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Challenges, strategies and opportunities for wind farm incorporated power systems: a review with bibliographic coupling analysis

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Abstract

Wind power is a rapidly developing energy source. Many nations use wind power to meet a considerable amount of their energy needs. Moreover, the technology of wind power has evolved over the period of time. As a result, the wind farm-incorporated power system has received more attention for its outstanding contributions. The purpose of this study is to review the research works published on four key topics within the theme of wind farm-incorporated power systems. We survey the research papers that are featured in the Web of Science database. We employ an approach called Methodi Ordinatio to filter the papers. The publication of papers related to wind farm-incorporated power system has increased significantly, especially between 2018 and 2022. Therefore, we conduct a database search during this period and select important papers. Then we review and describe the technical challenges and solutions of these papers. Furthermore, a bibliographic coupling analysis is presented. The analysis shows that the journals such as Energy, Energies, and Renewable Energy are the leading journals publishing papers on all four key topics. The analysis further demonstrates that the focus of the researchers is on wind power forecasting, followed by energy storage systems, and wind farm layout optimization. The least focus is on optimal power flow.

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The dataset used during this study is available from the corresponding author on reasonable request.

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References

  • Abedinia O, Lotfi M, Bagheri M, Sobhani B, Shafie-Khah M, Catalão JP (2020) Improved EMD-based complex prediction model for wind power forecasting. IEEE Transact Sustain Energy 11(4):2790–2802

  • Alfaro-García VG, Merigo´ JM, Pedrycz W, Gomez´ Monge R (2020) Citation analysis of fuzzy set theory journals: bibliometric insights about authors and research areas. Int J Fuzzy Syst

  • Aneke M, Wang M (2016) Energy storage technologies and real life applications – a state of the art review. Appl Energy 179:350–377. https://doi.org/10.1016/j.apenergy.2016.06.097

    Article  Google Scholar 

  • Antonini EGA, Romero DA, Amon CH (2018) Continuous adjoint formulation for wind farm layout optimization: a 2D implementation. Appl Energy 228:2333–2345

    Article  Google Scholar 

  • Aytun Ozturk U, Norman BA (2004) Heuristic methods for wind energy conversion system positioning. Elec Power Syst Res 70:179–185. https://doi.org/10.1016/j.epsr.2003.12.006

    Article  Google Scholar 

  • Aziz MJ, Gayme DF, Johnson K, Knox-Hayes J, Li P, Loth E, Pao LY, Sadoway DR, Smith J, Smith S (2022) A co-design framework for wind energy integrated with storage. Joule 6(9):1995–2015

  • Azlan F, Kurnia JC, Tan BT, Ismadi MZ (2021) Review on optimisation methods of wind farm array under three classical wind condition problems. Renew Sustain Energy Rev 135:110047

  • Baros S, Ilic MD (2019) A consensus approach to real-time distributed control of energy storage systems in wind farms. IEEE Transact Smart Grid 10(1):613–625

    Article  Google Scholar 

  • Biswas PP, Arora P, Mallipeddi R, Suganthan PN, Panigrahi BK (2020) Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network. Neural Comput Appl 33(12):6753–6774

  • Bonilla CA, Merigó JM, Torres-Abad C (2015) Economics in Latin America: a bibliometric analysis. Scientometrics 105(2):1239–1252

    Article  Google Scholar 

  • Bornmann L, Mutz R (2015) Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. J Am Soc Inf Sci 66(11):2215–2222

    CAS  Google Scholar 

  • Cao M, Xu Q, Qin X, Cai J (2020) Battery energy storage sizing based on a model predictive control strategy with operational constraints to smooth the wind power. Int J Electric Power Energy Syst 115:105471

  • Crosby PA (1987) Application of a Monte Carlo optimization technique to a cluster of wind turbines. J Sol Energy Eng 109:330–6. https://doi.org/10.1115/1.3268225

    Article  Google Scholar 

  • da Silva RG, Ribeiro MHDM, Moreno SR, Mariani VC, dos Santos Coelho L (2021) A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting. Energy 216:119174

  • da Silva RG, Moreno SR, Ribeiro MHDM, Larcher JHK, Mariani VC, dos Santos Coelho L (2022) Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach. Int J Electric Power Energy Syst 143:108504

  • Das T, Krishnan V, McCalley JD (2015) Assessing the benefits and economics of bulk energy storage technologies in the power grid. Appl Energy 139:104–118. https://doi.org/10.1016/j.apenergy.2014.11.017

