Given the high stochasticity of solar irradiance, the accurate forecasts of photovoltaic plants production can contribute to correctly manage power systems resources. In microgrids and grids, the availability of thorough forecasts allow to suitably operate generation, load and storage resources, avoiding grid imbalance conditions. In this work authors investigate the possibility to employ a Machine Learning approach that leverages the PV power forecast obtained by weather model and past observed generated power data. The proposed approach permits to obtain at least 3.7% improvement in photovoltaic production forecasting, demonstrating the methodology effectiveness.
This paper proposes and studies a DC power system with SMES and ultracapacitor energy storage modules and their control system for an electric ship. The system allows the storage modules to primarily regulate the voltage of the ship DC switchboard against high fluctuation caused by any transient loads onboard. As such, the ship battery as prime mover only provides the average component of the total dynamic power, which can be far below the peak power supplied frequently to the load. This procedure would eventually lead to extending the lifespan of the battery and to the deferral of its costly replacement. The paper also covers comparison of the power system efficiency when connecting the energy storage modules. The dynamic results, obtained via real-time hardware-in-the-loop simulation and control implementation, have validated the concept, and helped to assess the design characteristics and system performance.
Proper integration of Fuel Cells in DC microgrids requires meticulous current control of both the input and output of the power converter. A two - boost - buck converter topology was introduced in the literature for Fuel Cell applications; however, some limitations are to be pointed out in terms of a practical and easy-to-implement current control. This paper intends to propose an output current control for the two-switch boost-buck converter based on a cascade connection of digital PI controllers. Throughout the paper, previously published works are mentioned, and the control architecture is explained. Simulation results are presented, and the main conclusions are drawn.
In this paper, a modular distributed battery energy storage system for DC shipboard microgrids configuration is described. A modular solution with a battery-converter bank is analyzed, and a management system with a hierarchical control is investigated. The state of charge control is implemented for the modular battery-converter solution to achieve the module’s voltage and current balance. Several simulation results in waterborne transport applications are carried out to demonstrate the effectiveness of the modular solution and state of charge control approach presented.
The transition to clean energy sources, and the integration of renewable energy-based decentralized generation units (DG) in distribution grids have led to the development of smart grids. These intelligent power grids employ advanced technologies such as information and communication systems to manage the bidirectional flow of electricity and to stabilize the grid state variables. The Clustering Power System Approach (CPSA), together with the Cluster Fractal Model (CFM), provides a structured and decentralized implementation of smart grids. The Smart Grid Cluster Controller (SGCC) is a crucial component for operating and controlling smart grids based on CPSA. The SGCC is equipped with a software system that allows for network control, and it is connected to the power grid via sensors and actuators using the real-time EtherCAT Fieldbus master. The EtherCAT master is coupled with a NoSQL database for topology description and a time series database for process data storage. Real-time communication between decentralized controllers is achieved through a novel approach. Based on this solution, a structured, organized, and decentralized automation can be enabled, including data exchange between clusters.
