Power Grid Control Research Articles

Multiple Delayed Signal Cancellation Filter-Based Enhanced Frequency-Locked Loop Under Adverse Grid Conditions

An accurate and prompt estimation of phase angles, frequency, and magnitudes of the grid voltage is extremely important in paving a good platform for a fast and reliable operation of power grid monitoring, protection, and control. The phase-locked loop (PLL) served this purpose for decades, while the concept of the frequency-locked loop (FLL) has been proposed more recently. Even though the FLL and the PLL share some similarities, some differences still exist. Further research on FLLs can boost their capabilities and open up new windows in designing devices for grid synchronization. Multiple delayed signal cancellation (MDSC) filters are a peculiar system of filters. Their applications to FLLs can enrich FLL’s performance. This article focuses on applying the stationary reference frame MDSC filters at in-loop and prefiltering stages of the FLL and compares its effectiveness with widely used cascaded delayed signal cancellation (CDSC) and complex bandpass filter (CBF) based filtering techniques. The application of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -factor-methodology-based controller design to FLLs has also been explored for dynamic performance enhancements. A new cascaded multiple delayed signal cancellation (CMDSC) filter is proposed and is compared with its CDSC counterpart proposed in the literature to address the effects of dc offsets and imbalances under adverse grid conditions. The effectiveness of the proposed method is verified through OPAL-RT real-time simulations and dSPACE ds1104 and TMS320F28335 microcontroller experimental test bench.

Frequency synchronization adaptive control for power grids under topological uncertainty

In this paper, an adaptive control strategy is proposed to address the synchronization issue for weakly damped generators under topological uncertainty. A singular perturbation analysis is then adopted for strongly damped generators and a compensation control scheme is subsequently given to maintain synchronization under topological changes. Theoretical proof is laid out for the validity of the proposed control scheme. Besides, a power sharing strategy is supplemented for strongly damped generators based on the designed controller. Finally, simulation studies are carried out to verify the effectiveness of the control strategies. Results show that synchronization can be swiftly restored even when the power grid suffers a fatal topological change. The power sharing property can be achieved under the given restriction with the proposed controller.

Design of LDMOS Device Modeling Method Based on Neural Network.

The rapid development of power semiconductor devices is helping to realize a low-carbon society and provide a better life for everyone. Power semiconductors not only are used in many large-scale industrial control fields such as power transmission and control in power grids, rail transit traction systems, and defense weapons and equipment, but also play a vital role in daily equipment such as home appliances, medical electronics, and electronic communications; all devices such as power steering in cars, battery chargers, cell phones, and microwave ovens utilize power electronics. This research mainly focuses on the high-voltage LDMOS device model and its implementation. Based on the in-depth study of the structure and physical mechanism of high-voltage LDMOS devices, with the help of BSIM4 core model, which is now very mature and widely used in industry, the drift region of high-voltage LDMOS is mainly modeled, and the drift region of LDMOS is modeled as a variable resistance controlled by voltage. Finally, Verilog-A language and neural network method are used to establish a compact model of LDMOS. The improved model is applied to LDMOS and can better fit the output characteristics with self-heating effect.

A mathematical programming model for optimal fleet management of electric car-sharing systems with Vehicle-to-Grid operations

Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.

Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

The rapid development of electric vehicles (EVs) has posed challenges to power grids and transportation networks. Accurately capturing the usage patterns of EV users is a prerequisite for EVs’ interaction with electrified transportation networks. Thus, this paper proposes a full data chain (FDC) driven model to mine EVs’ comprehensive characteristics. By collecting the data of 150 private electric vehicles (PREVs), 100 commercial electric vehicles (CEVs), and 50 official electric vehicles (OEVs) in Chongqing, China, the driving characteristics are firstly mined by the adoption of origin-destination (OD) distribution and driving portrait. Moreover, the charging characteristics are extracted based on the state recognition for data chains. Then, vehicle usage characteristics of different types of users are comprehensively described based on the density-based spatial clustering of applications with noise (DBSCAN). Finally, the results of EV user characteristics are analyzed, and the effectiveness of the proposed model is verified by regional charging load analysis and urban road traffic flow comparison. The findings provide a data source and user behavior model for the planning, operation, and control of power grids and transportation networks.

