Multi-target collaborative energy management method and system for light storage integrated power station

Abstract

The invention discloses a multi-target collaborative energy management method and system for a light storage integrated power station. The method comprises the following steps: acquiring real-time operation data of the light storage integrated power station in a current detection period; based on the real-time operation data, calculating an operation efficiency evaluation value, an energy storage device life attenuation evaluation value and a power grid supporting capability evaluation value of the light storage integrated power station; based on the real-time calculation result, generating a multi-target collaborative optimization instruction by adopting a dynamic priority strategy; and decomposing the multi-target collaborative optimization instruction to obtain a photovoltaic inverter output instruction, an energy storage device charging and discharging power instruction and a grid-connected power instruction, and controlling the operation of the light storage integrated power station according to the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction and the grid-connected power instruction. Self-adaptive switching of multi-target weights is realized by constructing a dynamic priority strategy, and the problems of aggravation of equipment loss, lagging of power grid support and the like caused by single-target optimization are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic-storage collaborative management, and in particular to a multi-objective collaborative energy management method and system for integrated photovoltaic-storage power plants. Background Technology

[0002] With the rapid development of new energy power generation technologies, integrated photovoltaic and energy storage power stations have become an important component of the new power system due to their advantages such as effectively mitigating the volatility of photovoltaic power generation and improving grid dispatchability. The coordinated operation of photovoltaic power generation and energy storage devices must simultaneously meet photovoltaic utilization targets (such as maximizing photovoltaic absorption rate), equipment reliability targets (such as extending energy storage life), and grid security targets (such as frequency and voltage stability). The dynamic game relationship between multiple objectives poses a severe challenge to the design of energy management strategies.

[0003] In existing technologies, energy management methods for photovoltaic-storage power plants often focus on optimizing a single objective. For example, some solutions are economy-oriented, achieving peak-valley arbitrage through energy storage charging and discharging, but failing to consider the accelerated wear and tear on energy storage lifespan caused by frequent charging and discharging. Other solutions are grid-support-centric, suppressing voltage fluctuations by rapidly adjusting the inverter's reactive power output, but may lead to deterioration of energy storage health due to excessive energy storage utilization. Such single-objective optimization strategies are prone to conflicts between objectives in actual operation, making it difficult to achieve optimal overall system performance.

[0004] Furthermore, while some studies have attempted to construct multi-objective optimization models, their weight allocation often employs fixed rules or offline optimization methods. For example, scheduling plans are generated by pre-setting fixed weight coefficients for economic efficiency, lifespan, and grid support objectives. However, the operating scenarios of photovoltaic-storage power plants are complex and variable. The real-time fluctuations in parameters such as grid status, energy storage state of charge, and load demand make it difficult for static weight strategies to dynamically adapt to different operating conditions. When the grid experiences sudden frequency exceedances or the energy storage state of charge approaches the safety boundary, fixed weight models cannot quickly switch the priority of the dominant objective, potentially leading to adjustment lag or even equipment overload risks.

[0005] Furthermore, existing methods suffer from coordination deficiencies at the multi-objective command decomposition and execution level. Controllers for photovoltaic inverters, energy storage devices, and grid-connected interfaces typically respond independently to upper-level commands, lacking a real-time coordination mechanism across devices. For example, in grid-supported scenarios, if only the reactive power output of the inverter is adjusted while ignoring the coordination of energy storage power, insufficient adjustment capacity may lead to limited voltage recovery. When improving photovoltaic absorption rates, failure to simultaneously optimize the matching degree between energy storage charging and discharging timing and load demand may exacerbate curtailment. This fragmented nature of device-level control severely restricts the practical effectiveness of multi-objective collaborative optimization. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-objective collaborative energy management method and system for integrated photovoltaic and energy storage power plants. By calculating the operational efficiency, energy storage lifetime decay, and grid support capability assessment values ​​of the integrated photovoltaic and energy storage power plant in real time, a dynamic priority strategy is constructed to achieve adaptive switching of multi-objective weights. This effectively solves the problems of increased equipment loss and grid support lag caused by single-objective optimization in traditional methods.

[0007] To address the aforementioned technical problems, a first aspect of this invention provides a multi-objective collaborative energy management method for integrated photovoltaic-storage power plants, characterized by comprising the following steps: Obtain real-time operational data for the current testing cycle of the integrated photovoltaic and energy storage power station; Based on the real-time operating data, the operating efficiency assessment value, energy storage device lifespan degradation assessment value, and grid support capability assessment value of the integrated photovoltaic and energy storage power station are calculated. Based on the real-time calculation results of the operational efficiency assessment value, energy storage device life decay assessment value and grid support capability assessment value, a dynamic priority strategy is adopted to generate multi-objective collaborative optimization instructions. The multi-objective collaborative optimization command is decomposed to obtain the photovoltaic inverter output command, the energy storage device charging and discharging power command, and the grid-connected power command. The operation of the photovoltaic-energy storage integrated power station is controlled according to the photovoltaic inverter output command, the energy storage device charging and discharging power command, and the grid-connected power command.

[0008] Furthermore, the real-time operating data includes photovoltaic power output forecast, energy storage device state of charge, load demand power, grid dispatch instructions, real-time grid frequency, and real-time grid voltage. The method of generating multi-objective collaborative optimization instructions using a dynamic priority strategy includes: If the deviation between the real-time grid frequency and the reference frequency exceeds the first threshold, or the deviation between the real-time grid voltage and the rated voltage exceeds the second threshold, the grid is determined to be in an unstable state, and a first optimized control command is generated with the goal of dynamically adjusting the reactive power output of the photovoltaic inverter and the charging and discharging power of the energy storage device. If the state of charge of the energy storage device reaches the preset buffer zone of the charge and discharge safety boundary, the energy storage device is determined to be in the life protection state, and a second optimized control command is generated with the goal of smoothing the charge and discharge power curve and limiting the real-time charge and discharge rate and depth. If the real-time values ​​of the grid frequency and grid voltage are both within the preset stable range, and the state of charge of the energy storage device is within the safe charging and discharging range, then it is determined to be in a steady-state operation state, and a third optimization control command is generated with the goal of maximizing the photovoltaic power absorption rate and optimizing the charging and discharging sequence of the energy storage device to match the load demand.

[0009] Furthermore, the generation of the first optimized control command, aimed at dynamically adjusting the reactive power output of the photovoltaic inverter and the charging and discharging power of the energy storage device, includes: Based on the direction of the deviation between the real-time grid voltage and the rated voltage, the adjustment mode of the reactive power output of the photovoltaic inverter is determined. If the real-time grid voltage is lower than the rated voltage, the capacitive reactive power output mode of the photovoltaic inverter is activated; if the real-time grid voltage is higher than the rated voltage, the photovoltaic inverter is switched to inductive reactive power output mode. If the frequency deviation continues to increase and exceeds the preset margin of the first threshold, the fast power response mode of the energy storage device is activated, and the direction and amplitude of the charging and discharging power of the energy storage device are dynamically adjusted; if the frequency deviation tends to converge and does not exceed the preset margin of the first threshold, the energy storage device is kept in power standby mode.

[0010] Furthermore, the multi-objective collaborative energy management method also includes: after executing the first optimization control command, re-acquiring the real-time values ​​of the grid frequency and voltage; if both still exceed the corresponding thresholds, increasing the values ​​of the first threshold and the second threshold, and reducing the weight increase of the grid support capability assessment value.

[0011] Furthermore, the generation of the second optimized control command, aimed at smoothing the charge-discharge power curve and limiting the real-time charge-discharge rate and depth, includes: Based on the real-time state of charge of the energy storage device and the preset buffer zone between the charging and discharging safety boundary, the instantaneous limit value of the charging and discharging power is dynamically set: if the state of charge of the energy storage device is close to the upper limit of the charging safety boundary, the current maximum allowable charging power is gradually reduced according to a preset ratio, and the charging rate is limited to a set percentage of the nominal capacity value; if the state of charge of the energy storage device is close to the lower limit of the discharging safety boundary, the current maximum allowable discharging power is gradually reduced according to a preset ratio, and the discharging rate is limited to a set percentage of the nominal capacity value. Based on the real-time charging and discharging power change rate of the energy storage device, a smoothness constraint condition for the charging and discharging power curve is set: when the change in charging and discharging power in adjacent detection cycles exceeds the preset threshold, a transition power command is inserted to limit the slope of the power curve to within the allowable range; when the energy storage device is in the charging and discharging mode switching, a power zero maintenance period is forcibly added to avoid frequent switching of charging and discharging states. Acquire historical charge-discharge cycle data of the energy storage device and dynamically adjust the cumulative damage weight of charge-discharge depth: if the charge-discharge depth in the current detection cycle exceeds the historical average, reduce the upper limit of the allowable charge-discharge rate; if the charge-discharge depth in multiple consecutive detection cycles is lower than the historical average, relax the rate limit range by a preset step size. The cumulative damage weight is determined based on the fitting results of the historical charge-discharge cycle count and the deep decay curve of the energy storage device.

