Grid Control Research Articles

Blockchain Research and Development Activities Sponsored by the U.S. Department of Energy and Utility Sector

This article provides an in-depth analysis of blockchain research in the energy sector, focusing on projects funded by the U.S. Department of Energy (DOE) and comparing them with industry-funded initiatives. A total of 110 funded activities within the U.S. power industry were successfully tracked and mapped into a newly developed categorization framework. This framework is designed to help research agencies to systematically understand their funded portfolio. Such characterization is expected to help them make effective investments, identify research gaps, measure impact, and advance technological progress to meet national goals. In line with this need, the proposed framework proposes a 2-D categorization matrix to systematically classify blockchain efforts within the energy sector.Under the proposed framework, the Energy System Domain serves as the primary classification dimension, categorizing use cases into 30 distinct applications. The second dimension, Blockchain Properties, captures the specific needs and functionalities provided by Blockchain technology. The aim was to capture blockchain’s applicability and functionality: where and why blockchain? Principles behind the selection of the viewpoint dimensions were carefully defined based on consensus obtained through the Blockchain for Optimized Security and Energy Management (BLOSEM) project. The mapped results show that activities within the Grid Automation, Coordination, and Control (31.8%), Marketplaces and Trading (25.5%), Foundational Blockchain Research (19.1%), and Supply Chain Management (17.3%) domains have been actively pursued to date. The three leading specific use case applications were identified as Transactive Energy Management for Marketplaces and Trading, Asset Management for Supply Chain Management, and Fundamental Blockchain for Foundational Blockchain Research. The Marketplaces and Trading and Retail Services Enablement domains stood out as being favored by industry by a factor greater than 2 (2.3 and 2.6, respectively), yet there seemed to be little to zero investment from DOE. Approximately 76% of the total projects prioritized Immutability, Identity Management, and Decentralization and/or Disintermediation compared to Asset Digitization and/or Tokenization, Automation, and Privacy and/or Anonymity. The greatest discrepancies between DOE and industry were in Asset Digitization and/or Tokenization and Automation. The industry efforts (36% in Asset Digitization/Tokenization and 22% in Automation) was 14 times and 2.4 times, respectively, more intensive than the DOE-sponsored efforts, indicating a significant discrepancy in industry versus government priorities. Overall, quantifying DOE-sponsored projects and industry activities through mapping provides clarity on portfolio investments and opportunities for future research.

Graph-Based Topological Embedding and Deep Reinforcement Learning for Autonomous Voltage Control in Power System

With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids.

A Purely Real-Valued Fast Estimator of Dynamic Harmonics for Application in Embedded Monitoring Devices in Power-Electronic Grids

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion on the time-varying amplitude and phase angle. Secondly, the estimation filter bank is computed in the least-squares sense, and the corresponding estimation error is theoretically analyzed. Finally, the purely real-valued fast dynamic harmonics estimator is designed. The advantage includes significantly reducing the computational complexity and memory space consumption while maintaining high-precision estimation. The testing results show that the proposed estimator can achieve the highest harmonics estimation precision under dynamic conditions. The frequency error, magnitude error, and phase angle error are less than 5 × 10−2 Hz, 7 × 10−1%, and 8 × 10−2 degrees, respectively, which verifies the advantage of high-precision estimation. The proposed estimator achieves a computational speed-up of approximately 430, 396, and 330 times compared to the Prony method, ESPRIT method, and iterative Taylor Fourier transform method, respectively. The computational load rate for executing the proposed estimator on the embedded prototype using C6748 DSP for estimating 50 harmonics is approximately only 2.05%, which verifies the advantage of a low computational load rate.

Comparative analysis of PI and fuzzy logic controller for grid connected wind turbine under normal and fault conditions

This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine. Additionally, the selected membership functions were found to be highly compatible with the unique characteristics of wind energy. The optimization process is implemented for the controllers of both the grid side converter (GSC) and RSC. Through simulated analyses conducted using MATLAB/Simulink software, comprehensive assessments are carried out. The robustness of the FLC is evaluated compared to the optimized controllers across various wind profiles and challenging fault conditions. The results demonstrate satisfactory performance of the FLC in terms of steady-state time, stability, and precision under diverse wind speed profiles. The FLC achieves a significantly better settling time than the enhanced PI, improving by approximately 14–70% under normal conditions and 40–70% under various fault conditions. Additionally, the FLC outperforms the enhanced PI in fault conditions by reducing peak-to-peak oscillations by about 30–65%. It also delivers a smaller steady-state error, with improvements of around 2–4% under both normal conditions and most fault scenarios.

