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Review | Open Access

Research on complementarity of multi-energy power systems: A review

Wei Hu1( )Yu Dong2Lei Zhang3Yiting Wang4Yunchao Sun1Kexi Qian1Yuchen Qi1
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
National Power Dispatching & Control Center, State Grid Corporation of China, Beijing 100031, China
College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Qinghai Electric Power Company, Qinghai 810000, China
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Abstract

In the background of the large-scale development and utilization of renewable energy, the joint operation of a variety of heterogeneous energy sources has become an inevitable development trend. However, the physical characteristics of different power sources and the inherent uncertainties of renewable energy power generation have brought difficulties to the planning, operation and control of power systems. For now, the utilization of multi-energy complementarity to promote energy transformation and improve the consumption of renewable energy has become a common understanding among researchers and the engineering community. This paper makes a review of the research on complementarity of new energy high proportion multi-energy systems from uncertainty modeling, complementary characteristics, planning and operation. We summarize the characteristics of the existing research and provide a reference for the further work.

References

[1]
Schellekens, G., Battaglini, A., Lilliestam, J., Patt, A. (2010). 100% Renewable Electricity: A Roadmap to 2050 for Europe and North Africa. London, UK: PricewaterhouseCoopers.
[2]
Hand, M. M., Baldwin, S., DeMeo, E., (2012). Renewable electricity futures study. Volume 1: Exploration of high-penetration renewable electricity futures, office of scientific & technical information technical reports. Available at https://www.nrel.gov/analysis/re-futures.html.
[3]
Energy Research Institute National Development and Reform Commission (2015), China 2050 high renewable energy penetration scenario and roadmap study. Available at https://www.efchina.org/Reports-zh/china-2050-high-renewable-energy-penetration-scenario-and-roadmap-study-zh. (in Chinese).
[4]

Rozakis, S., Soldatos, P. G., Papadakis, G., Kyritsis, S., Papantonis, D. (1997). Evaluation of an integrated renewable energy system for electricity generation in rural areas. Energy Policy, 25: 337–347.

[5]

Al-Ashwal, A. M., Moghram, I. S. (1997). Proportion assessment of combined PV-wind generating systems. Renewable Energy, 10: 43–51.

[6]

Luo, Z., Yang, S., Xie, N., Xie, W., Liu, J., Souley Agbodjan, Y., Liu, Z. (2019). Multi-objective capacity optimization of a distributed energy system considering economy, environment and energy. Energy Conversion and Management, 200: 112081.

[7]

Sun, W., Harrison, G. P. (2019). Wind-solar complementarity and effective use of distribution network capacity. Applied Energy, 247: 89–101.

[8]

Wang, Q., Liu, J., Hu, Y., Zhang, X. (2019). Optimal operation strategy of multi-energy complementary distributed CCHP system and its application on commercial building. IEEE Access, 7: 127839–127849.

[9]
Yadav, A., Srivastava, L. (2014). Optimal placement of distributed generation: An overview and key issues. In: Proceedings of the 2014 International Conference on Power Signals Control and Computations (EPSCICON), Thrissur, India.
[10]
NDRC, NEA (2016). 13th five-year plan for electric power development. National Development and Reform Commission (NDRC), National Energy Administration (NEA). Available at https://www.gov.cn/xinwen/2016-12/22/content_5151549.htm. (in Chinese)
[11]
Li, J., Guo, B., Niu, M., Xiu, X., Tian, L. (2018). Optimal configuration strategy of energy storage capacity in wind/PV/storage hybrid system. Transactions of China Electrotechnical Society, 33(6): 1189–1196. (in Chinese)
[12]

Salgado Duarte, Y., Szpytko, J., del Castillo Serpa, A. M. (2020). Monte Carlo simulation model to coordinate the preventive maintenance scheduling of generating units in isolated distributed power systems. Electric Power Systems Research, 182: 106237.

[13]

Akdağ, S. A., Dinler, A. (2009). A new method to estimate Weibull parameters for wind energy applications. Energy Conversion and Management, 50: 1761–1766.

[14]

Youcef Ettoumi, F., Mefti, A., Adane, A., Bouroubi, M. Y. (2002). Statistical analysis of solar measurements in Algeria using beta distributions. Renewable Energy, 26: 47–67.

[15]

Holttinen, H. (2005). Impact of hourly wind power variations on the system operation in the Nordic countries. Wind Energy, 8: 197–218.

[16]

Lange, M. (2005). On the uncertainty of wind power predictions—Analysis of the forecast accuracy and statistical distribution of errors. Journal of Solar Energy Engineering, 127: 177–184.

