Electrochemical energy storage power station dispatching operation management system and dispatching adaptation method

Abstract

The application discloses an electrochemical energy storage power station dispatching operation management system and a dispatching adaptation method, and relates to the technical field of power station dispatching management.The application solves the technical problem that there is no systematic analysis and utilization of historical data and real-time operation parameters of the power station, and dynamic scheduling cannot be accurately performed according to the power consumption load characteristics and the energy storage battery state.Through real-time calculation and comparison based on SOC and physical constraint conditions in a normal period, it can be quickly judged whether scheduling is needed, scheduling signals are generated in time and the charging and discharging power is adjusted for the situation close to the upper and lower limits of the constraint to guarantee the safe operation of the battery.In a peak period, the four stages of power consumption load rising, peak, platform and falling are segmented, the discharging power is calculated in combination with the battery SOC state of each stage, and stepwise accurate scheduling is realized, so that the peak power consumption demand can be met, the battery capacity can be reasonably utilized, and the defect that the traditional scheduling lacks dynamic adjustment is made up.

Description

Technical Field

[0001] This invention relates to the field of power plant dispatch management technology, specifically to an electrochemical energy storage power plant dispatch operation and management system and a dispatch adaptation method. Background Technology

[0002] With the rapid development of new energy power generation and the increasing demand for power system flexibility, electrochemical energy storage power stations are playing an increasingly crucial role in the power system.

[0003] According to patent application CN110365114A, a comprehensive management system for energy storage power stations based on multi-module integration is disclosed. The management system includes a dispatch master station, a remote control workstation, an energy storage monitoring backend, and an energy storage grid-connected device. The remote control workstation is communicatively connected to the energy storage monitoring backend and can exchange information with it. The energy storage monitoring backend integrates an EMS module, an AGC module, an AVC module, and a conventional monitoring module. The energy storage grid-connected device is used to receive and respond to control commands processed by the energy storage monitoring backend and upload the execution results to the energy storage monitoring platform and the remote control workstation.

[0004] In the current dispatching and operation management of energy storage power stations, a relatively extensive dispatching model is usually adopted, or manual experience is relied upon for time period division and charge / discharge control, lacking systematic analysis and utilization of historical data and real-time operating parameters of the power station. At the same time, it is impossible to accurately perform dynamic dispatching based on the characteristics of electricity load and the status of energy storage batteries during different electricity consumption periods, resulting in low operating efficiency of energy storage power stations and making it difficult to fully realize their role in peak shaving and valley filling, ensuring grid stability, and improving economic benefits. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an electrochemical energy storage power station scheduling and operation management system and scheduling adaptation method, which solves the problem of lacking systematic analysis and utilization of historical data and real-time operating parameters of the power station, and the inability to accurately perform dynamic scheduling based on power load characteristics and energy storage battery status.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an electrochemical energy storage power station dispatch and operation management system, comprising:

[0007] The energy storage power station information analysis unit is used to classify the energy storage power station into different working periods based on the power station information transmitted by the power station information acquisition unit. It classifies the different periods into normal periods and peak periods based on electricity consumption, generates corresponding information, and transmits the information to both periods separately.

[0008] The normal period scheduling analysis unit is used to analyze the acquired normal period information, predict the battery charge state according to the formula, and judge it with physical constraints. If the conditions are met, normal regulatory information is generated; otherwise, a scheduling optimization signal is generated.

[0009] The scheduling optimization signal is analyzed, the difference between the predicted battery charge state and the upper and lower limits of physical constraints is calculated, and the difference between the two is compared to generate upper and lower scheduling signals and lower limit scheduling signals. At the same time, the charging and discharging power is adjusted according to the safety threshold as the standard to generate scheduling information, which is then transmitted to the scheduling management information output unit.

[0010] The peak-hour scheduling and analysis unit is used to analyze the acquired peak-hour information, classify it into different tiers based on the power load during peak hours, calculate the corresponding discharge power according to the battery charge of each tier, and generate scheduling information based on this information, which is then transmitted to the scheduling management information output unit.

