Automatic Generation Control Performance Research Articles

Cooperation Mode and Operation Strategy for the Union of Thermal Generating Unit and Battery Storage to Improve AGC Performance

With the growing penetration of intermittent renewable energy resources in power systems, it is a challenge for automatic generation control (AGC) to maintain the required control performance. In recent years, battery energy storage systems (BESSs) have been installed in thermal power plants to provide frequency regulation service bundled with the traditional thermal generating units (TGUs). However, in real projects, the installed BESS typically works as parasitic equipment of the generating unit, which limits the coordination between the TGU and the BESS. In this paper, a complementary cooperation pattern is proposed for the TGU-BESS union to improve the dispatchability of its response to the AGC signal. Then, the correlation between the BESS's energy demand and the TGU's operation states is described by a data-driven method, which combines fuzzy c-means cluster, r-vine copula, and random forest. Finally, a rolling-horizon energy balance strategy is developed to make the control decisions for the TGU-BESS union in response to the stochastic AGC signal. The effectiveness of the proposed method is demonstrated using the historical data from a real union with a 9MW/4.5MWh BESS and a 350MW coal-fired generator.

Modeling of battery energy storage systems for AGC performance analysis in wind power systems

Battery energy storage system (BESS) is being widely integrated with wind power systems to provide various ancillary services including automatic generation control (AGC) performance improvement. For AGC performance studies, it is crucial to accurately describe BESS’s power regulation behavior and provide a correct state of charge (SOC). In addition, BESS model is required to maintain a sufficiently high simulation speed, since it usually is involved in hour- or day-scale simulations. Different from existing models, this paper develops a new BESS model which considers DC-AC converter control, DC link circuit and switching loss, thus providing an accurate power regulation behavior. In addition, it also considers the nonlinear relationship between open-circuit voltage and SOC of battery so that it provides a correct SOC. As a whole, the BESS is simplified as a controlled current source with fundamental frequency (50/60 Hz) and described by algebraic phasor equations to reduce computational burden. This makes the proposed model capable of finishing hour- or day-scale simulations within a few minutes. Time-domain simulations are used to validate and compare the simulation accuracy with the classical first-order transfer function model and electromagnetic model. Based on the proposed model, this paper also introduces and verifies a new BESS-based strategy to improve the AGC performance of wind farms.

Optimization and Performance Improvement of Automatic Generation Control (AGC) for 330 MW Subcritical Thermal Power Unit

With the continuous assessment of the “Two Detailed Rules”, various power generation groups are paying increasing attention to AGC. A 350 MW subcritical unit has been in a poor performance state under AGC-R mode since the 2022 heating season, with a K1 value below 1.2 and a K2 value not reaching the qualified value. It is facing the cancellation of the qualification for AGC-R mode. In the face of the problem with the unit, this article analyzes three reasons. Firstly, the speed setting exceeds the requirements, but the actual unit action does not meet the standards. There is a deviation in power and a significant deterioration in control quality during the turning point and continuous climbing of AGC instructions. Based on the above analysis, this article proposes three renovation plans to optimize the instructions through nonlinear correction functions and achieve the elimination of instruction deviations. It calculates and judges the difference between the corrected AGC load command value and the actual power value of the unit. It calculates the deviation and sends the deviation to a saturated nonlinear link to generate the feedforward of the main turbine control, improving the output of the main turbine control. By setting a variable integral function, the main control output of the boiler is improved. After this technical transformation, the AGC performance of the unit has been significantly improved.

FOPTID+1 controller with capacitive energy storage for AGC performance enrichment of multi-source electric power systems

