Microgrid Control Research Articles

Real-Time Privacy-Preserving Average Consensus and Its Application to Secondary Control for AC Microgrid

Real-Time Privacy-Preserving Average Consensus and Its Application to Secondary Control for AC Microgrid

A microgrid control scheme for islanded operation and re-synchronization utilizing Model Predictive Control

Enhancing grid resilience is proposed through the integration of distributed energy resources (DERs) with microgrids. Due to the diverse nature of DERs, there is a need to explore the optimal combined operation of these energy sources within the framework of microgrids. As such, this paper presents the design, implementation and validation of a Model Predictive Control (MPC)-based secondary control scheme to tackle two challenges: optimal islanded operation, and optimal re-synchronization of a microgrid. The MPC optimization algorithm dynamically adjusts input signals, termed manipulated variables, for each DER within the microgrid, including a gas turbine, an aggregate photovoltaic (PV) unit, and an electrical battery energy storage (BESS) unit. To attain optimal islanded operation, the secondary-level controller based on Model Predictive Control (MPC) was configured to uphold microgrid functionality promptly following the islanding event. Subsequently, it assumed the task of power balancing within the microgrid and ensuring the reliability of the overall system. For optimal re-synchronization, the MPC-based controller was set to adjust the manipulated variables to synchronize voltage and angle with the point of common coupling of the system. All stages within the microgrid operation were optimally achieved through one MPC-driven control system, where the controller can effectively guide the system to different goals by updating the MPC’s target reference. More importantly, the results show that the MPC-based control scheme is capable of controlling different DERs simultaneously, mitigating potentially harmful transient rotor torques from the re-synchronization as well as maintaining the microgrid within system performance requirements.

Exponential stabilization of linear systems with feedback optimization and its application to microgrids control

This paper proposes a feedback-optimization-based control method for linear time-invariant systems, which is aimed to exponentially stabilize the system and, meanwhile, drive the system output to an approximate solution of an optimization problem with convex set constraints and affine inequality constraints. To ensure the exponential stability of the closed-loop system, the original optimization problem is first reformulated into a counterpart that has only convex set constraints. It is shown that the optimal solution of the new optimization problem is an approximate optimal solution of the original problem. Then, based on this new optimization problem, the projected primal–dual gradient dynamics algorithm is used to design the controller. By using the singular perturbation method, sufficient conditions are provided to ensure the exponential stability of the closed-loop system. The proposed method is also applied to microgrid control.

Improvement of an AC microgrid system’s stability by using a fuzzy logic-based PSS controller

This research article presents the low-frequency oscillation damping control of an AC Microgrid. It is integrated with four distributed generation units in which two DGs are renewable energy source based solar PV and wind energy using DFIG and the other two DGs are conventional synchronous generator-based hydro and diesel generators. There is insufficient generation through renewable-based DG then a diesel generator is employed as an emergency unit to fulfil the requirements and maintain and control the deviation of the frequency of the system. The use of renewable-based DG affected the system's dynamic stability and the characteristics of an integrated microgrid differ from conventional sources of generation. This investigation explores the impression of the deployment of multiple forms of the power oscillation damping (POD) controllers to distributed generation sources of grid forming AC Microgrid. The main various types of POD controllers are conventional lead-lag PSSs, MBPSS-4B, and proposed robust fuzzy logic PSS (FLPSS) controllers are implemented on distributed generation units of AC Microgrid for the elimination of low-frequency oscillation. Low oscillation frequencies between 0.1 and 2 Hz are the focus of this work. The system's damping ratios and stability margins because of MG integration are observed using eigenvalues analysis. The results of the simulation test demonstrate the accomplishment of the robust fuzzy logic PSS controller regarding conventional lead-lag PSSs and the MBPSS-4B controller during circumstances of fault.

