1. Introduction
Over the past few decades, fragmented and suboptimal energy utilization has resulted in the rapid depletion of fossil fuels and the excessive emission of greenhouse gases, leading to a severe ecological and environmental crisis [
1]. To meet this challenge, the integrated electricity–heat–gas energy system (IEHGES) has been widely promoted and deployed [
2]. Distinguishing from the conventional energy infrastructure, IEHGES enables complementarities and synergies between heterogeneous energy resources by coupling different energy systems through energy-conversion devices, thus improving the efficiency and flexibility of energy management [
3]. As an effective analytical tool for quantifying the interdependency within IEHGES, the steady-state energy-flow calculation (EFC) provides a rapid state snapshot of IEHGES at a given time step, and it has been widely applied in the long-term collaborative planning, economic dispatch, and security assessment of IEHGES [
4].
Focusing on the energy-flow modeling and model solution strategy, numerous studies have been conducted on the EFC framework of IEHGES [
5]. Nevertheless, most of the current research accomplishments remain largely theoretical, making them inadequate for engineering deployment. On the one hand, the portability of existing energy-flow models is limited due to their case-specific nature. The effectiveness of these models cannot be assured if the physical system changes. On the other hand, the coupling mode of IEHGES determined by the operation status of energy devices has often been fixed in prior studies, limiting the universality of the corresponding EFC solution strategies for diverse interdependencies. Last but not least, the traditional framework formulations are mostly presented in a recursive form with redundant intermediate variables, which is neither concise nor computationally friendly. Therefore, investigations on standardized modeling and solution approaches are desired to develop a universal steady-state EFC tool for IEHGES with which the energy-flow analysis of arbitrary IEHGESs can be easily implemented.
The IEHGES consists of coupling devices and energy subnetworks. The energy hub (EH) concept was introduced to describe the collection of coupling devices [
6], where the interactions between energy subnetworks were abstracted to a universal coupling matrix. To pragmatize this concept, several versions of standardized modeling methods have been proposed in [
7,
8], with which the energy flow of EH can be compactly automated into a matrix. However, the above studies only focused on EH itself, the detailed state of another important component of IEHGES—energy subnetworks—was ignored. Typically, the district electricity network (DEN), district heat network (DHN), and district gas network (DGN) constitute the subnetworks of IEHGES. Compared to others, studies on DEN models have been quite in-depth. As the mainstream model, the alternating current (AC) model portrays the operation state of the DEN with a set of nonlinear algebraic equations (AEs). The AC model is streamlined into a universal matrix form by introducing the nodal admittance matrix. To solve this model, the holomorphic embedded (HE) method [
9], Gauss–Seidel method, fast decoupled load-flow method [
10], etc. were developed, among which the Newton–Raphson (NR) algorithm [
11] and its variants have been widely adopted due to their robustness and quadratic convergence. In [
12], the matrix-based concise formula of an NR iterative matrix was directly derived from the streamlined AC model to avoid the traditional cumbersome expression form, realizing the standardized modeling and solution of the DEN model. The same EFC framework has been employed in commercial software such as MATPOWER [
13], demonstrating its practicality for engineering applications.
Since DHN transmits mass flow and thermal energy simultaneously, the DHN model is divided into hydraulic and thermal parts. The universal matrix form of the hydraulic model can be derived using graph theory [
14]. Specifically, there are three versions of hydraulic models, i.e., a branch-flow model (BFM) [
15], a nodal-pressure model (NPM) [
16], and a pressure-flow model (PFM) [
17]. In contrast to other models, BFM balances the initial condition sensitivity and solution efficiency when iterative solution approaches are applied, and it is thus broadly utilized for IEHGES modeling. As for the thermal part, a pseudo-dynamic thermal model that ignores the temperature variation over time was built. Due to the directional thermal power transmission, the thermal model was usually represented as a set of mixture-temperature equations in recursive form. Recently, the matrix-based thermal models for quality-regulated and quantity-regulated DHN were derived in [
18,
19] and [
20,
21,
22], respectively, laying the foundation for the standardized thermal model. However, these models are not inapplicable to arbitrary DHN topologies since their intrinsic nature cannot be strictly aligned with the original model. Additionally, models in any form fail to tackle the reversed mass flow (RMF) problem [
23,
24], which further undermines their universality. According to model complexity, different EFC solution strategies have been used. Gauss elimination and LU factorization were adopted to linearly calculate the decoupled thermal model [
19], which are effective but scene-limited. A HE-based, non-iterative approach was raised to solve the whole DHN model in [
22]. Although it takes advantage of analyticity, its reported superior convergence is subjected to suitable initial guesses generated via iterative methods. In comparison, the NR-based method remains the most prevalent, but its iterative matrix for DHN is either manually derived [
25] or incompletely generated from the flowchart [
15], which yields huge influences on the performance of the NR-based EFC framework. Hence, there are still some research gaps in the standardized modeling and solution of the DHN model that warrant further exploration.