[0011] As a further embodiment of the present invention, it also includes a power plant information acquisition unit and a dispatch management information output unit;

[0012] The power station information acquisition unit is used to collect power station information of the energy storage power station, including operating parameters and historical data, and transmit it to the energy storage power station information analysis unit.

[0013] The dispatch management information output unit is used to display the acquired normal regulatory information and dispatch information to the corresponding management personnel.

[0014] As a further aspect of the present invention, the energy storage power station information analysis unit classifies different time periods according to electricity consumption to obtain normal time periods and peak time periods, and generates corresponding information in the following specific way:

[0015] Data is extracted from the power plant’s historical data in cycles of time T, then broken down to the unit time granularity. The electricity consumption for each unit time period is obtained and compared with the preset value set by the operator.

[0016] Periods exceeding a preset value are marked as high-power consumption periods, while those below are marked as normal-power consumption periods. After classifying all time periods, peak and normal time period information is generated and then transmitted to the corresponding peak and normal time period scheduling and analysis units.

[0017] As a further aspect of the present invention, the specific method by which the normal time period scheduling analysis unit analyzes the acquired normal time period information is as follows:

[0018] Obtain the normal time period within the time period T, and simultaneously obtain the battery state of charge (SOC) of the energy storage power station at time t. t And the corresponding battery charging and discharging power Pt Then according to the formula The battery state of charge (SOC) at time t+1 was calculated. t+1 And η represents the battery's charge / discharge efficiency, Δt refers to the time interval, E rated Indicates the battery's rated capacity;

[0019] Battery State of Charge (SOC) t+1 The SOC is specifically determined by comparing it with the corresponding upper and lower limits of physical constraints. min ≤SOC t+1 ≤SOC max If the above physical constraints are met, normal regulatory information is generated; otherwise, a scheduling optimization signal is generated.

[0020] As a further aspect of the present invention, the specific method by which the normal time period scheduling analysis unit analyzes the scheduling optimization signal is as follows:

[0021] Calculate the battery state of charge (SOC) t+1 With SOC min and SOC max The difference between the upper and lower limits is used to determine the relationship between the battery charge state and the upper and lower limits. If the difference between the lower and upper limits is greater than the difference between the upper and lower limits, an upper limit scheduling signal is generated. Conversely, if the difference between the lower and upper limits is less than the difference between the upper and lower limits, a lower limit scheduling signal is generated.

[0022] As a further aspect of the present invention, the normal time period scheduling analysis unit adjusts the charging and discharging power based on a safety threshold, and the specific method for generating scheduling information is as follows:

[0023] The upper limit scheduling signal is analyzed to obtain the battery charging power corresponding to the energy storage power station, and the battery charging power is adjusted according to the safety threshold to generate scheduling information.

[0024] The lower limit scheduling signal is analyzed to obtain the battery discharge power corresponding to the energy storage power station, and the battery discharge power is adjusted according to the safety threshold to generate scheduling information.

[0025] As a further aspect of the present invention, the peak period analysis unit analyzes the acquired peak period information in the following specific manner:

[0026] Based on the trend of electricity load changes during peak hours, it is divided into four stages: the first stage is the rising period, the second stage is the peak period, the third stage is the peak plateau period, and the fourth stage is the falling period, which are marked as i = 1, 2, 3, and 4 respectively. At the same time, the SOC state of the energy storage battery in each stage is obtained, and dynamic adjustments are made according to the battery charge state.

[0027] As a further aspect of the present invention, the specific method by which the peak-hour analysis unit dynamically adjusts according to the battery charge state is as follows:

[0028] According to the formula The discharge power P corresponding to different steps i was calculated. dischaerge，i , where Δt i E represents the stair duration corresponding to stair i. capacity For energy storage power station capacity, SOC star，t The state of charge (SOC) of the battery at the start of the i-th step is given. end，t The battery charge state at the end of the i-th step is given by the calculated discharge power P. dischaerge，i To standardize the scheduling of energy storage power stations and generate scheduling information.

