Frequency Control Of Microgrid Research Articles

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Suppression of low‐frequency oscillations in hybrid/multi microgrid systems with an improved model predictive controller

AbstractGrid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method.

Voltage and frequency control of microgrid in presence of micro-turbine interfaced to matrix converter

The active and reactive load changes have a significant impact on voltage and frequency. In this paper, in order to stabilize the microgrid (MG) against load variations in islanding mode, the active and reactive power of all distributed generators (DGs), including energy storage (battery), diesel generator, and micro-turbine, are controlled. The micro-turbine generator is connected to MG through a three-phase to three-phase matrix converter, and the droop control method is applied for controlling the voltage and frequency of MG. In addition, a method is introduced for voltage and frequency control of micro-turbines in the transition state from grid-connected mode to islanding mode. A novel switching strategy of the matrix converter is used for converting the high-frequency output voltage of the micro-turbine to the grid-side frequency of the utility system. Moreover, using the switching strategy, the low-order harmonics in the output current and voltage are not produced, and consequently, the size of the output filter would be reduced. In fact, the suggested control strategy is load-independent and has no frequency conversion restrictions. The proposed approach for voltage and frequency regulation demonstrates exceptional performance and favorable response across various load alteration scenarios. The suggested strategy is examined in several scenarios in the MG test systems, and the simulation results are addressed.

Modified Tilt-integral active disturbance rejection controller with inertia emulated direct current link for frequency control of marine microgrid

The increasing integration of intermittent renewable sources (RSs) poses a dynamic frequency stability challenge for modern marine vessel microgrids. To address this issue, this paper proposes a novel control approach, specifically targeting frequency and tie-line power stabilization in a diverse source marine microgrid (MμG) with two intertied areas featuring renewable (wind-wave) sources. The suggested approach introduces a modified tilt-integral active disturbance rejection (TI-ADRC) controller designed to ensure effective damping of power frequency oscillations. As the control scheme depends on the optimal setting of the proposed controller, a recently developed marine predator technique (MPT) has been adopted. The performance of the proposed controller is compared with other recent controllers viz. PID, tilt-integral derivative (TID), two-degree-of-freedom (2DOF)-PID, fuzzy-PI, and ADRC to validate its superiority. To further enhance the system dynamics, a precise modeling of inertia emulated direct current (IEPDC) tie link is incorporated in microgrid system. The impact assessments, considering time delays with pre/post IEPDC link, demonstrate a substantial 57.79% and 81.53% reduction in peak frequency overshoot compared to DC link (conventional model) and AC link, respectively. The analysis of the eigen plot confirms the stability of the control system. Sensitivity assessments of the controller against ± 30% parametric variations and load fluctuations are conducted, affirming its robustness. Finally, the result from OPAL-RT confirm the practicality of the proposed method. It is asserted that the suggested controller is reliable and functions well in n the presence of diverse disruptions and parametric variations.

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.

Optimizing load frequency control in microgrid with vehicle-to-grid integration in Australia: Based on an enhanced control approach

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency.

Enhanced harris hawks optimization based load frequency control of multi area microgrid based water treatment plant with consideration of 3DOF-(FO-PIDN)/(TIDN) controller

Municipal water supply system (WSS) consist of different pumping stages viz. intake, water treatment plant (WTP) and intermediate pumping station (IPS). Usually, the power supply for WSS is obtained through public power tapping sources. However, this often leads to load shedding and disruption of the water supply. This paper focuses on the concept, considering WSS as a multi-source multi-area microgrid scheme, this includes renewable energy sources (RES) such as solar, wind, etc. Moreover, the study incorporates a Battery Energy Storage System (BESS) and a Diesel Engine Generator (DEG) to provide power supply during peak demand at each pumping station. Frequency control is essential for optimizing system performance. This paper proposes Enhanced Harris Hawks Optimization Algorithm (EHHO) based PID controller for regulating the frequency in the multi-microgrid-based water supply system. The proposed controller is implemented in MATLAB simulation software, and its response is compared with other optimization methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Moreover, implementation and comparison of higher degree order controller such as 3DOF-FOPIDN controller and 3DOF-TIDN controllers are tested under PSO method to observe the performance as well as robustness of the controller. The results indicate that the proposed controller provides better performance in controlling the load frequency deviation, thus improving the efficiency and reliability of the multi-microgrid system for consideration of municipal water supply.

