Assessment of Penalty-Free Multi-Objective Evolutionary Optimization Approach for the Design and Rehabilitation of Water Distribution Systems

This paper describes a penalty-free multi-objective evolutionary optimization approach for the phased whole-life design and rehabilitation of water distribution systems. The optimization model considers the initial construction, rehabilitation and upgrading costs. Repairs and pipe failure costs are included. The model also takes into consideration the deterioration over time of both the structural integrity and hydraulic capacity of every pipe. The fitness of each solution is determined from the trade-off between its lifetime costs and its actual hydraulic properties. The hydraulic analysis approach used, known as pressure-dependent modelling, considers explicitly the pressure dependency of the water supply consumers receive. Results for two sample networks in the literature are included that show the algorithm is stable and finds optimal and near-optimal solutions reliably and efficiently. The results also suggest that the evolutionary sampling efficiency is very high. In other words, the number of solutions evolved and analysed on average before finding a near-optimal solution is small in comparison to the total number of feasible and infeasible solutions. We found better solutions than those reported previously in the literature for the two networks considered. For the Kadu network, for example, the new best solution costs Rs125,460,980—a significant improvement. Additional statistics that are based on extensive testing are included.     

Evolving Effective Hydraulic Model for Municipal Water Systems

Constructing a robust hydraulic network model is vitally important, but a time-consuming task. Over last two decades, several approaches using optimization techniques have been developed for identifying model parameters. Although most of the methods can make the model agree with field observations, few are able to achieve a good level of accuracy in terms of determining the correct model parameters for a water distribution system. The previously developed methods appear to be lacking versatility for users to specify calibration tasks given real data for a real system. This paper proposes a comprehensive framework for evolving a hydraulic network model. Calibration tasks can be specified according to data availability and model application requirements. It allows an engineer to (1) flexibly choose any combination of the model parameters such as pipe roughness, junction demand and link (pipes, valves and pumps) operational status; (2) easily aggregate model parameters to reduce the problem dimension for expeditious calculation and (3) consistently specify boundary conditions and junction demand loadings that are corresponding to field data collection. A model calibration is then defined as an implicit nonlinear optimization problem, which is solved by employing a competent evolutionary algorithm. With this methodology, a modeler can be fully assisted to carry out not only a single parameter optimization run, but also a variety of calibration tasks in a progressive manner according to practical system conditions, thus it is possible to achieve a good model calibration with high level of confidence. The method has been applied to the model of a municipal water system to demonstrate the efficacy and robustness of the evolutionary modeling practices.     

A Derivative-Free Hybrid Optimization Model for Short-Term Operation of a Multi-Objective Reservoir System Under Uncertainty

Short-term operation of a multi-objective reservoir system under inflow uncertainty has been receiving increasing attention, however, major challenges for the optimization of this system still remain due to the multiple and often conflicting objectives, highly nonlinear constraints and uncertain parameters in which derivative information may not be directly available. Population-based optimization methods do not rely on derivatives while generally have a slow convergence. This study presents a hybrid optimization model for short-term operation of multi-objective reservoirs under uncertainty that is derivative free and has a relatively fast convergence. The model incorporates a local improvement method called Mesh Adaptive Direct Search (MADS) into a population-based method NSGA-II and has no requirement for differentiability, convexity and continuity of the optimization problem. The operation of a multi-objective and multi-reservoir system on the Columbia River under inflow uncertainty is used as a case study. Overall, the hybrid model outperforms optimization models based on either the NSGA-II only or the MADS only. The model is intended for conditions where derivative information of the optimization problem is unavailable, which could have a wide array of applications in water resources systems.     

