Identification-based power unit model for load-frequency control purposes

Hain, Y.; Kulessky, R.; Nudelman, G.

doi:10.1109/59.898107

Cited by 38 publications

(8 citation statements)

References 14 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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“…Over the years, various mathematical and intelligent control strategies such as optimal control [3]- [4], robust control [5]- [8], distributed control [9]- [11], soft computing model [12]- [16], and reinforcement learning [19]- [23] have been developed in literatures as existing AGC solutions. However, despite many works on AGC strategies, optimal activation rule for AGC signal are rarely investigated.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control

et al. 2019

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This paper proposes a novel hybrid intelligent approach to solve the dynamic optimization problem of activation rules for automatic generation control (AGC) based on multiobjective reinforcement learning (MORL) and small population-based particle swarm optimization (SPPSO). The activation rule for AGC is to dynamically allocate the AGC regulating commands among various AGC units, and subsequently, the secondary control reserve of those units can be activated. Therefore, the activation rule for AGC is vital to ensure the overall control performance of AGC schemes. In this paper, MORL is applied to provide a customized platform for interactive self-learning to maximize the long-run discounted reward, i.e., minimize the generation cost, regulating error, and emission from a long-term viewpoint. SPPSO is utilized to effectively and efficiently obtain the optimality of activation rule with a fast convergence speed to fulfill the real-time requirement of AGC activation. Furthermore, a novel analytic hierarchy processbased coordination factor is introduced to identify the optimum multi-objective tradeoff in various power system operation scenarios. At last, the validation of the proposed hybrid method has been demonstrated via comprehensive tests using practical data from the dispatch center of China Southern Power Grids.

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“…For the secondary control problem, many solution approaches have been proposed including optimal control [12][13][14][15] or adaptive controllers and robust controllers [16,17]. Often these approaches are combined with state observers [18] and system identification techniques [19]. Proactive action in the LFC can improve its performance and MPC is a control strategy enabling for it.…”

Section: Introductionmentioning

confidence: 99%

Load–frequency control in microgrids using target‐adjusted MPC

Banis

Guericke

Madsen

et al. 2019

IET Renewable Power Generation

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Model predictive control (MPC) has been applied in multiple ways to the load-frequency control problem. In this study, the authors illustrate and compare a target-adjusted MPC to a classical MPC formulation. The target-adjusted approach is also posed as optimal control law. The target-adjusted MPC is an alternative formulation that incorporates the system equilibrium into the control objective. The alternative derived controller can be used as substitute for classical MPCs. GELSD Lapack routine for solving least-squares problems c0 classic quadratic regulator formulation c1 target-adjusted regulator formulation

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Identification-based power unit model for load-frequency control purposes

Cited by 38 publications

References 14 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

Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control

Load–frequency control in microgrids using target‐adjusted MPC

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