Multi-Objective Coordinated Control of Smart Inverters and Legacy Devices

This work proposes multi-objective two-stage distribution optimal power flow (D-OPF) to coordinate the use of smart inverters (SIs) and existing voltage control legacy devices. The first stage of multi-objective D-OPF aims to solve a mixed-integer nonlinear programming (MINLP) formulation that minimizes both voltage variation and active power loss, with SI modes, SI settings, voltage regulator (VR) taps, and capacitor bank (CB) status as control variables. The Pareto Optimal Solutions obtained from the first-stage MINLP are used to determine the optimal active–reactive power dispatch from the SIs by solving a nonlinear programming formulation in the second stage of the proposed D-OPF. This model guarantees that the setpoints for active–reactive power align with the droop characteristics of the SIs, ensuring practicability and the autonomous dispatch of active–reactive power by the SIs according to IEEE 1547-2018. The effectiveness of the proposed method is tested on the IEEE 123 distribution network by contrasting the two proposed D-OPF models, with one prioritizing SIs for voltage control and power loss minimization and the other not prioritizing SIs. The simulation results demonstrate that prioritizing SIs with optimal mode and droop settings can improve voltage control and power loss minimization. The proposed model (with SI prioritization) also reduces the usage of traditional grid control devices and optimizes the dispatch of active–reactive power. The POS also shows that the SI modes, droops, and legacy device settings can be effectively obtained based on the desired objective priority.

Experimental Evaluation of a Proposed LQR-LU Optimal Grid Controller in the Applications of Grid-Tied PV Systems

This article aims to design a linear quadratic regulator (LQR)-based optimal current controller along with a Luenberger observer model in order to enhance power quality during the grid's dynamic conditions. To analyze the performance of the proposed control scheme, a three-phase photovoltaic-based gird-tied inverter is considered. A grid controller comprises a DC-link voltage controller to stabilize the capacitor voltage and a current controller to assure the output currents are well controlled according to the dynamic conditions of the grid as well as the source. In general, the capacitor-voltage and current controllers are regulated by traditional proportional Integral (PI) controllers which is associated with many demerits. In this work, a predictive LQR-based current controller with an observer algorithm and an adaptive capacitor voltage controller is proposed to improve the performance of the power grid at various dynamic conditions. The mathematical analogy is evaluated using MATLAB/SIMULINK software with different load and grid-disturbance conditions. Moreover, a laboratory prototype is designed to validate the simulation results. The outcomes prove that the proposed controller is dynamically robust to tackle any disturbance in load and grid.

Analysis and control of Wind-driven grid based power electronic interface for cathodic protection

Analysis and control of Wind-driven grid based power electronic interface for cathodic protection

Coordinated control of EAU-based MEA power grid using MPC method

Coordinated control of EAU-based MEA power grid using MPC method

Intelligent active and reactive power control using multi-layer neural network based MPPT controller for grid tied solar PV system under fault conditions

The integration of renewable energy sources, particularly grid-tied solar photovoltaic (PV) systems, into the modern power grid has become increasingly prevalent. However, ensuring the reliable and efficient operation of grid-tied PV systems under various grid conditions, including fault scenarios, poses a significant challenge. In the event of grid faults or disturbances, traditional control methods often fall short in maintaining stable and reliable operation. This paper introduces a multi-layer neural network (MLNN) based MPPT controller that adapts intelligently to grid fault conditions, mitigating the impact on the grid-tied PV system's performance and providing low voltage ride through (LVRT). The research employs a detailed simulation framework on MATLAB to validate the effectiveness of the proposed controller under fault conditions. The LVRT capability of the designed system was analyzed and validated according to Indian grid code. The proposed controller leverages its capacity to learn and make real-time decisions to optimize the active and reactive power outputs of the PV system as per the grid code. Simulation results demonstrate that the proposed controller not only improves the fault tolerance of grid-tied PV systems but also enhances their performance, ensuring a stable and continuous power supply in the face of grid disturbances.

