Best economical hybrid energy solution: Model development and case study of a WDS in Portugal

Energy Policy 39 (2011) 3361–3369

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

Best economical hybrid energy solution: Model development and case study of a WDS in Portugal F.V. Gonc- alves a, L.H. Costa b, H.M. Ramos c,n a

DECivil—Civil Engineering Department, Instituto Superior Te´cnico, Technical University of Lisbon, IST, Lisbon, Portugal ´, Brazil DEHA—Department of Hydraulic and Environmental Engineering, Federal University of Ceara c DECivil—Civil Engineering Department, Instituto Superior Te´cnico, Technical University of Lisbon, Avenue Rovisco Pais, 1049-001 Lisbon, Portugal b

a r t i c l e i n f o

abstract

Article history: Received 10 June 2010 Accepted 15 March 2011 Available online 6 April 2011

This work aims at the development of an integrated model for analysis and optimization of operating strategies of Water Distribution Systems (WDS), taking into consideration economical-hydraulic-power performances. For this purpose, a software tool has been developed based on the following procedures: (i) an Artificial Neural Network (ANN) to determine the best economical hybrid energy system; (ii) for the ANN training process an energy Configuration type and Economical base Simulator model (CES) is used; (iii) as well a Hydraulic and Power Simulator model (HPS) to describe the hydraulic system behaviour; (iv) a performance assessment tool based on an optimization module to minimize pumping costs and maximize the hydraulic reliability and energy efficiency is then implemented. The Artificial Neural Network uses scenarios with only grid supply, grid combined with hydro-turbine, or with wind turbine and a mutual solution, with hydro and wind turbine. The results obtained show how the model is useful for decision support solutions in the planning of sustainable hybrid energy solutions that can be applied to water distribution systems or others existent hydro-systems, allowing the improvement of the global energy efficiency. A real case study of a small WDS in Portugal is analyzed. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Hybrid energy ANN economical solution model WDS

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. 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 et al., 2009). In order to have enough energy to make its economy grow the development of new solutions to produce sustainable energy in a most feasible way is imperative, not only depending on conventional sources (i.e. fossil fuel). The increase 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 n

Corresponding author. E-mail addresses: [email protected] (F.V. Gonc-alves), [email protected] (L.H. Costa), [email protected], [email protected] (H.M. Ramos). 0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.03.031

solutions. In Koroneos et al.’s (2003) analysis, renewable sources are used to produce energy with high efficiencies, social and environmental significant benefits. Renewable energy includes hydro, wind, solar and many others resources. To avoid problems caused by weather and environment uncertainties that hinder the reliability of a continuous 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 production bringing a new perspective for the future. These hybrid solutions are feasible applications 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, 2009b; Vieira and Ramos, 2008, 2009), the option to mix complementary energy sources like wind, solar and hydropower seems to be a solution to mitigate the energy intermittency when comparing with only one source of renewable energy. So, the idea of a hybrid solution has the advantage of compensating the fluctuations between available sources with decentralized renewable generation technologies.

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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 affordable price, clean supply, in safe and reliable conditions. On the other hand, the typical characteristics 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. The case study corresponds to a small village of a water distribution system in Portugal.

price the renewable sources are used to power the system for pumping. Whenever there is water consumption the turbine is operates to supply pumping stations, treatment plants or to sell surplus energy to the national grid. Kenfack et al. (2009) designs a micro-hydro-PV hybrid system for rural electrification and the achieved results recommend applications in similar sites. They had compared different combinations of component sizes and quantities, and explored how variations in resource availability and system characteristics affect the costs of installing and operating different solutions concerning an electrification of a remote area in a developing country. Nowadays the use of hybrid power systems can be found in many ways, as an energy source for rural desalination plants (Setiawan et al., 2009), a renewable resource of power generation for grid connected applications in Iraq (Dihrab and Sopian, 2010) or as a multi-energy system in buildings (Fabrizio et al., 2010). 2.3. Modelling conditions

