Provincial Power Grid Research Articles

Static voltage stability margin calculation of power systems with high wind power penetration based on the interval optimisation method

Calculation of the static voltage stability margin (SVSM) of a power system with high wind-power penetration requires consideration of the uncertain fluctuation of wind farms' (WFs') output. Using interval numbers to describe the uncertain WF output fluctuation and based on the interval optimisation theory, a new SVSM interval calculation method is proposed. The method establishes two bi-level optimal power flow (OPF) models to calculate the SVSM interval, including a min–min model for the upper bound and a max–min model for the lower bound. Through the dual theory of convex programming, the max–min model is transformed into a max–max model, which is solved by transforming it into a one-layer optimisation model. Using correlation angles to describe the correlation of different WF output intervals and using linear transformation to decorrelate them into independent intervals, two bi-level OPF models of calculating the SVSM interval considering different WFs' correlation are established. Take the IEEE-39 bus system and an actual 964-bus provincial power grid as examples, compared with the Monte-Carlo method and the continuous power flow and affine interval integrated method, the results indicate the proposed method obtains more accurate SVSM interval and has better calculation efficiency.

Optimization Method of Annual Power Generation and Transmission Maintenance Plan for New Energy Consumption

Recently, with the rising proportion of new energy installed capacity in China, the problem of new energy consumption in the northwest region is particularly prominent, and the phenomenon of abandoning wind power and solar power is serious. In order to alleviate this situation, this paper proposes an annual maintenance plan optimization method for new energy consumption. Under the premise of considering the new energy output, the goal is to minimize the limited power of new energy generation and the minimum cost of maintenance. Transmission maintenance planning model is mixed integer linear programming model and can be solved by a solver such as CPLEX. The feasibility of the method is verified by a provincial power grid example, which effectively reduces the limited power of new energy generation.

Research on distributed photovoltaic power prediction based on centralized photovoltaic output

Distributed photovoltaic does not provide output data when it is connected to power grid due to cost constraints, which adversely affects power grid dispatching. Considering the solar irradiance correlation between output of centralized and distributed photovoltaic stations in a region, a method for predicting the output of distributed photovoltaic power generation is proposed. Sinusoid function is used to fit the output of unshielded photovoltaic system by using the least square method, and the unshielded coefficient is proposed to represent the variation of photovoltaic output. The output of distributed photovoltaic (DPV) can be predicted by combining the unshielded coefficient and the reference power curve. The method is verified by a provincial power grid in northern China, the results show that the method has high accuracy, especially for small fluctuations.

Research on Monthly Unified Scheduling Electricity Consumption Prediction Based on Temperature Gradient Change

With the improvement of residents’ living standards, the cooling and heating load are increasing rapidly, and the influence of temperature on the unified scheduling electricity consumption is increasing. Based on the temperature gradient change, the monthly unified scheduling electricity consumption prediction research is carried out to quantify the influence of temperature on the unified scheduling electricity consumption, and the unified scheduling electricity consumption prediction is performed for a provincial power grid. The example further proves the feasibility and effectiveness of the prediction method.

Optimal Dispatch Strategy for a Flexible Integrated Energy Storage System for Wind Power Accommodation

The application of the large-capacity energy storage and heat storage devices in an integrated energy system with a high proportion of wind power penetration can improve the flexibility and wind power accommodation capacity of the system. However, the efficiency and cost of the flexible resource should also be taken into consideration when improving the new energy accommodation capacity. Based on these considerations, the authors try to construct a joint optimal scheduling model for day-ahead energy storage and heat storage that considers flexibility. The power supplies and devices will be modeled separately, which enables a universal applicability. The objective function is the minimum cost and wind curtailment. Various practical constraints are taken into account. The mixed integer programming and software GLPK is used to program and solve. The actual operation data of a provincial power grid in northern China is used to conduct simulation analysis in four different working conditions. The results show that the model can maintain economical efficiency under different working conditions. In addition, it can adjust and dispatch various power supplies and devices efficiently, significantly improving wind power accommodation of the system.

