Integrated energy optimization management system and integrated energy management method

Abstract

The application discloses a comprehensive energy optimization management system and a comprehensive energy management method, and relates to the technical field of energy management. The comprehensive energy optimization management system and the comprehensive energy management method analyze and determine the conversion power value under the unit time by analyzing the parameter data in each power generation model, and evaluate and calculate the energy use in the period of time by fusing meteorological data, geographical data and load data. The optimization strategy in the current period of time is determined based on the power cost value. The analysis and evaluation of the multi-source model are combined with geographical environmental information, accurate meteorological data and energy transmission consumption data. Various factors of the energy system are fully considered to provide scientific and reasonable guidance for the operation of the system, and more accurate and low-cost energy power supply management operation is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, in particular to a comprehensive energy optimization management system and a comprehensive energy management method. BACKGROUND

[0002] With the continuous growth of energy demand and the increasing complexity of energy structure, coordinated and optimized management of comprehensive energy becomes particularly important. The rapid development of renewable energy such as wind energy and solar energy brings new challenges to the stable operation of energy systems due to its intermittent and uncertain output.

[0003] Referring to the patent entitled "A park comprehensive energy system optimization management system and its coordinated scheduling method" (patent publication number: CN116502921A, patent publication date: 2023-07-28), it includes an energy flow generation module, an energy flow conversion and storage module, and an energy flow comprehensive demand response module. The energy flow generation module is used to generate energy supply for park demand users. The heat network represents a centralized heating pipeline. The energy flow conversion and storage module establishes a system optimization model to convert and store the above-mentioned energy generated by the energy flow generation module into a transmittable energy within the park. The energy flow comprehensive demand response module: from the perspective of load-side energy consumption curve changes, uses energy consumption indicators of three energy forms (electricity, heat, and electricity) to measure the performance of comprehensive demand response considering electricity substitution, and generates comprehensive demand response information flow.

[0004] Based on the above-mentioned document, the existing energy management system has certain limitations in data processing, scheme generation, and execution adjustment. Since the specific power generation results of energy in the energy supply operation are easily affected by environmental factors, and the energy supply process is subject to changes in load location area, resulting in losses in the energy supply process, thereby causing changes in costs in the final power supply management operation. Therefore, the present application provides a comprehensive energy optimization management system and a comprehensive energy management method. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a comprehensive energy optimization management system and a comprehensive energy management method, which solves the problem of limitations of the existing energy management system in data processing, scheme generation, and execution adjustment. Since the specific power generation results of energy in the energy supply operation are easily affected by environmental factors, and the energy supply process is subject to changes in load location area, resulting in losses in the energy supply process, thereby causing changes in costs in the final power supply management operation.

[0006] To achieve the above-mentioned purposes, the present application realizes the following technical solutions: a comprehensive energy optimization management system, comprising:

[0007] The data acquisition and processing module is configured to acquire power grid data, load data, and renewable energy generation data, and to perform data preprocessing operations, data transmission, and data storage.

[0008] The multi-source model establishing module is configured to establish various generation models based on the renewable energy generation data and to associate the various generation models.

[0009] The energy analysis and evaluation module is configured to analyze and determine the conversion power value per unit time based on the parameter data in the various generation models using historical data, to evaluate and calculate the energy usage in a period of time by fusing meteorological data, geographical data, and load data, and to determine the optimization strategy in the current period of time based on the power cost value and transmit the optimization strategy.

[0010] The energy optimization and scheduling module is configured to perform energy optimization management operations based on the optimization strategy and to generate corresponding scheduling instructions.

[0011] The device control and management module is configured to manage and control various power supply devices and load devices based on the scheduling instructions.

[0012] Preferably, the data acquisition and processing module performs data preprocessing operations as follows:

[0013] The power grid data, load data, and renewable energy generation data are classified as the classification conditions of the main node.

[0014] Different parameter category data corresponding to the main node are classified as the classification conditions of the secondary node.

[0015] Different collection time stamps corresponding to the secondary node are classified as the classification conditions of the terminal node based on the collection period set under the secondary node.

