An operation optimization method for an electricity-hydrogen-mixed natural gas coupled integrated energy system

Abstract

The invention discloses an operation optimization method for an electric-hydrogen-mixed natural gas coupled integrated energy system, belonging to the technical field of integrated energy system operation; the optimization method comprises: establishing a hydrogen-mixed natural gas network model taking into account the influence of hydrogen blending ratio; establishing a hydrogen-mixed natural gas station model; establishing an optimization scheduling model for an electric-hydrogen-mixed natural gas coupled integrated energy system; using second-order cone relaxation and piecewise linearization to convert an original mixed integer non-convex quadratic constraint programming model into a second-order cone relaxation model; proposing a penalty function model based on a concave-convex process method and a boundary tightening method, and an iterative algorithm for solving the optimization scheduling model for an electric-hydrogen-mixed natural gas coupled integrated energy system; and constructing an improved hydrogen-mixed natural gas network steady-state model by establishing an improved gas network pipe section pressure drop equation and an improved gas network pipe section storage equation, considering the influence of hydrogen blending in the natural gas network on the fluid dynamics properties.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system operation, and specifically relates to an operation optimization method for an electric-hybrid hydrogen-natural gas coupled integrated energy system. Background Art

[0002] Hydrogen energy, as an important secondary energy source to promote the low-carbon transformation of the energy system, can achieve large-scale consumption of renewable energy, reduce the energy system's dependence on fossil energy and alleviate the pressure on the power system to consume renewable energy; hydrogen energy can be mixed with natural gas and injected into the natural gas pipeline network in the form of mixed hydrogen and natural gas, realizing its long-distance transmission and utilization, and solving the current dilemma of insufficient momentum for the development of hydrogen energy infrastructure.

[0003] However, current research on hydrogen-blended natural gas mostly focuses on simulation modeling and flow analysis. Injecting hydrogen into the natural gas network changes the fluid dynamics of the original natural gas pipeline network and complicates the physical properties of the compressed gas. If not handled properly, it will threaten the safe operation of the power and hydrogen-blended natural gas systems. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide an operation optimization method for an electric-hydrogen-hybrid natural gas coupled integrated energy system. Considering the impact of hydrogen addition to the natural gas network on the fluid dynamics properties, an improved hydrogen-hybrid natural gas network steady-state model is constructed, which has more accurate pipe section pressure drop equations and pipe section storage equations.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An operation optimization method for an electricity-hydrogen hybrid natural gas coupled integrated energy system, comprising:

[0007] S1, establish a hydrogen-blended natural gas network model taking into account the influence of hydrogen blending ratio;

[0008] S2, establish a hydrogen-mixed natural gas station model including renewable energy, electrolyzer, compressor, hydrogen storage tank, methane reactor, and gas mixing device;

[0009] S3, establish an optimization scheduling model for the electricity-hydrogen hybrid natural gas coupled integrated energy system;

[0010] S4, uses second-order cone relaxation and piecewise linearization to transform the original mixed integer non-convex quadratic constrained programming model into a second-order cone relaxation model;

[0011] S5, proposed a penalty function model based on the concave-convex process method and the boundary tightening method, and proposed an iterative algorithm to solve the optimal scheduling model of the electric-hydrogen hybrid natural gas coupled integrated energy system;

[0012] The step of establishing the hydrogen-mixed natural gas network model in S1 includes:

[0013] S11, establish the improved gas network pipe section pressure drop equation:

[0014]

[0015] Z=αp+β (3)

[0016]

[0017] Where λ represents the gas friction factor; M represents the molar mass of the gas. For mixed gases, it can be calculated according to formula (2); T em represents temperature; D represents the diameter of the pipe section; A represents the cross-sectional area of ​​the pipe section; α and β are the coefficients of the linear term and constant term obtained by linear regression of the relationship between the compressibility factor and pressure; L ij represents the length of the pipe segment ij; p i and p j represents the pressure at the beginning and end of pipe segment ij; is the average flow rate of the pipe section, expressed as the inlet flow rate of the pipe section and export traffic The mean of

[0018] S12, establish the improved gas network pipeline storage equation:

[0019]

[0020] in, Indicates pipe section storage; Indicates the average compressibility factor of the gas in the pipe section; Indicates the average pressure of the pipe section; when the gas transmitted in the network is mixed hydrogen natural gas, It can be expressed as Thus, equation (5) is transformed into equation (7).

[0021] Beneficial effects of the present invention:

[0022] 1. Taking into account the effect of hydrogen blending ratio on the physical and kinetic properties of gas, the present invention establishes a hydrogen-blended natural gas network model, including a pipe pressure drop equation and a pipe storage equation. Secondly, a hydrogen-blended natural gas station model is established, which includes renewable energy, electrolyzers, compressors, hydrogen storage tanks, methane reactors, and gas mixing devices. Thirdly, based on the hydrogen-blended natural gas network model, the power network model, and the hydrogen-blended natural gas station model, an optimization scheduling model for the electricity-hydrogen-blended natural gas coupled integrated energy system is constructed. The objective function is to optimize the overall economic efficiency of the system. The constraints include the operating status constraints of the equipment in the hydrogen-blended natural gas station, the multi-energy flow balance constraints, the hydrogen-blended natural gas network constraints, and the power network constraints.

