Energy management is a crucial aspect for achieving energy efficiency within a Hybrid Renewable energy power station. Load being unbalanced through the day, a reasonable power management can avoid energy dissipation and unnecessary grid solicitation. This article presents an energy management strategy in a real case scenario of a hybrid windsolar power station in the ENSET campus. The approach manages energy provided by wind turbines and multiple photovoltaic panels, using a power bank as backup source. in this study actual data involving wind speed, solar radiation, load profile and energy generation was collected. Different scenarios were simulated in order to synthesize an efficient energy management and load balancing system with possible load forecasting capability. In all the simulated scenarios the study emphasizes a minimal solicitation of the grid.
To improve efficiency and productivity of electric energy generators based on photovoltaic, wind or hybrid systems; several DC/AC conversion techniques have been developed and tested like multilevel inverters. Multilevel inverters are a performant solution for the ramp-up of converters. As soon as the DC supply voltage exceeds a few kV, it is necessary to combine switches, switching cells or converters. This paper presents a progressive study of an interesting type of these inverters namely flying capacitor multilevel inverters (FCMLI): architecture, evolutions, benefits and inconvenient. In fact, we processed 3-and 5-level FCMLI while presenting possible circuit schemes and simulation results on Matlab Simulink. Finally, a general formulation has been adopted and applied to a 17 level FCMLI.
Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.