Load Frequency Control Method Research Articles

Robust fractional-order load frequency control for hybrid power system concerning high wind penetration

Hybrid power systems concerning wind farms have witnessed significant dynamic characteristic changes due to unpredicted generation output and integration mode conversion between frequency-sensitive wind plants and non-scheduled ones. This work presents a robust fractional-order load frequency control (LFC) method for a multi-area hybrid power system in the presence of wind penetration uncertainties. To characterize the injection effect of wind farms, the power system is described by a model with an uncertain wind penetration factor in a pre-estimated range. Then, an enhancement of Levy’s identification method is developed to convert this model into a fractional-order reduced structure to design the load frequency controller, which is in a form of fractional-order PID with a filter for ease of engineering implementation. The closed-loop stability is analyzed by the stability boundary locus (SBL) method under different wind penetration levels. It is shown that superior frequency response performance and system robustness can be obtained if the controller is designed in terms of the model with the worse-case wind penetration. Illustrative examples including a three-area hybrid power system under a variety of wind penetration variations are given to show the effectiveness of the proposed method.

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.

Brain-Inspired Deep Meta-Reinforcement Learning for Active Coordinated Fault-Tolerant Load Frequency Control of Multi-Area Grids

This paper proposes an active coordinated fault tolerance load frequency control (AFCT-LFC) method, which effectively prevents sudden frequency changes caused by unit actuator failures or unplanned decommissioning in a multi-area interconnected grid subject to the performance-based frequency regulation market mechanism. It can also reduce regulation mileage payments and achieve multi-objective active fault-tolerant control. In addition, this paper proposes a brain-Inspired deep meta-deterministic policy gradient algorithm (BIMA-DMDPG), which adopts multi-agent centralized training, equates the controller of each area as an agent capable of independent decision making, and implements distributed training by dividing the environment into multiple environments. In addition, meta-reinforcement learning is employed to realize multi-task collaborative learning. The optimal policy is actively selected under different fault conditions to achieve active fault-tolerant control. The superior performance of the method is verified in a four-area LFC model of the China Southern Grid (CSG), in which it is tested alongside a selection of existing algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —AFCT-LFC is based on advanced artificial intelligence algorithm, which can effectively identify any fault in multi-area grids and make rapid response to achieve active fault-tolerant control. Compared with the existing model-based fault-tolerant control methods, the BIMA-DMDPG algorithm proposed in this paper does not need to rely on accurate mathematical models, and can be applied to practice through simple training, which is very suitable for practical applications. Therefore, AFCT-LFC is an advanced adaptive active fault-tolerant control method that can be truly applied in practice because of its fast-decision-making ability and performance.

VSG-LFC on multi-area power system under unsecured communication channel using hybrid trigger mechanism

This paper studies the frequency fluctuation problems of a multi-area power system with renewable energy sources (RESs) under denial-of-service (DoS) attacks. (i) Due to the decoupling of the RESs from power system using electronic devices, the RESs have no natural frequency response capability, which result in decreasing of entire power system equivalent inertia with large penetration of RESs. (ii) DoS attacks cause the insecurity of network communication, which can vanish data transmission and yield data packet dropout. To address the above issues, a virtual synchronous generator (VSG) incorporated with load frequency control (LFC) method (VSG-LFC) is proposed by introducing a hybrid-triggered mechanism (HTM) to (i) reduce the system sensitivity to the penetration level of RESs; (ii) compromise between frequency regulation performance and the reasonable usage of network resource under unsecured communication environments. Then, a switched system model is constructed considering non-identical DoS attack situations. Furthermore, theoretical analyses using Lyapunov functional related methods are carry out for asymptotical mean-square stability with H∞ performance on frequency regulation. Simulation evaluations of power system verify the effectiveness of this development. It shows that in a two-area power system frequency derivation performance indicators (IAE, ITAT, ISE) in Area1 are reduced by (82.996%, 86.273%, 85.823%) and in Area2 are reduces by (77.996%, 83.613%, 82.315%).

Quantum-inspired deep reinforcement learning for adaptive frequency control of low carbon park island microgrid considering renewable energy sources

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning’s experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost.

