Distributed comprehensive energy management system

Abstract

The invention discloses a distributed comprehensive energy management system. The management system is connected with a property management platform and an energy network; the management system obtains energy network operation data from an energy network; the property management platform is used for transmitting energy network operation data to the outside; the management system manages the energy network according to the energy network operation data; wherein the energy network operation data comprises an energy peak value, an energy distribution condition and an energy use condition. According to the invention, efficient utilization and flexible scheduling of distributed energy are realized, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid systems, and in particular to a distributed integrated energy management system. Background Art

[0002] Energy is the foundation for the survival and development of modern society. In order to cope with the energy crisis, countries are actively researching new energy technologies, especially renewable energy such as solar energy, wind energy, and bioenergy. Renewable energy is inexhaustible, clean and environmentally friendly, and is highly valued by countries around the world. Renewable energy has the characteristics of geographical dispersion, discontinuous production, randomness, volatility and uncontrollability. The centralized and unified management method of traditional power networks is difficult to adapt to the requirements of large-scale utilization of renewable energy. The effective way to utilize renewable energy is distributed "on-site collection, on-site storage, and on-site use".

[0003] However, the existing distributed power generation grid connection cannot fundamentally change the impact of distributed power generation on the power quality, fault detection, and fault isolation of the upper-level power grid under high penetration conditions. It is also difficult to maximize the utilization of renewable energy. Renewable energy generation information is difficult to share, which makes it inconvenient to control energy flow and thus causes energy waste. Therefore, this application proposes a distributed integrated energy management system. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed integrated energy management system, which solves the problem that the existing distributed power generation grid connection cannot fundamentally change the impact of distributed power generation on the power quality, fault detection, and fault isolation of the upper-level power grid under high penetration conditions, and it is difficult to maximize the utilization of renewable energy. Renewable energy generation information is difficult to share, which makes it inconvenient to control energy flow, thereby causing energy waste.

[0005] To achieve the above-mentioned purpose, the present invention provides a distributed integrated energy management system, which is respectively connected to a property management platform and an energy network; the management system obtains energy network operation data from the energy network and sends it to the property management platform; the property management platform transmits the energy network operation data to the outside world; the management system manages the energy network according to the energy network operation data; wherein the energy network operation data includes energy peak value, energy distribution, energy usage, energy flow data and energy network operation status; the management system comprises: an intelligent fusion terminal, which is connected to the property management platform; a distributed energy consumption device, which is respectively connected to the intelligent fusion terminal and the energy network, and is used to obtain energy network operation data and transmit it to the intelligent fusion terminal, and according to the energy network operation The distributed energy storage system is connected to the intelligent fusion terminal and the energy network respectively, for obtaining the operation data of the energy network and transmitting it to the intelligent fusion terminal, and storing the energy of the energy network when there is excess energy in the energy network according to the operation data of the energy network, and releasing the energy to the energy network when there is a shortage of energy in the energy network; the energy management system is connected to the intelligent fusion terminal and the energy network respectively, for obtaining the operation data of the energy network and transmitting it to the intelligent fusion terminal, and performing real-time monitoring, early warning, scheduling and optimization of the energy network; the intelligent fusion terminal receives the operation data of the energy network transmitted respectively from the distributed energy consumption device, the distributed energy storage system and the energy management system, and transmits it to the property management platform, and the property management platform transmits the operation data of the energy network to the outside world.

[0006] Preferably, the property management platform is connected to the information display system and the power supply monitoring system respectively; the information display system receives the energy network operation data from the property management platform, and displays the energy flow data in the energy network operation data in real time for management personnel to monitor and analyze; the power supply monitoring system receives the energy network operation data from the property management platform, and monitors and controls the energy network operation status of the energy network in real time, so as to improve the reliability and efficiency of the energy network power supply service.

