This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life.
The self-discharge phenomenon results in a decrease of the open-circuit voltage (OCV), which occurs when an electrochemical device is disconnected from the power source. Although the self-discharge phenomenon has widely been investigated for energy storage devices such as batteries and supercapacitors, no previous works have been reported in the literature about this phenomenon for electrolyzers. For this reason, this work is mainly focused on investigating the self-discharge voltage that occurs in a proton exchange membrane (PEM) electrolyzer. To investigate this voltage drop for modeling purposes, experiments have been performed on a commercial PEM electrolyzer to analyze the decrease in the OCV. One model was developed based on different tests carried out on a commercial-400 W PEM electrolyzer for the self-discharge voltage. The proposed model has been compared with the experimental data to assess its effectiveness in modeling the self-discharge phenomenon. Thus, by taking into account this voltage drop in the modeling, simulations with a higher degree of reliability were obtained when predicting the behavior of PEM electrolyzers.
An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used.
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