# Unified optimal power flow model for AC/DC grids integrated with natural gas systems considering gas-supply uncertainties

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## Abstract

A unified optimal power flow (OPF) model for AC/DC grids integrated with natural gas systems is proposed for the real-time scheduling of power systems. Herein, the primary physical couplings underlying this coordinated system are modeled and investigated. In addition, the uncertainties of gas loads are considered when studying the role of gas supply for gas-fired units in power system operations. The nonlinear gas system constraints are converted to the second-order cone forms that allow for the use of the Benders decomposition techniques and the interior-point method to obtain the optimal solution. The numerical results of the modified IEEE 118-bus test system that integrates the Belgium 20-node natural gas system demonstrate the effectiveness of the proposed model. The effects of gas demand uncertainties on the optimal schedule of thermal generators are investigated as well.

## Keywords

AC/DC optimal power flow (OPF) Natural gas system Benders decomposition Second-order cone programming## 1 Introduction

Currently, owing to their low prices and environment-friendly properties, gas-fired units (GFUs) have been widely used worldwide. For example, they accounted for \(42\%\) of the total installed capacity in the United States in 2015 [1]. Meanwhile, the development of power electronics and DC transmission technology enhance the capability of long-distance energy transmission [2]. Accordingly, interconnections and interactions between power system, DC connections, and natural gas systems are increasing rapidly. As a result, modern power systems have become an AC/DC and multi-energy coupled system, and any of them can have significant effects on the security operations of the system. Thus, the efficiency improvement of power utilization, accurate modeling, and stability analysis of multi-energy coupling systems are ongoing efforts. This paper aims to propose a unified optimal power flow (OPF) framework to improve the efficiency of power usage and the reliability of this multi-energy coupling system.

- 1)
Power/gas flow modeling for the power-natural gas integrated system

- 2)
Power flow modeling for AC/DC grids

- 3)
Solution method for OPF problem

The power flow equations are nonlinear and nonconvex, which pose challenges to the traditional deterministic OPF using the interior-point method. Recently, the convexity of the OPF problem has piqued great research interest [16, 17, 18, 19, 20]. The primary idea is to transform the initial nonlinear/nonconvex problem into a convex one by using convex relaxations, such as the semidefinite relaxation and second-order cone relaxation. In [18], the OPF problem for AC/DC grids was converted to a second-order cone programming (SOCP). However, the sufficient conditions that guarantee the exactness of this convex relaxation are not discussed. This was solved by using the semidefinite programming (SDP) approach [19]. Although the SDP formulation achieves a more accurate solution than the SOCP, its computation efficiency is too low to render the practical application capability. In this sense, the SOCP formulation of the OPF appears to be promising.

Table A1 in Appendix A shows a statistical study of different OPF formulations in the electricity-gas coupling system, where Y and N denote whether the subject is being considered. Most related works focus on the interdependency between AC grids and natural gas systems or the interconnection between the AC and DC power systems; none have studied the OPF problem for AC/DC grids integrated with gas systems. Since practical modern power systems are coupled with both DC grids and natural gas systems, neglecting any of them may yield suboptimal solutions. More importantly, according to a report [1], the gas supply of gas-fired generators could suffer shortages during winter peak hours in some American regions. This is because the gas supply of GFUs does not prioritize the natural gas loads and once the GFU generation is curtailed, the thermal generators need to increase their active power generation to compensate the decrease in the GFU generation. Thus, the gas-supply uncertainty plays an important role in the schedule of thermal generators.

To solve the aforementioned problems, this paper presents a unified OPF model (master-sub-OPF, denoted as MS-OPF in this paper) for the AC/DC grid coordinated with gas systems, considering gas-supply uncertainties. Specifically, a two-stage BD-based optimization problem is designed to handle data uncertainties. In the first stage, the nonlinear OPF model for AC/DC grids is reformulated as a master problem and solved by a large-scale nonlinear programming solver, i.e., the interior-point optimizer (IPOPT). Nevertheless, the SOCP formulation for a natural gas system is considered a subproblem and is solved by a convex optimization solver, i.e., the Gurobi. It is noteworthy that each subproblem represents a gas-supply scenario; as a result, the effects of gas-supply uncertainty on GFUs can be evaluated. In addition, owing to the SOCP formulation of the subproblems, the original nonconvex and nonlinear formulation is transformed to a convex optimization problem and can be solved in polynomial time.

