Demand flexibility models for urban water utilities

Urban water utilities face converging imperatives to adapt to water supply disruptions and to a decarbonizing electricity grid. Demand management offers a cost-effective source of flexibility to utilities -- allowing them to shape use of existing infrastructure for optimal service reliability and resource use efficiency with minimal investments in new, energy intensive infrastructure systems. Flexible management of the sector's energy demand can also provide scalable energy services to the grid as it accommodates more variable renewable electricity sources. Despite this promise, the realizable benefits of demand flexibility are limited by uncertainties about the complex behaviors of infrastructure users and systems. Planning for demand flexibility requires scalable, data driven modeling tools that can quantify these uncertainties. In this work, I explore two demand flexibility planning problems operating at different space and time scales relevant to a water utility's operations. The first is an urban water utility considering water supplies and conservation a semi-arid service area with frequent and prolonged seasonal water shortages. The second is a wastewater facility planning energy flexibility upgrades to limit power consumption during peak hours -- while ensuring reliable wastewater treatment. Chapters 2-3 address the first demand management problem. In Chapter 2, I develop a closed-loop water consumption analytics tool that uses customer billing data, ensemble learning, and unsupervised change detection to pinpoint the timing and magnitude of customer-level water use shifts during and after droughts. Case study results from Southern California's Mesa Water district during the severe 2012-2016 drought show consumption reductions averaging 35-40% among 80% of customers in 2013-2016. Roughly 75% of these occurred before mandatory urban watering restrictions in May 2015, coinciding instead with spikes in drought media coverage and public awareness. Chapter 3 builds on the tool developed in Chapter 2 to better understand consumption rebound after drought conditions improve. I apply survival analysis to detected 2014-2015 water savings, finding that only 25% of savers had rebounded to prior consumption levels by 2019. Survival models indicate that drought conservation persists for 8 years on average, is significantly longer in politically progressive neighborhoods, and is unrelated to receipt of water-efficiency upgrade rebates from Mesa Water. Chapter 4 explores solutions to the second demand management problem. I develop a data-driven energy flexibility planning tool for wastewater treatment facilities seeking to limit their power consumption during time-of-use hours. The tool uses statistical learning and process modeling to simulate how existing wet-weather water storage and short-term biogas storage systems can be reliably repurposed for load shifting and peak shaving. Techno-economic analysis of multiple simulation runs evaluates the commercial viability of energy flexibility upgrades given expected bill savings. Results find commercially viable investments in energy flexibility, with annual returns of 0.6-4.2% and payback periods less than 8 years. Tapping into existing, untapped sources of energy flexibility yields bill savings of as much as 6% at minimal upfront cost. Although they are different, these two demand flexibility problems are part of an integrated water infrastructure planning problem that encompasses multiple technology systems and their users. For water supply planners, the short-term drivers of customers' seasonal "drought demand response" buffers and subsequent readjustments are key to understanding the adequacy of current water supplies -- and the need for long-term infrastructure investments. For wastewater utilities, knowing the complementary value of indirect energy storage embedded in their operations allows more cost-effective use of existing treatment infrastructure, operationalizing a large and untapped source of energy flexibility that can be profitably deployed by energy infrastructure operators as they integrate variable renewable generation

Advisor: Rajagopal Ram
Ajami Newsha
Mauter Meagan