Electricity-heat-cold multi-energy complementary micro-grid cooperative operation control system

Abstract

The invention relates to the technical field of power distribution system control, in particular to an electricity-heat-cold multi-energy complementary micro-grid cooperative operation control system. The system comprises a power grid data acquisition module for acquiring output power of a micro-grid heat supply system and a micro-grid cold supply system as well as heat load power and cold load power of a demand side, and acquiring power generation power of a power supply side and power utilization power of a load side in the micro-grid; the power grid state evaluation module is used for acquiring a consistency coefficient between the power generation state of the micro-grid in each sliding window and the overall load, and calculating a change significant coefficient of the power consumption power in each time period to obtain a first characteristic value of the overall supply and demand balance state of the micro-grid in each time period; and the cooperative operation control module adjusts the droop coefficient according to the first characteristic value, and performs energy storage operation adjustment on the micro-grid by using a droop control technology. According to the invention, the precision of micro-grid cooperative operation control is improved.

Description

Technical Field

[0001] This application relates to the field of power distribution system control technology, specifically to a microgrid collaborative operation control system that is complementary in multiple energy sources including electricity, heat, and cooling. Background Technology

[0002] Multi-energy complementary industrial parks, as complex energy systems, involve the production, conversion, storage, and utilization of various energy sources, including electricity, heat, and cooling. They exhibit complex load characteristics and high load demand. However, with the increasing penetration rate of renewable energy generation, the uncertainty of new energy output and the complex fluctuations in load place enormous pressure on microgrid operations, easily leading to supply-demand imbalances. Furthermore, heating and cooling systems have significant thermal inertia, resulting in varying degrees of time delay in temperature transfer from heat sources to users, making it difficult to accurately capture dynamic processes. Consequently, under the influence of these factors, microgrids suffer from delayed energy optimization and dispatch response and insufficient regulation capabilities.

[0003] Publication No. CN115660142A discloses a source-load-storage coordinated optimization scheduling method for an integrated energy system in a park. When optimizing the integrated energy system, it considers that electricity-to-gas conversion units can achieve complementary advantages among units within the system, improve energy utilization efficiency, coordinate power supply optimization within the grid, and enable cogeneration units to operate more flexibly. However, due to the uncertainty of source-load status and the inertia of heating and cooling systems, there is a problem of insufficient optimization scheduling capability. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a microgrid collaborative operation control system that integrates electricity, heat, and cooling energy, with the specific technical solution as follows:

[0005] This application proposes a microgrid collaborative operation control system that integrates electric, thermal, and cooling multi-energy complementarity, the system comprising: The power grid data acquisition module is used to acquire the output power of the microgrid heating and cooling systems, as well as the heat load power and cooling load power on the demand side, and to acquire the power generation power on the power supply side and the power consumption power on the load side in the microgrid. The power grid status assessment module obtains the delay coefficient of the output power of the heating and cooling systems in each sliding window by measuring the time lag between the fluctuation of output power and demand-side load power within the sliding window. This leads to the average delay coefficient of the temperature response of the heating and cooling systems as a function of output power. Based on the correlation between power generation and power consumption, as well as the correlation between the heat and cooling load power in each sliding window and the power generation in each sliding window, the module obtains the consistency coefficient between the power generation status of the microgrid and the overall load in each sliding window. By utilizing the fluctuating and disordered characteristics of power consumption on the load side of the microgrid, the significant coefficient of power consumption change in each time period is obtained. Combined with the consistency coefficient, the first characteristic value of the overall supply and demand balance of the microgrid in each time period is obtained. The collaborative operation control module is used to adjust the droop coefficient according to the first feature value to obtain the optimized droop coefficient, and to use droop control technology to regulate the energy storage operation of the microgrid.

[0006] Preferably, a preset duration is used as a sliding window, and the first-order difference sequence of the heating system output power data and heat load power in each sliding window is statistically analyzed. The delay coefficient of the heating system output power in each sliding window is calculated based on the difference between the two first-order difference sequences.

[0007] Preferably, the delay coefficient of the heating system output power is obtained as follows: Calculate the first-order difference sequence of the output power of the i-th sliding window heating system and the first-order difference sequence of the output power of the i-th sliding window heating system. The distance between the first-order difference sequences of the heat load power of each sliding window, j takes values ​​from a first preset positive number to a second preset positive number. The value of j corresponding to the sliding window with the minimum distance is used as the delay coefficient of the output power of the heating system of the i-th sliding window, where the first preset positive number is less than the second preset positive number.