[0029] A method for dispatching and adapting electrochemical energy storage power stations, which specifically includes the following steps:

[0030] Step 1: Collect historical data and operating parameters of the energy storage power station, and classify different time periods according to electricity consumption to obtain normal time periods and peak time periods;

[0031] Step 2: Perform scheduling processing for normal periods, predict the battery charge state according to the formula, and judge it against physical constraints. If the conditions are met, generate normal monitoring information; otherwise, generate scheduling optimization signal.

[0032] Step 3: Analyze the scheduling optimization signal, calculate the difference between the predicted battery charge state and the upper and lower limits of physical constraints, compare the magnitude of the difference, generate upper and lower scheduling signals and lower limit scheduling signal, and adjust the charging and discharging power according to the safety threshold to generate scheduling information.

[0033] Step 4: Perform scheduling processing for peak periods. Based on the power load during peak periods, classify the load into different tiers and calculate the corresponding discharge power according to the battery charge of each tier. Simultaneously, use this as a standard to generate scheduling information.

[0034] This invention provides an electrochemical energy storage power station dispatch and operation management system and a dispatch adaptation method. Compared with the prior art, it has the following advantages:

[0035] This invention performs multi-level and refined processing on historical data of energy storage power stations, from periodic data to unit historical data, and then to electricity consumption records for unit time periods. By combining preset value comparison, it can accurately divide peak periods and normal periods, providing a reliable basis for subsequent targeted scheduling. Compared with traditional experience-based division methods, it improves the scientificity and accuracy of time period classification.

[0036] During normal periods, this invention can quickly determine whether scheduling is needed based on real-time calculation and comparison of SOC and physical constraints. When the battery is close to the upper or lower limit of the constraints, scheduling signals are generated in a timely manner and the charging and discharging power is adjusted to ensure the safe operation of the battery. During peak periods, the power load is divided into four stages: rising, peak, plateau, and falling. The discharge power is calculated based on the battery SOC status at each stage to achieve tiered and precise scheduling. This not only meets the peak power demand but also makes reasonable use of battery capacity, improving the operating efficiency and economic benefits of energy storage power stations at different times and making up for the lack of dynamic adjustment in traditional scheduling. Attached Figure Description

[0037] Figure 1 This is a block diagram illustrating the system principle of the present invention;

[0038] Figure 2 This is a diagram illustrating the steps and methods of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1

[0041] Please see Figure 1 This application provides an electrochemical energy storage power station dispatch and operation management system, including a power station information acquisition unit, an energy storage power station information analysis unit, a normal period dispatch analysis unit, a peak period dispatch analysis unit, and a dispatch management information output unit, and in conjunction with... Figure 1 It can be seen that the above functional units are connected electrically in one direction.

[0042] The power station information acquisition unit is used to collect power station information from the energy storage power station and transmit it to the energy storage power station information analysis unit. The power station information here specifically includes the historical data and operating parameters of the energy storage power station.

[0043] The energy storage power station information analysis unit is used to classify the operating periods of the energy storage power station based on the acquired power station information and generate time period classification information. The time period classification information includes peak period information and normal period information, and the specific classification processing method is as follows:

[0044] First, historical data is retrieved from the power plant information system. Using time period T as a cycle (e.g., T is set to one month, meaning data is collected based on calendar months), the corresponding periodic historical data is extracted. Next, the periodic historical data is further refined to obtain unit-based historical data. This unit-based historical data can be set according to actual needs, such as using one day as a unit (i.e., each day's data is treated as an independent unit). For each unit-based historical data, its corresponding electricity consumption record is obtained. The electricity consumption record details the electricity consumption at each moment within that unit of time.

[0045] The electricity consumption records in the historical data of each unit are divided according to preset time periods (e.g., one hour as a time period). Then, the electricity consumption corresponding to each time period is obtained and compared with the preset value set by the operator. For example, the operator sets the preset value to 50 kWh per hour (determined comprehensively based on the scale of the power plant, historical electricity consumption, etc.).