POWER QUALITY ENHANCEMENT USING GWO OPTIMIZED ANFIS CONTROLLER BASED STATCOM IN MG

In power systems, voltage stability and power quality are major problems. Hence, this work formulated a novel flexible control algorithm for STATCOM in order to mitigate power quality issues in micro grids. Further to improve the performance of the STATCOM, an intelligent optimised ANFIS controller has been incorporated. The suggested technique has two parts; GWO is employed in the first stage to learn the ANFIS parameters. The performance of the suggested ANFIS-GWO approach is assessed in the second stage. Thus, the efficacy of the proposed controller is examined under different case studies using both simulation and experimental results. From the results, it was found that when the system is subjected to load changes, the dynamic performance of the stem is improved and the dc link voltage remains constant. As the proposed controller achieves less fluctuation, it can be considered as suitable controller for micro-grids.

Distributed periodic event-triggered secondary control for islanded microgrids with switching topologies and communication delays

With the application of distributed generators (DGs), microgrids (MGs) have gained much attention. Combined with the distributed communication of networked systems, we investigates the secondary restoration problem in islanded microgrids. The periodic event-triggered (PET) controllers are designed for voltage restoration and frequency restoration with accurate active power allocation, which can naturally avoid Zeno behavior. By means of a common Lyapunov function, a PET protocol is designed that requires only the information of neighbors for event detection. Both event-checking sampling period and event trigger parameter are taken into account to reduce computation and communication redundancy by choosing appropriate parameters. Furthermore, considering the effect of communication delay, sufficient conditions are given to accomplish the secondary control objectives with allowable delays. Switching topologies are considered in an extra discussion. Finally, the effectiveness of the proposed PET control strategies is verified by several MATLAB simulations.

Optimal control of microgrid using ALO optimization MPPT with power quality enhancement

AbstractThe paper presents a normalized, varying step size-based least mean square-based control for a standalone microgrid and Ant Lion optimization-based maximum power point tracking. This modified version of the incremental conductance algorithm addresses issues like slow dynamic response, fixed step size issues, and steady state oscillations. Comparative analysis with well-known techniques shows that Ant Lion optimizes the tracking of maximum power points more accurately, with fewer oscillations and increased efficiency. Power management and power quality are maintained through proposed adaptive voltage source converter control. The proposed voltage control shows better performance under various dynamic and steady conditions.

Frequency control of an islanded microgrid with multi-stage PID control approach using moth-flame optimization algorithm

Frequency control of an islanded microgrid with multi-stage PID control approach using moth-flame optimization algorithm

Optimal hybrid resonant current controller for microgrids connected to an unbalanced IEEE test distribution network

Microgrids (MGs) based on renewable energies have emerged as a proficient strategy for tackling power quality issues in conventional distribution networks. Nonetheless, MG systems require a suitable control scheme to supply energy optimally towards the electrical grid. This paper presents an innovative framework for designing hybrid Proportional-Resonant (PR) controllers with Linear Quadratic Regulators (LQR), PR+LQR, which merge relevant properties of PR and LQR controllers. This method simultaneously determines the MG control parameters and the current unbalanced factor generated at the distribution network. We select the traditional IEEE 13-bus test feeder network and place two MGs at strategic locations to validate our approach. Moreover, we use the Grey Wolf Optimizer (GWO) to find control parameters through a reliable fitness function that leads to high-performance microgrids. Finally, we conceive several tests to assess the efficacy of GWO for tuning the hybrid controller and compare the resulting data across distinct realistic operation conditions representing power quality events. So, we choose four case studies considering different renewable energy penetration indexes and power factors and evaluate the effects of the MGs over the distribution grid. We also compare the proposed hybrid PR+LQR controller against closely-related alternatives from the literature and validate its robustness and stability through the disk margin approach and the Nyquist criterion. Our numerical simulations prove that hybrid controllers driven by GWO are highly reliable strategies, yielding an average unbalanced current reduction of 30.03%.

