Automatic Generation Control Dispatch Research Articles

Hierarchical AGC Dispatch With Detailed Modeling of Energy Storage System Behaviors

The key challenge for automatic generation control (AGC) dispatch lies in the contradiction between the detailed modeling required for optimal dispatch and the tight calculation time. The current method includes (1) the heuristics method that allocates real-time commands based on certain rules (fast but nonoptimal) and (2) the proactive dispatch method with a detailed AGC dispatch model (relatively optimal but the forecast error exists). The development of renewables and the prevalence of energy storage systems (ESSs) with costly degradation calls for combining the advantages of heuristics and proactive methods in AGC dispatch. With this in mind, a hierarchical AGC dispatch method is proposed. A proactive AGC dispatch model embedding state of charge (SOC) recovery is established to make the best use of regulating resources while avoiding the degradation of ESSs. A uniform SOC model considering comprehensive ramping circumstances of ESSs is proposed with low model complexity. A theoretical analysis demonstrates the desired modeling accuracy. To compensate for the forecast error, real-time AGC dispatch is considered. The dead band and forced SOC recovery of ESSs are used to alleviate their degradation. Time-domain simulations based on practical data demonstrate that the proposed method can improve the system frequency performance while obviously reducing the degradation of ESSs.

Dropout Deep Neural Network Assisted Transfer Learning for Bi-Objective Pareto AGC Dispatch

To balance the unexpected power disturbances, an independent system operator (ISO) should assign the dynamic power regulation commands to all the regulation resources via an automatic generation control (AGC) dispatch. It can be described as a bi-objective Pareto optimization by considering the minimizations of total power deviation and total regulation mileage payment. In this work, a novel dropout deep neural network assisted transfer learning (DDNN-TL) is proposed to rapidly approximate the high-quality Pareto optimal solutions for AGC dispatch. Firstly, the training data is generated from the Pareto optimal solutions and fronts obtained by various multi-objective optimization algorithms according to the anticipated total power regulation commands. Secondly, the network parameters of DDNN will be updated via an off-line training with these data at each frequency regulation service period. Finally, based on an efficient transfer learning with a correction of infeasible solutions, DDNN-TL can directly approximate the high-quality Pareto optimal solutions for on-line decision of AGC dispatch. Case studies of DDNN-TL for bi-objective Pareto AGC dispatch are carried out on a two-area load frequency control model and Hainan power grid of China Southern Power Grid (CSG), which demonstrates its superior performance on optimization speed and stability compared with other multi-objective optimization algorithms.

Line Overload Alleviations in Wind Energy Integrated Power Systems Using Automatic Generation Control

Modern power systems are largely based on renewable energy sources, especially wind power. However, wind power, due to its intermittent nature and associated forecasting errors, requires an additional amount of balancing power provided through the automatic generation control (AGC) system. In normal operation, AGC dispatch is based on the fixed participation factor taking into account only the economic operation of generating units. However, large-scale injection of additional reserves results in large fluctuations of line power flows, which may overload the line and subsequently reduce the system security if AGC follows the fixed participation factor’s criteria. Therefore, to prevent the transmission line overloading, a dynamic dispatch strategy is required for the AGC system considering the capacities of the transmission lines along with the economic operation of generating units. This paper proposes a real-time dynamic AGC dispatch strategy, which protects the transmission line from overloading during the power dispatch process in an active power balancing operation. The proposed method optimizes the control of the AGC dispatch order to prevent power overflows in the transmission lines, which is achieved by considering how the output change of each generating unit affects the power flow in the associated bus system. Simulations are performed in Dig SILENT software by developing a 5 machine 8 bus Pakistan’s power system model integrating thermal power plant units, gas turbines, and wind power plant systems. Results show that the proposed AGC design efficiently avoids the transmission line congestions in highly wind-integrated power along with the economic operation of generating units.

Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system

To balance the stochastic power disturbance in integrated energy system (IES), a novel automatic generation control (AGC) dispatch is proposed by taking account of the regulation rule that applies to a performance-based frequency regulation market, with the aim to reduce area control deviation and regulation mileage payment while complying with constraints of various regulation units. Thus, a multiple experience pool replay twin delayed deep deterministic policy gradient (MEPR-TD3) is put forward to improve the training efficiency and the action quality via four improvements including the multiple experience pool probability replay strategy. Finally, the performance of the proposed algorithm is verified on an extended two-area load frequency control (LFC) model and Hainan province IES with different demand of multiple energy.

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.

Optimal Mileage Based AGC Dispatch of a GenCo

This paper presents a new real-time optimal mileage based dispatch (OMD) algorithm for a generating company (GenCo) responding to the automatic generation control (AGC) dispatch signals in real time in context of a performance-based frequency regulation market. Distinguished from traditional methods, the proposed method innovatively incorporates the mileage based regulation service compensation criterion in dispatching multiple units to maximize the Genco's total profit. In addition, the proposed method adopts the model predictive control (MPC) framework to effectively address the real-time OMD given dynamic variations of AGC signals between adjacent dispatch intervals to further improve GenCo's profit. The effectiveness of the proposed method is thoroughly tested using the Hainan power grid as part of China Southern Power Grid (CSG) considering various operation scenarios, where a performance-based frequency regulation market is under implementation.