Fluctuation response patterns of network dynamics – An introduction

Networked dynamical systems, i.e., systems of dynamical units coupled via nontrivial interaction topologies, constitute models of broad classes of complex systems, ranging from gene regulatory and metabolic circuits in our cells to pandemics spreading across continents. Most of such systems are driven by irregular and distributed fluctuating input signals from the environment. Yet how networked dynamical systems collectively respond to such fluctuations depends on the location and type of driving signal, the interaction topology and several other factors and remains largely unknown to date. As a key example, modern electric power grids are undergoing a rapid and systematic transformation towards more sustainable systems, signified by high penetrations of renewable energy sources. These in turn introduce significant fluctuations in power input and thereby pose immediate challenges to the stable operation of power grid systems. How power grid systems dynamically respond to fluctuating power feed-in as well as other temporal changes is critical for ensuring a reliable operation of power grids yet not well understood. In this work, we systematically introduce a linear response theory (LRT) for fluctuation-driven networked dynamical systems. The derivations presented not only provide approximate analytical descriptions of the dynamical responses of networks, but more importantly, also allow to extract key qualitative features about spatio-temporally distributed response patterns. Specifically, we provide a general formulation of a LRT for perturbed networked dynamical systems, explicate how dynamic network response patterns arise from the solution of the linearised response dynamics, and emphasise the role of LRT in predicting and comprehending power grid responses on different temporal and spatial scales and to various types of disturbances. Understanding such patterns from a general, mathematical perspective enables to estimate network responses quickly and intuitively, and to develop guiding principles for, e.g., power grid operation, control and design.

Autonomous vehicle-to-grid design for provision of frequency control ancillary service and distribution voltage regulation

We develop a system-level design for the provision of Ancillary Service (AS) for control of electric power grids by in-vehicle batteries, suitably applied to Electric Vehicles (EVs) operated in a sharing service. An architecture for cooperation between transportation and energy management systems is introduced that enables us to design an autonomous Vehicle-to-Grid (V2G) for the provision of multi-objective AS: primary frequency control in a transmission grid and voltage amplitude regulation in a distribution grid connected to EVs. The design is based on the ordinary differential equation model of distribution voltage, which has been recently introduced as a new physics-based model, and is utilized in this paper for assessing and regulating the impact of spatiotemporal charging/charging of a large population of EVs to a distribution grid. Effectiveness of the autonomous V2G design is evaluated with numerical simulations of realistic models for transmission and distribution grids with synthetic operation data on EVs in a sharing service. In addition, we present a hardware-in-the-loop test for evaluating its feasibility in a situation where inevitable latency is involved due to power, control, and communication equipments.

Stability analysis of doubly-fed wind generation systems under weak power grid based on virtual synchronous control combined with adaptive robust control

Doubly-fed induction generator (DFIG) of wind generation system is generally located at the end of the distribution power-grid. Under the weak power grid, the grid connection of wind turbines is unstable, and the current quality is poor. Based on DFIG output impedance model, a stability analysis method combined with adaptive control method is proposed in this paper. The DFIG impedance model with power outer-loop is derived based on the virtual synchronous control of DFIG by using adaptive robust control method. Using generalized Nyquist criterion, the impacts of different power grid conditions and control parameters on the system stable operation under virtual synchronous control are analyzed and verified. The proposed control method in this paper is verified by experiments. The proposed control method improves the quality of grid-connected current.