[0008] Preferably, the mode of the delay coefficients of the output power of all sliding window heating systems on that day is taken as the average delay coefficient of the temperature response of the heating system as a function of the output power. Accordingly, the average delay coefficient of the temperature response of the cooling system as a function of the output power is calculated by using the process of obtaining the average delay coefficient of the temperature response of the heating system as a function of the output power.

[0009] Preferably, the process of obtaining the consistency coefficient between the power generation status of the microgrid and the overall load within any sliding window is as follows: Statistically calculate the correlation coefficient between the cooling load power data of any sliding window and the power generation power data of the previous B sliding windows, and the correlation coefficient between the heating load power data of any sliding window and the power generation power data of the previous A sliding windows, where B is the average delay coefficient of the cooling system temperature response changing with output power, and A is the average delay coefficient of the heating system temperature response changing with output power. Also, calculate the correlation coefficient between the power generation power data and the power consumption power data within any sliding window. Use these three correlation coefficients to calculate the consistency coefficient between the power generation status of the microgrid and the overall load within each sliding window.

[0010] Preferably, the consistency coefficient between the microgrid's generation state and the overall load within the i-th sliding window. The formula for obtaining it is: In the formula, , These are the weighting coefficients, , Let be the correlation coefficient between the cooling load power data in the i-th sliding window and the power generation data in the (iB)-th sliding window. Let be the correlation coefficient between the heat load power data of the i-th sliding window and the power generation data within the (iA)-th sliding window. Let be the correlation coefficient between power generation data and power consumption data within the i-th sliding window.

[0011] Preferably, the process of obtaining the significance coefficient of power consumption change in each time period includes: For the power consumption in the current time period, fit and extract all extreme points of the fitted curve, calculate the absolute value of the difference between any two adjacent extreme values, and calculate the fractal dimension of all absolute values; count the slope value of the fitted curve of power consumption in the current time period at each time point, and calculate the permutation entropy of all slope values; combine the fractal dimension and the permutation entropy to calculate the significance coefficient of power consumption change, where the duration of a time period is a preset duration.

[0012] Preferably, the formula for calculating the significance coefficient of power consumption change in each time period is as follows: In the formula, v represents a very small positive number, and E is the significance coefficient of the change in power consumption during the current time period. Let S be the fractal dimension and S be the permutation entropy.

[0013] Preferably, the formula for obtaining the first characteristic value of the overall supply and demand balance state of the microgrid in each time period is: In the formula, Let L be the first characteristic value of the overall supply and demand balance of the microgrid in the current time period, and let L be the mean of the consistency coefficients corresponding to all sliding windows in the current time period. This represents an exponential function with the natural constant as its base.

[0014] Preferably, the next time period corresponds to the optimized droop coefficient. for: ,in, This is the normalized result of the first feature value corresponding to the current time period. , These are the preset minimum and maximum values ​​of the droop coefficient, respectively.

[0015] This application has the following beneficial effects: This application conducts an in-depth analysis of the inertial delay characteristics of the heating and cooling systems in microgrids, thereby accurately assessing the consistency between power generation and various loads on the demand side. Furthermore, considering the random fluctuation characteristics of electrical loads in microgrids under the superposition of electricity consumption behaviors, it comprehensively analyzes the overall supply and demand balance characteristics of the microgrid. Based on the obtained supply and demand balance characteristic values, the relevant parameters of the droop control technology are optimized, and the operation and regulation of the energy storage system of the microgrid are regulated using the droop control technology, which helps to compensate for the shortcomings of delayed response and insufficient regulation capacity of energy optimization scheduling in microgrids. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A block diagram of a microgrid cooperative operation control system that provides an embodiment of this application, which is an example of an electric-thermal-cooling multi-energy complementary system. Figure 2 This is a schematic diagram of a microgrid collaborative operation control process for electric-thermal-cooling multi-energy complementarity, provided as an embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a microgrid cooperative operation control system with complementary electric-thermal-cooling multi-energy components proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the microgrid collaborative operation control system provided in this application, which features a multi-energy complementary system of electricity, heat, and cooling.

