Optical storage direct flexible multi-purpose cooperative optimization method, system, electronic device and storage medium

Abstract

The application discloses a kind of light storage straight flexible multi-energy collaborative optimization method, comprising: real-time acquisition of the operation data of building light storage straight flexible system, and establish unified power and electricity monitoring benchmark;Calculate the net power deviation value of current scheduling time, judge whether the fluctuation amplitude of net power deviation value exceeds the preset energy storage battery smooth running fluctuation threshold;When fluctuation amplitude does not exceed smooth running fluctuation threshold, control energy storage battery to maintain system power balance with constant small current charging and discharging;When fluctuation amplitude exceeds smooth running fluctuation threshold, generate flexible load calling sequence according to preset priority order and the like steps.The application also discloses a kind of light storage straight flexible multi-energy collaborative optimization system, electronic equipment and storage medium.The application solves the problem that energy storage battery is accelerated aging due to frequent deep charge and discharge caused by lack of dynamic perception and collaborative decision mechanism for the coupling relationship between energy storage health status and flexible load adjustment capability.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology, specifically a method, system, electronic device, and storage medium for the synergistic optimization of optical storage, direct current, flexible energy, and multi-energy systems. Background Technology

[0002] As the global energy structure transformation and goals are further advanced, the building sector, as a crucial end-point in energy consumption, has seen its energy use shift towards low-carbon and flexible methods become a key breakthrough in the energy revolution. The photovoltaic-storage-DC-flexible system (PV-SHS-Flexible System), a novel building energy system architecture integrating photovoltaic power generation, energy storage batteries, DC power distribution, and flexible loads, achieves efficient coupling of source, storage, and load through a DC bus, effectively enhancing the building's renewable energy absorption capacity and energy flexibility. In recent years, with the continuous expansion of distributed photovoltaic installations, the rapid decline in electrochemical energy storage costs, and the widespread electrification of building end-use energy equipment, PV-SHS-DC-Flexible Systems have been increasingly widely applied and promoted in public buildings, industrial parks, and residential communities. However, the operation and control of PV-SHS-DC-Flexible Systems face the combined effects of multiple complex factors, including random fluctuations in photovoltaic output, strong time-varying building loads, limited energy storage battery capacity and aging degradation, and significant differences in the energy consumption characteristics of various types of flexible loads. How to achieve multi-energy complementary coupling and coordinated optimization scheduling while ensuring real-time system power balance and DC bus voltage stability has become a core problem that urgently needs to be solved in this technological field.

[0003] In existing technologies, the control methods for building-integrated photovoltaic-storage-direct-flexible systems (BIPV-SHUPS) mainly have the following shortcomings: Existing control strategies mostly adopt a single path-dependent mode that prioritizes energy storage. That is, when the system experiences power imbalance, the energy storage battery suppresses the charging and discharging level, or prioritizes adjusting the flexible load to reduce power. There is a lack of dynamic perception and collaborative decision-making mechanism for the coupling relationship between the health status of energy storage and the adjustment capability of flexible load. This leads to the accelerated aging of energy storage batteries due to frequent deep charging and discharging, or the flexible load being over-adjusted, which damages the user's normal electricity experience.

[0004] Therefore, to address the above issues, a method and system for multi-energy coupling and collaborative optimization of building-integrated photovoltaic-storage-direct-flexible systems is provided. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method, system, electronic device, and storage medium for the coordinated optimization of photovoltaic, energy storage, direct current, and flexible energy sources. This solves the problem that the lack of a dynamic perception and collaborative decision-making mechanism for the coupling relationship between the health status of energy storage and the ability to adjust flexible loads leads to accelerated aging of energy storage batteries due to frequent deep charging and discharging, or that excessive adjustment of flexible loads damages the user's normal electricity experience.

[0006] The technical solution to achieve the above objectives is: One of the present inventions provides a method for synergistic optimization of optical storage, direct-drive, flexible, and multi-energy systems, comprising: Step S1: Collect real-time operating data of the building-integrated photovoltaic-storage-flexible system and establish a unified power and electricity monitoring benchmark; wherein, the operating data includes photovoltaic power generation, building electrical load power, terminal voltage and charging / discharging current and cell temperature of energy storage batteries, and real-time power consumption and operating status parameters of various types of flexible loads connected to the system. Step S2: Based on the collected operating data, calculate the net power deviation value at the current scheduling time, and determine whether the fluctuation range of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery. Step S3: When the fluctuation amplitude does not exceed the stable operation fluctuation threshold, control the energy storage battery to charge and discharge with a constant small current to maintain the system power balance, and execute step S8. Step S4: When the fluctuation amplitude exceeds the stable operation fluctuation threshold, a flexible load call sequence is generated according to a preset priority order; wherein, the preset priority order is preset based on the regulation response characteristics of each type of flexible load and the degree of impact on user comfort. Step S5: Based on the real-time adjustable capacity of each flexible load in the flexible load call sequence and combined with the pre-established user comfort elastic constraint model, dynamically allocate the adjustment power command of each called flexible load; wherein, the user comfort elastic constraint model is used to quantify the degree of impact of flexible load adjustment behavior on the user's normal electricity experience. Step S6: After flexible load adjustment, calculate the remaining stable power gap or surplus, and control the energy storage battery to supplement and absorb the remaining stable power gap or surplus with the charging and discharging current after dynamic current limiting; wherein, the charging and discharging current after dynamic current limiting is calculated in real time by a dynamic current limiting function. The dynamic current limiting function takes the real-time health status estimate of the energy storage battery, the real-time temperature of the cell and the accumulated effective cycle number as input variables, takes the rated current of the energy storage battery as the reference value, and outputs the maximum allowable charging and discharging current constrained by the battery aging state. Step S7: During the execution of energy storage battery charge and discharge control, a hybrid online health state estimation method that integrates electrochemical mechanism and data-driven approach is adopted to estimate the real-time health state value of the energy storage battery online and update the battery life loss model. Step S8: Record the operation data of this collaborative scheduling and the battery life loss model update results to form an optimization log; Step S9: Feedback the optimization strategy parameters in the optimization log to the system controller to correct the smooth operation fluctuation threshold of step S2, the preset priority order of step S4, and the dynamic current limiting function parameters of step S6 in the next scheduling cycle, forming a continuous closed-loop optimization control.

[0007] Preferably, in step S1, the real-time acquisition of operational data from the building's photovoltaic-storage-direct-flexible system specifically includes: With sampling period Real-time acquisition of DC output voltage of each MPPT branch of the photovoltaic array Charging and discharging current Calculate the total photovoltaic power generation : ; In the formula, For the first MPPT branch road The instantaneous output voltage at a given moment. For the first MPPT branch road Instantaneous direct current at a given moment This represents the total number of MPPT branches in the photovoltaic array; With sampling period Real-time acquisition of battery terminal voltage reported by the energy storage battery management system With DC current Calculate the total electrical load of the building : ; In the formula, For the first Road building power branch circuit DC terminal voltage at time 10:00 For the first Power supply branch circuits for road construction DC current at any given moment This represents the total number of electrical branch circuits in the building. With sampling period Real-time acquisition of battery terminal voltage reported by the energy storage battery management system Charging and discharging current Cell temperature State of charge and health status data; With sampling period The system collects real-time operating status parameters of various types of flexible loads connected to the system. These flexible loads include at least one or more of the following: air conditioners, water heaters, and charging piles. The operating status parameters of air conditioners include set temperature. Real-time indoor temperature Compressor operating power The operating mode and other parameters of the water heater include the set water temperature. Real-time water temperature in the water tank Heating power Including water tank volume, the charging pile's operating status parameters include charging demand. Current charge amount Rated charging power and the user-defined expected departure time .

