A low-carbon probability optimal energy flow method for an electro-mechanical coupled system based on cross-term decoupling

Abstract

The application discloses a kind of low-carbonization probability optimal energy flow methods of electricity-gas interconnection system based on cross term decoupling, steps are as follows: 1) obtain electricity-gas interconnection system parameter;2) to output random variable Y is carried out Wiener chaos polynomial expansion;3) establish electricity-gas interconnection system low-carbonization probability optimal energy flow model;4) solve electricity-gas interconnection system low-carbonization probability optimal energy flow model, obtain electricity-gas energy flow.The application is applicable to electricity-gas interconnection system low-carbonization operation, the application considers the recycling of carbon and hydrogen multi-mode utilization interconnection system low-carbonization probability optimum, effectively handle the uncertainty in electricity-gas interconnection system, reduce the operation cost of interconnection system.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power systems, and in particular to a method for optimizing the low-carbonization probability of an electricity-gas interconnected system based on cross-term decoupling. Background Art

[0002] Through the mutual conversion and coordinated utilization of multiple energy types, the electricity-gas interconnected system forms a tightly coupled, low-carbon, multi-energy flow structure, which is conducive to the low-carbon operation of the power energy system and promotes the strategic goal of carbon neutrality. Uncertainties such as electricity / natural gas load and intermittent energy output pose significant challenges to the safe and economic operation of the electricity-gas interconnected system. Probabilistic energy flow is an effective means of addressing the impact of uncertainties on the system and has been widely used. Therefore, under the dual-carbon strategic goal, research on probabilistic optimal energy flow for the low-carbonization of the electricity-gas interconnected system is of great significance.

[0003] Existing low-carbon electricity-gas coupling models for electricity-gas interconnected systems typically only consider electricity-hydrogen-natural gas models and fail to deeply couple and synergize hydrogen systems. Due to the additional methanation process involved in hydrogen-to-natural gas synthesis, the energy conversion efficiency is lower than that of electricity-to-hydrogen technologies. Therefore, if electricity-gas interconnected systems fail to consider the deep coupling and synergistic operation of hydrogen systems, the coordinated utilization of multiple energy sources within the interconnected system will be impossible, leading to problems such as low system energy utilization, wind and solar power curtailment, and increased operating costs. Considering that the natural gas grid can inject a certain volume of carbon-free and environmentally friendly hydrogen, it is necessary to explore models for the mutual conversion and recycling of different energy sources within the electricity-gas interconnected system to strengthen the coupling and synergy between multiple energy systems and contribute to the low-carbon economic operation of the system through the optimization of mixed energy flows such as hydrogen and natural gas. Furthermore, methods for calculating the probabilistic energy flow of electric-gas interconnected systems that account for uncertainty primarily rely on simulation and analytical methods, which struggle to balance accuracy and efficiency. While the random response surface method (SRSM) can address this trade-off, it is limited to small-scale, low-dimensional random variables. When faced with electric-gas interconnected systems containing large-scale, high-dimensional random variables, the efficiency of the solution significantly declines. In summary, there is currently a lack of a method that effectively addresses the uncertainties of electric-gas interconnected systems, specifically for their low-carbon operation. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for optimizing the energy flow of an electric-gas interconnected system with low carbonization probability based on cross-term decoupling, comprising the following steps:

[0005] 1) Obtain the parameters of the electrical-gas interconnection system.

[0006] The electricity-gas interconnection system parameters include electricity load, natural gas load, and wind speed of wind farms.

[0007] The probability density functions of electric load, natural gas load, and wind speed at wind farms are shown below:

[0008]

[0009] Where x represents the electricity / natural gas load value or wind speed. μ and σ represent the mean and standard deviation, respectively. α is the proportionality coefficient.

[0010] 2) Perform Wiener chaos polynomial expansion on the output random variable Y.

[0011] The steps of performing Wiener chaos polynomial expansion on the random variable Y of the random response surface include:

[0012] 2.1) Transform the input random variable X into an independent input random variable ξ=[ξ1,ξ2,…,ξ n ]. n is the number of input random variables; ξ n is the nth input random variable;

[0013] 2.2) Using the Hermite orthogonal polynomial basis, the output random variable Y is expanded into a Wiener chaos polynomial, and we obtain:

[0014]

[0015]

[0016] Where n is the number of input random variables. m (·) is the m-order Hermite orthogonal polynomial basis. a0, These are all undetermined coefficients. 0,2 、a i,2 、a ii,2 、a ij,2 、a 0,3 、a i,3 、a ii,3 、a iii,3 、a ij,3 、a ijj,3 、a ijk,3 , are all unknown coefficients; ξ i ,ξ j ,ξ k are the i-th, j-th, and k-th input random variables; Y2 is the second-order Wiener chaotic polynomial; Y3 is the third-order Wiener chaotic polynomial;

[0017] 2.3) For the cross term ξ i ξ j , cross term ξ i ξ j ξ k Perform decoupling processing, the steps include:

[0018] 2.3.1) For the cross term ξ i ξ j Performing the identity transformation, we get:

[0019]

[0020] Cross term ξ i ξ j ξ k Performing the identity transformation, we get:

[0021]

[0022] 2.3.2) In the parameter term (ξ i,0 ,ξ j,0 ) for the cross term (ξ i -ξ j ) 2 Expanding the Taylor series method and retaining the first-order terms yields:

[0023] (ξ i -ξ j ) 2 ≈2(ξ i,0 -ξ j,0 )(ξ i -ξ j )-(ξ i,0 -ξ j,0 ) 2 (6)

[0024] In the parameter term (ξ i,0 ,ξ j,0 ,ξ k,0 ), for the parameter f(ξ i ,ξ j ,ξ k ) is expanded using the Taylor series method and retaining the second-order terms, yielding:

[0025]

[0026] 2.3.3) Cross term ξ after decoupling i ξ j As shown below:

[0027]

[0028] The cross term ξ after decoupling i ξ j ξ k As shown below:

[0029]

[0030] Where b 0,3 is a constant term. b i,3 、b ii,3 and b iii,3 is the coefficient.

[0031] 2.4) Based on step 2.3), the second-order Wiener chaotic polynomial Y2 and the third-order Wiener chaotic polynomial Y3 of the output random variable Y are as follows:

[0032]

[0033]

[0034] Where b 0,2 is a constant term. b i,2 and b ii,2 These are all coefficients to be determined.

[0035] 3) Establish an optimal energy flow model for the low-carbonization probability of the electricity-gas interconnection system.