[0016] The data and the index unit are associated, that is, the required collection value is obtained by inputting the content part corresponding to the main node, the secondary node, and the terminal node through the index unit.

[0017] Preferably, the various generation models of the multi-source model establishing module include:

[0018] The hydroelectric power generation model simulates and restores the behavior of water flow through a hydroelectric power conversion digital model and tests the power generation efficiency under different water flow conditions in combination with the operation of the water turbine and the generator set.

[0019] The wind power generation model simulates and restores the power generation operation of the rotating wind blade through a wind power conversion digital model and tests the power generation efficiency under different wind speed conditions in combination with the operation of the wind wheel.

[0020] The solar energy power generation model simulates the power generation operation of a photovoltaic array by a light energy conversion digital model, and tests the power generation efficiency of the photovoltaic array under different light intensities;

[0021] The biomass energy power generation model simulates the power generation operation of high-temperature combustion by a biomass energy conversion digital model, and tests the power generation efficiency of different biomass materials under different combustion conditions;

[0022] The geothermal energy power generation model simulates the power generation operation of heat exchange by the flow of underground hot water or steam by a geothermal energy conversion digital model, and tests the power generation efficiency under different geological conditions.

[0023] Preferably, the energy analysis and evaluation module analyzes and determines the conversion power value per unit time in each power generation model as follows:

[0024] The historical hydropower data is extracted to determine the power generation efficiency of the hydropower model, and a hydropower mapping table is formed to reflect the conversion power value A corresponding to the flow speed within a cycle time T a , and the consumption cost N a corresponding to the cycle time T;

[0025] The historical wind power data is extracted to determine the power generation efficiency of the wind power model, and a wind power mapping table is formed to reflect the conversion power value B corresponding to the wind speed within a cycle time T b , and the consumption cost N b corresponding to the cycle time T;

[0026] The historical light energy power generation data is extracted to determine the power generation efficiency of the solar energy power generation model, and a light energy power generation mapping table is formed to reflect the conversion power value D corresponding to the light intensity within a cycle time T d , and the consumption cost N d corresponding to the cycle time T;

[0027] The historical biomass combustion power generation data is extracted to determine the power generation efficiency of the biomass energy power generation model, and a biomass energy mapping table is formed to reflect the conversion power value E corresponding to the combustion of different biomass materials within a cycle time T e , and the consumption cost N e corresponding to the cycle time T;

[0028] The historical geothermal energy power generation data is extracted to determine the power generation efficiency of the geothermal energy power generation model, and a geothermal energy mapping table is formed to reflect the conversion power value F corresponding to the flow within a cycle time T f , and the consumption cost N f corresponding to the cycle time T.

[0029] Preferably, the change of wind speed direction affects the power generation efficiency:

[0030] The direction of the wind is determined synchronously in the process of collecting wind speed data, so that the power generation efficiency is the highest when the wind direction is perpendicular to the blades;

[0031] The center of the wind wheel is taken as the starting point, and a spatial coordinate system for determining the wind direction is established, the direction of the support frame of the wind wheel is taken as the opposite direction of the Y axis and located in the plane of the blades, the X axis is established perpendicular to the Y axis from the starting point, and the Z axis is established perpendicular to the X axis and the Y axis from the starting point;

[0032] The angle between the wind direction and the projection line of the wind direction on the plane of the Y axis and the Z axis is α, the angle between the projection line and the Z axis is β, and the measured wind speed is V c ;

[0033] According to the determination of the wind direction, the adjustment instruction of the wind turbine unit is produced, and the direction adjusting angle β of the impeller of the wind turbine unit is adjusted to the perpendicular position relative to the projection line, and at this time, the wind speed of the wind direction perpendicular to the blades is V b =V c ×Cosα, V b is the bth actual wind speed value.

