Load Frequency Control Model Research Articles

Dropout Deep Neural Network Assisted Transfer Learning for Bi-Objective Pareto AGC Dispatch

To balance the unexpected power disturbances, an independent system operator (ISO) should assign the dynamic power regulation commands to all the regulation resources via an automatic generation control (AGC) dispatch. It can be described as a bi-objective Pareto optimization by considering the minimizations of total power deviation and total regulation mileage payment. In this work, a novel dropout deep neural network assisted transfer learning (DDNN-TL) is proposed to rapidly approximate the high-quality Pareto optimal solutions for AGC dispatch. Firstly, the training data is generated from the Pareto optimal solutions and fronts obtained by various multi-objective optimization algorithms according to the anticipated total power regulation commands. Secondly, the network parameters of DDNN will be updated via an off-line training with these data at each frequency regulation service period. Finally, based on an efficient transfer learning with a correction of infeasible solutions, DDNN-TL can directly approximate the high-quality Pareto optimal solutions for on-line decision of AGC dispatch. Case studies of DDNN-TL for bi-objective Pareto AGC dispatch are carried out on a two-area load frequency control model and Hainan power grid of China Southern Power Grid (CSG), which demonstrates its superior performance on optimization speed and stability compared with other multi-objective optimization algorithms.

Event-triggering-based [formula omitted] load frequency control for multi-area cyber–physical power system under DoS attacks

In this work, the event-triggering-based resilient H∞ load frequency control (LFC) scheme under denial-of-service (DoS) attacks is developed for multi-area cyber–physical power system (MA-CPPS) with electric vehicles (EVs). In MA-CPPS, the sampled measurements are transmitted through the communication network, which may be subjected to the energy-limited DoS attack with a resilient index of maximum allowable continuous data losses. Meanwhile, a novel adaptive event-triggering mechanism (AETM) characterized by parameters disturbances and dynamic adjustment mechanism of triggering threshold can effectively reduce the occupation of network bandwidth and computational burden in practical operation of sampled-data LFC in MA-CPPS. Hence a sampled-data LFC model of considering energy-limited DoS attack and AETM is established firstly. Then based on a novel AETM transmission interval-dependent loop functional approach, a resilient H∞ state-feedback controller is designed with guaranteed H∞ performance and resilient index of energy-limited DoS attack. Finally, the effectiveness and superiority of our proposed scheme are verified by one-area and three-areas LFC CPPS.

Frequency stability analysis of power system with photovoltaic frequency regulation considering communication delay

Photovoltaic (PV) grid-connected will reduce the inertia of the system, which has an effect on power system frequency reliability. In order to clarify the frequency stability situation of power system when photovoltaic participates in frequency regulation, this paper first establishes the load frequency control (LFC) model of the power system with photovoltaic based on the analysis of the traditional LFC model of the power system. Secondly, based on the Pade approximation method, the communication delay in the control loop is linearized. The frequency stability of power system with photovoltaic participation in frequency regulation is characterized by system frequency steady-state error, feedback system sensitivity, and closed-loop system stability margin. According to the index calculation and theoretical derivation, the influence of system frequency regulation related parameters on frequency characteristics is analyzed. Finally, on the matlab/simulink simulation environment, the frequency stability methodological approach presented in this research is validated. The results of the simulations demonstrate the effectiveness of this analytical approach in assessing the frequency stability of a power system with photovoltaic frequency regulation while taking communication latency into account. It can provide guidance for the power grid’s secure operation and control when photovoltaics participate in frequency regulation.

Encrypted Price-based Market Mechanism for Optimal Load Frequency Control⋆

The global trend of energy deregulation has led to the market mechanism replacing some functionality of load frequency control (LFC). Accordingly, information exchange among participating generators and the market operator plays a crucial role in optimizing social utility. However, privacy has been an equally pressing concern in such settings. This conflict between individuals’ privacy and social utility has been a long-standing challenge in market mechanism literature as well as in Cyber-Physical Systems (CPSs). In this paper, we propose a novel encrypted market architecture that leverages a hybrid encryption method and two-party computation protocols, enabling the secure synthesis and implementation of an optimal price-based market mechanism. This work spotlights the importance of secure and efficient outsourcing of controller synthesis, which is a critical element within the proposed framework. A two-area LFC model is used to conduct a case study.

