**4.1 Model input**

contribution [6, 7] addressed the adaption of *urbs*, an economic model, which was originally designed by the Chair of Renewable and Sustainable Energy Systems of the Technical University of Munich (ENS) for distributed energy systems. *Urbs* has a well-documented mathematical description; it is open-source and can be used for cross-sectoral models in any spatial and temporal resolution [28]. Hence, it is used

*Work flow to obtain least-cost design of decentralized energy-water-food systems with programming tool urbs. A*

*urbs* is a linear optimization tool programmed in Python and identifies the optimal system configuration based on the minimization of the total system costs resulting from the techno-economic modeling of each process and storage technologies in the system. **Figure 3** gives an overview of the *urbs* model for

It requires three kinds of input data. Site data is defined by the demand, solar and rainfall time series, techno-economic parameters of the processes and storages as depicted in the EWFS model schema (**Figure 2**), and lastly the market prices of the commodities that can be bought or sold between the system boundaries. This data is read by *urbs*, which already has an implemented script adapted to model EWFS with a linear approach [7], and the total system costs are optimized. The output data includes the installed capacities related to the three sectors, the commodity flows, total revenues, and costs. A pre-feasibility analysis can be conducted on the basis of these results to evaluate the business attractiveness and ensure

**4. Economic feasibility of decentralized energy-water-food systems:**

relatively high national electrification rate of 82.5% (2016), there is a drastic regional contrast between urban and rural areas within the country [29]. While the urban Greater Accra area has the highest regional electrification rate of 85%, the three northernmost, sparsely populated regions have an average electrification rate of only 30% [30]. These rural areas are the most expensive regions to be connected to the main grid and therefore particularly suitable for off-grid energy solutions. Since rural northern Ghana is characterized by high solar radiation and high agricultural activity, the use of solar photovoltaics and the coupling of the energy sector

to the water and food sectors promise great productivity potential.

The northern region of Ghana is selected as case study. Although Ghana has a

Kpori is a village of about 300 inhabitants in the West Gonja District in the north of Ghana. It is an off-grid village with no access to the national energy network,

to conduct the economic feasibility analysis aimed in this work.

decentralized EWFS.

*business analysis is derived from the output results.*

*Regional Development in Africa*

**Figure 3.**

a sustainable project operation.

**case study Kpori**

**104**

As depicted in **Figure 3**, *urbs* already includes the EWFS model and optimization script. The input data needed about Kpori are the following:


The community demand for residential electricity and domestic water is determined by the approx. 300 Kpori inhabitants distributed over 70 households with an average household size of 4.4 [32]. The hourly private power demand is obtained by a Monte Carlo simulation based on the hourly utilization probability of residential appliances and their rated power. This data was obtained from an on-site survey on the nearest electrified farming community. The results of **Figure 4** show a typical load profile of a farming community with a total annual consumption of 42.5 MWh or 138 kWh per inhabitant.

Domestic water demand is set to 50 liters per day and person based on the drinking and sanitation water right standards [23]. Daily food demand is modeled as 658 g of maize grain per inhabitant, which covers the minimum dietary calorie intake of 2400 kcal [33]. In Kpori, up to 263 tons of maize grain can be produced annually on the domestic farmland due to the maximum capacity of arable land of

15 ha. Mismatches in food supply and demand can be balanced by selling or purchasing maize grain on external markets for 200 USD per ton. Additionally, maize stover and chicken manure is fermented into biogas. The capacity of the biogas digestion process is limited to 367.5 kg/day due to the amount of manure available from approx. 3000 chickens in a nearby town. The solar and rainfall time series are obtained by data from geographical information systems (GIS) or online data bases.

The technical and economic parameters for all technologies depicted in **Figure 2** are listed in the Appendix. Lastly, the weighted average cost of capital is assumed to be at the market rate of 15% according to the study [17].
