Rare event sensitivity calculation method and system for integrated energy system

Abstract

The invention discloses a comprehensive energy system rare event sensitivity calculation method and system, and the method sequentially comprises the steps: building a comprehensive energy system probability energy flow model, building a comprehensive energy system rare event representation model considering the uncertainty of a random source model, decomposing a rare event into a larger probability event through subset simulation, reducing the number of samples, and improving the sensitivity of the rare event. The estimation of rare events is accelerated, and the calculation burden is reduced; the method comprises the steps of establishing a rare event probability multi-term chaos agent model, reducing the number of samples required for efficient global sensitivity, and efficiently calculating the global sensitivity of the parameter uncertainty of the integrated energy system model according to the rare event probability. Meanwhile, risk relief measures are also provided.

Description

Technical Field

[0001] This invention belongs to the technical field of uncertainty analysis of integrated energy systems, and mainly relates to a method and system for calculating the sensitivity of rare events in integrated energy systems. Background Technology

[0002] As a key form of integrated energy system, electrothermal coupling system can enhance the complementary utilization of electrical and thermal energy, improve overall energy efficiency, and increase the proportion of renewable energy consumption. Therefore, its related technology research has become a current hot topic.

[0003] However, the operation of integrated energy systems involves the coordination of power and regional heat networks. The volatility and strong correlation of loads in these networks significantly increase the uncertainty of system operation. This uncertainty leads to risk events in integrated energy systems such as line overload and voltage exceedances. This uncertainty can affect the operating status of the power system and the power distribution within the grid, thereby impacting overall power quality. To ensure the safe operation of the system, it is essential to quantitatively assess the impact of various uncertainties on the electro-thermal coupling system. Furthermore, the parameters of random variables such as load and renewable energy sources exhibit uncertainty, necessitating an assessment of their impact on the probability of rare events. Global sensitivity analysis is an effective quantitative assessment tool, with the Sobo method being a commonly used approach. This method, based on analysis of variance, quantifies the impact of each input variable on the output variable. By analyzing the relationship between the output and input, it assesses the degree of influence of uncertainties, helping operators identify factors with critical impact on system operation.

[0004] A key step in calculating the global sensitivity of a comprehensive energy system to risk events is accurately assessing the probability of rare events. Currently, Monte Carlo simulation (MCS) is a common sampling method; however, MCS struggles to sample rare events and capture the tail-end characteristics of the probability density function. Furthermore, Monte Carlo simulation requires an excessive number of samples for global sensitivity calculations, increasing the cost and complexity of sensitivity assessment. Summary of the Invention

[0005] This invention addresses the risks posed to the operation of integrated energy systems by rare events such as power outages in existing technologies. It provides a method and system for calculating the sensitivity of rare events in integrated energy systems. The method involves first establishing a probabilistic energy flow model for the integrated energy system, then establishing a representation model of rare events considering the uncertainties of the random source model. Subset simulation is used to decompose rare events into higher-probability events, reducing the sample size, accelerating the estimation of rare events, and alleviating the computational burden. Next, a multinomial chaotic proxy model of rare event probabilities is established to reduce the number of samples required for efficient global sensitivity. Based on the rare event probabilities, the global sensitivity of the integrated energy system model parameters with uncertainties is calculated efficiently. This invention is significant for improving the efficiency of uncertainty analysis in large systems and also proposes risk mitigation measures.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a method for calculating the sensitivity of rare events in a comprehensive energy system, comprising the following steps:

[0007] S1. Establish a probabilistic energy flow model for a comprehensive energy system, specifically as follows:

[0008] g = f(x, ε)

[0009] in, As an input to a comprehensive energy system, it represents the uncertainty in the system; P represents the heat load vector in the heating network system. G P represents the active power generation vector in a power system. D Q represents the active load vector in the power system. G Q represents the reactive power generation vector in a power system. D Let f represent the reactive load vector in the power system; ε = {Y, λ, L, c, K, B} represents the deterministic input in the system, Y represents the nodal admittance matrix of the power system, c represents the specific heat capacity of the heat flow, λ represents the heat transfer coefficient per unit length of the pipe, L represents the length of the regional heating network pipe, K represents the matrix composed of the resistance coefficients of the regional heating network pipe, B represents the loop relationship matrix, and f represents the steady-state energy flow model of the integrated energy system.