Research on Active Vibration Control and Environmental Performance Evaluation of Large Mechanical Grid Connected Systems

Research on Active Vibration Control and Environmental Performance Evaluation of Large Mechanical Grid Connected Systems

Aspects of Relevance of Hybrid Power Plants in Control and Stability of Weak Grids

This paper reviews the possible contributions of Hybrid Power Plants (HPPs) to support weak grids while maintaining the desired system stability. Moving towards a converter-dominant power system with less inherent inertia and distant connections to the nearest synchronous generator, frequency and voltage controls are becoming more critical to ensure the stability of the weak grid. In this regard, state-of-the-art literature is reviewed for frequency and voltage controllers in single-technology power plants, like wind and solar power plants. The contribution of this paper lies in providing a clear overview of available literature in terms of frequency and voltage control stages, regardless of the utilized control method. On the other hand, focus has been put on the increased utilization of HPPs to provide more flexibility, increased availability, and reduced variability through the combination of various sources, i.e., wind, solar, and storage. Furthermore, investigating the specific capabilities and challenges of HPPs, this review shows that very little literature has been conducted on voltage control using HPPs. Finally, the aspect of relevance of HPPs is discussed in the control and stability of modern power systems.

Experimental Tests and Simulations About The Efficiency of Commercial Hybrid Inverters Operating at Variable Power Factor

The need to ‘rethink’ the concept of Distribution, both at the planning and operational levels, arises from the emergence of the Distributed Generation paradigm, mostly installed in LV networks. The existing arrangements for the management, protection and control of distribution networks are typically inadequate for this ever-increasing presence of Distributed Generation. Consequently, national and international grid codes have been updated requiring distributed generators to be involved in grid control by supplying ancillary services, such as the voltage and frequency regulation. In this context, the Italian Standards, and in particular the CEI 0–21, require hybrid inverters of residential systems, which are usually only setup to supply active power at unity power factor, to include also the possibility of being able to absorb or supply reactive energy. This allows to limit the over / under voltages problems. To this aim, the inverter manufacturers have introduced the possibility for residential inverters to set the power factor. These new capabilities have been exploited in this paper in order to study the inverter performance with the variation of the power factor, considering both the inductive and the capacitive behaviour. In particular, the presented paper focuses on experimental tests on a hybrid inverter-based system for residential use with the aim of evaluating the dependence of the inverter efficiency on the power factor. Based on measured values, a Matlab/Simulink simulator has been developed for modelling the system, using the found dependence. The main aim is to evaluate the inverter control systems in order to participate in regulation services and to evaluate the inverter efficiency in case of a power factor different from unity.

GNN4GC—Graph Neural Networks for Grid Control

GNN4GC—Graph Neural Networks for Grid Control

VNN-DM: a vector neural network-based detection model for time synchronization attacks in park-level energy internet

Micro phasor measurement units (µPMUs) provide high-precision voltage and current phasor data, allowing real-time state estimation and fault detection, which are critical for the stability and reliability of modern power systems. However, their reliance on accurate time synchronization makes them vulnerable to time synchronization attacks (TSAs), which can disrupt grid monitoring and control by corrupting µPMU data. Addressing these vulnerabilities is essential to ensure the secure and resilient operation of smart grids and energy internet technologies. To address these challenges, intelligent detection methods are essential. Therefore, this paper proposes a µPMU measurement data TSA detection model based on vector neural networks (VNNs). This model initially employs a vector neural network to process raw data, effectively extracting and analyzing temporal features. During the same time, a capsule network is employed to classify these temporal features. On this basis, a reconstruction network is used to verify the representational capacity of the model. Simulations based on µPMU measurement data demonstrate that the model exhibits excellent detection capacity in various performance metrics, underscoring its precision and robustness.

Flexible DC Optimization Control Technology Based on Zonal Interconnection of Urban Power Grids

Aiming at the problems of insufficient power supply capacity, unbalanced load, and poor safety and reliability of the urban power grid, a flexible DC optimization control strategy is proposed to support the interconnection of power grid zones. Firstly, the steady-state and transient optimal control objectives are established to meet the safety and economic operation requirements of the grid; secondly, comprehensive evaluation indexes are constructed, covering load balancing, network loss, and voltage deviation, and a step-by-step approximation method is used to quickly solve the optimal power of the flexible DC, so as to realize the optimal control of the urban grid in the steady state. Meanwhile, a multi-terminal flexible DC adaptive sag control strategy based on the power margin of the converter station is designed to reasonably allocate the emergency power support in each partition after a fault and reduce the frequency deviation of the heavily loaded nodes. Taking the urban grid as an example, a simulation model of the grid structure with a 750 KV outer ring and a 220 KV inner ring is constructed, and the simulation results verify the effectiveness of the proposed method and its engineering practicability.

