**4.4 Sensitivity analysis: Fuel cost on NPV, cost of energy, and payback period**

The plot for sensitivity analysis of fuel costs and investment rate on the NPV for the PVwind-diesel-battery system is shown in Fig. 13. It can be seen that as the cost of fuel increases and the investment rate decreases, the NPV of the system increases. The NPV plays an important role in deciding on the type of the system to be installed. The NPV of a system includes the total spending on the installation, maintenance, replacement, and fuel cost for the type of system over the life-cycle of the project. Knowing the NPV for different system configurations, the user can install a system with minimum NPV.

The plot for sensitivity analysis of fuel costs and investment rate on the COE for the PVwind-diesel-battery system is shown in Fig. 14. It can be observed that as the cost of fuel increases and the investment rate increases, the COE increases.

In order to calculate the COE for the diesel-battery (high emissions plant) system and the PV-wind-diesel-battery (low emissions plant) system, it is necessary to know the A/P ratio for the system, where 'A' is the annual payment on a loan whose principal is 'P' at an interest rate *'i'* for a given period of 'n' years (Sandia, 1995).

The ratio A/P is given as follows:

$$\frac{\mathbf{A}}{\mathbf{P}} = \frac{i(1+i)^n}{(1+i)^n \cdot 1} \tag{17}$$

The annual COE for different systems given a fuel price in USD per liter (4.00 USD per gallon) and an investment rate (%) is calculated as follows:

$$\text{COE}\_{\text{L}} = \left(\frac{A}{P}\right)\_{\text{L}} \left(\text{C}\_{\text{PV-wind}} \cdot \text{C}\_{\text{DB}}\right) + \left(\frac{A}{P}\right)\_{\text{H}} \left(\text{C}\_{\text{DB}}\right) + \text{C}\_{\text{F}} \tag{18}$$

and

138 Fossil Fuel and the Environment

20-year LCC analysis of the Kongiganak Village hybrid power system using HOMER

The NPV of the system, with i = 7% and fuel cost = 0.79 USD per liter (3.0 USD per gallon), is 2,421,502 USD

power source used in HOMER. Therefore, the life of the battery bank is less in HARPSim due to the annual increase in charge/discharge cycles. This results in more efficient operation of the DEGs while reducing the fuel consumption and saving in the cost of the

The plot for sensitivity analysis of fuel costs and investment rate on the NPV for the PVwind-diesel-battery system is shown in Fig. 13. It can be seen that as the cost of fuel increases and the investment rate decreases, the NPV of the system increases. The NPV plays an important role in deciding on the type of the system to be installed. The NPV of a system includes the total spending on the installation, maintenance, replacement, and fuel cost for the type of system over the life-cycle of the project. Knowing the NPV for different

The plot for sensitivity analysis of fuel costs and investment rate on the COE for the PVwind-diesel-battery system is shown in Fig. 14. It can be observed that as the cost of fuel

In order to calculate the COE for the diesel-battery (high emissions plant) system and the PV-wind-diesel-battery (low emissions plant) system, it is necessary to know the A/P ratio for the system, where 'A' is the annual payment on a loan whose principal is 'P' at an

DEGs. Overall, the LCC analysis shows a lower NPV in HARPSim than in HOMER.

**4.4 Sensitivity analysis: Fuel cost on NPV, cost of energy, and payback period** 

Fig. 12. 20-year LCC analysis of the hybrid power system using HOMER.

system configurations, the user can install a system with minimum NPV.

increases and the investment rate increases, the COE increases.

interest rate *'i'* for a given period of 'n' years (Sandia, 1995).

The ratio A/P is given as follows:

DEGs Renewables Battery Bank

Switchgear + Controller

Miscellaneous

Switchgear + Controller = 2% Miscellaneous = 1%

DEGs = 91%

Renewables = 4%

Battery bank = 2%

$$\text{COE}\_{\text{H}} = \left(\frac{A}{P}\right)\_{\text{H}} \text{ (C}\_{\text{DB}}\text{)} + \text{C}\_{\text{F}} \tag{19}$$

where CPV-wind is the cost of the PV-wind-diesel-battery system from Table 2, CDB is the cost of the diesel-battery system from Table 2 and CF is the annual cost of fuel from Table 3.

