Real Grid Research Articles

Discharge image reconstruction and frequency domain analysis based on event data

AbstractOptical image method has been the earliest and most used direct method for observing gas discharge. Currently, research on gas discharge monitoring based on visible light mainly relies on high‐speed cameras, but the large size, significant data storage requirements, and susceptibility to interference from complex backgrounds and lighting conditions limit their further application. Dynamic vision sensing (DVS) technology is a neuromorphic sensing technology that asynchronously measures the luminance changes at each pixel. It offers advantages such as a large dynamic range (>120 dB), high temporal resolution (up to 1 µs), and small data volume (MB level). In this study, dynamic vision sensing technology was employed to monitor both 30 mm short‐gaps and 1080 mm long‐gaps discharge processes simultaneously. This study developed the CountImage encoding method for event data and conducted image reconstruction, time‐domain analysis, and frequency‐domain characteristic analysis based on the event data. The results show that the event‐reconstructed images are highly consistent with the high‐speed camera images, and the arc development process and its path can also be clearly observed. Additionally, this study discovered a correlation between the electrical characteristics and event information during the discharge process. In the time domain, the duration of the maximum DVS event count closely matches the duration during which the voltage drops to zero during flashover. In the frequency domain, the Pearson correlation coefficient between the event stream spectrum and the voltage signal spectrum is greater than 0.95. Both the maximum number of brightening events (ONmax) and the maximum number of darkening events (OFFmax) are positively correlated with the voltage applied between the electrodes. This study demonstrates that, compared to the GB/s data rate of high‐speed cameras, this approach can record the discharge process and accurately reconstruct the discharge process, arc morphology, and discharge path at MB/s data rates, while also adapting to changes in brightness without the need for exposure adjustment. Additionally, there is a positive correlation between the frequency‐domain characteristics of the event data and the voltage characteristics. These results indicate that dynamic vision sensing holds promise as a replacement for high‐speed cameras in laboratory discharge observations and could be even effectively applied to discharge monitoring in electrical equipment in real grid.

Limitations of using LCOE as economic indicator for solar power plants

With the increasing penetration of renewable energy, the importance of power plant dispatchability has become more relevant; an aspect that is not accounted in the Levelized Cost of Electricity (LCOE) parameter. This paper presents a mathematical model to optimize solar plant design based on optimized dispatching strategies considering real grid demand and prices. To demonstrate that the LCOE is not the correct optimization parameter of Photovoltaic and Concentrated Solar Power technologies, the application of the new approach is performed considering their installation in South Australia and Southern California. Sixteen different cases are proposed to assess the importance of the boundary conditions (location with corresponding electricity load curve, cost of the technology and possibility to purchase electricity from the grid) on the plant design.Results show that the optimal plant design significantly differs when considering the actual prices with respect to the common LCOE approach. The LCOE approach does not lead to any storage installation in PV plants and operates CSP plant around the clock. The optimized design based on the grid needs generates the electricity when the prices are high by introducing the storage in the PV and reducing the solar field area as well as the thermal storage size. Overall, the Net Present Value and the IRR can be increased by 10 times and 70 %, respectively, by adopting optimized design with respect to the one achieved with the typical LCOE approach.

An Optimized Strategy for the Integration of Photovoltaic Systems and Electric Vehicles into the Real Distribution Grid

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

Allocation of Microgrid-Forming resources for distribution systems reliability improvement

This paper proposes a novel method to allocate distributed generation and energy storage as Microgrid-Forming Resources (MFR) to improve the reliability of medium voltage (MV) grids. A graph-based approach is used, which simplifies the circuit analysis and reduces computational time when compared to exhaustive search methods. First, a medium voltage substation-level Nodal Fault-Effect Matrix (NFEM) is derived from the feeder’s topology, which is used for predictive reliability calculations, and calibrated with historical data. Second, a reduced set of scenarios of MFR allocation are generated using a graph community approach and evaluated in terms of reliability improvement and power and energy requirements of the distributed resources. Two case studies are presented based on real grids with different reliability characteristics and the results indicate that the proposed approach is an insightful tool for distribution system planners to evaluate the integration of microgrids to improve system reliability.

