An energy storage energy optimization scheduling method and system applied to a smart grid

Abstract

The present application relates to the technical field of smart grid, and provides an energy storage energy optimization scheduling method and system applied to smart grid, which comprises the following steps: receiving performance information from each energy storage unit in the smart grid; evaluating the additional available power capacity of each energy storage unit based on the performance information; adjusting the energy scheduling task of each energy storage unit according to the real-time demand of the power grid and the additional available power capacity of each energy storage unit; issuing the energy scheduling instruction corresponding to the adjusted energy scheduling task to each energy storage unit; obtaining the actual energy scheduling execution of each energy storage unit after the energy scheduling instruction is executed, and calculating the deviation between the actual energy scheduling execution and the energy scheduling instruction; and adjusting the energy scheduling strategy based on the deviation. The present application has the effect of improving the reliability and economy of power grid operation.

Description

Technical Field

[0001] This invention relates to the technical field of smart grids, and specifically to a method and system for optimizing energy storage scheduling in smart grids. Background Technology

[0002] In modern smart grids, energy storage systems play a crucial role in integrating large-scale intermittent renewable energy sources. Their purpose is to smooth power fluctuations, provide ancillary services such as peak shaving and frequency regulation, and participate in electricity market transactions to improve the overall stability and economic efficiency of the grid. However, existing energy storage optimization and dispatch methods often rely excessively on the initial design parameters of the energy storage system or periodically calibrated static performance descriptions when formulating dispatch plans. This approach fails to adequately consider that during long-term actual operation, the actual usable capacity, charge / discharge efficiency, and power output capability of the internal battery packs of the energy storage system will undergo continuous and non-linear performance degradation due to various complex factors (such as frequent charge / discharge cycles, different depths and rates of charge / discharge, and changes in ambient temperature).

[0003] This dynamic performance degradation causes a discrepancy between the instructions issued by the dispatch system and the actual carrying capacity of the energy storage system, thus affecting the reliable operation and economic benefits of the power grid. For example, in a large regional power grid, the central dispatch system generates an optimized 24-hour dispatch plan based on forecasts and allocates charging and discharging tasks to energy storage power stations. However, due to the long-term high-intensity operation of some energy storage power stations, the actual usable capacity and charging and discharging efficiency of their internal battery packs may have degraded, preventing them from completing the dispatch tasks as planned. When the dispatch system issues a maximum power charging instruction to an energy storage power station, the station may charge prematurely due to the decrease in its actual usable capacity, resulting in some clean energy not being stored and causing curtailment of solar power. Similarly, during peak electricity consumption periods, energy storage power stations may be unable to provide the expected peak power support due to increased internal resistance and decreased efficiency, leading to a power gap in the power grid and necessitating the activation of costly gas turbine generators for emergency response.

[0004] A further problem is that this performance degradation is not an isolated event; other energy storage power stations may also experience varying degrees of capacity decay, efficiency reduction, or power limitation. When the dispatch system attempts to re-optimize globally, if it lacks accurate real-time perception and prediction of the actual performance status of all energy storage power stations, any dispatch decision based on "ideal" or "static" parameters may lead to similar deviations, or even trigger a chain reaction, severely impacting the reliability and economy of the entire power grid. In this situation, although the dispatch system can detect deviations and make post-hoc adjustments, it is essentially "the blind men and the elephant," unable to fundamentally solve the dispatch dilemma caused by the discrepancy between the actual performance of the energy storage system and model assumptions.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This application discloses an energy storage energy optimization scheduling method and system for smart grids, aiming to solve the technical problem that existing energy storage energy optimization scheduling methods rely too much on static performance parameters and fail to fully consider the dynamic performance degradation of energy storage systems, resulting in a deviation between scheduling commands and actual carrying capacity, which in turn affects the reliability and economic benefits of grid operation.

[0007] The technical solution of this application is as follows: Firstly, this application discloses a method for optimizing the scheduling of energy storage in smart grids, specifically including: Receive performance information from each energy storage unit in the smart grid; the performance information is generated by each energy storage unit based on internal operating parameters, and the performance information is used to characterize the current actual carrying capacity of the corresponding energy storage unit; Based on various performance information, the additional available power capacity of each energy storage unit is evaluated; the additional available power capacity is the power capacity to cope with real-time changes in grid demand after meeting preset operating requirements. Based on the real-time demand of the power grid and the additional available power capacity of each energy storage unit, the energy scheduling tasks of each energy storage unit are adjusted. The steps for adjusting the energy scheduling tasks of each energy storage unit include: reallocating the reduced or unused additional available power capacity of some energy storage units to other energy storage units, and rearranging the priority of energy scheduling tasks. Issue the adjusted energy scheduling instructions corresponding to the energy scheduling tasks to each energy storage unit; After the energy scheduling command is executed, the actual energy scheduling execution status of each energy storage unit is obtained, and the deviation between the actual energy scheduling execution status and the energy scheduling command is calculated. Adjust the energy dispatch strategy based on the deviation.

[0008] This technical solution enables real-time sensing of the actual carrying capacity of energy storage units and dynamic adjustment of scheduling tasks, effectively solving the scheduling deviation problem caused by the performance degradation of energy storage systems and improving the reliability and economy of power grid operation.

[0009] Secondly, this application also discloses an energy storage energy optimization scheduling system for smart grids, used to perform energy storage energy optimization scheduling for smart grids, specifically including: The performance information receiving module is used to receive performance information from each energy storage unit in the smart grid. The performance information is generated by each energy storage unit based on internal operating parameters and is used to characterize the current actual carrying capacity of the corresponding energy storage unit. The available power assessment module is used to assess the additional available power capacity of each energy storage unit based on various performance information; the additional available power capacity is the power capacity to cope with real-time changes in grid demand after meeting preset operating requirements. The scheduling task adjustment module is used to adjust the energy scheduling tasks of each energy storage unit based on the real-time demand of the power grid and the additional available power capacity of each energy storage unit. The steps for adjusting the energy scheduling tasks of each energy storage unit include: reallocating the reduced or unused additional available power capacity of some energy storage units to other energy storage units, and rearranging the priority of energy scheduling tasks. The scheduling instruction issuing module is used to issue energy scheduling instructions corresponding to the adjusted energy scheduling tasks to each energy storage unit; The scheduling deviation calculation module is used to obtain the actual energy scheduling execution status of each energy storage unit after the energy scheduling command is executed, and to calculate the deviation between the actual energy scheduling execution status and the energy scheduling command. The scheduling strategy adjustment module is used to adjust the energy scheduling strategy based on the deviation.

[0010] This technical solution provides a system for implementing the aforementioned scheduling method. Through modular design, it ensures the integrity and scalability of system functions, providing reliable hardware and software support for the optimized scheduling of energy storage in smart grids.

[0011] Beneficial Effects: This application discloses an energy storage optimization scheduling method for smart grids. By receiving performance information from various energy storage units in the smart grid and evaluating the additional available power capacity of each unit based on this information, the method can accurately and in real-time grasp the actual carrying capacity and potential for responding to changes in grid demand for each energy storage unit. Based on this, the system dynamically adjusts energy scheduling tasks according to the real-time grid demand and the additional available power capacity of each energy storage unit, reallocating some of the reduced or unused additional available power capacity to other energy storage units and reordering the priority of energy scheduling tasks. This achieves refined and adaptive scheduling of energy storage resources. After the scheduling command is executed, the system can obtain the actual execution status and calculate the deviation from the command, adjusting the energy scheduling strategy based on this deviation to form a closed-loop optimization.