[0008] Preferably, in step S2, the net power deviation value at the current scheduling moment is calculated, and it is determined whether the fluctuation range of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery, specifically including: calculate Net power deviation at time : ; In the formula, for Total photovoltaic power generation at any given time for Total electrical load of the building at any time. This refers to the total number of flexible loads that previously participated in the regulation. For the first A flexible load in The power adjustment command value at any given time; a positive value indicates load reduction, and a negative value indicates load increase. Within the preset sliding time window Within, calculate the fluctuation range of the net power deviation value. The calculation formula is as follows: ; In the formula, The number of sampling points within the sliding time window. For the first in the sliding window Net power deviation at each sampling time. This is the arithmetic mean of the net power deviation values ​​within the sliding time window. It is a natural exponential function used to construct time decay weights. This is the time decay coefficient, and its value is chosen to give higher computational weight to sampling points that are closer to the current time. Fluctuation range With respect to the preset fluctuation threshold for stable operation of energy storage batteries Comparison, stable operation fluctuation threshold Based on the real-time health status of the energy storage battery Real-time temperature of the battery cell Dynamic correction is performed, and the correction formula is as follows: ; In the formula, The benchmark for stable operation fluctuation threshold, and These are the weighting coefficients for the influence of health status and the weighting coefficients for the influence of temperature, respectively. and These are the nonlinear correction indices for health status and temperature, respectively. This is the optimal operating temperature for energy storage batteries; when When the fluctuation amplitude does not exceed the preset fluctuation threshold for stable operation of the energy storage battery, it is determined that the fluctuation amplitude does not exceed the preset threshold for stable operation of the energy storage battery. when When the fluctuation exceeds the preset fluctuation threshold for stable operation of the energy storage battery, it is determined that the fluctuation amplitude exceeds the preset threshold for stable operation of the energy storage battery.

[0009] Preferably, in step S4, when the fluctuation amplitude exceeds the stable operation fluctuation threshold, a flexible load call sequence is generated according to a preset priority order, specifically including: The various types of flexible loads connected to the system are sorted from high to low according to the preset priority. The higher the priority, the more likely it is to be called to participate in power regulation. The preset priority order is as follows: air conditioning loads have the first priority, water heater loads have the second priority, and charging pile loads have the third priority. The reason why air conditioning loads have the highest priority is as follows: The building envelope of the room to which the air conditioning load is located has thermal energy storage characteristics. Short-term adjustment of the compressor's operating power has a slow impact on indoor temperature changes and has the least impact on the user's immediate perception of thermal comfort. The reason why water heater loads have a second priority is as follows: Water tanks containing water heaters have heat storage capacity. Adjusting the heating power has a time delay in affecting water temperature changes, but it has a certain degree of adjustment flexibility while ensuring basic hot water demand. The basis for giving charging piles the third priority is as follows: The charging power adjustment of charging piles directly affects the charging completion time, which has a rigid constraint on users' travel plans and has relatively little adjustment flexibility. When multiple flexible loads of the same type exist, the priority of the calling sub-loads within the same type is further determined based on the real-time operating status of each load: For air conditioning loads, the air conditioner whose indoor temperature is closer to the upper limit of the set temperature has a higher sub-priority for being called; For water heater loads, the closer the water temperature in the tank is to the lower limit of the set temperature, the higher the priority of the water heater. For charging pile loads, charging piles with later expected departure times have higher call priority. In step S5, based on the real-time adjustable capacity of each flexible load in the flexible load call sequence and in conjunction with the pre-established user comfort elastic constraint model, the adjustment power command of each called flexible load is dynamically allocated, specifically including: Establish user comfort elastic constraint factors for various types of flexible loads Used for quantization representation of the first Flexible loads in The impact of constantly adjusting power on user comfort. The value range is (0,1]. The smaller the value, the greater the impact of continuing to adjust the load in the current state on user comfort and the smaller the adjustability. Among them, for air conditioning loads, the user comfort elasticity constraint factor The calculation formula is: ; In the formula, This is the penalty coefficient for the cumulative adjustment amount of charging piles. For charging piles at all times Real-time power regulation amount, The cumulative adjustment is a nonlinear exponent. The penalty coefficient for the urgency of completing the charging time. The user's expected departure time. The total amount of electricity required for charging. for The current charge level is [not specified]. The rated charging power of the charging pile, A non-linear exponent for time urgency; Based on user comfort elastic constraint factor Preset priority weights for various types of flexible loads and real-time adjustable capacity The total adjustment target power that needs to be shared by flexible loads should be allocated proportionally. , No. Adjustment power command for flexible load sharing for: ; In the formula, For the first The availability status of flexible loads, with a value of 1 indicating that they are available and a value of 0 indicating that they are not available. This refers to the set of flexible load types currently being invoked for regulation.

[0010] Preferably, in step S6, the specific form of the dynamic current limiting function is as follows: ; In the formula, for The maximum allowable charging and discharging current after dynamic current limiting at all times. The rated charge and discharge current of the energy storage battery. The flow restriction weighting coefficient is based on the health status. for Real-time health status estimate of the energy storage battery. This is the health status threshold at the end of the battery's lifespan. This is the temperature-limited current-weighting coefficient. for Real-time temperature of the battery cells For the optimal operating temperature of the battery, For the battery's maximum allowable operating temperature, The current limiting index is a temperature-dependent nonlinear flow limiting index. The current limiting weight coefficient is the number of loop iterations. As of The cumulative equivalent cycle count of the energy storage battery at any given time. Design the battery to have a rated cycle life; Cumulative equivalent loop count The calculation formula is as follows: The actual throughput of electricity during charging and discharging is weighted and adjusted according to the depth of charge and discharge. ; In the formula, This refers to the rated capacity of the energy storage battery. for The actual charge and discharge current of the battery at any given time. for Battery terminal voltage at all times This is the depth of charge / discharge correction factor. for Battery charge and discharge depth at all times The nonlinear exponent for depth of charge / discharge; When the energy storage battery participates in charging and discharging, the actual charging and discharging current is... Get the current required for the scheduling command With the maximum allowable charge and discharge current after dynamic current limiting The smaller value in, that is: ; when When the power imbalance exceeds the limit, the excess power is automatically transferred to flexible load regulation or grid interaction.

[0011] Preferably, in step S7, during the execution of energy storage battery charge and discharge control, a hybrid online health state estimation method integrating electrochemical mechanisms and data-driven approaches is adopted, specifically including: Establish a one-dimensional electrochemical-thermal coupled reduced-order model for a single energy storage battery cell, and extract internal characteristic variables characterizing the battery's health state. These internal characteristic variables include at least the solid-phase lithium-ion diffusion time constant. and the lithium-ion concentration distribution gradient of the liquid electrolyte ; Construct a terminal voltage-internal state mapping observer based on a long short-term memory neural network to obtain real-time measurable battery terminal voltage. Charging and discharging current Cell temperature As network input, and using estimates of internal feature variables as network output, the number of hidden layer neurons in the Long Short-Term Memory (LSTM) neural network is... The tanh activation function and the sigmoid gate function are used. A dual extended Kalman filter algorithm is used to jointly estimate the state variables and aging-related model parameters of the reduced-order model online. The state variables include the battery state of charge. and polarization voltage Aging-related model parameters include the battery's ohmic internal resistance. and available capacity attenuation coefficient ; Based on the aging-related model parameters obtained from online estimation, the real-time health status estimate of the energy storage battery is calculated. The calculation formula is as follows: ; In the formula, This is the threshold of ohmic internal resistance at the end of battery life. for The battery's internal resistance is estimated online in real time. and These are weighting coefficients, and they satisfy... , This represents the initial ohmic internal resistance of the new battery. for The available capacity decay factor is estimated online at all times. This is the threshold value for the usable capacity degradation factor at the end of the battery's life. The initial usable capacity decay factor of the new battery; The real-time health status estimate obtained online Updated to the battery life loss model for dynamic correction of fluctuation thresholds and dynamic current limiting calculation of charging and discharging current in the next scheduling cycle.

