Electric power engineering intelligent design comprehensive service system and use method

Abstract

The invention discloses an electric power engineering intelligent design comprehensive service system and a use method, relates to the field of micro-grid redundancy design, and is used for solving the problem of dynamic resource allocation under load fluctuation and energy uncertainty. A dynamic joint probability field model capable of capturing load requirements, distributed energy output and environment variable time sequence and space characteristics is constructed, and the complex nonlinear dependency relationship is comprehensively reflected; on the basis of the model, an advanced nonlinear optimization algorithm is adopted, redundancy capacity threshold values in all time periods are deduced, it is ensured that redundancy configuration in different operation time periods can meet the high power supply reliability requirement, and economical efficiency and resource utilization efficiency are optimized; furthermore, redundant resources are dynamically allocated by using a Bayesian network, the multi-energy synergistic effect is fully mined, and scientific allocation and real-time response of the resources are realized; therefore, the stability and economy of the micro-grid in dealing with load fluctuation and energy uncertainty are obviously enhanced.

Description

Technical Field

[0001] The present invention relates to the field of microgrid redundancy design, and more specifically, to an integrated service system for intelligent design of electric power engineering and a method for using the system. Background Art

[0002] With the rapid development of distributed energy technology, microgrids have become an important form of meeting local regional energy needs. Microgrids achieve flexibility and efficiency in local power supply by combining multiple energy forms (such as solar energy, wind energy, energy storage batteries, etc.). However, in order to cope with sudden failures or load fluctuations, a certain amount of redundant capacity is usually reserved in the design stage to ensure the reliability of power supply. Reasonable planning of redundant capacity is a key link in microgrid design, which is directly related to resource utilization, operation economy and power supply security. Especially in the scenario of multi-energy synergy, how to achieve dynamic redundant configuration under limited resource constraints has become one of the core challenges in the design field.

[0003] Traditional microgrid redundancy design is usually based on static load forecasting and empirical rules, which makes it difficult to fully reflect the probability distribution characteristics of load fluctuations during actual operation. Especially when faced with the intermittent output of distributed energy, the randomness of load demand, and the complex influence of external environmental factors, static design solutions often have problems of insufficient redundancy or over-design. Insufficient redundancy may lead to reduced power supply reliability in emergencies, or even cause local power grid instability; while over-design may lead to waste of resources, increase initial investment and operating costs. In addition, existing methods rarely consider the dynamic change characteristics of load fluctuations and fault risks in the time dimension, and lack the ability to accurately evaluate and optimize high-risk scenarios during critical periods. This design defect not only limits the operating efficiency of the microgrid, but also increases the uncertainty and potential risks of the design.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiment of the present invention provides a comprehensive service system for intelligent design of power engineering and a method for using the system. The present invention integrates multi-source data through the system, first constructs a dynamic joint probability field model that can accurately capture the time series and spatial characteristics of load demand, distributed energy output and environmental variables, and fully reflects its complex nonlinear dependencies. Based on the model, an advanced nonlinear optimization algorithm is used to derive the redundant capacity threshold in each time period to ensure that the redundant configuration in different operating time periods can not only meet the high power supply reliability requirements, but also optimize the economy and resource utilization efficiency. Further, the Bayesian network is used to dynamically allocate redundant resources, fully explore the synergistic effect of multiple energy sources, and realize scientific allocation and real-time response of resources. In addition, the stability and economy of the microgrid in dealing with load fluctuations and energy uncertainties are significantly enhanced, and the problem of difficulty in dynamically balancing load and energy supply in the redundant design of traditional microgrids is effectively solved. Through multi-level and multi-dimensional optimization and coordination, the system reliability, economy and intelligence level are comprehensively improved, and the overall adaptability and operation efficiency of the microgrid in a complex operating environment are significantly enhanced to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for using a comprehensive service for intelligent design of electric power engineering, comprising the steps of:

[0008] S1. Construct a dynamic joint probability field model of load demand, distributed energy output and environmental variables, capture the time series dynamics through Markov chain, generate multi-scenario joint probability distribution in combination with Copula function, and identify the correlation and dynamic characteristics between variables.

[0009] S2. Based on the joint probability field model, the sensitive periods of load fluctuation and energy output are deduced, and the redundant capacity thresholds of different time periods are calculated using the dynamic programming algorithm to form a time-sensitive redundant configuration strategy.

[0010] S3. According to the redundant capacity threshold, a Bayesian network is constructed to infer the status of energy nodes, identify the synergistic relationship between various energy sources, generate a dynamic redundant resource allocation plan, and ensure the rational allocation of resources in multi-energy scenarios.

[0011] In a preferred embodiment, step S1 includes the following contents:

[0012] S1.1 First, through multi-source data collection and preprocessing, the real-time and historical data of various key parameters during the operation of the microgrid are obtained.

[0013] S1.2 In the time series dynamic characteristics modeling stage, the change of load demand is divided into several states, and the transition probability matrix of load demand is established using Markov chain;

[0014] The modeling of distributed energy output adopts the autoregressive hidden Markov model to define the hidden state and the corresponding output distribution, and trains the model parameters through the maximum expectation algorithm to characterize the intermittent and random nature of energy output.

[0015] The spatiotemporal modeling of environmental variables adopts the high Vickrigg interpolation method, which defines the spatial covariance matrix to describe the correlation of environmental variables between different geographical locations, and combines it with the time series analysis method to achieve dynamic adjustment of environmental variables.

[0016] In a preferred embodiment, step S1 further includes the following contents:

[0017] S1.3 In the process of constructing the joint probability distribution, a multi-layer Copula function architecture is used to capture the nonlinear dependencies between load demand, distributed energy output and environmental variables. First, the marginal distribution function F is defined for each type of data. L (L t ), F E (E t ), F V (V t ), corresponding to load demand, energy output and environmental variables respectively; then, a two-layer Copula structure is constructed. The first layer adopts a hybrid form of Gaussian Copula and Clayton Copula to capture the tail dependence and symmetry between different variables respectively; the second layer refines the dynamic dependency in the time series through conditional Copula; by introducing the time lag parameter τ, the joint probability distribution can reflect the dependency between the current moment and the past τ moments.

