Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks

The criticality of seismic robustness of the water pipe networks cannot be overstated. Current methodologies for optimizing seismic robustness of city‐scale water pipe networks are scarce. A very few studies that can be found are also prone to long optimization runtimes due to the requirement of repeated hydraulic analysis. Hence, there is a critical need for the identification of computationally efficient surrogate optimization methods for maximizing seismic robustness of water pipe networks. To address this need, this research was conducted to identify, for the first time, computationally efficient topological surrogates for hydraulic simulation‐based optimization. The computational efficiency of surrogate optimization was measured in terms of solution quality (i.e., post‐earthquake serviceability) and computational runtime. Ten different topological connectivity metrics were evaluated out of which five were considered computationally infeasible due to their prohibitive optimization runtime. Five remaining metrics were then used to formulate five surrogate objective functions for seismic robustness of water pipe networks. Each of these functions was optimized using a simulated annealing‐based algorithm. Application of the proposed approach to city‐level benchmark networks helped to identify two metrics out of ten that offered a substantial reduction in optimization runtime with a minimal loss in solution quality. These findings will be highly valuable to water distribution network managers for identifying economical rehabilitation policies for enhancing the seismic robustness at a city‐scale within a reasonable amount of time.     

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

This article develops an efficient methodology to optimize the timing of signalized intersections in urban street networks. Our approach distributes a network‐level mixed‐integer linear program (MILP) to intersection level. This distribution significantly reduces the complexity of the MILP and makes it real‐time and scalable. We create coordination between MILPs to reduce the probability of finding locally optimal solutions. The formulation accounts for oversaturated conditions by using an appropriate objective function and explicit constraints on queue length. We develop a rolling‐horizon solution algorithm and apply it to several case‐study networks under various demand patterns. The objective function of the optimization program is to maximize intersection throughput. The comparison of the obtained solutions to an optimal solution found by a central optimization approach (whenever possible) shows a maximum of 1% gap on a number of performance measures over different conditions.     

Optimal joint deployment of flow and pressure sensors for leak identification in water distribution networks

ABSTRACT

A multi-objective optimization methodology is proposed herein for accurate identification of leakage in water distribution networks (WDNs) using pressure and flow sensors. We first model leakage at potential nodes using the EPANET software, and then divide WDN into near-homogenous zones using k-means clustering algorithm based on geographic distribution of nodes. Finally, flow and pressure sensors locations are optimized using the NSGA-II algorithm to identify the leakage zone accurately. Novelty of the proposed approach lies in sequential optimization of flow and pressure sensors placement, which helps improve the accuracy of leakage zone identification in WDNs. The objective functions of this study are: 1) maximizing accuracy of identified leakage zone and 2) minimizing number of sensors (and hence operational costs). Simulation results of the Mesopolis WDN corroborate the efficiency and effectiveness of the proposed approach.     

Applied Energy

It is acknowledged that the conventional design methods can easily lead to oversized system or unsatisfactory performance for different design conditions. Most existing studies on design optimization of net zero energy building ( nZEB) are conducted based on deterministic data/information. However, the question is:How is the actual performance of a design nZEB in different years considering uncertainties? This study, therefore, proposed a robust design method for sizing renewable energy systems in nZEB concerning uncertainties in renewable resources and demand load. The proposed robust design method is applied to the planning of renewable energy system for the Hong Kong Zero Carbon Building. The annual performance of nZEB under the optimal design options are systematically investigated and compared using the proposed robust design method and the deterministic method. It is meaningful to obtain a fitting formula to identify the relationship between the probability of achieving annual zero energy balance and the design mismatch ratio. On the basis of Monte Carlo uncertainty propagation methods, the uncertainty of nZEB performance is quantified which provides flexibility for designers in selecting appropriate design options according to the required probability of achieving nZEB during the design stage.     

EXPLICIT CALCULATION OF WATER QUALITY PARAMETERS IN PIPE DISTRIBUTION SYSTEMS

Explicit algorithms have been developed for use in water quality studies and management of distribution networks with sources of dissimilar quality. The proposed algorithms can be effectively used for directly determining a variety of blended water quality parameters for a given set of network loading and operational conditions. The parameters include the spatial distribution of constituent concentrations, the flow influence characteristics of supply sources, and the water age distribution throughout the system. The developed algorithms are formulated analytically from mass balance relationships as contingent linear boundary value problems in conjunction with a topological sorting methodology. The resulting formulations yield coefficient matrices, of order equal to the number of junction nodes, that are triangular and positive definite. The parameter solutions sought are, thus, derived through a direct application of a one step substitution process. The proposed algorithms are both robust and efficient, and guaranteed to yield mathematically consistent solutions. Moreover, the algorithms are simple to understand and implement, and are well suited to the needs of those practicing engineers who wish to write and run their own programs. Two example applications are presented. Enhancement of distribution system water quality management is a principal benefit of the algorithms.     