[0007] Preferably, the management system also includes: a controller, one end of which is connected to the distributed energy consumption device, and the other end is connected to the intelligent fusion terminal; the controller obtains the device operation data of the distributed energy consumption device, and transmits it to the property management platform through the intelligent fusion terminal; the outside world can generate external instructions based on the device operation data of the distributed energy consumption device, and transmit them to the distributed energy consumption device through the property management platform, the intelligent fusion terminal and the controller in sequence; the distributed energy consumption device processes the energy network according to the external instructions to further optimize the management of the distributed energy consumption device.

[0008] Preferably, the management system also includes: an AC / DC power module, which is connected to the distributed energy consumption device and the energy grid respectively; it can convert the alternating current through the AC / DC power module into direct current and convert the direct current through the AC / DC power module into alternating current.

[0009] Preferably, the management system also includes: an energy storage DC / DC power module, which is connected to the distributed energy consumption device, receives and stores energy from the distributed energy consumption device, and releases energy to the distributed energy consumption device when the distributed energy consumption device needs energy.

[0010] Preferably, the energy management system includes: a data acquisition layer, which is connected to the energy grid, and the data acquisition layer acquires the energy grid operation data of the energy grid in real time; a data transmission layer, which is respectively connected to the intelligent fusion terminal and the data acquisition layer, and sends the energy grid operation data to the intelligent fusion terminal; preprocessing the collected energy grid operation data to obtain preprocessed energy grid operation data; a data processing layer, which is connected to the data transmission layer, receives the preprocessed energy grid operation data from the data transmission layer, and performs further cleaning, sorting, storage and analysis to obtain secondary processed energy grid operation data; an application layer, which is connected to the data processing layer, receives the secondary processed energy grid operation data from the data processing layer, and performs scheduling and optimization functions on the energy grid according to the secondary processed energy grid operation data.

[0011] Preferably, the energy management system adopts an algorithm to perform real-time monitoring, early warning, scheduling and optimization of the energy network; the algorithm includes: a data analysis algorithm or an optimization algorithm or an early warning algorithm; the data analysis algorithm adopts regression analysis and time series analysis; the optimization algorithm adopts a particle swarm algorithm; the early warning algorithm adopts a statistics-based early warning algorithm and a machine learning-based early warning algorithm.

[0012] Preferably, the regression analysis method is:

[0013] y=β0+β1x1+β2x2+…+β n x n +∈

[0014] Among them, y is the dependent variable, that is, the value of the energy numerical prediction; x1, x2, ..., x n is the independent variable used to predict the input value of the dependent variable; β0 is the intercept term, which indicates the expected value of the dependent variable when all independent variables are equal; β1, β2, …, β n is the regression coefficient, which indicates the influence of each variable on the dependent variable; ∈ is the error term, which indicates the random error that cannot be explained by the model; the time series analysis method is as follows:

[0015]

[0016] Where: y t is the observed value at time t; The autoregressive coefficient indicates the influence of past observations on the current observations; p is the order of the autoregressive part, indicating how many past observations to consider; θ1, θ2, …, θ q is the moving average coefficient, which indicates the influence of past error terms on the current observation; q is the order of the moving average part, which indicates how many past error terms should be considered; ε t is the error term at time t, usually assumed to be independent and identically distributed random variables.

[0017] Preferably, the particle swarm algorithm formula consists of a speed update formula and a position update formula; the speed update formula is:

[0018] vi(t+1)=wvi(t)+c1r1(pbesti-xi(t))+c2r2*(gbest-xi(t))

[0019] Where vi(t+1) is the velocity of the particle in the next iteration, w is the inertia factor, vi(t) is the velocity of the particle at time t; c1 is the learning factor of the individual particle, r1 is a random number between [0,1], pbest1 is the historical optimal position of the individual particle, xi(t) is the position at time t, c2 is the group learning factor of the particle, r2 is a random number between [0,1], gbest is the historical optimal position of the group; t is the current iteration number, t+1 is the next iteration;

[0020] The position update formula is as follows:

[0021] xi(t+1)=xi(t)+vi(t+1)

[0022] Among them, xi(t+) is the position of the particle in the next iteration, which is calculated by the velocity update formula.