Load Frequency Control Method for Cyber-Physical Power Systems With 100% Renewable Energy

Load Frequency Control Method for Cyber-Physical Power Systems With 100% Renewable Energy

A Reinforcement Learning Based Coordinated but Differentiated Load Frequency Control Method With Heterogeneous Frequency Regulation Resources

As the diversification of frequency regulation resources, it is increasingly important to find a load frequency control (LFC) scheme that can effectively coordinate different resources. This paper proposes a coordinated but differentiated LFC control method that utilizes heterogeneous frequency regulation resources with their characteristics fully considered. The optimal LFC models for the single area and multi-area power systems are formulated as partially observable Markov decision process model (POMDP) and partially observable Markov game (POMG) considering the accessibility of system information. A deep reinforcement learning method called recurrent proximal policy optimization is leveraged to solve the POMDP and POMG models. Simulations based on historical data demonstrate that the proposed control scheme can better coordinate different resources and achieve superior control performance.

Advanced load frequency control of microgrid using a bat algorithm supported by a balloon effect identifier in the presence of photovoltaic power source.

Due to the unpredictability of the majority of green energy sources (GESs), particularly in microgrids (μGs), frequency deviations are unavoidable. These factors include solar irradiance, wind disturbances, and parametric uncertainty, all of which have a substantial impact on the system's frequency. An adaptive load frequency control (LFC) method for power systems is suggested in this paper to mitigate the aforementioned issues. For engineering challenges, soft computing methods like the bat algorithm (BA), where it proves its effectiveness in different applications, consistently produce positive outcomes, so it is used to address the LFC issue. For online gain tuning, an integral controller using an artificial BA is utilized, and this control method is supported by a modification known as the balloon effect (BE) identifier. Stability and robustness of analysis of the suggested BA+BE scheme is investigated. The system with the proposed adaptive frequency controller is evaluated in the case of step/random load demand. In addition, high penetrations of photovoltaic (PV) sources are considered. The standard integral controller and Jaya+BE, two more optimization techniques, have been compared with the suggested BA+BE strategy. According to the results of the MATLAB simulation, the suggested technique (BA+BE) has a significant advantage over other techniques in terms of maintaining frequency stability in the presence of step/random disturbances and PV source. The suggested method successfully keeps the frequency steady over I and Jaya+BE by 61.5% and 31.25%, respectively. In order to validate the MATLAB simulation results, real-time simulation tests are given utilizing a PC and a QUARC pid_e data acquisition card.

Adaptive Active Disturbance Rejection Load Frequency Control for Power System with Renewable Energies Using the Lyapunov Reward-Based Twin Delayed Deep Deterministic Policy Gradient Algorithm

The substitution of renewable energy sources (RESs) for conventional fossil fuels in electricity generation is essential in addressing environmental pollution and resource depletion. However, the integration of RESs in the load frequency control (LFC) of power systems can have a negative impact on frequency deviation response, resulting in a decline in power quality. Moreover, load disturbances can also affect the stability of frequency deviation. Hence, this paper presents an LFC method that utilizes the Lyapunov reward-based twin delayed deep deterministic policy gradient (LTD3) algorithm to optimize the linear active disturbance rejection control (LADRC). With the advantages of being model-free and mitigating unknown disturbances, LADRC can regulate load disturbances and renewable energy deviations. Additionally, the LTD3 algorithm, based on the Lyapunov reward function, is employed to optimize controller parameters in real-time, resulting in enhanced control performance. Finally, the LADRC-LTD3 is evaluated using a power system containing two areas, comprising thermal, hydro, and gas power plants in each area, as well as RESs such as a noise-based wind turbine and photovoltaic (PV) system. A comparative analysis is conducted between the performance of the proposed controller and other control techniques, such as integral controller (IC), fractional-order proportional integral derivative (FOPID) controller, I-TD, ID-T, and TD3-optimized LADRC. The results indicate that the proposed method effectively addresses the LFC problem.

