Multi-element collaborative optimization methods, systems, electronic devices, and media for building microgrids

Abstract

This invention relates to the technical field of power operation and maintenance, and in particular to a multi-element collaborative optimization method, system, electronic equipment, and medium for building microgrids. The method includes: setting microgrid optimization benchmark indicators based on building energy consumption characteristics, equipment safety specifications, and grid connection requirements; collecting multi-element state characteristics of the building microgrid in real time based on a preset acquisition frequency; inputting the microgrid optimization benchmark indicators and multi-element state characteristics into a pre-constructed multi-element collaborative optimization model to obtain optimized control commands for each control unit; for each control unit, obtaining the corresponding current control command, and calculating the optimization magnitude based on the current control command and the optimized control command; generating an optimized adjustment strategy corresponding to the control unit based on the optimization magnitude, and executing the optimized adjustment strategy; it can deeply integrate building energy consumption characteristics, equipment operation constraints, and grid interaction requirements.

Description

Technical Field

[0001] This invention relates to the technical field of power operation and maintenance, and in particular to a multi-element collaborative optimization method, system, electronic equipment and medium for building microgrids. Background Technology

[0002] As the global energy structure transformation continues to deepen, distributed clean energy, represented by photovoltaics, is accounting for an increasingly larger share of building energy systems. Building microgrids, as comprehensive energy utilization units that integrate local renewable energy generation, energy storage systems, DC loads, and grid interaction, have become a key technological path to improve building energy efficiency, promote the local consumption of renewable energy, and reduce dependence on traditional power grids.

[0003] However, existing building microgrid energy management methods often focus on controlling single objectives, such as independently regulating photovoltaic (PV), energy storage, and load units, lacking a collaborative optimization framework. For example, some methods aim only at maximizing PV self-consumption, potentially neglecting energy storage lifespan losses or load regulation limitations; others only perform real-time balancing based on the current state, failing to proactively optimize by incorporating historical characteristics and future trends of building energy consumption. This often results in insufficient optimization depth and poor dynamic response characteristics when facing multiple constraints and conflicting objectives, impacting the overall economy and stability of the building microgrid.

[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 of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method, system, electronic device, and medium for multi-element collaborative optimization of building microgrids.

[0006] In a first aspect, the present invention provides a multi-element collaborative optimization method for building microgrids, including:

[0007] Based on building energy consumption characteristics, equipment safety standards, and grid connection requirements, microgrid optimization benchmark indicators are set.

[0008] Based on a preset acquisition frequency, the multi-state characteristics of the building microgrid are acquired in real time.

[0009] The microgrid optimization benchmark index and the multi-state characteristics are input into a pre-constructed multi-factor collaborative optimization model to obtain the optimization control commands of each control unit.

[0010] For each control unit, the corresponding current control command is obtained, and the optimization magnitude is calculated based on the current control command and the optimized control command.

[0011] Based on the optimization range, an optimized adjustment strategy corresponding to the control unit is generated and the optimized adjustment strategy is executed.

[0012] Furthermore, the microgrid optimization benchmark indicators include the minimum clean energy acceptance rate, the energy storage operation safety boundary, the flexible load regulation range limit, and the grid-connected power permit threshold.

[0013] Furthermore, 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.

[0014] Furthermore, the method for setting the preset sampling frequency includes:

[0015] Set basic acquisition frequencies for instantaneous output of photovoltaic units, remaining energy status of energy storage systems, real-time energy consumption of DC loads, and grid-connected interactive power respectively;

[0016] A dynamic correction factor system is constructed, which includes a fluctuation intensity correction factor, a regulation priority correction factor, and an equipment cost correction factor.

[0017] For each state feature, its basic acquisition frequency is multiplied by the product of each correction factor in the dynamic correction factor system to obtain the preset acquisition frequency of that state feature.

[0018] Furthermore, the fluctuation intensity correction factor is determined by calculating the unit time fluctuation amplitude of the state characteristics and comparing it with preset low fluctuation thresholds and high fluctuation thresholds;

[0019] The regulation priority correction factor is determined based on the priority set for each state characteristic by the microgrid optimization benchmark index;

[0020] The equipment cost correction factor is determined by monitoring the operating energy consumption and communication link bandwidth utilization of the data acquisition equipment.

[0021] Furthermore, the construction of the multivariate collaborative optimization model includes:

[0022] A dynamic equilibrium relationship is constructed based on the principle of energy conservation;

[0023] An association coupling matrix is ​​introduced to quantify the linkage relationship between photovoltaic, energy storage, load and grid state characteristics. The association coupling matrix includes photovoltaic energy storage association coefficient, photovoltaic load association coefficient, energy storage load association coefficient and energy storage grid association coefficient.

[0024] The microgrid optimization benchmark indicators are converted into mathematical constraints.

[0025] A planning algorithm is used to solve a function with the goal of minimizing the overall operating cost, to obtain the incremental adjustment amount of each control unit, and the optimized control command is generated by combining the current state characteristics.

[0026] Furthermore, the generation of the optimization adjustment strategy includes:

[0027] Based on the sign and magnitude of the optimization amplitude, combined with the equipment tolerance rate of the control unit, the current operating status, and external environmental conditions, the adjustment process is divided into multiple step steps, and the adjustment rate of each step is controlled.

[0028] On the other hand, this application also provides a multi-element collaborative optimization system for building microgrids, the system comprising:

[0029] The benchmark setting module is used to set microgrid optimization benchmarks based on building energy consumption characteristics, equipment safety specifications, and grid connection requirements.

[0030] The status feature acquisition module is used to acquire multi-dimensional status features of the building microgrid in real time according to a preset acquisition frequency.

[0031] An optimization instruction generation module is used to input the microgrid optimization benchmark index and the multi-state characteristics into a pre-constructed multi-coordinated optimization model to obtain the optimization control instructions of each control unit.

[0032] The optimization amplitude calculation module is used to obtain the corresponding current control command for each control unit, and calculate the optimization amplitude based on the current control command and the optimization control command.

[0033] The adjustment strategy execution module is used to generate an optimized adjustment strategy corresponding to the control unit based on the optimization magnitude, and to execute the optimized adjustment strategy.

[0034] Thirdly, this application provides an electronic device including 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 via the bus, and the computer program, when executed by the processor, implements the steps of any of the methods described above.

