A high-efficiency energy storage method and system based on multi-energy coordination

Abstract

The application discloses a kind of based on the efficient energy storage method and system of multi-energy coordination, it is related to electric energy storage technical field.The efficient energy storage method based on the multi-energy coordination, comprising the following steps: energy storage equipment pre-starting mark, energy storage equipment pre-starting real-time optimization and energy storage equipment pre-starting adjustment.The application is first to the next power cycle of specified power consumption area in each energy storage period Each scheduled energy storage equipment is pre-started mark, then real-time monitoring load demand condition to determine the pre-starting time of each scheduled energy storage equipment, while optimizing the pre-starting time based on the actual pre-starting situation of each scheduled energy storage equipment, finally based on the load demand change situation of adjacent energy storage period Determine whether the pre-starting adjustment of corresponding scheduled energy storage equipment is carried out, improve the coincidence degree of each energy storage equipment scheduling and actual power load demand, solve the problem of energy storage equipment scheduling and actual power charging and discharging period mismatch in prior art.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a high-efficiency energy storage method and system based on multi-energy synergy. Background Technology

[0002] To collaboratively manage and utilize various energy sources (such as electricity, heat, cooling, and gas), and achieve efficient energy storage and utilization through systematic scheduling and optimization, a multi-energy collaborative energy storage method needs to be designed. Currently, the implementation process of this method mainly covers key stages such as energy access, energy flow identification, collaborative control, energy storage execution, and feedback optimization. Energy access involves identifying accessible energy types and analyzing the supply periods, stability, cost, and carbon emission characteristics of each energy source, followed by configuring corresponding energy storage devices based on the characteristics of different energy sources. Energy flow identification involves establishing mathematical models for each energy source (energy balance equations, transmission loss models, etc.), collecting real-time data (power generation, load, status) for each energy source, and modeling the efficiency, capacity, and charge / discharge rate of various energy storage media. Collaborative control first sets system objectives, such as maximizing energy efficiency. The first step is to minimize carbon emissions. Then, optimization methods such as linear programming and genetic algorithms are selected. Finally, the multi-energy scheduling logic is determined. For example, electricity / heat storage is performed during off-peak hours, and discharge / heat is performed during peak hours. Energy storage execution means that the energy management system issues instructions to each energy subsystem and coordinates the electric-thermal conversion (such as electric boilers / heat pumps), heat-electric feedback (such as organic Rankine cycle power generation), and electric-cooling (such as cold storage and ice storage) through the controller to optimize the charging and discharging sequence and ratio among multiple energy storage devices. Feedback optimization is used for real-time feedback monitoring and adaptive optimization to quickly respond to emergencies, such as load fluctuations and intermittent changes in the output of new energy sources.

[0003] For example, Chinese invention patent CN114421536A discloses a multi-energy interactive control method based on energy storage, which includes: Step 1: Establishing a new energy output model, performing power calculation, and obtaining the output power of the unit; Step 2: Establishing an energy storage mathematical model that satisfies different energy sources; Step 3: Establishing an objective function and constraints; Step 4: Solving the objective function using a particle swarm optimization algorithm to obtain the annual total cost and daily output power of wind power and photovoltaic units under the conditions of this invention; Step 5: Based on the obtained objective function solution results, comparing and analyzing to further determine the rationality of the method of this invention.

[0004] For example, Chinese invention patent CN105305472B discloses a substation capacity optimization method based on multi-energy coordinated power supply, which includes: establishing an operational economic model based on historical data of the substation, according to the energy utilization efficiency of the combined heat and power (CHP) units in the combined heat and power (CHP) units and the COP value of the lithium bromide units; determining the capacity of the phase change energy storage device based on the supply and demand balance of a typical day, and determining the operating cost function of the phase change energy storage device based on the energy storage efficiency; solving for the economically optimal start-up and shutdown time arrangement of the CHP units using a multi-objective optimization method on the operational economic model and the operating cost function; performing multi-objective optimization of the annual electricity load characteristics using distributed photovoltaic module power generation; and obtaining the optimized substation capacity based on the optimized load-side characteristics, under the "N-1" principle and the capacity-to-load ratio standard.

[0005] The above-mentioned technology has at least the following technical problems:

[0006] In multi-energy collaborative energy storage systems, subsystems such as electricity, heat, cooling, and gas typically operate independently with low coupling, resulting in an inability to effectively share energy storage resources. This leads to poor coordination among the various energy subsystems. Furthermore, differences in the characteristics of energy storage devices also affect the coordination of each subsystem. For example, electrochemical energy storage (lithium batteries) is suitable for short-term rapid adjustment, thermal energy storage is suitable for long-term balancing, and hydrogen energy storage is suitable for cross-seasonal adjustment. Therefore, the available time window of energy storage devices is not synchronized with actual load demand. For instance, during the day when photovoltaic power is in surplus, the batteries are already full, thermal energy storage has not yet started, and hydrogen energy storage has a slow response time, resulting in wasted electricity resources. During peak electricity consumption at night, thermal energy storage cannot supply energy quickly, the batteries are not charged enough, and hydrogen energy storage is not yet ready. Delayed or lagging startup issues in some energy storage systems are also contributing factors. For example, thermal storage boilers require preheating time, and hydrogen energy systems have a slow response time. Summary of the Invention

[0007] To address the technical problem of mismatch between the scheduling of energy storage devices and the actual charging and discharging periods in existing technologies, this invention provides a high-efficiency energy storage method and system based on multi-energy synergy. The technical solution is as follows:

[0008] On the one hand, a high-efficiency energy storage method based on multi-energy synergy is provided. This method includes: marking the pre-start time of each energy storage device to be dispatched in each energy storage cycle of the next power consumption cycle in a designated power consumption area based on historical electricity consumption data. The pre-start time marking indicates that the pre-start time of each energy storage device that cannot respond to dispatch in time is marked for early start-up; monitoring the load demand of the designated power consumption area in real time to determine the pre-start time of each energy storage device to be dispatched, and optimizing the corresponding pre-start time based on the actual pre-start time of each energy storage device to improve the matching degree between the pre-start time of the energy storage device and the load demand; and determining whether to adjust the pre-start time of the corresponding energy storage device to be dispatched based on the load demand changes of adjacent energy storage cycles, thereby improving the accuracy of the pre-start time of each energy storage device to be dispatched.