Cloud–Edge Cooperative Load Frequency Control for Isolated Microgrid Using Emergent Computation-Based Large-Scale Meta-Machine Learning

In the isolated microgrid, each unit is managed by different operators, and their interests conflict greatly. To balance the interests of different stakeholders in the electric grid sector, this paper proposes an Edge-Cloud cooperative data-driven load frequency control (ECCDD-LFC) method for isolated microgrids, which can deliver improved frequency control performance and reduce regulation cost. This method replaces the LFC controller in the conventional microgrid control center with a Cloud-based agent and sets each unit as an independent-decision Edge-based agent. It enables the units to make decisions independently and realize Cloud-Edge cooperative control. In addition, an emergent computation large-scale multi-agent deep meta-deterministic policy gradient (ECLSMA-DMDPG) is proposed. In this algorithm, a centralized training and decentralized execution policy are used to achieve the collaboration of multiple agents, a meta-learning technique is used to improve the robustness of the algorithm, and emergent computation is used to produce high-value samples for a higher-performance policy. The effectiveness of the proposed method is demonstrated in a simulation of the Zhuzhou isolated microgrid of the China Southern Grid (CSG) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i>

Dual degree branched type-2 fuzzy controller optimized with a hybrid algorithm for frequency regulation in a triple-area power system integrated with renewable sources

The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation (DRG) sources such as solar and wind necessitate the use of an efficient load frequency control (LFC) technique. Therefore, a hybrid version of two metaheuristic algorithms (arithmetic optimization and African vulture's optimization algorithm) is developed. It is called the ‘arithmetic optimized African vulture's optimization algorithm (AOAVOA)’. This algorithm is used to tune a novel type-2 fuzzy-based proportional–derivative branched with dual degree-of-freedom proportional–integral–derivative controller for the LFC of a three-area hybrid deregulated power system. Thermal, electric vehicle (EV), and DRG sources (including a solar panel and a wind turbine system) are connected in area-1. Area-2 involves thermal and gas-generating units (GUs), while thermal and geothermal units are linked in area-3. Practical restrictions such as thermo-boiler dynamics, thermal-governor dead-band, and generation rate constraints are also considered. The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances. By functioning as energy storage elements, EVs and DRG units can enhance dynamic responses during peak demand. As a result, the effect of the aforementioned units on dynamic reactions is also investigated. To validate its effectiveness, the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature. It is determined that the suggested AOAVOA improves fitness by 40.20% over the arithmetic optimizer (AO), while frequency regulation is improved by 4.55% over an AO-tuned type-2 fuzzy-based branched controller.

Multiagent deep meta reinforcement learning for sea computing-based energy management of interconnected grids considering renewable energy sources in sustainable cities

In a sustainable city with a large amount of renewable energy, the difficulty inherent in the coordination of the load frequency control strategies of grid units and area units lead to severe frequency fluctuations. Conventional load frequency control has difficulty overcoming the above problems due to its lack of adaptive control ability and robustness. To overcome this problem, this paper proposes a sea computing-based grid-area coordinated load frequency control (SCGAC-LFC) method, which equates each grid unit and area unit in each area as independent agents. Instead of different units relying on different strategies, all units collectively obtain the LFC policy that suits the market requirements. In its online application, no communication is needed since each unit can arrive at its own decision. In addition, this paper proposes a curriculum multiagent deep meta-actor-critic (CMA-DMAC) algorithm, which introduces meta-reinforcement learning and curriculum learning to guide the agent training to improve the robustness and quality of the obtained SCGAC-LFC strategies. Using a simulation of the four-area LFC model for the China Southern Grid (CSG), our proposed method carries significantly lower frequency error and regulation mileage payment.

Evolutionary Multi-Agent Deep Meta Reinforcement Learning Method for Swarm Intelligence Energy Management of Isolated Multi-Area Microgrid With Internet of Things

In an isolated multi-area microgrid, a conventional centralized active control policy relies on excessive communication and therefore is incapable of coordinating the interests of multiple operators. For this reason, this paper proposes a swarm intelligence load frequency control (SI-LFC) method. Based on the swarm intelligence method, the proposed method equates the units in each area as independent agents and adopts the swarm intelligence centralized offline learning policy to achieve the balance of interests of different operators. In online application, each unit only needs to collect the frequency locally to achieve global optimal control, thereby reducing the communication burden across the network. In addition, this paper proposes an evolutionary multi-agent deep meta-actor critic (EMA-DMAC) algorithm, which introduces meta-reinforcement learning and evolutionary learning to achieve fast collaborative learning of swarm agents, thereby improving the robustness and quality of the obtained SI-LFC strategies. The effectiveness of the proposed method is demonstrated in a simulation of the four-area LFC model for Sansha island in the China Southern Grid (CSG).