[0018] S1.4 In order to generate the joint probability distribution of multiple scenarios, the Markov chain Monte Carlo method is used to sample the constructed dynamic joint Copula model to generate a multi-scenario data set covering different time periods and operating states.

[0019] The multi-scenario dataset generated by S1.5 is subjected to statistical tests to verify the accuracy and generalization ability of the model and ensure that the generated multi-scenario joint probability distribution can truly reflect the dynamic relationship of multiple variables in the operation of the microgrid.

[0020] In a preferred embodiment, step S2 includes the following contents:

[0021] S2.1.1 First, divide the day into time periods.

[0022] S2.1.2 In each divided time period, the dynamic joint probability field model is used to analyze the probability distribution characteristics of load demand and energy output, identify sensitive periods of high load fluctuations, low energy output and extreme changes in environmental variables, and determine their impact on redundant capacity configuration by calculating the joint probability density of variables in each time period.

[0023] S2.2.1 In order to derive the redundant capacity thresholds for each time period, a redundant capacity demand model is constructed. By comprehensively analyzing the joint probability distribution of multiple variables, the optimal configuration range of redundant capacity, i.e., the redundant capacity demand R, is determined. t Specifically, the redundant capacity requirement R t At time t it is expressed as: in: represents the load demand change of the oth power consumption node at time t; represents the output change of the oth distributed energy at time t; represents the change of the oth environmental variable at time t; N is the total number of power nodes, distributed energy resources and environmental variables.

[0024] S2.2.2 Based on the redundant capacity demand model, derive the redundant capacity threshold Θ for each time period t ; Use a multi-objective optimization framework to consider power supply reliability, economy and resource utilization efficiency, and construct an optimization objective function in: represents the power supply reliability function; represents the economic function; represents the resource utilization efficiency function; λ1, λ2, λ3 are weight parameters, reflecting the relative importance of each objective.

[0025] S2.2.3 In the optimization process, multi-level constraints and nonlinear optimization strategies are used to ensure the rationality and practicality of the redundant capacity threshold. The specific calculation formula is as follows: Where: P(L t,o ≤Θ t ) represents the probability that the load demand of the oth power consumption node does not exceed the redundancy threshold at time t; C(Θ t ) is the cost function of redundant capacity, which is usually a nonlinear function and reflects the economic cost of redundant capacity; Indicates resource utilization efficiency and encourages efficient allocation of redundant capacity.

[0026] In a preferred embodiment, step S2 further includes the following contents:

[0027] S2.3.1 Based on the derived redundant capacity threshold, construct a time-sensitive redundant configuration strategy matrix S, whose elements S t,k It represents the configuration amount of the kth type of redundant resources in time period t, which is specifically expressed as: Where: T is the total number of divided time periods, and K is the number of categories of redundant resources.

[0028] S2.3.2 uses a combination of hierarchical clustering and nonlinear optimization to generate a time-sensitive redundant configuration strategy. The specific steps are as follows:

[0029] Hierarchical clustering: Based on the redundant capacity threshold and load demand fluctuation characteristics in each time period, a hierarchical clustering algorithm is used to divide the time period into several clusters. The time periods in each cluster have similar redundant capacity demand characteristics.

[0030] Non-linear optimization: For each cluster, a non-linear optimization algorithm is used to optimize the configuration ratio of redundant resources to ensure that the redundant capacity requirements of the cluster are met at all times, while maximizing resource utilization efficiency and economy.

[0031] Strategy mapping: Map the optimization results to the redundant configuration strategy matrix, provide specific values ​​for the redundant resource configuration in each time period, and ensure that the redundant configuration strategy is targeted and sensitive in different time periods.

[0032] In a preferred embodiment, step S3 includes the following contents:

[0033] S3.1 First, identify and define various redundant resource nodes in the microgrid, including backup generators, energy storage equipment, renewable energy devices, and critical load nodes.

[0034] S3.2 Based on the redundant resource node modeling, a Bayesian network is constructed to describe the conditional dependency relationship between nodes.

[0035] S3.3 reflects the nonlinear dependency between the redundant resource nodes through the conditional probability table.

[0036] S3.4 After completing the construction of the Bayesian network and its conditional probability table, the Bayesian reasoning technology is used in combination with the redundant capacity threshold to optimize the allocation of dynamic redundant resources. The specific steps are as follows:

[0037] Observation data input and posterior probability calculation: The load demand L at the current moment t , energy output E t and environment variable V t As observation data, the Bayesian network is input and the posterior probability P(N i =s i|O), where O represents the set of observed data.

[0038] Energy synergy factor calculation: using energy synergy factor C ij , reflecting the synergistic effect between different energy types. The specific calculation formula is as follows: Where: ΔE i , ΔE j is the output change of energy types i and j at the current moment; is the attenuation parameter of the synergistic effect, which controls the attenuation rate of the synergistic effect with the output change difference; P(R i ) is the actual output power of energy type i; P max (R i ) is the maximum output power of energy type i.

[0039] S3.5 builds a multi-energy collaborative redundant resource allocation model, the goal of which is to maximize the overall power supply reliability and resource utilization efficiency of the system. The specific optimization objective function is defined as follows: Where: K is the number of redundant resource categories; S i , S j is the configuration ratio of redundant resource categories i and j; is the energy synergy factor; P(N i =ON|O), P(N j =ON|O) is the posterior probability that nodes i and j are in the running state.

[0040] In a preferred embodiment, step S3 further includes the following contents:

[0041] S3.6 Under the defined objective function and constraints, solve the redundant resource allocation ratio S i The specific optimization process includes:

[0042] Initialization: Generate an initial population or starting point.

[0043] Fitness evaluation: Calculate the fitness value of each configuration scheme, that is, the objective function The value of .

[0044] Selection and crossover: Select excellent individuals according to fitness, perform crossover and mutation operations, and generate a new generation of configuration solutions.

[0045] Iterative optimization: Repeat the fitness evaluation and selection crossover process until the preset convergence condition or number of iterations is reached.

[0046] Optimal solution extraction: Finally, the configuration scheme with the highest fitness is selected as the optimal redundant resource allocation scheme.

[0047] S3.7 generates a specific redundant resource allocation plan based on the optimization results, and organizes the optimal configuration ratio into a redundant resource configuration matrix, in which each element represents the configuration ratio of the redundant resource category at the current moment.

