I-Corps: Decision Support Tool to Assess Distributed Electricity Needs

Purdue University, West Lafayette IN

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project will have a direct impact on the energy industry, and indirect effects to all people connected to the energy grid. Energy generation, sources and distribution methods have been continuously evolving over the past decade. In 2018, solar-produced energy accounted for 1.6% of the electricity generated in the U.S., up from 0.11% in 2012. By 2020, solar-produced energy is forecasted to account for 5% of U.S. generated electricity. With the increased efficiency associated with solar energy production and distribution, local homeowners have also assumed the role of energy generators, even getting credit for access electricity supplied to the grid given the policy around net-metering. When planning their energy distribution frameworks, utility companies have to take these changes in energy consumption and generation into account when planning their energy distribution frameworks. Devising methods and tools which can accurately estimate near-future (5-10 days) local weather forecasts and its implications for changes in solar energy production will positively impact utility companies, reducing stakeholder costs currently associated with forecasting errors. This I-Corps project aims to conduct customer discovery and validation for a decision support tool which incorporates localized real-time weather data and near-future forecasted weather data to predict site-specific weather parameters for the purpose of estimating solar energy generation at the region level. This tool assists utility companies (who own solar arrays and/or have customers who own solar arrays) to level-out load demand and supply. In addition, this tool assists solar energy system owners to verify the system is working properly. This I-Corps project is an extension of prior research to develop a framework to model long-term efficiency and reliability of photovoltaic (PV) systems, and then verify accuracy through comparison to real-time PV system performance data. From a practical perspective, this produced a model to estimate performance and value of used solar energy systems. This I-Corps project leverages the previously developed framework but incorporates multiple weather data sets (e.g., real-time weather data, historical weather data, and near-future forecasted weather data) to estimate near-future (5-7 days) performance. It is proposed that this focus on big data will be beneficial to utility companies to level out demand and supply. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.