[0023] Preferably, the statistics-based early warning algorithm is to set thresholds according to the statistical laws of historical data; it can calculate the mean and standard deviation of historical data, and then set upper and lower thresholds based on these values; the machine learning-based early warning algorithm is specifically: using machine learning algorithms, including but not limited to support vector machines and neural networks to train models and identify abnormal patterns.

[0024] In summary, compared with the prior art, the distributed integrated energy management system provided by the present invention has the following beneficial effects:

[0025] First, the distributed integrated energy management system proposed in the present invention, in which the distributed energy consumption device realizes the efficient utilization and flexible scheduling of distributed energy through energy conversion technology and intelligent control strategy, thereby improving energy utilization efficiency.

[0026] Second, the distributed integrated energy management system proposed in the present invention can realize the reasonable distribution and scheduling of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a structural schematic diagram of the present invention.

[0028] Figure 2 It is a schematic diagram of the distributed energy consumption device and the intelligent fusion terminal of the present invention.

[0029] Reference numerals:

[0030] 1-intelligent fusion terminal, 2-distributed energy consumption device, 3-distributed energy storage system, 4-energy management system, 5-energy network, 6-property management platform, 7-information display system, 8-power supply detection system, 9-controller, 21-AC / DC conversion AC / DC power module, 22-energy storage DC / DC power module. DETAILED DESCRIPTION

[0031] The following will be combined with the attached embodiment of the present invention Figure 1 ~Attached Figure 2 , the technical solutions, structural features, objectives achieved and effects in the embodiments of the present invention are described in detail.

[0032] It should be noted that the drawings are in a very simplified form and use non-precise proportions. They are only used to conveniently and clearly assist in explaining the embodiments of the present invention, and are not used to limit the conditions for the implementation of the present invention. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention.

[0033] It should be noted that, in the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only the elements explicitly listed, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0034] The present invention provides a distributed integrated energy management system, which is connected to a property management platform 6 and an energy network 5 respectively, and obtains energy network operation data from the energy network 5 and sends it to the property management platform 6; the property management platform 6 transmits the energy network operation data to the outside world; the management system manages the energy network 5 according to the energy network operation data; wherein the energy network operation data includes but is not limited to energy peak value, energy distribution, energy usage, energy flow data and energy network operation status, etc., and in the following steps without exception, the energy network operation data is used as a reference; the outside world refers to external personnel, etc.

[0035] The present invention solves the drawbacks of the existing energy network 5 in fault detection, fault isolation, difficulty in maximizing the utilization of renewable energy, as well as the problem that energy operation data is difficult to share and energy flow is inconvenient to control, thereby causing energy waste.

[0036] like Figure 1 As shown, the present invention discloses a distributed integrated energy management system, the management system comprising:

[0037] The intelligent fusion terminal 1 is connected to the property management platform 6;

[0038] The distributed energy consumption device 2 is connected to the intelligent fusion terminal 1 and the energy network 5 respectively, and is used to obtain the energy network operation data and transmit it to the intelligent fusion terminal 1, and distribute energy to the energy network 5;

[0039] Energy consumption means that energy is basically in a state of "generated and used immediately". For example, the electricity generated by power plants is digested and absorbed so that the power generated by power plants and the power consumed by the load end can achieve real-time dynamic balance in the entire power system.

