Building micro-grid multi-element collaborative optimization method and system, electronic equipment and medium

Abstract

The invention relates to the technical field of electric power operation and maintenance, in particular to a building micro-grid multi-element collaborative optimization method and system, electronic equipment and a medium, and the method comprises the steps: setting a micro-grid optimization reference index based on building energy consumption characteristics, equipment safety specifications and power grid access requirements; based on a preset acquisition frequency, acquiring multivariate state characteristics of the building micro-grid in real time; inputting the microgrid optimization reference index and the multivariate state characteristics into a pre-constructed multivariate collaborative optimization model to obtain an optimization control instruction of each regulation and control unit; for each regulation and control unit, obtaining a corresponding current control instruction, and calculating an optimization amplitude according to the current control instruction and the optimization control instruction; based on the optimization amplitude, an optimization adjustment strategy corresponding to the regulation and control unit is generated, and the optimization adjustment strategy is executed; the method can deeply integrate building energy consumption characteristics, equipment operation constraints and power grid interaction requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power operation and maintenance, in particular to a building micro-grid multi-element collaborative optimization method, system, electronic device and medium. BACKGROUND

[0002] With the deepening of the global energy structure transformation, distributed clean energy represented by photovoltaic is increasingly high in the building energy system. As a comprehensive energy utilization unit integrating local new energy generation, energy storage system, direct current load and grid interaction, the building micro-grid has become a key technical path to improve building energy efficiency, promote on-site consumption of renewable energy and reduce dependence on traditional power grids.

[0003] However, the existing building micro-grid energy management method focuses on single target control, such as independent regulation and control of photovoltaic, energy storage, load and other units, and lacks a collaborative optimization framework. For example, some methods only aim to maximize photovoltaic self-generation and self-use, which may ignore energy storage life loss or load adjustment restrictions; some only balance in real time according to the current state, without combining historical characteristics and future trends of building energy consumption for forward-looking optimization, resulting in insufficient optimization depth and poor dynamic response characteristics when facing multiple constraints and target conflicts, affecting the overall economy and stability of the building micro-grid.

[0004] Therefore, there is an urgent need for a collaborative optimization method that can deeply integrate building energy consumption characteristics, equipment operation constraints and grid interaction requirements. SUMMARY

[0005] To solve the above technical problems, the present application provides a building micro-grid multi-element collaborative optimization method, system, electronic device and medium.

[0006] In a first aspect, the present application provides a building micro-grid multi-element collaborative optimization method, comprising: Based on building energy consumption characteristics, equipment safety specifications and grid access requirements, set micro-grid optimization benchmark indicators; Based on a preset collection frequency, real-time collection of multi-element state characteristics of the building micro-grid; Input the micro-grid optimization benchmark indicators and the multi-element state characteristics into a pre-constructed multi-element collaborative optimization model to obtain optimization control instructions for each control unit; For each control unit, obtain the corresponding current control instruction, and calculate the optimization amplitude according to the current control instruction and the optimization control instruction; Based on the optimization amplitude, generate an optimization adjustment strategy corresponding to the control unit, and execute the optimization adjustment strategy.

[0007] Further, the micro-grid optimization benchmark index comprises a minimum acceptance rate of clean energy, a safe operating boundary of energy storage, a flexible load regulation amplitude limit value, and a grid-connected power permission threshold.

[0008] Further, the multi-element state feature comprises instantaneous output of a photovoltaic unit, residual energy state of an energy storage system, real-time energy consumption of a direct current load, and grid interaction power.

[0009] Further, the setting method of the preset acquisition frequency comprises: a basic acquisition frequency is set for the instantaneous output of the photovoltaic unit, the residual energy state of the energy storage system, the real-time energy consumption of the direct current load, and the grid interaction power, respectively; a dynamic correction factor system is constructed, the dynamic correction factor system comprising a fluctuation intensity correction factor, a regulation priority correction factor, and a device cost correction factor; for each state feature, the product of the basic acquisition frequency and each correction factor in the dynamic correction factor system is multiplied to obtain the preset acquisition frequency of the state feature.

[0010] Further, the fluctuation intensity correction factor is determined by calculating the fluctuation amplitude per unit time of the state feature and comparing it with a preset low fluctuation threshold and a high fluctuation threshold; the regulation priority correction factor is determined based on the priority of each state feature set by the micro-grid optimization benchmark index; the device cost correction factor is determined by monitoring the operating energy consumption and communication link bandwidth occupancy rate of the acquisition device.

[0011] Further, the construction of the multi-element cooperative optimization model comprises: a dynamic balance relationship is constructed based on the principle of energy conservation; a correlation coupling matrix is introduced to quantify the linkage relationship between the state features of photovoltaic, energy storage, load, and grid, the correlation coupling matrix comprising a photovoltaic energy storage correlation coefficient, a photovoltaic load correlation coefficient, an energy storage load correlation coefficient, and an energy storage grid correlation coefficient; the micro-grid optimization benchmark index is converted into a mathematical constraint condition; a planning algorithm is used to solve a function with the objective of minimizing the comprehensive operating cost to obtain the incremental regulation amount of each regulation unit, and the optimization control instruction is generated in combination with the current state feature.

[0012] Further, the generation of the optimization regulation strategy comprises: according to the positive and negative and size of the optimization amplitude, in combination with the device tolerance rate of the regulation unit, the current operating state, and the external environmental conditions, the regulation process is divided into multiple ladder steps, and the regulation rate of each step is controlled.

[0013] In another aspect, the application also provides a building micro-grid multi-element collaborative optimization system, which comprises: a benchmark index setting module, configured to set a micro-grid optimization benchmark index according to building energy consumption characteristics, equipment safety specifications and grid access requirements; a state feature collection module, configured to collect multi-element state features of the building micro-grid in real time according to a preset collection frequency; an optimization instruction generation module, configured to input the micro-grid optimization benchmark index and the multi-element state features into a pre-constructed multi-element collaborative optimization model to obtain optimization control instructions of each regulation and control unit; an optimization amplitude calculation module, configured to obtain corresponding current control instructions for each regulation and control unit, and calculate optimization amplitudes according to the current control instructions and the optimization control instructions; an adjustment strategy execution module, configured to generate an optimization adjustment strategy corresponding to the regulation and control unit according to the optimization amplitudes, and execute the optimization adjustment strategy.

[0014] In a third aspect, the application provides an electronic device, comprising a bus, a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor, the transceiver, the memory and the processor being connected through the bus, and the computer program being executed by the processor to implement the steps in any of the above methods.

[0015] In a fourth aspect, the application also provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps in any of the above methods.

