Modelo de un sistema de administración de energía autónomo operado desde la nube para optimizar la gestión de un grupo de microrredes

Abstract

Organizing the generation, storage, and management of electrical energy from the perspective of renewable energies, as well as the parameterization of the energy consumption characteristics of communities with limited access to the interconnected electricity supply, has taken more relevance in recent years due the demands that define the social welfare of this century. Complementary to the demand increase, other factors require the improvement and updating of the utility grid infrastructure and its opening to other technologies that meet the needs of end users. The interest in renewable energy sources, the evolution of energy storage technologies, the continuous research in microgrid management systems, and the massification of technologies and tools available in cloud computing, machine learning, big data, and the internet of things environment motivated the development of this doctoral research.

This doctoral research focuses on three tasks linked to the operation of a cluster of microgrids. The first task is the fluctuating integration of heterogeneous energy generation devices and objects whose mobility and distribution characteristics are particular over various geographical areas. The second is the need for real-time operation and extensive information processing and storage capabilities. Finally, the third task considers the conservation factors for critical applications linked to advanced optimization techniques, especially the operational cost and the battery's lifespan. An autonomous and scalable energy management model that follows the hierarchical control structure and bases its operation on cloud computing, the internet of things, machine learning, and big data solves the aforementioned tasks.

This research defines the elements considered by the real-time autonomous and scalable energy management system framework in a cluster of microgrids. For this, it is necessary to emulate the behavior of a group of interconnected microgrids and test the framework under real scenarios with the assistance of power-hardware-in-the-loop platforms connected to a cloud server. The server programming must include the implementation of the framework management protocol that exploits the optimization algorithm and state of charge equalization. Also, the framework takes advantage of machine learning and big data tools available in a cloud computing environment, ensuring the scalability of the framework's operation based on the fluctuation of the available resources in a microgrid or extending its operation to a cluster microgrids in a transparent way by the incorporation of IoT sensors or other tools. This doctoral thesis summarizes the framework research results and the published evidence released in one book, two journal papers, two international conferences, and one national conference.