[0012] Compared to existing scheduling methods that rely excessively on static performance parameters, this application effectively solves the scheduling deviation problem caused by the performance degradation of energy storage systems during long-term operation. Through real-time sensing and dynamic adjustment, this application avoids problems such as curtailment of solar power and power gaps caused by insufficient actual capacity of energy storage units, significantly improving the operational stability and economic benefits of the power grid. For example, when an energy storage power station is unable to complete its intended task due to performance degradation, this application can promptly identify and reallocate its unused power capacity to other capable energy storage units, ensuring the overall supply and demand balance of the power grid and avoiding the emergency response of starting high-cost gas turbine units. Therefore, this application overcomes the "blind men and the elephant" scheduling dilemma in existing technologies, fundamentally solving the scheduling problem caused by the discrepancy between the actual performance of the energy storage system and the model assumptions, and realizing the efficient and reliable operation of energy storage systems in smart grids. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for optimizing energy storage scheduling applied to a smart grid, according to one embodiment of the present invention. Figure 2 This is a flowchart of a method for optimizing energy storage scheduling applied to a smart grid, according to another embodiment of the present invention. Figure 3 This is a system block diagram of an energy storage energy optimization scheduling system applied to a smart grid according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Energy storage energy optimization and scheduling system applied to smart grids; 11. Performance information receiving module; 12. Available power assessment module; 13. Scheduling task adjustment module; 14. Scheduling instruction issuance module; 15. Scheduling deviation calculation module; 16. Scheduling strategy adjustment module. Detailed Implementation

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] This application proposes a method for optimizing the scheduling of energy storage in smart grids, combining... Figure 1 As shown, it includes: S1 receives performance information from each energy storage unit in the smart grid; the performance information is generated by each energy storage unit based on internal operating parameters, and the performance information is used to characterize the current actual carrying capacity of the corresponding energy storage unit. S2, based on various performance information, evaluate the additional available power capacity of each energy storage unit; the additional available power capacity is the power capacity to cope with real-time changes in grid demand after meeting preset operating requirements; S3, based on the real-time demand of the power grid and the additional available power capacity of each energy storage unit, adjust the energy dispatching tasks of each energy storage unit; the steps of adjusting the energy dispatching tasks of each energy storage unit include: reallocating the reduced or unused additional available power capacity of some energy storage units to other energy storage units, and rearranging the priority of energy dispatching tasks. S4, issue energy scheduling instructions corresponding to the adjusted energy scheduling tasks to each energy storage unit; S5, after the energy scheduling command is executed, obtain the actual energy scheduling execution status of each energy storage unit, and calculate the deviation between the actual energy scheduling execution status and the energy scheduling command; S6, adjusts the energy dispatch strategy based on the deviation.

[0017] The term "energy storage unit" as used in this application refers to a device in a smart grid system that possesses energy storage and release capabilities, such as a battery energy storage system, a flywheel energy storage system, or a supercapacitor. These energy storage units can evaluate their own operating status through internal operating parameters and generate performance information reflecting their actual operating capabilities. This performance information can be comprehensively calculated based on operating parameters such as temperature, voltage, current, internal resistance, state of charge, and historical charge-discharge behavior, thereby reflecting the actual carrying capacity of the energy storage unit under the current state, including indicators such as maximum charge-discharge power, available capacity, and charge-discharge efficiency. "Additional available power capability" refers to the power margin that, under the premise of meeting the energy storage unit's safe operation constraints and lifespan management requirements, can still be used to respond to real-time changes in grid demand.

[0018] In the specific implementation process, the first step is to receive performance information from each energy storage unit in the smart grid. This performance information is generated by each energy storage unit based on its internal operating parameters and is used to characterize the current actual carrying capacity of the corresponding energy storage unit. For example, each energy storage unit can be configured with voltage sensors, current sensors, and temperature sensors to collect the operating parameters of the battery pack or energy storage module in real time. The collected data is transmitted to the local controller of the energy storage unit, which executes a preset state evaluation algorithm to calculate the health status, internal resistance changes, and capacity decay of the battery unit. For example, a state estimation algorithm based on Kalman filtering or a state prediction model based on machine learning can be used to evaluate the available capacity and maximum charge / discharge capacity of the battery, and these evaluation results are integrated into performance information. As one implementation method, the energy storage unit can also periodically upload its operation log data to the central dispatch system. The central dispatch system parses these operation logs and combines them with a preset performance degradation model to comprehensively evaluate the current operating performance of the energy storage unit, thereby obtaining more accurate performance information.

[0019] Subsequently, the system performs a step of evaluating the additional available power capacity of each energy storage unit based on its performance information. Additional available power capacity refers to the dispatchable power capacity that can be used to respond to real-time changes in grid demand, provided that preset safe operating constraints and lifetime management strategies are met. For example, after the central dispatch system receives the performance information uploaded by each energy storage unit, it can calculate the maximum charge and discharge power that each energy storage unit can currently withstand, based on preset safe operating boundaries such as maximum allowable temperature, maximum charge / discharge rate, and minimum allowable state of charge. Then, by subtracting the power consumed by the dispatching task currently being performed by the energy storage unit, the additional available power capacity that the energy storage unit can use to respond to changes in grid demand at the current moment can be obtained. As one implementation method, the central dispatch system can also maintain an energy storage unit performance database, which records the historical operating data, performance degradation curves, and operating environment parameters of each energy storage unit. When the system receives new performance information, it can compare it with historical data and use predictive models to predict the performance change trend of the energy storage unit over a future period, thereby dynamically evaluating its additional available power capacity in the short and medium term.

[0020] After obtaining the additional available power capacity of each energy storage unit, the next step is to adjust the energy dispatching tasks of each energy storage unit based on the real-time demand of the power grid and the additional available power capacity of each unit. In this step, the reduced or unused additional available power capacity of some energy storage units can be reallocated to other energy storage units, and the priority of energy dispatching tasks can be rearranged. For example, when a sudden power shortage occurs in the power grid, the central dispatching system first identifies energy storage units with additional available power capacity, and then calculates the optimal power allocation scheme using optimization algorithms based on the additional available power capacity, geographical location, response speed, and current load conditions of these energy storage units. For example, linear programming or mixed integer programming algorithms can be used to solve for the optimal power allocation scheme, thereby reallocating the unused or reduced power capacity of some energy storage units to other energy storage units, and adjusting the charging and discharging power or duration of each energy storage unit accordingly. Simultaneously, the execution priority of energy dispatching tasks is reordered based on the urgency of the power grid demand and the response capability of each energy storage unit to ensure that critical tasks are executed first.

[0021] After adjusting the scheduling tasks, the next step is to issue energy scheduling instructions corresponding to the adjusted energy scheduling tasks to each energy storage unit. For example, after determining a new energy scheduling scheme, the central scheduling system generates corresponding energy scheduling instructions and sends these instructions to the local controllers of each energy storage unit via a communication network. The energy scheduling instructions may include parameters such as target power value, power change rate, duration, and charging / discharging mode. As one implementation, the central scheduling system can send scheduling instructions to multiple energy storage units via broadcast or multicast, and the local controllers of each energy storage unit can selectively receive and execute the corresponding instructions based on their own identification information and task priority.

[0022] After the energy dispatch command is executed, the next step is to obtain the actual energy dispatch execution status of each energy storage unit and calculate the deviation between the actual execution status and the energy dispatch command. In this step, the local controller of each energy storage unit monitors its actual operating status in real time, such as actual output power, energy throughput, and execution duration, and feeds this data back to the central dispatch system. Upon receiving this feedback data, the central dispatch system compares the actual execution data with the previously issued energy dispatch command to calculate the deviation between the actual execution status and the command. For example, it can calculate the power deviation between actual power and target power, the energy deviation between actual output energy and planned energy, and the time deviation between execution time and planned time.

[0023] After obtaining the aforementioned deviations, the energy dispatch strategy is adjusted based on these deviations. The central dispatch system can dynamically optimize the current dispatch strategy based on the magnitude, duration, and cause of the deviations. For example, if some energy storage units fail to meet the commanded power requirements for an extended period, the system can determine that their performance evaluation results may be biased or that their operating status has changed. This allows for a reassessment of the performance model parameters of the energy storage unit and adjustments to the weighting coefficients in the dispatch algorithm. Simultaneously, the system can update the model parameters in the energy storage unit performance database to make subsequent dispatch decisions more accurate. Through this dynamic optimization mechanism based on execution feedback, the accuracy and adaptability of energy storage system dispatch strategies in smart grids can be continuously improved, thereby enhancing the stability and energy utilization efficiency of the entire power grid.