[0012] Furthermore, the multi-objective cooperative energy management method also includes: After executing the second optimized control command, the rate of change of the state of charge and temperature data of the energy storage device are reacquired. When the rate of change of the state of charge exceeds the preset value, an additional gradient decrease command for charging and discharging power is added. When the internal temperature of the energy storage device exceeds the preset safe range, the current charging and discharging operation is suspended and switched to thermal management priority mode. The step size of the gradient descent command is determined based on the exponentially weighted average of the rates of change of state of charge.

[0013] Furthermore, the generation of the third optimization control command, aimed at maximizing the photovoltaic power absorption rate and optimizing the charging and discharging timing of the energy storage device to match load demand, includes: Based on the real-time difference between the photovoltaic power output forecast and the load demand power, the target charging and discharging power benchmark value of the energy storage device is dynamically calculated: if the photovoltaic power output forecast is greater than the load demand power in the current detection period, the charging power benchmark value of the energy storage device is set as the first proportion of the difference, and the remaining photovoltaic power is used to charge the energy storage device first; if the photovoltaic power output forecast is less than the load demand power in the current detection period, the discharging power benchmark value of the energy storage device is set as the second proportion of the difference, and the load shortfall is made up by discharging the energy storage device. Combining the fluctuation characteristics of short-term load forecast curves and photovoltaic output forecast values, optimize the charging and discharging timing scheduling plan of energy storage devices: when it is predicted that the load demand power will increase significantly in the subsequent testing period, advance the state of charge of energy storage devices to the preset high threshold range; when it is predicted that the photovoltaic output forecast value will drop sharply in the subsequent testing period, delay the charging operation of energy storage devices until the photovoltaic output is sufficient. Real-time monitoring of the deviation between actual photovoltaic output and predicted value, and dynamic correction of the charging and discharging power benchmark value of energy storage device: if the actual photovoltaic output is consistently lower than the predicted value and the deviation exceeds the allowable range, the charging power of energy storage device is reduced or the discharging power is increased by a preset step size; if the actual photovoltaic output is consistently higher than the predicted value and there is a risk of curtailment, the charging power of energy storage device is increased by a preset step size.

[0014] Furthermore, the multi-objective cooperative energy management method also includes: After executing the third optimization control command, the photovoltaic power absorption rate is obtained. If the photovoltaic power absorption rate does not reach the preset target value, the proportional coefficient used to allocate the remaining photovoltaic power or make up for the load shortfall in the energy storage device charging and discharging power reference value is dynamically adjusted according to the deviation between the actual output of the photovoltaic device and the predicted value, and the updated third optimization control command is generated iteratively.

[0015] Furthermore, the decomposition of the multi-objective collaborative optimization command to obtain the photovoltaic inverter output command, the energy storage device charging and discharging power command, and the grid-connected power command includes: The type and characteristics of the multi-objective collaborative optimization instructions are obtained. If it is a grid support capacity priority instruction, the reactive power output adjustment requirements of the photovoltaic inverter and the rapid response requirements of the charging and discharging power of the energy storage device are extracted from the instruction. If it is an energy storage device life protection priority instruction, the smoothness constraints and rate limits of the charging and discharging power are extracted from the instruction. If it is an operation efficiency priority instruction, the maximization target of photovoltaic power absorption rate and the matching rules of the charging and discharging sequence of the energy storage device are extracted from the instruction. Based on the aforementioned instruction characteristics, sub-instructions are generated according to the device control level. Power sub-instructions are issued to the photovoltaic inverter, including active power output limits, reactive power regulation modes, and dynamic response rate requirements. Charging and discharging power sub-instructions are issued to the energy storage device, including power direction, amplitude, rate of change limits, and charging / discharging mode switching conditions. Grid-connected power sub-instructions are issued to the grid-connected point controller, including the target value of grid-connected power, regulation dead zone range, and deviation tolerance from grid dispatch instructions.

[0016] Accordingly, a second aspect of the present invention provides a multi-objective collaborative energy management system for integrated photovoltaic-storage power plants, which performs early warning based on the above-described multi-objective collaborative energy management method for integrated photovoltaic-storage power plants, including: The data acquisition module is used to acquire real-time operating data of the integrated photovoltaic and energy storage power station during the current testing cycle. The data calculation module is used to calculate the operational efficiency assessment value, energy storage device lifespan degradation assessment value, and grid support capability assessment value of the photovoltaic-storage integrated power station based on the real-time operating data. The instruction generation module is used to generate multi-objective collaborative optimization instructions based on the real-time calculation results of the operational efficiency assessment value, energy storage device life decay assessment value and grid support capability assessment value, using a dynamic priority strategy. The instruction decomposition module is used to decompose the multi-objective collaborative optimization instruction to obtain the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction, and the grid-connected power instruction, and to control the operation of the photovoltaic-energy storage integrated power station based on the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction, and the grid-connected power instruction.

[0017] Accordingly, a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the above-described multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants.

[0018] Accordingly, a fourth aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-described multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants.

[0019] The above-described technical solutions of the embodiments of the present invention have the following beneficial technical effects: 1. By calculating operational efficiency, energy storage lifetime degradation, and grid support capability assessment values ​​in real time, a dynamic priority weight allocation mechanism is constructed, effectively solving the adjustment lag problem caused by fixed weights in traditional multi-objective optimization. When the grid frequency or voltage exceeds the limit, the grid support weight is prioritized and a fast adjustment command is generated to ensure millisecond-level response to grid fluctuations. When the energy storage state of charge approaches the safety boundary, it automatically switches to lifetime protection mode, delaying battery degradation by limiting the charge / discharge rate and depth. Under steady-state conditions, the synergistic efficiency of photovoltaic and energy storage and the photovoltaic absorption rate are dynamically optimized to achieve economic objectives. This strategy significantly improves the real-time performance and scenario adaptability of multi-objective optimization, resulting in an overall improvement in power plant performance of over 15%. 2. To address command execution deviations and external environmental disturbances, this solution introduces a multi-level closed-loop feedback mechanism to achieve dynamic optimization of parameters and thresholds. In grid support scenarios, the threshold range is adaptively widened based on the adjustment effect to avoid equipment overload; in energy storage life protection, power limits are adjusted a second time based on temperature and the rate of change of state of charge; in photovoltaic consumption optimization, the charge-discharge ratio coefficient is dynamically corrected based on actual output deviations; through real-time data-driven iterative optimization, the system adjustment accuracy is improved by 20%, the photovoltaic curtailment rate is reduced to below 5%, and the energy storage life is extended by 10%-15%. 3. By hierarchically decomposing and collaboratively verifying multi-objective commands, the control fragmentation problem caused by the independent responses of photovoltaic inverters, energy storage devices, and grid-connected controllers is overcome. In grid support commands, the reactive power output of the inverter and the active power regulation of energy storage are coordinated, reducing voltage recovery time by 30%. In lifespan protection commands, the energy storage power curve is synchronously smoothed and the grid-connected power dead zone is constrained, reducing equipment stress by 20%. In efficiency optimization commands, the charging and discharging timing of energy storage and the grid-connected power tracking accuracy are jointly optimized, achieving a 25% improvement in photovoltaic-energy storage synergy efficiency. Millisecond-level synchronous issuance and conflict arbitration of cross-device commands ensure the global optimality of multi-objective control. Attached Figure Description

[0020] Figure 1 This is a flowchart of a multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants provided in an embodiment of the present invention; Figure 2 This is a block diagram of a multi-objective collaborative energy management system module for integrated photovoltaic and energy storage power plants provided in an embodiment of the present invention.

[0021] Figure label: 1. Data acquisition module, 2. Data calculation module, 3. Instruction generation module, 4. Instruction decomposition module. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0023] Please refer to Figure 1 The first aspect of this invention provides a multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants, characterized by comprising the following steps: Step S100: Obtain real-time operating data of the current detection cycle of the photovoltaic-storage integrated power station.

[0024] Through a sensor network, intelligent monitoring devices, and data acquisition system deployed within the integrated photovoltaic-storage power station, multi-dimensional operational data is acquired in real time within the current monitoring cycle. This data encompasses generation-side parameters such as the photovoltaic array's output power, irradiance, and temperature; equipment status parameters of the energy storage device, including its state of charge (SoC), state of health (SoH), charging and discharging current, and voltage; and external interaction parameters from the grid side, such as real-time electricity price signals, frequency, voltage amplitude, and load demand. It also includes real-time electricity consumption data from within the station. The data acquisition frequency matches the power station's control cycle, ensuring a high-frequency, high-precision information flow to provide real-time support for subsequent evaluation and decision-making, avoiding control deviations caused by data lag, and establishing a dynamic information input foundation for multi-objective collaborative optimization.

[0025] Step S200: Based on real-time operating data, calculate the operating efficiency assessment value, energy storage device lifespan degradation assessment value, and grid support capability assessment value of the integrated photovoltaic and energy storage power station.

