An energy management and distribution method and system for an integrated energy system

Abstract

The present invention discloses a method and system for energy management and distribution in an integrated energy system, belonging to the technical field of energy management and distribution in an integrated energy system. The method includes: obtaining the enterprise demand data of the target enterprise in real time, and generating a corresponding enterprise demand map according to the enterprise demand data; generating a corresponding enterprise historical demand map according to the enterprise historical demand data, and identifying each single energy of the target enterprise; determining the energy demand range of each single energy based on the enterprise historical demand map; setting a demand matching model according to the obtained energy demand range of each single energy; analyzing based on the demand matching model and the enterprise demand map to obtain each candidate method, evaluating and screening each candidate method to determine the target method, and performing integrated energy distribution according to the target method; obtaining the optimization evaluation parameters corresponding to each target method, generating each optimization curve based on each optimization evaluation parameter, determining the corresponding target optimization method based on each optimization curve, and performing optimization according to the target optimization method.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management and distribution in integrated energy systems, and specifically relates to a method and system for energy management and distribution in integrated energy systems. Background Art

[0002] With the increasingly serious global climate change problem, the carbon trading market, as an effective carbon emission trading mechanism, has become an important means to address climate change globally. Through the carbon trading market, enterprises can buy and sell carbon emission rights, thereby reducing their own carbon emission costs, and at the same time promoting the development and popularization of clean energy. Therefore, the development of the carbon trading market is of great significance for promoting global energy transformation and the development of a low-carbon economy.

[0003] With the diversification of the energy structure and the continuous increase in energy demand, integrated energy systems have become an important direction for future energy development. An integrated energy system refers to a new concept of an energy system formed by the coupling of multiple energy systems such as cooling, heating, electricity, and gas in the links of energy production, transmission, and use. By integrating various energy resources, the integrated energy system realizes the efficient utilization and optimal allocation of energy, improves energy utilization efficiency, and reduces environmental pollution. However, the integrated energy system also faces many challenges in the development process, such as energy supply-demand contradictions, energy structure adjustment, energy security and other issues. Therefore, how to achieve the optimal operation and management of the integrated energy system is an urgent problem to be solved. Based on this, the present invention provides a method and system for energy management and distribution in integrated energy systems. Summary of the Invention

[0004] In order to solve the problems existing in the above solutions, the present invention provides a method and system for energy management and distribution in integrated energy systems.

[0005] The object of the present invention can be achieved by the following technical solutions:

[0006] A method for energy management and distribution in an integrated energy system, the method comprising:

[0007] Step 1: Determine the target enterprise, obtain the enterprise demand data of the target enterprise in real time, and dynamically generate a corresponding enterprise demand map according to the obtained enterprise demand data;

[0008] Further, the method for generating the enterprise demand map includes:

[0009] Obtain the historical operation data of the target enterprise, identify and match the historical operation data based on a preset project matching table, and obtain each fixed item and dynamic item corresponding to the target enterprise;

[0010] Dynamically classify the enterprise demand data based on each of the fixed items and the dynamic items to obtain the fixed item data corresponding to each of the fixed items and the dynamic item data corresponding to each of the dynamic items; generate an initial demand graph for each item based on the obtained fixed item data, and adjust each initial demand graph for each item according to the dynamic item data to obtain a demand graph for each item.

[0011] Merge the demand graphs for each item based on time to form a comprehensive graph; mark the obtained comprehensive graph as the enterprise demand graph of the target enterprise.

[0012] Step 2: Obtain the enterprise historical demand data of the target enterprise, generate a corresponding enterprise historical demand graph based on the enterprise historical demand data, and identify each individual energy corresponding to the integrated energy system of the target enterprise; determine the energy demand interval for each individual energy based on the enterprise historical demand graph.

[0013] Furthermore, the method for determining the energy demand interval for each individual energy includes:

[0014] Generate a historical usage curve for each individual energy based on the enterprise historical demand graph; determine the historical energy usage interval for the corresponding individual energy according to the historical usage curve.

[0015] Preset the cycle period of the target enterprise, divide the historical usage curve according to the cycle period to form several cycle curve segments, and identify the lowest point and the highest point in each cycle curve segment.

[0016] Classify and integrate the obtained lowest points and highest points in chronological order to form a corresponding lowest point set and highest point set; input the lowest point set and the highest point set into the corresponding coordinate systems respectively to generate corresponding low value curves and high value curves.

