Control Performance Standards Research Articles

The design of an improved index system for frequency control in China Southern Power Grid

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode.Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line.Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets.

Load Frequency Control Strategy for Islanded Microgrid Based on SCQ(λ) Algorithm

In the evolving landscape of power grids, where green transportation and intermittent clean energy play a crucial role, ensuring the security and reliability of the urban network is of utmost importance. However, the increasing volatility associated with these new energy sources poses a challenge to the traditional control methods. The large-scale integration of new energy in microgrids often leads to frequency instability and deviation in control performance standards. Addressing these issues, this paper introduces the SCQ(λ) algorithm, which accurately estimates the system's state to enhance controller capabilities. To evaluate the effectiveness of the proposed SCQ(λ) algorithm, the authors employ a load frequency control model in our simulation. In this model, they introduce various load change disturbances, including sine waves, square waves, and step disturbances to simulate realistic scenarios encountered in power systems. Throughout the simulation, they observe a significant reduction in frequency deviation in the case of step perturbation, with the deviation value decreasing by 0.0096.

Design strategies for addressing COVID-19 issues in buildings

The past COVID-19 pandemic has created new demand and expectations for next-generation buildings. Besides proper indoor air quality (IAQ) and daylight in the indoor environment, the building should provide appropriate working conditions. Due to this pandemic, millions have spent longer hours indoors over the past few years. It is imperative to address a multi-objective and multi-function design to control air circulation in the building's shared and isolated spaces while maintaining energy efficiency, humidity control, and other performance standards. This paper analyzed two architectural design concepts that can eliminate energy usage and daylighting inefficiencies and increase the ventilation rate to reduce indoor transmission of airborne particulates and pathogens and unwanted indoor humidity. We have analyzed the impact of courtyards and atria in typical commercial buildings based on architectural principles and practical experience to create a safe and healthy indoor environment for working and living that will improve indoor protection against the past COVID-19 and future similar virus pandemics. The results show that the use of a courtyard or atrium in design builds a positive effect on controlling the indoor environment.

Adjustable Event-Triggered Load Frequency Control of Power Systems Using Control-Performance-Standard-Based Fuzzy Logic

This article proposesa control performance standard (CPS)-based fuzzy event-triggered scheme for load frequency control (LFC) of power systems with a limited communication bandwidth. First, a CPS-based fuzzy LFC system is established to reduce the wear and tear of the generating unit equipment. Then, based on the Lyapunov stability theory, a stability criterion of the LFC system is proposed to ensure the stable operation of the LFC system, which considers the threshold parameter of the event-triggered condition and the fuzzy gain in the fuzzy LFC system. Next, based on the stability criterion and the Gaussian-type curve-fitting method, a functional expression between the fuzzy gain and the threshold parameter is obtained. According to the expression, the threshold parameter is updated in real time with the change of fuzzy gain, so as to further save usage of the communication network bandwidth. Case studies based on a one-area power system and an IEEE 39-bus benchmark test system are undertaken. Simulation results show that the proposed scheme achieves three objectives: 1) to comply with CPS1 and CPS2 in the North American Electric Reliability Council; 2) to reduce wear and tear of the generating unit equipment; and 3) tosave more communication network resources.

Control Strategy and Performance Analysis of Electrochemical Energy Storage Station Participating in Power System Frequency Regulation: A Case Study of the Jiangsu Power Grid

Electrochemical energy storage stations (EESSs) have been demonstrated as a promising solution to mitigate power imbalances by participating in peak shaving, load frequency control (LFC), etc. This paper mainly analyzes the effectiveness and advantages of control strategies for eight EESSs with a total capacity of 101 MW/202 MWh in the automatic generation control (AGC) in the power system of the Jiangsu power grid. Firstly, an adaptive tracking strategy for electricity quantity that considers the state of charge (SOC) of EESSs is proposed to calculate the baseline power of the EESS participating in AGC. This strategy can simultaneously coordinate different time scale application requirements, such as peak shaving and LFC. Then, an adaptive strategy for regulation requirement allocation among AGC control groups with EESSs that considers different area regulation requirements (ARRs) is proposed to calculate the regulation power of EESS participating in AGC. This strategy can realize the balance transfer of fast and slow regulation capacity, ensure the complementary advantages of various frequency regulation resources and improve the dynamic regulation performance of the control area. Finally, the proposed strategy is validated via a test system to confirm its effectiveness and advantages, as well as via a quantitative analysis on the improvement of the control performance standard (CPS) of the Jiangsu power grid with the participation of EESSs in AGC.