2. Background review 2.1. Basic concepts The needs of water consumption, environmental targets and energy savings have become the main concerns of water managers over the last years and becoming more and more important in a near future. In delivering water to populations, the energy for pumping represents the main cost for water companies (Vieira and Ramos, 2008). Renewable energy creates multiple public benefits such as environmental improvement (reduction of power plant greenhouse emissions, thermal and noise pollution), reduction of energy price volatility effects on the economy, national economic security, since fossil energy is vulnerable to political instabilities, trade disputes, embargoes and other disruptions. On the other hand, renewable energy increases economic productivity through more efficient production processes (Menegaki, 2008). Renewable sources represent a viable option for power generation even though there are some geographical and environmental restrictions. Through the Directive 2001/77/EC, the indicative target for Europe to the production from renewable sources is 22% of the electricity consumed. This objective is expected to be achieved through quotas taken by different member states (European Commission, 2001) and Portugal is an excellent example in this subject. 2.2. Hybrid energy systems In the past decade the hybrid energy solution has received much attention since it is a viable alternative solution as compared to solutions based entirely on hydrocarbon fuel, providing flexibility on the energy management and a longer life cycle (Gupta et al., 2006; Vieira and Ramos, 2008). An example of a hybrid energy network integrated in drinking systems, e.g. the pumps can be supplied with electricity through most renewable sources, which would take into account the water consumption pattern, the electricity tariff rate, the environmental factors and the system characteristics, such as storage water regularization volume. Commonly these systems work only based in the national energy grid. However, alternative solutions can be adopted. During the peak hours, the higher costs in the electricity tariff are registered; the system could be supplied by renewable sources, such as micro-hydro-turbines installed in water gravity pipelines and complemented also by wind or solar energy. When the energy prices are lower, the national energy grid is used to pump the water, and when the energy has a higher

Gupta et al. (2006), Ramos and Ramos (2009b) and Vieira & Ramos (2008) reveal that the modelling of hybrid energy research systems and their applications in decentralized mode are still quite limited. The models currently applied are generally based on only one or two available sources. Further attempts for developing optimum energy solutions based on different sources for meeting the energy targets are also quite restricted. So, the application of models for matching the estimated future energy demand with a complementary combination of sources at decentralized level is one of the main subjects. Fattahi and Fayyaz (2010) presented a mathematical model using a compromise programming to optimize a multi-objective problem, integrated in water management. The objectives involving water distribution cost, leakage water and social satisfaction level were considered to evaluate the performance and efficiency of the model and the results demonstrated the capability to solve the problem. Compromise programming belongs to a class of multi-criteria analytical methods called ‘‘distance-based’’ methods, which identify solutions closest to the ideal one by some distance measure. Software models (e.g. HOMER and PVSYST) have been used by various researchers (Barsoum and Vacent, 2007; Gabrovska et al., 2004; Gupta et al., 2006; Ramos and Ramos, 2009a; Vieira and Ramos, 2008), for design and research alternative energy solutions. These tools are usually developed by the European or American research centres even supported by energy companies. MATLAB can be used for optimization modelling to manage the water and energy in water distribution systems. Vieira and Ramos (2009) use linear and non-linear programming to develop an optimization tool to obtain the best hourly operation, according to the electricity tariff, with water consumption and inlet discharge, for a pump–storage system supplied by wind energy. The need of a model for prediction and optimization of the energy management of hybrid type systems with specific operational controls is imperative. 2.4. Artificial neural networks Artificial Neural Networks (ANN) have been used in water distribution systems to model the degradation of water (Castronuovo and Lopes, 2004; Jafar and Shahrour, 2007; Sakarya and Mays, 2000; Turgeon, 2005). The research has been considered promising, providing a strong base for the development of a financial–economical model, which applied with the degradation model, is able to give an integrated approach for optimizing intervention strategies in water distribution systems.