[0036] The objective function of the low-carbon probability optimal energy flow model of the electricity-gas interconnection system is as follows:

[0037]

[0038] Where F represents the expected total operating cost of the electricity-gas interconnection system throughout the day. CGU is the power generation cost of coal-fired units. CCS.P2G is the cost of carbon capture-power-to-gas co-operation. GS is the gas purchase cost. CURT Penalty costs for curtailing wind and solar power. The cost of carbon emissions.

[0039] Among them, the power generation cost of coal-fired units is C CGU As shown below:

[0040]

[0041] Where T is the operating period, T=24. and are the coal consumption coefficients of the coal-fired unit at node i. is the output of the coal-fired unit. CGU Inject node collection for coal-fired units.

[0042] Carbon capture-power-to-gas co-operation cost C CCS.P2G As shown below:

[0043]

[0044] Where, and are the price coefficients of CO2 and H2 respectively.

[0045] Gas purchase cost C GS As shown below:

[0046]

[0047] Where, is the gas purchase price coefficient of the gas well at node i. is the gas purchase price coefficient of the gas storage station at node i;

[0048] Penalty cost for curtailing wind and solar power C CURT As shown below:

[0049]

[0050] Where, are the predicted output and actual output of the new energy electric field at node i respectively. is the penalty coefficient for curtailment of renewable energy. Δt is the operating time interval; Ω RE Inject node collection into new energy electric field. is the active power of the new energy electric field at node i;

[0051] Carbon emission costs As shown below:

[0052]

[0053] Where, is the price of carbon emissions. is the free carbon emission allocation corresponding to the unit power generation of the conventional unit at node i.

[0054] The constraints of the low-carbonization probability optimal energy flow model of the electricity-gas interconnected system include the low-carbonization coordinated operation constraints of carbon capture-power-to-gas, the operation constraints of the natural gas system, and the operation constraints of the power system.

[0055] The low-carbon coordinated operation constraints of carbon capture-power-to-gas include carbon capture equipment operation constraints, power-to-gas equipment operation constraints, carbon cycle constraints, gas flow constraints after hydrogen-natural gas mixing, and carbon-hydrogen balance constraints.

[0056] The operating constraints of the carbon capture plant are as follows:

[0057]

[0058] Where t is the counting variable of the running period. and are the unit output and carbon emission intensity of node i respectively. and are the power consumption per unit CO2 captured and the total operating energy consumption of the carbon capture equipment at node i, respectively. and are the carbon capture efficiency and maximum efficiency of the carbon capture equipment respectively.

[0059] The operating constraints of the power-to-gas equipment are as follows:

[0060]

[0061]

[0062]

[0063]

[0064] Where, is the hydrogen flow rate produced by the electrolyzer at node i. and are the efficiency and power consumption of the electrolyzer, respectively. is the gross calorific value of hydrogen. It is the upper limit of the electrolytic cell output. and are the flow rate and efficiency of synthetic natural gas of the power-to-gas equipment at node i, respectively. The hydrogen flow rate required for natural gas synthesis in power-to-gas equipment. and Divided into the upper and lower limits of hydrogen input to the methanation reactor.

[0065] The carbon cycle constraints are as follows:

[0066]

[0067] Where, The amount of natural gas synthesized from the CO2 captured by the carbon capture equipment at node i. The amount of hydrogen required to synthesize natural gas from captured CO2. and are the molar mass of CO2 and the molar volume of CH4, respectively.

[0068] The gas flow constraints after hydrogen-natural gas mixing are as follows:

[0069]

[0070]

[0071]

[0072] Where, and are the amount of hydrogen injected into natural gas node i and the gas flow rate after hydrogen is converted into natural gas. CH4 is the gross calorific value of CH4. The total gas flow injected into the power-to-gas device at node i. and are the gas purchase flows of the gas well and gas storage station at node i respectively. Ω GS and Ω GW are the injection node sets of gas wells and gas storage stations respectively. P2G is the set of injection nodes for the power-to-gas equipment. ε is the maximum hydrogen blending ratio allowed in the natural gas pipeline.

[0073] The carbon-hydrogen balance constraints are as follows:

[0074]

[0075] Where, and are the amount of CO2 and H2 purchased by the power-to-gas equipment at node i, respectively. is the molar mass of H2. and The amount of natural gas synthesized from purchased CO2 and the amount of hydrogen required for synthesizing natural gas.

[0076] The operating constraints of the natural gas system are as follows:

[0077]

[0078]

[0079]

[0080]

[0081] Where, and are the gas turbine output and required natural gas flow of natural gas node i respectively. GFU , β GFU , γ GFU is the energy consumption coefficient of the gas turbine. is the gas load consumption of node i. Ω i Represents the set of natural gas nodes connected to node i. ij.t and C ij are the gas flow rate and pipeline transmission capacity constant of the pipeline from node i to j respectively. i.t and p j.t are the air pressures at nodes i and j respectively. sign(p i.t 、p j.t ) is the direction of gas flow in pipeline ij. is the maximum flow limit of pipeline ij. and is the upper and lower limits of the gas flow rate of the gas source at node i. i.max and p i.min are the upper and lower limits of the air pressure at node i. is the compression ratio of the compressor at node i. and are the power consumption of compressor k and the air flow through the compressor respectively. GC and Z GC are all constants. and is the limit of the compression ratio.

[0082] The power system operation constraints are as follows:

[0083]

[0084]

[0085]

[0086] Where, and are the load active / reactive power and compressor power consumption of node i respectively. and are the reactive power of conventional power supply and new energy electric field at node i respectively. i.t and U j.t The voltage amplitudes at nodes i and j respectively. θ ij.t is the voltage phase angle difference between nodes i and j. G ij and B ij are the real and imaginary parts of the corresponding elements in the node admittance matrix Y. i.max and U i.min I is the upper and lower limits of the voltage at node i. ij.t and I ij.max is the current of line ij and the maximum current allowed. and They are the upper and lower limits of active output of conventional power source i respectively. and They are the upper and lower limits of reactive output of conventional power source i respectively.

[0087] 4) Solve the optimal energy flow model for the low-carbonization probability of the electricity-gas interconnection system and obtain the optimal energy flow for the low-carbonization probability of the electricity-gas interconnection system.

[0088] The steps for solving the probabilistic optimal energy flow model for the low-carbonization of the electricity-gas interconnection system include:

[0089] 4.1) Select samples of the standard normal input random variable ξ as collocation points and form several collocation point groups.

[0090] 4.2) Convert each set of collocations into samples of the original input random variables.

[0091] 4.3) Draw samples of input random variables and use the interior point method to solve the optimal energy flow model.