[0040] The distributed energy storage system 3 is connected to the intelligent fusion terminal 1 and the energy grid 5, respectively, and is used to obtain the energy grid operation data and transmit it to the intelligent fusion terminal 1, and store the energy of the energy grid 5 when the energy of the energy grid 5 is in excess, and release energy to the energy grid 5 when the energy of the energy grid 5 is in short supply;

[0041] The energy management system 4 is connected to the intelligent fusion terminal 1 and the energy network 5 respectively, and is used to obtain the energy network operation data and transmit it to the intelligent fusion terminal 1, and perform real-time monitoring, early warning, scheduling and optimization of the energy network 5;

[0042] The intelligent fusion terminal 1 receives energy network operation data transmitted from the distributed energy consumption device 2, the distributed energy storage system 3 and the energy management system 4 respectively, and transmits it to the property management platform 6, and the property management platform 6 transmits the energy network operation data to the outside world.

[0043] Specifically, the intelligent fusion terminal 1 can also process the energy network operation data transmitted by the distributed energy consumption device 2, the distributed energy storage system 3 and the energy management system 4 respectively, and generate a supply and demand status evaluation result.

[0044] Specifically, the distributed energy consumption device 2 realizes efficient utilization and flexible scheduling of energy through energy conversion technology and intelligent control strategy. At the same time, the distributed energy consumption device 2 can also automatically adjust its output power during energy distribution according to the grid demand and energy supply situation to ensure the stable operation of the energy network 5.

[0045] Specifically, the distributed energy storage system 3 stores energy when there is excess energy and releases energy when there is a shortage of energy, thereby achieving effective management and optimal utilization of energy. In addition, the distributed energy storage system 3 can also participate in the peak-shaving and valley-filling and frequency regulation functions of the power grid through intelligent scheduling strategies.

[0046] Specifically, the energy management system 4 performs real-time monitoring, early warning, scheduling and optimization functions on the energy management system 4; in addition, it can generate energy usage reports and decision support information from the received energy network operation data, helping users to better understand energy usage and formulate corresponding energy-saving measures.

[0047] like Figure 2 As shown, in a preferred embodiment, the management system further comprises:

[0048] A controller, one end of which is connected to the distributed energy consumption device 2, and the other end of which is connected to the intelligent fusion terminal 1;

[0049] The controller obtains the device operation data of the distributed energy consumption device 2, and transmits it to the property management platform 6 through the intelligent fusion terminal 1. The outside world can generate external instructions based on the device operation data, and transmit it to the distributed energy consumption device 2 through the property management platform 6, the intelligent fusion terminal 1 and the controller. The distributed energy consumption device 2 processes the energy network 5 according to the external instructions. In addition, the controller here is an additional setting, which is equivalent to a new line to further optimize the management of the distributed energy consumption device 2. The line can be called when needed. The specific method of calling is an existing technology and will not be repeated here.

[0050] In a preferred embodiment, the controller and the distributed energy consumption device 2 are connected through RS-485 (RS485 is a serial communication interface standard released by the American Electronics Industry Association in 1983) bus technology to achieve data uploading and external command reception.

[0051] In a preferred embodiment, the intelligent fusion terminal 1 is connected to the property management platform 6 via a 4G wireless private network or Ethernet to achieve remote transmission of energy network operation data, device operation data of the distributed energy consumption device 2, and external instructions.

[0052] In a preferred embodiment, the management system further comprises:

[0053] The AC / DC (AC=Alternating Current, DC=Direct Current, AC / DC is the conversion of AC into DC) power module is connected to the distributed energy consumption device 2 and the energy network 5 respectively; the AC / DC power module 21 can convert the AC power through the AC / DC power module 21 into DC power and convert the DC power through the AC / DC power module 21 into AC power to adapt to different energy consumption scenarios; the AC / DC power module 21 here is an additional setting, the same as the aforementioned, to further realize the operation of the distributed energy consumption device 2, and the line can be called when needed. The specific method of calling is the existing technology and will not be repeated here.

[0054] In a preferred embodiment, the management system further comprises:

[0055] The energy storage DC / DC power module 22 is connected to the distributed energy consumption device 2, receives and stores energy from the distributed energy consumption device 2 to further increase the capacity of the available energy of the distributed energy consumption device 2. When the distributed energy consumption device 2 needs to release energy, it releases the energy to the distributed energy consumption device 2, and the distributed energy consumption device 2 then utilizes the electrical energy. This design can better realize the reasonable allocation and scheduling of energy.

