The Battle of the Water Networks II (BWN-II)

1 The Battle of the Water Networks II (BWN-II) By 4Angela Marchi 1, Elad Salomons 2, Avi Ostfeld 3, Zoran Kapelan 4, Angus R Simpson 1, Aaron 5C Zecchin 1, Holger R Maier 1, Zheng Yi Wu 5, Samir M Elsayed 6, Yuan Song 6, Tom 6Walski 5, Christopher Stokes 1, Wenyan Wu 1, Graeme C Dandy 1, Stefano Alvisi 7, Enrico 7Creaco 7, Marco Franchini 7, Juan Saldarriaga 8, Diego Páez 8, David Hernández 8, Jessica 8Bohórquez 8, Russell Bent 9, Carleton Coffrin 10, David Judi 9, Tim McPherson 9, Pascal van 9Hentenryck 10, José Pedro Matos 11,12, António Jorge Monteiro 11, Natércia Matias 11, Do Guen 10Yoo 13, Ho Min Lee 13, Joong Hoon Kim 13, Pedro L Iglesias-Rey 14, Francisco J Martínez11Solano 14, Daniel Mora-Meliá 14, José V Ribelles-Aguilar 14, Michele Guidolin 4, Guangtao Fu 124, Patrick Reed 15, Qi Wang 4, Haixing Liu 4,16, Kent McClymont 4, Matthew Johns 4, Edward 13Keedwell 4, Venu Kandiah 17, Micah Nathanael Jasper 17, Kristen Drake 17, Ehsan Shafiee 17, 14Mehdy Amirkhanzadeh Barandouzi 17, Andrew David Berglund 17, Downey Brill 17, 15Gnanamanikam Mahinthakumar 17, Ranji Ranjithan 17, Emily Michelle Zechman 17, Mark S 16Morley 4, Carla Tricarico 18, Giovanni de Marinis 18, Bryan A Tolson 19, Ayman Khedr 19, 17Masoud Asadzadeh 19 18 19 201 School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, 21Australia; Email: angela.marchi@adelaide.edu.au 222 OptiWater, Amikam Israel St., Haifa 34385, Israel 233 Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, 24Haifa 32000, Israel 254 University of Exeter, Centre for Water Systems, Exeter, UK 265 Bentley Systems, Incorporated, 27 Siemon Company Drive, Suite200W, Watertown, 27CT06795, USA 286 Department of Computer Science and Engineering, University of Connecticut, Storrs, USA 297 Department of Engineering, University of Ferrara, 44122 Ferrara, Italy 308 Civil and Environmental Engineering Department, Universidad de los Andes, Bogotá, 31Colombia 329 Los Alamos National Laboratory, Los Alamos, New Mexico 3310 NICTA, Australia 3411 Techinical University of Lisbon, Instituto Superior Técnico, Lisbon, Portugal 3512 École Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland 3613 School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 37Korea 3814 Dpto Ingeniería Hidráulica y Medio Ambiente Universitat Politécnica de València, Spain 3915 Department of Civil and Environmental Engineering, Pennsylvania State University, 40University Park, Pennsylvania, PA 16802, USA 4116 School of Municipal and Environmental Engineering, Harbin Institute of Technology, 42Harbin, Heilongjiang, China 4317 Department of Civil, Construction, and Environmental Engineering, North Carolina State 44University, Raleigh, NC 27695, USA 4518 Dipartimento di Ingegneria Civile e Meccanica, Università di Cassino e del Lazio 46Meridionale, via Di Biasio, 43, Cassino, Frosinone, Italy 4719 Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, 48Ontario, Canada 49 50 51Keywords: water distribution systems, optimization, design, pump operation 52 1 53Abstract 54The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions 55related to the design and operation of water distribution systems (WDSs) undertaken 56within the Water Distribution Systems Analysis (WDSA) Symposium series The 57BWN-II problem specification involved a broadly defined design and operation 58problem for an existing network that has to be upgraded for increased future demands, 59and the addition of a new development area The design decisions involved addition 60of new and parallel pipes, storage, operational controls for pumps and valves, and 61sizing of backup power supply Design criteria involved hydraulic, water quality, 62reliability, and environmental performance measures Fourteen teams participated in 63the Battle and presented their results at the 14th Water Distribution Systems Analysis 64(WDSA 2012) conference in Adelaide, Australia, September 2012 This paper 65summarizes the approaches used by the participants and the results they obtained 66Given the complexity of the BWN-II problem and the innovative methods required to 67deal with the multi-objective, high dimensional and computationally demanding 68nature of the problem, this paper represents a snap-shot of state of the art methods for 69the design and operation of water distribution systems A general finding of this paper 70is that there is benefit in using a combination of heuristic engineering experience and 71sophisticated optimization algorithms when tackling complex real-world water 72distribution system design problems 73 74INTRODUCTION 75The Battle of the Networks II (BWN-II) is the third of a series of competitions 76undertaken within the Water Distribution System Analysis (WDSA) Symposium 77series, the previous competitions being the Battle of the Water Calibration Networks 78(BWCN) (Ostfeld et al 2011), and the Battle of the Water Sensor Networks (BWSN) 2 79(Ostfeld et al., 2008) All of these are predated by the original Battle of the Network 80Models (BNM) (Walski et al., 1987), which was organized as a part of the American 81Society of Civil Engineers (ASCE) conference "Computers in Water Resources" at 82Buffalo, New York, in June 1985 To celebrate the 25 