[0092] 4.4) Establish a system of linear equations about the unknown coefficients and solve them to determine the unknown coefficients in the chaotic polynomial.

[0093] 4.5) Estimate the statistical information of state variables such as the total operating cost of the electricity-gas interconnection system based on Wiener chaos polynomials.

[0094] The technical effect of the present invention is unquestionable. The present invention constructs a carbon capture-power-to-gas low-carbon coordinated operation mode, which can realize the recycling of carbon and the multi-mode coordinated utilization of hydrogen, making the interactive coupling of different energy systems closer. On this basis, an optimal energy flow model of the low-carbonization probability of the electric-gas interconnected system is established; then, considering that the existing probability analysis method of the electric-gas interconnected system is difficult to balance the calculation accuracy and solution efficiency, an electric-gas probabilistic energy flow analysis method based on the cross-term decoupling random response surface is proposed, and the Taylor series expansion is used to decouple and simplify the cross terms in the Wiener chaotic polynomials, thereby overcoming the defect that the random response surface method is difficult to handle high-dimensional input random variables, and providing guidance and methods for the low-carbon operation of the electric-gas interconnected system. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 This is a flow chart of the electric-gas probabilistic energy flow analysis method based on the cross-term decoupled random response surface;

[0096] Figure 2 is (ξ i ,ξ j ) Segmentation diagram;

[0097] Figure 3 This is a schematic diagram of the IEEE 39-NGS 20 electrical-gas interconnection system. DETAILED DESCRIPTION

[0098] The present invention will be further described below with reference to the following examples, but it should not be understood that the scope of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications can be made according to common technical knowledge and customary means in the art, and all should be included in the scope of protection of the present invention.

[0099] Example 1:

[0100] See also Figures 1 to 3A method for low-carbonization probability optimal energy flow of an electricity-gas interconnected system based on cross-term decoupling is proposed, comprising the following steps:

[0101] 1) Obtain the parameters of the electrical-gas interconnection system.

[0102] The electricity-gas interconnection system parameters include electricity load, natural gas load, and wind speed of wind farms.

[0103] The probability density functions of electric load, natural gas load, and wind speed at wind farms are shown below:

[0104]

[0105] Where x represents the electricity / natural gas load value or wind speed. μ and σ represent the mean and standard deviation, respectively. α is the proportionality coefficient.

[0106] 2) Perform Wiener chaos polynomial expansion on the output random variable Y.

[0107] The steps of performing Wiener chaos polynomial expansion on the random variable Y of the random response surface include:

[0108] 2.1) Transform the input random variable X into an independent input random variable ξ=[ξ1,ξ2,…,ξ n ]. n is the number of input random variables; ξ n is the nth input random variable;

[0109] 2.2) Using the Hermite orthogonal polynomial basis, the output random variable Y is expanded into a Wiener chaos polynomial, and we obtain:

[0110]

[0111]

[0112] Where n is the number of input random variables. m (·) is the m-order Hermite orthogonal polynomial basis. a0, These are all undetermined coefficients. 0,2 、a i,2 、a ii,2 、a ij,2 、a 0,3 、a i,3 、a ii,3 、a iii,3 、a ij,3 、a ijj,3 、a ijk,3 , are all unknown coefficients; ξ i ,ξ j ,ξ kare the i-th, j-th, and k-th input random variables; Y2 is the second-order Wiener chaotic polynomial; Y3 is the third-order Wiener chaotic polynomial;

[0113] 2.3) For the cross term ξ i ξ j , cross term ξ i ξ j ξ k Perform decoupling processing, the steps include:

[0114] 2.3.1) For the cross term ξ i ξ j Performing the identity transformation, we get:

[0115]

[0116] Cross term ξ i ξ j ξ k Performing the identity transformation, we get:

[0117]

[0118] 2.3.2) In the parameter term (ξ i,0 ,ξ j,0 ) for the cross term (ξ i -ξ j ) 2 Expanding the Taylor series method and retaining the first-order terms yields:

[0119] (ξ i -ξ j ) 2 ≈2(ξ i,0 -ξ j,0 )(ξ i -ξ j )-(ξ i,0 -ξ j,0 ) 2 (6)

[0120] In the parameter term (ξ i,0 ,ξ j,0 ,ξ k,0 ), for the parameter f(ξ i ,ξ j ,ξ k ) is expanded using the Taylor series method and retaining the second-order terms, yielding:

[0121]

[0122] 2.3.3) Cross term ξ after decoupling i ξ j As shown below:

[0123]

[0124] The cross term ξ after decoupling i ξ j ξ k As shown below:

[0125]

[0126] Where b 0,3 is a constant term. b i,3 、b ii,3 and b iii,3 is the coefficient.

[0127] 2.4) Based on step 2.3), the second-order Wiener chaotic polynomial Y2 and the third-order Wiener chaotic polynomial Y3 of the output random variable Y are as follows:

[0128]

[0129]

[0130] Where b 0,2 is a constant term. b i,2 and b ii,2 These are all coefficients to be determined.

[0131] 3) Establish an optimal energy flow model for the low-carbonization probability of the electricity-gas interconnection system.

[0132] The objective function of the low-carbon probability optimal energy flow model of the electricity-gas interconnection system is as follows:

[0133]

[0134] Where F represents the expected total operating cost of the electricity-gas interconnection system throughout the day. CGU is the power generation cost of coal-fired units. CCS.P2G is the cost of carbon capture-power-to-gas co-operation. GS is the gas purchase cost. CURT Penalty costs for curtailing wind and solar power. The cost of carbon emissions.

[0135] Among them, the power generation cost of coal-fired units is C CGU As shown below:

[0136]

[0137] Where T is the operating period, T=24. and are the coal consumption coefficients of the coal-fired unit at node i. For the output of coal-fired units.Ω CGU For the injection node set of coal-fired units.

[0138] Carbon capture-electricity to gas co-operation cost C CCS.P2G As follows:

[0139]

[0140] In the formula, And The price coefficient of CO2 and H2, respectively.

[0141] Gas purchase cost C GS As follows:

[0142]

[0143] In the formula, The gas purchase price coefficient of node i. The gas purchase price coefficient of node i gas station;

[0144] Wind and light abandoned penalty cost C CURT As follows:

[0145]

[0146] In the formula, The predicted output and actual output of new energy power plant of node i, respectively. The new energy abandoned electricity penalty coefficient.Δt is the operation interval interval, Δt = 1h.Ω RE The injection node set of new energy power plant. The active power of node i new energy power plant;

[0147] Carbon emission cost As follows:

[0148]

[0149] In the formula, The carbon emission price. The free carbon emission allocation quota corresponding to the unit power generation of node i conventional unit.