Direct Predictive Power Control of Grid Tied Distributed Generator Systems

Direct Predictive Power Control of Grid Tied Distributed Generator Systems

Control and Optimization Algorithms for Lattice Power Grids With Multiple Grid-Forming Converters

The growth of renewable energies, together with their power converter interfaces, reshapes power systems into more-electronics power systems. Along with this paradigm shift is the requirement of grid formation by grid-tied power converters. However, existing grid architectures only allow parallel operation of grid-forming converters, which excludes high-voltage applications. This article proposes novel lattice power grids that combine the advantages of multilevel converters and power grids, thereby allowing both serial and parallel connectivity with modularity and scalability. Further, we propose control and optimization algorithms for lattice power grids by use of graph theory. In particular, we investigate H-bridge-based lattice power grids and achieve several objectives, including desired voltages and currents between any two selective nodes in lattice power grids as well as efficiency optimization by minimizing switching actions. To achieve these objectives, this article details control and optimization methodology for square lattice power grids. Finally, the proposed algorithms and lattice power grids are validated via simulation results.

Time Delay of Wide Area Damping Control in Urban Power Grid: Model-Based Analysis and Data-Driven Compensation

Due to the rapid development of economies, large urban cities consume an increasing amount of energy and have a higher requirement for power quality. Voltage source converter based high voltage direct current (VSC-HVDC) is a promising device to transmit clean power from remote regions to urban power systems, while also providing wide area damping control (WADC) for frequency stabilization. However, the time-delay naturally existing in the VSC-HVDC system may degrade the performance of WADC and even result in instability. To address this issue, this paper develops a time-delay correction control strategy for VSC-HVDC damping control in urban power grids. First, a small signal model of WADC is built to analyze the negative impacts of time delay. Then, a data-driven approach is proposed to compensate for the inherent time delay in VSC-HVDC damping control. The extensive training data will be generated under various disturbances. After offline training, the long short-term memory network (LSTM) can be implemented online to predict the actual frequency deviation based on real-time measurements. Finally, the proposed method is validated through MATLAB-Simulink in a two-area four-machine system. The results indicate that the data-driven compensation has a strong generalization ability for random delay time constants and can improve the performance of WADC significantly.

Adaptive load control of electrolytic aluminum for power system frequency regulation based on the aluminum production operation state

The national ”double carbon” plan promotes the reform of the power system, and large-scale distributed energy is connected to the power system. It is difficult to control the fluctuation of new energy only by the reserve of thermal power units. Therefore, it is urgent to excavate new auxiliary service subjects and increase the flexibility of power grid frequency control. Taking electrolytic aluminum as an example, the traditional high load capacity load has large capacity continuous regulation ability, but it is seldom used in frequency stability control of power grid. Thus this paper proposes the precise coordinated frequency modulation control strategy of electrolytic aluminum load in the case of large N-1 failure. By means of updating the running status of electrolytic aluminum load regularly and calculating the load damping coefficient adaptively, this strategy can realize the rapid response to the system frequency change and ensure that the system frequency fluctuation does not exceed the limit. Finally, we built the regional power grid of Wenshan in Yunnan province and the results verify the feasibility of the effectiveness of the proposed strategy in dealing with DC locking.

Intelligent power grid monitoring and management strategy using 3D model visual computation with deep learning

The legacy grid is developing into an intelligently rooted grid system that can enhance and adapt its performance to learning experiences in its architectural components. Due to the evolution of artificial intelligence and in-depth learning techniques, decision-making and execution can become autonomous activities in intelligent power grids throughout the years. 3D visualization plays a vital role in simplifying monitoring, analysis, and reaction to events in the smart grid. The visualization of the 3D models for various energy system applications is first summarized, including the smart grid, power vehicles, and energy consumption of buildings. Accordingly, design principles are provided for wide-screen, personal computer, or mobile interfaces. Visualization technology for geographical information systems is described. 3D technology, animations, and AR&VR to visualize the energy system is discussed. In this article, a coherent 3D visualization approach for the control and monitoring of intelligent power grids (P.G.) via the deep learning (DL) method is examined in industry literature (3D-PGDL). Data visualization is gaining significance with improved measurement systems and Big Data Analytics in intelligent grids and low carbon energy systems. No data design study, accompanying technology, or visualization tools have been conducted. The analysis and practice of data viewing on energy and power systems are thoroughly addressed here. Because of the intrinsic properties of intelligent grids, all collected and processed data are heterogeneous to provide a more comprehensive, multifaceted control viewpoint for the energy sector by creating new ICT instruments and platforms. This section’s experiment resulted in a 95.3 % prediction rate and improved data management ratio of 95.7 %, a lower energy consumption ratio of 18.2%, a lower probability of 18.4 %, a lower CO2 emission level of 15.6 % and a humidity rate of 93.1 %.

