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A network-based virtual slack bus model for energy conversion units in dynamic energy flow analysis

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Abstract

Integrated energy system (IES) is a viable route to “carbon peak and carbon neutral”. As the basis and cornerstone of economic operation and security of IES, energy flow calculation (EFC) has been widely studied. Traditional EFC focuses on the single or distributed slack bus models, which results in the lack of unlimited power to maintain system operation, especially for electric power grid working in islanded or coupled mode. To deal with this problem, this paper proposes a network-based virtual-slack bus (VSB) model in EFC. Firstly, considering the anticipated growth of energy conversion units (ECUs) with power adjustment capacity, the generators and ECUs are together modeled as a virtual slack bus model to reduce the concentrated power burden of IES. Based on this model, a power sensitivity method is designed to achieve the power sharing among the ECUs, where the power can be allocated adaptively based on the network conditions. Moreover, the method is helpful to maintain the voltage and pressure profile of IES. With these changes, a dynamic energy flow analysis including virtual slack bus types is extended for IES. It can realize the assessment of the system state. Finally, simulation studies illustrate the beneficial roles of the VSB model.

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References

  1. Zhao X, Ma X, Chen B, et al. Challenges toward carbon neutrality in China: Strategies and countermeasures. Resour Conserv Recycl, 2020, 176: 105959

    Article  Google Scholar 

  2. Wang Y, Zhang J Y, Zhao Y C, et al. Exergy life cycle assessment model of “CO2 zero-emission” energy system and application. Sci China Tech Sci, 2011, 54: 3296–3303

    Article  Google Scholar 

  3. Xi L, Yu L, Zhang X, et al. Automatic generation control of ubiquitous power Internet of Things integrated energy system based on deep reinforcement learning (in Chinese). Sci Sin Tech, 2020, 50: 221–234

    Article  Google Scholar 

  4. Wu H F, Zhang B W, Qu W J, et al. Integration of a thermochemical energy system driven by solar energy and biomass for natural gas and power production. Sci China Tech Sci, 2022, 65: 1383–1395

    Article  Google Scholar 

  5. Zeng Q, Fang J, Li J, et al. Steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion. Appl Energy, 2016, 184: 1483–1492

    Article  Google Scholar 

  6. Cai N, Khatib A R. A universal power flow algorithm for industrial systems and microgrids-active power. IEEE Trans Power Syst, 2019, 34: 4900–4909

    Article  Google Scholar 

  7. Exposito A G, Ramos J L M, Santos J R. Slack bus selection to minimize the system power imbalance in load-flow studies. IEEE Trans Power Syst, 2004, 19: 987–995

    Article  Google Scholar 

  8. Tong S, Miu K N. A network-based distributed slack bus model for DGs in unbalanced power flow studies. IEEE Trans Power Syst, 2005, 20: 835–842

    Article  Google Scholar 

  9. Ortega J, Molina T, Muñoz J C, et al. Distributed slack bus model formulation for the holomorphic embedding load flow method. Int Trans Electr Energ Syst, 2020, 30: e12253

    Article  Google Scholar 

  10. Kamh M Z, Iravani R. A sequence frame-based distributed slack bus model for energy management of active distribution networks. IEEE Trans Smart Grid, 2012, 3: 828–836

    Article  Google Scholar 

  11. Chertkov M, Backhaus S, Lebedev V. Cascading of fluctuations in interdependent energy infrastructures: Gas-grid coupling. Appl Energy, 2015, 160: 541–551

    Article  Google Scholar 

  12. Qiao Z, Guo Q, Sun H, et al. An interval gas flow analysis in natural gas and electricity coupled networks considering the uncertainty of wind power. Appl Energy, 2017, 201: 343–353

    Article  Google Scholar 

  13. Martinez-Mares A, Fuerte-Esquivel C R. A unified gas and power flow analysis in natural gas and electricity coupled networks. IEEE Trans Power Syst, 2012, 27: 2156–2166

    Article  Google Scholar 

  14. Shabanpour-Haghighi A, Seifi A R. An integrated steady-state operation assessment of electrical, natural gas, and district heating networks. IEEE Trans Power Syst, 2016, 31: 3636–3647