[0010] S2. Establish a characterization model for rare events in a comprehensive energy system that considers the uncertainty of the random source model. The rare events include at least fluctuating load and wind speed random variables.

[0011] S3. Use subset simulation to decompose rare events into more probable events, thereby reducing the number of samples;

[0012] S4. Establish a multinomial chaotic proxy model based on the probability of rare events, and calculate the global sensitivity of the integrated energy system model parameters based on the probability of rare events; the multinomial chaotic proxy model can be expressed as:

[0013]

[0014] Where θ is a vector of random variables following a standard normal probability distribution; Φ n (θ) is the nth polynomial chaotic basis, b n N is the corresponding coefficient; c N is the number of polynomial chaotic bases involved in the polynomial chaotic expansion. c = (N+V)! / (N!V!)-1, where N is the dimension of the uncertain input variables and V is the maximum order of the polynomial chaotic basis functions.

[0015] As an improvement to the present invention, the calculation formula for the probabilistic energy flow of the integrated energy system in step S1 is as follows:

[0016] (P G -P D )-Real{U(YU) *} = 0

[0017] (Q G -Q D )-Imag{U(YU) *} = 0

[0018]

[0019] Where U represents the vector composed of the voltages at each node of the power system; Real{.} represents the real part of the complex number; Imag{.} represents the imaginary part of the complex number; (.) * T represents the conjugate function of a complex number; i - T i + T0 represents the temperature at which heat flows out of and into pipe i in the regional heating network; T0 represents the ambient temperature. This represents the mass flow rate of heat flowing from pipe i into node d; T represents the temperature at which heat flows from pipe i out of node d; d This represents the temperature after the heat flow mixes at node d; T represents the output heat power of the heat source at node i or the heat power transferred in pipe i; i s T i r Let represent the temperature of the heat flow in the input and return heat flow pipes of pipe i; c represent the specific heat capacity of the heat flow; λ represent the heat transfer coefficient per unit length of pipe; L represent the length of the regional heating network pipes; E represent the set of all pipes; V represent the set of all thermal nodes; V i This represents the set of thermal nodes connected to the regional heating network pipeline i; This represents the set of pipes into which heat flows to node d; This represents the set of pipes from which heat flows to node d.

[0020] As another improvement of the present invention, the probability distribution model of the fluctuating load and wind speed random variables in step S2 is as follows:

[0021] For the i-th heat load random variable that follows a normal distribution Its probability density function is expressed as:

[0022]

[0023] Where, λ μi express The mean; ρ i express The ratio of standard deviation to mean, ρ i λ μi express Standard deviation;

[0024] For the i-th wind speed random variable w that follows a Weibull distribution i Its probability density function is expressed as:

[0025]

[0026] Where, λ ki Indicates w i Position parameters; λ li Indicates w i The scale parameter; n rand-wind This indicates the number of wind farms.

[0027] As another improvement of the present invention, step S3 specifically includes the following steps:

[0028] S31. Under the condition of λ distribution, the set of rare events F(λ) is defined in the form of:

[0029]

[0030] F(λ) represents the probability space of x under the condition of the λ distribution that satisfies A set;

[0031] Define a subset of events with increasing probability space: The corresponding event's limit value meets the condition. The i-th event subset i = {1, 2, ..., K} is defined as follows:

[0032] S32. Under the condition of ξ distribution, the subset simulation method expresses the probability of rare events as:

[0033]

[0034] Where ∩ represents the intersection of different events; P(F i (λ)∣F i-1 (λ)) represents the value in F i-1 (λ) in the event space simultaneously in the event set F i The part of (λ);

[0035] S33. Represent the probability of rare events using a set of events with higher probabilities:

[0036] Let the conditional probability between a set of adjacent event sets be: P(F) i (λ)∣F i-1 (λ))=p0;

[0037] Estimated probability of rare events The expression is:

[0038]

[0039] Where, N SS This represents the number of samples drawn from the intermediate events; K represents the number of events in the event set. This represents the floor function.

