Smart Solution to Improve Water-energy Nexus for Water Supply Systems

• Published in: Procedia engineering 2017, Vol.186, p.101-109
• Main Authors: Helmbrecht, Jorge, Pastor, Jordi, Moya, Carolina
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Business Rules; Energy Management in water Distribution Systems; Machine Learning; Nexus; Pattern Recognition; smart water networks; urban water distribution network management; water energy; Water Utilities
• ISSN: 1877-7058; 1877-7058
• DOI: 10.1016/j.proeng.2017.03.215

In the last years, there has been a great interest in the complex relations between energy and water, known as the Water-Energy Nexus [1]. Natural resources, such as energy and water, enable economy growth and support quality of life. The Water-Energy Nexus is considered as one of the most important multidisciplinary challenges [2] that the global growing water market [3] has to face in the forthcoming years. Currently, many water systems are not managed sustainably enough. Water Utilities face other challenges, such as infrastructure aging and poor cost-recovery, leading to a lack of finance for O&M (Operation and Maintenance). Energy is required in all stages of water production and distribution, from pumping and treatment to transportation. Energy costs are a top-of-mind concern for water utilities, regardless of geography, size and level of water network efficiency [4]. On the other hand, Water Utilities are having a hard time to either improve their services or expand their network to unserved neighborhoods in developing countries. The current trend of water transmission system to the creation of DMAs (District Metered Areas) offers great possibilities of non-structural solutions that use existing data and transform them into useful information to support decision making. The Smart Metering and the use of large amounts of data from a network enhance the use of software for decision support, but it is not the only way. Smart Solutions can also be applied to networks with less recorded data, which would enhance operators’ knowledge to these data, turn them into useful information for decision-making either for the operation or the maintenance and network design. In this scope, a Smart Solution is presented. It is developed combining key factors of the energy consumption and the water supply into water networks management to obtain improvements from both water and energy fields. This non-structural solution increases resource efficiency and environmental performance of water distribution networks by using data acquisition and geographical visualization (real time & historical), weather and water demand forecasting, detection of networks events and hydraulic simulation of the network, and finally through a decision support system based on machine learning (pattern recognition and business rules techniques). As a conclusion, a non-structural solution for the Nexus issues can have a great impact on several matters (climate change, carbon footprint, WUs bala