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Advanced three-stage photovoltaic system phasor model for grid integration dynamic studies

As the penetration of photovoltaic (PV) generation increases at different system voltage levels, their impact on the power grid operation and control is more important to analyze. Hence, suitable PV models must be developed with which it is possible to carry out dynamic analysis for fair-sized electrical networks. This is a demanding task, computationally-wise, for detailed switching-based PV models using small numerical integration steps required by the associated power electronic devices. In this sense, an advanced three-stage PV system phasor model is proposed in this paper whose hallmark resides in the proper characterization of the PV arrays and DC/DC converter through a nonlinear potential regression technique in combination with the generatrix method. As a result, the PV operating surfaces, as function of irradiance and temperature, can be determined and used for the calculation of virtual resistances relating to the PV optimal operation and internal power losses. The virtual resistances are seamlessly combined with a developed AC/DC converter phasor model for efficient dynamic simulations of electrical networks.The new PV model has been thoroughly validated using Simscape Electrical of Simulink where an electrical network with one PV system is dynamically simulated. The outcomes of the developed model are compared to those obtained from switching-based PV models based on different maximum power tracking strategies, i.e., the incremental conductance (IC) and perturbation and observation (P&O) algorithms. The impact of the PV system on the power grid operation and the accuracy of results are assessed, demonstrating that the proposed model favorably agrees with the benchmark switching-based PV models as errors inferior to 2 % are obtained. Furthermore, it is confirmed that the new PV system model enables faster dynamic simulations than what it is possible to achieve with switching-based models since it is 28.82 times faster than the PV model based on the IC algorithm and 31.80 times faster than that using the P&O technique.

Authenticating source information of distribution synchrophasors at intra-state locations for cyber-physical resilient power networks

As power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information and communication technologies, they become increasingly reliant upon more accurate monitoring and fast control. Phasor Measurement Units (PMUs) collect high-precision Distribution Synchrophasors (DS) data regarding the system dynamics and provide real-time situational awareness for better monitoring and control of large-scale power grids. The accuracy and generalizability of the PMUs heavily rely upon the data quality of DS measurements, which is very susceptible to newly emerging “Source ID Mix” data spoofing attacks. Such attacks could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby jeopardizing most DS-based applications and even causing catastrophic power interruptions. This paper proposes a novel data-driven source authentication method to automatically identify the source information of DS collected from multiple intra-state locations and thus enhance the reliability and cybersecurity of power systems. The proposed method integrates Mathematical Morphological Decomposition (MMD) and Multi-Weighted Deep Stacking Forest (MLW-DSF) for providing accurate source authentication with a low computational cost which requires neither system’s models nor parameters. The effectiveness of the proposed method is corroborated through seven “state-of-the-art” data-driven models by using real-life DS measurements of power networks in Queensland state.© 2017 Elsevier Inc. All rights reserved.

