Hubei Power Grid Research Articles

Research on Reactive Power Optimization of Synchronous Condensers in HVDC Transmission Based on Reactive Power Conversion Factor

With the rapid development of high-voltage direct current (HVDC) transmission systems, the coupling between AC and DC grids is becoming increasingly close. Voltage disturbances in the grid can easily cause commutation failures in the DC system, threatening its safe and stable operation. The new generation of synchronous condensers (SCs) and modified synchronous condenser units are powerful reactive power support devices widely used in large-capacity DC transmission systems. To maximize the voltage support and commutation failure suppression of SCs, this paper proposes improvements in the initial operating state of SCs, using the Shanxi–Wuhan HVDC receiving end in the Hubei power grid as an example, to better support the HVDC commutation process. Additionally, a reactive power output optimization strategy for SCs is proposed, considering the reactive power equivalent factor of electrical connections between grid nodes. This strategy determines the optimal reactive power output limit of SCs near the converter station to suppress DC commutation failures. Simulation results show that this strategy effectively utilizes the dynamic support capabilities of SCs, prevents DC commutation failures, improves HVDC transmission capacity, and enhances the safety and stability of the receiving end power grid, providing theoretical guidance for reactive power output control.

A novel automatic generation control method with hybrid sampling for multi-area interconnected girds

Introduction: The emerging “net-zero carbon” police will accelerate the large-scale penetration of renewable energies in the power grid, which would bring strong random disturbances due to the unpredictable power output. It would affect the coordinated control performance of the distributed grids.Method: From the quadratic frequency modulation perspective, this paper proposes a fast Q-learning-based automatic generation control (AGC) algorithm, which combines full sampling with full expectation for multi-area coordination. A parameter σ is used to balance the state between the full sampling update and only the expectation update so as to improve the convergence accuracy. Meanwhile, fast Q-learning is incorporated by replacing the historical estimation function with the current state estimation function to accelerate the convergence speed.Results: Simulations on the IEEE two-region load frequency control model and Hubei power grid model in China have been performed to validate that the proposed algorithm can achieve optimal multi-area coordination and improve the control performance of frequency deviations caused by the strong random disturbances.Discussion: The proposed Q-learning-based AGC method outperforms the convergence accuracy, speed, and control performance compared with other reinforcement learning algorithms.

Study on transmission line icing prediction based on micro-topographic correction

For the requirement of refinement prediction of transmission line icing thickness, this paper presents an icing thickness correction method which combines the prediction using low spatial resolution with the correction of micro-topography features. By analyzing the regional characteristics of micro-topography, the areas can be divided into three categories: plains, high-altitude mountains, and low-altitude mountains. For each type of terrain, the quantitative correction model for the icing thickness is established, which is dependent on altitude, relief amplitude, slope angle, slope aspect, water system, valley line, ridge line, and saddle point. In this paper, according to the numerical prediction on the 3 × 3 km2 grids, the icing thickness of the transmission line on the 300 × 300 m2 grids with micro-topography is obtained using the correction method in Hubei Power Grid. The results show that the correction method is efficient and convenient to improve the accuracy of icing thickness prediction of the transmission line.

Load characteristics forecasting of Hubei power grid up to year 2030 with the development of electric vehicles

The load characteristics of a power grid significantly change with the increase in the number of electric vehicles (EVs). In this study, we present a method for predicting the long-term load characteristics of Hubei Power Grid (A large power grid in China with annual power consumption of 207.1 billion kWh in 2020) up to until year 2030 with the development of EVs. The predicted load characteristics include the annual peak loads and hourly load curves of typical days for different seasons in target years 2025, and 2030. The proposed long-term load forecasting method uses saturated load prediction with grey Verhulst model and Monte Carlo simulation. The future impacts of the charging load of EVs on the grid are discussed in detail.