[0012] Preferably, in steps S8 and S9, the running data of this coordinated scheduling and the battery life loss model update results are recorded to form an optimization log, and the optimization strategy parameters in the optimization log are fed back to the system controller to correct the control decision for the next scheduling cycle, specifically including: After each coordinated scheduling cycle ends, record the operational data for that scheduling session. The operational data should include at least the peak value of the net power deviation fluctuation. Total Regulating Power of Flexible Load Actual adjustment contribution of various types of flexible loads Actual charge and discharge capacity of energy storage batteries Increment of equivalent cycle number of energy storage batteries Changes in the health status of energy storage batteries User overall comfort loss evaluation value ; Based on the recorded operational data, a recursive least squares algorithm is used to update the fitting parameters in the battery aging model online. The fitting parameters include the current limiting weight coefficient for the health state. Temperature current limiting weighting coefficient and the current limiting weight coefficient for the number of loops The updated parameters are used to correct the calculation of the dynamic flow limiting function in the next scheduling cycle; Analysis of the actual adjustment contribution of various types of flexible loads With preset priority weight When the matching deviation between the two types of flexible loads is such that the actual adjustment contribution of a certain type of flexible load is consistently lower than the preset proportion of the expected contribution for a duration exceeding the preset number of consecutive scheduling cycles, a dynamic adjustment of the priority weight is triggered. The adjustment formula is as follows: ; In the formula, For the adjusted number Class load priority weight, For the first time before the adjustment Class load priority weight, Adjust the step size factor to reduce the weight. For the first The expected regulating contribution of flexible loads. For the first The actual contribution of flexible loads; When the actual adjustment contribution of a certain type of flexible load consistently exceeds the preset proportion of the expected contribution, the adjustment formula is: ; In the formula, Adjust the step size factor to increase the weight; The updated battery aging model fitting parameters and flexible load priority weights are fed back to the system controller for use in the next scheduling cycle. These parameters are used to calculate the stable operation fluctuation threshold in step S2, determine the preset priority order in step S4, and correct the dynamic current limiting function parameters in step S6, forming a continuous closed-loop optimization control of execution, observation, evaluation, and correction.

[0013] A second invention provides a photoelectric storage direct-drive flexible multi-energy synergistic optimization system, comprising: The multi-field data acquisition module is used to collect real-time operating data of the building-integrated photovoltaic-storage-DC-flexible system and establish a unified power and electricity monitoring benchmark. The operating data includes photovoltaic power generation, building electrical load power, terminal voltage and charging / discharging current and cell temperature of energy storage batteries, as well as real-time power consumption and operating status parameters of various types of flexible loads connected to the system. The fluctuation judgment and mode switching module is used to calculate the net power deviation value at the current scheduling time based on the operating data, and to determine whether the fluctuation amplitude of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery. When the fluctuation amplitude does not exceed the stable operation fluctuation threshold, the energy storage battery is controlled to charge and discharge with a constant small current to maintain the system power balance. When the fluctuation amplitude exceeds the stable operation fluctuation threshold, flexible load is triggered. The flexible load tiered call module is used to generate a flexible load call sequence according to a preset priority order, and dynamically allocate the adjustment power command of each called flexible load based on the real-time adjustable capacity of each flexible load and in combination with a pre-established user comfort elastic constraint model. The battery dynamic current limiting control module is used to calculate the remaining stable power gap or surplus after flexible load adjustment, and control the energy storage battery to supplement and absorb the remaining stable power gap or surplus with the charging and discharging current after dynamic current limiting; wherein, the charging and discharging current after dynamic current limiting is calculated in real time by a dynamic current limiting function. The battery health status online estimation module is used to estimate the real-time health status value of the energy storage battery and update the battery life loss model during the execution of energy storage battery charge and discharge control by adopting a hybrid online health status estimation method that integrates electrochemical mechanism and data driving. The log recording module has been optimized to record the running data of this collaborative scheduling and the battery life loss model update results, forming an optimization log. The closed-loop feedback correction module is used to feed back the optimization strategy parameters in the optimization log to the system controller, and to correct the smooth operation fluctuation threshold, preset priority order and dynamic current limiting function parameters in the next scheduling cycle, forming a continuous closed-loop optimization control.

[0014] The third invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the optical-storage-direct-flexible-multi-energy synergistic optimization method.

[0015] The fourth invention provides a computer-readable storage medium storing a computer program thereon, wherein the computer program is executed by a processor to perform the steps of the time-storage direct-flexible multi-functional collaborative optimization method.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention introduces a stable operation fluctuation threshold based on real-time health status and dynamic correction of cell temperature, enabling the system to adapt to the battery aging state when determining whether power fluctuations are borne by energy storage. When the battery is in good health, the fluctuation tolerance is appropriately relaxed to leverage the energy storage regulation advantage. When battery aging intensifies, the fluctuation threshold is automatically tightened to trigger flexible load sharing, thereby effectively slowing down the battery aging rate and extending the service life of the energy storage system while ensuring power balance. This invention designs a dynamic current limiting function for the battery with health status, cell temperature, and cumulative equivalent cycle count as input variables. This function achieves multi-dimensional, nonlinear adaptive adjustment of the charge and discharge current limits, ensuring that the current stress experienced by the battery in actual operation matches its current health margin. This avoids battery overstress damage or idle regulation capacity caused by a fixed current limiting strategy, effectively suppresses accelerated battery capacity decay, and improves the economic efficiency of the energy storage system throughout its entire life cycle. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for synergistic optimization of optical storage, direct-drive, flexible, and multi-energy systems according to the present invention; Figure 2 This is a block diagram of a multi-energy synergistic optimization system for optical storage, direct current, and flexible energy sources according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: like Figure 1 As shown, a method for synergistic optimization of optical storage, direct current, flexible energy, and multi-energy sources includes: Step S1: Collect real-time operating data of the building-integrated photovoltaic-storage-DC-flexible system and establish a unified power and electricity monitoring benchmark. The operating data includes photovoltaic power generation, building electrical load power, terminal voltage and charging / discharging current of the energy storage battery and cell temperature, as well as the real-time power consumption and operating status parameters of various types of flexible loads connected to the system.

[0020] In this embodiment, the real-time acquisition of operational data from the building's photovoltaic-storage-direct-flexible system specifically includes: With sampling period Real-time acquisition of DC output voltage of each MPPT branch of the photovoltaic array Charging and discharging current Calculate the total photovoltaic power generation : ; In the formula, For the first MPPT branch road The instantaneous output voltage at a given moment. For the first MPPT branch road Instantaneous direct current at a given moment This represents the total number of MPPT branches in the photovoltaic array; With sampling period Real-time acquisition of battery terminal voltage reported by the energy storage battery management system With DC current Calculate the total electrical load of the building : ; In the formula, For the first Power supply branch circuits for road construction DC terminal voltage at time 10:00 For the first Power supply branch circuits for road construction DC current at any given moment This represents the total number of electrical branch circuits in the building. With sampling period Real-time acquisition of battery terminal voltage reported by the energy storage battery management system Charging and discharging current Cell temperature State of charge and health status data; With sampling period The system collects real-time operating status parameters of various types of flexible loads connected to the system. These flexible loads include at least one or more of the following: air conditioners, water heaters, and charging piles. The operating status parameters of air conditioners include set temperature. Real-time indoor temperature Compressor operating power The operating mode and other parameters of the water heater include the set water temperature. Real-time water temperature in the water tank Heating power Including water tank volume, the charging pile's operating status parameters include charging demand. Current charge amount Rated charging power and the user-defined expected departure time .