The demand for power increases during the peak hours and the power supply can't exceed its limitations. This results into mismatch between the supplied and demanded powers causing fluctuations in the frequency. Extreme power-frequency deviations may cause power system blackouts. To maintain the constant frequency of power system, automatic generation control (AGC) should be embedded with a reliable controller. In this paper, a fractional order proportional tilt integral derivative plus one (FOPTID+1) controller is introduced in the design of AGC to retain frequency constancy. This controller has the ability to regulate the frequency hastily during load disruptions. The controller parameters are optimized by exploiting global neighbourhood algorithm (GNA). Firstly, the investigation is directed on a one-area multi-source thermal-hydro-gas generating system. To authenticate the expandability of the method, the study is then extended to multi-source interconnected two-area thermal-hydro-gas power system without/with nonlinearities. The dominance of the suggested controller is recognized over various recently published results of I/PI/PID/TID/FOTID and GNA tuned PID/FOPID controllers. Inspection of results authorizes the worthier performance of FOPTID+1 over other controllers regarding least overshoot/undershoot/settling time of frequency/tie-line power change and error criteria at disturbances. To further advance the system performance, capacitive energy storage (CES) units are incorporated to compensate the power demanded by the load disturbances. The obtained results demonstrate that CES is capable to improve system performance effectively. With CES, FOPTID+1 produces less cost function value for both one-area (9.52%) and two-area (8.97% for linear and 11.38% for nonlinear) systems. The control system stability is verified by the study of bode diagram. The resilience behaviour of the controller is confirmed by the stout conduct of controller under ±25% variation of system parameters and tie-line outages.

Performance Improvement of AGC Using Novel Controllers in a Multi-Area Solar Thermal System under Deregulated Environment

The performance improvement of Automatic Generation Control (AGC) in a Multi Area Solar Thermal Power System (MASTPS) under a deregulated environment is considered as MIPS and their performance is compared using various controllers to improve the performance of AGC in the proposed MIPS. This study takes into account a three-area conventional thermal power system in conjunction with a solar thermal power plant in each area, along with the proper generation rate constraints (GRC). The performance of the different controllers, including the Adaptive Neuro Fuzzy Inference System (ANFIS) controller and the Droop Controller (DC), Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Fractional Order PID (FOPID) controllers, is compared in the proposed AGC deregulated power system. The proposed MIPS is designed and implemented to make the system as realistic as possible with real world scenarios. The recommended MIPS is being simulated in MALTAB Simulink environment and simulated for multiple times to investigate the performance improvement in AGC using the adopted controllers with 0.1 per unit of step load perturbation to verify its dynamic characteristics. According to the investigation, the suggested ANFIS controller improves the AGC performance in proposed MIPS and offers better dynamic performance than other controllers.

Automatic Generation Control Strategy for Integrated Energy System Based on Ubiquitous Power Internet of Things

The integrated energy system based on Ubiquitous Power Internet of Things has the characteristics of ubiquitous connection of everything, complex energy conversion mode and unbalanced supply-demand relationship. It brings strong random disturbance to the power grid, which deteriorates the comprehensive control performance of automatic generation control. Therefore, a novel deep reinforcement learning algorithm, namely collaborative learning actor-critic strategy, is proposed. It is oriented to different exploration horizons, has the advantage on experience sharing mechanism and can continuously coordinate the key behavioral strategies. Simulation tests are performed on the two-area integrated energy system and the four-area integrated energy system based on ubiquitous power Internet of Things. Comparative analyses show that the proposed algorithm can efficiently solve the problem of strong random disturbance, and has better convergence characteristic and generalization performance. Besides, it can realize the optimal cooperative control of multi-area integrated energy system efficiently.

Optimal design of a cascade controller for frequency stability of photovoltaic–reheat thermal power systems considering nonlinearities

Automatic generation control (AGC) is essential for raising living standards because it enhances power supply quality. However, due to the constraints and challenges experienced in practice, an effective and computationally economical control method is necessary to improve AGC performance, particularly in the presence of renewable energy. Therefore, this study introduces a novel cascade controller (CC) of a proportional–integral–derivative (PIDn) controller followed by a proportional–integral (PI) controller, forming a PIDn-PI CC. This controller is used in a two-area model comprising a reheat thermal generator and a photovoltaic unit. The gains of the PI, PIDn, and PIDn-PI controllers are adjusted using the recently introduced chaos game optimization (CGO), which minimizes the objective function integral time multiplied absolute error. The CGO relies on chaos theory principals, wherein the organization of fractal geometry is perceived through the chaotic game and the fractals’ self-similarity properties are considered. At first, the CGO based PIDn controller is employed, to check the suitability of CGO in dealing with AGC problems. Furthermore, several scenarios are used to confirm the effectiveness of the CGO:PIDn-PI scheme when subjected to a high load disturbance and uncertainty, which can change system parameters by ± 50%. A random load pattern is used to ascertain the proposed method’s efficacy. Finally, nonlinearities, such as generation rate constraint and time delay, which have a significant impact on AGC performance, are considered. Compared with relevant current research, the suggested approach outperforms them in terms of settling time, frequency, and tie-line power deviations.