[0035] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0036] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention, by setting multi-dimensional optimization benchmark indicators, incorporates building energy consumption characteristics, equipment safety specifications, and grid connection requirements into a unified constraint framework. Under the premise of ensuring the safe operation of each unit, it achieves multi-objective balance, avoiding the loss of overall system benefits due to prioritizing a single objective. Through real-time data acquisition at a preset acquisition frequency, and based on benchmark indicators set according to building energy consumption characteristics, it ensures the timeliness of decision-making by relying on real-time multi-state characteristics, and achieves forward-looking adaptation to future scenarios through the historical energy consumption patterns implicit in the benchmark indicators. This allows optimization decisions to cope with real-time fluctuations and conform to the long-term characteristics of building energy consumption, avoiding a disconnect between short-term optimization and long-term benefits. Through multiple... The meta-cooperative optimization model deeply couples benchmark indicators with multi-dimensional state characteristics, outputting cooperative optimization instructions for each control unit. For example, when photovoltaic output suddenly increases, the model simultaneously calculates the energy storage charging power, the flexible load adjustment range, and the grid-connected power reduction, ensuring the synergistic effect of each control unit and avoiding system imbalance caused by insufficient adjustment capacity or conflicting actions of a single control unit, thus improving overall control efficiency. By calculating the optimization amplitude, the model avoids the impact of sudden changes in instructions on the control unit. For example, when photovoltaic output fluctuates significantly, it does not directly output extreme adjustment instructions, but generates step-like adjustment strategies based on the optimization amplitude of the current instructions and the optimized instructions, which can achieve system power balance and ensure equipment operation stability.

[0037] In this invention, the benchmark indicators are set based on static rules such as building energy consumption characteristics and equipment specifications to ensure the compliance and bottom-line safety of optimization decisions. Real-time multi-state characteristics are used to characterize dynamic variables such as photovoltaic fluctuations and load changes to ensure the adaptability of decisions. After the two are coupled through a multi-coordinated optimization model, they can meet the fixed needs of different building types and cope with sudden scenarios. Through the multi-coordinated model, this invention enables the actions of each control unit to support each other. For example, when the peak photovoltaic output and the peak load do not completely overlap, the photovoltaic absorption rate is improved by charging the energy storage in advance and combined with the coordinated action of flexible load peak shifting. This avoids deep charging and discharging of the energy storage and ensures the energy consumption of the load. In the long-term operation of the system, it can maintain high energy utilization efficiency and reduce equipment operation and maintenance costs, thereby maximizing the comprehensive benefits. Attached Figure Description

[0038] Figure 1 This is a flowchart of the multi-element collaborative optimization method for building microgrids of the present invention;

[0039] Figure 2 This is a structural diagram of the multi-element collaborative optimization system for building microgrids of the present invention. Detailed Implementation

[0040] As will be apparent to those skilled in the art from the description of this application, this application can be implemented as a method, apparatus, electronic device, and computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, this application can also be implemented as a computer program product contained in one or more computer-readable storage media, which includes computer program code.

[0041] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, optical disc read-only memory, optical storage devices, magnetic storage devices, or any combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0042] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a 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 that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0043] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction apparatus product that includes the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0044] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable data processing apparatus provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0045] This application will now be described with reference to the accompanying drawings.

[0046] Example 1: As Figure 1 As shown, the multi-element collaborative optimization method for building microgrids of the present invention specifically includes the following steps:

[0047] Step S1: Based on building energy consumption characteristics, equipment safety specifications, and grid connection requirements, set microgrid optimization benchmark indicators; the microgrid optimization benchmark indicators include minimum clean energy acceptance rate, energy storage operation safety boundary, flexible load regulation amplitude limit, and grid-connected power permit threshold.

[0048] Step S1 involves setting microgrid optimization benchmark indicators. These indicators are used in the multi-level collaborative optimization of building microgrids to anchor the safety operation baseline of each link, determine the renewable energy utilization target, and provide constraints and optimization directions for the multi-level collaborative optimization model. The microgrid optimization benchmark indicators include the minimum clean energy acceptance rate, the safety boundary of energy storage operation, the limit of flexible load regulation amplitude, and the grid-connected power permit threshold. The setting of each indicator needs to integrate the building's energy consumption characteristics, equipment safety specifications, and grid access requirements, and adopt the logic of setting basic values ​​and dynamic correction to ensure that the indicators comply with rigid specifications and are adapted to dynamic operating scenarios.

[0049] Specifically, the minimum clean energy integration rate refers to the proportion of distributed clean energy that a building microgrid must absorb out of its total power generation. The steps for setting this rate are as follows:

[0050] Step S111: Extract the total photovoltaic power generation of the building microgrid covering the complete seasonal cycle and the total electricity consumption of the building during the same period, calculate the monthly average self-consumption rate, and increase the monthly average self-consumption rate by a certain percentage as the basic acceptance rate, based on historical absorption capacity and with reserved room for improvement.

[0051] Step S112: Adjust the photovoltaic output based on the seasonal characteristics of building energy consumption and the pattern of photovoltaic output. The output of photovoltaic power is further increased on the basis during the peak season, maintained on the basis during the off-season, and moderately increased during the stable period to adapt to the output differences in different seasons.

[0052] Step S113: Retrieve the weather forecast for the next day every day, predict the photovoltaic power generation for the next day through the photovoltaic power output prediction model, and adjust the daily acceptance rate according to the prediction results; at the same time, count the adjustable power ratio of flexible loads in real time, and further adjust the current acceptance rate based on the ratio to take on more photovoltaic loads by utilizing load flexibility.

[0053] The safety boundary for energy storage operation refers to the allowable range of the remaining energy state of the energy storage system. The steps for setting it are as follows:

[0054] Step S121: Set the initial range according to the type of energy storage device and the manufacturer's specifications to avoid damage to the battery from deep charging and discharging.

[0055] Step S122: Collect data such as battery cycle count and charge / discharge depth in real time through the energy storage controller to assess the health of the energy storage, adjust the boundary range according to the changes in health, and reduce deep charge / discharge to delay aging;

[0056] Step S123: Monitor the ambient temperature of the energy storage device using a temperature sensor, and adjust the boundary range according to the temperature to avoid damage to the battery from extreme temperatures; at the same time, adjust the boundary range temporarily in conjunction with the short-term load demand of the building to reserve more discharge space to ensure power supply.

[0057] The flexible load regulation range limit refers to the power regulation range of the adjustable load. The setting steps are as follows:

[0058] Step S131: Determine the initial adjustment range according to the type of flexible load, and set it with reference to the rated power of the load and operating requirements;

[0059] Step S132: For older loads with long operating times, narrow the adjustment range to reduce adjustment failures caused by equipment aging; monitor the load operating status through sensors, and if an abnormality occurs, temporarily narrow the adjustment range and then revert it after the equipment returns to normal.