[0021] Please see Figure 1 It illustrates a block diagram of a microgrid cooperative operation control system with electric-thermal-cooling multi-energy complementarity according to an embodiment of this application. The system includes: The power grid data acquisition module is used to acquire the output power of the microgrid heating and cooling systems, as well as the heat load power and cooling load power on the demand side, and to acquire the power generation power on the power supply side and the power consumption power on the load side in the microgrid.

[0022] Multi-energy complementarity of electricity, heat, and cooling refers to the coupling and coordination of three energy forms—electricity, heat, and cooling—in an energy system. Through energy conversion, storage, and cascade utilization technologies, the mutual supplementation and optimized allocation of different energy sources are achieved. For example, this application takes combined heat and power (CHP) as an example. In terms of electricity supply, electricity is provided by gas turbine power generation, wind power, and photovoltaic power generation. In terms of heat supply, the heat source mainly comes from the high-temperature waste heat generated after gas turbine power generation, which is recovered and utilized through a waste heat boiler; the heat source can also be obtained through a gas boiler. In terms of cooling supply, the cooling source is jointly provided by an absorption chiller and an electric chiller. The absorption chiller can use the heat energy generated by the heat source to drive cooling, while the electric chiller directly consumes electricity for cooling.

[0023] In this embodiment, a centralized pipeline network is used to provide unified cooling and heating supply for the entire area. To monitor the heating and cooling power and the power status of the heat and cooling loads in the microgrid, taking the heating system as an example, flow rate and temperature data of the fluid in the heating system are collected through heat meters. Based on the collected data and the specific heat capacity of the fluid, the output power of the heating system can be calculated. The calculation method is a known technique. Using this method, the output power data of the cooling system can be obtained, as well as the heat load power and cooling load power data on the demand side. The time interval for collecting the above power data is set to 5 seconds. Simultaneously, to detect the power generation and electrical load status of the microgrid, in this embodiment, smart meters are used to collect power generation data on the power supply side and electrical consumption data on the load side of the microgrid. The time interval for collecting power generation and electrical consumption data is set to 5 seconds. The collected data are then subjected to moving average filtering. The specific filtering process is existing technology and will not be described in detail in this embodiment.

[0024] The power grid status assessment module obtains the delay coefficient of the output power of the heating and cooling systems in each sliding window by analyzing the time lag between the fluctuation of output power and demand-side load power within the sliding window. This leads to the average delay coefficient of the temperature response of the heating and cooling systems as a function of output power. Based on the correlation between power generation and power consumption, and the correlation between the heat and cooling load power in each sliding window and the power generation within each sliding window (based on the average delay coefficient), the module obtains the consistency coefficient between the microgrid's power generation status and the overall load within each sliding window. Utilizing the irregular fluctuations and disordered changes in the microgrid's load-side power consumption, the module obtains the significant coefficient of power consumption changes in each time period. Combined with the consistency coefficient, the module obtains the first characteristic value of the overall supply-demand balance state of the microgrid in each time period.

[0025] In microgrids with a high proportion of renewable energy integration, the inherent randomness and volatility on both the source and load sides can easily lead to supply-demand imbalances. Furthermore, the thermal inertia present in heating and cooling systems further increases the difficulty of achieving precise coordinated operation and control. Therefore, to improve the coordinated operation efficiency of multi-energy complementary microgrids, the impact of the above factors needs to be comprehensively considered, and the following analysis is conducted.

[0026] First, let's take a heating system as an example. Its thermal inertia provides the power system with flexible adjustment capabilities; however, the dynamic response delay caused by thermal inertia can easily lead to excessive temperatures within buildings, thus reducing system energy efficiency and increasing the difficulty of real-time power balancing. There is a time lag between the start of power adjustment by the heat source equipment and the actual heat change felt by the user, and the degree of this time lag varies under different temperature environments. Accurately assessing this time delay helps improve the accuracy of subsequent microgrid coordinated control.