The remainder of this paper is organized as follows. In Section 2, the detailed nonlinear OPF model for the AC/DC grid is presented and an SOCP formulation of the natural gas system constraints is proposed. The solution method for the MS-OPF is elaborated in Section 3. In Section 4, the details of the simulation results performed on a modified AC/DC grid-natural gas coupled system is presented. Finally, Section 5 draws the relevant conclusions.

## 2 Problem formulation

- 1)
The steady-state model is adopted in both power systems and natural gas systems.

- 2)
To convert nonlinear gas system constraints into SOCP forms, the natural gas systems are considered to be lossless, i.e., compressors are not considered. This is primarily because the gas consumption of compressors only accounts for a very small fraction of the total gas loads.

- 3)
The power demands are unchangeable during the dispatch interval, which means only the uncertainty of natural gas systems was considered. It is noteworthy that uncertainties exist in both power systems and natural gas systems. However, if the power system and gas system uncertainties are considered simultaneously, the sensitivity of the optimal solution to gas system uncertainties may not be evaluated.

### 2.1 OPF model of AC/DC grids

#### 2.1.1 Objective function

*i*; \(P_{i}^{R}\) is the active power generation of gas-fired generator

*i*. It is noteworthy that the gas-fired generators are supplied with low-priced natural gas.

#### 2.1.2 Power flow models of AC/DC grids

*i*; \(P_i^S\) and \(Q_i^S\) are the active/reactive power transformed from AC grid to the \(i^{\text {th}}\) VSC station; \(Q_i^G\) is the reactive power generation of thermal generator

*i*; \({N_{{\text {AC}}}}\) is the number of AC buses; \({G_{ij}}\) and \({B_{ij}}\) are the real/imaginary part of the AC grid admittance matrix; \({e_i}\) and \({f_i}\) is the real/imaginary part of the AC voltage.

If no generator is connected to bus *i*, the values of \(P_i^G\) and \(Q_i^G\) are 0 in (2) and (3). Rectangular coordinates are chosen because the Hessian matrix of the equality or inequality constraints will be constant, which is convenient for the interior-point-method-based solver.

### 2.2 Natural gas system formulations considering gas-supply uncertainty

#### 2.2.1 SOCP formulation of natural gas systems

*i*to node

*j*, we obtain:

*k*is the index of scenarios; \(f_{ij}^k\) is the gas flow in pipeline \(\left( {i,j} \right)\); \(p_i^k\) is the gas pressure at node

*i*; \({C_{ij}}\) is a constant determined by the length, diameter, absolute rugosity, and the gas composition of the pipeline [3]. Equations (21) and (22) are exactly in the form of a rotated quadratic cone by convex relaxations. For example, using convex relaxation, (21) can be rewritten as:

*i*to node

*j*, we have:

*i*; \(L_i^k\) is the gas load at node

*i*.

*k*th scenario. Natural gas systems are also constrained by the operating limits:

#### 2.2.2 Modeling of gas-supply uncertainty of GFUs

## 3 Solution methodology

The MS-OPF model proposed herein aims at finding an optimized schedule for thermal power plants and gas-fired generators under gas-load uncertainties. It is noteworthy that different scenarios of the natural gas system may affect the final solution of the MS-OPF. In this regard, the BD [24, 25] is adopted owing to its capability in handling uncertainties. Using the BD method, the MS-OPF is decomposed into a master problem, i.e., a nonlinear programming AC/DC OPF, and *S* subproblems, where each problem corresponds to a natural gas network load scenario.