Convex Neural Networks Based Reinforcement Learning for Load Frequency Control under Denial of Service Attacks

With the increase in the complexity and informatization of power grids, new challenges, such as access to a large number of distributed energy sources and cyber attacks on power grid control systems, are brought to load-frequency control. As load-frequency control methods, both aggregated distributed energy sources (ADES) and artificial intelligence techniques provide flexible solution strategies to mitigate the frequency deviation of power grids. This paper proposes a load-frequency control strategy of ADES-based reinforcement learning under the consideration of reducing the impact of denial of service (DoS) attacks. Reinforcement learning is used to evaluate the pros and cons of the proposed frequency control strategy. The entire evaluation process is realized by the approximation of convex neural networks. Convex neural networks are used to convert the nonlinear optimization problems of reinforcement learning for long-term performance into the corresponding convex optimization problems. Thus, the local optimum is avoided, the optimization process of the strategy utility function is accelerated, and the response ability of controllers is improved. The stability of power grids and the convergence of convex neural networks under the proposed frequency control strategy are studied by constructing Lyapunov functions to obtain the sufficient conditions for the steady states of ADES and the weight convergence of actor–critic networks. The article uses the IEEE14, IEEE57, and IEEE118 bus testing systems to verify the proposed strategy. Our experimental results confirm that the proposed frequency control strategy can effectively reduce the frequency deviation of power grids under DoS attacks.

Distributed Sliding Mode Fault‐Tolerant LFC for Multiarea Interconnected Power Systems under Sensor Fault

This paper focuses on the problem of load frequency control and sensor fault‐tolerant control in the multiarea power grid. To solve these problems, a sliding mode control strategy based on an interval observer is designed. First, an interval observer is designed to obtain the boundary estimation information of the system state online, which is used to reconstruct the load disturbance online. Based on the reconstructed values, an integral sliding mode controller is designed to keep the system frequency stable when the load is disturbed. Then, sensor fault is diagnosed by interval residual. An augmented observer is designed to estimate sensor fault online by equivalent transformation, and the sensor fault is compensated by the fault estimation to reduce the influence of fault on system performance and ensure the reliable and stable operation of the power system. The simulation results show the superiority of the proposed control strategy.

Frequency Control of Low Inertia Power Grids With Fuel Cell Systems in Distribution Networks

High penetration of variable renewable energy resources to distribution networks brings new challenges to frequency control in transmission systems; notwithstanding, coordinated exploitation of some widely distributed resources in distribution networks could support overall system frequency control. We developed a centralized control scheme to utilize existing solid oxide fuel cell systems in distribution networks to mitigate frequency deviation in the transmission system. This novel control scheme minimizes curtailing renewable resources. It appropriately prioritizes adjusting active or reactive power of medium voltage connected fuel cell systems to efficiently provides frequency support, while keeping the renewable sources connected. A co-simulation platform was also developed to allow for analyzing the interactions between transmission and distribution networks. It enhances simulation capability through parallel distributed real-time simulations. This platform enables ex-ante analysis of the impact of control strategies of distribution system on transmission system. A set of experimental results on European transmission and distribution network benchmarks demonstrated the performance of the developed primary frequency control scheme using solid oxide fuel cell systems.

Research on the Impacts of the Inertia and Droop Control Gains from a Variable-Speed Wind Turbine Generator on the Frequency Response

System frequency must be kept very close to its nominal range to ensure the stability of an electric power grid. Excessive system frequency variations are able to result in load shedding, frequency instability, and even generator damage. With increasing wind power penetration, there is rising concern about the reduction in inertia response and primary frequency control in the electric power grid. Converter-based wind generation is capable of providing inertia response and primary frequency response; nevertheless, the primary frequency and inertia responses of wind generation are different from those of conventional synchronous fleets; it is not completely understood how the primary frequency and inertia responses affect the given system under various disturbances and available kinetic energy levels. Simulations are used to investigate the influences of inertia and droop control strategies on the dynamic frequency responses, particularly the index of the second frequency drop under various disturbance and wind conditions. A quantitative analysis provides insight into setting of inertia and droop control coefficients for various wind and disturbance conditions to facilitate adequate dynamic frequency responses during frequency events.