Accounting for Greenhouse Gas Emissions in Multiobjective Genetic Algorithm Optimization of Water Distribution Systems

Increased awareness of climate change has shifted the focus of water distribution system (WDS) optimization research from cost minimization only to the incorporation of energy or associated greenhouse gas (GHG) minimization. In this study, a sensitivity analysis is conducted to investigate the effect of electricity tariff and generation (emission factors) on the results of multiobjective WDS optimization accounting for both total economic cost (both capital and operating costs) and GHGs. A multiobjective genetic algorithm-based optimization approach is used to conduct the analysis. The results show that electricity tariff has a significant effect on the total economic cost of WDSs and the selection of optimal solutions. In contrast, the changes of emission factors in the future have a significant effect on the total GHGs from WDSs. However, it does not alter the final solutions on the Pareto-optimal front. DOI: 10.1061/(ASCE)WR.1943-5452.0000169. © 2012 American Society of Civil Engineers. CE Database subject headings: Water distribution systems; Optimization; Emissions; Climate change; Sensitivity analysis; Algorithms. Author keywords: Water distribution systems; Multiobjective optimization; Greenhouse gas emissions; Climate change; Sensitivity analysis; Genetic algorithms.

Climate change, especially global warming caused by human activities presents serious global risks. Mitigating global warming by reducing greenhouse gas (GHG) emissions is a unique challenge facing our generation. In order to tackle this challenge, many measures are being developed, among which carbon trading is a popular one. In this paper, a new paradigm for the design of water distribution systems (WDSs) is being developed under a possible emission trading scheme. In this paradigm, minimisation of the costs of GHG emissions is incorporated into the optimisation of WDSs either as one part of the objective or as a second objective. A multi-objective genetic algorithm (MOGA) called WSMGA (water system multi-objective genetic algorithm) has been developed to solve this problem. The time value of both the system costs and the costs from GHG emissions has been taken into account by using present value analysis. Following the Stern Review Report there is controversy as to what discount rate should be used in present value analysis for mitigation of climate change, consequently two different discount rates have been used in this study. The impacts that the carbon prices used in the emission trading scheme have on the optimisation of WDSs have been explored for two hypothetical case studies. The optimisation results show that the different carbon prices used lead to different solutions in the single-objective optimisation formulation. In general, a network with larger pipes is chosen when a higher carbon price is used. In contrast, the carbon price used has no impact on the multi-objective optimisation results. However, different carbon prices lead to different amounts of savings in greenhouse gas costs resulting from the same amount of increase in system costs for the same ordered set of Paretooptimal solutions.

Previous research has demonstrated that there are significant trade-offs between the competing objectives of minimizing costs and greenhouse gas (GHG) emissions for water distribution system (WDS) optimization. However, upon introduction of an emission trading scheme, GHG emissions are likely to be priced at a particular level. Thus, a monetary value can be assigned to GHG emissions, enabling a single-objective optimization approach to be used. This raises the question of whether the introduction of carbon pricing under an emission trading scheme will make the use of a multiobjective optimization approach obsolete or whether such an approach can provide additional insights that are useful in a decision-making context. In this paper, the above questions are explored via two case studies. The optimization results obtained for the two case studies using both single-objective and multiobjective approaches are analyzed. The analyses show that the single-objective approach results in a loss of trade-off informa...

The multi-objective design optimization of water distribution systems (WDS) is to find the Pareto front of optimal designs of WDS for two or more conflicting design objectives. The most popular conflicting objectives considered for the design of WDS are minimization of cost and maximization of resilience index which are considered for the current study. Robust multi-objective optimization is to find the optimal set of the Pareto front considering demand is uncertain. The robustness is controlled by a single parameter that defines the size of the uncertainty set it can vary. The study explores ellipsoidal uncertainty set with different sizes and co-variance matrices. A combined simulation–optimization framework with a combination of self-adaptive multi-objective cuckoo search (SAMOCSA) and the fmincon optimization algorithm is proposed to solve the robust multi-objective design problem. The proposed algorithm is applied to medium and large WDS. The main contribution of this paper is to study the effect of demand uncertainty and the correlation on the WDS designs in a multi-objective framework. The study shows that the inclusion of correlation into the multi-objective design framework can significantly affect the optimal designs.

A water distribution system (WDS), being a system of interconnected hydraulic elements ensures water distribution and supply to satisfy demands while optimization is applied in many systems and situations, thus making it an important paradigm in technology. When we try to optimize, we either minimize (resource consumption, cost) or maximize (profit, system performance). This paper reviews optimization of water distribution systems and presents different aspects of WDSs. It also presents the different optimization methods in a detailed manner. Finally, modern sophisticated optimization methods are refined enough to solve complex real-world problems such as WDS design and operations. The categories of challenges being solved include: new WDSs design; expansion and revamping of existing WDSs; pump operation; water quality management; calibration and system partitioning. Reliability, robustness and resilience considerations are often encountered in WDS literature and are still open to analysis because there is no generally acceptable model for permanent inclusion in WDSs optimization.