[0023] 2. The original mixed integer non-convex quadratic constrained programming model is transformed into a second-order cone relaxation model using second-order cone relaxation and piecewise linearization;

[0024] 3. To reduce relaxation errors and obtain feasible solutions to the original non-convex optimization model, a penalty function model based on the concave-convex process method and the boundary tightening method was proposed. Furthermore, an iterative algorithm was proposed to solve the optimal scheduling model for the electric-hydrogen hybrid natural gas coupled integrated energy system, which can obtain a local optimal solution that is effective for the original optimal scheduling model.

[0025] 4. Through the coordinated optimization of the operating status of hydrogen production, storage, methanation and other equipment in the hydrogen-hybrid natural gas station, and the use of the pipeline and storage characteristics of the hydrogen-hybrid natural gas network, the electricity-hydrogen-hybrid natural gas coupled integrated energy system can flexibly respond to changes in load, renewable energy and external energy prices, and realize the coordinated economic dispatch of the power network and the hydrogen-hybrid natural gas network taking into account multiple links of the hydrogen-hybrid natural gas supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 This is a schematic diagram of the structure of the electricity-hydrogen-natural gas coupled integrated energy system of the present invention;

[0028] Figure 2 is a flow chart of the iterative solution algorithm of the present invention;

[0029] Figure 3 is the pressure at each node of the hydrogen-mixed natural gas network of the present invention;

[0030] Figure 4 is the flow rate of each pipe section of the hydrogen-mixed natural gas network of the present invention;

[0031] Figure 5 It is the pipe storage of each pipe section and the total pipe storage of the hydrogen mixed natural gas network of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] like Figure 1As shown, an electric-hydrogen-mixed natural gas coupled integrated energy system includes a hydrogen-mixed natural gas station, a 6-node power network and a 6-node hydrogen-mixed natural gas network; the hydrogen-mixed natural gas station includes a wind turbine, photovoltaics, an electrolyzer, a compressor, a methane reactor, a hydrogen storage tank and a gas mixing device; the hydrogen-mixed natural gas station integrates the functions of renewable energy power generation, electrolysis hydrogen production, compression, hydrogen storage, hydrogen methanation, and hydrogen and natural gas blending. It is coupled with the multi-energy flow network through the flow output of the gas mixing device to meet the load requirements of the hydrogen-mixed natural gas network and the power network. The two networks are coupled through a gas turbine.

[0034] Specific examples are as follows:

[0035] The network and load parameters of the power grid are shown in Tables 1 and 2, and those of the hydrogen-blended natural gas network are shown in Tables 3 and 4. The system uses peak-valley pricing for gas purchases from long-distance natural gas transmission lines, as shown in Table 5. The linear regression coefficient α and constant coefficient β of the compression factor Z at different hydrogen blending ratios are shown in Table 6. The scheduling time scale Δt is 1 hour, and the scheduling period is one day, meaning T is 24 hours.

[0036] Table 1 Network parameters of power network

[0037]

[0038] Table 2 Load data of power network (MW)

[0039]

[0040] Table 3 Network parameters of the 6-node gas network

[0041]

[0042] Table 4 Load data of 6-node gas network (1000m 3 / h)

[0043]

[0044] Table 5 Purchase price of natural gas

[0045]

[0046]

[0047] Table 6 Compression factor regression results at different hydrogen doping ratios

[0048] Hydrogen doping ratio <![CDATA[α(Pa -1 )]]> β 0 <![CDATA[-1.61×10 -8 ]]> 0.992665 0.01 <![CDATA[-1.57×10 -8 ]]> 0.992676 0.02 <![CDATA[-1.52×10 -8 ]]> 0.992692 0.03 <![CDATA[-1.48×10 -8 ]]> 0.992713 0.04 <![CDATA[-1.44×10 -8 ]]> 0.992738 0.05 <![CDATA[-1.40×10 -8 ]]> 0.992767 0.06 <![CDATA[-1.36×10 -8 ]]> 0.9928 0.07 <![CDATA[-1.32×10 -8 ]]> 0.992837 0.08 <![CDATA[-1.28×10 -8 ]]> 0.992877 0.09 <![CDATA[-1.24×10 -8 ]]> 0.992921 0.1 <![CDATA[-1.20×10 -8 ]]> 0.992967 0.11 <![CDATA[-1.16×10 -8 ]]> 0.993017 0.12 <![CDATA[-1.13×10 -8 ]]> 0.99307 0.13 <![CDATA[-1.09×10 -8 ]]> 0.993125 0.14 <![CDATA[-1.05×10 -8 ]]> 0.993183 0.15 <![CDATA[-1.02×10 -8 ]]> 0.993243 0.16 <![CDATA[-9.82×10 -9 ]]> 0.993305 0.17 <![CDATA[-9.48×10 -9 ]]> 0.99337 0.18 <![CDATA[-9.14×10 -9 ]]> 0.993436 0.19 <![CDATA[-8.81×10 -9 ]]> 0.993504 0.2 <![CDATA[-8.48×10 -9 ]]> 0.993574

[0049] An operation optimization method for an electricity-hydrogen hybrid natural gas coupled integrated energy system comprises the following steps:

[0050] S1, establish a hydrogen-blended natural gas network model taking into account the influence of hydrogen blending ratio:

[0051] The steps of establishing a hydrogen-mixed natural gas network model include:

[0052] S11, considering the effect of hydrogen doping on gas compressibility factor and molar mass and other parameters, an improved gas network pipe section pressure drop equation is established, as shown in equations (1)-(4):

[0053]

[0054] Z=αp+β (3)

[0055]

[0056] Where λ represents the gas friction factor; M represents the molar mass of the gas. For mixed gases, it can be calculated according to formula (2); T em represents temperature; D represents the diameter of the pipe section; A represents the cross-sectional area of ​​the pipe section; α and β are the coefficients of the linear term and constant term obtained by linear regression of the relationship between the compressibility factor and pressure, as shown in formula (3); L ij represents the length of the pipe segment ij; p i and p j represents the pressure at the beginning and end of pipe segment ij; is the average flow rate of the pipe section, expressed as the inlet flow rate of the pipe section and export traffic The mean of is shown in formula (4).

