Stable Grid Operation Research Articles

An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction

Accurate PV power prediction is crucial for stable grid operation and rational dispatch. However, due to the instability of PV power generation, PV power prediction still has great challenges. Therefore, an Autoformer model based on secondary decomposition, Bayesian optimization and error correction for PV power prediction. In order to reduce the complexity of the data and fully extract the features, two decomposition methods are employed. First, empirical mode decomposition (EMD) is applied to decompose the PV power series at the first level. Then, the sample entropy (SE) is introduced to measure the complexity of each component, and the variational mode decomposition (VMD) is employed to implement secondary decomposition of the component with the highest complexity. Secondly, a Bayesian optimization algorithm enhanced Autoformer model is developed for predicting each component, and the predicted component results are aggregated to obtain preliminary PV power prediction results. Finally, the preliminary prediction results are error corrected using a least squares support vector machine. A four-month PV dataset from a PV power plant in Hangzhou, China is utilized to validate the effectiveness of the proposed model. The experimental results show that the model after primary decomposition is superior to the single model, and the prediction accuracy is substantially improved after secondary decomposition. The proposed model has the best prediction performance in predicting the PV power for different seasons, which shows good robustness.

Addressing the limitation of Operational Flexibility in Power Generation with Pumped Hydro Energy Storage System – Indian Perspective

The global climate change scenario has impacted and taken corrective measures in shifting the sourcing of primary energy and efficient end-use pattern with more focus to utilise renewable energy resources to replace the depleting fossil fuels. Electric power generation capacity in the Indian subcontinent has gradually been improved to substantially contribute to intermittent and variable renewable power generation mix up to 40% of grid demand. However, the injection of grid-integrated renewable power as per the availability of generation potential will necessitate limiting the power generation from generating plants running on fossil fuel. But the operational flexibility of fossil fuel power plants is mostly designed to run efficiently with a 55% minimum technical load on the machines. In the above situation, stable grid availability and operation with a desirable spinning reserve may be taken care of suitably with an on-grid energy storage system, and in the Indian context, a pumped hydro energy storage system may play a critical role to take care of grid operational variability with adequate measures on frequency and voltage correction through periodic power generation and power draw. In this paper, the feasibility of a pumped hydro storage system from an Indian power sector perspective has been reviewed about the present national power scenario.

A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electricity Price

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

Improved sequential impedance modeling and stability analysis of virtual synchronous generators considering frequency coupling effects

With the increasing penetration rate of distributed power supply, the interaction between grid-connected inverters and power grid is prone to harmonic oscillation, which will seriously threaten the stable operation of power grid. At present, impedance analysis has been proved to be an effective tool to study the stability of grid-connected systems. However, few studies have considered the influence of frequency coupling effect on the output impedance characteristics of grid-connected inverters. To solve this problem, the sequence impedance model of a three-phase grid-connected inverter controlled by a virtual synchronous generator is established by harmonic linearization method based on the frequency coupling effect. In addition, an improved method of sequence impedance modeling based on grid impedance is proposed to effectively eliminate the influence of coupling terms. Finally, the correctness and practicability of the proposed simplified sequence impedance model are verified by simulation and experiment.

Saturated load forecasting based on improved logistic regression and affinity propagation

Saturated load forecasting (SLF) is a crucial guarantee for stable operation of power grid. However, insufficient data makes SLF collapse. Although logistic regression (LR) can be employed to tackle this problem, its non-linearity may lead to analytically unsolvable maximum likelihood (ML) function. Due to this dilemma, this paper proposes a sequential LR estimator. It depends on minimum variance unbiased estimator (MVUE) and maximum likelihood estimator (MLE) to form an iterative method: in which a linearized statistical model is established by inverse substitution to reduce complexity. Moreover, a new forecasting framework combining LR estimator with clustering algorithms is constructed. In this framework, load data are clustered based on affinity propagation (AP) before LR. Subsequently, SLF result is obtained from corresponding categories by iteration. Simulation results show that the method proposed in this paper has lower computational complexity and higher prediction accuracy.

