Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting

Authors

DOI:

https://doi.org/10.4108/ew.4653

Keywords:

composite electric power system, ship, energy management, model predictive control, power prediction, neural network, variational modal decomposition

Abstract

A proposed solution is presented to address the issue of rising energy loss resulting from inaccurate power prediction in the predictive energy management strategy for composite electric power electric ship. The solution involves the development of a power prediction model that integrates Archimedes' algorithm, optimized variational modal decomposition, and BiLSTM. Within the framework of Model Predictive Control, this predictive model is utilized for power forecasting, transforming the global optimization problem into one of optimizing the power output distribution among power sources within the predictive time domain, then the optimization objective is to minimize the energy loss of the composite electric power system, and a dynamic programming algorithm is employed to solve the optimization problem within the forecast time domain. The simulation findings demonstrate a significant enhancement in the forecast accuracy of the power prediction model introduced in this study, with a 52.61% improvement compared to the AOA-BiLSTM power prediction model. Concurrently, the energy management strategy utilizing the prediction model proposed in this research shows a 1.02% reduction in energy loss compared to the prediction model control strategy based on AOA-BiLSTM, and a 15.8% reduction in energy loss compared to the ruler-based strategy.

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References

Han, J.G.; Charpentier, J.F.; Tang, T.H. An Energy Man-agement System of a Fuel Cell/Battery Hybrid Boat. EN-ERGIES 2014, 7, 2799-2820.DOI: 10.3390/en7052799 DOI: https://doi.org/10.3390/en7052799

Halima, N. B.; Hadj, N. B.; Chaieb, M.; Neji, R. Energy Management of Parallel Hybrid Electric Vehicle Based on Fuzzy Logic Control Strategies. J CIRCUIT SYST COMP 2023, 32, 01.DOI: 10.1142/S021812662350007X DOI: https://doi.org/10.1142/S021812662350007X

Zhang, Z.; Guan, C.; Liu, Z. Real-time optimization energy management strategy for fuel cell hybrid ships considering power sources degradation. IEEE ACCESS 2020, 8, 87046-87059.DOI: 10.1109/ACCESS.2020.2991519 DOI: https://doi.org/10.1109/ACCESS.2020.2991519

Bukar, A. L.; Tan, C. W. A Review on Stand-alone Pho-tovoltaic-Wind Energy System with Fuel Cell: System Op-timization and Energy Management Strategy. J CLEAN PROD 2019, 221, 73-88. DOI: 10.1016/j.jclepro.2019.02.228 DOI: https://doi.org/10.1016/j.jclepro.2019.02.228

Yuan, Y.P.; Wang, J.X.; Yan, X.P.; Shen, B.Y.; Long, T. A review of multi-energy hybrid power system for ships. RENEW SUST ENERG REV 2020, 132, page range. DOI: 10.1016/j.rser.2020.110081 DOI: https://doi.org/10.1016/j.rser.2020.110081

Wilhelm, J.; Janßen, J.; Mergel, J.; Stolten, D. Energy management for a fuel cell/battery hybrid system. Emobil-ity - Electrical Power Train, Leipzig, Germany, 8-9 Nov 2010. DOI: 10.1109/EMOBILITY.2010.5668030 DOI: https://doi.org/10.1109/EMOBILITY.2010.5668030

Tang, R.L.; Li, X.; Lai, J.G.; A novel optimal ener-gy-management strategy for a maritime hybrid energy system based on large-scale global optimization. APPL ENERG 2018, 228, 254-264. DOI: 10.1016/j.apenergy.2018.06.092 DOI: https://doi.org/10.1016/j.apenergy.2018.06.092

Zhu, J.Y.; Chen, L.; Wang, X.F.; Yu, L. Bi-level optimal sizing and energy management of hybrid electric propulsion systems. APPL ENERG 2020, 260, 114134. DOI: 10.1016/j.apenergy.2019.114134 DOI: https://doi.org/10.1016/j.apenergy.2019.114134

Ge, Y.; Zhang, J.; Zhou, K.; Zhu, J.; Wang, Y. Research on Energy Management for Ship Hybrid Power System Based on Adaptive Equivalent Consumption Minimization Strategy. J MAR SCI ENG 2023, 11, 1271. DOI: 10.3390/jmse11071271 DOI: https://doi.org/10.3390/jmse11071271