SSA‐optimized cascade optimal‐PIDN controller for multi‐area power system with RFB under deregulated environment

AbstractIn today's rapidly evolving interconnected electric power system network, ancillary service such as automatic generation control (AGC) plays a critical and important role in safeguarding the system's power supply quality by establishing an equilibrium between generation and load demanded plus losses. A power system with a robust and smart AGC strategy is obligatory for better power quality and system security. Cascade control schemes cope up better with nonlinearity by tuning the gain of controller of power system amid continuous load changing. Therefore, in this article, a supplementary control approach that is, cascade optimal controller‐proportional integral with derivative filter (COC‐PIDN) is suggested for AGC performance advancement of the power systems under deregulated environment. The OC designed consuming full state feedback control approach acts as master controller and PIDN plays the role of slave controller. The nature inspired salp swarm algorithm is employed to adjust the PIDN parameters. Initially, the efficacy of the COC‐PIDN control‐method is explored on a two‐area restructured reheat thermal system and then to validate its excellence and extensibility, the study is protracted to restructured two‐area thermal‐hydro system. Extensive analysis of the findings establishes that the dynamic performance of the COC‐PIDN controller is more valuable than the OC and COC‐PID approaches. The results of COC‐PIDN get further improved in presence of redox flow battery. Finally, the sensitivity analysis as well the acquired results with the COC‐PIDN controller with system nonlinearities demonstrate its robustness.

Fractional order control strategy for superconducting magnetic energy storage to take part effectually in automatic generation control issue of a realistic restructured power system

Keeping frequency regulation (FR) while fulfilling bilateral contracts and pool-co transactions simultaneously in a competitive environment become a challenging task for solitary automatic generation control (AGC) owing to inherent nonlinear limitations like generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD) in realistic power systems. An effective solution to help AGC is employing rapid-response energy storage devices (ESDs) like superconducting magnetic energy storage (SMES). It is an appropriate choice for applications like AGC which need to deliver a large amount of power within such little time. Although employing the SMES in the AGC issue has been addressed widely in literature, however, the considered SMES models have fundamental weak points which are explained thoroughly in this paper. A comprehensive literature review reveals that the lack of an efficient control strategy implemented on the real dynamics of the SMES unit remains unsolved and needs further investigation. In this paper, a maiden supplementary control approach based on fractional order controllers (FOCs) is put forward for the SMES per its real dynamics to take part effectually in the deregulated AGC. The concurrent design of the proposed SMES and AGC is treated as a nonlinear minimization issue wherein an appropriate performance index is minimized utilizing different meta-heuristic algorithms. To achieve realistic and reliable results, a fully competitive two-area various-GENCOs multiple-DISCOs power system is taken into consideration for examinations with taking into account the nonlinearity sources of GDB and GRC. Detailed investigations are conducted under different contract violation scenarios and different CTDs to assess the competency of the suggested SMES model. The robust behavior of the proposed SMES-AGC coordinated control is verified using sensitivity analyses. The simulation results confirm the superiority of the presented SMES model and control strategy over the former models to enhance AGC performance.

Diagonal Stability of Systems With Rank-1 Interconnections and Application to Automatic Generation Control in Power Systems

We study a class of matrices with a rank-1 interconnection structure, and derive a simple necessary and sufficient condition for diagonal stability. The underlying Lyapunov function is used to provide sufficient conditions for diagonal stability of approximately rank-1 interconnections. The main result is then leveraged as a key step in a larger stability analysis problem arising in power systems control. Specifically, we provide the first theoretical stability analysis of automatic generation control (AGC) in an interconnected nonlinear power system. Our analysis is based on singular perturbation theory, and provides theoretical justification for the conventional wisdom that AGC stabilizes under the typical time-scales of operation. We illustrate how our main analysis results can be leveraged to provide further insight into the tuning and dynamic performance of AGC.

