Distributed Load Frequency Control Research Articles

Robust Distributed Load Frequency Control for Multi-Area Power Systems with Photovoltaic and Battery Energy Storage System

The intermittent power generation of renewable energy sources (RESs) interrupts the balance between power generation and demand load due to the increased frequency fluctuation, which challenges the frequency stability analysis and control synthesis of power generation systems. This paper proposes a robust distributed load frequency control (DLFC) scheme for multi-area power systems. Firstly, a multi-area power system is constructed by integrating photovoltaic (PV) and battery energy storage systems (BESSs). Then, by employing the linear matrix inequality (LMI) technique, the sufficient condition capable of ensuring that the proposed controller satisfies H∞ robust performance in the sense of asymptotic stability is derived. Finally, testing is conducted on a four-area renewable power system, and results verify the strong robustness of the proposed controller against load disturbance and intermittence of RESs.

Data-Driven Fully Distributed Load Frequency Control for an IoT-Based Interconnected Grid Considering a Performance-Based Frequency Regulation Market

Data-Driven Fully Distributed Load Frequency Control for an IoT-Based Interconnected Grid Considering a Performance-Based Frequency Regulation Market

Bio-inspired distributed load frequency control in Islanded Microgrids: A multi-agent deep reinforcement learning approach

To prevent errors in load frequency control (LFC) decision-making in an islanded microgrid and reduce the waste of frequency regulation resources, a fully distributed “starfish” load frequency control method is proposed. For this method, a novel deep reinforcement learning algorithm called the distributed decomposed multirole multiagent deep deterministic policy gradient (DDMR-MADDPG) algorithm is introduced. This algorithm treats each unit as an agent, enabling centralized training of all the agents. Through the coordination and control of multiple agents, a global optimal strategy can be obtained. During online application, the algorithm employs a distributed implementation strategy, enabling each unit to make decisions autonomously. This decision-making strategy is akin to the distributed neural network of a starfish, and it can fundamentally improve the efficiency and correctness of decision-making. In addition, it employs multiple techniques, including value function decomposition, multirole learning, imitation learning, and curriculum learning approaches. The performance of the proposed method compared with 24 existing methods is demonstrated on a simulated LFC model of the Zhuzhou Island microgrid in China.

Robust Distributed Load Frequency Control for Multiarea Wind Energy-Dominated Microgrids Considering Phasor Measurement Unit Failures

Robust Distributed Load Frequency Control for Multiarea Wind Energy-Dominated Microgrids Considering Phasor Measurement Unit Failures

Detection and Defense Method Against False Data Injection Attacks for Distributed Load Frequency Control System in Microgrid

Detection and Defense Method Against False Data Injection Attacks for Distributed Load Frequency Control System in Microgrid

Event-Based Distributed Fuzzy Load Frequency Control for Multiarea Nonlinear Power Systems With Switching Topology

This article investigates the event-based distributed fuzzy load frequency control approach for nonlinear interconnected power systems under a switching topology. Specifically, nonlinear factors and uncertain parameters existing in turbine and governor dynamics are modeled and analyzed under the interval type-2 fuzzy method. New memory-based adaptive event-triggered mechanisms, whose directions and threshold parameters are dynamically updated based on the output change trend and relative output error of multiple memories, are proposed to further save network energy. Aiming at communication topology changes in multiarea power systems, a novel switched control system is established by introducing a time-varying communication matrix. Then, the system’s asymptotic stability is ensured by the piecewise Lyapunov–Krasovskii method. Furthermore, to further relax stability conditions, slack matrices are considered with the boundary information of membership functions. Finally, three-area power systems are shown to demonstrate the effectiveness of proposed distributed load frequency control strategies.

Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control

The rise of microgrid-based architectures is modifying significantly the energy control landscape in distribution systems, making distributed control mechanisms necessary to ensure reliable power system operations. In this article, the use of Reinforcement Learning techniques is proposed to implement load frequency control (LFC) without requiring a central authority. To this end, a detailed model of power system dynamic behaviour is formulated by representing individual generator dynamics, generator rate and network constraints, renewable-based generation, and realistic load realisations. The LFC problem is recast as a Markov Decision Process, and the Multi-Agent Deep Deterministic Policy Gradient algorithm is used to approximate the optimal solution of all LFC layers, that is, primary, secondary and tertiary. The proposed LFC framework operates through centralised learning and distributed implementation. In particular, there is no information interchange between generating units during operation. Thus, no communication infrastructure is necessary and information privacy between them is respected. The proposed framework is validated through numerical results and it is shown that it can be used to implement LFC in a distributed and cost-efficient manner.

Credibility-based distributed frequency estimation for plug-in electric vehicles participating in load frequency control

The accurate, robust and economic acquisition of frequency signals of plug-in electric vehicles (PEVs) is a vital prerequisite to regulate the frequency of power systems with the participation of a large number of PEVs. While the communication links among PEVs are vulnerable to cyber attacks, we propose a credibility-based distributed frequency estimation scheme to detect and isolate compromised PEVs participating in the frequency regulation. In this scheme, each PEV can distinguish its neighbors’ misbehavior based on the distributed frequency estimation and/or frequency measurements taken only at distribution substations. Then, a distributed load frequency control (DLFC) strategy is designed to integrate the credibility-based resilient distributed frequency estimation scheme. Tests under a variety of communication topologies are conducted to verify the proposed credibility-based distributed frequency estimation scheme. Simulation results of a four-area power system validate the proposed DLFC strategy after detecting and isolating the compromised PEVs can regulate the frequency quickly when suffering load variations.