Deep reinforcement learning for adaptive frequency control of island microgrid considering control performance and economy

To achieve frequency stability and economic efficiency in isolated microgrids, grid operators face a trade-off between multiple performance indicators. This paper introduces a data-driven adaptive load frequency control (DD-ALFC) approach, where the load frequency controller is modeled as an agent that can balance different objectives autonomously. The paper also proposes a priority replay soft actor critic (PR-SAC) algorithm to implement the DD-ALFC method. The PR-SAC algorithm enhances the policy randomness by using entropy regularization and maximization, and improves the learning adaptability and generalization by using priority experience replay. The proposed DD-ALFC method based on the PR-SAC algorithm can achieve higher adaptability and robustness in complex microgrid environments with multiple performance indicators, and improve both the frequency control and the economic efficiency. The paper validates the effectiveness of the proposed method in the Zhuzhou Island microgrid.

Belbic frequency control of provisional microgrid with hybrid AC/DC microgrid and renewable energy sources

Nowadays, intelligent control methods play an important role in the advancement of technology and the human movement towards further evolution. The development of new frameworks for power generation and distribution systems by designing a microgrid structure with economic capabilities is one of these areas of progress. Therefore, this paper introduces a new practical method for controlling the frequency of provisional microgrid and is able to cover the following issues at the same time including (1) It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources; (2) Introduces a method for microgrid frequency control under different operational conditions that performs based on the brain emotional learning; (3) Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the first time; (4) Confirms the robustness of the proposed method under severe load changes and generation from renewable sources. So, the effects of wind turbines and solar energy are considered in the simulation scenario and under the influence of various changes in load and system uncertainties, the robustness and efficiency of the proposed method are well demonstrated.

Deep Meta-Reinforcement Learning-Based Data-Driven Active Fault Tolerance Load Frequency Control for Islanded Microgrids Considering Internet of Things

Deep Meta-Reinforcement Learning-Based Data-Driven Active Fault Tolerance Load Frequency Control for Islanded Microgrids Considering Internet of Things

Simultaneous Damping and Frequency Control in AC Microgrid Using Coordinated Control Considering Time Delay and Noise

The incorporation of converter-based generating sources in utility-scale microgrids causes frequency instability and low-frequency oscillations (LFOs), which is also a reason for degeneration in system stability. Damping frequency control is an essential part of alternating current (AC) microgrid system operation and control. Sudden changes in load, variations in renewable power outputs due to changes in solar insolation or wind speed, and so on factors cause the system frequency to deviate from the nominal value. Therefore, the role of a frequency controller is to maintain the dynamic stability in an AC microgrid by retaining the system frequency at the nominal value. Again, AC microgrids with high renewable power penetration face even more difficulty in maintaining frequency stability because of their poor inertial response. The research presented here proposes a novel approach for grid-connected AC microgrid oscillation damping and frequency control that simultaneously takes into consideration time delay and noise. To improve the frequency response and dampen LFOs by providing the voltage and frequency within the specified range, a coordinated technique-based control approach is adopted. In the developed hybrid control, the frequency controller relies on active power modulation, while the power oscillation damping controller is dependent on reactive power modulation. To improve stability and reduce communication consequences such as noise and signal latency (time delay), the developed power oscillation damping controller and frequency controller are coordinated along with the robust linear quadratic Gaussian controller. The comparative investigation of the effectiveness of the coordinated control technique is employed in the software of MATLAB/Simulink for grid-connected AC microgrid. The outcome of the simulation illustrates the superior effectiveness of the suggested controller over the traditional droop controller for huge power flows under disturbance with various operating conditions, under/overfrequency events, time delay, and noise. This grid encouragement capability for AC microgrids is anticipated to lead to novel possibilities for generating revenue.

An adaptive virtual inertia control design for energy storage devices using interval type-2 fuzzy logic and fractional order PI controller

This research paper introduces a novel methodology, referred to as the Optimal Self- Tuning Interval Type-2 Fuzzy-Fractional Order Proportional Integral (OSTIT2F-FOPI) controller for inverter-based energy storage system (ESS) to regulate the input and output power of ESSs, aimed at enhancing the frequency control of microgrids (MGs) with varying levels of renewable energy sources (RESs) integration. The proposed method entails the integration of a self-tuner interval type-2 fuzzy logic controller (IT2FLC) and FOPI controller within a virtual inertia (VI) control loop. Within the OSTIT2F-FOPI control framework, the IT2FLC block tunes the parameters of the FOPI controller in an online method to deal with problems associated with fixed gains of the FOPI controller. Consequently, the VI control unit can emulate varying degrees of inertia and damping based on the integrated levels of RESs or load variations. The self-tuning capability of the proposed method enables real-time performance improvement of the system, resulting in enhanced frequency stabilization and improved control efficiency, particularly in low-inertia MGs. Moreover, a different control methodology, referred to as EO-FOPI-based VI, is developed to compare and assess the results achieved by the suggested OSTIT2F-FOPI control framework. This alternative method incorporates Equilibrium Optimization (EO) to adjust VI and FOPI parameters and provide a comparative analysis of the obtained outcomes.