[0046] If the electricity consumption in a certain time period is greater than 50 kWh, then that time period is marked as a high electricity consumption period; if it is less than 50 kWh, then it is marked as a normal electricity consumption period. The above method is used to classify all time periods (24-hour periods) within a unit of time (e.g., a day).

[0047] All time periods marked as high electricity consumption periods are categorized to generate peak period information; all time periods marked as normal electricity consumption periods are categorized to generate normal period information. Finally, the peak period information is transmitted to the peak period scheduling and analysis unit for targeted scheduling analysis and the formulation of reasonable response strategies; the normal period information is transmitted to the normal period scheduling and analysis unit for corresponding scheduling analysis.

[0048] Assume we acquire historical data on a monthly basis (T = one month). We use one day as the unit of historical data to obtain daily electricity consumption records. We divide the day into hourly time periods. The operator sets a preset value of 40 kWh per hour. On a certain day, the electricity consumption from 18:00 to 19:00 is 45 kWh, exceeding the preset value of 40 kWh, and this period is marked as a high-consumption period; while the electricity consumption from 10:00 to 11:00 is 30 kWh, less than the preset value, and is marked as a normal-consumption period. After classifying all 24 time periods of this day in this way, all high-consumption periods are categorized as peak period information and transmitted to the peak period scheduling and analysis unit; all normal-consumption periods are categorized as normal period information and transmitted to the normal-consumption period scheduling and analysis unit.

[0049] The normal period scheduling analysis unit is used to perform scheduling management of energy storage power stations based on the acquired normal period information. The specific scheduling management method is as follows:

[0050] Obtain the normal time period within the time period T, and simultaneously obtain the battery state of charge (SOC) of the energy storage power station at time t. t And the corresponding battery charging and discharging power P t Then according to the formula The battery state of charge (SOC) at time t+1 was calculated. t+1 In the above formula, η represents the battery's charge / discharge efficiency, Δt refers to the time interval, i.e., the time elapsed from time t to time t+1, and E rated This indicates the battery's rated capacity, and also provides the battery's state of charge (SOC). t+1 The SOC is specifically determined by comparing it with the corresponding physical constraints. min ≤SOC t+1 ≤SOC max And the SOC here min and SOC max and are the lower and upper limits of the battery's state of charge, respectively, such as SOC. min =0.2, or 20%, SOC max =0.8, or 80%. If the above physical constraints are met, it means that the energy storage power station does not need to be dispatched and managed, and normal regulatory information is generated. Otherwise, if the above physical constraints are not met, a dispatch optimization signal is generated.

[0051] Next, the generated scheduling optimization signal is analyzed to calculate the battery state of charge (SOC). t+1 With SOC min and SOC max The difference is used to determine the relationship between the battery's state of charge and its upper and lower limits. If the SOC... t+1 With SOC min The difference is greater than SOC t+1 With SOC max The difference indicates that the battery charge state is close to the physical constraint limit, and an upper limit scheduling signal is generated; conversely, if the SOC is low, the battery charge state is close to the physical constraint limit. t+1 With SOC min The difference is less than SOC t+1 With SOC max The difference indicates that the battery charge state is close to the lower limit of the physical constraint, and a lower limit scheduling signal is generated.

[0052] The generated upper limit scheduling signal is analyzed to obtain the battery charging power corresponding to the energy storage power station, and the battery charging power is adjusted according to the safety threshold to generate scheduling information.

[0053] The generated lower limit scheduling signal is analyzed to obtain the battery discharge power corresponding to the energy storage power station, and the battery discharge power is adjusted according to the safety threshold to generate scheduling information.

[0054] For example, SOC t+1 =0.75, SOC min =0.2, SOC max =0.8, 0.75-0.2=0.55, 0.8-0.75=0.05, 0.55>0.05, at this point an upper limit scheduling signal is generated. Based on the upper limit scheduling signal, the battery charging power corresponding to the energy storage station is obtained, and then the battery charging power is adjusted according to a safety threshold. For example, if the current charging power is P... charge =10kW, and the safety threshold limits the charging power to no more than 8kW, then the charging power will be adjusted to 8kW and the corresponding scheduling information will be generated.

