Multi-energy complementary energy storage EMS cross-layer collaborative scheduling method and system

Abstract

The invention relates to the technical field of cross-layer cooperative scheduling, and particularly discloses a multi-energy complementary energy storage EMS cross-layer cooperative scheduling method and system. The method comprises the following steps: S1, acquiring energy data of each layer in real time; S2, carrying out collaborative optimization on the energy data of each layer; S3, matching an energy quantity with energy storage capacity; S4, matching the energy quantity with energy consumption, S5, generating a cross-layer collaborative scheduling strategy, and S6, optimizing the cross-layer collaborative scheduling strategy. Through establishing a three-layer cooperation mechanism of an energy supply layer, a storage layer and a demand layer, optimization and matching of energy data are realized, an electric energy quality parameter is introduced as a scheduling strategy adjustment basis, a formed closed-loop optimization mechanism solves the problems of insufficient inter-layer information interaction, poor scheduling flexibility and the like in a traditional method, the energy utilization efficiency is improved, and the scheduling cost is reduced. The adaptive capacity of the system to renewable energy fluctuation is enhanced, and technical support is provided for efficient operation of the multi-energy complementary system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cross-layer collaborative scheduling, in particular to a multi-energy complementary energy storage EMS cross-layer collaborative scheduling method and system. BACKGROUND

[0002] With the deepening of energy transformation, the proportion of renewable energy in the energy structure is increasing, but the inherent intermittency, volatility and uncertainty of renewable energy have brought serious challenges to the safe and stable operation of the power system. At the same time, the diversification and dynamic change of energy consumption load have also made the energy supply and demand balance problem increasingly complex. Under this background, multi-energy complementation and energy storage technology, as an important means to solve renewable energy grid-connection and improve energy utilization efficiency, have attracted widespread attention, but the traditional energy storage EMS scheduling method has many limitations.

[0003] Firstly, the traditional energy storage EMS scheduling method adopts single-layer or double-layer scheduling architecture, independently manages energy supply, energy storage control or demand response, and each layer operates independently, with prominent information barriers between the energy supply layer, the energy storage layer and the energy demand layer, serious lack of data interaction, resulting in scheduling decisions limited to local optimization and unable to achieve global optimization of energy flow. Secondly, the scheduling strategy lacks flexibility and adaptability, neither fully considers the differences in charge and discharge characteristics of different energy storage devices nor the time elasticity characteristics of the load, nor can it cope with the strong volatility of renewable energy, resulting in low energy utilization efficiency and weak intermittent energy consumption capacity. In addition, the influence of power quality parameters on system stability is generally ignored, and they are not included in the scheduling decision system, and there is a lack of dynamic adjustment mechanism based on real-time operating state, resulting in a lag in the system's response to sudden conditions and difficulty in ensuring continuous and stable operation. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a multi-energy complementary energy storage EMS cross-layer collaborative scheduling method and system to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a multi-energy complementary energy storage EMS cross-layer collaborative scheduling method, comprising the following steps: S1: Real-time acquisition of energy data of each layer: real-time acquisition of a first energy amount of an energy supply layer, a first energy storage amount of an energy storage layer and a first energy consumption amount of an energy demand layer; S2: Collaborative optimization of energy data of each layer: collaborative optimization processing of the first energy amount, the first energy storage amount and the first energy consumption amount to obtain an optimized second energy amount, a second energy storage amount and a second energy consumption amount; S3: Energy quantity and energy storage quantity matching: dynamically match the second energy quantity and the second energy storage quantity by establishing an energy storage matching model, and output an energy storage matching result; S4: Energy quantity and energy consumption quantity matching: dynamically match the second energy quantity and the second energy consumption quantity, and output an energy consumption demand matching result; S5: Generate cross-layer collaborative scheduling strategy: based on the energy storage matching result and the energy consumption demand matching result, analyze the energy storage and the energy consumption demand, and generate a cross-layer collaborative scheduling strategy; S6: Optimize the cross-layer collaborative scheduling strategy: execute the cross-layer collaborative scheduling strategy, and real-time monitor the demand side equipment, evaluate the power quality by acquiring power quality parameters, and dynamically adjust the cross-layer collaborative scheduling strategy based on the evaluation result.

[0006] Preferably, the real-time acquisition of each layer energy data includes the following contents: Through the metering instrument matched with each energy equipment of the energy supply layer, the current energy power value of each energy equipment is collected in real time, the current energy power value is summarized to obtain the total energy quantity that the energy supply layer can currently provide, the energy component affected by meteorological data is extracted, and the difference between the total energy quantity and the energy component is taken as the first energy quantity of the energy supply layer. Through the user end metering equipment of the energy consumption demand layer, the current energy consumption value of each user is collected in real time, the current energy consumption value is summarized to obtain the total energy consumption value of the energy consumption demand layer, the energy consumption component affected by time and seasonal factors is extracted, and the difference between the total energy consumption value and the energy consumption component is taken as the first energy consumption quantity of the energy consumption demand layer.

[0007] Preferably, the collaborative optimization of each layer energy data includes the following contents: Read the first energy quantity, the first energy storage quantity and the first energy consumption quantity, and perform collaborative optimization processing on the first energy quantity by establishing an energy influence optimization model to obtain the second energy quantity after optimization; Collaborative optimization processing on the first energy storage quantity is performed by establishing an energy storage influence optimization model to obtain the second energy storage quantity after optimization; Collaborative optimization processing on the first energy consumption quantity is performed by establishing an energy consumption influence optimization model to obtain the second energy consumption quantity after optimization; The content of the collaborative optimization processing on the first energy quantity is: acquiring the energy influence parameters of the energy supply layer, the energy influence parameters are the power generation fluctuation rates corresponding to each energy, and the power generation fluctuation rates corresponding to each energy are obtained by: acquiring the power generation quantity corresponding to each energy, analyzing the fluctuation amplitude of the power generation quantity corresponding to each energy, and calculating the standard deviation of the fluctuation amplitude of the power generation quantity corresponding to each energy to obtain the power generation fluctuation rate corresponding to each energy; Based on the corresponding power generation fluctuation rate of each energy, an energy influence optimization model is established, and an energy influence optimization coefficient is calculated, and the first energy quantity obtained is processed by collaborative optimization based on the energy influence optimization coefficient, and a second energy quantity after optimization is obtained.