[0040] As another improvement of the present invention, the Sobo L global sensitivity index of the probability of risk events in a comprehensive energy system based on a multinomial chaotic proxy model can be expressed as:

[0041]

[0042] Where, N i This indicates that all variables depend only on λ. i The set of polynomial chaotic bases, The global sensitivity index for rare events represents the ith uncertainty parameter. It represents the global sensitivity index for rare events, which is the i-th uncertainty parameter obtained by using a multinomial chaotic surrogate model.

[0043] As another improvement of the present invention, in step S4, the coefficients of each basis in the multinomial chaotic surrogate model are solved using the minimum angle regression method, specifically as follows:

[0044]

[0045] Where, ε l The tuning parameters are used to control the sparsity of PCE coefficients; D is the number of samples used to build the surrogate model; θ (d) It is the d-th sample of θ. Yes, it is the probability of rare events calculated from the corresponding samples; A penalty term added when performing minimum angle regression.

[0046] As another improvement of the present invention, it also includes step S5, which proposes risk mitigation measures for the high global sensitivity obtained in step S4, wherein the mitigation measures are specifically as follows:

[0047] S51. Construct models of different types of electrothermal coupling devices connecting the power grid and the regional heating network:

[0048]

[0049] in, These represent the heat energy generated by the combined heat and power unit, the electric furnace, and the heat pump, respectively; P chp P represents the electrical energy generated by a combined heat and power (CHP) unit. eb P hp These represent the electrical energy consumed by the electric furnace and the heat pump, respectively; η chp Indicates the heat and power ratio of a combined heat and power (CHP) unit; η eb COP and COP represent the heat production efficiency of the electric furnace and heat pump, respectively.

[0050] S52. Find the index of the sub-regional heat network with the highest Sobol global sensitivity:

[0051]

[0052] Where index_max is the value to be found The number of the largest λ in the range;

[0053] S53. Adjust the index of the sub-regional heating network with the maximum Sobol global sensitivity:

[0054]

[0055] Where R is the proportional coefficient for adjustment; η κ chp The heat and power ratio of the cogeneration unit numbered κ before adjustment; The adjusted heat and power ratio of the cogeneration unit numbered κ.

[0056] To achieve the above objectives, the present invention also adopts the following technical solution: a rare event sensitivity calculation system for an integrated energy system, comprising a computer program, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0057] Compared with existing technologies, this invention offers the following advantages: It provides a method and system for calculating the sensitivity of rare events in an integrated energy system. This method establishes a characterization model of rare events in an integrated energy system considering the uncertainty of the random source model. It uses subset simulation to decompose rare events into higher probability events to reduce the sample size and improve the efficiency of rare event probability estimation. It calculates the global sensitivity of the integrated energy system model parameter uncertainty based on the rare event probability. This invention significantly reduces the sample size while considering the uncertainty of the probability density function parameters of random variables, effectively solving the problem of excessive computation in traditional Monte Carlo sampling algorithms. It can quickly calculate the influence of uncertain parameters of the probability density function of random variables in an integrated energy system on the probability of rare events in the operation of grid state variables. This scheme is highly adaptable and can assess rare events of different degrees, as well as consider the uncertainty of multiple parameters. It provides a theoretical basis for studying the impact of probability model uncertainty on the frequency of risk events such as grid power exceeding limits and voltage exceeding limits. Attached Figure Description

[0058] Figure 1 This is a flowchart of the steps in the method for calculating the sensitivity of rare events in an integrated energy system according to the present invention;

[0059] Figure 2 This is a graph showing the global sensitivity Sobol exponent of rare events in the integrated energy system using a multi-chaotic proxy model in Embodiment 3 of the present invention.