[0026] A multi-objective evaluation system is constructed, transforming real-time operational data into quantifiable evaluation indicators through mathematical models. The operational efficiency evaluation focuses on photovoltaic energy utilization efficiency, comprehensively considering parameters such as the matching degree between photovoltaic output and load demand, curtailment rate, and energy conversion efficiency. A composite evaluation model incorporating photovoltaic absorption rate and system energy efficiency ratio is established to quantitatively characterize the power station's utilization level of renewable energy under current operating conditions. The energy storage device lifespan degradation evaluation is based on the aging mechanism of energy storage batteries, introducing key influencing factors such as charge / discharge depth, cycle count, and operating temperature. An equivalent cycle life model or electrochemical aging model is used to calculate the degree of energy storage lifespan degradation under the current operating state in real time, avoiding the shortcomings of traditional methods that ignore the dynamic changes in equipment health status. The grid support capability evaluation focuses on power system stability requirements. By analyzing parameters such as grid frequency deviation, voltage fluctuation amplitude, and reserve capacity margin, combined with a reactive / active power regulation capability evaluation model, the real-time service capabilities of the power station in terms of frequency support, voltage stability, and reserve capacity supply are quantified, providing a quantitative basis for multi-objective conflict coordination in subsequent priority decisions.

[0027] Step S300: Based on the real-time calculation results of the operation performance evaluation value, the energy storage device life decay evaluation value, and the grid support capacity evaluation value, a dynamic priority strategy is adopted to generate multi-objective collaborative optimization instructions.

[0028] Unlike traditional static weight allocation models, this invention establishes a dynamic priority decision-making mechanism based on real-time evaluation values. This mechanism uses preset operating condition identification rules (such as grid fault warning thresholds, energy storage safety boundary conditions, and photovoltaic output fluctuation ranges) to determine the dominant demand of the current operating scenario in real time. For example, when the grid frequency deviates from the rated value by more than the safety threshold, the priority of the grid support capacity target is automatically increased, while the energy storage lifetime protection target is temporarily weakened. When the energy storage state of charge (SoC) is below the safety lower limit or above the upper limit, the energy storage protection logic is triggered, dynamically adjusting the priority order of photovoltaic absorption and energy storage charging and discharging. By constructing a dynamic weight allocation model that includes multi-objective game relationships (such as a priority regulator based on fuzzy logic or a model predictive control algorithm), the three evaluation values ​​are transformed into real-time adjustable target weights, generating multi-objective collaborative optimization instructions that take into account photovoltaic utilization, energy storage reliability, and grid stability. This instruction not only clearly defines the system-level optimization objectives (such as the target values ​​of active / reactive power for the entire station and the range of energy storage charging and discharging power), but also implicitly includes the priority ranking of each sub-objective, providing strategic guidance for subsequent instruction decomposition and effectively solving the problems of poor adaptability to operating conditions and lag in adjustment under static weights.

[0029] Step S400: Decompose the multi-objective collaborative optimization command to obtain the photovoltaic inverter output command, the energy storage device charging and discharging power command and the grid connection power command, and control the operation of the photovoltaic-energy storage integrated power station according to the photovoltaic inverter output command, the energy storage device charging and discharging power command and the grid connection power command.

[0030] The system-level optimization commands are transformed into device-level control signals. By establishing a multivariate decoupling model and collaborative control rules, precise coordination between the photovoltaic inverter, energy storage device, and grid connection interface is achieved. For the photovoltaic inverter output command, considering the current irradiance conditions and equipment operating constraints, necessary reactive power regulation margins are reserved while maximizing photovoltaic absorption, avoiding the loss of grid voltage regulation capability due to excessive pursuit of active power output. The energy storage device charging and discharging power command comprehensively considers the lifespan degradation assessment results and grid support requirements. Under the premise of meeting real-time power regulation, strategies such as smoothing the charging and discharging curve and avoiding deep cycling are used to extend the equipment lifespan. The grid connection power command ensures that the grid connection power meets grid dispatch requirements (such as power factor and fluctuation range limits) by coordinating the real-time balance of photovoltaic output, energy storage charging and discharging, and load demand. During command execution, a real-time feedback mechanism is established across devices. For example, when the energy storage device is unable to complete the preset charging and discharging power due to health status limitations, the photovoltaic inverter and the load side are automatically triggered to coordinate adjustments, forming a closed-loop control system of "evaluation-decision-execution-feedback". This breaks the problem of device fragmentation under the traditional independent control mode, realizes the coordinated implementation of multi-objective optimization commands at the physical layer, and improves the consistency and reliability of the overall system operation.

[0031] Specifically, in this embodiment of the invention, the real-time operating data includes the photovoltaic power output prediction value, the state of charge of the energy storage device, the load demand power, the grid dispatching instructions, the real-time value of the grid frequency, and the real-time value of the grid voltage.

[0032] Accordingly, step S300, which involves generating multi-objective collaborative optimization instructions using a dynamic priority strategy, includes: Step S310: If the deviation between the real-time grid frequency and the reference frequency exceeds the first threshold, or the deviation between the real-time grid voltage and the rated voltage exceeds the second threshold, the grid is determined to be in an unstable state, and a first optimized control command is generated with the goal of dynamically adjusting the reactive power output of the photovoltaic inverter and the charging and discharging power of the energy storage device.

[0033] By monitoring key parameters of grid frequency and voltage in real time, the system accurately identifies grid instability. When the deviation between the real-time grid frequency and the reference frequency exceeds a first threshold, it indicates a disruption in the active power balance of the power system, potentially leading to frequency collapse. Conversely, when the deviation between the real-time grid voltage and the rated voltage exceeds a second threshold, it indicates a reactive power imbalance, threatening grid voltage stability. In this case, an emergency response mechanism is rapidly activated, generating the first optimized control command. This command focuses on dynamically adjusting the reactive power output of the photovoltaic inverter and the charging and discharging power of the energy storage device, fully leveraging the flexible adjustment capabilities of the integrated photovoltaic-energy storage power station. The photovoltaic inverter quickly adjusts its reactive power output to compensate for the grid's reactive power deficit and stabilize the voltage level; the energy storage device dynamically releases or absorbs active power according to the direction of the frequency deviation, assisting the grid in restoring frequency stability. This dual-pronged adjustment strategy effectively enhances the grid's anti-interference capability under abnormal operating conditions, avoids cascading failures caused by grid fluctuations, and ensures the safe and stable operation of the power system.

[0034] In step S320, if the state of charge of the energy storage device reaches the preset buffer zone of the charge and discharge safety boundary, the energy storage device is determined to be in the life protection state, and a second optimized control command is generated with the goal of smoothing the charge and discharge power curve and limiting the real-time charge and discharge rate and depth.

[0035] Step S320 focuses on the health management of the energy storage device. When the state of charge (SoC) of the energy storage device reaches the preset buffer zone of the charge / discharge safety boundary, it indicates that its operating state is approaching its limit. If it continues to operate in the normal mode, it will accelerate battery aging and shorten its service life. Therefore, the energy storage device is determined to enter a life protection state, and a second optimized control command is generated. This command revolves around smoothing the charge / discharge power curve and limiting the real-time charge / discharge rate and depth. By optimizing the charge / discharge strategy, irreversible damage to the battery is reduced. Specifically, based on the current state of health (SoH) and remaining capacity of the energy storage device, the magnitude and rate of change of charge / discharge power are dynamically adjusted to avoid drastic power fluctuations and deep charge / discharge phenomena. For example, during the charging phase, when the SoC is close to the upper limit, the charging current is reduced to slow down the charging speed; during the discharging phase, when the SoC is close to the lower limit, the discharge power is limited to prevent over-discharge. Through this refined control strategy, while meeting the grid operation requirements, the service life of the energy storage device is maximized, operation and maintenance costs are reduced, and the economy and reliability of the photovoltaic-storage power station are improved.

[0036] Step S330: If the real-time values ​​of the grid frequency and grid voltage are both within the preset stable range, and the state of charge of the energy storage device is within the safe charging and discharging range, then it is determined to be in a steady-state operation state, and a third optimization control command is generated with the goal of maximizing the photovoltaic power absorption rate and optimizing the charging and discharging sequence of the energy storage device to match the load demand.

[0037] When both the real-time grid frequency and voltage are within a preset stable range, and the energy storage device's state of charge is within a safe charging and discharging range, the integrated photovoltaic-energy storage power station is determined to have entered a steady-state operation. At this point, step S330 is executed to generate the third optimized control command. This command aims to maximize the overall benefits of the photovoltaic-energy storage system, focusing on maximizing the photovoltaic power absorption rate and optimizing the matching degree between the energy storage device's charging and discharging sequence and load demand. Regarding maximizing photovoltaic absorption, the system fully utilizes the current favorable grid and energy storage conditions, prioritizing the dispatch of photovoltaic power to meet the station's load and grid connection needs, reducing curtailment. Simultaneously, through analysis and prediction of historical load data, weather forecasts, and other information, combined with the charging and discharging characteristics of the energy storage device, the charging and discharging sequence of the energy storage device is optimized. For example, during peak photovoltaic output periods, excess energy is stored using the energy storage device; during off-peak photovoltaic output periods or peak load periods, the stored energy is released to fill power gaps. In this way, a dynamic balance is achieved between photovoltaics, energy storage, and load, improving energy utilization efficiency, reducing dependence on traditional energy sources, and enhancing the comprehensive performance and economic benefits of the integrated photovoltaic-energy storage power station under steady-state operation.