[0008] Preferably, the content of processing the first energy storage quantity by collaborative optimization through establishing an energy storage influence optimization model is as follows: An energy storage influence parameter of the energy reserve layer is obtained, the energy storage influence parameter being an energy storage efficiency decay factor, the energy storage efficiency decay factor being obtained by collecting the actual capacity and rated capacity corresponding to each energy storage device, calculating the ratio of the actual capacity and the rated capacity to obtain the capacity retention rate corresponding to each energy storage device, and combining the cycle number proportion corresponding to each energy storage device to calculate the energy storage efficiency decay factor corresponding to each energy storage device; Based on the energy storage efficiency decay factor corresponding to each energy storage device, an energy storage influence optimization model is established, and an energy storage influence optimization coefficient is calculated, and the first energy storage quantity is processed by collaborative optimization based on the energy storage influence optimization coefficient, and a second energy storage quantity after optimization is obtained. The content of processing the first energy quantity by collaborative optimization is as follows: obtaining an energy use influence parameter of the energy use demand layer, the energy use influence parameter being a load prediction error rate, the load prediction error rate being obtained by obtaining the actual energy use quantity and the predicted energy use quantity of the energy use demand layer, calculating the absolute value of the difference between the actual energy use quantity and the predicted energy use quantity, and calculating the ratio of the absolute value of the difference and the actual energy use quantity to obtain the load prediction error rate; Based on the load prediction error rate, an energy use influence optimization model is established, and an energy use influence optimization coefficient is calculated, and the first energy quantity is processed by collaborative optimization based on the energy use influence optimization coefficient, and a second energy use quantity after optimization is obtained.

[0009] Preferably, the execution steps of matching the energy quantity and the energy storage quantity are as follows: S31: obtaining a second energy quantity of the energy supply layer and a second energy storage quantity of the energy reserve layer, and extracting fluctuation characteristic parameters of each energy type in the second energy quantity and state parameters of each energy storage device in the second energy storage quantity; the fluctuation characteristic parameters include energy change rate and fluctuation duration; the state parameters include response time and maximum charge and discharge power of the energy storage device; S32: based on the fluctuation characteristic parameters and the state parameters, an energy storage matching model is established, the energy storage matching model adopting a bidirectional compensation matching mechanism, specifically: when the second energy quantity is greater than a preset upper threshold P_up, energy absorption distribution is performed according to the priority order of the energy storage devices; when the second energy quantity is less than a preset lower threshold P_down, energy release distribution is performed according to the priority order of the energy storage devices; S33: A piecewise linear matching model is used for energy distribution, the working range of the energy storage device is divided into a low capacity section, a medium capacity section and a high capacity section, different charging power curves are set for different capacity sections, and a storage capacity balanced distribution strategy is realized, and when multiple energy storage devices are available at the same time, the distribution ratio of each energy storage device is calculated; S34: A threshold triggering matching mechanism is set, the fluctuation of each energy and the state of each energy storage device are monitored, and intelligent matching of energy and energy storage is realized; Specifically, when the real-time fluctuation of a certain energy exceeds the preset fluctuation threshold, the system checks the current capacity state of each energy storage device; if the current capacity state of the energy storage device is lower than the preset lower limit threshold, the discharge operation of the energy storage device is triggered to supplement the energy of the system; if the current capacity state of the energy storage device is higher than the preset upper limit threshold, the charging operation of the energy storage device is triggered to absorb the excess energy of the system, and finally an energy storage matching result is output.

[0010] Preferably, the execution steps of matching the energy amount with the energy consumption amount are as follows: S41: Obtain the second energy amount of the energy supply layer and the second energy consumption amount of the energy consumption demand layer, extract the available power curve of the second energy amount and the quantified characteristic parameters of each load in the second energy consumption amount; The quantified characteristic parameters include the energy consumption elasticity index and the time value decay rate of the load; S42: Obtain the total available energy amount and the total energy consumption demand amount, calculate the ratio of the total available energy amount to the total energy consumption demand amount to obtain the system energy supply and demand ratio ESR, and determine the system regulation intensity SI based on the system energy supply and demand ratio; S43: Construct a load response function to obtain each load response f, and determine a load regulation strategy in combination with the system regulation intensity, execute the load regulation strategy, and output an energy consumption demand matching result.

[0011] Preferably, the content of determining the load regulation strategy and executing the load regulation strategy is as follows: Determine the load regulation strategy: multiply the system regulation intensity by the load response for each load v to obtain the actual regulation degree, which represents the degree of regulation required for the load, determine the power regulation amount and the time regulation amount based on the actual regulation degree and the quantified characteristic parameters, the determination method of the power regulation amount is: multiply the actual regulation degree by the energy consumption elasticity index to obtain the power reduction ratio, if the power reduction ratio exceeds the energy consumption elasticity index, take the energy consumption elasticity index as the upper limit; multiply the power reduction ratio by the original power of the load to obtain the specific power reduction amount; the determination method of the time regulation amount is: divide the actual regulation degree by the time value decay rate to obtain the time delay amount, if the time delay amount exceeds the maximum allowed delay time, take the maximum allowed delay time as the upper limit; Performing load regulation strategy: based on power regulation amount and time regulation amount, performing regulation operation on each load, for power regulation, the original power of the load is reduced by the power regulation amount to obtain the adjusted new power value; for time regulation, the original planned operation time of the load is added to the time regulation amount to obtain the adjusted new operation time.

