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Order reduction method for high-order dynamic analysis of heterogeneous integrated energy systems

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  • Wang, L.X.
  • Zheng, J.H.
  • Li, Z.G.
  • Jing, Z.X.
  • Wu, Q.H.

Abstract

The dynamics of heterogeneous integrated energy systems (HIES) coupling of electricity, gas, and heating/cooling subsystems is with high-order characteristics. The complex dynamics, including electromagnetic transients, electromechanical transients, hydraulic and thermal dynamics, exist in different system devices and interacts with others in different time scales. To deal with the difficulty of analysing the multi-time scale dynamics, this paper proposes an order reduction method (ORM) to map high-order modes into a lower dimensional space. Firstly, the complex model of HIES is partitioned and modelled by individuals, and the dynamic characteristics of each unit is revealed. Then modal synthesis is used to obtain feature modes of the system. Similar dynamics of individuals are synthesized whereas different modes are classified in order. When exploring dynamic process of a certain variable, the relevant modes are extracted from the synthesis model, with other modes handled as parametric or algebraic equations. Simulation studies are conducted to investigate dynamic characteristics of a test HIES using the proposed ORM. The results indicate that the proposed method is capable of simulating high-order dynamics of the test system. Furthermore, it has advantages in both computation accuracy and time, compared with equal-step method and conventional subsystem multi-step simulation method.

Suggested Citation

  • Wang, L.X. & Zheng, J.H. & Li, Z.G. & Jing, Z.X. & Wu, Q.H., 2022. "Order reduction method for high-order dynamic analysis of heterogeneous integrated energy systems," Applied Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:appene:v:308:y:2022:i:c:s0306261921015269
    DOI: 10.1016/j.apenergy.2021.118265
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    References listed on IDEAS

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    Cited by:

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    4. Zheng, J.H. & Liang, Z.T. & Li, Zhigang & Wang, F. & Wu, Q.H., 2023. "Online coal consumption characteristics fitting for daily economic dispatch using a data-driven hybrid sequential model," Applied Energy, Elsevier, vol. 341(C).

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