[0021] Step S2: Based on the collected operating data, calculate the net power deviation value at the current scheduling time, and determine whether the fluctuation range of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery.

[0022] In this embodiment, the net power deviation value at the current scheduling moment is calculated, and it is determined whether the fluctuation range of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery. Specifically, this includes: calculate Net power deviation at time : ; In the formula, for Total photovoltaic power generation at any given time for Total electrical load of the building at any time. This refers to the total number of flexible loads that previously participated in the regulation. For the first A flexible load in The power adjustment command value at any given time; a positive value indicates load reduction, and a negative value indicates load increase. Within the preset sliding time window Within, calculate the fluctuation range of the net power deviation value. The calculation formula is as follows: ; In the formula, The number of sampling points within the sliding time window. For the first in the sliding window Net power deviation at each sampling time. This is the arithmetic mean of the net power deviation values ​​within the sliding time window. It is a natural exponential function used to construct time decay weights. This is the time decay coefficient, and its value is chosen to give higher computational weight to sampling points that are closer to the current time. Fluctuation range With respect to the preset fluctuation threshold for stable operation of energy storage batteries Comparison, stable operation fluctuation threshold Based on the real-time health status of the energy storage battery Real-time temperature of the battery cell Dynamic correction is performed, and the correction formula is as follows: ; In the formula, The benchmark for stable operation fluctuation threshold, and These are the weighting coefficients for the influence of health status and the weighting coefficients for the influence of temperature, respectively. and These are the nonlinear correction indices for health status and temperature, respectively. This is the optimal operating temperature for energy storage batteries; when When the fluctuation amplitude does not exceed the preset fluctuation threshold for stable operation of the energy storage battery, it is determined that the fluctuation amplitude does not exceed the preset threshold for stable operation of the energy storage battery. when When the fluctuation exceeds the preset fluctuation threshold for stable operation of the energy storage battery, it is determined that the fluctuation amplitude exceeds the preset threshold for stable operation of the energy storage battery.

[0023] Step S3: When the fluctuation amplitude does not exceed the stable operation fluctuation threshold, control the energy storage battery to charge and discharge with a constant small current to maintain the system power balance, and then execute step S8.

[0024] Step S4: When the fluctuation amplitude exceeds the stable operation fluctuation threshold, a flexible load call sequence is generated according to a preset priority order; wherein, the preset priority order is preset based on the regulation response characteristics of each type of flexible load and the degree of impact on user comfort.

[0025] In this embodiment, when the fluctuation amplitude exceeds the stable operation fluctuation threshold, a flexible load call sequence is generated according to a preset priority order, specifically including: The various types of flexible loads connected to the system are sorted from high to low according to the preset priority. The higher the priority, the more likely it is to be called to participate in power regulation. The preset priority order is as follows: air conditioning loads have the first priority, water heater loads have the second priority, and charging pile loads have the third priority. The reason why air conditioning loads have the highest priority is as follows: The building envelope of the room to which the air conditioning load is located has thermal energy storage characteristics. Short-term adjustment of the compressor's operating power has a slow impact on indoor temperature changes and has the least impact on the user's immediate perception of thermal comfort. The reason why water heater loads have a second priority is as follows: Water tanks containing water heaters have heat storage capacity. Adjusting the heating power has a time delay in affecting water temperature changes, but it has a certain degree of adjustment flexibility while ensuring basic hot water demand. The basis for giving charging piles the third priority is as follows: The charging power adjustment of charging piles directly affects the charging completion time, which has a rigid constraint on users' travel plans and has relatively little adjustment flexibility. When multiple flexible loads of the same type exist, the priority of the calling sub-loads within the same type is further determined based on the real-time operating status of each load: For air conditioning loads, the air conditioner whose indoor temperature is closer to the upper limit of the set temperature has a higher sub-priority for being called; For water heater loads, the closer the water temperature in the tank is to the lower limit of the set temperature, the higher the priority of the water heater. For charging pile loads, charging piles with later expected departure times have higher call priority.

[0026] Step S5: Based on the real-time adjustable capacity of each flexible load in the flexible load call sequence and in conjunction with the pre-established user comfort elastic constraint model, dynamically allocate the adjustment power command of each called flexible load; wherein, the user comfort elastic constraint model is used to quantify the degree of impact of flexible load adjustment behavior on the user's normal electricity experience.

[0027] In this embodiment, based on the real-time adjustable capacity of each flexible load in the flexible load call sequence and combined with a pre-established user comfort elastic constraint model, the adjustment power command of each called flexible load is dynamically allocated, specifically including: Establish user comfort elastic constraint factors for various types of flexible loads Used for quantization representation of the first Flexible loads in The impact of constantly adjusting power on user comfort. The value range is (0,1]. The smaller the value, the greater the impact of continuing to adjust the load in the current state on user comfort and the smaller the adjustability. For air conditioning loads, the user comfort elasticity constraint factor The calculation formula is: ; In the formula, This is the penalty coefficient for the cumulative adjustment amount of charging piles. For charging piles at all times Real-time power regulation amount, The cumulative adjustment is a nonlinear exponent. The penalty coefficient for the urgency of completing the charging time. The user's expected departure time. The total amount of electricity required for charging. for The current charge level is [not specified]. The rated charging power of the charging pile, A non-linear exponent for time urgency; Based on user comfort elastic constraint factor Preset priority weights for various types of flexible loads and real-time adjustable capacity The total adjustment target power that needs to be shared by flexible loads should be allocated proportionally. , No. Adjustment power command for flexible load sharing for: ; In the formula, For the first The availability status of flexible loads, with a value of 1 indicating that they are available and a value of 0 indicating that they are not available. This refers to the set of flexible load types currently being invoked for regulation.

[0028] Step S6: After flexible load adjustment, calculate the remaining stable power gap or surplus, and control the energy storage battery to supplement and absorb the remaining stable power gap or surplus with the charging and discharging current after dynamic current limiting; wherein, the charging and discharging current after dynamic current limiting is calculated in real time by a dynamic current limiting function. The dynamic current limiting function takes the real-time health status estimate of the energy storage battery, the real-time temperature of the cell and the accumulated effective cycle number as input variables, takes the rated current of the energy storage battery as the reference value, and outputs the maximum allowable charging and discharging current constrained by the battery aging state.

[0029] In this embodiment, the specific form of the dynamic current limiting function is as follows: ; In the formula, for The maximum allowable charging and discharging current after dynamic current limiting at all times. The rated charge and discharge current of the energy storage battery. The flow restriction weighting coefficient is based on the health status. for Real-time health status estimate of the energy storage battery. This is the health status threshold at the end of the battery's lifespan. This is the temperature-limited current-weighting coefficient. for Real-time temperature of the battery cells For the optimal operating temperature of the battery, For the battery's maximum allowable operating temperature, The current limiting index is a temperature-dependent nonlinear flow limiting index. The current limiting weight coefficient is the number of loop iterations. As of The cumulative equivalent cycle count of the energy storage battery at any given time. Design the battery to have a rated cycle life; Cumulative equivalent loop count The calculation formula is as follows: The actual throughput of electricity during charging and discharging is weighted and adjusted according to the depth of charge and discharge. ; In the formula, This refers to the rated capacity of the energy storage battery. for The actual charge and discharge current of the battery at any given time. for Battery terminal voltage at all times This is the depth of charge / discharge correction factor. for Battery charge and discharge depth at all times The nonlinear exponent for depth of charge / discharge; When the energy storage battery participates in charging and discharging, the actual charging and discharging current is... Get the current required for the scheduling command With the maximum allowable charge and discharge current after dynamic current limiting The smaller value in, that is: ; when When the power imbalance exceeds the limit, the excess power is automatically transferred to flexible load regulation or grid interaction.