F.V. Gonc- alves et al. / Energy Policy 39 (2011) 3361–3369

Even with the limitations the prediction performance has proven to be rather good in a short and medium term. As part of the Potable Water Distribution Management (POWADIMA) research project, a study is developed to describe the technique used to predict the consequences of different control settings on the performance of a water distribution network system, in the context of real-time analyses and nearoptimal control (Jamieson et al., 2007; Rao and Alvarruiz, 2007). Since the use of a complex hydraulic simulation model is somewhat quite different for real-time operations, as a result of imposed computational time consumption, the approach adopted has been to capture its domain knowledge in a far more efficient way by means of ANN (Chaves et al., 2004). A neurofuzzy algorithm can be used as a powerful tool for riskof-failure analysis in two case studies where the combination of artificial neural networks and fuzzy logic is extremely effective for the detection of patterns in the underlying data and in the conversion of these patterns to knowledge and generic rules, which can assist in risk-of-failure analysis and preventive maintenance of water distribution (Christodoulou and Deligianni, 2010). Al-Alawi et al. (2007) developed an Artificial Neural Network based model for the optimum operation of an integrated hybrid renewable energy based water and power supply system (IRWPSS), demonstrating that an ANN can be used with high degree of confidence to predict the control strategies.

The conception of an ANN in order to capture the best energy model domain from a Configuration model and Economical Simulator (CES) in a much more efficient way is based on the following remarks: first of all, a robust data base has to be developed to create the input and output data set that will be used in ANN conception and training; the data has to be analyzed to determine a structure that fits the problem and then to train and validate the ANN. The CES uses hydraulic and energy formulas to calculate the Net Present Cost (NPC) of each hybrid solution and then compares each one to create a list from the best to the worst solution. To calculate the hydro-turbine power CES uses Eq. (3.1).

Zhyd  rwater  g  hnet  Qturbine 1000

ð3:1Þ

where, Phyd is the power output of the hydro-turbine (kW); Zhyd is the hydro-turbine efficiency (%); rwater is the density of water (1000 kg/ m3); g is the acceleration due to gravity (9.81 m/s2); hnet is the effective head (m); Qturbine is the hydro-turbine flow rate (m3/s). To calculate the output of the wind turbine in a particular hour, CES model follows a three-step process: 1. It takes that hour’s wind speed from the wind resource data and adjusts it to the hub height using either the logarithmic profile or the power law profile, as described in Wind Shear Inputs. 2. It refers to the wind turbines power curve to calculate the power output under standard conditions of temperature and pressure. 3. It multiplies that value by the air density ratio expressed by Eq. (3.2) 

r Bz ¼ 1 T0 r0

CNPC ¼

Cann,tot CRFði,Rproj Þ

ð3:3Þ

where, Cann,tot is the total annualized cost (h/yr); CRF( ) is the capital recovery factor; i is the interest rate (%); Rproj is the project lifetime (yr); The CRF was calculated based on Eqs. (3.4) and (3.5) CRFði,nÞ ¼

ið1 þ iÞN ð1 þ iÞN 1

ð3:4Þ

where, i is the real interest rate; N ¼number of years i¼

i0 f 1þ f

ð3:5Þ

where, i0 is the normal interest rate; f is the annual inflation rate. The algorithms for the ANN were demonstrated in Eq. (3.6) and the synaptic weight were recalculated until the output value in training process acquires an acceptable root mean square error (RMSE) net1 ¼

1 X

wij xj ¼ w10  x0 þ w11  x1 þ . . . þ wij  xj

ð3:6Þ

j¼0

where, w is the synaptic weight; x is the input value. Then 1 1 þenet1

ð3:7Þ

where, y is the output value calculated. The synaptic weight recalculation follows Eq. (3.8)