    Article  Google Scholar 

  • Del Rosso AD, Eckroad SW (2014) Energy storage for relief of transmission congestion. Smart Grid, IEEE Trans 5:1138–46. https://doi.org/10.1109/TSG.2013.2277411

    Article  Google Scholar 

  • Dhiman HS, Deb D, Guerrero JM (2019) Hybrid machine intelligent SVR variants for wind forecasting and ramp events. Renew Sustain Energy Rev 108:369–379

    Article  Google Scholar 

  • Divya KC, Østergaard J (2009) Battery energy storage technology for power systems—an overview. Elec Power Syst Res 79:511–520. https://doi.org/10.1016/j.epsr.2008.09.017

    Article  Google Scholar 

  • Dong Y, Zhang H, Wang C, Zhou X (2021) A novel hybrid model based on bernstein polynomial with mixture of gaussians for wind power forecasting. Appl Energy 286:116545

  • Du R, Zou P, Ma C (2021) Multi-objective optimal sizing of hybrid energy storage systems for grid-connected wind farms using fuzzy control. J Renew Sustain Energy 13(1):014103

    Article  Google Scholar 

  • Duan J, Wang P, Ma W, Fang S, Hou Z (2022) A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting. Int J Electric Power Energy Syst 134:107452

  • Dui X, Zhu G, Yao L (2018) Two-stage optimization of battery energy storage capacity to decrease wind power curtailment in grid-connected wind farms. IEEE Trans Power Syst 33(3):3296–3305

    Article  Google Scholar 

  • Duman S, Rivera S, Li J, Wu L (2020) Optimal power flow of power systems with controllable wind-photovoltaic energy systems via differential evolutionary particle swarm optimization. Int Transact Electric Energy Syst 30(4):e12270

  • Elkinton CN, Manwell JF, McGowan JG (2008) Optimizing the layout of offshore wind energy systems. https://doi.org/10.4031/002533208786829188

  • Fingersh LJ (2004) Wind-battery-hydrogen integration study wind-battery-hydrogen integration study. Golden, CO

  • Freire RR, Veríssimo JMC (2020) Mapping co-creation and co-destruction in tourism: a bibliographic coupling analysis. Anatolia, 1–11

  • García-Orozco D, Espitia-Moreno IC, Alfaro-García VG, Merigó JM (2020) Sustainability in Mexico a bibliometric analysis of the scientific research field presented in the last 28 years. Inquietud Empresarial 20(2):101–120

    Article  Google Scholar 

  • García-Orozco D, Alfaro-García VG, Merigó JM, Espitia Moreno IC, Gómez Monge R (2022) An overview of the most influential journals in fuzzy systems research. Expert Syst Appl 200:117090. https://doi.org/10.1016/j.eswa.2022.117090

    Article  Google Scholar 

  • Georgilakis Pavlos S (2008) Technical challenges associated with the integration of wind power into power systems. Renew Sustain Energy Rev 12(3):852–863

    Article  Google Scholar 

  • Gracio MCC (2016) Acoplamento bibliografico e analise de cocitaçao: revisao teorico-conceitual. Encontros Bibli: Revista Eletronica De Biblioteconomia e Ciencia Da Informaçao 21(47):82

    Article  Google Scholar 

  • Gu D, Li J, Li X, Liang C (2017) Visualizing the knowledge structure and evolution of big data research in healthcare informatics. Int J Med Inform 98:22–32

    Article  Google Scholar 

  • Guo N, Zhang M, Li B, Cheng Y (2021) Influence of atmospheric stability on wind farm layout optimization based on an improved Gaussian wake model. J Wind Eng Indus Aerodyn 211:104548

  • Hao Y, Tian C (2019) A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting. Appl Energy 238:368–383

    Article  Google Scholar 

  • Huang L, Tang H, Zhang K, Fu Y, Liu Y (2020) 3-D layout optimization of wind turbines considering fatigue distribution. IEEE Transact Sustain Energy 11(1):126–135

  • Huang S, Khajepour A (2022) A new adiabatic compressed air energy storage system based on a novel compression strategy. Energy 242:122883

    Article  Google Scholar 

  • Huh J-H, Seo K (2015) Hybrid advanced metering infrastructure design for micro grid using the game theory model. Int J Softw Eng Its Appl 9:257–268

    Google Scholar 

  • Ida Evangeline S, Rathika P (2019) Particle swarm optimization algorithm for optimal power flow incorporating wind farms. (2019) IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE

  • Ida Evangeline S, Rathika P (2021a) Real-time optimal power flow solution for wind farm integrated power system using evolutionary programming algorithm. Int J Environ Sci Technol 18(7):1893–1910

    Article  Google Scholar 

  • Ida Evangeline S, Rathika P (2021b) A real-time multi-objective optimization framework for wind farm integrated power systems. J Power Sources 498:229914

    Article  CAS  Google Scholar 

  • Ida Evangeline S, Rathika P (2022) Wind farm incorporated optimal power flow solutions through multi-objective horse herd optimization with a novel constraint handling technique. Expert Syst Appl 194:116544

    Article  Google Scholar 

  • Kaminsky FC, Kirchhoff RH, Sheu L-J (1987) Optimal spacing of wind turbines in a wind energy power plant. Sol Energy 39:467–471. https://doi.org/10.1016/0038-092X(87)90053-3

    Article  Google Scholar 

  • Khosravi M, Afsharnia S, Farhangi S (2021) Optimal sizing and technology selection of hybrid energy storage system with novel dispatching power for wind power integration. Int J Electr Power Energy Syst 127:106660

    Article  Google Scholar 

  • Kim D, Hur J (2018) Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method. Energy 157:211–226

    Article  Google Scholar 

  • Kleminski R, Kazienko P, Kajdanowicz T (2020) Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification. J Inform Sci 1–25

  • Li C, Shi H, Cao Y, Wang J, Kuang Y, Tan Y, Wei J (2015) Comprehensive review of renewable energy curtailment and avoidance: a specific example in China. Renew Sustain Energy Rev 41:1067–1079

    Article  Google Scholar 

  • Li S, Gong W, Wang L, Gu Q (2022) Multi-objective optimal power flow with stochastic wind and solar power. Appl Soft Comput 114:108045

  • Liu Y, Ćetenović D, Li H, Gryazina E, Terzija V (2022) An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems. Int J Electr Power Energy Syst 136:107764

  • Liu Z, Fan S, Wang Y, Peng J (2021) Genetic-algorithm-based layout optimization of an offshore wind farm under real seabed terrain encountering an engineering cost model. Energy Convers Manage 245:114610

  • Luo X, Wang J, Dooner M, Clarke J (2015) Overview of current development in electrical energy storage technologies and the application potential in power system operation. Appl Energy 137:511–536. https://doi.org/10.1016/j.apenergy.2014.09.081

    Article  Google Scholar 

  • Meho LI, Yang K (2007) Impact of data sources on citation counts and rankings of LIS faculty: web of science versus scopus and google scholar. J Am Soc Inform Sci Technol 58(13):2105–2125

    Article  Google Scholar 

  • Merigo JM, Gil-Lafuente AM, Yager RR (2015) An overview of fuzzy research with bibliometric indicators. Appl Soft Comput J 27:420–433

    Article  Google Scholar 

  • Merigo JM, Cancino CA, Coronado F, Urbano D (2016) Academic research in innovation: a country analysis. Scientometrics 108(2):559–593

    Article  Google Scholar 

  • Moghaddam IN, Chowdhury BH, Mohajeryami S (2018) Predictive operation and optimal sizing of battery energy storage with high wind energy penetration. IEEE Trans Industr Electron 65(8):6686–6695. https://doi.org/10.1109/tie.2017.2774732

    Article  Google Scholar 

  • Mokarram M, Pourghasemi HR, Mokarram MJ (2022) A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method. Environ Sci Pollut Res: 1–22

  • Moreno SR, da Silva RG, Ribeiro MHDM, Fraccanabbia N, Mariani VC, Coelho LDS (2019) Very short-term wind energy forecasting based on stacking ensemble. 14th Brazilian Computational Intelligence Meeting (CBIC). Belem Brazil

  • Moreno SR, Coelho LDS, Ayala HV, Mariani VC (2020a) Wind turbines anomaly detection based on power curves and ensemble learning. IET Renew Power Gener 14(19):4086–4093

  • Moreno SR, da Silva RG, Mariani VC, dos Santos Coelho L (2020b) Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Convers Manag 213:112869

  • Moreno SR, Mariani VC, dos Santos Coelho L (2021a) Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast. Renew Energy 164:1508–1526

    Article  Google Scholar 

  • Moreno SR, Pierezan J, dos Santos Coelho L, Mariani VC (2021b) Multi-objective lightning search algorithm applied to wind farm layout optimization. Energy 216:119214

  • Mosetti G, Poloni C, Diviacco B (1994) Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J Wind Eng Ind Aerod 51:105–116. https://doi.org/10.1016/0167-6105(94)90080-9

