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6th Asia Conference on power and Electrical Engineering, ACPEE 2021

作者： Wang, Hao Wang, Hongtao Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China

ISBN: (纸本)9781728191591

In the process of power system res toration after a large power outage, the power support of wind farms is taken into account that it can speed up the restoration of the system. However, the uncertainty of wind farm output also increases the risk of secondary power outages. To solve this problem, this paper adopts the uncertain bi-level programming theory to establish a load recovery optimization model that takes into account the uncertainties of wind power and load, and the model converts it into load power and wind power forecasting errors for processing. Furthermore, the traditional deterministic power grid safe operation constraint is changed to a chance constraint in the form of confidence. In order to meet the requirements of online rapid decision-making, the normal distribution theory is used to transform the uncertainty model into its definite equivalence model, and CPLEX is used to solve it. Finally, an IEEE39-bus system with wind farms is taken as an example to verify the effectiveness of the proposed method. © 2021 IEEE.

关键词： Recovery

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Interval Probability Estimation of Wind Farm Cluster Ramp Events Based on Credal Network

Interval Probability Estimation of Wind Farm Cluster Ramp Ev...

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IEEE IAS Conference on Industrial and Commercial power system Asia (IEEE I and CPS Asia)

作者： Wang Xinyi Han Xueshan Yang Ming Yu Yixiao Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781665434980

Large-scale wind power integration makes the impact of wind power ramp events more impossible to ignore. Compared with single station prediction, cluster prediction can reflect the impact of power mutation on the power system more intuitively, and the prediction results are more conducive to the decision-making of dispatchers. Therefore, this paper proposed an imprecise probabilistic prediction method for wind farm cluster. Data dimensionality reduction was carried out through correlation analysis and principal component analysis to avoid problems such as excessive data dimension caused by too many input variables and the influence of calculation speed. The credal network (CN) was established to express the dependent relationship between wind farm cluster ramp events and evidence variables, and the conditional dependent relationship was statistically quantified by using the imprecise Dirichlet model (IDM). Finally, combined with meteorological information, the ramp events were classified and inferred in the form of probability intervals, and the prediction performance was evaluated by using evaluation indexes. In this paper, the validity of the method was verified by using the data of a wind farm cluster in Xinjiang.

关键词： wind power ramp event principal component analysis ramp prediction credal network interval probability estimation

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Short-Term Wind Generation Combined Forecast Considering Meteorological Similarity

Short-Term Wind Generation Combined Forecast Considering Met...

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IEEE IAS Conference on Industrial and Commercial power system Asia (IEEE I and CPS Asia)

作者： Liu, Yating Yang, Ming Yu, Yixiao Ding, Tingting Si, Zhiyuan Li, Menglin Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781665434980

High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.

关键词： BP neural network algorithm Gaussian transform particle swarm optimization algorithm XGBoost short-term wind power combined forecast

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Cascading Failure Screening Based on Gradient Boosting Decision Tree for HVDC Sending-End systems with High Wind power Penetration

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IEEE Transactions on power systems 2025年

作者： Liu, Tianhao Yan, Jiongcheng Liu, Yutian Chung, Chi Yung Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong

In LCC-HVDC sending-end AC systems, cascading failures combined with the dynamic response of wind turbines (WTs) can lead to HVDC commutation failures. The resulting transient voltage disturbances cause WT tripping in sending-end systems. Cascading failures that involve the interaction between WTs and HVDC significantly limit the wind power transmitted by HVDC systems. This paper proposes an online cascading failure screening method based on gradient boosting decision tree (GBDT) for HVDC sending-end systems with large-scale WTs. First, a confidence level-based WT tripping model is proposed for cascading failure risk assessment considering a typical cascading failure propagation pattern. Then, Monte Carlo tree search is improved using a contrastive pruning technique to generate evenly distributed samples of cascading failures offline. Dynamic insecure scenarios are quickly identified using an improved support vector machine. Finally, GBDT is utilized to screen for cascading failures online by predicting subsequent high-risk failures using operating features. A dynamic weighting technique is proposed for GBDT to improve the fault prediction accuracy. Simulation results of a modified New England test system and the Ningxia provincial power grid in western China demonstrate that the proposed method can quickly screen for cascading failures considering the dynamic interaction between WTs and HVDC. © 1969-2012 IEEE.

关键词： Risk assessment

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Small-Signal Modeling and Analysis of Delayed Microgrids under Distributed control

Small-Signal Modeling and Analysis of Delayed Microgrids und...

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2021 IEEE Sustainable power and Energy Conference, iSPEC 2021

作者： Yang, Haoran Ye, Hua Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China

ISBN: (纸本)9781665414395

The distributed secondary control normally relies on the communication network to exchange information, yet non-negligible communication delays are thus introduced at the same time. Under the influence of communication delays, the well-designed distributed secondary controllers will have poor performance or even failed, making it difficult to meet the requirements for safe and stable operation of microgrids. To study the delay-related stability problem, a delay-concerned small-signal dynamic model of a microgrid under distributed control is developed. On the basis of this model, low-frequency eigenvalue spectrums of the microgrid system are calculated and a delay-related stability analysis is performed. Time-domain simulations are presented to verify the effectiveness of the proposed method. © 2021 IEEE.

关键词： Time domain analysis

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An Emergency Load Shedding Optimization Algorithm Based on Differential Evolution Driven by Deep Transfer Learning

An Emergency Load Shedding Optimization Algorithm Based on D...