Performance enhancement of renewable based deregulated system using distributed power flow controller, Redox flow battery, and Tesla power wall battery

ABSTRACT In the upcoming era, when conventional sources will mostly be replaced by renewable energy sources, engineers may face huge problem during controller design. For this large interconnected hybrid automatic generation control (AGC) system, high proficient and intelligent control techniques are needed. Hence, a newly developed fractional order tilt integral derivative double integral filter plus (FOTIDF-II) controller has been proposed in this research work. To overcome slow convergence feature of moth swarm algorithm (MSA), chaotic-based moth swarm algorithm (CMSA) is introduced by incorporating chaos into the MSA algorithm. Afterward, to upgrade the performance of AGC, the newly proposed controller is incorporated to the said system. The gains and fractional order parameters such as order of integrator, differentiator and filter coefficients of the proposed controller are optimized using CMSA optimizing technique. It is observed that the dynamic performance of the proposed CMSA optimized FOTIDF-II controller is better than the CMSA optimized PID. Furthermore, the performance of CMSA in the presence of different flexible AC transmission system (FACTs) and storage devices such as distributed power flow controller (DPFC), redox flow battery (RFB), and Tesla power wall (TPWA) battery (i.e Storage devices) along with proposed controller is compared with MSA-based PID controller with same combination of FACTs controller. Afterwards, superiority of the proposed controller has been judged in three area renewable-based AGC system under realistic environment.

Multi-Objective Mayfly Optimization-Based Frequency Regulation for Power Grid With Wind Energy Penetration

With the continuous development of society and under the background of sustainable development and resource conservation, the proportion of renewable energy in the global energy structure is increasing. At the same time, wind power has been widely used in many regions of the world because wind power technology is more advanced and mature than other renewable energy sources. In addition, with a large number of wind turbines connected to the grid, it not only helps automatic generation control (AGC) of power systems but also brings new challenges and difficulties. In this study, a multi-source cooperative control model of wind power participating in AGC frequency regulation is established to solve the dynamic problem of power distribution from real-time total power command to different AGC units. This study presents an optimal AGC-coordinated control method based on the multi-objective mayfly optimization (MMO) algorithm, which makes the fitting degree of power command output and actual output curve high and the adjustment mileage payment minimum, so as to achieve the best AGC performance. Finally, the simulation results show that this method can effectively decrease the total power deviation and adjustment mileage payment in the multi-source-coordinated control of AGC.

Stochastic Model Predictive Control of Hybrid Energy Storage for Improving AGC Performance of Thermal Generators

In order to improve the automatic generation control (AGC) performance of thermal generators, this paper presents a stochastic model predictive control (SMPC) approach for a battery/flywheel hybrid energy storage system (HESS) to distribute power. The approach combines an adaptive Markov chain for power demand prediction of HESS, a scenario tree generation and model predictive control strategy. To develop an effective prediction model to deal with the randomness of a thermal generator in response to AGC command, a Markov chain is used to describe the randomness of the HESS power demand, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> information is used to adapt to the fluctuation of the AGC command. A scenario tree generation approach is proposed to make better use of the Markov probability matrix. Based on these efforts, an SMPC approach is proposed for HESS energy management. Simulation results show that the regulation performance of the proposed approach outperforms conventional approaches, and that its performance is close to the model predictive control strategy with prescient information of the future power demand. In addition, compared with other approaches such as rule-based strategies, it does less damage to the battery and yields higher annual average net benefits in the whole life cycle.

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity.

Pseudo-Derivative Feedback Controller for Automatic Generation Control in a Deregulated Power System with Hydrogen Energy Storage

