This paper introduces a methodology for building synthetic electric grid data sets that represent fictitious, yet realistic, combined transmission and distribution (T&D) systems. Such data sets have important applications, such as in the study of the wide-area interactions of distributed energy resources, in the validation of advanced control schemes, and in network resilience to severe events. The data sets created here are geographically located on an actual North American footprint, with the end-user load information estimated from land parcel data. The grid created to serve these fictional but realistic loads is built starting with low-voltage and medium-voltage distribution systems in full detail, connected to distribution and transmission substations. Bulk generation is added, and a high-voltage transmission grid is created. This paper explains the overall process and challenges addressed in making the combined case. An example test case, syn-austin-TDgrid-v03, is shown for a 307,236-customer case located in central Texas, with 140 substations, 448 feeders, and electric line data at voltages ranging from 120 V to 230 kV. Such new combined test cases help to promote high quality in the research on large-scale systems, particularly since much actual power system data are subject to data confidentiality. The highly detailed, combined T&D data set can also facilitate the modeling and analysis of coupled infrastructures.

The spatiotemporal distribution of pollutants plays a significant role on their impact on ecosystems and human health. This paper presents a strategy for calculating spatiotemporal operational emissions of road transportation and the electric grid to quantify the impact of transportation electrification. Emissions from internal combustion engine vehicles and offset emissions from electric vehicles (EVs) are considered using actual transportation networks and travel data. The spatiotemporal charging demand for light-duty and medium- and heavy-duty EVs are estimated in a behaviorally-informed way and mapped to electrical buses within the power grid and an ac optimal power flow with unit commitment is solved. The methodology is demonstrated with ten scenarios simulated on a grid with 7000 electrical buses geographically sited in Texas, created with actual generator data for the 2020 grid, and a future case including anticipated generator updates by 2030. Results show overall transportation emissions reductions in daily operational emissions of up to 20–30% for all pollutants studied, outweighing the increase in emissions from the electric grid. Considering emissions on an hourly basis, up to approximately 1000% reduction in CO emissions is observed.

In this paper, we propose a strategy to model the required spatio-temporal charging demand from light-duty (LD) and medium- and heavy-duty (MHD) electric vehicles (EVs) using actual transportation data by mapping the demand for the required EV charging to a realistic and coordinated distribution and transmission electric grid at the predicted times of the day to study their impact on power system in a variety of load, weather and EV penetration scenarios. This work is the first study that includes the actual weather data and transportation data with realistic and coordinated distribution and transmission grid data in a large industry-scale level study. The main goal of this study is to identify possible issues and required upgrades in the electric grid, caused by an increase in EV integration. The transmission case study is a large grid with 6717 buses over Texas footprint and the distribution grid is over Houston, a city in Texas, covering over 3 million customers. The resulting overloads and voltage violations experienced in the system are discussed and required planning upgrades to avoid these issues are suggested.

We examine a 25 km resolution climate model dataset to evaluate how regional climate change impacts solar and wind energy under a high-emission scenario. Our study considers the Western Electricity Coordinating Council (WECC) region, which covers the western United States and southwestern Canada, focusing specifically on locations with existing solar and wind infrastructure. First, we conduct a historical model comparison of solar and wind energy capacity factors to highlight model uncertainties across the study area. Using future climate projections, we then assess the seasonal patterns of solar and wind capacity factors for three timeframes: historical, mid-century, and end of century. Additionally, we estimate the frequency of solar and wind resource droughts during these periods for the entire WECC and its five operational subregions, finding that certain subregions are more susceptible to energy droughts due to limited renewable resources. Finally, we present day-ahead capacity factor forecasts to support energy storage planning and provide estimates of offshore wind energy capacity within the WECC. Our results indicate that offshore wind capacity factors are nearly twice as high as onshore values, with less seasonal variation, which suggests that offshore wind could offer a more consistent renewable energy supply in the future.

Electrification and renewables deployment efforts are amplifying the interdependence of the climate and energy systems. Increases in climate model resolution, which is now approaching that of reanalysis datasets and operational weather forecast models, present a unique opportunity to use future climate projections for energy infrastructure planning. In this Perspective, we review recent developments in high-resolution climate modeling, which have been driven by increased computing power and advanced software tools. We then look ahead to discuss how high-resolution climate data can be used to plan for a renewable-dependent future, and envision a unified climate-energy model framework that captures the two-way feedbacks between these interdependent systems.

The increasing amount of power electronic devices and inertia-lacking generation have introduced more complexity in power system frequency stability analysis. For power system operations, quick analysis of system frequency response following contingencies is desired. Building upon the uniform frequency approximation, an approach commonly used in operator training simulators, this paper proposes a method of power system dynamic simulation using a uniform frequency approximation coupled with a dc power flow. This method is compared with the full frequency stability study using two case studies of different system sizes. In this paper, the computation time and performance of the two methods are compared, and the applications of the proposed method are also discussed.