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Wu, X.; Tang, Z.; Stroe, D.-I.; Kerekes, T. Overview and Comparative Study of Energy Management Strategies for Residential PV Systems with Battery Storage. Batteries 2022, 8, 279. https://doi /10.3390/batteries8120279
Wu X, Tang Z, Stroe D-I, Kerekes T. Overview and Comparative Study of Energy Management Strategies for Residential PV Systems with Battery Storage. Batteries. 2022; 8(12):279. https://doi /10.3390/batteries8120279
Wu, Xiangqiang, Zhongting Tang, Daniel-Ioan Stroe, and Tamas Kerekes. 2022. "Overview and Comparative Study of Energy Management Strategies for Residential PV Systems with Battery Storage" Batteries 8, no. 12: 279. https://doi /10.3390/batteries8120279
Wu, X., Tang, Z., Stroe, D. -I., & Kerekes, T. (2022). Overview and Comparative Study of Energy Management Strategies for Residential PV Systems with Battery Storage. Batteries, 8(12), 279. https://doi /10.3390/batteries8120279
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Himabindu et al. have developed an optimal energy management strategy. The main objective of the research was to satisfy the power demand by the load and to maintain the state of the charge of the energy storage systems, which included the battery and the hydrogen in a certain range. The results have shown that the method was able to optimize the utilization costs and the lifespan of the energy storage systems. The authors suggested the utilization of other methods to check the performance of the proposed energy management system and also include the grid-connected mode [8].
Kang et al. [9] have proposed a small-scale hybrid AC/DC microgrid energy management technique based on Artificial Neural Network control. An EMS operation mode was chosen, an operation profile was chosen in each operation mode, and then ANN training was implemented in each operation mode in order to develop the proposed EMS.
Bilbao et al. [11] focused on the optimal operation of hybrid microgrids, based generally on the mathematical modelling. To implement the management of this type of networks requires several challenges and various options. The main contribution of the research was the mathematical modelling of many hybrid microgrid components. This modelling can be utilized in various control and management methods of the network, and its feasibility has been shown in three methods, which include one based on the Decision Tree method, which belongs to the Machine Learning family. The results on a test system of 69 buses showed that it is possible to implement its management.
A new hybrid battery and PV-wind turbine power system was proposed by Reddy et al. [12] using Fuzzy Logic control. The dynamic behaviour of the recommended model was examined under various operating conditions. The developed system and its control strategy displayed exceptional performance. The proposed model offers a useful tool for enhancing the efficiency of the smart grid.
A hybrid micro-grid is composed of different distributed generation sources; the power from these DGs is collected, converted and distributed based on the load demands. To assure an effective operation of the system, a control strategy is required and it is important when power electronics interface with the system to constitute a single unit. The control system is very important because it also enables to conserve the specific energy supply and the power quality [17]. Figure 1 gives a typical representation of a hybrid micro-grid system, where it can be seen that a micro-grid could also be interconnected with the main grid and includes a diversity of assets and power sources, which provide different services to a range of facilities.
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