Power Grid Control Research Articles

A Gaussian hybrid clustering-based method for compensating for the loss of semi-persistent scheduling data in the middle station of large power grid regulation and control

In order to ensure the effectiveness of power grid scheduling decisions, ensure the stability, safety, and intelligence level of scheduling operations, and solve the semi persistent problem of station scheduling data loss in power grid scheduling, a Gaussian mixture clustering based method for compensating station scheduling data loss in power grid scheduling is proposed. Based on the role of the intermediate station and its semi persistent scheduling data generation mechanism in the control system of the large power grid, a Gaussian mixture model is constructed to calculate the conditional expected value of missing data as compensation value, and the final compensation result of the semi persistent scheduling data of the intermediate station is obtained. The experimental results show that in various types and degrees of scheduling data missing scenarios, this method performs well, and its Pearson correlation coefficient for compensating data is generally higher than 0.94, fully verifying the effectiveness and accuracy of this method. This achievement not only provides a practical and feasible solution to the problem of data loss in power grid scheduling, but also provides strong technical support for improving the accuracy of power grid regulation and ensuring the safe and stable operation of the power grid.

Adaptive Data Fusion for State Estimation and Control of Power Grids Under Attack

Adaptive Data Fusion for State Estimation and Control of Power Grids Under Attack

Optimal capacity configuration and operation strategy of typical industry load with energy storage in fast frequency regulation

As the potential and competent load-side resources for frequency response and control in modern power grids, typical industrial load can compensate for the deficiency of frequency response capability for new-type power systems. However, the operational flexibility is seriously enforced by the operation conditions uncertainties of industrial load. With “Online Calculation, and Real-time Matching” as the core, based on fuzzy mathematical theory, the coordinated operation strategy of typical industrial loads and energy storage systems (ESS) is proposed to finish fast frequency regulation (FFR) tasks. And an optimal capacity configuration model of industrial loads with ESSs is established to evaluate the whole economic profitability in FFR. Specially, in terms of algorithms, a novel Two-layer Parallel Particle Swarm Optimization and Genetic Algorithm (TPPSGA) is innovatively designed in this manuscript. Comparing with traditional heuristic algorithms, TPPSOGA improves computation speed by 6.84 times. Simulation results show that our proposed strategy can reasonably estimate the frequency capability of industrial load by membership degree function, and prove that the industrial load participating in FFR with the support of ESSs is able to get more revenue and stability than industrial load or ESSs separately is in regulation of FFR. The economic analyze in market price and ESS parameters can help the load agent to screen the suitable-type ESS and its capacity to serve for FFR and enhance the flexibility in load-side frequency regulation.

Power Flow Control Strategy of Wind Power Hybrid Power Grid Based on Digital Simulation Cloud Platform Architecture

Wind power hybrid is a comprehensive utilization method of new energy, which can greatly reduce energy pollution, but there is also the problem of tidal flow difference, so the power flow regulation of wind power energy is to be solved at present. In this paper, the digital simulation platform is used to comprehensively judge the grid-connected demand, collect data such as wind speed, power generation and energy storage, and regulate the power flow of the wind power hybrid grid in combination with the problem of wind speed randomness. Secondly, the intelligent control function is used to judge the wind turbine transmission power hybrid grid and the power flow of the distributed power grid to balance the stability of the overall power flow, voltage and current of the wind power hybrid power grid. The results show that the digital simulation cloud platform collects the power flow data of the wind power hybrid power grid, generates abnormal power flow points, and supplements the power flow through alternative methods, and its regulation and control effectiveness is greater than 90%, and the amplitude of maintaining the power flow is between 10~20%. The wind power grid's grid-connected power flow regulation time is within 10s, and there is no damage to the wind turbine and energy storage battery. Therefore, the digital simulation cloud platform can realize the power flow control of the wind power hybrid power grid, maintain its overall stability, and meet the needs of new energy grid expansion.