[0016] Compared with the prior art, the beneficial effects of the present application are: the present application sets multi-dimensional optimization benchmark indicators, integrates building energy characteristics, equipment safety specifications, and grid access requirements into a unified constraint framework, realizes multi-objective balance under the premise of ensuring the safe operation of each unit, and avoids the loss of overall system benefits caused by the priority of a single target; through real-time data collection with a preset collection frequency, and based on the benchmark indicators set according to the building energy characteristics, the real-time multi-element state characteristics are relied on to ensure the timeliness of the decision, and the historical energy consumption rules implied in the benchmark indicators are used to realize the forward-looking adaptation to future scenarios, so that the optimization decision can not only cope with real-time fluctuations, but also fit the long-periodicity characteristics of building energy consumption, avoiding the disconnection between short-term optimization and long-term benefits; through the multi-element collaborative optimization model, the benchmark indicators are deeply coupled with the multi-element state characteristics, and the collaborative optimization instructions of each control unit are output, for example, when the instantaneous output of photovoltaic suddenly increases, the model will simultaneously calculate the energy storage charging power, the flexible load adjustment amplitude, and the grid power reduction amount, to ensure the synergistic effect of each control unit, avoid system imbalance caused by insufficient adjustment capacity or action conflict of a single control unit, and improve the overall control efficiency; the optimization amplitude calculation is used to avoid the impact of the control unit caused by the sudden change of the instruction; for example, when the photovoltaic output fluctuates greatly, instead of directly outputting extreme adjustment instructions, a stepwise adjustment strategy is generated according to the optimization amplitude of the existing instruction and the optimization instruction, which can realize system power balance and ensure equipment operation stability. In the present application, the benchmark indicators are set based on static rules such as building energy characteristics and equipment specifications, to ensure the compliance and bottom line safety of the optimization decision; real-time multi-element state characteristics are used to represent dynamic variables such as photovoltaic fluctuations and load changes, to ensure the adaptability of the decision; after the two are coupled through the multi-element collaborative optimization model, they can meet the fixed needs of different building types and respond to unexpected scenarios; the present application uses the multi-element collaborative model to make the actions of each control unit support each other, for example, when the photovoltaic output peak and the load peak do not completely coincide, through the collaborative action of the advance charging of the energy storage combined with the peak-shifting adjustment of the flexible load, the photovoltaic consumption rate is improved, the deep charging and discharging of the energy storage is avoided, and the load energy consumption is ensured, so that the system can maintain high energy utilization efficiency and reduce equipment operation and maintenance costs in the long-term operation, and realize the maximization of comprehensive benefits. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of the building microgrid multi-element collaborative optimization method of the present application; Figure 2 is a structural diagram of the building microgrid multi-element collaborative optimization system of the present application. DETAILED DESCRIPTION

[0018] In the description of the present application, those skilled in the art shall understand that the present application can be implemented as a method, an apparatus, an electronic device and a computer readable storage medium. Therefore, the present application can be specifically implemented as follows: complete hardware, complete software (including firmware, resident software, microcode, etc.), and a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented as a computer program product in one or more computer readable storage media, which contains computer program code.

[0019] The computer readable storage medium described above can adopt any combination of one or more computer readable storage media. The computer readable storage medium includes an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or component, or any combination thereof. More specific examples of computer readable storage media include portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, compact disks read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used or combined with an instruction execution system, device or component.

[0020] It should be understood that each block of the flowchart and / or block diagram and combinations of blocks in the flowchart and / or block diagram can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine, so that the computer readable program instructions, when executed by the computer or other programmable data processing apparatus, generate an apparatus that implements the functions / operations specified in the block of the flowchart and / or block diagram.

[0021] These computer readable program instructions can also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to work in a specific manner. Thus, the instructions stored in the computer readable storage medium produce an instruction apparatus product that includes the functions / operations specified in the block of the flowchart and / or block diagram.

[0022] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus or other equipment, so that a series of operation steps are performed on the computer, other programmable data processing apparatus or other equipment to produce a computer implemented process, so that the instructions executed on the computer or other programmable data processing apparatus can provide the process specified in the block of the flowchart and / or block diagram.

[0023] The present application will be described below in conjunction with the drawings in the present application.

[0024] Example 1: Figure 1 As shown, the multi-element collaborative optimization method of a building microgrid of the present invention specifically includes the following steps: Step S1: Based on the building energy consumption characteristics, equipment safety specifications, and grid access requirements, set microgrid optimization benchmark indicators; the microgrid optimization benchmark indicators include the minimum clean energy acceptance rate, energy storage operation safety margin, flexible load regulation amplitude limit, and grid-connected power permission threshold; Step S1 sets microgrid optimization benchmark indicators. In the multi-element collaborative optimization of building microgrids, microgrid optimization benchmark indicators are used to anchor the bottom line of safe operation of each link, determine the renewable energy utilization target, and provide constraints and optimization directions for the multi-element collaborative optimization model. The microgrid optimization benchmark indicators include the minimum acceptance rate of clean energy, the safety boundary of energy storage operation, the flexible load regulation amplitude limit and the grid-connected power permission threshold. The setting of each indicator needs to integrate the building energy consumption characteristics, equipment safety specifications, and grid access requirements, and adopt the logic of basic value setting and dynamic correction to ensure that the indicators meet rigid specifications and adapt to dynamic operation scenarios.

[0025] Specifically, the minimum clean energy acceptance rate refers to the proportion of distributed clean energy that the building microgrid must at least absorb to the total power generation. The setting steps are as follows: Step S111: Extract the total photovoltaic power generation of the building microgrid covering the entire seasonal cycle and the total building electricity consumption during the same period, calculate the monthly average self-generation and self-consumption rate, and increase the monthly average self-generation and self-consumption rate by a certain percentage as the basic acceptance rate, based on the historical absorption capacity and reserving room for improvement; Step S112: Combine the seasonal characteristics of building energy consumption with the photovoltaic output pattern to further increase the photovoltaic output during the peak season based on the base value, maintain the base value during the off-season, and moderately increase it during the stable period to adapt to the output differences in different seasons; Step S113: retrieve the weather forecast for the next day every day, predict the photovoltaic power generation for the next day through the photovoltaic output prediction model, and adjust the acceptance rate for the day according to the prediction results; at the same time, count the proportion of adjustable power of flexible loads in real time, and further adjust the current acceptance rate based on the proportion, so as to take on more photovoltaic power by utilizing load flexibility.

[0026] The energy storage operation safety boundary refers to the allowable range of the remaining energy state of the energy storage system. The setting steps are as follows: Step S121: Set an initial range based on the energy storage device type and refer to the device factory specifications to avoid damage to the battery due to deep charging and discharging; Step S122: The energy storage controller collects data such as the number of battery cycles and charge / discharge depth in real time to evaluate the health of the energy storage system. The boundary range is adjusted according to changes in health, and the depth of charge / discharge is reduced to delay aging. Step S123, monitor the energy storage device environment temperature through the temperature sensor, adjust the boundary range according to the temperature condition, avoid damage to the battery caused by extreme temperature; at the same time, combine the short-term load demand of the building, temporarily adjust the boundary range, reserve more discharge space to ensure power supply.