[0034] Preferably, the change of the light receiving area of the photovoltaic array in the light energy power generation affects the power generation efficiency:

[0035] The light receiving area S m of the photovoltaic array under the same light intensity in the historical data is extracted, and the power generation amount corresponding to the light receiving area S m is K m ;

[0036] The power generation conversion coefficient k corresponding to the area is determined as k=(K1 / S1+K2 / S2+…+K m / S m ) / m, and then the real-time light energy power generation conversion power value is determined according to the conversion coefficient k.

[0037] Preferably, the operation of the energy analysis and evaluation module for fusing meteorological data, geographical data and load data to evaluate and calculate the energy use in a period of time is:

[0038] The daily load cycle interval is divided according to the load data in the historical data, so as to determine the low load interval and the high load interval;

[0039] The priority order of each power generation model is determined by determining the corresponding power generation and consumption cost of each power generation model, and then determining the influence degree of each power generation model by combining the changes of meteorological data, and finally determining the loss cost of each power generation model during scheduling by the required load position, so as to evaluate the priority of each power generation model.

[0040] Preferably, the determination of the priority order of each power generation model is as follows:

[0041] A period interval is selected, and the effective income of each model during the normal operation of the period time is calculated. The effective income calculation formula of hydraulic power generation in the corresponding period time T is: a =A a ×Q1-N a , P a represents the effective income under the current hydraulic power conversion value, Q1 is the value of power conversion, and the effective income P b of wind power generation, the effective income P d of light energy generation, the effective income P e of biomass energy generation and the effective income P f of geothermal energy generation are obtained in the same way, and it is determined whether all energy models exist in the current energy management area. After removing the energy models not in the range, the effective incomes of the remaining energy models are compared and sorted;

[0042] Then, it is determined whether the power conversion operation of each energy model is affected under the corresponding different interval nodes through the real-time changes of meteorological data, and the effective incomes of each energy model are sorted after the compensation of the combined meteorological data;

[0043] Finally, the loss of each energy model in power transmission is determined by the scheduling position of the load supply, and the current energy model loss cost and the subsequent energy model saving cost are determined by associating the energy model priority order with the load data. The saving cost is the difference between the effective income of the current energy model and the effective income of the subsequent adjacent energy model in the same time power supply minus the loss cost. If the loss cost is greater than the saving cost and less than the grid cost, the energy model with the smallest saving cost is selected as the first power supply energy model;

[0044] The sorting of the power supply energy model is realized in sequence and the power supply strategy is generated.

[0045] Preferably, the energy optimization and scheduling module realizes energy optimization management according to the optimization strategy, which is as follows:

[0046] After determining the priority of the corresponding power supply energy, the combined power supply operation of the load area is realized according to the priority, and when the previous power supply energy is insufficient to support the load, the subsequent power supply energy is combined for power supply.

[0047] Finally, when all power supply sources cannot meet the load, direct power supply operation is realized through the power grid.

[0048] The application also discloses a comprehensive energy management method, and specifically comprises the following steps.

[0049] S1, collecting and pre-processing renewable energy generation data through various sensors;

[0050] S2, establishing energy generation models and determining the power supply cost of the energy generation models according to the electric energy conversion, environmental impact, transmission loss and load data to determine the optimization strategy in the current period of time;

[0051] S3, finally, the scheduling and management of energy are completed according to the generated instructions of the optimization strategy.

[0052] The application provides a comprehensive energy optimization management system and a comprehensive energy management method.

[0053] 1. The comprehensive energy optimization management system and the comprehensive energy management method analyze and determine the conversion power value in unit time through historical data in the parameter data of various generation models, and evaluate and calculate the energy use in the period of time by fusing meteorological data, geographical data and load data, determine the optimization strategy in the current period of time based on the power cost value, use the analysis and evaluation of the multi-source model, combine the geographical environment information, accurate meteorological data and energy transmission consumption data, fully consider various factors of the energy system, provide scientific and reasonable guidance for the operation of the system, realize more accurate and low-cost energy power supply management operation.

[0054] 2. The comprehensive energy optimization management system and the comprehensive energy management method establish various generation models based on renewable energy generation data, and correlate the various generation models to compare and determine the power generation value and consumption cost in the energy data, so as to monitor the running state of the system in real time, monitor the wind and light power generation fluctuation in real time, so as to realize higher management and evaluation in the subsequent energy selection process, guarantee the accuracy of the data, and better adapt to the scheduling operation of the strategy.