[0060] Figure 3 This is a probability density diagram of the active power of the line before and after adjusting the connection nodes 37 and 38 in Embodiment 3 of the present invention. Detailed Implementation

[0061] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0062] Example 1

[0063] like Figure 1 As shown, a rare event sensitivity calculation for an integrated energy system includes the following steps:

[0064] S1. Establish a probabilistic energy flow model for the integrated energy system, as follows:

[0065] S11, The probability energy flow calculation formula for the integrated energy system is as follows:

[0066] P-Real{U(YU) *} = 0

[0067] Q-Imag{U(YU) *} = 0

[0068]

[0069] Where P represents the vector of active power at each node of the power system; Q represents the vector of active power at each node of the power system; Y represents the node admittance matrix of the power system; U represents the vector of voltages at each node of the power system; Real{.} represents the real part of the complex number; Imag{.} represents the imaginary part of the complex number; (.) * T represents the conjugate function of a complex number; i - T i + T0 represents the temperature at which heat flows out of and into pipe i in the regional heating network; T0 represents the ambient temperature. This represents the mass flow rate of heat flowing from pipe i into node d; T represents the temperature at which heat flows from pipe i out of node d; d This represents the temperature after the heat flow mixes at node d; T represents the output heat power of the heat source at node i or the heat power transferred in pipe i; i s T i r Let represent the temperature of the heat flow in the input and return heat flow pipes of pipe i; c represents the specific heat capacity of the heat flow; λ represents the heat transfer coefficient per unit length of pipe, in meters (m); L represents the length of the regional heating network pipes, in meters (m); K represents the matrix of resistance coefficients of the regional heating network pipes; B represents the loop relationship matrix; E represents the set of all pipes; V represents the set of all thermal nodes; V i This represents the set of thermal nodes connected to the regional heating network pipeline i; This represents the set of pipes into which heat flows to node d; This represents the set of pipes from which heat flows out to node d;

[0070] S12. Establish a probabilistic energy flow model for a comprehensive energy system:

[0071] This model can be simplified as follows:

[0072] g = f(x, ε)

[0073] in, Indicates the input of the integrated energy system; P represents the heat load vector in the heating network system. G P represents the active power generation vector in a power system. D Q represents the active load vector in a power system. G Q represents the reactive power generation vector in a power system. DLet f represent the reactive load vector in the power system; ε = {Y, λ, L, c, K, B} represents the deterministic input in the system, Y represents the nodal admittance matrix of the power system, c represents the specific heat capacity of the heat flow, λ represents the heat transfer coefficient per unit length of the pipe, L represents the length of the regional heating network pipe, K represents the matrix composed of the resistance coefficients of the regional heating network pipe, B represents the loop relationship matrix, and f represents the steady-state energy flow model of the integrated energy system.

[0074] P represents the state variable to be determined in the system, serving as the output of the integrated energy system; line Q represents the active power on the lines in a power system. line U represents the reactive power on the lines in the power system, and U represents the vector composed of the voltages of each node in the power system. T represents the mass flow rate of heat flowing from pipe i into node d; i s T i r This indicates the temperature of the heat flow in the input heat flow tube and the return heat flow tube in pipe i;

[0075] S2. Establish a characterization model for rare events in a comprehensive energy system that considers the uncertainties of the stochastic source model, as follows:

[0076] S21. The probability distribution model for fluctuating load and wind speed random variables in an integrated energy system is defined as follows:

[0077] For the i-th heat load random variable that follows a normal distribution Its probability density function is expressed as:

[0078]

[0079] Where, λ μi express The mean; ρ i express The ratio of standard deviation to mean, ρ i λ μi express Standard deviation;

[0080] For the i-th wind speed random variable w that follows a Weibull distribution i Its probability density function is expressed as:

[0081]

[0082] Where, λ ki Indicates w i Position parameters; λ li Indicates w i The scale parameter; nrand-wind The number of wind farms is represented by the number of wind turbines, and the output of the wind turbines is represented by:

[0083]

[0084] Among them, w ci The cutoff wind speed; w r Rated wind speed; w co To cut off the wind speed; P r C1 represents the rated active power output of the wind farm; C1 represents the slope.

