Automatic Generation Control Model Research Articles

Two-Area Automatic Generation Control for Power Systems with Highly Penetrating Renewable Energy Sources

Currently, renewable energy sources (RESs) are gradually replacing traditional power sources that use fossil fuels. In some countries, such as Vietnam, RESs are developed on a massive scale and are concentrated in some key areas. This causes negative impacts on a power system when its transmission system is not deployed synchronously to release their capacity from these new renewable energy plants. An important challenge today is to ensure frequency stability in power systems with high uncertainty in RES output power. Additionally, the system requires solutions to prevent transmission line overloads during periods when RESs make a substantial contribution to the electricity generation capacity. Therefore, this paper builds an automatic generation control (AGC) system for a two-area power system with high penetration of RESs. This AGC system model aims to maintain system frequency stability amid unpredictable changes in RESs while also ensuring that tie-lines transmit the predetermined power levels to mitigate frequent congestion. By continuously monitoring and adjusting the system’s frequency, the challenges posed by the inherent variability of RESs can be effectively mitigated. The AGC model is simulated on DIgSILENT PowerFactory software and tested with a 106-bus system. The simulation results of this study show that the AGC system operates effectively, ensuring that the frequency returns to the rated value and maintaining the exchange capacity on the tie-lines after occurrences of RES power decrease events.

Renewable sources with virtual inertia penetration in multi area interconnected AGC using a modified MPC controller

The challenge of controlling frequency deviation becomes more difficult as the complexity of a power plant increases. The robustness of the controller has a major impact on the stability of a complex power system (CPS). Due to the hybridization of CPS basic Automatic Generation Control (AGC) controllers (PID, FOPID, and TID), they are insufficient to give optimal performance to a plant. This requires a robust controller. So, a modified MPC controller has been proposed and evaluated by comparing it with several existing controllers, which gives optimal performance in terms of overshoot, undershoot, and settling time and improves its performance approximately 45%. This research discusses a combined AGC model and control for a three-area CPS, where each producing area consists of a thermal plant, a Diesel power plant, and a Solar Thermal Power Plant (STPP). The results of the modified MPC are superior to those of the basic controller compared to several existing controllers. An improved version of Sea-horse Optimization (SHO) has been proposed to optimize the different controller settings. The superiority of the SHO is shown by a comparison with some well-known, current meta-heuristic methods. The higher penetration levels of renewable energy sources (RESs) reduced system inertia which further deteriorate frequency response in CPS. To overcome these challenges, virtual inertia (VI) has been implemented with MPC. VI is applied to improve the performance of the AGC of the interconnected CPS along with emphasizing the nature of intermittent RESs of PV and wind energy. The thorough study findings provide compelling evidence for the effectiveness and efficiency of the recommended control strategies and also point to the possibility of applying them in actual power systems to improve stability and performance.

Automatic Generation Control in Modern Power Systems with Wind Power and Electric Vehicles

The modern power system is characterized by the massive integration of renewables, especially wind power. The intermittent nature of wind poses serious concerns for the system operator owing to the inaccuracies in wind power forecasting. Forecasting errors require more balancing power for maintaining frequency within the nominal range. These services are now offered through conventional power plants that not only increase the operational cost but also adversely affect the environment. The modern power system emphasizes the massive penetration of wind power that will replace conventional power plants and thereby impact the provision of system services from conventional power plants. Therefore, there is an emergent need to find new control and balancing solutions, such as regulation reserves from wind power plants and electric vehicles, without trading off their natural behaviors. This work proposes real-time optimized dispatch strategies for automatic generation control (AGC) to utilize wind power and the storage capacity of electric vehicles for the active power balancing services of the grid. The proposed dispatch strategies enable the AGC to appropriately allocate the regulating reserves from wind power plants and electric vehicles, considering their operational constraints. Simulations are performed in DIgSILENT software by developing a power system AGC model integrating the generating units and an EVA model. The inputs for generating units are considered by selecting a particular day of the year 2020, when wind power plants are generating high power. Different coordinated dispatch strategies are proposed for the AGC model to incorporate the reserve power from wind power plants and EVs. The performance of the proposed dispatch strategies is accessed and discussed by obtaining responses of the generating units and EVs during the AGC operation to counter the initial power imbalances in the network. The results reveal that integration of wind power and electric vehicles alongside thermal power plants can effectively reduce real-time power imbalances acquainted in power systems due to massive penetration of wind power that subsequently improves the power system security. Moreover, the proposed dispatch strategy reduces the operational cost of the system by allowing the conventional power plant to operate at their lower limits and therefore utilizes minimum reserves for the active power balancing services.