[17]

Bludszuweit, H., Dominguez-Navarro, J. A., Llombart, A. (2008). Statistical analysis of wind power forecast error. IEEE Transactions on Power Systems, 23: 983–991.

[18]
Zhao, W., Zhang, N., Kang, C., Wang, Y., Li, P., Ma, S. (2015). A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation. Automation of Electric Power Systems, 39(16): 8–15. (in Chinese)
[19]
Zhang, Z. S., Sun, Y. Z., Gao, D. W., Lin, J., Cheng, L. (2013). A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation. IEEE Transactions on Power Systems, 28, 3114–312539.
[20]
Chen, Y., Wang, Y., Kirschen, D., Zhang, B. (2019). Model-free renewable scenario generation using generative adversarial networks. In: Proceedings of the 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA.
[21]

Jiang, C., Mao, Y., Chai, Y., Yu, M., Tao, S. (2018). Scenario generation for wind power using improved generative adversarial networks. IEEE Access, 6: 62193–62203.

[22]
Li, K. P., Zhang, Z. Y., Wang, F., Jiang, L. H., Zhang, J. J., Yu, Y. L., Mi, Z. Q. (2019). Stochastic optimization model of capacity configuration for stand-alone microgrid based on scenario simulation using GAN and conditional value at risk. Power System Technology, 43(5): 1717–1725.
[23]

Zhanga, H., Hua, W., Yub, R., Tangb, M., Dingc, L. (2018). Optimized operation of cascade reservoirs considering complementary characteristics between wind and photovoltaic based on variational auto-encoder. MATEC Web of Conferences, 246: 01077.

[24]
Huang, N. T., Wang, W. T., Cai, G. W., Yang, D. F., Huang, D. W., Song, X. (2019). The joint scenario generation of multi source-load by modular denoising variational autoencoder considering the complex coupling characteristics of meteorology. Proceedings of the CSEE, 39(10): 2924–2934.
[25]

Jurasz, J., Canales, F. A., Kies, A., Guezgouz, M., Beluco, A. (2020). A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions. Solar Energy, 195: 703–724.

[26]

Patton, A. J. (2012). A review of copula models for economic time series. Journal of Multivariate Analysis, 110: 4–18.

[27]

Zhang, H., Lu, Z., Hu, W., Wang, Y., Dong, L., Zhang, J. (2019). Coordinated optimal operation of hydro-wind-solar integrated systems. Applied Energy, 242: 883–896.

[28]

Ávila R, L., Mine, M. R. M., Kaviski, E., Detzel, D. H. M., Fill, H. D., Bessa, M. R., Pereira, G. A. A. (2020). Complementarity modeling of monthly stream flow and wind speed regimes based on a copula-entropy approach: A Brazilian case study. Applied Energy, 259: 114127.

[29]

Bedford, T., Cooke, R. M. (2002). Vines—A new graphical model for dependent random variables. The Annals of Statistics, 30: 1031–1068.

[30]

Aas, K., Czado, C., Frigessi, A., Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44: 182–198.

[31]

Brechmann, E. C., Czado, C., Aas, K. (2012). Truncated regular vines in high dimensions with application to financial data. Canadian Journal of Statistics, 40: 68–85.

[32]

Engeland, K., Borga, M., Creutin, J. D., François, B., Ramos, M. H., Vidal, J. P. (2017). Space-time variability of climate variables and intermittent renewable electricity production–A review. Renewable and Sustainable Energy Reviews, 79: 600–617.

[33]

Gallardo, R. P., Ríos, A. M., Ramírez, J. S. (2020). Analysis of the solar and wind energetic complementarity in Mexico. Journal of Cleaner Production, 268: 122323.

[34]

Ren, G., Wan, J., Liu, J., Yu, D. (2019). Spatial and temporal assessments of complementarity for renewable energy resources in China. Energy, 177: 262–275.

[35]

Monforti, F., Huld, T., Bódis, K., Vitali, L., D'Isidoro, M., Lacal-Arántegui, R. (2014). Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach. Renewable Energy, 63: 576–586.

[36]

Cantão, M. P., Bessa, M. R., Bettega, R., Detzel, D. H. M., Lima, J. M. (2017). Evaluation of hydro-wind complementarity in the Brazilian territory by means of correlation maps. Renewable Energy, 101: 1215–1225.

[37]

Lei, Y., Sun, Y., Hou, K., Zhang, P., Zhu, L., Yang, X., Liu, X. (2022). An impact-increment-based hybrid reliability assessment method for transmission systems. CSEE Journal of Power and Energy Systems, 8: 317–328.