[0055] Conversely, if SOC t+1 With SOC min The difference is less than SOC t+1 With SOC max The difference indicates that the battery's state of charge (SOC) is close to the lower limit of physical constraints. For example, SOC... t+1 =0.25, SOC min =0.2, SOC max =0.8, 0.25-0.2=0.05, 0.8-0.25=0.55, 0.05<0.55, at this point a lower limit scheduling signal is generated. For the lower limit scheduling signal, the battery discharge power corresponding to the energy storage station is obtained, and adjustments are made based on the safety threshold. Assume the current discharge power is P. discharge =15kW. The safety threshold stipulates that the discharge power cannot exceed 12kW, so the discharge power will be adjusted to 12kW, and the corresponding scheduling information will be generated at the same time.

[0056] Simultaneously, the generated scheduling information is transmitted to the scheduling management information output unit.

[0057] The scheduling management information output unit is used to display the obtained scheduling information to the corresponding management personnel.

[0058] Example 2

[0059] As a second embodiment of the present invention, it is implemented based on the first embodiment, and the difference from the first embodiment is as follows:

[0060] The peak-hour scheduling analysis unit is used to perform scheduling optimization processing based on the acquired peak-hour information. The specific scheduling optimization processing method is as follows:

[0061] Obtain peak period information and segment the peak periods accordingly. The specific segmentation method is as follows:

[0062] The system acquires the electricity load corresponding to peak hours and segments the electricity load. The periods when the electricity load is rising are classified as the first tier, the periods when the electricity load is at its peak are classified as the second tier, the periods when the electricity load is at its peak plateau are classified as the third tier, and the periods when the electricity load is falling are classified as the fourth tier. Each tier is labeled with a number i, where i = 1, 2, 3, 4. Specifically, i = 1 represents the first tier, i = 2 represents the second tier, and so on. At the same time, the system acquires the battery charge state corresponding to the energy storage batteries in different tiers and dynamically adjusts the system based on the battery charge state.

[0063] First, obtain electricity load data for peak hours (e.g., 18:00-22:00 on summer weekdays). Based on this data, segment the electricity load according to its changing trends.

[0064] The period when electricity load is rising is categorized as the first tier. For example, between 6:00 PM and 7:00 PM, as people return home from get off work, various electrical appliances are gradually turned on, and the electricity load shows an upward trend; this period falls under the first tier.

[0065] The period when electricity load reaches its peak is classified as the second tier. For example, if most household electrical appliances are turned on between 7:00 PM and 8:00 PM, and the electricity load reaches its peak for the day, this period is considered the second tier.

[0066] During periods when the electricity load remains relatively stable near its peak, it falls into the third tier. For example, from 20:00 to 21:00, although electrical equipment continues to operate, the overall load fluctuation is small, and it is in the peak plateau period, which belongs to the third tier.

[0067] The period when electrical load begins to decrease is classified as the fourth tier. For example, from 21:00 to 22:00, as some appliances are turned off, the electrical load gradually decreases; this is the fourth tier.

[0068] According to the formula The discharge power P corresponding to different steps i was calculated. dischaerge，i , where Δt i E represents the stair duration corresponding to stair i. capacity For energy storage power station capacity, SOC star，t The state of charge (SOC) of the battery at the start of the i-th step is given. end，t The battery charge state at the end of the i-th step is given by the calculated discharge power P. dischaerge，i The system is used to schedule energy storage power stations according to standards, generate scheduling information, and transmit it to the scheduling management information output unit.

[0069] For example, in the first tier, SOC star，170%, SOC end，1 The energy storage power station capacity is 60%. capacity For 1000kWh, the step duration Δt i If the time is 1 hour, then the discharge power P calculated according to the formula is... dischaerge，1 If the power is 50kW, then the calculated value will be used as the standard for scheduling.

[0070] The scheduling management information output unit is used to display the generated scheduling information to the corresponding management personnel.