[0012] To achieve the above object, the present application provides the following technical scheme: a multi-energy complementary energy storage EMS cross-layer collaborative scheduling system, which implements the above-mentioned multi-energy complementary energy storage EMS cross-layer collaborative scheduling method, comprising: Each layer energy data acquisition module: used for acquiring the first energy amount of the energy supply layer, the first energy storage amount of the energy reserve layer and the first energy consumption amount of the energy demand layer in real time; Energy data collaborative optimization module: used for collaborative optimization processing of the first energy amount, the first energy storage amount and the first energy consumption amount, to obtain the second energy amount, the second energy storage amount and the second energy consumption amount after optimization; Energy amount and energy storage amount matching module: used for dynamically matching the second energy amount and the second energy storage amount by establishing an energy storage matching model, and outputting an energy storage matching result; Energy amount and energy consumption amount matching module: used for dynamically matching the second energy amount and the second energy consumption amount, and outputting an energy demand matching result; Cross-layer collaborative scheduling strategy generation module: based on the energy storage matching result and the energy demand matching result, analyzing the energy storage and energy demand, and generating a cross-layer collaborative scheduling strategy; Cross-layer collaborative scheduling strategy optimization module: used for executing the cross-layer collaborative scheduling strategy, and real-time monitoring the demand side equipment, evaluating the power quality by acquiring the power quality parameters, and dynamically adjusting the cross-layer collaborative scheduling strategy based on the evaluation result.

[0013] As described above, the multi-energy complementary energy storage EMS cross-layer collaborative scheduling method and system provided by the present application has at least the following beneficial effects: The application provides a multi-energy complementary energy storage EMS cross-layer collaborative scheduling method and system, which obtains a second energy amount, a second energy storage amount and a second energy consumption amount by real-time acquisition of a first energy amount of an energy supply layer, a first energy storage amount of an energy storage layer and a first energy consumption amount of an energy consumption demand layer, and collaborative optimization processing of the three-layer data; dynamically matches the second energy amount with the second energy storage amount and the second energy consumption amount to obtain an energy storage matching result and an energy consumption demand matching result; generates a cross-layer collaborative scheduling strategy based on the two matching results; and dynamically adjusts and optimizes the scheduling strategy by monitoring demand-side equipment and evaluating power quality. The application realizes data collaboration and optimized scheduling of the energy supply layer, the energy storage layer and the energy consumption demand layer, improves energy utilization efficiency, enhances system stability, reduces operation cost, and meets the efficient collaborative operation demand of a multi-energy complementary system. BRIEF DESCRIPTION OF DRAWINGS

[0014] The application is further described by using the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the application, and other drawings can be obtained by those skilled in the art without creative labor on the premise of not paying any creative labor.

[0015] Figure 1 The application is further described by using the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the application, and other drawings can be obtained by those skilled in the art without creative labor on the premise of not paying any creative labor.

[0016] Figure 2 The application is further described by using the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the application, and other drawings can be obtained by those skilled in the art without creative labor on the premise of not paying any creative labor. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be clearly and completely described in the embodiments of the application in combination with the accompanying drawings, obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0018] Embodiment 1 Please refer to Figure 1 The application provides a multi-energy complementary energy storage EMS cross-layer collaborative scheduling method, which comprises the following steps: S1: Real-time acquisition of energy data of each layer: real-time acquisition of a first energy amount of an energy supply layer, a first energy storage amount of an energy storage layer and a first energy consumption amount of an energy consumption demand layer; In this embodiment, it needs to be specifically pointed out that the content of the real-time acquisition of the energy data of each layer is as follows: The current energy power values of each energy device in the energy supply layer (such as photovoltaic power station, wind power station, gas turbine) are collected in real time by the metering instruments matched with the energy devices, the current energy power values are summarized, the total energy amount currently available in the energy supply layer is obtained, the energy component affected by meteorological data is extracted, and the difference between the total energy amount and the energy component is taken as the first energy amount of the energy supply layer. The current storage energy values of each energy storage device in the energy reserve layer (such as battery pack, heat storage tank) are read in real time by the monitoring devices of the energy storage devices, the current storage energy values are summarized, the total storage energy value currently in the energy reserve layer is obtained, the current used storage energy values of each energy storage device are read, the current used storage energy values are summarized, the total used storage energy value currently in the energy reserve layer is obtained, and the difference between the total storage energy value and the total used storage energy value is taken as the first storage energy of the energy reserve layer. The current energy consumption values of each user in the energy demand layer are collected in real time by the user-side metering devices (such as smart electricity meter, heat meter), the current energy consumption values are summarized, the total energy consumption value currently in the energy demand layer is obtained, the energy component affected by time and seasonal factors is extracted, and the difference between the total energy consumption value and the energy component is taken as the first energy consumption of the energy demand layer.

[0019] It should be specifically noted that the energy component affected by meteorological data is extracted by deploying meteorological monitoring devices (such as light sensor, anemometer, thermometer, etc.) in the energy supply layer to collect current meteorological data (including light intensity, wind speed, temperature, etc.) in real time, calling a preset meteorological influence coefficient model, the preset meteorological influence coefficient model is trained based on historical meteorological data and corresponding energy device output fluctuation data in the same period using a random forest algorithm, and the model contains energy fluctuation coefficients corresponding to different meteorological parameters; the real-time collected meteorological data is input into the meteorological influence coefficient model, the influence amount of each meteorological parameter on energy supply is calculated by using the weight and fluctuation coefficient of each meteorological parameter output by the model; and the influence amounts are summarized, that is, the energy component affected by meteorological data is obtained.

[0020] The content of extracting the energy consumption component affected by the time and seasonal factors includes: obtaining current time information (including time, date) and seasonal information (such as spring, summer, autumn, winter); determining the time period (such as peak period, flat period, and trough period) currently in according to the time information, and combining the seasonal information, calling a preset time and seasonal influence model, the preset time and seasonal influence model is trained based on historical same period energy fluctuation data at different times and in different seasons using an LSTM algorithm, and the model includes energy fluctuation coefficients corresponding to different times and seasons; input the current time information and seasonal information into the time and seasonal influence model, and calculate the influence of time and seasonal factors on energy demand through the model; adding the two influence amounts, that is, obtaining the energy consumption component affected by the time and seasonal factors.