Application of Improved Classification Algorithm with Binary Tree Support Vector Machine in Power Grid Prediction

Abstract To further improve the accuracy of the power grid prediction, the paper proposes an improved binary tree classification algorithm with the SVM and predicts the real time power grid. Based on the basic principles of SVM, the common multi-classifier classification algorithm and its characteristics are summarized. Incorporating the advantages of existing classification algorithms, an improved binary tree classification algorithm was used, addressing the limitations of conventional methods. From the simulation results, it can be seen that the improved binary tree algorithm can shorten the test time from the original 0.285 seconds to 0.267 seconds, and the measured category can also be increased from the original 94.42 to 95.27. This shows that the improved binary tree can not only improve the practicability of the support vector machine algorithm, but also better improve the ability of real-time prediction of power grid.

Flexible operation of nuclear hybrid energy systems for load following and water desalination

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The results demonstrate that nuclear power is capable of flexibly responding to changing grid demand while simultaneously producing clean water, particularly during periods of low electricity demand. The MSR-RO system was found to be the most efficient in electricity generation and water production, and all hybrid systems reduced CO2 emissions by 356,000 to 682,000 tons annually. Economic analysis reveals that nuclear desalination technologies are cost-competitive with conventional systems, especially when paired with RO. These findings confirm the technical feasibility and environmental benefits of nuclear hybrid systems for sustainable electricity and water production.

Construction and application of static stability intelligent evaluator for large power grid from the perspective of knowledge graph

Abstract Relying on information technology such as artificial intelligence, knowledge analysis on a large amount of high-dimensional real-time measurement and simulation information, and deep excavation of stable operation situations in the power grid are the core of real time intelligent security defense of large power grid. In this paper, from the perspective of the knowledge graph, considering the stability state evaluation index and optimizing control strategy information of different operation scenarios of power grid comprehensively, using the integration mechanism of information system and knowledge graph, the functional framework of intelligent evaluator for static stability in power grid is proposed, and an evaluator including static evaluation and decision-making, and general intelligent algorithm is designed. Introducing knowledge graph automation engine technology, visual display of intelligent evaluator for large power grid static stability is realized. An application case in a real power grid is given to prove the effectiveness of the proposed method. The research work is helpful to regulate and control operators to grasp the operation situation and improve the efficiency of evaluation and decision-making of the power grid. It has positive exploration application value to improve the construction of intelligent active security defense systems online in large power grids.

Distributed battery energy storage systems for deferring distribution network reinforcements under sustained load growth scenarios

Energy storage systems can be leveraged in electricity distribution network planning as mitigation alternatives to traditional grid reinforcements if they are strategically installed and operated to reduce congestion and voltage limit violations. This paper examines the technical and economic viability of distributed battery energy storage systems owned by the system operator as an alternative to distribution network reinforcements. The case study analyzes the installation of battery energy storage systems in a real 500-bus Spanish medium voltage grid under sustained load growth scenarios. The results show that, in general, dedicated battery energy storage systems are only a cost-efficient alternative in distribution system planning under very specific conditions, such as when low load growth rates are expected. Nevertheless, they are only required for peak shaving a few days per year. For the analyzed case study, the recoverable portion of their total cost through deferral of distribution system upgrades is higher than the fraction of cycles required for peak shaving under all sustained load growth scenarios. Therefore, it is also explored if mobile battery energy storage systems, capital grants, and revenue stacking can enable battery energy storage systems to become an efficient distribution system planning alternative.

Power Grids and Instrument Transformers up to 150 kHz: A Review of Literature and Standards.