[0048] A comprehensive service system for intelligent design of electric power engineering includes: a probability modeling module, a scenario generation module, a sensitive identification module, a threshold calculation module, a network reasoning module and a resource allocation module.

[0049] Probabilistic modeling module: constructs a dynamic joint probability field model of load demand, distributed energy output and environmental variables, identifies the correlation and dynamic characteristics between variables, and generates a dynamic joint probability field model as the basis for the scenario generation module.

[0050] Scenario generation module: Uses Markov chain and Copula function to generate multi-scenario joint probability distribution from the probability model; the output multi-scenario joint probability distribution is used for analysis by the sensitive recognition module.

[0051] Sensitive identification module: Based on the joint probability field model analysis, the sensitive periods of load fluctuation and energy output are deduced; the identification results are input into the threshold calculation module to determine the redundant capacity threshold.

[0052] Threshold calculation module: Use dynamic programming algorithm to calculate the redundant capacity thresholds in different time periods; the output of the threshold calculation is sent to the network reasoning module for network reasoning and inferring the energy node status.

[0053] Network reasoning module: Construct a Bayesian network to infer the state of energy nodes and identify the synergy relationship between various energy sources; the inference results of network reasoning are used by the resource allocation module to generate dynamic allocation plans.

[0054] Resource allocation module: Generates dynamic redundant resource allocation plans based on redundant capacity thresholds and inference results to ensure reasonable allocation of resources in multi-energy scenarios.

[0055] The technical effects and advantages of the electric power engineering intelligent design integrated service system and the use method of the present invention are as follows:

[0056] The present invention integrates multi-source data through the system, first constructs a dynamic joint probability field model that can capture the time and spatial characteristics of load demand, distributed energy output and environmental variables, and fully reflects their complex nonlinear dependencies. Based on this model, a nonlinear optimization algorithm is used to derive the redundant capacity threshold in each time period, ensuring that the redundant configuration in different operating time periods can not only meet the high power supply reliability requirements, but also optimize the economy and resource utilization efficiency. Furthermore, the Bayesian network is used to dynamically allocate redundant resources, fully tap the synergy effect of multiple energy sources, and realize scientific allocation and real-time response of resources. This significantly enhances the stability and economy of the microgrid in response to load fluctuations and energy uncertainties. It effectively solves the problem of the difficulty in dynamically balancing load and energy supply in the traditional microgrid redundant design. Through multi-level and multi-dimensional optimization and coordination, the system reliability, economy and intelligence level are comprehensively improved, and the overall adaptability and operational efficiency of the microgrid in a complex operating environment are significantly enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The present invention is a flowchart of a method for using the comprehensive service of intelligent design of electric power engineering.

[0058] Figure 2 The present invention is a schematic diagram of the structure of an integrated service system for intelligent design of electric power engineering. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1: Figure 1 The present invention provides a method for using a comprehensive service of intelligent design of electric power engineering, comprising:

[0061] S1. Construct a dynamic joint probability field model of load demand, distributed energy output and environmental variables, capture the time series dynamics through Markov chain, generate multi-scenario joint probability distribution in combination with Copula function, and identify the correlation and dynamic characteristics between variables.

[0062] S2. Based on the joint probability field model, the sensitive periods of load fluctuation and energy output are deduced, and the redundant capacity thresholds of different time periods are calculated using the dynamic programming algorithm to form a time-sensitive redundant configuration strategy.

[0063] S3. According to the redundant capacity threshold, a Bayesian network is constructed to infer the status of energy nodes, identify the synergistic relationship between various energy sources, generate a dynamic redundant resource allocation plan, and ensure the rational allocation of resources in multi-energy scenarios.

[0064] In modern microgrid design, the dynamic changes of load demand, distributed energy output and environmental variables have a crucial impact on the reliability and economy of the system. Traditional static modeling methods are difficult to fully capture the complex temporal and spatial dependencies between these variables, resulting in limited efficiency and effectiveness of redundant configuration strategies. To solve this problem, a dynamic joint probability field model construction method is used to fully reflect the multi-variable interaction dynamics in microgrid operation through advanced data processing technology and complex probability models, providing a scientific and accurate probabilistic basis for redundant configuration strategies.

[0065] Step S1 includes the following contents:

[0066] S1.1 First, through multi-source data collection and preprocessing, the real-time and historical data of various key parameters in the operation of the microgrid are fully obtained. Load demand data includes the load changes of different power nodes on different time scales; distributed energy output data covers the real-time power generation of renewable energy such as solar energy and wind energy; environmental variable data involves key environmental factors such as temperature, humidity, wind speed, and irradiance that affect energy output. In order to ensure the quality and consistency of the data, data cleaning technology is used, including statistical outlier detection and processing, spatiotemporal interpolation methods to fill missing data, and multivariate standardization processing to eliminate the impact of different dimensions on the model.

[0067] S1.2 In the time series dynamic characteristic modeling stage, the load demand changes are divided into several states (such as high load, medium load, and low load), and the load demand transition probability matrix T is established using the Markov chain. L , where T L (p1, p2) represents the probability of transitioning from state p1 to state p2. The transfer matrix uses the maximum likelihood estimation method to estimate parameters from historical load data to ensure that the time series dynamic characteristics of load demand can be accurately reflected. The modeling of distributed energy output uses the autoregressive hidden Markov model (AR-HMM), defining the hidden state S E,t And the corresponding output distribution, the model parameters are trained by the maximum expectation algorithm (EM algorithm) to characterize the intermittent and random nature of energy output. The spatiotemporal modeling of environmental variables uses the Kriging interpolation method, by defining the spatial covariance matrix Σ V Describe the correlation between environmental variables in different geographical locations, and combine time series analysis methods to achieve dynamic adjustment of environmental variables.