3.1. Basic concepts

Phyd ¼

The NPC was obtained from Eq. (3.3) and for the ANN modelling the NPC was converted to Net Present Value (NPV), which is the NPC value multiplied by  1

y1 ¼ f ðnet1 Þ ¼

3. Methodology

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g=ðRBÞ 

T0 T0 Bz

 ð3:2Þ

where, r is the air density (kg/m3); P is the pressure (Pa); R is the gas constant (287 J/kgK); T0 is the standard temperature (288.16 K); B is the lapse rate (0.00650 K/m); z is the altitude (m); g is the gravitational acceleration (9.81 m/s2).

w10 ðnewÞ ¼ w10 ðoldÞ þ aðd1 y1 Þ  x0  y1  ð1y1 Þ

ð3:8Þ

where, a is the learning rate value; d is the desired output. A flowchart describing the procedures of the designed ANN is shown in Fig. 1 and explains the process in the ANN creation and it could be interpreted as the ANN creation methodology. 3.2. Data set The data used on this study is calculated by means of a CES model that gives an optimized ranking of the best hybrid solution for each particular case, based on an economy analyses for the production and consumption of energy. This data set is organized with the subject that the study is concerned to evaluate the use of hybrid solutions in water distribution systems based on wind turbine, micro-hydro- and national grid. Hence, the range of data is defined in order to adequate the installation of such energy converters. The data range for flow, power head and water levels variation in reservoirs is used in a Hydraulic and Power Simulator (HPS) to determine the power consumed by the pump and the power produced in a micro-hydro-turbine installed in a gravity pipe branch whenever there is energy available in the system. Those data are included in the CES model with renewable resources performance characteristics to determine the best hybrid solution to be selected. One of the resources data used in CES is the wind turbine power curve of a selected wind turbine (Fig. 2a), the local wind source along an average year for the region under analysis (Fig. 2b) and the wind annual average speed applied to the wind turbine. In Table 1 the data set range is fixed and used with the CES model to determine the inputs and outputs of the developed ANN. Those data are used to calculate all energy and economy parameters to be included in the CES model to complete the data needed to train the ANN. Based on a basic data range, depending on the system characteristics (Table 1), to be used in the CES

F.V. Gonc- alves et al. / Energy Policy 39 (2011) 3361–3369

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ANN Development Determine the topology based on:

Target data

Input data

Input nodes Output nodes Hidden layers Transfer function

Result from HPS and CES to create the data set to use as input values in input nodes

Results from CES determine the data set to be used as output values in output nodes

Training Evaluation of the number of neurons in hidden layer with some thumb rules

Pump and turbine heads and respective power

Wind turbine power

Complete the data range of number of neurons obtained to make an try and error approach

NPV of each hybrid configuration, the type and number of renewable energy equipments to install

Optimization module - minimize pump cost and maximize hydraulic reliability

Train ANN for each hidden layer configuration

Hydraulic and Power Simulator - HPS

Configurator and Economic Simulator - CES

Calculate the hydraulic behavior of the system and the pump and turbine power

Economic analysis evaluation based on NPV (Net Present Value) for different hybrid system configuration

Keep the ANN with best correlation and lower RMSE Flow and water levels variation Head losses

Gross head (between water levels)

Pipe lenght

Pipe diameter variation

ANN testing Test the best ANN with new data set and analyse the correlation and RMSE

No

Validation data set New data set based on new behavior of the system

Acceptable correlation and lower RMSE

Yes New Input data

Validation

HPS and CES give new set of data for ANN validation

Validate the best ANN with new set of data

Run the ANN program with new inputs

No

Acceptable RMSE and good correlation on validation group output

Yes

ANN model

Fig. 1. Flowchart for the developed ANN model.