[0057] S12, considering the effect of hydrogen addition on the compression factor in the traditional pipeline storage equation, an improved pipeline storage equation for the gas network pipeline section is established, as shown in equation (5):

[0058]

[0059] in, Indicates pipe section storage; Indicates the average compressibility factor of the gas in the pipe section; represents the average pressure of the pipe section, as shown in formula (6):

[0060]

[0061] When the gas transmitted in the network is hydrogen-mixed natural gas, It can be expressed as Thus, formula (5) is transformed into formula (7):

[0062]

[0063] S2, establish a hydrogen mixing natural gas station model including renewable energy, electrolyzer, compressor, hydrogen storage tank, methane reactor, and gas mixing device:

[0064] The steps to establish a hydrogen-blended natural gas station model include:

[0065] S21, the mathematical model of theoretical electric power output of wind turbine power generation and photovoltaic power generation, as well as the actual utilization power constraints are shown in equations (8)-(11):

[0066]

[0067] in, and P wt,t The theoretical output power and actual utilization power of wind turbine generator; and P pv,t are the theoretical output power and actual utilization power of photovoltaic power generation respectively; η wt Indicates the power generation efficiency of the wind turbine, with a value of 0.55; ρ air Indicates the air density, the value is 1.23kg / m 3 ; N wt is the number of fans installed, which is 1000; R wt is the radius of the fan blade, which is 40m; u t is the wind speed at time t; is the solar radiation intensity; θ pv is the inclination angle of the photovoltaic panel, which is 35°; η MPPT is the MPPT controller efficiency, which is 0.75; η pv is the photovoltaic panel power generation efficiency, which is 0.16; A pv is the photovoltaic panel installation area, which is 5×10 6 m 2 .

[0068] S22, the mathematical model of the electrolytic cell operating state is shown in equations (12) and (13):

[0069]

[0070] Among them, Cap elz is the installed capacity of the electrolyzer, which is 2000MW; P elz,t is the input power of the electrolytic cell; η elz is the electrolytic cell conversion efficiency, which is 0.64; LHV H and LHV NG is the lower calorific value of hydrogen and natural gas, which are 2.792 and 9.886 kWh / m respectively. 3 η mr is the conversion efficiency of the methane reactor, which is 0.77; is the hydrogen flow rate output from the electrolyzer and injected into the methane reactor; The hydrogen flow rate output from the electrolyzer and injected into the hydrogen storage tank; It is the hydrogen flow rate output from the electrolyzer and directly injected into the hydrogen mixing device.

[0071] S23, the mathematical model of the compressor operating state is shown in equations (14) and (15):

[0072]

[0073] Among them, Cap cp is the installed capacity of the compressor; P cp,t is the input electrical power of the compressor; γ cp It is the ratio of compressor power consumption to compressed hydrogen flow rate, and its value is 0.05kWh / m 3 .

[0074] S24, the mathematical model of the hydrogen storage tank operating state is shown in equations (16)-(21):

[0075] S hs,1 =S hs,T =0.5·Cap hs (16)

[0076]

[0077] Among them, Cap hs is the capacity of the hydrogen storage tank, which is 2.5×106m 3 ;S hs,t is the gas storage capacity of the hydrogen storage tank; is the injection and production gas flow rate of the hydrogen storage tank; The hydrogen flow rate injected into the methane reactor from the hydrogen storage tank; is the hydrogen flow rate injected into the gas mixing device from the hydrogen storage tank; η hs,ch / dis is the gas injection and production efficiency of the hydrogen storage tank, and its value is 0.95; σ hs is the self-loss coefficient of the hydrogen storage tank, which is 0.001; μ hs,min / max is the minimum / maximum gas storage ratio of the hydrogen storage tank, which is 0.2 and 0.8 respectively; γ hs,ch / dis is the gas injection rate of the hydrogen storage tank, and the value is 0.042h -1 .

[0078] S25, the methane reactor operating state mathematical model is shown in equations (22) and (23):

[0079]

[0080] Among them, Cap mris the capacity of the methane reactor, which is 50MW; γ mr is the ratio of the power consumption of the methane reactor to the flow rate of methanogenic hydrogen, which is 1kWh / m 3 .

[0081] S26, the mathematical model of the operating state of the mixing device is shown in equations (24)-(26):

[0082]

[0083] in, and Respectively represent the total amount of hydrogen and natural gas injected into the gas mixing device; It is the natural gas flow purchased from the long-distance natural gas transmission line.