th year since the publication of 83the first BNM, the BWN-II focuses on the optimal design and operation of a water 84distribution system (WDS), where not only capital and operational costs are 85considered, but additional objectives, including water quality, reliability, and 86environmental considerations are considered 87Even in its most idealized form, the design of WDSs is a non-deterministic 88polynomial-time hard (NP-hard) problem (the definition of NP-hard problems can be 89found in Yates et al., 1984), which can be attributed to the non-linearity of the 90hydraulic equations, and the presence of discrete diameter size variables Note that the 91WDS design problem could be treated as a non-linear problem (NLP) (Duan et al 921990) if pipe sizes were assumed to be continuous, or as a linear problem (LP) 93(Alperovits and Shamir, 1977) if the decision variables were the pipe lengths 94However, in both cases, the resulting continuous solution has to be ‘rounded’ to 95discrete sizes, resulting in approximations (Savic and Walters, 1997) Note that the 96split-pipe solutions obtained using LP are often not allowed in WDS pipe design 97problems, where each pipe has to have one single diameter Moreover, the LP 98formulation requires the objective function to be a linear relationship of the pipe 99lengths: not all problems in WDSs can be expressed in this way In its original 100definition, the WDS problem is a mixed integer non linear problem (Bragalli et al 1012012) and belongs to the NP-hard category (Burer and Letchford, 2012) The practical 102implication of this is that no algorithm can guarantee an optimal design in polynomial 103time Typical of these problems is that full enumeration is impossible due to the size 3 104of the decision variable search space, motivating many researchers and research 105groups to develop algorithms and strategies aimed at finding good near-optimal 106solutions Building on this history, the aim of the BWN-II was to test the performance 107of a range of strategies on a large and complex multi-objective problem to gain insight 108into the state of the art of optimization algorithms applied to WDS problems The aim 109of this paper is to report on approaches, difficulties and results as outlined by the 110competition participants in solving the problem Note that our aim is not that of 111identifying the best approach to solve WDS problems, because i) no algorithm will 112necessarily perform best for each class of WDS problems (Wolpert and Macready, 1131997); and ii) the participants used different amounts of resources, hence a 114comparison on purely algorithmic grounds is not possible 115As in previous competitions, the BWN-II was advertised to teams/individuals from 116academia, consulting firms and utilities to submit their strategies and proposed design 117solutions The submissions from the participants were presented at a special session of 118the 14th Water Distribution Systems Analysis (WDSA 2012) conference in Adelaide, 119Australia, September 2012 120The objective of this paper is to summarize the major characteristics 121of the BWN-II design solutions and approaches and to highlight 122future research directions based on insights gained The BWII-II rules 123and data are presented in the next section, followed by a synopsis of 124each team’s design approach, a comparison of the optimization results, and 125conclusions and future research directions 126 127PROBLEM DESCRIPTION 4 128The aim of the competition was to identify the best long-term design improvements 129and associated operational strategy for D-Town (see Figure 1), given projected future 130water demand and development of a new area The aim was to identify a single 131strategy leading to minimized capital and operational costs whilst minimizing 132greenhouse gas (GHG) emissions and improving water age A summary description of 133D-Town is outlined below, followed by the design decision options, and the design 134constraints and performance criteria The full problem details can be found in the 135supplemental material of the paper 136D-Town Network Description 137As depicted in Figure 1, the D-Town network consists of five existing district metered 138areas (DMAs) requiring upgrades and an additional new zone to be designed In total, 139the D-Town network consists of 399 junctions, storage tanks, 443 pipes, 11 pumps, 1405 valves, and a single reservoir The pipe network properties, and other pump, valve 141and nodal data, used for the existing regions in D-Town were taken from the C-Town 142network used in the BWCN (Ostfeld et al 2011) The only changes for the existing D143Town regions were an increase in nodal water demands to reflect population growth in 144the regions and a few modifications to node elevations and pipe roughness All data 145for the existing network components were incorporated into the EPANET input file 146D-Town.inp (for version 2.00.12) available as supplemental material 147 148Design Decisions 149As outlined previously, the BWN-II involved the design of the new zone, and the 150upgrade of the existing zones For the new zone, pipes were required to be sized from 151one of 12 diameter options (varying from 102 mm to 762 mm) for each link The