**4.2. Prediction of hybrid energy solutions in Espite system**

Espite is located in Ourém and it is a small system that distributes water to Couções and Arneiros do Carvalhal villages and the average flow in this pipe system is approximately 7 l/s. This system is hydraulically analysed to determine the best hydro solution. Then ANN is applied to establish the best economical hybrid solution, employing the same data set used to developed ANN model. A simplified scheme of Espite water drinking system is present‐ ed in Figure 13.

**Figure 13.** Scheme of Espite water distribution system

od, always imposing tank levels' restriction to satisfy the minimum and maximum advisa‐ ble values for its good operation. Figure 15 shows the system behaviour regarding the water level variation and the optimized pump operation time. Table 7 shows the savings ach‐ ieved with the water level control and pump operation optimization for the energy tariff

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**Figure 15.** System behaviour with reservoir level control and pump operation optimization: water level variation in

Max. Power kW Saving (%)

Couções tank, electricity tariff and pump and turbine operation time.

Pump Station Use\*(%) Consumption

Energy Report

**Table 7.** Pump benefits with optimization of water level control and pump operation

kWh/m3

Carvalhal 1 65.09 0,55 3.24 58.19 Carvalhal 2 65.09 0,55 3.24 58.19

pattern adopted.

**Figure 14.** Elevation and length profile of Espite pipeline

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 Figure 14, 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, Tank Carvalhal, Pumps Carvalhal 1 & 2,; (10) Node 6; (11) Tank Couções and (12) Demand point Couções.

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 optimisation process to guarantee that the limit tank levels are always respected. In order to determine the most ad‐ equate hydro turbine in this water pipe system, regarding the importance to always main‐ tain 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 ade‐ quate turbine selection a key for the successful of this solution. The system is then analysed using the electricity tariff for the worst conditions. The energy report of the original situa‐ tion is shown in Table 6.


**Table 6.** Pump cost with original situation. \*basis reference

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 peri‐ od, always imposing tank levels' restriction to satisfy the minimum and maximum advisa‐ ble values for its good operation. Figure 15 shows the system behaviour regarding the water level variation and the optimized pump operation time. Table 7 shows the savings ach‐ ieved with the water level control and pump operation optimization for the energy tariff pattern adopted.

0 2000 4000 6000 8000 Length (m)

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 Figure 14, 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, Tank Carvalhal, Pumps Carvalhal 1 & 2,; (10) Node 6; (11) Tank Couções and (12) Demand point Couções.

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 optimisation process to guarantee that the limit tank levels are always respected. In order to determine the most ad‐ equate hydro turbine in this water pipe system, regarding the importance to always main‐ tain 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 ade‐ quate turbine selection a key for the successful of this solution. The system is then analysed using the electricity tariff for the worst conditions. The energy report of the original situa‐

Energy Report

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 peri‐

kWh/m3

Max. Power kW

Pump Station Use\*(%) Consumption

Carvalhal 1 100,00 0,78 4,51 Carvalhal 2 100,00 0,78 4,51

<sup>7</sup> <sup>8</sup>

9

10 11

12

1

tion is shown in Table 6.

**Table 6.** Pump cost with original situation. \*basis reference

2 3 4

96 Water Supply System Analysis - Selected Topics

**Figure 14.** Elevation and length profile of Espite pipeline

5

6

Elevat oi n (m)

**Figure 15.** System behaviour with reservoir level control and pump operation optimization: water level variation in Couções tank, electricity tariff and pump and turbine operation time.


**Table 7.** Pump benefits with optimization of water level control and pump operation

The energy production in the hydro power is calculated using the hydraulic turbine selected considering a sell rate of 0.10€/kWh for 24 hour production as shown in Table 8 as well as the saving achieved with this energy configuration. The operating point of the turbine corre‐ sponds 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 24h.

The negative value of NPV in Grid+Wind and Grid+Hydro+Wind is derived from initial in‐ stallation costs of the wind turbine and its small energy production. For the case study a big‐ ger wind turbine with a higher installed power capacity wasn't 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

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The feasibility of the developed HGA model in the search of the best operational strategy for a lowest energy cost in the real Fátima system was analysed. Two algorithms were devel‐ oped and linked to the GA. The first one, a repair algorithm from an analysis done in the unfeasible solutions generated by the GA, alters the decision variables in the attempt of making these solutions feasible. After finishing the generations of the GA, the second algo‐ rithm acts in these solutions, making a local search in the attempt of finding optimal locals. The efficiency of the algorithm developed HGA in the search of the solution with lowest op‐ erational cost is confirmed, whereas the convergence occurred six times faster. One of the biggest limitations of the GA is the treatment of problems with high quantity of constraints. The application of penalties only allows the identification of unfeasible solutions. In prob‐ lems of this kind it is probable that along the candidates' generation, the quantity of unfeasi‐ ble candidates does not decrease, making the search of good solutions very difficult. With the implementation of repair algorithms, the appearance of super-candidates occurs in less time, since the alterations in the individuals are done directly in its problematic genes.

An evaluation analysis about the necessity of use genetic operators, when these algorithms are applied directly in a large set of solutions generated randomly, also shows final good results. To determine the best strategy among thousands of possible solutions it must also be

The HGA model presented can be implemented in any network. Furthermore, its applica‐ tion is practical and useful, being able to be used by water supply companies, making easier

The current research work aims at the prediction analysis about the best energy system con‐ figuration, depending on the renewable available sources of the region, and the optimiza‐ tion 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 (ANN) to determine the best hybrid energy system configuration; (ii) for the ANN training process, a configuration and economical base

study that is a small system and without many resources to be invested.

**5.1. Optimization of the pumps' schedule in the Fátima system**

taken into consideration the hydraulic reliability of the system.

**5.2. Prediction of hybrid energy solutions in Espite system**

the best decision aiming at the energy efficiency in pumping systems.

**5. Conclusions**


**Table 8.** Energy production in the hydropower solution.

**Figure 16.** NPV results by ANN and CES models for the case study.

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 energy solution it takes into account that the wind speed in the region of this case study has an average value of 5 m/s. It was considered the wind turbine model SW Skystream 3.7 with a rated power of 1.8 kW and a market price of € 15,000 and a micro hydro turbine (or a pump as turbine – PAT) with a market price estimat‐ ed in € 2,500 with a nominal power of 3.14 kW. For a lifetime analysis of 25 years, the ANN results show that the best hybrid solution for this case study is a grid + hydro with an NPV of €18,966, and the CES results point out for the same solution a NPV of €18,950, with a rela‐ tive error of 0.08% and a correlation coefficient of 0.999996. Figure 16 presents the results for all configurations calculated by ANN and CES models showing clearly the best solution.

The negative value of NPV in Grid+Wind and Grid+Hydro+Wind is derived from initial in‐ stallation costs of the wind turbine and its small energy production. For the case study a big‐ ger wind turbine with a higher installed power capacity wasn't 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.