[0071] Example 3

[0072] As a third embodiment of the present invention, the focus is on combining the implementation processes of the first and second embodiments.

[0073] Example 4

[0074] Please see Figure 2 This application provides a method for scheduling and adapting electrochemical energy storage power stations, which specifically includes the following steps:

[0075] Step 1: Collect historical data and operating parameters of the energy storage power station, and classify different time periods according to electricity consumption to obtain normal time periods and peak time periods. The specific processing method is the same as that of the energy storage power station information analysis unit in Example 1.

[0076] Step 2: Perform scheduling processing for normal periods, predict the battery charge state according to the formula, and judge it against physical constraints. If the conditions are met, generate normal monitoring information; otherwise, generate scheduling optimization signal. The specific processing method is the same as the processing method of the normal period scheduling analysis unit in Implementation Example 1.

[0077] Step 3: Analyze the scheduling optimization signal, calculate the difference between the predicted battery charge state and the upper and lower limits of physical constraints, compare the magnitude of the difference, generate upper and lower scheduling signals and lower limit scheduling signal, and adjust the charging and discharging power according to the safety threshold to generate scheduling information. The specific processing method is the same as the processing method of the normal period scheduling analysis unit in Example 1.

[0078] Step 4: Perform scheduling processing for peak periods. Based on the power load during peak periods, classify the load into different tiers and calculate the corresponding discharge power according to the battery charge of each tier. Simultaneously, use this as a standard to generate scheduling information. The specific processing method is the same as that of the peak period scheduling analysis unit in Implementation Example 1.

[0079] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0080] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An electrochemical energy storage power station dispatch and operation management system, characterized in that, include: The energy storage power station information analysis unit is used to classify the energy storage power station into different working periods based on the power station information transmitted by the power station information acquisition unit. It classifies the different periods into normal periods and peak periods based on electricity consumption, generates corresponding information, and transmits the information to both periods separately. The normal period scheduling analysis unit is used to analyze the acquired normal period information, predict the battery charge state according to the formula, and judge it with physical constraints. If the conditions are met, normal regulatory information is generated; otherwise, a scheduling optimization signal is generated. The scheduling optimization signal is analyzed, the difference between the predicted battery charge state and the upper and lower limits of physical constraints is calculated, and the difference between the two is compared to generate upper and lower scheduling signals and lower limit scheduling signals. At the same time, the charging and discharging power is adjusted according to the safety threshold as the standard to generate scheduling information, which is then transmitted to the scheduling management information output unit. The peak-hour scheduling and analysis unit is used to analyze the acquired peak-hour information, classify it into different tiers based on the power load during peak hours, calculate the corresponding discharge power according to the battery charge of each tier, and generate scheduling information based on this information, which is then transmitted to the scheduling management information output unit.

2. The electrochemical energy storage power station dispatch and operation management system according to claim 1, characterized in that, It also includes a power plant information acquisition unit and a dispatch management information output unit; The power station information acquisition unit is used to collect power station information of the energy storage power station, including operating parameters and historical data, and transmit it to the energy storage power station information analysis unit. The dispatch management information output unit is used to display the acquired normal regulatory information and dispatch information to the corresponding management personnel.

3. The electrochemical energy storage power station dispatch and operation management system according to claim 1, characterized in that, The energy storage power station information analysis unit classifies different time periods based on electricity consumption to obtain normal time periods and peak time periods, and generates corresponding information in the following specific way: Data is extracted from the power plant’s historical data in cycles of time T, then broken down to the unit time granularity. The electricity consumption for each unit time period is obtained and compared with the preset value set by the operator. Periods exceeding a preset value are marked as high-power consumption periods, while those below are marked as normal-power consumption periods. After classifying all time periods, peak and normal time period information is generated and then transmitted to the corresponding peak and normal time period scheduling and analysis units.