Multi-agent deep reinforcement learning strategy for distributed energy

The strong random disturbance issues caused by the large-scale grid connections of distributed energy, such as wind energy, photovoltaic energy storage and electric vehicles, must be resolved. In this paper, we propose a Multi-agent deep reinforcement learning strategy, namely DDQN-CDP, which deeply integrate the improved actor-critic strategy with the neural network. This approach also solves the problem of the lack of continuous action controlling ability of traditional deep reinforcement learning, and obtains an optimal solution by multi-region collaboration. By simulating the modified IEEE standard two-area load frequency control power system model and Hubei power grid model, our results indicate that the proposed strategy can solve the strong random disturbance problem caused by the large-scale grid connections of distributed energy and achieve faster convergence and better control performance than other strategies.

Consider the Risk Constraints of Hydro-Thermal Power Generation in Real-Time Strategy of Control

Based on the problem of abandoned water and insufficient output caused by runoff randomness, the short-term runoff inflow interval prediction method combined with nonparametric kernel density estimation (NKDE)and Monte Carlo (MC)sampling is proposed. The method analyzes the impact of hydropower station abandonment risk and insufficient output risk caused by inflow uncertainty on hydropower station output and thermal power output in the power grid, and the corresponding output adjustment strategy is given. This paper mainly carries out these studies: real-time load distribution, short-term runoff interval prediction and real-time output adjustment of hydro-thermal power plant under uncertain incoming water: (1) Risk factors of water and electricity may be involved in the scheduling process, the process of considering runoff uncertainty on hydropower unit output, analyze the impact of hydropower abandoned water risk, estimate based on short-term nonparametric kernel density estimation (NKDE) runoff forecasting methods, given the range of the forecast runoff, reducing forecast error runoff, combine Monte Carlo (MC) sampling and propose a new real-time power control adjustment strategy. (2) Taking Qingjiang cascade power station as a case study, considering the increase of incoming water and the decrease of incoming water, the impact of water abandonment risk and insufficient output risk on short-term power generation plan is verified. The results show that the proposed hydropower and thermal power output adjustment can effectively deal with the risk of abandoned water and the risk of insufficient output. It is of positive significance to make full use of hydropower resources. It is helpful for the safe and stable operation of Hubei Power Grid.

Reliability prediction method and application in distribution system based on genetic algorithm–back‐propagation neural network

With the continuous expansion of distribution system, the structure of the power grid is becoming increasingly complex, and the limitations of traditional analysis methods are more and more obvious. In this study, a reliability prediction model of the distribution network based on back-propagation neural network and genetic algorithm is proposed. Strong correlation factors of reliability are extracted as the input of the neural network for training, and the trained model is used to predict the distribution system reliability level in the future. The neural network is improved by momentum and adaptive learning rate, and the initial weight and threshold are optimised by genetic algorithm to realise rapid and accurate prediction. The proposed prediction model is trained and validated by the actual data of Hubei power grid. The prediction results show that the method is effective. The sensitivity analysis of reliability-related factors is carried out by using the trained network to identify the key indicators that have a greater impact on the reliability of the distribution network. This research can provide the basis for reasonable decision making to improve the reliability of distribution system, and has certain practical significance for a cost-benefit analysis of distribution system reliability.

Dual‐loop SFC scheme for BTB‐VSC‐HVDC interconnecting asynchronous AC grids

This study proposes a dual-loop supplementary frequency control (SFC) scheme for back-to-back voltage source converter high-voltage DC (BTB-VSC-HVDC) interconnecting asynchronous AC grids, to provide frequency support for each other after a large disturbance. The proposed dual-loop SFC consists of the frequency-active power (f–P) loop and the frequency-reactive power (f–Q) loop. The former deployed on the P-loop of VSC-HVDC can provide frequency support for the disturbed grid during the primary frequency regulation and improve the steady-state characteristics of the frequency response, while the latter attached to the Q-loop of VSC-HVDC supports virtual inertia to ameliorate the transient characteristics of the frequency response. Simulation studies are conducted based on the equivalently simplified model of Southwest Power Grid and Hubei Power Grid interconnected by Chongqing-Hubei BTB-VSC-HVDC. Simulation results show that the proposed SFC scheme can effectively improve the transient and steady-state characteristics of the system frequency response under a wide range of operating condition.

