Linear Quadratic Regulator and Fuzzy Control for Grid-Connected Photovoltaic Systems

Mukhatov, Azamat; Thao, Nguyen Gia Minh; Duc, Ton

doi:10.3390/en15041286

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…In future work, the HEMS and optimization algorithm considered in this paper will be appropriately combined with the modified interval fuzzy model in [44] and fuzzy-based control method in [45] in order to further improve the performance and effectiveness.…”

Section: Discussionmentioning

confidence: 99%

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

Tran,

Ton-That,

Thao

2023

Electronics

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The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a self-managed system that encompasses these energy resources as well as interconnected consumers. It has the flexibility to function in both isolated and grid-connected configurations. This study aims to design an effective method of power management for a MG in the two operating modes. The proposed optimization model seeks to strike a balance between energy usage, protecting the life of batteries, and maximizing economic benefits for users in the MG, with consideration of the real-time electricity price and constraints of the power grid. Furthermore, in order to accurately account for the dynamic nature of not only the stationary battery banks used as the energy storage systems (ESS) but also the built-in batteries of electric vehicles (EVs), the model is presented as a multi-objective, multiparametric and constrained problem. The solution is proposed to be found using the Lagrange multiplier theory, which helps to achieve good performance with less computational burden. Lastly, simulation results from both the isolated and grid-connected modes also demonstrate the effectiveness of the designed method.

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Section: Discussionmentioning

confidence: 99%

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

Tran,

Ton-That,

Thao

2023

Electronics

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Optimal tracking for PV three-phase grid-connected inverter with LC filter

Al-Abri,

Shadoul,

Yousef

et al. 2024

Franklin Open

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A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System

Nouri

Lachheb

Amraoui

2023

EEENG

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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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Linear Quadratic Regulator and Fuzzy Control for Grid-Connected Photovoltaic Systems

Cited by 4 publications

References 39 publications

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

Optimal tracking for PV three-phase grid-connected inverter with LC filter

A Comparative Study of the Performances of the LQR Regulator versus the PI Regulator for the Control of a Battery Storage System

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