Computer‐Generated Pumping Schedules for Satisfying Operational Objectives

Chase, Donald V.; Ormsbee, Lindell

doi:10.1002/j.1551-8833.1993.tb06024.x

Cited by 32 publications

(9 citation statements)

References 1 publication

(3 reference statements)

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“…Initially, investigations related to cost optimization of pumping expenses relied on operational research techniques as, for instance, linear programming [6,7], integral linear programming [8], nonlinear programming [9,10], and dynamic programming [11,12]. Wood and Reddy (1994) [13] were the first to utilize genetic algorithms (GA) to reduce the energetic cost of pumping systems.…”

Section: Introductionmentioning

confidence: 99%

Energy Cost Optimization in a Water Supply System Case Study

Moreira¹,

Ramos²

2013

Journal of Energy

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The majority of the life cycle costs (LCC) of a pump are related to the energy spent in pumping, with the rest being related to the purchase and maintenance of the equipment. Any optimizations in the energy efficiency of the pumps result in a considerable reduction of the total operational cost. The Fátima water supply system in Portugal was analyzed in order to minimize its operational energy costs. Different pump characteristic curves were analyzed and modeled in order to achieve the most efficient operation point. To determine the best daily pumping operational scheduling pattern, genetic algorithm (GA) optimization embedded in the modeling software was considered in contrast with a manual override (MO) approach. The main goal was to determine which pumps and what daily scheduling allowed the best economical solution. At the end of the analysis it was possible to reduce the original daily energy costs by 43.7%. This was achieved by introducing more appropriate pumps and by intelligent programming of their operation. Given the heuristic nature of GAs, different approaches were employed and the most common errors were pinpointed, whereby this investigation can be used as a reference for similar future developments.

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Section: Introductionmentioning

confidence: 99%

Energy Cost Optimization in a Water Supply System Case Study

Moreira¹,

Ramos²

2013

Journal of Energy

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“…Recent papers describing the implementation of DSSs include a $120 million computer overflowregulation system for reducing sewage discharges into the Mississippi River (Batzel, 1994), several programs for managing and predicting the quality in various urban water distribution systems (Deininger et al, 1992;Grayman and Clark, 1993;Orr et al, 1992), a program for determining water distribution pumping schedules in Texas (Chase and Ormsbee, 1993), a program for integrating hydraulic models with a supervisory control and data acquisition (SCADA) simulator in Illinois (Schulte and Maim, 1993), a river-aquifer simulation program applied to water supply planning and operation in New Jersey (Wan and Levy, 1993;Frank, 1994), and a program for regional development and environmental planning in China (Fedra, 1992a;Fedra et al, 1992b), to cite only a few. DSSs for simulating river system design and operation include those developed by Andreu (1991), Basson et al (1994), the Center for Advanced Decision Support for Water and Environmental Systems (1992), Eichert (1992), Ford (1990), Hydrologic Engineering Center (1993, Hydrological Modelling Unit in New South Wales, Australia (1994)k Kuczera (1990Kuczera ( , 1993, Loucks et al (1995), Randall et al (1995), and Stockholm Environment Institute (1993).…”

Section: Impacts Of Dss In Environmental and Water Resources Managementmentioning

confidence: 99%

DEVELOPING AND IMPLEMENTING DECISION SUPPORT SYSTEMS: A CRITIQUE AND A CHALLENGE¹

Loucks

1995

J American Water Resour Assoc

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Computer‐based models together with their interactive interfaces are typically called decision support systems. DSSs are interactive computer‐based information providers. The common objective of all DSSs, regardless of the frameworks, methodologies, or techniques used, is to provide timely information that supports human decision makers ‐ at whatever level of decision making. The informational needs of the decision making process are the key considerations that motivate the development of DSSs. The growth of DSS development and use has been substantial. In spite of this impressive growth, computer‐aided decision support systems can still be improved and made more useful to those they are intended to support. Researchers and practitioners, and indeed the computer industry, continue to identify ways of doing this. This paper reviews some of these needs and opportunities by focusing on the process of successful DDS development and implementation. The paper outlines an approach and some guidelines for developing DSSs. The approach emphasizes and requires considerable interaction between the DSS developers (analysts) and the DSS users (decision makers). This interaction and feedback is required throughout the entire DSS building, testing and evaluation (debugging), and implementation processes. The paper concludes by identifying some research needs and opportunities affecting DSS development and effective use.

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“…Nonetheless, there are algorithms for operational optimisation of WDNs based on linear programming (Jowitt and Germanopoulos, 1992), nonlinear programming (Chase and Ormsbee, 1993;Yu et al, 1994), dynamic programming (Lansey and Awumah, 1994;Nitivattananon et al, 1996), and heuristics (Ormsbee and Reddy, 1995;Leon et al, 2000). However, these algorithms had limited success because of various reasons.…”

Section: Introductionmentioning

confidence: 99%

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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Computer‐Generated Pumping Schedules for Satisfying Operational Objectives

Cited by 32 publications

References 1 publication

Energy Cost Optimization in a Water Supply System Case Study

Energy Cost Optimization in a Water Supply System Case Study

DEVELOPING AND IMPLEMENTING DECISION SUPPORT SYSTEMS: A CRITIQUE AND A CHALLENGE¹

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

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Computer‐Generated Pumping Schedules for Satisfying Operational Objectives

Cited by 32 publications

References 1 publication

Energy Cost Optimization in a Water Supply System Case Study

Energy Cost Optimization in a Water Supply System Case Study

DEVELOPING AND IMPLEMENTING DECISION SUPPORT SYSTEMS: A CRITIQUE AND A CHALLENGE1

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

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DEVELOPING AND IMPLEMENTING DECISION SUPPORT SYSTEMS: A CRITIQUE AND A CHALLENGE¹