China Grid Research Articles

Optimal Flexibility Dispatching of Multi-Pumped Hydro Storage Stations Considering the Uncertainty of Renewable Energy

With the continuous increase in the penetration rate of renewable energy, the randomness and flexibility demand in the power system continues to increase. The main grid side of the power system vigorously develops pumped hydro storage (PHS) resources. However, the current PHS station scheduling method of a fixed time period and fixed power has lost a certain flexibility supply. In this paper, an optimal dispatching model of multi-pumped hydro storage stations is proposed to supply flexibility for different regions of the state grid in east China. Firstly, the credible predictable power (CPP) of renewable energy is calculated and the definition of flexibility demand of a power system is given. The calculation model for flexibility demand is established. Secondly, considering the regional allocation constraint in the state grid in east China, a non-centralized model of multi-PHS within the dispatch scope is established. In the model, the constraints of storage capacity of different hydropower conversion coefficients of each PHS station is considered. The flexibility supply model of PHS stations to each region of the state grid in east China is established to realize reasonable flexibility allocation. Then, by combining the PHS station models and the flexibility demand calculation model, the optimal dispatching model for the flexibility supply of multi-PHS stations is established. Finally, based on the network dispatching example, the effectiveness and superiority of the proposed strategy are verified by a case study.

Online Evaluation for the POI-Level Inertial Support to the Grid via Ambient Measurements

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5%.

Integrated resource strategic planning considering inter-regional flexibility supply–demand balance: A case study for the Northwest and Central Grid in China

China’s rapid advancement in renewable energy and regional power grid interconnection has highlighted the need for enhanced system flexibility to ensure the security and stability of regional power systems. To address this issue, this study presents a new framework for integrated resource strategic planning for inter-regional flexibility (IRSP-IF). This framework evaluates inter-regional flexibility demand and optimizes power resource allocation across regions. The northwest and central grids in China are used as case studies to evaluate inter-regional power resource allocation from 2022 to 2035. This analysis considers key factors including flexibility supply–demand balance constraints, regional power grid interconnections, and time scales for assessing flexibility demand. The results show that incorporating these constraints and interconnections into the strategic power plan enables greater integration of renewable energy while reducing both installed capacity and the overall cost of regional power resources. In the S3 scenario, which includes inter-regional flexibility supply–demand balance, the total integration of wind and solar power increases by 1.54%. Additionally, the combined average annual fixed and operating costs decrease by 1.44 billion yuan compared to the S2 scenario, which only considers single-region flexibility supply–demand balance. Furthermore, evaluating flexibility demand at a shorter time scale results in a higher proportion of flexible resources, with an average annual growth of 24.43%.

Tower and power line segmentation method based on RandLA-Net

Abstract The scale of the power grid of China has gradually expanded, and the number of long-distance and high-level transmission lines has also increased. However, it is very hard to detect the power infrastructure transmission lines because of the complex laying environment and various ground features. Lines and towers in the power system are important components of power transmission, and their safety directly affects the operation of the entire power system. Reasonable management of lines and towers can reduce the occurrence of faults and system safety risks. At present, both manual inspection efficiency and visual inspection processing accuracy are low. An airborne LiDAR point cloud has become a key means for monitoring towers and power lines. In response to the above problems, we propose a tower and power line segmentation algorithm based on RandLA-Net. Experimental results demonstrate that the proposed method gets precise detection of power poles and lines, and enhances the efficiency in identifying hazardous areas along power lines. The highest Mean Intersection over Union (mloU) recorded is 95.34%, establishing a clear correlation between power towers and lines. Power line data holds significant importance for the power sector, facilitating the monitoring and management of transmission lines.

Unit commitment of power systems considering system inertia constraints and uncertainties

AbstractLarge‐scale integration of renewable energy into the power grid results in a lack of system inertia, posing challenges to the optimal operation and scheduling of systems considering frequency stability. This article proposes a unit commitment model that considers both inertia constraints and the uncertainty of load and renewable energy. First, the time‐domain expression of the system frequency response is calculated based on the aggregated System Frequency Response (SFR) model, considering the system's maximum frequency deviation and the maximum Rate of Change of Frequency (RoCoF) limit. This calculation determines the minimum inertia requirement for the system. Furthermore, inertia constraints suitable for mixed‐integer programming model are derived to address the nonlinearity of conventional frequency constraints. Second, considering the uncertainties of load and wind energy from renewable sources, a unit commitment model with inertia constraints is constructed, and a robust method is used to solve the uncertainties. Finally, the accuracy of the proposed inertia constraints and unit commitment model is validated using case study of IEEE standard test cases and a provincial power grid in China.

