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2000
Volume 18, Issue 9
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

The coupling of multiple heterogeneous energy sources in an integrated energy system has led to difficulties in coordinating the optimal scheduling of various energy sources. As a typical cyber-physical system, the development scale of an integrated energy system is limited by the communication bandwidth.

Objective

A coordinated optimal scheduling method for integrated energy system based on finite-time event-triggered consensus algorithm is proposed in this paper to achieve the optimal operation of an integrated energy system and lower the burden on the communication network.

Methods

In this paper, the optimal scheduling model of integrated energy system is established, and the finite-time consensus algorithm is applied to solve the model, so that the operating costs of various energy sources can reach the optimal solution within a finite time. Then, a discrete system communication scheme is established so that neighbor nodes exchange state information only at the triggering instants. The stability of the system is analyzed using the Lyapunov stability theory, and it is verified that the system does not exhibit the Zeno phenomenon. Finally, the effectiveness of the proposed optimal scheduling method is verified by case analysis.

Results

The results show that the method can achieve the optimal operation of integrated energy system and effectively reduce the number of communications between neighbor nodes, lowering the burden on the communication network.

Conclusion

An integrated energy system composed of electric-heating-gas-cooling is given to verify the feasibility and effectiveness of the proposed method.

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2026-02-20
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References

  1. MitićP. FedajevA. RadulescuM. RehmanA. The relationship between CO2 emissions, economic growth, available energy, and employment in SEE countries.Environ. Sci. Pollut. Res. Int.2022306161401615510.1007/s11356‑022‑23356‑336175729
    [Google Scholar]
  2. MaH. ChenQ. HuB. SunQ. LiT. WangS. A compact model to coordinate flexibility and efficiency for decomposed scheduling of integrated energy system.Appl. Energy202128511647411647410.1016/j.apenergy.2021.116474
    [Google Scholar]
  3. LiJ.H. ZhuM.S. LuY.J. Review on optimal scheduling of integrated energy systems.Power Sys. Technol.2021452256226910.13335/j.1000‑3673.pst.2021.0020
    [Google Scholar]
  4. ZhangT. YaoZ. HuJ. HuangJ. Multi-time scale rolling optimization scheduling of “nealy-zero carbon park” based on stepped carbon allowance trading.Int. Trans. Electr. Energy Syst.20222022444951511210.1155/2022/4449515
    [Google Scholar]
  5. WangM.J. MuY.F. MengX.J. Optimal scheduling method for integrated electro-thermal energy system considering heat transmission dynamic characteristics.Power Sys. Technol.20204413214210.13335/j.1000‑3673.pst.2019.1097
    [Google Scholar]
  6. HuangS. TangW. WuQ. Network constrained economic dispatch of integrated heat and electricity systems through mixed integer conic programming.Energy201917946447410.1016/j.energy.2019.05.041
    [Google Scholar]
  7. LuoS. PengK. HuC. MaR. Consensus-based distributed optimal dispatch of integrated energy microgrid.Electronics2023126146810.3390/electronics12061468
    [Google Scholar]
  8. DebS. SachanS. MalikP. SinhaS. Local energy systems development in India and United Kingdom: A comprehensive review of latest developments and way forward.Wiley Interdiscip. Rev. Energy Environ.2024131e49610.1002/wene.496
    [Google Scholar]
  9. LiangY. DongH. LiD. SongZ. Adaptive eco-cruising control for connected electric vehicles considering a dynamic preceding vehicle.eTransportation20241910029910029910.1016/j.etran.2023.100299
    [Google Scholar]
