Protecting Water Quality and Public Health Using a Smart Grid
Affiliation(s)
Intelligent Water Solutions, CH2M HILL, Englewood, Colorado, USA. Intelligent Water Solutions, CH2M HILL, Oakland, California, USA.
Intelligent Water Solutions, CH2M HILL, Englewood, Colorado, USA. Intelligent Water Solutions, CH2M HILL, Oakland, California, USA.
ABSTRACT
After the attacks on September 11, 2001 and the follow-up risk assessments by utilities across the United States, securing the water distribution system against malevolent attack became a strategic goal for the U.S. Environmental Protection Agency. Following 3 years of development work on a Contamination Warning System (CWS) at the Greater Cincinnati Water Works, four major cities across the United States were selected to enhance the CWS development conducted by the USEPA. One of the major efforts undertaken was to develop a process to seamlessly process “Big Data” sets in real time from different sources (online water quality monitoring, consumer complaints, enhanced security, public health surveillance, and sampling and analysis) and graphically display actionable information for operators to evaluate and respond to appropriately. The most significant finding that arose from the development and implementation of the “dashboard” were the dual benefits observed by all four utilities: the ability to enhance their operations and improve the regulatory compliance of their water distribution systems. Challenge: While most of the utilities had systems in place for SCADA, Work Order Management, Laboratory Management, 311 Call Center Management, Hydraulic Models, Public Health Monitoring, and GIS, these systems were not integrated, resulting in duplicate data entry, which made it difficult to trace back to a “single source of truth.” Each one of these data sources can produce a wealth of raw data. For most utilities, very little of this data is being translated into actionable information as utilities cannot overwhelm their staffs with manually processing the mountains of data generated. Instead, utilities prefer to provide their staffs with actionable information that is easily understood and provides the basis for rapid decision-making. Smart grid systems were developed so utilities can essentially find the actionable needle in the haystack of data. Utilities can then focus on rapidly evaluating the new information, compare it known activities occurring in the system, and identify the correct level of response required. Solution: CH2M HILL was engaged to design, implement, integrate, and deploy a unified spatial dashboard/smart grid system. This system included the processes, technology, automation, and governance necessary to link together the disparate systems in real time and fuse these data streams to the GIS. The overall solution mapped the business process involved with the data collection, the information flow requirements, and the system and application requirements. With these fundamentals defined, system integration was implemented to ensure that the individual systems worked together, eliminating need for duplicate data entry and manual processing. The spatial dashboard was developed on top of the integration platform, allowing the underlying component data streams to be visualized in a spatial setting. Result: With the smart grid system in place, the utilities had a straightforward method to determine the true operating conditions of their systems in real time, quickly identify a potential non-compliance problem in the early stages, and improve system security. The smart grid system has freed staff to focus on improving water quality through the automation of many mundane daily tasks. The system also plays an integral role in monitoring and optimizing the utilities’ daily operations and has been relied on during recovery operations, such as those in response to recent Superstorm Sandy. CH2M HILL is starting to identify the processes needed to expand the application of the smart grid system to include real-time water demands using AMI/AMR and real-time energy loads from pumping facilities. Once the smart grid system has been expanded to include Quality-Quantity-Energy, CH2M HILL can apply optimization engines to provide utility operations staffs with a true optimization tool for their water systems.
After the attacks on September 11, 2001 and the follow-up risk assessments by utilities across the United States, securing the water distribution system against malevolent attack became a strategic goal for the U.S. Environmental Protection Agency. Following 3 years of development work on a Contamination Warning System (CWS) at the Greater Cincinnati Water Works, four major cities across the United States were selected to enhance the CWS development conducted by the USEPA. One of the major efforts undertaken was to develop a process to seamlessly process “Big Data” sets in real time from different sources (online water quality monitoring, consumer complaints, enhanced security, public health surveillance, and sampling and analysis) and graphically display actionable information for operators to evaluate and respond to appropriately. The most significant finding that arose from the development and implementation of the “dashboard” were the dual benefits observed by all four utilities: the ability to enhance their operations and improve the regulatory compliance of their water distribution systems. Challenge: While most of the utilities had systems in place for SCADA, Work Order Management, Laboratory Management, 311 Call Center Management, Hydraulic Models, Public Health Monitoring, and GIS, these systems were not integrated, resulting in duplicate data entry, which made it difficult to trace back to a “single source of truth.” Each one of these data sources can produce a wealth of raw data. For most utilities, very little of this data is being translated into actionable information as utilities cannot overwhelm their staffs with manually processing the mountains of data generated. Instead, utilities prefer to provide their staffs with actionable information that is easily understood and provides the basis for rapid decision-making. Smart grid systems were developed so utilities can essentially find the actionable needle in the haystack of data. Utilities can then focus on rapidly evaluating the new information, compare it known activities occurring in the system, and identify the correct level of response required. Solution: CH2M HILL was engaged to design, implement, integrate, and deploy a unified spatial dashboard/smart grid system. This system included the processes, technology, automation, and governance necessary to link together the disparate systems in real time and fuse these data streams to the GIS. The overall solution mapped the business process involved with the data collection, the information flow requirements, and the system and application requirements. With these fundamentals defined, system integration was implemented to ensure that the individual systems worked together, eliminating need for duplicate data entry and manual processing. The spatial dashboard was developed on top of the integration platform, allowing the underlying component data streams to be visualized in a spatial setting. Result: With the smart grid system in place, the utilities had a straightforward method to determine the true operating conditions of their systems in real time, quickly identify a potential non-compliance problem in the early stages, and improve system security. The smart grid system has freed staff to focus on improving water quality through the automation of many mundane daily tasks. The system also plays an integral role in monitoring and optimizing the utilities’ daily operations and has been relied on during recovery operations, such as those in response to recent Superstorm Sandy. CH2M HILL is starting to identify the processes needed to expand the application of the smart grid system to include real-time water demands using AMI/AMR and real-time energy loads from pumping facilities. Once the smart grid system has been expanded to include Quality-Quantity-Energy, CH2M HILL can apply optimization engines to provide utility operations staffs with a true optimization tool for their water systems.
