**1. Introduction**

120 Fossil Fuel and the Environment

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The cost and efficiency of fossil fuel based electric power and heat production in remote areas is an important topic, such as in Alaska with more than 250 remote villages, and developing countries such as Mexico, with approximately 85,000 villages, each with populations less than 1000 persons. The operating cost of fossil fuel based generators such as diesel electric generators (DEGs) is primarily influenced by the cost associated with the purchase, transportation, and storage of diesel fuel. It is very expensive to transport fuel for DEGs in some villages of Alaska (Denali Commission, 2003) due to the extreme remoteness of the site. Furthermore, there are issues associated with oil spills and storage of fuels (Drouhillet & Shirazi, 1997). As of the year 2010, the average subsidized cost of electricity (COE) for a remote Alaskan community is about 0.53 USD/kWh for the first 500 kWh per residential customer per month. The unsubsidized COEs are as high as 2.00 USD/kWh for some extremely isolated communities (Denali Commission, 2003). An extension of the main grid is not possible for such communities due to high cost and losses for the transmission lines.

Based on energy consumption studies compiled by the US Department of Energy, Alaska spends about 50% more (28.71 USD per million BTU) for electrical energy than the rest of the United States (19.37 USD per million BTU) (EIA, 2002). A Memorandum of Agreement (MOA) was signed between the Denali Commission, the Alaska Energy Authority, and the Regulatory Commission of Alaska to supply reliable and reasonably priced electricity to the rural communities of Alaska (Denali Commission, 2003). With the rising cost of fuel and the need for more efficient systems with higher reliability and lower emissions, integrating renewable energy sources and energy storage devices could prove to be more cost effective solutions for electrical power in remote communities (Fyfe, Powell, Hart, & Ratanasthien, 1993). Consequently, there is great need for energy-efficient standalone smart micro-grid systems in these remote communities that employ renewable power sources and energy storage devices.

Distributed power generation systems consisting of two or more generation and storage components, including solar PV arrays, WTGs, battery banks, DEGs, and microhydro, are widely used to supply energy needs. Renewable energy sources such as solar photovoltaics (PV) and wind turbine generators (WTGs) could be used in conjunction with DEGs to supply

Energy-Efficient Standalone Fossil-Fuel Based

Fig. 1. General distributed (hybrid) power generation system.

The sample standalone distributed (hybrid) electric power system used in this analysis consists of four DEGs rated at 235 kW, 190 kW, 190 kW, and 140 kW. The average electrical load is 95 kW with a minimum of 45 kW and a maximum of 150 kW. One DEG is sufficient to supply the village load. Currently, a PV array and a WTG are not installed in the system. In order to analyze the long-term performance of the system while integrating a PV array, a WTG, and a battery bank, simulations were performed using HARPSim for a PV-dieselbattery system, a wind-diesel-battery system, and a PV-wind-diesel-battery system. The simulation results were compared with those predicted by the HOMER software. The system performance is analyzed by incorporating a 100 kWh absolyte IIP battery bank, a 12 kW PV array, a 65 kW 15/50 AOC WTG, and a 100 kVA bi-directional power converter.

**2.2 Sample standalone village power system** 

Hybrid Power Systems Employing Renewable Energy Sources 123

electricity in remote Alaskan communities and other remote regions (Denali Commission, 2003), (Drouhillet & Shirazi, 1997), (Dawson & Dewan, 1989), (Wies, et al., 2005a), (Wies, et al., 2005b), (Wies, et al., 2005c), & (Borowy, 1996). Besides reducing fuel consumption, the use of renewable energy sources has been shown to increase system efficiency and reliability, while reducing emissions (Drouhillet & Shirazi, 1997), (Dawson & Dewan, 1989), (Wies, et al., 2005a), (Wies, et al., 2005b), (Wies, et al., 2005c), & (Borowy, 1996). It has been predicted that by the year 2050, despite the increase in the demand for electric power, the global CO2 level which is the major greenhouse gas would be reduced to 75% of its 1985 level due to the increase in the use of renewable energy sources for energy production (Johansson, et al., 1993).

This remainder of this chapter presents an economic and environmental model for standalone fossil fuel based micro-grid systems employing renewable energy sources based on an existing diesel-electric power generation systems in remote arctic communities. A simulator called the Hybrid Arctic Remote Power Simulator (HARPSim) was developed using MATLAB Simulink to estimate the reduction in fuel consumption of DEGs and the minimization in the cost of producing electricity in remote locations by integrating solar PV and WTGs into the system. HARPSim is used to predict the long-term economic and environmental performance of the system with and without the use of renewable sources in combination with the diesel electric power generation system. A battery bank is also included in the system to serve as a backup and a buffer/storage interface between the DEGs and the variable sources of power from solar PV and wind.

The economic part of the model calculates the fuel consumed, the kilowatt-hours (kWhrs) obtained per liter (gallon) of fuel supplied, and the total cost of fuel. The environmental part of the model calculates the CO2, particulate matter (PM), and the NOx emitted to the atmosphere. The Life Cycle Cost (LCC), net present value (NPV), efficiency, and air emissions results of the Simulink model are compared with those predicted by the Hybrid Optimization Model for Electric Renewables (HOMER) software developed at the National Renewable Energy Laboratory (NREL) (NREL HOMER, 2007). A sensitivity analysis of fuel cost and investment rate on the COE is also performed to illustrate the impact of rising fuel costs on the long-term system economics.