[0024] Optional, combined Figure 2 As shown, the steps for adjusting the energy dispatching tasks of each energy storage unit based on the real-time demand of the power grid and the additional available power capacity of each energy storage unit include: A1, internal operating parameters are monitored by the local control unit of the energy storage unit; internal operating parameters include at least temperature, charging and discharging current, voltage and internal resistance; A2, the local control unit estimates the rate of heat accumulation and temperature rise trend inside the battery cluster based on internal operating parameters, and determines whether the internal temperature exceeds the safe temperature threshold under the upcoming energy dispatch command based on the rate of heat accumulation and temperature rise trend inside the battery cluster, and obtains the safe temperature judgment result. A3, when the safe temperature judgment result indicates that the safe temperature threshold is exceeded, the local control unit initiates a power limitation assessment to calculate the maximum executable power that the energy storage unit can continuously output under the condition of meeting the safe operation requirements; A4. When the maximum executable power is lower than the command power corresponding to the energy dispatch command, the local control unit generates and reports a micro-limit signal. The micro-limit signal includes at least the energy storage unit identifier, the signal timestamp, the expected time of the power limit, the adjusted maximum executable power, the duration of the limit, the reason code for the limit, and the current key parameters. A5. The central dispatch system receives and parses the micro-limit signal to update the instantaneous available capacity table. The instantaneous available capacity table records the maximum executable charging and discharging power limit, the start and end time of the limit, and the limit reason identifier of each energy storage unit within a preset time window, and uses the signal timestamp as the basis for version update. A6. The central dispatch system triggers margin reassessment and task rearrangement based on the updated instantaneous available capacity table, identifies power gaps caused by micro-limiting signals, and queries the global margin pool to select energy storage units to make up for the power gaps, thus obtaining the selected energy storage units. The global margin pool is used to summarize the additional reserve power margin that each energy storage unit can release in the current dispatch cycle and the corresponding duration of sustainability, and serves as a candidate set for screening compensation units. A7. The central dispatch system issues compensation instructions to the selected energy storage units to provide additional power, while simultaneously issuing update instructions to the limited energy storage units to match their instantaneous carrying capacity.

[0025] Specifically, the local control unit of the energy storage unit is configured to monitor its internal operating parameters in real time. These parameters are key indicators for assessing the health status and operational safety of the device. For example, internal operating parameters may include multiple temperature points, charging / discharging current, voltage, and internal resistance. Temperature points can be understood as data collected by temperature sensors located at different positions within the battery cluster, reflecting the thermal state of the battery cluster in different areas; charging / discharging current and voltage reflect the intensity and direction of current energy flow; and changes in internal resistance reflect the degree of aging or the trend of changes in the health status of the battery's internal materials. By continuously collecting these operating parameters, the local control unit can obtain the real-time operating status of the energy storage unit and provide a data basis for subsequent safety assessments and power control.

[0026] After obtaining real-time operational data, the local control unit predicts and analyzes the thermal behavior of the energy storage unit based on the monitored internal operating parameters. For example, by analyzing temperature and charge / discharge current trends, the rate of heat accumulation within the battery cluster and the temperature rise trend over a future period can be estimated. This estimation process can be based on a battery thermal model or utilize machine learning algorithms to establish a temperature prediction model, thereby predicting the development trend of the battery's internal temperature under current or upcoming energy dispatch commands. Through this analysis, the local control unit can determine whether the internal temperature of the energy storage unit may exceed a preset safe temperature threshold during the execution of energy dispatch tasks. This safe temperature threshold is typically provided by the battery manufacturer or set according to the system's safe operation strategy, and is a crucial safety limit for preventing battery thermal runaway and ensuring long-term stable operation of the equipment. The corresponding safe temperature judgment result can be obtained through the above prediction and judgment process.

[0027] When the safety temperature assessment indicates that the internal temperature may exceed the safety temperature threshold under the current energy dispatch command, the local control unit will immediately initiate a power limitation assessment process. In this process, the local control unit calculates the maximum power that the energy storage unit can continuously output, taking into account the current operating status, thermal model predictions, and safe operating strategies. The resulting maximum executable power is dynamically calculated based on real-time internal parameters, aiming to ensure that the equipment can maintain safe operation under current conditions without causing excessively rapid temperature rise or equipment overload due to excessive power output.

[0028] When the calculated maximum executable power is lower than the command power corresponding to the energy dispatch instruction issued by the central dispatch system, the local control unit generates and reports a micro-limit signal. This micro-limit signal can be understood as a real-time warning message sent by the energy storage unit to the central dispatch system, which includes at least the energy storage unit identifier, signal timestamp, expected time of power limitation, adjusted maximum executable power, limitation duration, limitation reason code, and current key operating parameters. With this information, the central dispatch system can accurately understand the impending power limitation of the energy storage unit and adjust the overall dispatch strategy accordingly.

[0029] Upon receiving and parsing a micro-limit signal, the central dispatch system immediately updates its maintained instantaneous availability capacity table. This table records information such as the maximum executable charging / discharging power limit, start and end times of the limit, and the reason for the limit for each energy storage unit within a preset time window. The signal timestamp serves as the basis for version control updates, ensuring that the data in the instantaneous availability capacity table always reflects the latest system status.

[0030] After updating the instantaneous available capacity table, the central dispatch system will trigger a margin reassessment and task rescheduling process. In this process, the system first identifies the power gap caused by micro-constraint signals based on the updated capacity information; that is, the portion of power that constrained energy storage units cannot provide as originally planned. To compensate for this power gap, the central dispatch system queries the global margin pool. The global margin pool aggregates the additional reserve power margin that each energy storage unit can release within the current scheduling cycle, along with its duration, serving as a candidate set for compensation resources. The central dispatch system will then select one or more energy storage units with sufficient reserve capacity from this set to undertake the additional power output tasks.

[0031] After determining the compensation unit, the central dispatch system issues a compensation instruction to the selected energy storage unit, requiring it to provide additional power to make up for the system's power shortfall. Simultaneously, to ensure the safe operation of the restricted energy storage unit, the central dispatch system issues an updated energy dispatch instruction to the restricted unit, adjusting its output power to a level that matches its instantaneous executable capacity.

[0032] In one specific embodiment, the above-described operating mechanism can be illustrated by the following scenario. For example, in a smart grid system, the central dispatch system issues a high-power discharge command to a battery energy storage power station, requiring the station to supply power to the grid at a rate of five megawatts within the next ten minutes. Before executing this command, the local control unit of a battery cluster at the power station continuously monitors its internal temperature, charging and discharging current, and internal resistance, among other operating parameters. By performing trend analysis on these parameters, the local control unit predicts that under the five-megawatt discharge power condition, the internal heat accumulation rate of the battery cluster will be high, and it is expected that its internal temperature will exceed the safe temperature threshold after five minutes.

[0033] Based on the above predictions, the local control unit immediately initiated the power limitation assessment process and calculated that, under safe operating conditions, the battery cluster could only continuously output a maximum of three megawatts of power over the next ten minutes. Since this power was lower than the five megawatts required by the original dispatch command, the local control unit then generated a micro-limitation signal, which included the battery cluster identifier, the current timestamp, the expected time of power limitation, the maximum executable power of three megawatts, the limitation duration of five minutes, the reason for limitation being overheating risk, and key parameters such as the current temperature and current. This signal was then reported to the central dispatch system.

[0034] Upon receiving the micro-limitation signal, the central dispatch system immediately updated the instantaneous available capacity table and adjusted the maximum executable power of the battery cluster for the next five minutes to three megawatts. Subsequently, the system triggered a margin reassessment and task rescheduling process, identifying a two-megawatt power shortfall caused by the limitation. The system then queried the global margin pool for backup resources and found another healthy battery storage power station with a four-megawatt reserve power margin. The central dispatch system therefore selected this storage station as the compensation unit and issued a compensation instruction, requiring it to provide an additional two megawatts of power for the next five minutes. Simultaneously, the central dispatch system issued an updated dispatch instruction to the limited battery cluster, adjusting its discharge power to three megawatts. Through this dynamic dispatch mechanism, while ensuring the safe operation of the batteries, the overall five-megawatt power supply demand of the power grid can still be continuously met, thus achieving a coordinated balance between system security and dispatch stability.

