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Multi-objective Optimization of a Hydrogen-Battery Hybrid Storage System for Offshore Wind Farm Using MOPSO

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Abstract

Recently, offshore wind farms (OWFs) are gaining more and more attention for its high efficiency and yearly energy production capacity. However, the power generated by OWFs has the drawbacks of intermittence and fluctuation, leading to the deterioration of electricity grid stability and wind curtailment. Energy storage is one of the most important solutions to smooth the wind power and capture its surplus. In this paper, we provide a multi-objective optimization approach that combines multi-objective particle swarm optimization and rule-based energy management strategy for an on-gird offshore wind-hydrogen-battery system to simultaneously address the economic (Eco), the qualified rate of smoothing offshore wind power fluctuations (QRS), and the rate of offshore wind power curtailment (ROC). Results revealed that ROC and Eco, QRS and Eco are negatively correlated, but ROC and QRS are positively correlated. The hybrid storage system is more conducive to improve QRS and reduce ROC. Comparing with other three systems, the improvement range for ROC is between 13.6 and 46% when QRS is 100%. In addition, battery storage improves QRS by 2.6%, hydrogen storage deteriorates Eco by 86.8% and improve ROC by 38.5%, the change of ROC and QRS brings by transmission project are close to 100% and 4.4%.

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Abbreviations

BSS:

Battery storage system

\(C_{x}\) :

Capital cost per unit of component x

\(C_{om - px}\) :

O&M cost per unit of component x

\(C_{t,x}\) :

Annual cost of component x

\(C_{ci,x}\) :

Capital cost of component x

\(C_{om,x}\) :

Annual O&M cost of component x

\(C_{re,x}\) :

Replacement cost of component x

\(E_{b}\) :

Energy store in battery

HSS:

Hydrogen storage system

\(LHV_{h}\) :

Low heating value of hydrogen

\(\dot{m}_{h}\) :

Hydrogen mass flow rate

\(\dot{m}_{h,c}\) :

Hydrogen flowing through compressors

\(M_{ht}\) :

Hydrogen in gas tank

OWF:

Offshore wind farm

\(P_{a}\) :

Abandoned offshore wind power

\(P_{e}\) :

Power consumed by electrolyzers

\(P_{gpre}\) :

Present grid power

\(P_{g}\) :

Grid connected power

\(P_{i,b}\) :

Input power of battery storage

\(P_{o,b}\) :

Output power of battery storage

\(P_{L}\) :

Capacity of transmission project

\(P_{w}\) :

Capacity of offshore wind farm

\(P_{wrat}\) :

Rated power of each wind turbine

\(P_{wmax}\) :

Accessible maximal output power

QRS:

Qualified rate of smoothing power fluctuations

ROC:

Rate of power curtailment

\(s_{b}\) :

Scaling factor of battery

\(s_{e}\) :

Scaling factor of electrolyzers

\(s_{g}\) :

Scaling factor of installed capacity

\(v_{i}\) :

Cut-in speed of wind turbine

\(v_{o}\) :

Cut-out speed of wind turbine

\(v_{rat}\) :

Rated speed of wind turbine

\( \alpha_{e}\) :

Spot price of electricity

\( \alpha_{h}\) :

Selling price of hydrogen

\(\beta_{ \, c}^{ \, ref}\) :

Reference pressure

\( \beta_{c}\) :

Normal working pressure of compressor

\(\beta_{0}\) :

Standard atmospheric pressure

\(\eta_{bc}\) :

Charging efficiency of battery

\(\eta_{bd}\) :

Discharging efficiency of battery

\( \eta_{e}\) :

Decomposition efficiency of electrolyzers

\( \eta_{r}\) :

Verage efficiency of rectifier

\(\gamma_{c}\) :

Hydrogen dissipation rate

References

  1. Dong, H., et al., Carbon neutrality commitment for China: from vision to action. Sustainability Science, 2022.

  2. Jing K et al (2021) Analysis, Modeling and Control of a Non-grid-connected Source-Load Collaboration Wind-Hydrogen System. Journal of Electrical Engineering & Technology 16(5):2355–2365

    Article  Google Scholar 

  3. BP, Statistical Review of World Energy 2022. 2022.

  4. Wang, Z.X., J.J. Hu, and B.Z. Liu, Stochastic optimal dispatching strategy of electricity-hydrogen-gas-heat integrated energy system based on improved spectral clustering method. International Journal of Electrical Power & Energy Systems, 2021. 126.

  5. Franco, B.A., et al., Assessment of offloading pathways for wind-powered offshore hydrogen production: Energy and economic analysis. Applied Energy, 2021. 286.

  6. Tazay AF, Samy MM, Barakat S (2020) A Techno-Economic Feasibility Analysis of an Autonomous Hybrid Renewable Energy Sources for University Building at Saudi Arabia. Journal of Electrical Engineering & Technology 15(6):2519–2527

    Article  Google Scholar 

  7. Rashid MU et al (2022) Techno-Economic Analysis of Grid-Connected Hybrid Renewable Energy System for Remote Areas Electrification Using Homer Pro. Journal of Electrical Engineering & Technology 17(2):981–997

    Article  MathSciNet  Google Scholar 

  8. Elnozahy A et al (2021) Optimal Economic and Environmental Indices for Hybrid PV/Wind-Based Battery Storage System. Journal of Electrical Engineering & Technology 16(6):2847–2862

    Article  Google Scholar 

  9. Sandeep SR, Nandihalli R (2020) Optimal Sizing in Hybrid Renewable Energy System with the Aid of Opposition Based Social Spider Optimization. Journal of Electrical Engineering & Technology 15(1):433–440

    Article  Google Scholar 

  10. Barakat, S., et al. Viability Study of Grid Connected PV/Wind/Biomass Hybrid Energy System for a Small Village in Egypt. in 18th International Middle-East Power Systems Conference (MEPCON). 2016. Helwan Univ, Cairo, EGYPT.