[0068] S1.3 In the process of constructing the joint probability distribution, a multi-layer Copula function architecture is used to effectively capture the nonlinear dependencies between load demand, distributed energy output, and environmental variables. First, the marginal distribution function F is defined for each type of data. L (L t ), F E (E t ), F V (V t ), corresponding to load demand, energy output and environmental variables respectively. Next, a two-layer Copula structure is constructed. The first layer uses a hybrid form of Gaussian Copula and Clayton Copula to capture the tail dependency and symmetry between different variables. The second layer further refines the dynamic dependency in the time series through conditional Copula. Specifically, the joint Copula function is defined as:

[0069]

[0070] Among them, u L =F L (L t )、u E =F E (E t )、u V =F V (V t ) are the marginal cumulative distribution values ​​of load demand, energy output and environmental variables, respectively. and They represent the Gaussian Copula and Clayton Copula of the first layer respectively, and C2 is the mixed Copula of the second layer, which is used to integrate the output of the first layer and capture the higher-level dependency structure. By introducing the time lag parameter τ, the joint probability distribution can reflect the dependency relationship between the current moment and the past τ moments, and the formula is expressed as:

[0071]

[0072] in, They represent the historical information of load demand, energy output and environmental variables at time t-τ respectively.

[0073] S1.4 In order to generate the multi-scenario joint probability distribution, the Markov Chain Monte Carlo (MCMC) method is used to sample the constructed dynamic joint Copula model to generate a multi-scenario data set covering different time periods and operating states. The specific generation process is as follows:

[0074]

[0075] Where S is the number of generated scenes, usually S≥10 4 To ensure the adequacy and representativeness of the sample. represents the load demand, energy output and environmental variables in the sth scenario.

[0076] The multi-scenario dataset generated by S1.5 was subjected to statistical tests, including the Kolmogorov-Smirnov (KS) test and the Anderson-Darling (AD) test, to verify the consistency between the simulated joint probability distribution and the actual observed data:

[0077]

[0078] F sim (x) is the cumulative distribution function (CDF) of the simulated data.

[0079] F obs (x) is the cumulative distribution function of the observed data.

[0080] D KS is the Kolmogorov-Smirnov statistic, which indicates the maximum gap between the simulated distribution and the observed distribution.

[0081] A D Anderson-Darling statistic, which evaluates the goodness of fit of the distribution by the difference in weighted cumulative distribution functions.

[0082] n is the number of observed samples.

[0083] x i are the sorted observation data points.

[0084] Through these statistical tests, the accuracy and generalization ability of the model are verified, ensuring that the generated multi-scenario joint probability distribution can truly reflect the dynamic relationship of multiple variables in the operation of the microgrid.

[0085] Through these steps, the dynamic joint probability field model can accurately reflect the multivariable interaction dynamics in the operation of the microgrid, providing a reliable data basis and theoretical support for the subsequent deduction of redundant capacity and the formulation of resource allocation strategies.

[0086] The core technical feature of this step is the introduction of a dynamic joint probability model that combines multi-layer Copula functions with time lag parameters. The traditional Copula method usually only considers simple dependencies between variables, while this model captures dependencies at different levels by constructing a two-layer Copula structure, which significantly improves the model's ability to express complex nonlinear relationships. In addition, by combining Markov chains and autoregressive hidden Markov models (AR-HMM), accurate time series modeling of load demand and distributed energy output is achieved, further enhancing the model's adaptability to dynamic changes. The high Vickrickian interpolation method for modeling environmental variables ensures the continuity and accuracy of spatial characteristics in a dynamic environment.

[0087] Through the above-mentioned dynamic joint probability field model, the dynamic interaction between load demand, distributed energy output and environmental variables in the microgrid can be fully and accurately simulated. This model not only improves the efficiency of data integration and processing, but also significantly enhances the ability to predict the operating status of the microgrid through a complex nonlinear dependency capture mechanism. The generated multi-scenario joint probability distribution covers a variety of possible operating scenarios, providing a solid data foundation for subsequent redundant capacity deduction and resource allocation strategies, ensuring that the microgrid can maintain efficient and reliable power supply capabilities when facing changing environments and emergencies.

[0088] Through the implementation of this step, the dynamic balance problem in microgrid design can be fully supported and optimized at the data level, providing a scientific and accurate probabilistic basis for subsequent redundant configuration strategies, greatly improving the intelligence and adaptability of the microgrid system.

[0089] Based on the dynamic joint probability field model constructed in step S1, the dynamic change characteristics of load demand, distributed energy output and environmental variables in the microgrid system can be fully reflected. However, only an accurate probability model is not enough to directly guide the reasonable configuration of redundant capacity. It is necessary to further deduce the redundant capacity thresholds in different time periods and formulate corresponding redundant configuration strategies to cope with various complex situations and emergencies that the microgrid may face in actual operation.

[0090] Step S2 includes the following contents:

[0091] S2.1.1 First, divide a day into multiple time periods. According to the dynamic characteristics of load demand and energy output, 24 hours can usually be divided into different stages such as morning peak, midday trough, evening peak and night stable period. The specific division criteria can be based on the statistical analysis of historical load data and energy output data to identify key periods with large fluctuations in load demand and energy output.

[0092] S2.1.2 In each divided time period, the dynamic joint probability field model is used to analyze the probability distribution characteristics of load demand and energy output, and identify sensitive periods of high load fluctuations, low energy output, and extreme changes in environmental variables. By calculating the joint probability density of variables in each time period, the degree of their impact on the redundant capacity configuration is determined.

[0093] S2.2.1 In order to accurately derive the redundant capacity thresholds for each time period, a redundant capacity demand model is constructed. This model takes into account the volatility of load demand, the uncertainty of energy output, and the impact of environmental variables. By comprehensively analyzing the joint probability distribution of multiple variables, the optimal configuration range of redundant capacity is determined, that is, the redundant capacity demand R t Specifically, the redundant capacity requirement R t At time t it can be expressed as:

[0094]

[0095] in:

[0096] It represents the load demand change of the oth power consumption node at time t.

[0097] Represents the output change of the oth distributed energy at time t.

[0098] Represents the change of the oth environmental variable at time t.

[0099] N is the total number of power consumption nodes, distributed energy resources and environmental variables.

[0100] By integrating the changes in various variables, the demand for redundant capacity at time t is quantified.

[0101] S2.2.2 uses an adaptive dynamic optimization algorithm to derive the redundant capacity threshold Θ for each time period based on the redundant capacity demand model. t Using a multi-objective optimization framework, we consider power supply reliability, economy, and resource utilization efficiency to construct an optimization objective function.