[0027] Therefore, this embodiment obtains the output power of the heating system and the heat load power data of the return water pipe on the demand side for the previous day. When the output power experiences a significant jump, the heat load power of the return water pipe on the demand side will also exhibit similar fluctuation characteristics after a period of time. In this embodiment, a sliding window with a duration of 5 minutes and a sliding step size of 1 minute is set. Furthermore, the first-order difference sequence of the output power data and the first-order difference sequence of the heat load power are calculated for each sliding window. These sequence values ​​reflect the magnitude of power change between adjacent moments within the sliding window.

[0028] Furthermore, to assess the time lag relationship between output power fluctuations and heat load power fluctuations, using the first-order difference sequence of the output power of the i-th sliding window heating system as a benchmark, the first-order difference sequence of the output power of the i-th sliding window and the first-order difference sequence of the heat load power are calculated respectively. The DTW distance between the first-order difference sequences of heat load power in each sliding window is used, where j is set to a value between 10 and 50 to monitor the delay correlation within a maximum of approximately 50 minutes. Then, the magnitudes of all DTW distances are compared, and the value of j corresponding to the sliding window with the smallest DTW distance is used as the delay coefficient for the output power of the i-th sliding window heating system. For example, if the smallest DTW distance occurs in... The sliding window represents the first... The output power fluctuation of the i-th sliding window is most similar to the fluctuation of the heat load power of the i-th sliding window. This serves as the delay coefficient for the output power of the heating system in the i-th sliding window. Therefore, corresponding delay coefficients can be obtained for each sliding window throughout the day. The mode of the delay coefficients obtained for the day is taken as the average delay coefficient of the heating system's temperature response as a function of output power, denoted as A. This average delay reflects the degree of inertial delay in the microgrid heating system.

[0029] For cooling systems, the above steps can be used to obtain the corresponding average delay coefficient, denoted as B.

[0030] Real-time balance between power supply and demand is crucial for the efficient and stable operation of a system, and correlation analysis between power generation and power consumption can effectively reflect the instantaneous adjustment pressure and stability of the power grid. In this embodiment, both heat and cold supply in the microgrid are directly affected by the magnitude of power generation. Furthermore, unlike the immediate availability of electricity loads, heat and cold loads exhibit significant thermal inertia. Therefore, this embodiment will evaluate the correlation between power generation and power consumption, as well as the power of heat and cold loads.

[0031] Preferably, in this embodiment, taking the i-th sliding window as an example, the correlation coefficient between the power generation data and the power consumption data within the sliding window is calculated and denoted as . The result This reflects the correlation characteristics between real-time power generation and power consumption in a microgrid. Then, the correlation coefficient between the heat load power data in the i-th sliding window and the power generation data in the (iA)-th sliding window is calculated and denoted as... And the correlation coefficient between the cooling load power data of the i-th sliding window and the power generation data in the (iB)-th sliding window, denoted as The result , These figures reflect the correlation between thermal and cooling load power data and power generation under the influence of inertial delay. For missing data, this embodiment uses edge copying to fill in the gaps. The correlation coefficients between power generation and thermal and cooling loads are analyzed to quantify the time matching degree and simultaneity rate of source and load output.

[0032] It should be noted that, regarding the acquisition of the above correlation coefficients, this embodiment uses the Spearman correlation coefficient to measure the correlation between data. In actual application scenarios, implementers may use other existing methods for acquiring correlation coefficients, and this embodiment does not impose any special restrictions on this.

[0033] Therefore, the consistency coefficient between the microgrid's generation state and the overall load within the i-th sliding window is calculated using the three correlation coefficients mentioned above. The formula is as follows: In the formula , These are the weighting coefficients, The ratio of electrical load to heat / cooling load in the microgrid can be adjusted according to the proportion of electrical load to heat / cooling load. In this embodiment, they are respectively set as follows: , 0.4, Let be the correlation coefficient between the cooling load power data in the i-th sliding window and the power generation data in the (iB)-th sliding window. Let be the correlation coefficient between the heat load power data of the i-th sliding window and the power generation data within the (iA)-th sliding window. Let be the correlation coefficient between power generation data and power consumption data within the i-th sliding window, where B is the average delay coefficient of the cooling system's temperature response as a function of output power, and A is the average delay coefficient of the heating system's temperature response as a function of output power. The obtained... This reflects the power consistency characteristics between the generation status and the overall load in the microgrid within the sliding window.

