Documents, People & Other Projects

¹TEMOA is an open source optimization modeling framework for conducting energy system planning and operational analysis. Such models have emerged as critical tools for technology assessment and policy analysis at regional to country level. Energy system optimization models (ESOMs) enable a simultaneously broad and deep assessment of technology by considering the economic and technical characteristics of individual technologies as well as their interactions within a well-defined system. TEMOA is developed in Python using the algebraic modeling library Pyomo from Sandia National Labs. TEMOA code and datasets are available online at the TemoaProject Github repository.

²The OEO is an initiative of Carnegie Mellon University (CMU) and NC State University that aims to examine U.S. energy futures to inform energy and climate policy efforts by applying policy-focused academic modeling. One of the main goals of the OEO is to maximize transparency, and build a networked community. The project has been funded by the Alfred P. Sloan Foundation. The modeling effort is based on TEMOA developed at NC State University in previous grants. The first OEO technical report is available on CMU website.

³The fundamental contribution of the CyberSees project lies in developing a prototype cyberinfrastructure for improving water and power systems management utilizing climate forecasts. Using the framework, the study proposes to minimize the operational costs of these two interdependent systems by considering various scenarios for maximizing the renewable energy potential utilizing multimodel climate (precipitation, temperature and wind) forecasts. By utilizing the streamflow and power demand forecasts, a paradigm shift in water and power systems management is targeted that promotes various proactive strategies such as forward purchasing of fuels, reducing emissions and meeting target reservoir storage to ensure both systems reliability.

⁴The goal of this project is to develop a state-of-the-art forecasting tool that will safeguard U.S. borders against the entry, establishment, and spread of economically and environmentally significant pests and diseases arriving in passage luggage, cargo or mail shipments while facilitating safe travel and trade. We will use past pests and diseases threats interception data, passenger entry data and a trade information from different ports of entry (POEs) to perform time series modeling using Box Jenkins methods and Recurrent Neural Networks. At the end of the performance period, we will determine the usefulness of time series modeling for estimating risk patterns and their potential to reduce the time burdens screening of luggage and cargo in different POEs. Effort for Solicitation # W911SR-14-2-0001 RPP-1914.

⁵The goal of this project is to develop an information analytics portal about important variables associated with planning and operations of the Brazilian electricity market. We are interested (and developed a suite of predictive analytical models) in variables that directly affect the electricity prices in the market such as electricity demand forecasts, energy inflows at the aggregated reservoirs, water inflows at hydro plants, and others. MC&E has a track record of more than 15 years of experience in research & development projects and consultancy for companies in the electric power sector. The company’s mission is to support agents through the design and application of innovative ideas to real problems focusing in improving the knowledge basis for informed decision-making. We aim to collaborate and contribute with the development of a more robust, modern and efficient sector.