- 1)Master problem (NLP)$$\begin{aligned}&{\mathrm {min}} \ \sum \limits _{i=1}^{N_t}{f_{i}^{G}}P_{i}^{G}+\sum \limits _{i=1}^{N_g}{f_{i}^{R}P_{i}^{R}} \end{aligned}$$(32)$$\begin{aligned}&{\mathrm {s.t.}}\ (2)-(20) \end{aligned}$$(33)
- 2)Benders cut$$\begin{aligned} {\omega _k} +\sum \limits _{i=1}^{N_g}{{{\lambda }_{i}^k}}\left( P_{i}^{R}-P_{i}^{R,k} \right) \le 0 \end{aligned}$$(34)
- 3)The \(k^{\text {th}}\) subproblem (SOCP)$$\begin{aligned}&{\mathrm {min}} \ {\omega _k} =\sum \limits _{i=1}^{N_g}{\left( u_{i,+}^{R}+u_{i,-}^{R} \right) } \end{aligned}$$(35)$$\begin{aligned}&{\mathrm {s.t.}}\ (24)-(31) \end{aligned}$$(36)$$\begin{aligned}&P_{i}^{R,k}=P_{i,*}^{R}+u_{i,+}^{R}-u_{i,-}^{R}:{\lambda _{i}^k} \end{aligned}$$(37)

The detailed procedures of adopting the BD method to solve the proposed formulation can be summarized in the following steps.

*Step 1:* Solve the master problem, i.e., the real-time OPF problem for AC/DC grids. For a large-scale nonlinear programming (NLP) problem, this paper adopted the IPOPT [26] to obtain the solution of \(P_{i,*}^R\).

*Step 2:* Check the feasibility of subproblem 1, which is based on gas-load scenario 1, and substitute \(P_{i,*}^R\) into the subproblem (as referred to in (37)). Since the gas flow constraints have been transformed into SOCP formulations, we use the Gurobi [27] to calculate this convex optimization problem and obtain the optimized solution of \({\omega _1}\).

*Step 3:* If \({\omega _1} = 0\), go back to Step 2 and check the feasibility of the \(2{\mathrm {nd}}\) subproblem until the termination criteria is validated for all subproblems. Otherwise, add the Benders cut (34) constraint to the master problem and go to Step 1; subsequently, recalculate the solution of \(P_{i,*}^R\) and check the feasibility of the next subproblem.

*Step 4:* Once each of the total *S* subproblems is feasible, output the optimized schedule of the thermal generators and GFUs. It is noteworthy that the coordinated system is dispatched according to different gas demand scenarios.

The maximum order of constraints in the master problem is two, and they are calculated in parallel. If the bus number of the AC grids is *n*, the time complexity of the NLP model is \(\mathrm{O}( {{n^2}})\). Nevertheless, all the constraints in the subproblem are linear and can be calculated in parallel. If the gas node number is *m*, the time complexity of the SOCP model is \(\mathrm{O}( m )\). In summary, the time complexity of the solution algorithm is \(\mathrm{O}( {{n^2}} )\). Although the master problem can be simplified to be linear as well, the guarantee of computational accuracy for practical applications may be difficult.

## 4 Case studies

### 4.1 Test systems

The proposed MS-OPF was tested on a benchmark AC/DC grid coupled with a natural gas system. The overall system is shown in Fig. 4, which consists of a modified version of the IEEE 118-bus test system and the Belgium 20-node gas network. As shown in Fig. 4, some AC lines in the IEEE 118-bus system are substituted for the multi-terminal HVDC systems. There are 54 generators in total. To interconnect the two systems, thermal generators G10, G24, G25, G27, and G87 of the IEEE 118-bus system are replaced by gas-fired generators, and these generators are connected to nodes 16, 9, 6, 4, and 12 of the natural gas system, respectively. The total active power loads decreased to 2000 MW/h; the capacity of each gas-fired generator is 100 MW, while the remaining 49 thermal power generators’ maximum active power outputs are modified to 50 MW. In this condition, the GFUs account for approximately 1/6 of the total power generation capacity. The remaining detailed information of the IEEE 118-bus test system, including the power cost functions and the variables’ boundaries can be obtained from the MATPOWER library [28].