The plot for sensitivity analysis of fuel costs and investment rate on the payback period for the PV-wind-diesel-battery system is shown in Fig. 15. It can be seen that the payback period of the PV array decreases as a function of a fifth order polynomial with the increase in the cost of fuel.

The simple payback period (SPBT) for the PV array and WTG is calculated using data from Table 2 and Table 3 as

$$\text{SPBT} = \frac{\text{Extra cost of PV system}}{\text{rate of saving per year}} \,\,. \tag{20}$$

Fig. 13. Sensitivity analysis of fuel cost and investment rate on the NPV.

Energy-Efficient Standalone Fossil-Fuel Based

fuel consumption of the DEG, the energy costs, and emissions.

be utilized to calculate the avoided costs of emissions.

diesel-electric generator and battery bank, respectively.

Cogeneration", *Solar'89*, pp. 269-274.

future of non-renewable energy sources.

SalesSheet2005-1.pdf.

**6. Acknowledgment** 

**7. References** 

4th ed.

**5. Conclusion** 

Hybrid Power Systems Employing Renewable Energy Sources 141

A model called HARPSim was developed in MATLAB Simulink to demonstrate that the integration of WTGs and PV arrays into stand-alone hybrid electric power systems using DEGs in remote arctic villages improves the overall performance of the system. Improved performance results from increasing the overall electrical efficiency, while reducing the total

The LCC cost analysis and the percentage annualized cost from the Simulink® model were comparable to those predicted by HOMER. The Simulink® model calculates the CO2, NOx and the PM10 emitted to the atmosphere over the period of one year. These results can also

Distributed or hybrid energy systems which result in more economical and efficient generation of electrical energy could not only improve the lifetime and reliability of the diesel-electric generation systems in remote communities, but could also help to extend the

The authors would like to thank Peter Crimp of the Alaska Energy Authority and Dennis Meiners of Intelligent Energy Systems for providing the power system information and data for the sample village electric power system. The authors would also like to thank Siemens, Entegrity Wind Systems (formerly Atlantic Orient Corporation), Caterpillar (Detroit Diesel) and GNB Industrial for providing the design specifications for the PV panels, wind turbines,

Agrawal, A. (2006). "Hybrid Electric Power Systems in Remote Villages: Economic and

Borowy, B. (1996). "Design and Performance of a Stand Alone Wind/Photovoltaic Hybrid System," *Ph.D. Dissertation*, Dept. of Elect. Eng., Univ. of Massachusetts, Lowell. Cengel, Y. & Boles, M. (2002). "Engineering Thermodynamics", *McGraw Hill Publications*,

Dawson, F. & Dewan, S. (September 1989). "Remote Diesel Generator with Photovoltaic

Denali Commission (August 2003). Memorandum of Agreement Re Sustainability of Rural

*a report prepared by the National Renewable Energy Laboratory*.

Power Systems, *A Memorandum of Agreement between the Denali Commission, the Alaska Energy Authority and the Regulatory Commission of Alaska,* August 2003. Drouilhet, S. & Shirazi, M. (September 1997) "Performance and Economic Analysis of the

Addition of Wind Power to the Diesel Electric Generating Plant at Wales, Alaska",

*Dissertation*, Dept. of Elect. and Comp. Eng., Univ. of Alaska, Fairbanks. Atlantic Orient 15/50 Brochure (2005). *Official Website of Atlantic Orient Canada Inc.*, accessed

Environmental Analysis for Monitoring, Optimization, and Control," *Ph.D.* 

Aug 8th, 2011, Available from: http://www.atlanticorientcanada.ca/pdfs/AOCI-

Fig. 14. Sensitivity analysis of fuel cost and investment rate on the COE.

Fig. 15. Sensitivity analysis of fuel cost and investment rate on the payback period.

The extra cost of the PV array and WTG in the system is obtained as the difference between the system cost of the PV-wind-diesel-battery system and the diesel-battery system from Table 2 and the rate of savings per year is obtained from the savings in the cost of fuel per year as given in Table 3.
