Load Frequency Control Method Research Articles

Event-Triggered Data-Driven Load Frequency Control for Multiarea Power Systems

This article presents an event-triggered data-driven load frequency control (LFC) method for multiarea interconnected power systems via model-free adaptive control, where the dynamic model of the power system is assumed to be unknown completely. By introducing the dynamic linearization technique for the unknown power system, an equivalent data relationship model between the area-control-error (ACE) data and the input signal is established. Then, a data-driven LFC scheme is developed only relying on the input and output data of the power system. Meanwhile, an event-triggered strategy is also proposed in the design of data-driven LFC such that the communication and computation burden of the system can be reduced. Whether the current instant is the transmission instant is determined by judging the proposed triggering condition at each sampling instant. It is showed that the presented event-triggered data-driven LFC method is independent to any model information of the power system and does not need to measure any state signals. Simulation tests are carried out to verify the effectiveness of the presented control method.

Application of load frequency control method to a multi-microgrid with energy storage system

Active power and frequency control reflecting the stability of network operation is referred to as load frequency control (LFC). The area control error (ACE) is a combinatorial model between the tie-line in the interconnected system and its frequency deviations. The ACE is forced to be close to zero to achieve generation and load balance in network control and network balancing. The present research proposes introducing an energy storage system (ESS) in the control loop to regulate the frequency when the network is under an unstable condition or where the average frequency between the different areas connected is different to zero. The proposed method is developed to coordinate the different modes (charging and discharging) of the storage system. The storage parameters are unknown constants and are calculated online with a modified heuristic and directional derivative objective. The system consists of two tie-lines connected between the two areas, and each one is made up of a synchronous generator, renewable energy sources, ESS and load. The optimal frequency deviation in the areas and tie problem was modelled into a control problem and solved using the MPC. The dynamic model of ESS was modelled as a control bahaviour to inject energy at any time of the control horizon to balance the electrical network. Modified control of the ESS by a new approach used to estimate uncertainties parameters of the interconnected system. The effectiveness of the frequency control strategies is measured through simulation assessment.

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.

Data‐driven cooperative load frequency control method for microgrids using effective exploration‐distributed multi‐agent deep reinforcement learning

To reduce the total power generation cost and improve the frequency stability of an island microgrid integrating renewable energy generation sources, a data-driven cooperative load frequency control (DC-LFC) method is proposed for solving the coordination control problem occurring between the controller and power distributor of the system. A novel algorithm, termed the effective exploration-distributed multiagent twin-delayed deep deterministic policy gradient (EED-MATD3) algorithm, is further proposed, the design of which is structured based on the concepts of imitation learning, ensemble learning, and curriculum learning. The EED-MATD3 method employs various exploration strategies, and the controller and power distributor are treated as two agents. Through centralized training and decentralized execution, a robust cooperative control strategy is realized. The performance of the proposed algorithm is verified in an LFC model of Zhuhai Tandang Island, an island microgrid in the China Southern Power Grid.

A deep asynchronous actor‐critic learning‐based event‐triggered decentralized load frequency control of power systems with communication delays

AbstractThis article proposes a novel asynchronous advantage actor‐critic (A3C) learning‐based dynamic event‐triggered mechanism for the decentralized load frequency regulation to alleviate the local‐area communication burden and influence of the load fluctuations. The proposed dynamic event‐triggered mechanism applies the A3C algorithm to optimally adjust the threshold of the event‐triggered function in real time. In the A3C algorithm framework, the long short‐term memory (LSTM) network is used to estimate the policy function and value function. First, for each control area, a novel model of the decentralized load frequency control (LFC) system is established to design the event‐triggered communication mechanism and deal with the communication delay simultaneously. Then, based on the Lyapunov stability theory, the controller gain parameters of the decentralized LFC system and the margins of the even‐triggering thresholds are derived by solving a series of linear matrix inequalities (LMIs). Finally, a three‐area and four‐area power systems are used to evaluate the proposed decentralized LFC method. Simulation results show that the proposed method can greatly reduce the data transmission times and preserve a satisfactory system performance.