To date, vast types of gas energy-flow models have been proposed for different research purposes, but they are hardly compatible with each other. On the one hand, most of the available studies have only considered the hydraulic parts of gas networks. Similar to DHN, the formulations of gas hydraulic models can also be divided into BFM [
26], NPM [
25], and PFM [
27]. Aside from the model type, existing hydraulic models varied significantly in modeling gas compressors. The gas hydraulic model of IEHGES was established in [
16] while the compressor model was ignored. For those gas-network models that contained compressors [
9,
22,
25,
28,
29,
30], the compressors were usually modeled to operate in a constant-compression-ratio mode. To enhance the universality of gas compressor modeling, an IEHGES model considering three operation modes of compressors was proposed in [
31]. However, the difference in compressor-driving modes was still neglected. On the other hand, the widespread injection of alternative gas, such as hydrogen (H
2) and synthetic natural gas (SNG), has led to increasingly complex gas properties in DGN, significantly impacting the gas energy flow. The simulation method of gas networks with hydrogen injections was first proposed in [
32], where both gas density and gross calorific value (GCV) varied with the gas composition. On this basis, DGN models containing heterogeneous gas sources were established for the NR-based EFC and optimal EFC in [
28] and [
33], respectively. Since gas composition calculation depended on the gas-flow direction, the aforementioned models were represented using either flowcharts or recursive AEs, and the related iterative matrices were incompletely and manually derived. Additionally, although a compact DGN model considering hydrogen blending was formulated in [
34] based on the gas admittance matrix, compressors were ignored, and its implementation was still spatially and temporally inefficient since abundant dense matrices and matrix operations were involved. In sum, it is critical yet challenging to achieve a concise formulation and efficient computation of the DGN model while ensuring its universality.
The EFC solution approaches of IEHGES can be categorized as either integrated or disaggregated. The specific implementation of either approach relies on determining the coupling mode, but most of the current EFC methods were dedicated to certain preconfigured coupling modes, which lack portability. To address this issue, four typical coupling modes were classified in [
4] for an interaction analysis of IEHGES. Based on the operation modes of electricity–heat-coupling devices, the calculation sequence of the decomposed approach was classified into three types [
35]. In [
19], six coupling modes of the integrated electricity–heat system were sorted out through permutation and combination. All the existing determination algorithms were designed from the perspective of distinguishing specific devices. Given the vast number of possible combinations of coupling devices, those algorithms are evidently insufficient, resulting in the shallow revelation of the interdependency mechanism of IEHGES.
The taxonomy of the above literature is presented in
Table 1 for a more intuitive comparison and analysis.
Inspired by [
13], this paper aims to develop a universal framework for the automatic EFC of IEHGES. Unlike previous studies that overwhelmingly focused on enhancing the performance of energy-flow analysis through solution strategy improvements, this work was conducted from the perspective of revising and reformulating the EFC model (including the subnetwork model and its iterative matrix). Additionally, the interdependency mechanism of IEHGES is comprehensively elucidated in terms of the nature of coupling relationships. Depending on the imported system parameters, the steady-state analysis of IEHGES can be carried out automatically and efficiently under the proposed framework. The main contributions of this paper include the following:
Building upon the proposed generalized heat energy-flow direction, a standardized DHN model is formulated in a compact matrix representation, which is applicable to arbitrary heat networks with precision. By circumventing the RMF problem, its convergence domain is inherently broadened.
A novel matrix-based DGN model consisting of hydraulic and composition-tracking models is constructed in a standardized manner in which the diversity of gas-network components is comprehensively considered. The varying gas-compressibility factor is first incorporated into the static EFC of IEHGES, and its impact on the overall system state is fully demonstrated.
The iterative matrices of the proposed models are derived while ensuring conciseness and sparsity. All iterative matrices follow the gradient-descent direction and can be obtained directly through matrix operations, thereby improving the efficiency of the NR-based EFC process.
A coupling chain-based analysis method that fully reveals the interdependency mechanism of IEHGES under any combination of coupling devices is proposed, ensuring the automatic execution of an NR-based universal EFC framework for any IEHGES configuration.
In addition to its contributions to energy-flow analysis, the proposed methodology provides significant advantages for other research aspects of IEHGES in which the rapid and repetitive formulation of the energy-flow models or the iterative matrices is required. The application prospects of such standardized modeling methodology include, but are not limited to, optimal EFC, probabilistic EFC, stability analysis, and a risk assessment of IEHGES.
The remainder of this article is arranged as follows. The matrix-based IEHGES model is derived in
Section 2. The iterative matrices are compactly formulated in
Section 3. In
Section 4, a universal EFC framework is proposed based on the thorough discussion of the interdependency mechanism of IEHGES. Then,
Section 5 analyzes the effectiveness of the proposed framework by case studies. Finally,
Section 6 concludes this article.