  10. DongH. ZhuangW. WuG. LiZ. YinG. SongZ. Overtaking-enabled eco-approach control at signalized intersections for connected and automated vehicles.IEEE Trans. Intell. Transp. Syst.20242554527453910.1109/TITS.2023.3328022
    [Google Scholar]
  11. ZheH YangH AmrS. Harmonic sources modeling and characterization in modern power systems: a comprehensive overview.Electr. Power Syst. Res.202321810923410.1016/j.etran.2023.100299
    [Google Scholar]
  12. EhsanA. PreeceR. Probabilistic assessment of overloaded lines in integrated gas and electricity networks with heat electrification.Electr. Power Syst. Res.202422911019410.1016/j.epsr.2024.110194
    [Google Scholar]
  13. LiuD.L. ZhouX. DaiJ.F. Double layer optimization scheduling strategy for building integrated energy system considering virtual energy storage.J. Shanghai Jiaotong Univ.2024202412710.16183/j.cnki.jsjtu.2024.036
    [Google Scholar]
  14. HemamaliniS. SimonS.P. Maclaurin series-based Lagrangian method for economic dispatch with valve-point effect.IET Gener. Transm. Distrib.20093985987110.1049/iet‑gtd.2008.0499
    [Google Scholar]
  15. ZhanJ.P. WuQ.H. GuoC.X. ZhouX.X. Fast ${λ$-iteration method for economic dispatch with prohibited operating zones.IEEE Trans. Power Syst.201429299099110.1109/TPWRS.2013.2287995
    [Google Scholar]
  16. KimJ.S. EdgarT.F. Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming.Energy20147767569010.1016/j.energy.2014.09.062
    [Google Scholar]
  17. ZhuQ. LuoX. ZhangB. ChenY. Mathematical modelling and optimization of a large-scale combined cooling, heat, and power system that incorporates unit changeover and time-of-use electricity price.Energy Convers. Manage.201713338539810.1016/j.enconman.2016.10.056
    [Google Scholar]
  18. NaserM.Z. Machine Learning for Civil and Environmental Engineers: A Practical Approach to Data-Driven Analysis, Explainability, and Causality.Wiley2023
    [Google Scholar]
  19. LiangS.Q. BianX.Y. LiuT.W. Low-carbon optimal dispatching method for the medium and low voltage distribution system considering the aggregated power of station resources.Power System TechnologyElsevier202410.13335/j.1000‑3673.pst.2023.2011
    [Google Scholar]
  20. HuJ. LiuX. ShahidehpourM. XiaS. Optimal operation of energy hubs with large-scale distributed energy resources for distribution network congestion management.IEEE Trans. Sustain. Energy20211231755176510.1109/TSTE.2021.3064375
    [Google Scholar]
  21. HosseiniM.M. Rodriguez-garciaL. ParvaniaM. Hierarchical combination of deep reinforcement learning and quadratic programming for distribution system restoration.IEEE Transac. Sustain. Energy202314210881098
    [Google Scholar]
  22. ChengL. ZangH. WeiZ. Secure multi-party household load scheduling framework for real-time demand-side management.Transac. Sustain. Energy2022141602612
    [Google Scholar]
  23. AbomazidA.M. El-TaweelN.A. FaragH.E.Z. Optimal energy management of hydrogen energy facility using integrated battery energy storage and solar photovoltaic systems.IEEE Trans. Sustain. Energy20221331457146810.1109/TSTE.2022.3161891
    [Google Scholar]
  24. TiwariS. KumarA. Optimal micro-pmus placement with channel limits using dynamically controlled taguchi binary particle swarm optimization.Electr. Power Compon. Syst.202250181072108610.1080/15325008.2022.2145392
    [Google Scholar]
  25. HaoG.T. HanX.S. Decentralized DC optimal power flow model based on improved Lagrangian and consensus algorithm.Int. J. Elect. Power. Energy Sys.202415510955510.1016/j.ijepes.2023.109555
    [Google Scholar]
  26. LoiaV. VaccaroA. Decentralized economic dispatch in smart grids by self-organizing dynamic agents.IEEE Trans. Syst. Man Cybern. Syst.201444439740810.1109/TSMC.2013.2258909
    [Google Scholar]
  27. LiP. WuQ. YangM. LiZ. HatziargyriouN.D. Distributed distributionally robust dispatch for integrated transmission distribution systems.IEEE Trans. Power Syst.20213621193120510.1109/TPWRS.2020.3024673
