2020
DOI: 10.3390/en13215589
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The Energy Trade-Offs of Transitioning to a Locally Sourced Water Supply Portfolio in the City of Los Angeles

Abstract: Predicting the energy needs of future water systems is important for coordinating long-term energy and water management plans, as both systems are interrelated. We use the case study of the Los Angeles City’s Department of Water and Power (LADWP), located in a densely populated, environmentally progressive, and water-poor region, to highlight the trade-offs and tensions that can occur in balancing priorities related to reliable water supply, energy demand for water and greenhouse gas emissions. The city is on … Show more

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Cited by 8 publications
(6 citation statements)
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“…However, there is an urgent need for water‐energy nexus methods that can mimic the cross‐interactions of energy and water supply systems in a practical way so that the full value of integrative resource planning and management approaches can be identified for the water and energy industries. The importance of energy management becomes even more significant as some regions are forced to tap more energy‐intensive water supply sources such as water recycling and desalination in the face of water scarcity and climate change (Zohrabian & Sanders, 2020). Moreover, water utilities, which are typically among the largest municipal electricity consumers, can mitigate greenhouse gas emissions by coordinating operations that result in reduced renewable energy curtailment and increased utilization of carbon‐free electricity.…”
Section: Dr Challenges and Future Research Directionsmentioning
confidence: 99%
“…However, there is an urgent need for water‐energy nexus methods that can mimic the cross‐interactions of energy and water supply systems in a practical way so that the full value of integrative resource planning and management approaches can be identified for the water and energy industries. The importance of energy management becomes even more significant as some regions are forced to tap more energy‐intensive water supply sources such as water recycling and desalination in the face of water scarcity and climate change (Zohrabian & Sanders, 2020). Moreover, water utilities, which are typically among the largest municipal electricity consumers, can mitigate greenhouse gas emissions by coordinating operations that result in reduced renewable energy curtailment and increased utilization of carbon‐free electricity.…”
Section: Dr Challenges and Future Research Directionsmentioning
confidence: 99%
“…Furthermore, variations in the supply side of the energy–water nexus, such as temporal and spatial changes in the emission factors of energy sources and energy mix, the policies and regulations affecting water and energy utilities, and the use of renewable energy, can impact household GHG emissions and trade-offs with water consumption . Multiobjective optimization and trade-off analysis have been conducted on the production system; however, in residential consumption, there is a trade-off between the choice of various household appliances and their impacts on GHG emissions and water consumption because of the differences in the capital and operational costs of each appliance, as well as different opportunities for water reuse and recycling. Thus, system optimization at the household scale can help choose energy- and water-efficient appliances, technologies, and scenarios to promote a circular water economy.…”
Section: Introductionmentioning
confidence: 99%
“…For example, Ye et al [23] propose a combined multiobjective optimization method to examine five different scenarios of renewable energy systems. Zohrabian et al [24] used a case study to highlight the trade-offs and tensions that can occur in balancing priorities related to reliable water supply, energy demand for water, and greenhouse gas emissions. Besides the comprehensive analysis ability, these models also face some problems, such as the complexity of that model will have a significant increase if there are too many parameters and variables in the model, and sometimes the overall optimization results cannot even be achieved.…”
Section: Introductionmentioning
confidence: 99%