[0150] The constraint conditions of the low-carbon probability optimal energy flow model of the electricity-gas interconnection system include the low-carbon co-operation constraint of carbon capture-electricity to gas, the natural gas system operation constraint and the power system operation constraint.

[0151] The low-carbon co-operation constraint of carbon capture-electricity to gas includes the carbon capture equipment operation constraint, the electricity to gas equipment operation constraint, the carbon cycle constraint, the gas flow constraint after hydrogen-natural gas mixing, the carbon-hydrogen balance constraint.

[0152] The operating constraints of the carbon capture plant are as follows:

[0153]

[0154] Where t is the counting variable of the running period. and are the unit output and carbon emission intensity of node i respectively. and are the power consumption per unit CO2 captured and the total operating energy consumption of the carbon capture equipment at node i, respectively. and are the carbon capture efficiency and maximum efficiency of the carbon capture equipment respectively.

[0155] The operating constraints of the power-to-gas equipment are as follows:

[0156]

[0157]

[0158]

[0159]

[0160] Where, is the hydrogen flow rate produced by the electrolyzer at node i. and are the efficiency and power consumption of the electrolyzer, respectively. is the gross calorific value of hydrogen. It is the upper limit of the electrolytic cell output. and are the flow rate and efficiency of synthetic natural gas of the power-to-gas equipment at node i, respectively. The hydrogen flow rate required for natural gas synthesis in power-to-gas equipment. and Divided into the upper and lower limits of hydrogen input to the methanation reactor.

[0161] The carbon cycle constraints are as follows:

[0162]

[0163] Where, The amount of natural gas synthesized from the CO2 captured by the carbon capture equipment at node i. The amount of hydrogen required to synthesize natural gas from captured CO2. and are the molar mass of CO2 and the molar volume of CH4, respectively.

[0164] The gas flow constraints after hydrogen-natural gas mixing are as follows:

[0165]

[0166]

[0167]

[0168] Where, and are the amount of hydrogen injected into natural gas node i and the gas flow rate after hydrogen is converted into natural gas. CH4 is the gross calorific value of CH4. The total gas flow injected into the power-to-gas device at node i. and are the gas purchase flows of the gas well and gas storage station at node i respectively. Ω GS and Ω GW are the injection node sets of gas wells and gas storage stations respectively. P2G is the set of injection nodes for the power-to-gas equipment. ε is the maximum hydrogen blending ratio allowed in the natural gas pipeline.

[0169] The carbon-hydrogen balance constraints are as follows:

[0170]

[0171] Where, and are the amount of CO2 and H2 purchased by the power-to-gas equipment at node i, respectively. is the molar mass of H2. and The amount of natural gas synthesized from purchased CO2 and the amount of hydrogen required for synthesizing natural gas.

[0172] The operating constraints of the natural gas system are as follows:

[0173]

[0174]

[0175]

[0176]

[0177] Where, and are the gas turbine output and required natural gas flow of natural gas node i respectively. GFU , β GFU , γ GFU is the energy consumption coefficient of the gas turbine. is the gas load consumption of node i. Ω i Represents the set of natural gas nodes connected to node i.ij.t and C ij are the gas flow rate and pipeline transmission capacity constant of the pipeline from node i to j respectively. i.t and p j.t are the air pressures at nodes i and j respectively. sign(p i.t 、p j.t ) is the direction of gas flow in pipeline ij. is the maximum flow limit of pipeline ij. and is the upper and lower limits of the gas flow rate of the gas source at node i. i.max and p i.min are the upper and lower limits of the air pressure at node i. is the compression ratio of the compressor at node i. and are the power consumption of compressor k and the air flow through the compressor respectively. GC and Z GC are all constants. and is the limit of the compression ratio.

[0178] The power system operation constraints are as follows:

[0179]

[0180]

[0181]

[0182] Where, and are the load active / reactive power and compressor power consumption of node i respectively. and are the reactive power of conventional power supply and new energy electric field at node i respectively. i.t and U j.t The voltage amplitudes at nodes i and j respectively. θ ij.t is the voltage phase angle difference between nodes i and j. G ij and B ij are the real and imaginary parts of the corresponding elements in the node admittance matrix Y. i.max and U i.min I is the upper and lower limits of the voltage at node i. ij.t and I ij.max is the current of line ij and the maximum current allowed. and They are the upper and lower limits of active output of conventional power source i respectively. and They are the upper and lower limits of reactive output of conventional power source i respectively.

[0183] 4) Solve the optimal energy flow model for the low-carbonization probability of the electricity-gas interconnection system and obtain the optimal energy flow for the low-carbonization probability of the electricity-gas interconnection system.

[0184] The steps for solving the probabilistic optimal energy flow model for the low-carbonization of the electricity-gas interconnection system include:

[0185] 4.1) Select samples of the standard normal input random variable ξ as collocation points and form several collocation point groups.

[0186] 4.2) Convert each set of collocations into samples of the original input random variables.

[0187] 4.3) Draw samples of input random variables and use the interior point method to solve the optimal energy flow model.

[0188] 4.4) Establish a system of linear equations about the unknown coefficients and solve them to determine the unknown coefficients in the chaotic polynomial.

[0189] 4.5) Estimate the statistical information of state variables such as the total operating cost of the electricity-gas interconnection system based on Wiener chaos polynomials.

[0190] Example 2:

[0191] See also Figure 1 , an electric-gas probabilistic energy flow analysis method based on cross-term decoupled random response surface mainly includes the following steps:

[0192] 1) The main steps for obtaining information such as electrical-gas interconnection system parameters and random variable probability models are as follows:

[0193] 1.1) Assume that the electricity load, natural gas load, and wind speed of wind farms in the electricity-gas interconnection system all follow a normal distribution;

[0194] 1.2) Assuming the predicted value is the mean and α% of the mean is the standard deviation, the probability density functions of power load, natural gas load, and wind speed at wind farms are expressed as:

[0195]

[0196] Where x represents the electricity / natural gas load value or wind speed; μ and σ represent the mean and standard deviation, respectively.