    Article  Google Scholar 

  • Naidu DS (2002) Optimal control systems. CRC Press, Boca Raton

    Google Scholar 

  • Neto JXV, Junior EJG, Moreno SR, Ayala HVH, Mariani VC, dos Santos Coelho L (2018) Wind turbine blade geometry design based on multi-objective optimization using metaheuristics. Energy 162:645–658

  • Niknam T, Azizipanah-Abarghooee R, Sedaghati R, Kavousi-Fard A (2012) An enhanced hybrid particle swarm optimization and simulated annealing for practical economic dispatch. Energy Educ Sci Technol Part A J 30(1):553–564

    Google Scholar 

  • Niknam T, Golestaneh F (2012) Enhanced adaptive particle swarm optimization algorithm for dynamic non-convex economic dispatch. IET J Mag Gener Transm Distrib 6(5):424–435

    Article  Google Scholar 

  • Niu D, Sun L, Yu M, Wang K (2022) Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model. Energy 124384

  • Ongsakul W, Dieu VN (2016) Artificial intelligence in power system optimization. CRC Press, Boca Raton

    Book  Google Scholar 

  • Pagani RN, Kovaleski JL, Resende L (2015) Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics 105(3):2109–2135. https://doi.org/10.1007/s11192-015-1744-x

    Article  Google Scholar 

  • Patel MR (2005) Wind and solar power systems: design, analysis, and operation. second ed. CRC Press. https://doi.org/10.1201/9781420039924

  • Parada L, Herrera C, Flores P, Parada V (2018) Assessing the energy benefit of using a wind turbine micro-siting model. Renew Energy 118:591–601

  • Paul S, Nath AP, Rather ZH (2020) A multi-objective planning framework for coordinated generation from offshore wind farm and battery energy storage system. IEEE Transact Sustain Energy 11(4):2087–2097

    Article  Google Scholar 

  • Qu L, Qiao W (2011) Constant power control of DFIG wind turbines with supercapacitor energy storage. IEEE Trans Ind Appl 47:359–67. https://doi.org/10.1109/TIA.2010.2090932

    Article  Google Scholar 

  • Quan H, Lv J, Zhang W, Wang T (2021) Spatial correlation modeling for optimal power flow with wind power: feasibility in application of superconductivity. IEEE Transact Appl Supercond 31(8):1–5

  • Rabiee A, Nikkhah S, Soroudi A (2018) Information gap decision theory to deal with long-term wind energy planning considering voltage stability. Energy 147:451–463

    Article  Google Scholar 

  • Razmi AR, Soltani M, Ardehali A, Gharali K, Dusseault MB, Nathwani J (2021) Design, thermodynamic, and wind assessments of a compressed air energy storage (CAES) integrated with two adjacent wind farms: a case study at Abhar and Kahak sites, Iran. Energy 221:119902

  • Ribeiro MHDM, da Silva RG, Moreno SR, Mariani VC, dos Santos Coelho L (2022) Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting. Int J Electric Power Energy Syst 136:107712

  • Rivas RA, Clausen J, Hansen KS, Jensen LE (2009) Solving the turbine positioning problem for large offshore wind farms by simulated annealing. Wind Eng 33:287–297. https://doi.org/10.1260/0309-524X.33.3.287

    Article  Google Scholar 

  • Salkuti SR (2019) Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system. Int J Green Energy 16(15):1547–1561

    Article  CAS  Google Scholar 

  • Shilaja C, Arunprasath T (2019) Optimal power flow using moth swarm algorithm with gravitational search algorithm considering wind power. Futur Gener Comput Syst 98:708–715

    Article  Google Scholar 

  • Simpson JG, Hanrahan G, Loth E, Koenig GM, Sadoway DR (2021) Liquid metal battery storage in an offshore wind turbine: concept and economic analysis. Renew Sustain Energy Rev 149:111387

  • Song M, Wen Y, Duan B, Wang J, Gong Q (2017) Micro-siting optimization of a wind farm built in multiple phases. Energy 137:95–103. https://doi.org/10.1016/j.energy.2017.06.127

    Article  Google Scholar 

  • Sorensen P, Nielsen T (2006) Recalibrating wind turbine wake model parameters—validating the wake model performance for large offshore wind farms. EWEA: European Wind Energy Conference and Exhibition

  • Sun G, Jiang C, Cheng P, Liu Y, Wang X, Fu Y, He Y (2018) Short-term wind power forecasts by a synthetical similar time series data mining method. Renew Energy 115:575–584