[0085] The probability distribution parameter of uncertainty in an integrated energy system can be expressed as λ=[λ μ ,λ k ,λ l ],in λ μ It follows a relatively uniform distribution within a 10% range above and below the baseline value, λ k ,λ l It follows a relatively uniform distribution within a 20% range above and below the baseline value;

[0086] S22, the probability P of rare events in a comprehensive energy system with uncertain values ​​for random variable model parameters. r (λ) is defined as:

[0087]

[0088] in, Let P(.) represent the probability of a rare event under the λ distribution; where P(.) represents the probability of the event occurring. π represents the operating limits of the power system state variables; π(.) represents the probability density function of the random variable; X represents the complete probability space of the random variable x; ∫ X .dx represents the integral of the random variable x over the complete probability space; χ f (x) represents the 0-1 identification function corresponding to the input random variable x, defined as follows:

[0089]

[0090] S3. Use subset simulation to decompose rare events into a series of events with higher probabilities to reduce the sample size and the computational cost of estimating the probability of rare events, as follows:

[0091] S31. Under the condition of λ distribution, the set of rare events F(λ) is defined in the form of:

[0092]

[0093] F(λ) represents the probability space of x under the condition of the λ distribution that satisfies A set;

[0094] Define a subset of events with increasing probability space: The corresponding event's limit value meets the condition.

[0095] The i-th event subset i = {1, 2, ..., K} is defined as follows:

[0096] S32. Under the condition of ξ distribution, the subset simulation method expresses the probability of rare events as:

[0097]

[0098] Where ∩ represents the intersection of different events; P(F i (λ)∣F i-1 (λ)) represents the value in F i-1 (λ) in the event space simultaneously in the event set F i The part of (λ);

[0099] S33. Represent the probability of rare events using a set of events with higher probabilities:

[0100] Let the conditional probability between a set of adjacent event sets be: P(F) i (λ)∣F i-1 (λ))=p0;

[0101] Estimated probability of rare events The expression is:

[0102]

[0103] Where, N SS This represents the number of samples drawn from the intermediate events; K represents the number of events in the event set. This represents the floor function;

[0104] S4. Establish a multinomial chaotic surrogate model with rare event probabilities, and use this surrogate model to calculate the global sensitivity of the integrated energy system model parameters under uncertainty, as follows:

[0105] S41. The multinomial chaotic surrogate model for the probability of risk events in a comprehensive energy system considering the parameter uncertainty of the probability density function of the input uncertainty source can be expressed as:

[0106]

[0107] Where θ is a vector of random variables following a standard normal probability distribution; Φ n (θ) is the nth polynomial chaotic basis, b nN is the corresponding coefficient; c N is the number of polynomial chaotic bases involved in the polynomial chaotic expansion, where N c = (N+V)! / (N!V!)-1, where N is the dimension of the uncertain input variables, and V is the maximum order of the polynomial chaotic basis functions. Typically, V=2 is sufficient to produce sufficiently accurate results;

[0108] S42. Solve for the coefficients of each basis in the multinomial chaotic surrogate model using the minimum angle regression method:

[0109]

[0110] Where, ε l The tuning parameters are used to control the sparsity of PCE coefficients; D is the number of samples used to build the surrogate model; θ (d) It is the d-th sample of θ. Yes, it is the probability of rare events calculated from the corresponding samples; A penalty term added when performing minimum angular regression;

[0111] S43. The Sobol global sensitivity index for the probability of risk events in a comprehensive energy system considering the parameter uncertainty of the probability density function of the input uncertainty source can be expressed as:

[0112]

[0113] Where, N i This indicates that all variables depend only on λ. i The set of polynomial chaotic bases.

[0114] This invention relates to an integrated energy system that considers the electrothermal coupling of wind power as a renewable energy source. By decomposing a set of rare events into a series of larger event sets, this method reduces the computational burden and is significant for improving the efficiency of uncertainty analysis in large-scale systems.