[0197] 2) The main steps of improving the traditional random response surface method based on cross-term decoupling are as follows:

[0198] 2.1) In the random response surface method, the input random variable X can be transformed into an independent input random variable ξ=[ξ1,ξ2,…,ξ n], the output random variable Y is expanded into a Wiener chaos polynomial using the Hermite orthogonal polynomial basis, and the analytical expression is:

[0199]

[0200] Where n is the number of input random variables; H m (·) is the m-order Hermite orthogonal polynomial basis; a0, All are undetermined coefficients; the total number of undetermined coefficients is N a It can be expressed as:

[0201]

[0202] 2.2) When the order m of the Wiener chaos polynomial expansion is greater than or equal to 3, simply increasing the order will not significantly improve the calculation accuracy. Therefore, using second-order or third-order Wiener chaos polynomials can usually achieve a balance between calculation accuracy and solution efficiency. The second-order and third-order Wiener chaos expansions of the output random variable Y can be expressed as:

[0203]

[0204]

[0205] 2.3) Take the second-order Wiener chaos polynomial as an example to briefly introduce the basic idea of ​​cross-term decoupling technology. i ξ j The number of corresponding undetermined coefficients occupies the vast majority of the total number of undetermined coefficients. Therefore, if ξ i ξ j By decoupling it into a form that can be combined with other terms in the second-order Wiener chaotic polynomial, the purpose of reducing the number of undetermined coefficients can be achieved.

[0206] To maximize the preservation of cross terms ξ i ξ j The accuracy of decoupling processing is first i ξ j Make the following identity transformation:

[0207]

[0208] Because i With ξ j are independent of each other and all obey the standard normal distribution. According to the "3σ" principle, assuming that ξ i ,ξ j ∈[-3,3], taking this as an example, the binary variable (ξ i ,ξ j ) can be expressed as Figure 2The square region with side length 6 is further divided into M 2 small squares with side length Δξ, M is a large enough integer, at this time, for any (ξ i ,ξ j ), it can be divided into a small square, then it can be considered that (ξ i ,ξ j ) in each small square is located near the geometric center (ξ i,0 ,ξ j,0 ), so (ξ i -ξ j ) 2 At (ξ i,0 ,ξ j,0 ), Taylor series expansion is used and the first order term is retained, which can obtain:

[0209] (ξ i -ξ j ) 2 ≈2(ξ i,0 -ξ j,0 )(ξ i -ξ j )-(ξ i,0 -ξ j,0 ) 2 (7) At this point, the cross term ξ i ξ j can be approximately expressed by decoupling processing:

[0210]

[0211] Substituting equation (8) into equation (4), the second order Wiener chaos polynomial can be simplified as:

[0212]

[0213] In the formula, b 0,2 is a constant term; b i,2 and b ii,2 are coefficients to be solved.

[0214] 2.4) Based on the above method, the cross term ξ i ξ j in the third order Wiener chaos polynomial (5) can be decoupled and processed. In addition, the cross terms and ξ i ξ j ξ k also need to be decoupled and processed. For this purpose, the cross term ξ i ξ j ξ k is transformed as:

[0215]

[0216] Also based on the “3σ” principle, assuming that ξ i ,ξ j ,ξ k ∈[-3,3]. At this time, the ternary variable (ξ i ,ξ j ,ξ k ) is a space enclosed by a cube with a side length of 6, which is divided into M 3 A small cube with side length Δξ, M is a sufficiently large integer. Let (ξ i ,ξ j ,ξ k ) belongs to the central coordinates of the small cube (ξ i,0 ,ξ j,0 ,ξ k,0 ), and here we have f(ξ i ,ξ j ,ξ k ) Using Taylor series expansion and retaining the second-order terms, we can obtain:

[0217]

[0218] This completes the cross term ξ i ξ j ξ k Decoupling process, similarly, let ξ k =ξ j Cross terms can be obtained The decoupling form will not be described here.

[0219] In summary, the third-order Wiener chaos expansion is processed by cross-term decoupling technology to obtain:

[0220]

[0221] Where b 0,3 is a constant term; b i,3 、b ii,3 and b iii,3 These are all coefficients to be determined.

[0222] 3) The main steps for establishing a probabilistic optimal energy flow model for day-ahead dispatch of a low-carbon electricity-gas interconnected system are:

[0223] 3.1) The optimization objective is to minimize the expected total operating cost F of the interconnected system throughout the day. The total cost includes the power generation cost of conventional units, the cost of carbon capture-power-to-gas coordinated operation, the penalty cost for wind and solar power curtailment, the gas purchase cost, and the carbon emission cost, which can be expressed as:

[0224]

[0225] Where C CGU is the power generation cost of coal-fired units; C CCS.P2G is the cost of carbon capture-power-to-gas co-operation; C GS is the gas purchase cost; C CURT Penalty costs for curtailing wind and solar power; The cost of carbon emissions.

[0226] a) Power generation cost of coal-fired units

[0227]

[0228] Where, T is the operating cycle, T=24; and are the coal consumption coefficients of the coal-fired unit at node i; is the output of the coal-fired unit; Ω CGU Inject node collection for coal-fired units.

[0229] b) Carbon capture-power-to-gas co-operation costs

[0230] The cost of carbon capture-power-to-gas co-operation is caused by the purchase of CO2 and H2 in the co-operation, and its cost can be expressed as:

[0231]

[0232] Where, and are the price coefficients of CO2 and H2 respectively.

[0233] c) Gas purchase cost

[0234]

[0235] Where, is the gas purchase price coefficient of the gas well at node i.

[0236] d) Penalty costs for curtailing wind and solar power

[0237]

[0238] Where, are the predicted output and actual output of the new energy electric field at node i respectively; is the penalty coefficient for curtailment of renewable energy; Δt is the operating time interval, Δt = 1h; Ω RE Inject node collection into new energy electric field.

[0239] e) Carbon emission costs

[0240] Considering that both carbon capture and methanation can help reduce the total CO2 emissions of the interconnected system, the carbon emission cost can be expressed as:

[0241]

[0242] Where, is the price of carbon emissions; is the free carbon emission allocation corresponding to the unit power generation of the conventional unit at node i.

[0243] 3.2) Consider the low-carbonization coordinated operation constraints of carbon capture-power-to-gas, the operation constraints of the natural gas system, and the operation constraints of the power system.

[0244] a) Constraints on the coordinated operation of carbon capture and power-to-gas for low-carbonization

[0245] The carbon capture model is specifically expressed as:

[0246]

[0247] Where t is the counting variable of the running period; and are the unit output and carbon emission intensity of node i respectively; and are the power consumption per unit CO2 captured and the total operating energy consumption of the carbon capture equipment at node i, respectively; and are the carbon capture efficiency and maximum efficiency of the carbon capture equipment respectively.

[0248] P2G technology includes power-to-hydrogen conversion and hydrogen-to-methane conversion. The chemical equations and energy conversion relationships for power-to-hydrogen conversion are shown in Equations (20)-(23). Hydrogen is introduced into a methane reactor and reacts with carbon dioxide via a Sabatier catalytic reaction to synthesize methane. The corresponding chemical equations and energy conversion relationships are shown in Equations (24)-(26).