Comparison of PSO, DE and Hybrid DE-PSO for LFC of Wind Power Systems

Abstract—This paper investigates the performance of particle swarm optimization (PSO), differential evolution (DE), and hybrid particle swarm optimization- differential evolution (HDEPSO) for solving load frequency control (LFC) problems. Wind power systems LFC model is used to compare the performance of PSO, DE and HPSODE for solving LFC problems. All the simulation are carried out in MATLAB/SIMULINK environment. From the simulation results, it is noticeable that by designing LFC of wind power system using HPSODE, the overshoot and settling time of the wind power system can be reduced and accelerated.

A load frequency coordinated control strategy for multimicrogrids with V2G based on improved MA-DDPG

The capacity of a single microgrid is limited, so the multimicrogrid system (MMS) has been widely used to improve the reliability and stability. And the mobile energy storage unit electric vehicles (EVs) with vehicle-to-grid (V2G) capability can improve the flexibility of the microgrid frequency control. Considering the complex structure of the MMS, a load frequency control (LFC) strategy for multimicrogrids with V2G based on improved multiagent deep deterministic policy gradient (MA-DDPG) is proposed in this paper. Firstly, a multimicrogrid LFC model including distributed power generation, micro gas turbine (MT), EVs which consider the random behavior of EV users and the mobility of EVs as dynamic limitations is constructed, the contact relationship between the submicrogrid controllers is added. Secondly, the MA-DDPG algorithm is improved to significantly reduce transmission cost and computational complexity. Additionally, the state, action space and reward function of the multiagent controller is designed. Finally, the simulation results show that, compared with PID, Fuzzy and traditional MA-DDPG control, the improved MA-DDPG can ensure the overall stability of MMS through cooperative control in the face of various complex operating scenarios. And under the premise of ensuring the control effect, the loss caused by transmission is significantly reduced.

Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems

The ongoing pandemic due to novel coronavirus disease-2019 (COVID-19) has rapidly unsettled the health sector with a considerable fatality rate. The main factors that help minimize the spread of this deadly virus are the proper use of masks, social distancing and antibody growth rate in a person. Based on these factors, we propose a new nature-inspired meta-heuristic algorithm named COVID-19 Based Optimization Algorithm (C-19BOA). The proposed C-19BOA mimics the spread and control behavior of coronavirus disease centered on three containment factors: (1) social distancing, (2) use of masks, and (3) antibody rate. Initially, the mathematical models of containment factors are presented, and further, the proposed C-19BOA is developed. To ascertain the effectiveness of the developed C-19BOA, its performance is verified on standard IEEE mathematical benchmark functions for the minimization of these benchmark functions and convergence to the optimal values. These performances are compared with established bio-inspired optimization algorithms available in the literature. Finally, the developed C-19BOA is applied on an electrical power system load–frequency–control model to test its effectiveness in optimizing the power system parameters and to check its applicability in solving modern engineering problems. A performance comparison of the proposed C-19BOA and other optimization algorithms is validated based on optimizing the controller gains for reducing the steady-state errors by comparing the effective frequency and tie-line power regulation ability of an industrially applied Proportional–Integral–Derivative controller (PID) and Active Disturbance Rejection controller (ADRC). Moreover, the robustness of C-19BOA optimized PID and ADRC gains is tested by varying the system parameters from their nominal values.

Load frequency control of power system with energy storage based on disturbance observer

In view of the problem that load frequency fluctuation of the electrical power systems with energy storage when it is disturbed, a load frequency control (LFC) model is constructed, furthermore, a load frequency controller which bottom on disturbance observer is devised, the accommodate reaching law is used to improve the system performance. These actions have improved the immunity and robustness of the system. It is verified by MATLAB simulation that the control strategy can decrease load frequency deflection under conditions of load fluctuation with effect, and the Lyapunov principle is used to analyze the stability. The analysis shows the method proposed can effectively ameliorate the robustness and operational stability of power systems with energy storage.

A Robust Non-Integer Controller Design for Load Frequency Control in Modern Marine Power Grids

In this study, a new robust non-integer order controller is presented for the secondary load frequency control (LFC) of the shipboard microgrid (MG). During the design phase of the controller, the time delays between the controller and the sensor as well as the actuator are also taken into account, and a robust controller design against these time delays has been realized accordingly. The system frequency is regulated by a single input interval type-2 fuzzy fractional-order PI (SIT2-FFOPI) controller. The basic FOPI parameters of the SIT2 controller are determined offline with the help of the stability boundary locus (SBL) method depending on the time delay and integral order. For the determination of basic FOPI parameters, the characteristic equation of the shipboard MG LFC model is obtained, and the necessary arrangements are made. The geometric centroid is calculated to obtain the basic parameter values in the SBL curve obtained. The performance of the proposed controller design is assessed using real-world marine wave and solar irradiation data in the shipboard MG. Also, the process of verification of the applicability of the proposed method from a systemic perspective is done by real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT.