An AGC Dynamic Optimization Method Based on Proximal Policy Optimization

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.

Control Performance Standards-Oriented Event-Triggered Load Frequency Control for Power Systems Under Limited Communication Bandwidth

Load frequency control (LFC) of modern power systems tends to employ open communication networks to transmit measurement/control signals. Under a limited network bandwidth, the continuous and high-sampling-rate signal transmission will be prone to degradation of the LFC performance through network congestion. This brief proposes a decentralized control performance standards (CPSs)-oriented event-triggered (ET) LFC scheme for power systems under constrained communication bandwidth. The proposed scheme comprises the ET LFC scheme and the CPSs-oriented regulation scheme. In the CPSs-oriented regulation scheme, regulation rules are designed to adjust the threshold parameter of the ET LFC scheme based on the North American Electrical Reliability Council (NERC)’s CPS1 and CPS2. The rules generate a larger threshold parameter to lower the triggering frequency in order to reduce unnecessary transmission of measurement/control signals, while ensuring the frequency and tie-lie power of the power systems to meet the required CPS1 and CPS2 instead of the asymptotic stability requirement in the existing research. The reduced transmission of these signals lessens the communication burden. In addition, the decentralized control strategy is used to solve the problems of poor large scalability and computational dimension caused by the centralized control strategy. The effectiveness of the proposed scheme is evaluated on an IEEE 39-bus test system with renewable energy sources.

Intelligent Frequency Control Strategy Based on Reinforcement Learning of Multi-Objective Collaborative Reward Function

Large scale wind power integration into the power grid will pose a serious threat to the frequency control of power system. If only Control Performance Standard (CPS) index is used as the evaluation standard of frequency quality, it will easily lead to short-term centralized frequency crossing, which will affect the effect of intelligent Automatic Generation Control (AGC) on frequency quality. In order to solve this problem, a multi-objective collaborative reward function is constructed by introducing a collaborative evaluation mechanism with multiple evaluation indexes. In addition, Negotiated W-Learning strategy is proposed to globally optimize the solution of the objective function from multi dimensions, it avoids the poor learning efficiency of the traditional Greedy strategy. The AGC control model simulation of standard two area interconnected power grid shows that the proposed intelligent strategy can effectively improve the frequency control performance and improve the frequency quality of the system in the whole-time scale.

Construction of a Frequency Compliant Unit Commitment Framework Using an Ensemble Learning Technique

Frequency control is essential to ensure reliability and quality of power systems. North American Electric Reliability Corporation’s (NERC) Control Performance Standard 1 (CPS1) is widely adopted by many operating authorities to examine the quality of the frequency control. The operating authority would have a strong interest in knowing how the frequency-sensitive features affect the CPS1 score and finding out more effective unit-dispatch schedules for reaching the CPS1 goal. As frequency-sensitive features usually possess multi-variable and high-correlated characteristics, this paper employed an ensemble learning technique (the Gradient Boosting Decision Tree algorithm, GBDT) to construct Frequency Response Model (FRM) of the Taipower system in Taiwan to evaluate by CPS1 score. The proposed CPS1 model was then integrated with Unit Commitment (UC) program to determine the unit-dispatch that achieves the targeted CPS1 score. The feasibility and effectiveness of the proposed CPS1-UC platform were validated and compared with the other benchmark model-based UC methods by two operating cases. The proposed model shows promising results: the system frequency could be maintained well, especially in the periods of the early morning or the high renewable penetration.

Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards

In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid.

