To meet demand with unpredictable daily and seasonal variations, the power grid faces significant hurdles in transmission and distribution. Electrical Energy Storage (EES), in which energy is stored in a specific state, depending on the technology utilized, and is converted to electrical energy, is acknowledged as a technology involved with significant potential for solving these difficulties. This paper deals with the modeling and control of a renewable energy production system based on solar panel. To improve the performance of the investigated power generation system, a lithium-ion battery storage system and bidirectional converter are associated to a solar panel that is unable to compensate for rapid variations in load power demand. In this situation, to meet load power demand, a rule-based energy management algorithm is used to share energy between the grid and the energy production system. Furthermore, two solutions are developed and compared: VC (Variable Current) and CC-CV (Constant Current Constant Voltage). The VC approach is used in conjunction with an energy management and protection system, whereas the CC-CV method is used in conjunction with an artificial neural network (ANN). The simulation results show that the VC control strategy give greater energy performance and installation stability compared to the CC-CV strategy, but not improved safety and protection of lithium-ion batteries.
This paper is consecrated to the development of a new approach to control a bidirectional DC-DC converter dedicated to battery storage systems by applying an optimal control based on a linear quadratic regulator (LQR) combined with an artificial neural network (ANN) algorithm. A state representation of the Buck-boost converter is performed. Then the ANN-LQR control strategy is compared to a classical control based on the proportional-integral controller combined with an ANN algorithm. The ANN algorithm generates the reference charging or discharging current based on a comparison between the power generated and the power consumed. In order to obtain an accurate comparison, two identical systems are designed, each consisting of a photovoltaic system optimized by an incremental conductance algorithm (INC) that powers a dynamic load and a backup storage system consisting of a lithium-ion battery. A management and protection algorithm is developed to protect the batteries from overcharge and deep discharge and to manage the load availability on the DC bus. The simulation results show an improvement in the performances of the storage system by the ANN-LQR control compared to the ANN-PI method and an increase in the stability, accuracy, efficiency of the system is observed. Background: Photovoltaic (PV) energy is one of the most promising technologies for combating climate change and meeting the urgent need for green renewable energy and long-term development. PV energy generation has a number of advantages: Solar energy is limitless and available anywhere on the planet. However, photovoltaic energy is intermittent and depends on meteorological conditions; also, the energy consumed is unpredictable. For this reason, a storage system is necessary to overcome these problems. Objective: The objective of this study is to develop an optimal control using a Linear Quadratic Regulator (LQR) combined with a neural network algorithm (ANN) to improve the performance of an electrical energy storage system and compare the results obtained with the classical control based on the PI regulator. Method: The state representation of the bidirectional Buck-boost converter was performed in order to apply the optimal control and determine the gain K and the ANN algorithm allowed to determine the charge and discharge current according to a comparison between the power produced and consumed. Results: The simulation results obtained by two control methods can be used to compare and select the appropriate control method to achieve optimal efficiency of the storage system. Conclusion: The combined ANN-LQR technique offer better performances and stability of the installation compared to the ANN-PI controller.
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