[0056] In a preferred embodiment, the property management platform 6 is connected to the information display system 7 and the power supply monitoring system respectively;

[0057] The information display system 7 receives the energy network operation data from the property management platform 6 and displays the energy flow data in the energy network operation data in real time for monitoring and analysis by management personnel;

[0058] The power supply monitoring system receives the energy network operation data from the property management platform 6, and monitors and controls the energy network operation status of the energy network 5 in real time, thereby improving the reliability and efficiency of the power supply service of the energy network 5.

[0059] In a specific embodiment, the energy management system 4 includes:

[0060] A data collection layer is connected to the energy network 5, and the data collection layer collects energy network operation data of the energy network 5 in real time through sensors and intelligent instrument equipment;

[0061] The data transmission layer is connected to the intelligent fusion terminal 1 and the data collection layer respectively, and sends the energy network operation data to the intelligent fusion terminal 1; the collected energy network operation data is preprocessed to obtain preprocessed energy network operation data;

[0062] The data processing layer is connected to the data transmission layer, receives the pre-processed energy network operation data from the data transmission layer, and further cleans, organizes, stores and analyzes it to obtain the secondary processed energy network operation data;

[0063] The application layer is connected to the data processing layer, receives the secondary processed energy network operation data from the data processing layer, and performs scheduling and optimization functions on the energy network 5 according to the secondary processed energy network operation data, and generates energy usage reports and decision support information.

[0064] In a preferred embodiment, the energy management system 4 uses an algorithm to perform real-time monitoring, early warning, scheduling and optimization of the energy network 5; the algorithm includes:

[0065] Data analysis algorithm, optimization algorithm or early warning algorithm;

[0066] Specifically, the data analysis algorithm adopts regression analysis and time series analysis; the optimization algorithm adopts particle swarm algorithm; and the early warning algorithm adopts statistics-based early warning algorithm and machine learning-based early warning algorithm.

[0067] 1. Data Analysis Algorithm

[0068] (1) Specifically, the regression analysis method is as follows:

[0069] y=β0+β1x1+β2x2+…+β n x n +∈

[0070] Among them, y is the dependent variable, that is, the value of the energy numerical prediction; x1, x2, ..., x n are independent variables, which are the input values ​​used to predict the dependent variable; β0 is the intercept term, which represents the expected value of the dependent variable when all independent variables are 0; β1, β2, …, β n are regression coefficients, which represent the influence of each variable on the dependent variable; ∈ is the error term, which represents the random error that cannot be explained by the model.

[0071] (2) Specifically, the time series analysis method is as follows:

[0072]

[0073] Where: yt is the observed value at time t; The autoregressive coefficient indicates the influence of past observations on the current observations; p is the order of the autoregressive part, indicating how many past observations to consider; θ1, θ2, …, θ q is the moving average coefficient, which indicates the influence of past error terms on the current observation; q is the order of the moving average part, which indicates how many past error terms should be considered; ε t is the error term at time t, usually assumed to be independent and identically distributed random variables.

[0074] (II) Particle Swarm Optimization Formula

[0075] Specifically, the particle swarm algorithm formula consists of a velocity update formula and a position update formula;

[0076] Speed ​​update formula:

[0077] vi(t+1)=wvi(t)+c1r1(pbesti-xi(t))+c2r2*(gbest-xi(t))

[0078] Where vi(t+1) is the velocity of the particle in the next iteration, w is the inertia factor, vi(t) is the velocity of the particle at time t; c1 is the learning factor of the individual particle, r1 is a random number between [0,1], pbest1 is the historical optimal position of the individual particle, xi(t) is the position at time t, c2 is the group learning factor of the particle, r2 is a random number between [0,1], gbest is the historical optimal position of the group; t is the current iteration number, t+1 is the next iteration;

[0079] The position update formula is as follows:

[0080] xi(t+1)=xi(t)+vi(t+1)

[0081] Among them, xi(t+1) is the position of the particle in the next iteration, which is calculated by the velocity update formula.