Parameter settings of VSC station

VSC parameters | Value |
---|---|

\({{R}_{Tf}}\) | 0.0005 |

\({{X}_{Tf}}\) | 0.0125 |

\({{B}_{f}}\) | 0.2 |

\({{R}_{pc}}\) | 0.00025 |

\({{X}_{pc}}\) | 0.04 |

\({m_a}\) | 1.15 |

\(U_i^{\mathrm {DC}}\) | [ 0.9,1.1 ] |

\(P_i^S\) | [ − 0.5,0.5 ] |

\(Q_i^S\) | [ − 0.1,0.1 ] |

Nevertheless, for simplicity, the parallel pipelines of the Belgian 20-node natural gas system are modeled as a single equivalent pipeline. The total forecasted RCI gas loads are changed to 50 \({\mathrm {mm}}^3/{\hbox {h}}\). The gas fuel capacities of gas suppliers at nodes 1, 2, 5, 8, 13, and 14 are 18, 16, 9, 27, 7.5, and 7.5 \({\mathrm {mm}}^3/{\hbox {h}}\), respectively. The gas fuel cost of gas storages at nodes 2, 5, 13, and 14 is 210 \({\$}\)/h and that of gas sources at nodes 1 and 8 is 250 \({\$}\)/h. \({\rho _g}\) shown in (27) is 0.05 in the simulation. The nodal pressure bounds and the pipeline maximum capacities can be found in [23]. All tests are carried out on a PC (Intel i5-4210 Quad Core CPU, 2.90 GHz, 4-GB RAM). The problems are conducted on the Yalmip [29] platform built in MATLAB.

### 4.2 Simulation results and analysis

*Case 1:*Optimizing gas flow (OGF) of the Belgian natural gas system to determine the most economical schedule of gas suppliers. The SOCP formulation of the OGF proposed herein is compared with the NLP formulation of the OGF model adopted in most published papers [3, 8]. The NLP formulation is solved by the IPOPT and the SOCP formulation is solved by the Gurobi. The simulation results are shown in Table 2 and Table 3.

Simulation results of Case 1

Formulation | Time (\(\mathrm{{s}}\)) | Cost (\({{\$ /{\hbox {h}}}}\)) |
---|---|---|

NLP | 0.2018 | \(1.09 \times {10^4}\) |

SOCP (proposed formulation) | 0.0094 | \(1.09 \times {10^4}\) |

Gas suppliers output in Case 1

Formulation | Output (\({\mathrm {mm}}^3/{\hbox {h}}\)) | |||||
---|---|---|---|---|---|---|

Node 1 | Node 2 | Node 5 | Node 8 | Node 13 | Node 14 | |

NLP | 0.0002 | 16.0000 | 9.0000 | 9.9998 | 7.5000 | 7.5000 |

SOPC (proposed formulation) | 2.6818 | 16.0000 | 9.0000 | 7.3182 | 7.5000 | 7.5000 |

These results indicate that the computation time of the SOCP formulation is much shorter than that of the NLP models. In addition, we found that to obtain the lowest gas fuel cost, the gas storages of nodes 2, 5, 13, and 14 have to account for more gas supply. The mismatch in the gas suppliers’ generation schedule in the two formulations is primarily because the SOCP formulation transforms the initial NLP problem into a convex optimization problem, and the global optimal solution can be found. By contrast, the non-convex NLP problem only obtains the local optimal solution.

*Case 2:* The test for a deterministic OPF of the AC/DC grid integrated with the natural gas system, i.e., optimizing the schedule of generators and gas sources simultaneously but the gas-load uncertainties are not considered. This case was solved by the IPOPT solver.

*Case 3:* The test for the two-stage MS-OPF considering the gas-system uncertainty.

