Graph-Based Optimization for Technology Pathway Analysis: A Case Study in Decarbonization of University Campuses

Abstract

Industrial sectors such as urban centers, chemical companies, manufacturing facilities, and microgrids are actively exploring strategies to help reduce their carbon footprint. For instance, university campuses are complex urban districts (involving collections of buildings and utility systems) that are seeking to reduce carbon footprints that originate from diverse activities (e.g., transportation operations and production of heating, cooling, and power utilities). This work presents an optimization framework to identify technology pathways that enable decarbonization of complex industrial sectors. The framework uses a graph abstraction that compactly captures interdependencies between diverse products and technologies as well as diverse externalities (e.g., market, policy, and carbon prices). Duality analysis reveals that the formulation can be interpreted as an economy, market, or value chain that uses technologies to generate economic value (wealth) by transforming basic products into higher value products. This interpretation also reveals that the formulation identifies pathways that maximize the profit of stakeholders, helps reveal the inherent value (prices) of intermediate products, and helps analyze the impact of externalities and technology specifications on product values. Our developments are illustrated via a case study involving a prototypical university campus that seeks to identify pathways that reduce its carbon footprint (e.g., via electrification and deployment of hydrogen technologies). We use the framework to determine carbon tax values, technology specifications, and investment budgets that activate different technology pathways and that achieve different levels of decarbonization.

Figures (15)

• Figure 1: Illustration of a simple graph in which supplies of basic products (gasoline and solar radiation) are used by technologies (gas-powered vehicles, electricity vehicles, and solar panels) to satisfy demand of higher value products (travel distance) and that generate undesired products (CO${}_2$ emissions). In this case, electrical power generated by the photovoltaic panels is an intermediate product that is used by the electric vehicle to generate a value-added and final product (distance). If the cost of CO${}_2$ emissions is not taken into account (and/or gasoline is inexpensive), the gas vehicle pathway will be preferred. On the other hand, when emissions costs are taken into account and/or renewable power is inexpensive, the electric vehicle pathway will be preferred.
• Figure 2: Graph showing diverse suppliers, technologies, and consumers that participate in a university campus. Complex interdependencies/connectivity arises between products and technologies and it is thus nonobvious what are the best pathways. See Table \ref{['tab:Acronyms']} for technology descriptions.
• Figure 3: Schematic representation of optimization model, indicating elements involved as well as model inputs/outputs.
• Figure 4: Sankey diagram summarizing the annual CO2 emissions and their sources for a university campus. Generation of electrical power and heating are the dominant sources.
• Figure 5: Superstructure graph showing currently-available suppliers, technologies, and demands for the university campus. See Table \ref{['tab:Acronyms']} for technology descriptions.