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• An unprecedented very large-scale T&D dataset covering Texas is made publicly available. • The sheer size of this dataset is intended to provide a challenge for the scientific community. • This lays the foundation for demonstrating the coordination of T&D. • It can help foster the improvement of algorithms and tools for years to come. Distributed energy resources, such as rooftop solar, have rapidly expanded in recent years, given declining costs and the desire to reduce carbon emissions. With more energy resources located in the lower-voltage distribution system, it is increasingly helpful to utilize combined transmission and distribution (T&D) system models to analyze interactions between these normally-distinct subsystems. This paper proposes a methodology for creating very large-scale, highly detailed, combined T&D systems that are synthetic—that is, free from non-public data—yet still realistic. The methodology creates very large-scale combined T&D systems by merging the most up-to-date techniques for creating synthetic distribution feeder networks with the latest methods for building synthetic, meshed bulk-power transmission networks. This methodology is demonstrated on a T&D system geolocated in Texas, and benchmarked with co-simulation results. Validation demonstrates that the resulting syn -texas-TDgrid synthetic test system realistically represents characteristics found in actual networks, addressing the lack of available T&D test systems. With over 15,000 feeders and 46 million electrical nodes, this T&D dataset has applications for research in optimal power flow algorithms, voltage control, reconfiguration, and T&D coordination schemes under high adoption of distributed energy resources. [ABSTRACT FROM AUTHOR]

• We evaluate the impact of operational modeling details on resource adequacy results. • Multi-stage probabilistic assessments may better capture operational details. • Thermal generator outages may impact results more than solar forecast errors. • Flexibility provided by hybrids can reduce the number of load-shedding events. • Results are more sensitive to hybrid inverter sizing than other hybrid features. Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1–2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1–2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions. [ABSTRACT FROM AUTHOR]

As the share of solar photovoltaics (PV) in the power system increases, there is a growing need for flexibility from multiple, possibly interdependent sources to adjust to PV's variability, uncertainty, and diurnal dependence. This paper investigates how stochastic unit commitment leveraging probabilistic solar forecasts can support other flexibility measures under high solar shares. We consider two flexibility measures relevant to day-ahead scheduling: battery energy time-shifting and solar ancillary service provision. Unit commitment and economic dispatch simulations are conducted on a realistic test system based on Texas using day-ahead solar trajectories. The benefits of the two flexibility measures are pronounced when the instantaneous solar share is high, offering cost savings of 10%–20% in the spring. For a Texas-sized system, this translates to hundreds of millions of dollars in cost savings once the installed PV capacity enables instantaneous solar shares regularly exceeding 40%. Using probabilistic forecasts also greatly increases the reliability of upward reserve provision from solar PV, reducing unserved reserves by 50%–100%. Both day-ahead forecast resolution and errors can impact system reliability at high solar shares, but the stochastic formulation has significant value, mitigating reliability impacts on over-forecast days. • Forecast resolution as well as error has reliability impacts at high solar shares. • Flexibility measures considered are battery storage and solar ancillary services. • Stochastic unit commitment greatly improves reliability of solar upward reserves. • Cost savings from flexibility are pronounced for instantaneous solar shares >40%. • Demonstrated on Texas-sized system with realistic day-ahead solar trajectories. [ABSTRACT FROM AUTHOR]