Many people across Latin America still do not have access to reliable electricity. Although Chile exhibits a comparatively high electricity coverage, many barriers are still present for the development of sustainable energy supply solutions exploiting local renewable energy sources. To face this challenge, a coconstruction methodology is proposed, which considers a flexible and participatory design with continuous communication between the technical team of the project and the community, thus ensuring informed decision making around the project design. In this context, microgrid-based solutions offer an ideal opportunity to exploit the integration of energy sources adapted to the specific local characteristics. The coconstruction methodology allows the identification of local requirements, less often considered for design procedures based on a traditional approach, in a joint work with the communities so that the technological solution is tailored for it. Consequently, different technical solutions (design adaptations and innovations) have been proposed and developed under this framework, such as: energy management systems, demand response strategies, microgrid applications for Mapuche communities, microformers, a monitoring system that includes social aspects, and vehicle to grid for microgrids. This paper summarizes the experience of several microgrid projects in Chile, identifies risks, impacts, control actions, and discusses their replicability to the Latin American and the Caribbean region.

In this paper, two methods for generating the daily load profile and forecasting in isolated small communities are proposed. In these communities, the energy supply is difficult to predict because it is not always available, is limited according to some schedules and is highly dependent on the consumption behavior of each community member. The first method is proposed to be used before the implementation of the microgrid in the design state, and it includes a household classifier based on a Self Organizing Map (SOM) that provides load patterns by the use of the socio-economic characteristics of the community obtained in a survey. The second method is used after the implementation of the microgrid, in the operation state, and consists of a neural network with on-line learning for the load forecasting. The neural network model is trained with real-data of load and it is designed to stay adapted according to the availability of measured data. Both proposals are tested in a real-life microgrid located in Huatacondo, in northern Chile (project ESUSCON). The results show that the estimated daily load profile of the community can be very well approximated with the SOM classifier. On the other hand, the neural network can forecast the load of the community reasonably well two-days ahead. Both proposals are currently being used in a key module of the energy management system (EMS) in the real microgrid to optimize the real uninterrupted load for 24-hour energy supply service.

Distribution network Greenfield planning is recognized as an important task of the planning studies. This importance lies in its use as a regulatory tool to determine the distribution charges applied to final users. However, the Greenfield planning requires solving a combinatorial optimization problem classified as a NP-hard. Because of its complexity, different models based on heuristic strategies have been proposed to solve this problem. In this work, an evolutionary strategy -as a global search methodology-is proposed. The corresponding Greenfield problem is formulated considering the network capacity constraints and the voltage drop. The proposed algorithm assumes that the system elements costs and its optimal assignment are provided beforehand. Next, the individuals' generation is based on a heuristic biased construction of random spanning trees. Then, the fitness function penalizes the unfeasible solutions with a dynamic function. Finally, four crossover methodologies are proposed and evaluated on a test case. A comparison of the solutions and the algorithm performance is performed on a test case system.