[0030] Step S7: During the execution of energy storage battery charge and discharge control, a hybrid online health state estimation method that integrates electrochemical mechanisms and data-driven approaches is adopted to estimate the real-time health state value of the energy storage battery online and update the battery life loss model.

[0031] In this embodiment, during the execution of energy storage battery charge and discharge control, a hybrid online health state estimation method integrating electrochemical mechanisms and data-driven approaches is adopted, specifically including: Establish a one-dimensional electrochemical-thermal coupled reduced-order model for a single energy storage battery cell, and extract internal characteristic variables characterizing the battery's health state. These internal characteristic variables include at least the solid-phase lithium-ion diffusion time constant. and the lithium-ion concentration distribution gradient of the liquid electrolyte ; Construct a terminal voltage-internal state mapping observer based on a long short-term memory neural network to obtain real-time measurable battery terminal voltage. Charging and discharging current Cell temperature As network input, and using estimates of internal feature variables as network output, the number of hidden layer neurons in the Long Short-Term Memory (LSTM) neural network is... The tanh activation function and the sigmoid gate function are used. A dual extended Kalman filter algorithm is used to jointly estimate the state variables and aging-related model parameters of the reduced-order model online. The state variables include the battery state of charge. and polarization voltage Aging-related model parameters include the battery's ohmic internal resistance. and available capacity attenuation coefficient ; Based on the aging-related model parameters obtained from online estimation, the real-time health status estimate of the energy storage battery is calculated. The calculation formula is as follows: ; In the formula, This is the threshold of ohmic internal resistance at the end of battery life. for The battery's internal resistance is estimated online in real time. and These are weighting coefficients, and they satisfy... , This represents the initial ohmic internal resistance of the new battery. for The available capacity decay factor is estimated online at all times. This is the threshold value for the usable capacity degradation factor at the end of the battery's life. The initial usable capacity decay factor of the new battery; The real-time health status estimate obtained online Updated to the battery life loss model for dynamic correction of fluctuation thresholds and dynamic current limiting calculation of charging and discharging current in the next scheduling cycle.

[0032] Step S8: Record the operation data of this collaborative scheduling and the battery life loss model update results to form an optimization log.

[0033] Step S9: Feedback the optimization strategy parameters in the optimization log to the system controller to correct the smooth operation fluctuation threshold of step S2, the preset priority order of step S4, and the dynamic current limiting function parameters of step S6 in the next scheduling cycle, forming a continuous closed-loop optimization control.

[0034] In this embodiment, the operation data of this coordinated scheduling and the battery life loss model update results are recorded to form an optimization log. The optimization strategy parameters in the optimization log are fed back to the system controller to correct the control decision for the next scheduling cycle. Specifically, this includes: After each coordinated scheduling cycle ends, record the operational data for that scheduling session. The operational data should include at least the peak value of the net power deviation fluctuation. Total Regulating Power of Flexible Load Actual adjustment contribution of various types of flexible loads Actual charge and discharge capacity of energy storage batteries Increment of equivalent cycle number of energy storage batteries Changes in the health status of energy storage batteries User overall comfort loss evaluation value ; Based on the recorded operational data, a recursive least squares algorithm is used to update the fitting parameters in the battery aging model online. The fitting parameters include the current limiting weight coefficient for the health state. Temperature current limiting weighting coefficient and the current limiting weight coefficient for the number of loops The updated parameters are used to correct the calculation of the dynamic flow limiting function in the next scheduling cycle; Analysis of the actual adjustment contribution of various types of flexible loads With preset priority weight When the matching deviation between the two types of flexible loads is such that the actual adjustment contribution of a certain type of flexible load is consistently lower than the preset proportion of the expected contribution for a duration exceeding the preset number of consecutive scheduling cycles, a dynamic adjustment of the priority weight is triggered. The adjustment formula is as follows: ; In the formula, For the adjusted number Class load priority weight, For the first time before the adjustment Class load priority weight, Adjust the step size factor to reduce the weight. For the first The expected regulating contribution of flexible loads. For the first The actual contribution of flexible loads; When the actual adjustment contribution of a certain type of flexible load consistently exceeds the preset proportion of the expected contribution, the adjustment formula is: ; In the formula, Adjust the step size factor to increase the weight; The updated battery aging model fitting parameters and flexible load priority weights are fed back to the system controller for use in the next scheduling cycle. These parameters are used to calculate the stable operation fluctuation threshold in step S2, determine the preset priority order in step S4, and correct the dynamic current limiting function parameters in step S6, forming a continuous closed-loop optimization control of execution, observation, evaluation, and correction.

[0035] In a specific embodiment, a photovoltaic-storage-DC-flexible system is applied to an office building in an industrial park: The system's configuration parameters are as follows: the photovoltaic array has an installed capacity of 150kWp, with 5 MPPT branches connected in parallel to the 750V DC bus; the energy storage battery system uses lithium iron phosphate battery packs with a rated capacity of 100kWh, a rated charge / discharge power of 50kW, a rated DC voltage of 768V, and a rated charge / discharge current of 65A; the DC bus voltage level is 750V; the flexible loads connected to the system include central air conditioning systems, centralized electric water heaters, and electric vehicle charging piles.

[0036] The system collects the following four types of operational data in real time with a sampling period of 1 second: The first category is photovoltaic power generation data acquisition: collecting the DC output voltage and DC output current of each MPPT branch of the photovoltaic array, and calculating the total photovoltaic power generation.

[0037] The second category is building load data acquisition: collecting the DC voltage and DC current of each electrical branch of the building, and calculating the total electrical load power of the building.

[0038] The third category is energy storage battery data acquisition: through the communication interface of the energy storage battery management system, data such as battery terminal voltage, charging and discharging current, cell temperature, state of charge, and health status are read.

[0039] The fourth category is flexible load operation status data collection: air conditioning systems collect set temperature, real-time indoor temperature, compressor operating power and working mode; water heater systems collect set water temperature, real-time water tank temperature, heating power and water tank volume; charging pile systems collect charging demand, current charging amount, rated charging power and user-set expected departure time.

[0040] Based on the operational data, calculate the net power deviation value at the current scheduling moment, and determine whether the fluctuation range of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery. calculate Net power deviation at time ; Within the preset sliding time window Within 300 seconds, the net power deviation fluctuation range is obtained using the root mean square formula with time decay weighting. ; Fluctuation range With respect to the preset stable operation fluctuation threshold By comparison, the fluctuation threshold for stable operation is dynamically corrected based on the real-time health status of the energy storage battery and the real-time temperature of the cell.

[0041] When the fluctuation amplitude does not exceed the stable operation fluctuation threshold, the energy storage battery is controlled to charge and discharge with a constant small current to maintain the system power balance, and the operation data of this coordinated scheduling and the battery life loss model update results are recorded to form an optimization log.

[0042] In this embodiment, the constant small current is set to 20% of the rated current, that is... .

[0043] When the fluctuation amplitude exceeds the stable operation fluctuation threshold, a flexible load call sequence is generated according to the preset priority order. The preset priority order is as follows: air conditioning loads have the first priority, water heater loads have the second priority, and charging pile loads have the third priority. When there are multiple flexible loads of the same type, the priority of the calling sub-loads within the same type is further determined based on the real-time operating status of each load.

[0044] Based on the real-time adjustable capacity of each flexible load in the flexible load call sequence, and combined with the pre-established user comfort elastic constraint model, the adjustment power command of each called flexible load is dynamically allocated. Establish user comfort elastic constraint factors for various types of flexible loads , used to quantize the representation of the first Flexible loads in The extent to which constantly adjusting power affects user comfort.