    [Google Scholar]
  28. XuD. WuQ. Distributed multi-energy operation of coupled electricity, heating, and natural gas networks.IEEE Transac. Sustain. Energy.20201142457246910.1109/TSTE.2019.2961432
    [Google Scholar]
  29. ChengE.L. WeiZ.N. Distributed optimization of integrated electricity-heat energy system considering multiple energy hubs .Elect. Power Auto. Equip.2022421374410.16081/j.epae.202108020
    [Google Scholar]
  30. Liu J.Z. MaL.F. Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources.Energy202326412598110.1016/j.energy.2022.125981
    [Google Scholar]
  31. LiuJ.Z. WangY.L. A Stackelberg game-based approach to transaction optimization for distributed integrated energy system.Energy202328312847510.1016/j.energy.2023.128475
    [Google Scholar]
  32. WangK. GongP. A continuous algorithm for finite-time consensus of disturbed fractional-order multiagent systems over digraphs.IEEE Trans. Circuits Syst. II Express Briefs202370114148415210.1109/TCSII.2023.3270659
    [Google Scholar]
  33. WenG. Alsaadi F.E. A distributed finite-time consensus algorithm for higher-order leaderless and leader-following multiagent systems.IEEE Transac. Sys. Man, and Cybernetics: Sys.20174771625163410.1109/TSMC.2017.2651899
    [Google Scholar]
  34. HeY. ChenY. LiuY. Event based practical fixed-time consensus control for heterogeneous nonlinear multiagent systems.Inf. Sci.202365011939711939710.1016/j.ins.2023.119397
    [Google Scholar]
  35. LiL. HuangM. Event-triggered consensus control for singular multi-agent systems based on observers.Trans. Inst. Meas. Contr.20234591661167210.1177/01423312221138887
    [Google Scholar]
  36. YangJ. ZhuangX. Integrated energy management strategy based on finite time double consistency under non-ideal communication conditions.IEEE Trans. Netw. Sci. Eng.202310611110.1109/TNSE.2023.3277708
    [Google Scholar]
  37. XiongL.Y. ZhuY.F. Multi objective distributed control of WT-PV-BESS integrated weak grid via finite time containment.Int. J. Elect. Power. Energy Sys.202416510970910.1016/j.ijepes.2023.109709
    [Google Scholar]
  38. WangX. WangG. Distributed finite‐time optimisation algorithm for second‐order multi‐agent systems subject to mismatched disturbances.IET Cont. Theory Appl.202014182977298810.1049/iet‑cta.2020.0901
    [Google Scholar]
  39. LiC. YuX. Distributed event-triggered scheme for economic dispatch in smart grids.IEEE Transac. Smart Grid20161251775178510.1109/TII.2015.2479558
    [Google Scholar]
  40. WangL.Y. DingL. Distributed energy management for smart grids with an event-triggered communication scheme.IEEE Transac. Cont. Sys. Technol.20182751950196110.1109/TCST.2018.2842208
    [Google Scholar]
  41. SongY. CaoJ. RutkowskiL. A fixed-time distributed optimization algorithm based on event-triggered strategy.IEEE Trans. Netw. Sci. Eng.2022931154116210.1109/TNSE.2021.3133541
    [Google Scholar]
  42. BoydP. VandenbergheL. Convex Optimization.Cambridge, U.K.Cambridge University Press2004
    [Google Scholar]
  43. LinP. RenW. FarrellJ.A. Distributed continuous-time optimization: Nonuniform gradient gains, finite-time convergence, and convex constraint set.IEEE Trans. Automat. Contr.20176252239225310.1109/TAC.2016.2604324
    [Google Scholar]
  44. YiP. HongY. LiuF. Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and application to economic dispatch of power systems.Automatica20167425926910.1016/j.automatica.2016.08.007
    [Google Scholar]
  45. Olfati-SaberR. MurrayR.M. 32Olfati-Saber R, Murray R M. Consensus problems in networks of agents with switching topology and time-delays.IEEE Trans. Automat. Contr.20044991520153310.1109/TAC.2004.834113
    [Google Scholar]
  46. NaserM.Z. AlaviA.H. Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences.Architect. Struct. Construct.20233449951710.1007/s44150‑021‑00015‑8
    [Google Scholar]
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