The phenomenon of high-frequency distortion (HFD) in the electric grids, at both low-voltage (LV) and medium-voltage (MV) levels, is gaining increasing interest within the scientific and technical community due to its growing occurrence and the associated impact. These disturbances are mainly injected into the grid by new installed devices, essential for achieving decentralized generation based on renewable sources. In fact, these generation systems are connected to the grid through power converters, whose switching frequencies are significantly increasing, leading to a corresponding rise in the frequency of the injected disturbances. HFD represents a quite recent issue, but numerous scientific papers have been published in recent years on this topic. Furthermore, various international standards have also covered it, to provide guidance on instrumentation and related algorithms and indices for the measurement of these phenomena. When measuring HFD in MV grids, it is necessary to use instrument transformers (ITs) to scale voltages and currents to levels fitting with the input stages of power quality (PQ) instruments. In this respect, the recently released Edition 2 of the IEC 61869-1 standard extends the concept of the IT accuracy class up to 500 kHz; however, the IEC 61869 standard family provides guidelines on how to test ITs only at power frequency. This paper provides an extensive review of literature, standards, and the main outputs of European research projects focusing on HFD and ITs. This preliminary study of the state-of-the-art represents an essential starting point for defining significant waveforms to test ITs and, more generally, to achieve a comprehensive understanding of HFD. In this framework, this paper provides a summary of the most common ranges of amplitude and frequency variations of actual HFD found in real grids, the currently adopted measurement methods, and the normative open challenges to be addressed.

Tuning and testing an Online Feedback Optimization controller to provide curative distribution grid flexibility

Due to more volatile generation, flexibility will become more important in transmission grids. One potential source of this flexibility can be distribution grids. A flexibility request from the transmission grid to a distribution grid then needs to be split up onto the different Flexibility Providing Units (FPU)s in the distribution grid. One potential way to do this is Online Feedback Optimization (OFO). OFO is a new control method that steers power systems to the optimal solution of an optimization problem using minimal model information and computation power. This paper will show how to choose the optimization problem and how to tune the OFO controller. Afterward, we test the resulting controller on a real distribution grid laboratory and show its performance, its interaction with other controllers in the grid, and how it copes with disturbances. Overall, the paper makes a clear recommendation on how to phrase the optimization problem and tune the OFO controller. Furthermore, it experimentally verifies that an OFO controller is a powerful tool to disaggregate flexibility requests onto FPUs while satisfying operational constraints inside the flexibility providing distribution grid.

Experimental investigation and numerical simulation of intense plume impingement effects from a 10 N bipropellant thruster

The intense interaction between thruster plumes and spacecraft surfaces presents substantial challenges for the advancement of space technologies. The impingement effects from a 10 N bipropellant thruster plume on a vertical instrumented plate were experimentally and numerically investigated in this study. Impingement pressure and heat flux were measured using micro differential pressure transducers and Gardon gauges, respectively, while the spatial distribution of the plume was captured using the high-speed camera. The phenomena and the distribution characteristics of plume impingement have been summarized, thereby providing a comprehensive validation dataset for subsequent predictions. Numerical simulations are conducted utilizing the coupled hybrid Computational Fluid Dynamics and Direct Simulation Monte Carlo (CFD-DSMC) algorithm. The dependence of the simulation outcomes on grid resolution was critically assessed, and the accuracy of the results was validated by comparison with experimental data. An integrated methodology, combining experimental and numerical approaches, was developed to calculate the maximum heat flux under realistic grid configurations. This method has proven to be effective and supports the accurate and efficient evaluation of intense plume impingement effects for engineering applications.

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO.

Identification of an algorithm for the analysis and study of urban road network trajectories

The object of this study is a clustering algorithm using various technologies. This paper compares clustering algorithms that are more commonly used to analyze urban road network trajectories, the growth curve model, with the elbow method and the x-means algorithm. Experiments were conducted with various volumes of big data to determine calculation time, accuracy, and ways to increase calculation time. These results can be used to manage traffic jams in congested areas and city streets. Considering the widespread use of clustering algorithms for solving various problems, this study proposes to introduce GCM, SPGK methods for monitoring and analyzing the state of congestion on city roads. The work was carried out in the following steps: research and selection of methods based on efficiency and time, implementation of parallel computing technologies to improve computation speed, demonstration of the selected method based on collected data from a real city with visualization of the results. The growth curve model algorithm has been proven to be almost 5 times more effective than the elbow method and the x-means algorithm. The time allocated for data processing has been calculated. An increase in the volume of processed data showed an almost stable execution time t = 3 s for the GCM algorithm for data with a volume of up to almost 2,000 units. The effectiveness of SPGK-means was shown for different values of the number of points. Models of the Chengdu transport network obtained using a clustering algorithm with maximum grid density of neighborhoods are presented. There are some deviations between the grid and the road network due to the large grid size. This error is explained by an error of up to one between the points and the real grid. The results obtained clearly show how optimization of congested roads can be influenced. They provide information to obtain data on available routes, which allows you to analyze the road network individually and as a whole