Event-Triggered H∞ Load Frequency Control for Multi-Area Nonlinear Power Systems Based on Non-Fragile Proportional Integral Control Strategy

In this article, a new event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> load frequency control (LFC) approach with dynamic triggered algorithm (DTA) for multi-area nonlinear power systems (NPSs) based on non-fragile proportional integral control (NPI-control) strategy is addressed. Firstly, different from the existing linear single-area LFC model for power systems, an improved nonlinear multi-area model with the performance of large-scale adjustment frequency fluctuation is constructed by considering the phenomenon of overshoots and long-term oscillations. Due to the existence of control uncertainty, it is the first time that the NPI-control scheme is applied to LFC approach for NPSs. Then, the DTA is proposed to adjust the dynamic event-triggered parameters, which reduces the occupation of communication bandwidth and the data computation of NPSs. Furthermore, a modified quadratic form with time-varying matrix and two-side closed functional method are adopted to construct the relaxed Lyapunov-Krasovskii functional, where some slack matrices are unnecessarily positive definite. Based on Lyapunov method, some less-conservatism stability criteria are derived. Utilizing the linear matrix inequality toolbox, the allowable upper bound of time-varying delays and the NPI-controller are obtained. Finally, a numerical example is presented to demonstrate the availability of the approach developed in this work.

A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG

With the development of micro gas turbines (MT) and power-to-gas (P2G) technology, the electric–gas system plays an important role in maintaining the stable, economical, and flexible operation of the microgrid. When subjected to power load disturbance and natural gas load disturbance, the system controller needs to coordinately control the frequency of the microgrid and the gas pressure at the natural gas pipeline nodes. Additionally, the reliability and stability of a multi-microgrid system are much higher than that of a single microgrid, but its control technology is more complicated. Thus, a frequency–pressure cooperative control strategy of a multi-microgrid oriented to an electric–gas system is proposed in this paper. Firstly, based on the analysis of the operating characteristics of the natural gas network and the coupling equipment, the dynamic model of natural gas transmission is built. Secondly, a multi-microgrid load frequency control model including MT, P2G equipment, electric vehicles (EVs), distributed power sources and loads has been established. In addition, according to the three control objectives of microgrid frequency, node pressure and system coordination and stability, the structure of a Muti-Agent Deep Deterministic Policy Gradient (MADDPG) controller is designed, then the definition of space and reward functions are completed. Finally, different cases are set up in the multi-microgrid, and the simulation results are compared with PI control and fuzzy control. The simulation results show that, the proposed MADDPG controller can greatly suppress the frequency deviation caused by wind power and load disturbances and the air pressure fluctuations caused by natural gas network load fluctuations. Additionally, it can coordinate well the overall stability between the sub-microgrids of multi-microgrid.

Event-triggering load frequency control for multi-area power system based on random dynamic triggering mechanism and two-side closed functional

By taking into account sampled-data mechanism and transmission delay, the novel event-triggering load frequency control (LFC) strategy involving random dynamic triggering algorithm (RDTA) is developed for multi-area power systems in this paper. Firstly, an improved multi-area LFC model considering sampling and transmission delay (STD) simultaneously is addressed. Secondly, a modified event-triggering mechanism (ETM) with RDTA is proposed, considering parameter disturbances and a dynamic adjustment mechanism of the triggering threshold. Thirdly, a more advanced Lyapunov–Krasovskii functional (LKF) is constructed, introducing the delay-dependent matrices, more variable cross terms and the two-sided closed functional. Furthermore, two less conservative stability criteria are obtained according to the designed approach. Finally, two multi-area LFC systems are presented to verify the progressiveness of the proposed approach.

