ReMatch: data-driven software for planning distributed energy resources infrastructure

A team of Stanford researchers has developed ReMatch, an efficient, data-driven DER (distributed energy resources) planning and decision support framework that accounts for a range of complexities to optimize energy resource planning. Unlike previous frameworks, which rely solely on engineering simulations, ReMatch combines engineering, data science and real-world consumer behavior to systematically analyze DER construction and deployment. This approach enables ReMatch to capture socio-technical complexities related to infrastructure and human dynamics – uncertainty in supply and demand, synergistic effects of demand-side management programs, diversity in consumer behavior and deployment dynamics of infrastructure. The algorithm accounts for individual consumers, generation infrastructure and storage infrastructure to calculate the lowest cost system to meet all consumer demand (including capital and operational costs). Furthermore, ReMatch uses smart grid data to match groups of consumers with different kinds of distributed resources based on customer's actual energy use and the ability to construct resources in that area. In summary, ReMatch can be used to enable utilities to determine what kinds of energy resources and targeted incentive programs will have the most economic and environmental impact.

Stanford Engineering News, 9/6/17
Could 'power from the people' cut electric bills in half?

Stage of Development
The inventors demonstrated the ReMatch algorithm in a case study using real hourly data from smart meters of 10,000 energy customers in California. By accounting for a wide range of consumer profiles and infrastructure solutions, their analysis showed that constructing DER infrastructure could reduce levelized cost of electricity (LCOE) nearly 50% over the lifetime of the infrastructure.