Control Performance Standards Research Articles

Intelligent control schemes applied to Automatic Generation Control

Integrating ever increasing amount of renewable generating resources to interconnected power systems has created new challenges to the safety and reliability of today‟s power grids and posed new questions to be answered in the power system modeling, analysis and control. Automatic Generation Control (AGC) must be extended to be able to accommodate the control of renewable generating assets. In addition, AGC is mandated to operate in accordance with the NERC‟s Control Performance Standard (CPS) criteria, which represent a greater flexibility in relaxing the control of generating resources and yet assuring the stability and reliability of interconnected power systems when each balancing authority operates in full compliance. Enhancements in several aspects to the traditional AGC must be made in order to meet the aforementioned challenges. It is the intention of this paper to provide a systematic, mathematical formulation for AGC as a first attempt in the context of meeting the NERC CPS requirements and integrating renewable generating assets, which has not been seen reported in the literature to the best knowledge of the authors. Furthermore, this paper proposes neural network based predictive control schemes for AGC. The proposed controller is capable of handling complicated nonlinear dynamics in comparison with the conventional Proportional Integral (PI) controller which is typically most effective to handle linear dynamics. The neural controller is designed in such a way that it has the capability of controlling the system generation in the relaxed manner so the ACE is controlled to a desired range instead of driving it to zero which would otherwise increase the control effort and cost; and most importantly the resulting system control performance meets the NERC CPS requirements and/or the NERC Balancing Authority’s ACE Limit (BAAL) compliance requirements whichever are applicable.

Penetration Rate and Effectiveness Studies of Aggregated BESS for Frequency Regulation

With the increased environmental concern, the photovoltaic (PV) generation capacity is growing in today’s power systems. As the PV penetration rate increases, the intermittency and uncertainty of PV systems will cause frequency regulation issues. When rapid fluctuations take place, the system requires fast responding regulation to recover the frequency within a short period of time. Traditional power plants with slow dynamics are less capable of tracking the fast-changing regulation signal. In this context, a battery energy storage system (BESS) is considered as an effective regulation source to respond immediately to frequency deviations. This paper addresses the sizing issue of an aggregated BESS by a series of system level performance tests with different BESS penetration rates. The evaluation criteria are the control performance standards 1 and 2. Response effectiveness of the BESS with different levels of disturbances is also analyzed, with comparison to that of the traditional power plants. The proposed BESS aggregation controller is also validated using software simulations and a hardware testbed.

The Joint Adequacy of AGC and Primary Frequency Response in Single Balancing Authority Systems

This paper generalizes previous work on automatic generation control (AGC) gain in a single balancing authority system. In previous work, a control performance standard 1 (CPS1)-compliant AGC gain for a single balancing authority interconnection was proposed, assuming that frequency response can be decoupled from AGC dynamics by widening governor dead bands. However, current practices suggest that widening dead bands may not be a wise course to take as it may deteriorate primary frequency response. This paper formulates sufficient conditions for the adequacy of the joint operation of AGC control and primary frequency response to be consistent with frequency response adequacy. Historical data and a simulation of ERCOT are presented to show numerical results.

Multi-Agent Correlated Equilibrium Q(λ) Learning for Coordinated Smart Generation Control of Interconnected Power Grids

This paper proposes an optimal coordinated control methodology based on the multi-agent reinforcement learning (MARL) for the multi-area smart generation control (SGC) under the control performance standards (CPS). A new MARL algorithm called correlated Q(λ) learning (CEQ(λ)) is presented to form an optimal joint equilibrium strategy for the coordinated load frequency control of interconnected control areas, and a SGC framework is proposed to facilitate information sharing and strategic interaction among multi-areas so as to enhance the overall long-run performance of the control areas. Furthermore, a novel time-varying equilibrium factor is introduced into the equilibrium selection function to identify the optimum equilibrium policies in various power system operation scenarios. The performance of CEQ(λ) based SGC strategy has been fully tested and benchmarked on a two-area power system and the China Southern Power Grid. Comparative studies have not only demonstrated the superior equilibrium optimization and dynamic performance of the proposed SGC strategy but also confirmed its fast convergence and high flexibility in designing the equilibrium factor for the desirable operating state of correlated equilibria.

