Automatic Generation Control Units Research Articles

Ancillary services from wind and solar energy in modern power grids: A comprehensive review and simulation study

Renewable energy sources like wind and solar have increased demand for surplus power capacity. The demand is primarily fueled by the growing impact of forecasting errors associated with these intermittent energy sources. Implementing advanced control methods for automatic generation control (AGC) is essential to integrate wind and solar power with conventional generation sources to balance the power system and reduce reliance on traditional reserves. Therefore, this paper comprehensively overviews solar and wind energy integration in the AGC framework to provide optimal grid ancillary services. Initially, the paper presents an overview of the basic equations used to integrate reserve power from the photovoltaic (PV) system by employing the de-loading strategy. Subsequently, a comprehensive review is conducted on integrating the PV system in AGC strategies to provide grid ancillary services. The study also analyzes the contribution of wind power in AGC services using relevant equations and past practices. The paper presents a real-time dynamic control strategy to optimize the dispatch of the AGC unit by integrating the operating reserves from wind energy systems in conjunction with thermal power systems. The study simulates an 8-bus, 5-machine model using the Dig-SILENT Power Factory. The findings reveal that utilizing operating reserves from wind power can significantly reduce large-scale forecasting errors in massively renewable energy resources (RES) integrated power systems, thereby ensuring the necessary system operational security and reducing the reliance on traditional generating units.

Distributed Cooperative AGC Method for New Power System With Heterogeneous Frequency Regulation Resources

The inverter-interfaced renewable energy is increasing in future new power systems, both the system rotary inertia and frequency regulation capacity of traditional units are decreasing and becoming increasingly insufficient. It is urgent to explore various types of frequency regulation resources, especially for inverter-interfaced units that show fast response speed. The heterogeneous frequency regulation resources generally have different system models, capacities, and response speeds, which challenges the automatic generation control (AGC). In this paper, to make the heterogeneous frequency regulation resources provide improved frequency regulation performance, a novel distributed cooperative AGC method is proposed. Firstly, a distributed consensus-based area control error (ACE) discovery algorithm is proposed. Then, each frequency regulation unit can participate in the frequency regulation with an individual PI controller based on the discovered ACE information. Finally, by controlling the low-speed AGC units to generate more power, it can release the power capacity of the high-speed AGC units to prepare for the following round of AGC services. Furthermore, the communication delays are also considered for the proposed distributed AGC algorithm design. The improved performance of the proposed AGC method is verified based on the simulation study of a two-area power system with five different kinds of frequency regulation units.

Optimal performance evaluation of thermal AGC units based on multi-dimensional feature analysis

In modern energy system, automatic generation control (AGC) is the core technology of real-time output regulation for thermal power generator. The performance of thermal AGC units must be accurately evaluated to measure their actual contribution to the energy system. However, based on current conventional evaluation methods, the difficulty of the tasks undertaken by AGC units has not been distinguished and quantified. An optimal performance evaluation method based on multi-dimensional feature analysis is proposed. Firstly, a performance index describing the difference between the expected regulating energy and the actual regulated energy of AGC units is designed, which improves the universality of the evaluation to the actual engineering scenarios. Additionally, after data preprocessing and data cleaning, a sample space is constructed to significantly distinguish the difficulty of tasks performed by AGC units. Finally, a multi-dimensional feature analysis in the sample space is proposed to find the optimal performance points of AGC units. Based on historical data, the proposed methods were verified on real AGC units. The experimental results show that the proposed method obtains detailed evaluation results of thermal AGC units with different control requirements and solves the problem of evaluation failure in traditional method.

A new distributed collaborative control for double-layer dynamic optimal scheduling of energy network

In this paper, the problem of energy planning in energy network is studied, and an energy optimal scheduling strategy and a dynamic two-layer distributed cooperative control method are proposed. First of all, in the upper layer, the optimal scheduling model of primary energy including renewable energy is designed, and the thermal inertia of heat load is taken into account, which reduces the cost of energy supply, improves the consumption of new energy and promotes carbon neutralization, so that the micro-energy network can operate economically and environmentally friendly. From the point of view of automatic generation control (AGC), the lower layer uses Linear Quadratic Gaussian (LQG) to realize the real-time control of single-area AGC units and ensure the stable operation of the network. Secondly, according to the control deviation of each area, a dynamic stratification strategy is designed, which can not only ensure the effect, but also save resources.

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.