Distributed Secondary Frequency Control for AC Microgrids Using Load Power Forecasting Based on Artificial Neural Network

In islanded ac microgrids with heterogeneous distributed generations (DGs), secondary frequency control (SFC) can restore the frequency that deviates from the nominal level due to the primary P-f droop control. Traditionally, to realize SFC, the instantaneous frequency needs to be directly measured or estimated to produce frequency compensations, which increases system cost and complexity. In this paper, a new spatial load power forecasting for distributed secondary frequency control (SLPF-DSFC) based on artificial neural networks is proposed. Without frequency measurement, it can effectively eliminate frequency deviations and autonomously cope with various load changes by optimizing active power outputs from DGs. The distributed average and pinning protocols based on graph theory are also included. Comprehensive case studies demonstrate that the proposed SLPF-DSFC method shows superior performance over conventional SFC approaches in terms of frequency stabilization and voltage harmonics reduction during load variations, SFC startup/shutdown, as well as communication delays.

Blockchain-Based Decentralized Frequency Control of Microgrids Using Federated Learning Fractional-Order Recurrent Neural Network

This paper presents a blockchain-based decentralized frequency control of an islanded microgrid (MG) using a novel federated learning fractional order recurrent neural network (FL-FORNN). A self-adaptive proportional-integral-derivative (PID) controller is proposed using FL-FORNN to handle the uncertainties of power generation by the prosumers while participating in the trading of the islanded MG. In this work, the frequency oscillations during peer-to-peer (P2P) energy trading are regulated through the appropriate design of an adaptive controller and the implementation of a smart contract participation matrix (SCPM) for balancing the generation and consumer demand. A Proof-of-Authority (PoA) private blockchain-based SCPM has been developed to secure the contract among peers. The SCPM provides the power demand information from a consumer to a prosumer who participates in the P2P energy market. It also provides power reference to the prosumer distributed generation as a supplementary control, in addition to the secondary frequency control of the system. To study this, an islanded MG comprising four source nodes (prosumers) and three demand nodes (consumers) are tested with the implementation of the PoA-based blockchain framework. With its SCPM calculation running in the blockchain framework, the decentralized control of prosumers is implemented by interfacing the OPAL-RT with Raspberry Pi devices. The robustness of the proposed FL-FORNN controller has been verified with the FORNN tuned PID controller. Furthermore, a comparative analysis is made with the centralized scheme and traditional decentralized frequency control technique.

Voltage and Frequency Control of Microgrids Considering State Constraints and Attacks

Voltage and Frequency Control of Microgrids Considering State Constraints and Attacks

Coordinating the Participation of Energy Sources and Wind Units in Micro-grid Frequency Control by Delaying Micro-grid Parameter Measurement Systems

Coordinating the Participation of Energy Sources and Wind Units in Micro-grid Frequency Control by Delaying Micro-grid Parameter Measurement Systems

Comparison between PID and PSO-PID controllers in analysing the load frequency control in interconnected microgrids in a deregulated environment

Comparison between PID and PSO-PID controllers in analysing the load frequency control in interconnected microgrids in a deregulated environment

Decentralized Secondary Frequency Control of Autonomous Microgrids via Adaptive Robust -Gain Performance

This paper presents an innovative passivity-based L2-gain performance (L2-GP) adaptive robust control method for the design of primary and secondary frequency control in the low-inertia microgrid with lines and the inverter-interfaced generation. First, by exploiting and analyzing the internal structural properties, the Port-controlled Kuramoto-Hamiltonian model with dissipation for the microgrid system is derived. Combining the dynamics of Kuramoto oscillators with energy viewpoints and following the physical features of dynamic energy coordination in the microgrid, an adaptive robust L2-GP decentralized secondary control is developed to guarantee frequency restoration and active power sharing. An adaptive mechanism is adopted to estimate the electrical parameters so that the parameter randomness can be overcome and fast transient response, high performance, and robust stability can be achieved. Via local measurements, the decentralized framework accordingly obviates the need of any communication links for information broadcast or exchange. Finally, the comprehensive case studies are presented to validate satisfactory performance under rapid randomness and flexibility during plug-and-play operations.

A Stochastic Bayesian Game for Securing Secondary Frequency Control of Microgrids Against Spoofing Attacks With Incomplete Information

A Stochastic Bayesian Game for Securing Secondary Frequency Control of Microgrids Against Spoofing Attacks With Incomplete Information

Delay-Dependent Stability Evaluation for Temperature Control Load Participating in Load Frequency Control of Microgrid

Delay-Dependent Stability Evaluation for Temperature Control Load Participating in Load Frequency Control of Microgrid