4. EFC Solution Method
To address the complicated coupling relationships between subnetworks, a thorough discussion of the interdependency mechanism within IEHGES is presented, based on which the NR-based integrated and decomposed EFC methods (NRIM and NRDM) are developed in this section.
4.1. Coupling Chain of the Device
Coupling devices facilitate energy interactions between individual subnetworks. Therefore, to investigate the interdependency mechanism within IEHGES, the attributes of coupling devices are classified in
Table 3.
As described in
Table 3, a single device can serve as the slack units of multiple subnetworks, whereby defining its category. The node types of device ports and the coupling relationships established by devices are all reflected in device categories. For instance, if a CHP works in FEL mode, its category will be ‘ES’, and its coupling chain will be ‘G←E→H’, indicating that the operating states of the DGN and DHN depend on that of the DEN. For another example, an MC categorized as ‘NS’ may either be shut down or powered via an independent electric source, wherein no coupling chain or node type is present. Note that all the coupling chains are directional. The initiator of the arrow is the dominator of this coupling chain, influencing the state of the terminal of the arrow. Additionally, the coupling chains can be superimposed under certain constraints, creating more complicated interdependencies.
4.2. Interdependency Mechanism Analysis
Generally, each subnetwork has one slack unit. Following this assumption, coupling devices with varying numbers, types, and categories can be utilized to interconnect each subnetwork, leading to numerous possible combinations of devices. Although the physical connection schemes within IEHGES are innumerable, the potential combinations of superimposable coupling chains remain finite, which is generalized in
Figure 5. In
Figure 5, the DGN dominates the DEN via MC/P2G, the DHN influences the DGN and DEN via GB/CHP and EB/HP/CHP/CP, respectively, while the DEN regulates the DHN and DGN via CHP and GTG/CHP, respectively.
The generalized interdependency mechanism of IEHGES is further concreted into different coupling modes, as illustrated in
Figure 6. According to the numbers and types of closed loops formed by coupling chains, four coupling modes are identified from the limited combination possibilities of coupling chains, which are described as follows:
Mode 1: Subnetworks are unidirectionally coupled or decoupled with each other;
Mode 2: Mutual coupling exists merely between DEN and DGN;
Mode 3: Mutual coupling exists merely between DEN and DHN;
Mode 4: Mutual couplings exist simultaneously between DEN and DGN, and between DEN and DHN.
Specifically, mode 1 contains three scenarios: (1) the slack unit of DEN is an independent source, namely mode 1.1; (2) GTG belongs to ‘ES’, while MC and P2G belong to ‘NS’, namely mode 1.2; and (3) CHP works as ‘ES’, while MC, P2G, EB, HP, CHP, and CP work as ‘NS’, namely mode 1.3.
Mode 2 is determined when IEHGES satisfies one of the following two conditions: (1) GTG belongs to ‘ES’, while MC or P2G belongs to ‘GS’, namely mode 2.1; (2) CHP works as ‘ES’, MC, or P2G works as ‘GS’, while GB, EB, HP, CHP, and CP work as ‘NS’, namely mode 2.2.
Scenarios become unitary for modes 3 and 4. In mode 3, CHP works as ‘ES’, and MC and P2G work as ‘NS’, while EB, HP, CHP, or CP works as ‘HS’. In mode 4, CHP belongs to ‘ES’ or MC, and P2G belongs to ‘GS’, while GB, EB, HP, CHP, or CP belongs to ‘HS’.
On this basis, the interdependency category of IEHGES can be automatically recognized via Algorithm 1. The solution procedure for each coupling mode is identical in NRIM but quite different in NRDM. In modes 1.1–1.3, the EFC solution is implemented once for each subnetwork in three distinct sequences. The specific solution orders are determined according to the directions of coupling chains, as shown in
Figure 6a, which are DHN→DGN→DEN, DHN→DEN→DGN, and DEN→DHN→DGN, respectively. Things get more complicated in modes 2–4 due to mutual effects. Mode 2–4 are related to four different solution processes in NRDM, with the specific solution sequences indicated in
Figure 6b–d, which can be illustrated as DHN→DGN↔DEN for mode 2.1, DGN↔DEN→DHN for mode 2.2, DHN↔DEN→DGN for mode 3, and DHN→DGN→DEN→… for mode 4. Taking mode 3 as an instance, the energy flow of the DHN is solved first with an assumed heat power of the CHP working as ‘ES’. Subsequently, the energy flow of DEN is determined using the calculated electric power of the CHP working as ‘HS’. The EFC calculations of these two mutually coupled subnetworks are iteratively performed until the convergence is achieved. After that, the DGN model can be directly solved to finalize the EFC results of IEHGES.