[0017] Determine the corresponding interval lower value and interval upper value based on the obtained low value curve, high value curve and historical energy usage interval; form a corresponding energy demand interval according to the interval lower value and the interval upper value.

[0018] Furthermore, the method for determining the interval lower value and interval upper value includes:

[0019] Identify a monotonic curve containing the current time in the low value curve or high value curve; set several positioning points on the monotonic curve based on the current time, and calculate the slope between each adjacent positioning point; determine the corresponding representative slope according to the obtained slopes.

[0020] According to the formula XQ z =XQ z1 +F(x)×(|XQ z1 -XQz2 | + k d Calculate the corresponding lower and upper interval values by (× T);

[0021] Where: z = low or z = high; XQ z Represents the lower or upper interval value; XQ z1 Represents the lowest or highest value of the corresponding historical energy usage interval; F(x) represents the situation analysis model; XQ z2 Represents the energy usage value of the low - value curve or high - value curve at the current moment; T represents the time span from the XQ z1 corresponding time to the current time; k d Represents the representative slope.

[0022] Furthermore, the expression of the situation analysis model is ;

[0023] Where: x represents the input data, which is the corresponding low - value curve or high - value curve; FL1, FL2, and FL3 are the first classification, second classification, and third classification respectively; the output data is the situation value.

[0024] Furthermore, the method for establishing the situation analysis model includes:

[0025] Define the simulation classification, where the simulation classification includes the first classification, second classification, and third classification; obtain various simulation curves, label the corresponding simulation classification for each simulation curve to form the corresponding training data; establish the corresponding curve recognition model based on the training data; the input data of the curve recognition model is the simulation curve, and the output data is the simulation classification;

[0026] Establish the corresponding situation analysis model in combination with the curve recognition model.

[0027] Furthermore, the method for calculating the representative slope includes:

[0028] Mark the obtained slope as k a , a = 1, 2, ……, c, c is a positive integer; k1 represents the slope between the reference point and the previous fixed point;

[0029] According to the formula Calculate the corresponding representative slope;

[0030] Where: k d Represents the representative slope.

[0031] Step three: Set up a demand matching model according to the obtained energy demand intervals of each single energy source;

[0032] Furthermore, the method for establishing the demand matching model includes:

[0033] Determine the corresponding demand targets that can be achieved by each single energy according to each of the energy demand intervals, set corresponding simulated demand diagrams according to the obtained demand targets, conduct comprehensive pairing of each single energy according to the simulated demand diagrams, determine the corresponding simulated implementation methods, and associate the obtained simulated implementation methods with the simulated demand diagrams; establish a demand matching model after integration.

[0034] Step Four: Analyze based on the demand matching model and the enterprise demand diagram, obtain corresponding alternative methods, evaluate and screen each of the alternative methods, determine the target method, and conduct comprehensive energy distribution according to the target method.

[0035] Furthermore, the method for evaluating and screening each alternative method includes:

[0036] Conduct identification and analysis on each of the alternative methods, determine the application amount of each single energy, and obtain the unit price of the corresponding single energy in real time.

[0037] Estimate the carbon trading volume of each of the alternative methods, obtain the corresponding carbon trading curve; estimate the corresponding carbon trading price according to the carbon trading curve.

[0038] According to the formula QXW = b1×∑YL i ×DP i +b2×TL×TP calculate the implementation cost of the corresponding alternative plan.

[0039] In the formula: QXW is the implementation cost; i represents the corresponding single energy, i = 1, 2, ……, n, n is a positive integer; YL i is the application amount of the corresponding single energy; DP i represents the unit price of the corresponding single energy; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; TL represents the corresponding carbon trading volume; TP represents the corresponding carbon trading price.

[0040] Select the alternative plan with the lowest implementation cost as the target method.

[0041] Step Five: Identify each target method applied, obtain the corresponding optimization evaluation parameters of each target method, generate the corresponding optimization curves of each target method based on the optimization evaluation parameters, determine the corresponding target optimization method based on the optimization curves, and conduct optimization according to the obtained target optimization method.

[0042] Furthermore, the method for determining the target optimization method includes:

[0043] Mark the corresponding optimization benchmark points in each of the optimization curves, and determine each optimization method based on the optimization benchmark points.