Improved load frequency control considering dynamic demand regulated power system integrating renewable sources and hybrid energy storage system

Demand response (DR) has emerged as a key component of the future electric power system's reliability and frequency stability. This study explores the effect of DR regulation and hybrid energy storage (HES) on an identical two-area test power system that comprises of solar photovoltaic, wind turbine, biogas unit, and a thermal power plant for improved frequency regulation services. By including intrinsic communication latency in the DR loop, a generalised load frequency control (LFC) model of a hybrid hierarchical DR mechanism is developed. The stability of the analysed system before and after DR is compared in terms of stability margin. A Quasi- oppositional Harris Hawks Optimization (QOHHO) is suggested to optimize the coefficients of the proposed cascade fractional order two-degree-of-freedom CC[FO(TDOF)] controller. The supremacy of the proposed algorithm is affirmed by comparing its figure of demerit with different existing algorithms. The investigations also manifest the efficacy of proposed controller over proportional integral derivative with filter (PID) and two degree of freedom (tilt integral derivative with filter) [TDOF (TID)] controllers. QOHHO optimized designed controller performance is also evaluated for the studied system under random load disturbances. Furthermore, the suggested technique's practical feasibility is verified through experimental validation using OPAL-RT based real-time analysis.

Robust LFC of Power Systems With Wind Power Under Packet Losses and Communication Delays

Network-induced packet losses and communication delays, as well as load fluctuations and uncertain wind power have a negative effect on the frequency regulation of power system. This paper is concerned with designing a robust load frequency control (LFC) scheme for a multi-area power system penetrated by wind power via sampled-data control. Firstly, considering the effects of the inertia reduction, packet losses, communication delays and measurement noise, a decentralized aperiodic sampled-data LFC model with time delay is constructed, where the maximum sampling interval is characterized by limiting the maximum count of successive packet losses (MCSPL). Secondly, by applying Lyapunov theory and linear matrix inequality technique, a new exponential stability condition for the modeled LFC system with given controller is proposed based on a combination method of Lyapunov-Krasovskii functional and loop function, which is dependent on delay and MCSPL. It can be used to find the delay margin and allowable MCSPL of the LFC system. Thirdly, based on the <inline-formula> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula>-gain performance condition developed by the stability condition above, a design method of robust PI-type controller is proposed by maximizing an exponential decay rate (EDR). The larger EDR, the faster frequency response speed to cope with the effect of inertia reduction, but the worse robustness to load fluctuations, packet losses and delays, and vice versa. Finally, case studies are carried out by a one-area power system and a three-area power system with wind power. By comparing with the existing LFC schemes, simulation results show that the proposed LFC scheme for the tested system is more effective and has better robustness.

A multi-agent deep reinforcement learning-based “Octopus” cooperative load frequency control for an interconnected grid with various renewable units

With the aim of improving the frequency regulation performance of load frequency control (LFC) in an interconnected power system as well as reducing wastage of frequency regulation resources, an “octopus” cooperative load frequency control (OC-LFC) method is proposed. This method addresses the secondary frequency regulation coordination problem affecting different areas, as well as the coordination problem arising between the controller and power distributor within the same area. In addition, for this framework a tracking-explorer-based distributed multi-agent deep deterministic policy gradient (TED-MADDPG) algorithm is proposed. This algorithm imitates the edge-cloud cooperation framework, akin to an octopus. In the performance-based frequency regulation market, the controller and power distributor in each area are regarded as independent agents, which output the total generated power command and the generated power commands of different units. In the proposed method, centralized training and distributed implementation are employed so that a global optimal decision with higher robustness can be obtained. Furthermore, this algorithm also employs a tracking-explorer strategy which incorporate multiple learning techniques and in turn enhance the robustness of the proposed method. The effectiveness of the proposed method is demonstrated using a China Southern Grid (CSG) four-area load frequency control model.

A Sampling Load Frequency Control Scheme for Power Systems with Time Delays

In this study, we investigate the effectiveness of a robust sampled‐data H∞ load frequency control (LFC) scheme for power systems with randomly occurring time‐varying delays. By using the input‐delay technique, the sampled‐data LFC model is reformulated as a continuous time‐delay representation. Then, Bernoulli‐distributed white noise sequences are used to describe randomly occurring time‐varying delays in the sampled‐data LFC model. Some less conservative conditions are achieved by utilizing the Lyapunov–Krasovskii functional (LKF) and employing Jensen inequality and reciprocal convex combination lemma to ensure the considered power system has mean‐square asymptotic stability under the designed control scheme. The derived results are based on linear matrix inequalities (LMIs) that can readily be solved using the MATLAB LMI toolbox. The criteria obtained are used to analyze the upper bounds for time delays, and a comparison study to validate the efficacy of the presented method is presented.