Fig. 2. Wind energy: (a) Wind Turbine Power Curve for an Enercon E33 and (b) Wind source for one year at Lisbon region.

model and from auxiliary hydraulic and energy formulations, the complete input data is then obtained (Table 2) to be: (1) Pump power (kW); (2) Pump energy consumption (kWh); (3) Turbine power (kW)—average output; (4) Flow (m3/s)—annual average

flow; (5) Gross head (m); (6) Pumping head (m); (7) Head losses (m); (8) Power net head (m); (9) Design pumping flow rate (l/s); (10) Wind speed (m/s)—annual average; and (11) Wind turbine power (kW)—annual average output.

F.V. Gonc- alves et al. / Energy Policy 39 (2011) 3361–3369

At the end of the modelling process the input data set is built in a matrix of [11  19,602] (Table 2), which by the interaction of the wind velocity data and the water flow yields in the output matrix of [5  19,602] (Table 3), representing the Net Present Value of each hybrid solution configuration, as well as the number of wind turbines to be installed. The ANN data set created to be used in water distribution systems is then ready to determine the NPV of each hybrid system evaluated for each type of configuration (e.g. grid, gridþhydro, gridþwind and grid þhydroþwind). 3.3. ANN conception and validation In this research Matlabs is used for the ANN development. The creation of an ANN should comprise the following steps: (i) patterns definition; (ii) network implementation; (iii) identification of the

learning parameters; (iv) training, testing and validation processes. A new neural network model of hybrid energy must be compared with an energy Configuration model and Economical Simulator using the following procedures: CES is used to obtain data applied in the training process and in reliable neural network tests, together with a Hydraulic and Power Simulator model for a large range of flow rates, gross heads, pumping and power heads and wind velocities. That data, available in Ramos and Ramos (2009b) research, uses the HPS to hydraulically balance the water distribution system, in a village of Portugal, determining the hydraulic behaviour of all systems, including the most suitable pump and turbine operation for each flow condition. When the ANN code runs, the process of training and simulation for each system characteristic is analyzed. In the training

Table 3 Input data set for the best economic configuration used in ANN.

Table 1 Basic data set range used in CES.

NPVh grid

Wind speed annual average (m/s)

Flow (l/s)

Power net head (m)

Gross Head (m)

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0

10 20 30 40 50 60 70 80 90 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000

2 7 13 18 24 29 35 40 46 51 57 62 68 73 79 84 90 95 101 106 112 117 123 128 134 139 145 150

10 16 21 27 32 38 43 49 54 60 66 71 77 82 88 93 99 104 110 116 121 127 132 138 143 149 154 160

3365

NPVh Gridþ Hydro

NPVh Gridþ Wind

NPVh Grid þHydro þWind

Wind turbine installed

 59.00  78.00  96.00  115.00  133.00  152.00  170.00  189.00  207.00

1812.00 6617.00 12,391.00 17,197.00 22,971.00 27,776.00 33,550.00 38,356.00 44,130.00

 571,464.00  571,495.00  571,526.00  571,557.00  571,588.00  571,619.00  571,650.00  571,680.00  571,712.00

 569,553.00  564,747.00  558,973.00  554,168.00  548,394.00  543,588.00  537,814.00  533,009.00  527,235.00

1 1 1 1 1 1 1 1 1

 226.00  244.00  263.00  282.00  300.00  319.00  337.00  356.00  374.00

48,935.00 54,710.00 59,514.00 65,289.00 70,094.00 75,868.00 80,674.00 86,447.00 91,253.00

 316,043.00  316,077.00  316,111.00  316,146.00  316,180.00  316,214.00  316,248.00  316,282.00  316,317.00

 266,690.00  260,916.00  256,110.00  250,337.00  245,531.00  239,757.00  234,951.00  229,177.00  224,372.00

2 2 2 2 2 2 2 2 2

 393.00  411.00  430.00  448.00  467.00  485.00  504.00  522.00  541.00  559.00

97,027.00 101,832.00 107,606.00 112,411.00 118185.00 122,991.00 128,765.00 133,570.00 139,344.00 144,150.00