[0009] On the other hand, a high-efficiency energy storage system based on multi-energy synergy is provided, including: an energy storage device pre-start marking module, an energy storage device pre-start real-time optimization module, and an energy storage device pre-start adjustment module; wherein, the energy storage device pre-start marking module is used to mark the pre-start of each energy storage device to be dispatched in each energy storage cycle of the next power consumption cycle of a designated power consumption area based on historical power consumption data; the energy storage device pre-start real-time optimization module is used to monitor the load demand of the designated power consumption area in real time to determine the pre-start time of each energy storage device to be dispatched, and optimize the corresponding pre-start time based on the actual pre-start status of each energy storage device to be dispatched; the energy storage device pre-start adjustment module is used to determine whether to perform pre-start adjustment of the corresponding energy storage device to be dispatched based on the load demand changes of adjacent energy storage cycles.

[0010] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0011] 1. First, based on historical electricity consumption data, pre-start markers are set for each energy storage device to be dispatched in each energy storage cycle of the next electricity consumption cycle in the designated electricity area. This initially ensures that the energy storage devices that need to be dispatched in each time period can respond in a timely manner. Next, the load demand of the designated electricity area is monitored in real time to determine the pre-start time of each energy storage device to be dispatched. The pre-start time is further determined based on the real-time monitoring data, which helps to ensure the accuracy of the pre-start time. At the same time, the corresponding pre-start time is optimized based on the actual pre-start status of each energy storage device to be dispatched, thereby improving the matching degree between the pre-start time of the energy storage devices and the load demand. Finally, based on the load demand changes in adjacent energy storage cycles, it is determined whether to adjust the pre-start of the corresponding energy storage devices to be dispatched, which further improves the accuracy of the pre-start of each energy storage device to be dispatched. This achieves the matching degree between the dispatch of each energy storage device and the actual electricity load demand, effectively solving the problem of mismatch between the dispatch of energy storage devices and the actual charging and discharging time of electricity consumption in existing technologies.

[0012] 2. First, load demand data is input into the load demand weight mapping set to output the corresponding load demand assessment factor, quantifying the influence of each load demand assessment data on the load demand assessment index. Then, after the average load is normalized by maximum-minimum, the load factor is subjected to load demand logic fitting to ensure the correlation between the load factor and the load demand assessment index. Finally, after weighting the load demand assessment data based on the load demand assessment factor, the load demand assessment index is obtained by coupling, which accurately quantifies the actual electricity load demand of the specified electricity consumption area in the current energy storage cycle, thereby timely dispatching the corresponding energy storage equipment and improving the energy storage efficiency of each energy storage device.

[0013] 3. First, load demand data for each energy storage cycle in the designated power consumption area is acquired in real time, providing a data foundation for load demand assessment. Then, based on the load demand data, a load demand assessment index is derived to quantify the degree of power demand in the designated power consumption area within the energy storage cycle. This index is compared with the extracted load demand judgment threshold, enabling automated selection of corresponding optimization measures. If the load demand assessment index is not greater than the load demand judgment threshold, energy storage equipment startup optimization is performed, which helps reduce resource waste from early startup of energy storage equipment. If the load demand assessment index is greater than the load demand judgment threshold, the corresponding pre-start flag is optimized based on the actual pre-start status of each energy storage equipment to be dispatched, improving the accuracy of the pre-start flag and thus ensuring the consistency between the pre-start flag and the power load.

[0014] 4. First, obtain the load demand assessment index for the next energy storage cycle and calculate the deviation with the load demand assessment index of the adjacent previous energy storage cycle to obtain the load demand change, quantifying the load demand change trend. Then, compare the load demand change with the judgment change interval. If the load demand change is greater than the maximum value of the judgment change interval, the advance adjustment range of the pre-start time of the corresponding number of the energy storage device to be dispatched in the next energy storage cycle is obtained, thereby ensuring that the pre-start load of the corresponding energy storage device conforms to the growth trend. If the load demand change is within the judgment change interval, the load change of the next energy storage cycle is monitored to ensure that the dispatch of energy storage devices is reduced while maintaining stability. If the load demand change is less than the minimum value of the judgment change interval, the delay adjustment range of the pre-start time of the corresponding number of the energy storage device to be dispatched in the next energy storage cycle is obtained, thereby ensuring that the corresponding energy storage device conforms to the load decline trend, thus improving the timeliness of energy storage device dispatch in response to load demand changes. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a high-efficiency energy storage method based on multi-energy synergy provided in an embodiment of the present invention;

[0017] Figure 2 This is one of the flowcharts illustrating the optimized pre-start time provided in the embodiments of the present invention;

[0018] Figure 3 This is a schematic diagram of the load analysis interface of the energy storage management system provided in an embodiment of the present invention;

[0019] Figure 4 This is the second flowchart illustrating the optimized pre-start time provided in this embodiment of the invention;

[0020] Figure 5 This is a schematic diagram of the interface of the scheduling strategy of the energy storage management system provided in the embodiment of the present invention;

[0021] Figure 6 This is a schematic diagram of a high-efficiency energy storage system based on multi-energy synergy provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0024] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0025] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0026] This invention provides a high-efficiency energy storage method based on multi-energy synergy. For example... Figure 1The flowchart shown represents a high-efficiency energy storage method based on multi-energy synergy. The processing flow of this method may include the following steps:

[0027] Based on historical electricity consumption data, each energy storage device to be dispatched in the next electricity consumption cycle of the designated electricity consumption area is marked with a pre-start mark. The pre-start mark indicates that the pre-start time of each energy storage device that cannot respond to dispatch in time is marked so as to start it in advance.

[0028] Designated electricity consumption areas typically refer to regions with complex energy structures, large fluctuations in supply and demand, limited grid support capacity, and the need for comprehensive scheduling of multiple energy forms. These include areas with concentrated industrial loads, significant peak residential electricity consumption, and a significant difference between daytime and nighttime electricity consumption. Examples include large industrial parks or cities with high summer air conditioning loads or winter electric heating loads (such as Beijing, Shanghai, and Guangzhou).

[0029] Real-time monitoring of load demand in designated power consumption areas determines the pre-start time of each energy storage device to be dispatched, and optimizes the corresponding pre-start time based on the actual pre-start status of each energy storage device to improve the matching degree between the pre-start time of the energy storage device and the load demand.

[0030] Based on the load demand changes in adjacent energy storage cycles, it is determined whether to perform pre-start adjustment of the corresponding energy storage devices to be dispatched, thereby improving the accuracy of pre-start of each energy storage device to be dispatched.