[0055] 3、The integrated energy optimization management system and the integrated energy management method, through the determination of the corresponding power generation and consumption cost of each power generation model, the priority of each power generation model is determined, and the influence degree of each power generation model is determined by combining the change of meteorological data, and finally the loss cost of each power generation model is determined during scheduling, so as to evaluate the priority of each power generation model, and dynamically adjust the scheme according to the actual situation, ensure the stable operation of the system and the efficient use of energy, and reduce the cost loss. BRIEF DESCRIPTION OF DRAWINGS

[0056] Fig. 1 It is the principle block diagram of the energy optimization management system of the application;

[0057] Fig. 2 It is the logical judgment diagram of the energy management of the application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0059] Please refer to Figs. 1-2 The application provides three technical solutions:

[0060] Embodiment one, an integrated energy optimization management system, comprising:

[0061] A data acquisition and processing module is used for acquiring power grid data, load data and renewable energy power generation data, and realizing data preprocessing operation to complete data transmission and storage;

[0062] A multi-source model establishment module is used for establishing each power generation model based on renewable energy power generation data, and correlating each power generation model;

[0063] An energy analysis and evaluation module is used for analyzing and determining the conversion power value in unit time by using historical data to analyze the parameter data in each power generation model, and evaluating and calculating the energy use in a period of time by fusing meteorological data, geographical data and load data, and determining the optimization strategy in the current period of time based on the power cost value, and transmitting the optimization strategy;

[0064] An energy optimization and scheduling module is used for realizing energy optimization management operation according to the optimization strategy, and generating corresponding scheduling instructions;

[0065] The device control and management module completes management and regulation of each power supply device and load device based on the scheduling instruction.

[0066] The historical data are used to analyze and determine the conversion power value in unit time in each power generation model, and meteorological data, geographical data and load data are used to evaluate and calculate the energy use in a period of time, and the optimization strategy in the current period of time is determined based on the power cost value, and the analysis and evaluation of the multi-source model are used in combination with geographical environment information, accurate meteorological data and energy transmission consumption data, and various factors of the energy system are fully considered to provide scientific and reasonable guidance for the operation of the system, and more accurate and low-cost energy supply management operation is realized.

[0067] In the embodiment of the application, the data acquisition and processing module realizes the preprocessing operation of data as follows:

[0068] The power grid data, load data and renewable energy generation data are used as the classification conditions of the main node to realize classification;

[0069] Based on the parameter type set under the main node, the data corresponding to different parameter categories are classified as the classification conditions of the secondary node;

[0070] Based on the collection period set under the secondary node, the data corresponding to different collection time stamps are classified as the classification conditions of the end node;

[0071] The data are associated with the index unit, that is, the required collection value result is obtained by inputting the content part corresponding to the main node, secondary node and end node through the index unit.

[0072] In the embodiment of the application, the power generation models of the multi-source model establishment module include:

[0073] The hydraulic power generation model simulates and restores the water flow behavior through a hydraulic conversion digital model, and the power generation efficiency under different water flow conditions is tested in combination with the operation of the water turbine and generator set;

[0074] The wind power generation model simulates and restores the power generation operation of the wind blade rotation through a wind energy conversion digital model, and the power generation efficiency under different wind speed conditions is tested in combination with the operation of the wind wheel;

[0075] The solar power generation model simulates and restores the power generation operation of the photovoltaic array through a light energy conversion digital model, and the power generation efficiency under different light intensity conditions is tested in combination with the operation of the photovoltaic;

[0076] The biomass power generation model simulates and restores the power generation operation after high-temperature combustion through a biomass energy conversion digital model, and the power generation efficiency under different biomass material conditions is tested in combination with the combustion efficiency;

[0077] The geothermal energy power generation model simulates the power generation operation of heat exchange by reducing the flow of underground hot water or steam through the geothermal energy conversion digital model, tests the power generation efficiency under different geological conditions.