Optimal power peak shaving using hydropower to complement wind and solar power uncertainty

Booming renewable energy development, such as wind and solar power, with their intermittency and uncertainty characteristics, pose challenges for power grid dispatching, especially for power grid peak shaving. In this paper, coordinated operation of hydropower and renewable energy in a provincial power grid is explored to alleviate fluctuation and aid peak shaving. Considering their aggregate effect, this study aggregates wind power plants and solar power plants into a virtual wind power plant and a virtual solar power plant, respectively, and the forecasted error distribution of wind and solar power is analyzed with kernel density estimation. Then, based on the principles of using hydropower to compensate for fluctuating wind and solar power, a day ahead peak shaving model with the objective of minimizing residual load peak-valley difference is built, which introduces chance constraints for forecast errors and coordinate hydropower operation with wind and solar power. To simplify the solution, the proposed model is recast as a successive linear programming problem. Day-ahead scheduling case studies of a provincial power grid system indicate that the proposed model can conduct peak shaving effectively, hydropower can compensate wind and solar power fluctuation, improve the stability of combined output, and make better use of renewable energy. Therefore, this study provides an alternative approach for peak shaving operation of power system with hydropower and increasing integration of wind and solar power in China and other places worldwide.

Network partitioning approach for reactive power/voltage control using analytical nodes coupling expressions

Power-grid partitioning is an important prerequisite for power systems security analysis and control. In this study, a novel recursive grid bipartitioning strategy is proposed. First, two analytical expressions are derived from Kirchhoff's equations to represent coupling relationships between nodes. Then, generator nodes under study are divided into two groups by recursively applying the coupling relationships between generator nodes. Finally, the coupling relationships between generator and load nodes are used to merge relevant load nodes to the corresponding groups of generators. The stopping criterion of the grid bipartitioning process is also discussed. The proposed bipartitioning strategy is implemented on the IEEE 118-bus system and a practical provincial power grid in China to demonstrate its effectiveness.

Fast Cumulant Method for Probabilistic Power Flow Considering the Nonlinear Relationship of Wind Power Generation

Currently, the increasing wind power penetration, with consequent randomness and variability, presents great challenges to power system planning and operation. Probabilistic power flow (PPF) has been developed to calculate the power flow under uncertain circumstances. However, the current wind power models are subject to specific probability distributions, limiting their accuracies in wider applications. Additionally, the cumulant method (CM)-based PPF, if nonlinear relationship is considered in, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this article proposes a novel generalized density/distribution fitting method (GDFM) combining with the Copula function to establish a joint probability model for wind power generation. A special impulse- mixed probability density (IMPD) integration method is also introduced to derive the input cumulants from the model. Finally, a fast cumulant method (FCM) is proposed to reduce the computational burden of output cumulant calculation while retaining a high accuracy in a nonlinear context. Case study on the IEEE-118 test system validates the effectiveness of the proposed methods, and a real application to a provincial power grid in China provides some useful power flow risk information for decision making. The whole FCM-based PPF scheme can be helpful for future power flow examination in power system planning and operation.

Model-Free Fast Frequency Control Support With Energy Storage System

With the advantage of quick response and flexible ramp, energy storage system (ESS) offers a promising capability of fast frequency control for power systems, especially under a severe disturbance. This paper proposes an ESS control strategy using local measurement in order to provide fast frequency support right after a sufficiently severe disturbance is observed. The proposed strategy includes online frequency characteristic estimator and online optimization controller. Distinguished from existing approaches, no prior knowledge of power system model is required in this work, including generator configuration, operation condition, and frequency response of each component. Instead, the overall system frequency characteristic is approximated online by the proposed estimator. Then, the power setpoint of ESS is determined adaptively to participate in frequency control by the proposed model predictive controller. Finally, the effectiveness of the proposed method is verified via real-world data of a provincial power grid in China.

Multi-Objective Optimal Control Approach for Static Voltage Stability of Power System Considering Interval Uncertainty of the Wind Farm Output

The static voltage stability margin (SVSM) of a power system considering the uncertain fluctuation range of wind farm (WF) output can be described as an interval value called the SVSM interval. A multi-objective optimal control model for SVS of a power system considering the interval uncertainty of WF output is proposed. The objective functions of the model are to increase the central value and reduce the fluctuation range of the SVSM interval, and the decision variables are the active power output and terminal voltage of generators and the switching capacity of shunt capacitors. Thus, it is a multi-layer optimization model. A parametric approximation (PA) method is used to obtain the approximate functional relationship between the optimal objective function values and the decision variables of the inner-layer and mid-layer optimization models and convert the optimization model into a single-layer bi-objective optimization model. A method for obtaining the continuous Pareto frontier of the bi-objective optimization model is proposed based on the normalized normal constraint and PA methods, and the compromise optimal solution calculated from the continuous Pareto frontier is used as the optimal control scheme. Two methods for improving the calculation efficiency of the PA method are also proposed. Finally, results from experimentation on the IEEE 39-bus system and an actual provincial power grid demonstrate the effectiveness of the proposed method.