[0031] In this embodiment, by marking and pre-starting energy storage devices with slow startup responses, the delay time of energy storage devices in responding to scheduling commands is shortened, improving the system's ability to respond quickly to load changes. Furthermore, by dynamically optimizing the pre-start time by combining historical electricity consumption data with real-time load changes, the matching degree between the activation timing of energy storage devices and actual load demand is improved, thereby reducing energy waste and improving system operating efficiency. Moreover, by introducing a judgment mechanism for load trends in adjacent cycles, adjustments can be made in advance for future load fluctuations, which is conducive to improving the adaptive capability of the scheduling system and ensuring the degree of conformity between the scheduling timing of each energy storage system and the dynamic changes in electricity load.

[0032] Furthermore, based on historical electricity consumption data, each energy storage device to be dispatched in each energy storage cycle of the next electricity consumption cycle in the designated electricity area is pre-started. The specific process is as follows:

[0033] First, the scheduling data of energy storage devices in each energy storage cycle is obtained by using historical electricity consumption data of the designated electricity consumption area. The scheduling data of energy storage devices includes the number of the energy storage device to be scheduled and the corresponding start-up time. The start-up time represents the time it takes for the energy storage device to be scheduled to be able to output stable power and put into normal operation from the time it receives the start-up command.

[0034] Next, the average startup time of each energy storage device to be dispatched in each energy storage cycle during the same period of each electricity consumption cycle is calculated to obtain the pre-startup time of each energy storage device to be dispatched.

[0035] Finally, based on the pre-start duration, the pre-start time of the corresponding number of the energy storage device to be dispatched is marked in each energy storage cycle of the electricity consumption cycle. If the current time coincides with the pre-start time, the corresponding number of the energy storage device to be dispatched will be pre-started. If the current time does not coincide with the pre-start time, the corresponding number of the energy storage device to be dispatched will not be pre-started.

[0036] In this embodiment, by statistically analyzing the actual startup time of energy storage devices in historical electricity consumption cycles, the average pre-startup time is obtained, which improves the rationality and accuracy of the pre-startup timing. At the same time, the pre-startup mechanism effectively avoids the problem of energy storage devices missing the load response window due to the time spent in the startup process, ensuring that energy storage devices can enter the working state in a timely manner when scheduling is required, thereby improving the overall response speed and load tracking capability of the energy storage system. In addition, by averaging the historical data of each energy storage cycle in the same period, the required startup advance time of energy storage devices is more accurately estimated, thereby improving the stability and accuracy of energy storage device scheduling.

[0037] like Figure 2The diagram shown is one of the flowcharts for optimizing the pre-start time provided in this embodiment of the invention. Here, 2 indicates the start of the optimized pre-start marker. The specific logic is as follows: Real-time acquisition of load demand data for each energy storage cycle in a specified power consumption area; deriving a load demand assessment index based on the load demand data and comparing it with a load demand judgment threshold; if the load demand assessment index is not greater than the load demand judgment threshold, then energy storage device startup optimization is performed; if the load demand assessment index is greater than the load demand judgment threshold, then the corresponding pre-start marker is optimized based on the actual pre-start status of each energy storage device to be scheduled; The specific process of energy storage device startup optimization is as follows: Detecting whether there are any energy storage devices to be scheduled with a pre-start time before the current time in the current energy storage cycle, and classifying the energy storage devices to be scheduled for start / stop; if there is no pre-start time in the current energy storage cycle, then monitoring the load demand changes in the next energy storage cycle; if there is a pre-start time before the current time and the corresponding pre-start marker is already set up... If the energy storage device to be started is a non-emergency stop device, then based on the pre-start time marked by the pre-start time of the electricity consumption cycle, it checks whether there is a pre-start time for the corresponding numbered energy storage device to be started within a subsequent fixed time period. If there is a pre-start time before the current time, then the corresponding numbered energy storage device to be started will continue to be started; otherwise, a stop start command will be issued to the corresponding numbered energy storage device to be started. If there is a pre-started energy storage device to be started and it is an emergency stop device, then a stop start command will be issued directly. If there is no energy storage device to be started before the current time, then the load judgment difference is obtained. Based on the load judgment difference, the corresponding delay duration is obtained by querying the delay mapping table. The corresponding numbered energy storage device to be started with a delay is then started with a delay based on the delay duration. The above process helps to more accurately determine changes in load demand, thereby improving the timeliness of energy storage device scheduling in response to load demand based on load changes.

[0038] Furthermore, the specific process for real-time monitoring of load demand in designated electricity areas is as follows:

[0039] R1 acquires real-time load demand data for each energy storage cycle in a specified power consumption area. The load demand data includes average load, load factor, load rate, and peak ratio.

[0040] It should be added that the average load is obtained by averaging the load at all times within the energy storage cycle, the maximum load is obtained by comparing the load at each time within the energy storage cycle, and the load factor is obtained by averaging the average load and the maximum load. The load factor is the ratio of the actual electricity load to the theoretical maximum load of the system, and is often used for power dispatching and capacity assessment. The peak ratio is the ratio of the maximum load to the average load.

[0041] R2 is a load demand assessment index derived from load demand data to quantify the degree of electricity demand in a designated area during an energy storage cycle, and is compared with the extracted load demand judgment threshold. By collecting and calculating multi-dimensional data such as average load, load factor, load rate and peak ratio in real time, the load demand assessment index is generated, which can more comprehensively and accurately reflect the degree of electricity demand in a designated area during each energy storage cycle, and provides a reliable quantitative basis for pre-start-up and scheduling decisions.

[0042] Specifically, the load demand determination threshold represents the boundary value for judging the degree of change in load demand. By substituting the load demand assessment index of the energy storage equipment that needs to be dispatched from the historical data into the specific constraint expression of the load demand assessment index, a dataset of the load demand assessment index is obtained, and the load demand determination threshold is obtained by averaging the dataset.

[0043] R3 If the load demand assessment index is not greater than the load demand judgment threshold, it means that the load change fluctuation is small, and the energy storage equipment startup optimization is performed.

[0044] R4 If the load demand assessment index is greater than the load demand judgment threshold, it indicates that the load change fluctuates greatly. In this case, the corresponding pre-start flag is optimized based on the actual pre-start status of each energy storage device to be dispatched.

