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IEEE General Meeting power& Energy Society

作者： Shuai He Ming Yang Donghua Yang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The development of distributed photovoltaics (PV) has led to a sharp increase in the number of PV prosumers, forming a large-scale PV prosumer cluster. Energy sharing is an effective method to enhance the economics of PV prosumer clusters and promote the local consumption of distributed PV. Large-scale PV prosumer clusters will increase the computational cost of energy sharing. Meanwhile, the uncertainty of PV is also stronger and needs to be considered. So, an auction-based energy sharing model for large-scale PV prosumer clusters in uncertain environments is proposed. First, an energy sharing market framework based on a call auction mechanism is established. The clearing goal of the energy sharing service provider is to maximize social welfare. Secondly, proposes an energy sharing decision model for different types of prosumers with conditional value at risk (CVaR) and expected values as the objectives. Thirdly, consider the decision-making preferences of different types of prosumers and analyze the impact of the proportion of different types of prosumers on energy sharing. Finally, a distribution system containing 20 prosumers is used to verify the effectiveness and feasibility of the proposed method.

关键词： Economics Photovoltaic systems Uncertainty Electricity Computational modeling Decision making Computational efficiency

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Enhanced Robust Scheduling Approach Considering Dynamic Flexibility Support from District Heating systems

Enhanced Robust Scheduling Approach Considering Dynamic Flex...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Wenzhang Zheng Peng Li Ming Yang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Apart from flexibility resources, e.g., thermal generators and battery energy storage system in electrical power system, dynamic heating networks and flexible heat loads in district heating systems could also provide considerable reserve capacity support for electrical power system. To accurately quantify the reserve support capacity, two operating periods of district heating systems, i.e., reserve capacity support providing period (RCPP) and reserve capacity support recovery period are designed in the paper. A novel dynamic flexibility support interaction mechanism among multiple district heating systems is then presented, and a corresponding scheduling strategy is proposed to coordinate electrical power system and district heating system (DHS), where the concept of allowable regulating capacity exchange interval is introduced to make full use of reserve capacity in DHS. And then, an enhanced robust scheduling model is developed for electrical power systems, where both of the wind power allowable region and operational costs are involved. Case studies demonstrate the proposed method effectively tackles the flexibility coordination between electrical power system and multiple district heating systems.

关键词： Costs Battery energy storage system District heating power system dynamics Wind power generation Dynamic scheduling Generators Thermal loading Heat recovery Resistance heating

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A Distributionally Robust Optimal Allocation Method of Energy Storage for Resilience Enhancement of Distribution Network

A Distributionally Robust Optimal Allocation Method of Energ...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Cao, Kuan Liu, Yutian Wang, Chunyi Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China State Grid Shandong Electric Power Company Development Planning Department Jinan China

ISBN: (纸本)9781665471640

To solve the problem of active power imbalance in distribution network with high penetrations of photovoltaic (PV) under extreme weather, the paper proposes a distributionally robust optimal allocation method of energy storage considering extreme scenarios. Firstly, considering that the effect of energy storage on enhancing the resilience of distribution network is strongly related to its location, resilience indexes based on node evaluation are established to select the installation sites of energy storage. Secondly, in view of the correlation and randomness of PV output and load, the typical scenarios with the empirical probability distribution are generated based on Frank Copula joint probability distribution function. Then, Kullback-Leibler divergence is utilized to characterize the probability distribution ambiguity set of the typical scenarios. Based on the ambiguity set, a two-stage distributionally robust optimization model for energy storage capacity is established to minimize the cost of energy storage investment and distribution network operation. For further improving the resilience, an operational resilience index as a chance constraint considering extreme scenarios is embedded into the model. Finally, the case study shows that the proposed method can effectively improve PV accommodation capacity of distribution network and reduce load loss in extreme scenarios. © 2023 IEEE.

关键词： copula function distributionally robust optimization energy storage allocation Kullback-Leibler divergence resilience

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power Ramp Extremes Two Stage Forecast based on Auto Encoder and Bi-LSTM

Power Ramp Extremes Two Stage Forecast based on Auto Encoder...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Chuanqi Wang Ming Yang Yixiao Yu Menglin Li Shuaijie Shan Yuanhe Zhang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

power ramp is one of the stages with the largest error of renewable energy power forecast. The deviation in the forecast of the time and values of the start-end points will lead to a significant forecast error to the whole ramp process. Therefore, accurate power ramp forecast can effectively improve the renewable energy power forecast accuracy. In this paper, SDAE is applied on encoding the meteorological features of the power ramp process. Then, the coded part is then spliced with a classifier and fine-tune, promote the output with clear category identity. Finally, the coding is fed into a bidirectional neural network to forecast the power at the start-end points. In case study, the proposed method in this paper demonstrated an improvement in the accuracy of extreme forecast of start-end points power by 4.94%in comparison to the power forecast based ramp recognition method.

