Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.

On Real-Time Monitoring And Control For Efficient Management Of Drinking Water Networks: Barcelona Case Study

Drinking water utilities in urban areas are focused on finding smart solutions facing new challenges in their real-time operation because of limited water resources, intensive energy requirements, a growing population, a costly and ageing infrastructure, increasingly stringent regulations, and increased attention towards the environmental impact of water use. Such challenges force water managers to monitor and control not only water supply and distribution, but also consumer demand. This paper presents and discusses novel methodologies and procedures towards an integrated water resource management system based on advanced ICT technologies of automation and telecommunications for largely improving the efficiency of drinking water networks (DWN) in terms of water use, energy consumption, water loss minimization, and water quality guarantees. In particular, the paper addresses the first results of the European project EFFINET (FP7-ICT2011-8-318556) devoted to the monitoring and control of the DWN in Barcelona (Spain). Results are split in two levels according to different management objectives: (i) the monitoring level is concerned with all the aspects involved in the observation of the current state of a system and the detection/diagnosis of abnormal situations. It is achieved through sensors and communications technology, together with mathematical models; (ii) the control level is concerned with computing the best suitable and admissible control strategies for network actuators as to optimize a given set of operational goals related to the performance of the overall system. This level covers the network control (optimal management of water and energy) and the demand management (smart metering, efficient supply). The consideration of the Barcelona DWN as the case study will allow to prove the general applicability of the proposed integrated ICT solutions and their effectiveness in the management of DWN, with considerable savings of electricity costs and reduced water loss while ensuring the high European standards of water quality to citizens.

Hydropower Energy Simulation Using Mike 11 Model; A Case Study In South Germany's Small Run-Of-River Hydropower Plants

Renewable energy production is a basic supplement to stabilize rapidly increasing global energy demand and skyrocketing energy price as well as to balance the fluctuation of supply from non-renewable energy sources at electrical grid hubs. The European energy traders, government and private company energy providers and other stakeholders have been, since recently, a major beneficiary, customer and clients of Hydropower simulation solutions. The relationship between rainfall-runoff model outputs and energy productions of hydropower plants has not been clearly studied. In this research, association of rainfall, catchment characteristics, river network and runoff with energy production of a particular hydropower station is examined. The essence of this study is to justify the correspondence between runoff extracted from calibrated catchment and energy production of hydropower plant located at a catchment outlet; to employ a unique technique to convert runoff to energy based on statistical and graphical trend analysis of the two, and to provide environment for energy forecast. For rainfall-runoff model setup and calibration, MIKE 11 NAM model is applied, meanwhile MIKE 11 SO model is used to track, adopt and set a control strategy at hydropower location for runoff-energy correlation. The model is tested at two selected micro run-of-river hydropower plants located in South Germany. Two consecutive calibration is compromised to test the model; one for rainfall-runoff model and other for energy simulation. Calibration results and supporting verification plots of two case studies indicated that simulated discharge and energy production is comparable with the measured discharge and energy production respectively.

Evaluating And Optimizing Sustainable Drainage Design To Maximize Multiple Benefits: Case Studies In China

In the past, the focus of drainage design was on sizing pipes and storages in order to provide sufficient network capacity. This traditional approach, together with computer software and technical guidance, had been successful for many years. However, due to rapid population growth and urbanisation, the requirements of a “good” drainage design have also changed significantly. In addition to water management, other aspects such as environmental impacts, amenity values and carbon footprint have to be considered during the design process. Going forward, we need to address the key sustainability issues carefully and practically. The key challenge of moving from simple objectives (e.g. capacity and costs) to complicated objectives (e.g. capacity, flood risk, environment, amenity etc) is the difficulty to strike a balance between various objectives and to justify potential benefits and compromises. In order to assist decision makers, we developed a new decision support system for drainage design. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. The evaluation framework is used for the quantification of performance, life-cycle costs and benefits of different drainage systems. The optimisation tool can search for feasible combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will discuss real-world application of the decision support system. A number of case studies have been developed based on recent drainage projects in China. We will use the case studies to illustrate how the evaluation framework highlights and compares the pros and cons of various design options. We will also discuss how the design parameters can be optimised based on the preferences of decision makers. The work described here is the output of an EngD project funded by EPSRC and XP Solutions.