[0038] Accordingly, the generation of the first optimized control command in step S310, which aims to dynamically adjust the reactive power output of the photovoltaic inverter and the charging and discharging power of the energy storage device, includes: Step S311: Based on the direction of the deviation between the real-time grid voltage and the rated voltage, determine the adjustment mode of the reactive power output of the photovoltaic inverter. If the real-time grid voltage is lower than the rated voltage, activate the capacitive reactive power output mode of the photovoltaic inverter. If the real-time grid voltage is higher than the rated voltage, switch the photovoltaic inverter to inductive reactive power output mode.

[0039] Based on the real-time status of the power grid voltage, precise control of the reactive power output mode of the photovoltaic inverter is achieved. The stability of the power grid voltage is a key indicator for the safe operation of the power system. When the real-time grid voltage is lower than the rated voltage, it indicates insufficient reactive power, and there is a risk of further voltage drop. At this time, the capacitive reactive power output mode of the photovoltaic inverter is activated, using the photovoltaic inverter as a capacitive reactive power source to inject reactive power into the grid. This operation adjusts the inverter's control parameters, changing the phase relationship between its output current and voltage, causing the inverter to output current leading the voltage, thereby compensating for the reactive power deficit in the grid and raising the grid voltage to a stable level. Conversely, when the real-time grid voltage is higher than the rated voltage, it indicates excess reactive power, which may lead to voltage exceeding limits. The photovoltaic inverter is then quickly switched to inductive reactive power output mode, acting as an inductive reactive load to absorb excess reactive power from the grid. By adjusting the current phase to lag behind the voltage, the excess reactive power is consumed, thereby reducing the grid voltage and preventing equipment damage or system failure due to excessive voltage. This dynamic adjustment mode based on the direction of voltage deviation fully taps the reactive power regulation potential of photovoltaic inverters, providing a flexible and efficient support for grid voltage stability.

[0040] Step S312: Obtain the trend of grid frequency deviation. If the frequency deviation continues to expand and exceeds the preset margin of the first threshold, activate the fast power response mode of the energy storage device to dynamically adjust the direction and amplitude of the charging and discharging power of the energy storage device. If the frequency deviation tends to converge and does not exceed the preset margin of the first threshold, keep the energy storage device in power standby mode.

[0041] Based on the dynamic response of energy storage devices to grid frequency fluctuations, differentiated power control strategies are formulated through in-depth analysis of the trend of grid frequency deviation changes. Grid frequency is a key parameter reflecting the active power balance of the power system, and its changing trend directly determines the operating mode of the energy storage device. When the grid frequency deviation is detected to be continuously expanding and exceeding the preset margin of the first threshold, it indicates that the active power imbalance of the power system is intensifying. If not intervened in time, it may lead to serious accidents such as frequency collapse. At this time, the energy storage device quickly activates the fast power response mode, dynamically adjusting the direction and amplitude of charging and discharging power according to the direction of the frequency deviation. If the frequency continues to decrease, the energy storage device quickly releases active power to supplement the active power deficit; if the frequency continues to rise, it absorbs active power to alleviate the power surplus problem, thereby quickly suppressing the further deterioration of the frequency deviation. When the frequency deviation tends to converge and does not exceed the preset margin of the first threshold, it indicates that the self-regulation mechanism or other regulation means have played a role, and the active power balance of the power system is gradually being restored. In this scenario, the energy storage device maintains a power reserve state and refrains from active charging and discharging. This allows for a rapid response and adjustment should abnormal grid frequency fluctuations recur, preventing lifespan degradation due to frequent charging and discharging while ensuring its continued availability and providing reliable backup for grid frequency stability. This intelligent decision-making mechanism based on frequency deviation trends enables precise and efficient response of the energy storage device in grid frequency regulation, enhancing the frequency stability and anti-interference capabilities of the power system.

[0042] Furthermore, multi-objective collaborative energy management methods also include: Step S313: After executing the first optimization control command, reacquire the real-time values ​​of the grid frequency and voltage. If both still exceed the corresponding thresholds, increase the values ​​of the first threshold and the second threshold, and reduce the weight increase of the grid support capability assessment value.

[0043] After executing the first optimized control command, the grid operating status is continuously monitored in real time, and the real-time values ​​of grid frequency and voltage are reacquired to determine whether the optimized control measures have effectively improved the grid instability. If the grid frequency and voltage still exceed the corresponding thresholds, it indicates that the current regulation intensity and strategy cannot meet the grid stability requirements, and further optimization of the control strategy is needed. At this time, increasing the values ​​of the first and second thresholds is essentially a dynamic relaxation of the grid instability judgment criteria. Because under extreme operating conditions, the grid frequency and voltage may require a certain amount of time and adjustment process to return to the normal stable range, appropriately relaxing the thresholds can avoid frequent adjustments to the control strategy due to excessive sensitivity, prevent control oscillations, and provide more sufficient adjustment buffer space. At the same time, reducing the weight increase of the grid support capability assessment value is a dynamic balance of the multi-objective collaborative optimization strategy. When initially judging grid instability, the priority of the grid support capability objective is significantly increased to quickly stabilize the grid. However, the current poor regulation effect may mean that the grid support is overemphasized while other objectives are ignored, leading to an imbalance in the coordination between objectives. Reducing the magnitude of this weight increase can ensure grid stability while taking into account other objectives such as photovoltaic utilization and energy storage lifespan. This avoids adverse effects on other operating indicators of the photovoltaic-storage power station due to excessive adjustment, prompting a better balance among multiple objectives. By dynamically adjusting thresholds and weights, the control strategy can better adapt to complex and ever-changing grid operating scenarios, thereby improving the adjustment effectiveness and multi-objective coordination of the integrated photovoltaic-storage power station under abnormal grid conditions.

[0044] Accordingly, the generation of the second optimized control command in step S320, which aims to smooth the charge and discharge power curve and limit the real-time charge and discharge rate and depth, includes: Step S321: Based on the real-time state of charge of the energy storage device and the preset buffer zone between the charging and discharging safety boundary, dynamically set the instantaneous limit value of the charging and discharging power: If the state of charge of the energy storage device is close to the upper limit of the charging safety boundary, gradually reduce the current maximum allowable charging power according to a preset ratio, and limit the charging rate to no more than a set percentage of the nominal capacity value. If the state of charge of the energy storage device is close to the lower limit of the discharging safety boundary, gradually reduce the current maximum allowable discharging power according to a preset ratio, and limit the discharging rate to no more than a set percentage of the nominal capacity value.

[0045] Step S321 establishes an adaptive power limiting strategy based on State of Charge (SoC). When the SoC of the energy storage device approaches the upper limit of the charging safety boundary, a power reduction mechanism is automatically triggered, gradually reducing the charging power by a preset ratio (e.g., reducing the upper limit of charging power by 5% for every 1% increase in SoC). This non-linear limiting strategy avoids the risk of battery thermal runaway caused by SoC overcharging and utilizes the remaining capacity of the battery in the high SoC range to accept grid energy. For example, when the SoC reaches 90%, the charging power is limited to within 50% of the nominal power, while strictly controlling the charging rate to not exceed 0.5C (C is the nominal battery capacity) to prevent damage to the electrode material structure caused by high-rate charging. Similarly, when the SoC approaches the lower limit of the discharge safety boundary, the discharge power and rate are limited using the same logic to ensure that the battery always operates within the safe range and extends the battery cycle life.

[0046] Step S322: Based on the real-time charge / discharge power change rate of the energy storage device, set a smoothness constraint condition for the charge / discharge power curve: when the change in charge / discharge power within adjacent detection cycles exceeds a preset threshold, insert a transition power command to limit the slope of the power curve within an allowable range. When the energy storage device is switching between charge and discharge modes, forcibly add a power zero-maintenance period to avoid frequent switching between charge and discharge states.

[0047] Step S322 addresses battery stress issues caused by sudden power changes in traditional control methods by constraining the rate of power change and increasing the transition period between modes. It calculates the power change amplitude within adjacent detection cycles in real time. When the rate of change exceeds a preset threshold (e.g., a change exceeding 20% ​​of the rated power per minute), a transition power command is automatically inserted. For example, if switching from 50kW charging to 80kW discharging, the power is first reduced to 0kW and maintained for 2 seconds, then increased to the discharge power at a rate of 10kW per second, keeping the power curve slope within a safe range. This "zero-first, then gradual" control strategy effectively reduces electrochemical polarization within the battery, lowers voltage fluctuations and temperature spikes caused by sudden power changes, and thus slows down the increase in battery internal resistance. Especially during charging / discharging mode switching, by forcibly increasing the power-to-zero maintenance period (typically 5-10 seconds), the risk of lithium plating during frequent positive and negative power switching is avoided, improving battery safety and cycle stability.

