ReMatch: An integrated and data-driven distributed energy resource (DER) planning framework

Traditionally, centralized fossil fuel plants located far from population centers have supplied energy to communities.  Due to mounting concerns regarding the sustainability, environmental impact and reliability of such centralized systems, distributed energy systems are becoming an attractive alternative.  Distributed energy systems have the advantage of being built on a neighborhood scale and thus can be placed close to population centers where demand for energy is the highest.  Furthermore, the advancement of sensing technologies and data analytics provides an opportunity to understand how, where and when individual citizens consume energy and dynamically plan distributed energy infrastructure accordingly.

 

However, numerous complexities and uncertainties arise due to infrastructure and human dynamics in cities, including: uncertainty in energy supply and demand, synergistic effects of demand-side management programs, diversity in consumer behavior and deployment dynamics of infrastructure construction.  Such complexities and uncertainties must be accounted for in the planning process if the maximum benefits of urban-scale distributed energy systems are to be realized.  The objective of this research is to create ReMatch, an integrated and data-driven planning framework for distributed urban energy systems that is able to capture such complexities and uncertainties due to it’s novel formulation of energy system planning as a minimum-cost network flow problem. Individual consumers, generation infrastructure and storage infrastructure are represented as nodes on the flow network. Conceptually, the objective of the ReMatch framework is to find a feasible flow of energy from generation and storage infrastructure nodes to consumer nodes with the lowest cost (capital + operational) such that all demand from consumers is met. 

Collaborator(s): Prof. Ram Rajagopal (CEE, Stanford)

Funder(s): National Science Foundation under Grant No. 1461549

© 2016 by Stanford Urban Informatics Lab