Optimal Allocation of Flood Control Capacity for Multi-Reservoir Systems Using Multi-Objective Optimization Approach

Operation of multi-reservoir systems during flood periods is of great importance in the field of water resources management. This paper proposes a multi-objective optimization model with new formulation for optimal operation of multi-reservoir systems. In this model, the release rate and the flood control capacity of each reservoir is considered as decision variable and the resulting nonlinear non-convex multi-objective optimization problem is solved with ε-constraint method through the mixed integer linear programming (MILP). Objective functions of the model are minimizing the flood damage at downstream sites and the loss of hydropower generation. The developed model is used to determine optimal operating strategies for Karkheh multi-reservoir system in southwestern Iran. For this purpose, the model is executed in two scenarios based on “two-reservoir” and “six-reservoir” systems and for floods with return periods of 25 and 50 years. The results show that in two-reservoir system, flood damage is at least about 114 million dollars and cannot be mitigated any further no matter how hydropower generation is managed. But, in the case of developing all six reservoirs, optimal strategies of coordinated operation can mitigate and even fully prevent flood damage.     

Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy

In the present study the WEAP-NSGA-II coupling model was developed in order to apply the hedging policy in a two-reservoir system, including Gavoshan and Shohada dams, located in the west of Iran. For this purpose after adjusting the input files of WEAP model, it was calibrated and verified for a statistical period of 4 and 2 years respectively (2008 till 2013). Then periods of water shortage were simulated for the next 20 years by defining a reference scenario and applying the operation policy based on the current situation. Finally, the water released from reservoirs was optimized based on the hedging policy and was compared with the reference scenario in coupled models. To ensure the superiority of the proposed method, its results was compared with the results of two well-known multi-objective algorithms called PESA-II and SPEA-II. Results show that NSGA-II algorithm is able to generate a better Pareto front in terms of minimizing the objective functions in compare with PESA-II and SPEA-II algorithms. An improvement of about 20% in the demand site coverage reliability of the optimum scenario was obtained in comparison with the reference scenario for the months with a higher water shortage. In addition, considering the hedging policy, the demand site coverage in the critical months increased about 35% in compared with the reference scenario.     

Penalty-Free Feasibility Boundary Convergent Multi-Objective Evolutionary Algorithm for the Optimization of Water Distribution Systems

This paper presents a new penalty-free multi-objective evolutionary approach (PFMOEA) for the optimization of water distribution systems (WDSs). The proposed approach utilizes pressure dependent analysis (PDA) to develop a multi-objective evolutionary search. PDA is able to simulate both normal and pressure deficient networks and provides the means to accurately and rapidly identify the feasible region of the solution space, effectively locating global or near global optimal solutions along its active constraint boundary. The significant advantage of this method over previous methods is that it eliminates the need for ad-hoc penalty functions, additional ??boundary search?? parameters, or special constraint handling procedures. Conceptually, the approach is downright straightforward and probably the simplest hitherto. The PFMOEA has been applied to several WDS benchmarks and its performance examined. It is demonstrated that the approach is highly robust and efficient in locating optimal solutions. Superior results in terms of the initial network construction cost and number of hydraulic simulations required were obtained. The improvements are demonstrated through comparisons with previously published solutions from the literature.     

A Negotiation-Based Multi-Objective, Multi-Party Decision-Making Model for Inter-Basin Water Transfer Scheme Optimization

The planning and management decisions often involve multiple objectives and multiple parties with conflicting interests due to the complexity of inter-basin water transfer systems. In this paper, the objectives, the groups involved and the corresponding conflicting interests that characterize water transfer decisions are analyzed. A multi-party, multi-objective decision/bargaining model based on the ??satisfaction principle?? is developed for inter-basin water transfer system decision-making. In order to obtain an ideal multi-party decision, bargaining is first broken down into two stages, and then decision alternatives are chosen using fuzzy pattern recognition. This model is simple, and it is more adaptable for solving practical multi-objective and multi-party decision problems. Finally, an inter-basin water transfer scheme optimization example is demonstrated by using the developed model.     