A memristor‐sourced key for encrypted power and data synchronous transmission in switched mode power supplies

AbstractIn power and data synchronous transmission (PDST), it is crucial to modulate additional signals onto the voltage bus of the converters. This reduces the cost of monitoring and control of the power grid. There is thus the need to ensure that the data transmitted is well secured. This study, designs an electronic system that uses the common coupling point for PDST. Then Chua's memristive circuit is introduced as an entropy source for generating a random bit sequence for encrypted PDST in a switched mode power supply. The encrypted data signal is modulated using phase shift key modulation. Thereafter, the carrier signal is pulse width modulation modulated to control the switching operation of the power switch. The use of Chua's memristive circuit for generating a random number is not new; however, there is little study directly adopting its use in PDST. This allows for resource sharing since multiple signal sources can share the same bus without interference or security risk. The generated random bits are tested for randomness using National Institute of Standards and Technology test suite and autocorrelation test. MATLAB/Simulink and experimental results both confirm the effectiveness of the proposed data encryption method.

Research on the multi-area cooperative control method for novel power systems

As new energy sources increasingly penetrate novel power systems, the coordinated control of frequency regulation units has become more challenging, leading to the degraded performance of power grid control. The automatic generation control methods based on traditional reinforcement learning overly depend on exploration, making it difficult quickly obtain optimal solutions for multi-area cooperative control and resulting in suboptimal performance. To address this issue, this paper proposes a multi-area coordinated control method using a greedy actor-critic algorithm enhanced with expert experience replay. This method decouple the role of entropy for exploration and policy collapse, utilizing a proposal policy to secure high-value actions for policy updates. Thus the exploration and exploitation are balanced and the local optima is avoided. Additionally, the expert experience replay collects expert demonstration data with high-value to assist the learning of multi-agent. Therefore, the meaningless exploration in early training is reduced and the rapid attainment of optimal solutions is facilitated. This paper validates the proposed method through simulations on the model of the improved IEEE standard two-area load frequency control and the model of the Sichuan, Chongqing, and Hubei three-area. Compared with other various reinforcement learning methods, the proposed method is demonstrated with superior control performance.

Hybrid power plant in FCR-N provision: An analysis of storage sizing in connection with turbine stress reduction

This paper examines the capability of a linear time-invariant (LTI) filter to reduce the stress of turbine units in hybrid power plants providing Frequency Containment Reserve (FCR) for primary control of the power grid. The main focus is to analyze the effect of filter parametrization on the stress reduction under the dynamic performance criteria given by the Nordic FCR requirements. Analytic derivations are targeted to gain insight into the dependencies of attainable stress reduction, and storage sizing as a function of the FCR test and filter parameters. The study revealed that the filter utilizes an available storage system inefficiently, resulting in low performance in terms of turbine stress reduction and high energy usage. The obtained results are valid for any turbine and storage combination with suitable characteristics for FCR operation since the analysis focused on power behavior.

Fractional order pole fixed second order generalized integrator based control for grid connected solar photovoltaic system

AbstractIn this article, the development of multi‐functional grid connected solar photovoltaic (PV) system using improved Fractional order Control theory‐based Pole fixed Second Order Generalized Integrator (FO‐PFSOGI) is proposed. The fractional order (FO) control technique offers an advantage to adjust the fixed structure of the integer order and provide additional degree of freedom to achieve accurate response during the system operation. The FO‐PFSOGI control technique is used to extricate fundamental constituent of the non‐sinusoidal load current. The PV system is competent of feeding the local load requisite and may also inject surplus power into the grid. The voltage source converter (VSC) utilized in the grid‐connected system can also be operated as power quality compensator, taking care of harmonics, unbalancing and excess reactive power demand of the local load. The feasibility of multifunctional operations of the converter is established in this article, thus achieving maximum utilization of the power electronics used in the system and reducing the overall cost. A performance comparison of the developed control technique is presented with the conventional techniques in terms of harmonic compensation, weight convergence and computational complexity. The implementation of the controller is modest and extracts fundamental quantities without any phase delay under different loading conditions. The developed system is validated using both simulation and experimental study. A scaled down experimental setup of grid‐connected PV system is implemented in the laboratory and real time performance of the FO‐PFSOGI is demonstrated using variations in system load and PV irradiance.

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.