(432g) Graph-Based Optimization for Technology Pathway Analysis: A Case Study in the Decarbonization of University Campuses

The global mean surface temperature reached 1℃ above pre-industrial times in 2017 and is projected to reach 1.5℃ as early as 2030 [1]. Significant efforts need to be made to decrease greenhouse emissions to limit the impacts such as biodiversity loss and increased severity/frequency of severe weather events [2,3]. Large industrial sectors such as urban centers, chemical companies, manufacturing facilities, and microgrid systems are actively looking for technology pathways that can help minimize their carbon footprint. An important and representative set of industrial systems that is actively seeking to decarbonize operations are university campuses. Campuses involve large collections of buildings that consume heating, cooling, power, and transportation services. To give some perspective into the magnitude of these systems, we note that over 16.5 million students in the US reside on university campuses [4]. It is estimated US campuses emit 7.7 MTCO2e per student and are responsible for 2% of the 5,222 million MTCO2e of the US greenhouse gas emissions [5,6].

The number of new technologies that can be used to decrease greenhouse gas emissions is steadily increasing and complicates decision-making processes. In the context of university campuses, for instance, detailed mixed-integer optimization models have been developed to determine suitable system configurations [7-10]. Existing optimization models are typically intended to provide long-term investment plans to achieve decarbonization and are often hindered by the fact that there might not be sufficient data to evaluate emerging technologies.

In this work, we present a graph-based optimization framework for analyzing technology pathways that can provide desired product demand targets while minimizing supply and technology costs by seeking to maximize the total surplus. Our modeling approach aims to provide a unifying framework that captures diverse technologies and their interconnectivity via products. Moreover, the framework aims to use a minimum amount of data for technologies (e.g., efficiencies, capacities, operating costs, investment costs), thus providing a “high-level” picture on the technology landscape and on potential factors that influence the economic viability of diverse technologies. Moreover, this approach aims to enable fast screening of pathways and to conduct parametric studies under diverse market and policy scenarios.

We show that the proposed graph-based model can be interpreted as a value chain in which there is no transportation and spatial context; specifically, the model is a simplification of a multi-product supply chain model [11,12]. This observation is important, as the model proposed can provide valuable preliminary insights that can inform the development of more sophisticated infrastructure models (e.g., that capture transportation and spatial effects). Duality analysis of the proposed model reveals that this has a natural market (value chain) interpretation that can help identify technology pathways that maximize profit for stakeholders and that helps reveal the inherent value (prices) of intermediate products. This price information is key in market studies that involve sector coupling (e.g., hydrogen, ammonia, and power). Moreover, the graph-based abstraction reveals how externalities (e.g., carbon taxes or product demands) propagate through the value chain and affect prices of diverse products.

Our developments are illustrated via a case study involving a prototypical university campus that seeks to identify pathways to reduce its carbon footprint (e.g., via electrification and deployment of hydrogen technologies). Analysis conducted with our framework reveals carbon tax values, technology targets, and investment budgets that can help achieve different levels of decarbonization.

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