[0060] Step S133: Adjust the energy consumption based on the time-period characteristics of the building. Narrow the adjustment range during high-frequency user usage periods to avoid affecting user experience, and widen the adjustment range during low-frequency user usage periods to improve the load's energy carrying capacity.

[0061] The grid-connected power permissible threshold refers to the allowable range of power interaction between the building microgrid and the public grid. The setting steps are as follows:

[0062] Step S141: Based on the rated capacity of the connection point between the building microgrid and the public power grid, set an initial threshold with reference to the power grid safety access specifications to avoid power grid fluctuations caused by overcapacity operation;

[0063] Step S142: Connect with the local power grid's time-of-use pricing policy, adjust the threshold according to the electricity price situation in different time periods, reduce the cost of purchasing electricity at high prices, and increase the revenue from selling electricity at low prices;

[0064] Step S143: Receive the load warning signal issued by the regional power grid, and temporarily adjust the threshold according to the warning type to reduce the load pressure on the main grid or respond to the grid demand.

[0065] More specifically, to prevent contradictions caused by independently setting various benchmark indicators, it is necessary to establish cross-indicator linkage rules; when the minimum acceptance rate of clean energy is temporarily increased, the safety boundary of energy storage operation and the grid-connected power permit threshold should be adjusted simultaneously to prioritize the absorption of photovoltaic power through energy storage and load; when the health of energy storage declines to a certain level, the increase in the minimum acceptance rate of clean energy should be controlled to avoid overuse of energy storage; when the peak electricity price of the grid overlaps with the peak output of photovoltaic power, the minimum acceptance rate of clean energy and the limit of flexible load regulation should be adjusted simultaneously to maximize local absorption and reduce peak electricity purchases.

[0066] It should be noted that the core of setting the microgrid optimization benchmark indicators in step S1 lies in taking the multi-dimensional constraints and objectives of the building microgrid as the guide, and achieving deep coupling between the indicators and the system state and operating scenarios through the logic of basic value anchoring and dynamic factor correction. Its protection scope is not limited to the specific indicator calculation methods mentioned above, but also covers all variant implementation methods that conform to this core logic. For example, the basic value of the minimum clean energy acceptance rate can be calculated based on historical data of different durations, and the correction factor can be adjusted in combination with the energy consumption differences of building types; the energy storage operation safety boundary can be updated according to the iterative updates of energy storage technology, and the health assessment parameters... More dimensions can be added; the limit of flexible load regulation amplitude can be adapted to the characteristics of new loads; the grid-connected power permit threshold can be modified in combination with the microgrid scale or grid policy; the cross-index linkage rules can be dynamically adjusted according to the priority of multiple objectives; all technical solutions that achieve index quantification and coordination through basic settings and dynamic adaptation based on the three core dimensions of building energy consumption characteristics, equipment safety specifications, and grid access requirements fall within the protection scope of this step, aiming to cover the microgrid benchmark index setting requirements under different building scenarios, different equipment types, and different grid policies, and avoid limiting the protection scope due to differences in specific parameters or calculation details.

[0067] In this embodiment, photovoltaic consumption, equipment safety, grid compliance, and user experience are incorporated into a unified constraint through multi-dimensional benchmark indicators, avoiding the overall system benefit loss caused by prioritizing a single objective in existing methods. The basic values ​​and dynamic correction logic of the benchmark indicators are adapted to the seasonal and time-of-use characteristics of building energy consumption and can cope with sudden scenarios, solving the problem that fixed parameters in existing methods cannot adapt to dynamic scenarios. The benchmark indicators transform building energy consumption characteristics, equipment safety specifications, and grid access requirements into quantitative parameters, avoiding confusion in the optimization direction of the model due to fuzzy constraints. Cross-indicator linkage rules provide coordination logic between indicators in the model. Dynamic correction of energy storage safety boundaries can reduce deep charging and discharging, delay energy storage aging, and reduce equipment replacement costs. Time-of-use pricing correction of grid connection thresholds can reduce high-price electricity purchases during peak periods and increase low-price electricity sales during off-peak periods, improving the economics of microgrids. Scenario-based correction of flexible loads can avoid abnormal equipment adjustments and reduce operation and maintenance failures.

[0068] Step S2: Based on a preset acquisition frequency, collect multi-state characteristics of the building microgrid in real time; the multi-state characteristics include instantaneous output of photovoltaic units, remaining energy status of energy storage systems, real-time energy consumption of DC loads, and grid-connected interactive power.

[0069] The multi-state characteristics include instantaneous output of photovoltaic units, remaining energy status of energy storage systems, real-time energy consumption of DC loads and grid-connected interactive power. The acquisition of each characteristic requires professional hardware equipment and a stable communication architecture, following the principles of high-frequency acquisition, accurate metering and reliable transmission, and combining preset acquisition frequencies to achieve real-time monitoring throughout the entire period.

[0070] Specifically, for the instantaneous output of photovoltaic units, high-precision power sensors are installed at the photovoltaic array combiner box or inverter output terminal to cover the dynamic range of photovoltaic output and ensure metering accuracy; light intensity sensors and module temperature sensors are deployed around the photovoltaic array to synchronously collect environmental data to help determine the cause of photovoltaic output fluctuations; the power sensor collects photovoltaic output power at a preset frequency, while the light and temperature sensors synchronously collect environmental parameters; the collected data is filtered for noise by a signal conditioning module, and then converted from analog to digital signals by an A / D conversion module to ensure data accuracy; the processed instantaneous output data of the photovoltaic units and environmental parameters are transmitted to the building microgrid central control system using wired or wireless communication methods;

[0071] To address the remaining energy state of the energy storage system, a power monitoring module is integrated into the battery management system of the energy storage battery pack. This module is connected to the positive and negative terminals of each battery cell, collecting real-time data on individual cell voltage and current, as well as the total voltage and current of the battery pack. Temperature sensors are deployed inside the energy storage cabinet to monitor the ambient temperature of the battery operating environment, preventing temperature anomalies from affecting calculation accuracy. The power monitoring module collects voltage and current data at a preset frequency and calculates the remaining power using coulomb counting: based on the integral of the charging / discharging current over time, combined with the initial battery charge and charging / discharging efficiency, the real-time remaining power is obtained, and the ratio of this to the rated capacity of the energy storage system is the remaining energy state. If any abnormal values ​​are found in the collected data, a data verification mechanism is triggered to remove the abnormal values ​​and replace them with the valid values ​​from the previous moment, ensuring accurate calculation results. The battery management system communicates with the central control system via industrial Ethernet, uploading remaining energy state data and auxiliary data such as individual cell voltage, current, and temperature in real time.