[0102]

[0103] in:

[0104] Represents the power supply reliability function, ensuring that the redundant capacity can meet the power supply demand during high load fluctuations.

[0105] Represents the economic function that minimizes the initial investment and operating cost of the redundant configuration.

[0106] It represents the resource utilization efficiency function, optimizes the allocation of redundant capacity, and avoids resource waste.

[0107] λ1, λ2, and λ3 are weight parameters that reflect the relative importance of each objective.

[0108] The power supply reliability function aims to quantify the contribution of redundant capacity to the system power supply reliability. The function ensures the continuity and stability of power supply by calculating the probability of the system meeting the power supply demand during high load fluctuations under different redundant capacity thresholds.

[0109]

[0110] Where: P(L t,o ≤Θ t ) indicates that the load demand of the oth power node at time t does not exceed the redundancy threshold Θ t The probability of. N is the total number of power consumption nodes.

[0111] The overall power supply reliability of the system is reflected by averaging the probability of each node meeting the power supply demand. The goal is to maximize this probability, that is, to minimize the power supply reliability function.

[0112] The economic function is used to quantify the economic cost of redundant configuration. This function not only considers the initial investment cost, but also covers the operation and maintenance costs to ensure that the redundant configuration is economically feasible.

[0113]

[0114] Where: C init (S o ) represents the oth type of redundant resource S o The initial investment cost. op (S o ) represents the oth type of redundant resource S o The operation and maintenance cost of the system. β is the nonlinear growth coefficient, which reflects the exponential growth of the initial cost as the redundant capacity threshold increases. t +1) is the logarithmic function of the redundant capacity threshold, indicating that as the redundant capacity increases, the increasing speed of the operating cost gradually slows down.

[0115] This function accurately reflects the impact of redundant capacity on economic cost through nonlinear relationship, avoiding the limitation of simple linear superposition and ensuring the accuracy of economic evaluation.

[0116] The resource utilization efficiency function aims to optimize the allocation of redundant capacity, improve resource utilization efficiency, and avoid over-configuration and waste of resources.

[0117]

[0118] in:

[0119] Indicates the sum of all redundant capacity thresholds, which serves as a benchmark for resource utilization.

[0120] Indicates the ratio of the redundant capacity threshold to the demand, ensuring that the redundant capacity is not over-configured.

[0121] γ is a nonlinear adjustment parameter that controls the sensitivity and responsiveness of the resource utilization efficiency function.

[0122] This function nonlinearly measures resource utilization efficiency by exponentially operating the ratio of redundant capacity to demand, and encourages minimizing the configuration of redundant capacity and improving resource utilization while meeting demand.

[0123] S2.2.3 In the optimization process, multi-level constraints and nonlinear optimization strategies are used to ensure the rationality and practicality of the redundant capacity threshold. The specific calculation formula is as follows:

[0124]

[0125] in:

[0126] P(L t,o ≤Θ t ) represents the probability that the load demand of the oth power node does not exceed the redundancy threshold at time t.

[0127] C(θ t ) is the cost function of redundant capacity, which is usually a nonlinear function, such as an exponential function or a logarithmic function, reflecting the economic cost of redundant capacity.

[0128] Indicates resource utilization efficiency and encourages efficient allocation of redundant capacity.

[0129] By solving the above optimization problem, the redundant capacity threshold of each time period is obtained to ensure that the redundant capacity configuration in different time periods can not only meet the power supply reliability requirements, but also control the economic cost and improve the resource utilization efficiency.

[0130] S2.3.1 Based on the derived redundant capacity threshold, construct a time-sensitive redundant configuration strategy matrix S, whose elements S t,k It represents the configuration amount of the kth type of redundant resources (such as backup generators, energy storage equipment, etc.) in time period t. It is specifically expressed as:

[0131]

[0132] Where: T is the total number of divided time periods. K is the number of categories of redundant resources.

[0133] S2.3.2 uses a combination of hierarchical clustering and nonlinear optimization to generate a time-sensitive redundant configuration strategy. The specific steps are as follows:

[0134] Hierarchical clustering: Based on the redundant capacity threshold and load demand fluctuation characteristics in each time period, a hierarchical clustering algorithm (such as Ward's method) is used to divide the time period into several clusters. The time periods in each cluster have similar redundant capacity demand characteristics.

[0135] Nonlinear optimization: For each cluster, a nonlinear optimization algorithm (such as particle swarm optimization, genetic algorithm) is used to optimize the configuration ratio S of redundant resources. t,k , ensuring that redundant capacity requirements are met at all times within the cluster while maximizing resource utilization efficiency and economy.

[0136] Strategy mapping: Map the optimization results to the redundant configuration strategy matrix, provide specific values ​​for the redundant resource configuration in each time period, and ensure that the redundant configuration strategy is targeted and sensitive in different time periods.

[0137] By deducing the time-sensitive redundant configuration strategy, the microgrid design can realize the dynamic adjustment of redundant capacity in different time periods, ensuring that the system still maintains high power supply reliability during high load fluctuations and low energy output periods, while optimizing resource utilization efficiency during resource-sufficient periods and reducing the economic cost of redundant configuration. This not only improves the overall operating efficiency and economy of the microgrid, but also enhances its resilience to deal with uncertainties and emergencies.

[0138] In step S2, the time-period-sensitive redundant configuration strategy has been successfully derived, and the redundant capacity requirements in different time periods have been clarified. However, how to scientifically and reasonably allocate these redundant resources in a multi-energy synergy scenario is still a key issue to ensure the efficient operation of the microgrid. Traditional resource allocation methods often ignore the synergy between multiple energy sources, resulting in low resource utilization efficiency or insufficient power supply reliability. Therefore, it is necessary to introduce a more advanced Bayesian network model, combined with synergy factors, to solve the complexity of multi-energy synergy resource allocation.

[0139] In the redundancy design of microgrids, the coordinated operation of multiple energy sources is a key factor in improving system reliability and resource utilization efficiency. Based on the time-sensitive redundant configuration strategy derived in the previous step, this step aims to dynamically allocate redundant capacity thresholds by constructing and applying Bayesian networks to generate redundant resource allocation schemes that are suitable for multi-energy collaborative scenarios. This process covers redundant resource node modeling, Bayesian network structure construction, conditional probability tables, and the generation of dynamic optimization allocation strategies to ensure that redundant resources are scientifically and reasonably configured in a complex and changing operating environment.

