Control Strategy For Microgrid Research Articles

Research on Control Strategy of Multi-energy Complementary Microgrid for Wind-solar Hydrogen Production

The multi-energy complementary microgrid system is an effective supplement to the areas not covered by the large power grid, and can effectively solve the problem of electricity consumption in remote areas. Based on the research of wind power, photovoltaic, energy storage, hydrogen production and fuel cell systems, this paper builds a wind-solar hydrogen storage multi-energy complementary micro-grid DC network system, and puts forward cooperative control strategies of the system under different working conditions, so as to achieve stable operation of the system and maximize energy utilization. Finally, PSCAD simulation software is used to establish a simulation model for each distributed unit to verify the stability and rationality of the control strategy. The simulation results show that the control strategy can achieve efficient, cooperative and stable operation between different energy sources on the premise of ensuring the power quality of the microgrid.

Improved Droop Control Strategy for Microgrids Based on Auto Disturbance Rejection Control and LSTM

This thesis proposes an improved droop control strategy design based on active disturbance rejection control and LSTM. This strategy uses the droop control method to coordinately control the distributed generation units (DGs) in a microgrid to achieve stable operation of the microgrid system. Linear-Auto Disturbance Rejection Control (LADRC) is introduced and an improved LADRC is designed based on the error principle. A disturbance compensation link is introduced on the basis of traditional LADRC to form ILADRC and a droop control strategy is used. Instead of improving the PD controller in LADRC, an improved droop control strategy is formed, which not only achieves natural decoupling between powers, but also improves the system’s immunity and transient operation capabilities. At the same time, in order to achieve adaptive parameter tuning in the improved droop control strategy, this article introduces long short-term memory (LSTM) to form an adaptive improved droop control strategy which further improves the system’s immunity and robustness. This article builds a simulation model through the MATLAB/Simulink simulation experiment platform and tests PI control and traditional droop control. The strategy and the improved droop control strategy designed in this thesis are experimentally compared and verified, and simulation analysis and verification are conducted on the two working conditions. The simulation results clearly demonstrate the superiority of the improved droop control strategy over PI control and traditional droop control, indicating that the correctness and reliability under various working conditions are verified.

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

On adaptive resilient secondary control for DC microgrids under false data injection attacks

AbstractDistributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber‐attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non‐idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three‐node DC microgrid to show the effectiveness of the proposed method.

Voltage stability control strategy for DC microgrid based on adaptive virtual DC motor

The large-scale integration of distributed energy sources and power electronic devices results in the DC microgrid exhibiting significant low inertia and weak damping characteristics. This, in turn, leads to inevitable fluctuations in the DC bus voltage, which endanger the stable operation of the DC system. Energy storage devices can provide equivalent inertia. To enhance the inertia and response speed of the DC bus interface converter, this paper proposes a power allocation parameter adaptive virtual DC motor control strategy based on a hybrid energy storage unit. The strategy introduces power allocation control to regulate the energy storage converter on the basis of virtual inertia parameter adaptive control, thereby enabling the energy storage converter to simulate the inertia and damping characteristics of a DC generator. The small-signal stability of the system is analyzed by establishing a small-signal model of the photovoltaic energy storage system and utilizing the impedance ratio criterion. Finally, the proposed control strategy is validated through simulation. The results demonstrate that the strategy effectively mitigates the fluctuations in bus voltage under varying photovoltaic power and sudden load changes, ensures the power distribution in the hybrid energy storage system, and enhances the dynamic response of the system.

Control strategy of PV microgrid grid-connected inverter

Abstract Aiming at the limitation of a three-phase inverter system to access clean energy, a design scheme of a two-stage microgrid grid-connected inverter system is proposed. The traditional double closed-loop control strategy has some disadvantages such as slow response, large fluctuation, and delay of frequency and phase tracking. Therefore, this paper proposes an improved grid-connected control strategy for photovoltaic microgrids with the addition of prediction units, which is established and verified by simulation. The results show that compared with the traditional method, the proposed control method has the advantages of fast dynamic response, small pulsation, and high power quality.

Distributed economic control strategy based on reinforcement pinning control for microgrids

Traditional droop control allocates distributed generation (DG) power based on capacity proportion, which leads to high system operating costs. To address this issue, this study proposes a distributed economic control strategy for microgrids based on reinforcement pinning (RP) control. To minimize operating costs, reinforcement learning (RL) continuously updates the Q-value table through reward feedback to obtain the optimal strategy. The optimal action determined by RL is set as the pinning reference value and transmitted to the pinning agent. Other agents achieve marginal cost consistency through the pinning information iteration matrix. To correct frequency deviations, proportional-integral (PI) control is used to ensure frequency stability for the system's operation. Simulations under different scenarios were conducted in MATLAB/Simulink. The results show that the proposed approach can coordinate the output of distributed power sources, reduce the operating costs of the microgrid, and maintain frequency stability.