[0045] like Figure 3 The diagram shows the interface of the energy storage management system provided in this embodiment of the invention for load analysis. The left side of the interface is the navigation bar of the energy storage management system, including system overview, energy storage management, energy management, load analysis, scheduling strategy, operation and maintenance management, data reports, user permissions, and system configuration. A status bar is set at the top of the interface to display the corresponding power consumption area and the current energy storage cycle. In load analysis, the upper half of the interface presents the load demand assessment interface, which includes a progress bar comparison of the demand assessment index and the corresponding threshold, as well as the display of various data used for assessment, such as average load, load factor, and peak ratio. The lower half of the interface is the display area for the energy storage device list, specifically displaying the device number, device status, pre-start status, and corresponding pre-start time. This interface clearly shows the load demand level and the specific status of each energy storage device, helping system administrators to schedule each energy storage device more effectively and improve the efficiency of energy storage.

[0046] The specific process for optimizing the startup of energy storage equipment is as follows:

[0047] R31 detects whether there are any energy storage devices to be dispatched in the current energy storage cycle whose pre-start time is before the current time, and classifies the energy storage devices to be dispatched into start-up and shutdown categories, including emergency stop devices and non-emergency stop devices;

[0048] It should be added that the start-stop classification is determined based on the start-stop characteristics of each energy storage device to be dispatched. The energy storage devices to be dispatched include mechanical energy storage devices (pumped hydro storage, compressed air storage), thermal energy storage devices (thermal energy storage), and hydrogen energy storage devices (electrolysis, fuel cells). Among them, the energy storage devices to be dispatched that cannot be started or stopped at any time due to high start-up costs, long start-up times, high start-up energy consumption, and complex control include pumped hydro storage, compressed air storage, hydrogen energy storage (electrolysis + fuel cells), and thermal energy storage systems. If the energy storage device to be dispatched is one of the above types, it is classified as a device that cannot be stopped urgently; the rest are devices that can be stopped urgently.

[0049] R32: If there is no pre-start time in the current energy storage cycle, monitor the load demand changes in the next energy storage cycle.

[0050] R33: If there is a pre-start time before the current time and the corresponding pre-started energy storage device to be dispatched is a non-emergency stop device, then based on the pre-start time marked by the pre-start time of the electricity consumption cycle, it is checked whether there is a pre-start time of the corresponding numbered energy storage device to be dispatched within the subsequent fixed time period. If there is, the pre-start of the corresponding numbered energy storage device to be dispatched continues; otherwise, a stop start command is issued to the corresponding numbered energy storage device to be dispatched. The fixed time period represents the maximum duration for checking whether there is a pre-start time of the corresponding numbered energy storage device in the subsequent period of the current energy storage cycle.

[0051] R34 If there is a pre-start time before the current time and the corresponding pre-started energy storage device to be dispatched is an emergency stop device, then a stop start command is issued directly.

[0052] R35 If there are no scheduled energy storage devices whose pre-start time is before the current time, the difference between the load demand assessment index and the load demand judgment threshold is quantified. That is, the load demand assessment index and the load demand judgment threshold are subtracted to obtain the load judgment difference. Based on the load judgment difference, the corresponding delay duration is obtained by querying the delay mapping table. The scheduled energy storage device with the corresponding number is pre-started with a delay based on the delay duration.

[0053] It should be added that the delay mapping table is a data table set in the preset database to fit the mapping relationship between the load judgment difference and the delay duration. There is a one-to-one or many-to-one mapping relationship between the load judgment difference and the delay duration in the delay mapping table. The obtained load judgment difference is queried in the delay mapping table to obtain the delay duration that has a mapping relationship with it.

[0054] In summary, by classifying and handling emergency-stop and non-emergency-stop devices, and combining this with the determination of the subsequent pre-start time, the continuous operation of energy storage devices during low-demand or non-critical periods is avoided, thereby reducing energy consumption, minimizing equipment wear, and extending service life.

[0055] Furthermore, the specific steps for optimizing the corresponding pre-start flags based on the actual pre-start status of each energy storage device to be dispatched are as follows:

[0056] R41: If the pre-start time of a scheduled energy storage device coincides with the current time, then the scheduled energy storage device with the corresponding number will be pre-started. By real-time monitoring of the match between the pre-start time and the current time, it is helpful to adjust the pre-start timing of energy storage devices in a timely manner according to the dynamic changes in load demand, ensuring that energy storage devices are pre-started in time before the peak load demand, and avoiding the waste of energy resources caused by untimely start-up.

[0057] R42, if the pre-start time of the energy storage device to be scheduled is after the current time, then the pre-start time advance time is obtained by projecting the load demand difference between the load demand assessment index and the load demand judgment threshold into the set projection sequence. The pre-start time advance time is used to advance the pre-start time of the other energy storage devices to be scheduled. The load demand difference is obtained by subtracting the load demand assessment index from the load demand judgment threshold.

[0058] It should be explained that the load demand difference is input into a pre-trained projection sequence to output the corresponding pre-start time advance duration, i.e., the degree to which the load demand difference leads to the pre-start time. The training data used in this projection sequence comes from load demand differences collected within historical time periods, and pre-start time advance durations set by professionals based on empirical rules. This data is used to fit the mapping relationship between the load demand difference and the pre-start time advance duration, thereby obtaining a more accurate advance duration for the pre-start time.

[0059] R43 If the pre-start time of a scheduled energy storage device is before the current time and the scheduled energy storage device has been pre-started, then check whether the scheduled energy storage device with the corresponding number is in operation, and perform pre-start device operation judgment and optimization to reduce the resource waste of the corresponding scheduled energy storage device.

[0060] In this embodiment, when the load demand index is lower than the load demand judgment threshold, the system automatically optimizes startup. When the load demand index is higher than the load demand judgment threshold, the pre-start flag is dynamically adjusted based on the actual pre-start situation. This ensures that each energy storage device can better match changes in load demand, improving scheduling flexibility and accuracy. For cases where the startup conditions are not met, the delay mapping table is queried using the load judgment difference to more accurately determine the delayed pre-start duration, avoiding energy efficiency losses caused by premature or delayed startup and improving the timing accuracy of load response. Furthermore, regardless of whether the pre-start time coincides, is earlier, or is later, the system can automatically make judgments based on the current load demand and equipment status, dynamically adjusting the startup strategy of the energy storage devices. This not only improves the automation level of the system but also ensures the accuracy of energy storage device scheduling and the continuous and stable operation of the system.

