved by advancements in computational resources. In consequence, energy system modelers must tackle the complexity of their models daily and introduce various methods to manipulate the underlying data and model structure, with the ultimate goal of finding optimal solutions. As which complexity reduction method is suitable for which research question is often unclear, herein we review some approaches to handling complexity. Thus, we first analyze the determinants of complexity and note that many drivers of complexity could be avoided a priori with a tailored model design. Second, we conduct a review of systematic complexity reduction methods for energy system optimization models, which can range from simple linearization performed by modelers to sophisticated multi-level approaches combining aggregation and decomposition methods. Based on this overview, we develop a guide for modelers who encounter computational limitations.

To account for volatile renewable energy supply, energy systems optimization problems require high temporal resolution. Many models use time-series clustering to find representative periods to reduce the amount of time-series input data and make the optimization problem computationally tractable. However, clustering methods remove peaks and other extreme events, which are important to achieve robust system designs. We present a general decision framework to include extreme events in a set of representative periods. We introduce a method to find extreme periods based on the slack variables of the optimization problem itself. Our method is evaluated and benchmarked with other extreme period inclusion methods from the literature for a design and operations optimization problem: a residential energy supply system. Our method ensures feasibility over the full input data of the residential energy supply system although the design optimization is performed on the reduced data set. We show that using extreme periods as part of representative periods improves the accuracy of the optimization results by 3% to more than 75% depending on system constraints compared to results with clustering only, and thus reduces system cost and enhances system reliability.

The installation and operation distributed energy resources (DER) and the electrification of the heat supply significantly changes the interaction of the residential building stock with the grid infrastructure. Evaluating the mass deployment of DER at the national level would require analyzing millions of individual buildings, entailing significant computational burden. To overcome this, this work proposes a novel bottom-up model that consists of an aggregation algorithm to create a spatially distributed set of typical residential buildings from census data. Each typical building is then optimized with a Mixed-Integer Linear Program to derive its cost optimal technology adoption and operation, determining its changing grid load in future scenarios. The model is validated for Germany, with 200 typical buildings considered to sufficiently represent the diversity of the residential building stock. In a future scenario for 2050, photovoltaic and heat pumps are predicted to be the most economically and ecologically robust supply solutions for the different building types. Nevertheless, their electricity generation and demand temporally do not match, resulting in a doubling of the peak electricity grid load in the rural areas during the winter. The urban areas can compensate this with efficient co-generation units, which are not cost-efficient in the rural areas., Comment: Preprint submitted to Energy & Buildings

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., Comment: Submitted to Applied Energy

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.