    Article  Google Scholar 

  15. Hu Y, Lian H, Bie Z, et al. Unified probabilistic gas and power flow. J Mod Power Syst Clean Energy, 2017, 5: 400–411

    Article  Google Scholar 

  16. Huang Y, Sun Q, Zhang N, et al. A multi-slack bus model for bi-directional energy flow analysis of integrated power-gas systems. CSEE J Power Energy Sys, 2022, doi: https://doi.org/10.17775/CSEE-JPES.2020.04190

  17. Wu J, Yang X P. An area method for visualizing heat-transfer imperfection of a heat exchanger network in terms of temperature-heat-flow-rate diagrams. Sci China Tech Sci, 2016, 59: 1517–1523

    Article  Google Scholar 

  18. Qin X, Sun H, Shen X, et al. A generalized quasi-dynamic model for electric-heat coupling integrated energy system with distributed energy resources. Appl Energy, 2019, 251: 113270

    Article  Google Scholar 

  19. Choi D H, Park J W, Lee S H. Virtual multi-slack droop control of stand-alone microgrid with high renewable penetration based on power sensitivity analysis. IEEE Trans Power Syst, 2018, 33: 3408–3417

    Article  Google Scholar 

  20. Chen X, Kang C, O’Malley M, et al. Increasing the flexibility of combined heat and power for wind power integration in China: Modeling and implications. IEEE Trans Power Syst, 2015, 30: 1848–1857

    Article  Google Scholar 

  21. Sun Q, Dong Q, You S, et al. A unified energy flow analysis considering initial guesses in complex multi-energy carrier systems. Energy, 2020, 213: 118812

    Article  Google Scholar 

  22. Qadrdan M, Abeysekera M, Chaudry M, et al. Role of power-to-gas in an integrated gas and electricity system in Great Britain. Int J Hydrogen Energy, 2015, 40: 5763–5775

    Article  Google Scholar 

  23. Liu X. Combined analysis of electricity and heat networks. Dissertation for Doctoral Degree. Cardiff: Cardiff University, 2013

    Google Scholar 

  24. Liu X, Jenkins N, Wu J, et al. Combined analysis of electricity and heat networks. Energy Procedia, 2014, 61: 155–159

    Article  Google Scholar 

  25. Liu W, Li P, Yang W, et al. Optimal energy flow for integrated energy systems considering gas transients. IEEE Trans Power Syst, 2019, 34: 5076–5079

    Article  Google Scholar 

  26. Wang Y, You S, Zhang H, et al. Thermal transient prediction of district heating pipeline: Optimal selection of the time and spatial steps for fast and accurate calculation. Appl Energy, 2017, 206: 900–910

    Article  Google Scholar 

  27. Yao S, Gu W, Lu S, et al. Dynamic optimal energy flow in the heat and electricity integrated energy system. IEEE Trans Sustain Energy, 2021, 12: 179–190

    Article  Google Scholar 

  28. Beerten J, Cole S, Belmans R. Generalized steady-state VSC MTDC model for sequential AC/DC power flow algorithms. IEEE Trans Power Syst, 2012, 27: 821–829

    Article  Google Scholar 

  29. De Wolf D, Smeers Y. The gas transmission problem solved by an extension of the simplex algorithm. Manage Sci, 2000, 46: 1454–1465

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    YuJia Huang, QiuYe Sun, Rui Wang & GuangLiang Liu

Authors
  1. YuJia Huang
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  2. QiuYe Sun
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  3. Rui Wang
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  4. GuangLiang Liu
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Corresponding author

Correspondence to QiuYe Sun.

Additional information

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFA0702200), the National Natural Science Foundation of China (Grant Nos. U20A20190 and 62073065), and the Fundamental Research Funds for the Central Universities in China (Grant No. N2204003).

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Huang, Y., Sun, Q., Wang, R. et al. A network-based virtual slack bus model for energy conversion units in dynamic energy flow analysis. Sci. China Technol. Sci. 66, 243–254 (2023). https://doi.org/10.1007/s11431-022-2172-8

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  • DOI: https://doi.org/10.1007/s11431-022-2172-8

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