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2021 IEEE Sustainable power and Energy Conference, iSPEC 2021

作者： Chang, Yanzhao Gai, Chenhao Li, Changgang Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China

ISBN: (纸本)9781665414395

Emergency load shedding (ELS) optimization by the meta-heuristic algorithm is confronted with a heavy computing burden as time-domain simulation (TDS) is needed to evaluate individuals. In this paper, an ELS optimization algorithm based on differential evolution driven by deep transfer learning is proposed to decrease TDS times. First, an optimization model of ELS is established in a single scenario based on differential evolution. Then, a Deep Belief Network (DBN) model is deployed to evaluate load shedding schemes to speed up the optimization. Moreover, when scenarios are changed, transfer learning is utilized to update the DBN model, avoiding massive samples regeneration by TDS. Finally, the optimal load shedding scheme is rolling updated following scenarios change. A simplified provincial power grid verifies the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Optimization

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Low-Carbon dispatch Strategy for Virtual power Plant Based on Bagging Ensemble Learning Forecasting Method

Low-Carbon Dispatch Strategy for Virtual Power Plant Based o...

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IEEE Student Conference on Electric Machines and systems

作者： Guang Li Xin Dong Li Li Yue Yu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

Virtual power plants (VPPs) struggle with peak-shaving due to unreliable distributed generation (DG) data and the intermittent nature of wind and solar power, impacting carbon emissions. This paper proposes a low-carbon dispatch strategy for VPPs based on a Bagging ensemble forecasting model. Firstly, a VPP dispatch framework based on DG output and load forecasting is established, and a Bagging ensemble forecasting model with feature selection is proposed. Secondly, the control characteristics of flexible industrial, commercial, and residential loads are analyzed to establish a control cost model, which is then integrated with the balanced relationship between wind, photovoltaic, and energy storage, as well as the carbon emission mechanism, to formulate a low-carbon dispatch model. Finally, the simulation results show that the proposed strategy can improve the accuracy of DG output and load forecasting in complex feature environments while realizing the interaction between source-grid-load and reducing the generation and operation costs of the grid.

关键词： Analytical models Costs Carbon dioxide Predictive models Wind power generation Virtual power plants Forecasting Wind forecasting Bagging Load modeling

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Optimal Generator Start-up Sequence with Active Distribution Networks Considering Recovery Path 4

Optimal Generator Start-up Sequence with Active Distribution...

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4th IEEE International Conference on Electronics Technology, ICET 2021

作者： Zhai, Shibo Wang, Hongtao Shan, YueChen Zhang, Xiang Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China

ISBN: (纸本)9781728176734

The recovery process of power system after blackout can be divided into three stages: generator start-up, grid reconfiguration and load restoration. This paper focuses on generator start-up and proposes a new optimization model of generators start-up sequence (GSS). Recovery path constraints are formed considering line capacitance for avoiding node overvoltage and node importance for facilitating the next two recovery process. The participation of active distribution network is also considered in the model, and various constraints are formed according to characteristics of active distribution network. The GSS problem is transformed into MISOCP model. This paper verifies the effectiveness of recovery path constraints and the advantages of participation of distribution network in GSS problem in T39D33 case. © 2021 IEEE.

关键词： Recovery

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A Bi-level Approach to Transmission Network Planning Considering Operational Flexibility

A Bi-level Approach to Transmission Network Planning Conside...

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58th IEEE/IAS Industrial and Commercial power systems Technical Conference Asia (IEEE I and CPS Asia)

作者： Zhong, Wentao Cao, Jinjing Gai, Pengyu Liu, Jianan Wang, Mingqiang Binzhou Elect Co Binzhou Shandong Peoples R China Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (数字)9781665450669

ISBN: (纸本)9781665450669

With a large scale integration of fluctuating wind power, it is imperative to improve the operational flexibility in transmission network planning on the basis of considering system security and economy to achieve the goal of safe wind power consumption and reliable power supply. In this paper, a bi-level transmission network planning model considering operational flexibility is proposed to optimize the solution of transmission lines capacity and new thermal generator to be installed. The upper-level model minimizes the investment cost and the operation cost. The lower-level model calculates the lack of flexibility and loss of load of the system based on the line capacity and installed units from upper layer. Both upper and lower models can be transformed into mixed integer linear programming (MILP) problems. The effectiveness and validity of the proposed method are illustrated on the IEEE-RTS system.

关键词： operational flexibility bi-level model transmission network planning renewable energy

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Forecasting-Aided Voltage Estimation for Distribution Network Considering the Integrated Management of Virtual power Plants Platform

Forecasting-Aided Voltage Estimation for Distribution Networ...

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IEEE Student Conference on Electric Machines and systems

作者： Yujian Jia Zhao Liu Changtao Kan Yue Yu Changgang Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

As more distributed energy sources integrate into distribution networks, issues such as voltage fluctuations and reduced observability become increasingly prominent. Therefore, this paper proposes an algorithm for forecasting-aided state estimation (FASE) in virtual power plants' integrated operation management platform. Firstly, a Bidirectional Long Short-Term Memory (BiLSTM)-Extended Kalman Filter (EKF) algorithm for distribution network FASE is introduced. Secondly, the integrated management of virtual power plant platforms (IM-VPPP) enhances the observability of distribution networks. To evaluate the performance of the proposed method, simulations are conducted using the IEEE 33-bus standard distribution system. Results demonstrate significant advantages of the algorithm in terms of accuracy and robustness.

关键词： Voltage measurement Voltage fluctuations Noise Distribution networks Virtual power plants Robustness Kalman filters State estimation Observability Standards

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