Data-driven polynomial chaos expansion-based reliability evaluation method for electro-hydrogen comprehensive energy system

Abstract

The invention discloses a data-driven polynomial chaos expansion-based reliability evaluation method for an electro-hydrogen comprehensive energy system. The method comprises the following steps of 1) constructing a multi-moment-based orthogonal polynomial basis; 2) constructing a chaotic polynomial proxy model; 3) calculating the optimal load reduction quantity of the electro-hydrogen comprehensive energy system according to the sample values of the collocation points; 4) solving each coefficient of the chaos polynomial proxy model by using a least square method; 5) acquiring a current random variable, inputting the current random variable into the chaos polynomial agent model, and calculating the optimal load reduction quantity of the current electricity-hydrogen comprehensive energy system; and 6) calculating the reliability index of the electro-hydrogen comprehensive energy system. According to the method, the chaotic polynomial proxy model is constructed through the historical data of the random variables, a large number of mixed random variables are efficiently processed, information such as probability features of the random variables is not needed, and rapid and accurate reliability evaluation can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of reliability assessment, and in particular to a reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion. Background Art

[0002] With the continuous maturity of hydrogen production technology by electrolysis, the continuous reduction of costs, and the deep coupling of the entire process and chain of hydrogen energy "production-storage-transportation-use" and electric energy "generation-transmission-distribution-transformation", the electric-hydrogen integrated energy system has become an important support for the construction of a new power system. At the same time, the widespread application of hydrogen energy in energy consumption terminals has caused the reliability level of hydrogen energy supply to have a greater impact on social productivity. Therefore, by studying the reliability assessment method of the electric-hydrogen integrated energy system, it can provide theoretical support and decision-making basis for the stable operation and scientific planning of the electric-hydrogen integrated energy system, which is the prerequisite for building a safe and reliable electric-hydrogen integrated energy system.

[0003] In terms of reliability assessment methods for electric-hydrogen integrated energy systems, existing research mainly includes analytical methods represented by enumeration methods and simulation methods represented by Monte Carlo simulation methods. However, the assessment speed of these two methods drops significantly as the scale of the operating state of the electric-hydrogen integrated energy system increases. In the probability assessment problem, Chaos Polynomial Expansion (PCE) is a fast and high-precision global polynomial approximation method that represents the random variable input and the response output of the optimization model as a weighted sum of a set of orthogonal polynomials, avoiding multiple repeated calculations in probability analysis problems, thereby greatly improving the speed of solving probability analysis problems and has been widely used. In addition, the method based on Data-Driven Polynomial Chaos Expansion (DPCE) that constructs a standard orthogonal basis from data statistical moments can efficiently handle a large number of mixed (continuous and discrete) random inputs (such as wind and solar uncertainty and random line interruptions) without any probability distribution information. At present, there is no research on applying the Chaos Polynomial Expansion method to the reliability assessment problem of electric-hydrogen integrated energy systems.

[0004] In summary, in terms of reliability assessment methods for electric-hydrogen integrated energy systems, in order to address the problem of low efficiency in reliability assessment calculations due to the large scale of system operation, it is urgent to verify the applicability of the data-driven polynomial chaos expansion method in reliability assessment problems, and explore the efficient application of this method in reliability assessment problems for electric-hydrogen integrated energy systems, improve the speed of solving reliability assessments for electric-hydrogen integrated energy systems, provide reliability reference indicators for operation planning of electric-hydrogen integrated energy systems, and help build new power systems. Summary of the invention

[0005] The purpose of the present invention is to provide a reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion, comprising the following steps:

[0006] 1) Read the basic parameters of the electric-hydrogen integrated energy system;

[0007] 2) Based on the historical data of mixed random variables in the basic parameters of the electric-hydrogen integrated energy system, an orthogonal polynomial basis based on multi-order moments is constructed;

[0008] 3) Constructing a chaotic polynomial agent model based on linear combinations of orthogonal polynomials;

[0009] 4) Select the matching points based on the linear independence principle, and calculate the optimal load reduction of the electric-hydrogen integrated energy system according to the sample values ​​of the matching points;

[0010] 5) The historical data of mixed random variables and the optimal load reduction of the electric-hydrogen integrated energy system are used as the input and output of the chaotic polynomial proxy model, and the coefficients of the chaotic polynomial proxy model are solved by the least squares method;

[0011] 6) Obtain the current random variables and input them into the chaotic polynomial agent model to calculate the optimal load reduction of the current electric-hydrogen integrated energy system;

[0012] 7) Calculate the reliability index of the electric-hydrogen integrated energy system.

[0013] Furthermore, the basic parameters of the electric-hydrogen integrated energy system include electric-hydrogen integrated energy system parameters, equipment component reliability parameters, and mixed random variable historical data sets.

[0014] Furthermore, the parameters of the hydrogen comprehensive energy system include the resistance and reactance parameters of the power line of the power system, the upper and lower limit parameters of the generator output, the parameters of the wind turbine and photovoltaic units, the line topology connection parameters, the parameters of the electrolyzer, fuel cell, hydrogen storage tank, hydrogen refueling machine of the hydrogen energy system, and the data of the electric load and hydrogen load;

[0015] The equipment component reliability parameters include the failure rate and repair rate of the generator set, line, electrolyzer, fuel cell, hydrogen storage tank, and hydrogen filling machine;

[0016] The mixed random variable historical data set includes historical output data of wind turbines and photovoltaic units, and historical fault-operation data of generator sets, lines, electrolyzers, and fuel cell equipment.

[0017] Further, in step 2), the step of constructing an orthogonal polynomial basis based on multi-order moments includes:

[0018] 2.1) Construct a multidimensional PCE model, namely:

[0019]

[0020] In the formula, c κ The k-th expansion term Φ κ coefficient; M is the number of random responses Y; M = (H + N)! / (H! N!); Φ κ is the full tensor product of one-dimensional polynomials; κ=1,2,…,M; ξ1,ξ2,…,ξ N is a random variable; N is the number of random variables; H is the order;

[0021] 2.2) Use the multidimensional PCE model to construct an orthogonal polynomial basis based on multi-order moments, namely:

[0022]

[0023] In the formula, α κ,i is the order of the i-th one-dimensional polynomial.

[0024] Further, in step 3), the step of constructing the chaotic polynomial agent model includes:

[0025] 3.1) Convert the mixed random variable historical data set in the basic parameters of the electric-hydrogen integrated energy system into a multi-order rectangular form and construct a one-dimensional orthogonal polynomial P i (l) (ξ i ),Right now:

[0026]

[0027] In the formula, ξ i κ is the historical data of mixed random variables; l=0,1,...,H;

[0028] Among them, the basis of any polynomial of order 0 to l As shown below:

[0029]

[0030] In the formula, μ k,i is the k-order origin moment of the random variable; M p is the number of samples of the random variable;

[0031] 3.2) One-dimensional orthogonal polynomial Normalization is performed to construct a chaotic polynomial agent model Right now:

[0032]

[0033] In the formula, ||·|| represents the calculation of the second norm of the polynomial;.