[0078] In the embodiment of the present application, the energy analysis and evaluation module analyzes and determines the conversion power value per unit time in the parameter data of each power generation model as follows:

[0079] The historical hydroelectric power generation data is extracted to determine the power generation efficiency of the hydroelectric power generation model, and a hydroelectric power generation mapping table is formed to reflect the conversion power value under the corresponding flow speed within the cycle time T as A a , and the consumption cost corresponding to the cycle time T is N a .

[0080] The historical wind power generation data is extracted to determine the power generation efficiency of the wind energy power generation model, and a wind power generation mapping table is formed to reflect the conversion power value under the corresponding wind speed within the cycle time T as B b , and the consumption cost corresponding to the cycle time T is N b .

[0081] The historical solar power generation data is extracted to determine the power generation efficiency of the solar energy power generation model, and a solar power generation mapping table is formed to reflect the conversion power value under the corresponding light intensity within the cycle time T as D d , and the consumption cost corresponding to the cycle time T is N d .

[0082] The historical biomass combustion power generation data is extracted to determine the power generation efficiency of the biomass energy power generation model, and a biomass energy mapping table is formed to reflect the conversion power value under the corresponding combustion of different biomass materials within the cycle time T as E e , and the consumption cost corresponding to the cycle time T is N e .

[0083] The historical geothermal energy power generation data is extracted to determine the power generation efficiency of the geothermal energy power generation model, and a geothermal energy mapping table is formed to reflect the conversion power value under the corresponding flow within the cycle time T as F f , and the consumption cost corresponding to the cycle time T is N f .

[0084] By establishing each power generation model based on renewable energy power generation data and correlating each power generation model, the power generation values and consumption costs in each energy data are compared and determined, so as to monitor the running state of the system in real time, monitor the fluctuation of wind and solar power generation in real time, so as to realize higher management and evaluation in the subsequent energy selection process, guarantee the accuracy of data, and facilitate better adaptation to strategy scheduling operation.

[0085] In this embodiment of the invention, the effect of wind speed direction change on power generation efficiency is as follows:

[0086] During the process of acquiring wind speed data, the direction data of the wind force is determined simultaneously, so that the power generation efficiency is highest when the blades are facing the wind.

[0087] Starting from the center of the wind turbine, a spatial coordinate system is established to determine the wind direction. The direction of the support frame used for the wind turbine is taken as the opposite direction of the Y-axis. An X-axis is established in the plane where the blades are located, which is perpendicular to the Y-axis from the starting point. A Z-axis is also established in the plane where the starting point is located, which is perpendicular to both the X-axis and the Y-axis.

[0088] The angle between the wind direction and its projection onto the planes along the Y and Z axes is determined by the sensor as α, while the angle between the projection and the Z-axis is β. The measured wind speed is V. c ;

[0089] Based on the determined wind direction, the manufacturer issues adjustment instructions for the wind turbine unit, adjusting the rotor direction angle β of the wind turbine unit to a position perpendicular to the projected lines. At this point, the wind speed directly opposite the blades is V. b =V c ×Cosα,V b This represents the b-th actual wind speed value.

[0090] In this embodiment of the invention, the effect of changes in the illuminated area of ​​a photovoltaic array on power generation efficiency is described:

[0091] Extract the illuminated area S of the photovoltaic array under the same light intensity from historical data. m And the corresponding illuminated area S m The power generation is K m ;

[0092] Therefore, the power generation conversion coefficient for the corresponding area is determined as k = (K1 / S1 + K2 / S2 + ... + K m / S m ) / m, and then determine the real-time power conversion value of solar power generation based on the conversion coefficient k.

[0093] In this embodiment of the invention, the operation of the energy analysis and assessment module in integrating meteorological data, geographical data, and load data to assess and calculate energy usage within a given period is as follows:

[0094] Based on historical load data, the daily load is divided into periodic intervals to determine low load and high load intervals.