[0084] S3, establish an optimal scheduling model for the electricity-hydrogen hybrid natural gas coupled integrated energy system (P0);

[0085] The steps of establishing an optimized scheduling model include:

[0086] S31, establish the objective function of the optimal scheduling of the electricity-hydrogen hybrid natural gas coupled integrated energy system; as shown in equations (27)-(32):

[0087]

[0088] Among them, C NG,t is the natural gas purchase cost of the electricity-hydrogen-natural gas coupled integrated energy system at time t; is the carbon emission cost caused by the natural gas component in the hydrogen-mixed natural gas at time t; is the penalty cost for curtailing wind and solar power at time t; C om,t is the operation and maintenance cost at time t; C wheel,t P is the transmission fee that the hydrogen-mixed natural gas station needs to pay to the public grid for transmitting electricity to the multi-energy flow network at time t; grid,t represents the electric power transmitted from the hydrogen-mixed natural gas station to the multi-energy flow network at time t; is the purchase price of natural gas at time t; It is the unit carbon emission fee that the natural gas component in the hydrogen-mixed natural gas needs to pay, and the value is 0.28 yuan / m 3 ; and These are the unit penalty fees to be paid for curtailing wind and solar power, respectively; and The unit operation and maintenance costs are 0.17 yuan / kWh, 0.043 yuan / kWh, 0.043 yuan / kWh, 0.17 yuan / kWh, 0.17 yuan / kWh, 0.085 yuan / kWh, and 0.085 yuan / m 3 ; It is the unit transmission fee that the hybrid hydrogen natural gas station needs to pay to the public power grid when transmitting electricity to the multi-energy flow network, and the value is 0.17 yuan / kWh.

[0089] S32, establish the constraints for optimal scheduling of the electricity-hydrogen hybrid natural gas coupled integrated energy system; the specific steps include:

[0090] S321, establishing operating status constraints for equipment in the hydrogen-mixed natural gas station;

[0091] Specifically, they include: wind turbine and photovoltaic power generation power constraints, see equations (8)-(11); electrolyzer operating state constraints, see equations (12) and (13); compressor operating state constraints, see equations (14) and (15); hydrogen storage tank operating state constraints, see equations (16)-(21); methane reactor operating state constraints, see equations (22) and (23); and mixing device operating state constraints, see equations (24)-(26).

[0092] S322, establish multi-energy flow balance constraints for the electricity-hybrid hydrogen-natural gas coupled integrated energy system;

[0093] The power balance constraint in the hydrogen mixing station is shown in formula (33):

[0094]

[0095] The input and output energy balance constraint of the mixing device is shown in formula (34):

[0096]

[0097] Among them, q w,t It is the flow rate of mixed hydrogen natural gas injected into the gas source node of the mixed hydrogen natural gas network.

[0098] S323, establish the gas purchase flow constraint of the electricity-hydrogen hybrid natural gas coupled integrated energy system and the capacity constraint of electricity transmission via the public power grid, as shown in Equations (35) and (36), respectively:

[0099]

[0100] in, and P grid,max They are the upper limit of gas purchase flow and the upper limit of power transmission capacity, with values ​​of 150m 3 / s and 20MW.

[0101] S324: Establishing the network constraints of hybrid hydrogen and natural gas in the electricity-hybrid hydrogen and natural gas coupled integrated energy system, specifically including:

[0102] The improved pipe section pressure drop equation is shown in equations (1) and (4);

[0103] The improved pipe section storage equation is shown in formula (7);

[0104] The node traffic balance constraint is shown in formula (37):

[0105]

[0106] Among them, q gt,t represents the gas consumption of the gas turbine connected to node i, Indicates gas load;

[0107] The gas source injection flow constraint is shown in formula (38):

[0108]

[0109] Among them, q w,t represents the gas purchase flow of the gas source node at time t; q w,min and q w,max Indicates the minimum and maximum values ​​of the purchased gas flow. The minimum value is 0, and the maximum values ​​of gas source nodes 4 and 6 are 100 and 200m respectively. 3 / s;

[0110] The tube storage charge-discharge balance constraint is shown in formula (39):

[0111]

[0112] The periodic recovery constraint is shown in formula (40):

[0113]

[0114] S325: Establish power network flow constraints in the electricity-hydrogen hybrid natural gas coupled integrated energy system; specifically,

[0115] The node power balance constraint is shown in formula (41):

[0116]

[0117] Among them, F nl,t represents the power flow on line nl connected to node n; P gt,t represents the output electric power of the gas turbine connected to node n; represents the electrical load at node n in the power network;

[0118] The relationship between active power and node voltage phase angle is shown in formula (42):

[0119]

[0120] θ n,t and θ l,t represents the voltage phase angle between nodes n and l; X nl represents the reactance of line nl;

[0121] The upper and lower limit constraints of the line power flow are shown in formula (43):

[0122]

[0123] S326, establish the operating state constraints of the gas turbine in the electricity-hydrogen hybrid natural gas coupled integrated energy system, as shown in equations (44)-(46):

[0124]

[0125] in, Indicates the minimum output of the gas turbine; Cap gt Indicates the installed capacity of the gas turbine. The values ​​of No. 1 gas turbine and No. 2 gas turbine are both 100MW; Indicates the maximum value of the gas turbine ramp rate, which is 40MW / h.

[0126] S33, obtain the optimization scheduling model (P0) of the electricity-hydrogen hybrid natural gas coupled integrated energy system; the specific form is shown in formula (47):

[0127]

[0128] S4, using second-order cone relaxation and piecewise linearization to transform the original mixed integer non-convex quadratic constrained programming model (P0) into a second-order cone relaxation model (P1);

[0129] The specific steps include:

[0130] S41, using the second-order cone relaxation to process the bilinear terms in the optimal scheduling model (P0) of the electric-hydrogen hybrid natural gas coupled integrated energy system established in step S3; specifically:

[0131] For formula (1) Perform second-order cone relaxation, as shown in Equation (48):

[0132]

[0133] For formula (7) and Perform second-order cone relaxation, as shown in Equation (49):

[0134]

[0135] in, and is an auxiliary variable of the second-order cone relaxation; ζ is an auxiliary parameter introduced to avoid numerical problems. Both ζ and p are normalized, and ζ is taken as 1.