[38]
Li, J. L., Guo, B. Q., Niu, M., Xiu, X. Q., Tian, L. T. (2018). Optimal configuration strategy of energy storage capacity in wind/PV/storage hybrid system. Transactions of China Electrotechnical Society, 33(6): 1189–1196. (in Chinese)
[39]
Liu, Q., Xie, P., Ouyang, J., Zhu, J., Xiong, X., Xuan, P. (2018). Dynamic economic dispatch strategy based on multi-time scale complementarity of heterogeneous energy sources. Electric Power Automation Equipment, 38(06): 55–64. (in Chinese)
[40]
Ye, L., Qu, X., Yao, Y., Zhang, J., Wang, Y., Huang, Y., Wang, W. S. (2018). Analysis on intraday operation characteristics of hybrid wind-solar-hydro power generation system. Automation of Electric Power Systems, 42(4): 158–164. (in Chinese)
[41]
Qu, Z., Yu, J. (2013). Quantitative evaluation on consistency and complementarity of wind power variability. Power System Technology, 37(2): 507–513. (in Chinese)
[42]

Prasad, A. A., Taylor, R. A., Kay, M. (2017). Assessment of solar and wind resource synergy in Australia. Applied Energy, 190: 354–367.

[43]

Booth, R. (1972). Power system simulation model based on probability analysis. IEEE Transactions on Power Apparatus and Systems, PAS-91: 62–69.

[44]

Dominguez, R., Conejo, A. J., Carrion, M. (2015). Toward fully renewable electric energy systems. IEEE Transactions on Power Systems, 30: 316–326.

[45]

Alsayed, M., Cacciato, M., Scarcella, G., Scelba, G. (2013). Multicriteria optimal sizing of photovoltaic-wind turbine grid connected systems. IEEE Transactions on Energy Conversion, 28: 370–379.

[46]
Zhao, D., Yan, W., Luo, C., Luo, T. (2019). MILP model for annual power and energy balance of hydro-thermal-wind power system considering RPS mechanism. In: Proceedings of the 8th Renewable Power Generation Conference (RPG 2019), Shanghai, China.
[47]
Waiwong, S., Damrongkulkamjorn, P. (2016). Optimal sizing for stand alone power generating system with wind-PV-hydro storage by mixed-integer linear programming. In: Proceedings of the 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, UK.
[48]

Askarzadeh, A. (2013). Developing a discrete harmony search algorithm for size optimization of wind-photovoltaic hybrid energy system. Solar Energy, 98: 190–195.

[49]

Domínguez, R., Carrión, M., Oggioni, G. (2020). Planning and operating a renewable-dominated European power system under uncertainty. Applied Energy, 258: 113989.

[50]

Wang, P., Liang, F., Song, J., Jiang, N., Zhang, X. P., Guo, L., Gu, X. (2020). Impact of the PV location in distribution networks on network power losses and voltage fluctuations with pso analysis. CSEE Journal of Power and Energy Systems, 8: 523–534.

[51]

Du, E., Zhang, N., Hodge, B. M., Wang, Q., Kang, C., Kroposki, B., Xia, Q. (2018). The role of concentrating solar power toward high renewable energy penetrated power systems. IEEE Transactions on Power Systems, 33: 6630–6641.

[52]
Lu, Z., Li, H., Qiao, Y. (2016). Power system flexibility planning and challenges considering high proportion of renewable energy. Automation of Electric Power Systems, 40(13): 147–158. (in Chinese)
[53]

Tang, Y., Fang, G., Tan, Q., Wen, X., Lei, X., Ding, Z. (2020). Optimizing the sizes of wind and photovoltaic power plants integrated into a hydropower station based on power output complementarity. Energy Conversion and Management, 206: 112465.

[54]

Luz, T., Moura, P. (2019). 100% Renewable energy planning with complementarity and flexibility based on a multi-objective assessment. Applied Energy, 255: 113819.

[55]

Han, S., Zhang, L. N., Liu, Y. Q., Zhang, H., Yan, J., Li, L., Lei, X. H., Wang, X. (2019). Quantitative evaluation method for the complementarity of wind-solar-hydro power and optimization of wind-solar ratio. Applied Energy, 236: 973–984.

[56]

Zhang, T., Wang, J., Zhong, H., Li, G., Zhou, M., Zhao, D. (2023). Soft open point planning in renewable-dominated distribution grids with building thermal storage. CSEE Journal of Power and Energy Systems, 9: 244–253.