new 152zone was able to be connected via pipelines to either, or both, DMA and DMA 5 153For the pipe connection to DMA3, the design of a pressure-reducing valve (PRV) was 154permitted 155The improvement options available to adapt the existing DMAs involved: addition of 156parallel pipes for all existing pipes (12 diameter options); increasing of storage 157volumes by one of six tank sizes (500 to 10,000 m 3); addition of new pumps at the 158existing pumping stations (10 pump options were provided with varying head159discharge relationships); and sizing of backup power diesel generators for the pump 160stations (8 diesel generator options were available) For the existing DMAs, the valve 161settings for the existing valves were also allowed to be modified 162In addition to the design options, operational pump scheduling decisions were also 163required to be made As the network was specified to have a single week balancing 164period, the pump schedule for a single week needed to be determined Operational 165controls were allowed to be either time-based, or based on threshold tank elevations 166Design Constraints and Loading Scenarios 167Two operational scenario types for D-Town were specified, a normal operation 168scenario, for which the network was subject to normal demand loadings, and an 169emergency scenario, representing the event of a power failure The design constraints 170for the normal operating scenario were specified as nodal constraints for the balancing 171period of a single design week At each time point within this design week, the 172demand nodes were required to satisfy minimum head constraints, and the tanks were 173required to not empty The evaluation of these criteria clearly required an extended 174period simulation (a hydraulic time-step of 15 minutes, and a water quality time-step 175of minutes were specified for the EPANET simulations) 176The emergency scenarios were characterized by a power outage that can begin at any 177hour within the design week, and last for a duration of two hours (therefore resulting 6 178in a total of 167 independent emergency scenarios) Within the emergency scenario, 179all pumps not powered by diesel generators were required to be shut down The 180constraints of minimum head for demand nodes, and non-emptying of tanks were also 181required to be met 182Performance Criteria 183The evaluation of whether the BWN-II design solutions satisfied the design 184constraints outlined above was based on three performance criteria: total annualized 185cost; the environmental criterion of estimated green house gas (GHG) emissions; and 186water age as a surrogate indicator of water quality 187The total annualized cost was based on annualized capital costs and operational costs 188The capital costs consisted of component costs of pipes, pumps, valves, tanks and 189generators The operational costs were calculated from the total system power usage 190under normal operating conditions based on a single design week The electricity 191costs within the design week were specified according to normal peak and off-peak 192tariffs 193The total GHG emissions included the emissions associated with the energy required 194for manufacturing, transportation and installation of the new pipes and the power 195usage from the operation of pumps (GHGs caused by the increase in tank volume or 196replacement and addition of pumps were not considered) The capital GHG emissions 197were annualized considering a 0% discount rate, as suggested by the International 198Panel on Climate Change (IPCC) (Fearnside, 2002) 199The defined metric for water age WAnet (evaluated only within the design balancing 200week) was specified as the weighted average network water age (hours), given by 7 201 (1) 202where is WAij is the water age at demand node i at time tj, kij is a binary variable 203defined as if WAij is greater than the threshold WAth and zero otherwise, Qdem,ij is the 204demand at junction i and time tj, where tj is the simulation time, which is given by 205tj=jt, where t is the time step, Njunc is the number of system junctions and Ntime is the 206number of simulation time steps (equal to 168, as the extended period simulation time 207is one week) The water age threshold was set to 48 hours, and the time step to hour, 208resulting in all water age and demand variables to be computed only on the hour Note 209that, if all nodes always have a water age below the 48h threshold, the value of WA net 210is zero Decreasing the water age results in higher operational costs and GHG 211emissions Therefore, there is a trade-off between the three objectives analyzed: costs, 212GHGs and water quality 213Assessment of Participant Design Solutions 214Participants were required to submit an EPANET input file with the implemented 215design and operational options, and a spreadsheet file summarizing the modifications 216made to the original system (i.e replaced, duplicated and new pipes; replaced and 217added pumps; additional tank volumes; valves and diesel generators inserted) The 218spreadsheet contained the details necessary to compute the capital costs and capital 219GHGs of the solution (ID, size, cost and, if applicable, GHGs of the component) The 220spreadsheet