109,886.00 109,850.00 109,813.00 109,778.00 109741.00 109,706.00 109,669.00 109,633.00 109,597.00 109,561.00

207,679.00 212,483.00 218,258.00 223,062.00 228838.00 233,644.00 239,416.00 244,223.00 249,997.00 254,802.00

3 3 3 3 3 3 3 3 3 3

Table 2 Input data set for the system characteristics used in ANN. Pump power (kW/h) (1)

Pump primary load (kW/d) (2)

Annual average Turbine mean output power (kW) flow (m3/s) (4) (3)

DZ (m) (5)

Pumping head (m) (6)

Head loss (m) (7)

Power head (m) (8)

Design flow rate (L/s) (9)

Wind speed Wind turbine mean (m/s) (10) output power (kW) (11)

0.322 0.398 0.475 0.552 0.628 0.705 0.781 0.858 0.935 1.011 1.088 1.165 1.241 1.318 1.394

2.895 3.584 4.274 4.964 5.653 6.343 7.032 7.722 8.412 9.101 9.791 10.481 11.170 11.860 12.549

0.587 1.016 1.446 1.876 2.306 2.735 3.165 3.595 4.025 4.454 4.884 5.314 5.744 6.173 6.603

16 21 27 32 38 43 49 54 60 66 71 77 82 88 93

24 29 35 41 46 52 57 63 69 74 80 86 91 97 102

8 8 8 8 8 8 9 9 9 9 9 9 9 9 9

7 13 18 24 29 35 40 46 51 57 62 68 73 79 84

16 16 16 16 16 16 16 16 16 16 16 16 16 16 16

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

15 15 15 15 15 15 15 15 15 15 15 15 15 15 15

F.V. Gonc- alves et al. / Energy Policy 39 (2011) 3361–3369

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Table 4 Relative ANN error concerning to CES base model. Grid

GridþHydro

Grid þWind

Gridþ Hydroþ Wind

Correlation Relative error (best solution) (%) Mean relative error (%)

0.99999121 0.22 0.74

0.99996283 0.08 1.80

0.99999695 0.09 2.51

0.99999538 0.16 2.02

0.00E+00 -2.00E+02 -4.00E+02 -6.00E+02 -8.00E+02 -1.00E+03 -1.20E+03

3.00E+05

NPV € (CES) NPV € (ANN)

NPV €

NPV €

Hybrid system solutions

NPV € (CES) NPV € (ANN)

2.00E+05 1.00E+05 0.00E+00

2 13

2 13

24 35 46 57 68 79 Pow er H 90 ead - H ( 101112123 m) 134145

24 35 46 Pow 57 68 79 er H 90 ead - H ( 101112 m) 123 134 145

Fig. 3. NPV results for CES and ANN with grid configuration (a) and with grid and hydro-configuration (b).

4.00E+05 NPV € (CES) NPV € (ANN)

NPV €

NPV €

1.1100E+05 1.1000E+05 1.0900E+05 1.0800E+05 1.0700E+05

NPV € (CES) NPV € (ANN)

3.00E+05 2.00E+05 1.00E+05 0.00E+00

2 13 24 35 46 Pow 57 68 79 er H 90 ead - H ( 101112 123 m) 134145

2 13 24 35 46 Pow 57 68 79 er H 90 ead - H ( 101112123 m) 134

145

Fig. 4. NPV results for CES and ANN with grid and wind configuration (a) and with grid, hydro- and wind configuration (b).

Reservoir 02

Pump Carvalhal 1 Demand point Carvalhal 2

Pump R02 Reservoir 01

Node 2

Demand point Couções

Node 6

Node 5 Tank Carvalhal

Pump R01

Node 1 Tank ASJ

Node 3

Node 4

Turbine

Demand point Carvalhal 1

Tank Couções Pump Carvalhal 2

Fig. 5. Scheme of Espite water distribution system.

