Products and Tools

Climate change poses tremendous challenges to the world, requiring a fundamental transformation towards clean and sustainable energy sources. While the rapid decarbonization of our energy systems mitigates the effects of climate change, new challenges linked to the integration of renewables and grid stability emerge. This is because existing power systems have been designed to accommodate centralized power plants only. Yet, many renewables, such as photovoltaic panels and wind turbines, are of a distributed and intermittent nature. Moreover, renewable energy sources are generally installed in smaller generation units and connected to the distribution grids. As a result, the electricity system has become increasingly complex, making forecasting and system operations ever more challenging. With the EnergyAtlas, we provide a tool for addressing many of these challenges.

With the EnergyAtlas, we aim to establish a global database with respect to conventional and renewable energy systems. Covering the supply, transmission, and demand side, the EnergyAtlas database will provide highly granular data for all regions of the world.

The environmental quality of the electricity flowing through electric grids varies by location, season, and time of day. To encourage and guide decarbonization efforts, better tools are needed to monitor the emissions embodied in electricity consumption, production, imports, and exports. Whether for policymakers designing energy efficiency and renewable programs, regulators enforcing emissions standards, or large electricity consumers greening their supply, greater resolution is needed for electric sector emissions indices to evaluate progress against current and future goals.

The economic input–output model is adapted to track emissions flows through electric grids and quantify the pollution embodied in electricity production, exchanges, and, ultimately, consumption for the continental US balancing authorities in [1]. Subsequent work introduces a physics-informed data reconciliation framework to automate electric system operating data cleaning [2]. The data reconciliation framework enables researchers to perform similar analyses in near real-time and to continuously monitor emissions rates in the US electricity system.

[1] "Tracking emissions in the US electricity system", by Jacques A. de Chalendar, John Taggart and Sally M. Benson. Proceedings of the National Academy of Sciences Dec 2019, 116 (51) 25497-25502; DOI.

[2] "Physics-informed data reconciliation framework for real-time electricity and emissions tracking", by Jacques A. de Chalendar and Sally M. Benson. Applied Energy 304 (2021): 117761; DOI; arXiv preprint.

Electric vehicles (EV) and EV charging stations are expected to multiply over the next decade. Long-term planning for grid and charging network design depend on detailed forecasts of future charging demand. SPEECh, Scalable Probabilistic Estimates of Electric Vehicle Charging, is a novel, probabilistic framework for simulating very large-scale estimates of EV charging load that are grounded in real charging data and drivers' charging patterns.

This repository contains:

• The model
• Examples demonstrating how to use the model and adjust assumptions to run new scenarios yourself
• Code to run an interactive application where you can do this ^ through an interface
• Code to help you apply the model to your own data set
• The code used for the paper