A Novel Contingency-Aware Primary Frequency Control for Power Grids With High CIG-Penetration

A Novel Contingency-Aware Primary Frequency Control for Power Grids With High CIG-Penetration

Hierarchical Frequency and SOC Control of Power Grids With Battery Energy Storage Systems

Hierarchical Frequency and SOC Control of Power Grids With Battery Energy Storage Systems

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

Novel tuning rules for IMC-high-order PID load frequency controller of power systems

The problem of load frequency control (LFC) for power grids has received widespread attention in power industrial control. Ensuring the stability of the frequency is the primary concern that constrains the development of the electric power system. This paper presents a novel internal model control (IMC)- proportional-integral-derivative (PID) controller design method for LFC control systems, centered on pole-zero conversion. This method aims to simplify the system model's complexity and improve the system's performance. The IMC controller is approximated as a PID series compensator (high-order PID), with exact tuning formulas provided for various scenarios. These include single-area without or with reheat turbines, hydro turbines with transient droop compensators, wind turbines, and gas turbines. The tuning formulas are characterized by a single parameter, determined under robustness constraints and integrated time absolute error (ITAE). The proposed tuning method considers the generator rate constraint (GRC) and applies to multi-area single-source and multi-area multi-source power systems. The results of the simulation confirm that the controller tuning method presented in this paper has significant advantages over the existing design methods concerning robustness and anti-interference performance.

Distributed Energy Resources Management System (DERMS) and Its Coordination with Transmission System: A Review and Co-Simulation

Ever-increasing penetration of distributed energy resources (DERs) in the power grids, alongside their numerous benefits, brings new challenges that call for enhanced solutions in the field of control and management of power grids. The majority of the available research have considered either distribution or transmission grids in their studies. In this paper, a comprehensive review of the effects of DERs on the distribution and transmission grids is performed. The focus of this paper is on hierarchical management methods in order to categorize different approaches and highlight the gaps. Moreover, a review is conducted in the field of the newly introduced distributed energy resources management system (DERMS) concept. A DERMS can facilitate the hierarchical energy management procedure due to its functionalities and broad capabilities. Hence, its implementation in energy management and its impact on the power grid will be assessed with the aid of a co-simulation platform that considers both transmission and distribution grids.

Virtual Power Grid Flux-Oriented Vector Control for Three-Phase Voltage-Type PWM Rectifier

More considerable power distortions are produced by the non-reversible diode-bridge rectifiers in the power system’s AC-DC conversion technology. In addition, bridge-rectifiers worsen energy quality and decrease power factor. Three-Phase Pulse Width Modulation (3phase-PWM) rectifiers have seen rapid growth in use due to recent breakthroughs in power device technology. Its benefits include a unity power factor, no harmonic distortion, and pulsating direct currents. An actual power regulation technique based on a novel virtual power grid controller is suggested to cheaply regulate a 3phase-PWM converter with good performance. The study introduces a new sensorless control scheme to improve the 3phase-PWM-performance rectifier. The proposed control technique employs the virtual power grid flux-oriented vector control with a Second-Order Generic Integral with Instantaneous Phase Lock Loop (SOGI-IPLL) to overcome the problems inherent in relying only on low-pass filter estimates. The virtual power grid flux value may then determine the phase difference. The IPLL takes the phase angle as an input and is utilized to construct the power-oriented vector control. The proposed power grid flux-oriented control strategy combines the VFidea with SOGI-IPLL to circumvent the restrictions imposed by relying only on an integrator of low-pass filters (LPF) to provide an estimate. A refined Virtual flux (VF) estimator with direct power control can improve management at a cheaper cost and more efficiently. The modeling findings demonstrate that the DC voltage input and power grid throughput of the 3phase-PWM rectifier can be successfully regulated in both the rectification and inversion states, allowing for the effective functioning of the 3phase-PWM rectifier.

Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches

With the global energy transition of the electric power system, grid control, supervision, and protection is becoming more challenging. With the increasing integration of renewable energy sources (RES), the system dynamics are changing, causing traditional power system dynamic modeling with swing equation-based modeling approaches to fail. Additionally, the converter-dominated power grid is decreasing the system inertia, making the power system more fragile to the frequency swings. This paper first investigates and compares the application of a model-based Kalman filter state estimation approach with (i) a model-free machine learning approach --- neural ordinary differential equations (NODEs) --- and (ii) a data-driven system identification (SysId) approach to model and infer critical state values of the power system frequency dynamics. Then a model predictive control (MPC) framework is compared to a model-free Soft Actor-Critic (SAC) reinforcement learning (RL) control algorithm in providing efficient fast frequency response (FFR) to the power system frequency dynamics. The approaches are compared in terms of their performance goals as well as their per-timestep computational efficiency. The comparative study for state estimation shows that for the model-free requirement, both NODEs and SysId can provide accurate state estimates; however, with increasing model complexity, NODEs can be a better choice for model identification. Similarly, the results from the FFR comparative study show that the SAC RL-based FFR, once trained, outperforms MPC with better control signals and faster computation time, making the SAC RL-based FFR better option for providing FFR to the power system.

