A Probability Distribution Model to Simulate Wind Power Output Fluctuation

Electric vehicles (EVs) are flexible demand-side response resources in a power distribution system. Reasonable and orderly control of charging/discharging processes of aggregated EVs can improve their coordination and interaction with the distribution system and ensure its efficient and stable operation. Aiming at the problem that the fluctuation of wind power output may affect the stable operation of distribution system, a real-time control strategy for aggregated EVs to smooth the fluctuation of wind power is proposed. Firstly, considering the dispatchability of EVs, the charging/discharging energy boundary model is established to determine the charging/discharging margin of an EV at each moment. Then, first-order low-pass filtering is used to determine the total dispatching power of aggregated EVs. Finally, the total charging power of aggregated EVs is determined and power allocation is carried out. Simulation results show that the proposed strategy can achieve real-time smoothing for the fluctuation of wind power output while meeting the charging requirements of EVs, and the proposed strategy can not only reduce the fluctuation rate of total load, but also realize peak shaving and valley filling for the distribution system.

Under the background of the construction of “three types and two networks”, the introduction of a large number of heterogeneous intelligent terminals, ubiquitous open networks, interactive real-time control services and wide area interconnected power systems have transmitted the network security risks to all aspects of the power system. The security defense capability of power system is not only the superposition of security defense equipment, but also the excellent network security team. The continuous improvement of the level of network security is inseparable from the accumulation of network security attack and defense technology and practical experience, as well as practical tools and equipment with the characteristics of the power industry. In order to meet the increasingly severe security protection challenges, actively implement the security protection requirements of the power information system, ensure the safe and stable operation of the power system, build a network shooting range suited to the attributes of the power industry, and continuously improve the ability of network security protection, consolidate the foundation of digital transformation to ensure the safe and stable operation of power system and reliable power supply.The safe and stable operation of the information system needs to carry out comprehensive and in-depth vulnerability troubleshooting. In view of the lack of power business loophole mining and security event verification shooting range environment, this paper studies the power security shooting range construction technology.

Very-short-term wind power prediction is of great significance for the safe and stable operation of power system. As a well-known time series model, vector autoregressive (VAR) model is very suitable for very-short-term forecasting, and it can consider the temporal and spatial correlation among multiple wind farms to improve the forecasting accuracy. However, when there are a large number of wind farms, VAR model is prone to over-fitting. Slow calculation speed is also the problem of VAR model. This paper adds regularization terms in estimating coefficients to obtain a sparse coefficient matrix, which helps to avoid over-fitting. Online VAR model is used to improve the calculation speed, and a specific improved online VAR method is proposed for forecasting wind power output from new-built wind farms. This method is tested in a specific case, and the prediction accuracy and calculation speed are better than the traditional VAR model. In addition, the improved online VAR method can accurately predict wind power output of new-built wind farms with little historical data.

The damage of arc to gas insulation protection will affect the safe and stable operation of power system seriously in the field of high voltage insulation. Therefore, it is a great theoretical value and practical significance to study the mechanism of gas discharge and find out the law of gas discharge under different conditions for ensuring the safe and stable operation of power system. In order to study the variation law of arc channel complexity under different voltage levels, this paper adopts the discharging experiment of needle-plate electrode, adds different high voltage to the needle-plate electrode under other conditions unchanged, carries out many experiments, and collects five groups of discharging channel plans of needle-plate gap under different voltage. The are discharge channel image in the experiment is restored to the three-dimensional image from the space angle, and the fractal dimension of arc discharge channel under different voltage pressure is calculated by box dimension method of fractal dimension. The results show that the complexity of discharge channel decreases with the increase of voltage level, that is, the lower the voltage level and the larger the fractal dimension.

With the rapid development of wind power, there are two trends for wind power integration: distributed integration for small-scale wind power and concentrated integration for the large-scale. This paper concentrates on and discusses transmission planning methods for the centralized mode. Firstly, the paper analyzes the peak characteristics of wind power output and its impact on transmission planning. Considering the peak characteristics, the paper proposes an economic and reasonable transmission planning model for large-scale wind power. The object of the model is to minimize the sum of the new-built circuits investment and the expected wind power loss. And by adding wind power output loss constraints to the model, the benefits of wind power can be guaranteed. The new model can be solved by Benders decomposition. An example is used to verify the correctness and validity of the method. This paper can provide a new transmission planning mode for integrating large-scale wind power.

In the application scenario of smoothing the fluctuation of wind power output, an optimal model of energy storage is proposed. The wavelet packet optimization algorithm is used to optimize the wind power output curve, and the annual power response curve of the energy storage system is obtained. The improved scenario clustering algorithm based on the combination of the cloud model and PSO is used to aggregate the annual power response curve of energy storage and generate typical scenarios of energy storage power response. To minimize the annual comprehensive cost of energy storage, an optimal allocation model of energy storage is established. Finally, the proposed method and model are analyzed and verified through numerical simulation. The results show that the model and method can comprehensively consider the actual operating characteristics of wind farm energy storage and effectively guide the energy storage configuration and construction planning of wind farms.