[0027] The flexible load regulation range limit value refers to the power regulation range of the adjustable load, and the setting steps are as follows: Step S131, determine the initial adjustment range according to the flexible load type, and set according to the load rated power and operation requirements; Step S132, for the old load with long operation time, narrow the adjustment range to reduce the adjustment failure caused by equipment aging; monitor the load operation state through the sensor, temporarily reduce the adjustment range if an abnormality occurs, and restore the adjustment range when the equipment returns to normal; Step S133, adjust according to the time period characteristics of building energy consumption, narrow the adjustment range during the high-frequency use period of users to avoid affecting user experience, and widen the adjustment range during the low-frequency use period of users to improve the load's ability to absorb energy.

[0028] The grid-connected power permission threshold refers to the allowable range of the interaction power of the building microgrid and the public grid, and the setting steps are as follows: Step S141, take the rated capacity of the building microgrid and the public grid access point as the benchmark, set the initial threshold value according to the grid safety access specification to avoid over-capacity operation causing grid fluctuation; Step S142, connect to the local grid time-of-use electricity price policy, adjust the threshold value according to the electricity price of different time periods to reduce the cost of high-price electricity purchase and increase the income of low-price electricity sale; Step S143, receive the load warning signal issued by the regional grid, temporarily adjust the threshold value according to the warning type to reduce the load pressure on the main grid or respond to the demand of the grid.

[0029] More specifically, in order to prevent contradictions caused by independent setting of each benchmark index, cross-index linkage rules need to be established; when the minimum clean energy acceptance rate is temporarily increased, the energy storage operation safety boundary and the grid-connected power permission threshold are adjusted simultaneously to preferentially consume photovoltaic power through energy storage and load; when the energy storage health degree decreases to a certain extent, the increase amplitude of the minimum clean energy acceptance rate is controlled to avoid excessive use of energy storage; when the peak period electricity price of the grid overlaps with the photovoltaic output peak, the minimum clean energy acceptance rate and the flexible load regulation range limit value are adjusted simultaneously to maximize the local consumption to reduce peak period electricity purchase.

[0030] It should be noted that the setting of the micro-grid optimization benchmark index in this step S1 is to guide the multi-dimensional constraints and targets of the building micro-grid, and to realize the deep coupling of the index and the system state and the operation scene through the logic of the basic value anchoring and the dynamic factor correction. Its protection range is not only limited to the specific index calculation method, but also covers all the modified implementation manners that meet the core logic. For example, the basic value of the minimum acceptance rate of clean energy can be calculated based on historical data of different time lengths, and the correction factor can be adjusted according to the energy consumption difference of the building type. The safe boundary of the energy storage operation can be updated according to the iteration of the energy storage technology, and the health evaluation parameters can be increased in more dimensions. The flexible load regulation range limit value can be adjusted according to the characteristics of the new type of load. The grid-connected power threshold can be corrected in combination with the micro-grid scale or the grid policy. The cross-index linkage rule can be dynamically adjusted according to the multi-target priority. Any technical solution that realizes the index quantization and coordination through the basic setting and dynamic adaptation based on the three core dimensions of building energy consumption characteristics, equipment safety specifications and grid access requirements falls within the protection range of this step, aiming to cover the micro-grid benchmark index setting requirements in different building scenes, different equipment types and different grid policies, and avoid limiting the protection range due to differences in specific parameters or calculation details.

[0031] In this embodiment, through the multi-dimensional benchmark index, photovoltaic consumption, equipment safety, grid compliance and user experience are included in the unified constraint, avoiding the loss of overall system benefits caused by single target priority in existing methods. The basic value and dynamic correction logic of the benchmark index not only adapts to the seasonal and period characteristics of building energy consumption, but also responds to sudden scenes, solving the problem that the fixed parameters of existing methods cannot adapt to dynamic scenes. The benchmark index converts building energy consumption characteristics, equipment safety specifications and grid access requirements into quantitative parameters, avoiding the confusion of optimization direction caused by fuzzy constraints. The cross-index linkage rule provides coordination logic between indexes. The dynamic correction of the energy storage safety boundary can reduce deep charging and discharging, delay energy storage aging and reduce equipment replacement cost. The time-of-use price correction of the grid-connected threshold can reduce peak period high price power purchase and increase valley period low price power sale, improving the economic efficiency of the micro-grid. The scenario-based correction of the flexible load can avoid abnormal adjustment of the equipment and reduce operation and maintenance failures.

[0032] Step S2, based on the preset acquisition frequency, real-time acquisition of the multi-element state characteristics of the building micro-grid; the multi-element state characteristics include photovoltaic unit instantaneous output, energy storage system residual energy state, direct current load real-time energy consumption and grid interaction power; The multi-element state characteristics include photovoltaic unit instantaneous output, energy storage system residual energy state, direct current load real-time energy consumption and grid interaction power. The acquisition of each characteristic needs to rely on professional hardware equipment and stable communication architecture, and follow the principles of high-frequency acquisition, accurate metering and reliable transmission, and realize real-time monitoring in all time periods combined with the preset acquisition frequency.