From Smart Meters To Smart Decisions: Web-Based Support For The Water Efficient Household

Smart water metering technologies for residential buildings offer, in principle, great opportunities for sustainable urban water management. However, much of this potential is as yet unrealized. Despite that several ICT solutions have already been deployed aiming at optimum operations on the water utilities side (e.g. real time control for water networks, dynamic pump scheduling etc.), little work has been done to date on the consumer side. This paper presents a web-based platform targeting primarily the household end user. The platform enables consumers to monitor, on a real-time basis, the water demand of their household, providing feedback not only on the total water consumption and relevant costs but also on the efficiency (or otherwise) of specific indoor and outdoor uses. Targeting the reduction of consumption, the provided feedback is combined with notifications about possible leakages\bursts, and customised suggestions to improve the efficiency of existing household uses. It also enables various comparisons, with past consumption or even with that of similar households, aiming to motivate further the householder to become an active player in the water efficiency challenge. The issue of enhancing the platform’s functionality with energy timeseries is also discussed in view of recent advances in smart metering and the concept of “smart cities”. The paper presents a prototype of this web-based application and critically discusses first testing results and insights. It also presents the way in which the platform communicates with central databases, at the water utility level. It is suggested that such developments are closing the gap between technology availability and usefulness to end users and could help both the uptake of smart metering and awareness raising leading, potentially, to significant reductions of urban water consumption. The work has received funding from the European Union FP7 Programme through the iWIDGET Project, under grant agreement no318272.

Comparison Of Demand Pattern Calibration In Water Distribution Networks With Geographic And Non-Geographic Parameterization

Demands are one of the most uncertain parameters in a water distribution network model. A good calibration of the model demands leads to better solutions when using the model for any purpose. A demand pattern calibration methodology that uses a priori information has been developed for calibrating the behaviour of demand groups. Generally, the behaviours of demands in cities are mixed all over the network, contrary to smaller villages where demands are clearly sectorised in residential neighbourhoods, commercial zones and industrial sectors. Demand pattern calibration has a final use for leakage detection and isolation. Detecting a leakage in a pattern that covers nodes spread all over the network makes the isolation unfeasible. Besides, demands in the same zone may be more similar due to the common pressure of the area rather than for the type of contract. For this reason, the demand pattern calibration methodology is applied to a real network with synthetic non-geographic demands for calibrating geographic demand patterns. The results are compared with a previous work where the calibrated patterns were also non-geographic.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

A Resource Centric Approach For Advancing Collaboration Through Hydrologic Data And Model Sharing

HydroShare is an online, collaborative system being developed for open sharing of hydrologic data and models. The goal of HydroShare is to enable scientists to easily discover and access hydrologic data and models, retrieve them to their desktop or perform analyses in a distributed computing environment that may include grid, cloud or high performance computing model instances as necessary. Scientists may also publish outcomes (data, results or models) into HydroShare, using the system as a collaboration platform for sharing data, models and analyses. HydroShare is expanding the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated, creating new capability to share models and model components, and taking advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. One of the fundamental concepts in HydroShare is that of a Resource. All content is represented using a Resource Data Model that separates system and science metadata and has elements common to all resources as well as elements specific to the types of resources HydroShare will support. These will include different data types used in the hydrology community and models and workflows that require metadata on execution functionality. The HydroShare web interface and social media functions are being developed using the Drupal content management system. A geospatial visualization and analysis component enables searching, visualizing, and analyzing geographic datasets. The integrated Rule-Oriented Data System (iRODS) is being used to manage federated data content and perform rule-based background actions on data and model resources, including parsing to generate metadata catalog information and the execution of models and workflows. This presentation will introduce the HydroShare functionality developed to date, describe key elements of the Resource Data Model and outline the roadmap for future development.

Reservoir Management For Flood And Drought Protection Using Infinite Horizon Model Predictive Control

Model Predictive Control (MPC) is a control method that solves in real time an optimal control problem over a finite horizon. The finiteness of the horizon is both the reason of MPC's success and its main limitation. In operational water resources management, MPC has been in fact successfully employed for controlling systems with a relatively short memory, such as canals, where the horizon length is not an issue. For reservoirs, which have generally a longer memory, MPC applications are presently limited to short term management only. Short term reservoir management can be effectively used to deal with fast process, such as floods, but it is not capable of looking sufficiently ahead to handle long term issues, such as drought. To overcome this limitation, we propose an Infinite Horizon MPC (IH-MPC) solution that is particularly suitable for reservoir management. We propose to structure the input signal by use of orthogonal basis functions, therefore reducing the optimization argument to a finite number of variables, and making the control problem solvable in a reasonable time. We applied this solution for the management of the Manantali Reservoir. Manantali is a yearly reservoir located in Mali, on the Senegal river, affecting water systems of Mali, Senegal, and Mauritania. The long term horizon offered by IH-MPC is necessary to deal with the strongly seasonal climate of the region.

SESAME: Finding Ways Of Promoting SME Adaptation To Flood Risk

Small and medium-sized companies and other enterprises (SMEs) around the world are exposed to flood risk and many of the 4.5 million in the UK are at risk. As SMEs represent almost half of total business turnover in the UK, their protection is a vital part of the drive for greater climate change resilience. However, few have measures in place to ensure the continuity of their activities during a flood and its aftermath. The SESAME project aims to develop tools that encourage businesses to discover ways of becoming more resilient to floods and to appreciate how much better off they will be once they have adapted to the ongoing risk. By taking some of the mystery out of flooding and flood risk, it aims to make it susceptible to the same business acumen that enables the UK’s SMEs to deal with the many other challenges they face. In this paper we will report on the different aspects of the research in the project Understanding behaviour Changing behaviour Modelling impacts Economic impacts Through the above the project will advise government, local authorities and other public bodies on how to improve their responses to floods and will enable them to recommend ways to improve the guidelines provided to SMEs in flood risk areas.