[0045] For air conditioning loads, the user comfort elasticity constraint factor The cumulative adjustment penalty and the indoor temperature deviation penalty are taken into account.

[0046] For water heater loads, the user comfort elastic constraint factor The cumulative adjustment penalty and the water temperature deviation penalty are taken into account.

[0047] For charging pile-type loads, the user comfort elastic constraint factor The penalty is determined by a combination of the cumulative adjustment penalty and the penalty for the urgency of the charging completion time.

[0048] Based on the elastic constraint factor, preset priority weights, and real-time adjustable capacity, the total adjustment target power that needs to be shared by flexible loads is allocated proportionally.

[0049] After flexible load adjustment, the remaining stable power gap or surplus is calculated, and the energy storage battery is controlled to supplement and absorb the remaining stable power gap or surplus with the charging and discharging current after dynamic current limiting. The dynamic current limiting function takes the real-time health status estimate of the energy storage battery, the real-time temperature of the cell, and the cumulative equivalent cycle number as input variables, and the rated current of the energy storage battery as the reference value, and outputs the maximum allowable charge and discharge current constrained by the battery aging state.

[0050] When the required current exceeds the dynamic current limit, the actual current is taken as the current limit, and the power imbalance exceeding the limit is supplemented by the power grid.

[0051] During the execution of energy storage battery charge and discharge control, a hybrid online health state estimation method that integrates electrochemical mechanisms and data-driven approaches is adopted to estimate the real-time health state value of the energy storage battery online and update the battery life loss model. A one-dimensional electrochemical-thermal coupled reduced-order model of a single energy storage battery cell is established to extract internal characteristic variables representing the battery's health state. A terminal voltage-internal state mapping observer based on a long short-term memory neural network is constructed. A dual extended Kalman filter algorithm is used to jointly estimate the state variables and aging-related model parameters online. Real-time health state estimates are calculated based on the estimated aging-related model parameters.

[0052] Record the operational data and battery life loss model update results of this collaborative scheduling to form an optimization log; The recorded operational data includes the peak value of net power deviation fluctuation, total regulation capacity of flexible loads, actual regulation contribution of each type of flexible load, actual charge and discharge capacity of energy storage batteries, increase in equivalent cycle number of energy storage batteries, change in the health status of energy storage batteries, and evaluation value of overall user comfort loss.

[0053] The optimization strategy parameters in the optimization log are fed back to the system controller to correct the smooth operation fluctuation threshold, preset priority order and dynamic current limiting function parameters in the next scheduling cycle, forming a continuous closed-loop optimization control.

[0054] The recursive least squares algorithm is used to update the fitting parameters in the battery aging model online; the matching deviation between the actual adjustment contribution of each type of flexible load and the preset priority weight is analyzed, and the priority weight is dynamically adjusted; the updated model parameters and priority weight are fed back to the system controller.

[0055] By cyclically executing the above steps, the method in this embodiment realizes multi-energy coupling and collaborative optimization control of building-integrated photovoltaic-storage-flexible systems at multiple time scales. While ensuring real-time power balance and user comfort, it effectively slows down the aging rate of energy storage batteries and achieves continuous self-evolution optimization of scheduling strategies through a closed-loop feedback mechanism.

[0056] Example 2: like Figure 2 As shown, a photovoltaic-storage-direct-flexible-multi-energy synergistic optimization system includes: a multi-field data acquisition module 1, a fluctuation judgment and mode switching module 2, a flexible load tiered call module 3, a battery dynamic current limiting control module 4, a battery health status online estimation module 5, an optimization log recording module 6, and a closed-loop feedback correction module 7. The multi-field data acquisition module 1 is used to collect real-time operating data of the building photovoltaic-storage-DC-flexible system and establish a unified power and power monitoring benchmark. The operating data includes photovoltaic power generation, building power load, terminal voltage and charging / discharging current and cell temperature of energy storage batteries, as well as real-time power consumption and operating status parameters of various types of flexible loads connected to the system. The fluctuation judgment and mode switching module 2 is used to calculate the net power deviation value at the current scheduling time based on the operating data, and to determine whether the fluctuation amplitude of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery; wherein, when the fluctuation amplitude does not exceed the stable operation fluctuation threshold, the energy storage battery is controlled to charge and discharge with a constant small current to maintain the system power balance; when the fluctuation amplitude exceeds the stable operation fluctuation threshold, flexible load calling is triggered. The flexible load tiered call module 3 is used to generate a flexible load call sequence according to a preset priority order, and dynamically allocate the adjustment power command of each called flexible load based on the real-time adjustable capacity of each flexible load and in combination with a pre-established user comfort elastic constraint model. The battery dynamic current limiting control module 4 is used to calculate the remaining stable power gap or surplus after flexible load adjustment, and control the energy storage battery to supplement and absorb the remaining stable power gap or surplus with the charging and discharging current after dynamic current limiting; wherein, the charging and discharging current after dynamic current limiting is calculated in real time by a dynamic current limiting function. The battery health status online estimation module 5 is used to estimate the real-time health status value of the energy storage battery online and update the battery life loss model during the execution of energy storage battery charging and discharging control by adopting a hybrid online health status estimation method that integrates electrochemical mechanism and data driving. The log recording module 6 is optimized to record the running data of this collaborative scheduling and the battery life loss model update results, forming an optimization log; The closed-loop feedback correction module 7 is used to feed back the optimization strategy parameters in the optimization log to the system controller, and to correct the smooth operation fluctuation threshold, preset priority order and dynamic current limiting function parameters in the next scheduling cycle, forming a continuous closed-loop optimization control.

[0057] Example 3: An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the above-mentioned method for multi-energy coupling and collaborative optimization of a building-integrated photovoltaic-storage-flexible system by calling the computer program stored in the memory.

[0058] The electronic device may vary considerably due to different configurations or performance, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the multi-energy coupling and collaborative optimization method for building light-storage-direction-flexible systems provided in the above-described method embodiments.

[0059] Example 4: A computer-readable storage medium having a computer program stored thereon, which, when run on a computer device, causes the computer device to execute the aforementioned method for multi-energy coupling and collaborative optimization of a building-integrated light-storage-direction-flexible system.

[0060] For example, computer-readable storage media can be read-only memory, random access memory, read-only optical disc, magnetic tape, floppy disk, and optical data storage devices.

[0061] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for synergistic optimization of optical storage, direct current, flexible energy, and multi-energy sources, characterized in that: include: Step S1: Collect real-time operating data of the building-integrated photovoltaic-storage-flexible system and establish a unified power and electricity monitoring benchmark; wherein, the operating data includes photovoltaic power generation, building electrical load power, terminal voltage and charging / discharging current and cell temperature of energy storage batteries, and real-time power consumption and operating status parameters of various types of flexible loads connected to the system. Step S2: Based on the collected operating data, calculate the net power deviation value at the current scheduling time, and determine whether the fluctuation range of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery. Step S3: When the fluctuation amplitude does not exceed the stable operation fluctuation threshold, control the energy storage battery to charge and discharge with a constant small current to maintain the system power balance, and execute step S8. Step S4: When the fluctuation amplitude exceeds the stable operation fluctuation threshold, a flexible load call sequence is generated according to a preset priority order; wherein, the preset priority order is preset based on the regulation response characteristics of each type of flexible load and the degree of impact on user comfort. Step S5: Based on the real-time adjustable capacity of each flexible load in the flexible load call sequence and combined with the pre-established user comfort elastic constraint model, dynamically allocate the adjustment power command of each called flexible load; wherein, the user comfort elastic constraint model is used to quantify the degree of impact of flexible load adjustment behavior on the user's normal electricity experience. Step S6: After flexible load adjustment, calculate the remaining stable power gap or surplus, and control the energy storage battery to supplement and absorb the remaining stable power gap or surplus with the charging and discharging current after dynamic current limiting; wherein, the charging and discharging current after dynamic current limiting is calculated in real time by a dynamic current limiting function. The dynamic current limiting function takes the real-time health status estimate of the energy storage battery, the real-time temperature of the cell and the accumulated effective cycle number as input variables, takes the rated current of the energy storage battery as the reference value, and outputs the maximum allowable charging and discharging current constrained by the battery aging state. Step S7: During the execution of energy storage battery charge and discharge control, a hybrid online health state estimation method that integrates electrochemical mechanism and data-driven approach is adopted to estimate the real-time health state value of the energy storage battery online and update the battery life loss model. Step S8: Record the operation data of this collaborative scheduling and the battery life loss model update results to form an optimization log; Step S9: Feedback the optimization strategy parameters in the optimization log to the system controller to correct the smooth operation fluctuation threshold of step S2, the preset priority order of step S4, and the dynamic current limiting function parameters of step S6 in the next scheduling cycle, forming a continuous closed-loop optimization control.