[0034] Further, in step 4), the step of calculating the optimal load reduction amount of the electric-hydrogen integrated energy system according to the sample value of the distribution point includes:

[0035] 4.1) Calculate one-dimensional orthogonal polynomials The third root of

[0036] 4.2) Random combination of "0" and one-dimensional orthogonal polynomials The third-order root of the initial collocation point set Ω IC ;

[0037] 4.3) From the set Ω IC Select the same number of collocation points as the number of coefficients to be determined M in turn to form a collocation point combination Ω C ;

[0038] 4.4) Calculate the point combination Ω C The coefficient matrix Φ is:

[0039]

[0040] 4.4) Let the rank of the coefficient matrix Φ be R Φ , if R Φ =M, then the current point combination Ω C is the optimal point combination, the point selection operation ends and goes to step 4.5); otherwise, the current point combination Ω is eliminated. C Medium MR Φ linearly related collocation points, from the set Ω IC Select MR from the unselected points Φ The selected MR Φ Write the point combination Ω C In the new point combination Ω C , and return to step 4.4);

[0041] 4.5) Construct an optimal load reduction model; input the sample values ​​of the selected distribution points into the optimal load reduction model in turn to obtain the optimal load reduction amount of the electric-hydrogen integrated energy system corresponding to each distribution point.

[0042] Furthermore, the objective function f of the optimal load reduction model is as follows:

[0043]

[0044] In the formula, c DG is the unit penalty cost for abandoning wind and solar power; Ω WG ,Ω PV are the grid-connected node sets of wind and solar generators respectively; ΔP WG,it , ΔP PV,it are the abandoned wind and solar power respectively; T is the scheduling period; n is the number of nodes; c is the number of nodes;p The penalty cost for reducing the load unit, L p,it To reduce power for electrical load; c q Reducing penalty costs for hydrogen load units, L q,it is the power reduction of hydrogen load. The subscripts i and t represent the node i and time t respectively; Δt is the time step.

[0045] Furthermore, the constraints of the optimal load reduction model include electric power balance constraints, hydrogen balance constraints, electric-hydrogen load reduction constraints, operation constraints of the relationship between hydrogen production and power consumption of the electrolyzer, and operation constraints of the relationship between hydrogen consumption and output power of the fuel cell;

[0046] The electrical power balance constraints are as follows:

[0047] P GEN,it +P WG,it +P PV,it +P FC,it =P EL,it +P load,it -L p,it +Bθ i,t (10)

[0048] Where P GEN,it P is the active power output by the generator set; WG,it , P PV,it are the active power actually output by the wind and solar units; P FC,it , P EL,it are the active power output by the fuel cell and the active power consumed by the electrolyzer; P load,it , L p,it are active load and load reduction power respectively; B is the node susceptance matrix used for DC power flow calculation, θ i,t is the node voltage phase angle.

[0049] The hydrogen balance constraints are as follows:

[0050] Q DP,in,it =Q EL,it -Q HT,in,it +Q HT,out,it -Q FC,it (11)

[0051] Q DP,out,it =Q load,it -L q,it (12)

[0052] In the formula, Q DP,in,it , Q DP,out,it are the amount of hydrogen input and output of the hydrogenation unit; Q EL,it , Q FC,itare the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q HT,in,it , Q HT,out,it are the hydrogen filling and dehydration amounts of the hydrogen storage tank respectively; Q load,it , L q,it They are the demand and reduction of hydrogen load respectively.

[0053] The electric hydrogen load reduction constraints are as follows:

[0054] 0≤L p,it ≤P load,it (13)

[0055] 0≤L q,it ≤Q load,it (14)

[0056] The operating constraints of the electrolyzer on the relationship between hydrogen production and power consumption are as follows:

[0057]

[0058] μ EL,it P EL,min ≤P EL,it ≤μ EL,it P EL,max (17)

[0059] In the formula, is the standard density of hydrogen; μ EL,it is the start-stop state variable of the electrolytic cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; P EL,max and P EL,min are the upper and lower limits of the power consumption of the electrolyzer respectively; is the relationship function; m EL,t is the hydrogen production of the electrolyzer.

[0060] The operating constraints of the fuel cell's hydrogen consumption and output power are as follows:

[0061] Q FC,it =ρ H2 m FC,it (18)

[0062] P FC,it =g(m FC,it ) (19)

[0063] μ FC,it Q FC,min ≤Q FC,it ≤μ FC,it Q FC,max (20)

[0064] In the formula, μ FC,itis the start-stop state variable of the fuel cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; Q FC,max , Q FC,min are the upper and lower limits of fuel cell hydrogen consumption, is the standard density of hydrogen; g(m FC,it ) is the relationship function; m FC,it is the hydrogen consumption of the fuel cell.

[0065] Further, in step 5), the step of solving the coefficients of the chaotic polynomial proxy model using the least squares method includes:

[0066] 5.1) Substitute the sample values ​​of the random variable and the value of the response function into the following formula to solve the coefficient of the chaos expansion:

[0067]

[0068] 5.2) The coefficients of the chaos expansion are calculated by minimizing the residual sum of squares, as shown in the following formula:

[0069]

[0070] Where, J(C) is the residual sum of squares;

[0071] 5.3) Take the derivative of the chaos expansion coefficients to obtain the coefficients of each chaos expansion, namely:

[0072]

[0073] In the formula, are the coefficients of the chaotic expansion to be solved.

[0074] Further, in step 7), the step of calculating the reliability index of the electric-hydrogen integrated energy system includes:

[0075] 7.1) Use the Galerkin projection method to project Y(ξ) onto the orthogonal polynomial Φ1 and obtain:

[0076]

[0077] In the formula, E(·) is the expectation operation, Φ1(ξ) is the first expansion term of the polynomial, and c κ The k-th expansion term Φ κ The coefficient of

[0078] 7.2) Calculate the expected μ of the response function Y(ξ) as a reliability indicator of the electric-hydrogen integrated energy system;

[0079] The expected μ is as follows:

[0080]

[0081] The technical effect of the present invention is unquestionable. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion proposed in the present invention helps to improve the reliability assessment efficiency of the electric-hydrogen integrated energy system and realize fast and accurate reliability assessment.

[0082] The present invention applies the data-driven polynomial chaos expansion method to the reliability assessment problem of the electric-hydrogen integrated energy system. The arbitrary polynomial orthogonal basis and orthogonal polynomial are constructed through historical data, and then the chaotic polynomial proxy model is constructed. The proxy model can output the statistical moment information of random variables, can efficiently process a large number of mixed random variables, and does not require information such as the probability characteristics of random variables, thereby improving the solution efficiency of reliability assessment and efficiently assessing the reliability level of the electric-hydrogen integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 The flowchart of the reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion is shown in Figure 2.