Identifiability analysis of load model by estimating parameters' confidential intervals

The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation, analysis and control. In practical situations, the accuracy of load model parameters identification results is impacted by data quality and measurement accuracy, 1 which leads to the problem of identifiability. In this paper, an identifiability analysis methodology of load model parameters by estimating the confidential intervals (CIs) of the parameters is proposed. The load model structure and the combined optimization and regression method to identify the parameters are introduced first. Then, the definition and analysis method of identifiability are discussed. The CIs of the parameters are estimated through the profile likelihood method, based on which a practical identifiability index (PII) is defined to evaluate identifiability quantitatively. Finally, the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid. The results have shown that the impact of various disturbance magnitudes, measurement errors and data length can all be reflected by the proposed PII. Furthermore, the proposed PII can provide guidance in data length selection in practical load model identification situations.

Fault Statistics and Analysis of 220-kV and Above Transmission Lines in a Southern Coastal Provincial Power Grid of China

Because the causes of transmission line faults are not known clearly at present, this paper proposes fault classification for transmission lines, and analyses the characteristics of different faults. Using the transmission line faults data of southern coastal provincial power grid from 2007 to 2018, this paper analyses the influence and distribution of transmission line faults affected by lightning, typhoon, wildfire, foreign object and external damage. These five faults are the main factors affecting the safe and stable operation of transmission lines in this region. According to the different fault classification and statistical rules, the design standards for important transmission lines should be improved. Considering the factors of micro-topography, micro-meteorology and engineering cost, the inspection and maintenance of existing lines should be strengthened. The statistics and analysis will be helpful to the research, development and application of new theories, new technologies and new equipment in disaster prevention and reduction from the aspects of disaster prediction and monitoring.

Dynamic paid peak shaving benchmarks and bidding strategies adapted to the high proportion of new energy system

This article mainly analyze the shortcomings of the inherent peak shaving auxiliary service mechanism in the high-proportion of new energy access scenarios in Northeast China, which restricts the enthusiasm of thermal power units to participate in peak shaving. For this reason, a dynamic peak shaving compensation benchmark is proposed which follows load changes. At the same time, in order to standardize market behavior and facilitate market supervision, a guiding formula is proposed for quotation of thermal power units. Based on the above, a dynamic auxiliary service market mechanism is established which used actual operating data of Liaoning province power grid as a calculation example to verify that the mechanism can effectively improve the enthusiasm of thermal power units to participate in peak shaving, which is conducive to market operation and supervision as well.

Multi-Agent Deep Reinforcement Learning for Sectional AGC Dispatch

Aiming at the problem of coordinating system economy, security and control performance in secondary frequency regulation of the power grid, a sectional automatic generation control (AGC) dispatch framework is proposed. The dispatch of AGC is classified as three sections with the sectional dispatch method. Besides, a hierarchical multi-agent deep deterministic policy gradient (HMA-DDPG) algorithm is proposed for the framework in this paper. This algorithm, considering economy and security of the system in AGC dispatch, can ensure the control performance of AGC. Furthermore, through simulation, the control effect of the sectional dispatch method and several AGC dispatch methods on the Guangdong province power grid system and the IEEE 39 bus system is compared. The result shows that the best effect can be achieved with the sectional dispatch method.

Online Dynamic Total Transfer Capability Estimation Using Cotraining-Style Semi-Supervised Regression

With the increasing integration of wind power, the operating condition of the power system varies more rapidly. As the total transfer capability (TTC) of the transmission interface changes with the operating condition, the offline TTC estimation has become less suitable for online security control. In this paper, an efficient online dynamic TTC estimation method using semi-supervised learning approach is proposed. First, considering the high-order uncertainties of wind and load, a sample database of expected operating conditions with or without corresponding TTCs is generated. Then, the pivotal features which greatly correlate with the TTC are selected. Finally, the relationship between the TTC and pivotal features is learned, using the cotraining-style semi-supervised regression algorithm (COREG), thus the dynamic TTC estimation model is established. With real-time data inputting the model, the TTC can be estimated. The proposed method is validated on Gansu Province Power Grid in China, and the results and accuracy and efficiency comparison with other typical existing methods indicate that, the proposed method can provide accurate TTC estimation, and because of the high efficiency of the semi-supervised learning approach, the whole process of model establishment and TTC estimation can be refreshed every 15 minutes. Therefore, the proposed method of online dynamic TTC estimation is suitable for online security control.

