**5. Conclusions and outlook**

For WACC 0%, the system profits of 14.1 k USD are distributed to the domestic

Altogether, there is a strong impact of the costs of capital on the financial and technical parameters of the system. The maximum field capacity is utilized up to the WACC 20% scenario, and even for WACC 30%, the food production of a leastcost system would be sufficient to meet the domestic demand. An increase in the cost of capital by 1% leads to a decrease in system profits by 880 USD. The required funds to finance long-term assets amount to 139.0 k USD for WACC 0% and decrease from there on as CapEx-intensive technologies such as PV are increasingly

The costs, revenues, and profit for scenario S3 are depicted in **Figure 12**. For WACC 0%, the cost-minimizing system is designed in a dimension that the maximum farmland capacity is utilized, regardless of the available power generation technologies. Since revenues are directly proportional to the maize grain production, annual revenues are constant at 52.6 k USD. It can be clearly seen that system costs rise with the constraints on combination of power generation technologies. While the total annual costs for the fully fledged system amount to 38.5 k USD, the omission of biogas leads to a cost increase by 29%, while the omission of biogas and photovoltaics leads to an increase by 74% to 66.8 k USD. Hence, a system in which electricity is exclusively generated from diesel is not even net-profitable in a fully socially financed scenario and thus cannot sustainably provide the domestic community with energy and water free of charge. In order to cover the net losses, 46.4 USD per capita and year or 0.34 USD/kWh are charged for electricity. As the WACC increases to 15%, only the fully fledged EWF system operates at full food production, while the omission of biogas reduces the agricultural productivity by 16% and the absence of both renewable energy sources reduces the productivity by 68% to 70 tons per year, which is just sufficient to feed the domestic community. While the fully fledged EWF system breaks even, the unavailability of biogas prevents the systems from being profitable. Net losses for the DG + PV EWF system of 15.4 k USD and 17.3 k USD for the pure DG EWF system imply annual electricity and water expenses of 50 USD and 56.3 USD per capita, respectively; allocated to power consumption, this equals 0.36 USD/kWh and 0.41 USD/kWh, respectively. In the WACC 30% scenario, none of the EWF systems utilizes the maximum field capacity. While the fully fledged system still produces enough maize grain to provide for the domestic community (104 tons), the DG + PV EWF system and the pure DG EWF system produce just 13 tons and 8 tons, respectively. As the trend of declining profitability with an increase in WACC continues to proceed, even the

*Costs and revenues variation in power generation technology choice for WACC = 0, 15, and 30%.*

substituted with investment light technologies such as diesel.

**4.5 Results of technology variation scenario S3**

community, corresponding to 45.61 USD per capita—0.10 more than the total expenses required for food. The market-based financing scenario (WACC 15%) breaks even (net loss of 1.8 USD). A finance system with WACC 30% generates a loss of 10.4 k USD, which implies an electricity price of 0.25 USD/kWh or annual costs of 33.84 USD per capita for electricity and total costs of 79.45 USD per capita

for energy, water, and food.

*Regional Development in Africa*

**Figure 12.**

**112**

This contribution presents an economic analysis of decentralized energy-waterfood systems and their capability to provide economic-feasible solutions for rural electrification and thus the potential to enable economic development of the rural population in sub-Saharan Africa. Their decentralized design avoids the financial and governmental obstacles coming with electrification through grid extension. Biogas motors as controllable power generators substitute the costly and environmental unfriendly use of diesel generators. Although the deployment of water pumps increases the system investment costs, they lead to two major advantages compared to micro-grids without their utilization. Firstly, they are flexible loads opposite to most private power consumers (e.g., light bulbs). The water pumps are powered by cheap solar power during daytime with little or even without use of costly battery storage. Secondly, water pumps are productive power consumers opposite to private consumption, because their utilization enables year-round agriculture, which increases local productivity. Hence the local population is enabled to pay back the investment costs despite their formerly low purchase power. The leastcost modeling on the case study of the rural community Kpori, a 300-inhabitant farming village in northern Ghana, confirmed this hypothesis. The system integration of biogas generators and water pumps to closed-loop energy-water-food systems reduces the costs significantly compared to current electrification approaches with diesel generators only or diesel generators combined with solar photovoltaics and batteries. The decreased demand of costly batteries and diesel and increased profits from year-round agriculture lead to annual costs of 2 USD for the for electricity and water supply of the community compared to 17,326 USD for power supply just with diesel, assuming WACC of 15% and that the profits from agricultural sales subsidize the power supply. The cost analysis of these modeling results shows that 37% of the costs are spent for farming salaries and just 9% on CAPEX but 21% on capital costs due to the WACC of 15%. The remaining costs result from costs for fuel and other operation costs such as maintenance. The conducted variation of WACC showed on the one hand that this has a strong impact on the LCOE, which are 0.08 USD/kWh for WACC of 0%, 0.22 USD/kWh for WACC of 15%, and 0.29 USD/kWh for WACC of 30%. On the other hand, increasing WACC leads to significant reduction of installed PV capacities and increased share of power from diesel generators. The utilization of biogas is almost independent of the WACC

because of its low CAPEX and constrained maximum capacity due to shortage of livestock manure as input. Based on this model results, decentralized energy-waterfood systems have shown their potential to enable LCOE below state-of-the-art offgrid systems and local job creation through improved agricultural productivity.

In order to prove the potential of decentralized energy-water-food systems, they must be implemented on-ground including research on the optimal management and ownership structures; professional requirements for its managers, technicians, and farmers; as well as possible investment strategies. Also, the least-cost model shall be improved regarding more detailed modeling of groundwater availability, nutrients in the soil, water consumption of different crops, and biogas digestion of various inputs. After adding these improvements of the model, it shall be disseminated to and used by interested NGOs and social enterprises. Thereby, decentralized energy-water-food systems could prove their potential to improve access to reliable energy, water, and food supply, to create local jobs, and thus to fight extreme poverty of the population in rural sub-Saharan Africa.

**Unit Value**

**Unit Value**

**Unit Value**

**Unit Value**

**Unit Value**

Technology — Lead-acid Depth of discharge — 60 Energy investment cost capacity USD/kWh 350 Power investment costs USD/kW 300 Energy fixed costs USD/kWh/year 10 Power fixed costs USD/kW/year 30 Variable costs USD/kWh 0 Round-trip efficiency % 85 Lifetime Year 10

Load efficiency % 29 Minimum load % 40 Investment costs USD/kW 675 Fixed costs USD/kW/year 10 Variable costs USD/kWh 0.01 Lifetime Year 15

Maximum installed capacity ton/h 0.0153 Investment costs USD/ton/h 788.4 k

Variable costs USD/ton 2.1 Lifetime Year 20

Fixed costs % 3.5% of investment costs

Material — Plastic (PVC) Investment costs USD/m<sup>3</sup> 60

Fixed costs USD/kW/year 20 Variable costs USD/kWh 0 Lifetime Year 25

*Economic Development of Rural Communities in Sub-Saharan Africa through Decentralized…*

**Table A3.**

**Table A4.**

**Table A5.**

**115**

**Table A2.**

*Techno-economic parameters for battery.*

*Techno-economic parameters for biogas generator.*

*Techno-economic parameters for biogas digester.*

*Techno-economic parameters for solar photovoltaics.*

*DOI: http://dx.doi.org/10.5772/intechopen.90424*