[0033] Specifically, for the instantaneous output of photovoltaic units, high-precision power sensors are installed at the output end of the photovoltaic array combiner box or the inverter to cover the dynamic range of photovoltaic output and ensure the measurement accuracy; light intensity sensors and component temperature sensors are deployed around the photovoltaic array to synchronously collect environmental data to assist in determining the cause of photovoltaic output fluctuations; the power sensors collect photovoltaic output power at a preset frequency, and the light and temperature sensors synchronously collect environmental parameters; the collected data is filtered by a signal conditioning module to remove noise, and then converted into digital signals by an A / D conversion module to ensure data accuracy; the processed photovoltaic unit instantaneous output data and environmental parameters are transmitted to the building microgrid central control system through wired or wireless communication. For the remaining energy state of the energy storage system, an electric quantity monitoring module is integrated into the battery management system of the energy storage battery pack, which is connected to the positive and negative electrodes of each battery to collect real-time voltage, current, and total voltage and current of the battery pack; temperature sensors are deployed inside the energy storage cabinet to monitor the battery operating environment temperature and avoid temperature abnormalities affecting calculation accuracy; the electric quantity monitoring module collects voltage and current data at a preset frequency, and calculates the remaining electric quantity by the coulomb counting method: according to the integral of the charging and discharging current with respect to time, combined with the initial electric quantity and charging and discharging efficiency of the battery, the real-time remaining electric quantity is obtained, and the ratio of the real-time remaining electric quantity to the rated capacity of the energy storage system is the remaining energy state; if abnormal values are found in the collected data, a data verification mechanism is triggered to remove the abnormal values and replace them with the previous valid values to ensure the accuracy of the calculation results; the battery management system communicates with the central control system through industrial Ethernet to upload real-time remaining energy state data and auxiliary data such as individual battery voltage, current, and temperature. For real-time energy consumption of DC loads, a DC intelligent electric meter is installed at the input end of each DC load to adapt to different DC voltage levels to ensure measurement accuracy; for multi-load centralized power supply scenarios, a total power sensor is installed at the total input end of the distribution box, and branch electric meters are installed in each branch circuit to realize total and branch two-level metering, which can not only master the overall energy consumption but also locate the energy consumption of individual loads; the intelligent electric meter collects load power at a preset frequency and records load operating status simultaneously; the central control system classifies and counts the data according to load types to generate real-time energy consumption curves of each type of load; the electric meter transmits data to the central control system through a communication bus or wireless module. For grid-connected interactive power, a bidirectional power metering device is installed at the access point of the building microgrid and the public grid, which has bidirectional metering function, can measure the power sent by the microgrid to the grid and the power supplied by the grid to the microgrid, and is suitable for grid voltage and frequency fluctuation range; the bidirectional power metering device collects interactive power at a preset frequency and distinguishes power flow direction through a built-in power direction judgment module; voltage and frequency data of the grid side are synchronously collected to evaluate the grid operating state; the metering device transmits grid-connected interactive power, voltage and frequency data to the central control system and the grid dispatching center through a special communication link.

[0034] More specifically, all collection devices are calibrated by the clock synchronization module to ensure consistency of feature data collection timestamps and avoid data analysis errors caused by time deviation; key collection devices are redundantly deployed, and standby devices automatically switch when the main device fails to ensure uninterrupted data collection; the central control system stores data in real time according to the collection frequency, and uses local hard disk and cloud server dual backup to avoid data loss.

[0035] It should be noted that the real-time collection of multi-element state characteristics in this step S2 is aimed at real-time working condition perception of the building microgrid, and through professional collection devices, high-frequency data updating, and stable transmission architecture, accurate and real-time acquisition of multi-dimensional state characteristics is achieved. Its protection scope is not limited to the above specific collection device types, collection frequencies, and communication methods, but also covers all variations of the core logic: for example, photovoltaic unit instantaneous output collection can obtain data through the monitoring function of the photovoltaic inverter; the remaining energy state of the energy storage system can be collected in combination with more accurate calculation methods; real-time energy consumption of DC load can be collected using a wireless sensor network for low-cost deployment; and grid interactive power collection can be directly obtained from the grid dispatching center real-time data platform; the collection frequency can be dynamically adjusted according to the microgrid regulation and control requirements; any technical solution that achieves state characteristic acquisition based on the core requirement of real-time perception of the multi-dimensional operating state of the building microgrid is within the protection scope of this step, aiming to cover the collection needs of different microgrid scales, different device configurations, and different communication conditions, and avoid limiting the protection scope due to specific hardware selection or technical details.

[0036] In this step, high-frequency collection can capture system fluctuations within a short period of time, allowing the multi-element collaborative optimization model to quickly perceive changes in working conditions and generate immediate optimization instructions to avoid power imbalance caused by data lag; by collecting multi-dimensional features, it avoids decision-making bias caused by incomplete information; high-precision devices ensure small data errors; auxiliary data collected in real time can help determine the cause of system abnormalities, making it easier for maintenance personnel to quickly locate problems.

[0037] Further, in order to ensure that data collection meets the real-time and accuracy requirements of the multi-element collaborative optimization model, and to avoid redundant consumption of hardware resources and communication bandwidth, the method for setting the collection frequency of multi-element state characteristics includes: Step S21, based on the scale of the building microgrid, the characteristics of the equipment, and the conventional operating conditions, set the basic collection frequency for photovoltaic unit instantaneous output, energy storage system remaining energy state, DC load real-time energy consumption, and grid interactive power; among them, the basic collection frequency of photovoltaic unit instantaneous output is set to the highest level, the basic collection frequency of energy storage system remaining energy state and DC load real-time energy consumption is set to the medium level, and the basic collection frequency of grid interactive power is coordinated with photovoltaic unit instantaneous output and lower than the basic collection frequency of photovoltaic unit instantaneous output. Step S22, a dynamic correction factor system is constructed, including a fluctuation intensity correction factor, a regulation priority correction factor and a device cost correction factor: The central control system calls the data of each state characteristic in the recent multiple collection cycles, calculates the fluctuation amplitude per unit time, the fluctuation amplitude per unit time is the difference between the maximum value and the minimum value of the state characteristic in the recent multiple collection cycles divided by the time interval; low fluctuation threshold and high fluctuation threshold are set for each state characteristic, if the fluctuation amplitude per unit time is less than or equal to the low fluctuation threshold, the fluctuation intensity correction factor is 0.5, if the low fluctuation threshold is less than the fluctuation amplitude per unit time and the fluctuation amplitude per unit time is less than the high fluctuation threshold, the fluctuation intensity correction factor is 1.0, if the fluctuation amplitude per unit time is greater than or equal to the high fluctuation threshold, the fluctuation intensity correction factor is 1.5-2.0; The regulation priority of each state characteristic is determined based on the micro-grid optimization benchmark index, the regulation priority correction factor of the high-priority state characteristic is 1.2-1.5, the medium-priority is 1.0, and the low-priority is 0.8-0.9; The device cost correction factor is set: the running energy consumption of the monitoring and collecting device and the communication link bandwidth occupation rate are monitored, if the device energy consumption exceeds the preset energy saving threshold or the communication bandwidth occupation rate reaches the preset occupation threshold, the device cost correction factor is 0.8-0.9, otherwise it is 1.0; Step S23, for each state characteristic, multiply the basic collection frequency by the product of the fluctuation intensity correction factor, the regulation priority correction factor and the device cost correction factor to obtain the real-time collection frequency of the state characteristic; set the highest frequency threshold and the lowest frequency threshold for each state characteristic, if the real-time collection frequency exceeds the threshold range, take the corresponding threshold as the final collection frequency.

[0038] It should be noted that the core of the above collection frequency setting method is to construct a dynamic adjustment mechanism based on working condition fluctuation and multi-target demand, to realize the adaptation of the collection frequency to the micro-grid operating state, and the protection range is not limited to the above specific basic frequency values, fluctuation threshold values, correction factor values, but also covers all the modified implementation manners conforming to the core logic: for example, the basic frequency can be adjusted according to the scale of the building micro-grid; the fluctuation threshold can be modified in combination with the characteristics of different types of devices; the correction factor can increase the dimension of the grid dispatching demand; special scenarios can be extended to grid fault scenarios, device maintenance scenarios, etc.; any technical solution based on the core logic of dynamic adaptation to working conditions and multi-target balancing to realize the collection frequency setting by combining basic benchmarks with dynamic corrections, falls within the protection range of the method, aiming to cover different building micro-grid scenarios, device configurations and operating demands, and avoid limiting the protection range due to specific parameter differences.

