Review of wind power scenario generation methods for optimal operation of renewable energy systems
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Bo Chen | Jinghua Li | Jiasheng Zhou | Jinghua Li | Jiasheng Zhou | Bo Chen
[1] Michal Kaut,et al. A Heuristic for Moment-Matching Scenario Generation , 2003, Comput. Optim. Appl..
[2] Mohammad Shahidehpour,et al. Optimal Stochastic Operation of Integrated Low-Carbon Electric Power, Natural Gas, and Heat Delivery System , 2018, IEEE Transactions on Sustainable Energy.
[3] Taher Niknam,et al. Self-Scheduling Approach to Coordinating Wind Power Producers With Energy Storage and Demand Response , 2020, IEEE Transactions on Sustainable Energy.
[4] Georg Ch. Pflug,et al. Dynamic generation of scenario trees , 2015, Computational Optimization and Applications.
[5] M. Sami Fadali,et al. Smart Scheduling and Cost-Benefit Analysis of Grid-Enabled Electric Vehicles for Wind Power Integration , 2014, IEEE Transactions on Smart Grid.
[6] Alexandre Street,et al. Generating Joint Scenarios for Renewable Generation: The Case for Non-Gaussian Models With Time-Varying Parameters , 2018, IEEE Transactions on Power Systems.
[7] Zuyi Li,et al. Risk-Constrained Bidding Strategy With Stochastic Unit Commitment , 2007, IEEE Transactions on Power Systems.
[8] Jie Zhang,et al. Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method , 2015, IEEE Transactions on Sustainable Energy.
[9] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .
[10] Qiang Yang,et al. Scenario-based investment planning of isolated multi-energy microgrids considering electricity, heating and cooling demand , 2019, Applied Energy.
[11] Pouria Maghouli,et al. Transmission Expansion and Reactive Power Planning Considering Wind Energy Investment Using A Linearized AC Model , 2019, Journal of Electrical Engineering & Technology.
[12] Zita Vale,et al. Generation of realistic scenarios for multi-agent simulation of electricity markets , 2016 .
[13] Kit Po Wong,et al. Optimal Prediction Intervals of Wind Power Generation , 2014, IEEE Transactions on Power Systems.
[14] Jing Xiong,et al. Two-Stage Compensation Algorithm for Dynamic Economic Dispatching Considering Copula Correlation of Multiwind Farms Generation , 2017, IEEE Transactions on Sustainable Energy.
[15] Leonardo H. Macedo,et al. A strategy for transmission network expansion planning considering multiple generation scenarios , 2019 .
[16] Xueqian Fu,et al. Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric , 2017 .
[17] Yijia Cao,et al. Impact of uncertainty and correlation on operation of micro-integrated energy system , 2019, International Journal of Electrical Power & Energy Systems.
[18] Qian Ai,et al. Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network , 2020 .
[19] Li Yang,et al. Scenario tree generation approaches using K-means and LP moment matching methods , 2012, J. Comput. Appl. Math..
[20] Le Xie,et al. Scenario-Based Economic Dispatch With Uncertain Demand Response , 2017, IEEE Transactions on Smart Grid.
[21] G. Papaefthymiou,et al. MCMC for Wind Power Simulation , 2008, IEEE Transactions on Energy Conversion.
[22] Miguel A. Prósper,et al. Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations , 2017, Renewable Energy.
[23] Peng Li,et al. Optimal operation modes of photovoltaic-battery energy storage system based power plants considering typical scenarios , 2017 .
[24] Nikolaos G. Paterakis,et al. A Class-Driven Approach Based on Long Short-Term Memory Networks for Electricity Price Scenario Generation and Reduction , 2020, IEEE Transactions on Power Systems.
[25] Sanjay Mehrotra,et al. Generating Moment Matching Scenarios Using Optimization Techniques , 2013, SIAM J. Optim..
[26] Yang Lei,et al. Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy , 2020, Applied Energy.
[27] Xue Yushen. A Review on Impacts of Wind Power Uncertainties on Power Systems , 2014 .
[28] M. Shahidehpour,et al. Security-Constrained Unit Commitment With Volatile Wind Power Generation , 2008, IEEE Transactions on Power Systems.
[29] Antonio J. Conejo,et al. A methodology to generate statistically dependent wind speed scenarios , 2010 .
[30] De Zhi Chen,et al. A Wind Farm Capacity Credibility Calculation Method Based on Parabola , 2014 .
[31] Michal Kaut,et al. Evaluation of scenario-generation methods for stochastic programming , 2007 .
[32] Yi Chai,et al. Scenario Generation for Wind Power Using Improved Generative Adversarial Networks , 2018, IEEE Access.
[33] Xi-Yuan Ma,et al. Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability , 2013, IEEE Transactions on Sustainable Energy.
[34] Qi Li,et al. A scenario generation method based on the mixture vine copula and its application in the power system with wind/hydrogen production , 2019, International Journal of Hydrogen Energy.
[35] Peter B. Luh,et al. Grid Integration of Distributed Wind Generation: Hybrid Markovian and Interval Unit Commitment , 2015, IEEE Transactions on Smart Grid.
