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Charging and Discharging Strategies for Clustered Regional Energy Storage System

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With the massive expansion of decentralised renewable energy in electricity grid networks, the power supply system has been changed from centralised to decentralised one and from directional to bi-directional one. However, due to the regional energy structure difference in the power imbalance between electricity generation and consumption is becoming more and more serious. A grid-scale energy storage system (ESS) can be one solution to balance the local difference. In this paper, two charging/discharging strategies for the grid-scale ESS were proposed to decide when and with how much power to charge/discharge the ESS. In order to realise the two strategies, this paper focuses on the application of fuzzy logic control system. The proposed strategies aim to reduce the peak power generation, consumption and the grid fluctuation. In particular, this paper analysis the ratio between energy-capacity and rated power of ESS. The performance of the proposed strategies is evaluated from two aspects, the normalised power of ESS itself and the influence on the power grid. Simulation studies were carried out on the rule-based control systems with different energy-to-power (e2p) ratios, and the results show that the proposed charging strategy with combination of extreme situation of power imbalance and the rest capacity of ESS provides a smooth load curve for the regional power grid system while the external power exchange is reduced effectively.
Wydawca
Rocznik
Strony
56--67
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
  • Brandenburg University of Technology Cottbus-Senftenberg, Department of Energy Distribution and High Voltage Engineering, Cottbus, Germany
  • Wrocław University of Science and Technology, Department of Electrical Engineering Fundamentals, Wrocław, Poland
  • Brandenburg University of Technology Cottbus-Senftenberg, Department of Energy Distribution and High Voltage Engineering, Cottbus, Germany
  • Brandenburg University of Technology Cottbus-Senftenberg, Department of Energy Distribution and High Voltage Engineering, Cottbus, Germany
Bibliografia
  • Al-Shetwi, A. Q., Hannan, M. A., Jern, K. P., Mansur, M. and Mahlia, T. M. I. (2020). Grid-Connected Renewable Energy Sources: Review of the Recent Integration Requirements and Control Methods. Journal of Cleaner Production, 253, pp. 119831.
  • Arefifar, S. A. and Alam, M. S. (2019). Energy Management in Power Distribution Systems: Review, Classification, Limitations and Challenges. IEEE Access, 7, pp. 92979–93001.
  • Baltensperger, D., Buechi, A., Segundo Sevilla, F. R. and Korba, P. (2017). Optimal Integration of Battery Energy Storage Systems and Control of Active Power Curtailment for Distribution Generation. IFAC-PapersOnLine, 50(1), pp. 8856–8860.
  • Benadli, R., Bjaoui, M., Khiari, B. and Sellami, A. (2021). Sliding Mode Control of Hybrid Renewable Energy System Operating in Grid Connected and Stand-Alone Mode. Power Electronics and Drives, 6(41), pp. 144–166.
  • Bird, L., Lew, D., Milligan, M., Carlini, E. M., Estanqueiro, A., Flynn, D., Gomez-Lazaro, E., Holttinen, H., Menemenlis, N., Orths, A. and Eriksen, P. B. (2016). Wind and Solar Energy Curtailment: A Review of International Experience. Renewable and Sustainable Energy Reviews, 65, pp. 577–586.
  • Bremen, L. V. (2010). Large-Scale Variability of Weather Dependent Renewable Energy Sources. In: A. Troccoli, eds., Management of Weather and Climate Risk in the Energy Industry. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht, pp. 189–206.
  • Colson, C. M. and Nehrir, M. H. (2011). Algorithms for Distributed Decision-Making for Multi-Agent Microgrid Power Management. In: 2011 IEEE power and Energy Society General Meeting. Detroit, MI, USA.
  • El Bourakadi, D., Yahyaouy, A. and Boumhidi, J. (2020). Multi-Agent System Based on the Extreme Learning Machine and Fuzzy Control for Intelligent Energy Management in Microgrid. Journal of Intelligent Systems, 29(1), pp. 877–893.