[0035] Optionally, the steps for adjusting the energy dispatch strategy based on the bias include: The central dispatch system monitors the actual power output from each energy storage unit and the overall grid response parameters as performance information; based on the performance information and energy dispatch instructions, it calculates the expected power contribution of each energy storage unit within a specific time window, and calculates the actual grid response indicators based on the overall grid response parameters. When the deviation between the expected power contribution and the actual grid response index continues to exceed the preset threshold and the duration exceeds the preset time, the deviation analysis process is triggered. In the deviation analysis process, the central dispatch system queries the communication link quality log, obtains environmental sensor data of the area where the power plant is located, and checks the power grid fault information system to quantify and eliminate the degree of attributability of communication problems, environmental impacts, and power grid faults to the deviation, and obtains the quantitative elimination results; the degree of attributability is used to characterize the contribution ratio or contribution intensity of communication problems, environmental impacts, and power grid faults to the deviation. When the quantitative exclusion results indicate that external factors are insufficient to explain the deviation, the central dispatch system maintains the individual contribution deviation index and calculates the global compensation capacity index to determine whether other energy storage units can make up for the deviation through excess contribution. When the quantitative exclusion result indicates that the individual contribution deviation index of one of the energy storage units is consistently positive and exceeds the threshold, and the global compensation capability index is lower than the preset level, the central dispatch system reduces the confidence weight of the corresponding energy storage unit performance information. When the central dispatch system generates a new energy dispatch strategy, for energy storage units with confidence weights lower than the preset value, the corresponding maximum charging and discharging power and efficiency are corrected according to the confidence weights before participating in the dispatch calculation, so as to adjust the energy dispatch strategy.

[0036] Specifically, the central dispatch system is configured to continuously monitor the actual power output from each energy storage unit and the overall grid response parameters. This monitoring data is used as performance information to reflect the actual operating status of the energy storage units and the overall performance of the grid. Based on this performance information and previously issued energy dispatch commands, the system can calculate the expected power contribution of each energy storage unit within a specific time window. Simultaneously, based on the overall grid response parameters, it calculates the actual grid response indicators. The expected power contribution refers to the power that the energy storage units should provide according to the dispatch commands under ideal conditions; the actual grid response indicators reflect the actual operating status of the grid after receiving the power contributions from all energy storage units.

[0037] Specifically, when the deviation between the expected power contribution and the actual grid response index continuously exceeds a preset threshold, and this deviation persists for a preset duration, the system will automatically trigger a deviation analysis process. This means that the system will not react immediately to instantaneous or slight deviations, but will wait for the deviation to reach a certain level and persist for a period of time to avoid misjudgment.

[0038] In the deviation analysis process, the central dispatch system performs multi-faceted data queries and analyses to quantify and eliminate the impact of external factors on the deviation. Specifically, the system queries communication link quality logs to assess the reliability and integrity of data transmission; acquires environmental sensor data from the power plant's location, such as temperature and humidity, to determine whether environmental changes have affected the performance of the energy storage units; and checks the power grid fault information system to rule out the possibility that grid-related faults (such as line tripping or load surges) are causing the deviation. Through these analyses, the system obtains a quantitative elimination result, which characterizes the proportion or intensity of the contribution of communication problems, environmental impacts, and grid-related faults to the deviation. For example, if the communication link quality logs show a high packet loss rate, the contribution of communication problems to the deviation may be relatively high.

[0039] Furthermore, when the quantification and exclusion results indicate that external factors are insufficient to explain the observed deviation, the system assumes that the deviation may stem from performance issues within the energy storage unit itself. In this case, the central dispatch system maintains an individual contribution deviation index for each energy storage unit, reflecting the difference between the actual and expected contributions of a single unit. Simultaneously, the system calculates a global compensation capability index, used to assess whether other energy storage units are capable of compensating for the current deviation through excess contributions.

[0040] As a preferred implementation, when the quantitative exclusion results show that external factors cannot explain the deviation, and the individual contribution deviation index of one energy storage unit remains positive (indicating its contribution is lower than expected) and exceeds a preset threshold, while the global compensation capability index is lower than a preset level (indicating that other units cannot compensate for its deficiency), the central scheduling system will reduce the confidence weight of the corresponding energy storage unit's performance information. The confidence weight is a parameter reflecting the system's degree of trust in the performance information of that energy storage unit; reducing its weight means that the system is cautious about the performance data it reports.

[0041] Therefore, when the central dispatch system generates a new energy dispatch strategy, for energy storage units with confidence weights lower than the preset value, their reported maximum charge / discharge power and efficiency will be adjusted according to the confidence weights before being used in the dispatch calculation. For example, if the confidence weight is 0.8, its maximum charge / discharge power and efficiency will be multiplied by 0.8 before being used in the dispatch decision. This adjustment mechanism ensures that the dispatch strategy can more accurately reflect the actual available capacity of the energy storage units, thereby enabling intelligent adjustment of the energy dispatch strategy.

[0042] Optionally, the step of adjusting the corresponding maximum charge / discharge power and efficiency according to confidence weights before participating in the scheduling calculation includes: The central dispatch system obtains the maximum charging and discharging power and efficiency values ​​reported by the energy storage unit, and performs a multiplication operation with the corresponding confidence weight to obtain the corrected maximum charging and discharging power and efficiency values. The central dispatch system continuously monitors the corresponding actual power output and real-time operating parameters under the revised energy dispatch instructions; The central dispatch system determines whether the actual power output of the corresponding energy storage unit is continuously higher than the corrected command power, whether the real-time operating parameters of the corresponding energy storage unit are within the safe operating range, and whether the micro-limit signal has not been reported within the preset period, and obtains the real-time data judgment result. When the actual power output is consistently higher than the corrected commanded power and the real-time operating parameters are within the safe operating range, and no micro-limiting signal is reported within the preset period, the central dispatch system gradually increases the confidence weight of the corresponding energy storage unit, and adjusts the recovery rate of the confidence weight by combining the historical performance recovery record of the corresponding energy storage unit with the current overall power grid operating environment.

[0043] Specifically, when the central dispatch system receives the maximum charge / discharge power and efficiency values ​​reported by an energy storage unit, it multiplies these values ​​by the corresponding confidence weight for that energy storage unit. For example, if an energy storage unit has a confidence weight of 0.8, reports a maximum charge / discharge power of 100MW and an efficiency of 90%, then the corrected maximum charge / discharge power will be calculated as 80MW, and the corrected efficiency as 72%. The purpose is to adopt a more conservative strategy for energy storage units with unstable performance during dispatch calculations, thereby reducing their potential dispatch risks.

[0044] After the revised energy dispatch command is issued, the central dispatch system continuously monitors the actual power output and real-time operating parameters of the energy storage unit. Real-time operating parameters can be understood as indicators reflecting the health status and operational safety of the energy storage unit, such as temperature, voltage, and current. The purpose is to obtain the true performance of the energy storage unit in actual operation through real-time and detailed monitoring, providing data support for subsequent confidence weight adjustments.

[0045] In practical applications, the central dispatch system determines whether the actual power output of the energy storage unit consistently exceeds the corrected commanded power, and whether its real-time operating parameters are within a safe operating range and whether it has not reported any minor limitation signals within a preset period. For example, if the corrected commanded power is 80MW, and the energy storage unit consistently outputs 90MW, with its internal temperature, voltage, and other parameters within safe thresholds, and it has not reported any minor limitation signals in the past 24 hours, then the energy storage unit can be considered to be performing well. The purpose is to ensure that the performance recovery of the energy storage unit is genuine, stable, and safe through multi-dimensional and rigorous conditional judgments.

[0046] Furthermore, when the above judgment result is affirmative, the central dispatch system will gradually increase the confidence weight of the energy storage unit. For example, the confidence weight can be increased in increments of 0.01 or 0.05 at the end of each dispatch cycle. Simultaneously, the recovery rate of the confidence weight is not fixed but is adjusted based on the energy storage unit's historical performance recovery record and the current overall grid operating environment. For example, if the energy storage unit has a history of multiple rapid recoverys, or if the current grid load is low and the demand for energy storage is not urgent, the recovery rate can be appropriately accelerated. The aim is to encourage performance improvement of energy storage units through a gradual and adaptive weight recovery mechanism, while avoiding the introduction of new dispatch risks due to overly rapid recovery, and simultaneously considering the actual operational needs of the grid.

