SCIENCE CHINA Information Sciences, Volume 64 , Issue 9 : 191201(2021) https://doi.org/10.1007/s11432-020-3176-x

A review of system modeling, assessment and operational optimization for integrated energy systems

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  • ReceivedMay 5, 2020
  • AcceptedDec 31, 2020
  • PublishedAug 23, 2021



This work was supported by National Key RD Program of China (Grant No. 2017YFA0700300), National Natural Sciences Foundation of China (Grant Nos. 61833003, 61533005, U1908218), Fundamental Research Funds for the Central Universities (Grant No. DUT18TD07), and Outstanding Youth Sci-Tech Talent Program of Dalian (Grant No. 2018RJ01).


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  • Figure 1

    (Color online) A structure of a typical IES.

  • Figure 2

    (Color online) An illustration of the EH model.

  • Figure 3

    The resilience triangle associated to an event, modified from [92].

  • Figure 4

    (Color online) Conceptual resilience trapezoid associated to an event, modified from [93].

  • Figure 5

    (Color online) Operational optimization of IES in different levels.

  • Table 1  

    Table 1Physical laws and state variables of energy subsystems

    Energy subsystem Physical laws State variables
    Nodes Line/pipeline
    Power Kirchhoff's law Magnitude of voltage, phase angle, Active and reactive power
    Ohm's law injection power, etc.
    Natural gas Law of hydromechanics Pressure, etc. Flow in pipeline subsystems
    Law of hydromechanics Heat power, pressure, Flow in pipeline subsystems
    Heating Law of thermodynamics supply and return temperature,
    output temperature, etc.
  • Table 2  

    Table 2Overview of the IES modeling methods

    Category Issues Methods Characteristics
    IES modeling
    Strong nonlinearity
    The mechanism-based methods: local linearization, such as the piecewise linearization [14,15] and the first-order Taylor expansion [16-18]. (1) Easy to implement; (2) strong explainability; (3) difficult to avoid the decrease of the model accuracy in linearization approaches.
    The data-driven methods: the linear regression (LR) [19-21], the support vector regression (SVR) [22-24], and the artificial neural network (ANN) [25-27], etc. (1) Easy to implement; (2) weak explainability; (3) higher modeling accuracy; (4) requiring the measured data of high quality.
    Combining the mechanism-based and data-driven methods: the serial integration [28], the parallel integration [29], and the embedded [30]. (1) Offering advantages of the former two; (2) the time-variant feature should be further investigated based on real-time data.
    Energy conversion processes The energy hub methods [31-36]. (1) Characterized by the energy conversion efficiency; (2) constant assumption might be inappropriate.
    IES modeling
    (multi-time scale
    Considering the transient process of the electric power subsystem The singular perturbation methods [37-39]. Higher computational burden when considering the transient process of the power systems.
    Ignoring the transient
    process of the electric
    power subsystem
    For the natural gas subsystem: the implicit finite difference methods [40,41] and the Wendroff finite difference methods [42-44]. Sensitiveness to the step size in difference methods.
    For the heating subsystem: the quasi-steady methods [45,46] and the quasi-dynamic ones [47,48]. Compared to the quasi-steady methods, the quasi-dynamic ones could produce more accurate models with a higher computational burden.
  • Table 3  

    Table 3Overview of the IES assessment methods

    Category Issues Methods Characteristics
    evaluation index
    For electric power systems: the load index and the system index [77-79]. For multi-energy IES: the indices of energy conversion devices [80-82]. (1) Maturely developed for power systems; (2) remaining to be studied for multi-energy IES.
    Assessment methods The Monte Carlo (MC) simulation [83-85], the analytical methods [86-88], and the combination of the former two [89-91]. In the IES assessment, the multiple-time-scale behavior of different energy resources should be further considered.
    Description of the
    The resilience triangle [92] and the conceptual resilience trapezoid [93]. The conceptual resilience trapezoid is more informative than the resilience triangle.
    Assessment methods The power flow-based performance simulation approach [94], the graph-theory-based method [95], etc. The influence of the multi-energy coupling and conversion on the anti-interference ability of the IES should be further considered.
    Fault issues Fault diagnosis The model-based methods [96,97] and the data-based ones [98,99]. (1) High computational burden and the ability of revealing the nature of failure processes for model-based methods; (2) weak expandability for the data-based ones.
    Cascading failures analysis For electric power systems: the pattern search theory-based methods [100,101], the methods based on self-organized criticality theory [102,103], the complex network theory-based approaches [104,105], etc. (1) Maturely developed for power systems; (2) A systematic methodology remains to be built for the multi-energy IES.
    For multi-energy IES: propagation rule of the faults among different energy subnetworks [106], simulation for the heating and power coupling network [107], etc.
    System restoration For electric power systems: the intelligent optimization algorithms [108], the dynamic programming [109], the bi-level programming [110], etc. (1) Maturely developed for power systems; (2) the multi-energy complementation should be considered for IES system restoration.
    For multi-energy IES: recovery strategy for multi-energy resources [111], sequential operation scheme [112], etc.
  • Table 4  

    Table 4The $\Phi~\Lambda~E\Pi$ resilience metric system [120]

    Resilience metric
    2*Disturbance progress How fast resilience drops? $\Phi$
    How low resilience drops? $\Lambda$
    2 Post-disturbance degraded How extensive is the post-disturbance degraded state? $E$
    3 Restorative How promptly dose the system recover? $\Pi$
  • Table 5  

    Table 5Overview of the IES operational optimization methods

    Category Issues Methods Characteristics
    The device-level Control of renewable energy devices The fuzzy control [153], the output feedback control [154], the sliding mode control (SMC) [155], etc. (1) Impacted by environmental conditions greatly; (2) the dynamics of energy devices should be payed more attention to.
    The system-level
    Considering the multi-energy
    For the IPHS: mixture integer linear programming [156], following a hybrid electric-thermal load [157], mixed-integer non-linear programming [158], etc. Difficult to achieve the optimal allocation for multi-energy resources simultaneously in the IPHS optimization.
    For the IPGS: the methods with P2G technology [159-161].
    Considering the uncertainties of the source side and the demand side The stochastic optimization [162], the robust optimization [163,164], the prediction-based optimization [165,166], etc. (1) High computational load in stochastic optimization; (2) lower computational load in robust optimization; (3) requirement of high prediction accuracy in prediction-based ones.
    The entity-level
    Considering the energy
    The steady-state constraints: the multi-agent GA [167], the dynamic programming [168], etc. High computational complexity in transient-state constraint optimizations.
    The transient-state constraints: the bi-level programming [169,170], etc.
    Considering the production processes The industrial demand response (IDR) method [171,172], the combination of the IDR and the energy storage [173,174], etc. (1) Currently focusing on the microgrids; (2) the IDR for multi-energy optimization needs to be further studied in the future.
    The park-level Optimization with multiple entities The non-interaction methods [175-177], the non-cooperation methods [178,179], and the cooperation methods [180-182]. (1) Currently focusing on the interaction of microgrids; (2) multiple-time-scale dynamics should be further investigated in multi-energy operational optimization.

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