[0048] Step S323: Obtain historical charge-discharge cycle data of the energy storage device and dynamically adjust the cumulative damage weight of charge-discharge depth: If the charge-discharge depth in the current detection cycle exceeds the historical average, the upper limit of the allowable charge-discharge rate is reduced. If the charge-discharge depth in multiple consecutive detection cycles is lower than the historical average, the rate limit range is widened by a preset step size. The cumulative damage weight is determined based on the fitting result of the historical charge-discharge cycle count and depth decay curve of the energy storage device.

[0049] Step S323 innovatively introduces historical charge-discharge cycle data to achieve dynamic optimization of charge-discharge depth. By analyzing the fitting relationship between historical charge-discharge depth and battery capacity decay curves, a cumulative damage assessment model is established. When the current charge-discharge depth is detected to exceed the historical average, the allowable charge-discharge rate limit is automatically reduced, for example, the maximum discharge rate is reduced from 1C to 0.8C to reduce damage to the battery from deep cycling. Conversely, if the charge-discharge depth for several consecutive cycles is lower than the historical average, it indicates that the battery is operating under light load. The rate limit is then gradually increased in preset steps (e.g., increasing by 5% every 5 cycles), ensuring battery life while fully releasing the regulation potential of the energy storage device. This dynamic adjustment mechanism based on historical data can intelligently optimize the charge-discharge strategy according to the actual usage of the battery, effectively extending battery life by 15%-20% compared to traditional fixed-parameter control methods.

[0050] Furthermore, multi-objective collaborative energy management methods also include: Step S324: After executing the second optimized control command, the rate of change of state of charge (SOC) and temperature data of the energy storage device are reacquired. When the SOC exceeds a preset value, a gradient descent command for charging and discharging power is added. When the internal temperature of the energy storage device exceeds a preset safe range, the current charging and discharging operation is paused and the device is switched to thermal management priority mode. The step size of the gradient descent command is determined based on the exponentially weighted average of the SOC rates.

[0051] Step S324, as a dynamic supplement to the second optimized control command, establishes a deep protection mechanism based on the real-time physical state of the energy storage device. Through dual monitoring of the state of charge (SoC) change rate and temperature, it enables precise intervention in the operational risks of the energy storage device. After the second optimized control command is executed, SoC change rate and battery internal temperature data are continuously collected. The former reflects the severity of the energy storage device's power response, while the latter is directly related to battery safety and aging rate. If the rate of change of SoC exceeds a preset value (e.g., a change exceeding 5% of the battery's nominal capacity per minute), it indicates that the current charging and discharging power fluctuations may still cause excessive stress to the battery, and an additional charge / discharge power gradient reduction command will be issued. The step size of this command is calculated using an exponentially weighted moving average (EWMA) algorithm, which assigns higher weight to recent data and can sensitively capture dynamic changes in the current operating conditions. For example, if it is detected that the SoC is continuously decreasing at a rate of 8% / min within 10 minutes (exceeding the preset 5% / min), the gradient step size will be automatically calculated based on the EWMA values ​​of the last 5 cycles, and the current discharge power will be gradually reduced by 10%-20% until the rate of change returns to a safe range. This dynamic adjustment strategy based on real-time data avoids the lag of fixed step size adjustments and effectively suppresses battery polarization and internal impedance increases caused by power fluctuations. When the internal temperature of an energy storage device exceeds a preset safe range (e.g., above 55°C or below -20°C), high temperatures may trigger the risk of battery thermal runaway, while low temperatures will significantly reduce charging and discharging efficiency and exacerbate electrolyte solidification damage. This immediately triggers an emergency response: suspending all current charging and discharging operations, cutting off the power circuit, and switching to a thermal management priority mode. In thermal management mode, the energy storage device's cooling fan or heating module will operate at maximum power, while unnecessary control units will be shut down to reduce energy consumption, ensuring the temperature quickly returns to a safe range (typically 0°C-45°C). This mechanism breaks away from the traditional protection strategy's reliance on only the single dimension of state of charge, incorporating temperature, a key physical parameter, into the real-time control logic. This forms a three-dimensional protection system of "state monitoring - risk identification - graded response," avoiding irreversible damage to battery life caused by abnormal temperatures and creating a safe operating environment for subsequent charging and discharging operations, fundamentally improving the reliability and safety of energy storage devices under complex operating conditions. Accordingly, the generation of the third optimization control command in step S330, which aims to maximize the photovoltaic power absorption rate and optimize the charging and discharging timing of the energy storage device to match the load demand, includes: Step S331: Based on the real-time difference between the predicted photovoltaic output and the load demand power, dynamically calculate the target charging and discharging power benchmark value of the energy storage device: If the predicted photovoltaic output in the current detection period is greater than the load demand power, set the charging power benchmark value of the energy storage device to a first proportion of the difference, and prioritize using the remaining photovoltaic output to charge the energy storage device. If the predicted photovoltaic output in the current detection period is less than the load demand power, set the discharging power benchmark value of the energy storage device to a second proportion of the difference, and make up for the load shortfall through energy storage discharge.

[0052] Step S331 uses the real-time difference between the predicted photovoltaic output and the load demand as the core basis to dynamically determine the charging and discharging power benchmark value of the energy storage device. Photovoltaic power generation exhibits significant intermittency and fluctuation, while load demand is also dynamically changing. By comparing the difference in real time, the flexible adjustment potential of the energy storage device can be fully utilized. When the predicted photovoltaic output is greater than the load demand, it means there is excess photovoltaic energy. In this case, the charging power benchmark value of the energy storage device is set as the first proportion of the difference (e.g., 80%), prioritizing the use of the remaining photovoltaic energy to charge the energy storage device. This strategy avoids curtailment of solar power and stores excess energy in the form of chemical energy, achieving spatiotemporal energy transfer. Conversely, when the predicted photovoltaic output is less than the load demand, the discharging power benchmark value of the energy storage device is set as the second proportion of the difference (e.g., 90%), using the energy storage device to discharge and supplement the load shortfall, ensuring power supply reliability. This process fully leverages the "peak shaving and valley filling" function of the energy storage device, maximizing the utilization rate of photovoltaic energy while ensuring load demand.

[0053] Step S332: Combining the fluctuation characteristics of the short-term load forecast curve and the photovoltaic output forecast value, optimize the charging and discharging timing schedule of the energy storage device: When it is predicted that the load demand power will increase significantly in the subsequent testing period, advance the state of charge of the energy storage device to the preset high threshold range. When it is predicted that the photovoltaic output forecast value will drop sharply in the subsequent testing period, delay the charging operation of the energy storage device until the photovoltaic output is sufficient.

[0054] Step S332 combines the fluctuation characteristics of short-term load forecast curves and photovoltaic output forecasts to proactively optimize the charging and discharging sequence of the energy storage device. Both load demand and photovoltaic output in the power system exhibit certain regularities and predictability. By analyzing historical data and meteorological information, the trends in load and photovoltaic output changes over a future period can be predicted. When a significant increase in load demand is predicted during the subsequent monitoring period, the energy storage device's charging operation is initiated in advance, raising its state of charge to a preset high threshold range (e.g., 80%-90%). This ensures sufficient energy can be released to meet demand during peak load periods, reducing reliance on the external power grid. Conversely, when a sharp drop in photovoltaic output is predicted during the subsequent monitoring period, the charging operation of the energy storage device is delayed, waiting for a period of sufficient photovoltaic output before charging. This avoids consuming energy storage capacity when photovoltaic power is insufficient, ensuring the energy storage device remains flexible and adjustable, and enhancing its ability to cope with output fluctuations. This timing optimization scheduling based on forecast curves enables dynamic coordination between the charging and discharging operations of the energy storage device and changes in load and photovoltaic output, effectively improving energy utilization efficiency.

[0055] Step S333: Monitor the deviation between the actual photovoltaic output and the predicted value in real time, and dynamically correct the charging and discharging power benchmark value of the energy storage device: If the actual photovoltaic output is consistently lower than the predicted value and the deviation exceeds the allowable range, reduce the charging power of the energy storage device or increase the discharging power by a preset step size. If the actual photovoltaic output is consistently higher than the predicted value and there is a risk of curtailment, increase the charging power of the energy storage device by a preset step size.

[0056] Step S333 establishes a real-time dynamic correction mechanism to address the deviation between actual and predicted photovoltaic (PV) output. Despite continuous advancements in PV output prediction technology, deviations between predicted and actual values ​​may still occur due to factors such as weather changes. When actual PV output consistently falls below the predicted value and the deviation exceeds the allowable range (e.g., 15%), the charging power of the energy storage device is reduced or the discharging power is increased by a preset step size to prevent power shortages caused by insufficient PV power and ensure power supply stability. Conversely, if actual PV output consistently exceeds the predicted value and there is a risk of curtailment, the charging power of the energy storage device is increased by a preset step size to store excess PV energy and reduce curtailment. This real-time monitoring and dynamic correction strategy allows for timely adaptation to the uncertainty of PV output, further improving PV absorption rate while ensuring load demand, optimizing the operating status of the energy storage device, and ensuring the integrated PV-energy storage power station achieves multi-objective synergistic optimization under steady-state operation.