  • Sun W, Zamani M, Zhang HT, Li Y (2019b) Probabilistic optimal power flow with correlated wind power uncertainty via Markov Chain Quasi-Monte-Carlo Sampling. IEEE Transact Indus Inform 15(11):6058–6069

  • Sun H, Yang H, Gao X (2019a) Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines. Energy 168:637–650

  • Tang XY, Yang Q, Stoevesandt B, Sun Y (2022) Optimization of wind farm layout with optimum coordination of turbine cooperations. Comput Indus Eng 164:107880

  • Tsvetkova O, Ouarda TBMJ (2021) A review of sensitivity analysis practices in wind resource assessment. Energy Convers Manage 238:114112. https://doi.org/10.1016/j.enconman.2021.114112

    Article  Google Scholar 

  • Von Krannichfeldt L, Wang Y, Zufferey T, Hug G (2022) Online ensemble approach for probabilistic wind power forecasting. IEEE Transact Sustain Energy 13(2):1221–1233

  • VOSviewer (2022) The VOSviewer software version 1.6.18, www.vosviewer.com. Accessed 11 Oct 2022

  • Wagner M, Day J, Neumann F (2013) A fast and effective local search algorithm for optimizing the placement of wind turbines. Renew Energy 51:64–70. https://doi.org/10.1016/j.renene.2012.09.008

    Article  Google Scholar 

  • Waltman L, van Eck NJ, Noyons ECM (2010) A unified approach to mapping and clustering of bibliometric networks. J Informet 4(4):629–635

    Article  Google Scholar 

  • Wan C, Wang J, Yang G, Zhang X (2010) Optimal micro-siting of wind farms by particle swarm optimization. In: Tan Y, Shi Y, Tan KC, (Eds) Advances in swarm intelligence. Springer Berlin Heidelberg; p. 198–205

  • Wang C, Zhang H, Ma P (2020) Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre Neural Network. Appl Energy 259:114139

    Article  Google Scholar 

  • Wu Y, Zhang S, Wang R, Wang Y, Feng X (2020) A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner. Renew Energy 146:687–698

  • Yang K, Kwak G, Cho K, Huh J (2019) Wind farm layout optimization for wake effect uniformity. Energy 183:983–995

  • Yao L, Wang X, Li Y, Duan C, Wu X (2020) Distributionally robust chance-constrained AC-OPF for integrating wind energy through multi-terminal VSC-HVDC. IEEE Transact Sustain Energy 11(3):1414–1426

  • Yazdani S, Deymi-Dashtebayaz M, Salimipour E (2019) Comprehensive comparison on the ecological performance and environmental sustainability of three energy storage systems employed for a wind farm by using an emergy analysis. Energy Convers Manage 191:1–11

    Article  Google Scholar 

  • Yekini Suberu M, Wazir Mustafa M, Bashir N (2014) Energy storage systems for renewable energy power sector integration and mitigation of intermittency. Renew Sustain Energy Rev 35:499–514. https://doi.org/10.1016/j.rser.2014.04.009

    Article  Google Scholar 

  • Yildiz C, Acikgoz H, Korkmaz D, Budak U (2021) An improved residual-based convolutional neural network for very short-term wind power forecasting. Energy Convers Manag 228:113731

  • You L, Ma H, Saha TK, Liu G (2022) Risk-based contingency-constrained optimal power flow with adjustable uncertainty set of wind power. IEEE Transact Indus Inform 18(2):996–1008

  • Zhang Z, Liu X, Zhao D, Post S, Chen J (2022) Overview of the development and application of wind energy in New Zealand. Energy Built Environ

  • Zhao E, Sun S, Wang S (2022) New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight. Data Sci Manag 5:84–95

    Article  Google Scholar 

  • Zhao Y, Ye L, Li Z, Song X, Lang Y, Su J (2016) A novel bidirectional mechanism based on time series model for wind power forecasting. Appl Energy 177:793–803. https://doi.org/10.1016/j.apenergy.2016.03.096

    Article  Google Scholar 

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S. Ida Evangeline: Conceptualization, methodology, analysis, writing original draft.

P. Rathika: Supervision, validation, review, and editing.

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Correspondence to Ida Evangeline Sundarapandi Edward.

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Sundarapandi Edward, I.E., Ponpandi, R. Challenges, strategies and opportunities for wind farm incorporated power systems: a review with bibliographic coupling analysis. Environ Sci Pollut Res 30, 11332–11356 (2023). https://doi.org/10.1007/s11356-022-24658-2

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