[0115] Example 2

[0116] The difference between this embodiment and Embodiment 1 is that, after obtaining the global sensitivity through the steps of Embodiment 1, a risk mitigation measure is specifically proposed for those with low sensitivity, as follows:

[0117] S51. Construct models of different types of electrothermal coupling devices connecting the power grid and the regional heating network:

[0118]

[0119] in, These represent the heat energy generated by the combined heat and power unit, the electric furnace, and the heat pump, respectively; P chp P represents the electrical energy generated by a combined heat and power (CHP) unit.eb P hp These represent the electrical energy consumed by the electric furnace and the heat pump, respectively; η chp Indicates the heat and power ratio of a combined heat and power (CHP) unit; η eb COP and COP represent the heat production efficiency of the electric furnace and heat pump, respectively.

[0120] S52. Find the index of the sub-regional heat network with the highest Sobol global sensitivity:

[0121]

[0122] Where index_max is the value to be found The number of the largest λ in the range;

[0123] S53. Adjust the index of the sub-regional heating network with the maximum Sobol global sensitivity:

[0124]

[0125] Where R is the proportional coefficient for adjustment; η κ chp The heat and power ratio of the cogeneration unit numbered κ before adjustment; The adjusted heat and power ratio of the cogeneration unit numbered κ.

[0126] Example 3

[0127] The integrated energy system structure of this embodiment includes a 118-node power grid and eight 35-node heating networks, as well as two new energy wind turbine generators. There are two wind farms, WF1 and WF2, connected to nodes 59 and 88 respectively. The installed capacity of both WF1 and WF2 is 300MW. The baseline values ​​for location parameters are 1.5 and 1.7, respectively. The baseline values ​​for scale parameters are 8.1 and 8.5, respectively. The uncertain distribution parameters of the location and scale parameters of the random variable wind speed at the two new energy wind turbine generator sites are denoted as λ1 to λ4, respectively. All heat loads in each heating network are considered as a single large load, and the uncertain distribution parameters of the eight loads are denoted as λ5 to λ6. 12The coupling facilities consist of four CHP units connected to nodes 10, 25, 49, and 65; two heat pumps connected to nodes 11 and 15; and two electric boilers connected to nodes 59 and 90. The ratio of thermal power to electrical power for the electric boilers is set to -0.8, -3 for the heat pumps, and 1.3 for the CHP units. The average total heat load for each DHN is 324.6 MW. The correlation coefficient between the heat loads of each two regional heating networks is set to 0.4. The correlation coefficient between WF1 and WF2 is set to 0.4. The sensitivity of the line active power connecting nodes 37 and 38 in the power grid to rare events under the uncertainty of the heating network load and the random variable parameters of the new energy sources is analyzed.

[0128] Table 1 compares the computation time required using polynomial chaotic expansion and 5000 Monte Carlo sampling. As can be seen after running the steps according to the invention, the computation time using polynomial chaotic expansion is only 0.15 hours. Compared to Monte Carlo sampling, the algorithm efficiency is improved by thousands of times.

[0129] Table 1. Sensitivity calculation time for different methods

[0130] method Calculation time (hours) Multivariate Chaos 0.15 Monte Carlo 287.6

[0131] The results of the global sensitivity Sobo L exponent for rare events in the integrated energy system based on a multinomial chaotic surrogate model in this embodiment. Figure 2 As shown.

[0132] Depend on Figure 2 It is evident that parameter λ9, the parameter of the fifth regional heating network, has the largest Sobol sensitivity index, used to reduce risks based on the integrated energy system. To reduce the risk of active power overload on the lines connecting nodes 37 and 38 in the grid, only the heat-to-power ratio of the cogeneration units serving as coupling facilities in the fifth regional heating network was changed. The heat-to-power ratio was initially 1.3, and it was adjusted to 1.17. The line over-limit probability before adjustment was 7.60 × 10⁻⁶. -3 The adjusted line over-limit probability is 6.16 × 10 -4 . Figure 3 The probability density diagram of the active power of the line connecting nodes 37 and 38 before and after adjustment is shown.