[0021] S2: Collaborative optimization of each layer of energy data: collaborative optimization processing is performed on the first energy amount, the first energy storage amount and the first energy consumption amount to obtain the second optimized energy amount, the second optimized energy storage amount and the second optimized energy consumption amount; In this embodiment, it needs to be specifically pointed out that the content of the collaborative optimization of each layer of energy data is as follows: The first energy amount, the first energy storage amount and the first energy consumption amount are read, the first energy amount is collaboratively optimized by establishing an energy influence optimization model to obtain the second optimized energy amount; The first energy storage amount is collaboratively optimized by establishing an energy storage influence optimization model to obtain the second optimized energy storage amount; The first energy consumption amount is collaboratively optimized by establishing an energy consumption influence optimization model to obtain the second optimized energy consumption amount; The content of the collaborative optimization processing of the first energy amount is: obtaining energy influence parameters of the energy supply layer, the energy influence parameters are the corresponding power generation fluctuation rates of each energy, and the corresponding power generation fluctuation rates of each energy are obtained in the following manner: obtaining the corresponding power generation of each energy, analyzing the fluctuation amplitude of the corresponding power generation of each energy, and obtaining the corresponding power generation fluctuation rate of each energy by calculating the standard deviation of the fluctuation amplitude of the corresponding power generation of each energy; It needs to be specifically pointed out that the greater the corresponding power generation fluctuation rate of each energy, the lower the accuracy of the obtained energy amount corresponding to the energy.

[0022] Based on the corresponding power generation fluctuation rate of each energy, an energy influence optimization model is established, an energy influence optimization coefficient is calculated, and the obtained first energy amount is collaboratively optimized based on the energy influence optimization coefficient to obtain the second optimized energy amount; The calculation formula of the energy influence optimization model is: Wherein, A represents the energy influence optimization coefficient, represents a preset maximum allowed power generation fluctuation rate, wi represents the weight of the i-th energy type, wi represents the power generation fluctuation rate corresponding to the i-th energy type, i is the number of each energy type, i = 1, 2, 3, …, n, n is the total number of energy types, ε represents a very small positive number to avoid the denominator being zero, wi represents the average power generation fluctuation rate of the energy supply layer; In the formula, the smaller the power generation fluctuation rate corresponding to the i-th energy type, the larger the energy impact optimization coefficient, indicating that the optimization space required by the energy is small.

[0023] The energy types include but are not limited to wind energy, light energy, and gas.

[0024] The second energy amount = the first energy amount x the energy use impact optimization coefficient.

[0025] It needs to be specifically pointed out that the content of the collaborative optimization processing of the first energy storage amount by establishing the energy storage impact optimization model is as follows: An energy storage impact parameter of the energy reserve layer is obtained, the energy storage impact parameter is a storage efficiency decay factor, and the energy storage efficiency decay factor is obtained in the following manner: the actual capacity and the rated capacity corresponding to each energy storage device are collected, the ratio of the actual capacity to the rated capacity is calculated, and the capacity retention rate corresponding to each energy storage device is obtained , and the cycle number proportion corresponding to each energy storage device is combined to calculate the energy storage efficiency decay factor corresponding to each energy storage device , and the calculation formula is: , wherein temperature correction coefficient j represents the number of each energy storage device, i = 1, 2, 3, …, m, and m is the total number of energy storage devices. In the formula, the larger the energy storage efficiency decay factor, the more serious the energy storage device efficiency decay, when the device is in a brand-new state, the capacity retention rate and the cycle number proportion are both zero, and the energy storage efficiency decay factor is also zero; when the energy storage device reaches the rated cycle life and the capacity retention rate is 80%, it indicates that the energy storage device efficiency decay, at this time, the device maintenance or replacement warning is triggered.

[0026] It needs to be specifically pointed out that the larger the energy storage efficiency decay factor, the less accurate the energy storage amount obtained from the energy reserve layer.

[0027] Based on the energy storage efficiency decay factor corresponding to each energy storage device, an energy storage impact optimization model is established, and an energy storage impact optimization coefficient is calculated, the first energy storage amount is collaboratively optimized based on the energy storage impact optimization coefficient, and an optimized second energy storage amount is obtained; The calculation formula of the energy storage impact optimization model is: Wherein, B represents the energy storage impact optimization coefficient, represents the weight of the jth energy storage device, represents the preset critical attenuation factor.

[0028] In the formula, the greater the energy storage efficiency attenuation factor corresponding to the jth energy storage device, the greater the energy storage impact optimization coefficient, indicating that the optimization demand of the energy storage device is large.

[0029] The second energy storage quantity = the first energy storage quantity x the energy storage impact optimization coefficient. The content of the collaborative optimization processing of the first energy consumption quantity is: obtaining the energy consumption impact parameter of the energy consumption demand layer, the energy consumption impact parameter is the load prediction error rate, and the load prediction error rate is obtained in the following manner: obtaining the actual energy consumption quantity and the predicted energy consumption quantity of the energy consumption demand layer, calculating the absolute value of the difference between the actual energy consumption quantity and the predicted energy consumption quantity, and calculating the ratio of the absolute value to the actual energy consumption quantity to obtain the load prediction error rate Er. It needs to be specifically pointed out that the greater the load prediction error rate, the more inaccurate the obtained energy consumption demand prediction of the energy consumption demand layer is. Based on the load prediction error rate, an energy consumption impact optimization model is established, and an energy consumption impact optimization coefficient is calculated, and the first energy consumption quantity is collaboratively optimized based on the energy consumption impact optimization coefficient to obtain the second energy consumption quantity after optimization. The calculation formula of the energy consumption impact optimization model is: Wherein, C represents the energy consumption impact optimization coefficient, represents the preset maximum allowable load prediction error rate.

