Moya, Carolina

English

Hydraulic Engineering Repository

Article, Published VersionMoya, CarolinaNext generation information and communicationtechnology for smart water utilitiesHydrolinkVerfügbar unter/Available at: https://hdl.handle.net/20.500.11970/109359Vorgeschlagene Zitierweise/Suggested citation:Moya, Carolina (2017): Next generation information and communication technology for smartwater utilities. In: Hydrolink 2017/1. Madrid: International Association for Hydro-EnvironmentEngineering and Research (IAHR). S. 6-9. https://iahr.oss-accelerate.aliyuncs.com/library/HydroLink/HydroLink2017_01_Sensors.pdf.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.6 hydrolink number 1/2017Water distribution systems face socio-economic, sustainability and resilience relatedchallenges which include: overuse due topopulation growth, underestimation of the valueof water, lack of coordination among actors,operational issues (ageing, leakages, quality,pressure, etc.), increasing energy prices and theneed to respond to climate change issues.Large amounts of water-related environmentaldata are already being reported from local tosupranational level. However, there is still a lackof a holistic approach to the assessment ofresource efficiency and environmentalperformance of water utilities. Hydraulic, economic, social, environmental andquality variables are highly interrelated. Anoptimization of energy consumption, supply risk,water storage levels or pressure might causelow performances in other variables or even notreliable scenarios. Therefore, to be able to takereliable operational decisions in waterdistributions systems, it seems unavoidable toturn to multi-criteria approaches.Next Generation ICT support forWater UtilitiesNowadays, the definition of Smart WaterNetworks is associated with Supervisory Controland Data Acquisition (SCADA), hydraulicmodelling, Geographic Information Systems(GIS), Big Data analysis, etc. At best, all thosesoftware and datasets work collaboratively toachieve a better understanding of what ishappening in the water supply system. But, isthis really smart or is it all about data ingestion?Does always having a large quantity of datalead to smarter decisions? Data collection iscurrently useful, but the key factor should be tobe able to extract all the knowledge that isactually available in each piece of data. A Smart Water Network involves evaluation ofproblems, elaboration of solving strategies anddecision making and action taking. This articleis introducing an application of innovative ICTdeveloped for and applied to the waterdistribution sector. WatEner represents a non-structural solution for Water Utilitiesmanagement having a great impact on severalmatters (e.g. climate change, carbon footprint,balance sheets, water losses, non-revenuewater, long-term infrastructure planning, watersources) leveraging past investments insensors, simulation models or GIS data.WatEner is a next generation ICT solution thatcombines the key factors of energyconsumption with further operationalrequirements of drinking water supply, toimprove the management of water supplynetworks. In this regard, the results of the testcase of the Karlsruhe Water Utility in Germanywill be next outlined.To achieve its objectives, WatEner makes use ofinnovative technologies in the generation ofknowledge. Such knowledge is expressed asrelations and foreseeable evolutions fromexisting information: sensor data, simulationmodels, GIS or infrastructure’s technicalcharacteristics. A set of Artificial Intelligencetechniques is applied to provide short-term(real-time, operation) and long-termNEXT GENERATION INFORMATION AND COMMUNICATION TECHNOLOGY FOR SMART WATER UTILITIESBY CAROLINA MOYAThe process of making operational and maintenance decisions ina water network management involves the analysis of a hugenumber of highly interrelated variables. Current managementsoftware systems are based on data gathering and modellingtechnologies. Nevertheless, an increased complexity in monitoringis rarely a synonym of a better efficiency. Having taken this intoaccount, it can be certainly suggested that new approaches andtechnologies are required to leverage the already existing ones. Acombined work of artificial intelligence, machine learning and real-time predictive analytics forms a decisive part of the nextgeneration of Information and Communication Technology (ICT)systems for Water Utilities.Figure 1. WatEner. From information toknowledgeSensors, data acquisition & analysis software7hydrolink number 1/2017IAHRequipped with a special dashboard for thepublic/customers, intended to transparentlyprovide information on the supply system, thequality of the service and the state of the waterresources. The case of Karlsruhe cityIn 2009 the city of Karlsruhe (Germany) togetherwith all its companies committed themselves toreduce their energy consumption and carbonfootprint by 2% each year. Since then a lot ofeffort was made in drinking water supply already,e.g. investments in more energy efficientgroundwater pumps feeding the water treatmentfacilities, power transformers, compressors andelectric lighting. Stadtwerke Karlsruhe GmbH(SWKA) is the local energy (electricity, naturalgas and long distant district heat) and drinkingwater supplier for the about 400,000 inhabitantsof the region of Karlsruhe in Germany. Its sharesare majority-owned by the city of Karlsruhe.The SWKA network has four water works locatedaround Karlsruhe that produce drinking waterfrom the upper layer of an aquifer. The treatmentof the raw water comprises the aeration and theremoval of iron and manganese by biologicalsand filtration. Each of the water works has fournetwork pumps with constant pump rates. Thetotal drinking water consumption in the region ofKarlsruhe is around 24 Hm3 per year, or 65,000m3 per day.The drinking water network has a length of morethan 900 kilometers. Most of the city is located inone pressure zone, fed by all four water works.Attached to that pressure zone, there is a waterstorage tank of approximately 20,000 m3located about 50 m above street level, thatstabilizes and controls the water pressure in thismain pressure zone.In this context, one of the present tasks forSWKA is the optimization of the daily pumpingschedules of the treated drinking water fed intothe distribution network