3.2.3 Battery bank technical data

sector. This information must be validated each time the optimization model is used

fuel curve parameters. This table was built using information supplied by the Colombian Regulation Commission of Energy and Gas (CREG—Comisión de Regulación de Energía y Gas) in [23]. The cost per kW presented in Table 5 includes the direct and indirect costs related to the installation of a Diesel plant in

> Derate factors of the initial capital cost invested [%]

 2724.09 31.83 1.4 2.6 0.020 0.240 1697.26 32.43 3.4 6.05 0.037 0.265 1540.12 31.63 3.6 6.4 0.032 0.224 1934.44 23.00 6.8 10.96 0.088 0.277 1654.09 23.71 8.69 15.12 0.056 0.321 1434.92 25.12 9.825 16.63 0.060 0.272 1343.75 25.26 10.96 18.14 0.063 0.239 1788.83 18.13 11.43 19.77 0.044 0.238 1686.08 18.56 11.9 21.4 0.030 0.237 1723.40 17.24 12.85 23.06 0.026 0.204 1587.11 17.92 18.9 34.4 0.027 0.248 1572.63 17.55 22.3 41.2 0.022 0.252 1373.73 19.32 29.11 54.43 0.019 0.253

Battery bank input data Symbol Description Value Vdcbc Battery voltage [V] 2 Vdcsist DC system voltage [V] 48 Crate Capacity rate [h] 5 ηbat\_<sup>c</sup> Charge efficiency 0.9 ηbat\_<sup>d</sup> Discharge efficiency 1 σ Self-discharge rate 0.000083 Lbat Lifecycle [years] 10 δbat Factor of the initial capital cost invested for the battery bank 0.7 ρbat Fixed OM factor as ratio of the battery bank initial investment 0.02 DODmax Maximum depth of discharge 0.5

Table 5 shows a database of diesel generation units with the cost per kW and the

1/2 load 1 hour in liters

Full-load 1 hour in liters

f0 [L/ kWh]

f1 [L/ kWh]

since it can vary depending on the studied case.

Wind Solar Hybrid Renewable Energy System

non-interconnected zones.

Cost per kW installed [USD/ kW]

DG power [kW]

Table 5.

Table 6.

198

Battery bank technical inputs.

Diesel genset unit database.

In this chapter book, vented lead-acid battery banks only are considered. This kind of battery cells are often selected for large energy storage banks due the low cost, low maintenance, and high cycle stability. Table 6 shows the input data required by the battery bank. The battery bank charge and discharge efficiency and the self-discharge ratio is taken from [24]. The maximum depth of discharge is set in 0.5 since the battery bank can accomplish 3000 cycles during its life service


#### Table 7.

Battery cell database.


Table 8. System input parameters.


#### Table 9.

PSO input parameters.

according the datasheet. Other values as maintenance cost, ρbat, and the fraction of reposition cost, δbat, are set according to the recommendation of experts in the energy sector.

The main characteristics and price of the battery cells of the reference used in this work are presented in Table 7. The information was obtained from inquiries to local companies.

5. Conclusions

Results of the case study.

Table 10.

energy storage.

fiscal incentive factor.

DG units are commonly underestimated.

resolution.

201

In this work, an optimization methodology was developed and described in detail to help sizing HRSE integrated by photovoltaic and diesel generation with

Component Design Unit Indicator Value Unit Npv 13 Units CCpv 7800.00 USD Ppvstc 3.9 [kWp] CCDG 48257.99 USD wDG 25 [kW] CCbat 3864.00 USD NDG 2 Units O&Mpv 78.00 USD/year PDG 50 [kW] O&MDGf 4825.80 USD/year Nbp 1 Units O&MDGv 26884.74 USD/year Nbs 24 Units O&MDG 31710.54 USD/year Nbat 24 Units O&Mbat 77.28 USD/year Ebcell, nom 1.04 [kWh] RCDG 7019.48 USD Ebat, <sup>n</sup> 24.96 [kWh] RCbat 1243.60 USD

Methodology for Sizing Hybrid Battery-Backed Power Generation Systems in Off-Grid Areas