[0030] In the formula, the greater the load prediction error rate, the greater the energy consumption impact optimization coefficient, indicating that the greater the deviation degree of the load prediction error rate from the preset maximum allowable load prediction error rate, the greater the optimization demand.

[0031] The second energy consumption quantity = the first energy consumption quantity x the energy consumption impact optimization coefficient.

[0032] S3: Energy quantity and energy storage quantity matching: the second energy quantity and the second energy storage quantity are dynamically matched by establishing an energy storage matching model, and an energy storage matching result is output. In this embodiment, it needs to be specifically pointed out that the execution steps of the energy quantity and energy storage quantity matching are as follows: S31: Obtain a second energy amount of the energy supply layer and a second energy storage amount of the energy storage layer, extract fluctuation characteristic parameters of each energy type in the second energy amount and state parameters of each energy storage device in the second energy storage amount; the fluctuation characteristic parameters include energy change rate and fluctuation duration; the state parameters include response time and maximum charge and discharge power of the energy storage device; and the extracted fluctuation characteristic parameters and state parameters are standardized to eliminate the influence of units; S32: Based on the fluctuation characteristic parameters and the state parameters, an energy storage matching model is established, which adopts a bidirectional compensation matching mechanism, specifically: when the second energy amount is greater than a preset upper threshold P_up, energy absorption distribution is performed according to the priority order of the energy storage device; when the second energy amount is less than a preset lower threshold P_down, energy release distribution is performed according to the priority order of the energy storage device. In one specific embodiment, for a system with a total installed capacity of 10 MW, the preset upper threshold P_up is 85% of the rated power of the system, i.e. 8.5 MW; and the preset lower threshold P_down is 30% of the rated power of the system, i.e. 3 MW.

[0033] The calculation formula of the energy storage matching model is: , wherein MSC ij represents the matching degree score of energy i and energy storage device j, RT j represents the response time of energy storage device j, RT max represents the maximum response time among all energy storage devices, CR i represents the change rate of energy i, CR max represents the maximum change rate among all energies, R j represents the maximum charge and discharge power of energy storage device j, R max represents the maximum charge and discharge power among all energy storage devices, DT i represents the fluctuation duration of energy i, DT max represents the maximum fluctuation duration among all energies, are weight coefficients, and .

[0034] In the formula, the first term evaluates the matching degree of the response speed of the energy storage device and the change rate of the energy; the second term evaluates the matching degree of the power of the energy storage device and the fluctuation duration of the energy; the higher the matching degree score, the more suitable the energy storage device is for processing the energy fluctuation of this type.

[0035] wherein the priority of the energy storage device j is determined by the following calculation formula: , wherein PI j represents the priority index of the jth energy storage device, SOC jR represents the current capacity state percentage of the jth energy storage device, j T represents the maximum charging and discharging power per unit time of the jth energy storage device, j T represents the response time of the jth energy storage device, respectively represent the weight coefficients of the current capacity state SOC, the maximum charging and discharging power, and the response time, and .

[0036] S33: A piecewise linear matching model is used for energy distribution, the working range of the energy storage device is divided into a low capacity section (0%-30%), a medium capacity section (30%-70%), and a high capacity section (70%-100%), different charging power curves are set for different capacity sections, so that a higher charging power coefficient is used in the low capacity section, a medium charging power coefficient is used in the medium capacity section, and a lower charging power coefficient is used in the high capacity section, so as to protect the energy storage device and prolong its service life; the energy storage capacity balancing distribution strategy is realized, when multiple energy storage devices are available at the same time, the distribution proportion of each energy storage device is calculated , the calculation formula is: , wherein, represents the sum of the values of all available energy storage devices k in the system, and the formula makes the energy storage device with lower SOC obtain a higher charging distribution proportion, so as to realize the equalization of the capacities of the energy storage devices in the system; S34: A threshold triggering matching mechanism is set, the intelligent matching of energy and energy storage is realized by monitoring the fluctuation of each energy and the state of each energy storage device; specifically, when the real-time fluctuation of a certain energy exceeds the preset fluctuation threshold, the system checks the current capacity state of each energy storage device; if the current capacity state of the energy storage device is lower than the preset lower threshold, the discharging operation of the energy storage device is triggered to supplement the energy of the system; if the current capacity state of the energy storage device is higher than the preset upper threshold, the charging operation of the energy storage device is triggered to absorb the excess energy of the system; in this way, the system can automatically adjust the energy flow direction according to the energy fluctuation and the energy storage state, and maintain stable operation of the system; finally, the energy storage matching result is output, which includes the real-time charging and discharging power distribution value, the operation time window, and the priority order of each energy storage device, serving as the input parameter for subsequent cross-layer collaborative scheduling.

[0037] S4: Energy amount and energy consumption amount matching: dynamically matching the second energy amount and the second energy consumption amount, and outputting an energy consumption demand matching result; In this embodiment, it needs to be specifically explained that the execution steps of the energy amount and energy consumption amount matching are as follows: ​S41: Obtain the second energy amount of the energy supply layer and the second energy consumption amount of the energy consumption layer, extract the available power curve of the second energy amount and the quantified characteristic parameters of each load in the second energy consumption amount; the quantified characteristic parameters include the energy consumption elasticity index and the time value decay rate of the load; The extracted available power curve and quantified characteristic parameters are standardized to eliminate the influence of different dimensions and ensure the comparability of the parameters in subsequent calculations; The energy consumption elasticity index represents the proportion of the adjustable range of load power, and the value range is 0-1. The larger the value, the larger the power adjustment space. The time value decay rate represents the value loss proportion caused by each unit of time delay of the load, and the value range is 0-1. The smaller the value, the higher the acceptability of time delay. S42: Obtain the total available energy amount and the total energy consumption demand amount, calculate the ratio of the total available energy amount to the total energy consumption demand amount to obtain the system energy supply and demand ratio ESR, and determine the system regulation intensity SI based on the system energy supply and demand ratio. The calculation formula is: The system regulation intensity SI represents the degree of demand side regulation required. SI is 0, indicating that no regulation is required. SI is close to 1, indicating that strong regulation is required. S43: Construct a load response function to obtain each load response f, and determine a load control strategy in combination with the system regulation intensity, execute the load control strategy, and output an energy demand matching result, which includes the adjusted power value of each load and the adjusted operation time of each load.