  11. Deng ZH, Jiang YW (2020) Optimal sizing of wind-hydrogen system considering hydrogen demand and trading modes. Int J Hydrogen Energy 45(20):11527–11537

    Article  Google Scholar 

  12. Naderipour, A., et al., Comparative evaluation of hybrid photovoltaic, wind, tidal and fuel cell clean system design for different regions with remote application considering cost. Journal of Cleaner Production, 2021. 283.

  13. Samy MM, Mosaad MI, Barakat S (2021) Optimal economic study of hybrid PV-wind-fuel cell system integrated to unreliable electric utility using hybrid search optimization technique. Int J Hydrogen Energy 46(20):11217–11231

    Article  Google Scholar 

  14. Dong J et al (2022) Optimization of Capacity Configuration of Wind-Solar-Diesel-Storage Using Improved Sparrow Search Algorithm. Journal of Electrical Engineering & Technology 17(1):1–14

    Article  MathSciNet  Google Scholar 

  15. Priya PPR et al (2023) Optimal Allocation of Distributed Generation Using Evolutionary Multi-objective Optimization. Journal of Electrical Engineering & Technology 18(2):869–886

    Article  Google Scholar 

  16. Momen, S., J. Nikoukar, and M. Gandomkar, Multi-objective Optimization of Energy Consumption in Microgrids Considering CHPs and Renewables Using Improved Shuffled Frog Leaping Algorithm. Journal of Electrical Engineering & Technology, 2022.

  17. Samy MM et al (2021) Reliability Support of Undependable Grid Using Green Energy Systems: Economic Study. Ieee Access 9:14528–14539

    Article  Google Scholar 

  18. Ramadan, A., et al., Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System. Electronics, 2021. 10(23).

  19. Elbaz A, Guneser MT (2021) Multi-Objective Optimization Method for Proper Configuration of Grid-Connected PV-Wind Hybrid System in Terms of Ecological Effects, Outlay, and Reliability. Journal of Electrical Engineering & Technology 16(2):771–782

    Article  Google Scholar 

  20. Fonseca, J.D., et al., Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach. Applied Energy, 2021. 290.

  21. Tebibel, H., Methodology for multi-objective optimization of wind turbine/battery/ electrolyzer system for decentralized clean hydrogen production using an adapted power management strategy for low wind speed conditions. Energy Conversion and Management, 2021. 238.

  22. Chen H et al (2021) Optimal Energy Management Strategy for an Islanded Microgrid with Hybrid Energy Storage. Journal of Electrical Engineering & Technology 16(3):1313–1325

    Article  Google Scholar 

  23. Alidrissi Y et al (2021) An Energy Management Strategy for DC Microgrids with PV/Battery Systems. Journal of Electrical Engineering & Technology 16(3):1285–1296

    Article  Google Scholar 

  24. Singh S, Chauhan P, Singh N (2020) Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm. Int J Hydrogen Energy 45(16):10070–10088

    Article  Google Scholar 

  25. Marocco, P., et al., An MILP approach for the optimal design of renewable battery-hydrogen energy systems for off-grid insular communities. Energy Conversion and Management, 2021. 245.

  26. Bornapour, M., et al., Probabilistic optimal coordinated planning of molten carbonate fuel cell-CHP and renewable energy sources in microgrids considering hydrogen storage with point estimate method. Energy Conversion and Management, 2020. 206.

  27. Yang GM, Jiang YW (2020) Siting and sizing of the hydrogen refueling stations with on-site water electrolysis hydrogen production based on robust regret. Int J Energy Res 44(11):8340–8361

    Article  Google Scholar 

  28. Shen, X.J., et al., Study on Hybrid Energy Storage Configuration and Control Strategy of Grid-connected Wind Hydrogen System, in 2019 the 2nd International Symposium on Hydrogen Energy and Energy Technologies, Y. Guo, Editor. 2020.

  29. Jiang, Y., Z. Deng, and S. You, Size optimization and economic analysis of a coupled wind-hydrogen system with curtailment decisions. International Journal of Hydrogen Energy, 2019. 44(36).

  30. Zhang, Y., et al., Life Cycle Optimization of Renewable Energy Systems Configuration with Hybrid Battery/Hydrogen Storage: A Comparative Study. Journal of Energy Storage, 2020. 30.

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Acknowledgements

This research is supported by Science and Technology Major Project of Guangdong Province (220311085850132), the Key Research and Development Project of Guangdong Province (2021B0101230004), Special fund for Science and technology innovation of Jiangsu Province (BE2022610), Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development (E1390702) and the Science and Technology Project in Guangzhou (202102020051).

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Tian Tian

  2. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, China

    Tian Tian, Zetao Ma, Qiong Cui, Jie Shu & Hao Wang

  3. Shanwei Branch of Advanced Energy Science and Technology Guangdong Laboratory, Shanwei, China

    Lei Tan

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Tian, T., Ma, Z., Cui, Q. et al. Multi-objective Optimization of a Hydrogen-Battery Hybrid Storage System for Offshore Wind Farm Using MOPSO. J. Electr. Eng. Technol. 18, 4091–4103 (2023). https://doi.org/10.1007/s42835-023-01574-0

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  • DOI: https://doi.org/10.1007/s42835-023-01574-0

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