Co-Design of Observer-Based Fault Detection Filter and Dynamic Event-Triggered Controller for Wind Power System Under Dual Alterable DoS Attacks

In this article, a novel co- design approach of observed-based fault detection filter (FDF) and dynamic event-triggered controller is proposed for multi-area wind power system under dual alterable aperiodic (DAA) denial-of-service (DoS) attacks. It is the first attempt to design an observed-based FDF for wind power system considering actuator fault signal. And a tolerable actuator fault threshold function is constructed to warn the occurrence of fault signal. Then, considering the bandwidth limitation of network communication, a novel dynamic event-triggered scheme is proposed to decrease the occupation of communication channel. Furthermore, the DAA DoS attacks presented in this work have different blocking effects on data transmission in different network channel. On account of the existence of aperiodic DoS attacks, the general load frequency control model for wind power system is reconstructed as a new switched system. Based on the reconstructed model, the stability with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance index is demonstrated by constructing appropriate polynomial Lyapunov-Krasovskii functionals. Besides, the FDF and dynamic event-triggered controller can be achieved by solving linear matrix inequalities. Finally, some contradistinctive case studies are given to illustrate the feasibility and effectiveness of the approaches proposed in this article.

Distributed deep reinforcement learning for integrated generation‐control and power‐dispatch of interconnected power grid with various renewable units

To mismatch various power disturbances of interconnected power grid, an integrated generation-control and power-dispatch (IGCPD) for performance-based frequency regulation market is proposed. Unlike the conventional automatic generation control with an independent controller and a power distributor, IGCPD can significantly improve the dynamic control performance via combining these two parts into an integrated decision agent. The IGCPD eliminates the waste of frequency regulation resources and repeated regulation of the units caused by the mismatch of the controller and power distributor. In addition, by setting the reward function reasonably, IGCPD can save regulatable capacity of fast units for regulation in the next large-scale disturbance. In order to enhance the decision ability, a swarm intelligence-based deep deterministic policy gradient (SI-DDPG) algorithm is proposed to acquire the control knowledge and implement a high-quality decision for this agent. Moreover, the swarm intelligence-based training sample can achieve a high learning robustness for SI-DDPG which solve the problem of low robustness in conventional deep reinforcement learning. The proposed technique is verified on an England two-area load frequency control model and the west China two-area LFC model. IGCPD improves the area control error control performance and reduce the frequency regulation mileage payment of the interconnected power grid.

A load frequency control strategy based on disturbance reconstruction for multi-area interconnected power system with hybrid energy storage system

In order to handle the problem of frequency fluctuations caused by load disturbances in multi-area interconnected power systems, a sliding mode control strategy for hybrid energy storage system has been designed by combining interval observer and disturbance reconstruction technology. The hybrid energy storage system composed of a battery and a super capacitor has been used to improve the performance of load frequency control systems. Using the load frequency control model of the multi-area interconnected power system, the interval observers are designed for each area to get the information about interval bounds of the system state in each area online. Then, the load disturbance in each area is reconstructed online using the estimation information of system state, and a sliding mode controller based on interval disturbance reconstruction is designed for power consumption and storage of the hybrid energy storage system, so as to stabilize the power grid frequency. Simulation results have shown that the proposed control strategy can effectively suppress the frequency and tie-line power fluctuations caused by load disturbances, and ensure the reliability and stability of the power system.

Coordinated Frequency Regulation of Smart Grid by Demand Side Response and Variable Speed Wind Turbines

Frequency stability of the power system is impacted by the increasing penetration of wind power because the wind power is intermittent. Meanwhile, sometimes the demand side loads increase quickly to require more power than total power produced. So balancing the active power in the power system to maintain the frequency is the main challenge of the high penetration of wind power to the smart grid. This paper proposes coordination rotor speed control (RSC), pitch angle control (PAC) and inertial control (IC) to control wind turbines, together with demand side response (DSR) participating in frequency regulation to balance active power in the power system. Firstly, the model of a single area load frequency control (LFC) system is obtained, which includes variable-speed wind turbines (VSWT) and DSR containing aggregated air conditioners and plug-in electric vehicles (PEVs). Then the RSC, PAC and IC, which controls wind turbines participating in frequency regulation in the power system, are introduced, respectively. Finally, the coordination of these three methods for wind turbines in different wind speeds is proposed. Case studies are carried out for the single area LFC system with a wind farm and DSR supported grid frequency. Coordination RSC and PAC combined IC are used to control wind turbines with DSR to balance active power in the power system. The proposed method used in the power system with high penetration of wind power and fluctuation of demand load is tested, respectively. Coordinated RSC or PAC with DSR can increase penetration of wind power and reduce peak load.