Reduce Frequency Deviations Research Articles

A Novel VSG with Adaptive Virtual Inertia and Adaptive Damping Coefficient to Improve Transient Frequency Response of Microgrids

This paper proposes a combined adaptive virtual Inertia and adaptive damping control of a virtual synchronous generator (AID-VSG) to improve the dynamic frequency response of microgrids. In the proposed control scheme, the VSG’s virtual inertia and damping coefficients adapt themselves during the transients to, respectively, reduce frequency deviations and increase the oscillations’ damping. In addition, as an important feature, the proposed AID-VSG is suitable for distributed control scheme applications and is designed to not rely on phase-locked loop (PLL) measurements, which avoids PLL stability issues on weak grids. The control parameters of the proposed AID-VSG are tuned by the particle swarm optimization (PSO) algorithm to minimize the overshoot and settling time of the microgrid’s frequency during an islanding event. The AID-VSG is validated by a comparative analysis with three existing VSG control schemes, also tuned by the stated optimization algorithm. The performance of each compared VSG strategy is evaluated through the simulation of a set of 10,000 initial conditions, using the islanded microgrid’s nonlinear model. The best response among the VSG strategies was achieved by the proposed AID-VSG control for both the optimization problem and the set of initial conditions’ simulations.

Knowledge-shareable adaptive deep dynamic programming for hierarchical generation control of distributed high-percentage renewable energy systems

The increased proportion of distributed renewable energy sources connected to systems leads to frequency regulation difficulty, such as uncoordinated algorithms, inconsistent optimization and control objectives, and long training time. This work proposes a knowledge-shareable adaptive deep dynamic programming (KSADDP) to improve the active control accuracy and generation economy of the proposed hierarchical generation control framework, which contains two-layer control structures. In the first layer, droop control is employed to optimize the outputs of controllable generators, follow the frequency changes, reduce frequency deviation, and improve the operation economy of battery energy storage systems. In the second layer, a KSADDP is proposed to mitigate local optimum and accelerate the hybrid cooperative structure optimization by sharing the parameters of neural networks. These two layers synergistically optimize control performance and reduce generation costs. The simulation results of a three-high-percentage renewable energy system show that: with two layers of cooperation and sharing training information, the KSADDP outperforms the comparison algorithms in seven evaluation indices and reduces the generation cost by 4.85%–7.41 % compared to the comparison algorithms. The proposed KSADDP cooperates the time scales between multiple layers, cooperates the objectives between multiple layers, increases the parameter-sharing process, and reduces training time.

Multi-timescale analysis and quantification of dynamic regulation characteristics of DFIM-based variable-speed pumped storage units in alleviating wind power fluctuations

To comprehensively clarify the regulating role of variable-speed pumped storage system (VSPS) in alleviating wind power fluctuations, this paper quantitatively evaluates the multi-timescale frequency regulation performance of VSPS in a typical wind-hydro-pumped storage hybrid system (WHPHS). Firstly, the flexible equivalent frequency response models of VSPS and WHPHS are established. Then, the effectiveness of fixed-speed pumped storage system (FSPS) and VSPS in enhancing the stability of dominant hydropower system (HS) in WHPHS is compared, and the dynamic regulation behaviors between VSPS and FSPS are quantitatively evaluated. Based on the operational characteristics, the differences in the allocation of regulation responsibilities between VSPS/FSPS and HS for frequency regulation are clarified. The results indicate that VSPS contributes to an exhaustive improvement in HS stability, reduces the power fluctuation and frequency deviation of WHPSHS. Specifically, in the established simulation scenarios, WHPSHS with VSPS outperforms an equivalent FSPS in reducing frequency deviation frms by 20 %, power fluctuation Is by 40 %, and complementarity Ic by 50 %. Particularly effective in suppressing power fluctuations in range of 0.01 Hz to 0.8 Hz, which decreases the resonant peak of frequency deviation by 61.3 % compared to FSPS. It further highlights the regulatory advantages of VSPS in responding to low-amplitude high-frequency power variations. These findings can provide novel insights into the role of VSPS in mitigating RES fluctuations and energy management strategies in multi-type hybrid systems.

Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems

With the continuous development of integrated energy systems (IESs), various distributed power is continuously connected to IESs. Uncertainty and volatility of renewable energy could increase power deviations of power systems and put pressure on the frequency control of the grid. A decomposition prediction fractional-order PID reinforcement learning (DPFOPIDRL) algorithm is proposed to reduce frequency deviations of IES and improve power quality in this study. The DPFOPIDRL-based unified timescale intelligent generation controller, which sends regulation commands to the automatic generation control every four seconds, continuously collects time series signals of frequency deviations; and then, the time series signals are predicted after modal decomposition. The DPFOPIDRL applies state-action-reward-state-action to control prediction signals with intense fluctuations, and fractional order proportional-integral-derivative to control prediction signals with gentle fluctuations. The DPFOPIDRL, proportional-integral-derivative, Q learning, and sliding mode are compared in four cases in an equivalent simplified and complex IES based on IEEE 39-node-Belgium 20-node system. Results under complex IES show that the frequency deviation and total generation cost of the DPFOPIDRL are at least 35.66% and 16.13% smaller than PID, Q learning, and sliding mode, respectively.

Power instruction correction based frequency response strategy for grid forming inverter in islanded microgrids

Grid forming (GFM) inverter interfaced energy storage system can offer frequency support for islanded microgrids (IMGs), and the frequency response relies on the GFM inverter’s power output and power instruction interaction. However, droop control, a primary GFM method, faces challenges in reducing frequency deviations during significant disturbances due to the constant power instruction, potentially triggering the relay. To enhance the security of IMGs, this paper proposes a power instruction correction (PIC) based frequency response strategy for GFM inverter in IMGs. Firstly, the fundamental principle of the PIC strategy that originates the basic droop control is theoretically analyzed. When disturbances are insignificant, the PIC strategy maintains the basic droop control structure. In the event of a significant disturbance, the PIC strategy promptly corrects the active power instruction by estimating the disturbance approximately to reduce frequency deviations. Secondly, by analyzing specific operating modes and switching logic, the detailed implementation of the PIC strategy is presented. Finally, the superiority of the PIC strategy is verified through simulation and hardware-in-the-loop experiments.

Integrating Demand Response for Enhanced Load Frequency Control in Micro-Grids with Heating, Ventilation and Air-Conditioning Systems

Heating, ventilation and air-conditioning (HVAC) systems constitute the majority of the demands in modern power systems for aggregated buildings. However, HVAC integrated with renewable energy sources (RES) face notable issues, such as uneven demand–supply balance, frequency oscillation and significant drop in system inertia owing to sudden disturbances in nearby generation for a longer period. To overcome these challenges, load frequency control (LFC) is implemented to regulate the frequency, maintain zero steady-state error between the generation and demand, reduce frequency deviations and balance the active power flow with neighboring control areas at a specified value. In view of this, the present paper investigates LFC with a proposed centralized single control strategy for a micro-grid (µG) system consisting of RESs and critical load of a HVAC system. The proposed control strategy includes a newly developed cascaded two-degree-of-freedom (2-DOF) proportional integral (PI) and proportional derivative filter (PDF) controller optimized with a very recent meta-heuristic algorithm—a modified crow search algorithm (mCSA)—after experimenting with the number of performance indices (PICs). The superiority of both the proposed optimization algorithm and the proposed controller is arrived at after comparison with similar other algorithms and similar controllers, respectively. Compared to conventional control schemes, the proposed scheme significantly reduces the frequency deviations, improving by 27.22% from the initial value and reducing the performance index criteria (ƞISE) control error to 0.000057. Furthermore, the demand response (DR) is implemented by an energy storage device (ESD), which validates the suitability of the proposed control strategy for the µG system and helps overcome the challenges associated with variable RESs inputs and load demand. Additionally, the improved robustness of the proposed controller for this application is demonstrated through sensitivity analysis with ±20% μG coefficient variation.

Modified Fractional Order PID Controller for Load Frequency Control of Four Area Thermal Power System

This paper presents the development of a modified Fractional Order Proportional Integral Derivative (FOPID) controller to mitigate frequency deviation in a four-area thermal power system. Change in load demand and noisy power system environment can cause frequency deviation. Reducing high-frequency deviation is very paramount in load frequency control. This is because large frequency deviation can cause the transmission line to be overloaded, which may damage transformers at the transmission level, damage mechanical devices at the generating stations and also damage consumer devices at the distribution level. The conventional PID has been widely used for this problem. However, the parameter values of the various generating units of the power system like generators, turbines and governors keep changing due to numerous on/off witching in the load side. As such, it is essential that the control strategy applied should have a good capability of handling uncertainties in the system parameters and good disturbance rejection. Fractional order PID controller is known to give a higher phase margin resulting in very good disturbance rejection, robustness to high-frequency noise and elimination of steady-state error. A four-area power system was designed, and FOPID was used as the supplementary controller to mitigate frequency deviation. Ant Lion Optimizer (ALO) algorithm was used to optimize the gains of the FOPID controller by minimizing Integral Square Error (ISE) as the objective function. Results obtained outperformed other designed methods available in the literature in terms of reducing frequency deviation, tie-line power deviation and area control error.