The search method for key transmission sections based on an improved spectral clustering algorithm

With the increased complexity of power systems stemming from the connection of high-proportion renewable energy sources, coupled with the escalating volatility and uncertainty, the key transmission sections that serve as indicators of the power grid’s security status are also subject to frequent changes, posing challenges to grid monitoring. The search method for key transmission sections based on an improved spectral clustering algorithm is proposed in this paper. A branch weight model, considering the impact of node voltage and power flow factors, is initially established to comprehensively reflect the electrical connectivity between nodes. Subsequently, a weighted graph model is constructed based on spectral graph theory, and an improved spectral clustering algorithm is employed to partition the power grid. Finally, a safety risk indicator is utilized to identify whether the partitioned sections are key transmission sections. Results from case studies on the IEEE39-node system and actual power grid examples demonstrate that the proposed method accurately and effectively searches for all key transmission sections of the system and identifies their security risks. The application in real power grid scenarios validates its ability to screen out some previously unrecognized key transmission sections.

Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.

Hierarchical-power-flow-based energy management for alternative/direct current hybrid microgrids

Modern microgrids are systems comprising both Alternative Current (AC) and Direct Current (DC) subgrids, integrated with Distributed Generations (DGs), storage systems, and Electric Vehicles (EVs) parking facilities. Achieving stable and reliable load flow control amidst varying load, generation, and charging/discharging strategies requires a hierarchical control scheme. This paper proposes an hourly power flow (PF) analysis within an Energy Management System (EMS) for AC/DC Hybrid Microgrids interconnected via an Interlinking Converter (IC) in both grid-connected and islanded modes. The framework operates within a two-level hierarchically controlled platform. Tertiary control at the top level optimizes DGs' reference power for generation and consumption, minimizing power purchase costs and load shedding in grid-connected and islanded modes, respectively. DG converters employ current control mode to share their power references as the primary controller. While no secondary controller is adopted in this scheme, the Battery Energy Storage System (BESS) in islanded mode utilizes P/Q droop control to maintain voltage and frequency in the AC subsystem. Power sharing between AC and DC subgrids through IC is determined by the difference between AC grid frequency and DC link voltage. Integration of controlled converters’ buses into PF equations enables solving the unified system using the traditional Newton-Raphson (NR) method. A segment of a real distribution grid planned for installation in Italy under the HYPERRIDE project serves as a case study. Comparison with MATLAB/Simulink results confirms the effectiveness, precision, and convergence speed of the proposed model and control schemes, demonstrating efficient load distribution and voltage/frequency restoration in islanded mode.

Multi-point method using effective demodulation and decomposition techniques allowing identification of disturbing loads in power grids

The paper presents an innovative approach to identifying voltage fluctuation sources in power networks, also considering the localization understood as the indication of supply points of disturbing loads. The presented approach considers disturbance sources that change their operating state with a frequency higher than the power frequency. The implementation of the proposed solution is also proposed in such a way that its implementation in the smart meter infrastructure allows automatic localization of disturbance sources without additional expert knowledge. In the proposed approach, the modulation signal is estimated using a carrier signal estimator, which allows the estimation of a modulation signal with a frequency higher than the power frequency. The estimated modulating signal is decomposed into component signals associated with individual disturbing loads by decomposition by approximation using pulse waves. The decomposition process allows for the estimation of selected parameters associated with disturbing loads, on the basis of which the assessment of propagation of voltage fluctuations associated with the impact of individual disturbance sources is performed, which allows for the indication of their supply point. The proposed approach was verified in numerical simulation studies using MATLAB/SIMULINK and in experimental studies carried out in a real low-voltage power grid.

Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power Grids

The growth in distributed energy resources (DERs) is an important step toward solving the global climate crisis. However, many DERs, such as wind and solar power, are random and intermittent, causing the data in power grids to be perturbed with high uncertainty. Such perturbations degrade the performance of data-driven algorithms introduced in power grids for sensing and control. Existing approaches can strengthen machine learning models against well-designed malicious attacks. However, such physics-agnostic efforts fail to ensure the robustness to natural perturbations in power grids, that occur due to varying loads and changes in control inputs. This paper proposes a novel physics-constrained adversarial training method for robustifying neural networks for the crucial problem of locating edge-faults in power grids. Our approach relies on first deriving analytical physical laws that are satisfied by state perturbations in realistic grids, and then using the descriptive sets during adversarial training. Compared to state of the art methods, our perturbation-robust neural networks have higher robust accuracy as well as training efficiency. Also, we demonstrate that the proposed approach achieves a tighter upper bound of robust risks than traditional efforts. The numerical experimental results in the IEEE 68-bus benchmark system validate our adversarial training in two scenarios when loads and control inputs vary randomly. The proposed method shows superior performance in accuracy and efficiency for different perturbed datasets. In addition, we testify the influence of different inputs on the robustness.

Research and Application of Power Grid Data Anomaly Tracing Method Based on Time Series Correlation

Abstract Power grid data is a “barometer” that reflects the operational trend of the power grid system, so conducting a traceability study on abnormal data generated by the power grid system is crucial to maintaining the stable operation of the power grid system and preventing further malfunctions. This article proposes a maximum time series correlation ring tracing algorithm based on time series correlation: firstly, for a large amount of time series data collected from measurement points in the power grid system, a correlation coefficient matrix is calculated, and then a time series correlation graph is constructed using graph theory knowledge. The Kruskal algorithm is used to search for the longest spanning tree in the time series correlation graph. Finally, based on the spanning tree, the breadth-first search (BFS) algorithm is further used to obtain the maximum time series correlation ring[1]. By using the time series correlation of each node within the ring and the physical topological relationship between nodes, the abnormal data can be traced back. Distinguish whether the generation of abnormal data is due to a single point failure or system failure. Most of the existing fault tracing techniques are based on machine learning algorithms, which are difficult to widely apply in high-dimensional time series data due to their high complexity.Through experiments on real power grid data, the results verify the effectiveness of this method in anomaly tracing of high-dimensional time series data. Through comparative experiments, this method is superior to machine learning model algorithms in terms of efficiency and stability. At the same time, this method does not require additional calculations of the statistical distribution of the samples to be tested, thus greatly saving computational costs.

Comparison of conducted emissions due to electric vehicle charging processes under isolated and on-line conditions in the 9–500 kHz frequency range

This paper aims at comparing the conducted emissions generated by Electric Vehicle Charging Processes (EVCPs) under isolated and on-line conditions in the frequency and time domains, covering the 9–500 kHz frequency range. The isolated conditions correspond to the use of a Line Impedance Stabilization Network (LISN), whereas measurements in the Low Voltage (LV) grid where the EV is connected are referred to as on-line conditions. Regarding the frequency analysis, the results lead to conclude that different amplitude and spectral features of the Non-Intentional Emissions (NIEs) are measured depending on the measurement conditions, registering higher-amplitude NIEs when the measurements are conducted in the LV grid. Moreover, the paper also shows that the spectral characteristics of the NIEs correspond to tonal and narrowband emissions at specific frequencies in the 9–150 kHz frequency range, while background noise with low-amplitude emissions (in the case of isolated conditions) and colored noise (in the case of on-line conditions) are reported in the 150–500 kHz band. In the time domain, a periodic time-dependent behavior within the fundamental cycle of the mains (20 ms) is reported in all the analyzed cases, except for the tonal emissions with oscillating central frequency generated by a specific EV model. The time variability between both measurement configurations only differs by more than 3 dB for three out of the twelve analyzed EVCPs, where a higher variability is observed if the measurements are carried out using a LISN. As a conclusion, considering the differences in the frequency and time characteristics of the NIEs under isolated and on-line conditions, evaluating the emissions generated by EVCPs might lead to an underestimation of the NIEs in real LV grids.