Optimized generation of gas suppliers

Node | Generation (\({\mathrm {mm}}^3/\mathrm {h}\)) | |
---|---|---|

Case 2 | Case 3 (proposed method) | |

1 | 10.2343 | 15.0674 |

2 | 16.0000 | 16.0000 |

5 | 9.0000 | 9.0000 |

8 | 24.7657 | 19.9326 |

13 | 7.5000 | 7.5000 |

14 | 7.5000 | 7.5000 |

Optimized generation of GFUs (\(\mathrm{{MW/h}}\))

Bus | Generation (MW/h) | |
---|---|---|

Case 2 | Case 3 (proposed method) | |

10 | 100 | 78.6346 |

24 | 100 | 88.2868 |

25 | 100 | 86.9162 |

27 | 100 | 87.3553 |

87 | 100 | 86.4071 |

It is noteworthy that once the gas suppliers’ schedule is determined, it is fixed for hours since the gas transmission is very slow. Hence, when gas loads are increased in a short time, such as during winter, the GFUs will experience supply shortages, yielding power system security problems. Furthermore, as observed from Fig. 5, the thermal generators in Case 3 increase their generation to provide additional power owing to the decrease in GFU generation compared to Case 2. These results indicate that if the gas-load uncertainty is not considered when the generator schedules are developed, once the GFU gas supply is not sufficient, the thermal generators would not satisfy the current power demand and the power system security is threatened. Thus, the proposed MS-OPF ensures the balance between the supply and demand of the integrated system and reduces the variability impact in natural gas systems on the safety of power systems.

In addition, Fig. 6 shows that the gas flow in pipelines is redistributed when the gas-load uncertainty is considered. It is noteworthy that the additional flexibility provided by VSC stations and DC connections does not vary consistently in the optimal solution of the two cases. This is primarily due to the action of the storage devices included in the gas network, which effectively decouples the gas operation from the power grid. Clearly, the higher the control capability of the electrical system, the lower the topological changes.

### 4.3 Sensitivity analysis

*X*-axis represents the proportion of the GFUs’ generation in the total power generation. The different-colored lines represent the different standard deviations of the gas-load forecast errors (i.e., the percent of gas-load forecast value). The greater the standard deviation, the higher is the degree of the gas system uncertainty. The amount of the thermal generators’ generation adjustment is found to be proportional to the gas system uncertainty level. Thus, the gas-load uncertainties are to be considered in the presence GFUs.

### 4.4 Evaluating robustness of proposed method

Gas load scenarios

Scenario | Gas load | Probability | System cost |
---|---|---|---|

(\({\mathrm {mm}}^3/{\hbox {h}}\)) | ($/h) | ||

1 | 49.612 | 0.1577 | 63948 |

2 | 50.887 | 0.1498 | 66314 |

3 | 52.717 | 0.0884 | 67759 |

4 | 47.727 | 0.1055 | 63477 |

5 | 45.731 | 0.0371 | 62978 |

6 | 50.555 | 0.1557 | 64478 |

7 | 49.778 | 0.1590 | 63990 |

8 | 48.170 | 0.1221 | 63588 |

9 | 54.824 | 0.0248 | 67846 |

Table 6 shows that the expected value of the total system operation cost calculated by the SB approach is 64702 \({\$}\)/h, which is lower than 66888 \({\$}\)/h obtained by the BD method. Comparing with the SB method gives variable optimized solutions; the BD approach adopted in this paper gives an appropriate schedule plan for power system operators.

## 5 Conclusion

- 1)
The SOCP formulation of the OGF problem proposed herein can effectively improve the computational efficiency for solving the subproblem.

- 2)
Although the total fuel cost poses a slight increase when the gas-load uncertainties are considered, thermal generators are able to increase their generation to mitigate the variability of gas demands. Thus, the proposed MS-OPF provides a tradeoff between economy and safety.

- 3)
The thermal generators’ adjustment of power generation shows a positive correlation with the proportion of GFUs in the total power generation and the uncertainty degree of the gas supply.

The technological progress in power-to-gas (PtG) has made the power and natural gas systems more closely linked, while the integration of renewable energy resources causes security problems to the power system. For future work, the authors plan to study the static stability of power-gas bidirectional coupled systems with renewable resource penetration.

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