[36] P Pinson,et al. Conditional Prediction Intervals of Wind Power Generation , 2010, IEEE Transactions on Power Systems.
[37] N. D. Hatziargyriou,et al. Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks , 2012, IEEE Transactions on Power Systems.
[38] Arman Alahyari,et al. A hybrid storage-wind virtual power plant (VPP) participation in the electricity markets: A self-scheduling optimization considering price, renewable generation, and electric vehicles uncertainties , 2019, Journal of Energy Storage.
[39] Robin Girard,et al. Evaluating the quality of scenarios of short-term wind power generation , 2012 .
[40] P. Jirutitijaroen,et al. Latin Hypercube Sampling Techniques for Power Systems Reliability Analysis With Renewable Energy Sources , 2011, IEEE Transactions on Power Systems.
[41] Shijie Cheng,et al. Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling , 2013, IEEE Transactions on Sustainable Energy.
[42] Jinghua Li,et al. Combination of moment-matching, Cholesky and clustering methods to approximate discrete probability distribution of multiple wind farms , 2016 .
[43] Zhaohong Bie,et al. Scenario generation and reduction methods for power flow examination of transmission expansion planning , 2017, 2017 IEEE 7th International Conference on Power and Energy Systems (ICPES).
[44] Guzmán Díaz,et al. Wind power scenario generation through state-space specifications for uncertainty analysis of wind power plants , 2016 .
[45] Guangyi Liu,et al. Reliability based power systems planning and operation with wind power integration: A review to models, algorithms and applications , 2014 .
[46] Chongqing Kang,et al. Scenario Map Based Stochastic Unit Commitment , 2018, IEEE Transactions on Power Systems.
[47] Raik Becker,et al. Generation of Time-Coupled Wind Power Infeed Scenarios Using Pair-Copula Construction , 2018, IEEE Transactions on Sustainable Energy.
[48] Paulo Rotela Junior,et al. Wind power generation: An impact analysis of incentive strategies for cleaner energy provision in Brazil , 2016 .
[49] Mehdi Ehsan,et al. A scenario-based planning framework for energy storage systems with the main goal of mitigating wind curtailment issue , 2019, International Journal of Electrical Power & Energy Systems.
[50] Jie Zhang,et al. Probabilistic wind power ramp forecasting based on a scenario generation method , 2017, 2017 IEEE Power & Energy Society General Meeting.
[51] René Henrion,et al. Discrepancy distances and scenario reduction in two-stage stochastic mixed-integer programming , 2008 .
[52] Jian Xu,et al. Efficient scenario generation of multiple renewable power plants considering spatial and temporal correlations , 2018, Applied Energy.
[53] Yongqian Liu,et al. Reviews on uncertainty analysis of wind power forecasting , 2015 .
[54] M. O'Malley,et al. Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland , 2011, IEEE Transactions on Power Systems.
[55] Piet M. T. Broersen,et al. Finite sample properties of ARMA order selection , 2004, IEEE Transactions on Instrumentation and Measurement.
[56] Suvrajeet Sen,et al. The Scenario Generation Algorithm for Multistage Stochastic Linear Programming , 2005, Math. Oper. Res..
[57] Zuyi Li,et al. Comparison of Scenario-Based and Interval Optimization Approaches to Stochastic SCUC , 2012, IEEE Transactions on Power Systems.
[58] Hua Wei,et al. A Scenario Optimal Reduction Method for Wind Power Time Series , 2016, IEEE Transactions on Power Systems.
[59] Jinyu Wen,et al. Generating wind power time series based on its persistence and variation characteristics , 2014, Science China Technological Sciences.
[60] Abdel Lisser,et al. Partial sample average approximation method for chance constrained problems , 2018, Optimization Letters.
[61] J.H. Zhang,et al. Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition , 2009, IEEE Transactions on Power Systems.
[62] Benjamin F. Hobbs,et al. Comparing scenario reduction methods for stochastic transmission planning , 2019 .
[63] Mousumi Basu,et al. Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources , 2020 .
[64] Li Li,et al. Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach , 2019, Applied Energy.
[65] R. Buizza,et al. Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models , 2009, IEEE Transactions on Energy Conversion.
[66] A. Papavasiliou,et al. Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework , 2011, IEEE Transactions on Power Systems.
[67] Goran Strbac,et al. An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources , 2018 .
[68] John Boland,et al. Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models , 2019, Applied Energy.
[69] Xiaoming Zha,et al. A combined multivariate model for wind power prediction , 2017 .
[70] Seyedmostafa Hashemi,et al. A Scenario-Based Approach for Energy Storage Capacity Determination in LV Grids With High PV Penetration , 2014, IEEE Transactions on Smart Grid.
[71] Taher Niknam,et al. Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties , 2013 .
[72] Ali Ehsan,et al. Scenario-based planning of active distribution systems under uncertainties of renewable generation and electricity demand , 2019, CSEE Journal of Power and Energy Systems.
[73] Paresh Date,et al. An algorithm for moment-matching scenario generation with application to financial portfolio optimisation , 2015, Eur. J. Oper. Res..