[0034] From the above calculation formula, we can see that the parameter... This reflects the degree of real-time synchronization between changes in power generation and power consumption. Parameters , Specifically targeting hot and cold loads with significant inertia, this method uses pre-determined average delay coefficients A and B to perform correlation analysis between the current sliding window's hot and cold load power data and the power generation data from the previous A and B sliding windows. This accurately reflects the synchronization characteristics between changes in power generation and changes in hot and cold load power over time delays. The resulting... The larger the value, the better the matching between power generation and consumption within the sliding window, and the more consistent the changes in power generation power with the changes in heat and cold loads driven by electricity.

[0035] Furthermore, the demand for electricity in a microgrid is typically greater than the demand for heating and cooling loads. Electricity load varies significantly in real time due to changes in user behavior, exhibiting considerable randomness. The uncertainty of power fluctuations on the demand side of the microgrid can severely impact the balance between power generation and consumption. Therefore, accurately analyzing the specific fluctuation characteristics of demand-side electricity load is crucial for maintaining the stable operation of the microgrid.

[0036] While electrical load exhibits a clear intraday periodicity, its consumption patterns also change randomly throughout the day. This embodiment uses a preset duration of 1 hour as a time period to analyze the intraday electrical load variation characteristics. Specifically, influenced by instantaneous changes in user behavior, the start-up and shutdown of high-power electrical equipment, and sudden environmental changes, the power consumption data of the microgrid experiences rapid increases or decreases in the short term. Furthermore, the superposition of different factors further causes the power consumption to exhibit strong random fluctuations. Therefore, for ease of understanding and description, this embodiment uses the current time period as an example for subsequent detailed explanation. In this embodiment, a 5th-order polynomial fitting algorithm is used to fit the power consumption data within the current time period, and all extreme points of the obtained fitted curve are obtained. Then, the absolute value of the difference between any two adjacent extreme values ​​is calculated, and the fractal dimension of all absolute values ​​is calculated using the Higuchi algorithm, denoted as H. The obtained H reflects the random characteristics of the fluctuation amplitude of the power consumption data within the current time period. Furthermore, the more stable the fluctuation of electrical load, the more consistent the overall trend of change in the short term. However, random fluctuations caused by the superposition of multiple factors will exacerbate the disorder of the trend of power consumption change. Therefore, the slope value of the power consumption fitting curve at each moment in the current period is calculated. This value reflects the magnitude of the instantaneous fluctuation rate of the power consumption data. Then, the permutation entropy of all the slope values ​​is calculated, denoted as S. The resulting S reflects the degree of disorder of the rate of change of power consumption data in the current period.

[0037] Therefore, the significance coefficient of the change in power consumption within the current time period is obtained. In this embodiment, the specific calculation formula is as follows: In the formula, v represents a very small positive number, which is taken as 0.01 in this example to avoid the situation where H and S being 0 directly leads to E being zero, thus ignoring the influence of the other term. E is the significance coefficient of the change in power consumption during the current period. Let S be the fractal dimension and S be the permutation entropy. The resulting significance coefficient of change... This reflects the irregular fluctuation range and disordered, random characteristics of the rate of change in electricity consumption data during the current period.

[0038] As can be seen from the above process, the fractal dimension H, based on the difference between adjacent extreme values ​​of the fitted curve, quantifies the randomness and irregularity of the short-term electrical load fluctuation amplitude; while the permutation entropy S, based on the slope value of the fitted curve at each moment, reflects the disorder of the rate of change in the sequential pattern. In this embodiment, the two are combined to reflect the random characteristics of the electrical load in the microgrid under the superimposed influence of user behavior from different perspectives.

[0039] Therefore, the first characteristic value of the overall supply and demand balance state of the microgrid in each time period is further calculated. In this embodiment, the specific formula for obtaining the value is as follows: In the formula, Let E be the first characteristic value of the overall supply and demand balance of the microgrid during the current period, E be the significance coefficient of the change in power consumption during the current period, and L be the mean of the consistency coefficients corresponding to all sliding windows during the current period, with the value of L ranging from [-1, 1]. This represents an exponential function with the natural constant as its base, ensuring that the result is always positive while avoiding a denominator of 0. The larger the resulting first eigenvalue R, the more unstable the overall supply and demand balance of the microgrid is within the current time period.

