Wind energy integration: Variability analysis and power system impact assessment

The progressive increase of renewable energy sources (RES) share during past decade strongly reflects on power system operating conditions. Romania is the South Eastern European country with the largest RES generating capacity. This paper proposes a novel method for assessing the impact on national power systems operation of integrating high shares of RES. Specifically, the method addresses first a variability analysis of RES production, based on historical records over 10 years for the Romanian power system. Wind energy is selected as the RES with the widest confidence interval, thus generating the largest power output fluctuations. Further, different simulation scenarios are defined in terms of variable wind energy share. The values obtained are integrated in a scaled simplified model, implemented in PSS/E. The impact of generating sets based on wind energy employment on voltage magnitude and transient time is assessed in fault conditions. Considering that the simulation scenarios defined comprise variable wind energy generation shares, this study emphasizes the impact of its dynamics on power systems. Simulation results highlight the negative influence of increasing wind energy share on the analyzed quantities. The outcomes of this research can suggest a particular analysis approach useful in studying power systems operation.

[1]  Brian Vad Mathiesen,et al.  Large-scale integration of wind power into the existing Chinese energy system , 2011 .

[2]  Faruk Ugranli,et al.  Comparison of single- and multiple-distributed generation concepts in terms of power loss, voltage profile, and line flows under uncertain scenarios , 2015 .

[3]  Rodrigo Palma-Behnke,et al.  A multi-service approach for planning the optimal mix of energy storage technologies in a fully-renewable power supply , 2018, Energy Conversion and Management.

[4]  Danny Pudjianto,et al.  Method for assessing impact of large‐scale wind power integration on reserves , 2008 .

[5]  James D. Weber,et al.  Generic Dynamic Models for Modeling Wind Power Plants and Other Renewable Technologies in Large-Scale Power System Studies , 2017, IEEE Transactions on Energy Conversion.

[6]  Piotr Wais,et al.  A review of Weibull functions in wind sector , 2017 .

[7]  Mohamed A. Farahat,et al.  Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies , 2019, Energy.

[8]  R.G. Harley,et al.  Effect of grid-connected DFIG wind turbines on power system transient stability , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[9]  N. Kiefer.  Economic Duration Data and Hazard Functions , 1988 .

[10]  Nayeem Rahmat Ullah,et al.  Effect of operational modes of a wind farm on the transient stability of nearby generators and on power oscillations: a Nordic grid study , 2008 .

[11]  Humberto Verdejo,et al.  Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile , 2017 .

[12]  B. Efron,et al.  Bootstrap confidence intervals , 1996 .

[13]  Prasenjit Basak,et al.  A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid , 2012 .

[14]  Mohammed Aggour,et al.  Steady State and Dynamic Analysis of Renewable Energy Integration into the Grid using PSS/E Software , 2017 .

[15]  V. Modi,et al.  Impact of deep wind power penetration on variability at load centers , 2019, Applied Energy.

[16]  Ersan Kabalci,et al.  Design and analysis of a hybrid renewable energy plant with solar and wind power , 2013 .

[17]  Imre M. Jánosi,et al.  Limitations of wind power availability over Europe: a conceptual study , 2008 .

[18]  Qian Zhang,et al.  Impacts of Integration of Wind Farms on Power System Transient Stability , 2018, Applied Sciences.

[19]  Andreas Diekmann,et al.  The Log-Logistic Rate Model , 1995 .

[20]  V. Katinas,et al.  Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania , 2017 .

[21]  Thomas Huld,et al.  Simulating European wind power generation applying statistical downscaling to reanalysis data , 2017 .

[22]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[23]  M. Thring.  World Energy Outlook , 1977 .

[24]  Brian Vad Mathiesen,et al.  Simulation versus Optimisation: Theoretical Positions in Energy System Modelling , 2017 .

[25]  O. P. Rahi,et al.  A comprehensive review of wind resource assessment , 2017 .

[26]  Dana-Alexandra Ciupageanu.  VARIABILITY ASSESSMENT OF RENEWABLE ENERGY SOURCES BASED ON POWER GENERATION RECORDINGS , 2018, 18th International Multidisciplinary Scientific GeoConference SGEM2018, Energy and Clean Technologies.

[27]  Yuan-Kang Wu,et al.  Impact on critical clearing time after integrating large-scale wind power into Taiwan power system , 2016 .

[28]  Carlos Matrán,et al.  Resampling schemes with low resampling intensity and their applications in testing hypotheses , 2009 .

[29]  Bryan W. Karney,et al.  System design and operation for integrating variable renewable energy resources through a comprehensive characterization framework , 2017 .

[30]  Paulina Jaramillo,et al.  A review of large-scale wind integration studies , 2015 .

[31]  Christian Pavese.  Wind energy literature survey no. 33 , 2014 .

[32]  Daren Yu,et al.  Overview of wind power intermittency: Impacts, measurements, and mitigation solutions , 2017 .

[33]  Gheorghe Lăzăroiu,et al.  Carbon dioxide emissions reduction by renewable energy employment in Romania , 2017, 2017 International Conference on Electromechanical and Power Systems (SIELMEN).

[34]  David Infield,et al.  A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe , 2017 .

[35]  Ramesh C. Bansal,et al.  Integration of renewable distributed generators into the distribution system: a review , 2016 .

[36]  Linda Barelli,et al.  Challenges in load balance due to renewable energy sources penetration: The possible role of energy storage technologies relative to the Italian case , 2015 .

[37]  Ana Estanqueiro,et al.  Impacts of large amounts of wind power on design and operation of power systems, results of IEA collaboration , 2008 .

[38]  Jinfu Liu,et al.  Analysis of wind power intermittency based on historical wind power data , 2018 .

[39]  Peter Wolfs,et al.  Potential challenges of integrating large-scale wind energy into the power grid–A review , 2013 .

[40]  Xiangyun Qing.  Statistical analysis of wind energy characteristics in Santiago island, Cape Verde , 2018 .

[41]  Sajjad Golshannavaz,et al.  Optimal wind energy penetration in power systems: An approach based on spatial distribution of wind speed , 2016 .

[42]  Linda Barelli,et al.  A micro-grid operation analysis for cost-effective battery energy storage and RES plants integration , 2016 .

[43]  Gheorghe Lazaroiu,et al.  Modeling and control of a low power wind turbine , 2018, 2018 International Conference on Development and Application Systems (DAS).

[44]  Nurulkamal Masseran.  Modeling the fluctuations of wind speed data by considering their mean and volatility effects , 2016 .

[45]  Masoud Aliakbar Golkar,et al.  Using Copulas for analysis of large datasets in renewable distributed generation: PV and wind power integration in Iran , 2010 .

[46]  Abderrazak Ouali,et al.  Voltage control of a variable speed wind turbine connected to an isolated load: Experimental study , 2012 .

[47]  Stavros A. Papathanassiou,et al.  A review of grid code technical requirements for wind farms , 2009 .