Battery Energy Storage Participation in Automatic Generation Control of Island Systems, Coordinated with State of Charge Regulation

Efficient storage participation in the secondary frequency regulation of island systems is a prerequisite towards their complete decarbonization. However, energy reserve limitations of storage resources pose challenges to their integration in centralized automatic generation control (AGC). This paper presents a frequency control method, in which battery energy storage systems (BESSs) participate in automatic frequency restoration reserve (aFRR) provision, through their integration in the AGC of an island system. A local state of charge (SOC) controller ensures safe operation of the BESS in case of disturbances, without jeopardizing system security when available energy reserves are diminishing. The aFRR participation factors of regulating units are altered when the storage systems approach their SOC limits, re-allocating their reserves to other load-following units. Restoration of BESS energy reserves is achieved by integrating SOC regulation in the real-time economic dispatch of the system, formulated as a mixed-integer linear programming problem and solved every few minutes to determine the base points of the AGC units. A small autonomous power system, comprising conventional units, renewable energy sources and a BESS, is used as a study case to evaluate the performance of the proposed method, which is compared with alternative approaches to secondary regulation with BESS participation.

Distributed optimal-tracking control to coordinate the optimization of active distribution networks with automatic generation control

To the shortage of automatic generation control (AGC) resources in power systems with high proportions of renewable generators, this paper proposes an optimal-tracking control strategy to integrate the optimization of active distribution networks (ADNs) into AGC. Different ADNs can coordinate to absorb the power fluctuations beyond AGC adjustment ranges in a distributed way. First, we build a time-varying model to integrate the optimization of ADNs into AGC, which gives the power allocation mechanism among different ADNs and AGC units in real time. Second, we design a distributed strategy to continuously drive the operation of ADNs to the solutions of the time-varying model using the optimal-tracking sensitivities calculated based on the feedback of measurements. In this strategy, the measurements of net load powers are calculated using the integral of the area control error in AGC; moreover, the optimal-tracking sensitivities are decomposed into several items of which each one only relates to the inner information of the transmission network or an ADN, which is deduced according to the physical laws of power flow of transmission and distribution networks. Based on the decomposition, each ADN can form the optimal-tracking sensitivities only based on its inner information and some coordination information from the transmission network. Thus, the distributed optimal-tracking controller is achieved. Finally, the effectiveness of the proposed control strategy is verified by case studies: ADNs are regulated to effectively restrain the excessive frequency deviations, caused by the sharp fluctuations of renewable generator outputs, with meeting the operational constraints of ADNs (e.g. the constraints on voltages) in real time.

Intra‐day Dynamic Optimal Dispatch for Power System Based on Deep Q‐Learning

AbstractSource‐load stochasticity affects the safety, stability and economical operation of power grid, and makes it difficult to design optimal dispatch. Currently, due to the low accuracy of day‐ahead forecast for wind power and load, dynamic optimal dispatch based on ultra‐short‐term wind power prediction is an effective way to reduce the power deviation in power system and to relieve the operating pressure on automatic generation control (AGC) unit. In this paper, by comprehensively considering the source‐load stochasticity, the dispatch properties of unit, and the constraints of AGC regulating range, we establish a dynamic optimal dispatch model to minimize the running cost of the underlying power system, including the coal consumption cost of thermal unit, and the output adjustment cost of both the dispatchable unit and the AGC unit. Since each dynamic dispatch order has an impact, direct or indirect, on the subsequent system operation, we formulate the dynamic optimal dispatch problem as a Markov decision process (MDP) problem. In this framework, a deep Q‐learning method is provided to optimize the dispatch strategy. Finally, the feasibility and the effectiveness of the proposed method is validated by means of numerical experiments on IEEE 30‐node system. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Analytical solution of stochastic real‐time dispatch incorporating wind power uncertainty characterized by Cauchy distribution

Real-time power dispatch can coordinate wind farms, automatic generation control units and non-automatic generation control units. In real-time power dispatch, the probable wind power forecast errors should be appropriately formulated to ensure system security with high probability and minimize operational cost. Previous studies and the authors' onsite tests show that Cauchy distribution effectively fits the “leptokurtic” feature of small-timescale wind power forecast errors distributions. In this paper, a chance-constrained real-time dispatch model with the wind power forecast errors represented by multivariate Cauchy distribution is proposed. Since the Cauchy distribution is stable and has promising mathematical characteristics, the proposed chance-constrained real-time dispatch model can be analytically transformed to a convex optimization problem considering the dependence among wind farms’ outputs. Moreover, the proposed model incorporates an affine control strategy compatible with automatic generation control systems. This strategy makes the chance-constrained real-time dispatch adaptively take into account both the potential power ramping requirement and power variation on transmission lines caused by the generation adjustment to offset the wind power forecast errors in real-time power dispatch stage. Numerical test results show that the proposed method is reliable and effective. Meanwhile it is very efficient and suitable for real-time application.