[0039] Step S3, input the micro-grid optimization benchmark index and the multi-element state characteristics into the pre-constructed multi-element collaborative optimization model to obtain the optimization control instructions of each regulating unit; In order to handle multi-objective conflicts and multi-constraint coupling, for example, when the photovoltaic output is surplus, the charging of the energy storage and the power transmission of the grid may be triggered at the same time, causing resource waste; when the photovoltaic output is insufficient, the discharge of the energy storage may be relied on too much, and the economy of the grid power transmission is ignored, causing the overall system benefit loss; the multi-element collaborative optimization model is needed to operate, the benchmark index of step S1 and the state characteristics of step S2 are deeply coupled, the multi-objective optimization problem is converted into a solvable model, and the collaborative instructions suitable for each regulating unit are generated, solving the problem that the existing method is not collaborative and difficult to balance, and ensuring that the regulating action not only meets the constraints, but also realizes the multi-objective optimization.

[0040] The multi-element collaborative optimization model takes energy conservation as the bottom logic, and is constructed by a basic balance model, a correlation coupling mechanism and constraint integration. The micro-grid optimization benchmark index and the multi-element state characteristics are taken as inputs, and the optimization control instructions of each regulating unit are output through model operation. The specific steps are as follows: Step S31, based on the principle of energy conservation, a dynamic balance relationship formula covering the interaction of photovoltaic, energy storage, load and grid is constructed to quantify the energy flow logic of each link. The sum of the instantaneous output of the photovoltaic unit, the discharge power of the energy storage system and the power supplied by the grid to the micro-grid is equal to the sum of the real-time energy consumption of the DC load, the charging power of the energy storage system and the power transmitted from the micro-grid to the grid. The relationship formula is used as the bottom logic of the model to ensure the dynamic balance of energy supply and demand.

[0041] Step S32, introduce the photovoltaic energy storage correlation coefficient, the photovoltaic load correlation coefficient, the energy storage load correlation coefficient and the energy storage grid correlation coefficient to form a correlation coupling matrix to quantify the linkage rules between each state characteristic; The photovoltaic energy storage correlation coefficient is used to reflect the matching relationship between the photovoltaic output fluctuation rate and the energy storage charging and discharging rate. The ratio of the photovoltaic output fluctuation amount to the energy storage charging and discharging power change amount at the same period is calculated through historical data, and the coefficient value is determined in combination with the influence of the current remaining energy state of the energy storage on the charging and discharging capacity. The photovoltaic load correlation coefficient is used to reflect the coincidence degree of the photovoltaic output peak and the flexible load power consumption peak. The product integral of the instantaneous output of the photovoltaic unit and the flexible load power in the statistical period is calculated, and then divided by the square root product of the square integral of the power of the two. The energy storage load correlation coefficient is used to reflect the linkage relationship between the energy storage remaining energy state and the load regulation demand. The correlation coefficient is calculated in combination with the ratio of the load regulation demand power to the rated power according to the relative position of the current remaining energy state of the energy storage to the safe operation boundary. The energy storage grid correlation coefficient is used to reflect the collaborative relationship between the energy storage remaining energy state and the grid power. When the energy storage remaining energy is low, the coefficient is small, and when the energy storage remaining energy is high, the coefficient is large.

[0042] Step S33, convert the basic energy balance model into incremental form, introduce four types of incremental adjustment quantities, namely photovoltaic output fine-tuning quantity, energy storage charging and discharging correction quantity, load adjustment quantity, and grid-connected power correction quantity, realize the coupling and correlation among increments through the correlation coupling matrix; at the same time, convert the microgrid optimization benchmark index set in step S1 into mathematical constraints; Among them, the minimum clean energy acceptance rate constraint is that the ratio of the sum of the instantaneous output of the photovoltaic unit and the photovoltaic output fine-tuning quantity minus the difference after the increment of the microgrid power sent to the grid to the sum of the instantaneous output of the photovoltaic unit and the photovoltaic output fine-tuning quantity is not less than the minimum clean energy acceptance rate; The energy storage operation safety boundary constraint is that the sum of the product of the difference between the remaining energy state of the energy storage system and the difference between the energy storage charging correction quantity and the energy storage discharging correction quantity and the time step and the rated capacity of the energy storage system is between the upper and lower limits of the energy storage operation safety boundary; The flexible load regulation amplitude limit constraint is that the load adjustment quantity is between the negative flexible load regulation amplitude limit and the flexible load regulation amplitude limit; The grid-connected power permission threshold constraint is that the increment of the microgrid power sent to the grid is between the negative grid-connected power permission threshold and zero, and the increment of the grid power supplied to the microgrid is between zero and the grid-connected power permission threshold.

[0043] Step S34, calibrate the model parameters using the historical operation data of the building microgrid, verify the model accuracy through simulation operation and result comparison, input the historical state characteristics and benchmark index into the model, compare the deviation between the optimization instructions output by the model and the actual optimal control action, adjust the correlation coupling matrix coefficients if the deviation exceeds the preset range, and continue until the model accuracy meets the requirements.

[0044] More specifically, in the input data preprocessing process, the microgrid optimization benchmark index of step S1 is converted into a mathematical constraint parameter recognizable by the model, and the multiple state characteristics of step S2 are subjected to outlier rejection and normalization processing to ensure the uniformity of the input data; in the model solving and optimization instruction generation process, the linear programming or quadratic programming algorithm is used to solve the incremental adjustment quantity equation set containing constraints, the objective function is set as the weighted sum of minimizing the photovoltaic light rejection, minimizing the energy storage charging and discharging loss, and minimizing the grid interaction cost, and the weights are set according to the optimization priority of the building microgrid: if the solving result meets all the constraints, the sum of the current state characteristics and the incremental adjustment quantity is taken as the optimization control instruction of each regulation unit; if one of the incremental adjustment quantities exceeds the constraint, the constraint relaxation mechanism is triggered, the correlation coupling matrix coefficients are adjusted, and the solving is restarted until all the adjustment quantities meet the constraints, to ensure the safety and feasibility of the instructions.

[0045] After the operation of the multi-element collaborative optimization model is completed, classified instructions are output according to the types of regulation units, including: Photovoltaic regulation module instruction: determine the target output power and output adjustment range of the photovoltaic unit; Energy storage charging and discharging module instruction: determine the target charging and discharging power and time length of the energy storage; Flexible load management module instruction: determine the power regulation direction and range of the flexible load; Bidirectional converter instruction: determine the target power of grid interaction.