[0095] The priority order of each power generation model is determined by determining the corresponding power generation and consumption cost of each power generation model, and then determining the influence degree of each power generation model by combining the change of meteorological data, and finally determining the loss cost of each power generation model during scheduling by the required load position, so as to evaluate the priority of each power generation model.

[0096] In the embodiment of the application, the determination of the priority order of each power generation model is as follows:

[0097] A period interval is selected, and the effective income of each model during the normal operation of the period time is calculated. The effective income calculation formula of hydraulic power generation in the corresponding period time T is: a =A a ×Q1-N a , P a represents the effective income under the current hydraulic conversion power value, Q1 is the value of power conversion, and the effective income P b of wind power generation, the effective income P d of light energy power generation, the effective income P e of biomass power generation and the effective income P f of geothermal power generation are obtained in the same way. Whether all energy models exist in the current energy management area is determined, and after removing the energy models not in the range, the effective incomes of the remaining energy models are compared and sorted.

[0098] Then, it is determined whether the power conversion operation of each energy model is affected under the corresponding different interval nodes through the real-time change of meteorological data, and the effective income of each energy model is sorted after the compensation of the combined meteorological data.

[0099] Finally, the loss of each energy model during power transmission is determined by the scheduling position of the load supply, and the current energy model loss cost and the subsequent energy model saving cost are determined according to the association of the energy model priority order and the load data. The saving cost is the difference between the effective income of the current energy model and the effective income of the subsequent adjacent energy model in the same time supply minus the loss cost. If the loss cost is greater than the saving cost and less than the grid cost, the energy model with the minimum saving cost is selected as the first power supply energy model.

[0100] The sorting of the power supply energy model is realized in sequence, and the power supply strategy is generated.

[0101] By determining the corresponding power generation and consumption costs of each power generation model, the priority of each power generation model is determined. Then, by combining changes in meteorological data, the degree of impact on each power generation model is determined. Finally, the loss cost of scheduling each power generation model is determined by the required load location. In this way, the priority of each power generation model is evaluated and the plan is dynamically adjusted according to the actual situation to ensure the stable operation of the system and the efficient use of energy, while reducing cost losses.

[0102] In this embodiment of the invention, the energy optimization and scheduling module performs energy optimization management operations based on optimization strategies as follows:

[0103] After determining the order of the corresponding power supply energy, the combined power supply operation of the load area is realized according to the order, and when the preceding power supply energy is insufficient to support the load, the subsequent power supply energy is used to combine the power supply.

[0104] Ultimately, when all available power sources are unable to meet the load, direct power supply is achieved through the power grid.

[0105] Example 2 differs from Example 1 in that: the present invention also discloses a comprehensive energy management method, specifically including the following steps:

[0106] S1. Collect and preprocess data on various renewable energy power generation through various sensors;

[0107] S2. Establish various energy generation models and determine the power supply cost of each energy generation model based on power conversion, environmental impact, transmission loss and load data, and determine the optimization strategy for the current period.

[0108] S3. Finally, after generating instructions based on the optimization strategy, the scheduling and management of energy are completed.

[0109] Example 3 differs from Examples 1 and 2 in that: An experimental operation was conducted on a designated small area using an existing energy management system that ranks power supply according to energy conversion efficiency and an energy management system that uses various models to correlate power supply and considers factors based on power supply changes. The load time period and specific conditions of the small area were kept consistent. The final comprehensive energy conversion benefits and any abnormal power supply situations were then recorded. The specific results are shown in Table 1.

[0110] Table 1. Comparison of Experiments

[0111]

[0112] The results show that the existing energy management system needs to participate in power supply through the power grid in the process of energy power supply, so the final comprehensive benefit is lower than that of the energy management system of the application, and the existing energy management system is obviously frequent in abnormal times of not timely in realizing power supply scheduling operation due to environmental fluctuations, so compared with the energy management system of the application, the energy management system of the application can better realize the application in actual operation.

[0113] Meanwhile, the contents not described in detail in the specification all belong to the prior art known by the person skilled in the art.