[0140] Step S3 includes the following contents:

[0141] S3.1 First, identify and define various redundant resource nodes in the microgrid, including backup generators, energy storage equipment, renewable energy devices (such as solar panels and wind turbines), and critical load nodes. Each redundant resource node N i It has unique attributes and state variables to describe its operating characteristics and availability. i The state variables of the energy storage device node B can be defined as "run" (ON) and "stop" (OFF); j The state variables of can be defined as “charging”, “discharging” and “idle”; the renewable energy node R k The state variables include "high output", "medium output" and "low output". By systematically modeling these nodes, a complex network structure is formed to reflect the dependencies and synergies between the resource nodes.

[0142] S3.2 Based on the redundant resource node modeling, a Bayesian network (BN) is constructed to describe the conditional dependencies between nodes. A Bayesian network consists of nodes and directed edges, where each node represents a redundant resource or load node, and the edge represents the conditional dependencies between nodes. j The state of the backup generator node G may depend on i The operating status of the renewable energy node R k The output level of the microgrid can be determined by the network structure. Through this network structure, the complex dynamic interaction between resource nodes in the multi-energy system can be captured. The structural design of the Bayesian network needs to be based on the actual operation data and resource characteristics of the microgrid to ensure that the model can accurately reflect the actual operation status of the system.

[0143] S3.3 reflects the nonlinear dependency between redundant resource nodes through a conditional probability table. Traditional CPT usually adopts a simple conditional probability distribution, while the present invention introduces a multi-level, nonlinear conditional probability formula to enhance the expression ability of the model. Specifically, the following conditional probability formula is adopted:

[0144]

[0145] in:

[0146] N i is the target node.

[0147] s i For node N i status.

[0148] Pa(N i ) is node N iThe parent node collection of .

[0149] pa is the specific status of the parent node.

[0150] M is the number of characteristic functions.

[0151] φ j is the characteristic function f j The associated weight parameter.

[0152] f j (s i ,pa) is the characteristic function that defines the nonlinear relationship between the target node state and the parent node state.

[0153] Through the multi-level feature function f j , such as interactive characteristic functions and nonlinear cumulative characteristic functions, can capture the complex synergy between different redundant resources. For example, the following characteristic functions are defined:

[0154]

[0155] in:

[0156] P(R k ) is renewable energy R k Output power.

[0157] P max (R k ) is renewable energy R k Maximum output power.

[0158] I is the indicator function. When the standby generator G i The value is 1 when in running state, otherwise it is 0.

[0159] Θ j The energy storage device B corresponding to the redundant capacity threshold j configuration ratio.

[0160] V t is the value of the environmental variable at time t (such as temperature).

[0161] V opt for the best working value of the environment variable.

[0162] σ V The standard deviation of the variation in the environmental variable.

[0163] These characteristic functions enhance the Bayesian network's ability to model multi-energy synergy effects by introducing nonlinear transformations and interactions, ensuring that the redundant resource allocation scheme can fully consider the dynamic interactions between various energy types.

[0164] S3.4 After completing the construction of the Bayesian network and its conditional probability table, use Bayesian reasoning technology and combine the redundant capacity threshold to optimize the allocation of dynamic redundant resources. The specific steps are as follows:

[0165] Observation data input and posterior probability calculation: The load demand L at the current moment t , energy output E t and environment variable V t As observation data, the Bayesian network is input and the posterior probability P(N i =s i |O), where O represents the set of observed data.

[0166] Energy synergy factor calculation: using energy synergy factor C ij , reflecting the synergistic effect between different energy types. The specific calculation formula is as follows:

[0167]

[0168] in:

[0169] ΔE i , ΔE j is the output change of energy types i and j at the current moment.

[0170] is the attenuation parameter of the synergistic effect, which controls the attenuation speed of the synergistic effect with the output change difference.

[0171] P(R i ) is the actual output power of energy type i.

[0172] P max (R i ) is the maximum output power of energy type i.

[0173] This formula calculates the energy synergy factor by combining the output change difference and the current output level to dynamically reflect the synergy relationship between different energy types.

[0174] S3.5 builds a multi-energy collaborative redundant resource allocation model, with the goal of maximizing the overall power supply reliability and resource utilization efficiency of the system. The specific optimization objective function is defined as follows:

[0175]

[0176] in:

[0177] K is the number of redundant resource categories.

[0178] S i , S jis the configuration ratio of redundant resource categories i and j.

[0179] It is an energy synergistic factor.

[0180] P(N i =ON|O), P(N j =ON|O) is the posterior probability that nodes i and j are in the running state.

[0181] This objective function optimizes the overall power supply reliability and resource utilization efficiency of the system by maximizing the product of the redundant resource configuration ratio and the energy synergy factor, comprehensively considering the synergy effects and operating state probabilities among various resource types.

[0182] S3.6 uses a nonlinear optimization algorithm (such as a genetic algorithm, simulated annealing algorithm, or particle swarm optimization algorithm) to solve the redundant resource allocation ratio S under the defined objective function and constraints. i The specific optimization process includes:

[0183] Initialization: Generate an initial population or starting point.

[0184] Fitness evaluation: Calculate the fitness value of each configuration scheme, that is, the objective function The value of .

[0185] Selection and crossover: Select excellent individuals according to fitness, perform crossover and mutation operations, and generate a new generation of configuration solutions.

[0186] Iterative optimization: Repeat the fitness evaluation and selection crossover process until the preset convergence condition or number of iterations is reached.

[0187] Optimal solution extraction: Finally, the configuration scheme with the highest fitness is selected as the optimal redundant resource allocation scheme.

[0188] Through nonlinear optimization algorithms, the redundant resource configuration scheme is ensured to achieve the optimal balance between power supply reliability and resource utilization efficiency in multi-energy synergy scenarios.

[0189] S3.7 generates a specific redundant resource allocation plan based on the optimization results. i The redundant resource configuration matrix is ​​formed, in which each element represents the configuration ratio of the redundant resource category at the current moment. The allocation plan is transmitted to the actual resource management system through the system control module to guide the dynamic allocation and operation of redundant resources and ensure the efficient and stable operation of the microgrid in different energy synergy scenarios.