Elevation (m)

4

320 280 240

11

10

360

12

3

200

9

5

2

6

1

7

8

160 0

2000

4000 Length (m)

6000

Fig. 6. Elevation and length profile of Espite pipeline.

8000

F.V. Gonc- alves et al. / Energy Policy 39 (2011) 3361–3369

mode is introduced the configuration parameters to the ANN. Those parameters are standard limits (max and min), number of neurons on the hidden layer, limit number of epochs, final error desired, validation rate and activation function used in the hidden layer. With the best ANN configuration for each possible hybrid system and new data set for inputs, a validation process is made and the results are verified in terms of correlation and relative error among the values of CES base model and the ANN, as presented in Table 4. The best economical solutions (based on Net Present Value index) compared between the CES base model and ANN developed are shown in Figs. 3 and 4. In this comparison process, for a configuration of grid, hydroand 3 wind turbines installed the ANN gives a NPV of h3.99E þ 5. CES results for the same situation present a NPV of h4.00E þ 5, for a wind speed of 5 m/s, a flow of 20 l/s and a power net head of 150 m. The relative error is 0.16% and the ANN solved the problem in no more than 2 s against 45 min of CES model for the same number of variables.

4. WDS case study in Portugal Espite is located in Oure´m, in the centre of Portugal, with geographical coordinates 391 460 000 North, 81 380 000 West. This is a ~ small system that distributes water to Couc- oes and Arneiros do Carvalhal villages and the average flow in this pipe system is approximately 7 l/s. This system is hydraulically analyzed to determine the best hydro-solution. Then ANN is applied to establish the best economical hybrid solution, employing the same data set used to develop the ANN model. A simplified scheme of Espite water drinking system is presented in Fig. 5. The pump station considered in the analysis is Pump Carvalhal 1 and 2 and the micro-hydro-power plant will be installed in the gravity pipe system between node 5 and Tank Carvalhal. The population consumption (i.e. demand points) must be guaranteed and the tanks water level variation should vary between recommended limits. The elevation profile of Espite system is established in Fig. 6, where (1) Reservoir 01; (2) Pump R01; (3) Node 1; (4) Tank ASJ; (5) Node 2; (6) Node 3; (7) Node 4; (8) Node 5; (9) Turbine, Table 5 Pump cost with original situation. Pump station Energy report Carvalhal1 Carvalhal2 a

a

Use (%)

100.00 100.00

Consumption (kWh/m3)

Max. power (kW)

0.78 0.78

4.51 4.51

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Tank Carvalhal, Pumps Carvalhal 1 & 2; (10) Node 6; (11) Tank ~ and (12) Demand point Couc- oes. ~ Couc- oes The HPS model is used to verify all hydraulic parameters and the system behaviour when a hydropower is installed. Rule-based controls are defined in the optimization process to guarantee that the limit tank levels are always respected. In order to determine the most adequate hydro-turbine in this water pipe system, regarding the importance to always maintain a good system operation management and the satisfactory demand flows, the evaluation of the available energy and the characteristic turbine curve compatible with the all operating and hydraulic constrains must be developed. According to Araujo (2005) and Ramos et al. (2010), a characteristic curve for the turbine is evaluated to define the most adequate turbine selection, a key for the success of this solution. The system is then analyzed using the electricity tariff for the worst conditions. The energy report of the original situation is shown in Table 5. To reduce the pump consumption, the optimization of the time pumping is considered, turning it on in the low electricity tariff period and turning it off in the higher tariff period, always imposing tank levels’ restriction to satisfy the minimum and maximum advisable values for its good operation. Fig. 7(a) and (b) shows the system behaviour regarding the water level variation and the optimized pump operation time. Table 6 shows the savings achieved with the water level control and pump operation optimization for the energy tariff pattern adopted. The energy production in the hydro-power is calculated using the hydraulic turbine selected considering a sell rate of 0.10 h/ kWh for 24 h production as shown in Table 7 as well as the saving achieved with this energy configuration. The operating point of the turbine corresponds to a power net head of 40 m and an average flow of 6.6 l/s determined by the HPS model based on extended period simulations of 24 h. After the calculation of the pump consumption and the turbine production, the values are inserted in the ANN model developed and compared with the results obtained with the CES model. For the analysis of the best hybrid solution it takes into account that the wind speed in the region of this case study has an average value of 5 m/s. The wind turbine model SW Skystream 3.7 with a rated power of 1.8 kW and a market price of h15,000 and a micro-hydro-turbine

