Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems

Li, Hongyue; Wang, Xihuai; Xiao, Jiang‐Wen

doi:10.3390/en11071686

Cited by 18 publications

(12 citation statements)

References 41 publications

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“…The dynamic responses of the system with and without the action of the controller along with the comprehensive comparison for the proposed DEO-ANN controller and the well-known benchmark classical PID controller is shown in Figures 12-25. In fact, the classical PID controller is widely used as a benchmark controller, and many papers in the literature compare the performance of their proposed controllers with the performance of the classical PID controller [34][35][36][37]. The PID block in MATLAB SIMULINK was utilized which had the control formula shown in Equation (18).…”

Section: Resultsmentioning

confidence: 99%

“…However, the performance of the above two proposed energy storage-based controllers was not satisfactory. In Reference [36], a robust control approach based on structural singular value theory was employed in order to design the MG frequency controller. The SCESS based controller gains were optimized using differential evolution optimization (DEO) techniques.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids

2019

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Modern power systems rely on renewable energy sources and distributed generation systems more than ever before; the combination of those two along with advanced energy storage systems contributed widely to the development of microgrids (MGs). One of the significant technical challenges in MG applications is to improve the power quality of the system subjected to unknown disturbances. Hence innovative control strategies are vital to cope with the problem. In this paper, an innovative online intelligent energy storage-based controller is proposed to improve the power quality of a MG system; in particular, voltage and frequency regulation at steady state conditions are targeted. The MG system under consideration in this paper consists of two distributed generators, a diesel synchronous generator, and a photovoltaic power system integrated with a battery energy storage system. The proposed control approach is based on hybrid differential evolution optimization (DEO) and artificial neural networks (ANNs). The controller parameters have been optimized under several operating conditions. The obtained input and output patterns are consequently used to train the ANNs in order to perform an online tuning for the controller parameters. Finally, the proposed DEO-ANN methodology has been evaluated under random disturbances, and its performance is compared with a benchmark controller.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids

2019

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“…Li, Wang and Xiao [26] investigated the secondary load frequency controller of the power systems with renewable energies taking into account internal parameter perturbations and stochastic disturbances induced by the integration of renewable energies and the power unbalance caused between the supply and the demand side. The robust µ-synthesis approach was used for load frequency control in a microgrid power system.…”

Section: Brief Overview Of the Contributions To This Special Issuementioning

confidence: 99%

Special Issue “Intelligent Control in Energy Systems”

Dounis

2019

Energies

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The editor of this special issue on "Intelligent Control in Energy Systems" have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (and Czech Republic), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust µ-synthesis for microgrid, and neuro-fuzzy systems in energy storage.

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“…In [17], the robust load frequency controller considered the H2-norm index and H∞-norm index is proposed. In [18]- [20], the load frequency controller based on robust µ-synthesis theory is presented. The controllers designed by robust control theory have strong robust stability and excellent robust performance, but the orders of the controller are too high, and difficulty to realization.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Event-Triggered Load Frequency Control for Interconnected Microgrids by Observer-Based Sliding Mode Control

Wang

Xiao

2019

IEEE Access

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This paper focuses on the load frequency control (LFC) for a multi-area interconnected microgrid power system with the introduction of communication networks, a robust sliding mode control strategy based on the adaptive event-triggered mechanism is proposed against the frequency deviation caused by power unbalance or time delays. First, a three-area power system attached to renewable energies and energy storage is considered, the corresponding LFC model is established. Second, the networked control is introduced into the LFC scheme, and the adaptive trigger mechanism which can adaptively adjust the event-triggered threshold is designed to improve the data transmission efficiency, the LFC scheme with network induced delays is formulated. Third, the Luenberger observer is designed to estimate the state errors and to facilitate the design of the sliding surface, the overall closed-loop system asymptotically stable and robust performance are analyzed by solving linear matrix inequalities. Then the control law is proved that the system state trajectory can be driven into the designed sliding surface in a limited time. Finally, some simulation experiments are given, and the results show that the proposed method is effective and has excellent robust performance. INDEX TERMS Microgrid power system, load frequency control, event-triggered control, robust performance, sliding mode observer.

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Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems

Cited by 18 publications

References 41 publications

An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids

An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids

Special Issue “Intelligent Control in Energy Systems”

Adaptive Event-Triggered Load Frequency Control for Interconnected Microgrids by Observer-Based Sliding Mode Control

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