Classification of power loads based on an improved denoising deconvolutional auto-encoder

Machine learning has a wide range of applications in the recognition of power loads (PLs). In the light of the problems, such as poor generalization and the ease of falling into the local optima existing in the current PL classification algorithms, an improved algorithm based on the denoising deconvolutional auto-encoder was proposed to classify the field PL data. With the mirror symmetric structure of the network, the convolutional module can extract the distinctive features, while the deconvolutional module can reduce data redundancy and maintain high activation pixels. The data preprocessing accomplishes data dimensionality reduction. In order to accelerate convergence and improve classification accuracy, the unsupervised pre-training and ℓ2, regularization were used. The experimental results in the field data of a provincial power grid demonstrate that the proposed algorithm has a better generalization performance and a higher recognition rate than other algorithms, thus providing an efficient and objective way for PLs recognition.

A data-driven approach for fault time determination and fault area location using random matrix theory

This paper proposes a wide-area measurement-based data-driven approach for fault time determination and fault area location in the power system. To avoid the influence of bad data, the random matrix theory (RMT) is applied to the proposed approach. The measurements obtained from wide-area measurement system (WAMS) are used to form the raw data matrix, its mean spectral radius (MSR) index and the correlation among entries are employed to determine the fault time and locate the fault area using RMT. Since the proposed approach only requires the data obtained from WAMS and is a data-driven method, no physical model and topology information is needed compared with the existing model-based methods. Case studies are carried out on the IEEE 39-bus power system and a practical provincial power grid of China respectively, and a decision-support tool is programmed using the proposed method to help operators obtain fault information more timely and accurately. Simulation results show that the proposed method can determine the fault time precisely and locate the fault line, whether there is bad data in wide-area measurements or not.

Evaluating the Investment Efficiency of China’s Provincial Power Grid Enterprises under New Electricity Market Reform: Empirical Evidence Based on Three-Stage DEA Model

The new round of electricity market reform in 2015 completely changed the profit pattern of power grid enterprises (PGEs) in China, and directly affected their investment plans. Under the new electricity market reform (NEMR), the government regulatory authority made higher requirements for the investment efficiency of PGEs, and the investment effectiveness hence became the core criterion for investment plans. Therefore, the PGEs are now attaching great importance to the investment efficiency. According to their geographical differences, this paper divides the Chinese provincial PGEs into three groups, namely eastern, central and western region enterprises. Based on the NEMR, we developed an evaluation system of investment efficiency for the above-mentioned enterprises. Moreover, this paper selects GDP per capita, electricity consumption in industry, and electrification rate as external environment variables, and conducts an empirical research on the investment efficiency of 31 provincial PGEs in China in 2017. The analysis reveals that three external environment variables have considerable impacts on the investment efficiency. Though the increase of GDP per capita and electricity consumption in industry are not conducive to improving investment efficiency, the advancement of electrification plays a positive role in its improvement. And from the real efficiency results, Tianjin, Liaoning, Jiangsu, and Fujian have relatively higher investment efficiency, while Henan, Shandong, and Shanghai exhibit lower investment efficiency. By comparing the investment efficiency of PGEs in the first and third stage, conclusions can be drawn that in the first stage the investment efficiency of PGEs was overestimated, and the inefficient investments prevailed some provincial PGEs, which caused by low scale efficiency.

Medium and long-term power quantity check and adjustment method based on power quantity margin

Medium and long-term power trade is increasing, and the adjustable margin of power grid is reducing. Therefore, medium and long-term power quantity security check is necessary. Firstly, a power quantity margin model is constructed, and power quantity constraints and the future state grid model are considered. The power quantity limits of principal parts such as unit and line section are calculated, and quantity adjustment method is given.Then the medium and long-term power quantity check and adjustment are realized through the correlation coefficient matrix. Finally, the actual provincial power grid is taken as an example to verify the effectiveness of the proposed model and method. Through the calculation of the quantity margin by power, the verification of medium and long-term transactions and the control of the safety margin of the power grid are realized.

Reactive power dynamic multi-objective optimization for multi-terminal AC/DC interconnected power systems under wind power fluctuation

In view of the reactive power coordination difficulties caused by reactive power strong coupling, the provincial power grids in the interconnected system are formed by the multi-AC/DC transmission. Wind power channels are under the conditions of large-scale long-distance transmission of wind power and other forms of renewable power generation. The AC-DC hybrid power flow equation of the interconnected system, including the AC-DC tie lines, is presented in this paper, along with the robust dynamic evolutionary optimization of the reactive power system in interconnected systems under fluctuating and uncertain wind power conditions. Therefore, the rapid collaborative optimization of reactive power flow and the exchange of reactive power between tie lines between provincial power grids are realized. The analysis was made by taking four interconnected large-scale provincial power grids of Eastern Mongolia, Jilin, Liaoning and Shandong as an example. The simulation results demonstrate the effectiveness and superiority of the proposed reactive power dynamic multi-objective optimization method for interconnected power grids.