Secondary Frequency Regulation from Variable Generation Through Uncertainty Decomposition: An Economic and Reliability Perspective

The rapid integration of variable generation (VG), such as photovoltaics (PV), necessitates an increase in the secondary frequency regulation (SFR) to handle the system intra-dispatch interval imbalance because of VG's variation. Although PV has the control capability to provide SFR, the main challenge is in guaranteeing the delivery of PV's energy and SFRs capacities in the automatic generation control (AGC) within each dispatch interval considering its uncertainty. This paper proposes a deliverable VG SFR provision model with endogenous VG's power uncertainty decomposition. First, the uncertainty of VG is decomposed using distributionally robust chance constraints through which the deliverable SFR provision is guaranteed. Next, the intra-interval frequency response of PV's SFR is validated with a user defined AGC model including the PV power plant. Finally, the economic benefits and the reliability improvements—such as the frequency deviation reduction with PV providing SFR—can be integrated and evaluated. The proposed model is tested in a modified 18-bus system and in the 240-bus simplified Western Electricity Coordinating Council test system. Results demonstrate that with the deliverable SFR from PV, the system cost and the frequency reliability can be improved simultaneously. PV's SFR performance can be guaranteed with the proposed model.

A hybrid fuzzy-PID controller based on gray wolf optimization algorithm in power system

This paper addresses a grey wolf optimizer to optimally set the PID controller with low pass filter for simulating multi area restructured power system with considered superconducting magnetic energy storage plant that works under various loading conditions and uncertainties. To make a real model, the reheat effect nonlinearity of the steam turbine and governor is considered into automatic generation control (AGC) model. The proposed AGC model is formulated as a nonlinear constrained optimization problem which is solved by an intelligent algorithm so that it shows powerful search in optimization problem especially the proposed AGC. The effectiveness of the proposed technique is demonstrated on a deregulated power system with possible contracted scenarios under large load demand and area disturbances in comparison with other method through several performance indices. Obtained results show that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers.

Optimizing the AGC system of a three-unequal-area hydrothermal system based on evolutionary algorithms

This paper focuses on expanding and evaluating an automatic generation control (AGC) system of a hydrothermal system by modelling the appropriate generation rate constraints to operate practically in an economic manner. The hydro area is considered with an electric governor and the thermal area is modelled with a reheat turbine. Furthermore, the integral controllers and electric governor parameters are optimized using integral squared error (ISE) criterion. Also, a novel Teaching-Learning-Based Optimization (TLBO) algorithm, Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) with controller are proposed for optimizing AGC. Investigations have been conducted for the selection of a suitable value for governor speed regulation parameter R for the hydro and thermal areas, to explore the effect of tie-line power on the dynamic response. The advantages of the proposed approach are demonstrated by comparing the results of optimizing the AGC system of a three-unequal-area hydrothermal system with mentioned algorithms for the first one in comparison with other recently published techniques. The results confirm the flexibility and the suitability of the proposed AGC model for optimizing the different approaches. Moreover, it is more practical to use the proposed method to make a wide variety of changes in the system parameters using sensitivity analysis.

Wind power penetration impact on power system frequency

ABSTRACTThe incorporation of large amounts of wind power in power systems poses huge challenges. Especially with the growth of wind power generation, more rule reserves would be essential, the capability of the power system to offer classical regulating power would be reduced and the dynamic stability of the grid frequency under large oscillations would be compromised. In this paper, to investigate the integration of large-scale wind power generation in power systems and its active power control, a novel method and solution dealing specifically with the electric frequency stability and high wind power penetration is presented. So, the impacts of wind power on a balancing area's frequency control specification and studies the wind power impacts power system control. The proposed method has been applied to a three-area Automatic Generation Control model to verify these impacts. The simulation results obtained show that power system control indices deteriorate as wind power penetration increases.

Virtual Synchronous Power Strategy for Multiple HVDC Interconnections of Multi-Area AGC Power Systems

Automatic generation control (AGC) in multi-area interconnected power systems is experiencing several adaptions due to increasing level of power converter based components in the system. The concept of virtual synchronous power (VSP) to simulate the dynamic effects of virtual inertia emulations by HVDC links for higher level control applications is introduced and reflected in the multi-area AGC model. By using this proposed combination in the AGC model, the dynamic performance of the studied system shows a significant improvement. The proposed formulation is generalized for multi-area systems with multiple HVDC links. The active power loop control in VSP-based HVDC links has a second-order characteristic, which makes a simultaneous enabling of damping and inertia emulations into the system. Trajectory sensitivities are also used to analyze the effects of VSP's parameters on the system stability. The effectiveness of the proposed concept on dynamic improvements is tested through MATLAB simulation of a four-area system.