2. The optical-storage-direct-flexible multi-energy synergistic optimization method according to claim 1, characterized in that, In step S1, the operational data of the building's photovoltaic-storage-direct-flexible system is collected in real time, specifically including: With sampling period Real-time acquisition of DC output voltage of each MPPT branch of the photovoltaic array Charging and discharging current Calculate the total photovoltaic power generation : ； In the formula, For the first MPPT branch road The instantaneous output voltage at a given moment. For the first MPPT branch road Instantaneous direct current at a given moment This represents the total number of MPPT branches in the photovoltaic array; With sampling period Real-time acquisition of battery terminal voltage reported by the energy storage battery management system With DC current Calculate the total electrical load of the building : ； In the formula, For the first Power supply branch circuits for road construction DC terminal voltage at time 10:00 For the first Power supply branch circuits for road construction DC current at any given moment This represents the total number of electrical branch circuits in the building. With sampling period Real-time acquisition of battery terminal voltage reported by the energy storage battery management system Charging and discharging current Cell temperature State of charge and health status data; With sampling period The system collects real-time operating status parameters of various types of flexible loads connected to the system. These flexible loads include at least one or more of the following: air conditioners, water heaters, and charging piles. The operating status parameters of air conditioners include set temperature. Real-time indoor temperature Compressor operating power The operating mode and other parameters of the water heater include the set water temperature. Real-time water temperature in the water tank Heating power Including water tank volume, the charging pile's operating status parameters include charging demand. Current charge amount Rated charging power and the user-defined expected departure time .

3. The optical-storage-direct-flexible multi-energy synergistic optimization method according to claim 2, characterized in that, In step S2, the net power deviation value at the current scheduling moment is calculated, and it is determined whether the fluctuation range of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery. Specifically, this includes: calculate Net power deviation at time : ； In the formula, for Total photovoltaic power generation at any given time for Total electrical load of the building at any time. This refers to the total number of flexible loads that previously participated in the regulation. For the first A flexible load in The power adjustment command value at any given time; a positive value indicates load reduction, and a negative value indicates load increase. Within the preset sliding time window Within, calculate the fluctuation range of the net power deviation value. The calculation formula is as follows: ； In the formula, The number of sampling points within the sliding time window. For the first in the sliding window Net power deviation at each sampling time. This is the arithmetic mean of the net power deviation values ​​within the sliding time window. It is a natural exponential function used to construct time decay weights. This is the time decay coefficient, and its value is chosen to give higher computational weight to sampling points that are closer to the current time. Fluctuation range With respect to the preset fluctuation threshold for stable operation of energy storage batteries Comparison, stable operation fluctuation threshold Based on the real-time health status of the energy storage battery Real-time temperature of the battery cell Dynamic correction is performed, and the correction formula is as follows: ； In the formula, The benchmark for stable operation fluctuation threshold, and These are the weighting coefficients for the influence of health status and the weighting coefficients for the influence of temperature, respectively. and These are the nonlinear correction indices for health status and temperature, respectively. This is the optimal operating temperature for energy storage batteries; when When the fluctuation amplitude does not exceed the preset fluctuation threshold for stable operation of the energy storage battery, it is determined that the fluctuation amplitude does not exceed the preset threshold for stable operation of the energy storage battery. when When the fluctuation exceeds the preset fluctuation threshold for stable operation of the energy storage battery, it is determined that the fluctuation amplitude exceeds the preset threshold for stable operation of the energy storage battery.

4. The optical-storage direct-drive flexible multi-energy synergistic optimization method according to claim 3, characterized in that, In step S4, when the fluctuation amplitude exceeds the stable operation fluctuation threshold, a flexible load dispatch sequence is generated according to a preset priority order, specifically including: The various types of flexible loads connected to the system are sorted from high to low according to the preset priority. The higher the priority, the more likely it is to be called to participate in power regulation. The preset priority order is as follows: air conditioning loads have the first priority, water heater loads have the second priority, and charging pile loads have the third priority. The reason why air conditioning loads have the highest priority is as follows: The building envelope of the room to which the air conditioning load is located has thermal energy storage characteristics. Short-term adjustment of the compressor's operating power has a slow impact on indoor temperature changes and has the least impact on the user's immediate perception of thermal comfort. The reason why water heater loads have a second priority is as follows: Water tanks containing water heaters have heat storage capacity. Adjusting the heating power has a time delay in affecting water temperature changes, but it has a certain degree of adjustment flexibility while ensuring basic hot water demand. The basis for giving charging piles the third priority is as follows: The charging power adjustment of charging piles directly affects the charging completion time, which has a rigid constraint on users' travel plans and has relatively little adjustment flexibility. When multiple flexible loads of the same type exist, the priority of the calling sub-loads within the same type is further determined based on the real-time operating status of each load: For air conditioning loads, the air conditioner whose indoor temperature is closer to the upper limit of the set temperature has a higher sub-priority for being called; For water heater loads, the closer the water temperature in the tank is to the lower limit of the set temperature, the higher the priority of the water heater. For charging pile loads, charging piles with later expected departure times have higher call priority. In step S5, based on the real-time adjustable capacity of each flexible load in the flexible load call sequence and in conjunction with the pre-established user comfort elastic constraint model, the adjustment power command of each called flexible load is dynamically allocated, specifically including: Establish user comfort elastic constraint factors for various types of flexible loads Used for quantization representation of the first Flexible loads in The impact of constantly adjusting power on user comfort. The value range is (0,1]. The smaller the value, the greater the impact of continuing to adjust the load in the current state on user comfort and the smaller the adjustability. Among them, for air conditioning loads, the user comfort elasticity constraint factor The calculation formula is: ； In the formula, This is the penalty coefficient for the cumulative adjustment amount of charging piles. For charging piles at all times Real-time power regulation amount, The cumulative adjustment is a nonlinear exponent. The penalty coefficient for the urgency of completing the charging time. The user's expected departure time. The total amount of electricity required for charging. for The current charge level is [not specified]. The rated charging power of the charging pile, A non-linear exponent for time urgency; Based on user comfort elastic constraint factor Preset priority weights for various types of flexible loads and real-time adjustable capacity The total adjustment target power that needs to be shared by flexible loads should be allocated proportionally. , No. Adjustment power command for flexible load sharing for: ； In the formula, For the first The availability status of flexible loads, with a value of 1 indicating that they are available and a value of 0 indicating that they are not available. This refers to the set of flexible load types currently being invoked for regulation.