[0072] To address real-time energy consumption of DC loads, a DC smart meter is installed at the input of each DC load, adapting to different DC voltage levels to ensure metering accuracy. For scenarios with centralized power supply to multiple loads, a total power sensor is installed at the main incoming line of the distribution box, and branch meters are installed on each branch circuit, achieving two-level metering to monitor both overall energy consumption and individual load energy consumption. The smart meter collects load power at a preset frequency and records the load's operating status. After receiving the data, the central control system categorizes and statistically analyzes it according to load type, generating real-time energy consumption curves for each type of load. The meters transmit data to the central control system via a communication bus or wireless module.

[0073] For grid-connected interactive power, a bidirectional power metering device is installed at the connection point between the building microgrid and the public power grid. This device has bidirectional metering capabilities, simultaneously metering the power supplied from the microgrid to the grid and the power supplied from the grid to the microgrid, and is adaptable to the voltage and frequency fluctuation range of the power grid. The bidirectional power metering device collects interactive power at a preset frequency and distinguishes the power flow direction through a built-in power direction determination module. It also collects voltage and frequency data from the grid side to assess the grid's operating status. The metering device transmits grid-connected interactive power, voltage, and frequency data to the central control system and the power grid dispatch center via a dedicated communication link.

[0074] More specifically, all acquisition devices are calibrated through a clock synchronization module to ensure that the timestamps of each feature data acquisition are consistent, avoiding data analysis errors caused by time deviations; key acquisition devices are redundantly deployed, and backup devices automatically switch when the main device fails, ensuring uninterrupted data acquisition; the central control system stores data in real time according to the acquisition frequency, and uses dual backups on local hard drives and cloud servers to avoid data loss.

[0075] It should be noted that the core logic of the real-time acquisition of multi-dimensional state characteristics in step S2 is to achieve accurate and real-time acquisition of multi-dimensional state characteristics by using professional acquisition equipment, high-frequency data updates, and a stable transmission architecture, with the goal of real-time perception of the building microgrid's operating conditions. Its protection scope is not limited to the specific acquisition equipment types, acquisition frequencies, and communication methods mentioned above, but also covers all variations of this core logic: for example, instantaneous output acquisition of photovoltaic units can obtain data through the built-in monitoring function of the photovoltaic inverter; residual energy status acquisition of energy storage systems can be combined with more accurate calculation methods; real-time energy consumption acquisition of DC loads can be achieved through low-cost deployment using wireless sensor networks; grid-connected interactive power acquisition can be directly obtained by accessing the real-time data platform of the power grid dispatch center; the acquisition frequency can be dynamically adjusted according to the microgrid's control needs. All technical solutions that achieve state characteristic acquisition through the above logic based on the core requirement of real-time perception of the multi-dimensional operating status of the building microgrid fall within the protection scope of this step, aiming to cover acquisition needs under different microgrid scales, equipment configurations, and communication conditions, and avoiding limitations on the protection scope due to specific hardware selections or technical details.

[0076] In this step, high-frequency data acquisition can capture short-term system fluctuations, enabling the multi-dimensional collaborative optimization model to quickly perceive changes in operating conditions, generate immediate optimization instructions, and avoid power imbalances caused by data lag. By collecting multi-dimensional features, decision-making biases caused by incomplete information are avoided. High-precision equipment ensures small data errors. Real-time collected auxiliary data can help determine the cause of system anomalies, making it easier for maintenance personnel to quickly locate problems.

[0077] Furthermore, to ensure that data acquisition meets the real-time and accuracy requirements of the multivariate collaborative optimization model while avoiding redundant consumption of hardware resources and communication bandwidth, the method for setting the acquisition frequency of multivariate state features includes:

[0078] Step S21: Based on the scale, equipment characteristics, and normal operating conditions of the building microgrid, set the basic acquisition frequencies for the instantaneous output of the photovoltaic unit, the remaining energy status of the energy storage system, the real-time energy consumption of the DC load, and the grid-connected interactive power. Among them, the basic acquisition frequency for the instantaneous output of the photovoltaic unit is set to the highest level, the basic acquisition frequencies for the remaining energy status of the energy storage system and the real-time energy consumption of the DC load are set to a medium level, and the basic acquisition frequency for the grid-connected interactive power is coordinated with the instantaneous output of the photovoltaic unit and is lower than the basic acquisition frequency for the instantaneous output of the photovoltaic unit.

[0079] Step S22: Construct a dynamic correction factor system, which includes a fluctuation intensity correction factor, a regulation priority correction factor, and an equipment cost correction factor.

[0080] The central control system retrieves data from multiple recent acquisition cycles for each state characteristic and calculates the unit time fluctuation amplitude. The unit time fluctuation amplitude is the ratio of the difference between the maximum and minimum values ​​of the state characteristic in multiple recent acquisition cycles to the rated value, divided by the time interval. Low fluctuation thresholds and high fluctuation thresholds are set for each state characteristic. If the unit time fluctuation amplitude 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 unit time fluctuation amplitude and less than the high fluctuation threshold, the fluctuation intensity correction factor is 1.0. If the unit time fluctuation amplitude is greater than or equal to the high fluctuation threshold, the fluctuation intensity correction factor is 1.5-2.0.

[0081] The control priority of each state characteristic is determined based on the microgrid optimization benchmark index. The control priority correction factor for high priority state characteristics is 1.2-1.5, for medium priority it is 1.0, and for low priority it is 0.8-0.9.

[0082] Set the equipment cost correction factor: Monitor the energy consumption of the equipment and the bandwidth utilization of the communication link. If the equipment energy consumption exceeds the preset energy saving threshold or the communication bandwidth utilization reaches the preset utilization threshold, the equipment cost correction factor is set to 0.8-0.9; otherwise, it is set to 1.0.

[0083] Step S23: For each state feature, multiply its basic acquisition frequency by the product of the fluctuation intensity correction factor, the control priority correction factor, and the equipment cost correction factor to obtain the real-time acquisition frequency of that state feature; set a maximum frequency threshold and a minimum frequency threshold for each state feature. If the real-time acquisition frequency exceeds the threshold range, take the corresponding threshold as the final acquisition frequency.

[0084] It should be noted that the core of the above-mentioned frequency setting method lies in constructing a dynamic adjustment mechanism based on operating condition fluctuations and multi-objective requirements to achieve the adaptation of the frequency setting to the microgrid's operating status. Its protection scope is not limited to the specific base frequency value, fluctuation threshold, and correction factor value mentioned above, but also covers all variations that conform to this core logic. For example, the base frequency can be adjusted according to the scale of the building microgrid; the fluctuation threshold can be corrected in combination with the characteristics of different types of equipment; the correction factor can add the dimension of grid dispatching requirements; special scenarios can be extended to grid fault scenarios, equipment maintenance scenarios, etc. All technical solutions that achieve frequency setting through a base benchmark combined with dynamic correction based on the core logic of dynamic adaptation to operating conditions and multi-objective balance fall within the protection scope of this method. It aims to cover different building microgrid scenarios, equipment configurations, and operating requirements, and avoid limiting the protection scope due to differences in specific parameters.

