Several studies have been conducted on Hybrid Power Generation Systems (HPGS) to reduce the existing gap in access to electricity, especially in isolated and difficult access areas. The appropriate use of HPGS allows to power a community, reduces environmental pollution, and improves the performance of a system that only uses Fossil Fuel Generators (FFG). However, despite the efforts made in several countries, there are still isolated areas that remain without electricity due to several factors, such as the high cost of generation, transmission, and energy distribution. For this reason, this work seeks to generate new viable solutions to improve access to electricity especially in isolated areas of Ecuador, although the tool can be applied to any community without constant electricity access. For this purpose, the modeling of an HPGS is proposed, such system is composed of an FFG, a photovoltaic generator, and a hybrid energy storage system (i.e., electrical and thermal). A control algorithm for the FFG is used to adapt the motor rotational speed by using a minimum fuel consumption to supply the load profile. The results are obtained from simulations with real data of three case studies. A reduction in fuel consumption, greenhouse gas emissions, and loss power from renewable energy is demonstrated.

This paper proposes a distributed predictive secondary controller to tackle together frequency and voltage regulation, realize the economic dispatch and reactive power sharing of generation units in isolated AC microgrids. Contrary to most approaches, the proposed predictive controller achieves consensus objectives (economic dispatch of generation and reactive power sharing) with soft constraints (keep both frequency and average voltage within predefined bands instead of restoring them to their nominal values). Extensive simulation work validates the effectiveness of the predictive controller for communication problems and in the presence of plug-and-play scenarios.

This paper presents the comparison of two modulated model predictive control (M2PC) strategies (the classical M2PC approach, and the M2PC with optimized overmodulation) applied to a three-phase three-level voltage source inverter (3Ph-3L-VSI). The control strategies are tested under various scenarios: no-load, a linear load connection, and a nonlinear load connection. Although each algorithm has its advantages, simulations show that the overmodulated version presents a substantially better performance than the classical version since the classical M2PC creates gaps between switching regions, making rough transitions between them. In contrast, the overmodulation version can produce a continuous transition between those regions, resulting in lower THD and control effort.

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.