[0190] Through the implementation of this step, the microgrid can realize the scientific and dynamic allocation of redundant resources in the multi-energy synergy scenario, significantly improving the reliability of power supply and resource utilization efficiency. Specifically, during periods of high load fluctuations and low energy output, redundant resources can respond quickly to ensure power supply continuity; during periods of sufficient resources, the economic cost of redundant resources can be reduced by optimizing the configuration and avoiding resource waste. At the same time, the introduction of multi-energy synergy has enhanced the ability of microgrids to cope with complex operating environments and emergencies, and improved the overall resilience and intelligence level of the system.

[0191] Embodiment 2: Figure 2 The invention provides an integrated service system for intelligent design of electric power engineering, comprising: a probability modeling module, a scenario generation module, a sensitive identification module, a threshold calculation module, a network reasoning module and a resource allocation module.

[0192] Probabilistic modeling module: constructs a dynamic joint probability field model of load demand, distributed energy output and environmental variables, identifies the correlation and dynamic characteristics between variables, and generates a dynamic joint probability field model as the basis for the scenario generation module.

[0193] Scenario generation module: Uses Markov chain and Copula function to generate multi-scenario joint probability distribution from the probability model; the output multi-scenario joint probability distribution is used for analysis by the sensitive recognition module.

[0194] Sensitive identification module: Based on the joint probability field model analysis, the sensitive periods of load fluctuation and energy output are deduced; the identification results are input into the threshold calculation module to determine the redundant capacity threshold.

[0195] Threshold calculation module: Use dynamic programming algorithm to calculate the redundant capacity thresholds in different time periods; the output of the threshold calculation is sent to the network reasoning module for network reasoning and inferring the energy node status.

[0196] Network reasoning module: Construct a Bayesian network to infer the state of energy nodes and identify the synergy relationship between various energy sources; the inference results of network reasoning are used by the resource allocation module to generate dynamic allocation plans.

[0197] Resource allocation module: Generates dynamic redundant resource allocation plans based on redundant capacity thresholds and inference results to ensure reasonable allocation of resources in multi-energy scenarios.

[0198] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0199] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0200] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0201] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for using comprehensive services for intelligent design of electric power engineering, characterized in that: include: S1. Construct a dynamic joint probability field model of load demand, distributed energy output and environmental variables, capture the time series dynamics through Markov chain, generate multi-scenario joint probability distribution in combination with Copula function, and identify the correlation and dynamic characteristics between variables; S2. Based on the joint probability field model, the sensitive periods of load fluctuation and energy output are deduced, and the redundant capacity thresholds of different time periods are calculated using the dynamic programming algorithm to form a time-sensitive redundant configuration strategy; S3. According to the redundant capacity threshold, a Bayesian network is constructed to infer the status of energy nodes, identify the synergistic relationship between various energy sources, generate a dynamic redundant resource allocation plan, and ensure the rational allocation of resources in multi-energy scenarios.

2. A method for using a comprehensive service for intelligent design of electric power engineering according to claim 1, characterized in that: Step S1 includes the following contents: S1.1 First, through multi-source data collection and preprocessing, the real-time and historical data of various key parameters in the operation of the microgrid are obtained; S1.2 In the time series dynamic characteristics modeling stage, the change of load demand is divided into several states, and the transition probability matrix of load demand is established using Markov chain; The modeling of distributed energy output uses an autoregressive hidden Markov model to define hidden states and corresponding output distributions, and trains model parameters through the maximum expectation algorithm to characterize the intermittent and random nature of energy output. The spatiotemporal modeling of environmental variables adopts the high Vickrigg interpolation method, which defines the spatial covariance matrix to describe the correlation of environmental variables between different geographical locations, and combines it with the time series analysis method to achieve dynamic adjustment of environmental variables.

3. A method for using a comprehensive service for intelligent design of electric power engineering according to claim 2, characterized in that: Step S1 also includes the following contents: S1.3 In the process of constructing the joint probability distribution, a multi-layer Copula function architecture is used to capture the nonlinear dependencies between load demand, distributed energy output and environmental variables. First, the marginal distribution function F is defined for each type of data. L (L t )、F E (E t ), F V (V t ), corresponding to load demand, energy output and environmental variables respectively; then, a two-layer Copula structure is constructed. The first layer adopts a hybrid form of Gaussian Copula and Clayton Copula to capture the tail dependence and symmetry between different variables respectively; the second layer refines the dynamic dependency in the time series through conditional Copula; by introducing the time lag parameter τ, the joint probability distribution can reflect the dependency between the current moment and the past τ moments; S1.4 In order to generate the multi-scenario joint probability distribution, the Markov chain Monte Carlo method is used to sample the constructed dynamic joint Copula model to generate a multi-scenario data set covering different time periods and operating states; The multi-scenario dataset generated by S1.5 is subjected to statistical tests to verify the accuracy and generalization ability of the model and ensure that the generated multi-scenario joint probability distribution can truly reflect the dynamic relationship of multiple variables in the operation of the microgrid.

4. A method for using integrated services for intelligent design of electric power engineering according to claim 3, characterized in that: Step S2 includes the following contents: S2.1.1 First, divide the day into multiple periods; S2.1.2 In each divided time period, the dynamic joint probability field model is used to analyze the probability distribution characteristics of load demand and energy output, identify sensitive periods of high load fluctuations, low energy output and extreme changes in environmental variables, and determine the degree of influence on redundant capacity configuration by calculating the joint probability density of variables in each time period; S2.2.1 In order to derive the redundant capacity thresholds for each time period, a redundant capacity demand model is constructed. By comprehensively analyzing the joint probability distribution of multiple variables, the optimal configuration range of redundant capacity, i.e., the redundant capacity demand R, is determined. t Specifically, the redundant capacity requirement R t At time t it is expressed as: in: represents the load demand change of the oth power consumption node at time t; represents the output change of the oth distributed energy at time t; represents the change of the oth environmental variable at time t; N is the total number of power nodes, distributed energy resources and environmental variables; S2.2.2 Based on the redundant capacity demand model, derive the redundant capacity threshold Θ for each time period t ; Use a multi-objective optimization framework to consider power supply reliability, economy and resource utilization efficiency, and construct an optimization objective function in: represents the power supply reliability function; represents the economic function; represents the resource utilization efficiency function; λ1, λ2, λ3 are weight parameters, reflecting the relative importance of each goal; S2.2.3 In the optimization process, multi-level constraints and nonlinear optimization strategies are used to ensure the rationality and practicality of the redundant capacity threshold. The specific calculation formula is as follows: Where: P(L t,o ≤Θ t ) represents the probability that the load demand of the oth power consumption node does not exceed the redundancy threshold at time t; C(Θ t ) is the cost function of redundant capacity, which is usually a nonlinear function and reflects the economic cost of redundant capacity; Indicates resource utilization efficiency and encourages efficient allocation of redundant capacity.

