**1. Introduction**

In the last decades, the managers of water distribution systems have been concerned with the reduction of energy consumption and the strong influence of climate changes on water patterns. The subsequent increase in oil prices has increased the search for alternatives to generate energy using renewable sources and creating hybrid energy solutions, in particular associated to the water consumption.

According to Watergy (2009), about two or three percent of the energy consumption in the world is used for pumping and water treatment for urban and industrial purposes. The con‐ sumption of energy, in most of water systems all over the world, could be reduced at least 25%, through performance improvements in the energy efficiency. Hence, it is noticeable the importance of development of models which define operational strategies in pumping sta‐ tions, aiming at their best energy efficiency solution.

The consumption of electric energy, due to the water pumping, represents the biggest part of the energy expenses in the water industry sector. Among several practical solutions, which can enable the reduction of energy consumption, the change in the pumping operational procedures shows to be very effective, since it does not need any additional investment but it is able to induce a significant energy cost reduction in a short term. As well known, the tasks of operators from the drinking network systems are very complex because several distinct goals are involved in this process. To determine, among an extensive set of possibil‐ ities, the best operational rules that watch out for the quality of the public service and also provide energy savings, through the utilization of optimization model tools which take into consideration all the system parameters and components, is undoubtedly a priority.

© 2013 Ramos et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The technological advances in the computational area enabled, in the last years, the intensifi‐ cation of the quality of scientific works related to the optimization tools, as well as aiming at the reduction of the energy costs in the operation of drinking systems. Nevertheless, most of the optimization models developed was applied to specific cases.

generated by a GA to make them feasible, through the development of a hybrid genetic al‐

Energy Efficiency in Water Supply Systems: GA for Pump Schedule Optimization and ANN for Hybrid Energy Prediction

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77

This new model determines, in discrete intervals (every hours) the best programming to be followed by the pumps switch on / off, in a daily perspective of operation. In this way, the decisions start to be orientated from the research of thousands of possible combinations, be‐ ing chosen, through an iterative process, the best energy management strategy that presents

The world's economy is directly connected to energy and it is the straight way to produce life quality for society. China is nowadays one of the biggest consumers of energy in the world (Wu, 2009). In order to have enough energy to make its economy grow the prediction of new solutions to produce sustainable energy in a most feasible way is imperative, not on‐ ly depending on conventional sources (i.e. fossil fuel) but using renewable sources. The in‐ crease of energy consumption and the desired reduction of the use of fossil fuels and the raise of the harmful effects of pollution produced by non-renewable sources is one of the most important reasons for conducting research in renewable and sustainable solutions. In Koroneos (2003) analysis, renewable sources are used to produce energy with high efficien‐

Renewable energy includes hydro, wind, solar and many others resources. To avoid prob‐ lems caused by weather and environment uncertainties that hinder the reliability of a con‐ tinuous production of energy from renewable sources, when only one source production system model is considered, the possibility of integrating various sources, creating hybrid energy solutions, can greatly reduce the intermittences and uncertainties of energy produc‐ tion bringing a new perspective for the future. These hybrid solutions are feasible applica‐ tions for water distribution systems that need to decrease their costs with the electrical component. These solutions, when installed in water systems, take the advantage of power production based on its own available flow energy, as well as on local available renewable sources, saving on the purchase of energy produced by fossil sources and contributing for the reduction of the greenhouse effect. In recent studies (Moura and Almeida, 2009; Ramos and Ramos, 2009a; Ramos and Ramos, 2009b; Vieira and Ramos, 2008, 2009), the option to mix complementary energy sources like hydropower, wind or solar seems to be a solution to mitigate the energy intermittency when comparing with only one source. So, the idea of a hybrid solution has the advantage of compensating the fluctuations between available sour‐

In literature review, a sustainable energy system has been commonly defined in terms of its energy efficiency, its reliability, and its environmental impacts. The basic requirements for an efficient energy system is its ability to generate enough power for world needs at an afforda‐ ble price, clean supply, in safe and reliable conditions. On the other hand, the typical charac‐ teristics of a sustainable energy system can be derived from policy definitions and objectives since they are quite similar in industrialized countries. The improvement of the efficiency in the energy production and the guarantee of reliable energy supply seem to be nowadays common interests of the developed and developing countries (Alanne and Saari, 2006).

gorithm (HGA) (i.e. genetic algorithm plus repair algorithms).

cies, social and environmental significant benefits.

ces with decentralized renewable generation technologies.

the best energy savings.

The first studies to optimize the energy costs of pumping have been used for operational re‐ search techniques, such as linear programming (Jowitt and Germanopoulos, 1992), integer linear programming (Little and Mccrodden, 1989), non-linear programming (Ormsbee et al., 1989) and dynamic programming (Lansey and Awumah, 1994). The limitation of using these models to real cases is mainly due to the complexity of the equations' resolution to ensure the hydraulic balance and the difficulty of generalizing such optimization models in any wa‐ ter supply system (WSS).