Table 6 Pump benefits with optimization of water level control and pump operation. Pump station Energy report Carvalhal1 Carvalhal2

Use* (%)

Consumption (kWh/m3)

Max. power (kW)

Saving (%)

65.09 65.09

0.55 0.55

3.24 3.24

58.19 58.19

Basis reference.

~ tank and energy tariff; (b) pump and turbine Fig. 7. System behaviour with reservoir level control and pump operation optimization: (a) water level variation in Couc- oes operation time.

F.V. Gonc- alves et al. / Energy Policy 39 (2011) 3361–3369

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Table 7 Energy production in the hydro-power. Turbine

Production (kWh/m3)

Energy report Carvalhal 0.07

Max. power (kW)

Power/day (kW)

Saving (%)

2.12

49.04

63.35

In this case study the installation of a micro-hydro-using water level controls and pump operation optimization shows the improvement of the energy efficiency in 63.35%. In this methodology to determine the best hybrid energy solution, the ANN has demonstrated significant reduction in time modelling, with a good correlation and mean relative error.

Acknowledgments The authors gratefully acknowledge the financial support of: ‘‘Coordenac- a~ o de Aperfeic- oamento de Pessoal de Nı´vel Superior’’ (CAPES, Brazil), who provided a doctoral scholarship to the first author; the support of ‘‘Fundac- a~ o para a Ciˆencia e a Tecnologia’’ (FCT, Portugal) through the project PTDC/ECM/65731/2006 and CEHIDRO, the Hydro-systems research centre from the Department of Civil Engineering, at Instituto Superior Te´cnico, Lisbon.

References

Fig. 8. NPV results by ANN and CES models for the case study.

(or a pump as turbine—PAT) with a market price estimated in h2500 with a nominal power of 3.14 kW was considered. For a lifetime analysis of 25 years, the ANN results show that the best hybrid solution for this case study is a gridþhydro with an NPV of h18,966, and the CES results point out for the same solution a NPV of h18,950, with a relative error of 0.08% and a correlation coefficient of 0.999996. Fig. 8 presents the results for all configurations calculated by the ANN and CES models showing clearly the best solution. The negative value of NPV in gridþwind and gridþhydroþwind is derived from initial installation costs of the wind turbine and its small energy production. For the case study a bigger wind turbine with a higher installed power capacity was not chosen because the wind speed in the case study area is very low and wind turbines that have a satisfactory energy production for these wind speeds are extremely expensive, being inadequate to the case study that is a small system and without many resources to be invested.

5. Conclusions The current research work aims at the prediction analysis about the best energy system configuration, depending on the renewable available sources of the region, and the optimization of operating strategies for the Water Distribution Systems (WDS), which have about 80% of their costs associated to the energy consumption. Hence an integrated methodology based on economical, technical and hydraulic performances has been developed using the following steps: (i) Artificial Neural Network to determine the best hybrid energy system configuration; (ii) for the ANN training process, a Configuration and Economical base Simulator model is used; (iii) as well a Hydraulic and Power Simulator model to describe the hydraulic behaviour; (iv) an optimization based model to minimize pumping costs and maximize hydraulic reliability and energy efficiency is then applied. The objective is to capture the knowledge domain in a much more efficient way than a CES, ensuring a good reliability and best economical hybrid energy solution in the improvement of energy efficiency and sustainability of WDS.

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