[0040] The collaborative operation control module is used to adjust the droop coefficient according to the first feature value to obtain the optimized droop coefficient, and to use droop control technology to regulate the energy storage operation of the microgrid.

[0041] Based on the above process in this embodiment, the inertial delay characteristics of the heating and cooling systems in the microgrid can be analyzed in depth, and the correlation between power generation and various loads on the demand side can be evaluated. Furthermore, the random fluctuation characteristics of the electrical load in the microgrid under the superposition of electricity consumption behaviors can be considered, comprehensively reflecting the overall supply and demand balance characteristics of the microgrid. However, in practical applications, it is difficult to achieve a complete balance between power supply and demand, requiring energy storage systems for peak shaving and valley filling. The discharge state of these energy storage systems needs to be adjusted according to the actual power supply and demand balance in the microgrid.

[0042] Specifically, this embodiment uses droop control technology to regulate the energy storage system. A larger first characteristic value indicates a worse supply-demand balance in the microgrid, requiring timely response and regulation from the energy storage system. Therefore, a smaller droop coefficient is needed for more timely response and regulation. Conversely, a smaller first characteristic value corresponds to a better supply-demand balance in the microgrid, requiring a larger droop coefficient to avoid excessive steady-state deviation. In this embodiment, the adjustment range of the droop coefficient is set to […]. , In this embodiment, , The droop coefficient for the next time period is optimized based on the first feature value of the current time period. To do this, the first feature values ​​obtained from the current time period and the M previous time periods are first normalized using the minimax method. In this embodiment, M is set to 20.

[0043] Therefore, the optimized formula for calculating the droop coefficient for the next time period after the current time period is: ,in This is the normalized result of the first feature value corresponding to the current time period. Specifically, if the first feature value of the current time period and the previous M time periods are all equal, that is, if the historical maximum value equals the minimum value during the normalization process, then this is directly set. =0.5, , These are the preset minimum and maximum droop coefficients, which in this embodiment are 2% and 7%, respectively. Based on the obtained droop coefficient for the next time period, droop control technology is used to regulate the operation of the microgrid's energy storage system. The control in this embodiment belongs to the slow optimization layer, that is, it uses system state statistics from past time periods to adjust the droop coefficient of the energy storage system for the next time period. Adjusting the droop coefficient for the next time period based on historical time period state statistics belongs to the slow optimization layer control of the microgrid energy management system. This is used to update the steady-state operating reference point, thereby helping to compensate for the shortcomings of delayed response and insufficient regulation capacity in microgrid energy optimization scheduling.

[0044] Specifically, the schematic diagram of the microgrid collaborative operation control process in this embodiment is as follows: Figure 2 As shown.

[0045] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0046] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0047] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A microgrid collaborative operation control system with complementary electric, heating, and cooling energy sources, characterized in that, The system includes: The power grid data acquisition module is used to acquire the output power of the microgrid heating and cooling systems, as well as the heat load power and cooling load power on the demand side, and to acquire the power generation power on the power supply side and the power consumption power on the load side in the microgrid. The power grid status assessment module obtains the delay coefficient of the output power of the heating and cooling systems in each sliding window by measuring the time lag between the fluctuation of output power and demand-side load power within the sliding window. This leads to the average delay coefficient of the temperature response of the heating and cooling systems as a function of output power. Based on the correlation between power generation and power consumption, as well as the correlation between the heat and cooling load power in each sliding window and the power generation in each sliding window, the module obtains the consistency coefficient between the power generation status of the microgrid and the overall load in each sliding window. By utilizing the fluctuating and disordered characteristics of power consumption on the load side of the microgrid, the significant coefficient of power consumption change in each time period is obtained. Combined with the consistency coefficient, the first characteristic value of the overall supply and demand balance of the microgrid in each time period is obtained. The collaborative operation control module is used to adjust the droop coefficient according to the first feature value to obtain the optimized droop coefficient, and to use droop control technology to regulate the energy storage operation of the microgrid.

2. The microgrid collaborative operation control system with electric-thermal-cooling multi-energy complementarity as described in claim 1, characterized in that, Using a preset duration as a sliding window, the first-order difference sequence of the heating system output power data and heat load power within each sliding window is statistically analyzed. Based on the difference between the two first-order difference sequences, the delay coefficient of the heating system output power in each sliding window is calculated.