[0136] S42, using piecewise linearization to process the logarithmic term in the optimal scheduling model (P0) of the electricity-hydrogen hybrid natural gas coupled integrated energy system established in step S3, the specific process is as follows:

[0137] For the lnp in the equation (1) of the electric-hydrogen hybrid natural gas coupled integrated energy system optimization scheduling model (P0) established in step S3, the node pressure p is set in the interval [p min ,p max ] is evenly divided into N p Segments, with intervals of d p , as shown in formula (50):

[0138]

[0139] In step S43, the McCormick envelope is used to reduce the error generated by the second-order cone relaxation of the bilinear term in step S41, as shown in equation (51):

[0140]

[0141] in, and is the minimum and maximum value of the pipe storage; p min and p max are the minimum and maximum values ​​of the node pressure; and are the minimum and maximum flow rates in the pipe section.

[0142] S44, obtain the second-order cone relaxation model (P1), as shown in formula (52):

[0143]

[0144] S5, in order to reduce the relaxation error of the model (P1) and obtain a feasible solution to the original non-convex optimization model (P0), a penalty function model (P2) based on the concave-convex process method and the boundary tightening method is proposed, and an iterative algorithm is proposed to solve the optimal scheduling model of the electric-hydrogen hybrid natural gas coupled integrated energy system;

[0145] S51, a penalty function model (P2) based on the concave-convex process method and the boundary tightening method is proposed, which specifically includes:

[0146] S511, by considering the penalty term to account for the non-convex constraints ignored in the second-order cone relaxation model (P1), the penalty function model (P2) for the optimal scheduling of the electric-hydrogen hybrid natural gas coupled integrated energy system is obtained. The specific process is as follows:

[0147] In model (P1), the mirrored graph of quadratic equality constraints (Equations (48) and (49)) is used to replace the original quadratic equality constraints without considering non-convex constraints, as shown in Equation (53):

[0148]

[0149] Taking the first inequality in the non-convex constraint (53) as an example, this constraint can be regarded as a linear function and a convex function The difference is shown in formula (54):

[0150]

[0151] For convex functions Constraints (55) must be satisfied:

[0152]

[0153] in, express At the point The tangent plane at and

[0154] They are At the point Place and the partial derivatives of p;

[0155] Therefore, Equation (54) can be relaxed to Equation (56):

[0156]

[0157] Where s is a slack variable that can be added to the objective function as a penalty term to reduce the slack error. Furthermore, the slack form of Equation (53) is shown in Equation (57):

[0158]

[0159] Therefore, the second-order cone relaxation model (P1) can be improved into a penalty function model (P2), as shown in Equation (58):

[0160]

[0161] Among them, κ (k)Represents the penalty coefficient in the kth iteration process; s sum The specific expression of is shown in formula (59):

[0162]

[0163] In the iterative solution process, equations (60) and (61) serve as the convergence criteria of the objective function;

[0164]

[0165] Among them, δ1 and δ2 are the allowable errors of GAP1 and GAP2, and their values ​​are 10 -4 and 10 -3 .

[0166] S512, using the boundary tightening method to determine the boundary value of the piecewise linearization interval of the logarithmic term in the penalty function model (P2), the specific process is as follows:

[0167] Under the same number of segments, the accuracy of the piecewise linearization of the logarithmic term lnp in formula (1) is the same as the segment interval [p min ,p max ]; if the segmented interval boundary value deviates greatly from the true value of p, then the linearization result will also deviate greatly from the true value of lnp; therefore, the boundary tightening method is used to improve the piecewise linearization result of the logarithmic term. The specific rules are shown in formula (62):

[0168]

[0169] Among them, ε (k) It is the control parameter of the boundary tightening method, which is a constant that decreases as the number of iterations k increases and is greater than 0 and less than 1;

[0170] In the iterative solution process, Equation (63) serves as the convergence criterion of the boundary tightening method:

[0171]

[0172] Among them, δ3 is the allowable error of GAP3, which is 10 -3 .

[0173] S52, an iterative algorithm is proposed to solve the optimization scheduling model of the electric-hydrogen hybrid natural gas coupled integrated energy system. The detailed process is as follows: Figure 2 As shown, specifically including:

[0174] S521, initialize the number of iterations k=0,κ (0) =106, {ε k Kk=1=0.97 4k / 2.8, μ=1.05, K=50, κmax =2×10 6 , δ1, δ2, δ3, p(0)min, p(0)max;

[0175] S522, solve the second-order cone relaxation model (P1) and obtain the initial solution

[0176] S523, based on the current solution Establish penalty function model (P2);

[0177] S524, solve the penalty function model (P2) and obtain the optimal solution

[0178] S525, calculating convergence criteria GAP1, GAP2 and GAP3;

[0179] S526, determine whether the convergence condition [(GAP1≤δ1)∪(GAP2≤δ1)]∩(GAP3≤δ3)=1 is met. If so, end the iteration and output the optimal solution Otherwise, go to S527;

[0180] S527, update the current solution: p (k+1) =p*,q (k+1) =q*,

[0181] S528, update the penalty term coefficient and the control parameter of the boundary tightening method: κ (k+1) =min{μκ (k) ,κ max}, p(k+1)min, p(k+1)max;

[0182] S529, update k=k+1 and go to step S523.

