A Systematic Review on Selected Applications and Approaches of Wind Energy Forecasting and Integration

Engineers are facing challenges related to secure and steady operation of power system by virtue of the fact that wind power is variable due to its intermittent and unpredictable behaviour. In the present paper, the focus has been laid down on the latest and ongoing advancements in the arena of wind-energy forecasting methods. It is seen that there are various forecasting techniques recommended and implemented to predict the level of fluctuations of the wind. To estimate the wind energy and wind speed numerous measures have been evolved and implemented by the researchers. The literature exhibits that a lot of research is under way, in operation, accompanying, and proceeding to estimate wind-speed and power incorporating intelligent, biologically and naturally inspired computing techniques, and mathematically developed models to minimize the forecasting error. This review paper also explores the techniques utilized for integration of wind power and its economic influences on the present power systems and various techniques and guidelines for balancing the electricity power market. Various models for validation and simulation of electricity power-market to integrate wind production are also highlighted. The paper intents to be beneficial for the researchers and engineers particularly those who are working in this particular field.

[1]  P. Varaiya,et al.  Bringing Wind Energy to Market , 2012, IEEE Transactions on Power Systems.

[2]  Gabriela Hug,et al.  Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation , 2017, IEEE Transactions on Sustainable Energy.

[3]  Robert Pitz-Paal,et al.  Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems , 2020, Energies.

[4]  Bri-Mathias Hodge,et al.  Wind power forecasting: IEA Wind Task 36 & future research issues , 2016 .

[5]  Ahad Kazemi,et al.  Economic-environmental active and reactive power scheduling of modern distribution systems in presence of wind generations: A distribution market-based approach , 2015 .

[6]  Jianzhou Wang,et al.  Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm , 2015 .

[7]  Tao Li,et al.  Review of Evaluation Criteria and Main Methods of Wind Power Forecasting , 2011 .

[8]  A. N. Andersen,et al.  Drivers of imbalance cost of wind power: A comparative analysis , 2010, 2010 7th International Conference on the European Energy Market.

[9]  Frank A. Wolak,et al.  Measuring Unilateral Market Power in Wholesale Electricity Markets: The California Market, 1998-2000 , 2003 .

[10]  Ehab F. El-Saadany,et al.  Impact of wind farm integration on electricity market prices , 2009 .

[11]  Pritam Das,et al.  Sizing and Placement of Battery Energy Storage Systems and Wind Turbines by Minimizing Costs and System Losses , 2019 .

[12]  Zhenyuan Zhang,et al.  Market-oriented optimal dispatching strategy for a wind farm with a multiple stage hybrid energy storage system , 2018, CSEE Journal of Power and Energy Systems.

[13]  Wan Azlan Wan Zainal Abidin,et al.  Different Models of Wind Speed Prediction; A Comprehensive Review , 2014 .

[14]  Mucun Sun,et al.  Aggregated Probabilistic Wind Power Forecasting Based on Spatio-Temporal Correlation , 2019, 2019 IEEE Power & Energy Society General Meeting (PESGM).

[15]  David C. Yu,et al.  Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System , 2017 .

[16]  Zakir Husain,et al.  D, Q reference frames for the simulation of multiphase (six phase) wound rotor induction generator driven by a wind turbine for disperse generation , 2019, IET Electric Power Applications.

[17]  Oliver Browne,et al.  How does market power affect the impact of large scale wind investment in 'energy only' wholesale electricity markets? , 2015 .

[18]  Xiuli Wang,et al.  Behavior analysis of wind power producer in electricity market , 2016 .

[19]  Jun-Hao Li,et al.  The use of wavelet theory and ARMA model in wind speed prediction , 2011, 2011 1st International Conference on Electric Power Equipment - Switching Technology.

[20]  Matthias Schmid,et al.  Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid , 2012 .

[21]  Pierre Pinson,et al.  Impact of renewable energy forecast imperfections on market-clearing outcomes , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[22]  P. Chaurasiya,et al.  Wind energy development and policy in India: A review , 2019, Energy Strategy Reviews.

[23]  Qi-Rong Qiu,et al.  Short-term wind speed forecasting combined time series method and arch model , 2012, 2012 International Conference on Machine Learning and Cybernetics.

[24]  Eva Onaindia,et al.  On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market , 2019, Energies.

[25]  Junshan Zhang,et al.  Wind Power Dispatch Margin for Flexible Energy and Reserve Scheduling With Increased Wind Generation , 2015, IEEE Transactions on Sustainable Energy.

[26]  Oliver Kramer,et al.  Wind energy prediction and monitoring with neural computation , 2013, Neurocomputing.

