Fundamental optimization models can determine normative cost-optimal systems following theoretical assumptions of markets with perfect competition and perfect foresight. However, in real markets, deviations from ideal assumptions need to be considered for designing efficient policies: they may lead to higher costs than theoretically expected and contribute to the so-called "efficiency gap". Agent-based simulation models can consider market inefficiencies but are not ideally suited for determining normative systems. This work aims to reduce the efficiency gap between theoretically cost-optimal system configurations and their implementations in non-perfect markets by coupling a fundamental electricity market model and an agent-based model. A fully automated iterative coupling is demonstrated, and its convergence behavior is investigated for two coupling parameters. Fast convergence is observed when integrating peak capacity usage from the previous agent-based simulation into the fundamental model. However, the efficiency gap is reduced only to limited extent as no information on hourly dispatch is transferred between the models. Conversely, when using hourly storage dispatch as coupling parameter, it takes longer to achieve convergence. In return, the efficiency gap is reduced to larger extent. The proposed iterative coupling thus allows the identification of realizable cost-optimal systems, which means that they are achievable under non-perfect market conditions.

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. Many powerful tools exist to find optimal capacity expansion. In a stylized comparison of six models, we evaluate the capacity expansion results of basic power sector technologies. The technologies under investigation include base- and peak load power plants, electricity storage, and transmission. We define four highly simplified and harmonized test cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all test cases. Fixed energy-to-power ratios of storage can entirely change optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and final-period storage levels can affect the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure substantially deviate from linear optimization models, as missing foresight and limited flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions, and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there.

This paper presents the results of a multi-model comparison to determine outcome deviations resulting from differences in power system models. We apply eight temporally and spatially resolved models to 16 stylized test cases. These test cases differ in their renewable energy supply share, technology scope, and optimization scope. We focus on technologies for balancing the variability of power generation, such as controllable power plants, energy storage, power transmission, and flexibility related to sector coupling. We use harmonized input data in all models to separate model-related from data-related outcome deviations. We find that our approach allows for isolating and quantifying model-related outcome deviations and robust effects concerning system operation and investment decisions. Furthermore, we can attribute these deviations to the identified model differences. Our results show that trends in the use of individual flexibility options are robust across most models. Moreover, our analysis reveals that differences in the general modeling approach and the modeling of specific technologies lead to comparatively small deviations. In contrast, a heterogeneous model scope can cause substantially larger deviations. Due to a large number of models and scenarios, our analysis can provide important information on which investment and operation decisions are robust to the model choice, and which modeling approaches have an exceptionally high impact on results. Our findings may guide both modelers and decision-makers in properly evaluating the results of similarly designed power system models.

Model-based scenario analyses of future energy systems often come to deviating results and conclusions when different models are used. This may be caused by heterogeneous input data and by inherent differences in model formulations. The representation of technologies for the conversion, storage, use, and transport of energy is usually stylized in comprehensive system models in order to limit the size of the mathematical problem, and may substantially differ between models. This paper presents a systematic comparison of nine power sector models with sector coupling. We analyze the impact of differences in the representation of technologies, optimization approaches, and further model features on model outcomes. The comparison uses fully harmonized input data and highly simplified system configurations to isolate and quantify model specific effects. We identify structural differences in terms of the optimization approach between the models. Furthermore, we find substantial differences in technology modeling primarily for battery electric vehicles, reservoir hydro power, power transmission, and demand response. These depend largely on the specific focus of the models. In model analyses where these technologies are a relevant factor, it is therefore important to be aware of potential effects of the chosen modeling approach. For the detailed analysis of the effect of individual differences in technology modeling and model features, the chosen approach of highly simplified test cases is suitable, as it allows to isolate the effects of model-specific differences on results. However, it strongly limits the model's degrees of freedom, which reduces its suitability for the evaluation of fundamentally different modeling approaches.

A novel experimental approach of inter- and intramodel comparisons is conducted with five power market models to give recommendations for modelers working on decarbonization pathways of Europe until 2050. The experiments investigate the impact of model type (optimization vs. simulation), planning horizon (intertemporal vs. myopic), temporal resolution (8760 vs. 384 hours), and spatial resolution (28 countries vs. 12 mega-regions). The model type fundamentally determines the evolution of capacity expansion. Planning horizon (assumed foresight of firms) plays a minor role for scenarios with high carbon prices. For low carbon prices in turn, results from myopic models deviate considerably from those of intertemporal models. Lower temporal and spatial resolutions foster wind power via storage and via neglected transmission boundaries, respectively. Using simulation instead of optimization frameworks, a shorter planning horizon of firms, or lower temporal and spatial resolutions might be necessary to reduce the computational complexity. This paper delivers recommendations on how to limit the discrepancies in such cases.

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. In a model comparison, we examine the optimal expansion of such technologies with six capacity expansion power system models. The technologies under investigation include base- and peak-load power plants, electricity storage, and transmission. We define four highly simplified and harmonized use cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all use cases. Fixed energy-to-power-ratios of storage can entirely change model optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and end storage levels can impact the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure significantly deviate from linear optimization models, as limited foresight and flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there.

A reliable and cost-effective electricity system transition requires both the identification of optimal target states and the definition of political and regulatory frameworks that enable these target states to be achieved. Fundamental optimization models are frequently used for the determination of cost-optimal system configurations. They represent a normative approach and typically assume markets with perfect competition. However, it is well known that real systems do not behave in such an optimal way, as decision-makers do not have perfect information at their disposal and real market actors do not take decisions in a purely rational way. These deficiencies lead to increased costs or missed targets, often referred to as an "efficiency gap". For making rational political decisions, it might be valuable to know which factors influence this efficiency gap and to what extent. In this paper, we identify and quantify this gap by soft-linking a fundamental electricity market model and an agent-based simulation model, which allows the consideration of these effects. In order to distinguish between model-inherent differences and non-ideal market behavior, a rigorous harmonization of the models was conducted first. The results of the comparative analysis show that the efficiency gap increases with higher renewable energy shares and that information deficits and policy instruments affect operational decisions of power market participants and resulting overall costs significantly.