4. The electrochemical energy storage power station dispatch and operation management system according to claim 1, characterized in that, The specific method by which the normal time period scheduling analysis unit analyzes the acquired normal time period information is as follows: Obtain the normal time period within the time period T, and simultaneously obtain the battery state of charge (SOC) of the energy storage power station at time t. t And the corresponding battery charging and discharging power P t Then according to the formula The battery state of charge (SOC) at time t+1 was calculated. t+1 And η represents the battery's charge / discharge efficiency, Δt refers to the time interval, E rated Indicates the battery's rated capacity; Battery State of Charge (SOC) t+1 The SOC is specifically determined by comparing it with the corresponding upper and lower limits of physical constraints. min ≤SOC t+1 ≤SOC max If the above physical constraints are met, normal regulatory information is generated; otherwise, a scheduling optimization signal is generated.

5. The electrochemical energy storage power station dispatch and operation management system according to claim 1, characterized in that, The specific method by which the normal period scheduling analysis unit analyzes the scheduling optimization signal is as follows: Calculate the battery state of charge (SOC) t+1 With SOC min and SOC max The difference between the upper and lower limits is used to determine the relationship between the battery charge state and the upper and lower limits. If the difference between the lower and upper limits is greater than the difference between the upper and lower limits, an upper limit scheduling signal is generated. Conversely, if the difference between the lower and upper limits is less than the difference between the upper and lower limits, a lower limit scheduling signal is generated.

6. The electrochemical energy storage power station dispatch and operation management system according to claim 1, characterized in that, The normal time period scheduling analysis unit adjusts the charging and discharging power based on a safety threshold, and the specific method for generating scheduling information is as follows: The upper limit scheduling signal is analyzed to obtain the battery charging power corresponding to the energy storage power station, and the battery charging power is adjusted according to the safety threshold to generate scheduling information. The lower limit scheduling signal is analyzed to obtain the battery discharge power corresponding to the energy storage power station, and the battery discharge power is adjusted according to the safety threshold to generate scheduling information.

7. The electrochemical energy storage power station dispatch and operation management system according to claim 1, characterized in that, The specific method by which the peak period analysis unit analyzes the acquired peak period information is as follows: Based on the trend of electricity load changes during peak hours, it is divided into four stages: the first stage is the rising period, the second stage is the peak period, the third stage is the peak plateau period, and the fourth stage is the falling period, which are marked as i = 1, 2, 3, and 4 respectively. At the same time, the SOC state of the energy storage battery in each stage is obtained, and dynamic adjustments are made according to the battery charge state.

8. The electrochemical energy storage power station dispatch and operation management system according to claim 7, characterized in that, The specific method by which the peak-hour analysis unit dynamically adjusts based on the battery charge state is as follows: According to the formula The discharge power P corresponding to different steps i was calculated. dischaerge，i , where Δt i E represents the stair duration corresponding to stair i. capacity For energy storage power station capacity, SOC star，t The state of charge (SOC) of the battery at the start of the i-th step is given. end，t The battery charge state at the end of the i-th step is given by the calculated discharge power P. dischaerge，i To standardize the scheduling of energy storage power stations and generate scheduling information.

9. A method for scheduling and adapting an electrochemical energy storage power station, wherein the method is executed by the electrochemical energy storage power station scheduling and operation management system according to any one of claims 1-8, characterized in that, The method specifically includes the following steps: Step 1: Collect historical data and operating parameters of the energy storage power station, and classify different time periods according to electricity consumption to obtain normal time periods and peak time periods; Step 2: Perform scheduling processing for normal periods, predict the battery charge state according to the formula, and judge it against physical constraints. If the conditions are met, generate normal monitoring information; otherwise, generate scheduling optimization signal. Step 3: Analyze the scheduling optimization signal, calculate the difference between the predicted battery charge state and the upper and lower limits of physical constraints, compare the magnitude of the difference, generate upper and lower scheduling signals and lower limit scheduling signal, and adjust the charging and discharging power according to the safety threshold to generate scheduling information. Step 4: Perform scheduling processing for peak periods. Based on the power load during peak periods, classify the load into different tiers and calculate the corresponding discharge power according to the battery charge of each tier. Simultaneously, use this as a standard to generate scheduling information.

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