design of electrification programs for remote areas

In decentralized rural areas, where access to the electrical grid is limited or nonexistent, obtaining reliable electricity is essential for improving quality of life and enabling basic socio-economic activities. One common solution is the deployment of simple solar home systems (SHSs), which are capable of providing electricity for essential household services such as lighting and small appliances. These systems are particularly important in rural regions, especially in developing countries, where they can serve as a stepping stone towards broader electrification and community development.

A notable example of such implementation is Morocco, where a company installed SHSs in remote villages. However, the company significantly underestimated the costs associated with both corrective and preventive maintenance as specified in their contracts. This case underscores the critical importance of designing an effective maintenance network that ensures the long-term sustainability of these systems. Proper network design involves strategic decisions such as the optimal location of logistics centers, the appropriate allocation and sizing of personnel and vehicles, and the planning of maintenance schedules, all of which are necessary to accurately budget and execute service contracts.

To address these challenges, (Carrasco et al., 2016) presents a mixed-integer linear optimization model that allows the efficient dimensioning of maintenance networks based on real operational data, while also determining the maintenance schedule by considering both preventive and corrective maintenance. The model provides a systematic approach to allocate resources and design the network structure in a way that minimizes costs while ensuring service reliability. Complementing this approach, (León et al., 2019) offers a statistical study using classification techniques to tailor the maintenance network to the specific characteristics of the terrain and the potential customer base. By combining optimization and data-driven statistical methods, these studies provide a comprehensive framework for designing robust and cost-effective maintenance networks for solar home systems in rural, decentralized regions.