Analysis of AC Filter Tripping Accident based on UHV Converter Station

Abstract An AC filter tripping accident occurred in a converter station of the North China Power Grid in 2019, which seriously affected the safe and stable operation of the power grid. In this paper, combined with the configuration of the AC filter in the converter station and the principle of capacitor unbalance protection, the tripping mechanism of the AC filter is explained. It is considered that the direct cause of the tripping of the AC filter is the short-circuit discharge phenomenon between the layers caused by the dead trees (grass) branches on the capacitor tower, which causes the instantaneous fault between the layers of the low-voltage capacitor, and finally leads to the action of the capacitor unbalance protection. From the perspective of operation and maintenance, some practical preventive measures are put forward, which will help to improve the safety of AC filter operation in the future, and then improve the reliability of DC transmission system, especially during the peak summer, peak winter and special power protection period, to ensure that there will be no fluctuation in DC load transmission and protect the safe and stable operation of power grid.

A pyramidal residual attention model of short‐term wind power forecasting for wind farm safety

AbstractWind power fluctuation significantly impacts the safe and stable operation of the wind farm power grid. As the installed capacity of grid‐connected wind power expands to a certain threshold, these fluctuations can detrimentally affect the wind farm's operations. Consequently, wind power prediction emerges as a critical technology for ensuring safe, stable and efficient wind power generation. To optimize power grid dispatching and enhance wind farm operation and maintenance, precise wind power prediction is essential. In this context, we introduce a joint deep learning model that integrates a compact pyramid structure with a residual attention encoder, aiming to bolster wind farm operational safety and reliability. The model employs a compact pyramid architecture to extract multi‐time scale features from the input sequence, facilitating effective information exchange across different scales and enhancing the capture of long‐term sequence dependencies. To mitigate vanishing gradients, the residual transformer encoder is applied, augmenting the original attention mechanism with a global dot product attention pathway. This approach improves the gradient descent process, making it more accessible without introducing additional hyperparameters. The model's efficacy is validated using a dataset from an actual wind farm in China. Experimental outcomes reveal a notable enhancement in wind power prediction accuracy, thereby contributing to the operational safety of wind farms.

Grid control strategy of wind turbines based on LADRC voltage control

Traditional grid-controlled wind turbines are required to rely on AC power grid operation, but when the power grid fluctuates, it will cause a series of oscillation instability problems in wind turbines, such as low-frequency oscillation, subsynchronous oscillation, etc. The safe and stable operation of the crisis power grid needs to be ensured. In this study, the virtual synchronous machine is supposed to be adopted as the main control strategy for grid-type control, transforming the traditional system into a more robust and reliable one. Additionally, a converter model of the grid-type control network is set up. By implementing grid-type control, voltage support and frequency support can be offered without relying solely on AC power grid operation. This approach effectively addresses issues caused by preventive such as reliance on wind turbines. The stability of the networked control system is dependent on maintaining stable system power. When there are fluctuations in system power, it impacts the stability of the networked control system. To mitigate these effects and enhance overall stability, this paper proposes using linear active disturbance rejection control (LADRC) based voltage and grid controls to suppress voltage fluctuations through linear active disturbance rejection techniques. The overall system performance is demonstrated through simulations conducted using Matlab/Simulink platform.

Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory

The precise forecasting of wind power output is crucial for the integration of large-scale renewable energy generation into the power grid. It plays a vital role in ensuring safe and stable operation of power grid, diminishing fuel consumption, and minimizing environmental impacts. This research presents a machine learning prediction model that integrates intelligent optimization algorithms and data decomposition techniques for short-term wind power output forecasting. The initial phase of this research develops a prediction model utilizing variational mode decomposition and long short-term memory networks. To mitigate the randomness and uncertainty of wind energy, and improve prediction accuracy, the butterfly optimization algorithm is introduced to optimize the parameters of variational mode decomposition and long short-term memory networks. Several in-depth case studies are carried out on the actual wind power generation dataset in mainland China to confirm the feasibility and effectiveness of the proposed hybrid model. Experimental results demonstrate that the proposed model outperforms the compared model in terms of prediction accuracy across different seasons, showing good practicality and generalizability.

Quantifying residential energy flexibility potential for demand response programs using observational data from grid outages: Evidence from Pakistan

Over the past few years, incentive- or price-based demand response programs targeting residential energy demand have become increasingly popular in high-income countries. Such programs aim to shift energy demand from times when there is limited supply to times of abundance, in order to ensure stable grid operation. However, despite their cost-effectiveness, these schemes have gained little traction in low-income countries, where the amount of residential flexibility cannot be accurately quantified due to a lack of existing demand response studies, and grid operators continue to rely on the blunt instrument of grid outages to manage demand. In this paper, we propose a framework to quantify the flexibility using existing grid outages. Applied to high-resolution sub-metered data from 42 households in Lahore (Pakistan), we show that the proposed method is able to quantify available flexibility at the residential or feeder-level in an automated manner. The proposed method can also be used to evaluate the efficacy of grid outages as a strategy to shift or curtail demand, which can in turn be used to design better demand response schemes. Our results provide evidence that load-shedding causes significant rebound peaks due to shifted demand, while overall energy demand is actually increased rather than curtailed due to the mitigation strategies adopted by households. The paper concludes with the most important policy implications, especially with the rapid proliferation of distributed solar PV systems.