[0249]

[0250]

[0251]

[0252]

[0253]

[0254]

[0255] Where, is the hydrogen flow rate produced by the electrolyzer at node i; and are the efficiency and power consumption of the electrolyzer, respectively; is the gross calorific value of hydrogen; is the upper limit of the electrolytic cell's output; and are the flow rate and efficiency of synthetic natural gas of the power-to-gas equipment at node i; The hydrogen flow rate required to synthesize natural gas for power-to-gas equipment; and Divided into the upper and lower limits of hydrogen input to the methanation reactor.

[0256] In the carbon capture-power-to-gas synergistic operation mode, based on the carbon capture device, the greenhouse gas CO2 emitted by the capture system is captured to provide the carbon raw material required for hydrogen methanation, thereby realizing the recycling of carbon, thereby reducing carbon emission costs and carbon procurement costs for hydrogen methanation, while avoiding the carbon storage and sequestration costs in the carbon capture single operation mode. The balance relationship in the carbon recycling mode can be expressed as:

[0257]

[0258] Where, The amount of natural gas synthesized from the CO2 captured by the carbon capture equipment at node i; The amount of hydrogen required to synthesize natural gas from captured CO2; and are the molar mass of CO2 and the molar volume of CH4, respectively.

[0259] Based on the traditional hydrogen methanation utilization model, the potential of hydrogen blending in gas pipelines is explored and utilized, the hydrogen system and the natural gas system are coupled, hydrogen is injected into the natural gas pipeline, and the gas load is supplied through the hydrogen-natural gas mixed energy flow, avoiding the energy waste caused by the multi-channel conversion of hydrogen methanation, reducing the carbon emissions of the gas load and the amount of fossil energy natural gas used at the gas source. Based on the principle of calorific value equivalence, the total gas flow rate after hydrogen-natural gas mixing is expressed as equations (28)-(29). The hydrogen injected into the natural gas system must not exceed a certain limit of the total amount of natural gas, and its constraint is expressed by equation (30). The details are as follows:

[0260]

[0261]

[0262]

[0263] Where, and are the amount of hydrogen injected into natural gas node i and the gas flow after hydrogen is converted into natural gas; GHV CH4 is the gross calorific value of CH4; The total gas flow injected into the power-to-gas device at node i. and are the gas purchase flows of the gas well and gas storage station at node i respectively; ΩGS and Ω GW are the injection node sets of gas wells and gas storage stations respectively; Ω P2G is the set of injection nodes for the power-to-gas equipment; ε is the maximum hydrogen blending ratio allowed in the natural gas pipeline.

[0264] In addition, considering that there may be a mismatch between the amount of carbon captured and the amount of hydrogen produced by water electrolysis during the coordinated operation of carbon capture and power-to-gas, it may be necessary to purchase CO2 and H2 from outside to participate in the methanation reaction during the methane synthesis process. The carbon-hydrogen balance under the coordinated operation of carbon capture and power-to-gas can be established as:

[0265]

[0266] Where, and are the amount of CO2 and H2 purchased from the power-to-gas equipment at node i, respectively; is the molar mass of H2; and The amount of natural gas synthesized from purchased CO2 and the amount of hydrogen required for synthesizing natural gas.

[0267] b) Natural gas system operation constraints

[0268] The energy conversion relationship of the gas turbine is shown in Equation (32), the airflow balance constraint and pipeline transmission flow constraint are shown in Equation (33), the gas outlet limit constraint and node gas pressure constraint are shown in Equation (34), and the compressor constraint is shown in Equation (35). The details are as follows:

[0269]

[0270]

[0271]

[0272]

[0273] Where, and are the gas turbine output and required natural gas flow of natural gas node i respectively; α GFU , β GFU , γ GFU is the energy consumption coefficient of the gas turbine; is the gas load consumption of node i; Ω i represents the set of natural gas nodes connected to node i; f ij.t and C ij are the gas flow rate and pipeline transmission capacity constant of the pipeline from node i to j respectively; p i.t and p j.t are the air pressures at nodes i and j respectively; sign(pi.t 、p j.t ) is the direction of gas flow in pipeline ij; is the maximum flow limit of pipeline ij; and is the upper and lower limits of the gas flow rate of the gas source at node i; p i.max and p i.min are the upper and lower limits of the air pressure at node i; is the compression ratio of the compressor at node i; and are the power consumption of compressor k and the air flow through the compressor respectively; B GC and Z GC are all constants; and is the limit of the compression ratio.

[0274] c) Power system operation constraints

[0275] Equation (36) represents the power flow constraint, Equation (37) represents the node voltage constraint and branch current constraint, and Equation (38) represents the conventional power output constraint. The details are as follows:

[0276]

[0277]

[0278]

[0279] Where, and are the load active / reactive power and compressor power consumption of node i respectively; and are the reactive power of conventional power supply and new energy electric field at node i respectively; U i.t and U j.t The voltage amplitudes at nodes i and j, respectively; θ ij.t is the voltage phase angle difference between nodes i and j; G ij and B ij are the real and imaginary parts of the corresponding elements in the node admittance matrix Y; U i.max and U i.min is the upper and lower limits of the voltage at node i; I ij.t and I ij.max is the current of line ij and the maximum current allowed; and They are respectively the upper and lower limits of active output of conventional power source i; and They are the upper and lower limits of reactive output of conventional power source i respectively.

[0280] 4) Based on the stochastic response surface method with cross-term decoupling, the optimal energy flow model for the low-carbonization probability of the electricity-gas interconnection system is solved. The main steps are as follows:

[0281] 4.1) Select collocations, i.e., samples of the standard normal input random variable ξ, to form collocation combinations;

[0282] 4.2) Using correlation processing and normalization techniques, each set of collocation points is converted into a sample of the original input random variable;

[0283] 4.3) Sample the input random variables sequentially and use the interior point method to solve the optimal energy flow model;

[0284] 4.4) Establish a system of linear equations with respect to the undetermined coefficients and solve and determine the undetermined coefficients in the chaotic polynomials;

[0285] 4.5) Estimate the statistical information of state variables such as the total operating cost of the electricity-gas interconnection system based on Wiener chaos polynomials.

