The management and delivery of water and wastewater consume significant amounts of energy, mostly in the form of electricity. With increasing populations, climate change, water quality issues and increasing energy prices, it is more important than ever to understand energy consumption patterns. Energy usually represents the largest operational cost in water utilities around the world, yet there is limited work aiming to quantify the specific relationship between water and its associated energy, and understand its implications for future decision-making. This thesis presents variousmethodological approachesto quantify and understand energy use in water infrastructure systems, as well as how to incorporate them in decision-making processes. The main hypotheses are as follows: firstly, a detailed understanding of the use of energy in water infrastructure systems can facilitate more efficient and sustainable water infrastructure systems and, secondly, that incorporating energy into planning for water and wastewater resources can help understand the impacts of decisions and establish trade-offs between actions. To test these hypotheses, the thesis presents an analytical approach to various areas. Firstly, it identifies, maps and quantifies the energy consumption patterns within a water infrastructure system. This is then used to identify inefficiencies and areas of potential energy saving. Secondly, it incorporates detailed energy costs into short and long-term water resources management and planning. Thirdly, it evaluates trade-offs between energy costs and changing effluent quality regulations in wastewater resources. The Thames River basin, in the south-east of England, is used as a case study to illustrate the approach. The results demonstrate that a systematic approach to the quantification of energy use in a water infrastructure system can identify areas of inefficiencies that can be used to make decisions with regards to infrastructure planning. For example, water systems have significant geo-spatial variations in energy consumption patterns that can be addressed specifically to reduce negative trade-offs. The results also show that incorporating detailed energy information into long-term water resources planning can alter the choices made in water supply options, by providing more complete information. Furthermore, methodologically, they show how several methodological approaches can be used to support more complete decision-making in water utilities to reduce short and long-term costs. In this particular case study, the results show that there are important differences in energy consumption by region, and significant differences in the seasonal and energy patterns of water infrastructure systems. For example, water treatment was shown to be the largest consumer of energy within the whole system, compared with pumping or wastewater treatment; but wastewater treatment energy consumption was shown to be the fastest growing over time due to changes in water quality regulatory frameworks. The results show that more stringent effluent standards could result in at least a doubling of electricity consumption and an increase of between 1.29 and 2.30 additional million tonnes of CO2 a year from treating wastewater in large works in the UK. These are projected to continue to increase if the decarbonisation of the electricity grid does not occur fast enough. Finally, the thesis also shows that daily energy consumption can be reduced by up to 18% by optimally routing water through a water network. optimization of water networks, and that a change in discount rates could change the daily operating costs by 19%, that in turn leads to a resulting different set of optimal investment options in a water supply network.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:740950 |
| Date | January 2017 |
| Creators | Trujillo, Iliana Cardenes |
| Contributors | Eyre, Nick ; Hall, Jim |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:df481801-cce1-4824-986c-612f4673b8eb |
Page generated in 0.01 seconds