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.

Current Transmission Network Expansion Planning (TNEP) models are usually strongly simplified in order to achieve optimal solutions within reasonable execution times and computational resources. Examples of these simplifications are the use of reduced network equivalents and the consideration of only a few years within the planning horizon. Due to these simplifications, current TNEP models do not always meet the requirements needed for practical applications. This is particularly true in case of power systems with increased use of renewable energies in which case the levels of variability and uncertainty require a better representation of the system. To meet these new challenges and achieve a time-effective increase of the transmission capacity for the integration of renewable energies, models that consider more accurate representations of power systems are needed. These models must also be able to handle realistic-size power systems in order to serve as supporting tool for real planning processes. In this article, a novel heuristic model based on Ant Colony Optimization for the multi-year TNEP is presented. The characteristics of ACO algorithms make the proposed model especially suitable for considering several expansion options, larger planning horizons, and several load-generation profiles. For validating the model, 25 years plans were calculated in the Garver's 6-bus system and in the IEEE 118-bus system. In both cases, several independent simulations were executed and the results were compared with the ones obtained using a traditional MILP approach. The results showed that all runs found the optimal solution within reasonable computation times, which enables us to validate our model.

This paper analyses and compares the performance of a number of approaches implemented for the detection of capacity regeneration phenomena (measured in ampere-hours) in the degradation trend of energy storage devices, particularly Lithium-Ion battery cells. All implemented approaches are based on a combination of information-theoretic measures and sequential Monte Carlo methods for state estimation in nonlinear, non-Gaussian dynamic systems. Properties of information measures are conveniently used to quantify the impact of process measurements on the posterior probability density function of the state, assuming that sub-optimal Bayesian estimation algorithms (such as classic or risk-sensitive particle filters) are to be used to obtain an empirical representation of the system uncertainty. The proposed anomaly detection strategies are tested and evaluated both in terms of (i) detection time (early detection) and (ii) false alarm rates. Verification of detection schemes is performed using simulated data for battery State-Of-Health accelerated degradation tests, to ensure absolute knowledge on the time instant where a regeneration phenomenon occurs.

A battery storage system connected to a micro-grid through a Voltage Source Inverter (VSI) can have at least two operating modes (dual-mode). In the first one the VSI is connected to a synchronous generator which could be considered as a large capacity grid or infinite busbar. The second operating mode is obtained when the VSI is feeding an isolated load and several small micro-sources. Therefore, for a proper operation of this system, the VSI has to be controlled in order to operate in the grid-connected or stand-alone modes, with seamless transfer between them. This paper proposes a master-slave control strategy for the main energy storage VSI operating in a microgrid. The proposed control strategy achieves seamless transfer between these two situations with minimal communication requirements between the VSI and the micro-grid controller, and allows the VSI to operate in parallel with virtually any type of generator or inverter. The control scheme is formulated for a micro-grid operated using conventional droop strategy. A control strategy is proposed for each of the VSI control levels. The control strategies have been experimentally tested in a full practical micro-grid implemented in a small village located in the Chilean Andes. Experimental results are presented and analyzed in this paper.

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.

This paper deals with a steady state load-shedding strategy considering the effect of uncertain data on generation supplies and loads. Uncertainty data are modeled by fuzzy numbers and a genetic algorithm is used to solve the load-shedding problem. The method is applied on two test case networks. Results prove additional enhancements for improving continuity of service in power systems during emergencies.

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.