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.

AGC unit capacity planning model considering auxiliary service market under the background of China’s new electricity reform

With the advancement of a new round of power system reform and the continuous access to renewable energy, the auxiliary service market has received extensive attention as an important service to ensure the safe and reliable operation of the power grid. However, it is difficult for the automatic generation control (AGC) units in the system to meet real-time fastness requirements of the power market. Based on the premise that “market electricity” and “planned electricity” coexist under the background of new electricity reform, this paper focuses on the upgrade and reconstruction of AGC units in “planned electricity”. With capacity matching and speed matching as the planning objectives, the optimization and reconstruction algorithm and model of the AGC units are established. Taking capacity matching and speed matching as research targets, the optimization and reconstruction algorithm and model of the AGC units are established. Finally, the effectiveness of the model and the specific scheme of the AGC unit optimization and reconstruction are established through simulation.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control (AGC). Thus, there is an urgent demand to coordinate AGC units with active equipment such as energy storage. Current dispatch decision-making methods often ignore the intermittent effects of renewable energy. This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units. This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units, improve the adjustment potential, and respond to the problems of intermittent power generation from renewable energy. A column and constraint generation algorithm is used to solve the model. In an example analysis, the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency

Adaptive distributed auction-based algorithm for optimal mileage based AGC dispatch with high participation of renewable energy

In a performance-based frequency regulation market, increasing number of controllable renewable energy will actively participate in automatic generation control (AGC) of interconnected power girds. Under such circumstance, this paper develops a new optimal mileage based AGC dispatch (OMD) to optimally distribute the real-time overall AGC dispatch command among different AGC units, including traditional units (e.g., coal-fired unit) and renewable energy (e.g., wind farm). It aims to minimize the power deviation between the dispatch command and the actual power regulation output. To rapidly find a high-quality dispatch scheme in a distributed manner, a novel adaptive distributed auction-based algorithm (ADAA) is used for handling OMD due to its fast convergence speed and model-free feature. Compared with the original auction-based algorithm (AA) with multiple fixed swap sizes, ADAA can converge faster and reduce communication traffic since it only employs an adaptive swap size according to the immediate optimization results. Finally, an extended two-area load frequency control model and a practical Hainan power grid of southern China are employed to evaluate the performance of ADAA for OMD.

Mitigation of Frequency and Voltage Disruptions in Smart Grid During Cyber-Attack

In this paper, impacts of cyber-attack on proportional integral derivative controlled automatic generation control unit and automatic voltage regulator have been analyzed individually. A controlled switching unit has been proposed for ensuring frequency stability during an attack. Also, another controlled switching unit, designed for automatic voltage regulator, has been proposed for mitigating voltage disruption during a cyber-attack. The protective model proposed in this paper offers combined protection for frequency and voltage disruptions in a power system during cyber-attack.

Cost Consensus Algorithm Applications for EV Charging Station Participating in AGC of Interconnected Power Grid

In order to more effectively reduce the regulation costs of power grids and to improve the automatic generation control (AGC) performance, an optimization mathematical model of generation command dispatch for AGC with an electric vehicle (EV) charging station is proposed in this paper, in which a cost consensus algorithm for AGC is adopted. Particularly, virtual consensus variables are applied to exchange information among different AGC units. At the same time, the actual consensus variables are utilized to determine the generation command, upon which the flexibility of the proposed algorithm can be significantly enhanced. Furthermore, the implement feasibility of such an algorithm is verified through a series simulation experiments on the Hainan power grid in southern China, where the results demonstrate that the proposed algorithm can effectively realize an autonomous frequency regulation of EVs participating in AGC.

Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration

With the high degree of wind power penetration integrated into multi-area AC/DC interconnected power grids, the frequency regulation capacity of automatic generation control (AGC) units is not sufficient in the wind power-penetrated area, making it difficult to effectively suppress the frequency stability caused by the fluctuation of wind power. Therefore, a coordinated control strategy for AGC units across areas based on bi-level model predictive control is proposed in this paper to achieve resource sharing. The control scheme uses economic model predictive control to realize steady power optimal allocation of the AGC units across areas in the upper layer and distributed model predictive control to realize dynamic frequency optimization control of the multi-area AGC units in the lower layer. Taking a three-area AC/DC interconnected power grid with a wind farm as an example, the simulation results show that, compared with model predictive control using tie-line frequency bias control (TBC) mode, the proposed control strategy can not only effectively maintain tie-line safety and frequency stability, but can also reduce the regulation cost of multi-area AGC units.