[0085] Step S3: Input the microgrid optimization benchmark index and the multi-state characteristics into the pre-constructed multi-coordinated optimization model to obtain the optimization control commands of each control unit;

[0086] To address the conflicts and coupling of multiple objectives and constraints—for example, when photovoltaic output is excessive, it may simultaneously trigger energy storage charging and grid power transmission, leading to resource waste; when photovoltaic output is insufficient, it may over-rely on energy storage discharge while neglecting the economics of grid power extraction, resulting in overall system efficiency losses—it is necessary to use a multi-dimensional collaborative optimization model to deeply couple the baseline index of step S1 with the state characteristics of step S2. This transforms the multi-objective optimization problem into a solvable model, generates collaborative instructions adapted to each control unit, and solves the problems of lack of coordination and difficulty in balancing existing methods. This ensures that the control actions not only comply with constraints but also achieve multi-objective optimization.

[0087] The multi-element collaborative optimization model is based on energy conservation as its underlying logic. It is constructed through a basic balance model, correlation coupling mechanism, and constraint integration. It takes the microgrid optimization benchmark index and multi-element state characteristics as inputs, and outputs the optimized control commands of each control unit after model calculation. The specific steps are as follows:

[0088] Step S31: Based on the principle of energy conservation, construct a dynamic balance relationship covering photovoltaic, energy storage, load, and grid interaction to quantify the energy flow logic of each link; the relationship is 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 microgrid, which 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 supplied by the microgrid to the grid; this relationship serves as the underlying logic of the model to ensure the dynamic balance of energy supply and demand.

[0089] Step S32: Introduce photovoltaic energy storage correlation coefficient, photovoltaic load correlation coefficient, energy storage load correlation coefficient, and energy storage grid correlation coefficient to form a correlation coupling matrix and quantify the linkage pattern between various state characteristics;

[0090] Among them, the photovoltaic energy storage correlation coefficient is used to reflect the matching relationship between the photovoltaic power output fluctuation rate and the energy storage charging and discharging rate. It is calculated by using historical data, statistically analyzing the ratio of the photovoltaic power output fluctuation to the change in energy storage charging and discharging power during the same period, and determining the coefficient value by combining the impact of the current remaining energy state of energy storage on the charging and discharging capacity.

[0091] The photovoltaic load correlation coefficient is used to reflect the overlap between the peak photovoltaic output and the peak power consumption of flexible loads. It is calculated by dividing the integral of the product of the instantaneous output of the photovoltaic unit and the power of the flexible load within the statistical period by the product of the square root of the integral of the square of their respective power.

[0092] The energy storage load correlation coefficient is used to reflect the linkage between the remaining energy state of energy storage and the load adjustment demand. It is calculated by combining the relative position of the current remaining energy state of energy storage and the safe operation boundary with the ratio of the load adjustment demand power to the rated power.

[0093] The energy storage grid correlation coefficient is used to reflect the synergistic relationship between the remaining energy status of energy storage and the grid-connected power. The coefficient is small when the remaining energy of energy storage is low and large when the remaining energy of energy storage is high.

[0094] Step S33: Transform the basic energy balance model into an incremental form, and introduce four types of incremental adjustment quantities: photovoltaic power output fine-tuning quantity, energy storage charging and discharging correction quantity, load adjustment quantity, and grid-connected power correction quantity. The coupling relationship between each increment is realized through the correlation coupling matrix. At the same time, the microgrid optimization benchmark index set in step S1 is transformed into mathematical constraints.

[0095] The minimum acceptance rate constraint for clean energy is: the ratio of the difference between the sum of the instantaneous output of the photovoltaic unit and the fine-tuning amount of photovoltaic output, minus the incremental power transmission from the microgrid to the grid, and the sum of the instantaneous output of the photovoltaic unit and the fine-tuning amount of photovoltaic output, shall not be less than the minimum acceptance rate for clean energy.

[0096] The safety boundary constraint for energy storage operation is: the sum of the difference between the remaining energy state of the energy storage system and the energy storage charging correction amount minus the energy storage discharging correction amount, multiplied by the time step and divided by the rated capacity of the energy storage, must be between the upper and lower limits of the energy storage operation safety boundary.

[0097] The constraint on the flexible load regulation amplitude limit is: the load regulation amount must be between the negative flexible load regulation amplitude limit and the flexible load regulation amplitude limit;

[0098] The grid-connected power permit threshold constraint is as follows: the incremental power supply from the microgrid to the grid must be between the negative grid-connected power permit threshold and zero, and the incremental power supply from the grid to the microgrid must be between zero and the grid-connected power permit threshold.

[0099] Step S34: Use historical operating data of building microgrid to calibrate model parameters, and verify model accuracy through simulation calculation and result comparison; input historical state characteristics and benchmark indicators into the model, compare the deviation between the optimized instructions output by the model and the actual optimal control action, and if the deviation exceeds the preset range, adjust the correlation coupling matrix coefficients until the model accuracy meets the requirements.

[0100] More specifically, during the input data preprocessing, the microgrid optimization benchmark indicators of step S1 are transformed into mathematical constraint parameters that the model can recognize, and the multivariate state characteristics of step S2 are subjected to outlier removal and normalization to ensure the uniformity of the input data. During the model solving and optimization command generation process, linear programming or quadratic programming algorithms are used to solve the constrained incremental regulation equation set. The objective function is set as the weighted sum of minimizing photovoltaic curtailment, minimizing energy storage charging and discharging losses, and minimizing grid interaction costs. The weights are set according to the optimization priority of the building microgrid. If the solution result satisfies all constraints, the sum of the current state characteristics and the incremental regulation is used as the optimization control command for each control unit. If a certain incremental regulation exceeds the constraint, the constraint relaxation mechanism is triggered, the coefficients of the correlation coupling matrix are adjusted, and the solution is re-solved until all regulation quantities satisfy the constraints, ensuring the safety and feasibility of the command.

[0101] After the multi-element collaborative optimization model is completed, classification instructions are output according to the type of control unit, including:

[0102] Photovoltaic regulation module instructions: Determine the target output power and output adjustment range of the photovoltaic unit;

[0103] Energy storage charging and discharging module instructions: Determine the target charging and discharging power and duration of the energy storage;

[0104] Flexible load management module instructions: Determine the direction and magnitude of power adjustment for the flexible load;

[0105] Bidirectional converter command: Determine the target power for grid connection.