[0044] Bring the above optimization method into each optimization curve, determine the corresponding single simulation point, and identify the simulation attributes of each single simulation point; determine the corresponding optimization value according to each simulation attribute;

[0045] Estimate the optimization cost of each optimization method;

[0046] Calculate the corresponding optimization rate according to the formula YLH = QWZ ÷ UYB; where: YLH is the optimization rate; QWZ is the expected value; UYB is the optimization cost;

[0047] Eliminate the optimization methods with an optimization rate lower than the threshold X1, and select the optimization method with the highest optimization rate among the remaining optimization methods as the target optimization method.

[0048] An integrated energy system energy management and distribution system, including a demand analysis module, a distribution module, and an optimization module;

[0049] The demand analysis module is used to obtain the enterprise demand data of the target enterprise in real time, dynamically generate a corresponding enterprise demand map according to the obtained enterprise demand data, obtain the enterprise historical demand data of the target enterprise, generate a corresponding enterprise historical demand map according to the enterprise historical demand data, identify each single energy corresponding to the integrated energy system of the target enterprise; determine the energy demand interval of each single energy based on the enterprise historical demand map.

[0050] The distribution module is used to perform integrated energy distribution, set a demand matching model according to the energy demand intervals of each single energy obtained, analyze based on the demand matching model and the enterprise demand map, obtain corresponding alternative methods, evaluate and screen each alternative method, determine the target method, and perform integrated energy distribution according to the target method.

[0051] The optimization module is used to optimize according to each target method, identify each target method applied, obtain the optimization evaluation parameters corresponding to each target method, generate an optimization curve corresponding to each target method based on each optimization evaluation parameter, determine the corresponding target optimization method based on each optimization curve, and perform optimization according to the obtained target optimization method.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] By obtaining the enterprise demand data of the target enterprise in real time, the real-time demand of the target enterprise is dynamically analyzed according to the enterprise demand data, and then the corresponding enterprise demand map is generated, which is convenient for subsequent comprehensive energy adjustment based on the obtained enterprise demand map; it provides data support for the intelligent management of energy; at the same time, according to the distinction between fixed items and dynamic items, it is convenient for the staff of the target enterprise to dynamically adjust the enterprise demand according to the actual situation, and directly update the enterprise demand map according to the corresponding dynamic item data, improving the update efficiency of the enterprise demand. By analyzing and optimizing based on various target methods, it is convenient to assist the enterprise in optimizing energy management and achieving a reduction in energy cost expenditure. Brief Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0055] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0056] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0057] As Figure 1 shown, a method for energy management and distribution of a comprehensive energy system includes:

[0058] Step 1: Determine the target enterprise, obtain the enterprise demand data of the target enterprise in real time, and dynamically generate the corresponding enterprise demand map according to the obtained enterprise demand data;

[0059] Enterprise demand data refers to the demand data related to enterprise production, management, etc. Specifically, it is the data that reaches the corresponding demand target through the use of relevant comprehensive energy, such as lighting, heating, operation of production equipment, etc.

[0060] The method for generating the enterprise demand map includes:

[0061] Obtain the historical operation data of the target enterprise, and set each fixed item and dynamic item according to the obtained historical operation data. Fixed items refer to fixed requirements, such as items corresponding to basic unchanged requirements like lighting. Dynamic items refer to items with dynamic change properties such as cooling, heating, production tasks, etc. The active adjustment by staff also belongs to the corresponding dynamic item scope. The specific division of fixed items and dynamic items does not affect the accuracy of the finally generated enterprise demand curve; it is only for better generating and managing the enterprise demand curve. Therefore, to facilitate setting each fixed item and dynamic item, a corresponding project matching table can be preset according to the possible enterprise demand data in each field, and the applicable scope corresponding to each fixed item and dynamic item in the project matching table is marked. Analyze the historical operation data of the target enterprise based on the project matching table to obtain the corresponding fixed items and dynamic items.

[0062] Classify the enterprise demand data according to the obtained fixed items and dynamic items to obtain each fixed item data and dynamic item data. Generate each single-item initial demand diagram according to the obtained fixed item data, such as lighting demand at different times, which is generated item by item according to the fixed item data. Adjust each single-item initial demand diagram according to the dynamic item data, that is, accumulate according to the corresponding dynamically supplemented demand; obtain the single-item demand diagram. Combine each single-item demand diagram according to the corresponding time to form a comprehensive diagram; that is, superimpose the demands at the corresponding time. Mark the obtained comprehensive diagram as the enterprise demand diagram.