[0082] 3. Early warning algorithm

[0083] (1) Specifically, the statistical-based early warning algorithm sets the threshold value according to the statistical law of historical data. It can calculate the average value and standard deviation of historical data, and then set the upper and lower threshold values ​​according to these values;

[0084] (2) Specifically, the early warning algorithm based on machine learning is: using machine learning algorithms, including but not limited to support vector machines and neural networks to train models and identify abnormal patterns.

[0085] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A distributed integrated energy management system, characterized in that: The management system is connected to the property management platform (6) and the energy network (5) respectively; The management system obtains energy network operation data from the energy network (5) and sends it to the property management platform (6); The property management platform (6) transmits energy network operation data to the outside world; The management system manages the energy network (5) according to the energy network operation data; The energy network operation data includes energy peak, energy distribution, energy usage, energy flow data and energy network operation status; The management system comprises: The intelligent fusion terminal (1) is connected to the property management platform (6); the distributed energy consumption device (2) is connected to the intelligent fusion terminal (1) and the energy network (5) respectively, and is used to obtain the energy network operation data and transmit it to the intelligent fusion terminal (1), and distribute energy to the energy network (5) according to the energy network operation data; The distributed energy storage system (3) is connected to the intelligent fusion terminal (1) and the energy network (5) respectively, and is used to obtain the energy network operation data and transmit it to the intelligent fusion terminal (1), and store the energy of the energy network (5) when the energy network (5) has excess energy according to the energy network operation data, and release the energy to the energy network (5) when the energy network (5) has a shortage of energy; The energy management system (4) is connected to the intelligent fusion terminal (1) and the energy network (5) respectively, and is used to obtain the energy network operation data and transmit it to the intelligent fusion terminal (1), and to perform real-time monitoring, early warning, scheduling and optimization of the energy network (5); The intelligent fusion terminal (1) receives energy network operation data transmitted respectively from the distributed energy consumption device (2), the distributed energy storage system (3) and the energy management system (4), and transmits the data to the property management platform (6), and the property management platform (6) transmits the energy network operation data to the outside world.

2. A distributed integrated energy management system according to claim 1, characterized in that: The property management platform (6) is connected to the information display system (7) and the power supply monitoring system respectively; The information display system (7) receives the energy network operation data from the property management platform (6) and displays the energy flow data in the energy network operation data in real time for monitoring and analysis by management personnel; The power supply monitoring system receives energy network operation data from the property management platform (6), and monitors and controls the energy network operation status of the energy network (5) in real time, thereby improving the reliability and efficiency of the power supply service of the energy network (5).

3. A distributed integrated energy management system according to claim 1, characterized in that: The management system also includes: A controller, one end of which is connected to the distributed energy consumption device (2) and the other end of which is connected to the intelligent fusion terminal (1); The controller obtains device operation data of the distributed energy consumption device (2), and transmits the data to the property management platform (6) through the intelligent fusion terminal (1); The outside world can generate external instructions based on the device operation data of the distributed energy consumption device (2), and transmit them to the distributed energy consumption device (2) through the property management platform (6), the intelligent fusion terminal (1) and the controller in sequence. The distributed energy consumption device (2) processes the energy network (5) according to the external instructions, thereby further optimizing the management of the distributed energy consumption device (2).

4. A distributed integrated energy management system according to claim 1, characterized in that: The management system also includes: The AC / DC power module (21) is connected to the distributed energy consumption device (2) and the energy network (5) respectively; the AC power passing through the AC / DC power module (21) can be converted into DC power, and the DC power passing through the AC / DC power module (21) can be converted into AC power.