关键词： Degradation Renewable energy sources Accuracy Neural networks Asia Weather forecasting Encoding Timing

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Energy Storage Configuration Method for Distribution Network Resilience Enhancement Using Two-Stage Game Optimization 8

Energy Storage Configuration Method for Distribution Network...

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8th International Conference on power and Renewable Energy, ICPRE 2023

作者： Liu, Shuai Li, Yuwen Zhou, Heng Cao, Kuan Liu, Yutian State Grid Weihai Power Supply Company Development Planning Department Weihai China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China

ISBN: (纸本)9798350328813

In order to cope with the impact of extreme weather on distribution network, a two-stage multi-objective game optimization-based method of energy storage configuration for distribution network resilience enhancement is proposed in this paper. First, typical scenarios are generated based on N-k contingency as well as distributed photovoltaic (DPV) output characteristics, and evaluation indexes of resilience and economy are established. Then a two-stage multi-objective optimal configuration model is proposed, considering the operating state and resilience demand of distribution network before and after the power supply transfer. Finally, the model is solved by multi-objective particle swarm (MOPSO) and Nash game. Simulation results on the modified IEEE33-node model demonstrate that the proposed method can achieve good equilibrium solution between resilience and economy. © 2023 IEEE.

关键词： MOPSO Nash game power supply transfer resilience two-stage optimal configuration

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A Novel Current Reference Transformation-Based Positive and Negative Sequence Rotor Current control Method of DFIGs

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第2期16卷 1283-1300页

作者： Gao, Xuesong Qin, Shiyao Sun, Xianzhuo Wang, Zhihao Cui, Rongde Xu, Shuai Ding, Lei Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China China Elect Power Res Inst Natl Key Lab Renewable Energy Grid Integrat Beijing 100192 Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy Dept Elect & Elect Engn Hong Kong 999077 Peoples R China

The existing rotor current control methods, despite achieving simultaneous control on the positive and negative sequence rotor currents for the doubly-fed induction generator (DFIG)-based wind turbine, are still facing challenges. Specifically, some works introduce the sequence current decomposition into the classical control structure, which can deteriorate the dynamic performance. While others with high-order regulator embedded into the classical control structure can increase the risk of instability. To this end, this paper proposes a novel current reference transformation-based positive and negative sequence rotor current control method. Firstly, the negative sequence response of the DFIG under the classical single dq-PI rotor current control method is studied, pointing out its satisfactory dynamic performance but poor steady-state performance. Based on which, a transformation formula for the negative sequence rotor current reference is analytically derived to compensate for the steady-state performance. The corresponding analysis indicates that negative sequence rotor current static errors from parameter deviations can be well limited. Comparative simulations illustrated an improved dynamic performance and stability of the DFIG rotor current control with the proposed method. The experimental test of a prototype DFIG system has also been conducted to verify the feasibility of the proposed method in practical implementation.

关键词： Rotors Doubly fed induction generators Current control Regulators Voltage control Stators Vectors control systems power system stability Angular velocity Current reference transformation asymmetrical fault doubly-fed induction generator positive and negative sequence rotor current control

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Hybrid Solar Forecasting Method Using Satellite Visible Images and Modified Convolutional Neural Networks

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第1期57卷 5-16页

作者： Si, Zhiyuan Yu, Yixiao Yang, Ming Li, Peng Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250000 Peoples R China

With the increase of solar photovoltaic (PV) permeability in power systems, high-precision PV power forecasting is critical to secure the economic operation of the smart grid. Solar irradiance is the most important factor affecting PV power. Accurately estimating the amount of solar irradiance leads to better production estimates from solar panels. Meanwhile, satellite images provide information on current and future cloud coverage, and are potentially useful in inferring solar irradiance. To accurately capture the effect of cloud on irradiance, this article develops a real-time mapping model between satellite image and solar irradiance. Based on the mapping model, a new ultrashort-term global horizontal irradiance (GHI) forecasting method combining satellite visible images and meteorological information is proposed. First, a modified method is utilized for reducing the influence of the solar zenith angle on pixel intensity and extracting the features in the image processing stage. Then, the cloud cover factors are extracted from satellite visible images by using the modified convolutional neural network. After that, the GHI forecasting model is developed, which is based on the combined use of meteorological information and cloud cover factors. Meanwhile, a cloud motion forecasting method using predicted wind speeds is developed for discussion. The better performance of the proposed method is demonstrated by comparing it with several benchmark models.

