## 4 Modeling of the Microgrid  
Urban and rural areas provide prospects for solar PV systems, but with different considerations [46]. Rooftop solar in urban areas focuses on distributed energy generation, while rural areas can cater to larger-scale utility installations. Optimizing solar PV potential involves considering geographical factors, energy demands, infrastructure, and regulatory frameworks specific to each setting. Urban areas have a substantial potential for rooftop solar installations on buildings [47]. However, shading from other buildings, limited roof space, and obstructions like vents or HVAC systems can reduce the effectiveness of solar panels. Urban areas generally have higher energy demands, making rooftop solar PV a viable option for meeting some of this demand. It also reduces transmission losses by generating electricity closer to where it is consumed. Rural areas often have more open land available, making them suitable for utility-scale solar farms. This allows for larger installations without the constraints of urban space limitations. Rural areas tend to have fewer obstructions like buildings and trees, which can maximize solar panels' exposure to sunlight. However, the demand is limited, burdening DER projects' return on investment, especially under the NEM billing mechanism. For this purpose, we decided to consider the case of a rural micro-gird that includes two prosumers and four consumers. This microgrid is tested using three billing mechanisms: NEM, FiT, and P2P energy trading. The same conditions are applied for the three scenarios and KPIs such as unused energy, financial gain for prosumers, and annual bill reduction for consumers are used to compare the three models. In the simulated model, we considered two prosumers for every four consumers. This reflects an optimistic perspective where 1/3 of the population would have a renewable source of generation. Moreover, the simulated microgrid considers as sources of energy the utility grid for all participants and rooftop PV solar systems for prosumers. At the same time, the consumers are residential, small-commercial, and industrial loads (<100 kW). The use case microgrid, presented in Figure 4, is detailed hereafter:  
* Prosumer A is a residential house occupied for the entire year and equipped with a 12.8 kWp solar PV system;* Prosumer B is a residential summer house occupied only during the summer and equipped with a 12.8 kWp solar PV system;
* Consumer #1 is a residential house occupied for the entire year. Prosumer A and Consumer #1 have been modeled with the same load profile. They are a household of 4 family members where one family member works during the day. The house covers an area of around 180 m\({}^{2}\);
* Consumer #2 is an industrial load; it is a vehicle bodywork workshop that is operational during the entire year;
* Consumer #3 is a commercial load; it is a supermarket that is operational during the entire year;
* Consumer #4 is a residential house that is occupied during the entire year. Since an hourly load profile for an entire year was unavailable for all the loads, we decided to have an hourly load profile for over 4 weeks, each week corresponding to a different season. Then, the results of each week were extrapolated to 13 weeks to cover an entire season, and the overall result would be 52 weeks to represent an entire year. The household used as a benchmark, Prosumer A, has a rooftop solar PV system. However, the current trend in the market is to replace traditional equipment with more efficient options. This includes switching from traditional lamps to light emitting diode (LED) lamps, using converter type air conditioning units, and upgrading to higher efficiency appliances. Given this trend, we have chosen to examine the scenario where Prosumer A is substituted with Prosumer A\({}^{\prime}\). This substitution allows us to evaluate the effects of increased energy efficiency on our three billing models, resulting in lower energy demand.