[0038] The formula of the load response function is: wherein, represents each load response, represents the energy consumption elasticity index of each load, represents the time value decay rate of each load, is a weight coefficient, and v represents the number of each load.

[0039] It should be specifically noted that the content of determining the load control strategy and executing the load control strategy is as follows: Determine the load regulation strategy: for each load v, multiply the system regulation intensity by the load response, to get the actual regulation degree, which represents the degree of regulation required for the load, the numerical range is 0-1, based on the actual regulation degree and the quantitative characteristic parameters, determine the power regulation amount and the time regulation amount, the determination method of the power regulation amount is: multiply the actual regulation degree by the energy elasticity index to get the power reduction ratio, if the power reduction ratio exceeds the energy elasticity index, then take the energy elasticity index as the upper limit; multiply the power reduction ratio by the original power of the load to get the specific power reduction amount; the determination method of the time regulation amount is: divide the actual regulation degree by the time value decay rate to get the time delay amount, if the time delay amount exceeds the maximum allowed delay time, then take the maximum allowed delay time as the upper limit; Execute the load regulation strategy: based on the power regulation amount and the time regulation amount, execute the regulation operation for each load, for power regulation, subtract the power regulation amount from the original power of the load to get the new power value after adjustment; for time regulation, add the time regulation amount to the original planned operation time of the load to get the new operation time after adjustment.

[0040] It needs to be specifically pointed out that in the execution of the regulation, the main regulation mode of the load is preferentially selected: for the load with the energy elasticity index higher than the time value decay rate, power regulation is preferentially implemented; for the load with the time value decay rate lower than the energy elasticity index, time regulation is preferentially implemented. Through this differentiated regulation execution, it is ensured that the total energy demand of the system is reduced to a level matching the available energy amount.

[0041] S5: Generate a cross-layer collaborative scheduling strategy: based on the energy storage matching result and the energy demand matching result, analyze the energy storage and energy demand to generate a cross-layer collaborative scheduling strategy; In this embodiment, it needs to be specifically pointed out that the content of generating the cross-layer collaborative scheduling strategy is: obtaining the energy storage matching result and the energy demand matching result, determining three optimization targets of the system economy, stability and environmental protection, constructing the collaborative relationship matrix among the energy supply layer, the energy storage layer and the energy demand layer, formulating the basic scheduling strategy and balancing the possible scheduling conflicts among the layers, finally generating a time-of-use scheduling plan, outputting a complete cross-layer collaborative scheduling strategy containing control instructions of each layer, collaborative optimization parameters and execution time arrangement, realizing the collaborative optimization operation of the system as a whole.

[0042] S6: Optimize the cross-layer collaborative scheduling strategy: execute the cross-layer collaborative scheduling strategy and monitor the demand side equipment in real time, evaluate the power quality by obtaining the power quality parameters, and dynamically adjust the cross-layer collaborative scheduling strategy based on the evaluation result.

[0043] In this embodiment, it needs to be specifically pointed out that the content of optimizing the cross-layer collaborative scheduling strategy is as follows: The cross-layer cooperative scheduling strategy is executed, real-time monitoring of demand side equipment is performed through deployment of power quality monitoring devices, power quality parameters are collected, the power quality parameters include voltage deviation coefficients and frequency deviation coefficients, the obtained power quality parameters are standardized and pretreated, and weighted calculation is performed to obtain power quality deviation coefficients, power quality is evaluated based on the power quality deviation coefficients, power quality evaluation results are output, and the cross-layer cooperative scheduling strategy is dynamically adjusted based on the power quality evaluation results.

[0044] The voltage deviation coefficient is obtained by obtaining a voltage actual value and a voltage rated value, calculating an absolute value of a difference between the voltage actual value and the voltage rated value, and calculating a ratio of the absolute value to a preset maximum voltage deviation to obtain the voltage deviation coefficient. The frequency deviation coefficient is obtained by obtaining a frequency actual value and a frequency rated value, calculating an absolute value of a difference between the frequency actual value and the frequency rated value, and calculating a ratio of the absolute value to a preset maximum frequency deviation to obtain the frequency deviation coefficient. The power quality is evaluated based on the power quality deviation coefficient by comparing the power quality deviation coefficient with a preset power quality deviation coefficient threshold value, determining that the power quality is normal if the power quality deviation coefficient is less than the preset power quality deviation coefficient threshold value, otherwise determining that the power quality is abnormal, issuing a warning information for the abnormal result, and outputting the power quality evaluation result.

[0045] In the embodiment, it is specifically pointed out that the above formulas are all dimensionless values calculated, the formulas are obtained by software simulation of a large amount of data to obtain a formula of the nearest real situation, and preset parameters in the formulas are set by a person skilled in the art according to actual conditions.

[0046] Embodiment 2 Please refer to Figure 2 As shown in the figure, the present application provides a multi-energy complementary energy storage EMS cross-layer cooperative scheduling system, which comprises a layer energy data acquisition module, an energy data cooperative optimization module, an energy quantity and energy storage quantity matching module, an energy quantity and energy consumption quantity matching module, a cross-layer cooperative scheduling strategy generation module and a cross-layer cooperative scheduling strategy optimization module.