[27]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .

[28]  Feng Ji,et al.  Wind power prediction interval estimation method using wavelet-transform neuro-fuzzy network , 2015, J. Intell. Fuzzy Syst..

[29]  I. González-Aparicio,et al.  Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain , 2015 .

[30]  Fazıl Gökgöz,et al.  Deep Learning for Renewable Power Forecasting: An Approach Using LSTM Neural Networks , 2018 .

[31]  Muhammad Bashar Anwar,et al.  Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review , 2016 .

[32]  Hua Han,et al.  Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform , 2019, Applied Sciences.

[33]  P. K. Katti,et al.  A comparative analysis for wind speed prediction , 2011, 2011 International Conference on Energy, Automation and Signal.

[35]  Atif Iqwal,et al.  Modeling and analysis of novel six-phase DFIG through asymmetrical winding structure for disperse generation , 2020 .

[36]  Chao Chen,et al.  A hybrid statistical method to predict wind speed and wind power , 2010 .

[37]  Georges Kariniotakis,et al.  Probabilistic short-term wind power forecasting based on kernel density estimators , 2007 .

[38]  Chi-Yo Huang,et al.  Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method , 2011, 2011 IEEE Green Technologies Conference (IEEE-Green).

[39]  Sampson Sampson Uko,et al.  Adaptive Neuro-Fuzzy Inference System (ANFIS) Model for Forecasting and Predicting Industrial Electricity Consumption in Nigeria , 2019, Advances in Energy and Power.

[40]  Jianzhou Wang,et al.  A novel hybrid model for short-term wind power forecasting , 2019, Appl. Soft Comput..

[41]  H. Madsen,et al.  On the market impact of wind energy forecasts , 2010 .

[42]  Jay Zarnikau,et al.  Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets , 2016 .

[43]  Xiaogang Wu,et al.  Optimal sizing of a wind-energy storage system considering battery life , 2020 .

[44]  Marc A. Rosen,et al.  Short-term wind speed forecasting using artificial neural networks for Tehran, Iran , 2016, International Journal of Energy and Environmental Engineering.

[45]  Hwachang Song,et al.  Real-Time Peak Shaving Algorithm Using Fuzzy Wind Power Generation Curves for Large-Scale Battery Energy Storage Systems , 2014, Int. J. Fuzzy Log. Intell. Syst..

[46]  S.Vidya,et al.  A Review On The Hybrid Approaches For Wind Speed Forecasting , 2019 .

[47]  Heng Nian,et al.  Impedance Aggregation Method of Multiple Wind Turbines and Accuracy Analysis , 2019, Energies.

[48]  Machteld van den Broek,et al.  Impacts of large-scale Intermittent Renewable Energy Sources on electricity systems, and how these can be modeled , 2014 .

[49]  P. S. Dokopoulos,et al.  Wind speed and power forecasting based on spatial correlation models , 1999 .

[50]  M. Benini,et al.  Comparison of the economic impact of different wind power forecast systems for producers , 2014 .

[51]  Li Hou,et al.  A New Volumetric CNN for 3D Object Classification Based on Joint Multiscale Feature and Subvolume Supervised Learning Approaches , 2020, Comput. Intell. Neurosci..

[52]  Mayur Barman,et al.  A fuzzy logic controller based mid-term load forecasting with renewable penetration in Assam, India , 2017 .

[53]  Anirban Mukherjee,et al.  Dynamic economic dispatch using hybrid metaheuristics , 2020 .

[54]  Devashish Jha.  A Comprehensive Review on Wind Energy Systems for Electric Power Generation: Current Situation and Improved Technologies to Realize Future Development , 2017, International Journal of Renewable Energy Research.

[55]  Ioannis B. Theocharis,et al.  A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation , 2007, Neurocomputing.

[56]  Yongqian Li,et al.  A New Wind Power Forecasting Approach Based on Conjugated Gradient Neural Network , 2016 .

[57]  Joao P. S. Catalao,et al.  Neuro-Fuzzy Approach to Forecast Wind Power in Portugal , 2010 .

[58]  Julian Meng,et al.  Short-Term Wind Speed Forecasting Based On Fuzzy Artmap , 2011 .

[59]  Georges Kariniotakis,et al.  Strategies for Wind Power Trading in Sequential Short-Term Electricity Markets , 2009 .

[60]  Gregory P. Swinand,et al.  Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland , 2015 .

[61]  Z. Husain,et al.  Novel Six Phase Doubly Fed Induction Generator through Modeling and Simulation-A comparison with Conventional Doubly Fed Induction Generator , 2019, 2019 International Conference on Power Electronics, Control and Automation (ICPECA).