[0047] Optionally, after the step of the central dispatch system monitoring the actual power output from each energy storage unit and the overall grid response parameters as performance information, the following may also be included: The central dispatch system monitors the transmission integrity and delay of communication links, obtains link monitoring results, and improves the sensitivity to anomalies in link transmission data when the link monitoring results indicate a decline in communication link quality. Based on anomaly detection sensitivity, the central dispatch system establishes a logical consistency verification mechanism for physical constraints on the overall power grid response parameters; In the logical consistency verification mechanism, when the actual power output or the overall grid response parameters are marked as potentially abnormal, the central dispatch system initiates the multi-source cross-verification process. The multi-source cross-verification process includes requesting the local control unit of the energy storage unit to retransmit the original data of the most recent period and comparing it with the operating data of the adjacent energy storage unit to obtain the multi-source cross-verification result. When the results of multi-source cross-validation indicate that a potential anomaly cannot be explained by multi-source cross-validation, the central dispatch system filters out the abnormal data in the performance information and fills it with the most recent valid data, or infers and adjusts the performance information based on the performance tags of the energy storage units and the current energy dispatch instructions.

[0048] Specifically, the central dispatch system continuously monitors the transmission integrity and latency of the communication links between itself and each energy storage unit. Transmission integrity refers to whether data is lost or corrupted during transmission, while latency refers to the time required for data to travel from the sender to the receiver. Real-time monitoring of these communication link parameters yields link monitoring results. When link monitoring results indicate a deterioration in communication link quality, such as high packet loss rates, high latency, or unstable connections, the central dispatch system will correspondingly increase its sensitivity to anomalies in the transmitted data. This means the system will apply stricter review standards to received data and more easily flag data as potentially anomaly-prone.

[0049] Based on this, the central dispatch system establishes a logical consistency verification mechanism for the overall power grid response parameters, which is constrained by physical limitations. This mechanism aims to utilize the physical laws and topology of power grid operation to verify the logical rationality of the received overall power grid response parameters. For example, it can compare power flow calculation results with actual monitoring parameters to determine whether the data conforms to basic power grid physical constraints.

[0050] During the operation of the logical consistency verification mechanism, once the actual power output or overall grid response parameters are marked as potentially abnormal, the central dispatch system will immediately initiate a multi-source cross-verification process. This process aims to verify the authenticity of the anomaly by acquiring and comparing data from different sources. Specifically, the central dispatch system will request the local control unit of the energy storage unit marked as abnormal to retransmit the original data from the most recent time period. Simultaneously, the system will compare this data with the operating data of adjacent energy storage units to check for local or systemic anomalies. Through this multi-source comparison, the multi-source cross-verification results can be obtained.

[0051] If the results of multi-source cross-validation indicate that a potential anomaly cannot be reasonably explained by multi-source cross-validation, thus ruling out simple communication retransmission errors or local data inconsistencies, the central dispatch system will take further data processing measures. At this point, the system will filter out anomalous data in the performance information to prevent it from negatively impacting subsequent dispatch decisions. To maintain data continuity and integrity, the system will populate the data with the most recent valid data or infer from historical performance tags of energy storage units and current energy dispatch instructions, thereby adjusting the performance information to better reflect actual operating conditions and physical constraints.

[0052] Optional, logical consistency verification mechanisms for physical constraints include: The central dispatch system receives real-time grid topology information, load data of each node, and real-time output data of new energy power generation units. The central dispatch system constructs a node admittance matrix based on real-time power grid topology information; The central dispatch system performs power flow calculations based on the node admittance matrix, load data of each node and real-time output data of new energy power generation units, and obtains the real-time power flow of each line and the real-time voltage amplitude and phase angle of each node as the power flow calculation results. The central dispatch system compares the power flow calculation results with the actual monitored overall power grid response parameters to obtain the comparison deviation; the comparison deviation includes at least one of the instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation. When the comparison deviation continues to exceed the preset threshold, an inconsistency alarm is triggered and the alarm information is recorded.

[0053] Specifically, the real-time topology information of the power grid received by the central dispatch system refers to the connection relationships and operating status between various generators, transformers, transmission lines, load nodes, and other equipment in the power grid. This information forms the basis for constructing the power grid mathematical model. Load data for each node refers to the electricity demand of each load point in the power grid at real-time or near-real-time, typically expressed in the form of active and reactive power. Real-time output data of new energy power generation units refers to the actual power generation of new energy units such as wind power and photovoltaic power at the current moment. These data collectively constitute a comprehensive input to the current operating status of the power grid.

[0054] Constructing the nodal admittance matrix is ​​a crucial step in power system analysis. This matrix describes the admittance relationships between nodes in the power grid and is a necessary prerequisite for power flow calculations. The elements of the nodal admittance matrix are typically determined by the impedance and admittance parameters of the lines and the parameters of the transformers.

[0055] In practical applications, power flow calculation refers to solving a set of nonlinear equations to calculate the steady-state operating parameters of the power grid under given topology, load, and power output conditions. The results of the power flow calculation include the real-time power flow of each line (i.e., the active and reactive power flowing through each line) and the real-time voltage magnitude and phase angle of each node. These calculation results represent the expected operating state of the power grid under an ideal physical model.

[0056] Furthermore, the comparison deviation refers to the difference between the theoretical results obtained from power flow calculations and the actual monitored overall power grid response parameters. Overall power grid response parameters typically include key operating indicators such as frequency, voltage amplitude, and phase angle. The comparison deviation can specifically manifest as at least one of the instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation. These deviations directly reflect the inconsistency between actual operation and the theoretical model. When these comparison deviations consistently exceed preset thresholds, it indicates that there may be an anomaly in the actual power grid operation. At this point, the system will trigger an inconsistency alarm and record the alarm information for subsequent fault diagnosis and handling.

[0057] Optional steps to troubleshoot communication problems include: The central dispatch system receives communication link quality log data and processes it in real time; real-time processing includes identifying the log data format and converting it into structured data. The central dispatch system uses timestamps and device identifiers in structured data to associate log records from the same energy storage unit or the same communication link to form a sequence of communication events; The central dispatch system checks the integrity of the communication event sequence and determines whether there are timestamp jumps or missing key fields, thus obtaining the integrity judgment result; When the integrity judgment result indicates that there are missing or format errors, the central dispatch system infers and fills in the missing data or marks the corresponding log as an unreliable record according to the preset log repair rules.

[0058] The central dispatch system is configured to receive communication link quality log data from various energy storage units or communication network infrastructures. This log data typically exists in raw, unstructured form, such as text files or binary streams. To facilitate subsequent analysis, real-time processing aims to parse this raw log data, identify its inherent data format, and convert it into standardized, computer-processable structured data, such as JSON, XML, or database records, for efficient querying and analysis.

[0059] Furthermore, the central dispatch system utilizes key information from the transformed structured data—namely, timestamps and device identifiers—to correlate log records. Timestamps determine the precise moment an event occurred, while device identifiers uniquely identify the energy storage unit or communication link that generated the log. By aggregating log records with the same device identifier and sequential temporal occurrences, a complete sequence of communication events can be formed. This sequence reflects the communication activities and status changes of a specific communication link or energy storage unit over a period of time.

[0060] Based on this, the central dispatch system performs an integrity check on the resulting communication event sequence. This check aims to detect potential data loss or anomalies, such as unexpected timestamp jumps (i.e., discontinuous or backward timestamps), or missing key fields (such as packet size, transmission status codes, etc.). Through this assessment, an integrity judgment result can be obtained regarding the completeness and reliability of the communication link data.

[0061] When the integrity assessment indicates that a communication event sequence is missing or formatted incorrectly, the central scheduling system will initiate the corresponding processing mechanism. Specifically, the system will infer and fill in the missing data based on preset log repair rules. For example, it can interpolate based on adjacent valid data or make predictions based on historical patterns. For log records that cannot be repaired or still have high uncertainty after repair, the system will mark them as unreliable records to avoid these abnormal data misleading subsequent deviation analysis.

[0062] Optionally, after the step of triggering an inconsistency alarm and recording alarm information when the comparison deviation continues to exceed a preset threshold, the method further includes: When recording alarm information, the central dispatch system associates and stores the alarm information with the corresponding real-time topology information version number, node admittance matrix version number, and power flow calculation input data time window identifier to obtain the associated storage result. Based on the associated storage results, the central dispatch system determines whether the inconsistency alarm is caused by topology change lag, decreased timeliness of input data, or abnormal node measurement, and outputs the corresponding cause judgment result.