[0106] In this step, the linkage relationship between each link is quantified through the correlation coupling matrix, transforming multiple objectives into a unified mathematical optimization problem. This avoids system imbalances caused by prioritizing a single objective in existing methods. For example, when photovoltaic output is abundant, coordinated instructions for energy storage charging, load adjustment, and reduction of grid-connected power transmission are generated simultaneously. The incremental adjustment mechanism, combined with high-frequency input data, enables the model to respond quickly to fluctuations in operating conditions, achieving power balance through small incremental adjustments and avoiding voltage fluctuations caused by large actions. The correlation coupling matrix is ​​calibrated based on historical data, ensuring that the instructions are highly adapted to actual operating conditions and that the control error is small. The model construction incorporates historical operating data, and the parameters of the correlation coupling matrix implicitly adapt to future trends. At the same time, real-time input data ensures that the instructions fit the current operating conditions, solving the problem that existing methods only achieve real-time balance and lack foresight, thus improving long-term operational economy. The model automatically completes constraint verification and instruction generation, reducing operation and maintenance costs. Constraint conditions strictly limit the operating boundaries of equipment, avoiding equipment lifespan loss caused by over-adjustment.

[0107] Step S4: For each control unit, obtain the corresponding current control command, and calculate the optimization magnitude based on the current control command and the optimized control command;

[0108] Existing methods often directly issue optimized control commands without considering the current operating status of the control unit, which may lead to equipment overload or accelerated aging. For example, when the current output of the photovoltaic control module is close to the rated value, directly executing a command to significantly increase the output can easily cause equipment failure. By calculating the optimization range, the difference between the current control command and the optimized control command is quantified, providing a basis for optimizing the regulation strategy. This solves the problems of rigid command execution and poor equipment adaptability in existing methods, ensuring that the regulation actions meet the optimization objectives and are adapted to the actual operating capacity of the control unit.

[0109] Specifically, for the photovoltaic regulation module, energy storage charging and discharging module, flexible load management module, and bidirectional converter, the current control commands are obtained respectively. Combined with the optimized control commands generated in step S3, the final optimization range is determined through difference quantization and boundary verification. The specific steps are as follows:

[0110] Step S41: Current control commands refer to the operating parameters currently executed by the control unit. These parameters need to be retrieved in real time through a dedicated communication interface or control platform to ensure that the data is consistent with the actual operating status. Current commands for the photovoltaic regulation module obtain the target output power and output limit threshold of the current photovoltaic unit through the photovoltaic inverter monitoring interface, and simultaneously record the operating status of the photovoltaic array. Current commands for the energy storage charging and discharging module obtain the target charging and discharging power and charging and discharging cutoff voltage of the current energy storage through the energy storage battery management system, and simultaneously retrieve auxiliary data such as the remaining energy status and health of the energy storage system. Current commands for the flexible load management module obtain the current target power and operating mode of each flexible load through the flexible load control terminal, and record the current operating status of the load. Current commands for the bidirectional converter obtain the target power and power factor setpoint of the current grid connection interaction through the bidirectional converter control platform, and simultaneously collect operating parameters such as grid-side voltage and frequency.

[0111] Step S42: For different control unit operating characteristics, adopt differentiated calculation logic to ensure that the optimization range can reflect the optimization requirements and adapt to equipment constraints;

[0112] The calculation of the photovoltaic regulation module's optimization range involves parameters including the current target output, the optimized target output, the rated photovoltaic output, and the minimum safe output. The calculation first determines the basic optimization range, which is the difference between the optimized target output and the current target output. Then, it combines this with photovoltaic unit output boundary verification: if the basic optimization range is positive, the output needs to be increased, and the final optimization range is the smaller of the basic optimization range and the difference between the rated photovoltaic output and the current target output; if the basic optimization range is negative, the output needs to be decreased, and the final optimization range is the larger of the basic optimization range and the difference between the minimum safe output and the current target output; if the basic optimization range is zero, the optimization range is zero.

[0113] The calculation of the optimization range of the energy storage charging and discharging module involves parameters including the current charging and discharging power, the optimized charging and discharging power, the rated charging and discharging power of the energy storage, the current remaining energy state, and the energy storage operation safety boundary. The calculation first determines the basic optimization range, which is the difference between the optimized charging and discharging power and the current charging and discharging power. Then, it is combined with energy storage safety constraint verification: if the basic optimization range is positive, charging needs to be increased, and the final optimization range is the smaller of the following: the basic optimization range, the difference between the rated charging and discharging power and the current charging and discharging power, the difference between the upper limit of the energy storage operation safety boundary and the current remaining energy state, multiplied by the rated energy storage capacity, and then divided by the adjustment time step. If the basic optimization range is negative, discharging needs to be increased, and the final optimization range is the larger of the following: the basic optimization range, the difference between the negative rated charging and discharging power and the current charging and discharging power, the difference between the current remaining energy state and the lower limit of the energy storage operation safety boundary, multiplied by the rated energy storage capacity, and then divided by the adjustment time step. If the basic optimization range is zero, the optimization range is zero.

[0114] The optimization range calculation for the flexible load management module involves parameters including the current load power, optimized load power, flexible load adjustment range limit, and the current operating status of the load. The calculation first determines the basic optimization range, which is the difference between the optimized load power and the current load power. Then, it is verified against the flexible load adjustment range limit: if the basic optimization range is positive, the load needs to be increased, and the final optimization range is the smaller of the basic optimization range and the flexible load adjustment range limit; if the basic optimization range is negative, the load needs to be decreased, and the final optimization range is the larger of the basic optimization range and the negative flexible load adjustment range limit; if the load is in a high-frequency usage period, the optimization range needs to be further narrowed to finally determine the feasible optimization range.

[0115] The calculation of the optimization magnitude of a bidirectional converter involves parameters including the current grid-connected power, the optimized grid-connected power, and the grid-connected power permit threshold. The calculation first determines the basic optimization magnitude, which is the difference between the optimized grid-connected power and the current grid-connected power. Then, it is verified using the grid-connected power permit threshold: if the basic optimization magnitude is positive, power extraction needs to be increased, and the final optimization magnitude is the smaller of the difference between the upper limit of power extraction and the current grid-connected power permit threshold; if the basic optimization magnitude is negative, power supply needs to be increased, and the final optimization magnitude is the larger of the difference between the upper limit of power supply and the current grid-connected power permit threshold; if the basic optimization magnitude is zero, the optimization magnitude is zero.