also contained a summary of the operational costs, GHG emissions and 221the water age metric Pump controls and valve settings had to be implemented in the 222EPANET file directly All design submissions were independently evaluated using 223EPANET2 for the normal loading scenario and the power outage scenarios Only 8 224solutions satisfying the design constraints for these loading scenarios were considered 225eligible to be evaluated based on the performance criteria 226 227COMPETITOR CONTRIBUTIONS 228Fourteen competitors submitted solutions for BWN-II The methodologies used to 229find these solutions differed significantly; however, a common consideration was that 230heuristic engineering judgment strategies had to be incorporated to deal with the size 231and complexity of the problem If formulated purely as an optimization problem, the 232search space could easily reach over 7,500 decision variables, depending on the 233options considered (Iglesias-Rey et al 2012) As mentioned in the Introduction, all 234WDS optimization problems are NP-hard and are therefore difficult to solve, even for 235a relatively small number of decision variables However, solving an NP-hard 236problem with such a large search space, and likely high correlation among the 237variables (e.g the tank sizes are related to the pump sizes and controls), was not the 238only challenge experienced by competitors Checking the design solution for 239adherence to the power outage scenario required multiple simulations, as the power 240outage could occur at any time during the simulation week This emergency scenario 241evaluation represented a significant computational burden 242To overcome the difficulties of high dimensionality and computational complexity, 243different approaches were adopted, from the use of solely engineering experience 244(Walski, 2012) to the use of parallel computing (Wu et al 2012, Matos et al 2012, 245Guidolin et al 2012, Wang et al 2012, Kandiah et al 2012 and Morley et al 2012) In 246addition, modifications to the EPANET code were made by Matos et al (2012), 247Guidolin et al (2012) and Kandiah et al (2012) to speed up computation or to define 9 248ad-hoc functions suitable for the specific problem (Kandiah et al 2012, Guidolin et al 2492012) 250Many authors further reduced the computational effort required by reducing the 251number of decision variables (Wu et al 2012, Iglesias-Rey et al 2012, Kandiah et al 2522012, Wang et al 2012, Stokes et al 2012) or the range of the possible values for each 253decision variable (Wu et al 2012, Iglesias-Rey et al 2012, Kandiah et al 2012) 254When the decision variables were pipes, engineering judgment was often used, such 255as the adoption of larger diameter options for pipes with large headlosses A slightly 256different approach was used by Wu et al (2012), where the number of possible 257parallel pipes was limited, considering that, in practice, only a small number of pipes 258would need to be replaced in a network In this case, the optimization algorithm was 259used to define which pipes were critical and which diameter was to be assigned to the 260parallel pipe Yoo et al (2012) and Iglesias-Rey et al (2012) skeletonized the network 261to decrease the number of decision variables related to pipes, thereby reducing the 262number of nodes and pipes by 40% and 30%, respectively (Iglesias-Rey et al 2012) 263Other common considerations were related to the capacity of the initial pumping 264stations S1 compared to the system demand: as the existing pumps could barely 265provide the required flow, additional pumps were inserted (Alvisi et al 2012; Kandiah 266et al 2012; Iglesias-Rey et al 2012; Morley et al 2012; Stokes et al 2012; Walski, 2672012; Wang et al 2012) 268To reduce the number of decision variables and the computational time required to 269evaluate a single solution, the power outage was usually left as a final evaluation, in 270which the installation of diesel generators could be optimized separately from the rest 271of the system, or, as in Matos et al (2012) and in Morley et al (2012), simulated once 272a feasible solution for normal operating conditions was found An exception to this is 10 10 421generator is proportional to the sum of the power generated It should be noted that 422these relative sizes are not at the same scale as those in Figures – 423Additional pumps were included at the first pumping station (S1) in solutions 424(Guidolin et al 2012) and (Kandiah et al 2012), while there was no increase in 425pumping capacity of the system in solution (Tolson et al 2012), only an increase in 426pump efficiency The pumps that are added and replaced in each solution are shown in 427Table It can be seen that, in general, the approaches that used a larger degree of 428engineering judgment resulted in a limited number of pump modifications Also, 429although use of the engineering approach often required the addition of pumps at the 430first pumping station, some solutions did not include any modification to the pumps, 431as in Saldarriaga et al (2012) and Tolson et al (2012) 432Different types of pump controls were implemented in the solutions: pump scheduling 433was used to reduce energy consumption (Stokes et al 