Computational frameworks for decarbonizing urban water supply systems
Abstract/Contents
- Abstract
- Major countries around the world have set ambitious timelines to reach 'Net Zero' in the next few decades. Such policy push is steadily tightening regulations on carbon emission and increasing the operating costs for sectors that are slow in decarbonizing their supply chains. Supplying clean water to urban areas is energy-intensive and has a significant carbon footprint. With population growth and the depletion of readily available freshwater resources, it is becoming increasingly common to utilize water resources that are deeper from the surface, more distant from the demand, and more saline. The transport and purification of such unconventional water resources will likely further intensify the energy use for water supply and increase the upstream carbon emission associated with power generation. Consequently, the water supply sector, especially the urban water supply systems (UWSS), must actively seek ways to decarbonize their electricity consumption in order to reduce their operating cost (e.g., energy cost) and prepare for a more stringent regulatory environment. Motivated by the need to better inform UWSS in their decarbonization efforts, we introduce several computational frameworks that aim to aid the UWSS operators in identifying, quantifying, and operationalizing the opportunities of decarbonizing their water supply. Chapter 2 demonstrates a flow backtracking algorithm that can be applied to trace energy flows embedded in water flows in a UWSS. More importantly, we introduce a new metric called 'marginal energy intensity' (MEI) of water supply in this chapter, which paves the way for the following studies. Following Chapter 2, Chapter 3 quantifies the temporally averaged MEI values of individual consumers in a single-source UWSS and demonstrates the energy-saving benefit of using MEI values to inform the siting of decentralized wastewater recycling (a form of raw water conservation). Building upon the flow backtracking algorithm introduced in Chapter 2, Chapter 4 shows a high-resolution carbon accounting framework that traces the embedded carbon footprint of water supply. In addition, we demonstrate the scalability of the flow backtracking algorithm in the water supply system serving a coastal city with a population of nearly 100,000. In Chapter 5, we propose an optimization framework that guides water utilities of different risk tolerance levels on DR participation. This optimization framework also introduces a novel convex relaxation technique that is invented to accelerate the computation of the rigorous mixed-integer linear programming (MILP) model. Chapter 6 applies a bottom-up approach to assess the DR potential of drinking water treatment plants (DWTP) across the entire U.S. with high spatio-temporal resolution. In this assessment, we analyze data of all major DWTPs in the U.S. from the Safe Drinking Water Information System (SDWIS) and multiple energy use surveys of UWSS.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Liu, Yang, (Researcher in civil and environmental engineering) |
|---|---|
| Degree supervisor | Mauter, Meagan |
| Thesis advisor | Mauter, Meagan |
| Thesis advisor | Jain, Rishee |
| Thesis advisor | Rajagopal, Ram |
| Degree committee member | Jain, Rishee |
| Degree committee member | Rajagopal, Ram |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yang Liu. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/pc169wb4533 |
Access conditions
- Copyright
- © 2022 by Yang Liu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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