Optimal allocation of energy storage capacity for hydro-wind-solar multi-energy renewable energy system with nested multiple time scales

Multi-energy supplemental renewable energy system with high proportion of wind-solar power generation is an effective way of “carbon neutral”, but the randomness and volatility of wind-solar power generation seriously affects the safe and stable operation of power grid. With the development of energy storage technology, this problem can be effectively solved by configuring energy storage, but how to reasonably configure energy storage is one of the key issues due to the expensive energy storage. To this end, a multi-timescale nested energy storage capacity optimization model for multi-energy supplemental renewable energy system with pumped storage hydro plant based on a three-battery group control operation strategy is proposed. First, the electrochemical energy storage is added to the supplemental renewable energy system containing hydro-wind-solar to form a hybrid energy storage system with pumped storage hydro units, and its group control strategy and charging/discharging coordinated operation are investigated. Then, a double-layer energy storage capacity optimization model nested in multiple time scales is developed. The inner layer optimizes hydropower and pumped storage output to smooth out the more fluctuating wind power output with large time scales. The outer layer optimizes energy storage taking into account factors such as output deviation, cost, attenuation, and dynamic prediction life to smooth out the less volatile and smaller time-scale wind power. Finally, simulation demonstrates that the proposed model can make full use of the flexible regulation capability of pumped storage hydro units and the fast response capability of batteries to maximize the fluctuation of the supplemental system into the grid, and further reduce the peaking pressure of the receiving end of the grid. In addition, the superiority of the proposed three-battery grouping strategy over the traditional battery control method in terms of saving investment cost and mitigating battery life loss is also verified. The advantages of the strategy in terms of carbon reduction and cleaner production are also illustrated in quantifiable terms.

Optimal Operation Strategy of Electric Vehicle Cluster in the Electricity Spot Market Considering Scheduling Capability

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

Ultra-short-term wind power forecasting techniques: comparative analysis and future trends

In recent years, the integration of wind power into the grid has steadily increased, but the volatility and uncertainty of wind power pose significant challenges to grid planning, scheduling and operation. Ultra-short term wind power forecasting technology as the basis of daily scheduling decision can accurately predict the future hourly wind power output, and has important research significance for ensuring the safe and stable operation of power grid. Although research on ultra-short-term wind power forecasting technology has reached maturity, practical engineering applications still face several challenges. These challenges include the limited potential for improving the accuracy of numerical weather forecasts, the issue of missing historical data from new wind farms, and the need to achieve accurate power prediction under extreme weather scenarios. Therefore, this paper aims to critically review the current proposed ultra-short-term wind power forecasting methods. On this basis, analyze the combined power forecasting method under extreme weather scenarios, and illustrate its effectiveness through wind farm case studies. Finally, according to the development trend and demand of future power systems, future research directions are proposed.

Online Estimation of Disturbance Size and Frequency Nadir Prediction in Renewable Energy Integrated Power Systems

The displacement of conventional synchronous generation by Inverter-Based Resources (IBRs) poses critical challenges to the frequency stability of Renewable Energy (RE) integrated power systems. An increased shift towards green energy by integrating RE sources that provide little or no inertia results in a high Rate of Change of Frequency (RoCoF) and deteriorated frequency nadir/zenith following a credible system disturbance. Prediction of frequency metrics, such as frequency nadir and RoCoF, immediately after disturbances will help grid operators to take preventive/corrective actions, such as the deployment of faster frequency control, to ensure secure and stable grid operation. This paper presents an easy-to-implement analytical method to estimate disturbance size in a power system immediately following a contingency which is then used for predicting frequency nadir. The proposed estimation method uses active power measurements from a limited number of monitoring nodes and an adaptive bus admittance matrix of the system for the disturbance size estimation. The estimated disturbance size is then used to predict frequency nadir using a Neural Network (NN) based method. The performance and accuracy of the presented approach are evaluated using a standard IEEE 39 bus system and a real-life Gujarat power system in India through extensive simulations in DIgSILENT PowerFactory, considering various cases of disturbance size, type, and location.