Bassam, A.M.; Phillips, A.B.; Turnock, S.R.; Wilson, P.A. Development of a multi-scheme energy management strategy for a hybrid fuel cell driven passenger ship. INT J HYDROGEN ENERG 2017, 42, 623-635.DOI: 10.1016/j.ijhydene.2016.08.209 DOI: https://doi.org/10.1016/j.ijhydene.2016.08.209

Lakshmanarao, A.; Kumar, G. V.; Kiran, T. S. R. An Ef-fective Multiple Linear Regression Model for Power Load Prediction. JETIR 2018, 5, 756-760.

Yang, X.Y.; Wang, S.C.; Yan, P.; Chen, J.W.; Meng, L.Z.C. Short-term photovoltaic power prediction with sim-ilar-day integrated by BP-AdaBoost based on the Grey-Markov model. ELECTR POW SYST RES 2023, 215, part A. DOI: 10.1016/j.epsr.2022.108966. DOI: https://doi.org/10.1016/j.epsr.2022.108966

Xiang, L.; Liu, J.N.; Yang, X.; Hu, A.J.; Su, H. Ultra-short term wind power prediction applying a novel model named SATCN-LSTM. ENERG CONVERS MANAGE 2022, 252, 115036. DOI: 10.1016/j.enconman.2021.115036 DOI: https://doi.org/10.1016/j.enconman.2021.115036

Hu, K.Y.; Wang, L.D.; Li, W.J.; Cao, S.H.; Shen, Y.Y. Forecasting of solar radiation in photovoltaic power station based on ground-based cloud images and BP neural net-work. IET GENER TRANSM DIS 2022, 16, 333-350.DOI: 10.1049/gtd2.12309 DOI: https://doi.org/10.1049/gtd2.12309

Yan, A.Y.; Gu, J.B.; Mu, Y.H.; Li, J.J.; Jin, S.W.; Wang, A.X. Research on photovoltaic ultra short-term power pre-diction algorithm based on attention and LSTM. IOP Conference Series: Earth and Environmental Science, Xiamen, China, 17-19 Nov 2020.DOI: 10.1088/1755-1315/675/1/012078 DOI: https://doi.org/10.1088/1755-1315/675/1/012078

Zhao, H.X.; Zhou, Z.L.; Zhang, P.Z. Forecasting of the Short-Term Electricity Load Based on WOA-BILSTM. INT J PATTERN RECOGN 2023, 37, 11. DOI: 10.1142/S0218001423590188 DOI: https://doi.org/10.1142/S0218001423590188

Putz, D.; Gumhalter, M.; Auer, H. A novel approach to multi-horizon wind power forecasting based on deep neural architecture. RENEW ENERG 2021, 178, 494-505.DOI: 10.1016/j.renene.2021.06.099 DOI: https://doi.org/10.1016/j.renene.2021.06.099

Houran, M.A.; Bukhari, S.M.S.; Zafar, M.H.; Mansoor, M.; Chen, W.J. COA-CNN-LSTM: Coa-ti optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. APPL ENERG 2023, 349, 121638.DOI: 10.1016/j.apenergy.2023.121638 DOI: https://doi.org/10.1016/j.apenergy.2023.121638

Chen, H.; Zhang, Z.H.; Guan, C.; Gao, H.B. Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship. ENERGY 2020, 197, 117285.DOI: 10.1016/j.energy.2020.117285 DOI: https://doi.org/10.1016/j.energy.2020.117285

Wang, S.; Ma, H.Y.; Zhang, Y.D.; Li, S.Y.; He, W. Re-maining useful life prediction method of lithium-ion bat-teries is based on variational modal decomposition and deep learning integrated approach. ENERGY 2023, 282, 128984.DOI: 10.1016/j.energy.2023.128984 DOI: https://doi.org/10.1016/j.energy.2023.128984

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Published

03-04-2024

How to Cite

1.
Chen H, Huang X. Research on Predictive Control Energy Management Strategy for Composite Electric Ship Based on Power Forecasting . EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 3 [cited 2024 Apr. 25];11. Available from: https://publications.eai.eu/index.php/ew/article/view/4653

Issue

Section

Intelligent Energy Monitoring System Using Internet of Things (IoT)