Load frequency control and dynamic response improvement using energy storage and modeling of uncertainty in renewable distributed generators

Nowadays to improve the dynamic performance of the power system and control the frequency stability, load frequency control (LFC) based on droop strategy must be provided. PID controller is also used in addition to the droop method to maintain the stable load frequency. In the new power systems such as microgrids (MGs) with different low and high-inertial distributed generators (DGs), using only LFC method is not sufficient to provide the stable load frequency and balanced power sharing. Thus, modified LFC approach is needed. Energy storage element is a precious solution presented to combat the non-desirable transient conditions on load frequency and power sharing. Among different storage elements, superconducting magnetic energy storage (SMES) is selected in this paper because of fast dynamic response and desirable inertial characteristic. Using accurate modeling of SMES, the reserved power in off-peak times can be exploited in on-peak times to inject the required power in sudden load changes to provide stable frequency and balanced power sharing. Moreover, the uncertainty of renewable DGs such as photovoltaic (PV) and wind turbine (WT) can be assumed as disturbance input to the MG. Applying SMES in the MG, accurate modeling of SMES dynamic behavior in control block and modeling of DGs uncertainties with suitable probability functions in the proposed control structure leads to more stable LFC approach shown in simulation results of this paper. Inertial property improvement by SMES is also analyzed while comparing with other LFC methods, proposed modified LFC approach presents more stable load frequency. In addition, the frequency fluctuation in the proposed control structure is reduced 15–20% in average compared to other methods.

Frequency variations management in deregulated environment using intelligent controller

Information of the frequency variations is critical to restore the dynamics of power system network. In this paper a modified load frequency control method in the deregulated power system to restore back the frequency is proposed and analysed. The research is done using industry standard modelling tool and the frequency variations are investigated through the construction of the power system network. The distribution company participation matrix is used in the deregulated environment with number of generation company (GENCO) and distribution company (DISCO). Three types of controllers are applied to the proposed two-area system through load frequency control including the conventional and intelligent controllers. The deviation of the output frequency in each area and tie line exchange are studied. It is concluded that the modified load frequency control method using intelligent controller shows 10% improvement on the settling time and about 20% improvement on undershooting.

Frequency variations management in deregulated environment using intelligent controller

Information of the frequency variations is critical to restore the dynamics of power system network. In this paper a modified load frequency control method in the deregulated power system to restore back the frequency is proposed and analysed. The research is done using industry standard modelling tool and the frequency variations are investigated through the construction of the power system network. The distribution company participation matrix is used in the deregulated environment with number of generation company (GENCO) and distribution company (DISCO). Three types of controllers are applied to the proposed two-area system through load frequency control including the conventional and intelligent controllers. The deviation of the output frequency in each area and tie line exchange are studied. It is concluded that the modified load frequency control method using intelligent controller shows 10% improvement on the settling time and about 20% improvement on undershooting.

Technical Measures to Mitigate Load Fluctuation for Large-Scale Customers to Improve Power System Energy Efficiency

Industrial equipment such as electric arc furnaces and steel mills are often associated with rapid and high load disturbances, so their power systems require additional control equipment to limit the frequency. However, proper ancillary service fees are not paid in these cases with extreme and variable load demands. The frequency regulation reserve equipment adds to power generation costs. Therefore, variable power generation loads lead to increases in the cost of energy production. We propose a load frequency control method that is applied on the customer end instead of the power supply end to reduce the operating reserve required to improve the energy efficiency of the power system. We analyzed the load fluctuation of steel mill customers using real data sampled at two-second intervals from the energy management system in Korea. We developed an automatic generation control program to simulate the power system’s frequency characteristics. We also propose compensation techniques for mitigation of the system’s frequency deviation at the customer end based on an energy storage system, pump storage hydro generator, customer generator, and plant process adjustment. To recover the frequency deviation, we calculated the compensation facility capacity and analyzed static characteristics, and we proved the feasibility via simulations.

Memory-Based Event-Triggering H∞ Load Frequency Control for Power Systems Under Deception Attacks.

This article proposes a memory-based event-triggering H∞ load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory H∞ LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.

A novel adaptive LFC based on MPC method

This article presents a novel load frequency control (LFC) method using an adaptive internal model of a power system, where model predictive control (MPC) technique is applied to the internal model, which is being updated on‐line. The proposed method will improve the LFC performance by reducing model identification error and by handling the disturbance effectively. Novelty lies in the combination of MPC and the effective use of the internal model to meet the response time of real world LFC control, which is typically equivalent to generation dispatch control cycle. The effectiveness of the proposed control is confirmed by simulations using a three‐area power system model. The results show that the proposed method can accurately identify the target plant and successfully handle disturbances to realize a reliable LFC. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Model predictive control for load frequency of hybrid power system with wind power and thermal power

With the increase of wind power penetration in generation profile, the contribution of wind power to load frequency control (LFC) has become more significant. To improve the frequency characteristics of a wind-power-contained power system, this paper establishes an analytical linearized model for the frequency response characteristics of wind turbine generator (WTG) during LFC which is helpful to the design of frequency controller. Furthermore, the gap metric measure to the above models under different wind speed ranges is calculated. Under the per unit system, it can be extended as an equivalent model for the whole wind farm. Combining the derived wind farm model and a known thermal power model, an integrated model of hybrid power system can be built. To optimize the frequency-response performance to the power system, an improved LFC method based on model predictive control (MPC) is presented and applied to a multi-areas hybrid system. Then, the wind farms and thermal power plants in the same area can be controlled simultaneously and obtain their reference orders from the predictive controller. The simulation results show that the proposed method can effectively raise the frequency response level of the both power supplies and then improve the frequency performance of power system.