3. The microgrid collaborative operation control system with electric-thermal-cooling multi-energy complementarity as described in claim 2, characterized in that, The delay factor for the output power of the heating system is obtained as follows: Calculate the first-order difference sequence of the output power of the i-th sliding window heating system and the first-order difference sequence of the output power of the i-th sliding window heating system. The distance between the first-order difference sequences of the heat load power of each sliding window, j takes values ​​from a first preset positive number to a second preset positive number. The value of j corresponding to the sliding window with the minimum distance is used as the delay coefficient of the output power of the heating system of the i-th sliding window, where the first preset positive number is less than the second preset positive number.

4. The microgrid collaborative operation control system with electric-thermal-cooling multi-energy complementarity as described in claim 3, characterized in that, The mode of the delay coefficients of the output power of all sliding window heating systems on that day is taken as the average delay coefficient of the heating system temperature response as a function of the output power. Correspondingly, the average delay coefficient of the cooling system temperature response as a function of the output power is calculated by using the process of obtaining the average delay coefficient of the heating system temperature response as a function of the output power.

5. The microgrid collaborative operation control system with electric-thermal-cooling multi-energy complementarity as described in claim 1, characterized in that, The process of obtaining the consistency coefficient between the power generation status of the microgrid and the overall load within any sliding window is as follows: The correlation coefficient between the cooling load power data of any sliding window and the power generation data of the previous B sliding windows is calculated, as well as the correlation coefficient between the heating load power data of any sliding window and the power generation data of the previous A sliding windows. Here, B is the average delay coefficient of the cooling system temperature response as a function of output power, and A is the average delay coefficient of the heating system temperature response as a function of output power. The correlation coefficient between the power generation data and the power consumption data within any sliding window is then calculated. These three correlation coefficients are used to calculate the consistency coefficient between the power generation status of the microgrid and the overall load within each sliding window.

6. The microgrid collaborative operation control system for electric-thermal-cooling multi-energy complementarity as described in claim 5, characterized in that, The consistency coefficient between the microgrid's generation state and the overall load within the i-th sliding window. The formula for obtaining it is: In the formula, , These are the weighting coefficients, , Let be the correlation coefficient between the cooling load power data in the i-th sliding window and the power generation data in the (iB)-th sliding window. Let be the correlation coefficient between the heat load power data of the i-th sliding window and the power generation data within the (iA)-th sliding window. Let be the correlation coefficient between power generation data and power consumption data within the i-th sliding window.

7. The microgrid collaborative operation control system for electric-thermal-cooling multi-energy complementarity as described in claim 1, characterized in that, The process of obtaining the significance coefficient of power consumption change in each time period includes: For the power consumption in the current time period, fit and extract all extreme points of the fitted curve, calculate the absolute value of the difference between any two adjacent extreme values, and calculate the fractal dimension of all absolute values; count the slope value of the fitted curve of power consumption in the current time period at each time point, and calculate the permutation entropy of all slope values; combine the fractal dimension and the permutation entropy to calculate the significance coefficient of power consumption change, where the duration of a time period is a preset duration.

8. The microgrid collaborative operation control system for electric-thermal-cooling multi-energy complementarity as described in claim 7, characterized in that, The formula for calculating the significance coefficient of power consumption changes in each time period is as follows: In the formula, v represents a very small positive number, and E is the significance coefficient of the change in power consumption during the current time period. Let S be the fractal dimension and S be the permutation entropy.

9. A microgrid collaborative operation control system with electric-thermal-cooling multi-energy complementarity as described in claim 8, characterized in that, The formula for obtaining the first characteristic value of the overall supply and demand balance state of the microgrid in each time period is: In the formula, Let L be the first characteristic value of the overall supply and demand balance of the microgrid in the current time period, and let L be the mean of the consistency coefficients corresponding to all sliding windows in the current time period. This represents an exponential function with the natural constant as its base.

10. A microgrid collaborative operation control system with complementary electric-thermal-cooling multi-energy systems as described in claim 1, characterized in that, The next time period corresponds to the optimized droop coefficient. for: ,in, This is the normalized result of the first feature value corresponding to the current time period. , These are the preset minimum and maximum values ​​of the droop coefficient, respectively.

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