[0061] like Figure 4 The diagram shown is a second flowchart illustrating the optimization of pre-start time provided in this embodiment of the invention. "2" indicates the start of the optimization of the pre-start marker. The specific logic is as follows: Based on the actual pre-start status of each scheduled energy storage device, the specific steps for optimizing the corresponding pre-start marker are as follows: If the pre-start time of a scheduled energy storage device coincides with the current time, then the scheduled energy storage device with the corresponding number is pre-started; if the pre-start time of a scheduled energy storage device is after the current time, then based on the load demand difference between the load demand assessment index and the load demand judgment threshold, the pre-start time advance duration is projected, and the pre-start time of the remaining scheduled energy storage devices is advanced using the pre-start time advance duration; if the pre-start time of a scheduled energy storage device is before the current time and the scheduled energy storage device has already been pre-started, then it is detected whether the scheduled energy storage device with the corresponding number is in operation, and pre-start device operation judgment and optimization are performed to reduce resource waste of the corresponding scheduled energy storage device. The specific steps for determining and optimizing pre-start equipment operation are as follows: If the scheduled energy storage device with the corresponding number is in operation, its runtime is compared with the resource waste limit duration. If the runtime is greater than the resource waste limit duration, the difference between the runtime and the resource waste limit duration is recorded as the delay duration. If the runtime is not greater than the resource waste limit duration, the pre-start time is not adjusted. If the scheduled energy storage device involved in the current energy storage cycle has not reached the operating state, the difference between its runtime and the corresponding start-up duration is recorded as the advance duration. Through the above process, the start-up strategy of the energy storage device can be automatically and dynamically adjusted according to the current load demand and the operating status of the device, ensuring the accuracy of energy storage device scheduling.

[0062] like Figure 5The diagram shows the interface of the scheduling strategy of the energy storage management system provided in this embodiment of the invention. The left side of the interface is the navigation bar of the energy storage management system, and the top has a status bar. The upper half of the interface displays the scheduling control strategy details, showing the scheduling status of each energy storage device, such as "ESU-03 detected as an emergency stop device, stop command issued." Below this area, system administrators are provided with "Manual Trigger Scheduling Optimization," "Historical Scheduling Log," and "Alarm Records," providing more ways for administrators to schedule the system's energy storage devices. The lower half of the interface displays the device status and scheduling adjustment list. By querying this list, administrators can better understand the pre-start status of each energy storage device, whether it is an emergency stop device, and the corresponding operating suggestions. System administrators can also control the start and stop of each energy storage device, making the management of each energy storage device more accurate and convenient, thereby improving the efficiency of scheduling each energy storage device and ultimately improving the energy storage efficiency of each energy storage device.

[0063] As a further embodiment, the specific method for obtaining the load demand assessment index is as follows:

[0064] The first step is to input the load demand data into the load demand weight mapping set to output the corresponding load demand assessment factors. The load demand assessment factors include the average load assessment factor, the load factor assessment factor, the load rate assessment factor, and the peak ratio assessment factor.

[0065] Specifically, load demand data is input into a pre-set load demand weight mapping set to output corresponding load demand assessment factors, i.e., the degree of influence of this type of data on the load demand assessment index. This weight mapping set is used to fit the mapping relationship between load demand data and load demand assessment factors, thereby accurately quantifying the degree of influence of each load demand data point on the load demand assessment index analysis.

[0066] The second step is to perform maximum-minimum normalization on the average load, and then perform load demand logical fitting on the load factor to ensure the correlation between the load factor and the load demand assessment index. Here, load demand logical fitting means performing an inverse proportional operation on the load factor to ensure that the load factor and the load demand assessment index are negatively correlated.

[0067] The third step involves weighting the load demand assessment data based on the load demand assessment factors and then coupling them together to obtain the load demand assessment index.

[0068] The specific constraint expression for the load demand assessment index is as follows:

[0069]

[0070] In the formula, X(1) represents the average load, X(2) represents the load factor, X(3) represents the load rate, X(4) represents the peak ratio, λ(1) represents the average load assessment factor, λ(2) represents the load factor assessment factor, λ(3) represents the load rate assessment factor, λ(4) represents the peak ratio assessment factor, and Y represents the load demand assessment index.

[0071] In this embodiment, the algorithm combines load demand data with load demand assessment factors to obtain a load demand assessment index. In the formula, as the average load increases, the load demand increases, and the load demand assessment index increases accordingly. Conversely, as the load factor increases, the load fluctuation is smaller, and the impact on load demand changes is smaller, resulting in a smaller load demand assessment index. Furthermore, when the load factor and peak ratio increase, it indicates increased electricity load demand, and the corresponding load demand assessment index also increases. Improving the analysis of the load demand assessment index enables accurate quantification of load demand changes, ensuring that energy storage devices can be pre-activated in a timely manner when load demand changes, thereby improving the matching degree between energy storage devices and electricity load demand.

[0072] Furthermore, the specific details of the pre-start equipment operation judgment and optimization are as follows, which are divided into four categories:

[0073] In the first category, if the energy storage device to be dispatched with the corresponding number is in operation, its running time is compared with the resource waste limit time. The resource waste limit time represents the maximum limit value of the time during which the energy storage device to be dispatched can be pre-started and run idle. The resource waste limit time is preset by the staff based on the specific operating standards and requirements of the corresponding energy storage device.

[0074] The second type is defined as follows: if the runtime is longer than the resource waste limit, the difference between the runtime and the resource waste limit is recorded as the delay duration.

[0075] The third category states that if the runtime is not greater than the resource waste limit, it means that the pre-start time of the corresponding numbered energy storage device to be scheduled is accurate, and therefore no adjustment will be made to the pre-start time.

[0076] The fourth category is that if the energy storage equipment to be dispatched in the current energy storage cycle has not yet reached the operating state, the difference between its running time and the corresponding start-up time is recorded as the advance time.

[0077] In this embodiment, when the operating time of an energy storage device exceeds the resource waste limit, the system uses the difference as a "delay time" to postpone the subsequent pre-start time. Conversely, for energy storage devices that are not yet in operation, the difference between their start-up time and operating time is used as a "pre-start time," realizing dynamic bidirectional correction of the start-up timing and thus improving the start-up time control accuracy of the energy storage system. Furthermore, by introducing the "resource waste limit time" parameter, the system can determine whether the energy storage device is in an idle state and dynamically adjust the subsequent scheduling strategy based on the difference between the operating time and the limit time, which helps to reduce energy waste caused by premature start-up. Moreover, by classifying and judging operating and non-operating devices and calculating the difference, the system can automatically optimize the processing according to the actual status of the devices, reducing human intervention and improving the automation scheduling level and overall efficiency of the energy storage system.