[0063] And perform dynamic adjustment according to the obtained enterprise demand data subsequently.

[0064] By obtaining the enterprise demand data of the target enterprise in real time, realize the dynamic analysis of the real-time demand of the target enterprise according to the enterprise demand data, and then generate the corresponding enterprise demand diagram, which is convenient for subsequent comprehensive energy adjustment based on the obtained enterprise demand diagram; provide data support for realizing the intelligent management of energy; at the same time, according to the distinction between fixed items and dynamic items, it is convenient for the staff of the target enterprise to perform dynamic adjustment of enterprise demand according to the actual situation, and directly update the enterprise demand diagram based on the corresponding dynamic item data, improving the update efficiency of enterprise demand.

[0065] Step 2: Obtain the enterprise historical demand data, generate the corresponding enterprise historical demand diagram according to the enterprise historical demand data, identify each comprehensive energy corresponding to the comprehensive energy system of the target enterprise, and mark it as single energy, such as electricity, gas, heat, etc.; determine the energy demand interval of each single energy based on the enterprise historical demand diagram.

[0066] The method for determining the energy demand interval of each single energy includes:

[0067] Generate the historical usage curves of each individual energy according to the enterprise's historical demand diagram; determine the historical minimum and maximum values of the corresponding individual energy's historical energy usage based on the historical usage curves, and form the historical energy usage range of this individual energy;

[0068] Mark several lowest points and highest points in the historical usage curve. Both the lowest points and the highest points are evaluated periodically, such as once a week, once a month, etc. Specifically, it is set according to the corresponding field. However, the set benchmark is that the time span can be small and cannot be large. That is, for a monthly cycle, it can be various time periods such as one month, one week, etc., because the error brought by a small span is smaller;

[0069] Classify and integrate the obtained lowest points and highest points in chronological order to form the corresponding set of lowest points and set of highest points; input the obtained set of lowest points and set of highest points into the corresponding coordinate systems respectively to generate the corresponding low-value curve and high-value curve; determine the corresponding lower value and upper value of the interval according to the obtained low-value curve, high-value curve and historical energy usage range; form the corresponding energy demand interval according to the obtained lower value and upper value of the interval.

[0070] Among them, the methods for determining the lower value and upper value of the interval include:

[0071] Obtain various possible low-value curves and high-value curves that enterprises in this field may have according to historical data, and uniformly mark them as simulation curves; divide the simulation curves into three simulation classifications, namely the first classification, the second classification and the third classification, corresponding to the trend-rising curve, the swing trend curve and the trend-decreasing curve; the trend-rising curve refers to a curve that changes upward as a whole, such as increasing, fluctuating and increasing, etc.; the trend-decreasing curve refers to a curve that changes downward as a whole, such as decreasing, fluctuating and decreasing, etc.; the swing trend curve refers to a curve that fluctuates up and down and basically does not deviate from the middle line; mark each simulation curve according to each simulation classification to form the corresponding training data, and train according to the existing relevant technologies combined with the corresponding training data to form a corresponding curve recognition model that can identify that each simulation curve belongs to the corresponding simulation classification.

[0072] Exemplarily, a sample data set is created based on training data. The sample data set includes material samples and an artificial annotation sample set. The material samples are the original picture set, and the artificial annotation sample set is the picture set after format conversion and artificial annotation simulation classification processing of the original pictures. The ratio of material samples to the artificial annotation sample set in the sample data set is 2:1. The pictures in the artificial annotation sample set are binarized and saved in a single-channel form. The photo sample set and the artificial annotation sample set are split according to the ratio to form a second photo sample set and a second artificial annotation sample set. Under the Pytorch deep learning framework, a Linknet network model is built based on the Linknet network structure, the parameters of the Linknet network model are set, the second photo sample set and the second artificial annotation sample set are input into the Linknet network model, and the Linknet network model is trained based on the Pytorch deep learning framework. During the training process, multiple models are saved, and the model with the smallest error is selected using the validation set data as the curve recognition model.