Design and analysis of multi-source multi-area deregulated power system for automatic generation control using quasi-oppositional harmony search algorithm

The present article focuses on the study of automatic generation control (AGC) of a realistic power system having a distinct combination of multi-area multi-source generating units in each control area under deregulated framework. An attempt is made in this paper to integrate reheat thermal, hydro and gas generating unit in a single control area and, then, extended this combination to five control areas. In this work, six reheat thermal, six hydro and three gas generating units are taken into account for the modeling of five-area power system. Some important physical constraints like time delay, governor dead band and generation rate constraint are imposed in the power system dynamics to get an accurate perception of the deregulated AGC subject. The highlighting features of the present work are to model, simulate, optimize and co-relate their inter-related dynamic performances for the purpose of AGC study. For such a complex AGC model, the vital role of the proposed quasi-oppositional harmony search (QOHS) algorithm, as an optimizing tool, is signified while solving the AGC problem in deregulated regime. The simplicity of the structure and acceptability of the responses of the well-known proportional–integral–derivative controller, inherently, enforces to employ in this work. The three classes of extensive deregulated cases (in the presence of load following and physical constraints) are demonstrated by examining the closed loop performance of the studied model. The simulation results show that the designed power system model may be a feasible one and the proposed QOHS algorithm may be a promising optimization technique under these circumstances.

Power Frequency Oscillation Suppression Using Two-Stage Optimized Fuzzy Logic Controller for Multigeneration System

This paper attempts to develop a linearized model of automatic generation control (AGC) for an interconnected two-area reheat type thermal power system in deregulated environment. A comparison between genetic algorithm optimized PID controller (GA-PID), particle swarm optimized PID controller (PSO-PID), and proposed two-stage based PSO optimized fuzzy logic controller (TSO-FLC) is presented. The proposed fuzzy based controller is optimized at two stages: one is rule base optimization and other is scaling factor and gain factor optimization. This shows the best dynamic response following a step load change with different cases of bilateral contracts in deregulated environment. In addition, performance of proposed TSO-FLC is also examined for±30%changes in system parameters with different type of contractual demands between control areas and compared with GA-PID and PSO-PID. MATLAB/Simulink® is used for all simulations.

Optimal design of a sliding mode AGC controller: Application to a nonlinear interconnected model

This paper presents a design of a sliding mode controller (SMC) with chattering reduction feature applied to interconnected automatic generation control (AGC). After formulating the design of SMC as an optimization problem, the proposed method utilizes the genetic algorithms (GA) to find the optimal feedback gains and switching vector values of the controller. In order to guarantee the enhancement of the system dynamical performance and a reduction in the SMC chattering, two objective functions were investigated in the optimization process. The tested two-interconnected AGC model incorporates nonlinearities in terms of generation rate constraint (GRC) and a limiter on the integral control value. Comparison with previous AGC methods reported in literature validates the significance of the proposed SMC design.

A reinforcement learning approach to automatic generation control

This paper formulates the automatic generation control (AGC) problem as a stochastic multistage decision problem. A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach. This method of obtaining an AGC controller does not depend on any knowledge of the system model and more importantly it admits considerable flexibility in defining the control objective. Two specific RL based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting area control error (ACE) excursions, where as, in the second algorithm, the controller can restore the load-generation balance by only monitoring deviation in tie line flows and system frequency and it does not need to know or estimate the composite ACE signal as is done by all current approaches. The effectiveness and versatility of the approaches has been demonstrated using a two area AGC model.

Stochastic inclusion principle applied to decentralized automatic generation control

An input-state-output inclusion principle for linear stochastic systems is proposed, with the emphasis on restriction and aggregation conditions for estimators and dynamic controllers. Inclusion of the LQG (linear quadratic Gaussian) optimal design is formulated and applied to the decentralized overlapping control of large-scale interconnected systems. Applications of the proposed methodology are illustrated using a stochastic model of automatic generation control (AGC) for interconnected power systems. Three types of overlapping decentralized and fully decentralized dynamic controllers, consisting of state estimators and feedback gains, are proposed for the cases of full and reduced measurement sets. An extensive analysis of both steady-state and transient regimes under a variety of operating conditions shows the superiority of the proposed AGC scheme with respect to the standard AGC designs.

Decentralized Automatic Generation Control Based on a Stochastic Inclusion Principle

An inclusion principle is formulated for stochastic systems, including the LQG optimal design. The obtained results are applied to decentralized overlapping control of large-scale interconnected systems. The efficiency of the proposed methodology is illustrated on a stochastic model of automatic generation control (AGC) for interconnected electric power systems. Two types of decentralized sub-optimal dynamic controllers consisting of state estimators and feedback gains are proposed. An extensive analysis of both steady-state and transient regimes under a variety of operating conditions shows the efficiency of the proposed AGC scheme.