[0116] It should be noted that the core logic of the optimization amplitude calculation in step S4 is based on the current state of the control unit and equipment constraints, quantifying the difference between the optimized control command and the current control command to generate a safe and feasible adjustment amplitude. Its protection scope is not limited to the specific calculation parameters, calculation logic and verification dimensions mentioned above, but also covers all modified implementation methods that conform to this core logic: for example, the optimization amplitude calculation can add the equipment health weight, the verification dimension can be extended to the grid dispatch requirements, and the calculation logic can be replaced with the relative proportion method; for new control units, the core parameters and constraints can be adjusted according to their operating characteristics; all technical solutions that calculate the adjustment amplitude of the control unit based on the core logic of quantifying the difference between the current and optimized commands and combining equipment constraints to ensure safety fall within the protection scope of this step, aiming to cover the amplitude calculation requirements under different building microgrid equipment configurations, operating conditions and optimization needs, and avoid limiting the protection scope due to differences in specific parameters, logic or control unit types.

[0117] In this step, boundary checks strictly limit the optimization range to prevent equipment from operating beyond its rated capacity or safety range, reducing equipment overload and deep damage, and delaying equipment aging. The optimization range is based on the quantification of differences in the current state to avoid sudden changes in instructions, reduce DC bus voltage fluctuations and grid interaction impacts, and ensure stable system operation. Differentiated calculation logic is used for the different characteristics of photovoltaic, energy storage, load, and grid-connected equipment to adapt to the operating constraints of various types of equipment and avoid instructions failing to execute due to incompatible equipment characteristics. By combining basic range calculation with boundary checks, it is ensured that the optimization range reflects the optimization target of step S3 without exceeding the necessary range, avoiding over-adjustment or under-adjustment, and balancing accuracy and efficiency.

[0118] Step S5: Based on the optimization amplitude, generate the optimization adjustment strategy corresponding to the control unit, and execute the optimization adjustment strategy;

[0119] Step S5, based on the optimization range calculated in step S4, combines the operating characteristics of each control unit, building energy consumption scenario constraints, and equipment safety requirements to generate a highly adaptable optimization and regulation strategy; and, based on the optimization range of step S4 and the characteristics of the control units, generates differentiated strategies for the four types of core control units.

[0120] Specifically, the optimization and regulation strategy of the photovoltaic regulation module is as follows: based on the sign of the optimization magnitude and the current operating status of the photovoltaic system, the regulation steps are broken down and the regulation rate is controlled.

[0121] If the optimization magnitude is positive: first check the temperature of the photovoltaic module. If the temperature is lower than the safety threshold, increase the output stepwise according to the equipment's tolerance rate until the target output is reached; if the temperature is higher than the safety threshold, reduce the adjustment rate and simultaneously activate the cooling system to prevent the module from overheating.

[0122] If the optimization magnitude is negative: firstly, reduce the rated slightly by adjusting the maximum power point tracking strategy of the photovoltaic inverter. If the magnitude is insufficient, then reduce the output in steps according to the equipment's tolerance rate to avoid a sudden drop in output caused by direct string disconnection.

[0123] Step adjustment commands are issued through the photovoltaic inverter monitoring interface. After each adjustment, the output is allowed to stabilize before proceeding to the next step to avoid fluctuations.

[0124] Specifically, the optimization and adjustment strategy for the energy storage charging and discharging module is as follows: based on the positive or negative value of the optimization magnitude, the current remaining energy state and health of the energy storage, the adjustment process is broken down and dynamically adjusted in conjunction with the energy storage operation safety boundary;

[0125] If the optimization magnitude is positive: First calculate the difference between the current remaining energy state and the safety limit. If the difference is large, increase the charging power in steps according to the device's tolerance rate. If the difference is small, reduce the rate to avoid rapidly approaching the safety limit. At the same time, monitor the energy storage temperature. If the temperature exceeds the safety threshold, pause charging to cool down before resuming low-speed operation.

[0126] If the optimization magnitude is negative: calculate the difference between the current remaining energy state and the safety lower limit. If the difference is large, increase the discharge power stepwise according to the equipment's tolerance rate. If the difference is small, reduce the rate to avoid over-discharge. If the energy storage health is low, further narrow the discharge rate to delay aging.

[0127] The energy storage battery management system issues charge and discharge rate commands, collects remaining energy status and temperature data after each adjustment, and continues execution after confirming that there are no abnormalities.

[0128] Specifically, the optimization and adjustment strategy of the flexible load management module is as follows: based on the positive or negative value of the optimization magnitude, the load type, and the user's energy consumption period, the adjustment magnitude is divided into priorities, and non-critical loads are adjusted first.

[0129] If the optimization magnitude is positive: prioritize non-critical flexible loads and increase the power in steps according to the equipment's tolerance rate; if the adjustment space of non-critical loads is insufficient, then slightly increase the critical loads, and only execute during non-sensitive periods for users to ensure that the critical loads function normally.

[0130] If the optimization magnitude is negative: first reduce the non-critical loads by lowering the power in steps according to the equipment's tolerance rate; if the magnitude is insufficient, then slightly reduce the critical loads, and ensure that the critical loads function normally after the reduction.

[0131] The flexible load control terminal issues priority-based adjustment commands, and the load operation status is fed back after each adjustment. The process continues after confirming that there are no abnormalities.

[0132] Specifically, the optimization and adjustment strategy for bidirectional converters is as follows: adjust the execution rhythm and magnitude according to the sign of the optimization magnitude, the real-time status of the power grid, and the grid-connected power allowable threshold.

[0133] If the optimization magnitude is positive: First, query the current time period of the power grid, increase the power extraction during the valley period according to the equipment's tolerance rate, and decrease the power extraction during the peak period, while ensuring that the final power extraction does not exceed the grid-connected power permit threshold; when the power grid issues a load warning, suspend the increase of power extraction, or even reduce the power extraction in the opposite direction.

[0134] If the optimization magnitude is negative: during the valley period, increase the power supply in steps according to the equipment's tolerance rate; during the peak period, decrease the power supply, but not exceeding the grid-connected power allowable threshold; at the same time, monitor the grid voltage and frequency, and suspend power supply adjustment when fluctuations occur;

[0135] The grid-connected power adjustment command is issued through the bidirectional converter control platform. After each adjustment, grid-side data is collected, and the process continues after compliance is confirmed.