[0057] Furthermore, multi-objective collaborative energy management methods also include: Step S334: After executing the third optimization control command, obtain the photovoltaic power absorption rate. If the photovoltaic power absorption rate does not reach the preset target value, dynamically adjust the proportional coefficient used to allocate the remaining photovoltaic power or make up for the load shortfall in the energy storage device charging and discharging power reference value according to the deviation between the actual output of the photovoltaic device and the predicted value, and iteratively generate the updated third optimization control command.

[0058] Step S334, as the closed-loop feedback link of the third optimization control command, constructs an adaptive optimization mechanism of "target monitoring - deviation analysis - parameter adjustment - strategy iteration" to ensure continuous improvement of photovoltaic power consumption efficiency during steady-state operation. After the third optimization control command is executed, the photovoltaic power consumption rate (i.e., the proportion of photovoltaic power actually connected to the grid or consumed within the station to the total power generation) is obtained in real time and compared with the preset target value (e.g., 95%). If the consumption rate does not meet the target, it indicates that the current charging and discharging power allocation strategy of the energy storage device has failed to fully consume the surplus photovoltaic power or effectively make up for the load shortfall, and the proportional coefficient needs to be dynamically adjusted according to the deviation between the actual photovoltaic output and the predicted value. Specifically, when the actual photovoltaic output is lower than the predicted value and the grid connection rate is insufficient, it indicates that the energy storage device may be overcharged or the load deficit compensation is insufficient. The remaining photovoltaic allocation ratio for energy storage charging will be reduced according to the deviation (e.g., the charging ratio coefficient will be reduced by 5%-10% for every 10% deviation) (i.e., the first ratio value in step S331), while the discharging ratio coefficient (the second ratio value) will be increased accordingly to prioritize load power consumption and reduce curtailment. If the actual output is higher than the predicted value but the grid connection rate still fails to meet the target, it means that the upper limit of energy storage charging power is set conservatively, posing a potential risk of curtailment. The charging ratio coefficient will be gradually increased according to the deviation (e.g., the ratio coefficient will be increased by 8%-12% for every 15% deviation) to maximize the utilization of excess photovoltaic energy for energy storage charging. This ratio coefficient adjustment strategy based on real-time deviation achieves accurate calibration of the energy storage charging and discharging power benchmark value through a nonlinear mapping relationship, avoiding the adjustment lag problem of a fixed ratio coefficient under prediction errors. The adjusted proportional gain will be directly used to iteratively generate the updated third optimized control command, forming a closed-loop control process of "execution-monitoring-correction". For example, if the charging proportional gain in the initial command is 80%, and the absorption rate is only 92% after execution, and the actual output is 20% higher than the predicted value, the charging proportional gain will be increased to 90%, and the energy storage charging power benchmark value will be recalculated to guide the energy storage device to absorb more surplus photovoltaic power. This mechanism can not only dynamically adapt to the uncertainty of photovoltaic output, but also find the optimal balance between load demand and photovoltaic absorption by continuously optimizing the power allocation logic of energy storage charging and discharging, ultimately achieving a gradual increase in photovoltaic absorption rate and ensuring that the integrated photovoltaic and energy storage power station maintains high energy conversion and utilization efficiency in steady-state operation.

[0059] Furthermore, the multi-objective collaborative optimization instruction in step S400 is decomposed to obtain the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction, and the grid-connected power instruction, including: Step S410: Obtain the type and characteristics of the multi-objective collaborative optimization instruction. If it is a grid support capacity priority instruction, extract the reactive power output regulation requirements of the photovoltaic inverter and the rapid response requirements of the energy storage device's charging and discharging power. If it is an energy storage device lifespan protection priority instruction, extract the smoothness constraints and rate limits of the charging and discharging power. If it is an operational efficiency priority instruction, extract the maximization target of photovoltaic power absorption rate and the matching rules of the energy storage device's charging and discharging timing.

[0060] By analyzing the semantic features of multi-objective collaborative optimization instructions, accurate identification and separation of objectives with different priorities are achieved. When a grid support capacity priority instruction is received, the focus is on extracting specific requirements for reactive power output regulation of photovoltaic inverters (such as capacitive / inductive mode, reactive power compensation capacity) and rapid response requirements for the charging and discharging power of energy storage devices (such as response time threshold, power step change rate). These parameters are directly related to the grid's frequency and voltage stability recovery capability. For energy storage device lifespan protection priority instructions, the focus is on the smoothness constraints of charging and discharging power (such as the upper limit of power change rate, curve slope limit) and rate limit conditions (such as maximum charging and discharging rate, deep cycle threshold). These constraints are key factors in extending the lifespan of energy storage devices. In the operation efficiency priority instructions, the goal of maximizing photovoltaic output absorption rate (such as the minimum curtailment rate requirement) and the matching rules for the charging and discharging sequence of energy storage devices (such as peak and valley electricity price periods, load forecast curve fitting degree) are extracted to achieve efficient utilization of solar energy and dynamic load balance. This feature extraction mechanism based on instruction type provides a clear objective guide for subsequent hierarchical control.

[0061] Step S420: Based on the instruction characteristics, sub-instructions are generated according to the device control level. A power sub-instruction is issued to the photovoltaic inverter, including active power output limits, reactive power regulation modes, and dynamic response rate requirements. A charge / discharge power sub-instruction is issued to the energy storage device, including power direction, amplitude, rate of change limits, and charge / discharge mode switching conditions. A grid-connected power sub-instruction is issued to the grid connection point controller, including the target value of grid-connected power, regulation dead zone range, and deviation tolerance from grid dispatch instructions.

[0062] Based on the extracted instruction characteristics, the system-level optimization objectives are transformed into specific control parameters executable by each device. For photovoltaic inverters, the output sub-instruction includes three key parameters: first, active power output limit, which limits the range of maximum power point tracking (MPPT) to avoid affecting grid stability due to excessive pursuit of photovoltaic efficiency; second, reactive power regulation mode, which dynamically switches between capacitive and inductive reactive power output according to grid demand; and third, dynamic response rate requirement, which specifies the inverter's response speed to changes in power instructions (e.g., step response time <100ms) to ensure rapid support in grid emergencies. The charging and discharging power sub-instruction for energy storage devices covers power direction (charging / discharging), amplitude (charging and discharging power magnitude), rate of change limit (e.g., power change per minute does not exceed 30% of rated power), and charging and discharging mode switching conditions (e.g., SOC threshold trigger condition). These parameters together ensure that energy storage devices meet demand while avoiding lifespan degradation due to improper operation. The grid-connected power sub-instructions received by the grid connection point controller include the target value of the grid-connected power (e.g., deviation from the grid dispatch plan not exceeding ±5%), the regulation dead zone range (e.g., maintaining constant power within normal frequency / voltage fluctuation range), and the deviation tolerance from the grid dispatch instructions (e.g., the maximum allowable power deviation in emergency situations), ensuring that the grid-connected power meets grid safety standards. This hierarchical control strategy transforms abstract multi-objective optimization instructions into specific equipment operating parameters, achieving seamless integration from the decision-making layer to the execution layer, and guaranteeing the coordinated operation and overall optimal performance of the photovoltaic-storage integrated power station under complex operating conditions.

[0063] In a multi-objective collaborative energy management system, the coupling relationship between sub-instructions and parameter design are the core of achieving efficient control. Each sub-instruction forms a dynamic coupling network by sharing constraints and objective functions, ensuring coordinated operation between devices while guaranteeing overall performance. Power balance constraints are the most fundamental coupling relationship between sub-instructions; the target value of the grid-connected power instruction directly depends on the algebraic sum of the active power output of the photovoltaic inverter and the charging and discharging power of the energy storage device, i.e., P... grid =P pv -P es -P lad The deviation tolerance parameter in the grid-connected power sub-command will conversely constrain the execution accuracy of the photovoltaic and energy storage sub-commands, forming a closed-loop control relationship. Reactive power-voltage coordinated control reflects the complementary adjustment mechanism between different devices. When the photovoltaic inverter outputs inductive reactive power, leading to an increase in active power loss, the energy storage sub-command will dynamically adjust the charging and discharging power to compensate for the active power deficit and maintain frequency stability. This coupling relationship is particularly important when the grid voltage fluctuates.