A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm for smart generation control of interconnected complex power grids

This paper proposes a multi-agent smart generation control scheme for the automatic generation control coordination in interconnected complex power systems. A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm is developed, which can effectively identify the optimal average policies via a variable learning rate under various operation conditions. Based on control performance standards, the proposed approach is implemented in a flexible multi-agent stochastic dynamic game-based smart generation control simulation platform. Based on the mixed strategy and average policy, it is highly adaptive in stochastic non-Markov environments and large time-delay systems, which can fulfill automatic generation control coordination in interconnected complex power systems in the presence of increasing penetration of decentralized renewable energy. Two case studies on both a two-area load–frequency control power system and the China Southern Power Grid model have been done. Simulation results verify that multi-agent smart generation control scheme based on the proposed approach can obtain optimal average policies thus improve the closed-loop system performances, and can achieve a fast convergence rate with significant robustness compared with other methods.

Multiagent Stochastic Dynamic Game for Smart Generation Control

AbstractThis paper proposes a multiagent (MA) smart generation control (SGC) scheme for the coordination of automatic generation control (AGC) in power grids with system uncertainties. Under the control performance standards, SGC will undergo a non-Markov random process, of which the optimal solution can be resolved online by the reinforcement learning. Therefore, an MA decentralized correlated equilibrium Q(λ)-learning algorithm, and an MA stochastic dynamic game-based SGC simulation platform (SGC-SP) have been proposed for its implementation, which can achieve AGC coordination in a highly uncertain environment resulting from the increasing penetration of renewable energy. Single-agent Q-learning, Q(λ)-learning, R(λ)-learning, and proportional integral control are implemented and embedded in SGC-SP for the control performance analysis. Two case studies on both a two-area power system and the China Southern Power Grid model have been done, which verify its effectiveness and scalability.

Optimized PI+ load–frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units

Abstract This paper investigates a renewable energy resource’s application to the Load–Frequency Control of interconnected power system. The Proportional-Integral (PI) controllers are replaced with Proportional-Integral Plus (PI+) controllers in a two area interconnected thermal power system without/with the fast acting energy storage devices and are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network (BWNN) approaches. The energy storing devices Hydrogen generative Aqua Electroliser (HAE) with Fuel cell and Redox Flow Battery (RFB) are incorporated to the two area interconnected thermal power system to efficiently damp out the electromechanical oscillations in the power system because of their inherent efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE and RFB designed with BWNN controller reveals that the PI+ controller designed using BWNN approach is found to be superior than that of output response obtained using PI+ controller. Moreover the BWNN based PI+ controller exhibits a better transient and steady state response for the interconnected power system with Hydrogen generative Aqua Electroliser (AE) unit than that of the system with Redox Flow Battery (RFB) unit.

CPS1 Compliance-Constrained AGC Gain Determination for a Single-Balancing Authority

The integration of non-dispatchable generation has increased the need for ancillary services to maintain frequency control performance, and the determination of adequacy to ensure reliability has become an important operating concern. This work determines the AGC gain of a single balancing authority interconnection in order for system frequency to comply with NERC control performance standards, ensuring secondary control adequacy. The particular case of wind expansion is considered, and a simulation of ERCOT is presented to evaluate the performance of the formulation.

Impact of short-term storage on frequency response under increasing wind penetration

In this paper, the effort is to study the impact of short-term storage technology in stabilizing the frequency response under increasing wind penetration. The frequency response is studied using Automatic Generation Control (AGC) module, and is quantified in terms of Control Performance Standards (CPS). The single area IEEE Reliability Test System (RTS) was chosen, and battery storage was integrated within the AGC. The battery proved to reduce the frequency deviations and provide good CPS scores with higher penetrations of wind. The results also discuss the ability of the short term storage to benefit the system by reducing the hourly regulation deployment and the cycling undergone by conventional units, by dint of their fast response; and sheds light on the economic implications of their benefits.

Current approaches and future role of high content imaging in safety sciences and drug discovery.