[0084] Figure 2 This is the topology diagram of the electric-hydrogen integrated energy system based on IEEE-RTS79. DETAILED DESCRIPTION

[0085] The present invention is further described below in conjunction with the embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to the common technical knowledge and customary means in the art, which should all be included in the protection scope of the present invention.

[0086] Embodiment 1:

[0087] See also Figure 1 to Figure 2 , a reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion includes the following steps:

[0088] 1) Read the basic parameters of the electric-hydrogen integrated energy system;

[0089] 2) Based on the historical data of mixed random variables in the basic parameters of the electric-hydrogen integrated energy system, an orthogonal polynomial basis based on multi-order moments is constructed;

[0090] 3) Constructing a chaotic polynomial agent model based on linear combinations of orthogonal polynomials;

[0091] 4) Select the matching points based on the linear independence principle, and calculate the optimal load reduction of the electric-hydrogen integrated energy system according to the sample values ​​of the matching points;

[0092] 5) The historical data of mixed random variables and the optimal load reduction of the electric-hydrogen integrated energy system are used as the input and output of the chaotic polynomial proxy model, and the coefficients of the chaotic polynomial proxy model are solved by the least squares method;

[0093] 6) Obtain the current random variables and input them into the chaotic polynomial agent model to calculate the optimal load reduction of the current electric-hydrogen integrated energy system;

[0094] 7) Calculate the reliability index of the electric-hydrogen integrated energy system.

[0095] The basic parameters of the electric-hydrogen integrated energy system include electric-hydrogen integrated energy system parameters, equipment component reliability parameters, and mixed random variable historical data sets.

[0096] The parameters of the hydrogen comprehensive energy system include the resistance and reactance parameters of the power line of the power system, the upper and lower limit parameters of the generator output, the parameters of the wind turbine and photovoltaic units, the line topology connection parameters, the parameters of the electrolyzer, fuel cell, hydrogen storage tank, hydrogen refueling machine of the hydrogen energy system, and the data of the electric load and hydrogen load;

[0097] The equipment component reliability parameters include the failure rate and repair rate of the generator set, line, electrolyzer, fuel cell, hydrogen storage tank, and hydrogen filling machine;

[0098] The mixed random variable historical data set includes historical output data of wind turbines and photovoltaic units, and historical fault-operation data of generator sets, lines, electrolyzers, and fuel cell equipment.

[0099] In step 2), the steps of constructing an orthogonal polynomial basis based on multi-order moments include:

[0100] 2.1) Construct a multidimensional PCE model, namely:

[0101]

[0102] In the formula, c κ The k-th expansion term Φ κ coefficient; M is the number of random responses Y; M = (H + N)! / (H! N!); Φ κ is the full tensor product of one-dimensional polynomials; κ=1,2,…,M; ξ1,ξ2,…,ξ N is a random variable; N is the number of random variables; H is the order;

[0103] 2.2) Use the multidimensional PCE model to construct an orthogonal polynomial basis based on multi-order moments, namely:

[0104]

[0105] In the formula, α κ,iis the order of the i-th one-dimensional polynomial.

[0106] In step 3), the steps of constructing the chaotic polynomial agent model include:

[0107] 3.1) Convert the mixed random variable historical data set in the basic parameters of the electric-hydrogen integrated energy system into a multi-order rectangular form and construct a one-dimensional orthogonal polynomial P i (l) (ξ i ),Right now:

[0108]

[0109] In the formula, ξ i κ is the historical data of mixed random variables; l=0,1,...,H;

[0110] Among them, the basis of any polynomial of order 0 to l As shown below:

[0111]

[0112] In the formula, μ k,i is the k-order origin moment of the random variable; M p is the number of samples of the random variable;

[0113] 3.2) One-dimensional orthogonal polynomial Normalization is performed to construct a chaotic polynomial agent model Right now:

[0114]

[0115] In the formula, ||·|| represents the calculation of the second norm of the polynomial;.

[0116] In step 4), the step of calculating the optimal load reduction amount of the electric-hydrogen integrated energy system according to the sample value of the distribution point includes:

[0117] 4.1) Calculate one-dimensional orthogonal polynomials The third root of

[0118] 4.2) Random combination of "0" and one-dimensional orthogonal polynomials The third-order root of the initial collocation point set Ω IC ;

[0119] 4.3) From the set Ω IC Select the same number of collocation points as the number of coefficients to be determined M in turn to form a collocation point combination Ω C ;

[0120] 4.4) Calculate the point combination Ω CThe coefficient matrix Φ is:

[0121]

[0122] 4.4) Let the rank of the coefficient matrix Φ be R Φ , if R Φ =M, then the current point combination Ω C is the optimal point combination, the point selection operation ends and goes to step 4.5); otherwise, the current point combination Ω is eliminated. C Medium MR Φ linearly related collocation points, from the set Ω IC Select MR from the unselected points Φ The selected MR Φ Write the point combination Ω C In the new point combination Ω C , and return to step 4.4);

[0123] 4.5) Construct an optimal load reduction model; input the sample values ​​of the selected distribution points into the optimal load reduction model in turn to obtain the optimal load reduction amount of the electric-hydrogen integrated energy system corresponding to each distribution point.

[0124] The objective function f of the optimal load reduction model is as follows:

[0125]

[0126] In the formula, c DG is the unit penalty cost for abandoning wind and solar power; Ω WG ,Ω PV are the grid-connected node sets of wind and solar generators respectively; ΔP WG,it , ΔP PV,it are the abandoned wind and solar power respectively; T is the scheduling period; n is the number of nodes; c is the number of nodes; p The penalty cost for reducing the load unit, L p,it To reduce power for electrical load; c q Reducing penalty costs for hydrogen load units, L q,it is the power reduction of hydrogen load. The subscripts i and t represent the node i and time t respectively; Δt is the time step.

[0127] The constraints of the optimal load reduction model include electric power balance constraints, hydrogen balance constraints, electric and hydrogen load reduction constraints, operating constraints on the relationship between hydrogen production and power consumption of the electrolyzer, and operating constraints on the relationship between hydrogen consumption and output power of the fuel cell;

[0128] The electrical power balance constraints are as follows:

[0129] P GEN,it +P WG,it +PPV,it +P FC,it =P EL,it +P load,it -L p,it +Bθ i,t (10)

[0130] Where P GEN,it P is the active power output by the generator set; WG,it , P PV,it are the active power actually output by the wind and solar units; P FC,it , P EL,it are the active power output by the fuel cell and the active power consumed by the electrolyzer; P load,it , L p,it are active load and load reduction power respectively; B is the node susceptance matrix used for DC power flow calculation, θ i,t is the node voltage phase angle.