[0063] Specifically, "associative storage" refers to binding alarm events with multiple key contextual information pieces and persistently saving them. Among these, the "real-time topology information version number" refers to the version identifier of the power grid topology data at a specific point in time, used to track the historical changes in the physical connection status of the power grid, ensuring that the valid power grid structure at that time can be referenced when analyzing alarms. The "node admittance matrix version number" refers to the version identifier of the node admittance matrix constructed based on a specific topology, reflecting the current state of the power grid electrical parameters, and it is usually strongly correlated with the topology version number. The "power flow calculation input data time window identifier" refers to the time range or timestamp of the input data such as load data and renewable energy output data used for power flow calculation, used to characterize the timeliness of the data. By storing this information together with alarm information, comprehensive background information can be provided for subsequent fault diagnosis.

[0064] Furthermore, "determining whether the inconsistency alarm is caused by topology change lag, decreased input data timeliness, or node measurement anomaly" refers to the central dispatch system logically inferring the potential causes of the alarm based on the context information stored in the associated database. "Topology change lag" means that the actual power grid topology has changed, but the topology information used by the central dispatch system for power flow calculation has not been updated, leading to a discrepancy between the calculation results and the actual monitoring data. "Decreased input data timeliness" means that the input information used for power flow calculation, such as load data or renewable energy output data, is outdated and cannot accurately reflect the real-time state of the power grid, thus introducing calculation errors. "Node measurement anomaly" means that the sensors of one or more nodes in the power grid malfunction or have measurement errors, resulting in inaccurate overall power grid response parameters reported by these nodes, thus causing inconsistency with the power flow calculation results. "Outputting the corresponding cause judgment result" means that after completing the above judgment, the system clearly provides the specific cause of the inconsistency alarm, such as "topology change lag," "decreased input data timeliness," or "node measurement anomaly," so that maintenance personnel or automation systems can take targeted measures.

[0065] Optionally, the central dispatch system compares the power flow calculation results with the actual monitored overall power grid response parameters to obtain the comparison deviation. The steps include: The central dispatch system performs time synchronization processing on the overall power grid response parameters that are actually monitored; The central dispatch system compares the power flow calculation results with the overall power grid response parameters after time synchronization point by point to obtain the instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation. The central dispatch system analyzes the duration, rate of change, and correlation with grid parameters of instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation as deviation characteristics; The central dispatch system makes judgments based on deviation characteristics, classifying deviations as fluctuations caused by the dynamic characteristics of the power grid itself, or as anomalies caused by sensor failures or communication errors. The central dispatch system performs suppression processing on fluctuations classified as being caused by the dynamic characteristics of the power grid itself; The central dispatch system issues alarms and records anomalies classified as sensor malfunctions or communication errors.

[0066] Specifically, the central dispatch system performs time synchronization processing on the overall power grid response parameters actually monitored. The purpose is to ensure that the overall power grid response parameters obtained from different data sources (e.g., various sensors and measuring devices) are consistent across the time axis. Since there may be inherent delays or sampling frequency differences in data acquisition and transmission within the power grid, high-precision time synchronization mechanisms, such as Network Time Protocol (NTP) or Precise Time Protocol (PTP), can eliminate these time inconsistencies, thereby providing a unified and reliable time reference for subsequent accurate data comparison.

[0067] The central dispatch system performs a point-by-point comparison between the power flow calculation results and the overall grid response parameters after time synchronization. This means that, while ensuring time synchronization, the power flow calculation results for each sampling point or each preset time step are compared one-to-one with the actual monitored overall grid response parameters. This point-by-point comparison can reveal instantaneous and subtle differences in the grid's operating state, thereby more accurately capturing the real-time deviation between the grid's operation and the theoretical model. From this, instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation can be obtained. These deviation values ​​directly quantify the difference between the actual operating condition of the grid at a specific moment and the theoretical calculation value.

[0068] In practical applications, the central dispatch system analyzes the duration, rate of change, and correlation with grid parameters of instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation as deviation characteristics. Duration can distinguish between transient disturbances and persistent problems; the rate of change reflects the severity of the deviation; and the correlation with grid parameters (e.g., load changes, generator output changes) helps determine whether the deviation is caused by normal grid operation. The extraction of these deviation characteristics provides rich data dimensions for subsequent intelligent judgment, enabling the system to gain a deeper understanding of the nature of the deviation.

[0069] Furthermore, the central dispatch system makes judgments based on deviation characteristics, classifying deviations as fluctuations caused by the dynamic characteristics of the power grid itself, or as anomalies caused by sensor malfunctions or communication errors. For example, if the deviation is characterized by a short duration, a rapid rate of change, and a high correlation with known power grid load fluctuation patterns or changes in renewable energy output, it may be classified as a fluctuation caused by the dynamic characteristics of the power grid. Conversely, if the deviation is characterized by a long duration, an abnormal rate of change, or exhibits fixed values ​​or abrupt changes that do not conform to physical laws, and has a weak correlation with power grid parameters, it may be classified as an anomaly caused by sensor malfunctions or communication errors. This intelligent classification judgment is the core of this application's solution, enabling the system to take differentiated handling measures for deviations of different natures.

[0070] Specifically, the central dispatch system performs suppression processing on fluctuations categorized as being caused by the dynamic characteristics of the power grid itself. This means that when a deviation is identified as an instantaneous fluctuation within the normal operating range of the power grid, the system will not immediately trigger an alarm or take aggressive dispatch adjustments. Instead, it may use smoothing algorithms, filtering, or short-term predictions to internally digest or softly process these fluctuations without affecting system stability, thereby avoiding overreaction and improving the robustness of the system.

[0071] Meanwhile, the central dispatch system alerts and records anomalies categorized as sensor malfunctions or communication errors. When a deviation is definitively determined to be caused by equipment failure or data transmission problems, the system immediately triggers a high-priority alarm and records detailed alarm information, including the anomaly type, time of occurrence, involved equipment, and deviation characteristics, so that maintenance personnel can intervene in a timely manner to investigate and repair, ensuring the accuracy of power grid operation data and the reliability of dispatch decisions.

[0072] This application also discloses a specific embodiment of an energy storage energy optimization scheduling system for smart grids, used to perform energy storage energy optimization scheduling for smart grids, combined with... Figure 3 As shown, the energy storage energy optimization and scheduling system 1 applied to the smart grid includes: The performance information receiving module 11 is used to receive performance information from each energy storage unit in the smart grid. The performance information is generated by each energy storage unit based on internal operating parameters and is used to characterize the current actual carrying capacity of the corresponding energy storage unit. The available power assessment module 12 is used to assess the additional available power capacity of each energy storage unit based on various performance information; the additional available power capacity is the power capacity to cope with real-time changes in grid demand after meeting preset operating requirements. The scheduling task adjustment module 13 is used to adjust the energy scheduling tasks of each energy storage unit according to the real-time demand of the power grid and the additional available power capacity of each energy storage unit. The steps of adjusting the energy scheduling tasks of each energy storage unit include: reallocating the reduced or unused additional available power capacity of some energy storage units to other energy storage units, and rearranging the priority of energy scheduling tasks. The scheduling instruction issuing module 14 is used to issue energy scheduling instructions corresponding to the adjusted energy scheduling tasks to each energy storage unit. The scheduling deviation calculation module 15 is used to obtain the actual energy scheduling execution status of each energy storage unit after the energy scheduling command is executed, and to calculate the deviation between the actual energy scheduling execution status and the energy scheduling command. The scheduling strategy adjustment module 16 is used to adjust the energy scheduling strategy based on the deviation.

[0073] This system achieves refined management and dynamic optimization of energy storage in the smart grid through a modular architecture. The system acquires real-time operational status data of each energy storage unit through a performance information receiving module, and the available power assessment module dynamically evaluates the available power capacity of each energy storage unit based on this data. The scheduling task adjustment module optimizes and adjusts energy scheduling tasks based on real-time grid demand and assessment results, and sends corresponding instructions to each energy storage unit through a scheduling instruction issuance module. Furthermore, the system monitors the execution of scheduling instructions in real-time and calculates execution deviations through a scheduling deviation calculation module. Finally, the scheduling strategy adjustment module adaptively optimizes the scheduling strategy based on deviation feedback, thus forming a closed-loop control mechanism. This closed-loop management process effectively solves the problem of mismatch between scheduling instructions and actual carrying capacity caused by energy storage system performance degradation in existing technologies, thereby significantly improving the stability and economy of smart grid operation.