[0073] A corresponding situation analysis model is established according to the obtained curve recognition model. The situation analysis model is used to analyze low-value curves and high-value curves to determine their corresponding situation values. The input data is a low-value curve or a high-value curve, that is, a simulated curve. The output data is the situation value. The expression of the situation analysis model is ; where x is the input data, that is, the simulated curve; FL1, FL2, and FL3 are the first classification, the second classification, and the third classification respectively; 1, 0, and -1 are the situation values; that is, in combination with the curve recognition model, determine the simulated classification to which the simulated curve belongs, and then determine the corresponding situation value.

[0074] The obtained low-value curve and high-value curve are respectively input into the situation analysis model to obtain the situation values corresponding to the low-value curve and the high-value curve respectively, which are respectively marked as the low situation value and the high situation value;

[0075] When the low situation value or the high situation value is 0, the lower value or the upper value of the corresponding interval does not need to be adjusted, that is, the lowest value and the highest value corresponding to the original historical energy usage interval are still used;

[0076] When the low situation value or the high situation value is not 0, identify the monotonic curve containing the current time in the low-value curve or the high-value curve, that is, based on the coordinate corresponding to the current time, determine a curve of a monotonic function; using the coordinate corresponding to the current time as the reference point, select several positioning points with equal time spans on the monotonic curve, such as one day; calculate the slope between adjacent positioning points, marked as k a , a = 1, 2,..., c, c is a positive integer; starting from the reference point, that is, k1 represents the slope between the reference point and the previous positioning point;

[0077] According to the formula Calculate the corresponding representative slope;

[0078] In the formula: k d represents the representative slope; k1 represents the slope corresponding to the corresponding reference point; for k a+1 the a + 1 in it is at most equal to c;

[0079] The representative slopes obtained by other existing methods can also be applied.

[0080] According to the formula XQ z = XQ z1 + F(x) × (|XQ z1 - XQ z2 | + k d × T) calculate the corresponding lower value and upper value of the interval;

[0081] In the formula: z represents the representative of low and high, that is, z = low or z = high; XQ z represents the lower value or upper value of the interval; XQ z1 represents the lowest value or highest value of the corresponding historical energy usage interval; F(x) represents the situation analysis model; XQ z2 represents the energy usage value of the low value curve or high value curve at the current moment; T represents the time span from the time corresponding to XQ z1 to the current time; k d represents the representative slope.

[0082] Step 3: Set up a demand matching model according to the obtained energy demand intervals of each individual energy;

[0083] The method for establishing the demand matching model includes:

[0084] Determine the corresponding demand targets that can be achieved by each individual energy according to each energy demand interval, set the corresponding simulated demand diagrams according to the obtained demand targets, and the simulated demand diagrams are set according to the demands that can be comprehensively achieved after integrating each demand target, forming a statistical chart for the entire range; perform comprehensive pairing of each individual energy according to different demands in the simulated demand diagrams, that is, according to a certain demand in the simulated demand diagrams, based on the demand targets that can be achieved by each individual energy, determine the output combination modes of each individual energy that have various ways to achieve this demand, form the corresponding simulated implementation modes for this demand, and associate the obtained simulated implementation modes with the corresponding demands in the simulated demand diagrams; and then establish a demand matching model after integration; that is, the demand matching model is used to output the implementation modes that can achieve the demand according to the input demand data.

[0085] Step 4: Analyze the enterprise demand diagram through the established demand matching model to obtain corresponding implementation methods, mark them as alternative methods, evaluate and screen each alternative method to determine the target method, and perform integrated energy distribution according to the obtained target method.

[0086] The methods for evaluating and screening each alternative method include:

[0087] Identify and analyze each alternative method to determine the application amount of each single energy, and obtain the unit price of this single energy in real time for the corresponding time.

[0088] Estimate the carbon trading volume of each alternative method. If the carbon emission rights are insufficient, they need to be purchased; if there are excess carbon emission rights, they need to be sold. The corresponding purchase volume or sales volume is the corresponding carbon trading volume. Connect to the corresponding carbon trading system to obtain the corresponding carbon trading curve, that is, the corresponding price curve. Estimate the corresponding carbon trading price according to the carbon trading curve, which respectively corresponds to the lowest price that can be obtained when purchasing and the highest price that can be obtained when selling, and estimate according to the corresponding allowed time.

[0089] According to the formula QXW = b1×∑YL i ×DP i +b2×TL×TP to calculate the implementation cost of the corresponding alternative plan.