5. The optical-storage-direct-flexible multi-energy synergistic optimization method according to claim 4, characterized in that, In step S6, the specific form of the dynamic current limiting function is as follows: ； In the formula, for The maximum allowable charging and discharging current after dynamic current limiting at all times. The rated charge and discharge current of the energy storage battery. The flow restriction weighting coefficient is based on the health status. for Real-time health status estimate of the energy storage battery. This is the health status threshold at the end of the battery's lifespan. This is the temperature-limited current-weighting coefficient. for Real-time temperature of the battery cells For the optimal operating temperature of the battery, For the battery's maximum allowable operating temperature, The current limiting index is a temperature-dependent nonlinear flow limiting index. The current limiting weight coefficient is the number of loop iterations. As of The cumulative equivalent cycle count of the energy storage battery at any given time. Design the battery to have a rated cycle life; Cumulative equivalent loop count The calculation formula is as follows: The actual throughput of electricity during charging and discharging is weighted and adjusted according to the depth of charge and discharge. ； In the formula, This refers to the rated capacity of the energy storage battery. for The actual charge and discharge current of the battery at any given time. for Battery terminal voltage at all times This is the depth of charge / discharge correction factor. for Battery charge and discharge depth at all times The nonlinear exponent for depth of charge / discharge; When the energy storage battery participates in charging and discharging, the actual charging and discharging current is... Get the current required for the scheduling command With the maximum allowable charge and discharge current after dynamic current limiting The smaller value in, that is: ； when When the power imbalance exceeds the limit, the excess power is automatically transferred to flexible load regulation or grid interaction.

6. The optical-storage direct-drive flexible multi-energy synergistic optimization method according to claim 5, characterized in that, In step S7, during the execution of energy storage battery charge and discharge control, a hybrid online health state estimation method integrating electrochemical mechanisms and data-driven approaches is adopted, specifically including: Establish a one-dimensional electrochemical-thermal coupled reduced-order model for a single energy storage battery cell, and extract internal characteristic variables characterizing the battery's health state. These internal characteristic variables include at least the solid-phase lithium-ion diffusion time constant. and the lithium-ion concentration distribution gradient of the liquid electrolyte ; Construct a terminal voltage-internal state mapping observer based on a long short-term memory neural network to obtain real-time measurable battery terminal voltage. Charging and discharging current Cell temperature As network input, and using estimates of internal feature variables as network output, the number of hidden layer neurons in the Long Short-Term Memory (LSTM) neural network is... The tanh activation function and the sigmoid gate function are used. A dual extended Kalman filter algorithm is used to jointly estimate the state variables and aging-related model parameters of the reduced-order model online. The state variables include the battery state of charge. and polarization voltage Aging-related model parameters include the battery's ohmic internal resistance. and available capacity attenuation coefficient ; Based on the aging-related model parameters obtained from online estimation, the real-time health status estimate of the energy storage battery is calculated. The calculation formula is as follows: ； In the formula, This is the threshold of ohmic internal resistance at the end of battery life. for The battery's internal resistance is estimated online in real time. and These are weighting coefficients, and they satisfy... , This represents the initial ohmic internal resistance of the new battery. for The available capacity decay factor is estimated online at all times. This is the threshold value for the usable capacity degradation factor at the end of the battery's life. The initial usable capacity decay factor of the new battery; The real-time health status estimate obtained online Updated to the battery life loss model for dynamic correction of fluctuation thresholds and dynamic current limiting calculation of charging and discharging current in the next scheduling cycle.

7. The optical-storage direct-drive flexible multi-energy synergistic optimization method according to claim 6, characterized in that, In steps S8 and S9, the operational data of this coordinated scheduling and the battery life loss model update results are recorded to form an optimization log. The optimization strategy parameters in the optimization log are then fed back to the system controller to correct the control decisions for the next scheduling cycle. Specifically, this includes: After each coordinated scheduling cycle ends, record the operational data for that scheduling session. The operational data should include at least the peak value of the net power deviation fluctuation. Total Regulating Power of Flexible Load Actual adjustment contribution of various types of flexible loads Actual charge and discharge capacity of energy storage batteries Increment of equivalent cycle number of energy storage batteries Changes in the health status of energy storage batteries User overall comfort loss evaluation value ; Based on the recorded operational data, a recursive least squares algorithm is used to update the fitting parameters in the battery aging model online. The fitting parameters include the current limiting weight coefficient for the health state. Temperature current limiting weighting coefficient and the current limiting weight coefficient for the number of loops The updated parameters are used to correct the calculation of the dynamic flow limiting function in the next scheduling cycle; Analysis of the actual adjustment contribution of various types of flexible loads With preset priority weight When the matching deviation between the two types of flexible loads is such that the actual adjustment contribution of a certain type of flexible load is consistently lower than the preset proportion of the expected contribution for a duration exceeding the preset number of consecutive scheduling cycles, a dynamic adjustment of the priority weight is triggered. The adjustment formula is as follows: ； In the formula, For the adjusted number Class load priority weight, For the first time before the adjustment Class load priority weight, Adjust the step size factor to reduce the weight. For the first The expected regulating contribution of flexible loads. For the first The actual contribution of flexible loads; When the actual adjustment contribution of a certain type of flexible load consistently exceeds the preset proportion of the expected contribution, the adjustment formula is: ； In the formula, Adjust the step size factor to increase the weight; The updated battery aging model fitting parameters and flexible load priority weights are fed back to the system controller for use in the next scheduling cycle. These parameters are used to calculate the stable operation fluctuation threshold in step S2, determine the preset priority order in step S4, and correct the dynamic current limiting function parameters in step S6, forming a continuous closed-loop optimization control of execution, observation, evaluation, and correction.

8. A multi-energy collaborative optimization system for the optical-storage direct-drive-flexible multi-energy collaborative optimization method according to claims 1-7, characterized in that, include: The multi-field data acquisition module is used to collect real-time operating data of the building-integrated photovoltaic-storage-DC-flexible system and establish a unified power and electricity monitoring benchmark. The operating data includes photovoltaic power generation, building electrical load power, terminal voltage and charging / discharging current and cell temperature of energy storage batteries, as well as real-time power consumption and operating status parameters of various types of flexible loads connected to the system. The fluctuation judgment and mode switching module is used to calculate the net power deviation value at the current scheduling time based on the operating data, and to determine whether the fluctuation amplitude of the net power deviation value exceeds the preset fluctuation threshold for stable operation of the energy storage battery. When the fluctuation amplitude does not exceed the stable operation fluctuation threshold, the energy storage battery is controlled to charge and discharge with a constant small current to maintain the system power balance. When the fluctuation amplitude exceeds the stable operation fluctuation threshold, flexible load is triggered. The flexible load tiered call module is used to generate a flexible load call sequence according to a preset priority order, and dynamically allocate the adjustment power command of each called flexible load based on the real-time adjustable capacity of each flexible load and in combination with a pre-established user comfort elastic constraint model. The battery dynamic current limiting control module is used to calculate the remaining stable power gap or surplus after flexible load adjustment, and control the energy storage battery to supplement and absorb the remaining stable power gap or surplus with the charging and discharging current after dynamic current limiting; wherein, the charging and discharging current after dynamic current limiting is calculated in real time by a dynamic current limiting function. The battery health status online estimation module is used to estimate the real-time health status value of the energy storage battery and update the battery life loss model during the execution of energy storage battery charge and discharge control by adopting a hybrid online health status estimation method that integrates electrochemical mechanism and data driving. The log recording module has been optimized to record the running data of this collaborative scheduling and the battery life loss model update results, forming an optimization log. The closed-loop feedback correction module is used to feed back the optimization strategy parameters in the optimization log to the system controller, and to correct the smooth operation fluctuation threshold, preset priority order and dynamic current limiting function parameters in the next scheduling cycle, forming a continuous closed-loop optimization control.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the optical-storage-direct-flexible-multi-energy synergistic optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the optical-storage-direct-flexible-multi-energy synergistic optimization method as described in any one of claims 1-7.

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