Failure probability minimization of buildings through passive friction dampers

This paper proposes a methodology for robust optimization of the failure probability of buildings subjected to stochastic earthquakes, using a less common type of passive energy dissipation device: the friction dampers. There is a lack of studies on optimal positions and parameters of passive friction dampers, and additionally, the few studies found in the literature consider the problem in a deterministic way. The robust optimization proposed in this paper is carried out through the recently developed backtracking search optimization algorithm, which is able to deal with optimization problems involving mixed discrete (positions) and continuous (friction forces) design variables. In order to take into account uncertainties present in both the system and the dynamic excitation (earthquakes), some parameters are modeled as random variables, and consequently, the structural response becomes stochastic. For illustration purposes, a 10‐story building is analyzed. The results showed that the proposed method was able to reduce the failure probability in approximately 99% with only three friction dampers, installed in their best positions and with their optimized friction forces. The proposed methodology is quite general, and it is believed that it can be recommended as an effective tool for optimum design of friction dampers. Copyright © 2016 John Wiley & Sons, Ltd.     

Use of numerical models for determination of best sampling locations for monitoring of large lakes

The implementation of the European Union Water Framework Directive (WFD) that requires all surface waters to achieve at least good status by year 2015 presents a challenge to local authorities responsible for the monitoring programmes. Obtaining a single representative value for each water quality parameter may be extremely difficult to achieve in the case of large lakes, which may be characterised by significant spatial and temporal variation of water quality parameters. The aim of this paper is to present a methodology, which can support the decision making as to the best locations for water quality sampling when developing monitoring systems of large lakes. The methodology is based on three-dimensional hydrodynamic and a conservative tracer transport numerical modelling and utilises the concept of flushing and residence time. The 3D numerical model of Lough Corrib located in the west of Ireland was created and used to calculate its residence time and flushing pathways. Based on the results it was possible to identify the areas in the lake that are characterised by the local residence time values close to the lake's average as well as by the values significantly lower and higher. Three water classes have been introduced and the authors recommend that sampling takes place in all three classes at various frequencies. Spatial distribution of the flushing pathways allows identifying the areas in the lake that best represent the lake's average conditions both in the long term and seasonally, where monitoring can be carried out at the required frequency; worst affected areas can also be identified. The methodology can be used to improve robustness of the monitoring programme and may also contribute towards the reduction of both sampling and analytical costs, both at the design and operational stages.     

Fast network multi-objective design algorithm combined with an a posteriori procedure for reliability evaluation under various operational scenarios

This paper presents a methodology for the optimal design of water supply networks. It features a multi-objective optimisation (aimed at minimising costs and maximising resilience) and a subsequent ‘retrospective’ evaluation of network reliability under various operational scenarios. The multi-objective optimisation is based on an algorithm specifically developed for the design of real networks which feature a very high number of nodes and pipes. The ‘retrospective’ evaluation of network reliability is assessed considering resilience contrasted with several other indexes adopted to describe the operational performance of the network under critical scenarios such as segment isolation or hydrant activation, and different water demand conditions. In the applications two case studies, made up of a simple benchmark network and a real network respectively, are considered for the multi-objective optimisation; the ‘retrospective’ evaluation of reliability is performed only on the real network. The latter example clearly highlights that the procedure proposed allows reliability and performance to be offset against cost, consenting informed choice of the optimal network configuration.     

Robust optimal design of renewable energy system in nearly/net zero energy buildings under uncertainties

It is acknow ledged that the conventional design methods can easily lead to oversized system or unsatisfactory performance for different design conditions. M ost existing studies on design optimization of net zero energy building( nZ EB) are conducted based on deterministic data / information. How ever,the question is: How is the actual performance of a design nZ EB in different years considering uncertainties? This study,therefore,proposed a robust design method for sizing renew able energy systems in nZ EB concerning uncertainties in renew able resources and demand load. The proposed robust design method is applied tothe planning of renew able energy system for the Hong Kong Zero Carbon Building. The annual performance of nZ EB under the optimal design options are systematically investigated and compared using the proposed robust design method and the deterministic method. It is meaningful to obtain a fitting formula to identify the relationship betw een the probability of achieving annual zero energy balance and the design mismatch ratio. On the basis of M onte Carlo uncertainty propagation methods, the uncertainty of nZ EB performance is quantified w hich provides flexibility for designers in selecting appropriate design options according to the required probability of achieving nZ EB during the design stage.     

