Determining Pump Operations using Particle Swarm Optimization

Wegley, Chad; Eusuff, Muzaffar; Lansey, Kevin

doi:10.1061/40517(2000)206

Cited by 45 publications

(29 citation statements)

References 3 publications

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“…A dynamic optimization technique is introduced in [17]. Recent efforts have also been conducted to implement the problem using the heuristic or meta-heuristic optimization techniques such as ant colony [18], particle swarm [19], and evolutionary algorithms [20]. While the heuristic-based methods provide flexibility to modeling of the problem and are often more scalable than deterministic optimization techniques, however; they do not guarantee finding a global optimum.…”

Section: Related Literaturementioning

confidence: 99%

An MILP-based formulation for minimizing pumping energy costs of oil pipelines: beneficial to both the environment and pipeline companies

Abbasi

Garousi

2010

Energy Syst

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Optimal scheduling of pumps operation in fluid distribution networks (e.g., oil or water) is an important optimization problem. This is due to the fact that the dollar cost and also global carbon footprints of such a major transportation are in mega scales. For example, one of our industrial partners, a Canadian oil pipeline operator, spent more than $18.11 million dollars in 2008 for pumping costs. According to our calculations, this would lead to up to 182,460 tons of CO 2 emissions annually. Therefore, even slight improvements in operation of a pipeline system can lead to considerable savings in costs and also reducing carbon footprints emitted to the environment (by introducing air pollutions needed to generate those huge amounts of electricity). In this paper, a methodology for determining optimal pump operation schedule for a fluid distribution pipeline system with multi-tariff electricity supply is presented. The optimization problem at hand is a complex task as it includes the extended period hydraulic model represented by algebraic equations as well as mixed-integer decision variables. Obtaining a strictly optimal solution involves excessive computational effort; however, a near optimal solution can be found at significantly reduced effort using heuristic simplifications. The problem is efficiently formulated in this paper based on Mixed-Integer Linear Programming. The proposed model is evaluated on a typical oil pipeline network. The numerical results indicate the effectiveness and computationally efficient performance of the proposed formulation.Power charged by second rate for pump j at time t Incidence i,j The (i, j ) entry of the network incidence matrix Lim t j Power limit that the rate of electricity is changed for pump j at time t LP t j Power charged by the first rate for pump j at time t Op t Binary variable which is equal to zero in case the system is shut down at time t and one otherwise P t j Power consumed by pump j at time t P Max j The power that pump j consumes to add the maximum possible head to a fluid with maximum flow rate PH t j Head added to the network by pump j at time t PL t j Pressure loss of segment j at time t Q t j Average flow rate associated with pipeline segment j at time t Q * j The flow rate associated with the maximum efficiency of pump j Q j The flow rate associated with zero efficiency point for pump j Q Max j Maximum acceptable flow rate of segment j Q min j Minimum acceptable flow rate of segment j QSink t i Discharge flow rate from node i at time t QSource t i Flow rate of fluid incoming to node i at time t S t j

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Section: Related Literaturementioning

confidence: 99%

An MILP-based formulation for minimizing pumping energy costs of oil pipelines: beneficial to both the environment and pipeline companies

Abbasi

Garousi

2010

Energy Syst

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“…As far as we know, not even studies proposing specialised representations for pump scheduling have performed a comparative analysis of different representations using the same algorithm and on the same network instances. In some cases, no comparison is done at all (Wegley et al, 2000). In other cases, a new algorithm using the new representation is compared with unoptimised settings or with a previous algorithm using a different representation (Kazantzis et al, 2002).…”

Section: Introductionmentioning

confidence: 98%

Representations and Evolutionary Operators for the Scheduling of Pump Operations in Water Distribution Networks

López-Ibáñez

Prasad

Paechter

2011

Evolutionary Computation

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Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels; or explicitly, by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain fewer than the maximum number of switches. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations is an improvement over the results obtained by a recent state of the art hybrid genetic algorithm for pump scheduling using level-controlled triggers.

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“…Ant colony optimisation, a nature inspired optimisation technique, has also been applied optimise pump scheduling (López-Ibáñez et al 2008;Afshar et al 2015). Other evolutionary methods applied to pump schedule optimisation in WDS include harmony search optimisation (Kougias and Theodossiou 2013) and particle swarm optimisation (Wegley et al 2000;Ostadrahimi et al 2012). Heuristic methods can provide optimised pump schedules for hydraulic systems, but cannot provide bounds on the global optimality of the solution.…”

Section: Review Of Heuristic Methodsmentioning

confidence: 99%

Exploring Optimal Pump Scheduling in Water Distribution Networks with Branch and Bound Methods

Menke

Abraham

Parpas

et al. 2016

Water Resour Manage

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Water utilities can achieve significant savings in operating costs by optimising pump scheduling to improve efficiency and shift electricity consumption to low-tariff periods. Due to the complexity of the optimal scheduling problem, heuristic methods that cannot guarantee global optimality are often applied. This paper investigates formulations of the pump scheduling problem solved using a branch and bound method. Piecewise linear component approximations outperform non-linear approximations within application driven accuracy bounds and demand uncertainties. It is shown that the reduction of symmetry through the grouping of pumps significantly reduces the computational effort, whereas loops in the network have the opposite effect. The computational effort of including convex, non-linear pump operating, and maintenance cost functions is investigated. Using case studies, it is shown that linear and fixed-cost functions can be used to find schedules which, when simulated in a full hydraulic simulation, have performances that are within the solver optimality gap and the uncertainty of demand forecasts.

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Determining Pump Operations using Particle Swarm Optimization

Cited by 45 publications

References 3 publications

An MILP-based formulation for minimizing pumping energy costs of oil pipelines: beneficial to both the environment and pipeline companies

An MILP-based formulation for minimizing pumping energy costs of oil pipelines: beneficial to both the environment and pipeline companies

Representations and Evolutionary Operators for the Scheduling of Pump Operations in Water Distribution Networks

Exploring Optimal Pump Scheduling in Water Distribution Networks with Branch and Bound Methods

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