Distributed quantum multiagent deep meta reinforcement learning for area autonomy energy management of a multiarea microgrid

This paper presents a distributed area autonomy load frequency control (DAA-LFC) method capable of balancing the interests of different grid operators and achieving fast frequency recovery. The method treats each area controller in a multiarea microgrid as an agent. During offline training, the agents enter into gameplay with each other to obtain a global optimization policy. The agents involved in this method are capable of independent decision-making and need not communicate with each other during online operation. In addition, this paper presents a distributed quantum multiagent deep meta-deterministic policy gradient (DQMA-DMDPG) algorithm, which employs both large-scale learning and meta-learning to achieve collaborative multitask learning by setting reasonable exploration parameters under different tasks. A quantum method is used to set the exploration action noise as a set of superposition states to obtain richer samples. These innovations deliver better performance in terms of frequency deviation and total generation cost, thus satisfying the requirements of different grid operators. A simulation based on a four-area microgrid of the China Southern Grid (CSG) demonstrates that the proposed method can simultaneously reduce the frequency deviation and power generation costs and balance the interests of multiple operators.

Optimized Non-Integer Load Frequency Control Scheme for Interconnected Microgrids in Remote Areas with High Renewable Energy and Electric Vehicle Penetrations

Renewable energy systems (RESs) have introduced themselves as vital solutions for energy supply in remote regions, wherein main utility supply systems are not available. The construction of microgrid (MG) systems is useful candidate for proper control and management with hybrid RESs. However, RESs-based MGs face reduced power system inertia due to the dependency of RESs on power electronic converter systems. Accordingly, preserving nominal operating frequency and reduced deviations in tie-line power are crucial elements for proper operation of interconnected MGs in remote areas. To overcome this problem, load frequency control (LFC) systems have proven featured solutions. Therefore, this paper proposes a new non-integer LFC method based on the fractional order (FO) control theory for LFC in interconnected MGs in remote areas. The proposed control is based on the three degree of freedom (3DoF) cascaded 1+proportional-integral-derivative-accelerated (PIDA) controller with FOPI controller, namely 3DoF 1+PIDA/FOPI LFC scheme. The proposed 3DoF 1+PIDA/FOPItakes the advantages of the accelerated term of PIDA control to improve power system transients, regarding maximum overshoot/undershoot and settling times. Additionally, it employs outer loop to reduce errors and faster inner loop to mitigate disturbances effects. The contribution of plug-in controlled electric vehicles (EVs) are considered to enhance the frequency regulation functions. An optimized design of the proposed 3DoF 1+PIDA/FOPI LFC scheme is proposed using the newly developed hybrid equilibrium optimizer (EO)-slime mould optimization (SMA) algorithm (namely EOSMA optimizer). The EOSMA combines the features of the EO and SMA powerful optimization algorithms. A two interconnected MGs in remote areas with RESs and EVs inclusions with high penetration levels is selected to verify the proposed 3DoF 1+PIDA/FOPI LFC scheme and the EOSMA optimizer. The results show high ability of the proposed controller and design scheme to minimize MGs’ frequency and tie-line power fluctuations and to preserve frequency stability and security.

Modified Frequency Regulator Based on TIλ-TDμFF Controller for Interconnected Microgrids with Incorporating Hybrid Renewable Energy Sources