A Dynamic Route Choice Model Considering Uncertain Capacities

Abstract: The standard assumption in (dynamic) traffic assignment models is that route choice is solely determined by a (perceived) deterministic travel time. However, recently, there is a growing interest in (dynamic) equilibrium route choice models in which travelers not only select their paths based on an estimated deterministic travel time, but also based on travel time reliability, in this article defined as the probability that the actual travel time deviates from the anticipated value. We extend the linear programming cell transmission model‐based dynamic traffic assignment (LP CTM‐DTA) model to account for travelers’ consideration of uncertainty regarding saturation flow rates (in this article referred to as capacities). It is shown that these reliability considerations can be accounted for by simply reducing the road capacities appearing in the constraint set of the classical LP CTM‐DTA model. More importantly, we provide results on the amount of capacity reduction necessary to ensure a certain reliability level. Although in the proposed model any probability distribution can be used to model the uncertainty, the selection of a specific probability distribution can potentially be burdensome for the modeler. To this end, we also present results on the class of symmetric probability distributions that has been particularly popular in the robust optimization literature. Properties for this broad class of distributions will be derived within the context of the introduced model. In numerical case studies, the model predicts that travel patterns can be significantly different when accounting for travelers’ reliability considerations.     

Multi-objective modelling of simultaneous reconfiguration of NMG-based distribution network and operation of different DERs

ABSTRACT

A Micro-Grid (MG) is envisaged with ever increasing distributed energy resources (DERs) e.g. distributed generation, demand response and electrical vehicles. This paper suggests a method based on multi-objective modelling for day-ahead scheduling of Networked-MGs based distribution network in the presence of different DERs and also this method is able to find the daily reconfiguration instants. The proposed scheme is solved using NSGA-II from distribution network operator viewpoints, who is responsible for providing power demand in higher reliability level and lower costs. Besides, in the suggested scheme voltage deviation and voltage stability as efficient power quality criteria in distribution networks and emission pollutant reduction are incorporated as independent objective functions. Moreover, to quantify the influence of different load models, a 33-node distribution network is adopted with a load class mix of residential, industrial and commercial loads. Eventually, the obtained results are reported which verify the efficiency of the proposed method.     

事故状态下供水管网的拓扑分析

当供水管网发生类似于爆管或水质污染等事故时,通过拓扑分析快速找到并关闭相应阀门以孤立事故源进而开展事故抢修是非常必要的。结合事故发生时管网的拓扑结构特点,以图论的理论和方法为基础,提出了一种可用于事故状态下供水管网拓扑分析的方法。该方法首先利用深度优先搜索算法找到距离事故源最近的阀门,然后通过剔除冗余阀门而确定最优关阀方案及关阀后的事故影响区域,最后调整管网水力模型以适应关阀后管网需水量和拓扑结构的变化。该方法不仅可用于供水管网事故时的关阀调度,还可用于供水系统污染控制分析以及供水管网的性能评价。     

Ant-colony optimization of pumping schedule to minimize the energy cost using variable-speed pumps in water distribution networks

Despite considerable pumping energy costs in pumping stations of water distribution networks (WDNs), there are not many studies offering pumping schedule in order to optimize pumping energy costs regarding the WDN design. This paper aims to focus on the optimization of the WDN pumping station schedule by means of variable-speed pump (VSP) and using a combination of the ant system iteration best algorithm (AS_ib) and EPANET2.0. In fact, the VSP is implemented to increase the flexibility of pumping station and obtain the pumping schedule with optimized energy cost as the water demand changes during a day. Given that vast search space in optimization process leads to a decrease in the quality of final results, in this study, two heuristic methods coupled with AS_ib are proposed in order to reduce the number of feasible solutions in the search space. The optimization results showed that the proposed heuristic approaches have considerably improved the quality of solutions produced by the AS_ib and enhanced the navigation of the optimization process. The results of optimization for the Richmond network, similar to the genetic algorithm (GA), showed that the AS_ib was capable of improving pumping energy costs. Besides, using the variable-speed pump in an optimized pump scheduling could lead to greater savings (about 10%) in pumping energy costs compared with the single-speed pump (SSP).     

Assessment of predictive uncertainty within the framework of water demand forecasting using the Model Conditional Processor (MCP)

This paper presents an application of the Model Conditional Processor (MCP), originally proposed by Todini (2008) within the hydrological framework, to assess the predictive uncertainty in water demand forecasting related to water distribution systems. The MCP enables us to assess the probability distribution of the future water demand conditional on the forecasts provided by two or more deterministic forecasting models. In the numerical application described here, where two years of hourly water demand data for a town in northern Italy are considered, two forecasting models are applied in order to forecast hourly water demands from 1 to 24 hours ahead: the first model has a modular structure comprising a periodic component which reflects the long-term effects and a persistence component which represents the short-term memory of the process; the latter is based on neural networks. The results highlight the effectiveness of the approach, provided that the data set used for the MCP parameterization is properly selected so as to be actually representative of the accuracy of the real-time water demand forecasting models.     