Toward Fair Power Grid Control: A Hierarchical Multiobjective Reinforcement Learning Approach

Toward Fair Power Grid Control: A Hierarchical Multiobjective Reinforcement Learning Approach

Analysing Smart Power Grid against different Cyber Attacks on SCADA System

The integration of information and communications technology (ICT) with traditional power grid is transforming the power grid into a more reliable and smarter cyber physical system. A supervisory control and data acquisition (SCADA) system is used to implement the cyber layer for power grid, which is responsible for real-time monitoring and control of power grid and power delivery network. A SCADA system combined with other devices creates smart power grid environment, but it also makes power grid vulnerable against different cyber-attacks as it requires connection with various kinds of open networks. Smart power grids are a extremely critical infrastructure and attack of any kind can affect socio economical condition of the region. In this paper we present a non-complex co-simulation environment for analysing smart power grid and also includes two different cyber-attack scenarios. Index Terms- co-simulation; smart power grid; SCADA system; cyber-attack scenarios.

Calculating Global Minimum Points to Binary Polynomial Optimization Problem: Optimizing the Optimal PMU Localization Problem as a Case-Study

State estimation (SE) is an algorithmic function of an energy management system (EMS). SE provides an actual-time monitoring and control of modern electrical power grids. State Estimation can be worked with sufficiency using Phasor Measurement Units optimally placed within a power grid. This paper concerns the implementation of proper algorithms embedded in optimization solvers to the optimal PMU localization problem solving globally. The optimization model is formulated as a 0 - 1 nonlinear minimization problem. The problem is transformed to a polyhedron using linearization methods and B&B tree. In this model, we use a linear cost function under polynomial constraints and binary restrictions on the design variables in a symbolic format. This mathematical model is programmed in the YALMIP environment which is fully compatible with MATLAB. The 0 - 1 Nonlinear Programming (NLP) model is suitable for getting concisely global optimal solutions. The optimal solution is given by a wrapped optimization engine including a local optimizer routine performing together with a mixed-Integer-Linear Programming routine. The solution is achieved within a zero-gap precisely encountered during the iterative process. This tolerance criterion is a necessity for a successful implementation of the B&B tree because it ensures global optimality with an acceptance relative gap. The minimization model is implemented in a YALMIP code fully compatible with MATLAB in two stages. Initially, an objective function with one term is minimized to discover a number of sensors for wide-area monitoring, control and state estimator applications. Then, an extra product is considered in the objective to suffice maximum reliability for observing the network buses. The numerical minimization models are applied to standard power networks in the direction to be solved globally.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

Research on robust adaptive control of strong nonlinear complex large power grids.

In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance.

A-Priori Reduction of Scenario Approximation for Automated Generation Control in High-Voltage Power Grids With Renewable Energy

A-Priori Reduction of Scenario Approximation for Automated Generation Control in High-Voltage Power Grids With Renewable Energy

Distributed Optimization Scheduling Consistency Algorithm for Smart Grid and its Application in Cost Control of Power Grid

Distributed Optimization Scheduling Consistency Algorithm for Smart Grid and its Application in Cost Control of Power Grid

Continuous Optimization of Business Environment for Power Grid Timing Control

How to optimize the business environment is a very difficult issue. Now the stability of the timing control of the power grid is poor and the energy cost is high. This paper uses literature reviews and case studies to study the optimization of the business environment of the timing control of the power grid. Through collecting and analyzing relevant literature and combining actual cases, the current business environment problems of timing control of power grids are discussed, and corresponding solutions are proposed. The research results show that the stability of the power grid of this technology is up to 99%, and the continuous optimization of the business environment of the power grid timing control can reduce energy costs. It is necessary to conduct business interconnection and share timing signals, and through the formulation of unified technical standards, data security protection, management and supervision mechanism improvement, and fair investment and cost sharing mechanism, enhance the efficiency of power grid timing control and energy exploitation.

Multi-DC commutation safety and system stability coordination control evaluation method

Due to the influence of multiple DC commutation failure after AC failure and stable interaction of system frequency, voltage and power Angle, the operation control of power grid is greatly difficult. The control scenario of multi-feed DC commutation failure or system instability is constructed, the comprehensive control performance cost ratio index of emergency control measures to the safety of multi-turn DC commutation and system stability is proposed, and the search method of coordinated control strategy based on global optimal is developed to avoid the risk of chain failure caused by negative control effects. The evaluation of control search path and control target after joint optimization is realized, which is conducive to the construction of system-level defense method and improves the adaptability of strategy.