NDVI With Artificial Neural Networks For SRTM Elevation Model Improvement – Hydrological Model Application

Digital elevation model (DEM) plays a substantial role in hydrological study, from understanding the catchment characteristics, setting up a hydrological model to mapping the flood risk in the region. Depending on the nature of study and its objectives, high resolution and reliable DEM is often desired to set up a sound hydrological model. However, such source of good DEM is not always available and it is generally high-priced. Obtained through radar based remote sensing, Shuttle Radar Topography Mission (SRTM) is a publicly available DEM with resolution of 92m outside US. It is a great source of DEM where no surveyed DEM is available. However, apart from the coarse resolution, SRTM suffers from inaccuracy especially on area with dense vegetation coverage due to the limitation of radar signals not penetrating through canopy. This will lead to the improper setup of the model as well as the erroneous mapping of flood risk. This paper attempts on improving SRTM dataset, using Normalised Difference Vegetation Index (NDVI), derived from Visible Red and Near Infra-Red band obtained from Landsat with resolution of 30m, and Artificial Neural Networks (ANN). The assessment of the improvement and the applicability of this method in hydrology would be highlighted and discussed.

Calibration Strategies Designed For Long Term Sediment Transport Modelling

The presented work deals with the calibration of a 2D numerical model for the simulation of long term bed load transport. A settled basin along an alpine stream was used as a case study. The focus is to parameterise the used multi fractional transport model such that a dynamically balanced behavior regarding erosion and deposition is reached. The used 2D hydrodynamic model utilizes a multi-fraction multi-layer approach to simulate morphological changes and bed load transport. The mass balancing is performed between three layers: a top mixing layer, an intermediate subsurface layer and a bottom layer. Using this approach bears computational limitations in calibration. Due to the high computational demands, the type of calibration strategy is not only crucial for the result, but as well for the time required for calibration. Brute force methods such as Monte Carlo type methods may require a too large number of model runs. All here tested calibration strategies used multiple model runs utilising the parameterization and/or results from previous run. One concept was to reset to initial bed elevations after each run, allowing the resorting process to convert to stable conditions. As an alternative or in combination, the roughness was adapted, based on resulting nodal grading curves, from the previous run. Since the adaptations are a spatial process, the whole model domain is subdivided in homogeneous sections regarding hydraulics and morphological behaviour. For a faster optimization, the adaptation of the parameters is made section wise. Additionally, a systematic variation was done, considering results from previous runs and the interaction between sections. The used approach can be considered as similar to evolutionary type calibration approaches, but using analytical links instead of random parameter changes.

Reducing The Risk Of Floods In Urban Areas With Combined Inland-River System

With the change of the water environment in accordance with climate change, the loss of lives and properties has increased due to urban flood. Although the importance of urban floods has been highlighted quickly, the construction of advancement technology of an urban drainage system combined with inland-river water and its relevant research has not been emphasized in Korea. In addition, without operation in consideration of combined inland-river water, it is difficult to prevent urban flooding effectively. This study, therefore, develops the uncertainty quantification technology of the risk-based water level and the assessment technology of a flood-risk region through a flooding analysis of the combination of inland-river. The study is also conducted to develop forecast technology of change in the water level of an urban region through the construction of very short-term/short-term flood forecast systems. This study is expected to be able to build an urban flood forecast system which makes it possible to support decision making for systematic disaster prevention which can cope actively with climate change.

Real-Time Control Of Floods Along The Demer River, Belgium, By Means Of MPC In Combination With GA And A Fast Conceptual River Model

Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

Application Of Real-Time Control Strategy To Improve Nitrogen Removal In Wastewater Treatment

Biological nitrogen removal is an important task in the wastewater treatment. However, the actual removal of total nitrogen (TN) in the wastewater treatment plant (WWTP) is often unsatisfactory due to several causes, one of which is the insufficient availability of carbon source. One possible approach to improve the nitrogen removal therefore is addition of external carbon source, while the amount of which is directly related to operation cost of a WWTP. It is obviously necessary to determine the accurate amount of addition of external carbon source according to the demand depending on the influent wastewater quality. This study focused on the real-time control of external carbon source addition based on the on-line monitoring of influent wastewater quality. The relationship between the influent wastewater quality (specifically the concentration of COD and ammonia) and the demand of carbon source was investigated through experiments on a pilot-scale A/O reactor (1m3) at the Nanjing WWTP, China. The minimum doses of carbon source addition at different situations of influent wastewater quality were determined to ensure the effluent wastewater quality meets the discharge standard. The obtained relationship is expected to be applied in the full-scale WWTPs. .