An intelligent analysis method for power flow of bulk power grid based on fusion of indicator system feature and image feature

Abstract At present, the adjustment of power flow non-convergence in large power grid is still mostly based on manual trial and error, which relies heavily on expert experience and is inefficient. Therefore, an intelligent power flow analysis method based on index system and image feature fusion is proposed. Firstly, the power flow index system is constructed according to the actual demand, which can provide data support for the following power flow convergence judgment problems. Secondly, taking into account the constraints of similarity and ternary loss, the image feature information is used to guide the output of the information feature representation and to realize the organic interaction of the two modal features. On this basis, the calculation result of the index is correlated with the actual state of power flow to judge the convergence of power flow. Finally, the proposed method is verified on the CEPRI36 nodes system and a large power grid in China. The simulation results show that the accuracy of power flow convergence judgment is 100% for CEPRI36 nodes system and 99.83% for some domestic large power grid. It can effectively predict the convergence of the power flow before the calculation of the power flow required by the project and can provide the basis and theoretical support for the power flow optimization adjustment based on the indicator data.

An optimizated additional damping control for suppressing ultra‐low frequency oscillation suppression based on SVC

AbstractThe ultra‐low frequency oscillation (ULFO) imposes an emerging stability challenge to the high proportion of hydropower grids. To suppress ULFO without reducing the primary frequency regulation of the hydro governor, a novel idea to exploit the voltage regulation effect of load is implemented. First, the additional damping controller is designed and configured in static var compensator (SVC), and the frequency analysis model considering the configuration of SVC as well as damping controller is established. Based on the model, the specific influence of SVC and controller on ULFO is analysed through eigenvalue calculation. In addition, the influence of various load models and SVC location on the damping level are further studied. Consequently, a parameter optimization design method for SVC additional damping control is proposed, it is modelled as the optimization problem of damping ratio in ULFO mode under multi‐operation conditions, which is solved by a particle swarm optimization algorithm. Finally, the effectiveness of the designed SVC additional damping controller is verified in the improved four‐machine two‐area power system and the actual power grid in China.

Optimal allocation of offshore wind power and energy storage considering source-load power stochasticity

Abstract Large-scale offshore wind generation has been integrated to power grids in China. The annual increase in electric vehicles, air conditioning systems, and other electrical facilities has intensified the randomness and volatility of power supply and demand, presenting significant challenges to the safe and economical operation of power systems. Energy storage systems serve as regulators in the power grid, yet the electrical performance and costs associated with various storage technologies differ considerably. Consequently, the strategic planning and development of energy storage solutions tailored to specific local conditions have emerged as critical areas of research. This paper presents an in-depth analysis of power characteristics across source loads, explores an optimized configuration approach for energy storage, and validates this method through a numerical example. The findings confirm the effectiveness of the proposed configuration strategy and offer pertinent recommendations for the implementation of energy storage solutions.

Intelligent Substation Noise Monitoring System: Design, Implementation and Evaluation

In recent years, the State Grid of China has placed significant emphasis on the monitoring of noise in substations, driven by growing environmental concerns. This paper presents a substation noise monitoring system designed based on an end-network-cloud architecture, aiming to acquire and analyze substation noise, and report anomalous noise levels that exceed national standards for substation operation and maintenance. To collect real-time noise data at substations, a self-developed noise acquisition device is developed, enabling precise analysis of acoustic characteristics. Moreover, to subtract the interfering environmental background noise (bird/insect chirping, human voice, etc.) and determine if noise exceedances are originating from substation equipment, an intelligent noise separation algorithm is proposed by leveraging the convolutional time-domain audio separation network (Conv-TasNet), dual-path recurrent neural network (DPRNN), and dual-path transformer network (DPTNet), respectively, and evaluated under various scenarios. Experimental results show that (1) deep-learning-based separation algorithms outperform the traditional spectral subtraction method, where the signal-to-distortion ratio improvement (SDRi) and the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of Conv-TasNet, DPRNN, DPTNet and the traditional spectral subtraction are 12.6 and 11.8, 13.6 and 12.4, 14.2 and 12.9, and 4.6 and 4.1, respectively; (2) DPTNet and DPRNN exhibit superior performance in environment noise separation and substation equipment noise separation, respectively; and (3) 91% of post-separation data maintains sound pressure level deviations within 1 dB, showcasing the effectiveness of the proposed algorithm in separating interfering noises while preserving the accuracy of substation noise sound pressure levels.