Fuzzy H ∞ control of nonlinear DC microgrids under aperiodic DoS attacks – an event-triggered approach

This paper studies the fuzzy H ∞ control of nonlinear DC microgrids subject to the dynamic event-triggered mechanism (ETM), aperiodic DoS attacks, noises and mismatching premises. First, using the information of DC microgrid's T-S fuzzy model and aperiodic DoS attacks, a discrete-time resilient dynamic ETM is proposed, which can save constrained system resources, as well as actively exclude attack-induced dropouts and Zeno behaviour. Second, a fuzzy switched system model is built, which provides a unified platform to evaluate effects of all the affecting factors such as the dynamic ETM and DoS attacks. Third, by constructing a piecewise Lyapunov functional, criteria for exponential stability with guaranteed H ∞ performance are obtained, and an event-triggered fuzzy injection current controller is further designed. Simulation results confirm that, in the presence of aperiodic DoS attacks and noises, the proposed controller can stabilise the nonlinear DC microgrids, while the dynamic ETM works well in reducing the triggering rate without dropouts. Tradeoffs can be made between control and communication resources, and the proposed fuzzy controller achieves shorter settling times and smaller overshoots than the robust 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.

Security control of direct current microgrids under deception attacks—A dynamic event‐triggered approach

AbstractThis paper studies the security control of direct current (DC) microgrids under deception attacks, and a dynamic event‐triggered mechanism (DETM) is proposed to save constrained network bandwidth. The DETM can reduce the frequency of data communications and exclude Zeno behavior inherently. Then, a time‐delay closed‐loop system model is built, which integrates parameters of the DC microgrid, the DETM, deception attacks, injection current controller, and network‐induced delays in one unified framework. Sufficient conditions are derived for globally exponentially ultimately bounded stability in mean square, which establish the relationship between system performance and the contributing factors such as the DETM, deception attacks, and network delays. Further, a co‐design method is presented to derive the parameters of the controller and the DETM in just one step. Simulation results confirm the effectiveness of the proposed method for security control of DC microgrids, achieving 21.5% savings of communication resources while effectively stabilizing the system even 10.7% of the transmitted data that are manipulated by the deception attacks.

Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage

Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs and elevated power density requirements necessitate alternative strategies to balance power supply and demand. In recent years, substantial attention has turned towards harnessing Electric Vehicle (EV) batteries as Mobile EV Energy Storage (MEVES) units to counteract frequency variations in IUMGs. Integrating MEVES into the IUMG infrastructure introduces complexity and demands a robust control mechanism for optimal operation. Therefore, this paper introduces a robust, high-order degree of freedom cascade controller known as the 1PD-3DOF-PID (1 + Proportional + Derivative—Three Degrees Of Freedom Proportional-Integral-Derivative) controller for Load Frequency Control (LFC) in IUMGs integrated with MEVES. The controller’s parameters are meticulously optimized using the Coati Optimization Algorithm (COA) which mimics coati behavior in nature, marking its debut in LFC of IUMG applications. Comparative evaluations against classical controllers and algorithms, such as 3DOF-PID, PID, Reptile Search Algorithm, and White Shark Optimizer, are conducted under diverse IUMG operating scenarios. The testbed comprises various renewable energy sources, including wind turbines, photovoltaics, Diesel Engine Generators (DEGs), Fuel Cells (FCs), and both Mobile and Fixed energy storage units. Managing power balance in this entirely renewable environment presents a formidable challenge, prompting an examination of the influence of MEVES, DEG, and FC as controllable units to mitigate active power imbalances. Metaheuristic algorithms in MATLAB-SIMULINK platforms are employed to identify the controller’s gains across all case studies, ensuring the maintenance of IUMG system frequency within predefined limits. Simulation results convincingly establish the superiority of the proposed controller over other counterparts. Furthermore, the controller’s robustness is rigorously tested under ± 25% variations in specific IUMG parameters, affirming its resilience. Statistical analyses reinforce the robust performance of the COA-based 1PD-3DOF-PID control method. This work highlights the potential of the COA Technique-optimized 1PD-3DOF-PID controller for IUMG control, marking its debut application in the LFC domain for IUMGs. This comprehensive study contributes valuable insights into enhancing the reliability and stability of Islanded Urban Microgrids while integrating Mobile EV Energy Storage, marking a significant advancement in the field of Load-Frequency Control.