5. A distributed integrated energy management system according to claim 1, characterized in that: The management system also includes: The energy storage DC / DC power module (22) is connected to the distributed energy consumption device (2), receives energy from the distributed energy consumption device (2) and stores it, and releases energy to the distributed energy consumption device (2) when the distributed energy consumption device (2) needs energy.

6. A distributed integrated energy management system according to claim 1, characterized in that: The energy management system (4) comprises: A data collection layer connected to the energy network (5), wherein the data collection layer collects energy network operation data of the energy network (5) in real time; The data transmission layer is connected to the intelligent fusion terminal (1) and the data collection layer respectively, and sends the energy network operation data to the intelligent fusion terminal (1); the collected energy network operation data is pre-processed to obtain pre-processed energy network operation data; The data processing layer is connected to the data transmission layer, receives the pre-processed energy network operation data from the data transmission layer, and further cleans, organizes, stores and analyzes it to obtain the secondary processed energy network operation data; The application layer is connected to the data processing layer, receives the secondary processed energy network operation data from the data processing layer, and performs scheduling and optimization functions on the energy network (5) according to the secondary processed energy network operation data.

7. A distributed integrated energy management system according to claim 6, characterized in that: The energy management system (4) uses an algorithm to perform real-time monitoring, early warning, scheduling and optimization of the energy network (5); the algorithm includes: a data analysis algorithm or an optimization algorithm or an early warning algorithm; the data analysis algorithm uses a regression analysis method and a time series analysis method; the optimization algorithm uses a particle swarm algorithm; the early warning algorithm uses a statistics-based early warning algorithm and a machine learning-based early warning algorithm.

8. A distributed integrated energy management system according to claim 7, characterized in that: The regression analysis method is: y=β0+β1x1+β2x2+…+β n x n +∈ Among them, y is the dependent variable, that is, the value of the energy numerical prediction; x1, x2, ..., x n is the independent variable used to predict the input value of the dependent variable; β0 is the intercept term, which indicates the expected value of the dependent variable when all independent variables are 0; β1, β2, …, β n is the regression coefficient, which indicates the influence of each variable on the dependent variable; ∈ is the error term, which indicates the random error that cannot be explained by the model; The time series analysis method is as follows: Where: y t is the observed value at time t; The autoregressive coefficient indicates the influence of past observations on the current observations; p is the order of the autoregressive part, indicating how many past observations to consider; θ1, θ2, …, θ q is the moving average coefficient, which indicates the influence of past error terms on the current observation; q is the order of the moving average part, which indicates how many past error terms should be considered; ε t is the error term at time t, usually assumed to be independent and identically distributed random variables.

9. A distributed integrated energy management system according to claim 8, characterized in that: The particle swarm algorithm formula consists of a speed update formula and a position update formula; Speed ​​update formula: vi(t+1)=wvi(t)+c1r1(pbesti-xi(t))+c2r2*(gbest-xi(t)) Where vi(t+1) is the velocity of the particle in the next iteration, w is the inertia factor, vi(t) is the velocity of the particle at time t; c1 is the learning factor of the individual particle, r1 is a random number between [0,1], pbest1 is the historical optimal position of the individual particle, xi(t) is the position at time t, c2 is the group learning factor of the particle, r2 is a random number between [0,1], gbest is the historical optimal position of the group; t is the current iteration number, and t+1 is the next iteration; The position update formula is as follows: xi(t+1)=xi(t)+vi(t+1) Among them, xi(t+1) is the position of the particle in the next iteration, which is calculated by the velocity update formula.

10. A distributed integrated energy management system according to claim 9, characterized in that: The statistical-based early warning algorithm sets the threshold value according to the statistical law of historical data, can calculate the average value and standard deviation of historical data, and then set the upper and lower threshold values ​​according to these values; The machine learning-based early warning algorithm specifically includes: using machine learning algorithms, including but not limited to support vector machines and neural networks to train models and identify abnormal patterns.

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