[0114] It should be noted that, in this paper, the relationship terms such as first and second 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 the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0115] Although the embodiments of the application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the application, and the scope of the application is defined by the appended claims and their equivalents.

Claims

1. A comprehensive energy optimization management system, characterized by: The utility model relates to a kind of energy optimization management system, including: Data acquisition and processing module, for collecting power grid data, load data and renewable energy generation data, and realizing the transmission and storage of data pre-processing operation; Multi-source model establishment module, based on renewable energy generation data, establishes each generation model, and each generation model is associated; Energy analysis and evaluation module, using historical data to analyze the parameter data in each generation model to determine the conversion power value in unit time, and fuse meteorological data, geographic data and load data to evaluate and calculate the energy use in periodic time, based on power cost value to determine the optimization strategy in current periodic time, and the optimization strategy is transmitted; Energy optimization and scheduling module, according to optimization strategy, realizes energy optimization management operation, and generates corresponding scheduling instruction; Equipment control and management module, based on scheduling instruction, completes the management and control of each power supply equipment and load equipment; The generation model of the multi-source model establishment module includes: Hydropower model, through the simulation of water flow behavior by hydraulic conversion digital model, the power generation efficiency under different water flow conditions is tested by combining the operation of water turbine and generator set; Wind power generation model, through the simulation of wind blade rotation by wind energy conversion digital model, the power generation efficiency under different wind speed conditions is tested by combining the operation of wind wheel; Solar energy generation model, through the simulation of photovoltaic array power generation operation by light energy conversion digital model, the power generation efficiency under different light intensity conditions is tested by combining photovoltaic operation; Biomass energy generation model, through the simulation of high-temperature combustion by biomass energy conversion digital model, the power generation efficiency under different biomass material conditions is tested by combining combustion efficiency; Geothermal energy generation model, through the simulation of underground hot water or steam flow for heat exchange by geothermal energy conversion digital model, the power generation efficiency under different geological conditions is tested; The energy analysis and evaluation module analyzes the parameter data in each generation model to determine the conversion power value in unit time, which includes: The extracted historical hydroelectric power generation data determines the power generation efficiency of the hydroelectric power generation model, and forms a hydroelectric power generation mapping table reflecting the converted power value A under the corresponding flow velocity within the cycle time T a , and the consumption cost N a corresponding to the cycle time T; The wind power generation data of the extraction history determines the power generation efficiency of the wind power generation model, and forms a wind power generation mapping table reflecting the converted power value B under the corresponding wind speed within the cycle time T b , and the consumption cost N b corresponding to the cycle time T; The extracted historical light energy power generation data determines the power generation efficiency of the solar power generation model, and forms a light energy power generation mapping table reflecting the converted power value D under the corresponding light intensity within a cycle time T d , and the consumption cost N d corresponding to the cycle time T; The extracted historical biomass combustion power generation data determines the power generation efficiency of the biomass power generation model, and forms a biomass energy mapping table. The conversion power value E under combustion corresponding to different biomass materials in a reaction period of time T is formed e , and the consumption cost corresponding to the period of time T is N e ; The extracted historical geothermal energy power generation data determine the power generation efficiency of the geothermal energy power generation model, and form a geothermal energy mapping table to reflect the converted power value F corresponding to the flow under the period time T f , and the consumption cost N f corresponding to the period time T; The influence of wind speed direction change on power generation efficiency: In the process of wind speed data acquisition, the wind direction data is determined simultaneously to achieve the highest power generation efficiency when the wind blade faces the wind; The center of wind wheel is taken as the starting point, and a spatial coordinate system for determining wind direction is established, the direction of support frame of wind wheel is taken as the opposite direction of Y axis, and X axis perpendicular to Y axis is established in the plane where the blade is located, and Z axis perpendicular to X axis and Y axis is established from the starting point; According to the sensor, the angle between the wind direction and the projection line of the wind direction on the plane where the Y axis and the Z axis are located is α, the angle between the projection line and the Z axis is β, and the measured wind speed is V c ; And according to the determination of the wind direction, the adjustment instruction about the wind turbine set is produced, the adjusting angle β of the impeller direction of the wind turbine set is adjusted to the vertical opposite position of the projection line, and the wind direction at this time is opposite to the wind speed V of the blade b =V c × Cos α, V b is the bth actual wind speed value.