[0131] The hydrogen balance constraints are as follows:

[0132] Q DP,in,it =Q EL,it -Q HT,in,it +Q HT,out,it -Q FC,it (11)

[0133] Q DP,out,it =Q load,it -L q,it (12)

[0134] In the formula, Q DP,in,it , Q DP,out,it are the amount of hydrogen input and output of the hydrogenation unit; Q EL,it , Q FC,it are the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q HT,in,it , Q HT,out,it are the hydrogen filling and dehydration amounts of the hydrogen storage tank respectively; Q load,it , L q,it They are the demand and reduction of hydrogen load respectively.

[0135] The electric hydrogen load reduction constraints are as follows:

[0136] 0≤L p,it ≤P load,it (13)

[0137] 0≤L q,it ≤Q load,it (14)

[0138] The operating constraints of the electrolyzer on the relationship between hydrogen production and power consumption are as follows:

[0139]

[0140] μ EL,it P EL,min ≤P EL,it ≤μ EL,it P EL,max (17)

[0141] In the formula, is the standard density of hydrogen; μ EL,it is the start-stop state variable of the electrolytic cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; P EL,max and P EL,min are the upper and lower limits of the power consumption of the electrolyzer respectively; is the relationship function; m EL,t is the hydrogen production of the electrolyzer.

[0142] The operating constraints of the fuel cell's hydrogen consumption and output power are as follows:

[0143] Q FC,it =ρ H2 m FC,it (18)

[0144] P FC,it =g(m FC,it ) (19)

[0145] μ FC,it Q FC,min ≤Q FC,it ≤μ FC,it Q FC,max (20)

[0146] In the formula, μ FC,it is the start-stop state variable of the fuel cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; Q FC,max , Q FC,min are the upper and lower limits of fuel cell hydrogen consumption, is the standard density of hydrogen; g(m FC,it ) is the relationship function; m FC,it is the hydrogen consumption of the fuel cell.

[0147] In step 5), the step of solving the coefficients of the chaotic polynomial proxy model using the least square method includes:

[0148] 5.1) Substitute the sample values ​​of the random variable and the value of the response function into the following formula to solve the coefficient of the chaos expansion:

[0149]

[0150] 5.2) The coefficients of the chaos expansion are calculated by minimizing the residual sum of squares, as shown in the following formula:

[0151]

[0152] Where, J(C) is the residual sum of squares;

[0153] 5.3) Take the derivative of the chaos expansion coefficients to obtain the coefficients of each chaos expansion, namely:

[0154]

[0155] In the formula, are the coefficients of the chaotic expansion to be solved.

[0156] In step 7), the steps of calculating the reliability index of the electric-hydrogen integrated energy system include:

[0157] 7.1) Use the Galerkin projection method to project Y(ξ) onto the orthogonal polynomial Φ1 and obtain:

[0158]

[0159] In the formula, E(·) is the expectation operation, Φ1(ξ) is the first expansion term of the polynomial, and c κ The k-th expansion term Φ κ The coefficient of

[0160] 7.2) Calculate the expected μ of the response function Y(ξ) as a reliability indicator of the electric-hydrogen integrated energy system;

[0161] The expected μ is as follows:

[0162]

[0163] Embodiment 2:

[0164] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion includes the following steps:

[0165] 1) Read the basic parameters of the electric-hydrogen integrated energy system;

[0166] 2) Based on the historical data of mixed random variables in the basic parameters of the electric-hydrogen integrated energy system, an orthogonal polynomial basis based on multi-order moments is constructed;

[0167] 3) Constructing a chaotic polynomial agent model based on linear combinations of orthogonal polynomials;

[0168] 4) Select the matching points based on the linear independence principle, and calculate the optimal load reduction of the electric-hydrogen integrated energy system according to the sample values ​​of the matching points;

[0169] 5) The historical data of mixed random variables and the optimal load reduction of the electric-hydrogen integrated energy system are used as the input and output of the chaotic polynomial proxy model, and the coefficients of the chaotic polynomial proxy model are solved by the least squares method;

[0170] 6) Obtain the current random variables and input them into the chaotic polynomial agent model to calculate the optimal load reduction of the current electric-hydrogen integrated energy system;

[0171] 7) Calculate the reliability index of the electric-hydrogen integrated energy system.

[0172] Embodiment 3:

[0173] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion has the same technical content as Example 2. Furthermore, the basic parameters of the electric-hydrogen integrated energy system include electric-hydrogen integrated energy system parameters, equipment component reliability parameters, and mixed random variable historical data sets.

[0174] Embodiment 4:

[0175] A reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion, the technical content of which is the same as any one of Embodiments 2-3, and further, the parameters of the hydrogen integrated energy system include the resistance and reactance parameters of the power line of the power system, the upper and lower limit parameters of the generator output, the parameters of the wind turbine and photovoltaic units, the line topology connection parameters, the parameters of the electrolyzer, fuel cell, hydrogen storage tank, hydrogenator of the hydrogen energy system, and the electric load and hydrogen load data;

[0176] The equipment component reliability parameters include the failure rate and repair rate of the generator set, line, electrolyzer, fuel cell, hydrogen storage tank, and hydrogen filling machine;

[0177] The mixed random variable historical data set includes historical output data of wind turbines and photovoltaic units, and historical fault-operation data of generator sets, lines, electrolyzers, and fuel cell equipment.

[0178] Embodiment 5:

[0179] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion has the same technical content as any one of Embodiments 2-4. Further, in step 2), the step of constructing an orthogonal polynomial basis based on multi-order moments includes:

[0180] 2.1) Construct a multidimensional PCE model, namely:

[0181]

[0182] In the formula, c κ The k-th expansion term Φ κcoefficient; M is the number of random responses Y; M = (H + N)! / (H! N!); Φ κ is the full tensor product of one-dimensional polynomials; κ=1,2,…,M;

[0183] 2.2) Use the multidimensional PCE model to construct an orthogonal polynomial basis based on multi-order moments, namely:

[0184]

[0185] In the formula, α κ,i is the order of the i-th one-dimensional polynomial.

[0186] Embodiment 6:

[0187] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion has the same technical content as any one of Embodiments 2-5. Further, in step 3), the step of constructing the chaotic polynomial agent model includes:

[0188] 3.1) Convert the mixed random variable historical data set in the basic parameters of the electric-hydrogen integrated energy system into a multi-order rectangular form and construct a one-dimensional orthogonal polynomial, namely:

[0189]

[0190] In the formula, ξ i κ is the historical data of mixed random variables; l=0,1,...,H;

[0191] Among them, the basis of any polynomial of order 0 to l As shown below:

[0192]

[0193] In the formula, μ k,i is the k-order origin moment of the random variable; M p is the number of samples of the random variable;

[0194] 3.2) One-dimensional orthogonal polynomial Normalization is performed to construct a chaotic polynomial agent model Right now:

[0195]

[0196] In the formula, ||·|| represents the calculation of the second norm of the polynomial;.