[0047] The layer energy data acquisition module is used for acquiring a first energy quantity of an energy supply layer, a first energy storage quantity of an energy storage layer and a first energy consumption quantity of an energy consumption demand layer in real time. The energy data cooperative optimization module is used for cooperative optimization processing of the first energy quantity, the first energy storage quantity and the first energy consumption quantity to obtain an optimized second energy quantity, a second energy storage quantity and a second energy consumption quantity. An energy quantity and energy storage quantity matching module is configured to dynamically match the second energy quantity and the second energy storage quantity by establishing an energy storage matching model, and output an energy storage matching result; An energy quantity and energy consumption quantity matching module is configured to dynamically match the second energy quantity and the second energy consumption quantity, and output an energy consumption demand matching result; A cross-layer collaborative scheduling strategy generation module is configured to analyze energy storage and energy consumption demand based on the energy storage matching result and the energy consumption demand matching result, and generate a cross-layer collaborative scheduling strategy; A cross-layer collaborative scheduling strategy optimization module is configured to execute the cross-layer collaborative scheduling strategy, monitor demand-side devices in real time, evaluate power quality by acquiring power quality parameters, and dynamically adjust the cross-layer collaborative scheduling strategy based on the evaluation result.

[0048] Finally, the above is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

[0049] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for EMS cross-layer collaborative scheduling of multiple energy types, characterized in that, The method comprises the following steps: S1: Real-time acquisition of each layer energy data: Real-time acquisition of the first energy amount of the energy supply layer, the first energy storage amount of the energy storage layer, and the first energy consumption amount of the energy consumption demand layer; S2: Collaborative optimization of each layer energy data: Collaborative optimization processing of the first energy amount, the first energy storage amount, and the first energy consumption amount to obtain the second energy amount, the second energy storage amount, and the second energy consumption amount after optimization; S3: Energy amount and energy storage amount matching: Dynamically matching the second energy amount and the second energy storage amount through the establishment of an energy storage matching model to output an energy storage matching result; S4: Energy amount and energy consumption amount matching: Dynamically matching the second energy amount and the second energy consumption amount to output an energy consumption demand matching result; S5: Generation of cross-layer collaborative scheduling strategy: Based on the energy storage matching result and the energy consumption demand matching result, analyzing the energy storage and the energy consumption demand to generate a cross-layer collaborative scheduling strategy; S6: Optimization of the cross-layer collaborative scheduling strategy: Executing the cross-layer collaborative scheduling strategy and real-time monitoring of the demand side equipment, evaluating the power quality by acquiring power quality parameters, and dynamically adjusting the cross-layer collaborative scheduling strategy based on the evaluation result. 2.The multi-energy complementary EMS cross-layer collaborative scheduling method of claim 1, wherein: The content of the real-time acquisition of each layer energy data is as follows: Through the metering instruments matched with each energy equipment of the energy supply layer, the current energy power value of each energy equipment is collected in real time, the current energy power value is summarized to obtain the total energy amount that can be provided by the energy supply layer at present, the energy component affected by meteorological data is extracted, and the difference between the total energy amount and the energy component is taken as the first energy amount of the energy supply layer; Through each user end metering device of the energy consumption demand layer, the current energy consumption value of each user is collected in real time, the current energy consumption value is summarized to obtain the total energy consumption value of the energy consumption demand layer at present, the energy consumption component affected by time and seasonal factors is extracted, and the difference between the total energy consumption value and the energy consumption component is taken as the first energy consumption amount of the energy consumption demand layer.

3. The multi-energy complementary energy storage EMS cross-layer collaborative scheduling method according to claim 1, characterized in that: The content of the collaborative optimization of each layer energy data is as follows: Reading the first energy amount, the first energy storage amount, and the first energy consumption amount, and collaboratively optimizing the first energy amount through the establishment of an energy influence optimization model to obtain the second energy amount after optimization; Collaboratively optimizing the first energy storage amount through the establishment of an energy storage influence optimization model to obtain the second energy storage amount after optimization; Collaboratively optimizing the first energy consumption amount through the establishment of an energy consumption influence optimization model to obtain the second energy consumption amount after optimization; The content of the collaborative optimization of the first energy amount is: acquiring the energy influence parameters of the energy supply layer, the energy influence parameters being the power generation fluctuation rates corresponding to each energy, the power generation fluctuation rates corresponding to each energy being acquired in the following manner: acquiring the power generation amounts corresponding to each energy, analyzing the fluctuation amplitudes of the power generation amounts corresponding to each energy, and obtaining the power generation fluctuation rates corresponding to each energy by calculating the standard deviation of the fluctuation amplitudes of the power generation amounts corresponding to each energy; Based on the corresponding power generation fluctuation rate of each energy, an energy influence optimization model is established, and an energy influence optimization coefficient is calculated. The first energy amount obtained is processed by collaborative optimization based on the energy influence optimization coefficient, and a second optimized energy amount is obtained.

4. The multi-energy complementary energy storage EMS cross-layer collaborative scheduling method according to claim 3, characterized in that: The content of the collaborative optimization processing of the first energy reserve amount by establishing the energy reserve influence optimization model is as follows: An energy reserve influence parameter of the energy reserve layer is obtained, which is an energy reserve efficiency decay factor. The energy reserve efficiency decay factor is obtained by collecting the actual capacity and rated capacity of each energy reserve device, calculating the ratio of the actual capacity and the rated capacity, obtaining the capacity retention rate of each energy reserve device, and combining the cycle number proportion of each energy reserve device to calculate the energy reserve efficiency decay factor of each energy reserve device; Based on the corresponding energy reserve efficiency decay factor of each energy reserve device, an energy reserve influence optimization model is established, and an energy reserve influence optimization coefficient is calculated. The first energy reserve amount is processed by collaborative optimization based on the energy reserve influence optimization coefficient, and a second optimized energy reserve amount is obtained. The content of the collaborative optimization processing of the first energy amount is as follows: the energy consumption demand layer is obtained, and the energy consumption influence parameter is obtained, which is the load prediction error rate. The load prediction error rate is obtained by obtaining the actual energy consumption and the predicted energy consumption of the energy consumption demand layer, calculating the absolute value of the difference between the actual energy consumption and the predicted energy consumption, and calculating the ratio of the absolute value and the actual energy consumption to obtain the load prediction error rate; Based on the load prediction error rate, an energy consumption influence optimization model is established, and an energy consumption influence optimization coefficient is calculated. The first energy amount is processed by collaborative optimization based on the energy consumption influence optimization coefficient, and a second optimized energy consumption amount is obtained.

