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Currently, around 1.1 billion people lack access to electricity, mainly in developing countries. Solar photovoltaic systems can provide electricity, but the design is complex, having to size and site the equipment. Besides, since forecasting consumption habits for newly electrified populations is complex, the estimation of the electricity needs is uncertain. This work addresses, for the first time, demand uncertainty to assist promoters in designing electricity access projects, simultaneously solving the sizing and siting problems, using fuzzy logic. In particular, a mathematical model is proposed, introducing uncertainty through five modelling approaches with different fuzzy logic assumptions: three based on the literature and two novel ones according to the conflicting problem nature (project cost minimisation vs electricity supply maximisation). The five approaches are compared and the new ones obtain solutions achieving a higher satisfaction regarding the cost and electricity supply. Then, the most efficient approach is applied in two Peruvian communities, comparing the solutions with those obtained without uncertainty. The results of the proposal show a better balance between the project cost and the demand supplied. Hence, the proposal can help promoters in developing countries to better design electricity access projects where the demand estimation is complex.

Isolated electrification systems based on renewable energy are a suitable option for providing many rural communities in developing countries with access to electricity. In particular, the design of wind-photovoltaic systems that combine stand-alone and micro-grid systems has proved an efficient but challenging optimization problem. Decision support tools and solution procedures have been proposed in the literature, most of them focused on techno-economic aspects. However, social aspects related to the management of the systems are a key issue for users’ satisfaction and project sustainability. Only one procedure based on mixed integer linear programming takes the management aspects into account, but excessive computing time may be required or no solutions may be found for non-small communities. Thus, a fast procedure is proposed. The validation is carried out by solving 640 instances, randomly generated from real project characteristics. Our procedure always finds a feasible solution in less than 7 s on average; and even for instances with hundreds of possible locations for equipment, the solutions are found in less than 1 min. Moreover, it finds cheaper solutions than those in the literature. Thus, the procedure represents a very efficient process for optimizing the design of electrification projects considering the technical and management constraints.