Algorithm 1: Coupling mode determination. |
1 | Initialization: Input parameters of the coupling devices |
2 | if GTG & CHP belong to ‘NS’ then |
3 | Coupled in mode 1.1; break |
4 | else |
5 | if GTG belongs to ‘ES’ then |
6 | | if MC & P2G belong to ‘NS’ then |
7 | | | Coupled in mode 1.2; break |
8 | | else |
9 | | | Coupled in mode 2.1; break |
10 | else |
11 | | if MC & P2G belong to ‘NS’ then |
12 | | | if EB & HP & CHP & CP belong to ‘NS’ then |
13 | | | | Coupled in mode 1.3; break |
14 | | | else |
15 | | | | Coupled in mode 3; break |
16 | | else |
17 | | | if GB & EB & HP & CHP & CP belong to ‘NS’ then |
18 | | | | Coupled in mode 2.2; break |
19 | | | else |
20 | | | | Coupled in mode 4; break |
21 | | | end if |
22 | | end if |
23 | end if |
24 | end if |
25 | Output the coupling mode of the IEHGES. |
4.3. NR-Based Solution Strategy
The general iteration form of the NR method is given as follows:
where the following applies:
λ is the damping factor, which is usually default as 1;
x is the unknown variable vector;
F is the mismatch vector;
k is the iteration index; and
J is the iterative matrix.
For NRIM, the state variables is
x = [
xe;
xh;
xg], the mismatch equations is
F = [
Fe;
Fh;
Fg], and
J is formulated as follows:
The iterative matrix
J consists of two parts. The formulations for diagonal block matrices
Jee,
Jhh, and
Jgg have been derived in
Section 3. For the remaining part,
xy denotes the effect of subnetwork y to subnetwork x, whose formulation depends not only on the coupling device model but also on the category and node type of the device [
25]. The dimension of
J equals the number of state variables, which is (
Ne +
Npq – 1) + (
Nhp + 2
Nhn) + (3
Ng + N
ge).
Npq/
Nhp/
Nhn/
Ng/
Nge is the number of
PQ buses/heat pipelines/heat non-source nodes/gas nodes/gas equipment. Algorithm 2 outlines the detailed EFC solution procedures for NRIM. The solution starts from the system parameter initialization and elementary incidence matrix generation. Then, based on the proposed numerical model, the NR iteration and intermediate variable update are executed continuously until either the maximum value of |
F| falls below
ζ or the iteration number exceeds
K.
Algorithm 2: NR-based integrated method (NRIM) |
1 | Initialization: Input convergence tolerance ζ, maximum iteration number K, initial value of x = [θ; |V|; m; ′; ′; Π; fE; ρ; q], parameters of networks and coupling devices; the iteration index k = 0. |
2 | Compute the elementary incidence matrices. |
3 | while k ≤ K do |
4 | Compute mismatch vector Fk according to Equations (40), (42) and (45). |
5 | if max(|Fk|) ≤ ζ then |
6 | | break |
7 | end if |
8 | Compute iterative matrix Jk according to Equation (63). |
9 | Update xk+1 according to Equation (62). |
10 | Update Ahd and Agd according to Equations (8) and (20). |
11 | Update mq, ′ and ′ according to Equations (3), (14) and (17). |
12 | Update ε, ZP, ZC,in and θC according to Equations (28), (30)–(32). |
13 | Update fP, HC, and τC according to Equations (19)–(22). |
14 | Update k = k + 1. |
15 | end while |
16 | Output the energy-flow results of the IEHGES. |
Compared with NRIM, NR calculation is individually applied to each subnetwork for NRDM. The specific EFC process for each subnetwork can be obtained by excluding other subnetworks from Algorithm 2. Subsequently, NRDM is implemented by performing the EFC solution for each subnetwork in particular orders described in
Section 4.2.
In summary, a matrix-based universal framework for the steady-state analysis of IEHGES has been established, which achieves the standardized modeling and solution of multi-energy flow. The flow chart of the framework is presented in
Figure 7, and the main steps are as follows:
Step 1: Input the system data in a standard format [
44], including the network data, device data, and solution algorithm data;
Step 2: Initialize the values of state variables
θ,
|V|,
m,
′,
′,
Π fE,
ρ, and
q. In this work, the commonly adopted initialization method in [
31] is employed;
Step 3: Generate the elementary incidence matrices
Ah,
Bh,
Ag,
Bg,
W, and
TC according to the input data. Classify the categories and node types of the coupling devices according to
Table 2;
Step 4: Execute the corresponding algorithm according to the solution instruction. If NRIM is selected, formulate the expression of J based on the device data, and then implement Algorithm 2; if NRDM is chosen, determine the coupling mode by implementing Algorithm 1 and then implement the corresponding NRDM algorithm;
Step 5: Output the steady-state analysis results of IEHGES.
5. Case Studies
To validate the performance of the proposed framework, a medium test system comprising the IEEE 30-bus DEN [
45], 32-node Barry Island DHN [
15], 14-node DGN [
40], and several coupling devices is established, and its schematic is shown in
Figure 8. The CHP, GB, and natural gas well serve as the slackers of DEN, DHN, and DGN, respectively. Circulation pumps at nodes H1, H31, and H32 are driven via buses E8, E2, and E5. Compressors 1 and 4 are powered via buses E15 and E20, while the remaining compressors are TCs extracting from the inlet nodes. Compressors 1–4 all work in mode I. Hydrogen storage, SNG storage, and P2G constitute the distributed gas sources. Accordingly, the described system is judged to be operating in the most complex mode, i.e., mode 4.