[0074] The energy storage units mentioned in this application refer to devices in smart grids that possess energy storage and release capabilities, such as battery energy storage systems, flywheel energy storage systems, or supercapacitors. These energy storage units can generate performance information based on internal operating parameters, including data such as temperature, voltage, current, and internal resistance. This performance information reflects the current actual carrying capacity of the energy storage unit, such as maximum charge / discharge power, available capacity, and charge / discharge efficiency. The additional available power capacity, under the premise of meeting the energy storage unit's safe operation constraints and lifespan management requirements, is the power margin that can be used to respond to real-time changes in grid demand.

[0075] The performance information receiving module receives performance information from various energy storage units in the smart grid. This module can be configured with multiple communication interfaces, such as wired Ethernet interfaces, fiber optic communication interfaces, and wireless communication modules, to adapt to different deployment environments and communication protocols of energy storage devices. In one implementation, the module can use a polling mechanism to periodically send data requests to each energy storage unit and obtain its performance information. In another implementation, the module can use an event-triggered mode, proactively pushing performance information to the central dispatch system when significant changes occur in the internal parameters of the energy storage unit. The received performance information can be either raw operating data streams or structured data preprocessed by the local controller of the energy storage unit.

[0076] The available power assessment module evaluates the additional available power capacity of each energy storage unit based on its performance information. This module can be configured with high-performance processing units and storage units and runs preset assessment algorithms. For example, it can combine performance information with preset safe operating boundaries, such as maximum allowable temperature, maximum charge / discharge rate, and minimum state of charge, to calculate the maximum charge / discharge power that each energy storage unit can currently provide. After obtaining the maximum charge / discharge capacity, the power consumed by the currently executing scheduling task is subtracted to obtain the additional available power capacity of the energy storage unit at the current moment. This module can employ data calculation methods based on physical mechanism models, data-driven prediction models, or a hybrid model combining both to improve the accuracy of the assessment results.

[0077] The scheduling task adjustment module dynamically adjusts energy scheduling tasks based on real-time grid demand and the additional available power capacity of each energy storage unit. In its implementation, this module can integrate an optimization algorithm engine to perform global scheduling calculations. For example, when the grid experiences power shortages or load fluctuations, the system can perform optimization calculations based on the additional available power capacity of each energy storage unit, its geographical location, response speed, and the current operating status of the grid, and reallocate unused or reduced power capacity from some energy storage units to others. Simultaneously, this module can also reorder the execution priority of energy scheduling tasks based on the urgency of grid operations. The scheduling task adjustment module can select different optimization strategies based on different operational objectives, such as scheduling strategies aimed at reducing operating costs or improving grid stability.

[0078] The scheduling instruction issuance module is used to issue adjusted scheduling instructions corresponding to energy scheduling tasks to each energy storage unit. This module can generate, encode, encrypt, and transmit instructions to ensure that the scheduling instructions can be accurately parsed and securely executed by the energy storage devices. For example, after completing the scheduling task calculation, the central scheduling system generates energy scheduling instructions containing parameters such as target power, execution time, and charging / discharging mode, and sends them to the local controllers of each energy storage unit via the communication network. This module can support multiple communication protocols, such as Modbus, IEC61850, or other industrial communication protocols, to meet the interface requirements of different energy storage devices.

[0079] The scheduling deviation calculation module is used to acquire the actual execution data of each energy storage unit after the scheduling command is executed, and to calculate the deviation between the actual execution and the scheduling command. In its implementation, this module can be configured with a data acquisition interface and a time synchronization mechanism to receive actual operating data fed back by the energy storage units, such as actual power output, energy throughput, and execution time information. Subsequently, the module compares this data with the original scheduling command to calculate indicators such as power deviation, energy deviation, or time deviation. To improve the accuracy of the analysis, this module can also use statistical analysis methods or machine learning methods to quantify and trend analyze the deviation.

[0080] The scheduling strategy adjustment module dynamically optimizes the current energy scheduling strategy based on scheduling deviations. This module can incorporate a strategy optimization engine to analyze the causes of deviations and adjust the scheduling strategy accordingly. For example, when the system detects that certain energy storage units exhibit significant long-term execution deviations, it may indicate errors in their performance evaluation models or changes in their operating status. In this case, the scheduling strategy adjustment module can update the relevant performance model parameters and adjust the weight coefficients or safe operating boundaries in the scheduling algorithm. Furthermore, this module can also learn strategies by combining historical operating data, continuously optimizing the scheduling strategy through a combination of online learning and offline training, thereby improving the accuracy and efficiency of future scheduling decisions.

[0081] Through the coordinated operation of the aforementioned modules, the smart grid energy storage dispatching system proposed in this application can achieve real-time perception of the operating capacity of energy storage devices, dynamic optimization of dispatching tasks, and continuous improvement of dispatching strategies, thereby forming a closed-loop dispatching and control system with adaptive capabilities. This system architecture not only enhances the responsiveness of energy storage systems in complex grid environments but also reduces the risk of equipment overload and improves energy utilization efficiency, thus possessing significant technical advantages and practical application value in the field of smart grid operation and management.

[0082] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing energy storage scheduling applied to smart grids, characterized in that, include: Receive performance information from various energy storage units in the smart grid; The performance information is generated by each energy storage unit based on internal operating parameters, and the performance information is used to characterize the current actual carrying capacity of the corresponding energy storage unit. Based on the aforementioned performance information, the additional available power capacity of each energy storage unit is evaluated; the additional available power capacity is the power capacity to cope with real-time changes in grid demand after meeting preset operating requirements. Based on the real-time demand of the power grid and the additional available power capacity of each energy storage unit, adjust the energy scheduling tasks of each energy storage unit. The steps for adjusting the energy scheduling tasks of each energy storage unit include: reallocating the additional available power capacity of some energy storage units that have been reduced or are not used to other energy storage units, and rearranging the priority of energy scheduling tasks; Issue the adjusted energy scheduling instructions corresponding to the energy scheduling tasks to each energy storage unit; After the energy scheduling command is executed, the actual energy scheduling execution status of each energy storage unit is obtained, and the deviation between the actual energy scheduling execution status and the energy scheduling command is calculated. Based on the aforementioned deviation, the energy scheduling strategy is adjusted.

2. The energy storage energy optimization scheduling method for smart grids according to claim 1, characterized in that, The steps for adjusting the energy scheduling tasks of each energy storage unit based on the real-time demand of the power grid and the additional available power capacity of each energy storage unit include: The internal operating parameters are monitored by the local control unit of the energy storage unit; the internal operating parameters include at least temperature, charging and discharging current, voltage and internal resistance. Based on the internal operating parameters, the local control unit estimates the rate of heat accumulation and the temperature rise trend inside the battery cluster, and determines whether the internal temperature exceeds the safe temperature threshold under the upcoming energy dispatch command, thus obtaining the safe temperature judgment result. When the safe temperature judgment result indicates that the safe temperature threshold is exceeded, the local control unit initiates a power limitation assessment to calculate the maximum executable power that the energy storage unit can continuously output under the condition of meeting safe operation requirements. When the maximum executable power is lower than the instruction power corresponding to the energy scheduling instruction, the local control unit generates and reports a micro-limit signal; the micro-limit signal includes at least the energy storage unit identifier, the signal timestamp, the expected time point when the power limit occurs, the adjusted maximum executable power, the limit duration, the limit reason code, and the current key parameters; The central dispatch system receives and parses the micro-limitation signal to update the instantaneous available capacity table; the instantaneous available capacity table is used to record the maximum executable charging and discharging power limit, the start and end time of the limitation, and the limitation reason identifier of each energy storage unit within a preset time window, and uses the signal timestamp as the basis for version update; The central dispatch system triggers margin reassessment and task rearrangement based on the updated instantaneous available capacity table, identifies power gaps caused by micro-limiting signals, and queries the global margin pool to select energy storage units to compensate for the power gaps, thus obtaining the selected energy storage units. The global margin pool is used to summarize the additional reserve power margin that each energy storage unit can release in the current dispatch cycle and the corresponding sustainability duration, and serves as a candidate set for screening compensation units. The central dispatch system issues compensation instructions to the selected energy storage units to provide additional power, while simultaneously issuing update instructions to the constrained energy storage units to match their instantaneous carrying capacity.

