Overview of Energy Management and Leakage Control Systems for Smart Water Grids and Digital Water
Abstract
:1. Introduction
2. Smart Water Management
2.1. Technologies and Improvements
2.1.1. Smart Pipe and Sensors
2.1.2. Smart Water Metering
2.1.3. Geographic Information System
2.1.4. Cloud Computing and Scada
2.2. Management Models and Decision Support Systems
2.2.1. Near Real-Time Models
2.2.2. Asset Management
3. Energy Recovery Systems
- The main instance of impulse turbines is the Pelton impeller-turbine [57]. These turbines are characterised by the flow passing through, ending up at atmospheric pressure. The flow input by the injectors is shot at the bucket impeller. This makes the runner spin to create kinetic energy. The turbine can lose its efficiency if the water jet is not correctly directed to the buckets.
- Reaction turbine examples include the Francis and the Kaplan turbines. The Francis turbine is a radial-inflow hydraulic turbine that can efficiently work for a large range of head and flow values [58]. Kaplan turbines, on the other hand, are adapted for high flow rates with low heads. This type of propeller turbine has the advantage of having variable vanes that adjust to the flow [57].
- Geometric similarity: Dimension of the turbine cannot be reduced to a smaller scale which can induce scale effects in the prototype.
- Kinematic similarity: The triangle of velocities is equivalent in the inlet and outlet and dynamic similarity the polygon of forces must be similar both in the prototype as in the model.
- Dynamic similarity: This implies that geometric and kinematic similarities are already met. It implies a constant ratio of fluid forces, , for the flow-metering system.
- The first corresponding to , standstill curve, in which values of flow and head lower than this curve do not produce torque.
- Where , shows the values from which the torque is not transmitted to the shaft.
4. Water Network Partitioning and Leakage Control
4.1. Water Network Partitioning
- Graph clustering and graph spectral clustering: Graph clustering finds groups in a graph based on different areas of connectivity between nodes. A similarity measure of nodes based on algorithms such as the shortest paths is applied often. There are relevant examples of graph clustering application in the urban water literature [80,81,82]. Spectral clustering has its basis on the Laplacian matrix of the graph representing the water network. The physical properties of the graph Laplacian spectrum, in terms of network connectivity, are further considered to apply traditional clustering algorithms to the subset of eigenvectors associated with the top eigenvalues [83,84,85,86,87].
- Breadth and depth first search: Breadth-first search is a widely used algorithm for traversing a graph, starting from a randomly chosen node of the graph, the procedure then checks its neighbouring nodes (those nodes linked to the initial node) and points to the shorter distance nodes to continue the checking process. A counter of the accumulated distance travelled is recorded if notes are found. A backtrack, depth-first process allows one to return to previous nodes in the path to double check that the route is following the shortest path in this endeavour. This algorithm has been adapted to find groups in networks and is used in urban water studies for network partitioning purposes [88,89].
- Community detection: Community detection algorithms in water networks are based on similarities on such a water network to a social network. Clustering algorithms in social networks are based on detection of areas in which individuals are strongly related (linked) and information is easily spread. This is the fundamental idea, as that adopted in water networks considers consumption points for information sharing [90,91].
- Agent-based systems: Agent-based modelling can model and simulate the evolution of a dynamic, complex system from a distributed approach. That is, by decomposing the system into a collection of autonomous decision-making entities, called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules which allow them to reach individual and common objectives. To this end, the agents evolve themselves, interacting with the environment and with other agents via coordination, cooperation and competition, among other actions. To divide a water network into DMAs, agents are considered the consumption nodes and have varying properties (demand, pressure, cluster membership, etc.). Agents communicate/negotiate with others by links representing pipes and valves to finally find a distributed notion of similarity and distance that make the higher similarity and/or lower distance network nodes to form clusters [92,93].
4.2. “Less Is More” in Hydraulic Resilience through Wnp
- Robustness: Ability of the system and system elements, to withstand disaster-induced damage and disruption without significant degradation or loss of performance. WNP allows one to divide the system in areas of a similar hydraulic head, uniformly all over the system, and limiting the oscillations between night and day. This leads to a significant reduction in stress on pipes and devices that consequently preserves, for a longer period, their mechanical performance.
- Redundancy: Extent to which system elements are replaceable, or equivalently, extent to which alternative paths and modes can be employed if some elements lose function. Due to the closure of some boundary pipes, WNP leads to a loss in both topological and energy redundancy, since some alternative paths are precluded. However, the possibility to isolate districts from the rest of the system during an abnormal event (e.g., pipe breakage, contamination) allows one to isolate the problem as well as guarantee normal functioning.
- Resourcefulness: Ability to diagnose and prioritise problems and to initiate solutions by identifying and mobilising material, monetary, informational, technological, and human resources. By splitting the system in monitored sub-areas, WNP allows one to separately manage them. Thus, efficiency is increased by the prioritisation of interventions.
- Rapidity: Capacity to restore functionality in a timely way, containing losses and avoiding disruptions. WNP’s inherent division of the system in smaller areas, allows to focus each to operate in a more efficient way and adapt rapidly to any problem in corresponding districts.
5. Emergent Paradigm of Digital Water and Future Research Directions
- Distributed systems management as an efficient methodology to deal with big data streams coming at near real-time from the cyber-physical structure controlling and monitoring the smart water grid.
- Innovative and necessary data analysis methodologies for increasing the efficiency and benefit of the decision-making processes. These methodologies can be found within the subjects of machine learning and artificial intelligence, big data, complexity science, and robotics.