Brion and Mays (1991), in the attempt to reduce the operational costs in a drinking pipeline in Austin, Texas (USA), had tested a model of optimization and simulation, achieving a re‐ duction of 17.3 % in the operational costs. Ormsbee and Reddy (1995) applied an optimiza‐ tion algorithm in Washington - DC and obtained significant results with the management implementation provided by the model, observing a reduction of 6.9% in the costs with elec‐ tric energy. During this period, the use of evolutionary algorithms was quite limited. Wood and Reddy (1994) were the pioneers in the use of such algorithms.

The remarkable use of evolutionary algorithms in this research topic in recent years is main‐ ly due to Genetic Algorithm (GA) provides a great flexibility in exploring the search space and allows an easy link to other simulation models. However, in contrast the GA does not solve problems with constraints. Once the operation in WSS is considered a complex proce‐ dure, with many constraints, there remains the doubt about the speed of the modelling and the convergence for optimal solutions between the GA and hydraulic simulators.

Additionally the concern with the reduction of the computational time is due to the applica‐ bility of energy optimization models in real time (Martinez, et al., 2007; Jamieson, et al., 2007, Salomons et al., 2007; Rao and Alvarruiz, 2007; Rao and Salomons, 2007; Alvis et al., 2007). To reduce the computational time for seeking solutions with reduced energy costs, these authors used the technique of Artificial Neural Networks (ANN) to reproduce the re‐ sults by the hydraulic simulator obtained by the EPANET (Rossman, 2000). Then, this new tool based on ANN for the hydraulic simulation was connected with a GA model. After sev‐ eral analyses done in a hypothetical system and in two real case studies, the authors con‐ cluded the model GA-ANN found optimal solutions in a period 20 times lower when compared to GA-EPANET. Shamir and Salomons (2008) have searched for reducing the computational simulation time based on a scale model of a real case system for different op‐ erating conditions.

At the present research a different resolution was adopted. In order to reduce the computa‐ tional simulation time in the search for optimal solutions, a change in the GA algorithm type was made, instead of replacing the hydraulic simulator model (EPANET) as former referen‐ ces. Thus, new algorithms were created which work directly with the infeasible solutions generated by a GA to make them feasible, through the development of a hybrid genetic al‐ gorithm (HGA) (i.e. genetic algorithm plus repair algorithms).

The technological advances in the computational area enabled, in the last years, the intensifi‐ cation of the quality of scientific works related to the optimization tools, as well as aiming at the reduction of the energy costs in the operation of drinking systems. Nevertheless, most of

The first studies to optimize the energy costs of pumping have been used for operational re‐ search techniques, such as linear programming (Jowitt and Germanopoulos, 1992), integer linear programming (Little and Mccrodden, 1989), non-linear programming (Ormsbee et al., 1989) and dynamic programming (Lansey and Awumah, 1994). The limitation of using these models to real cases is mainly due to the complexity of the equations' resolution to ensure the hydraulic balance and the difficulty of generalizing such optimization models in any wa‐

Brion and Mays (1991), in the attempt to reduce the operational costs in a drinking pipeline in Austin, Texas (USA), had tested a model of optimization and simulation, achieving a re‐ duction of 17.3 % in the operational costs. Ormsbee and Reddy (1995) applied an optimiza‐ tion algorithm in Washington - DC and obtained significant results with the management implementation provided by the model, observing a reduction of 6.9% in the costs with elec‐ tric energy. During this period, the use of evolutionary algorithms was quite limited. Wood

The remarkable use of evolutionary algorithms in this research topic in recent years is main‐ ly due to Genetic Algorithm (GA) provides a great flexibility in exploring the search space and allows an easy link to other simulation models. However, in contrast the GA does not solve problems with constraints. Once the operation in WSS is considered a complex proce‐ dure, with many constraints, there remains the doubt about the speed of the modelling and

Additionally the concern with the reduction of the computational time is due to the applica‐ bility of energy optimization models in real time (Martinez, et al., 2007; Jamieson, et al., 2007, Salomons et al., 2007; Rao and Alvarruiz, 2007; Rao and Salomons, 2007; Alvis et al., 2007). To reduce the computational time for seeking solutions with reduced energy costs, these authors used the technique of Artificial Neural Networks (ANN) to reproduce the re‐ sults by the hydraulic simulator obtained by the EPANET (Rossman, 2000). Then, this new tool based on ANN for the hydraulic simulation was connected with a GA model. After sev‐ eral analyses done in a hypothetical system and in two real case studies, the authors con‐ cluded the model GA-ANN found optimal solutions in a period 20 times lower when compared to GA-EPANET. Shamir and Salomons (2008) have searched for reducing the computational simulation time based on a scale model of a real case system for different op‐

At the present research a different resolution was adopted. In order to reduce the computa‐ tional simulation time in the search for optimal solutions, a change in the GA algorithm type was made, instead of replacing the hydraulic simulator model (EPANET) as former referen‐ ces. Thus, new algorithms were created which work directly with the infeasible solutions

the convergence for optimal solutions between the GA and hydraulic simulators.

the optimization models developed was applied to specific cases.

and Reddy (1994) were the pioneers in the use of such algorithms.

ter supply system (WSS).