Self-Adaptive Solution-Space Reduction Algorithm for Multi-Objective Evolutionary Design Optimization of Water Distribution Networks

An effective way to improve the computational efficiency of evolutionary algorithms is to make the solution space of the optimization problem under consideration smaller. A new reliability-based algorithm that does this was developed for water distribution networks. The objectives considered in the formulation of the optimization problem were minimization of the initial construction cost and maximization of the flow entropy as a resilience surrogate. After achieving feasible solutions, the active solution space of the optimization problem was re-set for each pipe in each generation until the end of the optimization. The algorithm re-sets the active solution space by reducing the number of pipe diameter options for each pipe, based on the most likely flow distribution. The main components of the methodology include an optimizer, a hydraulic simulator and an algorithm that calculates the flow entropy for any given network configuration. The methodology developed is generic and self-adaptive, and prior setting of the reduced solution space is not required. A benchmark network in the literature was investigated, and the results showed that the algorithm improved the computational efficiency and quality of the solutions achieved by a considerable margin.     

黄河三角洲水资源承载力多目标优化计算

为促进黄河三角洲地区水资源的可持续利用,构建了水资源承载力多目标优化计算模型,选用人口、经济和粮食承载指数量化水资源承载力,得出2010年、2015年和2020年不同水平年基本方案和节水方案下水资源系统能够支撑的最大人口和经济社会发展规模,利用相对承载指数法评价了水资源承载力的相对状态。结果表明:2010年研究区整体处于弱度超载状态;2015年、2020年由于用水功效系数的提高、南水北调工程通水以及非常规水源的利用,因此水资源承载能力有了较大的提高;节水方案下研究区水资源承载力更强,节水是提高研究区水资源承载力的重要措施。     

Development of a Fuzzy Multi-Objective Heuristic Model for Optimum Water Allocation

Water Resources Management - A challenging issue in optimal allocating water resources is uncertainty in parameters of a model. In this paper, a fuzzy multi-objective model was proposed to maximize...     

Optimization of Investments in the Resilience of Water Distribution Systems Subject to Interruptions

In this paper, we propose an optimization model to support decisions related to the design of water distribution systems (WDS) that are subjected to interruptions caused by disruptive events, emphasizing their resilience capabilities, namely: absorption, adaptation, and recovery. Considering the exposure of WDS operation to random interruptions, we aim at minimizing the total investment considering the possibility of implementing actions that improve these capabilities, which can be put in place prior or posterior to the occurrence of a disruptive event. An application example is discussed as a way to understand the nature of the problem and to support the formulation of the proposed model. The results demonstrate the need to invest in resilient capacities adequate to each interruption probability associated to the disruptive scenarios, characterizing these considerations as of great importance to support managerial decisions, thus constituting a guideline for the allocation of investments before and/or after the occurrence of the event.

     

Conjunctive Operation of Reservoirs and Ponds Using a Simulation-Optimization Model of Irrigation Systems

Water resource management in arid agricultural irrigation regions is a great challenge for managers and decision makers. In some of those regions, many ponds have been built to ensure an adequate water supply for irrigation. Therefore, reservoirs and ponds should be managed conjunctively to minimize shortages of water. In this study, a new integrated mathematical model of conjunctive, or integrated, operation of reservoirs and ponds to maximize the water supply has been proposed for a reservoir-pond irrigation system. This objective has been achieved via the use of two models: an optimal model, which is used to determine the optimal discharge of reservoirs, and a simulation model, which considers the regulatory role of ponds and reservoirs and simulates their water supply to the irrigation system. An adaptive genetic algorithm has been employed in this study to solve the nonlinear and multi-dimensional reservoirs optimization problem. This methodology has been applied to the Yarkant River Basin to demonstrate its applicability, and three scenarios are presented. The main objective of the simulation-optimization model in the Yarkant River Basin is to minimize shortages in meeting irrigation demands for nine sub-irrigation systems subject to the constraint of ecological water transfer to the Tarim River. The optimizing effect of the model was particularly prominent under the third scenario, i.e., the XBD, MMK, and ART Reservoirs and 16 ponds conjunctively operated to meet the water demand of the YKB. The frequency of success (FS) in meeting agricultural water demands reaches up to 75%, and the value for ecological demand is 50.98%. The results demonstrate the importance of the conjunctive combined use approach for management of water resources in irrigation system of arid regions.     