This paper is focused on design and application of Pseudo-Derivative Feedback (PDF) controller for Automatic Generation Control (AGC) of a two-area thermal reheat interconnected power system treated in deregulated condition. The proposed controller gains are tuned simultaneously using Flower Pollination Algorithm (FPA) in order to achieve the optimal transient response of the test system. The control performance of the PDF controller is compared with Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Further to improve the AGC performance, Hydrogen Energy Storage (HES) are included in its control area. The execution of HES unit captures the underlying fall in frequency as well as the tie line control power deviations after a sudden load unsettling influence. The simulation results demonstrate that the proposed PDF controller enhance the dynamic response of the deregulated power system as compared with PI and PID contrtoller. The frequency oscillation and tie-line power deviations in the control zones are reduced and the settling time is additionally enhanced when HES unit takes an interest in the frequency regulation along with the traditional generators. Additionally, the Power System Restoration Indices (PSRI) is figured in view of system dynamic performances and the remedial measures to be taken can be decreed. These PSRI shows that the ancillary service requirement to enhances the effectiveness of physical task of the power system with the expanded transmission limit in the system. The presence of an Hydrogen Energy Storage (HES) water electrolyser coupled to a fuel cell improves significantly the control and operation of an energy system and provides good margin of stability of the grid system compared to that a system without HES unit.

A hybrid evolutionary algorithm for stability analysis of 2-area multi-non-conventional system with communication delay and energy storage

The integration of renewable energy with the grid is essential given the environmental and economic constraints. In this work, the dynamic performance of Automatic Generation Control (AGC) was studied with a dynamic steam turbine and renewable sources. A 2-area multi-non-conventional source (Thermal-Hydro-Solar-wind-Gas-battery storage) system with communication time delay (τ) is implemented with the proposed cascaded PID (C-PID) controller scheme. The parameters of the C-PID controller were tuned by a hybrid Improved Teaching Learning Based Optimization and Differential Evolution (hITLBO-DE).Root locus, Bode plot, and eigenvalue analysis were used to demonstrate the stability of the system. Various controllers (i.e. I, PI, PID, and C-PID) were implemented with the designed system under varying operating conditions of step (1% to 10%) and dynamic load variation (1%, ±1%). The C-PID controller is able to maintain a stable system response even for τ of 4 s. Finally, the obtained Amelioration (%) of 55.93%, 49.03%, and 72.84% for Over Shoot emphases the efficacy of the proposed C-PID controller and hITLBO-DE algorithm.

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

Large-scale renewable energy sources connected to the grid bring new problems and challenges to the automatic generation control (AGC) of the power system. In order to improve the dynamic response performance of AGC, a biobjective of complementary control (BOCC) with high-participation of energy storage resources (ESRs) is established, with the minimization of total power deviation and the minimization of regulation mileage payment. To address this problem, the strength Pareto evolutionary algorithm is employed to quickly acquire a high-quality Pareto front for BOCC. Based on the entropy weight method (EWM), grey target decision-making theory is designed to choose a compromise dispatch scheme that takes both of the operating economy and power quality into account. At last, an extended two-area load frequency control (LFC) model with seven AGC units is taken to verify the effectiveness and the performance of the proposed method.

Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

With the real-time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs’ output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs’ DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF.

AGC performance amelioration in multi-area interconnected thermal and thermal-hydro-gas power systems using a novel controller

Due to varying structure, random load demands, nonlinearities, parameters ambiguity, steadily escalating size and intricacy of the interconnected power system (IPS), automatic generation control (AGC) is treated as one of the biggest crucial issues in IPS. Hence, expert, intelligent and robust control scheme is indispensable for stable operation of IPS and supply of electricity under sudden load demand disturbances. In vision of this, in this work, a novel cascade fuzzy-proportional integral derivative incorporating filter (PIDN)-fractional order PIDN (FPIDN-FOPIDN) controller is offered as an expert control strategy to deal effectively with AGC issue of IPS. Imperialist competitive algorithm is prolifically utilized for optimizing the controller parameters. Initially, a two area non-reheat thermal IPS is studied in detail and next to attest the efficacy of the technique, the study is extended to realistic two-area multi-source thermal-hydro-gas and reheat thermal three-area systems. The prominent benefit of cascade FPIDN-FOPIDN strategy comprises its great lethargy to large load demands and its supremacy over various latest intelligent classical/fuzzy controllers. The control strategy beats several techniques concerning significant lesser settling time, oscillations, over/under shoots and different performance index values. Finally, a robustness investigation is performed in order to validate the robustness of the controller.

Improvement of the Performance of Automatic Generation Control of a Power System

Improvement of the Performance of Automatic Generation Control of a Power System