Optimum Load-Frequency Sampled-Data Control with Randomly Varying System Disturbances

Bohn, E. V.; Miniesy, Samir M.

doi:10.1109/tpas.1972.293519

Cited by 45 publications

(7 citation statements)

References 5 publications

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“…In last decade, modern optimal control theory is used for designing load frequency controller that can optimally control both frequency and tie-line power deviations. Some LFC schemes based on modern optimal control theory are presented in [190][191][192][193][194][195][196]. In [197], optimal linear regulator theory is used to design a linear regulator for load frequency control in power systems.…”

Section: Optimal Control Methodsmentioning

confidence: 99%

“…The prescribed requirements of optimal control theory makes this theory unrealistic in some cases, but with the developments in the dynamic state estimation methods, the unavailable states can be obtained by well-designed observers. Many state estimation construction methods have been introduced in [190][191][192][193][194][195]. State estimator with decaying error using a nonlinear transformation based on optimal observer for AGC schemes is presented in [194].…”

Section: Optimal Control Methodsmentioning

confidence: 99%

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Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

et al. 2018

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Power systems are the most complex systems that have been created by men in history. To operate such systems in a stable mode, several control loops are needed. Voltage frequency plays a vital role in power systems which need to be properly controlled. To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems. Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance. Likewise, the power exchanges between different control areas are controlled by LFC approaches. In recent decades, many control approaches have been suggested for LFC in power systems. This paper presents a comprehensive literature survey on the topic of LFC. In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems. Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups. The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC.Energies 2018, 11, 2497 2 of 35 before triggering the under/over frequency protection relays. The primary frequency control is usually implemented by the governor droop which results in steady stated errors. The secondary frequency control, which is called load frequency control (LFC) or automatic generation control (AGC), is responsible for regulating the frequency in power systems and has two main goals: (i) maintaining the frequency into a desirable range; and (ii) controlling the interchange power through major tie-lines between the different control areas. The main task of tertiary control level is re-dispatching generating units and ancillary reserve after a sever disturbance.With increasing the penetration level of renewable resources such as wind farms and photovoltaic plants in power systems, the uncertainties of active power production is highly increased, thus determining frequency variations. Such increase in active power fluctuation, beside the demand stochasticity, the frequency of power system would be highly oscillated. Therefore, future power systems need more robust and optimal LFC approaches that can handle such problems.Many control approaches have been suggested for LFC in interconnected power systems. These approaches can be categorized into four groups: (i) classical control approaches focus on designing proportional-integral-derivative (PID) controllers for controlling the frequency and tie-lines power flows; (ii) modern control approaches including optimal control method, sliding mode control schemes, and adaptive control systems; (iii) intelligent control schemes, such as fuzzy control systems; and (iv) soft computing-based approaches for controllers' parameter tuning which had a considerable attention from researchers in the last decade. Survey MethodologySeveral methodologies can be followed for conduc...

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Section: Optimal Control Methodsmentioning

confidence: 99%

Section: Optimal Control Methodsmentioning

confidence: 99%

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

et al. 2018

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“…At present, to solve these two problems, scholars have proposed many control strategies. For calculating the total adjustment power, proposed strategies include the classical proportional−integral (PI) control (Concordia and Kirchmayer, 1953), proportional−integral-derivative (PID) control (Sahu et al, 2015;Dahiya et al, 2016), optimal control (Bohn and Miniesy, 1972;Yamashita and Taniguchi, 1986;Elgerd and Fosha, 2007), adaptive control (Talaq and Al-Basri, 1999;Olmos et al, 2004), model predictive control (Atic et al, 2003;Mcnamara and Milano, 2017), robust control (Khodabakhshian and Edrisi, 2004;Pan and Das, 2016), variable structure control (Erschler et al, 1974;Sun, 2017), and intelligent control technologies such as neural network (Beaufays et al, 1994;Zeynelgil et al, 2002), fuzzy control (Talaq and Al-Basri, 1999;Feliachi and Rerkpreedapong, 2005), and genetic algorithm (Abdel-Magid and Dawoud, 1996;Chang et al, 1998). In terms of allocating total adjustment power to each AGC unit, a baseline allocation approach is proposed according to the adjustable capacity ratio and installed capacity ratio of each unit without considering the differences of dynamic characteristic among units.…”

Section: Introductionmentioning

confidence: 99%

An AGC Dynamic Optimization Method Based on Proximal Policy Optimization

Liu

Li²,

Zhang

et al. 2022

Front. Energy Res.

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The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.

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“…DeMello et aI [3,4] have presented digital control technique for AGC and have considered a sampling period of 2 seconds. Bohn and Miniesy [5] have studied the effect of sampling period on system dynamic performance using linear discrete time optimal control strategy for a single area nonreheat thermal system connected to an infinite bus. Their analysis reveals that there is no significant deterioration in dynamic performance by using sampling rate of one second with discrete optimal controller as compared to the one obtained with continuous time controller.…”

Section: Introductionmentioning

confidence: 99%

Selection of Sampling Period for Automatic Generation Control

Kothari

Nanda

Hari

1997

Electric Machines & Power Systems

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The paper presents the analysis of automatic generation control (AGC) of a twoequal area reheat thermal system in the presence of generation rate constraints considering a discrete-continuous time mathematical model. The effect of variation of sampling period on optimum integral gain setting and system dynamic performance has been analyzed considering supplementary controllers based on conventional area control errors (ACEs) and new area control errors (ACENs). Investigations reveal that the optimum integral gain setting and system dynamic performance are hardly affected over a wide range of sampling period T for controllers based on conventional ACEs. Studies also reveal that the permissible sampling period is further enhanced with integral controllers based on new area control errors (ACENs).

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Optimum Load-Frequency Sampled-Data Control with Randomly Varying System Disturbances

Cited by 45 publications

References 5 publications

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

An AGC Dynamic Optimization Method Based on Proximal Policy Optimization

Selection of Sampling Period for Automatic Generation Control

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