76 Water Supply System Analysis - Selected Topics

erating conditions.

This new model determines, in discrete intervals (every hours) the best programming to be followed by the pumps switch on / off, in a daily perspective of operation. In this way, the decisions start to be orientated from the research of thousands of possible combinations, be‐ ing chosen, through an iterative process, the best energy management strategy that presents the best energy savings.

The world's economy is directly connected to energy and it is the straight way to produce life quality for society. China is nowadays one of the biggest consumers of energy in the world (Wu, 2009). In order to have enough energy to make its economy grow the prediction of new solutions to produce sustainable energy in a most feasible way is imperative, not on‐ ly depending on conventional sources (i.e. fossil fuel) but using renewable sources. The in‐ crease of energy consumption and the desired reduction of the use of fossil fuels and the raise of the harmful effects of pollution produced by non-renewable sources is one of the most important reasons for conducting research in renewable and sustainable solutions. In Koroneos (2003) analysis, renewable sources are used to produce energy with high efficien‐ cies, social and environmental significant benefits.

Renewable energy includes hydro, wind, solar and many others resources. To avoid prob‐ lems caused by weather and environment uncertainties that hinder the reliability of a con‐ tinuous production of energy from renewable sources, when only one source production system model is considered, the possibility of integrating various sources, creating hybrid energy solutions, can greatly reduce the intermittences and uncertainties of energy produc‐ tion bringing a new perspective for the future. These hybrid solutions are feasible applica‐ tions for water distribution systems that need to decrease their costs with the electrical component. These solutions, when installed in water systems, take the advantage of power production based on its own available flow energy, as well as on local available renewable sources, saving on the purchase of energy produced by fossil sources and contributing for the reduction of the greenhouse effect. In recent studies (Moura and Almeida, 2009; Ramos and Ramos, 2009a; Ramos and Ramos, 2009b; Vieira and Ramos, 2008, 2009), the option to mix complementary energy sources like hydropower, wind or solar seems to be a solution to mitigate the energy intermittency when comparing with only one source. So, the idea of a hybrid solution has the advantage of compensating the fluctuations between available sour‐ ces with decentralized renewable generation technologies.

In literature review, a sustainable energy system has been commonly defined in terms of its energy efficiency, its reliability, and its environmental impacts. The basic requirements for an efficient energy system is its ability to generate enough power for world needs at an afforda‐ ble price, clean supply, in safe and reliable conditions. On the other hand, the typical charac‐ teristics of a sustainable energy system can be derived from policy definitions and objectives since they are quite similar in industrialized countries. The improvement of the efficiency in the energy production and the guarantee of reliable energy supply seem to be nowadays common interests of the developed and developing countries (Alanne and Saari, 2006).

This work aims to present an artificial neural network model by the optimization of the best economical hybrid solution configuration applied to a typical water distribution system.

Pressure: for each time-step of operational time, the pressures in all the junction nodes must

Energy Efficiency in Water Supply Systems: GA for Pump Schedule Optimization and ANN for Hybrid Energy Prediction

Levels of storage tanks: The levels of storage tanks must be between the minimum and max‐ imum limits for each time-step. Besides at the end of the operational time duration, they must be superior to the levels at the beginning of the time duration. This last constraint as‐ sures the levels of the tanks do not lessen with the repetitions of the operational cycles.

e Smax <sup>j</sup>

*P*min*<sup>i</sup>* ≤*Pit* ≤*P*max*<sup>i</sup>* ∀*<sup>i</sup>* , ∀*<sup>t</sup>* (2)

and Pmax <sup>i</sup> are the mini‐

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79

∀ *<sup>j</sup>* , ∀*<sup>t</sup>* (3)

(0*h* )∀ *<sup>j</sup>* (4)

*PPkt* ≤*PP*max*<sup>k</sup>* ∀*<sup>k</sup>* (5)

*N Ak* ≤ *NA*max*<sup>k</sup>* (6)

: minimum and maximum levels of

be between the minimum and maximum limits.

mum and maximum pressures required for node i.

where Sjt: level of tank j in time-step t; Smin <sup>j</sup>

be inferior to its maximum capacity.

lowable pump start-ups for the pump k.

storage tank j.

tem operation.

where Pit represents the pressure on node i in time-step t, Pmin <sup>i</sup>

*S*min *<sup>j</sup>* ≤*S jt* ≤*S*max *<sup>j</sup>*

(24*h* )≤*Sj*

Pumping power capacity: the power used by each pump during the operational time must

where PPkt : used power by pump k in time-step t; PPmaxk: maximum capacity of the pump k.

Actuation of the pumps: The number of pumping start-ups in the operational strategy must be inferior to a pre-established limit. This constraint, presented by Lansey and Awumah (1994), influences in the maintenance of each pump, since the more it is put into action in a same operational cycle, the bigger will be its wear. Lansey and Awumah (1994) suggest the maximum pump start-ups 3 in 24 hours. A greater value can cause problems on the pumps inducing the need of maintenance and repair and consequently the interruption of the sys‐

where: NAk represents the number of start-ups for pump k and NAmaxk the maximum al‐

*Sj*