The detailed parameters of the subnetworks and coupling devices are provided in [
44]. The convergence and iteration tolerances for NR calculation are 10
−8 and 100 times, respectively. The DEN and DHN are initialized at |
V| = 1 p.u.,
θ = 0°,
m = 100 kg/s,
′ = 100 °C and
′ = 30 °C, while a pressure difference of 5–10% between the receiving and sending node is considered for initializing
p.
fE is then calculated from
p. The gas property values of the natural gas are adopted to initialize
ρ and
q, which are 0.610 6 and 41.04 MJ/m
3, respectively. Gas temperature is fixed at 288.15 K, and STP is defined as 293.15 K and 1.013 25 bar. Other characteristic parameters of heterogeneous gas sources are listed in
Table 4. Simulations in this section are solved by MATLAB 2023a on a laptop with Intel(R) Core(TM) i9-13900H CPU @2.60GHz and 16.0 GB memory.
5.1. Accuracy Analysis
Due to the lack of a control group, the effectiveness of the proposed gas composition-tracking model is first verified through quantitative analysis. The gas property results of the proposed matrix-based models (PM) solved via NRIM and NRDM are depicted in
Table 5 and
Table 6. It can be observed that both NRIM and NRDM yield identical results, further validated by calculations from Equations (30)–(32) and (46). Unlike conventional DGN models with constant gas characteristics, multiple gas properties vary across the network, demonstrating the gas composition-tracking capability of PM. As illustrated in
Table 5, specific gravity and GCV are closer to discrete variables compared with the continuously varying pressure, as their values merely depend on the gas molar fraction.
Table 6 shows that compressors 1 and 3, as well as 2 and 4, have the same polytropic coefficients since the gas compositions are identical at their inlet ends, which is consistent with Equation (32). Conversely, the gas-compressibility factors for pipelines and compressors differ from each other. The main reason is that they are affected not only by the gas molar fraction but also by pipeline pressure, as indicated in Equations (30) and (32).
After that, the traditional EFC models (TM) for DHN [
15] and DGN [
28], whose mismatch vectors and iterative matrices are formulated in recursive form, are built for comparison. The proposed gas composition-tracking model is also added in TM but programmed recursively. Since no modification has been made for DEN, the modeling of DEN in PM and TM refers to [
12]. PM and TM are tested on the medium test system, and the corresponding absolute errors of EFC results between PM and TM are given in
Table 7. As depicted in
Table 7, the maximum absolute errors in NRIM and NRDM are 1.94 × 10
−9 MJ/m
3 and 1.89 × 10
−9 MJ/m
3, respectively, which are less than 10
−8, indicating the accuracy of PM. In summary, the proposed framework is capable of characterizing the energy-flow distribution of IEHGES while precisely tracking the gas composition.
5.2. Universality Analysis
In this subsection, the proposed framework is applied to various IEHGES operation scenarios to demonstrate its universality. All the designed scenarios below are generated by revising the input files of the benchmark scenario, i.e., the original medium test system.
5.2.1. Diversity of Heat Sources
In the benchmark scenario, loads at heat nodes H7 and H17 are replaced with two thermal storage devices to represent the diversity of heat sources. The charging/discharging power of thermal storage equals the original nodal load power. To manifest the applicability of PM across different heat sources, the following two scenarios are considered:
The heat energy flow of Scenario 1 is modeled through PM and TM, with their supply temperature results in
Figure 9a. A notable deviation in supply temperature is observed between TM and PM around H7. This discrepancy arises because TM incorrectly defaults the supply temperature at H7 to
, whereas H7 is actually a mixing node. This miscalculation of DHN propagates through other subnetworks along the coupling chains, ultimately leading to an inaccurate assessment of the IEHGES operation status. Furthermore, other DHN formulations in [
18,
19,
20,
21,
22] encounter similar issues, as they lack nodal temperature equations for source nodes.
Once the discharging thermal storage transfers from H7 to H17 in Scenario 2, PM remains effective in characterizing the energy flow distribution, whereas TM fails to converge as reported in [
22]. The mass flow results for both the benchmark scenario and Scenario 2 are shown in
Figure 9b. It is observed that the mass flow in pipeline 16 reverses from 20.97 kg/m
3 to −20.56 kg/m
3 due to the state switching of the thermal storage. Since existing DHN models do not account for the generalized heat energy-flow direction, they are more sensitive to inappropriate hydraulic initialization, resulting in their failure in Scenario 2.
5.2.2. Diversity of Gas Properties Considered in DGN
Although PM is established towards heterogeneous gas-source mixing, it is versatile enough to be applied in various scenarios where different categories of gas properties are considered:
Scenario 3: Based on the benchmark, part of the gas properties are treated as constants by setting
ZP,
ZC,in, and
ω to 0.8, 0.8, and 1.309 [
25], respectively.