[0090] In the formula: QXW is the implementation cost; i represents the corresponding single energy, i = 1, 2,..., n, and n is a positive integer; YL i is the application amount of the corresponding single energy; DP i represents the unit price of the corresponding single energy; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; TL represents the corresponding carbon trading volume, negative for selling and positive for purchasing; TP represents the corresponding carbon trading price.

[0091] Select the alternative plan with the lowest implementation cost as the target method.

[0092] Step 5: Identify each target mode of the application and obtain the optimization evaluation parameters corresponding to each target mode, which refer to the product of the implementation cost, the unit price and the application amount of a single energy source, and the product between the carbon trading volume and the carbon trading price; obtain the optimization curve of each target mode according to the optimization evaluation parameters; that is, determine the allocation based on the above target mode and the change curve of the implementation cost in combination with the current various application modes, technologies, etc. For example, the implementation based on more advanced and energy-saving equipment will lead to a decrease in the application amount of the corresponding single energy source; for example, if an enterprise generates a large amount of carbon emissions during the logistics transportation process, in order to reduce carbon emissions, the enterprise decides to take some emission reduction measures, such as using more efficient transportation tools, optimizing transportation routes, etc. Conduct a simulation evaluation according to the available reduction methods to determine the implementation cost under this method, and generate a corresponding implementation cost curve based on the obtained implementation costs, marked as the optimization curve. One target mode corresponds to one optimization curve; the optimization curve is dynamically updated as the technologies and equipment in the corresponding field are updated;

[0093] Mark the implementation cost corresponding to the corresponding target mode in the optimization curve, marked as the optimization reference point;

[0094] Determine the corresponding optimization segment according to the optimization reference point and the corresponding optimization curve, that is, the curve segment below the optimization reference point, and identify each optimization method corresponding to the corresponding curve segment; conduct a feasibility evaluation on each optimization method, and determine the corresponding target optimization method according to the obtained feasibility evaluation results, and conduct optimization based on the obtained target optimization method.

[0095] By analyzing and optimizing based on each target mode, it is convenient to assist the enterprise in optimizing energy management and achieving a reduction in energy cost expenditure.

[0096] The methods for conducting a feasibility evaluation on each optimization method include:

[0097] Substitute the obtained optimization method into each optimization curve to determine the corresponding single simulation point, that is, the point in the optimization curve; calculate the implementation cost difference corresponding to each single simulation point. The implementation cost difference lower than the corresponding optimization reference point is positive, and vice versa is negative; mark it as the simulation attribute of the single simulation point; determine the corresponding optimization value according to each simulation attribute, that is, the cumulative sum of each difference; determine the corresponding expected value according to the obtained optimization value; that is, calculate according to the estimated application period of the optimization method multiplied by the corresponding optimization value; determine according to the existing conventional application duration;

[0098] Estimate the optimization cost of the corresponding optimization method, the total expenditure cost such as equipment cost for applying this optimization method, and the depreciation recovery income of the corresponding replaced equipment needs to be deducted;

[0099] Calculate the corresponding optimization rate according to the formula YLH = QWZ ÷ UYB; where: YLH is the optimization rate; QWZ is the expected value; UYB is the optimization cost;

[0100] Eliminate the optimization methods with an optimization rate lower than the threshold X1, and select the optimization method with the highest optimization rate among the remaining optimization methods as the target optimization method.

[0101] An integrated energy system energy management and distribution system, including a demand analysis module, a distribution module, and an optimization module;

[0102] The demand analysis module is used to obtain the enterprise demand data of the target enterprise in real time, dynamically generate the corresponding enterprise demand diagram according to the obtained enterprise demand data, obtain the enterprise historical demand data of the target enterprise, generate the corresponding enterprise historical demand diagram according to the enterprise historical demand data, identify each single energy corresponding to the integrated energy system of the target enterprise; determine the energy demand interval of each single energy based on the enterprise historical demand diagram.

[0103] The distribution module is used to perform integrated energy distribution, set a demand matching model according to the obtained energy demand intervals of each single energy, analyze based on the demand matching model and the enterprise demand diagram, obtain the corresponding alternative methods, evaluate and screen each alternative method, determine the target method, and perform integrated energy distribution according to the target method.

