The micro synchronous phasor measurement unit (μPMU) and remote terminal unit (RTU) provide high precision measurement data for distribution network. This paper presents a forecasting aided state estimation (FASE) method based on hybrid measurement data, which uses data imputation and the cubature Kalman filter to improve the synchronous and filtering profile of μPMU and RTU. Firstly, to improve the synchronous profile of RTU with the shorter updating period, the historical data and linear interpolation are considered comprehensively to realize the data imputation by weighted average interpolation. Then, the cubature Kalman filter is used to construct a state prediction equation, measurement prediction equation, and filter correction equation. A FASE for distribution network with mixed data is proposed. Finally, the validity of the proposed method is verified in the IEEE 37-node system.
With the development of new energy sources, higher requirements are put forward for the regulation and operation of grids with PV and wind power. The typical scenario is one of the main ways to deal with this problem. The traditional method for generating typical scenarios is prone to data information loss and feature extraction inaccuracy. This paper proposes an uncertain wind-PV-load typical scenario generation method based on residual convolution auto-encoders. First, the residual convolution auto-encoders network is used to extract the characteristics of wind-PV-load data to reduce the data dimension while reducing the loss of data information and taking into account the coupling of wind and solar power. Then, reducing the influence of noise data on the experimental results, k-medoids is used for clustering to generate typical scenarios. The actual data collected from a power grid in northwest China is taken as the research object. Comparison with traditional clustering methods such as DBI (Davies-Bouldin Index), CHI (Calinski-Harabasz Index) and other indicators, the feasibility of the proposed method is verified.
With the increase of uncertainty caused by the single phase access of distributed renewable generation (DRG) in the distribution power grid, the static safety analysis for overvoltage and imbalance of the distribution power grid with DRG faces new challenges. This paper proposes to use the extreme learning machine to analyze the complex relationship between the input and output of the three-phase power flow calculations in distribution network. The trained network can greatly improve the efficiency of power flow calculation in different scenarios caused by DRG fluctuations with this topology. On this basis, a voltage safety analysis method considering multiple scenarios of distribution power grid is proposed, which can quickly analyze and judge the safety of grid nodes. Finally, this paper uses IEEE 13-node,IEEE 33-node and IEEE 118-node systems with DRG for simulation calculation. The experimental results show that the method proposed in this paper is more effective than the traditional three-phase power flow calculation method without any convergence problem, and has higher efficiency and accuracy than BP neural network. The effectiveness of the proposed method is verified.
Electric energy substitution has become an important trend and key path for the development of energy transition. The rapid and accurate estimation of energy saving is conductive to the promotion of electric energy substitution projects. To make full use of a small amount of the operation data during the electric energy adjustment period to achieve the purpose of rapid energy-saving estimation, this paper proposes an online unit energy-saving estimation method based on transfer learning. In this method, we firstly use regression algorithm to train a large amount of base period samples to obtain a base period energy consumption model. Secondly, we use the regression algorithm based on transfer learning to train a large amount of base period samples and a small amount of adjustment period samples together, and use different weight updating strategies to iteratively adjust the weights of the based period samples and the adjustment period samples to obtain an adjustment period energy consumption model. Finally, the normalization method is used to obtain the energy consumption difference under the reference conditions, that is, the unit energy-saving amount. In this paper, the simulation analysis of electric energy substitution in the drying field proves the effectiveness of the proposed method, and shows the influence of the iteration times, sample number, the way of sample combination on the prediction errors of the proposed algorithm.
In order to reduce the operating cost of power companies, for non-technical loss (NTL), this paper proposes a belief rule-based (BRB) and long short-term memory (LSTM) based method for diagnosing the user’s electricity theft behavior. This method firstly extracts the power fluctuation coefficient and the burr width of the power consumption curve from the electric power dataset, and then the input pre-attribute conversion method for BRB anomaly feature is established. The final confidence is output by evidential reasoning (ER) method, and the confidence-rule base for NTL anomaly detection is established, thus, the labeled positive and negative training data sets with high robustness can be obtained automatically. Then, on this basis, a multi-LSTM network detection model is proposed to realize the effective extraction and detection of abnormal electrical features. The experimental results show that, compared with existing mainstream fault detection networks, the proposed method can better accurately diagnose the abnormal electrical behavior of users from the power big data.