Chance-Constrained Optimal Configuration of BESS Considering Uncertain Power Fluctuation and Frequency Deviation Under Contingency

With the accelerating integration of variable renewable energies (VREs), power systems become more vulnerable to active power disturbances, and more drastic frequency dynamics emerge. The battery energy storage system (BESS) is able to handle the uncertainties of VREs, and the decreasing system inertia and frequency regulation capability. This paper proposes a chance-constrained optimal configuration scheme for the BESS to maintain both the uncertain power fluctuations and frequency deviation within predefined limits. First, the required frequency regulation capability of the BESS constrained by the maximum transient frequency deviation (MTFD) and quasi-steady-state frequency deviation (QSSFD) is estimated. Then, the kernel density estimation method is utilized to model the net power fluctuations of VREs and load. A multi-objective chance-constrained programming model accounting for the life cycle cost, energy arbitrage, uncertain power fluctuation, MTFD, and QSSFD is established to optimize the capacity of the BESS. Furthermore, the Bernstein approximation is utilized to process the chance constraint, and transform the optimization model into a deterministic form. Based on the linear weighted method and Benders decomposition, the optimization model is solved through alternating iteration. Case studies were conducted to validate the proposed scheme, showing superior performance in smoothing uncertain power fluctuations, and reducing frequency deviation under contingencies.

H ∞ Robust Frequency Control of Wind Turbine Generators and Electric Vehicles in Isolated grid

Wind turbine generators (WTGs) occupy an increasingly larger share in power system. WTGs in isolated grid lead to fluctuation in power and decrease system inertia. To reduce frequency deviation due to power fluctuation or wind speed change, model of WTGs is established based on deloading control. Considering that WTGs will have institutional wear because of frequent frequency regulation, which would short the life of WTGs, a robust frequency control strategy is proposed. WTGs and electric vehicles (EVs) are used in this control strategy. To suppress disturbances due to wind speed and uncertainty of system parameters, a H ∞ robust controller is solved using linear matrix inequality (LMI) methodology. Time-domain simulation results shows that the adopted control methodology can decrease frequency deviation during power fluctuations and wind speed changes.

Fuzzy vector reinforcement learning algorithm for generation control of power systems considering flywheel energy storage

To reduce carbon emissions, the proportion of renewable energy in power systems is increased. However, the volatility and uncertainty of renewable energy cause power deviations. To reduce frequency deviations caused by power deviations, a fuzzy vector reinforcement learning (FVRL) is developed for the generation control of power systems considering flywheel energy storage systems (FESSs). The FVRL algorithm consists of two independent fuzzy controls, two independent Q-learnings (QLs), and vector operation. Area control error and control performance standard 1 index are the inputs of one QL; the input of the other QL is the output power of the FESS. Fuzzy control fuzzifies the inputs of QL and provides the row number of the Q-table. Vector operation is acted for the output values of two QLs; the amplitude of the obtained vector is the final power regulation command of automatic generation control. The FVRL, proportional–integral, five reinforcement learnings, and deep Q-network are compared in two cases. Case studies show that the proposed FVRL obtains the lowest frequency deviation, the lowest area control error, the lowest generation cost, and the highest control performance standard 1 index.

Multi-Objective Decentralized Model Predictive Control for Inverter Air Conditioner Control of Indoor Temperature and Frequency Stabilization in Microgrid

Microgrid (MG) is a novel concept for a future distribution power system that enables renewable energy sources (RES). The intermittent RES, such as wind turbines and photovoltaic generators, can be connected to the MG via a power electronics inverter. However, the inverter interfaced RESs reduce the total inertia and damping properties of the traditional MG. Consequently, the system exhibits steeper frequency nadir and the rate of change of frequency (RoCoF), which may degrade the dynamic performance and cause the severe frequency fluctuation of the system. Smart loads such as inverter air conditioners (IACs) tend to be used for ancillary services in power systems. The power consumption of IACs can be regulated to suppress frequency fluctuation. Nevertheless, these IACs, regulating power, can cause the deviation of indoor temperature from the temperature setting. The variation in indoor temperature should be controlled to fulfill residential comfort. This paper proposes a multi-objective decentralized model predictive control (DMPC) for controlling the power consumption of IACs to reduce MG frequency fluctuation and control the variation in indoor temperature. Simulation results on the studied microgrid with the high penetration of wind and photovoltaic generator demonstrate that the proposed DMPC is able to regulate frequency deviation and control indoor temperature deviation as a user preference. In addition, the DMPC has a superior performance effect to the proportional-integral (PI) controller in terms of reducing frequency deviation, satisfying indoor temperature preferences, and being robust to the varying numbers of IACs.