Transmission line ice disaster early warning method based on ice thickness prediction using GM(1, 6) model

The operation of the electricity system faces the difficulty of minimizing ice damage to transmission lines. Due to the uncertainty of transmission line icing thickness, accurate prediction of icing thickness is very important to guide transmission line planning and power grid anti-icing design effectively. Scientific and reasonable early warning assessment of conductor icing is also helpful to make timely and accurate response measures to the possible freezing disaster risk, so as to effectively ensure the safe and stable operation of power grid and reduce the occurrence of the freezing disaster. Based on this, this paper proposes a multi-factor prediction model based on GM (1, 6) grey theory, which considers the transmission line conductor icing model under the influence of multiple meteorological factors. Based on the conductor icing model, the conductor icing degree can be predicted in real time according to meteorological parameters, so as to realize the purpose of transmission line conductor icing disaster risk early warning. It is worth noting that the paper improves the traditional grey model by adding random data functions, which makes the model solve the problem of inaccurate prediction of small samples. Finally, a case study is carried out, and the icing disaster risk is divided into five levels. It is found that the maximal prediction error of icing thickness based on GM (1, 6) grey theory multi-factor prediction model is 10.06%(The average value is only 4.22%), and the accuracy of transmission line conductor icing disaster risk early warning is 88.9%. In addition, a certain safety margin value is added to the predicted value near the critical value of icing thickness, reducing the probability of judging the high-risk level as low-risk. Applying risk early warning method in ice areas can guide the anti-ice work of transmission line.

Predicting Braess's paradox of power grids using graph neural networks.

As an increasing number of renewable energy generators are integrated into the electrical grid, the necessity to add new transmission lines to facilitate power transfer and ensure grid stability becomes paramount. However, the addition of new transmission lines to the existing grid topology can lead to the emergence of Braess's paradox or even trigger grid failures. Hence, predicting where to add transmission lines to guarantee stable grid operation is of utmost importance. In this context, we employ deep learning to address this challenge and propose a graph neural network-based method for predicting Braess's paradox in electrical grids, framing the problem of adding new transmission lines causing Braess's paradox as a graph classification task. Taking into consideration the topological and electrical attributes of the grid, we select node features such as degree, closeness centrality, and power values. This approach assists the model in better understanding the relationships between nodes, enhancing the model's representational capabilities. Furthermore, we apply layered adaptive weighting to the output of the graph isomorphism network to emphasize the significance of hierarchical information that has a greater impact on the output, thus improving the model's generalization across electrical grids of varying scales. Experimental results on the IEEE 39, IEEE 57, and IEEE 118 standard test systems demonstrate the efficiency of the proposed method, achieving prediction accuracies of 93.8%, 88.8%, and 88.1%, respectively. Model visualization and ablation studies further validate the effectiveness of this approach.

Evaluation method of composite insulator aging status based on hyperspectral imaging technology

Silicone rubber insulators' long-term operation in complex environments can lead to accelerated aging, causing the risk of flashovers and grid outages. Timely detection of the insulators' aging status is crucial to ensure the stable operation of grid. Therefore, this paper proposes a pixel-level fast, non-contact detection method based on hyperspectral imaging (HSI) technology for insulators' aging state. Firstly, the hyperspectral spectral line of the samples' six aging levels was extracted, and 14 features were extracted using pre-processing methods like segmented principal component analysis (SPCA) to reduce irrelevant information interference. Secondly, the aging state classification model was established, and through comparison using overall accuracy, F1 score, Precision, and Recall, the random forest (RF) model demonstrated optimal performance with a classification accuracy of 96.81%. Finally, this study utilized HSI technology to achieve a pixel-level evaluation of composite insulators' aging status, and the technique's feasibility was verified through specific parameters such as roughness.

