Genetic Algorithm Optimization of Operational Costs and Greenhouse Gas Emissions for Water Distribution Systems

Blinco, Lisa J.; Simpson, Angus R.; Lambert, Martin F.; Auricht, C.A.; Hurr, N.E.; Tiggemann, S.M.; Marchi, Angela

doi:10.1016/j.proeng.2014.11.246

Cited by 22 publications

(7 citation statements)

References 3 publications

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“…Changes in electricity and GHG emissions can be an important factor to consider during an optimization problem, as shown in the following examples. Blinco et al (2014) studied the optimization of pump operations in WDSs in relation to the minimization of GHG emissions and the use of different power source scenarios, showing that optimal tank trigger levels can be influenced by the variation in emission factors. Wu et al (2012a) considered three different electricity tariff scenarios, which increased over time, and three different GHG emission factor scenarios, which decreased over time, in the optimization of WDS design.…”

Section: Power Source(s) [P1] Electricity Tariffs and Costs [P2]mentioning

confidence: 99%

Framework for the optimization of operation and design of systems with different alternative water sources

et al. 2017

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Water security has become an increasing concern for many water system managers due to climate change and increased population. In order to improve the security of supply, alternative sources such as harvested stormwater, recycled wastewater and desalination are becoming more commonly used. This brings about the need for tools to analyze and optimize systems that use such sources, which are generally more complex than traditional water systems. Previous methodologies have been limited in their scope and cannot be applied to all types of water sources and systems. The framework presented in this paper has been developed for holistic analysis and optimization of water supply and distribution systems that use alternative water sources. It includes both design and operational decision variables, water and energy infrastructure, simulation of systems, analysis of constraints and objectives, as well as policies and regulations which may affect any of these factors. This framework will allow users to develop a comprehensive analysis and/or optimization of their water supply system, taking into account multiple types of water sources and consumers, the effect of their own design and operational decisions, and the impact of government policies and different energy supply options. Two case study systems illustrate the application of the framework; the first case study is a harvested stormwater system that is used to demonstrate the importance of simulation and analysis prior to optimization, the second utilizes four different water sources to increase security of supply and was optimized to reduce pump energy use.

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Section: Power Source(s) [P1] Electricity Tariffs and Costs [P2]mentioning

confidence: 99%

Framework for the optimization of operation and design of systems with different alternative water sources

et al. 2017

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“…The production of clean water (product) requires the implementation of processes (water intake, water treatment) and the organization of the entire water supply system in a sustainable, effective, and economical manner while respecting the natural environment [4]. Energy costs account for approximately 65% of the operating costs of water supply companies [5]. One way to introduce changes is to reduce electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Use of the PV Installation in the Power Supply of the Water Pumping Station

Średziński

Świętochowska²,

Świętochowski³

et al. 2022

Energies

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Delivering water to consumers uses a lot of electricity. In the era of limited fossil fuel resources, we are increasingly looking at the possibilities of using renewable energy sources to power residential buildings or industries. The research aimed to analyze the potential of producing electricity from photovoltaic panels, ensuring electricity supplies for pumping water, and reducing costs. The tests were carried out using the existing photovoltaic (PV) installation with a capacity of 12.3 kW connected to the installation monitoring the production of electricity and monitoring the water-pumping station (WPS). An analysis of the daily electricity production from the PV installation, an analysis of the settlement unit’s demand for water, and an analysis of the costs of pumping water, including the unit cost of pumping 1 m3 of water, were carried out. Studies have shown the possibility of reducing water-pumping costs by more than 77.8% after a PV installation. The tested installation provided 100% of the electricity necessary for WPS for 167 days a year.

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“…Most of the cited works above-considered wind turbines reliability at a single level, to provide an increased power output, this system reliability should be considered at two or multiple levels; this is still a challenge for existing mechanisms such as ant colony algorithm and genetic algorithm (Wang et al, 2004;Bianchi & Dorigo., 2006;Blinco et al, 2014). Some of the attempts that have been reported when these algorithms were used to solve this problem were computationally expensive, especially when different combinations of multiple parameters were considered.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

Aikhuele¹,

Periola²,

Ighravwe³

2019

10.5267/j.ijdns

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The increased role of wind turbine systems makes it important for its operational states to be continuously monitored and optimized. This goal can be achieved using existing methods, which relies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10-3 , 4.2 ×10-3 , and 4.0 × 10-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.

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Genetic Algorithm Optimization of Operational Costs and Greenhouse Gas Emissions for Water Distribution Systems

Cited by 22 publications

References 3 publications

Framework for the optimization of operation and design of systems with different alternative water sources

Framework for the optimization of operation and design of systems with different alternative water sources

Analysis of the Use of the PV Installation in the Power Supply of the Water Pumping Station

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

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