[0197] Embodiment 7:

[0198] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion has the same technical content as any one of Embodiments 2-6. Further, in step 4), the step of calculating the optimal load reduction amount of the electric-hydrogen integrated energy system according to the sample value of the distribution point includes:

[0199] 4.1) Calculate one-dimensional orthogonal polynomials The third root of

[0200] 4.2) Random combination of "0" and one-dimensional orthogonal polynomials The third-order root of the initial collocation point set Ω IC ;

[0201] 4.3) From the set Ω IC Select the same number of collocation points as the number of coefficients to be determined M in turn to form a collocation point combination Ω C ;

[0202] 4.4) Calculate the point combination Ω C The coefficient matrix Φ is:

[0203]

[0204] 4.4) Let the rank of the coefficient matrix Φ be R Φ , if R Φ =M, then the current point combination Ω C is the optimal point combination, the point selection operation ends and goes to step 4.5); otherwise, the current point combination Ω is eliminated. C Medium MR Φ linearly related collocation points, from the set Ω IC Select MR from the unselected points Φ The selected MR Φ Write the point combination Ω C In the new point combination Ω C , and return to step 4.4);

[0205] 4.5) Construct an optimal load reduction model; input the sample values ​​of the selected distribution points into the optimal load reduction model in turn to obtain the optimal load reduction amount of the electric-hydrogen integrated energy system corresponding to each distribution point.

[0206] Embodiment 8:

[0207] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion has the same technical content as any one of Embodiments 2-7. Furthermore, the objective function f of the optimal load reduction model is as follows:

[0208]

[0209] In the formula, cDG is the unit penalty cost for abandoning wind and solar power; Ω WG ,Ω PV are the grid-connected node sets of wind and solar generators respectively; ΔP WG,it , ΔP PV,it are the abandoned wind and solar power respectively; T is the scheduling period; n is the number of nodes; c is the number of nodes; p The penalty cost for reducing the load unit, L p,it To reduce power for electrical load; c q Reducing penalty costs for hydrogen load units, L q,it is the power reduction of hydrogen load. The subscripts i and t represent the node i and time t respectively; Δt is the time step.

[0210] Embodiment 9:

[0211] A reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion, the technical content of which is the same as any one of Embodiments 2-8, and further, the constraints of the optimal load reduction model include electric power balance constraints, hydrogen balance constraints, electric-hydrogen load reduction constraints, operating constraints on the relationship between hydrogen production and power consumption of the electrolyzer, and operating constraints on the relationship between hydrogen consumption and output power of the fuel cell;

[0212] The electrical power balance constraints are as follows:

[0213] P GEN,it +P WG,it +P PV,it +P FC,it =P EL,it +P load,it -L p,it +Bθ i,t (10)

[0214] Where P GEN,it P is the active power output by the generator set; WG,it , P PV,it are the active power actually output by the wind and solar units; P FC,it , P EL,it are the active power output by the fuel cell and the active power consumed by the electrolyzer; P load,it , L p,it are the active load and load reduction power respectively; B is the node susceptance matrix used for DC power flow calculation, and θ is the node voltage phase angle.

[0215] The hydrogen balance constraints are as follows:

[0216] Q DP,in,it =Q EL,it -Q HT,in,it +Q HT,out,it -Q FC,it (11)

[0217] Q DP,out,it =Q load,it -L q,it (12)

[0218] In the formula, Q DP,in,it , Q DP,out,it are the amount of hydrogen input and output of the hydrogenation unit; Q EL,it , Q FC,it are the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q HT,in,it , Q HT,out,it are the hydrogen filling and dehydration amounts of the hydrogen storage tank respectively; Q load,it , L q,it They are the demand and reduction of hydrogen load respectively.

[0219] The electric hydrogen load reduction constraints are as follows:

[0220] 0≤L p,it ≤P load,it (13)

[0221] 0≤L q,it ≤Q load,it (14)

[0222] The operating constraints of the electrolyzer on the relationship between hydrogen production and power consumption are as follows:

[0223]

[0224] μ EL,it P EL,min ≤P EL,it ≤μ EL,it P EL,max (17)

[0225] In the formula, is the standard density of hydrogen; μ EL,it is the start-stop state variable of the electrolytic cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; P EL,max and P EL,min are the upper and lower limits of the power consumption of the electrolyzer respectively; is the relationship function; m EL , t is the hydrogen production of the electrolyzer.

[0226] The operating constraints of the fuel cell's hydrogen consumption and output power are as follows:

[0227] Q FC,it =ρ H2 m FC,it (18)

[0228] P FC,it =g(mFC,it ) (19)

[0229] μ FC,it Q FC,min ≤Q FC,it ≤μ FC,it Q FC,max (20)

[0230] In the formula, μ FC,it is the start-stop state variable of the fuel cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; Q FC,max , Q FC,min are the upper and lower limits of fuel cell hydrogen consumption, is the standard density of hydrogen; g(m FC,it ) is the relationship function; m FC,it for.

[0231] Embodiment 10:

[0232] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion has the same technical content as any one of embodiments 2-9. Further, in step 5), the step of solving each coefficient of the chaotic polynomial proxy model by the least squares method includes:

[0233] 5.1) Substitute the sample values ​​of the random variable and the value of the response function into the following formula to solve the coefficient of the chaos expansion:

[0234]

[0235] 5.2) The coefficients of the chaos expansion are calculated by minimizing the residual sum of squares, as shown in the following formula:

[0236]

[0237] 5.3) Take the derivative of the chaos expansion coefficients to obtain the coefficients of each chaos expansion, namely:

[0238]

[0239] In the formula, are the coefficients of the chaotic expansion to be solved.

[0240] Embodiment 11:

[0241] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion has the same technical content as any one of Embodiments 2-10. Further, in step 7), the step of calculating the reliability index of the electric-hydrogen integrated energy system includes:

[0242] 7.1) Use the Galerkin projection method to project Y(ξ) onto the orthogonal polynomial Φ1 and obtain:

[0243]

[0244] In the formula, E(·) is the expectation operation, Φ1(ξ) is the first expansion term of the polynomial, and c κ The k-th expansion term Φ κ The coefficient of

[0245] 7.2) Calculate the expected μ of the response function Y(ξ) as a reliability indicator of the electric-hydrogen integrated energy system;

[0246] The expected μ is as follows:

[0247]

[0248] Embodiment 12:

[0249] The reliability assessment method of the electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion mainly includes the following steps:

[0250] 1) Read information such as electric-hydrogen integrated energy system parameters, equipment component reliability parameters, and mixed random variable historical data sets;

[0251] The electric-hydrogen integrated energy system parameters, equipment component reliability parameters, mixed random variable historical data sets and other information, wherein the electric-hydrogen integrated energy system parameters include the power line resistance and reactance parameters of the power system, the upper and lower limit parameters of the generator output, the wind turbine and photovoltaic unit parameters, the line topology connection parameters, the electrolyzer, fuel cell, hydrogen storage tank, hydrogen filling machine parameters of the hydrogen energy system, as well as the electric load and hydrogen load data; the equipment component reliability parameters include the failure rate and repair rate of the generator set, line, electrolyzer, fuel cell, hydrogen storage tank, hydrogen filling machine and other parameters; the mixed random variable historical data set includes the output historical data of the wind turbine and photovoltaic unit, and the equipment failure-operation historical data of the generator set, line, electrolyzer, and fuel cell.