岸边溢洪道水工模型试验与设计优化

笔者以江西四方井水利枢纽溢洪道为例,通过整体水工模型试验,研究溢洪道泄流能力、水流流态、沿程水深、时均压强及消能效果等;通过优化进水渠形式,调整宽顶堰为WES实用堰,同时增加消力池长度等措施,提出了优化布置方案.结果表明:原方案溢洪道泄流能力不足,控制段流态紊乱、消力池长度偏短;优化方案泄流能力满足设计要求,能够保证各...     

Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.     

Robust Parameter Set Selection for a Hydrodynamic Model Based on Multi-Site Calibration Using Multi-Objective Optimization and Minimax Regret Approach

A robust parameter set (ROPS) selection method for a hydrodynamic flow model was proposed based on the multi-site calibration by combining multi-objective optimization with the minimax regret approach (MRA). The multi-site calibration was defined by a multi-objective optimization problem for which individual objective functions were used to measure errors at each site. In the hydrodynamic model, coefficients of power functions that show the changing relationships between Manning’s roughness and discharge in each sub-reach were optimized by minimizing the residuals of multiple sites. Different combinations of weights were assigned to sites in the application of an aggregation approach to solve the multi-objective function, and the corresponding Pareto optimal parameter sets were assumed as the ROPS candidates. All performance measures to individual Pareto optimal parameter sets were calculated and the ROPS was determined using MRA. The set which has the lowest maximum regret obtained by averaging the results from calibration and validation was determined as the only ROPS. It was found that the estimated variable roughness and the corresponding computed water levels varied considerably depending on the weights assigned to sites. Using the proposed method, the task to assign proper weights on multiple sites can be easily achieved for multi-site calibration problems. This study provides a multi-criteria decision making method to choose a ROPS that has the lowest potential regret among various alternatives for hydrologic and hydraulic models.     

Multi-Objective Differential Evolution for Design of Cascade Hydropower Reservoir Systems

After Paris Agreement and obligation made by various countries to decrease greenhouse gases, generation of clean energy with low carbon was taken into consideration. Hydropower plant is considered as a clean, cheap and renewable energy source for generating electrical energy. Through the construction of the multipurpose dams and their optimal planning and management, we may decrease the potential losses sustained by aquatic ecosystem in addition to supplying the energy and fulfilling the industrial, agricultural and drinking water demands. In the present study, a multi-objective optimization model was proposed for determination of design parameters in cascade hydropower multi-purpose reservoir systems. Considering the significant number of constraints and decision variables and non-convex form of the objective functions and constraints, particularly in multi-reservoir systems, a multi-objective evolutionary algorithm (MOEA) known as non-dominated sorting differential evolution (NSDE) was developed to solve the problem and reduce the computational costs. Karkheh River basin was selected as a case study in order to make an assessment on the capabilities and strength of the model. This basin is capable of generating hydropower energy and agricultural development with high environmental considerations due to Hurolazim International Wetland. Based on the results, we may supply various demands such as environmental demands of the aquatic ecosystem with high reliability as well as generating firm hydropower energy through optimal design of cascade hydropower reservoirs.     

A Graph-Theoretic Framework for Assessing the Resilience of Sectorised Water Distribution Networks

Water utilities face a challenge in maintaining a good quality of service under a wide range of operational management and failure conditions. Tools for assessing the resilience of water distribution networks are therefore essential for both operational and maintenance optimization. In this paper, a novel graph-theoretic approach for the assessment of resilience for large scale water distribution networks is presented. This is of great importance for the management of large scale water distribution systems, most models containing up to hundreds of thousands of pipes and nodes. The proposed framework is mainly based on quantifying the redundancy and capacity of all possible routes from demand nodes to their supply sources. This approach works well with large network sizes since it does not rely on precise hydraulic simulations, which require complex calibration processes and computation, while remaining meaningful from a physical and a topological point of view. The proposal is also tailored for the analysis of sectorised networks through a novel multiscale method for analysing connectivity, which is successfully tested in operational utility network models made of more than 100,000 nodes and 110,000 pipes.     