Scenario 4: Based on Scenario 3, only natural gas sources are injected into the DGN. It is achieved by setting ρ and q to constants 0.610 6 and 41.04 MJ/m3, respectively, while the injection amount of alternative gas sources is adjusted to be energy-equivalent to that in other scenarios.
Note that gas loads in different scenarios are also energy-equivalent in this subsection.
Table 8 and
Table 9 and
Figure 10 display the EFC results of different scenarios solved through PM.
As depicted in
Table 8, the nodal pressures in Scenarios 3 and 4 differ from the benchmark due to the consideration of various gas properties. Gas flow in the DGN is primarily affected by load demand. A reduced GCV, combined with a constant load energy demand, increases gas flow. According to the gas pipeline equation
= Δ
Π/(
Cpipeρ), although the gas density
ρ decreases, the pressure drop, Δ
Π, increases because of the rising gas flow,
fP, and the fixed pipeline parameter,
Cpipe. This explains why the nodal pressure in Scenario 3 is lower than that in Scenario 4. Furthermore, according to
Table 6, the pipeline compressibility factors in the benchmark scenario are greater than those in Scenario 3. As defined in the gas pipeline equation, this difference further facilitates the pressure drop in the benchmark. Analyzing the pressure differences between Scenario 3/4 and the benchmark in
Table 8 reveals that the pressure deviations caused by each pipeline accumulate along the gas-flow direction, eventually reaching a maxima value at node G14. In terms of gas flow,
Figure 10 illustrates that Scenario 4 has the lowest flow rate due to its highest GCV. The gas flow of Scenario 3 is similar to that of the benchmark since their load energy demands and GCVs are comparable. Per Equation (21), the higher the compressor flow or compressibility factor, the larger the energy consumption of the compressor. This is why
PC and
τC in
Table 9 show a descending trend from the benchmark to Scenario 3 and then to Scenario 4. The energy-flow discrepancies in the DGN across different scenarios propagate to the DEN through MCs, causing fluctuations in the output of CHP, which subsequently alters the output of GB. Consequently, the operation states of the DEN and DHN also vary in different scenarios due to the interdependencies within IEHGES.
In brief, PM has been proven to be scalable for various application scenarios. Unlike traditional DGN models, PM avoids inaccurate estimations of nodal gas pressure by comprehensively considering the impacts of physical characteristics on gas-flow distribution. This capability is crucial for developing rational gas-network scheduling strategies and safeguarding the gas supply quality of downstream customers.
5.2.3. Diversity of Compressor Operating and Driving Modes
To further illustrate the universality of PM, scenarios featuring various operating and driving modes of compressors are designed:
Scenario 5: Compressors 1–4 are replaced with TCs from the benchmark, operating in modes II, I, III, and V, respectively.
Scenario 6: Compressors 1–4 are replaced with MCs from the benchmark, operating in modes I, III, II, and IV, respectively.
Figure 11 represents the EFC results of each subnetwork in different scenarios. As shown in
Figure 11a, the gas pressures of Scenarios 5 and 6 are overall lower and higher than those of the benchmark, respectively, due to the different operation modes and control parameters of compressors. The number of MCs driven via the DEN in benchmark, Scenario 5, and Scenario 6 are 2, 0, and 4, respectively. Consequently, the voltage angles in the benchmark lag behind those in Scenario 5 but lead those in Scenario 6, as depicted in
Figure 11b. According to the varying mass flow of DHN given in
Figure 11c, this influence on DEN is further extended to DHN. Therefore, it can be deduced that the universality of the IEHGES energy-flow analysis method for diverse compressors is necessary.
5.3. Robustness Analysis
The convergence performance of PM is compared with that of TM by adjusting the initial values (including |V|, m, p, and the initial guess ΦCHP in NRDM) and load levels of the benchmark through scaling factors, thereby validating the robustness of the proposed framework.
5.3.1. Effectiveness with Different Initializations
The distinction in iterations between PM and TM under different initializations is summarized in
Table 10. As can be seen, TM solved by NRIM converges only with default initial values, whereas PM exhibits a broader convergence range of 80–160% of the original initialization. This difference arises because improper initializations, such as excessively high or low values of |
V|, or inappropriately initialized
p, may result in the negative output of the slack CHP during the iteration, which reverses the heat source of DHN simultaneously, eventually facilitating the singular Jacobian matrix of TM. The same problem occurs in other DHN models [
18,
19,
20,
21,
22]. Since the bidirectional DHN model is established, this defect can be overcome in PM. Moreover, by comparing the convergence curves of different subnetworks under the original initialization in
Figure 12, it can be found that each subnetwork in PM satisfies the mismatch tolerance of 10
−8 within 10 steps. In contrast, the DHN and DGN in TM require 23 and 32 iterations to converge, respectively. This is because
∂Δ
′/
∂m and
∂Δ
′/
∂m of
Jhh,
∂Δ
ρ/
∂q and
∂Δ
q/
∂ρ of
Jgg are both omitted in TM. Thus, the non-gradient-descent direction of the iterative matrix in TM causes a slower reduction in mismatches, further reflecting the correctness of the iterative matrix derivation for PM.