As high-density, decentralized and network-wide power electronic nonlinear devices make it difficult to effectively estimate the harmonic pollution, a data-driven method for modeling the harmonic emission level of decentralized harmonic source group is proposed. Firstly, by comprehensively considering the characteristics and applicability of multiple harmonic source models, and selecting the Norton equivalent harmonic model to present harmonic source load device, a typical harmonic emission characterization is formed. Then, non-intrusive load monitoring (NILM) technology is introduced to decompose user’s electricity consumption data to obtain status of the devices, and then obtain the total number of running devices at each time. Finally, the Markov Chain (MC) is used to simulate the dynamic changes of the number of running devices in the time sequence, and the time-series characteristic model of user’s electricity consumption is established. The time-series characteristic model is combined with the harmonic source model to obtain the collective harmonic emission model. Compared with Monte Carlo simulation results and measured data, the proposed method has a more efficient modeling process and effectively solves the problem of group harmonic estimation with a large number of dispersed harmonic sources.
Aiming at that the difference of unit cost between standard capacity and customized capacity being not considered in the current research of voltage sag mitigation strategy, the relationship between different capacities (customized and standardized) and initial cost of governance equipment such as uninterruptible power supply (UPS ) and dynamic voltage restorer (DVR) is analyzed in this paper. On the other hand, according to the economic losses caused by a single sag event of different sensitive equipment, a more reasonable method to measure the severity of voltage sag is proposed. Moreover, according to the overall severity of voltage sag and the user’s attention to voltage sag, this paper analyzes the investment willingness of users. Combined with the investment ability of users, the prospect theory is used to quantify the investment willingness of voltage sag mitigation. Then, the mitigation cost accords with the user’s investment willingness is obtained as the constraint condition. Taking the maximum return on investment of the mitigation strategy as the objective function, and taking the mitigation cost and the area of the treatment equipment as constraints, the simulated annealing algorithm is used to optimize the configuration of user voltage sag mitigation equipment. Finally, an example is given to verify the effectiveness of the proposed method.
The uncertainty of wind power output and charging demand of electric vehicles (EVs) brings great challenge for power system dispatching. Taking day-ahead dispatch as the target, this paper proposes a stochastic optimization scheduling model and corresponding reserve model on the basis of traditional unit commitment model. Firstly, for the uncertainty of wind power output, a two-stage stochastic unit commitment model is established in this paper according to scenario analysis based on generative adversarial network (GAN), while electric vehicles are divided into two categories: schedulable and non-schedulable EVs. Monte-Carlo method is adopted to simulate the behavior and the dynamic change of schedulable EVs on the basis of probability distribution of the travel patterns, and the model of EV aggregators is established in this paper. As for non-schedulable EVs, K-means cluster analysis is adopted to get a typical load curve, and then the charging demand is viewed as part of conventional load. Case study demonstrates the validity of the proposed model.
The efficient and economical operation of combined cooling, heat and power (CCHP) system depends on the overall optimization of the system’s equipment capacity and operating strategy. In order to improve the practicality of the CCHP, a two-layer collaborative optimization configuration method for the CCHP system considering wind power, solar power and energy storage is proposed. The optimized configuration of the outer layer aims at the best economic net present value and the lowest pollutant emissions as the objective function, and the NSGAⅡ algorithm is used to obtain the capacity configuration of the devices. The dual theory is used in the inner optimization configuration to build a robust model, and the optimal annual operating cost considering pollutant emissions and energy purchase costs is the objective function to obtain the optimal scheduling of each device. Finally, taking the CCHP system of a certain park as the research object, the double-layer collaborative optimization configuration method proposed in this paper is used to optimize the configuration of the system. The simulation results verify the effectiveness of the proposed method, which can effectively realize the collaborative optimization of the system with both economy and environmental protection.
To reduce the probability of commutation failure in practical engineering, the dynamic turn-off angle model of thyristor is established in PSCAD, and the actual criterion of commutation failure is obtained by determining the dynamic minimum turn-off angle. Then, the variation of turn-off angle of the thyristor valve is introduced into the control system, and a dynamic control method for turn-off angle is proposed. The proposed control method was improved on the CIGRE standard test model and realized by PSCAD/EMTDC simulation. The simulation results show that the actual criterion of commutation failure can effectively improve the accuracy of commutation failure identification. The dynamic control method for turn-off angle can mitigate the commutation failure caused by single-phase or three-phase short-circuit faults better. It can be beneficial for the control and protection system of DC transmission to adjust the control margin of turn-off angle of converters, which has practical engineering significance.