Integrating Bayesian inference and neural ODEs for microgrids dynamics parameters estimation

The integration of solar and wind energy sources in microgrids has witnessed significant growth, giving rise to distinct challenges due to their intermittent nature when it comes to achieving efficient microgrid control. However, estimating the parameters of the dynamic microgrid components facilitates capturing the complex and time-varying characteristics of renewable energy generation. This requires an accurate estimation of the parameters from the dynamic differential equations for effective modeling and control. In this research paper, we presented a novel methodology based on the integration of Bayesian inference and Neural ODEs. The Bayesian inference quantifies the uncertainty, and the Neural ODEs model the dynamic systems. By combining the strengths of both methods, we aimed to achieve a precise and robust parameter estimation of the dynamic microgrid components. The methodology is validated on a simulated microgrid that consists of a diesel generator, Solar PV array, double-fed induction generator, and a battery energy storage system. The results showed promised inferences estimation obtained from the parameter posterior distribution even in the presence of uncertainty. This can enhance our understanding of the dynamics of renewable energy systems and can contribute to the advancement of decision-making microgrid control strategies.

Design of Universal Control Structure for Regulation of Voltage and Frequency in Hybrid Microgrid

Hybrid microgrid (HMG) control strategies reveal several critical research gaps that must be addressed. Current approaches often fall short in efficiency and reliability due to the inherent complexities of transitioning between grid-connected mode (GCM) and islanded mode (IM), resulting in increased losses and operational difficulties. Additionally, managing overloads, especially in IM, poses significant challenges for maintaining stable voltage and frequency regulation. This paper seeks to address these issues by introducing a novel universal control structure (UCS) for HMGs, which includes a frequency compensation unit (FCU) and a three-stage bidirectional AC/DC droop. It presents a streamlined yet robust solution for handling overloads, maintaining power balance, and ensuring precise voltage and frequency regulation while promising smooth maneuver in both GCM and IM. The proposed UCS provides flexible and reliable operation, allowing for the versatile placement of DC and AC sources and loads. This will reduce power transformation stages thereby lowering cost and increasing efficiency of system. Through experimental validation, this study demonstrates the effectiveness of the proposed control in addressing these critical research gaps and advancing the field of HMG control strategies.

Proportional‐integral‐differential‐inspired acceleration in distributed optimal control strategy for direct current microgrids

AbstractA PID‐inspired accelerated distributed optimal control algorithm is proposed for the economic dispatch problem of a multi‐bus DC microgrid, which contains both conventional generators (CGs) and renewable generators (RGs). Firstly, a constrained optimization problem with the aim of minimizing the power generation cost of the DC microgrid is established. To solve the optimization problem, an accelerated distributed optimal control algorithm in the discrete‐time domain is proposed. The convergence speed of the proposed algorithm is significantly improved compared to the existing distributed optimization algorithms without acceleration terms. More importantly, the communication cost is greatly reduced. The proposed algorithm is in a fully distributed manner, which means each controller only relies on the limited information from neighbouring controllers to achieve optimal cooperative control and bus voltage regulation across multiple buses. Finally, the effectiveness of the proposed algorithm is validated through numerical simulations.

Islanded micro-grid under variable load conditions for local distribution network using artificial neural network

ABSTRACT This research proposes an intelligent artificial neural network (ANN) controller designed for a micro-grid featuring solar photovoltaic, wind, and Battery Energy Storage (BES) systems, integrated with a shunt voltage source converter. The primary goal of this proposed method is to minimize Total Harmonic Distortion (THD) and ensure a stable Distribution Line Commutation Voltage (DLCV) amidst load variations, achieving a rapid settling period. The STF not only facilitates phase synchronization but also effectively separates harmonic and fundamental components. This research contributes to advancing micro-grid control strategies, particularly in enhancing stability and reducing distortion during variable operating conditions. Based on an analysis of the four test cases, it is evident that the proposed strategy lowered the THDs to 4.63%, 3.45%, 3.05%, and 4.70% for each test case, as well as the PF to a level that was similar. The proposed controller shows better results compared with other controllers like Sliding Mode Controller (SMC) and the Proportional-Integral Controller (PIC).

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.