Multi-Agent Deep Reinforcement Learning for Sectional AGC Dispatch

Aiming at the problem of coordinating system economy, security and control performance in secondary frequency regulation of the power grid, a sectional automatic generation control (AGC) dispatch framework is proposed. The dispatch of AGC is classified as three sections with the sectional dispatch method. Besides, a hierarchical multi-agent deep deterministic policy gradient (HMA-DDPG) algorithm is proposed for the framework in this paper. This algorithm, considering economy and security of the system in AGC dispatch, can ensure the control performance of AGC. Furthermore, through simulation, the control effect of the sectional dispatch method and several AGC dispatch methods on the Guangdong province power grid system and the IEEE 39 bus system is compared. The result shows that the best effect can be achieved with the sectional dispatch method.

Virtual Generation Alliance Automatic Generation Control Based on Deep Reinforcement Learning

This article proposes a distributed hierarchical automatic generation control (AGC) framework with multiple regulation units in the performance-based frequency regulation market, named virtual generation alliance automatic generation control (VGA-AGC), aiming to achieve the coordination of control algorithm and AGC dispatch algorithm and adapt to the development trend of AGC from centralized framework to centralized-decentralized framework. The framework also involves a multi agent distributed multiple improved deep deterministic policy gradient (MADMI-TD3) algorithm that is characterized by excellent global search capability and optimizing speed. The algorithm can help create an optimal AGC strategy in a randomization environment so as to obtain an optimal cooperative control of AGC. According to a simulation verification on the LFC model for an interconnected power grid of a province, the algorithm is superior to the current algorithms and conventional engineering methods in terms of control performance and economic benefits. In other words, the algorithm can improve control performance and reduce the regulation mileage payment.

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.

Optimal Bidding in Performance-Based Regulation Markets: An MPEC Analysis With System Dynamics

In this paper, we address the problem of optimal bidding in performance-based regulation markets for a large price-maker regulation resource. Focusing on the case of the California Independent System Operator (ISO), detailed market components are considered, such as regulation capacity payment, regulation mileage payment, performance accuracy adjustment, automatic generation control dispatch, and participation factor. Our analysis also incorporates system dynamics of the regulation resource for different resource types and technologies. In principle, our problem formulation is a mathematical program with equilibrium constraints (MPEC). However, our fundamentally new formulations introduce several new challenges in solving the MPEC problem in the context of performance-based regulation markets that are not previously addressed. In fact, global optimization techniques fail to solve the original nonlinear program due to its complexity. Therefore, we undergo several innovative steps to transform the problem into a mixed-integer linear program which is solved with accuracy, reliability, and computational efficiency. Insightful case studies are presented using data from a California ISO regulation market project.

Dynamic performance-based automatic generation control unit allocation with frequency sensitivity identification

With the alteration of energy structures in power system, the allocation of automatic generation control (AGC) is facing new challenges. The emergence of high penetration of manufacturing sectors and renewable energy sources has increased the demand for faster-ramping resources to participate in the frequency regulation service. Additionally, the current regulation service does not properly arrange the output of the resources considering the actual performance while they follow the AGC allocation signals, which affects the accuracy of frequency regulation. The fast-ramping capacity and response accuracy of AGC units are supposed to be considered in the dispatch. Meanwhile, the power outputs of different governors have different impacts on the system frequency, which has important guiding significance for the AGC dispatch. With the purpose of improving frequency regulation service, this paper proposes a dynamic performance-based dispatch model considering the above issues. We first prove that there is a linear relation between the output of the generators and system frequency, defined as frequency sensitivity. Then, the frequency sensitivity of each generator can be identified using the least square (LS) method. Furthermore, a dynamic multi-objective optimization allocation model is established, which considers the units’ economy, ramping capacity and accuracy. Finally, the proposed identification method and allocation model are simulated in the IEEE-9 bus system, and the simulation results verify their validity and feasibility

Real-time AGC dispatch units considering wind power and ramping capacity of thermal units

The high penetration of wind energy sources in power systems has substantially increased the demand for faster-ramping thermal units participating in the frequency regulation service. To fulfill the automatic generation control (AGC) and compensate the influence of wind power fluctuations simultaneously, ramping capacity should be considered in the dispatch model of thermals. Meanwhile, conventional methods in this area do not take the impact of transmission loss into the dispatch model, or rely on offline network model and parameters, failing to reflect the real relationships between the wind farms and thermal generators. This paper proposes an online approach for AGC dispatch units considering the above issues. Firstly, the power loss sensitivity is online identified using recursive least square method based on the real-time data of phasor measurement units. It sets up power balance constraint and results in a more accurate dispatch model. Then, an improved multi-objective optimization model of dispatch is proposed and a connection is established between the thermal units with fast ramping capacity and the wind farms with rapid fluctuations. Genetic algorithm is used to solve the dispatch model. The proposed method is compared with conventional methods in simulation case in the IEEE 30-bus system. Finally, simulation results verify the validity and the feasibility of identification method and optimization model.

Determination of AGC capacity requirement and dispatch considering performance penalties

A continuous and near instantaneous balance between generation supply and load demand must be maintained in power system operation. As a kind of regulation reserve, automatic generation control (AGC) is widely employed for maintaining system security and reliability, especially for controlling frequency deviation and supporting commercial transactions. In a competitive electricity market, AGC services are either contracted via negotiations between the market operator and generation companies concerned, or procured via a certain form of auction. A new framework to determine AGC capacity requirement and to deal with regulation dispatch under uncertain demand in a competitive electricity market is presented in this paper. First, the stochastic characteristics of AGC capacity requirement and services are analyzed. A stochastic optimization decision-making model for determining optimal AGC capacity requirement is then developed, followed by a pricing analysis which reveals that a trade-off between economic efficiency and system security must be made by the market operator. Finally, an optimal AGC dispatch algorithm is suggested. A numerical study is given to further demonstrate the essential features of the proposed model and method.