2012) and tank trigger levels 434were used to reduce the number of variables and to have a set of controls that can 435better adapt to the variability in demand (Walski, 2012; Bent et al 2012; Iglesias-Rey 436et al 2012; Wang et al 2012) However, most authors used a combination of these 437two types of controls, so that pumps are operated according to schedules and tank 438levels (Wu et al 2012; Alvisi et al 2012; Saldarriaga et al 2012; Matos et al 2012; 439Yoo et al 2012; Guidolin et al 2012; Kandiah et al 2012; Morley et al 2012; Tolson 440et al 2012) 441In general, as can be seen in Table 6, many of the optimal solutions included only a 442limited increase in tank capacity, because, as reported by several participants, tanks 443were found to be more expensive than diesel generators, and larger tank volumes were 444found to increase water age For example, the third best solution did not include any 445increases in tank capacity (Kandiah et al 2012), similarly to many other solutions, 17 17 446while the best solution (Guidolin et al 2012) and the second best solution (Tolson et 447al 2012) increased the capacity of tank T4 by 1,000 m3 and the capacity of tank T2 by 448500 m3, respectively 449The solutions differ in the way they provide water to the new zone For example, 450Kandiah et al (2012) achieved this by connecting the new zone to DMAs and and 451using a PRV; Guidolin et al (2012) connected it to both DMAs, but did not use the 452PRV; and Tolson et al (2012) only linked the new zone to DMA As shown in Table 4537, some of the submitted solutions only include a link between the new zone and 454DMA2 455In the top three solutions, all of the pipes in the new area were set to the minimum 456diameters; however, different pipe sizes, which were generally small, were used by 457the other authors Nine pipes, with a total length LTot equal to 2,150 m and an average 458diameter Dave equal to 208.8 mm, were replaced or duplicated in Tolson et al (2012), 45928 pipes (LTot = 2,689 m, Dave = 215.8 mm) were modified in Kandiah et al (2012), 460and 38 pipes (LTot = 4,901 m, Dave = 270.0 mm) were modified in Guidolin et al 461(2012) The number of pipes modified and their total length are very small compared 462with the overall number of pipes in the network and with the potential for duplication 463or replacement As can be seen in Table 7, many optimal solutions included a limited 464number of replaced or parallel pipes In particular, many solutions where engineering 465judgment was used to find an initial good solution have fewer than 40 pipes replaced 466In contrast, when all pipes had the possibility to be duplicated or replaced by the 467algorithm, the number of pipes changed usually exceeded one hundred 468The number of pumps backed up by diesel generators varied from for a total pump 469power of 217.15 kW (Wu et al 2012 and Tolson et al 2012) to 17 for a total pump 470power of 513.47 kW (Morley et al 2012) The cost for the diesel generators ranged 18 18 471from $38,910 to $56,130: the first cost corresponds to a total diesel generator capacity 472of 250 kW (Wu et al 2012 and Tolson et al 2012); the latter cost corresponds to the 473solution of Stokes et al (2012), where the diesel generator power installed is 650 kW 474to back up a total pump power of 497.64 kW (Table 8) 475Operational costs in the form of energy costs associated with pumping was a 476significant component of total costs for most solutions (e.g exceeding 60% of the 477total cost in Tolson et al and Kandiah et al.’s solutions) In contrast, in Guidolin et 478al.’s solution, where larger capital costs were introduced to reduce operational costs 479and GHGs, the energy costs were only 40% of the total costs This is also reflected in 480the GHG emissions of the solutions; however, in this case, the operational GHG 481emissions are always greater than or equal to 90% of the total GHG emissions 482Finally, it has to be noted that, despite the differences in the design options adopted 483and in the methodology used to solve the BWNII problem, the solutions had similar 484values of the performance criteria 485 486GENERAL OBSERVATIONS 487The BWN-II is a challenging problem in the optimization of WDSs, because of the 488large number of decision variables and related optional choices, their correlation and 489the large computational effort required to properly evaluate each solution In addition, 490the BWN-II raised issues that go beyond the application of optimization algorithms, 491including i) the different potential interpretations of the problem, and ii) the different 492ways of ranking the solutions in the competition 493Misunderstanding the decision variables and constraints results in a different problem 494to be optimized and in unexpected ranking values Obviously, it is important to clearly 495define the problem, but this task is difficult to achieve From this perspective, the 19 19 496BWN-II is similar to real-life problems, for which objective functions, constraints and 497design options are usually not clearly defined Problems were also encountered when 498the hydraulic simulation software was used in a LINUX operating system (simulation 499files were not deleted, Stokes et al.) and when a