5. The multi-energy complementary energy storage EMS cross-layer collaborative scheduling method according to claim 1, characterized in that: The execution steps of the energy amount and energy reserve amount matching are as follows: S31: Obtain the second energy amount of the energy supply layer and the second energy reserve amount of the energy reserve layer, extract the fluctuation characteristic parameters of each energy type in the second energy amount and the state parameters of each energy reserve device in the second energy reserve amount; the fluctuation characteristic parameters include energy change rate and fluctuation duration; The state parameters include the response time and maximum charge and discharge power of the energy reserve device; S32: Based on the fluctuation characteristic parameters and the state parameters, an energy storage matching model is established, which adopts a bidirectional compensation matching mechanism. Specifically, when the second energy amount is greater than a preset upper threshold P_up, energy absorption distribution is performed according to the priority order of the energy reserve device; when the second energy amount is less than a preset lower threshold P_down, energy release distribution is performed according to the priority order of the energy reserve device; S33: A segmented linear matching model is used for energy distribution, the working range of the energy reserve device is divided into a low capacity segment, a medium capacity segment and a high capacity segment, different charge power curves are set for different capacity segments, and a balanced distribution strategy of energy reserve capacity is realized. When multiple energy reserve devices are available at the same time, the distribution proportion of each energy reserve device is calculated; S34: Set the threshold trigger matching mechanism, by monitoring the fluctuation of each energy and the state of each energy storage device, realize the intelligent matching of energy and energy storage; Specifically: when the real-time fluctuation of a certain energy exceeds the preset fluctuation threshold, the system will check the current capacity state of each energy storage device; If the current capacity state of the energy storage device is lower than the preset lower limit threshold, the discharge operation of the energy storage device is triggered to supplement the energy to the system; If the current capacity state of the energy storage device is higher than the preset upper limit threshold, the charging operation of the energy storage device is triggered to absorb the excess energy of the system, and finally the energy storage matching result is output.

6. The multi-energy complementary energy storage EMS cross-layer collaborative scheduling method according to claim 1, characterized in that: The execution steps of matching the energy amount with the energy consumption amount are as follows: S41: Obtain the second energy amount of the energy supply layer and the second energy consumption amount of the energy consumption demand layer, extract the available power curve of the second energy amount and the quantified characteristic parameters of each load in the second energy consumption amount; The quantified characteristic parameters include the energy consumption elasticity index and the time value decay rate of the load; S42: Obtain the total available energy amount and the total energy consumption demand amount, calculate the ratio of the total available energy amount to the total energy consumption demand amount to obtain the system energy supply and demand ratio ESR, and determine the system regulation intensity SI based on the system energy supply and demand ratio; S43: Construct a load response function to obtain each load response f, and determine the load regulation strategy in combination with the system regulation intensity, execute the load regulation strategy, and output the energy consumption demand matching result.

7. The multi-energy complementary energy storage EMS cross-layer collaborative scheduling method according to claim 6, characterized in that: The content of determining the load regulation strategy and executing the load regulation strategy is as follows: Determine the load regulation strategy: for each load v, multiply the system regulation intensity by the load response to obtain the actual regulation degree, which represents the degree of regulation required for the load, determine the power regulation amount and the time regulation amount based on the actual regulation degree and the quantified characteristic parameters, the determination method of the power regulation amount is: multiply the actual regulation degree by the energy consumption elasticity index to obtain the power reduction ratio, if the power reduction ratio exceeds the energy consumption elasticity index, the energy consumption elasticity index is taken as the upper limit; Multiply the power reduction ratio by the original power of the load to obtain the specific power reduction amount; The determination method of the time regulation amount is: divide the actual regulation degree by the time value decay rate to obtain the time delay amount, if the time delay amount exceeds the maximum allowed delay time, the maximum allowed delay time is taken as the upper limit; Execute the load regulation strategy: based on the power regulation amount and the time regulation amount, execute the regulation operation on each load, for power regulation, subtract the power regulation amount from the original power of the load to obtain the adjusted new power value; For time regulation, add the time regulation amount to the original planned operation time of the load to obtain the adjusted new operation time.

8. A multi-energy complementary energy storage EMS cross-layer collaborative scheduling system for implementing the multi-energy complementary energy storage EMS cross-layer collaborative scheduling method of any one of claims 1-7. It includes: Each layer of energy data acquisition module: used for real-time acquisition of the first energy amount of the energy supply layer, the first energy storage amount of the energy storage layer and the first energy consumption amount of the energy consumption demand layer; Energy data collaborative optimization module: used for collaborative optimization processing of the first energy amount, the first energy storage amount and the first energy consumption amount, to obtain the second energy amount, the second energy storage amount and the second energy consumption amount after optimization; An energy quantity and energy storage quantity matching module is configured to dynamically match the second energy quantity and the second energy storage quantity by establishing an energy storage matching model, and output an energy storage matching result; An energy quantity and energy consumption quantity matching module is configured to dynamically match the second energy quantity and the second energy consumption quantity, and output an energy consumption demand matching result; A cross-layer collaborative scheduling strategy generation module is configured to analyze the energy storage and energy consumption demand based on the energy storage matching result and the energy consumption demand matching result, and generate a cross-layer collaborative scheduling strategy; A cross-layer collaborative scheduling strategy optimization module is configured to execute the cross-layer collaborative scheduling strategy, and monitor the demand side equipment in real time, evaluate the power quality by acquiring power quality parameters, and dynamically adjust the cross-layer collaborative scheduling strategy based on the evaluation result.

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