  • Hesse, H. C., Schimpe, M., Kucevic, D. and Jossen, A. (2017). Lithium-Ion Battery Storage for the Grid - A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids. Energies, 10(12), pp. 2107.
  • Hossain, R., Maung, A., Than Oo, A. and Ali, A. S. (2013). A Hybrid Machine Learning using Mamdani Type Fuzzy Inference System (FIS) for Solar Power Prediction. Annals of Fuzzy Sets, Fuzzy Logic and Fuzzy Systems, 2(3), pp. 73–113.
  • IEA. (2021). Renewables 2021 Analysis and forecast to 2026. International Energy Agency Publications. Available at: https://www.iea.org/reports/renewables-2021 [Accessed January 2022].
  • Li, J., Wei, W. and Xiang, J. (2012). A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids. Energies, 5(12), pp. 5307–5323.
  • Mohamed, A., Refaat, S. S. and Abu-Rub, H. (2019). A Review on Big Data Management and Decision-Making in Smart Grid. Power Electronics and Drives, 4(39), pp. 1–13.
  • Park, S. W., Cho, K. S., Hoefter, G. and Son, S. Y. (2022). Electric Vehicle Charging Management Using Location-Based Incentives for Reducing Renewable Energy Curtailment Considering the Distribution System. Applied Energy, 305, pp. 117680.
  • Rabbani, M. G., Devotta, J. B. X. and Elangovan, S. (1997). A Fuzzy Set Theory Based Control of Superconductive Magnetic Energy Storage Unit to Improve Power System Dynamic Performance. Electric Power Systems Research, 40(2), pp. 107–114.
  • Ren, Y., Yao, X., Liu, D., Qiao, R., Zhang, L., Zhang, K., Jin, K., Li, H., Ran, Y. and Li, F. (2022). Optimal Design of Hydro-Wind-PV Multi-Energy Complementary Systems Considering Smooth Power Output. Sustainable Energy Technologies and Assessments, 50, pp. 101832.
  • Shams, M. H., Niaz, H., Na, J., Anvari-Moghaddam, A. and Liu, J. J. (2021) Machine Learning-Based Utilization of Renewable Power Curtailments Under Uncertainty by Planning of Hydrogen Systems and Battery Storages. Journal of Energy Storage, 41, pp. 103010.
  • Skfuzzy 0.2 documentation. (2022). Available at: https://pythonhosted.org/scikit-fuzzy/index.html [Accessed January 2022].
  • Song, F., Yu, Z., Zhuang, W. and Lu, A. (2021). The Institutional Logic of Wind Energy Integration: What can China Learn from the United States to Reduce Wind Curtailment? Renewable and Sustainable Energy Reviews, 137, pp. 110440.
  • Steiner, A., Köhler, C., Metzinger, I., Braun, A., Zirkelbach, M., Ernst, D., Tran, P. and Ritter, B. (2017). Critical Weather Situations for Renewable Energies – Part A: Cyclone Detection for Wind Power. Renewable Energy, 101, pp. 41–50.
  • Teo, T. T., Logenthiran, T., Woo, W. L. and Abidi, K. (2016). Fuzzy Logic Control of Energy Storage System in Microgrid Operation. In: 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). Melbourne, VIC, Australia.
  • Vargas, L. S., Bustos-Turu G. and Larran, F. (2015). Wind Power Curtailment and Energy Storage in Transmission Congestion Management Considering Power Plants Ramp Rates. IEEE Transactions on Power Systems, 30(5), pp. 2498–2506.
  • Wang, Y., Song, F., Ma, Y., Zhang, Y., Yang, J., Liu, Y., Zhang, F., Zhu, J. (2020). Research on Capacity Planning and Optimization of Regional Integrated Energy System Based on Hybrid Energy Storage System. Applied Thermal Engineering, 180, pp. 115834.
  • Wu, K. and Zhou, H. (2014). A Multi-Agent-Based Energy-Coordination Control System for Grid-Connected Large-Scale Wind–Photovoltaic Energy Storage Power-Generation Units. Solar Energy, 107, pp. 245–259.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6f427747-92bf-4cb0-82d7-edc45d696adb
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