[0286] Example 3:

[0287] An experiment to verify the probabilistic optimal energy flow method for low-carbonization of electric-gas interconnected systems based on cross-term decoupling is conducted. The main steps are as follows:

[0288] 1) Obtain information such as the parameters of the power-to-gas interconnection system and the probability model of random variables. Taking the IEEE 39-NGS 20 power-to-gas interconnection system as an example, the IEEE 39-NGS 20 power-to-gas interconnection system consists of an IEEE 39-node system and a 20-node natural gas system. The power / natural gas load and wind speed all follow a normal distribution with a standard deviation of 2% of the mean. The parameters for the coordinated operation of carbon capture and power-to-gas are detailed in Table 1, and the economic parameters of the power-to-gas interconnection system are detailed in Table 2.

[0289] Table 1 Carbon capture-power-to-gas coordinated operation parameters

[0290]

[0291]

[0292] Table 2 Economic parameters of the electricity-gas interconnection system

[0293]

[0294] 2) Improve the traditional random response surface method based on cross-term decoupling.

[0295] 3) Establish an optimal energy flow model for the low-carbonization probability of the electricity-gas interconnection system.

[0296] 4) The low-carbon probabilistic optimal energy flow model of the electricity-gas interdependent system is solved by the random response surface method based on the decoupling of cross terms, and the calculation results are shown in Table 3.

[0297] Table 3 Expected value of test system cost

[0298] 10 4 USD

[0299]

[0300]

[0301] As can be seen from Table 3, the total operating cost of the electricity-gas interdependent system is 23.49 million $, and since the method in this paper can directly inject part of the hydrogen produced by electrolysis of water into the natural gas system, avoiding further loss caused by the methanation process, it makes the available energy greater than the synthesized methane. For example: the volume of hydrogen obtained by the electrolytic cell is V H2 , according to the principle of equivalent calorific value, it can be equivalent to about 0.46V CH4 of natural gas; if the volume of V H2 hydrogen is all input into the methane reactor to synthesize natural gas, according to the methanation reaction formula, the maximum volume of natural gas generated is about 0.2V CH4 . From the energy point of view, the hydrogen directly injected into the system can replace about 2.3 times the natural gas generated by the methane reactor. Therefore, the method in this paper can reduce the gas purchase quantity of the natural gas system to the gas source point and reduce the gas purchase cost. In addition, the hydrogen produced by electrolysis of water directly injected into the natural gas system can reduce the hydrogen injection amount constraint in the methanation process, avoiding the phenomenon that a large amount of hydrogen generated by electrolysis of water cannot be synthesized into methane. Based on the advantages of small energy utilization rate and small constraint effect of hydrogen injection into the natural gas system, the method in this paper uses part of the electricity generated by the coal-fired unit to generate hydrogen by electrolysis of water, which is injected into the natural gas system, while capturing the carbon emitted by the coal-fired unit for use as raw material for synthesizing methane. Although it increases the power generation cost of the coal-fired unit by a small amount, it can reduce the gas purchase cost of the interdependent system and the carbon procurement cost in the hydrogen methanation process, and finally the total operating cost of the system is also reduced. It can be seen that the method in this paper can convert fossil energy coal into carbon-free and environmentally friendly hydrogen energy, and use the emitted carbon to synthesize natural gas, which helps to clean the use of traditional fossil energy.

Claims

1. A method for the optimal energy flow of low-carbonization probability of electric-gas interconnected systems based on cross-term decoupling, characterized by: The following steps are involved: 1) Obtaining electrical-gas interconnection system parameters; 2) Perform Wiener chaos polynomial expansion on the output random variable Y; 3) Establishing a probabilistic optimal energy flow model for the low-carbonization of the electricity-gas interconnection system; 4) Solve the optimal energy flow model for the low-carbonization probability of the electricity-gas interconnection system and obtain the optimal energy flow for the low-carbonization probability of the electricity-gas interconnection system; The electricity-gas interconnection system parameters include electricity load, natural gas load, and wind speed of wind farms; The probability density functions of electric load, natural gas load, and wind speed at wind farms are shown below: Where x represents the electricity / natural gas load value or wind speed; μ and σ represent the mean and standard deviation, respectively; α is the proportionality coefficient; The steps of performing Wiener chaos polynomial expansion on the random variable Y of the random response surface include: 2.1) Transform the input random variable X into an independent input random variable ξ=[ξ1,ξ2,…,ξ n ]; n is the number of input random variables; ξ n is the nth input random variable; 2.2) Using the Hermite orthogonal polynomial basis, the output random variable Y is expanded into a Wiener chaos polynomial, and we obtain: Where n is the number of input random variables; a 0,2 、a i,2 、a ii,2 、a ij,2 、a 0,3 、a i,3 、a ii,3 、a iii,3 、a ij,3 、a ijj,3 、a ijk,3 , are all unknown coefficients; ξ i ,ξ j ,ξ k are the i-th, j-th, and k-th input random variables; Y2 is the second-order Wiener chaotic polynomial; Y3 is the third-order Wiener chaotic polynomial; 2.3) For the cross term ξ i ξ j , cross term ξ i ξ j ξ k Perform decoupling processing, the steps include: 2.3.1) For the cross term ξ i ξ j Performing the identity transformation, we get: Cross term ξ i ξ j ξ k Performing the identity transformation, we get: 2.3.2) In the parameter term (ξ i,0 ,ξ j,0 ) for the cross term (ξ i -ξ j ) 2 Expanding the Taylor series method and retaining the first-order terms yields: (x) i -x j ) 2 ≈2(ξ i,0 -x j,0 (x) i -x j )-(ξ i,0 -x j,0 ) 2 (6) In the parameter term (ξ i,0 ,ξ j,0 ,ξ k,0 ), for the parameter f(ξ i ,ξ j ,ξ k ) is expanded using the Taylor series method and retaining the second-order terms, yielding: 2.3.3) Cross term ξ after decoupling i ξ j As shown below: The cross term ξ after decoupling i ξ j ξ k As shown below: Where b 0,3 is a constant term; b i,3 、b ii,3 and b iii,3 is the coefficient; 2.4) Based on step 2.3), the second-order Wiener chaotic polynomial Y2 and the third-order Wiener chaotic polynomial Y3 of the output random variable Y are as follows: Where b 0,2 is a constant term; b i,2 and b ii,2 All are coefficients to be determined; The objective function of the low-carbon probability optimal energy flow model of the electricity-gas interconnection system is as follows: Where F represents the expected total operating cost of the electricity-gas interconnection system throughout the day; C CGU is the power generation cost of coal-fired units; C CCS.P2G is the cost of carbon capture-power-to-gas co-operation; C GS is the gas purchase cost; C CURT Penalty costs for curtailing wind and solar power; is the cost of carbon emissions; in, Coal-fired unit power generation cost C CGU As shown below: Where, T is the operating cycle, T=24; and are the coal consumption coefficients of the coal-fired unit at node i; is the output of the coal-fired unit; Ω CGU Inject node sets for coal-fired units; Carbon capture-power-to-gas co-operation cost C CCS.P2G As shown below: Where, and are the price coefficients of CO2 and H2 respectively; and are the amount of CO2 and H2 purchased from the power-to-gas equipment at node i, respectively; Gas purchase cost C GS As shown below: Where, is the gas purchase price coefficient of the gas well at node i; and are the gas purchase flows of the gas well and gas storage station at node i respectively; is the gas purchase price coefficient of the gas storage station at node i; Penalty cost for curtailing wind and solar power C CURT As shown below: Where, are the predicted output and actual output of the new energy electric field at node i respectively; is the penalty coefficient for curtailment of renewable energy; Δt is the operating time interval; Ω RE Injecting nodes into the new energy electric field; is the active power of the new energy electric field at node i; Carbon emission costs As shown below: Where, is the price of carbon emissions; is the free carbon emission allocation corresponding to the unit power generation of the conventional unit at node i; and are the unit output and carbon emission intensity of node i respectively; The carbon capture efficiency of the carbon capture equipment; is the flow rate of natural gas synthesized by the power-to-gas equipment at node i; and are the molar mass of CO2 and the molar volume of CH4 respectively; The constraints of the low-carbonization probability optimal energy flow model of the electricity-gas interconnected system include the low-carbonization coordinated operation constraints of carbon capture-power-to-gas, the operation constraints of the natural gas system, and the operation constraints of the power system.