[74] T. Y. Ji,et al. Quasi-Monte Carlo Based Probabilistic Optimal Power Flow Considering the Correlation of Wind Speeds Using Copula Function , 2018, IEEE Transactions on Power Systems.
[75] Liu Ye,et al. A scenario-based robust transmission network expansion planning method for consideration of wind power uncertainties , 2016 .
[76] Chenxu Wang,et al. A scenario-based analytical method for probabilistic load flow analysis , 2020 .
[77] W.L. Kling,et al. Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.
[78] Ali Elkamel,et al. Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology , 2020 .
[79] Chaitanya Swamy,et al. Sampling-Based Approximation Algorithms for Multistage Stochastic Optimization , 2012, SIAM J. Comput..
[80] David L. Woodruff,et al. Multi-stage scenario generation by the combined moment matching and scenario reduction method , 2014, Oper. Res. Lett..
[81] Goran Strbac,et al. A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration , 2018, Applied Energy.
[82] M. Sklar. Fonctions de repartition a n dimensions et leurs marges , 1959 .
[83] Zhao Xu,et al. Direct Interval Forecasting of Wind Power , 2013, IEEE Transactions on Power Systems.
[84] Behnam Mohammadi-Ivatloo,et al. Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment , 2020 .
[85] Jie Zhang,et al. A clustering-based scenario generation framework for power market simulation with wind integration , 2020 .
[86] Ponnuthurai N. Suganthan,et al. Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach , 2019, Appl. Soft Comput..
[87] Yang Xiyun,et al. Wind power probability interval prediction based on Bootstrap quantile regression method , 2017, 2017 Chinese Automation Congress (CAC).
[88] Yun Zhang,et al. Asymptotic Mean and Variance of Gini Correlation Under Contaminated Gaussian Model , 2016, IEEE Access.
[89] Kit Po Wong,et al. Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.
[90] Mark Z. Jacobson,et al. A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables. , 2011 .
[91] Duehee Lee,et al. Load and Wind Power Scenario Generation Through the Generalized Dynamic Factor Model , 2017, IEEE Transactions on Power Systems.
[92] Hassan Barati,et al. Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response , 2019, Energy.
[93] Vladimiro Miranda,et al. Finding representative wind power scenarios and their probabilities for stochastic models , 2011, 2011 16th International Conference on Intelligent System Applications to Power Systems.
[94] Daniel Kirschen,et al. Model-Free Renewable Scenario Generation Using Generative Adversarial Networks , 2017, IEEE Transactions on Power Systems.
[95] Manish Kumar Goyal,et al. Evaluation of Rule and Decision Tree Induction Algorithms for Generating Climate Change Scenarios for Temperature and Pan Evaporation on a Lake Basin , 2014 .
[96] Jian Xu,et al. Lagrangian Relaxation With Incremental Proximal Method for Economic Dispatch With Large Numbers of Wind Power Scenarios , 2019, IEEE Transactions on Power Systems.
[97] Joao P. S. Catalao,et al. ANN-based scenario generation methodology for stochastic variables of electric power systems , 2016 .
[98] Guangzhong Dong,et al. Sequential Monte Carlo Filter for State-of-Charge Estimation of Lithium-Ion Batteries Based on Auto Regressive Exogenous Model , 2019, IEEE Transactions on Industrial Electronics.
[99] Tao Shi,et al. Research on the Optimal Configuration of Regional Integrated Energy System Based on Production Simulation , 2020 .
[100] Saeed Lotfifard,et al. Spatiotemporal Modeling of Wind Generation for Optimal Energy Storage Sizing , 2015, IEEE Transactions on Sustainable Energy.
[101] Birgitte Bak-Jensen,et al. ARIMA-Based Time Series Model of Stochastic Wind Power Generation , 2010, IEEE Transactions on Power Systems.
[102] Mahdi Pourakbari-Kasmaei,et al. An Unambiguous Distance-Based MIQP Model to Solve Economic Dispatch Problems with Disjoint Operating Zones , 2016, IEEE Transactions on Power Systems.
[103] R. Billinton,et al. Reliability-Based Transmission Reinforcement Planning Associated With Large-Scale Wind Farms , 2007, IEEE Transactions on Power Systems.
[104] R. Billinton,et al. Probing the Intermittent Energy Resource Contributions From Generation Adequacy and Security Perspectives , 2012, IEEE Transactions on Power Systems.
[105] Hongtao Wang,et al. A scenario probability based method to solve unit commitment of large scale energy storage system and thermal generation in high wind power penetration level system , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).
[106] Dongyuan Shi,et al. Probabilistic load flow computation with polynomial normal transformation and Latin hypercube sampling , 2013 .
[107] Antonio J. Conejo,et al. Correlated wind-power production and electric load scenarios for investment decisions , 2013 .
[108] N. M. Muhamad Razali,et al. Backward reduction application for minimizing wind power scenarios in stochastic programming , 2010, 2010 4th International Power Engineering and Optimization Conference (PEOCO).
[109] Fei Teng,et al. Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications , 2019, Applied Energy.
[110] I. Erlich,et al. A Stochastic Model for the Optimal Operation of a Wind-Thermal Power System , 2009, IEEE Transactions on Power Systems.