Synchrophasor-Based Emergency Generation Control for Area Balancing

In North America, balancing authority (BA) maintains the demand-supply balance and frequency stability within its responsible area. In case of an emergency, such as loss of supervisory control and data acquisition (SCADA), automatic generation control (AGC), state estimation and energy management system (EMS), system operators lose the automatic monitoring and control capability, as a result of which they have to manually dispatch units to maintain area balancing. This could be inefficient and at the risk of failure to comply with NERC’s balancing control performance standards. We propose an emergency control strategy to help BA balance the system cost-effectively based on the independent synchrophasor infrastructure in the event of SCADA/EMS failure. Depending on the availability of electronic dispatch network, the proposed emergency control decides if a synchrophasor-based AGC-like function should be performed every 4 s to regulate area control error. It also executes economic dispatch on a 5-min basis by solving the desired dispatch point of units with PMU installed through a linear programming problem to meet forecasted load change in 5 min. Closed-loop simulations demonstrate that the proposed scheme is able to successfully balance the system at a lower cost to fulfill the BA’s obligations under emergency operation conditions.

Capacity Scheduling of Energy Storage and Conventional Generation for Frequency Regulation Based on CPS1

This paper proposes and evaluates a systematic method of scheduling energy storage and conventional generation capacities in a day-ahead frequency regulation market, based on compliance to control performance standard 1 (CPS1), during each hour of the following day. The salient feature of this method is the development of two simple piecewise linear curves that represent the relationship between first, conventional generation and energy storage power capacities, and second, energy storage power and energy capacities, to satisfy CPS1 compliance in the presence of stochastic load variations. These curves are then modeled inside a mixed integer linear program to solve for resource allocations by minimizing total capacity cost. As such, the formulation avoids complex algorithms that are otherwise required to model frequency dynamics inside the optimization problem. To reduce computation time, a scenario reduction algorithm is used to obtain a small set of scenarios to represent the stochastic load. The proposed method is evaluated using comprehensive dynamic simulations of the two-area IEEE reliability test system implemented in PSS/E. The results prove that the proposed method is effective for scheduling capacities to meet CPS1 compliance with minimum cost.

Imaging Quality Control in the Era of Artificial Intelligence

The advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experience has shown that quality tools alone are often not sufficient to bring about consistent excellent performance. Specifically, rather than assuming outcome targets are consistently met, in quality control, managers assume that wide variation is likely present unless proven otherwise with objective performance data. In this article, we discuss what we consider to be the eight essential elements required to achieve comprehensive process control, necessary to deliver consistent quality in radiology: a process control framework, performance measures, performance standards and targets, monitoring applications, prediction models, optimization models, feedback mechanisms, and accountability mechanisms. We consider these elements to be universally applicable, including in the application of AI-based models. We also discuss how the lack of specific elements of a quality control program can hinder widespread quality control efforts. We illustrate the concept using the example of a CT radiation dose optimization and process control program previously developed by one of the authors and provide several examples of how AI-based tools might be used for quality control in radiology.

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.

Study on the balancing authority ACE limit standard of North American power grid

The control performance standard 2 (CPS2), once adopted in North American power grid, has some disadvantages, such as having insufficient evaluation time and ignoring the correlations between area control errors (ACEs). It was not suitable to restrain the tie‐line power flow and had been replaced by the balancing authority ACE limit (BAAL) standard. It is found that the design mechanism of BAAL standard is derived from reliability requirement. Its purpose is to restrict the duration of over‐limit frequency to be less than 30 min by managing the fluctuation of each ACE. The role of the new standard in co‐operation with control performance standard 1 (CPS1) is analyzed, and some of its technical characteristics, including short‐time operation security, setting dynamic ACE limit, and permitting the correlations between ACEs, are discussed. The simulation results indicate that even in the case where each area performance meets the requirements of CPS1 and CPS2, the short‐time frequency performance still cannot meet the requirement of operation security. BAAL standard co‐operating with CPS1 can not only restrict the standard deviation of frequency fluctuation over a long term but also maintain the short‐time frequency quality, which is beneficial for real‐time operation security and reliability. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Estimation of Regulation Reserve Requirement Based on Control Performance Standard

This paper proposes a method to estimate the real-time regulation reserve requirement based on the NERC Control Performance Standard (CPS). This method is constructed via three steps: first, a Multiple Linear Regression (MLR) model is applied to abstract the relationship between CPS and regulation reserve and other system conditions using training observations generated from a load frequency control model; second, a stepwise method with cross validation is used to select the most relevant features of MLR; and third, the regulation reserve requirement is computed by the MLR model as a function of the predicted system conditions and target CPS score. The recursive least square (RLS) method is used to update the model parameters in an online environment. Testing on a single area automatic generation control model with load and wind data from CAISO 33% Renewable Portfolio Standard scenario indicates the method outperforms methods used in the industry today.