High content imaging combines automated microscopy with image analysis approaches to simultaneously quantify multiple phenotypic and/or functional parameters in biological systems. The technology has become an important tool in the fields of safety sciences and drug discovery, because it can be used for mode-of-action identification, determination of hazard potency and the discovery of toxicity targets and biomarkers. In contrast to conventional biochemical endpoints, high content imaging provides insight into the spatial distribution and dynamics of responses in biological systems. This allows the identification of signaling pathways underlying cell defense, adaptation, toxicity and death. Therefore, high content imaging is considered a promising technology to address the challenges for the "Toxicity testing in the 21st century" approach. Currently, high content imaging technologies are frequently applied in academia for mechanistic toxicity studies and in pharmaceutical industry for the ranking and selection of lead drug compounds or to identify/confirm mechanisms underlying effects observed in vivo. A recent workshop gathered scientists working on high content imaging in academia, pharmaceutical industry and regulatory bodies with the objective to compile the state-of-the-art of the technology in the different institutions. Together they defined technical and methodological gaps, proposed quality control measures and performance standards, highlighted cell sources and new readouts and discussed future requirements for regulatory implementation. This review summarizes the discussion, proposed solutions and recommendations of the specialists contributing to the workshop.

Beta Wavelet Neural Network Based Load-Frequency Controller for an Interconnected Reheat Power system with Hydrogen Electrolyser

This paper investigates a renewable energy resources application to the Load-Frequency Control of interconnected power system. The Proportional plus Integral(PI) controller gains of the two area interconnected thermal power system with the fast acting energy storage devices are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network(BWNN) approaches. The energy storing device Hydrogen generative Aqua Electrolizer (HAE) with fuel cell can efficiently damp out the electromechanical oscillations in the power system because of their efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% and 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE designed with BWNN Controller are found to be superior than that of output response obtained using PI Controller.

Impact of wind power on control performance standards

As the penetration of wind power continuously increases, the impacts of wind power on frequency control have become of great concern. Frequency control requires real time balance between system generation and load, with system frequency deviation maintained within a certain range. Control Performance Standards (CPS) are indices for evaluating a balancing area’s frequency control performance in an interconnected system. They are important for quantifying the frequency control performance of the interconnected system and the relative distribution of frequency control responsibility among areas. Without mitigating action, the increasing wind power may inhibit a balancing area’s ability to comply with CPS due to variability in power output from wind plants and reduced system frequency response. This paper assesses the impacts of wind power on a balancing area’s frequency control characteristics and studies the wind power impacts on CPS. A two area Automatic Generation Control (AGC) model is used to verify these impacts. AGC changes the valve reference positions of units and restores system frequency to nominal values. Both theoretical study and simulation results show that CPS indices deteriorate as wind power penetration increases. Measures to improve CPS are provided.

Control performance standards based load-frequency controller considering redox flow batteries coordinate with interline power flow controller

This paper proposes a sophisticate application of redox flow batteries (RFB) coordinate with Interline Power Flow Controller (IPFC) for the improvement of Load Frequency Control (LFC) of a multiple units two- area power system. The Interline Power Flow Controller is to stabilize the frequency oscillations of the inter-area mode in the interconnected power system by the dynamic control of tie-line power flow. The redox flow batteries, which are not aged to the frequent charging and discharging, have a quick response and outstanding function during overload conditions. In addition to leveling load, the battery is advantageous for secondary control in the power system and maintenance of power quality of distributes power resources. The Bacterial Foraging Optimization (BFO) algorithm is use to optimize the parameters of the cost functions for designing the integral controller. Compliance with North American Electric Reliability Council (NERC) standards for Load Frequency Control has also been establishes in this work. Simulation studies reveal that the RFB coordinate with IPFC units has greater potential for improving the system dynamic performance.

R([formula omitted]) imitation learning for automatic generation control of interconnected power grids

The goal of average reward reinforcement learning is to maximize the long-term average rewards of a generic system. This coincides with the design objective of the control performance standards (CPS) which were established to improve the long-term performance of an automatic generation controller (AGC) used for real-time control of interconnected power systems. In this paper, a novel R(λ) imitation learning (R(λ)IL) method based on the average reward optimality criterion is presented to develop an optimal AGC under the CPS. This R(λ)IL-based AGC can operate online in real-time with high CPS compliances and fast convergence rate in the imitation pre-learning process. Its capability to learn the control behaviors of the existing AGC by observing system variations enable it to overcome the serious defect in the applicability of conventional RL controllers, in which an accurate power system model is required for the offline pre-learning process, and significantly enhance the learning efficiency and control performance for power generation control in various power system operation scenarios.