[0078] Furthermore, pre-start equipment operation judgment and optimization are performed, which also includes:

[0079] On the one hand, the delay durations of the energy storage devices to be dispatched in the same time period during each power consumption cycle are statistically analyzed and integrated to obtain the corresponding delay duration dataset. When the number of data in the delay duration dataset reaches the delay quantity limit, the average value of the delay duration dataset is calculated to obtain the pre-start time delay duration. Based on the pre-start time delay duration, the pre-start time of the corresponding numbered energy storage devices to be dispatched is delayed, that is, the pre-start time of the corresponding numbered energy storage devices to be dispatched is delayed to the time corresponding to the time after the pre-start time delay duration is increased. The delay quantity limit value is the maximum number of delay durations set by the preset staff.

[0080] On the other hand, the advance time is obtained by statistically analyzing the energy storage cycles of the energy storage devices to be dispatched in the same time period in each power consumption cycle, and integrating them to obtain the corresponding advance time dataset. When the number of data in the advance time dataset reaches the advance quantity limit, the average value of the advance time dataset is calculated to obtain the pre-start time advance time. Based on the pre-start time advance time, the pre-start time of the corresponding numbered energy storage devices to be dispatched is advanced, that is, the pre-start time of the corresponding numbered energy storage devices to be dispatched is advanced to the time corresponding to the pre-start time advance time. The advance quantity limit is the maximum number of advance times set by the preset staff.

[0081] In this embodiment, by statistically analyzing the pre-start time and pre-delay time of the energy storage devices to be scheduled in different power consumption cycles and within the same time period, a corresponding dataset is constructed. This allows for dynamic adjustment of the pre-start time based on the periodic operating pattern, thereby improving the long-term optimization capability of the energy storage system. Furthermore, by employing delay quantity limits and advance quantity limits as triggering mechanisms, the mean is calculated only when the sample size of the corresponding dataset is sufficient, thus avoiding erroneous adjustments due to individual outliers and enhancing the robustness of the energy storage system.

[0082] Furthermore, the mean of the delay duration dataset is calculated to obtain the pre-start delay duration. This is followed by: performing linear fitting on the delay duration dataset and performing point slope calculation based on the slopes corresponding to two adjacent delay durations to obtain the pre-start delay duration.

[0083] Specifically, the coordinates of the delay durations are obtained by combining the time sequence numbers of two adjacent delay durations with the corresponding energy storage cycle. The time sequence number of the energy storage cycle is the x-axis, and the corresponding delay duration is the y-axis. It is worth noting that since the x-axis represents the time sequence number of the energy storage cycle, the x-axis coordinates will not be the same. Then, the delay duration coordinates are input into the slope formula to obtain the corresponding slope, which is the ratio of the difference between the y-axis coordinates and the difference between the x-axis coordinates of the two delay duration coordinates. Once the slope is determined, a corresponding linear function is assumed, and the x-axis coordinate of the next delay duration coordinate is directly input into the linear function to calculate the corresponding y-axis coordinate, thus obtaining the corresponding pre-start delay duration.

[0084] Simultaneously, the mean calculation of the advance duration dataset is performed to obtain the advance duration of the pre-start time. This also includes: performing linear fitting on the advance duration dataset, and performing point slope calculation based on the slopes corresponding to two adjacent advance durations to obtain the advance duration of the pre-start time. Point slope calculation represents a calculation method that can obtain another unknown data by calculating the slope of a straight line with one data.

[0085] Similarly, based on the time series numbers of the two adjacent advance durations and the corresponding energy storage cycles, the coordinates of the advance durations are obtained respectively. That is, the time series number of the energy storage cycle is the x-axis, and the corresponding advance duration is the y-axis. It is worth noting that since the x-axis also represents the time series number of the energy storage cycle, the x-axis will not be the same. Then, the advance duration coordinates are input into the slope formula to obtain the corresponding slope. That is, the slope is obtained by taking the ratio of the difference between the y-axis and the difference between the x-axis of the two advance duration coordinates. Once the slope is determined, a corresponding linear function is assumed, and the x-axis of the next advance duration coordinate is directly input into the linear function to calculate the corresponding y-axis, thus obtaining the corresponding advance duration of the pre-start time.

[0086] In this embodiment, based on the slope calculation result between two adjacent adjustment durations and combined with a determined data point, the pre-start adjustment duration corresponding to the current moment is obtained through point slope calculation. This makes the correction of the pre-start time no longer dependent on the static average value, but generated according to dynamic trend prediction, which is more in line with actual operation requirements. At the same time, through the slope-guided point slope prediction calculation mechanism, the system can capture the rate and direction of duration change, and can still respond in a timely manner when the load demand change trend fluctuates greatly, thus improving the real-time adaptability of the mechanism in complex environments.

[0087] Furthermore, based on the load demand changes in adjacent energy storage cycles, it is determined whether to perform pre-start adjustment of the corresponding energy storage devices to be dispatched. The specific process is as follows:

[0088] Q1. Obtain the load demand assessment index for the next energy storage cycle and perform a deviation calculation with the load demand assessment index of the adjacent previous energy storage cycle to obtain the load demand change. Here, the deviation calculation means performing a difference calculation between the load demand assessment index of the next energy storage cycle and the load demand assessment index of the adjacent previous energy storage cycle, and then performing a ratio calculation with the load demand assessment index of the adjacent previous energy storage cycle.

[0089] Q2 compares the change in load demand with the determined change interval, which contains the determined change value and is usually the median of the determined change interval. If the change in load demand is greater than the maximum value of the determined change interval, it indicates that the load demand is increasing. The load change difference between the change in load demand and the determined change value is then input into the pre-start advance adjustment function to obtain the advance adjustment range of the pre-start time of the corresponding number of the energy storage equipment to be dispatched in the next energy storage cycle. The pre-start advance adjustment function is used to fit the mapping relationship between the load change difference value and the advance adjustment range of the pre-start time. The determined change interval is preset by the preset staff and represents the stability range of the load demand change fluctuation. The determined change value is also obtained from this.

[0090] It needs to be explained that the advance magnitude fitting function is obtained by linearly fitting a linear function based on known advance magnitude fitting training data. The advance magnitude fitting training data includes the load change difference value and the advance adjustment magnitude set for the preset staff. First, a linear function (such as a linear function y = ax + b) is assumed based on the advance magnitude fitting training data. Then, the parameters (i.e., parameters a and b in the aforementioned linear function) are calculated using the least squares method. Finally, the corresponding advance magnitude fitting function is obtained by fitting the load change difference value and the advance adjustment magnitude at the pre-start time using numpy.polyfit() and / or scipy.optimize.curve_fit() in Python. This function is used to fit the mapping relationship between the load change difference value and the advance adjustment magnitude at the pre-start time.