double-precision version of the 500EPANET library was used (Morley et al.) In the latter case, the small numerical 501difference resulted in a different pump operation, affecting nodal pressures: the 502different solver precision adopted caused the failure of the pressure requirements in 503the solution reported by Morley et al (2012) 504Defining when constraints are satisfied was also a topic of discussion From a 505practical perspective, pressures that are slightly lower than the target values not 506compromise the design, but in an optimization competition, it is not possible to allow 507for constraint violations, as it is necessary to compare the solutions on an equal basis 508A small difference in the constraint values could make a large difference in the 509searching procedure of the algorithm, and can change the optimal solution to the 510problem 511The ranking of the submitted solutions raised some criticism among participants 512First, as the problem could have been formulated as a multi-objective optimization, 513participants were left with the hard task of selecting the solution with the best trade514off among the objectives The submission of a single solution was justified by the 515need to simplify the submission procedure and solution checking, although it would 516be possible to improve these aspects Secondly, the method chosen to rank the 517solutions considered all feasible solutions, regardless of whether they were non518dominated or not The solutions on the optimal front computed using all received 519solutions could have been used for the ranking, but it was preferred to use a ranking 20 20 520method that was not restricted to the use of multi-objective optimization methods, as 521the problem was meant to be open to all approaches 522Although the ranking method did not use a multi-objective approach, three non523dominated solutions were selected as winners for the BWN-II Figures 6, and 524present the partial scores of the solutions (obtained using the recomputed values of the 525objectives) in multi-objective space From the plot of cost vs GHG emissions (Figure 5266), it can be seen that the three winning solutions dominate the others Two of the 527three winning solutions are also non-dominated in the Sc - SWAnet space (Figure 7) 528Here, the solution from Alvisi et al (2012) has a lower cost than Guidolin et al.’s 529solution This is different from the original data reported by the authors because of the 530lower recomputed energy cost The other non-dominated solution when only costs and 531water age are considered is the solution from Wang et al (2012) that, similarly to the 532solution of Alvisi et al., had larger GHGs emissions 533The plot of the scores for water age vs GHG emissions suggests that the final ranking 534among the three best solutions was probably most strongly influenced by the SGHG and 535SWAnet scores, as the order of the solution ranking matches the order of the non536dominated ranking (Figure 8) 537 538FUTURE RESEARCH DIRECTIONS 539The Battle of the Water Networks (BWN-II) provided a great opportunity for both 540researchers and practitioners in this field to solve a challenging real-world WDS 541optimization problem and, in particular, to test a wide range of methodologies 542involving traditional engineering experience and modern computing techniques 543Although, the scale of the network is still small compared with that of the WDS of a 544major city, the BWN-II network is larger than those of past benchmark problems, such 21 21 545as the New York Tunnels (Schaake and Lai, 1969), Anytown (Walski et al., 1987) and 546Hanoi (Fujiwara and Khang, 1990) problems and captures many real world features 547covering both design and operation 548However, the problem still contains a number of simplifications, compared to real 549problems, which limit the applicability of the proposed methodologies These 550simplifications will be highlighted in the following We hope that the realism of future 551test problems will be increased so that the results obtained, and the optimization 552methods used, are more widely applicable in practice The ultimate objective is to 553obtain better solutions (in terms of all real world design objectives) more quickly 554Note that the emphasis on the development of algorithms capable of dealing with 555more realistic problems does not mean that engineering judgment can be eliminated or 556its quality decreased 557One of the most important simplifications is the use of a constant pump efficiency, 558instead of an efficiency curve The assumption of a constant pump efficiency 559eliminated the necessity of operating the pumps near their best efficiency point (as 560energy cost was a major component of this problem) and represents a large 561simplification compared with reality 562Other simplifications (also frequently assumed in other case studies) are related to the 563fact that demands are assumed to be known with certainty and no reliability issues, 564other than the power outage, are considered, e.g the effect of demand variations, pipe 565breaks and equipment failure in general is not evaluated In addition, all pipes and 566other facilities are installed contemporaneously at time zero, without