The convergence of PM and TM are also tested through NRDM, as shown in
Table 10. Similarly, the excessively high value of
ΦCHP can also cause the reversed mass flow of the heat source so that TM fails to converge. In contrast, owing to its iterative matrix with a gradient-descent direction, PM converges successfully for each scaling factor with fewer average inner-layer iterations.
5.3.2. Effectiveness with Different Initializations
All kinds of loads in the benchmark are varied with the scaling factor, and the corresponding iteration results of PM and TM are presented in
Table 11. It is observed that the iteration number of TM increases with rising load levels until the scaling factor exceeds 120%, whereas PM effectively operates across all load levels with a faster convergence rate. However, as the scaling factor increases, the mass flow of heat slack source GB may decrease and even reverse as the increment in
ΦCHP surpasses that of the heat loads. Hence, although PM converges to scaling factors greater than 120%, the values of
ΦGB turn negative, which is not allowable under real-world conditions. This indicates that PM is applicable not only for normal operation states but also for some extreme impermissible scenarios, thereby helping avoid potential operation risks of IEHGES.
Additionally, the convergence performance of PM in DHNs containing small communities with light loads is also a critical indicator of its robustness [
23]. Herein, three types of models, including PM, TM, and PM without ∂Δ
′/∂
m and ∂Δ
′/∂
m in
Jhh, are tested by reducing the heat load at H10 based on the benchmark. The simulation results solved via NRIM are depicted in
Figure 13. PM successfully converges after 10 steps, while oscillations appear in the iteration processes of other models.
Figure 14 presents the partial heatmap of |
Jhh| calculated through PM in the final iteration. The reduction in load H10 causes a significant decrease in the diagonal element of the correlative row of
Jhh. In this circumstance, only the accurate calculation of ∂Δ
′/∂
m ensures the iteration progresses in the correct direction, highlighting PM’s superior robustness under light load conditions.
5.4. Efficiency Analysis
To demonstrate the superiority of PM in computational efficiency, the average execution time of PM and TM across various coupling modes and solution methods is compared in
Table 12. The IEHGESs coupled in modes 1–3 are obtained by modifying system parameters from the benchmark:
Mode 1: MCs and CPs are powered independently.
Mode 2: CPs and GB are powered independently.
Mode 3: MCs are powered independently.
Note that the neglected elements in the iterative matrix of TM are supplemented in recursive form to prevent discrepancies in computational efficiency caused by iteration numbers.
The efficiency-gain ratio is defined as the multiple of TM consumption time relative to PM consumption time. The shorter the calculation time of PM, the greater the gain ratio, and the higher the efficiency of PM. As shown in
Table 12, the computation efficiency of PM is consistently higher than that of TM in each coupling mode, even though both models utilize iterative matrices with gradient-descent direction in this test. Specifically, the execution time of NRIM remains unaffected by the coupling mode since the energy-flow distributions and iteration numbers are similar in modes 1–4, maintaining a gain ratio of PM to TM at around 1.6. Conversely, the time consumption of NRDM fluctuates across modes 1–4 due to the use of different solution sequences. As the degree of interdependency deepens, the efficiency of NRDM decreases while the gain ratio of PM to TM rises from 1.718 7 to 1.994 0. This is because more subnetworks are involved in the inner-layer iteration as the coupling effect strengthens. It should be remarked that no code optimization or additional acceleration algorithms are applied in this test. Compared with the recursive loops used in TM, PM has the potential of fully utilizing the hardware capabilities and optimization techniques of modern computers owing to its matrix-based structure [
13], reducing efficiency losses caused by repeated updating of mismatch equations and the Jacobian matrix in NR-based methods.
5.5. Large-Scale Capability
To further validate the effectiveness of the proposed framework, a larger test system consisting of IEEE 118-bus DEN [
45], 51-node DHN in Jilin Province [
20] and 48-node DGN [
31] with 8 compressors operating in mode I, is established, and its schematic is shown in
Figure 15. The system parameters provided in [
44] are set as the benchmark scenario of this larger-scale system, where CHPI, CHPII, and P2G, as well as two natural gas wells, serve as the slackers of DEN, DHN, and DGN, respectively.
5.5.1. Effect of Compressibility Factor on Energy-Flow Analysis
Given that the variation in the compressibility factor is commonly ignored in previous studies, Scenarios 7–9 and the benchmark scenario of the large system are adopted for investigating the impact of compressibility factor on EFC, whereby demonstrating the suitability of PM on large-scale IEHGES. Specifically, all gas properties in the benchmark scenario are determined using the proposed gas composition-tracking model. Based on the benchmark, Scenarios 7–9 set
ZP/
ZC,in to 0.8, the average value in the benchmark scenario, and 1, respectively, and set
ω to 1.309. The comparisons of EFC results under different scenarios are shown in
Figure 16.