[62]  Mariam Ibrahim,et al.  Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms , 2020, Comput. Intell. Neurosci..

[63]  Milkias Berhanu,et al.  LOAD FORECASTING OF ADAMA UNIVERSITY BYIMPLEMENTING FUZZY LOGIC CONTROLLER , 2013 .

[64]  Isabel Praça,et al.  Elspot: Nord Pool Spot Integration in MASCEM Electricity Market Simulator , 2014, PAAMS.

[65]  Ruili Ye,et al.  An optimal sizing method for energy storage system in wind farms based on the analysis of wind power forecast error , 2016 .

[66]  Arup Kumar Goswami,et al.  Wind power: Existing status, achievements and government's initiative towards renewable power dominating India , 2019, Energy Strategy Reviews.

[67]  Georgios Tzanes,et al.  Forecasting of Wind Power Generation with the Use of Artificial Neural Networks and Support Vector Regression Models , 2019, Energy Procedia.

[68]  BABAK MOZAFARI,et al.  Optimal sizing of energy storage system for microgrids , 2014 .

[69]  Sven-Erik Thor,et al.  Long‐term research and development needs for wind energy for the time frame 2000–2020 , 2002 .

[70]  D. M. Vinod Kumar,et al.  Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review , 2020, Engineering Reports.

[71]  Pierre Pinson,et al.  Trading wind energy on the basis of probabilistic forecasts both of wind generation and of market quantities , 2013 .

[72]  S. Tewari,et al.  A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets , 2011, IEEE Transactions on Power Systems.

[73]  Daniel Ambach,et al.  A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting , 2017, 1707.03258.

[74]  Agus Naba.  Fuzzy logic principles for wind speed estimation in wind energy conversion systems , 2014, 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE).

[75]  A. Berizzi,et al.  Optimal Siting and Sizing of Energy Storage Systems for Wind Integration , 2017 .

[76]  A. Chroneos,et al.  Defects and Dopants in CaFeSi2O6: Classical and DFT Simulations , 2020, Energies.

[77]  Yong Sun,et al.  Economic dispatch with CHP and wind power using probabilistic sequence theory and hybrid heuristic algorithm , 2017 .

[78]  Pierre Courbin,et al.  Smoothing of renewable energy generation using Gaussian-based method with power constraints , 2017 .

[79]  Ricardo J. Bessa,et al.  Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model , 2017 .

[80]  Muhammad Khalid.  Wind Power Economic Dispatch – Impact of Radial Basis Functional Networks and Battery Energy Storage , 2019, IEEE Access.

[81]  Xinxin Zhu,et al.  Space-time wind speed forecasting for improved power system dispatch , 2014 .

[82]  Hamid A. Toliyat,et al.  Multiphase induction motor drives - : a technology status review , 2007 .

[83]  J.C. Palomares-Salas,et al.  ARIMA vs. Neural networks for wind speed forecasting , 2009, 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[84]  Ehab E. Elattar,et al.  A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem , 2015 .

[85]  P.J. Zucatelli,et al.  Short-term wind speed forecasting in Uruguay using computational intelligence , 2019, Heliyon.

[86]  Saad Mekhilef,et al.  Long-Term Wind Speed Forecasting and General Pattern Recognition Using Neural Networks , 2014, IEEE Transactions on Sustainable Energy.

[87]  Hannele Holttinen,et al.  Agent-Based Simulation of Day-Ahead Energy Markets: Impact of Forecast Uncertainty and Market Closing Time on Energy Prices , 2016, 2016 27th International Workshop on Database and Expert Systems Applications (DEXA).

[88]  Aysin Ertüzün,et al.  Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[89]  Yongqian Liu,et al.  Interpolation of missing wind data based on ANFIS , 2011 .

[90]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[91]  Huang Hui,et al.  Ultra-Short-Term Prediction of Wind Power Based on Fuzzy Clustering and RBF Neural Network , 2018, Adv. Fuzzy Syst..

[92]  Haibo Lan,et al.  Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration , 2019, IEEE Transactions on Power Systems.

[93]  M. A. Abido.  Environmental/economic power dispatch using multiobjective evolutionary algorithms , 2003 .

[94]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[95]  Rob J Hyndman,et al.  Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond , 2016 .

[96]  Ioannis P. Panapakidis,et al.  A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence , 2019, Electronics.

[97]  Ravindra B. Sholapurkar,et al.  Review of Wind Energy Development and Policy in India , 2015 .