LOAD FREQUENCY CONTROL STRATEGY FOR MICROGRID SYSTEMS USING COMPUTATIONAL INTELLIGENCE OPTIMIZED PID CONTROLLER

This paper presents Load Frequency Control (LFC) strategy for Microgrid (MG) systems, proposing a strategy based on a PID optimized controller using computational intelligence (CI) technique. The MG system, relying solely on Renewable Energy Sources(RES) is modeled in SIMULINK. A novel multi-objective function comprising of Weighted Integral Square Error, WISE and Weighted Integral Absolute Square Error WIASE (WISE+WIASE) is proposed to enhance system performance. Using Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO), the Proportional, Integral and Derivative controller parameters are tuned. Simulation results revealed the superiority of the PSO-PID with the least steady state error of 4.1560e-07 and overshoot 0.0611 respectively over the APSO-PID with steady state error value of 4.8144e-07 and overshoot of 0.1334 respectively. PSO-PID also outperformed APSO-PID across other indices (ITAE, IAE, ITSE and ISE) making the PSO-PID to excel over the APSO-PID in terms of steady state error and overshoot. The controllers proved to be robust in various conditions with the PSO-PID controller exhibiting more robustness and stability than the APSO-PID controller. Comparing PSO and APSO algorithms, PSO outperformed APSO in terms of robustness because for five number of runs in both algorithms, the PSO algorithm presents a more quality and consistent results and also display a better convergence than the APSO algorithm . Conversely, APSO surpasses PSO in fast computational time and convergence speed. These findings underscore the effectiveness of the proposed LFC strategy for RES based Microgrid systems, providing valuable insights into comparative performance of PSO and APSO algorithms.

Neural Network Based Control Scheme for Micro-Grid Integrated HRES

Neural Network Based Control Scheme for Micro-Grid Integrated HRES

A control strategy for peak clipping and valley filling based on DC microgrid

Abstract In this paper, the peak clipping and valley filling control strategy for DC microgrid is proposed. The purpose of the proposed control strategy is to make the power system run more smoothly while reducing the cost of electricity. The working mode of the DC microgrid is determined by the peak-valley time of the electricity cost. By distinguishing different periods of time, the charging and discharging of the battery are controlled, and the difference between the peak and valley electricity prices is earned for the users. The maximum power was extracted from photovoltaic panels by P&O (Perturb and Observe) MPPT algorithm, and SPWM technology based on droop control strategy was adopted. The system is simulated in matlab/simulink environment, and the feasibility of the control strategy is verified.

Two-Stage Robust Voltage Control Strategy for Seaport Microgrids With Health-Aware All-Electric Ships

Two-Stage Robust Voltage Control Strategy for Seaport Microgrids With Health-Aware All-Electric Ships

Secondary Control Strategies in the DC Microgrids

Now, DC microgrids have become more popular for several reasons, including the lack of issues related to reactive power and frequency control, the direct integration of energy storage devices and solar photovoltaic, and the higher utilization of DC loads. A DC microgrid using several sources (distributed generation) is a popular research area. The main issue in such a DC microgrid is to provide good voltage regulation and proportional power sharing among all sources. Control strategy is very important to solve the above issue in order to maintain the reliability and stability of DC microgrids. Hence, a comprehensive review of DC microgrid control techniques is essential. Compared to other methods, hierarchical control is widely used to solve the aforementioned issue. It consists of three layers of control: primary control, secondary control, and tertiary control. At the primary level, to improve current sharing performance, droop control is usually applied. Secondary control is used for voltage regulation of the DC bus. A tertiary control is a higher-level control to achieve Optimization and economical grid operation. In traditional primary control, it is not possible to attain accurate power sharing and voltage regulation simultaneously. Thus, secondary control is required. So, this paper reviews the secondary level control techniques in the hierarchical control strategy for DC microgrids. Precisely, Centralized, distributed, and decentralized approach-based secondary control are reviewed. Several secondary control techniques have been thoroughly examined in terms of their advantages and disadvantages making this an excellent resource for both academics and business executives.

A Stabilization Control Strategy for Wind Energy Storage Microgrid Based on Improved Virtual Synchronous Generator

In high-penetration renewable-energy grid systems, conventional virtual synchronous generator (VSG) control faces a number of challenges, especially the difficulty of maintaining synchronization during grid voltage drops. This difficulty may lead to current overloads and equipment disconnections, and it has an impact on the security and reliability of the system, as well as limiting the dynamic reactive power support capability of the system. To solve this problem, in this study, a wind–solar hybrid power generation system is designed with a battery energy storage device connected on the DC side, and proposes a low voltage ride-through (LVRT) control strategy for the grid-connected inverter based on an improved VSG. The control strategy employs an integrated current limiting technique combining virtual impedance and vector current limiting to ensure that the VSG exhibits good dynamic power support characteristics during symmetrical faults by adjusting the setpoint value of reactive power. At the same time, it maintains the synchronization and power angle stability of the VSG itself to achieve the goal of LVRT. Simulation results show that the proposed control strategy can effectively suppress the renewable power fluctuations (about 30% reduction in fluctuations compared to the conventional strategy) and ensure the safe and reliable operation of the renewable energy sources and VSGs during grid-side faults. In addition, it provides a given reactive power support and stable grid voltage control (voltage dips reduced by about 20%), which significantly enhances the LVRT capability of the hybrid wind–solar-storage generation system.

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An Improved Nonlinear Droop Control Strategy in DC Microgrids

An Improved Nonlinear Droop Control Strategy in DC Microgrids