Lost in optimisation of water distribution systems? A literature review of system operation

Water distribution networks are crucial for supplying consumers with quality and adequate water. A water distribution system comprises connected hydraulic components which ensure water supply and distribution to meet demand. Optimization of water distribution networks is carried out to minimize resource utilization and expenditure or maximize the system’s efficiency and higher benefits. Genetic algorithms signify an effective search technique for non-linear optimization problems and have gained acceptance among water resources planners and managers. This paper reviews various developments in the optimization of water distribution systems using the technique of genetic algorithms. These developments are pertinent to creating novel systems for distributing water and the expansion, reinforcement, and rehabilitation process for prevailing water supply mechanisms.Graphical

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.

Water distribution systems are an integral part of the economic infrastructure of modern-day societies. However, previous research on the design optimization of water distribution systems generally involved few decision variables and consequently small solution spaces; piecemeal-solution methods based on pre-processing and search space reduction; and/or combinations of techniques working in concert. The present investigation was motivated by the desire to address the above-mentioned issues including those associated with the lack of high-performance computing (HPC) expertise and limited access in developing countries. More specifically, the article’s aims are, firstly, to solve a practical water distribution network design optimization problem and, secondly, to develop and demonstrate a generic multi-objective genetic algorithm capable of achieving optimal and near-optimal solutions on complex real-world design optimization problems reliably and quickly. A multi-objective genetic algorithm was developed that applies sustained and extensive exploration of the active constraint boundaries. The computational efficiency was demonstrated by the small fraction of 10-245 function evaluations relative to the size of the solution space. Highly competitive solutions were achieved consistently, including a new best solution. The water utility’s detailed distribution network model in EPANET 2 was used for the hydraulic simulations. Therefore, with some additional improvements, the optimization algorithm developed could assist practitioners in day-to-day planning and design.

Global warming caused by human activities presents serious global risks. Individuals, governments, and industries need to be more energy efficient and contribute to the mitigation of global warming by reducing their greenhouse gas (GHG) emissions. In previous research, GHG emission reduction has been identified as one important criterion in improving the sustainability of urban infrastructure and urban water systems. Within the water industry, opportunities exist for reducing GHG emissions by improving pumping efficiency via the use of variable-speed pumps (VSPs). Previously, VSPs have been used in the optimization of the operation of existing water distribution systems (WDSs). However, in WDS design optimization problems, fixed-speed pumps (FSPs) are commonly used. In this study, a pump power estimation method, developed using a false position method based optimization approach, is proposed to incorporate VSPs in the conceptual design or planning of water transmission systems (WTSs), using optimization. This pump power estimation method is implemented within the solution evaluation process via a multiobjective genetic algorithm approach. A case study is used to demonstrate the application of the pump power estimation method in estimating pump power and associated energy consumption of VSPs and FSPs in WTS optimization. In addition, comparisons are made between variable-speed pumping and fixed-speed pumping in multiobjective WTS optimization accounting for total cost and GHG emissions. The results show that the use of variable-speed pumping leads to significant savings in both total cost and GHG emissions from WTSs for the case study considered.

Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details.

A multicriteria maximum-entropy approach to the joint layout, pipe size and reliability optimization of water distribution systems is presented. The capital cost of the system is taken as the principal criterion, and so the trade-offs between cost, entropy, reliability and redundancy are examined sequentially in a large population of optimal solutions. The novelty of the method stems from the use of the maximum-entropy value as a preliminary filter, which screens out a large proportion of the candidate layouts at an early stage of the process before the designs and their reliability values are actually obtained. This technique, which is based on the notion that the entropy is potentially a robust hydraulic reliability measure, contributes greatly to the efficiency of the proposed method. The use of head-dependent modelling for simulating pipe failure conditions in the reliability calculations also complements the method in locating the Pareto-optimal front. The computational efficiency, robustness, accuracy and other advantages of the proposed method are demonstrated by application to a sample network.

Key Points Three‐objective WDS optimization considering cost, reliability and GHGs Shape of solution space formed by the objectives is a U‐shaped curve Location of Pareto front in the solution space and its practical implications

Optimization of water distribution systems (WDSs) to minimize operating costs becomes an important concern for water utilities, especially due to the continually increasing energy price. Besides the energy cost, water utilities worldwide are also facing with the water loss problem. More water loss occurring in WDSs will require pumps to work much more to supply extra flows and pressures for compensating the water loss amount. As a result, clean water is wasted and at the same time more energy cost is expensed without any benefit for water utilities. For this reason, optimizing operations of pump stations for minimizing only the energy cost by taking advantage of electrical tariff structure is indeed inadequate since the water loss cost may be higher than the energy cost. To be capable of practical implementation and preserve clean water resources, operational optimization of WDSs should take both energy cost and water loss cost into account for deducing optimal pump scheduling. This paper investigated the potential application of variable speed pumps (VSPs) for decreasing both energy cost and water leakage cost for operations of WDSs with storage tanks. The comparisons of the uses of VSPs and fixed speed pumps (FSPs) for many scenarios are also carried out demonstrating that the application of VSPs outperforms the use of FSPs in decrease of the total cost of energy and water loss.

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.