Coordinated Control Strategy and Validation of Vehicle-to-Grid for Frequency Control

The increased penetration of renewable energy sources (RES) and electric vehicles (EVs) is resulting in significant challenges to the stability, reliability, and resiliency of the electrical grid due to the intermittency nature of RES and uncertainty of charging demands of EVs. There is a potential for significant economic returns to use vehicle-to-grid (V2G) technology for peak load reduction and frequency control. To verify the effectiveness of the V2G-based frequency control in a microgrid, modeling and simulations of single- and multi-vehicle-based primary and secondary frequency controls were conducted to utilize the integrated components at the Canadian Centre for Housing Technology (CCHT)-V2G testing facility by using MATLAB/Simulink. A single-vehicle-based model was validated by comparing empirical testing and simulations of primary and secondary frequency controls. The validated conceptual model was then applied for dynamic phasor simulations of multi-vehicle-based frequency control with a proposed coordinated control algorithm for improving frequency stability and facilitating renewables integration with V2G-capable EVs in a microgrid. This proposed model includes a decentralized coordinated control of the state of charge (SOC) and charging schedule for five aggregated EVs with different departure times and SOC management profiles preferred by EV drivers. The simulation results showed that the fleet of 5 EVs in V2B/V2G could effectively reduce frequency deviation in a microgrid.

Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

Stability properties of non‐linear model predictive control of variable speed hydropower

This study presents a method for small-signal analysis of an advanced, multi-variable control system for variable speed hydropower (VSHP) plants. A model predictive controller (MPC) optimises the power plant performance. In parallel, a virtual synchronous generator-type (VSG) converter control ensures that the VSHP contributes to virtual inertia and frequency control of the power system. The aim of the small-signal analysis is to parametrise the cost function of the MPC to minimise oscillatory modes between the VSHP hydraulic system and the power system. A state-space representation of the MPC is developed by assuming a stable steady-state operating point equal to the reference values of the MPC cost function, and that no constraints are active. This state-space representation allows for small-signal analysis of the power system, including the MPC. The results show that the modes between the hydraulic system and the power system are well-damped and negligible when the costs of deviations in the hydraulic system are low compared to the cost of deviations in the VSG power reference. Thus, these modes do not constrain the tuning of the VSG. The VSHP power output can, therefore, be optimised independently through the VSG controller to damp power oscillations and reduce frequency deviations.

Emulation of Virtual Inertia to Accommodate Higher Penetration Levels of Distributed Generation in Power Grids

A novel control scheme based on a new stochastic equivalent model of the power system, which provides flexible inertia constant to enable high penetration levels of microgrid (MG) generation, is proposed. The approach combines an adaptive energy storage system (ESS) dispatch strategy with an MG-controlled islanding scheme to provide frequency support for the host power grid. First, a center-of-gravity (COG) formulation of the inertia-control scheme is proposed, in which the unbalance torques between the control areas and the COG are used to reduce frequency deviations. An application example-based equivalent model of ESSs as seen from the grid side is then proposed that takes advantage of the ESS special attributes to contribute to inertial response. Simulation results on two test systems demonstrate the efficiency of the proposed control scheme to increase the host grid ability to efficiently integrate multiple MGs. Numerical tests suggest that the proposed control scheme increases system penetration level at least by 12%, which justifies the use of inertia emulation techniques to accommodate higher levels of distributed generation.

Simultaneous Fast Frequency Control and Power Oscillation Damping by Utilizing PV Solar System as PV-STATCOM

This paper presents a novel fast frequency response and power oscillation damping control by large-scale PV plants controlled as STATCOM, termed PV-STATCOM, to simultaneously enhance frequency regulation and small signal stability of power systems. Frequency deviations typically occur together with power oscillations in large power systems. The proposed controller comprises: first, power oscillation damping controller based on reactive power modulation and second, fast frequency response controller based on real power modulation, both of which are applied to the plant level controller of PV-STATCOMs. The proposed composite control is shown to successfully reduce frequency deviations, damp power oscillations, and provide voltage regulation both during over-frequency and under-frequency events. The proposed smart inverter control makes effective utilization of the PV inverter capacity and available solar power. For large power flows, the proposed control is shown to be superior than the conventional droop control recommended by North American Electric Reliability Corporation for generating plants. MATLAB/Simulink-based simulations are conducted on two-area power system using generic PV plant dynamic models developed by Western Electricity Coordinating Council, for a wide range of system operating conditions. Such grid support functionality is expected to bring new revenue making opportunities for PV solar farms.