Planning of Groundwater Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced Models and Multi-Objective Evolutionary Optimization

The typical modeling approach to groundwater management relies on the combination of optimization algorithms and subsurface simulation models. In the case of groundwater supply systems, the management problem may be structured into an optimization problem to identify the pumping scheme that minimizes the total cost of the system while complying with a series of technical, economical, and hydrological constraints. Since lack of data on the subsurface system most often reflects upon the development of groundwater flow models that are inherently uncertain, the solution to the groundwater management problem should explicitly consider the tradeoff between cost optimality and the risk of not meeting the management constraints. This work addresses parameter uncertainty following a stochastic simulation (or Monte Carlo) approach, in which a sufficiently large ensemble of parameter scenarios is used to determine representative values selected from the statistical distribution of the management objectives, that is, minimizing cost while minimizing risk. In particular, the cost of the system is estimated as the expected value of the cost distribution sampled through stochastic simulation, while the risk of not meeting the management constraints is quantified as the expected value of the intensity of constraint violation. The solution to the multi-objective optimization problem is addressed by combining a multi-objective evolutionary algorithm with a stochastic model simulating groundwater flow in confined aquifers. Evolutionary algorithms are particularly appropriate in optimization problems characterized by non-linear and discontinuous objective functions and constraints, although they are also computationally demanding and require intensive analyses to tune input parameters that guarantee optimality to the solutions. In order to drastically reduce the otherwise overwhelming computational cost, a novel stochastic flow reduced model is thus developed, which practically allows for averting the direct inclusion of the full simulation model in the optimization loop. The computational efficiency of the proposed framework is such that it can be applied to problems characterized by large numbers of decision variables.     

Multi-Objective Planning Model for Large Scale Irrigation Systems: Method and Application

An increase in demand, and droughts in recent years have resulted in the need for tools to allocate limited water between users in different regions in order to achieve economic, social and environmental benefits. Multi-objective planning is an important decision support tool for natural resource management. Planners, decision makers and stakeholders use this approach in the decision-making process. In this research, a multi-objective planning model was developed and applied on the Menemen Left Bank Irrigation System of the Lower Gediz Basin in Turkey. The aims of the model were to increase the benefit from production, to increase the size of the total area irrigated, and to reduce the water losses occurring at network level. The model was applied to an open channel system consisting of 44 tertiary channels receiving water from three secondaries, serving an area of 3,606 ha. The model predicted a 20.63% increase in income, and a 29.26% decrease in the total irrigation water requirements of crops dependent on projected changes in the actual crop pattern of the research area. This decrease caused a reduction of 29.90% in expected water losses over the network as a whole. The operation of the model enabled optimum productivity and income at the system level per unit of land and water resources.     

Multi-Objective Design Optimization of Branched Pipeline Systems with Analytical Assessment of Fire Flow Failure Probability

This paper develops a multi-objective optimization approach for incorporating the conditional probability of fire flow failure into the design of branched water networks. To this end, a new analytical probabilistic model was developed to quantify the conditional probability of fire flow failure in branched networks and incorporated into the non-dominated sorting genetic algorithm (NSGA-II). The optimization sought to minimize capital cost through pipe diameter and pump selection and to minimize the conditional probability of fire flow failure. The NSGA-II was applied to two branched networks to generate Pareto-optimal solutions. Results indicated a strategic allocation of pipe and pump capacity with limited fiscal resources and with a reduction in uncertainty of fire flow failure. Interestingly, optimization results for a real branched network supported the industry practice of using a minimum 150 mm distribution main sizing to provide fire flow protection.