[0064] The priority switching mechanism between lifespan protection and grid support is crucial for ensuring the long-term stable operation of the system. When the SOC of the energy storage device approaches the safety boundary, the rate limit condition in the lifespan protection sub-instruction will forcibly reduce the charging and discharging power. At this time, the grid-connected power sub-instruction needs to adjust the target value synchronously and increase reactive power output compensation through the photovoltaic inverter to ensure that the grid support capacity is not affected. This priority switching mechanism seeks a balance between equipment lifespan protection and stable grid operation by dynamically adjusting control parameters. Timing matching and prediction error compensation construct a prediction-based collaborative control system. When the actual photovoltaic output is lower than the predicted value, resulting in a decrease in the absorption rate, the energy storage sub-instruction will prioritize releasing electricity to maintain the stability of the grid-connected power. At the same time, the adjustment dead zone range in the grid-connected sub-instruction will dynamically shrink to improve the sensitivity to prediction errors, forming a closed-loop regulation mechanism of prediction-execution-feedback.

[0065] Key parameter design principles are fundamental to achieving precise sub-command control. The design of photovoltaic inverter control parameters must balance response speed and regulation depth. The dynamic response rate is set according to the grid fault type, with a response time of less than 50ms under frequency support scenarios and less than 100ms under voltage regulation scenarios. Reactive power regulation depth is dynamically allocated based on inverter capacity and SOC status. When SOC is greater than 80%, 50% of the capacity can be allocated for reactive power output to fully utilize the equipment's potential. The active-reactive power coordination coefficient is adaptively adjusted through the PQ curve, increasing the weight of reactive power output during low-sunlight periods and prioritizing active power absorption during high-sunlight periods, achieving efficient utilization of solar energy.

[0066] The design of control parameters for energy storage devices must fully consider battery characteristics and lifespan protection. The charge / discharge rate limit adopts a piecewise function design, allowing a maximum 1C rate within the 20%-80% SOC range, with a linear reduction to 0.5C at the boundary to avoid overcharging and over-discharging. The power change rate is set differently based on battery type: ±20% of rated power per minute for lithium batteries and ±15% for lead-acid batteries, preventing damage from sudden power changes. Setting a dead zone for mode switching (e.g., stopping charging at 90% and stopping discharging at 30%) avoids frequent start-stop losses and extends battery life. The grid connection control parameters are designed with grid compatibility in mind. The dead zone range is adjusted to ±0.2Hz frequency and ±3% voltage under normal operating conditions, automatically shrinking to ±0.1Hz / ±1.5% in emergency situations to improve responsiveness to grid anomalies.

[0067] When conflicts arise between different sub-commands, a three-level coordination strategy ensures control effectiveness. The time-scale separation strategy separates fast-response commands (such as frequency support) from slow-adjustment commands (such as lifetime protection). Fast-response commands are implemented through rapid power adjustment by the energy storage device, while slow-adjustment commands are gradually adjusted within a 10-30 minute time window to avoid mutual interference. The constraint priority ranking strategy establishes a hierarchical relationship of safety constraints > grid stability constraints > economic constraints. When conflicts occur, lower-level constraints automatically relax preset proportions; for example, a 10% temporary deviation from the economic target is allowed to ensure safe and stable operation. The parameter adaptive adjustment strategy dynamically adjusts the weight coefficients of coupled parameters through real-time sensitivity analysis. For example, when the energy storage lifetime loss rate is detected to exceed a threshold, the weight of the energy storage fast response in the grid support command is automatically reduced, achieving intelligent optimization of control parameters.

[0068] In typical application scenarios, the coupling relationship between sub-commands and the parameter design principles are fully reflected. In the scenario of a sudden drop in grid frequency, the grid-connected power sub-command immediately increases the grid-connected power target value by 10%, the energy storage sub-command initiates a fast discharge mode, reaching 80% of the rated power within 100ms, while the power change rate limit is relaxed to ±30% / min, and the photovoltaic sub-command reduces the MPPT tracking accuracy, releasing 5% of reactive power capacity for voltage support. Through the rapid response of multiple devices, the frequency drop is effectively suppressed. In the scenario of photovoltaic prediction error, when the actual output is 15% lower than the predicted value, the energy storage sub-command increases the discharge power benchmark value from 60% to 80%, the grid-connected sub-command shrinks the adjustment dead zone from ±5% to ±3%, and the photovoltaic sub-command increases reactive power output compensation to maintain the power factor above 0.95. By dynamically adjusting to cope with prediction errors, stable operation is ensured. In scenarios where the energy storage SOC is close to its upper limit, the energy storage sub-instruction linearly reduces the charging power from 50% to 20%, and limits the charging rate to 0.5C. The photovoltaic sub-instruction initiates active power control, shifting the MPPT operating point to the suboptimal efficiency point. The grid connection sub-instruction increases the grid connection power target value, prioritizing the delivery of excess power to the grid. Through coordinated control, overcharging of the energy storage is avoided, ensuring equipment safety.

[0069] Accordingly, please refer to Figure 2 The second aspect of this invention provides a multi-objective collaborative energy management system for integrated photovoltaic-storage power plants, which performs early warning based on the aforementioned multi-objective collaborative energy management method for integrated photovoltaic-storage power plants, including: Data acquisition module 1 is used to acquire real-time operating data of the photovoltaic-storage integrated power station during the current testing cycle; Data calculation module 2 is used to calculate the operational efficiency assessment value, energy storage device lifespan degradation assessment value, and grid support capability assessment value of the photovoltaic-storage integrated power station based on real-time operational data. The instruction generation module 3 is used to generate multi-objective collaborative optimization instructions based on the real-time calculation results of the operation performance evaluation value, the energy storage device life decay evaluation value and the grid support capacity evaluation value, using a dynamic priority strategy. The instruction decomposition module 4 is used to decompose the multi-objective collaborative optimization instruction to obtain the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction and the grid-connected power instruction. Based on the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction and the grid-connected power instruction, the operation of the photovoltaic-energy storage integrated power station is controlled.

[0070] Accordingly, a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the above-described multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants.

[0071] Accordingly, a fourth aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-described multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants.

[0072] The embodiments of the present invention aim to protect a multi-objective collaborative energy management method and system for integrated photovoltaic and energy storage power plants, which has the following effects: 1. By calculating operational efficiency, energy storage lifetime degradation, and grid support capability assessment values ​​in real time, a dynamic priority weight allocation mechanism is constructed, effectively solving the adjustment lag problem caused by fixed weights in traditional multi-objective optimization. When the grid frequency or voltage exceeds the limit, the grid support weight is prioritized and a fast adjustment command is generated to ensure millisecond-level response to grid fluctuations. When the energy storage state of charge approaches the safety boundary, it automatically switches to lifetime protection mode, delaying battery degradation by limiting the charge / discharge rate and depth. Under steady-state conditions, the synergistic efficiency of photovoltaic and energy storage and the photovoltaic absorption rate are dynamically optimized to achieve economic objectives. This strategy significantly improves the real-time performance and scenario adaptability of multi-objective optimization, resulting in an overall improvement in power plant performance of over 15%. 2. To address command execution deviations and external environmental disturbances, this solution introduces a multi-level closed-loop feedback mechanism to achieve dynamic optimization of parameters and thresholds. In grid support scenarios, the threshold range is adaptively widened based on the adjustment effect to avoid equipment overload; in energy storage life protection, power limits are adjusted a second time based on temperature and the rate of change of state of charge; in photovoltaic consumption optimization, the charge-discharge ratio coefficient is dynamically corrected based on actual output deviations; through real-time data-driven iterative optimization, the system adjustment accuracy is improved by 20%, the photovoltaic curtailment rate is reduced to below 5%, and the energy storage life is extended by 10%-15%. 3. By hierarchically decomposing and collaboratively verifying multi-objective commands, the control fragmentation problem caused by the independent responses of photovoltaic inverters, energy storage devices, and grid-connected controllers is overcome. In grid support commands, the reactive power output of the inverter and the active power regulation of energy storage are coordinated, reducing voltage recovery time by 30%. In lifespan protection commands, the energy storage power curve is synchronously smoothed and the grid-connected power dead zone is constrained, reducing equipment stress by 20%. In efficiency optimization commands, the charging and discharging timing of energy storage and the grid-connected power tracking accuracy are jointly optimized, achieving a 25% improvement in photovoltaic-energy storage synergy efficiency. Millisecond-level synchronous issuance and conflict arbitration of cross-device commands ensure the global optimality of multi-objective control.

[0073] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective collaborative energy management method for integrated photovoltaic-storage power plants, characterized in that, Includes the following steps: Obtain real-time operational data for the current testing cycle of the integrated photovoltaic and energy storage power station; Based on the real-time operating data, the operating efficiency assessment value, energy storage device lifespan degradation assessment value, and grid support capability assessment value of the integrated photovoltaic and energy storage power station are calculated. Based on the real-time calculation results of the operational efficiency assessment value, energy storage device life decay assessment value and grid support capability assessment value, a dynamic priority strategy is adopted to generate multi-objective collaborative optimization instructions. The multi-objective collaborative optimization command is decomposed to obtain the photovoltaic inverter output command, the energy storage device charging and discharging power command, and the grid-connected power command. The operation of the photovoltaic-energy storage integrated power station is controlled according to the photovoltaic inverter output command, the energy storage device charging and discharging power command, and the grid-connected power command.

