Abstract:

Autonomous electrification systems based on the use of renewable energies are suitable to electrify isolated rural communities. For its design there are decision-making support tools, but they do not cover some technical and social constraints and do not consider the specific detail of the project. In this context, the aim of this PhD is to develop a methodology for helping to the design of autonomous electrification systems based on wind and solar energies, which is appropriate to the economic, technical and social aspects of rural communities in developing countries, and which considers the views of all stakeholders involved in the project.

The proposed design methodology is divided into 3 principal stages:

• In the first stage characteristics from the community to electrify are gathered through some initial assessments (socio-economic, resource and technical).

• The second stage is the design process itself and is divided into 3 decision levels, ordered according to the importance of the decisions to take. Specifically the influence on the cost of modifications on the demand (level 1), the system management (level 2) and the security of supply (level 3) are studied. Each decision level is divided into 2 steps: first several electrification alternatives are generated through a linear programming model, including technical and social considerations; second the most appropriate alternative is selected based on economic, technical and social criteria, through compromise programming multicriteria technique.

• The third stage, optional, allows trying to improve the cost of the obtained solutions, maintaining previously taken decisions.

To validate this research work, two experts in the field of rural electrification have acted as hypothetical users of the design methodology, and have carried out all the process for the real communities of El Alumbre and Alto Peru, both in Cajamarca (Peru). Thus, it is validated that the proposed methodology is suitable to design autonomous electrification systems. Specifically, it allows personalizing decision-making in a clear and structured way, evaluating many electrification alternatives and obtaining results that match user’s preferences up.