[57]

Qi, Y., Hu, W., Dong, Y., Fan, Y., Dong, L., Xiao, M. (2020). Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder. Applied Energy, 274: 115124.

[58]
Chattopadhyay, D., Bankuti, M., Bazilian, M. D., de Sisternes, F., Oguah, S., Sanchez, M. J. M. (2018). Capacity planning model with CSP and battery. In: Proceedings of the 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA.
[59]

Su, C., Cheng, C., Wang, P., Shen, J., Wu, X. (2019). Optimization model for long-distance integrated transmission of wind farms and pumped-storage hydropower plants. Applied Energy, 242: 285–293.

[60]

Xiao, B., Wang, J., Xiao, Z., Yan, G., Dong, L., Wang, M., Yang, H. (2023). Power source flexibility margin quantification method for multi-energy power system based on blind number theory. CSEE Journal of Power and Energy Systems, 9: 2321–233.

[61]

Gebretsadik, Y., Fant, C., Strzepek, K., Arndt, C. (2016). Optimized reservoir operation model of regional wind and hydro power integration case study: Zambezi Basin and South Africa. Applied Energy, 161: 574–582.

[62]

Yang, Y., Guo, S., Liu, Q., Li, R. (2019). Day-ahead scheduling for a new wind-CSP hybrid system. Energy Procedia, 158: 6254–6259.

[63]

Zhang, S., Xiang, Y., Liu, J., Liu, J., Yang, J., Zhao, X., Jawad, S., Wang, J. (2022). A regulating capacity determination method for pumped storage hydropower to restrain PV generation fluctuations. CSEE Journal of Power and Energy Systems, 8: 304–316.

[64]

Hetzer, J., Yu, D. C., Bhattarai, K. (2008). An economic dispatch model incorporating wind power. IEEE Transactions on Energy Conversion, 23: 603–611.

[65]
Zhang, B., Wu, W., Zheng, T., Sun, H. (2011). Design of a multi-time scale coordinated active power dispatching system for accommodating large scale wind power penetration. Automation of Electric Power Systems, 35(1): 1–6. (in Chinese)
[66]

Wang, X., Chang, J., Meng, X., Wang, Y. (2018). Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems. Applied Energy, 229: 945–962.

[67]

Du, E., Zhang, N., Hodge, B. M., Wang, Q., Lu, Z., Kang, C., Kroposki, B., Xia, Q. (2019). Operation of a high renewable penetrated power system with CSP plants: A look-ahead stochastic unit commitment model. IEEE Transactions on Power Systems, 34: 140–151.

[68]

Fu, Y., Lu, Z., Hu, W., Wu, S., Wang, Y., Dong, L., Zhang, J. (2019). Research on joint optimal dispatching method for hybrid power system considering system security. Applied Energy, 238: 147–163.

[69]

Xi, L., Zhang, L., Xu, Y., Wang, S., Yang, C. (2020). Automatic generation control based on multiple-step greedy attribute and multiple-level allocation strategy. CSEE Journal of Power and Energy Systems, 8: 281–292.

[70]

Yang, H., Hao, Z., Zhang, D., Ma, Y. (2020). An inverse proportion technique based scheduling strategy of energy storage system considering electrical load demand difference. CSEE Journal of Power and Energy Systems, 8: 1487–1496.

[71]
Zhou, H., Chen, C. (2010). Fuzzy programming based daily dispatch of power system with wind power. In: Proceedings of the 2010 Conference Proceedings IPEC, Singapore.
[72]
Elshahed, M., Elmarsafawy, M. M., El-din, H. Z. (2015). Stochastic chance constraint with discrete probabilities of wind sources in economic dispatch. In: Proceedings of the International Conference on Renewable Power Generation (RPG 2015), Beijing, China.
[73]
Le, J., Zhao, L., Wang, C., Zhou Q., Wang, Y. (2020). Leader optimal selection method for the distributed control system of active distribution networks. CSEE Journal of Power and Energy Systems, https://doi.org/10.17775/CSEEJPES.2020.02410.
[74]

Li, H., Liu, P., Guo, S., Ming, B., Cheng, L., Yang, Z. (2019). Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization. Applied Energy, 238: 863–875.

iEnergy
Pages 275-283
Cite this article:
Hu W, Dong Y, Zhang L, et al. Research on complementarity of multi-energy power systems: A review. iEnergy, 2023, 2(4): 275-283. https://doi.org/10.23919/IEN.2023.0042

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Received: 13 November 2023
Revised: 22 December 2023
Accepted: 22 December 2023
Published: 29 December 2023
© The author(s) 2023.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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