Impact of natural disasters on the western Hubei power grid and its anti‐disaster enhancement measures

The western mountain area of Hubei Province has obvious vertical climate and micro-climate features. It is a place where natural disasters occur frequently. The natural disasters have recently caused several large-scale and long-time power blackouts in the western mountain area of Hubei Province. This study analyses the damage to the power grid with voltage level from 220 to 10 kV in western Hubei Province caused by natural disasters from 2008 to 2016. The influence features of natural disasters on the western Hubei power grid are researched from four aspects, including natural disaster type, grid voltage level, occurrence time of natural disaster and line outage frequency. The causes of damage to the western Hubei power grid are studied from five aspects, including grid structure, design, equipment, construction, operation and maintenance. Also, the anti-disaster enhancement measures of the western Hubei power grid are proposed from the aspects of grid planning, design, equipment transformation, construction, operation and maintenance. The research results can provide a reference for other regional power grids to carry out research on resisting natural disasters and provide a guidance to improve anti-disaster ability of the power grid.

Research on Hierarchical and Distributed Control for Smart Generation Based on Virtual Wolf Pack Strategy

Nowadays, haze has become a big trouble in our society. One of the significant solutions is to introduce renewable energy on a large scale. How to ensure that power system can adapt to the integration and consumption of new energy very well has become a scientific issue. A smart generation control which is called hierarchical and distributed control based on virtual wolf pack strategy is explored in this study. The proposed method is based on multiagent system stochastic consensus game principle. Meanwhile, it is also integrated into the new win-lose judgment criterion and eligibility trace. The simulations, conducted on the modified power system model based on the IEEE two-area load frequency control and Hubei power grid model in China, demonstrate that the proposed method can obtain the optimal collaborative control of AGC units in a given regional power grid. Compared with some smart methods, the proposed one can improve the closed-loop system performances and reduce the carbon emission. Meanwhile, a faster convergence speed and stronger robustness are also achieved.

Influence of Deep Earth Resistivity on HVDC Ground-Return Currents Distribution

During monopolar operation or commissioning of high-voltage direct-current (HVdc) links, ground-return current (GRC) leads to power transformer dc bias issues. A GRC model is proposed in this paper, and the penetration depth, penetration ratio, and earth surface potential of various earth models are simulated. The result indicates that the influence of deep earth resistivity increases rapidly by the increasing distance from substations to HVdc electrodes. The simulation results are compared with measured data of GRCs distribution in the Hubei power grid of China. The result demonstrates that the simulation considering deep earth resistivity is credible and the shallow earth model could lead to poor accuracy. Furthermore, earth resistivity measurements are carried out at different geological regions and the deep earth characteristics and the GRCs distribution are quite different. Thus, it can be concluded that to calculate GRCs distribution using uniform earth structure without measurement is not appropriate. Finally, the relationship between accurately measured earth depth and the accuracy ratio of GRCs calculation is analyzed. The result of this paper contributes to determine the depth of earth model in GRCs simulation modeling and earth resistivity measurements.

Influence of back‐to‐back VSC‐HVDC project on the operation characteristics of Hubei power grid

The synchronous interconnection between Chongqing and Hubei power grid is planned to be unsynchronised by back-to-back voltage source converter based high-voltage direct current (VSC-HVDC) project, which has great impact on the operation characteristics of Hubei power grid. In this study, the basic information of back-to-back VSC-HVDC project was introduced. The influence of this project on the operation characteristics of Hubei power grid was analysed in detail, including power flow, short-circuit current, transient stability and frequency characteristic. The corresponding grid reinforcement measures were proposed to solve power flow over-limit and transient instability problems caused by this project. The study has some reference value for the planning and operation of Hubei power grid and the design of other similar HVDC transmission projects.