[0104] The optimization module is used to optimize according to each target method, identify each target method applied, obtain the optimization evaluation parameters corresponding to each target method, generate the optimization curve corresponding to each target method based on each optimization evaluation parameter, determine the corresponding target optimization method based on each optimization curve, and perform optimization according to the obtained target optimization method.

[0105] Specifically, for the parts not disclosed in this embodiment, refer to the embodiment of a method for energy management and distribution of an integrated energy system.

[0106] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is a formula obtained by collecting a large amount of data for software simulation to be the closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained by simulating a large amount of data.

[0107] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for energy management and distribution in an integrated energy system, characterized in that, The method includes: Step 1: Determine the target enterprise, obtain the enterprise demand data of the target enterprise in real time, and dynamically generate a corresponding enterprise demand diagram according to the obtained enterprise demand data; Step 2: Obtain the enterprise historical demand data of the target enterprise, generate a corresponding enterprise historical demand diagram according to the enterprise historical demand data, and identify each single energy corresponding to the integrated energy system of the target enterprise; Based on the enterprise historical demand diagram, determine the energy demand intervals of each single energy; Step 3: Set up a demand matching model according to the obtained energy demand intervals of each single energy; The method for establishing the demand matching model includes: Determine the demand targets that can be achieved by each single energy according to each energy demand interval, set corresponding simulated demand diagrams according to the obtained demand targets, conduct comprehensive pairing of each single energy according to the simulated demand diagrams, determine the corresponding simulated implementation methods, and associate the obtained simulated implementation methods with the simulated demand diagrams; establish a demand matching model after integration; Step 4: Analyze based on the demand matching model and the enterprise demand diagram, obtain corresponding alternative methods, evaluate and screen each alternative method, determine the target method, and conduct integrated energy distribution according to the target method; Step 5: Identify each target method applied, obtain the optimization evaluation parameters corresponding to each target method, generate corresponding optimization curves for each target method based on each optimization evaluation parameter, determine the corresponding target optimization method based on each optimization curve, and conduct optimization according to the obtained target optimization method; The method for determining the energy demand intervals of each single energy includes: Generate the historical usage curves of each single energy according to the enterprise historical demand diagram; determine the historical energy usage intervals corresponding to each single energy according to the historical usage curves; Preset the cycle period of the target enterprise, divide the historical usage curve according to the cycle period to form several cycle curve segments, and identify the lowest point and the highest point in each cycle curve segment; Classify and integrate the obtained lowest points and highest points in chronological order to form a corresponding lowest point set and highest point set; input the lowest point set and the highest point set into corresponding coordinate systems respectively to generate corresponding low value curves and high value curves; Determine the corresponding interval lower value and interval upper value according to the obtained low value curve, high value curve and historical energy usage interval; form a corresponding energy demand interval according to the interval lower value and the interval upper value; The method for determining the interval lower value and the interval upper value includes: Identify the monotonic curve containing the current time in the low value curve or the high value curve; set several positioning points on the monotonic curve based on the current time, and calculate the slopes between adjacent positioning points; determine the corresponding representative slope according to the obtained slopes; According to the formula XQ z = XQ z1 + F(x) × (|XQ z1 - XQ z2 | + k d × T), calculate the lower value and upper value of the corresponding interval; where: z = low or z = high; XQ z represents the lower value or upper value of the interval; XQ z1 represents the lowest value or highest value corresponding to the historical energy usage interval; F(x) represents the situation analysis model; XQ z2 represents the energy usage value of the low-value curve or high-value curve at the current moment; T represents the time span from the XQ z1 corresponding time to the current time; k d represents the representative slope; The expression of the situation analysis model is: ； In the formula: x represents the input data, which is the corresponding low value curve or high value curve; FL1, FL2 and FL3 are the first classification, the second classification and the third classification respectively; the output data is the situation value; The first classification, the second classification, and the third classification respectively correspond to an upward trend curve, a swing trend curve, and a downward trend curve.