Credibility‐based secure distributed load frequency control for power systems under false data injection attacks

In the distributed load frequency control (DLFC) scheme, the compromised areas by attackers may endanger the stable and reliable operation of the entire power system. A credibility-based secure DLFC strategy is in this work proposed to sustain the stable operation of the power system under false data injection attacks. In this secure DLFC method, the gradientvariation- based credibility metrics are defined to detect potential attacked measurements. Then, the communication isolation decisions are made based on the adaptive thresholds. To compensate the impact of the communication isolation of misbehaving areas, predictions of future measurements by using the average gradient variation of stored healthy data are feeded back to the area controller. Simulation results of a four-area power system show the proposed secure DLFC can identify and cut off the communication of the misbehaving areas and still maintain the stability of the power system.

Impact of communication delay on distributed load frequency control (dis-LFC) in multi-area power system (MAPS)

AIn this paper, impact of communication delay on distributed load frequency control (dis-LFC) of multi-area interconnected power system (MAIPS) is investigated. Load frequency control (LFC), as one of ancillary services, is aimed at maintaining system frequency and inter-area tie-line power close to the scheduled values, by load reference set-point manipulation and consideration of the system constraints. Centralized LFC (cen-LFC) requires inherent communication bandwidth limitations, stability and computational complexity, as such, it is not a good technique for the control of large-scale and geographically wide power systems. To decrease the system dimensionality and increase performance efficiency, distributed and decentralized control techniques are adopted. In distributed LFC (dis-LFC) of MAIPS, each control area (CA) is equipped with a local controller and are made to exchange their control actions by communication with controllers in the neighboring areas. The delay in this communication can affect the performance of the LFC scheme and in a worst case deteriorates power system stability. To investigate the impact of this delay, model predictive controller (MPC) is employed in the presence of constraints and external disturbances to serve as LFC tracking control. The scheme discretizes the system and solves an on-line optimization at each time sample. The system is subjected to communication delay between the CAs, and the response to the step load perturbation with and without the delay. Time-based simulations were used on a three-area MAIPS in MATLAB/SIMULINK environment to verify the investigations. The overshoot and settling time in the results reveals deterioration of the control performance with delay. Also, the dis-LFC led to zero steady states errors for frequency deviations and enhanced the MAIPS’ performance. With this achievement, MPC proved its constraints handling capability, online rolling optimization and ability to predict future behavior of systems.

Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems

How to design a set of optimal distributed load frequency controllers for a multi-area interconnected power system is an important but still challenging issue in the field of modern electric power systems. This paper presents an adaptive population extremal optimization-based extended distributed model predictive load frequency control method called PEO-EDMPC for a multi-area interconnected power system. The key idea behind the proposed method is formulating the dynamic load frequency control issue of each area power system as an extended distributed discrete-time state-space model based on an extended state vector, obtaining a distributed dynamic extended predictive model, and rolling optimization of real-time control output signal by adopting an adaptive population extremal optimization algorithm, where the fitness is evaluated by the weighted sum of square predicted errors and square future control values. The superiority of the proposed PEO-EDMPC method to a traditional distributed model predictive control method, a population extremal optimization-based distributed proportional-integral control algorithm and a traditional distributed integral control method is demonstrated by the simulation studies on two-area and three-area interconnected power systems in cases of normal, perturbed system parameters and dynamical load disturbances.

Distributed load frequency control in an interconnected power system using ecological technique and coefficient diagram method

This paper presents a load frequency control (LFC) design, using both of coefficient diagram method (CDM) and ecological optimal technique (ECO) in a multi-area power system. The proposed controller has been designed to reduce the effect of uncertainties owing to variations in the parameters of governors and turbines as well as load disturbance. Each local area controller is designed independently, such that, stability of the overall closed-loop system is guaranteed. The CDM structure is built on the frequency response model of multi-area power system, and physical constraints of the governors and turbines are considered. Also, the standard Kalman filter technique has been employed to estimate the full states of the system including the area frequency deviation, these states has been employed by the (ECO) state feedback optimal controller to produce the optimal control signal. Digital simulations for three-area power systems are provided to validate the effectiveness of the proposed scheme. From the simulation results, it is shown that, considering the overall closed-loop system performance with the proposed CDM+ECO technique, robustness is demonstrated in the face of uncertainties due to governors, turbines parameters variation and loads disturbances. A performance comparison between the proposed controller, CDM alone, and a classical integral controller (I) scheme is carried out, confirming the superiority of the proposed CDM+ECO technique.

Application of Linear Quadratic Gaussian and Coefficient Diagram Techniques to Distributed Load Frequency Control of Power Systems

This paper presented both the linear quadratic Gaussian technique (LQG) and the coefficient diagram method (CDM) as load frequency controllers in a multi-area power system to deal with the problem of variations in system parameters and load demand change. The full states of the system including the area frequency deviation have been estimated using the Kalman filter technique. The efficiency of the proposed control method has been checked using a digital simulation. Simulation results indicated that, with the proposed CDM + LQG technique, the system is robust in the face of parameter uncertainties and load disturbances. A comparison between the proposed technique and other schemes is carried out, confirming the superiority of the proposed CDM + LQG technique.