[0046] In this step, the linkage relationship of each link is quantified by the associated coupling matrix, and the multiple targets are converted into a unified mathematical optimization problem, avoiding the system imbalance caused by the priority of a single target in the existing method, for example, when the photovoltaic output is rich, the cooperative instructions of energy storage charging, load adjustment, and reduction of grid power transmission are generated simultaneously; the incremental regulation mechanism is combined with high-frequency input data, so that the model can quickly respond to working condition fluctuations, and power balance is achieved through small incremental adjustment, avoiding voltage fluctuations caused by large actions; the associated coupling matrix is calibrated based on historical data to ensure that the instructions are highly adapted to the actual working conditions and the regulation error is small; the model construction incorporates historical operation data, and the associated coupling matrix parameters implicitly adapt to future trends, while real-time input data ensure that the instructions fit the current working conditions, solving the problem of real-time balancing without foresight in existing methods and improving long-term operation economy; the model automatically completes constraint verification and instruction generation, reducing operation and maintenance costs; the constraint conditions strictly limit the operating boundaries of the equipment to avoid equipment life loss caused by excessive regulation.

[0047] Step S4, for each regulation unit, obtain the corresponding current control instruction, and calculate the optimization range according to the current control instruction and the optimization control instruction; The existing method directly issues an optimization control instruction without considering the current running state of the regulation unit, which may cause equipment overload or accelerated aging, for example, when the current output of the photovoltaic regulation module is close to the rated value, directly executing a large output instruction may cause equipment failure; by calculating the optimization range, the difference between the current control instruction and the optimization control instruction is quantified, which provides a basis for optimization regulation strategy, solves the problem of rigid instruction execution and poor equipment adaptability in existing methods, and ensures that the regulation action meets the optimization target and adapts to the actual running capacity of the regulation unit.

[0048] Specifically, for the photovoltaic regulation module, the energy storage charging and discharging module, the flexible load management module, and the bidirectional converter, the current control instruction is obtained, the optimization control instruction generated in step S3 is combined, the final optimization range is determined through difference quantification and boundary verification, and the specific steps are as follows: Step S41, the current control instruction refers to the running parameter currently executed by the regulation unit, which needs to be real-time called through a special communication interface or a control platform to ensure that the data is consistent with the actual running state; the current instruction of the photovoltaic regulation module is obtained through the photovoltaic inverter monitoring interface to obtain the target output power and output limit threshold of the current photovoltaic unit, and the running state of the photovoltaic array is recorded synchronously; the current instruction of the energy storage charge-discharge module is obtained through the energy storage battery management system to obtain the target charge-discharge power and charge-discharge cutoff voltage of the current energy storage, and the remaining energy state, health degree and other auxiliary data of the energy storage system are called at the same time; the current instruction of the flexible load management module is obtained through the flexible load control terminal to obtain the current target power and running mode of each flexible load, and the current running state of the load is recorded; the current instruction of the bidirectional converter is obtained through the bidirectional converter control platform to obtain the target power and power factor setting value of the current grid interaction, and the running parameters such as grid side voltage and frequency are synchronously collected.

[0049] Step S42, for the running characteristics of different regulation units, differential calculation logic is adopted to ensure that the optimization amplitude can reflect the optimization demand and adapt to the equipment constraints; The optimization amplitude calculation of the photovoltaic regulation module involves parameters including the current target output, the optimization target output, the photovoltaic rated output and the minimum safe output; the basic optimization amplitude, that is, the difference between the optimization target output and the current target output, is obtained first; then the photovoltaic unit output boundary is checked: if the basic optimization amplitude is positive, the output needs to be improved, and the final optimization amplitude is the smaller one of the basic optimization amplitude and the difference between the photovoltaic rated output and the current target output; if the basic optimization amplitude is negative, the output needs to be reduced, and the final optimization amplitude is the larger one of the basic optimization amplitude and the difference between the minimum safe output and the current target output; if the basic optimization amplitude is zero, the optimization amplitude is zero; The optimization amplitude calculation of the energy storage charge-discharge module involves parameters including the current charge-discharge power, the optimization charge-discharge power, the rated charge-discharge power of the energy storage, the current remaining energy state and the safe boundary of the energy storage running; the basic optimization amplitude, that is, the difference between the optimization charge-discharge power and the current charge-discharge power, is obtained first; then the energy storage safety constraint is checked: if the basic optimization amplitude is positive, the charging needs to be increased, and the final optimization amplitude is the smaller one of the basic optimization amplitude, the difference between the rated charge-discharge power of the energy storage and the current charge-discharge power, the difference between the upper limit of the safe boundary of the energy storage running and the current remaining energy state multiplied by the rated capacity of the energy storage and then divided by the adjustment time step; if the basic optimization amplitude is negative, the discharging needs to be increased, and the final optimization amplitude is the larger one of the basic optimization amplitude, the difference between the negative rated charge-discharge power of the energy storage and the current charge-discharge power, the difference between the current remaining energy state and the lower limit of the safe boundary of the energy storage running multiplied by the rated capacity of the energy storage and then divided by the adjustment time step; if the basic optimization amplitude is zero, the optimization amplitude is zero; The flexible load management module optimization amplitude calculation involves parameters including current load power, optimized load power, flexible load regulation amplitude limit, and current load operation state. The basic optimization amplitude, i.e., the difference between the optimized load power and the current load power, is first calculated. Then, the flexible load regulation amplitude limit is checked. If the basic optimization amplitude is positive, the load needs to be increased, and the final optimization amplitude is the smaller value between the basic optimization amplitude and the flexible load regulation amplitude limit. If the basic optimization amplitude is negative, the load needs to be decreased, and the final optimization amplitude is the larger value between the basic optimization amplitude and the negative flexible load regulation amplitude limit. If the load is in a high-frequency use period, the optimization amplitude needs to be narrowed, and the final feasible optimization amplitude is determined. The bidirectional converter optimization amplitude calculation involves parameters including current grid-connected power, optimized grid-connected power, and grid-connected power permission threshold. The basic optimization amplitude, i.e., the difference between the optimized grid-connected power and the current grid-connected power, is first calculated. Then, the grid-connected power permission threshold is checked. If the basic optimization amplitude is positive, power needs to be increased, and the final optimization amplitude is the smaller value between the basic optimization amplitude and the difference between the grid-connected power permission threshold and the current grid-connected power. If the basic optimization amplitude is negative, power needs to be decreased, and the final optimization amplitude is the larger value between the basic optimization amplitude and the difference between the grid-connected power permission threshold and the current grid-connected power. If the basic optimization amplitude is zero, the optimization amplitude is zero.