Reducing the emissions of greenhouse gases has directed energy sectors toward using renewable energy sources (RESs) and decreasing the dependency on conventional energy sources. Recently, developing efficient load frequency control (LFC) schemes has become essential to face the reduced inertia due to RESs installations. This paper presents a modified tilt fractional order (FO) integral–tilt FO derivative with a fractional filter (TFOI-TFODFF or namely TIλ-TDμFF) LFC method. Although the proposed controller uses the same elements of standard controllers, it adopts FO control capabilities and flexibilities, including the tilt, FO integral, FO derivative, and FO filter. Thence, a new control structure is obtained, merging the advantages of both controllers. Moreover, the proposed TFOI-TFODFF controller employs two control loops to be able to mitigate low-frequency as well as high-frequency disturbances in power grids. Additionally, a new modified marine predator algorithm (MMPA) is proposed for optimally tuning the parameters of the proposed TFOI-TFODFF LFC method. The performance of the MMPA is enhanced in terms of initialization and exploitation phases using the chaotic maps and weighting factor. A two-area interconnected power system case study is implemented with wind and photovoltaic RESs and electric vehicles (EVs) contribution. The proposed TFOI-TFODFF LFC is compared with the FOPID, TID, TI-DF, and FOTPID controllers, wherein the proposed TFOI-TFODFF has offered superior performance of the proposed controller. Moreover, the proposed modified MPA is compared with the original MPA and other competitive optimization algorithms, and statistical analyses are carried out through parametric and nonparametric tests.

A novel adaptive model predictive frequency control using unscented Kalman filter

This paper proposes a novel adaptive load frequency control (LFC) method based on model predictive control (MPC) using an unscented Kalman filter (UKF). The objective is to develop a robust LFC controller against the change in frequency characteristics of a power system. An adaptive MPC (AMPC) control is realized by updating the prediction model on-line to capture the frequency characteristics of a target system. A simplified prediction model is identified in the preliminary examination of the closed-loop performance of the target system, and then the system parameters are estimated in real-time circumstances. The estimated parameters include equivalent inertia of the target system. An interconnected three-area test system is used in the simulations to confirm the effectiveness of the proposed approach. The simulation results verify that the proposed MPC method can work successfully and efficiently handle the disturbances of the power system. The successful results also confirm that the proposed control scheme can be applied to a microgrid (MG) project, which is being promoted by the authors.

Frequency Regulation of Power Systems With a Wind Farm by Sliding-Mode-Based Design

Load frequency regulation is an essential auxiliary service used in dealing with the challenge of frequency stability in power systems that utilize an increasing proportion of wind power. We investigate a load frequency control method for multiarea interconnected power systems integrated with wind farms, aimed to eliminate the frequency deviation in each area and the tie-line power deviation between different areas. The method explores the derivative and integral terminal sliding mode control technology to solve the problem of load frequency regulation. Such technology employs the concept of relative degrees. However, the subsystems of wind-integrated interconnected power systems have different relative degrees, complicating the control design. This study develops the derivative and integral terminal sliding-mode-based controllers for these subsystems, realizing the load frequency regulation. Meanwhile, closed-loop stability is guaranteed with the theory of Lyapunov stability. Moreover, both a thermal power system and a wind power system are applied to provide frequency support in this study. Considering both constant and variable external disturbances, several numerical simulations were carried out in a two-area thermal power system with a wind farm. The results demonstrate the validity and feasibility of the developed method.

A Time-Varying Hierarchical Load Frequency Control Method for Multi-area Power System with Wind Power Participation and HVDC Tie-Line based on MAS Structure

A Time-Varying Hierarchical Load Frequency Control Method for Multi-area Power System with Wind Power Participation and HVDC Tie-Line based on MAS Structure

Optimal Reset-Control-Based Load Frequency Regulation in Isolated Microgrids

In isolated ac microgrids, multiple controllable distributed energy resources (DERs) may simultaneously participate in load frequency control (LFC). To improve system frequency dynamics and reduce the frequency deviation for such a multiple-DER microgrid,this paper presents a novel LFC method based on an optimal reset control (ORC) scheme. The proposed ORC comprises a baseline linear controller and resetting elements, i.e., the integrator outputs, determined by a linear quadratic regulation problem. The overshoot in frequency regulation induced by integral control is reduced, and the settling time is shortened. Besides, the simple structure facilitates its real-time application, making ORC a simple yet effective solution for the LFC of microgrids. By coordinating the reset controllers of all controllable DERs, the ORC further improves the overall frequency control performance. The asymptotic stability of the closed-loop system is theoretically proved. Finally, the effectiveness of the presented LFC method is validated by switch-level simulation studies.