3. The energy storage optimization scheduling method for smart grids according to claim 1, characterized in that, The step of adjusting the energy dispatch strategy based on the deviation includes: The central dispatch system monitors the actual power output from each energy storage unit and the overall grid response parameters as performance information; based on the performance information and energy dispatch instructions, it calculates the expected power contribution of each energy storage unit within a specific time window, and calculates the actual grid response index based on the overall grid response parameters. When the deviation between the expected power contribution and the actual power grid response index continues to exceed a preset threshold and the duration exceeds a preset time, the deviation analysis process is triggered. In the deviation analysis process, the central dispatch system queries the communication link quality log, obtains environmental sensor data of the area where the power plant is located, and checks the power grid fault information system to quantify and eliminate the degree of attributability of communication problems, environmental impacts, and power grid faults to the deviation, and obtains the quantitative elimination results; the degree of attributability is used to characterize the contribution ratio or contribution intensity of communication problems, environmental impacts, and power grid faults to the deviation. When the quantitative exclusion results indicate that external factors are insufficient to explain the deviation, the central dispatch system maintains the individual contribution deviation index and calculates the global compensation capability index to determine whether other energy storage units can make up for the deviation through excess contribution. When the quantification exclusion result indicates that the individual contribution deviation index of one of the energy storage units is continuously positive and exceeds the threshold, and the global compensation capability index is lower than the preset level, the central scheduling system reduces the confidence weight of the corresponding energy storage unit performance information. When the central dispatch system generates a new energy dispatch strategy, for energy storage units with a confidence weight lower than the preset value, the corresponding maximum charging and discharging power and efficiency are corrected according to the confidence weight and then used in the dispatch calculation to adjust the energy dispatch strategy.

4. The energy storage optimization scheduling method for smart grids according to claim 3, characterized in that, The step of adjusting the corresponding maximum charge / discharge power and efficiency according to the confidence weight before participating in the scheduling calculation includes: The central dispatch system obtains the maximum charging and discharging power and efficiency values ​​reported by the energy storage unit, and performs a multiplication operation with the corresponding confidence weight to obtain the corrected maximum charging and discharging power and efficiency values. The central dispatch system continuously monitors the corresponding actual power output and real-time operating parameters under the revised energy dispatch instructions; The central dispatch system determines whether the actual power output of the corresponding energy storage unit is continuously higher than the corrected command power, whether the real-time operating parameters of the corresponding energy storage unit are within the safe operating range, and whether the micro-limit signal has not been reported within the preset period, and obtains the real-time data judgment result. When the actual power output is consistently higher than the corrected command power and the real-time operating parameters are within the safe operating range, and no micro-limiting signal is reported within the preset period, the central dispatch system gradually increases the confidence weight of the corresponding energy storage unit, and adjusts the recovery rate of the confidence weight by combining the historical performance recovery record of the corresponding energy storage unit with the current overall power grid operating environment.

5. The energy storage optimization scheduling method for smart grids according to claim 3, characterized in that, The central dispatch system monitors the actual power output from each energy storage unit and the overall grid response parameters as performance information. Following this step, the system further includes: The central dispatch system monitors the transmission integrity and delay of the communication link, obtains the link monitoring results, and improves the sensitivity to anomalies in the link transmission data when the link monitoring results indicate a decline in the quality of the communication link. Based on the aforementioned anomaly detection sensitivity, the central dispatch system establishes a logical consistency verification mechanism for physical constraints on the overall power grid response parameters; In the logical consistency verification mechanism, when the actual power output or the overall grid response parameters are marked as potentially abnormal, the central dispatch system initiates a multi-source cross-verification process. The multi-source cross-verification process includes requesting the local control unit of the energy storage unit to retransmit the original data of the most recent time period and comparing it with the operating data of the adjacent energy storage unit to obtain the multi-source cross-verification result. When the multi-source cross-validation results indicate that a potential anomaly cannot be explained by multi-source cross-validation, the central scheduling system filters the abnormal data in the performance information and fills it with the most recent valid data, or infers and adjusts the performance information based on the performance tags of the energy storage units and the current energy scheduling instructions.

6. The energy storage optimization scheduling method for smart grids according to claim 5, characterized in that, The logical consistency verification mechanism for the physical constraints includes: The central dispatch system receives real-time grid topology information, load data of each node, and real-time output data of new energy power generation units. The central dispatch system constructs a node admittance matrix based on the real-time topology information of the power grid; The central dispatch system performs power flow calculations based on the node admittance matrix, load data of each node and real-time output data of new energy power generation units to obtain the real-time power flow of each line and the real-time voltage amplitude and phase angle of each node, which are used as the power flow calculation results. The central dispatch system compares the power flow calculation results with the actual monitored overall power grid response parameters to obtain the comparison deviation; the comparison deviation includes at least one of the instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation; When the comparison deviation continues to exceed a preset threshold, an inconsistency alarm is triggered and the alarm information is recorded.

7. The energy storage energy optimization scheduling method for smart grids according to claim 3, characterized in that, The steps to troubleshoot communication problems include: The central dispatch system receives communication link quality log data and processes it in real time; the real-time processing includes identifying the log data format and converting it into structured data. The central dispatch system uses timestamps and device identifiers in structured data to associate log records from the same energy storage unit or the same communication link to form a sequence of communication events; The central dispatch system checks the integrity of the communication event sequence and determines whether there are timestamp jumps or missing key fields, thus obtaining the integrity judgment result; When the integrity judgment result indicates that there is a missing or format error, the central scheduling system infers and fills in the missing data or marks the corresponding log as an unreliable record according to the preset log repair rules.

8. The energy storage optimization scheduling method for smart grids according to claim 6, characterized in that, After the step of triggering an inconsistency alarm and recording alarm information when the comparison deviation continues to exceed a preset threshold, the method further includes: When recording alarm information, the central dispatch system associates and stores the alarm information with the corresponding real-time topology information version number, node admittance matrix version number, and power flow calculation input data time window identifier to obtain the associated storage result. Based on the associated storage results, the central scheduling system determines whether the inconsistency alarm is caused by topology change lag, decreased timeliness of input data, or abnormal node measurement, and outputs the corresponding cause judgment result.

9. The energy storage optimization scheduling method for smart grids according to claim 6, characterized in that, The central dispatching system compares the power flow calculation results with the actual monitored overall power grid response parameters to obtain the comparison deviation. The steps include: The central dispatch system performs time synchronization processing on the overall power grid response parameters that are actually monitored; The central dispatch system compares the power flow calculation results with the overall power grid response parameters after time synchronization point by point to obtain the instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation. The central dispatch system analyzes the duration, rate of change, and correlation with grid parameters of the instantaneous frequency deviation, instantaneous voltage amplitude deviation, and instantaneous phase angle deviation as deviation characteristics; The central dispatch system makes a judgment based on the aforementioned deviation characteristics, classifying the deviation as fluctuations caused by the dynamic characteristics of the power grid itself, or as anomalies caused by sensor failures or communication errors. The central dispatch system performs suppression processing on fluctuations classified as being caused by the dynamic characteristics of the power grid itself; The central dispatch system issues alarms and records anomalies classified as sensor malfunctions or communication errors.

10. An energy storage optimization scheduling system for smart grids, used to perform energy storage optimization scheduling for smart grids, characterized in that, include: The performance information receiving module is used to receive performance information from various energy storage units in the smart grid; The performance information is generated by each energy storage unit based on internal operating parameters, and the performance information is used to characterize the current actual carrying capacity of the corresponding energy storage unit. The available power assessment module is used to assess the additional available power capacity of each energy storage unit based on the aforementioned performance information; the additional available power capacity is the power capacity to cope with real-time changes in grid demand after meeting preset operating requirements. The scheduling task adjustment module is used to adjust the energy scheduling tasks of each energy storage unit based on the real-time demand of the power grid and the additional available power capacity of each energy storage unit. The steps for adjusting the energy scheduling tasks of each energy storage unit include: reallocating the additional available power capacity of some energy storage units that have been reduced or are not used to other energy storage units, and rearranging the priority of energy scheduling tasks; The scheduling instruction issuing module is used to issue energy scheduling instructions corresponding to the adjusted energy scheduling tasks to each energy storage unit; The scheduling deviation calculation module is used to obtain the actual energy scheduling execution status of each energy storage unit after the energy scheduling command is executed, and to calculate the deviation between the actual energy scheduling execution status and the energy scheduling command. The scheduling strategy adjustment module is used to adjust the energy scheduling strategy based on the deviation.

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