[0133] To reduce the sample size, a subset simulation decomposition method is used to decompose rare events into higher probability events, thereby improving the efficiency of rare event probability estimation. A multinomial chaotic surrogate model for rare event probabilities is established, and the global sensitivity of the integrated energy system model parameter uncertainty is calculated using this surrogate model.

[0134] Therefore, by simulating the decomposition of rare events into higher probability events to reduce the number of samples and improve the efficiency of rare event probability estimation, a multinomial chaotic surrogate model of rare event probability can be established to efficiently solve the global sensitivity of rare events in integrated energy systems. This is of great significance for improving the efficiency of analyzing the influencing factors of rare event probability in large systems and provides an important guarantee for the safe, efficient and economical operation of integrated energy systems.

[0135] It should be noted that the above content merely illustrates the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, various improvements and modifications can be made without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the sensitivity of rare events in an integrated energy system, characterized in that... It includes the following steps: S1. Establish a probabilistic energy flow model for a comprehensive energy system, specifically as follows: g = f(x, ε) in, Indicates the input of the integrated energy system; P represents the heat load vector in the heating network system. G P represents the active power generation vector in a power system. D Q represents the active load vector in a power system; G Q represents the reactive power generation vector in a power system; D ε represents the reactive load vector in the power system; ε = {Y, λ, L, c, K, B} represents the deterministic input in the system; Y represents the nodal admittance matrix of the power system; c represents the specific heat capacity of the heat flow; λ represents the heat transfer coefficient per unit length of the pipe; L represents the length of the regional heating network pipe; K represents the matrix composed of the resistance coefficients of the regional heating network pipe; B represents the loop relationship matrix; f represents the steady-state energy flow model of the integrated energy system. S2. Establish a characterization model for rare events in a comprehensive energy system that considers the uncertainty of the random source model. The rare events include at least fluctuating load and wind speed random variables. S3. Use subset simulation to decompose rare events into more probable events, thereby reducing the number of samples; S4. Establish a multinomial chaotic proxy model based on the probability of rare events, and calculate the global sensitivity of the integrated energy system model parameters based on the probability of rare events; the multinomial chaotic proxy model can be expressed as: Where θ is a vector of random variables following a standard normal probability distribution; Φ n (θ) is the nth polynomial chaotic basis, b n N is the corresponding coefficient; c λ is the number of polynomial chaotic bases involved in the multinomial chaotic expansion, N is the dimension of the uncertain input variables, and λ represents the uncertainty parameter of the input.

2. The method for calculating the sensitivity of rare events in an integrated energy system as described in claim 1, characterized in that: The formula for calculating the probabilistic energy flow of the integrated energy system in step S1 is as follows: (P G -P D )-Real{U(YU) * }=0 (Q G -Q D )-Imag{U(YU) * }=0 Where U represents the vector composed of the voltages at each node of the power system; Real{.} represents the real part of the complex number; Imag{.} represents the imaginary part of the complex number; (.) * T represents the conjugate function of a complex number; i - T i + T0 represents the temperature at which heat flows out of and into pipe i in the regional heating network; T0 represents the ambient temperature. This represents the mass flow rate of heat flowing from pipe i into node d; T represents the temperature at which heat flows from pipe i out of node d; d This represents the temperature after the heat flow mixes at node d; T represents the output heat power of the heat source at node i or the heat power transferred in pipe i; i s T i r Let represent the temperature of the heat flow in the input and return heat flow pipes of pipe i; c represent the specific heat capacity of the heat flow; λ represent the heat transfer coefficient per unit length of pipe; L represent the length of the regional heating network pipes; E represent the set of all pipes; V represent the set of all thermal nodes; V i This represents the set of thermal nodes connected to the regional heating network pipeline i; This represents the set of pipes into which heat flows to node d; This represents the set of pipes from which heat flows to node d.

