Power System Voltage Stability Evaluation Considering Renewable Energy With Correlated Variabilities

Xu, Xiaoyuan; Yan, Zheng; Shahidehpour, Mohammad; Wang, Han; Chen, Sijie

doi:10.1109/tpwrs.2017.2784812

Cited by 104 publications

(51 citation statements)

References 27 publications

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“…Many literatures have reported impressive experimental results of various machine learning algorithms with applications in oscillation detection, voltage stability, fault or transient detection and restoration, islanding detection and restoration, postevent analysis, etc. [80][81][82][83]. With the emergence of the Big Data analytics and smart grid technology, the above-mentioned monitoring and detection methods have been greatly improved, and an increasing number of novel approaches are being studied.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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“…It was reported in [26] that the multivariate Gaussian copula is the most effective and accurate for representation of the correlations between uncertainties involved in modelling load demand, wind generation and PV generation for power system small-disturbance stability analysis. In [28], the Gaussian T copula has been employed for voltage stability analysis. The multivariate Gaussian copula is applied here first to uncertainty modelling for transient stability analysis and then for voltage and small disturbance stability analysis.…”

mentioning

confidence: 99%

Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

Hasan

Milanović

2019

IEEE Trans. Power Syst.

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The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviours of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index (TSI) have been chosen as the relevant stability indices for voltage stability, small-disturbance stability and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behaviour due to its correlation with other influential parameters.

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“…To make optimal decisions in the presence of uncertainties and tackle the uncertainties in electricity markets, two-stage stochastic programming is widely used [1,5,11,[15][16][17][18]. First stage decisions, which are also known as ''here-and-now decisions'', are made before the wind generation is realized.…”

Section: Turkish Electricity Marketmentioning

confidence: 99%

“…2) When generation company's real-time generation is less than the amount of DA power schedule, generation deficit is bought from balancing market with imbalance down price q À t as in (16).…”

Section: ) When Generation Company's Real-time Generation Ismentioning

confidence: 99%

CVaR-based stochastic wind-thermal generation coordination for Turkish electricity market

Aydoğdu

Tör

Güven

2018

J. Mod. Power Syst. Clean Energy

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Uncertainties in wind power forecast, day-ahead and imbalance prices for the next day possess a great deal of risk for the profit of generation companies participating in a day-ahead electricity market. Generation companies are exposed to imbalance penalties in the balancing market for unordered mismatches between associated day-ahead power schedule and real-time generation. Coordination of wind and thermal power plants alleviates the risks raised from wind uncertainties. This paper proposes a novel optimal coordination strategy by balancing wind power forecast deviations with thermal units in the Turkish dayahead electricity market. The main focus of this study is to provide an optimal trade-off between the expected profit and the risk under wind uncertainty through conditional value at risk (CVaR) methodology. Coordination problem is formulated as a two-stage mixed-integer stochastic programming problem, where scenario-based wind power approach is used to handle the stochasticity of the wind power. Dynamic programming approach is utilized to attain the commitment status of thermal units. Profitability of the coordination with different day-ahead bidding strategies and trade-off between expected profit and CVaR are examined with comparative scenario studies.

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Power System Voltage Stability Evaluation Considering Renewable Energy With Correlated Variabilities

Cited by 104 publications

References 27 publications

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

CVaR-based stochastic wind-thermal generation coordination for Turkish electricity market

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