and of the water storagetanks management. Furthermore, someimportant requirements have to be fulfilled: thewater supply must always be assured, thehighest possible water quality is not to be put atany risk and the energy costs should also be aslow as possible.In this case, the main objective of theimplementation of WatEner sought to respondto the needs and requirements in terms of(infrastructure planning, maintenance)recommendations. Moreover, all the data,information and knowledge are shown in a veryuser-friendly web application, designed by andfor water managers.On the one hand, pattern recognitiontechniques are used to provide a ten-minutewater demand forecasting for each districtmetering area of the network, based on themost similar day methodology. On the otherhand, they are also applied for searchingreliable operational strategies, taking intoaccount customizable key performanceindicator (KPI) which results related to keyfactors in water supply systems (e.g. energyefficiency, economic cost, water usage, qualityof service, water quality).Moreover, business rules inference engines arevery powerful instruments in charge of theassessment of the operational strategy. Theexpert knowledge of the Water Utility team iscaptured and adopted in evaluating policies,best practices, supply risk assessment,feasibility and any other operational matter.Also, these techniques allow to gather andimprove the expert knowledge and to maintain itwithin the organization.Machine learning procedures provide thecapability to continually improve the operationaland planning recommendations. Throughtraining modules designed for both expert watermanagers and operators, the ICT system andthe expert staff learn how to operate better.Training modules will collect and assessoperational strategies to face normal, abnormalor even theoretical situations.Finally, the aim of the real-time predictiveanalysis is to check if during operation, thenetwork situation evolution is proceeding asexpected or if any anomaly can compromise theservice. This module helps operators to tracknetwork events, detect service anomalies andcompare them with the expected service KPI,not only in terms of quality of service, but also inenergy, water and economic-cost efficiency.Furthermore, WatEner incorporates a rich set ofdashboards designed for water managers, aptto enable easy access to events, operationalrecommendations and KPI. They are designedto incorporate the indispensable knowledgerequired by the distinct job profiles and theoperational and business management in aWater Utility to perform their tasks in the bestway possible. In addition, the platform isCarolina Moya is thetechnical director ofWatEner. As ComputerScience Engineer she hasdealt with differenttechnologies, alwaysinvolving and focused on geographicalinformation systems, geographical locationsystems, OGC services, sensoring and soon. Carolina specializes in Hydrological andHydraulic modeling software for themanagement of resources and real timesystems. She is constantly looking for theperfect match between high-performance/high- availability systems and the main coreof the hydraulic and hydrological know-how.  Figure 2. Average daily consumption daySensors, data acquisition & analysis software8 hydrolink number 1/2017                                                                  Low demand (January, 1st)                                     Average demand (March, 10th)                                       High demand (July, 3rd)Consumption                                                          42,200 m3                                                                       54,500 m3                                                                  75,000 m3                                                  Original                                Optimized                         Original                               Optimized                       Original                                 OptimizedNetwork pressure             Tank filled to 7.7m           Tank filled to 4.1m         Tank filled to 7.5m          Tank filled to 5m.         Tank filled to 7.4m           Tank filled to 4mLosses by friction              14 switching                     6 switching                      20 switching                     8 switching                    12 switching                      5 switching                                               operations.                        operations.                      operations.                       operations.                    operations.                        operations.                                                                                            Outflow close to                                                         Outflow close to                                                        Outflow close to                                                                                             the average.                                                              the average.                                                               the average.                                                  Energy efficiency               0.2263 kWh/m3                 7.6% lower                      0.2237 kWh/m3                7.5% lower                    0.2300 kWh/m3                 6.2% lowerCost efficiency                     0.0379 €/m3                      7.7% lower                      0.0374 €/m3                     7.2% lower                    0.0385 €/m3                       6.0% lowerenergy saving and emission reduction, focusingon the following areas: (i) demand forecast ofdrinking water, (ii) management of the waterstorage tanks, (iii) pumping schedule of pumpsfeeding the network and, as a result, (iv)improvement of energy efficiency of the networkpumps.In this network, there are three main factors thatcan be used to adjust the energy efficiency. Firstof all, the water level in the main storage tankhas a dominant effect on the entire networkpressure. A lower water level reduces thepressure in the network and at the output of thewater works’ pumps, and it also increases thewater output for a given energy use.Secondly, it is possible to affect the energylosses by friction in pipes by maintaining thewater velocity in the entire network close to theaverage