[0050] It should be noted that the core logic of the optimization amplitude calculation in step S4 is to quantify the difference between the optimization control instruction and the current control instruction based on the current state of the control unit and the device constraints, and to generate a safe and feasible adjustment amplitude. The protection range is not limited to the specific calculation parameters, calculation logic, and verification dimensions mentioned above, but also covers all variations of the core logic: for example, the optimization amplitude calculation can add a device health weight, the verification dimension can be extended to grid scheduling requirements, and the calculation logic can be replaced by a relative proportion method. For new control units, the core parameters and constraints can be adjusted according to their operating characteristics. Any technical solution that calculates the adjustment amplitude of the control unit based on the quantification of the difference between the current and optimization instructions and the device constraints to ensure safety falls within the protection range of this step, aiming to cover the amplitude calculation requirements of different building microgrid device configurations, operating conditions, and optimization needs, and to avoid limiting the protection range due to differences in specific parameters, logic, or control unit types.

[0051] In this step, the optimization amplitude is strictly limited by boundary check to avoid over-rating operation, over-safety interval work, reduce device overload, deep damage, and other situations, and to delay device aging; the optimization amplitude is based on the current state quantitative difference to avoid sudden changes in instructions, reduce direct current bus voltage fluctuations, grid interaction impact, and other problems, and to ensure smooth system operation; different calculation logics are adopted for different characteristics of photovoltaic, energy storage, load, and grid-connected equipment to adapt to the operation constraints of various equipment and avoid the situation that the instructions cannot be executed due to inconsistent equipment characteristics; the combination of basic amplitude calculation and boundary check ensures that the optimization amplitude can reflect the optimization target of step S3 and does not exceed the necessary range, avoiding excessive regulation or insufficient regulation, and balancing accuracy and efficiency.

[0052] Step S5, based on the optimization amplitude, an optimization adjustment strategy corresponding to the control unit is generated, and the optimization adjustment strategy is executed. Step S5 generates an optimized adjustment strategy with strong adaptability based on the optimization amplitude calculated in step S4, in combination with the operation characteristics of each control unit, building energy consumption scene constraints, and equipment safety requirements; and generates a differentiated strategy for the four types of core control units in combination with the optimization amplitude of step S4 and the characteristics of the control unit.

[0053] Specifically, the optimization adjustment strategy of the photovoltaic adjustment module is: according to the positive and negative of the optimization amplitude and the current operating state of the photovoltaic, the adjustment steps are split, and the adjustment rate is controlled. If the optimization amplitude is positive: first check the temperature of the photovoltaic module, when the temperature is lower than the safety threshold, increase the output according to the device tolerance rate ladder, until the target output is reached; when the temperature is higher than the safety threshold, reduce the adjustment rate, and at the same time, start the cooling system to avoid overheating of the module; If the optimization amplitude is negative: preferentially reduce the power of the photovoltaic inverter by adjusting the maximum power point tracking strategy, and when the amplitude is insufficient, reduce the output according to the device tolerance rate ladder, to avoid sudden output drop caused by direct string interruption; The ladder adjustment instruction is issued through the photovoltaic inverter monitoring interface, and after each adjustment, the output is stabilized before the next step is executed to avoid fluctuations.

[0054] Specifically, the optimization adjustment strategy of the energy storage charging and discharging module is: according to the positive and negative of the optimization amplitude, the current remaining energy state of the energy storage, and the health degree, the adjustment process is split, and the dynamic adjustment is combined with the safety boundary of the energy storage operation; If the optimization amplitude is positive: first calculate the difference between the current remaining energy state and the safety upper limit, when the difference is large, increase the charging power according to the device tolerance rate ladder; when the difference is small, reduce the rate to avoid quickly approaching the safety upper limit; at the same time, monitor the temperature of the energy storage, when the temperature exceeds the safety threshold, suspend charging and cool down before executing at a low speed; If the optimization amplitude is negative: calculate the difference between the current remaining energy state and the lower limit of safety, and increase the discharge power according to the device tolerance rate ladder when the difference is larger; reduce the rate when the difference is smaller to avoid excessive discharge; when the energy storage health is low, further narrow the discharge rate to delay aging; Through the energy storage battery management system, the charge and discharge rate command is issued, and the remaining energy state and temperature data are collected after each step adjustment. After confirming that there is no abnormality, it continues to execute.

[0055] Specifically, the optimization adjustment strategy of the flexible load management module is: according to the positive and negative of the optimization amplitude, the load type and the user energy consumption period, the adjustment amplitude is divided into priorities, and the non-critical load is adjusted first; If the optimization amplitude is positive: preferentially select non-critical flexible load, and adjust the power according to the device tolerance rate ladder; when the non-critical load adjustment space is insufficient, the critical load is adjusted by a small amplitude, and only in the user non-sensitive period, to ensure the normal function of the critical load; If the optimization amplitude is negative: first, reduce the non-critical load, and reduce the power according to the device tolerance rate ladder; when the amplitude is insufficient, the critical load is adjusted by a small amplitude, and the critical load function needs to be ensured after adjustment; Through the flexible load control terminal, the priority adjustment command is issued, and the load running state is fed back after each step adjustment. After confirming that there is no abnormality, it continues.

[0056] Specifically, the optimization adjustment strategy of the bidirectional converter is: according to the positive and negative of the optimization amplitude, the real-time state of the power grid and the grid-connected power permission threshold, adjust the execution rhythm and amplitude; If the optimization amplitude is positive: first, query the current period of the power grid, increase the power taking power according to the device tolerance rate ladder in the valley section, and reduce the power taking power in the peak section, and ensure that the final power taking power does not exceed the grid-connected power permission threshold; when the power grid issues a load warning, suspend power taking increase, or even reduce power taking in reverse; If the optimization amplitude is negative: increase the power sending power according to the device tolerance rate ladder in the valley section, and reduce the power sending power in the peak section, and do not exceed the grid-connected power permission threshold; at the same time, monitor the power grid voltage and frequency, and suspend power sending adjustment when fluctuations occur; Through the bidirectional converter control platform, the grid-connected power adjustment command is issued, and the grid side data is collected after each step adjustment. After confirming compliance, it continues.

[0057] In this step, the generation and execution of the optimization regulation strategy is based on the optimization amplitude. In combination with the characteristics of the control unit, the constraints of the building energy scenario and the safety requirements of the equipment, an adaptability strategy is generated and executed through closed-loop control to ensure that the optimization goal is achieved, rather than being limited to the above-mentioned specific strategy splitting method, adjustment rate or monitoring dimension. The reason for adopting the above logic is to solve the common problems of rough execution and poor adaptability of existing methods. No matter how the types of control units of the building microgrid are added, how the building energy scenario is expanded, how the equipment characteristics are iterated, and how the grid policy is adjusted, it is necessary to pass the logic of characteristic adaptation, safe execution, and closed-loop verification to ensure that the regulation strategy is consistent with the actual scenario. Step S5 splits the regulation action through scenario constraints to avoid interference with key energy demand; splits the regulation amplitude according to the dynamic characteristics of the equipment, controls the regulation rate, avoids equipment damage caused by sudden changes, and extends the life of the equipment; step-by-step regulation and dynamic response reduce system fluctuations during the regulation process and ensure stable power supply.