As illustrated in
Figure 16a–c, the nodal pressure differs markedly among scenarios, while the gas flow remains relatively constant. In Scenarios 7 and 9, compressibility factors that are either too low or too high result in the overestimation and underestimation of nodal pressure relative to the benchmark, respectively, which is particularly pronounced in the downstream region of the DGN. Compared with the medium system, the pressure deviations here are more severe. This is not only due to the greater accumulation of pressure errors but also because compressors operating in mode I exhibit a more pronounced bias amplification effect. In contrast, the state deviation of the DGN in Scenario 8 is considerably smaller compared to Scenarios 7 and 9 since the compressibility factors of the benchmark and Scenario 8 are similar. However, according to
Figure 16d–f, the interdependencies of the IEHGES, as well as the states of the DEN and DHN, vary noticeably from the benchmark in Scenarios 7–9, regardless of the state deviation degree of the DGN. The main reason is that even a slight variation in gas production can be converted into a more substantial energy difference through P2G, ultimately leading to different operation states of the IEHGES in each scenario. Additionally, since the power consumption of P2G is bigger than that of MC, the energy-flow deviations in the large IEHGES are greater than that of the medium system. Hence, it can be deduced that a precise compressibility factor is necessary for the accurate EFC of DGN and even the whole IEHGES, especially when the energy interaction between the DGN and other subnetworks is considerable.
5.5.2. Computational Efficiency for Large-Scale System
Additionally, the computational efficiency of PM for the large-scale system is also verified in the same manner as in
Section 5.4. Different system-coupling modes are accomplished by adjusting the driving or operating modes of coupling devices. The comparison of computational efficiency under each coupling mode is shown in
Table 13.
As described in
Table 13, despite the increase in the running time of both PM and TM as the system scale expands, PM can still achieve better computational efficiency in every coupling mode and solution strategy. The time consumption of NRIM is relatively stable, and its average value is around 0.32 s, while the efficiency of NRDM varies obviously with the coupling modes, indicating the necessity of choosing appropriate EFC strategies for specific operating conditions. Furthermore, the efficiency gain ratios here are all higher than the corresponding values in
Table 12, and the highest one reaches 2.278 6. It can be inferred that the proposed matrix structure also has advantages when dealing with large systems, not to mention the efficiency improvement potential of PM created using acceleration techniques.
6. Conclusions
This paper has proposed a universal EFC framework for fast static analysis of IEHGES, which is a systematic revision and reformulation of the classic NR-based EFC framework. By adopting a compact matrix form, the standardized modeling of energy-flow models and iterative matrices is achieved. Compared to the previous investigations on the interdependency mechanism of IEHGES, a more penetrating analysis method based on the superimposable coupling chains has been raised. On this basis, detailed EFC results of any IEHGES can be obtained from the proposed framework by inputting system parameters in a specific data format, which is fast and convenient to employ.
The effectiveness of the proposed framework was verified using two test systems of different scales. Through quantitative comparison and qualitative analysis, the established compact energy-flow model was proven to be accurate with an error of less than the convergence tolerance, i.e., 1 × 10−8. Following the generalized heat energy-flow direction, the proposed heat network model is universal to arbitrary DHNs, and its convergence is inherently improved since the RMF problem is fundamentally resolved. The simulation results under various scenarios demonstrate the applicability of the constructed novel gas-network model to diverse components of DGN. It also reflects the impact of multiple varying gas properties on energy-flow analysis, which cannot be ignored in the planning and operation of IEHGES. The robustness of the proposed framework is promoted owing to the precise iterative matrices with gradient-descent direction. Furthermore, the interdependency of IEHGES can be comprehensively categorized into four types of coupling modes. Across coupling types, more coupling chains indicate a deeper coupling degree; within the same type, greater interaction energy indicates a higher coupling degree. Finally, the efficiency gain ratios are all greater than 1.5 for two test systems, proving that the proposed matrix-based formulation has advantages in computational efficiency under each coupling mode and solution strategy. As the highest and lowest gain ratios increase from 1.99 to 2.28 and from 1.6 to 1.69, respectively, it can be concluded that this advantage becomes more pronounced with an increasing system scale and iteration numbers.
The efficiency enhancement of the proposed framework is achieved primarily at the model formulation level. Considering the potential for improvement in the temporal and spatial efficiency of matrix operation, programming schemes that leverage accelerating technologies, such as parallel computing, sparse storage, algorithm optimization, etc., should be developed in future research. Additionally, despite some minor modifications, the convergence nature of the proposed framework is largely inherited from the conventional framework. Therefore, strengthening the convergence of the proposed framework from the perspective of the solution algorithm to enhance its practical applicability constitutes another challenge for future work.