关键词： Satellites Clouds Forecasting Predictive models Image processing Wind forecasting Convolutional neural networks (CNNs) forecasting hybrid methods image processing irradiance satellite images

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A Generalized Wind Turbine Anomaly Detection Method Based on Combined Probability Estimation Model

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Journal of Modern power systems and Clean Energy 2023年第4期11卷 1136-1148页

作者： Xiangjun Zeng Ming Yang Chen Feng Yaohua Tang Key Laboratory of Power System Intelligent Dispatch and Control Shandong UniversityJinan 250061Chinaand X.Zeng is also with the College of Electrical Engineering&New EnergyChina Three Gorges UniversityYichang 443002China Rundian Energy Science and Technology Co. Ltd.Zhengzhou 450046China

Anomaly detection based on the data collected from the supervisory control and data acquisition(SCADA)system is crucial to reduce the failure rate of wind turbines(WTs).The difficulty of this kind of methods is to dynamically identify the threshold for anomaly detection under changing operating *** this paper,a generalized WT anomaly detection method based on the combined probability estimation model(CPEM)is *** CPEM can estimate the conditional probability density function(PDF)of the target variable given changing *** generalization and accuracy are better than those of the independent probability estimation model because it combines the advantages of various kinds of probability estimation models through linear *** using the CPEM,the normal operating bounds under different operating conditions can be obtained,which dynamically form the thresholds for anomaly ***,with respect to the thresholds,hypothesis testing(HT)is adopted to identify the anomaly by inspecting whether the observations exceed the thresholds at a given significance level,providing sound mathematical support for anomaly detection and making the detection results more *** effectiveness of the proposed method is tested by using the actual data of WTs with known *** results illustrate that the proposed method can detect the abnormal operating state of the gearbox and generator much more early than the system fault alarm.

关键词： Anomaly detection combined probability estimation generalization hypothesis testing(HT)

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An SVM-Based Anomaly Detection Method for power system Security Analysis Using Particle Swarm Optimization and t-SNE for High-Dimensional Data Classification

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PROCESSES 2025年第2期13卷 549-549页

作者： Tao, Ye Yan, Jiongcheng Niu, Enquan Zhai, Pengming Zhang, Shuolin Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Shandong Luruan Digital Technol Co Ltd Jinan 250101 Peoples R China Wenshan Power Supply Bur Yunnan Power Grid Co Ltd Wenshan 663000 Peoples R China

Research on the detection and identification of anomalies in electric power systems is crucial for ensuring their secure and stable operation. Anomaly detection models based on Support Vector Machines (SVMs) effectively process high-dimensional data while maintaining strong generalization capabilities. However, the performance of SVMs significantly depends on the choice of parameters, where improper parameter settings can lead to overfitting or underfitting, consequently decreasing the accuracy of anomaly detection. Furthermore, the dimensions of anomaly data in electric power systems are often unknown, making it difficult for existing methods to maintain a high precision in multidimensional data detection, and the segmentation of such data lacks intuitive display. In response, this article proposes an improved SVM model for electric power system anomaly detection, enhanced by parameter optimization algorithms, alongside a method for nonlinear dimension reduction and visualization using t-Distributed Stochastic Neighbor Embedding (t-SNE). Initially, traditional SVM parameters are optimized using the following four algorithms: Grid Search (GS), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Artificial Bee Colony (ABC) algorithms, in order to establish the optimized SVM model for electric power system anomaly detection. Finally, the effectiveness of the proposed method is verified through simulations. The simulation results indicate that, in the IEEE-14 node system case study, the accuracy for normal data reaches 97.58%, the accuracy for load step change detection reaches 99.52%, the accuracy for bad data detection reaches 99.92%, and the accuracy under fault conditions reaches 100%.

关键词： anomaly data detection SVM particle swarm optimization parameter optimization t-SNE

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Stochastic MPC based double-time-scale voltage regulation for unbalanced distribution networks with distributed generators

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2024年 155卷

作者： Song, Guocheng Wu, Qiuwei Tan, Jin Jiao, Wenshu Chen, Jian Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China

With the increasing penetration of renewable energy resources, the uncertainty of renewable energy resources output introduces challenges to voltage control in distribution networks (DNs). To solve these problems, this paper proposes a double-time-scale voltage control scheme for unbalanced DNs based on stochastic model predictive control (SMPC) to coordinate multiple voltage control devices. In fast-time-scale control, the active and reactive power outputs of distributed generators (DGs) are optimized to minimize voltage deviation, active power curtailment and reactive power fluctuation. In the slow-time-scale control, optimal tap changes of on-load tap changers (OLTC), step voltage regulators (SVR), and switched capacitor banks (CBs) are calculated to reduce voltage deviation and tap operations. Additionally, in the fast-time-scale control, the SMPC-based scheme adopts a scenario generation approach to represent the uncertainty of DG output. Considering the temporal correlation of the DG output, different prediction scenarios and scenario probabilities are obtained by the empirical cumulative distribution function fitted by the historical measurement data. In this paper, the effectiveness of the proposed double-time-scale voltage control scheme is verified by the unbalanced IEEE123 node system. The case study demonstrates that the proposed voltage scheme can effectively keep the three-phase voltages in unbalanced DN within the secure operation range and achieve better voltage profiles.

关键词： Distributed generator Unbalanced distribution network Stochastic model predictive control Voltage control

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