velocity. This can be achieved bypumping homogeneous outflows in the waterworks, which causes less variable flow rates,lower pressure, lower friction and thereby lowerenergy consumption.The third main factor is infrastructural; not all thepumps have the same energy efficiency. Inmost of the operational scenarios, using moreefficient pumps increases the average energyefficiency.Another important element not directly related tothe supply network is the electricity tariff. Theenergy prices in Germany are most expensivefrom 8 am to 10 am and from 5 pm to 9 pm, andthe cheapest ones are between midnight and 6am. Regrettably, there is a coincidence betweenhigher prices and higher water consumptionperiods.In the daily operation before WatEner, thestorage tank was filled during night time tobenefit from the lower energy prices and fed thedistribution network during the day to supply thewater demand and avoid pumping duringperiods of higher energy prices. Also, the pumpschedules were created in real time by the waterworks operators, reacting to the observedbehavior of the system. The multi-criteriaapproach of WatEner was adopted to combinethe effects described above to design newpump schedules which optimize energy and donot compromise the quality and level of service.Hereafter, the real effects of the new pumpschedules are shown using three significantdays of the year 2015: the 1st of January as thelowest water consumption day, the 3rd of July asa very hot summer day and consequently withthe highest water demand and the 3rd of Marchas an average working day.The Figure 2 depicts the average consumptionday management, showing the scenario beforeoptimization (solid lines) and WatEner optimizedone (dashed lines). The optimized scenariorecommends a pumping schedule where theevolution of total outflow is close to the averageduring all day and the main tank fills less duringthe night, avoiding losses by friction andoverpressure.  Against what would have seemedto be an adequate option, this enhancedscenario through a minor pumping during valleytariff provides an energy saving of 7.5% and costsavings of 7.2% and ensures that water andservice quality remain constant and unaltered.The minimum and maximum consumption dayspresent a similar situation. The WatEnerrecommendation is to apply a pumpingschedule which fills less the main tank during thenight and feeds the network with a total outflowclose to the average, providing a more efficientFigure 3. Minimum and maximum consuption daysTable 1Sensors, data acquisition & analysis softwareIAHRNext generation of ICT tools foster a change inthe operational paradigm of water utilities bydealing wit  relevant matters from a differentperspective, specifically by:• Leveraging of previous investments inmonitoring, modelling, GIS and other tools.• Adopting a multi-criteria and holistic visionof operations. These tools facilitate theassessment of different managementstrategies in the supply network, improvingthe savings and preventing inefficiencies inthe entire network.• Dynamically adapting in real-time to thecontinuous operational issues that canhappen in a water supply network, such astemporary halts or services restrictions insome parts of the network for maintenanceor failure.• Adding flexibility to provide usefulrecommendations, regardless of thetechnical monitoring level of the water utility.In conclusion, through the analysis of the aboveillustrated obtained results, it is reasonable toaffirm that non-structural solutions inr theoperation water utilities can have a beneficialimpact on several significant matters of our age,such as climate change and carbon footprint, aswell as affect positively business balance sheetsand the control of water losses, all within therange of a reasonable investment.Acknowledgments We would also like to thank Dirk Kühlers,Wolfgang Deinlein and Prof. Matthias Maier ofStadtwerke Karlsruhe for the contributionsduring the WatEner developing and testing andthe trust in our team and solution. nReferences[1] Dirk Kühlers (2016) Software WatEner: Ergebnisse des Testlaufs beiden Stadtwerken Karlsruhe, 8. Diskussionsreihe, DVGW-Technologiezentrum Wasser "Energieeffizienter betrieb vonWasserversorgungs-anlagen"[2] Royan Fredrick, Global Env. Water Markets. (2012). Global SmartWater Market ‘Unearthing the REAL Value of Water and theIndustry’.[3] Helmbrecht, Jorge et al. (2016). Improvement of the water-energynexus and the environmental performance of water supply systemsusing smart ICT solutions. Current World Environment - AnInternational Research Journal of Environmental Science. (Underreview for publishing). [4] www.watener.com (2016)[5] Helmbrecht, Jorge et al. (2016) Smart solution to improve water-energy nexus for water supply systems. 18th Conference on WaterDistribution System Analysis, WDSA 2016.www.watener.cominfo@watener.comoperation in terms of energy and cost. Also,water quality and service quality are ensured.Table 1 summarizes the three scenarios; all thenumerical data is extracted from WatEner KPIset. The energy cost reduction for the averageconsumption day is 147€ (7.2%). With this dayas reference the platform saves, only taking intoaccount water production pumping costs,54,000€ and 334.34 MWh (7.5%) per year.ConclusionThe size of the population served by a WaterUtility, the energy costs and other factors relatedto particular situation/region, have a majorinfluence on the return on investment (ROI) andwater/energy savings that can be achieved withthe next generation of ICT solutions.This article described the implementation ofWatEner in a medium-sized Water Utility(400,000 inhabitants) in Germany. In larger andmegacities in developed countries, as well as incities in developing countries too, the savingsare expected to be quite similar as in theNorthwestern Europe pilot.Sensors, data acquisition & analysis software

Next generation information and communication technology for smart water utilities

https://henry.baw.de/bitstream/handle/20.500.11970/109359/HydroLink_2017_01_Next%20generation%20information%20and%20communication%20technology%20for%20smart%20water%20utilities.pdf?sequence=1&isAllowed=y

Next generation information and communication technology for smart water utilities

Abstract

Similar works

Full text

Available Versions

Hydraulic Engineering Repository