DOI: http://dx.doi.org/10.5772/intechopen.88830

FC 38406.77 [l] ACSadj 38737.05 USD/year LPSP 1.25 % COEadj 0.26 USD/kWh ACloss 475.03 USD/year LPVG 0.00 % Cost 0.21 USD/kWh

The main features of the sizing methodology developed were as follows: (a) it allows the simulation of hybrid renewable systems and the evaluation of its economic and reliability integrated by diesel and photovoltaic generation with energy storage, (b) the dispatch strategy developed prioritize the use of renewable energy among other energy sources, and (c) fiscal incentives granted by the Act 1715 of 2014 in Colombia were considered on the calculation of the cost of energy using the

The reliability of the system was included in the objective function of the PSO algorithm through the annual cost of the energy not supplied. Also a fiscal incentive factor was used to include the financial benefits granted by the Act 1715 of 2014 in Colombia to non-conventional renewable source of energy. The results were obtained after simulating the energy flow of the system for 1 year with 1-hour

Dispatch strategy was described in detail, prioritizing the use of renewable resource over diesel generation to supply the load. Also diesel generation cannot be used to charge the battery bank. This condition was based on the fact that, in offgrid areas, the complications associated to supply the fuel and the maintenance of

### 3.2.4 System inputs

The system input parameters are shown in Table 8. The cost of energy lost is assumed in 0.2 USD/kWh. This value depends on the necessities and characteristics of the users of the select location. The interest rate considered in this work is 8.08% taken in [25].

Fiscal incentive factor is calculated applying an effective corporate tax income rate of 33%. The resulting incentive factor is 0.938.

The parameters for the PSO algorithm and the boundaries for each decision variable are shown in Table 9.

## 4. Results of the case study

Table 10 summarized the obtained results after applying the proposed sizing methodology. The best cost achieved was 0.2090 USD/kWh being the lowest obtained. The optimization results deliver no only the design (number of components) but also economic and reliability indicators.


Methodology for Sizing Hybrid Battery-Backed Power Generation Systems in Off-Grid Areas DOI: http://dx.doi.org/10.5772/intechopen.88830

Table 10. Results of the case study.

according the datasheet. Other values as maintenance cost, ρbat, and the fraction of reposition cost, δbat, are set according to the recommendation of experts in the

PSO input parameters Symbol Description Value NPVl Lower bound number of PV modules 0 wDGl Lower bound nominal power of diesel 0 NBp <sup>l</sup> Lower bound number of battery cell in parallel 0 Ebcell, noml Lower bound nominal capacity of battery cell [kWh] 0 NPVu Upper bound number of PV modules 20,000 wDGu Upper bound nominal power of diesel unit in [kW] 200 NBp <sup>u</sup> Upper bound number of battery cell in parallel 10 Ebcell, nomu Upper bound nominal capacity of battery cell [kWh] 9.40 Maxit Maximum number of iterations 50 nPop Population size 200 w Inertia coefficient 1 wmax Inertia coefficient max 0.9 wmin Inertia coefficient min 0.5 c<sup>1</sup> Personal acceleration coefficient 2.5 c<sup>2</sup> Social acceleration coefficient 1.5

Wind Solar Hybrid Renewable Energy System

The main characteristics and price of the battery cells of the reference used in this work are presented in Table 7. The information was obtained from inquiries to

The system input parameters are shown in Table 8. The cost of energy lost is assumed in 0.2 USD/kWh. This value depends on the necessities and characteristics of the users of the select location. The interest rate considered in this work is 8.08%

Fiscal incentive factor is calculated applying an effective corporate tax income

The parameters for the PSO algorithm and the boundaries for each decision

Table 10 summarized the obtained results after applying the proposed sizing methodology. The best cost achieved was 0.2090 USD/kWh being the lowest obtained. The optimization results deliver no only the design (number of compo-

rate of 33%. The resulting incentive factor is 0.938.

nents) but also economic and reliability indicators.

energy sector.

PSO input parameters.

Table 9.

local companies.

3.2.4 System inputs

taken in [25].

200

variable are shown in Table 9.

4. Results of the case study