A Toolbox for generalized pumped storage power station based on terrain in ArcGIS Environment

China proposed the goal of carbon peak by 2030 and carbon neutrality by 2060 at the 75th session of the United Nations General Assembly. Accelerating the development of clean energy is the key to achieve this carbon peak and neutrality goal. However, large-scale grid connection of new energy brings great challenges to the stable and safe operation of power grid. As a regulating power source and energy storage power source, pumped hydro energy storage (PHES) has strong regulating ability and is characterized as a reliable operation with broad prospects for development. However, the current field-survey-based method of site selection for PHES is time consuming, labour intensive, and costly. Improper site selection would cause ecological environment damages among other problems. A scheme or protocol that enables the automated site selection of PHES is therefore urgently required. The objective of this study was therefore to develop a new tool based on digital elevation model (DEM) and geographic information system (GIS) hydrological analysis function to screen out the potential PHES sites. ArcGIS, as a leading geospatial software, provides a set of geoprocessing (GP) tools for raster analysis. ArcPy is a Python package that runs in the ArcGIS environment. It can quickly invoke existing tools in ArcGIS to create custom extension modules. In this study, ArcPy was used to screen the site of PHES based on reservoir capacity and dam height, etc. Taking the southern Shaanxi Province, China as an example, comprehensive evaluation method integrating objective weighting method and multi-factor statistical analysis method is proposed and tested. This new method can promote the solution of the PHES site selection planning and preliminary reserve of PHES, and provide scientific reference and theoretical basis for the development and construction of PHES.

Fast and Reliable Sending of Generic Object Oriented Substation Event Frames between Remote Locations over Loss-Prone Networks.

WAMPAC (Wide Area Monitoring Protection and Control) applications are becoming crucial for granting a stable operation of the electricity transmission grid. These systems use a set of sensors distributed between different electrical substations to gather real-time measurements from the field. These sensors are called Phasor Measurement Units (PMUs). Using the gathered data, different monitoring, protection, and control algorithms are run in a Phasor Data Concentrator (PDC) located in a central location. These algorithms close the loop via the generation of remedial commands, which are sent back to the field level with stringent delay, security, and reliability requirements. GOOSE (Generic Object Oriented Substation Events) protocol, defined by IEC 61850 (IEC stands for International Electrotechnical Commission), is used for that aim and also considers the option of sending these commands over IP networks (this option is called Routed-GOOSE). The present article proposes two alternatives for the tunneling of GOOSE frames over IP. Both options allow the decoupling of the transmission and the security aspects, thus increasing flexibility and allowing for easier deployment. The first option, called VX-GOOSE, is a combination of standard protocols, allowing the sending of these frames over UDP/IP tunnels. The tests that have been carried out demonstrate that, under certain network conditions, the transmission of GOOSE frames over UDP may fail, and in some extreme cases, even a whole burst of GOOSEs could be lost. This may have very bad consequences for a distributed electrical system. It should be noted that this limitation affects both VX-GOOSE and Routed-GOOSE. To overcome these limitations, the second option, called Simplemux blast mode, includes a novel mechanism that provides delivery guarantees and a reduced delay, with the counterpart of a certain degree of redundancy. As shown in the experiments, the incurred delays can be significantly reduced when remote locations are connected via unreliable networks, whereas the bandwidth increase caused by redundancy can be kept at reasonable levels. Finally, it should be remarked that although GOOSE is a relevant example use case, this approach can be applied in other fields where flows require very low delay and delivery guarantees.

A novel hybrid model for short-term prediction of PV power based on KS-CEEMDAN-SE-LSTM

Accurate prediction of photovoltaic (PV) power plays a pivotal role in ensuring safe and stable grid operation, enhancing power supply quality, and facilitating proactive grid management to mitigate power fluctuations. This paper introduces a novel hybrid model named KS-CEEMDAN-SE-LSTM, which combines K-Shape (KS) clustering, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample entropy (SE), and Long Short-Term Memory (LSTM) techniques. To improve the accuracy of PV power prediction, three advanced preprocessing techniques, KS clustering, CEEMDAN and SE, are employed for feature extraction before prediction. In the proposed approach, KS clustering is employed to capture the similarity features of PV power. Subsequently, CEEMDAN decomposes the PV power into multiple intrinsic mode functions (IMFs) to reduce the influence of non-stationary and noisy features in power prediction. To further enhance prediction accuracy and streamline computational complexity, SE methods and K-means clustering are utilized to reconstruct the decomposed IMFs into new sub-sequences with refined granularity features. Finally, the LSTM model is applied to predict each reconstructed sub-sequence, and the ultimate prediction results are obtained by aggregating the predictions from all sub-sequences. The effectiveness of this method is demonstrated using real-world PV power data collected from China. Comprehensive experiments substantiate that the proposed KS-CEEMDAN-SE-LSTM model outperforms benchmark methods in short-term PV power prediction.