[0252] 2) Based on the historical measured data of mixed random variables, an orthogonal polynomial basis based on multi-order moments is established, and a chaotic polynomial proxy model is constructed based on the linear combination of orthogonal polynomials;

[0253] The historical measured data based on mixed random variables is used to establish an orthogonal polynomial basis based on multi-order moments, and a chaotic polynomial proxy model is constructed based on the linear combination of orthogonal polynomials, as shown below:

[0254] Multidimensional independent random variables ξ=[ξ1,...,ξ N ] As the input of the PCE model, the random response Y can be expressed as an H-order truncated multidimensional PCE model:

[0255]

[0256] In the formula, c κ The k-th expansion term Φ κ coefficients, M is the number of terms in the expansion Y, M = (H + N)! / (H! N!), the k-th standard orthogonal polynomial Φ κ (κ=1,2,…,M) is the full tensor product of one-dimensional polynomials.

[0257]

[0258] In the formula, α κ,i is the order of the i-th one-dimensional polynomial.

[0259] The standard orthogonal polynomial basis described above is constructed by using the multi-order moments of the original data. i One-dimensional orthogonal polynomials Defined as an arbitrary polynomial basis p of order 0 to l (l)κ,i The sum of (l=0,1,...,H).

[0260]

[0261] Any polynomial basis p (l)κ,i (l=0,1,...,H) is obtained by solving the moment matching equation shown below:

[0262]

[0263] In the formula, μ k,i is the k-order origin moment of the random variable.

[0264] One-dimensional orthogonal polynomial Normalized as follows:

[0265]

[0266] In the formula, ||·|| represents the calculation of the second norm of the polynomial.

[0267] 3) Select the matching points based on the linear independence principle, and calculate the optimal load reduction of the electric-hydrogen integrated energy system according to the sample values ​​of the matching points;

[0268] The distribution points are selected based on the linear independence principle, and the optimal load reduction amount of the electric-hydrogen integrated energy system is calculated according to the sample values ​​of the distribution points, as shown below:

[0269] Step 1) Solve the one-dimensional orthogonal polynomial The third-order roots of are combined as the matching points to be selected, and "0" and one-dimensional orthogonal polynomials are randomly combined. The third-order root of the initial collocation point set Ω IC ;

[0270] Step 2) From the set Ω IC Select the same number of collocation points as the number of coefficients to be determined M in turn to form a collocation point combination Ω C , and calculate the coefficient matrix Φ;

[0271]

[0272] Step 3) Let the rank of the coefficient matrix Φ be R Φ , if R Φ =M, then the current point combination Ω C is the optimal point combination, the point selection operation ends; otherwise, the current point combination Ω is eliminated C Middle (MR Φ ) linearly related collocation points, and then from the set Ω IC Select from the remaining points (MR Φ ) points form a new point combination Ω C , repeat step 3).

[0273] According to the selected distribution points, the optimal load reduction model calculation is performed in turn, and the model output response Y is recorded. The optimal load reduction model is as follows:

[0274] Objective function:

[0275]

[0276] In the formula, c DG is the unit penalty cost for abandoning wind and solar power; Ω WG ,Ω PV are the grid-connected node sets of wind and solar generators respectively; ΔP WG,it , ΔP PV,it are the abandoned wind and solar power respectively; T is the scheduling period; n is the number of nodes; c is the number of nodes; p The penalty cost for reducing the load unit, L p,it To reduce power for electrical load; c q Reducing penalty costs for hydrogen load units, L q,it The subscripts i and t represent the node i and time t, respectively.

[0277] Constraints:

[0278] Electric power balance constraints:

[0279] P GEN,it +P WG,it +P PV,it +P FC,it =P EL,it +P load,it -L p,it +Bθ i,t (10)

[0280] Where P GEN,it P is the active power output by the generator set; WG,it , P PV,it are the active power actually output by the wind and solar units; P FC,it , P EL,it are the active power output by the fuel cell and the active power consumed by the electrolyzer; P load,it , L p,it are the active load and load reduction power respectively; B is the node susceptance matrix used for DC power flow calculation, and θ is the node voltage phase angle.

[0281] Hydrogen balance constraints:

[0282] Q DP,in,it =Q EL,it -Q HT,in,it +Q HT,out,it -Q FC,it (11)

[0283] Q DP,out,it =Q load,it -L q,it (12)

[0284] In the formula, Q DP,in,it , Q DP,out,it are the amount of hydrogen input and output of the hydrogenation unit; Q EL,it , Q FC,it are the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q HT,in,it , Q HT,out,it are the hydrogen filling and dehydration amounts of the hydrogen storage tank respectively; Q load,it , L q,it They are the demand and reduction of hydrogen load respectively.

[0285] Electric hydrogen load reduction constraints:

[0286] 0≤L p,it ≤P load,it (13)

[0287] 0≤L q,it ≤Q load,it (14)

[0288] The operating constraints of the electrolyzer on the relationship between hydrogen production and power consumption are as follows:

[0289]

[0290] μ EL,it P EL,min ≤P EL,it ≤μ EL,it P EL,max (17)

[0291] In the formula, ρ H2 is the standard density of hydrogen (kg / Nm 3 ); μ EL,it is the start-stop state variable of the electrolytic cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; P EL,max and P EL,min are the upper and lower limits of the power consumption of the electrolytic cell respectively. EL,t is the hydrogen production of the electrolyzer.

[0292] The operating constraints of the fuel cell's hydrogen consumption and output power are as follows:

[0293] Q FC,it =ρ H2 m FC,it (18)

[0294] P FC,it =g(m FC,it ) (19)

[0295] μ FC,it Q FC,min ≤Q FC,it ≤μ FC,it Q FC,max (20)

[0296] In the formula, μ FC,it is the start-stop state variable of the fuel cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; Q FC,max , Q FC,min are the upper and lower limits of fuel cell hydrogen consumption, ρ H2 is the standard density of hydrogen (kg / Nm 3 ). g(m FC,it ) is the relationship function; m FC,it for.