[0091] Q3. If the change in load demand falls within the defined range, it indicates that the load demand is trending steadily. In this case, we will continue to monitor the load changes for the next energy storage cycle.

[0092] Q4. If the change in load demand is less than the minimum value of the determination range, it indicates that the load demand is declining. The load change difference value is then input into the pre-start delay amplitude fitting function to obtain the delay adjustment amplitude of the pre-start time of the corresponding number of the energy storage device to be dispatched in the next energy storage cycle. The pre-start delay amplitude fitting function is used to fit the mapping relationship between the load change difference value and the delay adjustment amplitude of the pre-start time.

[0093] It needs to be explained that the delay amplitude fitting function is obtained by linearly fitting a linear function based on known delay amplitude fitting training data. This training data includes the load change difference values ​​and the preset delay adjustment ranges for each worker. First, insert the delay amplitude fitting training data into an Excel chart and select a scatter plot. Then, select the scatter plot and add a trend line. In the trend line options, select the function type, usually a linear function, and check "Show Formula" and "R". 2 The Excel chart automatically displays the corresponding fitting function, which is the delay amplitude fitting function. This function represents a linear function that maps the difference in the fitted load change value to the delay adjustment amplitude at the pre-start time.

[0094] In this embodiment, the load change is obtained by calculating the ratio of the load demand assessment index between adjacent energy storage cycles, and the pre-start time is adjusted accordingly to determine whether it needs to be adjusted. This enables the energy storage dispatch system to respond quickly to periodic load fluctuations. Simultaneously, setting a change range as a threshold condition helps avoid dispatch actions caused by minor load fluctuations, thereby improving the stability and reliability of the dispatch strategy and reducing equipment wear and energy waste caused by frequent starts. Furthermore, by fitting the nonlinear mapping relationship between the load change difference and the adjustment range using the pre-start advance amplitude fitting function and the delay amplitude fitting function, the energy storage dispatch system can make corresponding time adjustment strategies according to different change trends, further ensuring the matching degree between energy storage device dispatch and load electricity demand.

[0095] like Figure 6 As shown, Figure 6 This is a schematic diagram of a high-efficiency energy storage system based on multi-energy synergy, including: an energy storage device pre-start marking module, an energy storage device pre-start real-time optimization module, and an energy storage device pre-start adjustment module.

[0096] Among them, the energy storage equipment pre-start marking module is used to pre-start each energy storage device to be dispatched in each energy storage cycle of the next power consumption cycle of the specified power consumption area based on historical power consumption data.

[0097] The real-time optimization module for pre-start of energy storage equipment is used to monitor the load demand of a designated power consumption area in real time to determine the pre-start time of each energy storage device to be dispatched, and optimize the corresponding pre-start time based on the actual pre-start status of each energy storage device to be dispatched.

[0098] The energy storage equipment pre-start adjustment module is used to determine whether to perform pre-start adjustment of the corresponding energy storage equipment to be dispatched based on the load demand changes of adjacent energy storage cycles.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0100] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0101] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0102] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0103] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0105] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0108] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A high-efficiency energy storage method based on multi-energy synergy, characterized in that, Includes the following steps: Based on historical electricity consumption data, each energy storage device to be dispatched in each energy storage cycle of the next electricity consumption cycle in the designated electricity consumption area is marked for pre-start. The pre-start mark means that the pre-start time of each energy storage device to be dispatched that cannot respond to dispatch in time is marked so as to start it in advance. Real-time monitoring of load demand in designated power consumption areas determines the pre-start time of each energy storage device to be dispatched, and optimizes the corresponding pre-start time based on the actual pre-start status of each energy storage device to improve the matching degree between the pre-start time of the energy storage device and the load demand; The specific process of real-time monitoring of load demand in a designated electricity consumption area is as follows: The load demand data for each energy storage cycle in a specified power consumption area is acquired in real time. The load demand data includes average load, load factor, load rate and peak ratio. Based on the load demand data, a load demand assessment index is derived to quantify the degree of demand for electricity load in the specified power consumption area within the energy storage cycle, and compared with the extracted load demand judgment threshold. If the load demand assessment index is not greater than the load demand judgment threshold, then energy storage device startup optimization is performed; if the load demand assessment index is greater than the load demand judgment threshold, then the corresponding pre-start flag is optimized based on the actual pre-start status of each energy storage device to be dispatched; the specific process of energy storage device startup optimization is as follows: detect whether there are any energy storage devices to be dispatched with a pre-start time before the current time in the current energy storage cycle, and classify the energy storage devices to be dispatched into start / stop categories, including emergency stop devices and non-emergency stop devices; if there is no pre-start time in the current energy storage cycle, then monitor the load demand changes in the next energy storage cycle; if there are energy storage devices to be dispatched with a pre-start time before the current time and the corresponding pre-started energy storage devices are non-emergency stop devices, then the pre-start flag is optimized based on the electricity consumption cycle. The pre-start time detection checks whether there is a pre-start time for the corresponding numbered energy storage device to be scheduled within a subsequent fixed time period. If there is, the pre-start of the corresponding numbered energy storage device to be scheduled continues; otherwise, a stop start command is issued to the corresponding numbered energy storage device to be scheduled. If there is a pre-start time before the current time and the corresponding pre-started energy storage device to be scheduled is an emergency stop device, a stop start command is issued directly. If there is no energy storage device to be scheduled before the current time, the difference between the load demand assessment index and the load demand judgment threshold is quantified to obtain the load judgment difference. Based on the load judgment difference, the corresponding delay duration is obtained by querying the delay mapping table. The corresponding numbered energy storage device to be scheduled is then pre-started with a delay based on the delay duration. Based on the load demand changes in adjacent energy storage cycles, it is determined whether to perform pre-start adjustment of the corresponding energy storage devices to be dispatched, thereby improving the accuracy of pre-start of each energy storage device to be dispatched.

2. The efficient energy storage method based on multi-energy synergy according to claim 1, characterized in that, The process of pre-starting each energy storage device to be dispatched in each energy storage cycle of the next electricity consumption cycle in the designated electricity consumption area based on historical electricity consumption data is as follows: The scheduling data of energy storage devices in each energy storage cycle is obtained by using historical electricity consumption data of a specified electricity consumption area. The scheduling data of energy storage devices includes the number of the energy storage device to be scheduled and the corresponding start-up time. The average startup time of each energy storage device to be dispatched in each energy storage cycle during the same period of each electricity consumption cycle is calculated to obtain the pre-startup time of each energy storage device to be dispatched. Based on the pre-start duration, the pre-start time of the corresponding number of the energy storage device to be dispatched is marked in each energy storage cycle of the electricity consumption cycle. If the current time coincides with the pre-start time, the corresponding number of the energy storage device to be dispatched will be pre-started. If the current time does not coincide with the pre-start time, the corresponding number of the energy storage device to be dispatched will not be pre-started.