Cite this paper
Thompson, K. and Kadiyala, R. (2013) Protecting Water Quality and Public Health Using a Smart Grid. Computational Water, Energy, and Environmental Engineering, 2, 73-80. doi: 10.4236/cweee.2013.22B013.
Thompson, K. and Kadiyala, R. (2013) Protecting Water Quality and Public Health Using a Smart Grid. Computational Water, Energy, and Environmental Engineering, 2, 73-80. doi: 10.4236/cweee.2013.22B013.
References
[1] CH2M HILL, “Contamination Warning System Demon-stration Pilot Project: Implementation and Assessment,” 2013. [2] EPA. (n.d.) “The Effectiveness of Disinfec-tant Residuals in the Distribution System,” Washington, DC: US EPA Office of Water, Office of Ground Water and Drinking Water. http://www.epa.gov/ogwdw/disinfection/tcr/pdfs/issuepaper_effectiveness.pdf [3] EPA, “Revised Guidance Manual for Selecting Lead and Copper Control Strate-gies,” Washington, DC: US EPA Office of Water, Office of Ground Water and Drinking Water, 2003. [4] AWWA(n.d.) Partnership for Safe Water Program Fact Sheet: Treatment Plant and Distribution System Optimization. http://www.awwa.org/Portals/0/files/resources/water%20utii-ty%20management/partnership%20safe%20water/files/FactSheetRevSept20102.pdf [5] EPA, “Water Sentinel Online Water Quality Monitoring as an Indicator of Drinking Water Contamination,” Washington, DC: US EPA Water Security Division, 2005. [6] J. Dilling and K. Kaiser, “Estimation of the hydrophobic fraction of dissolved organic matter in water samples using UV photometry,” Water Resourse, Vol. 36, No. 20, 2002, pp. 5037-5044. doi:10.1016/S0043-1354(02)00365-2 [7] J. Hall and J. Szabo, “Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Re-sults,” Cincinnati, OH: EPA, 2009. [8] J. van den Broeke, “Spectral RICIN Measurement - Detection Lim-its,” s: can Messtechnik, GmbH, 2009. [9] EPA, “Wa-ter Security Initiative: Interim Guidance on Planning for Contamination Warning System Deployment,” Cincin-nati, OH: EPA Water Security Division, 2007. [10] J. E. Berry, TEVA-SPOT Toolkit and User's Manual. Wash-ington, DC: EPA, 2008.
[1] CH2M HILL, “Contamination Warning System Demon-stration Pilot Project: Implementation and Assessment,” 2013. [2] EPA. (n.d.) “The Effectiveness of Disinfec-tant Residuals in the Distribution System,” Washington, DC: US EPA Office of Water, Office of Ground Water and Drinking Water. http://www.epa.gov/ogwdw/disinfection/tcr/pdfs/issuepaper_effectiveness.pdf [3] EPA, “Revised Guidance Manual for Selecting Lead and Copper Control Strate-gies,” Washington, DC: US EPA Office of Water, Office of Ground Water and Drinking Water, 2003. [4] AWWA(n.d.) Partnership for Safe Water Program Fact Sheet: Treatment Plant and Distribution System Optimization. http://www.awwa.org/Portals/0/files/resources/water%20utii-ty%20management/partnership%20safe%20water/files/FactSheetRevSept20102.pdf [5] EPA, “Water Sentinel Online Water Quality Monitoring as an Indicator of Drinking Water Contamination,” Washington, DC: US EPA Water Security Division, 2005. [6] J. Dilling and K. Kaiser, “Estimation of the hydrophobic fraction of dissolved organic matter in water samples using UV photometry,” Water Resourse, Vol. 36, No. 20, 2002, pp. 5037-5044. doi:10.1016/S0043-1354(02)00365-2 [7] J. Hall and J. Szabo, “Distribution System Water Quality Monitoring: Sensor Technology Evaluation Methodology and Re-sults,” Cincinnati, OH: EPA, 2009. [8] J. van den Broeke, “Spectral RICIN Measurement - Detection Lim-its,” s: can Messtechnik, GmbH, 2009. [9] EPA, “Wa-ter Security Initiative: Interim Guidance on Planning for Contamination Warning System Deployment,” Cincin-nati, OH: EPA Water Security Division, 2007. [10] J. E. Berry, TEVA-SPOT Toolkit and User's Manual. Wash-ington, DC: EPA, 2008.