OPTIMAL DESIGN OF WATER DISTRIBUTION NETWORKS USING FUZZY OPTIMIZATION

A new heuristic approach for the design of water distribution networks involving a robust fuzzy linear program optimization in which the capital costs of the network are minimized while maintaining the nodal heads at demand nodes within a satisfactory region as defined by the customers at those nodes is presented. Iterative interaction between the fuzzy linear program and a network solver is used to ensure hydraulic consistency. Level of service is modelled by the residual nodal head available at demand nodes with the subjective nature of customers’ satisfaction with the nodal head being represented through fuzzy sets which reflect more realistically consumers’ attitudes toward pressure variations in the supply of water. Non-probabilistic uncertainty in the demand is modelled by a trapezoidal possibility distribution function. The model is demonstrated by application to an example network.     

Optimal Design of Water Distribution Systems Using Genetic Algorithms

This article proposes an optimal design methodology for the design of water distribution systems based on genetic algorithms. The objective of the optimization is to minimize the capital cost, subject to ensuring adequate pressures at all nodes. The proposed method differs from those of previous workers who have applied genetic algorithms in that the strings in the genetic algorithm model are coded using real variables, and this avoids the problem of redundant states often found when using binary (and Gray) coding schemes. A fitness function is also proposed that incorporates a variable penalty coefficient that depends on the degree of violation of the pressure constraints. The method also differs from those of previous workers in that it does not require solution of the nonlinear equations governing the flows and pressures in the distribution system for each individual member within the population. Hence this method shows a significant advantage compared with previously published techniques in terms of computational efficiency. The method has been tested on several networks, including networks used for benchmark testing least-cost design algorithms, and has been shown to be very efficient and robust.     

Steady-State Analysis of Water Distribution Networks Including Pressure-Reducing Valves

Hydraulic networks that contain controlling elements such as pressure-reducing valves (PRVs) are difficult to simulate. Limited literature exists on the explicit modeling of PRVs in a general solution procedure for steady-state analysis of water distribution systems. It is also known that inclusion of PRVs may lead to numerical difficulties. The objective of this article is to develop and present in sufficient detail the modeling of PRVs in combination with the linear theory method for steady-state analysis of water distribution networks. The presentation is explicit enough and leads to a robust algorithm that can be directly implemented in a computer program. The general methodology for simulating water distribution networks that embodies graph theoretic concepts, hydraulic theory, and numerical algorithms is reviewed.     

Robust Transportation Network Design Under Demand Uncertainty

Abstract:   This article addresses the problem of a traffic network design problem (NDP) under demand uncertainty. The origin–destination trip matrices are taken as random variables with known probability distributions. Instead of finding optimal network design solutions for a given future scenario, we are concerned with solutions that are in some sense "good" for a variety of demand realizations. We introduce a definition of robustness accounting for the planner's required degree of robustness. We propose a formulation of the robust network design problem (RNDP) and develop a methodology based on genetic algorithm (GA) to solve the RNDP. The proposed model generates globally near-optimal network design solutions, f, based on the planner's input for robustness. The study makes two important contributions to the network design literature. First, robust network design solutions are significantly different from the deterministic NDPs and not accounting for them could potentially underestimate the network-wide impacts. Second, systematic evaluation of the performance of the model and solution algorithm is conducted on different test networks and budget levels to explore the efficacy of this approach. The results highlight the importance of accounting for robustness in transportation planning and the proposed approach is capable of producing high-quality solutions.     

A state-of-the-art review of an optimal sensor placement for contaminant warning system in a water distribution network

ABSTRACT

Protection of water distribution networks (WDNs) is at the forefront because of the negative implications of the use of the contaminated water on the public health. In order to safeguard the water distribution networks from accidental and intentional attacks, a water quality sensor should be installed across the network. Remarkably, the budget constraints to procure and maintain sensors have limited the number of sensors deployed in networks. These constraints make the optimal sensor placement receive notable attention. Over time, researchers have devised various methodologies to tackle sensor placement in a water distribution network. Investigations have shown that each of the methodologies has a research gap which must be addressed. In this work, a state-of-the-art review of optimal sensor placement in a water distribution network is presented. The review results show technical challenges, possible solutions, and future research directions in this domain.     

Efficiency assessment of household water use

This paper presents a novel methodology for assessing the overall efficiency of indoor water uses in the household. The methodology comprises three main components of assessment: the evaluation based on efficient patterns, the comparison with peers grouped by clustering techniques and the performance of water use devices based on penalty functions. A water use index is calculated for individual households for each component; this index can be used to compare and to rank the performance of different households. The most representative results from a real life case study composed of 43 households are presented herein. The proposed methodology can support urban demand managers to identify low efficiencies, to set viable water efficiency targets, to calculate potential water savings and, subsequently, to implement water demand management strategies customized to less-efficient water use devices and to groups of consumers with specific socio-demographic characteristics.