considering the 567time scheduling of interventions over some pre-defined long-term planning horizon 568The problem does not consider construction times or the possibility of future 569expansions In addition, the BWN-II problem uses a fairly low target value for 22 22 570maximum water age (48 hours) This resulted in making the installation of additional 571storage in the system undesirable in terms of providing reliability Note that for this 572case study, the water age of most nodes reached a dynamic equilibrium after the first 57324-48 hours However, the assumption that a single week simulation is sufficient for 574equilibrium of the water quality results (as was assumed in this competition for 575practical reasons of not making the water quality evaluation to onerous for the 576contestants) has to be tested in future competitions Moreover, water leakages and 577their costs are not taken into account, as well as the presence of allowances for asset 578deterioration over time Another simplification is with regard to energy costs, which 579were represented by a simple cost per kilowatt hour as a function of time However, 580real energy tariffs often contain a peak demand charge that is based on the peak 581kilowatts used during some period of time (e.g peak 15 minutes between noon and 582pm during summer months) 583Most countries account for fire flow during the design of WDSs because, although 584they are rare events, they have a significant influence on pipe, pump and storage 585sizing Despite this, provision for fire flow was not included in this problem 586Other simplifications compared to the real world are: i) the pump wear and tear 587(typically approximated by counting the number of pump switches) is not considered; 588ii) pump replacement is limited to fixed speed pumps without the possibility of 589evaluating variable speed pumps, iii) pump cavitation is not taken into account and 590the only requirement is to have a pressure larger than zero if the node has demand 591equal to zero; iv) the cost of the diesel generators does not take into account 592maintenance costs; v) only GHG emissions from pipe construction or pump 593operations are considered 23 23 594Finally, the absence of a scale map that shows road layouts and other physical features 595and of previous information related to the operational costs of the network provided 596limitations in terms of solving the problem using engineering judgment Therefore, it 597would be desirable to provide this information in future competitions 598For the next battle, we also suggest the inclusion of some measure of the personnel 599and computational time required to reach the solution This information could lead to 600interesting results by analyzing the trade-off between engineering experience and 601computational time Unfortunately, we did not have such data for the BWN-II In 602addition, in order to solve the issues related to constraint precision and to improve the 603applicability of solutions, practicing engineering could be involved in the problem 604definition and solution evaluation steps 605 606CONCLUSIONS 607Following the successful series of “Battle Competitions” in past years, the Battle of 608the Water Networks II (BWN-II) provided an opportunity for both researchers and 609practitioners in this field to solve a challenging real-world WDS optimization 610problem A wide range of methodologies involving traditional engineering experience 611and modern computing techniques was employed by the participants to tackle the 612problem However, in general, the problem was divided into multiple phases, at least 613two, to account for the power outage scenario Thus, the involvement of practical 614experience and/or expert opinion played an important role in determining the most 615suitable solution The results of the Battle show that, given a precise definition of a 616problem in terms of decision variables, objective functions and constraints, i) the use 617of optimization can enhance the solutions found using engineering expertise; ii) the 618use of large computational resources can overcome relatively small amounts of 24 24 619engineering judgment, as shown by the Guidolin et al (2012) solution; iii) the use of 620limited computational resources can be successful if a larger amount of engineering 621judgment is used, as shown by the Tolson et al (2012) solution Note that this does 622not mean that engineering judgment can be completely avoided – we believe that this 623will never be the case – but it means that there is a trade-off between the engineering 624experience and computational resources needed for solving a problem The results 625also show that there is no one algorithm that is universally better than the others, as 626very different methods 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Battle of the Water Calibration Networks 78(BWCN) (Ostfeld et al 2011), and the Battle of the Water Sensor Networks (BWSN) 2 79(Ostfeld et al., 2008) All of these are predated by the original Battle. .. of the Networks II (BWN -II) is the third of a series of competitions 76undertaken within the Water Distribution System Analysis (WDSA) Symposium 77series, the previous competitions being the Battle