On the Quantitatively Characterization of Intermittent Power Sources Uncertainty

This paper designs a statistical quantification towards the intermittent power uncertainty in power systems. A negative-exponential forecast uncertainty function is constructed to represent the relationship between the statistics of forecast error of a single intermittent power source and time advance. Subsequently, other kinds of statistical functions are proposed to characterize the statistical uncertainty of multiple intermittent power sources and all power sources, namely the sum statistical functions, the equivalent statistical functions, and the contour statistical functions. Based on a large amount of historical observations, these functions are employed to statistically quantify the forecast uncertainty of a single intermittent power source, multiple intermittent power sources as well as all power sources. Historical data sampled from real wind farms and solar sites demonstrates the effectiveness of the proposed method.

[1]  Ming Fu,et al.  A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search , 2015 .

[2]  Yiqi Chu,et al.  A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction , 2016 .

[3]  Lei Chen,et al.  Stability mechanism and emergency control of power system with wind power integration , 2017 .

[4]  Jin Lin,et al.  A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch , 2013, IEEE Transactions on Power Systems.

[5]  Jan Kühnert.  Development of a photovoltaic power prediction system for forecast horizons of several hours , 2016 .

[6]  Yang Wang,et al.  A two-stage method for assessment of voltage stability in power system with renewable energy , 2013, 2013 IEEE Electrical Power & Energy Conference.

[7]  Xu Fei.  Research on statistical modeling of large-scale wind farms output fluctuations in different spacial and temporal scales , 2012 .

[8]  N.D. Hatziargyriou,et al.  An Advanced Statistical Method for Wind Power Forecasting , 2007, IEEE Transactions on Power Systems.

[9]  Xiaodong Liang,et al.  Probabilistic reliability evaluation for power systems with high penetration of renewable power generation , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[10]  Deger Saygin,et al.  The Implications for Renewable Energy Innovation of Doubling the Share of Renewables in the Global Energy Mix between 2010 and 2030 , 2015 .

[11]  Hongbin Sun,et al.  Multi-time interval power system state estimation incorporating phasor measurements , 2015, 2015 IEEE Power & Energy Society General Meeting.

[12]  Tsung-Ying Lee.  Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO , 2007, IEEE Transactions on Power Systems.

[13]  Lei Zhang,et al.  Strategy Design of Hybrid Energy Storage System for Smoothing Wind Power Fluctuations , 2016 .

[14]  Michela Longo,et al.  Improvement of Wind Energy Production through HVDC Systems , 2017 .

[15]  Jari Miettinen,et al.  Wind power forecasting accuracy and uncertainty in Finland , 2013 .

[16]  Mahmoud-Reza Haghifam,et al.  Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages , 2017 .

[17]  P. Denholm,et al.  Renewable Electricity Futures for the United States , 2014, IEEE Transactions on Sustainable Energy.

[18]  Mathieu David,et al.  Solar Forecasting in a Challenging Insular Context , 2016 .

[19]  Kai Heussen,et al.  Unified System-Level Modeling of Intermittent Renewable Energy Sources and Energy Storage for Power System Operation , 2012, IEEE Systems Journal.

[20]  Eduard Muljadi,et al.  Synchrophasor-Based Auxiliary Controller to Enhance the Voltage Stability of a Distribution System With High Renewable Energy Penetration , 2015, IEEE Transactions on Smart Grid.

[21]  Hui Liu,et al.  Research on the periodicity of wind power based on the maximum entropy spectrum estimation , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[22]  Xu Andy Sun,et al.  Multistage Robust Unit Commitment With Dynamic Uncertainty Sets and Energy Storage , 2016, IEEE Transactions on Power Systems.

[23]  Hideharu Sugihara,et al.  Evaluation Method for Real-Time Dynamic Line Ratings Based on Line Current Variation Model for Representing Forecast Error of Intermittent Renewable Generation , 2017 .

[24]  Jin Zhong,et al.  A Generation Adjustment Methodology Considering Fluctuations of Loads and Renewable Energy Sources , 2016, IEEE Transactions on Power Systems.