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

The prevalence of fraud activities in telecommunication industry has reached a critical point so that efficient algorithms to identify such activities are greatly needed. In this article, we propose a new fraud detection algorithm using Gaussian mixed model (GMM), a probabilistic model successfully used in speech recognition problem. The expectation maximization algorithm is used to estimate the parameter of the model such that the initial values of the algorithm is determined using the kernel method. Using data obtained from one of the leading telecommunication companies in Malaysia, we show that the proposed algorithm has successfully not only detected fraud calls as suspected by the company, but also to identify suspicious calls which can be candidates of fraud call. The proposed algorithm is easy to implement with a great potential to be extended to detect (billed/outgoing) fraud calls and hence reduces the lost incurred by the telecommunication companies.

In the distributed load frequency control (DLFC) scheme, the compromised areas by attackers may endanger the stable and reliable operation of the entire power system. A credibility‐based secure DLFC strategy is in this work proposed to sustain the stable operation of the power system under false data injection attacks. In this secure DLFC method, the gradient‐variation‐based credibility metrics are defined to detect potential attacked measurements. Then, the communication isolation decisions are made based on the adaptive thresholds. To compensate the impact of the communication isolation of misbehaving areas, predictions of future measurements by using the average gradient variation of stored healthy data are feeded back to the area controller. Simulation results of a four‐area power system show the proposed secure DLFC can identify and cut off the communication of the misbehaving areas and still maintain the stability of the power system.

In view of the large fluctuation of photovoltaic output power affected by different weather, accurate prediction of photovoltaic output power is particularly important for the safe and stable operation of power system. Firstly, the pelican optimization algorithm ( POA ) is improved in the following three aspects : adding Circle chaotic map to make the population evenly distributed, introducing mutation factor to expand the search range of pelican when approaching prey, adding adaptive weight and firefly disturbance to avoid falling into local optimum in the water surface flight stage ; then, in order to improve the prediction accuracy of BP algorithm, the improved pelican algorithm ( IPOA ) is used to optimize the weights and thresholds of BP neural network, and the IPOA-BP photovoltaic power prediction model is built to improve the accuracy of power prediction. Finally, this paper tests the prediction performance of IPOA-BP, POA-BP and basic BP power prediction models in sunny, cloudy and rainy days through experiments. The experimental results demonstrate that the IPOA-BP prediction model outperforms both the POA-BP and traditional BP neural network models under various weather conditions.

Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles

As power big data plays an increasingly important role in the operation, maintenance, and management of power systems, complex and covert false data attacks pose a serious threat to the safe and stable operation of the power system. This article first explores the characteristics of new power systems, and the challenges posed by false data attacks. The application of big data technology in power production optimization, energy consumption analysis, and user service improvement is then investigated. The article classifies typical attacks against the four stages of power big data systems in detail and analyzes the characteristics of the attack types. It comprehensively summarizes the attack detection technologies used in the four key stages of power big data, including state estimation, machine learning, and data-driven attack detection methods in the data collection stage; clock synchronization monitoring and defense strategies in the data transmission stage; data processing and analysis, data integrity verification and protection measures of blockchain technology in the third stage; and traffic supervision, statistics and elastic computing measures in the control and response stage. Finally, the limitations of attack detection mechanisms are proposed and discussed from three dimensions: research problems, existing solutions, and future research directions. It aims to provide useful references and inspiration for researchers in power big data security to promote technological progress in the safe and stable operation of power systems.

With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.

With the increasing penetration of wind power, how to accurately analyze characteristics of wind power becomes significant to the safe and stable operation of power system. This paper proposes a data-driven based wind power characteristic analysis and climbing identification method. Wind power climbing threshold is extracted firstly to construct climbing event dataset based on k-means clustering. 2D convolutional neural network-based climbing identification method is then proposed, with network parameters trained by transforming 1-dimensional wind power output records into a 2-dimensional matrix to identify future climbing events. Test results on practical wind farm show that the proposed method can effectively analyze characteristics of wind power, which has better climbing identification accuracy compared with traditional methods.

In today's world, with the rapid development of green and clean energy, the comparison of wind power in power network has been gradually improved. However, wind power has also brought new challenges to the stable operation of power system due to its inherent volatility and randomness. In this paper, the economic dispatching model of power system including wind power grid connection is studied. On the basis of stochastic expectation and chance-constrained programming, the specific economic dispatch model with wind power integration is given. The core of this paper is that we simulate the degree of wind power output fluctuation by setting different confidence levels. Simulation results show that a larger confidence interval can achieve better security, while a smaller confidence interval can achieve better economy.