Adaptive Load Frequency Control in Microgrids Considering PV Sources and EVs Impacts: Applications of Hybrid Sine Cosine Optimizer and Balloon Effect Identifier Algorithms

The negative impacts of microgrids (µGs) on the load frequency highlight the importance of implementing a robust, efficient, and adaptable controller to ensure stability. This work introduces an adaptive load frequency control (LFC) for an isolated µG that includes a PV system and electric vehicles (EVs), which have a significant impact on frequency. This control utilizes a combination of sine cosine optimization (SCO) and balloon effect identifier (BEI) algorithms. The controller presented in this work transforms the LFC process into an optimization problem that is highly compatible with various random situations encountered in the control process. The suggested control method is a novel approach by utilizing SCO+BEI for adaptive LFC application, resulting in a highly efficient response. The effectiveness of the proposed adaptive controller is assessed under the conditions of 17 MW variable load, system parameters uncertainties, and installed PV systems of 6 MW. MATLAB / Simulink package is rummage-sale as a digital test environment. According to simulation results, the proposed adaptive controller succeeds in regulating the frequency and power of an islanded µG. To measure the efficiency of the proposed control scheme, a comparison between other control techniques (such as adaptive controller using Jaya+BEI and classical integral controller) is done. The findings of the studied scenarios assured that the not compulsory control method using (SCO+BEI) has an obvious superiority over other control methods in terms of frequency solidity in case of random load instabilities and parameter uncertainties. Finally, it can be said that the proposed controller can better ensure the safe operation of the µGs.

A wideband harmonic self-mitigation controller of the VSG-based islanded microgrid without harmonic extraction

In virtual synchronous generator (VSG)-based islanded microgrid, voltage harmonics induced by nonlinear loads seriously degrade system power quality. Existing harmonic mitigation controllers mainly rely on the extracted harmonic components to control, and generally have the weakness that can only mitigate harmonics with known frequencies. To better improve the power quality of the islanded microgrid, a wideband harmonic self-mitigation controller (WHSMC) is proposed in this paper, which can effectively mitigate wideband voltage harmonics above the fundamental frequency without extracting harmonics. Firstly, based on the impedance method, the reason why nonlinear loads will cause voltage harmonics in the islanded microgrid is analyzed in detail. Secondly, by establishing the impedance model of VSG with the proposed WHSMC, the mechanism how the proposed WHSMC mitigates voltage harmonics without knowing the harmonic frequency in advance is theoretically revealed. Finally, the effectiveness of the proposed WHSMC is verified through simulation and experimental results. The experiments indicate that the voltage total harmonic distortion (THD) can decrease from 7.02 % to 3.41 % under nonlinear load disturbance and from 8.27 % to 3.99 % under pulse load disturbance.

Distributed Control of Hydrogen-Based Microgrids for the Demand Side: A Multiagent Self-Triggered MPC-Based Strategy

Distributed Control of Hydrogen-Based Microgrids for the Demand Side: A Multiagent Self-Triggered MPC-Based Strategy

Learning-Based Virtual Inertia Control of an Islanded Microgrid With High Participation of Renewable Energy Resources

Learning-Based Virtual Inertia Control of an Islanded Microgrid With High Participation of Renewable Energy Resources

Data-Driven Distributed H ∞ Current Sharing Consensus Optimal Control of DC Microgrids via Reinforcement Learning

Data-Driven Distributed <i>H</i> _∞ Current Sharing Consensus Optimal Control of DC Microgrids via Reinforcement Learning

Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network

Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi-feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi-feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi-feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.

Practical Considerations for the Design and Control of Networked Microgrids: Enabling effective operation

Practical Considerations for the Design and Control of Networked Microgrids: Enabling effective operation