[0183] Five sub-scenarios were developed for the aforementioned electricity-hydrogen hybrid natural gas coupled integrated energy system. Scenario 1 is the baseline scenario, with a zero hydrogen blending ratio. This means that hydrogen hybrid natural gas stations and the addition of hydrogen to natural gas are not considered, and the multi-energy flow network uses only natural gas from long-distance natural gas transmission lines. In scenarios 2 through 5, the hydrogen blending ratio at the hydrogen hybrid natural gas station varies from 5% to 20% in 5% increments. To ensure consistency in load size, all loads in the five scenarios are expressed in energy units.

[0184] Table 7 compares the computational performance of the operation optimization method proposed in the present invention under four hydrogen doping scenarios, namely, scenario 2 to scenario 5.

[0185] Table 7 Computational performance under different hydrogen doping scenarios:

[0186]

[0187]

[0188] In the four hydrogen-doping scenarios (Scenarios 2 to 5), the proposed iterative solution algorithm met the convergence criteria after 3 to 5 iterations, with the shortest solution time being 855.56 seconds (14.26 minutes) and the longest solution time being 2501.18 seconds (41.69 minutes). The algorithm performed well.

[0189] The optimized scheduling results of the five scenarios are shown in Table 8:

[0190] Table 8 Optimization results under different hydrogen blending ratios

[0191]

[0192] Because it doesn't involve hydrogen production, storage, methanation, and blending equipment within the hydrogen-blended natural gas station, Scenario 1's daily equipment maintenance costs are significantly lower than the other four scenarios, accounting for less than 10% of the four hydrogen-blended scenarios. Because it doesn't use clean renewable energy, Scenario 1's daily gas purchase costs are 12.4% to 14.4% higher than the other four hydrogen-blended scenarios, and it also requires paying higher carbon emissions fees in the carbon trading market. Taking into account the energy losses in the hydrogen-to-hydrogen / methane conversion process within the hydrogen-blended natural gas station equipment and the cost advantage of low natural gas prices, the baseline scenario still has a certain advantage in total daily operating costs, approximately 96.45% of Scenario 2 and 97.44% of Scenario 5.

[0193] In the four hydrogen blending scenarios, as the hydrogen blending ratio increases, the economic efficiency of the system gradually improves. Although producing more hydrogen increases the operating costs of the equipment in the hydrogen blending natural gas station, it also reduces the consumption of natural gas. Benefiting from the carbon tax system, when the hydrogen blending is 20% (Scenario 5), the daily operating cost of the system can be reduced to 98.98% of the scenario of 5% hydrogen blending (Scenario 2). In addition, as the hydrogen blending ratio increases, the system's ability to absorb renewable energy is also enhanced. In the 5% hydrogen blending scenario, there is still a penalty of 4,917.89 yuan for wind and solar power abandonment. When the hydrogen blending ratio increases to 10%, the system can already achieve 100% renewable energy absorption.

[0194] Figure 3 and Figure 4 The node pressure and pipeline flow in the hydrogen-blended natural gas network with a hydrogen blending ratio of 20% are shown (normalized). Figure 3 In the figure, the pressure of gas source nodes 4 and 6 is represented by dotted lines, which varies between 95% and 100% of the baseline value. The pressure of other nodes can drop to as low as 73.05% of the baseline value during the peak load. The pressure change trend of each node is basically the same, which is consistent with the Figure 4The pressure at each node is lower during peak flow periods and higher during low flow periods. This is because the flow rate at a pipe section is related to the pressure difference between its ends, as shown in Equation (1). When the flow rate is high, a larger pressure difference is required to meet the demand. Figure 4 In the figure, the flow amplitude of pipe section 2 is significantly lower than that of the other four pipe sections. This is because, under the hydrogen-mixed natural gas network parameters of this embodiment, load node 1 is primarily supplied by gas source node 4, and load node 3 is primarily supplied by gas source node 6. Due to the limited gas supply capacity of gas source node 4, part of the load at node 1 is supplemented by gas source node 6 via pipe section 2.

[0195] Pipeline storage conditions Figure 5 As shown, the pipeline storage trends for the five pipeline sections are essentially the same, with the thick dashed line representing the total pipeline storage in the network. This indicates that the hydrogen-blended natural gas network releases pipeline storage during peak natural gas price periods and accumulates pipeline storage during low natural gas price periods, demonstrating its ability to leverage gas inertia to smooth out peak demand and fill valleys.