5. A method for using integrated services for intelligent design of electric power engineering according to claim 4, characterized in that: Step S2 also includes the following contents: S2.3.1 Based on the derived redundant capacity threshold, construct a time-sensitive redundant configuration strategy matrix S, whose elements S t,k It represents the configuration amount of the kth type of redundant resources in time period t, which is specifically expressed as: Where: T is the total number of time periods divided, K is the number of categories of redundant resources; S2.3.2 uses a combination of hierarchical clustering and nonlinear optimization to generate a time-sensitive redundant configuration strategy. The specific steps are as follows: Hierarchical clustering: Based on the redundant capacity threshold and load demand fluctuation characteristics in each time period, a hierarchical clustering algorithm is used to divide the time period into several clusters. The time periods in each cluster have similar redundant capacity demand characteristics. Non-linear optimization: For each cluster, a non-linear optimization algorithm is used to optimize the configuration ratio of redundant resources to ensure that the redundant capacity requirements of the cluster are met at all times, while maximizing resource utilization efficiency and economy. Strategy mapping: Map the optimization results to the redundant configuration strategy matrix, provide specific values ​​for the redundant resource configuration in each time period, and ensure that the redundant configuration strategy is targeted and sensitive in different time periods.

6. A method for using integrated services for intelligent design of electric power engineering according to claim 5, characterized in that: Step S3 includes the following contents: S3.1 First, identify and define various redundant resource nodes in the microgrid, including backup generators, energy storage equipment, renewable energy devices, and critical load nodes; S3.2 Based on the redundant resource node modeling, a Bayesian network is constructed to describe the conditional dependency relationship between nodes; S3.3 reflects the nonlinear dependency relationship between each redundant resource node through a conditional probability table; S3.4 After completing the construction of the Bayesian network and its conditional probability table, the Bayesian reasoning technology is used in combination with the redundant capacity threshold to optimize the allocation of dynamic redundant resources. The specific steps are as follows: Observation data input and posterior probability calculation: The load demand L at the current moment t , energy output E t and environment variable V t As observation data, the Bayesian network is input and the posterior probability P(N i =s i |O), where O represents the set of observation data; Energy synergy factor calculation: using energy synergy factor C ij , reflecting the synergistic effect between different energy types. The specific calculation formula is as follows: Where: ΔE i , ΔE j is the output change of energy types i and j at the current moment; is the attenuation parameter of the synergistic effect, which controls the attenuation rate of the synergistic effect with the output change difference; P(R i ) is the actual output power of energy type i; P max (R i ) is the maximum output power of energy type i; S3.5 builds a multi-energy collaborative redundant resource allocation model, the goal of which is to maximize the overall power supply reliability and resource utilization efficiency of the system. The specific optimization objective function is defined as follows: Where: K is the number of redundant resource categories; S i , S j is the configuration ratio of redundant resource categories i and j; is the energy synergy factor; P(N i =ON|O), P(N j =ON|O) is the posterior probability that nodes i and j are in the running state.

7. A method for using integrated services for intelligent design of electric power engineering according to claim 6, characterized in that: Step S3 also includes the following: S3.6 Under the defined objective function and constraint conditions, find the optimal value of the redundant resource configuration ratio Si. The specific optimization process includes: Initialization: Generate the initial population or starting point; Fitness evaluation: Calculate the fitness value of each configuration scheme, that is, the objective function The value of Selection and crossover: Select excellent individuals according to fitness, perform crossover and mutation operations, and generate a new generation of configuration solutions; Iterative optimization: Repeat the fitness evaluation and selection crossover process until the preset convergence condition or number of iterations is reached; Optimal solution extraction: Finally, the configuration scheme with the highest fitness is selected as the optimal redundant resource allocation scheme; S3.7 generates a specific redundant resource allocation plan based on the optimization results, and organizes the optimal configuration ratio into a redundant resource configuration matrix, in which each element represents the configuration ratio of the redundant resource category at the current moment.

8. An integrated service system for intelligent design of electric power engineering, used to implement an integrated service method for intelligent design of electric power engineering as claimed in any one of claims 1 to 7, characterized in that: include: Probabilistic modeling module, scenario generation module, sensitive identification module, threshold calculation module, network reasoning module and resource allocation module; Probabilistic modeling module: constructs a dynamic joint probability field model of load demand, distributed energy output and environmental variables, identifies the correlation and dynamic characteristics between variables, and generates a dynamic joint probability field model as the basis of the scenario generation module; Scenario generation module: uses Markov chain and Copula function to generate multi-scenario joint probability distribution from the probability model; the output multi-scenario joint probability distribution is used for analysis by the sensitive recognition module; Sensitive identification module: Based on the joint probability field model analysis, it deduces the sensitive periods of load fluctuation and energy output; The identified result is input into the threshold calculation module to determine the redundant capacity threshold; Threshold calculation module: uses dynamic programming algorithm to calculate the redundant capacity thresholds in different time periods; the output of the threshold calculation is sent to the network reasoning module for network reasoning and inferring the energy node status; Network reasoning module: constructs a Bayesian network to reason about the state of energy nodes and identify the synergistic relationship between various energy sources; the inference results of network reasoning are used by the resource allocation module to generate dynamic allocation plans; Resource allocation module: Generates dynamic redundant resource allocation plans based on redundant capacity thresholds and inference results to ensure reasonable allocation of resources in multi-energy scenarios.

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