2. The method for optimizing the probabilistic energy flow for low-carbonization of an electricity-gas interconnected system based on cross-term decoupling according to claim 1, wherein the constraints for the coordinated low-carbonization operation of carbon capture and power-to-gas include carbon capture equipment operation constraints, power-to-gas equipment operation constraints, carbon cycle constraints, gas flow constraints after hydrogen-natural gas mixing, and carbon-hydrogen balance constraints. The operating constraints of the carbon capture plant are as follows: Where t is the counting variable of the running period; and are the unit output and carbon emission intensity of node i respectively; and are the power consumption per unit CO2 captured and the total operating energy consumption of the carbon capture equipment at node i, respectively; and are the carbon capture efficiency and maximum efficiency of the carbon capture equipment respectively; The operating constraints of the power-to-gas equipment are as follows: Where, is the hydrogen flow rate produced by the electrolyzer at node i; and are the efficiency and power consumption of the electrolyzer, respectively; is the gross calorific value of hydrogen; is the upper limit of the electrolytic cell's output; and are the flow rate and efficiency of synthetic natural gas of the power-to-gas equipment at node i; The hydrogen flow rate required to synthesize natural gas for power-to-gas equipment; and Divided into upper and lower limits for hydrogen input to the methanation reactor; The carbon cycle constraints are as follows: Where, The amount of natural gas synthesized from the CO2 captured by the carbon capture equipment at node i; The amount of hydrogen required to synthesize natural gas from captured CO2; and are the molar mass of CO2 and the molar volume of CH4 respectively; The gas flow constraints after hydrogen-natural gas mixing are as follows: Where, and are the amount of hydrogen injected into natural gas node i and the gas flow after hydrogen is converted into natural gas; GHV CH4 is the gross calorific value of CH4; The total gas flow injected into the power-to-gas device at node i; and are the gas purchase flows of the gas well and gas storage station at node i respectively; Ω GS and Ω GW are the injection node sets of gas wells and gas storage stations respectively; Ω P2G is the set of injection nodes for the power-to-gas equipment; ε is the maximum hydrogen blending ratio allowed in the natural gas pipeline; The carbon-hydrogen balance constraints are as follows: Where, and are the amount of CO2 and H2 purchased from the power-to-gas equipment at node i, respectively; is the molar mass of H2; and The amount of natural gas synthesized from purchased CO2 and the amount of hydrogen required for synthesizing natural gas.

3. The method for low-carbonization probability optimal energy flow of electric-gas interconnected system based on cross-term decoupling according to claim 1 is characterized in that: The operating constraints of the natural gas system are as follows: Where, and are the gas turbine output and required natural gas flow of natural gas node i respectively; α GFU , β GFU , γ GFU is the energy consumption coefficient of the gas turbine; is the gas load consumption of node i; Ω i represents the set of natural gas nodes connected to node i; f ij.t and C ij are the gas flow rate and pipeline transmission capacity constant of the pipeline from node i to j respectively; p i.t and p j.t are the air pressures at nodes i and j respectively; sign(p i.t 、p j.t ) is the direction of gas flow in pipeline ij; is the maximum flow limit of pipeline ij; and is the upper and lower limits of the gas flow rate of the gas source at node i; p i.max and p i.min are the upper and lower limits of the air pressure at node i; is the compression ratio of the compressor at node i; and are the power consumption of compressor k and the air flow through the compressor respectively; B GC and Z GC are all constants; and is the limit of the compression ratio.

4. The method for low-carbonization probability optimal energy flow of electric-gas interconnected system based on cross-term decoupling according to claim 1 is characterized in that: The power system operation constraints are as follows: Where, and are the load active / reactive power and compressor power consumption of node i respectively; and are the reactive power of conventional power supply and new energy electric field at node i respectively; U i.t and U j.t The voltage amplitudes at nodes i and j, respectively; θ ij.t is the voltage phase angle difference between nodes i and j; G ij and B ij are the real and imaginary parts of the corresponding elements in the node admittance matrix Y; U i.max and U i.min is the voltage upper and lower limits of node i; I ij.t and I ij.max is the current of line ij and the maximum current allowed; and They are respectively the upper and lower limits of active output of conventional power source i; and They are the upper and lower limits of reactive output of conventional power source i respectively.

5. The method for optimizing the energy flow of low-carbonization probability of an electric-gas interconnected system based on cross-term decoupling according to claim 1 is characterized by: The steps for solving the probabilistic optimal energy flow model for the low-carbonization of the electricity-gas interconnection system include: 1) Select samples of the standard normal input random variable ξ as collocation points and form several collocation point groups; 2) Convert each set of collocation points into a sample of the original input random variable; 3) Extract input random variable samples and use the interior point method to solve the optimal energy flow model; 4) Establishing a system of linear equations with respect to the undetermined coefficients and solving and determining the undetermined coefficients in the chaotic polynomials; 5) Based on Wiener chaos polynomials, the statistical information of state variables such as the total operating cost of the electricity-gas interconnection system is estimated.

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