[0297] 4) Solving the coefficients of the chaotic polynomial proxy model corresponding to the input mixed random variables and the output load reduction response according to the least squares method;

[0298] The coefficients of the chaotic polynomial proxy model corresponding to the input mixed random variables and the output load reduction response are solved according to the least square method as follows:

[0299] Substitute the sample values ​​of the random variable and the value of the response function into the following formula to solve the coefficient of the chaos expansion:

[0300]

[0301] The chaos expansion coefficient is calculated by minimizing the residual sum of squares, as shown in the following formula:

[0302]

[0303] By taking the derivative of the above formula, we can get the least squares solution as follows:

[0304]

[0305] In the formula, are the coefficients of the chaotic expansion to be solved.

[0306] 5) Based on the constructed chaotic polynomial agent model, the statistical moment information of the required random variables is calculated and output, and the reliability index of the electric-hydrogen integrated energy system is calculated.

[0307] Based on the constructed chaotic polynomial agent model, the statistical moment information of the desired random variable is calculated and output, and the reliability index of the electric-hydrogen integrated energy system is calculated as follows:

[0308] Use the Galerkin projection method to project Y(ξ) onto the orthogonal polynomial Φ1. According to the definition of the inner product and the orthogonality of the orthogonal polynomial, we can get

[0309]

[0310] Then the expected μ of the response function Y(ξ) can be calculated as follows:

[0311]

[0312] The expectation μ of the response function Y(ξ) of the chaotic polynomial agent model is the expected electricity load reduction (Expected Electricity Not Supplied, EENS) or the expected hydrogen load reduction (Expected Hydrogen Not Supplied, EHNS) of the electric-hydrogen integrated energy system.

[0313] Embodiment 13:

[0314] See also Figure 2 , a simulation example of a reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion is presented. Based on the IEEE-RTS79 system, an electric-hydrogen integrated energy system consisting of a hydrogen energy system configured at nodes 5, 8, 17, 18, and 20 is taken as an example to verify the feasibility and effectiveness of the reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion. The following four methods are used for comparative analysis to verify the advantages of the proposed method:

[0315] Method 1: Monte Carlo simulation method;

[0316] Method 2: Monte Carlo simulation based on Latin hypercube sampling;

[0317] Method 3: Chaotic polynomial expansion method (PCE method);

[0318] Method 4: The proposed data-driven chaotic polynomial expansion method (DPCE method).

[0319] The load reduction expectation calculated by method 1 is used as a comparison benchmark to verify the accuracy and speed of the proposed method. The calculation results and solution speed are shown in Tables 1 and 2.

[0320] Table 1 Comparison of system power supply / hydrogen reliability evaluation results by different methods

[0321]

[0322] Table 2 Comparison of evaluation speed of different methods

[0323]

[0324] In method 1, the expected variance coefficient of load reduction energy is used as the convergence criterion. When 2×105 sampling calculations are performed, method 1 achieves convergence. Method 2 uses the Latin hypercube sampling method to accelerate the convergence speed of the Monte Carlo simulation method while ensuring the accuracy of the solution. However, the essence of method 2 is still the same as method 1, which is to achieve the purpose of reliability assessment through a large number of sampling calculations.

[0325] In contrast, the proxy model construction method based on the chaotic polynomial expansion method can complete the calculation of the expected value of load reduction faster, such as Method 3 and Method 4. However, Method 3 cannot directly use historical data for evaluation calculations and cannot handle mixed random variables, so it is not applicable. Method 4 is the data-driven chaotic polynomial expansion method proposed in this paper. The random variables processed include the output of photovoltaic units and wind turbines connected to nodes 5, 8, 17, 18 and 20, as well as the random failures of equipment such as electrolyzers and fuel cells, and the random failures of 33 generators and 38 lines in the IEEE-RTS79 system. This method can handle continuous and discrete random variables at the same time, and does not require the probability distribution information of random variables to be known in advance. From the results in Table 1, it can be seen that the data-driven chaotic polynomial expansion method proposed in this paper has a significantly improved calculation speed compared with method 1 and method 2 when the solution accuracy is within 10%. This is because the data-driven chaotic polynomial expansion method only needs to extract some point combinations (4000 groups in this paper) to find the undetermined coefficients of the data-driven chaotic polynomial expansion method, and then the expected electricity and hydrogen load reduction values ​​can be calculated. This method based on the proxy model greatly reduces the number of times the optimal load reduction model is solved, thereby saving calculation time.

Claims

1. A reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion, characterized by , including the following steps: 1) Read the basic parameters of the electric-hydrogen integrated energy system; 2) Based on the historical data of mixed random variables in the basic parameters of the electric-hydrogen integrated energy system, an orthogonal polynomial basis based on multi-order moments is constructed; 3) Constructing a chaotic polynomial agent model based on linear combinations of orthogonal polynomials; 4) Select the matching points based on the linear independence principle, and calculate the optimal load reduction of the electric-hydrogen integrated energy system according to the sample values ​​of the matching points; 5) The historical data of mixed random variables and the optimal load reduction of the electric-hydrogen integrated energy system are used as the input and output of the chaotic polynomial proxy model, and the coefficients of the chaotic polynomial proxy model are solved by the least squares method; 6) Obtain the current random variables and input them into the chaotic polynomial agent model to calculate the optimal load reduction of the current electric-hydrogen integrated energy system; 7) Calculate the reliability index of the electric-hydrogen integrated energy system.

2. According to claim 1, a reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion is characterized in that: The basic parameters of the electric-hydrogen integrated energy system include electric-hydrogen integrated energy system parameters, equipment component reliability parameters, and mixed random variable historical data sets.

3. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion according to claim 2 is characterized in that: The parameters of the hydrogen comprehensive energy system include the resistance and reactance parameters of the power line of the power system, the upper and lower limit parameters of the generator output, the parameters of the wind turbine and photovoltaic units, the line topology connection parameters, the parameters of the electrolyzer, fuel cell, hydrogen storage tank, hydrogen refueling machine of the hydrogen energy system, and the data of the electric load and hydrogen load; The equipment component reliability parameters include the failure rate and repair rate of the generator set, line, electrolyzer, fuel cell, hydrogen storage tank, and hydrogen filling machine; The mixed random variable historical data set includes historical output data of wind turbines and photovoltaic units, and historical fault-operation data of generator sets, lines, electrolyzers, and fuel cell equipment.

4. According to claim 1, a reliability assessment method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion is characterized in that: In step 2), the steps of constructing an orthogonal polynomial basis based on multi-order moments include: 2.1) Construct a multidimensional PCE model, namely: In the formula, c κ The k-th expansion term Φ κ coefficient; M is the number of random responses Y; M = (H + N)! / (H! N!); Φ κ is the full tensor product of one-dimensional polynomials; κ=1,2,…,M; ξ1,ξ2,…,ξ N is a random variable; N is the number of random variables; H is the order; 2.2) Use the multidimensional PCE model to construct an orthogonal polynomial basis based on multi-order moments, namely: In the formula, α κ,i is the order of the i-th one-dimensional polynomial.

5. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion according to claim 1 is characterized in that: In step 3), the steps of constructing the chaotic polynomial agent model include: 3.1) Convert the mixed random variable historical data set in the basic parameters of the electric-hydrogen integrated energy system into a multi-order rectangular form and construct a one-dimensional orthogonal polynomial P i (l) (ξ i ),Right now: In the formula, ξ i κ is the historical data of mixed random variables; l=0,1,...,H; Among them, the basis of any polynomial of order 0 to l is As shown below: In the formula, μ k,i is the k-order origin moment of the random variable; M p is the number of samples of the random variable; 3.2) One-dimensional orthogonal polynomial Normalization is performed to construct a chaotic polynomial agent model Right now: In the formula, ||·|| represents the calculation of the second norm of the polynomial.

6. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion according to claim 1 is characterized in that: In step 4), the step of calculating the optimal load reduction amount of the electric-hydrogen integrated energy system according to the sample value of the distribution point includes: 4.1) Calculate one-dimensional orthogonal polynomials The third root of 4.2) Random combination of "0" and one-dimensional orthogonal polynomial The third-order root of the initial collocation point set Ω IC ; 4.3) From the set Ω IC Select the same number of collocation points as the number of coefficients to be determined M in turn to form a collocation point combination Ω C ; 4.4) Calculate the point combination Ω C The coefficient matrix Φ is: 4.4) Let the rank of the coefficient matrix Φ be R Φ , if R Φ =M, then the current point combination Ω C is the optimal point combination, the point selection operation ends and goes to step 4.5); otherwise, the current point combination Ω is eliminated. C Medium MR Φ linearly related collocation points, from the set Ω IC Select MR from the unselected points Φ The selected MR Φ Write the point combination Ω C In the new point combination Ω C , and return to step 4.4); 4.5) Construct an optimal load reduction model; input the sample values ​​of the selected distribution points into the optimal load reduction model in turn to obtain the optimal load reduction amount of the electric-hydrogen integrated energy system corresponding to each distribution point.

7. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion according to claim 6 is characterized in that: The objective function f of the optimal load reduction model is as follows: In the formula, c DG is the unit penalty cost for abandoning wind and solar power; Ω WG ,Ω PV are the grid-connected node sets of wind and solar generators respectively; ΔP WG,it , ΔP PV,it are the abandoned wind and solar power respectively; T is the scheduling period; n is the number of nodes; c is the number of nodes; p The penalty cost for reducing the load unit, L p,it To reduce power for electrical load; c q Reducing penalty costs for hydrogen load units, L q,it is the power reduction of hydrogen load. The subscripts i and t represent the node i and time t respectively; Δt is the time step.

8. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion according to claim 6 is characterized in that: The constraints of the optimal load reduction model include electric power balance constraints, hydrogen balance constraints, electric and hydrogen load reduction constraints, operating constraints on the relationship between hydrogen production and power consumption of the electrolyzer, and operating constraints on the relationship between hydrogen consumption and output power of the fuel cell; The electrical power balance constraints are as follows: Q GEN,it +P WG,it +P PV,it +P FC,it =P EL,it +P load,it -L p,it +Bθ i,t (10) Where P GEN,it P is the active power output by the generator set; WG,it , P PV,it are the active power actually output by the wind and solar units; P FC,it , P EL,it are the active power output by the fuel cell and the active power consumed by the electrolyzer; P load,it , L p,it are active load and load reduction power respectively; B is the node susceptance matrix used for DC power flow calculation, θ i,t is the node voltage phase angle. The hydrogen balance constraints are as follows: Q DP,in,it =Q EL,it -Q HT,in,it +Q HT,out,it -Q FC,it (11) Q DP,out,it =Q load,it -L q,it (12) In the formula, Q DP,in,it , Q DP,out,it are the amount of hydrogen input and output of the hydrogenation unit; Q EL,it , Q FC,it are the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q HT,in,it , Q HT,out,it are the hydrogen filling and dehydration amounts of the hydrogen storage tank respectively; Q load,it , L q,it They are the demand and reduction of hydrogen load respectively. The electric hydrogen load reduction constraints are as follows: 0≤L p,it ≤P load,it (13) 0≤L q,it ≤Q load,it (14) The operating constraints of the electrolyzer on the relationship between hydrogen production and power consumption are as follows: μ EL,it P EL,min ≤P EL,it ≤μ EL,it P EL,max (17) In the formula, is the standard density of hydrogen; μ EL,it is the start-stop state variable of the electrolytic cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; P EL,max and P EL,min are the upper and lower limits of the power consumption of the electrolyzer respectively; is the relationship function; m EL,t is the hydrogen production of the electrolyzer. The operating constraints of the fuel cell's hydrogen consumption and output power are as follows: Q FC,it =ρ H2 m FC,it (18) P FC,it =g(m FC,it ) (19) μ FC,it Q FC,min ≤Q FC,it ≤μ FC,it Q FC,max (20) In the formula, μ FC,it is the start-stop state variable of the fuel cell. If it is 1, it indicates the start state, and if it is 0, it indicates the stop state; Q FC,max , Q FC,min are the upper and lower limits of fuel cell hydrogen consumption, is the standard density of hydrogen; g(m FC,it ) is the relationship function; m FC,it is the hydrogen consumption of the fuel cell.

9. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion according to claim 1 is characterized in that: In step 5), the step of solving the coefficients of the chaotic polynomial proxy model using the least square method includes: 5.1) Substitute the sample values ​​of the random variable and the value of the response function into the following formula to solve the coefficient of the chaos expansion: 5.2) The coefficients of the chaos expansion are calculated by minimizing the residual sum of squares, as shown in the following formula: Where, J(C) is the residual sum of squares; 5.3) Take the derivative of the chaos expansion coefficients to obtain the coefficients of each chaos expansion, namely: In the formula, are the coefficients of the chaotic expansion to be solved.

10. The reliability assessment method of an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion according to claim 1 is characterized in that: In step 7), the steps of calculating the reliability index of the electric-hydrogen integrated energy system include: 7.1) Use the Galerkin projection method to project Y(ξ) onto the orthogonal polynomial Φ1 and obtain: In the formula, E(·) is the expectation operation, Φ1(ξ) is the first expansion term of the polynomial, and c κ The k-th expansion term Φ κ The coefficient of 7.2) Calculate the expected μ of the response function Y(ξ) as a reliability indicator of the electric-hydrogen integrated energy system; The expected μ is as follows:

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