Research on the Supplementary Analysis System Based on CPS Standard

CPS test standard has put forward new demands to the management of power grids. This paper analyzes the main influence factors of CPS, and introduces the way of building data warehouse and constructing supplementary analysis system on this basis, which can effectively help power grid workers to research the pros and cons in dispatch and operation. The application of the supplementary analysis system is helpful for Shanghai power grid to enhance AGC control and frequency control ability under the control performance standard.

Special Issue on Restructuring and Sustainable Development for China’s Electric Power Industry

Special Issue on Restructuring and Sustainable Development for China’s Electric Power Industry

Stochastic Optimal Relaxed Automatic Generation Control in Non-Markov Environment Based on Multi-Step $Q(\lambda)$ Learning

This paper proposes a stochastic optimal relaxed control methodology based on reinforcement learning (RL) for solving the automatic generation control (AGC) under NERC's control performance standards (CPS). The multi-step Q(λ) learning algorithm is introduced to effectively tackle the long time-delay control loop for AGC thermal plants in non-Markov environment. The moving averages of CPS1/ACE are adopted as the state feedback input, and the CPS control and relaxed control objectives are formulated as multi-criteria reward function via linear weighted aggregate method. This optimal AGC strategy provides a customized platform for interactive self-learning rules to maximize the long-run discounted reward. Statistical experiments show that the RL theory based Q(λ) controllers can effectively enhance the robustness and dynamic performance of AGC systems, and reduce the number of pulses and pulse reversals while the CPS compliances are ensured. The novel AGC scheme also provides a convenient way of controlling the degree of CPS compliance and relaxation by online tuning relaxation factors to implement the desirable relaxed control.

Stochastic optimal generation command dispatch based on improved hierarchical reinforcement learning approach

This study presents an improved hierarchical reinforcement learning (HRL) approach to deal with the curse of dimensionality in the dynamic optimisation of generation command dispatch (GCD) for automatic generation control (AGC) under control performance standards. The AGC committed units are firstly classified into several different groups according to their time delay of frequency control, and the core problem of GCD is decomposed into a set of subtasks for search of the optimal regulation participation factors with the solution algorithm. The time-varying coordination factor is introduced in the control layer to improve the learning efficiency of HRL, and the generating error, hydro capacity margin and AGC regulating costs are formulated into Markov decision process reward function. The application of the improved hierarchical Q-learning (HQL) algorithm in the China southern power grid model shows that the proposed method can reduce the convergence time in the pre-learning process, decrease the AGC regulating cost and improve the control performance of AGC systems compared with the conventional HQL, genetic algorithm and a engineering method.

Q-Learning Approach for Hierarchical AGC Scheme of Interconnected Power Grids

This paper formulates automatic generation control (AGC) for the power dispatch center as a two-layer hierarchical control framework, which can be divided into two discrete-time Markov decision process (DTMDP) sub-problems. The first one focuses on the solution of optimum AGC regulating commands under control performance standards, while the second works on the dynamic optimization allocation of the commands to various types of AGC units. The model-free Q-learning and multicriteria reward function are proposed and designed specifically for two DTMDP sub-problems, respectively. The proposed methodology can enhance the overall performance of the hierarchical AGC scheme from the viewpoint of long-term optimal objective. The effectiveness and efficiency of the AGC scheme are fully studied via simulation tests on a two-area interconnected hydro-thermal power system model, and test results are benchmarked against another heuristic algorithm and practical engineering approaches.

Stochastic Optimal CPS Relaxed Control Methodology for Interconnected Power Systems Using Q-Learning Method

This paper presents the application and design of a novel stochastic optimal control methodology based on the Q-learning method for solving the automatic generation control (AGC) under the new control performance standards (CPS) for the North American Electric Reliability Council (NERC). The aims of CPS are to relax the control constraint requirements of AGC plant regulation and enhance the frequency dispatch support effect from interconnected control areas. The NERC’s CPS-based AGC problem is a dynamic stochastic decision problem that can be modeled as a reinforcement learning (RL) problem based on the Markov decision process theory. In this paper, the Q-learning method is adopted as the RL core algorithm with CPS values regarded as the rewards from the interconnected power systems; the CPS control and relaxed control objectives are formulated as immediate reward functions by means of a linear weighted aggregative approach. By regulating a closed-loop CPS control rule to maximize the long-term discounted...