A New Control Logic for a Wind-Area on the Balancing Authority Area Control Error Limit Standard for Load Frequency Control

Nowadays, the Balancing Authority Area Control Error (ACE) Limit (BAAL) Standard has been adopted to replace the Control Performance Standard 2 (CPS2) in the North American power grid. According to the new standard’s mechanism, a new control logic, named “Triggered Monitoring and Graded Regulation” (TM-GR) is proposed. Its purpose is to improve wind power utilization, with good BAAL Standard compliance for load frequency control (LFC). With the TM logic, according to the real-time regulating ability of areas and forecasting results of wind power output, the triggering moments to give orders are found and a defined monitoring interval is set to track the succeeding fluctuation of Area Control Error (ACE). With the GR logic, based on whether or not over-limit frequency and over-limit ACE occur simultaneously, unit output is regulated in different grades. In cooperation with the existing control logic of Control Performance Standard 1 (CPS1), the proposed logic has a higher priority. From the test results, with the proposed control logic, the utilization of wind power output increases and, meanwhile, the area’s control performance meets the Standard BAL-001-2 requirements. The standard deviation of the frequency deviation is less than the target value, and the duration of over-limit ACE and over-limit frequency can both be restricted to be less than 30 min.

NERC’s control performance standards based load frequency controller for a multi area deregulated power system with ANFIS approach

Abstract This paper presents the design of a North American Reliability Council (NERC) standards BAL-001-2 based Adaptive Neuro-Fuzzy Interface System (ANFIS) controller for multi-area deregulated power system under different contract scenarios. The proposed controller is tested on the Indian regional grid system and its control performance standards are compared with the conventional PID controller and ANFIS controller. The major objectives are (i) To find a suitable control for mitigating the diverse LFC problems in a deregulated power system (ii) to comply with the NERC control performance standards which are CPS1, CPS2 and BAAL, and (iii) to reduce the wear and tear of the generating units. Dynamic modelling of controllers, test system and simulation studies are performed using MATLAB/Simulink environment. The simulation results validate that the NERC standards based ANFIS controller is one of the best controller for the four area Indian regional grid under deregulated environment.

Distributed coordination regulation marginal cost control strategy for automatic generation control of interconnected power system

The area-level coordination of regulation capacity by automatic generation control (AGC) is an effective approach to mitigate wind power fluctuations in interconnected power system with high wind penetration. However, the applicability of this method is restricted by the existing AGC strategy. For this reason, power systems in China are experiencing serious wind curtailments to meet the power balance. In this study, a distributed coordination AGC strategy based on regulation marginal cost (RMC) is proposed for coordinating regulation capacity in different areas to remove frequency deviations. Detailed regulating characteristics of the AGC strategy in the existing power systems are discussed. Based on the above analysis, a distributed coordination RMC controller is designed to meet the proposed multi-area coordination rule using a partial primal-dual sub-gradient algorithm. In this proposed controller, the coordination signal containing RMC is exchanged with adjacent controllers through communication networks. The simulation results prove that the distributed coordination RMC strategy can economically coordinate the regulation capacity of an interconnected power system and satisfy the frequency control performance standard.

A novel NERC compliant automatic generation control in multi-area power systems in the presence of renewable-energy resources

In this paper, a novel automatic generation control for a multi-area power system is developed. The proposed controller is based on the Immune-Reinforcement-Learning Algorithm and is capable of real-time adjustment to comply with North American Electric Reliability Council (NERC) control performance standards as well as decreasing the generators’ oscillations. High-renewable penetration, such as wind power, reduces the total inertia of the system which makes the system unable to recover from perturbation causing deterioration in the control performance standards. Because of the intermittency of the renewables, constant gain controllers do not perform well and tend to violate the NERC standards. The proposed algorithm acts in real time and adapts with changes in the renewable power as well as changes in the load to always comply with the standards while smoothly running the generators to reduce wear and tear. The proposed controller is implemented on a four-area power system and compared to different fixed value cases.