2. The multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants according to claim 1, characterized in that, The real-time operating data includes photovoltaic power output prediction, energy storage device state of charge, load demand power, grid dispatch instructions, real-time grid frequency, and real-time grid voltage. The method of generating multi-objective collaborative optimization instructions using a dynamic priority strategy includes: If the deviation between the real-time grid frequency and the reference frequency exceeds the first threshold, or the deviation between the real-time grid voltage and the rated voltage exceeds the second threshold, the grid is determined to be in an unstable state, and a first optimized control command is generated with the goal of dynamically adjusting the reactive power output of the photovoltaic inverter and the charging and discharging power of the energy storage device. If the state of charge of the energy storage device reaches the preset buffer zone of the charge and discharge safety boundary, the energy storage device is determined to be in the life protection state, and a second optimized control command is generated with the goal of smoothing the charge and discharge power curve and limiting the real-time charge and discharge rate and depth. If the real-time values ​​of the grid frequency and grid voltage are both within the preset stable range, and the state of charge of the energy storage device is within the safe charging and discharging range, then it is determined to be in a steady-state operation state, and a third optimization control command is generated with the goal of maximizing the photovoltaic power absorption rate and optimizing the charging and discharging sequence of the energy storage device to match the load demand.

3. The multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants according to claim 2, characterized in that, The generation of the first optimized control command, aimed at dynamically adjusting the reactive power output of the photovoltaic inverter and the charging and discharging power of the energy storage device, includes: Based on the direction of the deviation between the real-time grid voltage and the rated voltage, the adjustment mode of the reactive power output of the photovoltaic inverter is determined. If the real-time grid voltage is lower than the rated voltage, the capacitive reactive power output mode of the photovoltaic inverter is activated; if the real-time grid voltage is higher than the rated voltage, the photovoltaic inverter is switched to inductive reactive power output mode. If the frequency deviation continues to increase and exceeds the preset margin of the first threshold, the fast power response mode of the energy storage device is activated, and the direction and amplitude of the charging and discharging power of the energy storage device are dynamically adjusted; if the frequency deviation tends to converge and does not exceed the preset margin of the first threshold, the energy storage device is kept in power standby mode.

4. The multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants according to claim 3, characterized in that, Also includes: After executing the first optimization control command, the real-time values ​​of the grid frequency and voltage are reacquired. If both still exceed the corresponding thresholds, the values ​​of the first and second thresholds are increased, and the weight increase of the grid support capability assessment value is reduced.

5. The multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants according to claim 2, characterized in that, The generation of the second optimized control command, aimed at smoothing the charge / discharge power curve and limiting the real-time charge / discharge rate and depth, includes: Based on the real-time state of charge of the energy storage device and the preset buffer zone between the charging and discharging safety boundary, the instantaneous limit value of the charging and discharging power is dynamically set: if the state of charge of the energy storage device is close to the upper limit of the charging safety boundary, the current maximum allowable charging power is gradually reduced according to a preset ratio, and the charging rate is limited to a set percentage of the nominal capacity value; if the state of charge of the energy storage device is close to the lower limit of the discharging safety boundary, the current maximum allowable discharging power is gradually reduced according to a preset ratio, and the discharging rate is limited to a set percentage of the nominal capacity value. Based on the real-time charging and discharging power change rate of the energy storage device, a smoothness constraint condition for the charging and discharging power curve is set: when the change in charging and discharging power in adjacent detection cycles exceeds the preset threshold, a transition power command is inserted to limit the slope of the power curve to within the allowable range; when the energy storage device is in the charging and discharging mode switching, a power zero maintenance period is forcibly added to avoid frequent switching of charging and discharging states. Acquire historical charge-discharge cycle data of the energy storage device and dynamically adjust the cumulative damage weight of charge-discharge depth: if the charge-discharge depth in the current detection cycle exceeds the historical average, reduce the upper limit of the allowable charge-discharge rate; if the charge-discharge depth in multiple consecutive detection cycles is lower than the historical average, relax the rate limit range by a preset step size. The cumulative damage weight is determined based on the fitting results of the historical charge-discharge cycle count and the deep decay curve of the energy storage device.

6. The multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants according to claim 5, characterized in that, Also includes: After executing the second optimized control command, the rate of change of the state of charge and temperature data of the energy storage device are reacquired. When the rate of change of the state of charge exceeds the preset value, an additional gradient decrease command for charging and discharging power is added. When the internal temperature of the energy storage device exceeds the preset safe range, the current charging and discharging operation is suspended and switched to thermal management priority mode. The step size of the gradient descent command is determined based on the exponentially weighted average of the rates of change of state of charge.

7. The multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants according to claim 2, characterized in that, The generation of the third optimized control command, aimed at maximizing the photovoltaic power absorption rate and optimizing the charging and discharging timing of energy storage devices to match load demand, includes: Based on the real-time difference between the photovoltaic power output forecast and the load demand power, the target charging and discharging power benchmark value of the energy storage device is dynamically calculated: if the photovoltaic power output forecast is greater than the load demand power in the current detection period, the charging power benchmark value of the energy storage device is set as the first proportion of the difference, and the remaining photovoltaic power is used to charge the energy storage device first; if the photovoltaic power output forecast is less than the load demand power in the current detection period, the discharging power benchmark value of the energy storage device is set as the second proportion of the difference, and the load shortfall is made up by discharging the energy storage device. Combining the fluctuation characteristics of short-term load forecast curves and photovoltaic output forecast values, optimize the charging and discharging timing scheduling plan of energy storage devices: when it is predicted that the load demand power will increase significantly in the subsequent testing period, advance the state of charge of energy storage devices to the preset high threshold range; when it is predicted that the photovoltaic output forecast value will drop sharply in the subsequent testing period, delay the charging operation of energy storage devices until the photovoltaic output is sufficient. Real-time monitoring of the deviation between actual photovoltaic output and predicted value, and dynamic correction of the charging and discharging power benchmark value of energy storage device: if the actual photovoltaic output is consistently lower than the predicted value and the deviation exceeds the allowable range, the charging power of energy storage device is reduced or the discharging power is increased by a preset step size; if the actual photovoltaic output is consistently higher than the predicted value and there is a risk of curtailment, the charging power of energy storage device is increased by a preset step size.

8. The multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants according to claim 7, characterized in that, Also includes: After executing the third optimization control command, the photovoltaic power absorption rate is obtained. If the photovoltaic power absorption rate does not reach the preset target value, the proportional coefficient used to allocate the remaining photovoltaic power or make up for the load shortfall in the energy storage device charging and discharging power reference value is dynamically adjusted according to the deviation between the actual output of the photovoltaic device and the predicted value, and the updated third optimization control command is generated iteratively.

9. The multi-objective collaborative energy management method for integrated photovoltaic-storage power plants according to any one of claims 1-8, characterized in that, The decomposition of the multi-objective collaborative optimization command yields the photovoltaic inverter output command, the energy storage device charging and discharging power command, and the grid-connected power command, including: The type and characteristics of the multi-objective collaborative optimization instructions are obtained. If it is a grid support capacity priority instruction, the reactive power output adjustment requirements of the photovoltaic inverter and the rapid response requirements of the charging and discharging power of the energy storage device are extracted from the instruction. If it is an energy storage device life protection priority instruction, the smoothness constraints and rate limits of the charging and discharging power are extracted from the instruction. If it is an operation efficiency priority instruction, the maximization target of photovoltaic power absorption rate and the matching rules of the charging and discharging sequence of the energy storage device are extracted from the instruction. Based on the aforementioned instruction characteristics, sub-instructions are generated according to the device control level. Power sub-instructions are issued to the photovoltaic inverter, including active power output limits, reactive power regulation modes, and dynamic response rate requirements. Charging and discharging power sub-instructions are issued to the energy storage device, including power direction, amplitude, rate of change limits, and charging / discharging mode switching conditions. Grid-connected power sub-instructions are issued to the grid-connected point controller, including the target value of grid-connected power, regulation dead zone range, and deviation tolerance from grid dispatch instructions.

10. A multi-objective collaborative energy management system for integrated photovoltaic and energy storage power plants, characterized in that, Early warning is based on the multi-objective collaborative energy management method for integrated photovoltaic and energy storage power plants as described in any one of claims 1-9, including: The data acquisition module is used to acquire real-time operating data of the integrated photovoltaic and energy storage power station during the current testing cycle. The data calculation module is used to calculate the operational efficiency assessment value, energy storage device lifespan degradation assessment value, and grid support capability assessment value of the photovoltaic-storage integrated power station based on the real-time operating data. The instruction generation module is used to generate multi-objective collaborative optimization instructions based on the real-time calculation results of the operational efficiency assessment value, energy storage device life decay assessment value and grid support capability assessment value, using a dynamic priority strategy. The instruction decomposition module is used to decompose the multi-objective collaborative optimization instruction to obtain the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction, and the grid-connected power instruction, and to control the operation of the photovoltaic-energy storage integrated power station based on the photovoltaic inverter output instruction, the energy storage device charging and discharging power instruction, and the grid-connected power instruction.

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