- Water companies are an important, active stakeholder in digital water. This brings to the overall process the need of a business reconfiguration, implying a multiscale thinking in which individual assets play an important role in the global physical and digital infrastructure. Along with their interrelationship to other utilities and the society network as a customer.
- There is a disruption of new markets and business models around the digital water management. Data again plays a pivotal role in this and new technologies, such as blockchain, allow for secure data sharing and decision making in case multiple companies and stakeholders can be involved in the overall approach.
5.1. Future Technologies for Smart Water Grids
- Cyber-physical systems: The concept of a smart water grid is directly related to a CPS [110]. In the case of a water distribution system, these are the set of sensors, smart monitors, and actuators distributed along the physical infrastructure. Internet of Things (IoT) is the main vehicle for communication in a CPS and for endowing the system with an optimal decision-making capability both in a hierarchical and a distributed manner [111]. Directly related to SCADA and a near real-time water operation and management [112], the benefits of a CPS expand to an online knowledge of the hydraulic state of a water distribution system [113], safety and security [114,115], contamination detection in a water distribution system [116], and optimal performance [117], among others.
- Digital twins: The paradigm of the digital twin is closely related to digital water. DT models map near real-time data and updated models from the assets of the CPS to a high resolution digital system reproduction. The aim for digital twins’ models of water distribution systems is to reproduce disruption scenarios for resilience assessment purposes, to validate beforehand new solutions for network configurations, and to analyse asset prognosis and health-status to determine proactive maintenance models. DTs are still a paradigm to explore further in urban water. Conejos et al. [118] presented one of the first scientific articles in this regard, as more of the development so far in DTs for urban water is directly approached by innovation teams within water utilities. DTs have been an enormous development in the last years by the deployment of twins. The challenge for water utilities and urban water researchers is to consider the complexity of dealing with a network of assets, along with their interaction models and data sharing and management.
- Blockchain technologies: Blockchain can be considered a decentralised database with the records partitioned into blocks forming a chain configuration. The information at each block is only a partial view of the data. Therefore, the partition of the records into blocks guarantees a safe and secure access to information, when it is shared by multiple stakeholders. This is a step-forward to address data security issues associated with the new business of digital water. The blocks of information can be considered as network nodes and the operations between them are network links. This complex network approach to blockchain technologies generates new paradigms about the database synchronisation and control [119] and the further use of machine learning models for an optimal database management and representation [120].
5.2. Future Model Analytics for Smart Water Grids
- Network dynamics: Now to consider the difference between “dynamic of networks” (as a variation on time of configuration; network topology—and number of the nodes and links of a network) and “dynamics on networks” as a variation on time of nodes and links weights or values; likely depending on the network flow and node demand. Multilayer networks can be defined as multiple interconnected networks, each can represent another network or a variation of the original network (regarding its topology or element status) [121]. In the latter, through a snapshot of the network at each time and per individual network layer. Considering network dynamics within the formal framework of multilayer networks is, therefore, an open research avenue for researchers and practitioners in smart water grids, where complex network analysis already plays a pivotal role. The challenge of time series processes in networks can be extended to other statistical and machine learning methods approached on networks [122].
- Geometric deep learning: It is a new emergent topic in which convolutional neural networks (CNN), mainly focused so far on image analysis, are used for manifolds and graph-structured databases [123]; having inherited the name for the latest of graph-CNN or directly graph neural networks (GNN). During an image analysis, a series of convolutional filters and pooling CNN layers extract relevant features and patterns in an image. The operations are of similar nature in geometric deep learning but approached over the adjacency or the Laplacian matrix of the graph representing, in this case, the smart water grid. The main characteristic to adopt and adapt CNNs to complex networks is to consider those matrices representing such networks that preserve spatial meaning of that, along with another properties of interest. Problems related to a smart water grid monitor and control can be approached through geometric deep learning in addition to a number of other important applications [124].
6. Conclusions
- Technologies: smart pipe and sensors, smart water metering, GIS and SCADA have been introduced in this manuscript as key technologies to accomplish the paradigm of a smart water grid. Cyber-physical systems, digital twins, and blockchain have been identified as future breakthrough technologies.
- Models: The importance of counting on near real-time models has also been highlighted in the document, showing how they may lead to an improved water efficiency and more reliable operations. In this regard, the role of asset management models in smart water grids have also been discussed. Complex networks (dynamics) and geometric deep learning were foreseen as key basis for any future model development in smart water grids.
- Strategies: A main part of the document is dedicated to describe and propose the use of strategies for energy recovery and water network partitioning. Both points have the common objective of improving the water network efficiency.
Author Contributions
Funding
Conflicts of Interest
References
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Giudicianni, C.; Herrera, M.; Nardo, A.d.; Adeyeye, K.; Ramos, H.M. Overview of Energy Management and Leakage Control Systems for Smart Water Grids and Digital Water. Modelling 2020, 1, 134-155. https://doi.org/10.3390/modelling1020009
Giudicianni C, Herrera M, Nardo Ad, Adeyeye K, Ramos HM. Overview of Energy Management and Leakage Control Systems for Smart Water Grids and Digital Water. Modelling. 2020; 1(2):134-155. https://doi.org/10.3390/modelling1020009
Chicago/Turabian StyleGiudicianni, Carlo, Manuel Herrera, Armando di Nardo, Kemi Adeyeye, and Helena M. Ramos. 2020. "Overview of Energy Management and Leakage Control Systems for Smart Water Grids and Digital Water" Modelling 1, no. 2: 134-155. https://doi.org/10.3390/modelling1020009