2. The integrated energy optimization management system of claim 1, wherein: The data acquisition and processing module realizes the pre-processing operation of data, which includes: Taking power grid data, load data and renewable energy generation data as the classification condition of main node to realize classification; Based on the parameter type set under main node, different parameter category data are classified as secondary node classification condition; Based on the collection period set under secondary node, different collection time stamps are classified as the classification condition of end node. And the data associated with the index unit, that is, by index unit input main node, secondary node and end node corresponding content part of the required acquisition of numerical results.

3. The integrated energy optimization management system of claim 1, wherein: The influence of the change of the light receiving area of the photovoltaic array on the power generation efficiency in the light energy power generation: extracting the light receiving area S of the photovoltaic array under the same light intensity in the historical data m , and the power generation under the corresponding light receiving area S m is K m ; The power generation conversion coefficient under the corresponding area is determined as k=(K1 / S1+K2 / S2+…+K m / S m ) / m, and then the real-time light energy power generation conversion power value is determined according to the conversion coefficient k.

4. The integrated energy optimization management system of claim 1, wherein: The operation of the energy analysis and evaluation module for evaluating and calculating the energy usage in the period of time by fusing meteorological data, geographic data and load data is: According to the load data under the historical data, the daily load cycle interval is divided to determine the low load interval and the high load interval; The priority order of each power generation model is determined by determining the corresponding power generation and consumption cost, and the influence degree of each power generation model is determined by combining the change of meteorological data, and finally the loss cost of each power generation model is determined by the required load position to determine the scheduling time, so as to evaluate the priority of each power generation model.

5. The integrated energy optimization management system of claim 4, wherein: The operation of determining the priority order of each power generation model is: Select a period interval, calculate the effective income of each model in the period of normal operation, and the effective income calculation formula of hydraulic power generation in the corresponding period T is: a =A a ×Q1-N a , P a represents the effective income under the current hydraulic conversion power value, Q1 is the value of power conversion, and the effective income P b , the effective income P d of wind power generation, the effective income P e of biomass power generation and the effective income P f of geothermal power generation are obtained in the same way, and it is determined whether all energy models exist in the current energy management area. After removing the energy models not in the range, the effective income of the remaining energy models is compared and sorted. Then, through the real-time change of meteorological data, it is determined whether the power conversion operation of each energy model is affected under the corresponding different interval nodes, and the effective benefits of each energy model are sorted after the compensation of meteorological data; Finally, the loss of each energy model in power transmission is determined by the scheduling position of load supply, and the current energy model loss cost and the saving cost of subsequent energy model are determined by associating the energy model priority order with the load data, the saving cost is the difference between the effective benefit of the current energy model and the effective benefit of the subsequent adjacent energy model in the same time supply under the order, if the loss cost is greater than the saving cost and less than the grid cost, the energy model with the minimum saving cost is selected as the first power supply energy model; The sorting of power supply energy model is realized in turn and the power supply strategy is generated.

6. The integrated energy optimization management system of claim 5, wherein: The operation of energy optimization management in the energy optimization and scheduling module according to the optimization strategy is: After determining the sorting of the corresponding power supply energy, the combined power supply operation of the load area is realized according to the sorting, and when the previous power supply energy is not enough to support the load, the subsequent power supply energy is combined for power supply; Finally, when all power supply energies cannot meet the load, direct power supply operation is realized through the grid.

7. A method for integrated energy management, using the integrated energy optimization management system according to any one of claims 1-6, characterized in that: Specifically, the following steps are included: S1, collecting and pre-processing the renewable energy power generation data through various sensors; S2, establishing each energy generation model and determining the power supply cost of each energy generation model according to the power conversion, environmental impact, transmission loss and load data to determine the optimization strategy in the current period of time; S3, finally, the scheduling and management of energy are completed after generating instructions according to the optimization strategy.

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