[98]  P. Siano,et al.  Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs , 2017 .

[99]  Milde M. S. Lira,et al.  Models Based on Neural Networks and Neuro-Fuzzy Systems for Wind Power Prediction Using Wavelet Transform as Data Preprocessing Method , 2012, EANN.

[100]  Gregory P. Swinand,et al.  Estimating the impact of wind generation on balancing costs in the GB electricity markets , 2012, 2012 9th International Conference on the European Energy Market.

[101]  Andrés Feijóo,et al.  Wind power distributions: A review of their applications , 2010 .

[102]  Andrey V. Savkin,et al.  On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting , 2018 .

[103]  Miao He,et al.  Support-Vector-Machine-Enhanced Markov Model for Short-Term Wind Power Forecast , 2015, IEEE Transactions on Sustainable Energy.

[104]  Mashood Nasir,et al.  Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review , 2019 .

[105]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[106]  Paramjeet Kaur,et al.  A Comprehensive Study of Economic Dispatch Problems by Different Evolutionary Techniques , 2019 .

[107]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[108]  Bidesh Roy.  Performance prediction of Savonius Wind Turbine using adaptive neuro-based fuzzy inference system (ANFIS) , 2019 .

[109]  Abbas Khosravi,et al.  Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[110]  Yilan Luo,et al.  Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada , 2019 .

[111]  P. Jirutitijaroen,et al.  Optimal sizing of energy storage system for wind power plants , 2012, 2012 IEEE Power and Energy Society General Meeting.

[112]  Claudia Kemfert,et al.  Gone with the Wind? Electricity Market Prices and Incentives to Invest in Thermal Power Plants under Increasing Wind Energy Supply , 2009 .

[113]  V Ranganayaki,et al.  An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems , 2016, TheScientificWorldJournal.

[114]  Tanuj Sen,et al.  A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm , 2016 .

[115]  Joao P. S. Catalao,et al.  Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information , 2015 .

[116]  Behrooz Vahidi,et al.  A probabilistic method for cost minimization in a day-ahead electricity market considering wind power uncertainties , 2017 .

[117]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[118]  Mathieu David,et al.  A heuristic approach for optimal sizing of ESS coupled with intermittent renewable sources systems , 2016 .

[119]  Yingtao Jiang,et al.  Wind Power Forecasting Methods Based on Deep Learning: A Survey , 2020, Computer Modeling in Engineering & Sciences.

[120]  Moncho Gómez-Gesteira,et al.  Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula , 2014 .

[121]  Yuan-Kang Wu,et al.  Economic dispatch in an offshore island system with high wind power penetration , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[122]  Manoj Datta,et al.  Reactive Power Management in Renewable Rich Power Grids: A Review of Grid-Codes, Renewable Generators, Support Devices, Control Strategies and Optimization Algorithms , 2018, IEEE Access.

[123]  Zita Vale,et al.  A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context , 2015 .

[124]  David Barbosa de Alencar,et al.  Different Models for Forecasting Wind Power Generation: Case Study , 2017 .

[125]  Christopher Heard,et al.  Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model , 2016 .

[126]  Ignacio Algredo-Badillo,et al.  Performance Analysis of ANFIS in short term Wind Speed Prediction , 2012, ArXiv.

[127]  Binsu C. Kovoor,et al.  Deep learning based multi-step short term wind speed forecasts with LSTM , 2019, DATA '19.

[128]  S. Iniyan,et al.  Applications of fuzzy logic in renewable energy systems – A review , 2015 .

[129]  P. K. Dash,et al.  Short term wind speed prediction using multiple kernel pseudo inverse neural network , 2018, Int. J. Autom. Comput..

[130]  Fausto Pedro García Márquez,et al.  A survey of artificial neural network in wind energy systems , 2018, Applied Energy.

[131]  Jianzhou Wang,et al.  A novel hybrid system based on multi-objective optimization for wind speed forecasting , 2020 .

[132]  Hussain Alazki,et al.  An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico , 2019, Energies.

[133]  C. Shanmuga Sundaram,et al.  Tabu search-enhanced artificial bee colony algorithm to solve profit-based unit commitment problem with emission limitations in deregulated electricity market , 2017, Int. J. Metaheuristics.

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

[135]  Helge V. Larsen,et al.  Area price and demand response in a market with 25% wind power , 2011 .

[136]  Jan Kleissl,et al.  Forecast value considering energy pricing in California , 2014 .

[137]  T. Oku,et al.  Fabrication and Characterization of CH 3 NH 3 PbI 3 x y Br x Cl y Perovskite Solar Cells , 2016 .

[138]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.