2. The energy management and distribution method of an integrated energy system according to claim 1, characterized in that, The method for generating an enterprise demand map includes: Obtaining the historical operation data of the target enterprise, identifying and matching the historical operation data based on a preset project matching table, and obtaining each fixed item and dynamic item corresponding to the target enterprise; Dynamically classifying the enterprise demand data based on each of the fixed items and the dynamic items to obtain fixed item data corresponding to each of the fixed items and dynamic item data corresponding to each of the dynamic items; generating an initial demand map for each item according to the obtained fixed item data, and adjusting each initial demand map for each item according to the dynamic item data to obtain a demand map for each item; Merging each of the single-item demand maps based on time to form a comprehensive map; marking the obtained comprehensive map as the enterprise demand map of the target enterprise.

3. A method for energy management and distribution of an integrated energy system according to claim 1, characterized in that The method for establishing a situation analysis model includes: Defining simulation classifications, where the simulation classifications include a first classification, a second classification, and a third classification; obtaining various simulation curves, marking corresponding simulation classifications for each of the simulation curves to form corresponding training data; establishing a corresponding curve recognition model based on the training data; the input data of the curve recognition model is a simulation curve, and the output data is a simulation classification; Establishing a corresponding situation analysis model in combination with the curve recognition model.

4. A method for energy management and distribution of an integrated energy system according to claim 1, characterized in that The calculation method for the representative slope includes: Mark the obtained slope as k a , where a = 1, 2, ……, c, and c is a positive integer; k1 represents the slope between the reference point and the previous fixed point According to the formula calculate the corresponding representative slope; Where: k d represents the slope; for k a+1 the maximum value of a + 1 is equal to c.

5. A method for energy management and allocation of an integrated energy system according to claim 1, characterized in that, The method for evaluating and screening each candidate method includes: Identifying and analyzing each of the candidate methods to determine the application amount of each single energy source, and obtaining the unit price of the corresponding single energy source in real time; Estimating the carbon trading volume of each of the candidate methods, obtaining a corresponding carbon trading curve; estimating the corresponding carbon trading price according to the carbon trading curve; According to the formula QXW = b1 × ∑YL i × DP i + b2 × TL × TP to calculate the implementation cost of the corresponding alternative Where: QXW is the implementation cost; i represents the corresponding single energy, i = 1, 2,..., n, and n is a positive integer; YL i is the application amount of the corresponding single energy; DP i represents the unit price of the corresponding single energy; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; TL represents the corresponding carbon trading volume; TP represents the corresponding carbon trading price; Selecting the candidate solution with the lowest implementation cost as the target method.

6. A method for energy management and distribution of an integrated energy system according to claim 1, characterized in that The method for determining the target optimization method includes: Marking corresponding optimization reference points in each of the optimization curves, and determining each optimization method based on the optimization reference points; Substituting the optimization method into each optimization curve to determine a corresponding single simulation point, identifying the simulation attributes of each of the single simulation points; determining the corresponding optimization value according to each of the simulation attributes; Estimating the optimization cost of each of the optimization methods; Calculating the corresponding optimization rate according to the formula YLH = QWZ ÷ UYB; where: YLH is the optimization rate; QWZ is the expected value; UYB is the optimization cost; Eliminating the optimization methods with an optimization rate lower than the threshold X1, and selecting the optimization method with the highest optimization rate among the remaining optimization methods as the target optimization method.

7. An energy management and distribution system for an integrated energy system, characterized in that, Implementing an integrated energy system energy management and distribution method according to any one of claims 1 to 6, including a demand analysis module, a distribution module, and an optimization module; The demand analysis module is used to obtain the enterprise demand data of the target enterprise in real time, dynamically generate a corresponding enterprise demand map according to the obtained enterprise demand data, obtain the enterprise historical demand data of the target enterprise, generate a corresponding enterprise historical demand map according to the enterprise historical demand data, and identify each single energy source corresponding to the integrated energy system of the target enterprise; Determine the energy demand intervals of each of the single energy sources based on the enterprise historical demand diagram; The allocation module is used for comprehensive energy allocation. A demand matching model is set according to the obtained energy demand intervals of each of the single energy sources, analyzed based on the demand matching model and the enterprise demand diagram to obtain corresponding alternative methods, evaluated and screened for each of the alternative methods to determine the target method, and comprehensive energy allocation is carried out according to the target method; The optimization module is used for optimization according to each target method, identify each target method applied, obtain the optimization evaluation parameters corresponding to each of the target methods, generate optimization curves corresponding to each of the target methods based on each of the optimization evaluation parameters, determine the corresponding target optimization methods based on each of the optimization curves, and perform optimization according to the obtained target optimization methods.

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