Spatiotemporal carbon sequestration by forests among counties and grids in China

Top-down emission reduction target setting results in inability of local governments to develop differentiated plans for emission reduction. Forests have received considerable attention as an important source of carbon sequestration, especially following China's commitment to carbon neutrality. However, studies have paid little attention to spatiotemporal differences of downscale forest carbon sequestration in China over the long term due to data limitations, while also ignoring the large differences in forest carbon sequestration per unit area among different regions. Here, a basic dataset of forest carbon sequestration among counties and grids in China is constructed and the temporal drivers and spatial differences of forest carbon sequestration were investigated from the perspectives of counties' administrative boundaries and 0.5 by 0.5 degree grid combining modelling, GIS spatial analysis and a decomposition method. Changes in forest land use between 2000 and 2020 were also analysed. The results highlight the importance of forests in managing carbon sequestration among all types of terrestrial vegetation and the differences in forest carbon sequestration per unit area. The scale effect increased forest carbon sequestration in the first two periods (2000–2005 and 2005–2010), followed by a decrease since 2010. The intensity effect promoted carbon sequestration; in particular, a dramatic increase was observed from the second (2005–2010) to the third period (2010–2015). The heterogeneity of the scale effect of forest carbon sequestration increased compared to that of the intensity effect, both from administrative and geographical divisions. The scale and intensity effects of counties located in the northern regions of northeast China enhanced forest carbon sequestration, while counties located in southern and central China inhibited it during the study period. The scale effect of grids changed from enhanced to inhibited forest carbon sequestration mainly in northeast China near 120°E, whereas the intensity effect of grids increased from inland regions in central China to eastern coastal regions during the study period. This study provides references for policymakers to set differentiated emission reduction targets considering the local forest carbon sequestration.

Neural network predictive controller based on the improved TPA-LSTM model for ultra-supercritical units

To mitigate the impact of large-scale renewable energy power on the national grid in China, it is imperative to enhance the flexible peaking capability of coal-fired thermal power units. The coordinated control system, central to the load control of coal-fired units, faces challenges such as multivariable coupling, sluggish response, and uncertain coal quality parameters. This paper introduces a neural network predictive controller based on the improved TPA-LSTM model, aimed at addressing these issues. Initially, a data-driven control model is established to break through the limitations of traditional linear predictive control and effectively handle disturbance uncertainties. Then, a multivariable coordinated control strategy based on the neural network controller is designed, achieving effective decoupling of multiple parameters and ensuring high adaptability across all load conditions. Additionally, by integrating an automatic model updating mechanism, the system can recalibrate in real-time when model mismatches occur due to equipment aging, maintenance, or changes in coal quality, thereby enhancing overall control performance. Simulation results demonstrate that this strategy has excellent control effectiveness, meeting the flexible peaking demands of 1000 MW ultra-supercritical units. The calibration feature of the data-driven model significantly improves control performance following model mismatches.

Optimization of tilt angle for PV in China with long-term hourly surface solar radiation

The optimal tilt angle for photovoltaic (PV) systems is crucial for maximizing solar energy capture. China's diverse climate and geography pose challenges for tilt angle optimization. This study addresses the challenges by using a data-driven approach to determine grid-specific optimal tilt angles across China. Long-term ERA5 hourly solar radiation data and an optimization procedure are used to calculate the annual and monthly tilt angle that maximizes the total solar radiation received over different time periods (up to ten years) for every grid in China. Sensitivity analyses indicate that the optimized tilt angle varies by up to 10° depending on the time period considered, highlighting the importance of long-term radiation data. It is shown that four latitude-dependent schemes in China cannot accurately match the optimized tilt angles, emphasizing the need for optimization through the use of local solar radiation. The difference between our optimized tilt angles and ones via a best-performing latitude scheme makes for an estimated PV power loss of approximately 1.11 TWh/year based on China's PV installations in 2018, equivalent to the PV power generation of Philippines or Portugal in the same year. The presented data-driven framework contributes to China's PV industry by determination of optimal fixed tilt angles.