Virtual inertia control strategy of doubly-fed induction generator with additional inertia and damping torque

Doubly-fed induction generator (DFIG) with dual Pulse-Width Modulation (PWM) converter cannot maintain the active power balance or voltage stability of the power grid self-synchronously because of lacking mechanical inertia and mechanical damping. The virtual synchronous generator (VSG) technology can help solve this problem through simulating the mechanical equation and electromagnetic equation of synchronous generator to control the grid-connected converter. However, under the common first-order virtual inertia link used in the VSG control scheme, while DFIG providing active power support, there exists second frequency dip and the rotor speed recovers slowly, which is not conducive for DFIG to provide continuous and effective virtual inertia support. In order to overcome the drawbacks as well as avoid second frequency dip, this paper puts forward a virtual inertia control strategy of DFIG with additional inertia and damping torque. The additional torque is achieved by proportional-derivative (PD) controller with rotor slip as an input signal. Simulation results on DIgSILENT/powerfactory platform show that the proposed control strategy is effective to reduce frequency deviation and recover the rotor speed faster without second frequency dip phenomenon.

Sliding mode load frequency control for multi‐area time‐delay power system with wind power integration

The interconnected time-delay power system has become an important issue for the open communication network. Meanwhile, due to the output power fluctuation of integrated wind energy, load frequency control (LFC) for power system with variable sources and loads has become more complicated. The novel decentralised sliding mode (SM) LFC strategy is proposed for multi-area time-delay power system with significant wind power penetration. The appropriate switching surface gain is selected to assure the stability of power system with mismatched uncertainties. The SM controller is constructed to satisfy the hitting condition. At last, the SM controller is proved by using the real-time digital simulator device under different case of time delay, wind penetration, load disturbance and operating point. The test results show that the proposed SM LFC can reduce frequency deviation and tie-line power fluctuation effectively.

Primary Frequency Response From Electric Vehicles in the Great Britain Power System

With the increasing use of renewable energy in the Great Britain (GB) power system, the role of electric vehicles (EVs) contributes to primary frequency response was investigated. A tool was developed to estimate the EV charging load based on statistical analysis of EV type, battery capacity, maximum travel range and battery state of charge. A simplified GB power system model was used to investigate the contribution of EVs to primary frequency response. Two control modes were considered: disconnection of charging load (case I) and discharge of stored battery energy (case II). For case II, the characteristic of the EV charger was also considered. A case study shows results for the year 2020. Three EV charging strategies: “dumb” charging, “off-peak” charging, and “smart” charging, were compared. Simulation results show that utilizing EVs to stabilize the grid frequency in the GB system can significantly reduce frequency deviations. However the requirement to schedule frequency response from conventional generators is dynamic throughout the day.

Enhancement of micro-grid performance during islanding mode using storage batteries and new fuzzy logic pitch angle controller

Power system deregulation, shortage of transmission capacities and needing to reduce green house gas have led to increase interesting in distributed generations (DGs) especially renewable sources. This study developed a complete model able to analysis and simulates in details the transient dynamic performance of the Micro-Grid (MG) during and subsequent islanding process. Wind speed fluctuations cause high fluctuations in output power of wind turbine which lead to fluctuations of frequency and voltages of the MG during the islanding mode. In this paper a new fuzzy logic pitch angle controller is proposed to smooth the output power of wind turbine to reduce MG frequency and voltage fluctuations during the islanding mode. The proposed fuzzy logic pitch controller is compared with the conventional PI pitch angle controller which usually used for wind turbine power control. Results proved the effectiveness of the proposed fuzzy controller in improvement of the MG performance. Also, this paper proposed using storage batteries technique to reduce the frequency deviation and fluctuations originated from wind power solar power fluctuations. Results indicate that the storage batteries technique is superior than fuzzy logic pitch controller in reducing frequency deviation, but with more expensive than the fuzzy controller. All models and controllers are built using Matlab® Simulink® environment.