[0136] In this step, the generation and execution of the optimized regulation strategy are based on the optimization magnitude, combined with the characteristics of the control unit, building energy consumption scenario constraints, and equipment safety requirements. An adaptive strategy is generated and executed through closed-loop control to ensure the optimization goal is achieved, rather than being limited to the specific strategy decomposition methods, regulation rates, or monitoring dimensions mentioned above. This logic is adopted to address the common problems of existing methods, such as rough execution and poor adaptability. Regardless of how the type of control unit in the building microgrid is added, how the building energy consumption scenario is expanded, how equipment characteristics are iterated, or how grid policies are adjusted, the logic of characteristic adaptation, safe execution, and closed-loop verification must be used to ensure that the regulation strategy fits the actual scenario. Step S5 decomposes regulation actions according to scenario constraints to avoid interfering with critical energy demand; it decomposes the regulation magnitude according to the dynamic characteristics of the equipment to control the regulation rate, avoiding equipment damage caused by sudden changes and extending equipment life; and it uses tiered regulation and dynamic response to reduce system fluctuations during the regulation process and ensure stable power supply.

[0137] Example 2: Figure 2 As shown, the multi-module collaborative optimization system for building microgrids of the present invention specifically includes the following modules;

[0138] The benchmark setting module is used to set microgrid optimization benchmarks based on building energy consumption characteristics, equipment safety specifications, and grid connection requirements.

[0139] The status feature acquisition module is used to acquire multi-dimensional status features of the building microgrid in real time according to a preset acquisition frequency.

[0140] An optimization instruction generation module is used to input the microgrid optimization benchmark index and the multi-state characteristics into a pre-constructed multi-coordinated optimization model to obtain the optimization control instructions of each control unit.

[0141] The optimization amplitude calculation module is used to obtain the corresponding current control command for each control unit, and calculate the optimization amplitude based on the current control command and the optimization control command.

[0142] The adjustment strategy execution module is used to generate an optimized adjustment strategy corresponding to the control unit based on the optimization magnitude, and to execute the optimized adjustment strategy.

[0143] The various variations and specific embodiments of the building microgrid multi-element collaborative optimization method in the aforementioned Embodiment 1 are also applicable to the building microgrid multi-element collaborative optimization system of this embodiment. Through the foregoing detailed description of the building microgrid multi-element collaborative optimization method, those skilled in the art can clearly understand the implementation method of the building microgrid multi-element collaborative optimization system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0144] In addition, this application also provides an electronic device, including 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 respectively connected via the bus. When the computer program is executed by the processor, it implements the various processes of the above-described method embodiment for controlling output data and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0145] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it causes the processor to execute the building microgrid multi-element collaborative optimization method in Embodiment 1 of the present invention, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0146] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-element collaborative optimization method for building microgrids, characterized in that, The method includes: Based on building energy consumption characteristics, equipment safety standards, and grid connection requirements, microgrid optimization benchmark indicators are set. Based on a preset acquisition frequency, the multi-state characteristics of the building microgrid are acquired in real time. The method for setting the preset acquisition frequency includes: setting a basic acquisition frequency for each multi-state characteristic; constructing a dynamic correction factor system, which includes a fluctuation intensity correction factor, a control priority correction factor, and an equipment cost correction factor; and for each state characteristic, multiplying its basic acquisition frequency by the product of each correction factor in the dynamic correction factor system to obtain the preset acquisition frequency of that state characteristic. The microgrid optimization benchmark index and the multi-state characteristics are input into a pre-constructed multi-factor collaborative optimization model to obtain the optimization control commands of each control unit. For each control unit, the corresponding current control command is obtained, and the optimization magnitude is calculated based on the current control command and the optimized control command. Based on the optimization range, an optimized adjustment strategy corresponding to the control unit is generated and the optimized adjustment strategy is executed. The microgrid optimization benchmark indicators include the minimum clean energy acceptance rate, the energy storage operation safety boundary, the flexible load regulation range limit, and the grid-connected power permit threshold. The multi-state characteristics include instantaneous output of photovoltaic units, remaining energy status of energy storage systems, real-time energy consumption of DC loads, and grid-connected interactive power. The construction of the multivariate collaborative optimization model includes: A dynamic equilibrium relationship is constructed based on the principle of energy conservation; An association coupling matrix is ​​introduced to quantify the linkage relationship between photovoltaic, energy storage, load and grid state characteristics. The association coupling matrix includes photovoltaic energy storage association coefficient, photovoltaic load association coefficient, energy storage load association coefficient and energy storage grid association coefficient. The microgrid optimization benchmark indicators are converted into mathematical constraints. A planning algorithm is used to solve a function with the goal of minimizing the overall operating cost, to obtain the incremental adjustment amount of each control unit, and the optimized control command is generated by combining the current state characteristics.

2. The multi-element collaborative optimization method for building microgrids as described in claim 1, characterized in that, The fluctuation intensity correction factor is determined by calculating the unit time fluctuation amplitude of the state characteristics and comparing it with preset low fluctuation threshold and high fluctuation threshold. The regulation priority correction factor is determined based on the priority set for each state characteristic by the microgrid optimization benchmark index; The equipment cost correction factor is determined by monitoring the operating energy consumption and communication link bandwidth utilization of the data acquisition equipment.

3. The multi-element collaborative optimization method for building microgrids as described in claim 1, characterized in that, The generation of the optimization adjustment strategy includes: Based on the sign and magnitude of the optimization amplitude, combined with the equipment tolerance rate of the control unit, the current operating status, and external environmental conditions, the adjustment process is divided into multiple step steps, and the adjustment rate of each step is controlled.

4. A building microgrid multi-element collaborative optimization system, wherein the system is applied to the building microgrid multi-element collaborative optimization method as described in claim 1, characterized in that, The system includes: The benchmark setting module is used to set microgrid optimization benchmarks based on building energy consumption characteristics, equipment safety specifications, and grid connection requirements. A state feature acquisition module is used to acquire multiple state features of a building microgrid in real time according to a preset acquisition frequency. The method for setting the preset acquisition frequency includes: setting a basic acquisition frequency for the instantaneous output of photovoltaic units, the remaining energy status of the energy storage system, the real-time energy consumption of DC loads, and the grid-connected interactive power; constructing a dynamic correction factor system, which includes a fluctuation intensity correction factor, a regulation priority correction factor, and an equipment cost correction factor; for each state feature, multiplying its basic acquisition frequency by the product of each correction factor 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 multi-state characteristics into a pre-constructed multi-coordinated optimization model to obtain the optimization control instructions of each control unit. The optimization amplitude calculation module is used to obtain the corresponding current control command for each control unit, and calculate the optimization amplitude based on the current control command and the optimization control command. The adjustment strategy execution module is used to generate an optimized adjustment strategy corresponding to the control unit based on the optimization magnitude, and to execute the optimized adjustment strategy.

5. 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, characterized in that, When the computer program is executed by the processor, it implements the steps of the method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-3.

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