Low-carbon development of urban power grids in China: Quality assessment, obstacle analysis, and potential release

Urban power grid planning plays a pivotal role in the process of reducing carbon dioxide emissions from power grids. Traditional grey target models and analytic hierarchy processes fail to reflect the diversity of resource endowments in urban areas and cannot provide targeted solutions for carbon reduction strategies in urban power grids, representing a critical issue that urgently needs addressing. This paper introduces an enhanced grey target model based on the ‘source-grid-load’ system, establishes a quality evaluation index system for low-carbon development, and employs the Analytic Hierarchy Process - entropy weight method to account for the differentiated impacts of urban power grid partition resources. It conducts a comprehensive assessment of overall potential through multi-dimensional relative target centrality and identifies and examines key factors in low-carbon development potential using single-dimensional relative target centrality. The findings indicate that the refined grey target model, focusing on relative target centrality, effectively identifies areas with significant low-carbon development opportunities and assesses their developmental scope across various metrics, thereby providing strategic insights for carbon reduction planning in urban power grids under multi-objective decision-making conditions. This method will decrease CO2 emission by 21.38 % in 2030.

Event-Driven Day-Ahead and Intra-Day Optimal Dispatch Strategy for Sustainable Operation of Power Systems Considering Major Weather Events

As the proportion of renewable energy installations in modern power systems increases, major weather events can easily trigger significant fluctuations in new energy generation and electricity load, presenting the system with the dual challenges of ensuring power supply and renewable energy consumption. Traditional dispatch models need more coordination and optimization of flexible resources under major weather events and risk management of system operations. This study focuses on provincial-level transmission systems, aiming to achieve the coordinated and optimized dispatch of flexible resources across multiple time scales in response to the complex and variable environments faced by the system. Firstly, by profoundly analyzing the response mechanisms of power systems during major weather events, this study innovatively proposes an event-driven day-ahead and intra-day optimal dispatch strategy for power systems. This strategy can sense and respond to major weather events in the day-ahead phase and adjust dispatch decisions in real time during the intra-day phase, thereby comprehensively enhancing the adaptability of power systems to sudden weather changes. Secondly, by considering the variability of renewable energy sources and electricity demand in the day-ahead and intra-day dispatch plans, the strategy ensures efficient and reliable power system operation under normal and major weather event scenarios. Finally, the method’s effectiveness is validated using actual data from a provincial-level power grid in China. The proposed dispatch strategy enhances the resilience and adaptability of power systems to major weather events, which are becoming increasingly frequent and severe due to climate change. The research demonstrates that an event-driven day-ahead and intra-day optimal dispatch strategy can enhance the economic efficiency and robustness of power system operations through the coordinated dispatch of flexible resources during major weather events, thereby supporting the transition toward sustainable energy systems that are resilient against the challenges of a changing climate.

A Multi-Rate Simulation Strategy Based on the Modified Time-Domain Simulation Method and Multi-Area Data Exchange Method of Power Systems

Accurate modeling for power-electronic devices requires power systems to be simulated with considerably small step sizes (typically several microseconds), which causes unnecessary computational burden and reduces efficiency, especially for large-scale power systems. To achieve a balance between simulation precision and efficiency, this paper introduces an innovative multi-rate interface strategy based on the modified time-domain simulation (TDS) method and multi-area data exchange method. The modified TDS method transforms the initialization process into exchange of electric data among different subsystems, while the multi-area data exchange method is able to ensure numerical stability and simulation universality during the multi-rate simulation. The proposed strategy provides a robust interface that allows different subsystems to be engaged in simulations with different step sizes while exchanging data. To validate this strategy, simulations on an integrated system of IEEE 14-bus and 33-bus systems is conducted. In addition, the strategy is further applied to a real-world scenario of the subsystem in the Guangxi Power Grid in China. Analysis of the results indicates that the proposed multi-rate fast simulation strategy can significantly boost simulation efficiency while maintaining accuracy, which marks a notable improvement compared with the traditional single step size simulation.

Fast Coordinated Predictive Control for Renewable Energy Integrated Cascade Hydropower System Based on Quantum Neural Network

The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control.