3. The method for calculating the sensitivity of rare events in an integrated energy system as described in claim 2, characterized in that: The probability distribution model for the fluctuating load and wind speed random variables in step S2 is as follows: For the i-th heat load random variable that follows a normal distribution Its probability density function is expressed as: Where, λ μi express The mean; ρ i express The ratio of standard deviation to mean, ρ i λ μi express Standard deviation; For the i-th wind speed random variable w that follows a Weibull distribution i Its probability density function is expressed as: Where, λ ki Indicates w i Position parameters; λ li Indicates w i The scale parameter; n rand-wind This indicates the number of wind farms.

4. The method for calculating the sensitivity of rare events in an integrated energy system as described in claim 3, characterized in that: Step S3 specifically includes the following steps: S31. Under the condition of λ distribution, the set of rare events F(λ) is defined in the form of: F(λ) represents the probability space of x under the condition of the λ distribution that satisfies A set; Define a subset of events with increasing probability space: The corresponding event's limit value meets the condition. The i-th event subset i = {1, 2, ..., K} is defined as follows: S32. Under the condition of ξ distribution, the subset simulation method expresses the probability of rare events as: Where ∩ represents the intersection of different events; P(F i (λ)∣F i-1 (λ)) represents the value in F i-1 (λ) in the event space simultaneously in the event set F i The part of (λ); S33. Represent the probability of rare events using a set of events with higher probabilities: Let the conditional probability between a set of adjacent event sets be: P(F) i (λ)∣F i-1 (λ))=p0; Estimated probability of rare events The expression is: Where, N SS This represents the number of samples drawn from the intermediate events; K represents the number of events in the event set. This represents the floor function.

5. The method for calculating the sensitivity of rare events in an integrated energy system as described in claim 3, characterized in that: In step S4, the Sobol global sensitivity index of the probability of risk events in the comprehensive energy system based on multiple chaotic proxy models is expressed as: Where, N i This indicates that all variables depend only on λ. i The set of polynomial chaotic bases, The global sensitivity index for rare events represents the ith uncertainty parameter. It represents the global sensitivity index for rare events, which is the i-th uncertainty parameter obtained by using a multinomial chaotic surrogate model.

6. The method for calculating the sensitivity of rare events in an integrated energy system as described in claim 5, characterized in that: In step S4, the coefficients of each basis in the multinomial chaotic surrogate model are solved using the minimum angle regression method, specifically as follows: Where, ε l The tuning parameters are used to control the sparsity of PCE coefficients; D is the number of samples used to build the surrogate model; θ (d) It is the d-th sample of θ. Yes, it is the probability of rare events calculated from the corresponding samples; A penalty term added when performing minimum angle regression.

7. The method for calculating the sensitivity of rare events in an integrated energy system as described in claim 6, characterized in that: The method also includes step S5, which proposes risk mitigation measures for the high global sensitivity calculated in step S4. The specific mitigation measures are as follows: S51. Construct models of different types of electrothermal coupling devices connecting the power grid and the regional heating network: in, These represent the heat energy generated by the combined heat and power unit, the electric furnace, and the heat pump, respectively; P chp P represents the electrical energy generated by a combined heat and power (CHP) unit. eb P hp These represent the electrical energy consumed by the electric furnace and the heat pump, respectively; η chp Indicates the heat and power ratio of a combined heat and power (CHP) unit; η eb COP and COP represent the heat production efficiency of the electric furnace and heat pump, respectively. S52. Find the index of the sub-regional heat network with the highest Sobol global sensitivity: Where index_max is the value to be found The number of the largest λ in the range; S53. Adjust the index of the sub-regional heat network with the maximum global Sobol sensitivity: Where R is the proportional coefficient for adjustment; η κ chp The heat and power ratio of the cogeneration unit numbered κ before adjustment; The adjusted heat and power ratio of the cogeneration unit numbered κ.

8. A rare event sensitivity calculation system for an integrated energy system, comprising a computer program, characterized in that: When the computer program is executed by a processor, it implements the steps of any of the methods described above.

Images