[0058] Example 2: Figure 2 As shown, the building microgrid multi-element collaborative optimization system of the present invention specifically includes the following modules: Benchmark indicator setting module, used to set microgrid optimization benchmark indicators based on building energy characteristics, equipment safety specifications and grid access requirements; The state feature acquisition module is used to collect the multivariate state features of the building microgrid in real time according to the preset acquisition frequency; An optimization instruction generation module is used to input the microgrid optimization benchmark index and the multivariate state characteristics into a pre-built multivariate collaborative optimization model to obtain optimization control instructions for each control unit; An optimization range calculation module is used to obtain the corresponding current control instruction for each control unit, and calculate the optimization range according to the current control instruction and the optimization control instruction; The adjustment strategy execution module is used to generate an optimization adjustment strategy corresponding to the control unit according to the optimization range and execute the optimization adjustment strategy.

[0059] The various variations and specific embodiments of the multi-element collaborative optimization method for building microgrids in the aforementioned embodiment 1 are also applicable to the multi-element collaborative optimization system for building microgrids in this embodiment. Through the aforementioned detailed description of the multi-element collaborative optimization method for building microgrids, those skilled in the art can clearly understand the implementation method of the multi-element collaborative optimization system for building microgrids in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0060] In addition, the application further provides an electronic device, comprising a bus, a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor, the transceiver, the memory and the processor are connected through the bus, the computer program is executed by the processor to realize each process of the method for controlling output data and achieve the same technical effects, to avoid repetition, here is not repeated.

[0061] The application further provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by the processor to make the processor execute the building micro-grid multi-element collaborative optimization method in the embodiment one of the application, and can achieve the same technical effects, to avoid repetition, here is not repeated.

[0062] The above only is the preferred embodiment of the application, it should be pointed out, for ordinary skilled in the art, without departing from the technical principles of the application, can make a number of improvements and variations, these improvements and variations should be considered as the protection scope of the application.

Claims

1. A multi-element collaborative optimization method for building microgrids, characterized in that: The method comprises: Set microgrid optimization benchmark indicators based on building energy characteristics, equipment safety specifications, and grid access requirements; Based on a preset collection frequency, multiple state characteristics of the building microgrid are collected in real time; the method for setting the preset collection frequency includes: setting a basic collection frequency for each multiple state characteristic; constructing a dynamic correction factor system, the dynamic correction factor system including a fluctuation intensity correction factor, a control priority correction factor, and an equipment cost correction factor; for each state characteristic, multiplying its basic collection frequency by the product of the correction factors in the dynamic correction factor system to obtain the preset collection frequency for the state characteristic; Inputting the microgrid optimization benchmark index and the multivariate state characteristics into a pre-built multivariate collaborative optimization model to obtain the optimization control instructions of each control unit; For each control unit, obtain the corresponding current control instruction, and calculate the optimization range according to the current control instruction and the optimized control instruction; Based on the optimization range, an optimization adjustment strategy corresponding to the control unit is generated, and the optimization adjustment strategy is executed.

2. The multi-element collaborative optimization method for building microgrids according to claim 1, characterized in that: The microgrid optimization benchmark indicators include the minimum clean energy acceptance rate, energy storage operation safety boundary, flexible load regulation amplitude limit and grid-connected power permission threshold.

3. The multi-element collaborative optimization method for building microgrids according to claim 2, characterized in that: The multi-state characteristics include the instantaneous output of the photovoltaic unit, the remaining energy state of the energy storage system, the real-time energy consumption of the DC load and the grid-connected interactive power.

4. The multi-element collaborative optimization method for building microgrids according to claim 1, wherein: The fluctuation intensity correction factor is determined by calculating the fluctuation amplitude per unit time of the state characteristic and comparing it with the preset low fluctuation threshold and high fluctuation threshold; The control priority correction factor is determined based on the priority set for each state feature by the microgrid optimization benchmark indicator; The equipment cost correction factor is determined by monitoring the operating energy consumption of the collection equipment and the communication link bandwidth occupancy rate.

5. The multi-element collaborative optimization method for building microgrids according to claim 3, characterized in that: The construction of the multivariate collaborative optimization model includes: Construct a dynamic equilibrium relationship based on the principle of energy conservation; A correlation coupling matrix is ​​introduced to quantify the linkage relationship between photovoltaic, energy storage, load and grid state characteristics. The correlation coupling matrix includes the photovoltaic energy storage correlation coefficient, the photovoltaic load correlation coefficient, the energy storage load correlation coefficient and the energy storage grid correlation coefficient. Converting the microgrid optimization benchmark into mathematical constraints; A planning algorithm is used to solve a function with the goal of minimizing the comprehensive operating cost, to obtain the incremental adjustment amount of each control unit, and to generate the optimized control instruction in combination with the current state characteristics.

6. The multi-element collaborative optimization method for building microgrids according to claim 1, characterized in that: The generation of the optimization adjustment strategy includes: According to the positive and negative and size of the optimization amplitude, combined with the equipment tolerance rate, current operating status and external environmental conditions of the control unit, the adjustment process is divided into multiple steps, and the adjustment rate of each step is controlled.

7. A building microgrid multi-element collaborative optimization system, characterized in that: The system comprises: Benchmark indicator setting module, used to set microgrid optimization benchmark indicators based on building energy characteristics, equipment safety specifications and grid access requirements; A state feature acquisition module is configured to acquire multiple state features of the building microgrid in real time according to a preset acquisition frequency. The preset acquisition frequency is set by setting a base acquisition frequency for each of the instantaneous output of the photovoltaic unit, the remaining energy state of the energy storage system, the real-time energy consumption of the DC load, and the grid-connected interactive power; constructing a dynamic correction factor system comprising a fluctuation intensity correction factor, a control priority correction factor, and an equipment cost correction factor; and for each state feature, multiplying its base acquisition frequency by the product of the correction factors in the dynamic correction factor system to obtain the preset acquisition frequency for that state feature. An optimization instruction generation module is used to input the microgrid optimization benchmark index and the multivariate state characteristics into a pre-built multivariate collaborative optimization model to obtain optimization control instructions for each control unit; An optimization range calculation module is used to obtain the corresponding current control instruction for each control unit, and calculate the optimization range according to the current control instruction and the optimization control instruction; The adjustment strategy execution module is used to generate an optimization adjustment strategy corresponding to the control unit according to the optimization range and execute the optimization adjustment strategy.

8. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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