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.

Adaptive Event-Triggering ${H}_{\infty }$ Load Frequency Control for Network-Based Power Systems

Load frequency control (LFC) is a very important method to keep the power systems stable and secure. However, due to the introduction of communication networks in multi-area power systems, the traditional LFC method is not effective again. This motivates us to investigate an adaptive event-triggering ${H}_{\infty }$ LFC scheme for multi-area power systems. Compared with the existing time-invariant event-triggering communication scheme, an adaptive event-triggering communication scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance. Compared with the existing emulation-based method, where the controller must be known a priori , the stability and stabilization criteria derived in this work can provide a tradeoff to balance the required communication resources and the desired control performance. The effectiveness of the proposed method is verified by two numerical examples.

An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations

As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

LMI-Based Robust Predictive Load Frequency Control for Power Systems With Communication Delays

This paper presents a robust predictive load frequency control for power systems with uncertain parameters and time delays in communication networks. The goal of the proposed approach is to achieve good performance for the closed-loop system under practical problems of the network including uncertainties in the dynamic model, time delays in the system, and time-varying model. To this end, a decentralized state-feedback control law is obtained by solving an linear matrix inequality based optimization. The aim of the optimization problem is to regularize the frequency deviation with the minimum control effort. It is shown that the stability of the system is guaranteed with respect to the Lyapunov stability theorem. Moreover, the problem is reformulated as a centralized load frequency control (LFC) approach for single-area power systems, and also as a non-predictive LFC method with lower computational complexity. The performance and robustness of the proposed strategy are studied through simulation results in different cases of uncertain and time-varying single-area and multi-area power systems with time delays.

A New Load Frequency Control Method of Multi-Area Power System via the Viewpoints of Port-Hamiltonian System and Cascade System

The existed control methods of the multi-area load frequency control (LFC) system fail to decouple the total tie-line power flow. This defect can be addressed as a problem on how to effectively utilize the total tie-line power flow. To improve that defect, the energy and structure of multi-area LFC system have been carefully studied, such that, the energy goes through the systemic cascade parts and the systemic structure matrix is partially skew symmetry. Namely, the energy and structural properties of the multi-area LFC system are similar to the properties of Port-Hamiltonian (PH) system and cascade system. Inspired by the above properties, a new method based on the PH system and cascade system that is proposed to design some PID control laws for the multi-area LFC system successfully works out the aforementioned problem. Compared with the existed PID methods for the multi-area LFC system, the proposed method has two advantages, which are the decoupling of total tie-line power flow and the robust disturbance rejection. At last, simulations results demonstrate the validity and advantages of the proposed method.

Supplementary Load Frequency Control by Use of a Number of Both Electric Vehicles and Heat Pump Water Heaters

A large integration of renewable energy sources such as wind power generation and photovoltaic generation causes some problems in power systems, e.g., distribution voltage rise and frequency fluctuation. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to a high cost of the BESS, an application of controllable loads such as electric vehicle (EV) and heat pump water heater (HPWH) to the power system control is considered in this paper for reduction of the required capacity of the BESS. This paper proposes a new supplementary load frequency control (LFC) method by use of a number of both the EVs and the HPWHs as controllable loads. The effectiveness of the proposed LFC method is shown by numerical simulations conducted on the power system model with a large integration of wind power generation and photovoltaic generation.

Robust analysis and design of load frequency controller for power systems

Robust load frequency control for power systems is discussed. A detailed robustness analysis of the existing control laws shows that parameter variation is not a critical issue but more attention should be paid to the unmodeled dynamics in robust load frequency controller design. A new robust load frequency control method is then proposed considering the unmodeled dynamics of power systems. Finally, a new configuration is proposed to overcome the effects of generation rate constraints (GRC). Simulation results show that the design method and the anti-GRC configuration are effective.

Robust load-frequency control for uncertain nonlinear power systems: A fuzzy logic approach

A new robust load-frequency control (LFC) methodology is proposed for controlling uncertain nonlinear power systems. Critical nonlinearity in the power system—the valve position limit on the governor, and the parametric uncertainty are concerned. The Takagi–Sugeno fuzzy model of the power system under consideration is first constructed to design the robust fuzzy-model-based LFC. Sufficient conditions for the robust asymptotic convergence of the frequency deviation are then provided in terms of linear matrix inequalities. Boundedness of the other system variables is also studied to ensure justifiable grounds for use of the proposed LFC method. Simulation results convincingly validate the effectiveness of the novel LFC design scheme and the theoretical discussions, which give a positive answer to the quality control of the electric energy.