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Energy System Optimization Models are applied to inform decision making processes and therefore must account for the uncertainty as it exists in the real world. In the recent decade, Modeling to Generate Alternatives gained popularity for considering structural uncertainty in Energy System Optimization Models. Modeling to Generate Alternatives finds numerous near-optimal alternatives, which could give the impression that all alternatives are comparably good. However, if one could compare near-optimal shadow prices of energy demand, certain alternatives would be superior to other solutions. Nevertheless, shadow prices of near-optimal solutions reflect the effort of being different and no approach to calculate expedient shadow prices of near-optimal solutions (comparable to the optimal one) has been presented. A simple approach is suggested and discussed with its implications, using an example scenario. The obtained shadow prices further differentiate the quality of solutions in the near-optimal solution space. Deeper analysis reveals regional or temporal bottlenecks of solutions and can help identify better search directions for alternative solutions. To some extent, the shadow prices highlight effects of parametric uncertainty, even though the proposed method cannot counteract this uncertainty. • Energy System Optimization Modeling benefits from analysis of near-optimal solutions. • Approach to enable expedient comparison of near-optimal shadow prices is presented. • Numerous near-optimal solutions are evaluated using said shadow prices. • Shadow prices differentiate the quality of Modeling to Generate Alternatives' solutions. [ABSTRACT FROM AUTHOR]

The energy system decarbonization leads to a decentralization of generation and flexibility. Among the new concepts to integrate distributed flexibility are local energy markets. While a broad range of research investigates local energy markets on a distribution grid level in detail, research on their system impact has been limited. Therefore, we develop a framework to investigate different configurations of local markets as well as the operation of distributed energy resources and their interaction with the wholesale markets. This framework is applied to a pan-European case study with detailed bottom-up modeling of local energy markets in France and Germany. Additionally, transmission grid simulations are carried out to investigate the impact of local trading on grid congestions. Our results show that a direct coordination of distributed flexibility within the wholesale market is more efficient than a preference for local trading. Nonetheless, local energy markets can be used as an incentive for the expansion of distributed flexibility as well as a way to break down the complexity of coordinating millions of distributed assets in a single market. Additionally, our results show that local trading relieves the transmission grid. • Bottom-up modeling of distributed flexibility in France and Germany. • Large-Scale-Modeling of LEMs. • Impact Assessment on Day-ahead market and transmission grid. [ABSTRACT FROM AUTHOR]

Integrating increasing shares of weather-dependent renewable energies into energy systems while maintaining high levels of security of supply constitutes a challenge for system operators. The system integration of large shares of variable renewable energy generation requires an effective operation of electricity grids and efficient coordination among grid operators. Hence, the detailed modelling of grid operation has increasingly become important in recent years. Methods for modelling the operation of (extra) high-voltage grids are undergoing persistent enhancements in academia and industry. Existing approaches vary in data granularity and computational methods. Moreover, assumptions on technical details in grid models vary. The results are thus subject to differences in input data and modelling methods. The paper at hand aims to identify the most relevant differences that exist amongst grid simulation models and methods for studying congestion management in a European context. Differences are studied based on a comparison of grid simulation models from eight German energy modelling institutions. The effects of model parameterization and formulation on congestion management results are investigated based on three different case studies focusing on outage simulation, line constraint relaxation and the modelling of cross-border measures. Results indicate that data parametrization can have large impacts on model results with respect to congestion management volumes and the geographic distribution of the respective measures. The analysis also highlights the need to thoroughly calibrate key model parameters. The research findings will assist the grid modelling community and power system planners in simulating congestion management and increase the validity and explorative power of grid simulation models. • Various differences in model formulations for grid operation exist in academia. • Soft-constraints are widely used to detect data inconsistencies and infeasibilities. • Simplified outage simulations particularly impact congestion results in meshed grids. • Parametrization of cross-border measures largely affects congestion volumes. [ABSTRACT FROM AUTHOR]