[0196] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0197] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for optimizing the operation of an electricity-hydrogen-natural gas coupled integrated energy system, characterized in that: include: S1, establish a hydrogen-blended natural gas network model taking into account the influence of hydrogen blending ratio; S2, establish a hydrogen-mixed natural gas station model including renewable energy, electrolyzer, compressor, hydrogen storage tank, methane reactor, and gas mixing device; S3, establish an optimization scheduling model for the electricity-hybrid hydrogen-natural gas coupled integrated energy system; S4, using second-order cone relaxation and piecewise linearization to transform the original mixed integer non-convex quadratic constraint programming model into a second-order cone relaxation model; S5, a penalty function model based on the concave-convex process method and the boundary tightening method is proposed, and an iterative algorithm is proposed to solve the optimal scheduling model of the electric-hybrid hydrogen-natural gas coupled integrated energy system; The step of establishing the mixed hydrogen natural gas network model in S1 includes: S11, establish the improved gas network pipe section pressure drop equation: Z=αp+β(3) Where λ represents the gas friction factor; M represents the molar mass of the gas. For mixed gases, it is calculated according to formula (2); T em represents temperature; D represents the diameter of the pipe section; A represents the cross-sectional area of ​​the pipe section; α and β are the coefficients of the first-order term and the constant term respectively obtained by linear regression of the relationship between the compressibility factor and the pressure; L ij represents the length of the pipe segment ij; p i and p j represents the pressure at the beginning and end of pipe segment ij; is the average flow rate of the pipe section, expressed as the inlet flow rate of the pipe section and export flow The mean of S12, establish the improved gas network pipeline storage equation: in, Indicates the pipe section storage; Indicates the average compressibility factor of the gas in the pipe section; Indicates the average pressure of the pipe section; when the gas transmitted in the network is mixed hydrogen natural gas, Expressed as Thus, equation (5) is transformed into equation (7); The steps of establishing the hydrogen mixed natural gas station model in S2 include: S21, establish the theoretical power output mathematical model of wind turbine power generation and photovoltaic power generation: in, and P wt,t The theoretical output power and actual utilization power of wind turbine generator; and P pv,t are the theoretical output power and actual utilization power of photovoltaic power generation respectively; η wt Represents the power generation efficiency of the wind turbine; ρ air Indicates air density; N wt is the number of fans installed; R wt is the fan blade radius; u t is the wind speed at time t; is the solar radiation intensity; θ pv is the photovoltaic panel inclination angle, η MPPT is the MPPT controller efficiency; η pv is the power generation efficiency of photovoltaic panels; A pv is the photovoltaic panel installation area; S22, establish the mathematical model of the electrolytic cell operation status: Among them, Cap elz is the installed capacity of the electrolytic cell; P elz,t is the input power of the electrolytic cell; η elz is the electrolyzer conversion efficiency; LHV H and LHV NG is the lower calorific value of hydrogen and natural gas; η mr is the conversion efficiency of the methane reactor; is the hydrogen flow rate from the electrolyzer output to the methane reactor; The hydrogen flow rate output from the electrolyzer and injected into the hydrogen storage tank; The hydrogen flow rate directly injected into the hydrogen mixing device from the electrolyzer output; S23, establish a mathematical model of the compressor operation state: Among them, Cap cp is the installed capacity of the compressor; P cp,t is the input electrical power of the compressor; cp It is the ratio of compressor power consumption to compressed hydrogen flow rate; S24, establish a mathematical model of the operating status of the hydrogen storage tank: S hs,1 =S hs,T =0.5·Cap hs (16) Among them, Cap hs is the capacity of the hydrogen storage tank; S hs,t is the gas storage capacity of the hydrogen storage tank; is the injection and production gas flow rate of the hydrogen storage tank, The hydrogen flow rate injected into the methane reactor from the hydrogen storage tank; is the hydrogen flow rate injected into the gas mixing device from the hydrogen storage tank; η hs,ch / dis is the gas injection and production efficiency of the hydrogen storage tank; σ hs is the self-loss coefficient of the hydrogen storage tank; μ hs,min / max is the minimum / maximum gas storage ratio of the hydrogen storage tank; γ hs,ch / dis The gas filling rate of the hydrogen storage tank; S25, establish a mathematical model of the methane reactor operation state: Among them, Cap mr is the capacity of the methane reactor; γ mr is the ratio of the power consumption of the methane reactor to the methanogenic hydrogen flow rate; S26, establish a mathematical model of the operating status of the mixing device in, and Respectively represent the total amount of hydrogen and natural gas injected into the gas mixing device; is the natural gas flow purchased from the natural gas long-distance transmission line; In S3, the step of establishing an optimization scheduling model includes: S31, establish the objective function for optimizing the dispatch of the electricity-hybrid hydrogen-natural gas coupled integrated energy system: Among them, C NG,t is the natural gas purchase cost of the electricity-hydrogen-natural gas coupling integrated energy system at time t; is the carbon emission cost caused by the natural gas component in the hydrogen-mixed natural gas at time t; C is the penalty cost for abandoning wind and solar power at time t; om,t is the operation and maintenance cost at time t; C wheel,t is the network fee that the hydrogen-mixed natural gas station needs to pay to the public grid for transmitting electricity to the multi-energy flow network at time t; P grid,t represents the electric power transmitted from the hydrogen-mixed natural gas station to the multi-energy flow network at time t; is the purchase price of natural gas at time t; It is the unit carbon emission fee that the natural gas component in the hydrogen-mixed natural gas needs to pay; and These are the unit penalty fees that need to be paid for abandoning wind power and abandoning solar power; and They are the unit operation and maintenance costs of gas turbines, fans, photovoltaics, electrolyzers, methane reactors, hydrogen storage tanks and gas mixing devices. It is the unit transmission fee that the hybrid hydrogen natural gas station needs to pay to the public grid when transmitting electricity to the multi-energy flow network; S32, establish the constraints for optimal scheduling of the electricity-hybrid hydrogen-natural gas coupled integrated energy system; S33, obtaining an optimal scheduling model for an electricity-hydrogen-mixed natural gas coupled integrated energy system; The steps to establish constraints in S32 include: S321, establishing operating status constraints