A Continuation Power Flow Model of Multi-Area AC/DC Interconnected Bulk Systems Incorporating Voltage Source Converter-Based Multi-Terminal DC Networks and Its Decoupling Algorithm

Existing continuation power flow (CPF) models mainly focus on the regional independent systems, which are not suitable for multi-area AC/DC interconnected systems because the market trading behaviors and security control for power allocation of tie-lines are ignored. This study presents a novel CPF model and its decoupling algorithm for multi-area AC/DC interconnected systems incorporating a voltage source converter (VSC)-based multi-terminal direct current (MTDC) network. This CPF model includes the following unique features: (1) In view of the bilateral power trading contracts among regional subsystems, the nonlinear constraint equations of directional trading active power via interface are derived, and the multi-balancing machine strategy is introduced to realize the active power balance of each subsystem. (2) An accurate simulation method for the security control behaviors of the power allocation in tie-lines is proposed, which includes a specific selection strategy for automatic generation control units and a generation re-dispatch strategy. These two strategies work together to prevent the serious overload in tie-lines during load growth and improve the voltage stability margin of the interconnected bulk systems. (3) The switching characteristic of reactive power control behaviors of VSC stations is simulated in the CPF calculation. In the end, a novel decoupling CPF algorithm based on bi-directional iteration is presented to realize the decomposition and coordination calculation. This decoupling algorithm preserves the precision and convergence of integrated CPF algorithms, and it has an apparent advantage on the calculation speed. Furthermore, this decoupling algorithm also can easily reflects the effects of the control mode changes of VSC stations to the voltage stability margin of AC system. Case studies and comparative analysis on the IEEE two-area RTS-96 system indicate the effectiveness and validity of the proposed CPF model and corresponding decoupling algorithm.

Chance constrained dynamic optimisation method for AGC units dispatch considering uncertainties of the offshore wind farm

The continuing growth of an offshore wind farm integrated into a power grid via high-voltage DC has posed great challenges for automatic generation control (AGC) of the power system. To address these challenges, a new concept of ‘dynamic dispatch of AGC units (DDA)’ under control performance standards from the view of economic dispatch (ED) has been proposed in the previous work, and proved to be an effective technique to co-operate the AGC units with different ramping rates and to fill the gap between ED and AGC. However, the existing DDA model is deterministic in nature, which can hardly deal with the uncertain forecasting error of the offshore wind power output. A novel stochastic DDA model based on chance-constrained programming is proposed considering a random offshore wind power forecasting error, and a hybrid algorithm combining the evolutionary programming algorithm and point estimate method is then developed to solve the stochastic model. Numerical results from a two-area test system with additional offshore wind power generation demonstrate the accuracy and computation efficiency of the hybrid algorithm and the benefits offered by the stochastic AGC dispatch method.

Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control

This paper proposes a novel hybrid intelligent approach to solve the dynamic optimization problem of activation rules for automatic generation control (AGC) based on multiobjective reinforcement learning (MORL) and small population-based particle swarm optimization (SPPSO). The activation rule for AGC is to dynamically allocate the AGC regulating commands among various AGC units, and subsequently, the secondary control reserve of those units can be activated. Therefore, the activation rule for AGC is vital to ensure the overall control performance of AGC schemes. In this paper, MORL is applied to provide a customized platform for interactive self-learning to maximize the long-run discounted reward, i.e., minimize the generation cost, regulating error, and emission from a long-term viewpoint. SPPSO is utilized to effectively and efficiently obtain the optimality of activation rule with a fast convergence speed to fulfill the real-time requirement of AGC activation. Furthermore, a novel analytic hierarchy process-based coordination factor is introduced to identify the optimum multi-objective tradeoff in various power system operation scenarios. At last, the validation of the proposed hybrid method has been demonstrated via comprehensive tests using practical data from the dispatch center of China Southern Power Grids.

Set-theoretic detection of data corruption attacks on cyber physical power systems

This paper addresses a set-theoretic method for the detection of data corruption cyber-attacks on the load frequency control loop of a networked power system. The system consists of several interconnected control areas forming a power grid. Based on the overall discrete-time network dynamics, a convex and compact polyhedral robust invariant set is extracted and is used as a set-induced anomaly detector. If the state vector exits the invariant set, then an alarm will be activated, and the potential threat is considered disclosed. The attack scenario used to assess the efficiency of the proposed anomaly detector concerns corrupted frequency sensor measurements transmitted to the automatic generation control unit of a compromised control area. Simulation studies highlight the ability of a set-theoretic approach to disclose persistent and intermittent attack patterns even when they occur at the same time with changes in the power load demand.