Technical research and demonstration projects of the intelligent building for smart grid in China

Through six demonstration projects of intelligent building connected to the smart grid via State Grid Corporation of China(SGCC), which are located in different areas of China with different sizes, all kinds of methods based on the smart grid for improving building power efficiency and integrated technologies are studied, changing previous energy-saving practices that mainly depend on a single product or partial technological transformation. The results of more than ten years of demonstration projects operation research indicate that the use of distributed renewable energy and power technologies such as energy storage, energy efficiency management, and demand response has enabled buildings to save energy consumption, improve the proportion and quality of green electricity, meet the peak shaving and valley filling of the power grid, and ultimately reduce carbon emissions. Not only does it reduce energy consumption by more than 15% for users, but it also provides a more comfortable and convenient building environment. The most important thing is to verify the effectiveness and long-term reliability of smart grid technology in promoting green and low-carbon buildings on the power user side. Furthermore, it provides valuable experience for the renovation and construction of low-carbon and flexible electricity-load buildings in the future.

Bias Analysis of PMU-Based State Estimation and Its Linear Bayesian Improvement

With the rapidly changing states of modern power systems, real-time state estimation based on phasor measurement units (PMUs) plays an increasingly important role in energy management systems (EMSs). However, due to the complicated distribution of PMU measurement errors, conventional state estimation methods such as the weighted least squares (WLS) estimator suffer from the estimation bias. Consequently, they do not represent the least variance unbiased estimators. To solve the estimation bias, a state estimation method combining the PMU linear measurement model and the linear Bayesian estimation theory is proposed. Specifically, considering the prior information on estimated parameters and the complicated probability distribution of PMU measurement errors, the linear Bayesian theory is applied to PMU-based state estimation to improve the performance of state estimation. To support this method, the correlation between real and imaginary errors is analyzed, and the prior information of estimated parameters is determined by the volatility of system states. When the real states of practical power systems are unavailable, performance indicators for state estimation are proposed based on the estimation residuals. The efficacies of the proposed estimation and evaluation methods are verified using IEEE 14 bus system and a large-scale power grid in China.

Hybrid attention-based improved temporal convolutional BiGRU approach for short-term load forecasting

Accurate load forecasting can ensure the safe and reliable operation of power systems, reduce generation costs, and improve economic efficiency. To improve the accuracy and performance of short-term load forecasting, this paper proposes a hybrid short-term load forecasting method composed of an improved temporal convolutional network (TCNPlus) with an attention mechanism and a bidirectional gated recurrent unit (BiGRU). Firstly, the collected pre-processed training data is reconstructed using a fixed-length sliding window. Secondly, using the self-attention mechanism (SA) in the improved TCN to further enhance the weight of key features, and introducing residual connections can allow the input to propagate forward faster and improve the representation ability and efficiency of error backpropagation of the network, to eliminate the impact of interference signals. Finally, BiGRU is used to learn the forward and backward dependencies of the load sequence in both directions and predict the true load value. Based on the real load data of a national power grid in South China, through experimental comparison of multiple models, the results show that this model still has higher short-term load forecasting accuracy with fewer input features.

Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy

Accurate extraction of photovoltaic (PV) model parameter is significant for the evaluation of PV system. Most of existing methods for extracting the PV parameters suffer from low extraction accuracy and are prone to fall into local optima. To solve these shortcomings, a chaos learning butterfly optimization algorithm (CLBOA) is proposed. First, the introduced Cauchy mutation increases the perturbation to butterflies and helps the algorithm to jump out of local optima. Second, a chaos learning strategy is proposed to lead the butterflies within the population to learn from the optimal individual using the improved Tent chaos mapping, which strengthens the learning exchange of the population, enhances the ability of global exploration and local exploitation, and optimizes the convergence speed and accuracy. Third, the three worst individual positions were randomly initialized using a terminal elimination mechanism to increase population diversity. After evaluating CLBOA through the CEC2022 benchmark suite, it was used to extract parameters for a total of 12 PV cell and module models for 5 materials. Compared with 9 well-known algorithms, CLBOA performs excellent in terms of convergence performance, parameters extraction accuracy, running time and improvement index. Finally, CLBOA was applied on YL PV power station model of Guizhou Power Grid in China under different irradiations and temperatures. The I-V and P-V curves reconstructed by CLBOA are highly fit to the synthesized curves, and the individual absolute error (IAE) and relative error (RE) also confirm the satisfactory accuracy of parameters extraction. Comprehensive analysis shows that CLBOA can extract parameters better than the comparison algorithms, demonstrating its strong potential for PV parameters extraction.