for equipment in the hydrogen-mixed natural gas station; Including: wind turbine power generation and photovoltaic power generation power constraints, electrolyzer operating state constraints, compressor operating state constraints, methane reactor operating state constraints and mixing device operating state constraints; S322, establish the multi-energy flow balance constraints of the electricity-hybrid hydrogen-natural gas coupled integrated energy system; Power balance constraints in hydrogen mixing stations: Input and output energy balance constraints of the mixing device: Among them, q w,t is the flow rate of hydrogen-blended natural gas injected into the gas source node of the hydrogen-blended natural gas network; S323, establish the gas purchase flow constraints of the electricity-hybrid hydrogen natural gas coupled integrated energy system and the capacity constraints of electric energy transmission via the public power grid: in, and P grid,max They are the upper limit of gas purchase flow and the upper limit of electric energy transmission capacity; S324: Establishing the network constraints of the hybrid hydrogen natural gas in the electricity-hybrid hydrogen natural gas coupled integrated energy system, specifically including: improved pipe section pressure drop equation, improved pipe section pipe storage equation, node flow balance constraint, gas source injection flow constraint, pipe storage charge and discharge balance constraint and pipe storage periodic recovery constraint; Node traffic balance constraints: Among them, q gt,t represents the gas consumption of the gas turbine connected to node i, Indicates gas load; Gas source injection flow constraints: Among them, q w,t represents the gas purchase flow of the gas source node at time t; q w,min and q w,max Indicates the minimum and maximum value of purchase flow; Tube storage charge and discharge balance constraints: Periodic recovery constraints: S325, establish power network flow constraints in the electricity-hybrid hydrogen-natural gas coupled integrated energy system; including: Node power balance constraints: Among them, F nl,t represents the power flow on line nl connected to node n; P gt,t represents the output electric power of the gas turbine connected to node n; represents the electrical load at node n in the power network; Relationship between active power and node voltage phase angle: θ n,t and θ l,t represents the voltage phase angle between nodes n and l; X nl represents the reactance of line nl; Line power flow upper and lower limit constraints: S326, establish the operating state constraints of the gas turbine in the electricity-hydrogen-natural gas coupling integrated energy system: in, Indicates the minimum output of the gas turbine; Cap gt represents the installed capacity of gas turbines, Indicates the maximum value of the gas turbine ramp rate; In S33, the optimization scheduling model of the electricity-hybrid hydrogen natural gas coupled integrated energy system is: The model transformation steps in S4 are: S41, using second-order cone relaxation to deal with the bilinear terms in the optimal scheduling model of the electricity-hydrogen-natural gas coupled integrated energy system: For formula (1) Perform a second-order cone relaxation: For formula (7) and Perform a second-order cone relaxation: Among them, θ and is the auxiliary variable of the second-order cone relaxation; ζ is an auxiliary parameter introduced to avoid numerical problems; S42, piecewise linearization is used to process the logarithmic terms in the optimal scheduling model of the electricity-hydrogen-natural gas coupled integrated energy system: For lnp in equation (1), the node pressure p is in the interval [p min ,p max ] is evenly divided into N p Segments, with intervals of d p : S43, using the McCormick envelope to reduce the error generated by the second-order cone relaxation of the bilinear term in step S41: in, and is the minimum and maximum value of the pipe storage; p min and p max are the minimum and maximum values ​​of the node pressure; and are the minimum and maximum flow rates of the pipe section; S44, obtain the second-order cone relaxation model: The step of establishing the penalty function model in S5 includes: S511, by considering the penalty term to account for the non-convex constraints ignored in the second-order cone relaxation model, a penalty function model for the optimal scheduling of the electric-hybrid hydrogen-natural gas coupled integrated energy system is obtained; In the second-order cone relaxation model, equations (48) and (49) are used to replace the original quadratic equality constraints without considering non-convex constraints: Taking the first inequality in the non-convex constraint (53) as an example, the constraint is regarded as a linear function and a convex function The difference: For convex functions Constraints (55) must be satisfied: in, express At the point The tangent plane at and They are At the point Right and the partial derivatives of p; Therefore, equation (54) is relaxed to equation (56): Among them, s is the slack variable; The relaxed form of formula (53) is shown in formula (57): The second-order cone relaxation model can be improved into a penalty function model, as shown in formula (58): Among them, κ (k) Represents the penalty term coefficient during the kth iteration; S512, using a boundary tightening method to determine the boundary value of the piecewise linearization interval of the logarithmic term in the penalty function model; The steps of solving the optimization scheduling model of the electricity-hydrogen-natural gas coupled integrated energy system using an iterative algorithm include: S522, solve the second-order cone relaxation model and obtain the initial solution S523, based on the current solution Establish a penalty function model; S524, solve the penalty function model and obtain the optimal solution S525, calculating convergence criteria GAP1, GAP2 and GAP3; S526, determine whether the convergence condition [(GAP1≤δ1)∪(GAP2≤δ1)]∩(GAP3≤δ3)=1 is met. If so, end the iteration and output the optimal solution. Otherwise, go to S527; S527, update current solution: p (k+1) =p*,q (k+1) =q*, S528, update the penalty term coefficient and the control parameter of the boundary tightening method: κ (k+1) =min{μκ (k) ,κ max }, p(k+1)min, p(k+1)max; S529, update k=k+1 and go to step S523.

2. The method for optimizing the operation of an electricity-hydrogen-mixed natural gas coupled integrated energy system according to claim 1, characterized in that: In the S511, s sum The specific expression is: In the iterative solution process, equations (60) and (61) are used as the convergence criteria of the objective function; Where δ1 and δ2 are the allowable errors of GAP1 and GAP2.

3. The method for optimizing the operation of an electricity-hydrogen-mixed natural gas coupled integrated energy system according to claim 1, characterized in that: In S512, the boundary value selection process is: Among them, ε (k) is the control parameter of the boundary tightening method, which is a constant that decreases as the number of iterations k increases and is greater than 0 and less than 1; In the iterative solution process, equation (63) is used as the convergence criterion of the boundary tightening method: Here, δ3 is the allowable error of GAP3.

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