With the development of demand response technology, demand-side resources with flexible regulation characteristics such as temperature-controlled loads and electric vehicles can be used as generalized energy storage to participate in the control of power fluctuations in island distribution networks. This paper is oriented to two types of generalized energy storage, air conditioners and electric vehicles, and proposes a collaborative control strategy for distribution network that considers the participation of generalized energy storage clusters. Firstly, the load aggregator is used as the control center to build a multi-dimensional generalized energy storage cluster control architecture. Secondly, the generalized energy storage control model for the air-conditioning cluster and the electric vehicle cluster is established separately, and the number of controlled times of each air-conditioning is considered in the air-conditioning group. In the electric vehicle group, a power distribution strategy based on the state of charge is proposed. Then, according to the power response characteristics of the air-conditioning cluster and the electric vehicle cluster, a low-pass filtering-based multiple generalized energy storage coordinated control strategy is given. Finally, the simulation results based on Matlab/Simulink verify the effectiveness of this control strategy.
With the continuous deepening of green travel concept, the number of electric vehicles (EVs) has increased year by year, and the large-scale EV charging load will have an adverse impact on the distribution network. In this background, this paper proposes a fast/slow charging station planning model that includes charging and discharging management. Firstly, according to the Wardrop user equilibrium theory, this paper establishes a mixed user equilibrium model for traffic network. Secondly, it proposes a charging and discharging management strategy based on the load transfer matrix and its modeling method; then, a fast/slow charging station planning model based on user equilibrium and charging and discharging management is proposed, and the piecewise linearization method, big-M method and second order cone relaxation technique are used to deal with the original problem. Finally, an IEEE 33-node distribution network and a 12-node typical traffic network are used to verify the effectiveness of the planning method proposed in this paper.
With China already committing to peak carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060, the evolution to a high-proportion new energy power system will be accelerated. Ensuring the flexible operation of is the core of the power system transformation towards a high proportion of renewable energy. As a clean, carbon-neutral, and multi-functional secondary energy carrier, hydrogen will play an important role on the flexibility adjustment of a high-proportion renewable energy power system due to its ability to inter-convert with electrical energy and achieve long-term storage. This paper systematically analyzes the evolution of power system characteristics and flexible resource requirements at various stages in the future, analyzes electricity-hydrogen conversion and hydrogen storage technology, and studies and prospects the application scenarios of hydrogen participating in the flexible regulation of renewable energy power systems.
Current available transfer capability (ATC) decision-making methods fail to combine security and economy, and the power grid companies also lack effective risk-management mechanism for compensation losses. Therefore, an ATC decision-making method based on security risk assessment and insurance mechanism is proposed. Firstly, a comprehensive risk-assessment index is introduced as a reference and an ATC decision-making method taking the security risk of N-1 contingencies into consideration is proposed. Then, to calculate power grid companies’ risk and benefit after each ATC decision-making, evaluation indexes of risk and benefit of ATC which consider the influence of uncertain factors in power system is established. Finally, the insurance theory is introduced into the risk management of power grid companies, and the insurance mechanism is used to appropriately transfer and avoid the risk of compensation losses caused by inevitable load interruption. The results show that the presented method is able to improve the economic benefit for power grid companies when certain security constrains are met; that insurance mechanism can effectively helps to reduce the risk and improve the stability of economic benefit. The proposed method can provide a reference for power grid companies’ ATC decision-makings.
In order to meet the lean management needs of power grid enterprises, improve the activity-based standard cost (ABSC) system of power grid maintenance, a differentiated allocation method for power grid ABSC is researched considering the differences in economic level, management level and natural environment among regions in China. Firstly, the calculation method based on the decision-making trial and evaluation method laboratory (DEMATEL) and combination weighting method is put forward according to the characteristics analysis of the maintenance activity and the maintenance work. Then, taking substation maintenance as an example, the differentiated allocation result of power grid ABSC is acquired on the basis of the engineering data. Finally, the rationality of the calculation results is verified on the basis of J-T nonparametric test method. The results show that the proposed method is effective and reasonable, and four key factors affecting the power grid ABSC are obtained. The results provide a theoretical and practical foundation for promoting management quality and production efficiency of power grid enterprises.