3. The efficient energy storage method based on multi-energy synergy according to claim 1, characterized in that, The specific method for obtaining the load demand assessment index is as follows: The load demand data is input into the load demand weight mapping set to output the corresponding load demand evaluation factors, which include the average load evaluation factor, the load factor evaluation factor, the load rate evaluation factor, and the peak ratio evaluation factor. After performing maximum-minimum normalization on the average load, the load factor is then subjected to load demand logical fitting to ensure the correlation between the load factor and the load demand assessment index. After weighting the load demand assessment data based on the load demand assessment factors, the load demand assessment index is obtained by coupling.

4. The efficient energy storage method based on multi-energy synergy according to claim 1, characterized in that, The specific steps for optimizing the corresponding pre-start flag based on the actual pre-start status of each energy storage device to be scheduled are as follows: If the pre-start time of a scheduled energy storage device coincides with the current time, then the scheduled energy storage device with the corresponding number will be pre-started. If the pre-start time of the energy storage device to be scheduled is after the current time, the pre-start time advance time is obtained by projecting the load demand difference between the load demand assessment index and the load demand judgment threshold into the set projection sequence. The pre-start time advance time is used to advance the pre-start time of the other energy storage devices to be scheduled. If the pre-start time of a scheduled energy storage device is before the current time and the scheduled energy storage device has already been pre-started, then check whether the scheduled energy storage device with the corresponding number is in operation, and perform pre-start device operation judgment and optimization to reduce the resource waste of the corresponding scheduled energy storage device.

5. The efficient energy storage method based on multi-energy synergy according to claim 4, characterized in that, The specific details of the pre-startup equipment operation determination and optimization are as follows: If the energy storage device to be dispatched with the corresponding number is in the operation phase, its running time is obtained and compared with the resource waste limit time, wherein the resource waste limit time represents the maximum limit value of the time during which the energy storage device to be dispatched can be pre-started and run idle. If the runtime is longer than the resource waste limit, the difference between the runtime and the resource waste limit is recorded as the delay duration. If the runtime is not greater than the resource waste limit, the pre-start time will not be adjusted. If the energy storage device to be dispatched in the current energy storage cycle has not yet reached the operating state, the difference between its running time and the corresponding start-up time is recorded as the advance time.

6. The efficient energy storage method based on multi-energy synergy according to claim 5, characterized in that, The process of pre-starting equipment operation determination and optimization then includes: The delay duration of the energy storage devices to be dispatched is obtained by statistically analyzing the energy storage cycles in the same time period of each power consumption cycle, and then integrating them to obtain the corresponding delay duration dataset. When the number of data in the delay duration dataset reaches the delay quantity limit, the average value of the delay duration dataset is calculated to obtain the pre-start time delay duration, and the pre-start time of the corresponding numbered energy storage devices to be dispatched is delayed based on the pre-start time delay duration. The advance start time is obtained by statistically analyzing the energy storage cycles of the energy storage devices to be dispatched in the same time period in each power consumption cycle, and integrating them to obtain the corresponding advance start time dataset. When the number of data in the advance start time dataset reaches the advance number limit, the average value of the advance start time dataset is calculated to obtain the advance start time. Based on the advance start time, the advance start time of the corresponding numbered energy storage devices to be dispatched is advanced.

7. The efficient energy storage method based on multi-energy synergy according to claim 6, characterized in that, The step of averaging the delay duration dataset to obtain the pre-start delay duration further includes: performing linear fitting on the delay duration dataset and performing point slope calculation based on the slopes corresponding to two adjacent delay durations to obtain the pre-start delay duration. The step of averaging the advance duration dataset to obtain the advance duration of the pre-start time further includes: performing linear fitting on the advance duration dataset and performing point slope calculation based on the slopes corresponding to two adjacent advance durations to obtain the advance duration of the pre-start time.

8. The efficient energy storage method based on multi-energy synergy according to claim 1, characterized in that, The process of determining whether to perform pre-start adjustment of the corresponding energy storage equipment to be dispatched based on the load demand changes of adjacent energy storage cycles is as follows: Obtain the load demand assessment index for the next energy storage cycle and calculate the deviation between it and the load demand assessment index of the adjacent previous energy storage cycle to obtain the load demand change. The load demand change is compared with the judgment change interval, which includes the judgment change. If the load demand change is greater than the maximum value of the judgment change interval, the load change difference value between the load demand change and the judgment change value is input into the pre-start advance magnitude fitting function to obtain the advance adjustment magnitude of the pre-start time of the corresponding number of the energy storage device to be dispatched in the next energy storage cycle. The pre-start advance magnitude fitting function is used to fit the mapping relationship between the load change difference value and the advance adjustment magnitude of the pre-start time. If the change in load demand falls within the determined change range, then continue to monitor the load change in the next energy storage cycle. If the change in load demand is less than the minimum value of the determined change range, the load change difference value is input into the pre-start delay amplitude fitting function to obtain the delay adjustment amplitude of the pre-start time of the corresponding number of the energy storage device to be dispatched in the next energy storage cycle. The pre-start delay amplitude fitting function is used to fit the mapping relationship between the load change difference value and the delay adjustment amplitude of the pre-start time.

9. A system applying the high-efficiency energy storage method based on multi-energy synergy as described in any one of claims 1-8, comprising: Energy storage equipment pre-start marking module, energy storage equipment pre-start real-time optimization module, and energy storage equipment pre-start adjustment module; The energy storage device pre-start marking module is used to pre-start each energy storage device to be dispatched in each energy storage cycle of the next power consumption cycle of a specified power consumption area based on historical power consumption data. The energy storage device pre-start real-time optimization module is used to monitor the load demand of a designated power consumption area in real time to determine the pre-start time of each energy storage device to be dispatched, and optimize the corresponding pre-start time based on the actual pre-start status of each energy storage device to be dispatched. The energy storage equipment pre-start adjustment module is used to determine whether to perform pre-start adjustment of the corresponding energy storage equipment to be scheduled based on the load demand changes of adjacent energy storage cycles.

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