The expansion of electric microgrids has led to the incorporation of new elements and technologies into the power grids, carrying power management challenges and the need of a well-designed control architecture to provide efficient and economic access to electricity. This paper presents the development of a flexible hourly day-ahead power dispatch architecture for distributed energy resources in microgrids, with cost-based or demand-based operation, built up in a multi-class Python environment with SQLExpress and InfluxDB databases storing the dispatcher and microgrid data, and its experimental implementation using Modbus communication. The experimental power dispatch architecture is described and each operation stage is detailed, including the considered mathematical models of the energy resources, the database management, the linear-programming optimization of power dispatch, and the Modbus setpoint writing. Validation studies of the proposed control system are presented for real-time digital-simulated devices and physical resources as a real application at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The simulated and physical microgrid characteristics are described and the hourly dispatch results for generation, storage and load devices are presented, standing out as a reliable power management architecture for economic commitment and load peak shaving in simulated and real microgrids. The proposed architecture demonstrates its readiness for present and future electrical system challenges, effectively incorporating meteorological variations, renewable energy sources, and power demand fluctuations into the control framework, with a strong dependence on the quality of the meteorological forecast.

Microgrids deal with challenges presented by intermittent distributed generation, electrical faults and mode transition. To address these issues, to understand their static and dynamic behavior, and to develop control systems, it is necessary to reproduce their stable operation and transient response through mathematical models. This paper presents a data-driven low-order model identification methodology applied to voltage characterization in a photovoltaic system of a real campus microgrid for secondary voltage regulation. First, a literature review is presented focusing on secondary voltage modeling strategies and control. Then, experimental data is used to estimate and validate a low-order MIMO (multiple input–multiple output) model of the microgrid, considering reactive power, solar irradiance, and power demand inputs and the voltage output of the grid node. The obtained model reproduced the real system response with an accuracy of 88.4%. This model is used for dynamical analysis of the microgrid and the development of a secondary voltage control system based on model predictive control (MPC). The MPC strategy uses polytopic invariant sets as terminal sets to guarantee stability. Simulations are carried out to evaluate the controller performance using experimental data from solar irradiance and power demand as the system disturbances. Successful regulation of the secondary voltage output is obtained with a fast response despite the wide range of disturbance values.

Due to the increasing size of electric power systems and its monitoring and operation needs, people at control rooms face complex situations, analyzing great amounts of information, protocols, operation states, alarms, and control parameters. Additionally, new renewable energy sources data is taking its place nowadays, hindering the process of quickly reading and interpreting information. This is known as the loss of Situational Awareness, SA, of system operators. This work presents proposals for displaying useful information in control rooms and visualization techniques to support accurate SA. Grid data displays, maps, trending graphics, lists, bars graphics, table plots, hierarchical levels, proximity regions, and connections are also proposed and analyzed, presenting an application case in a microgrid control room.