A power grid scheduling method against grid-connected instability

Abstract

The application provides a power grid scheduling method for resisting grid instability. Step 1, the grid subsystem reports the power stability and grid maintenance time length information of each distributed grid site; Step 2, the terminal management subsystem evaluates the terminal device storage efficiency according to the internet information; Step 3, the scheduling subsystem detects that there is an under-capacity distributed grid site, selects the first N terminal devices according to the storage efficiency, uses the terminal energy storage to supply power, and schedules the under-capacity distributed grid site to reduce the corresponding grid integration. The method of the application improves the total power reserve construction capability of the whole grid, realizes the purpose of energy storage reconstruction of the under-capacity grid site, effectively solves the problem of green power generation capacity fluctuation affecting power supply stability, greatly reduces the green grid capacity stability access threshold, and effectively promotes the construction and development of green power grid.

Description

Technical Field

[0001] This invention relates to the field of dynamic power grid dispatching, and in particular to a power grid dispatching method and system for combating grid instability. Background Technology

[0002] With the rapid development of human informatization and industrialization, human society's energy consumption has been growing at a high speed for a long time, posing a great challenge to energy supply, especially for electricity supply, which involves social welfare and industrial foundation.

[0003] To alleviate the pressure on power supply, the government has introduced relevant policies to encourage distributed generation and grid connection. This mobilizes private resources in various regions to widely utilize energy sources such as hydropower, wind power, solar power, bioenergy (biogas), and tidal energy to generate electricity, build distributed grid systems, and provide an effective supplement to electricity supply. Ultimately, the goal is to alleviate the pressure on power supply through the widespread development of green energy.

[0004] Because green power generation primarily relies on hydropower, wind power, solar power, bioenergy (biogas), and tidal energy, and these resources are largely dependent on the transient nature of the environment, green power generation often suffers from inconsistent production capacity, posing a significant challenge to power supply. As a bottleneck issue in green power generation, this problem has been extensively discussed in the industry. Currently, the main solutions adopted by the industry to address the issue of inconsistent green power generation capacity fall into two categories:

[0005] First, the cluster operation method: This method constructs a multi-site cluster power generation with complementary capacity characteristics, and the complementary site group achieves neutralization, thereby mitigating the degree of capacity fluctuation.

[0006] Second, the independent caching method: This method sets up independent energy storage pools for distributed sites to cache and re-output excess power during peak periods, thereby achieving constant-level compensation for off-peak capacity and improving the stability of power output.

[0007] However, the two main approaches mentioned above share a common problem: they both assume that the constructed capacity curves are highly accurate to achieve internal system neutralization; otherwise, large fluctuations in output will occur. In practical applications, due to the rapidly changing natural environment, capacity curve predictions are merely probabilistic simulations and cannot achieve highly accurate forecasts. Therefore, in most cases, this leads to significant fluctuations after grid connection, affecting power supply quality or the effectiveness of traditional power plant intervention. This situation greatly hinders the development of green grids. Therefore, since green grid connection cannot be self-balancing, the key issue for further development of green grids is how to move beyond the design concept of a completely self-balancing grid subsystem. This involves constructing third-party regulating factors and maximizing their influence to further offset and neutralize the fluctuations in the existing grid structure, thereby improving stable capacity output and lowering the entry barrier for stable output in green grid connection. This is a problem that the industry needs to solve. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a power grid dispatching method to combat grid instability.

[0009] The technical solution to the technical problem to be solved by the present invention is: a power grid dispatching method for combating grid instability, characterized by comprising the following steps:

[0010] Step 1: The grid connection subsystem reports the duration of stable power connection at each distributed grid connection point;

[0011] Step 2: The end-point management subsystem evaluates the recharge efficiency of end-point devices based on IoT information;

[0012] Step 3: The scheduling subsystem detects the presence of underpowered distributed grid points, selects the top N terminal devices based on energy storage efficiency to self-supply electricity using terminal energy storage, and schedules the underpowered distributed grid points to reduce their power supply accordingly.

[0013] Better still, in step 1, the reporting of the grid connection maintenance duration information is actively reported by the distributed grid connection points in the grid connection subsystem to the end management subsystem and the scheduling subsystem after the stable grid connection maintenance duration is less than the preset threshold value Threshold1.

[0014] Even better, the preset threshold value Threshold1 is the response time from detecting a site's power shortage to switching power supply from a traditional site.

[0015] Better still, in step 1, the calculation method for the duration of stable grid connection maintenance is as follows:

[0016] The remaining energy storage capacity of the distributed grid-connected power station is divided by the grid-connected power output requirement per unit time to obtain the stable grid connection maintenance time of the power station.

[0017] Better still, in step 1, the calculation method for the duration of stable grid connection maintenance is as follows:

[0018] The minimum power generation per unit time required for the distributed grid-connected site is minus the minimum power generation per unit time within the preset time period of the site, Min_dep, to obtain Delta_out2in. Then, the duration for which the grid-connected site's power is stably connected is obtained by dividing the remaining power storage capacity of the grid-connected site by Delta_out2in.

[0019] More preferably, the method for calculating the preset time period includes:

[0020] The preset time period refers to the time period between the current time point T_current and T_last.

[0021] Better still, the X1 time periods P1 included in the time period between the current time point T_current and T_last are then used as references based on the X1 time points RefPoint(i) = T_current-i*P1, where i takes the values ​​0, ..., X1-1, to obtain the union of the X1 time periods [RefPoint(i)-Rang2, RefPoint(i)].

[0022] Better still, in step 2, the end-point management subsystem evaluates the recharge efficiency of the end-point device based on IoT information. The end-point management subsystem obtains IoT information and evaluates the recharge efficiency of the end-point device after detecting the occurrence of a low-energy distributed grid point.

[0023] In step 2, the end-user management subsystem evaluates the recharge efficiency of the end-user devices based on IoT information. Specifically, the process is as follows:

[0024] Step 2.1: The end-point management subsystem reads the sensor data and power generation equivalent area of ​​each end device;

[0025] Step 2.2: The end-point management subsystem obtains the power generation per unit area per unit time by looking up a table based on the sensor data.

[0026] Step 2.3: The end-point management subsystem calculates the recharge efficiency of the end-point device by multiplying the "equivalent area" by the "power generation per unit equivalent area per unit time".

[0027] Better still, in step 3, the method for determining the occurrence of the underpowered distributed grid site is that the duration of stable grid connection of the grid site is less than a preset threshold value Threshold1. Preferably, the preset threshold value Threshold1 is the response time from the discovery of the underpowered site to the switching of power supply from the traditional site.

[0028] In step 3, the scheduling subsystem detects the presence of under-powered distributed grid connection points, selects the top N end devices based on energy storage efficiency to utilize end-point energy storage for self-power supply, and schedules the under-powered distributed grid connection points to reduce their power supply accordingly. The specific steps are as follows:

[0029] Step 3.1: Investigate the under-powered distributed sites according to their energy storage capacity from low to high, and store them in ListA;

[0030] Step 3.2: Determine if ListA is empty. If it is, proceed to step 3.10. If not, retrieve the first site First_site from ListA and delete it from ListA.

[0031] Step 3.3: The scheduling subsystem continuously reads the remaining power storage of the underpowered distributed station at Delta_interval intervals to obtain the remaining power values ​​Surplus0 and surplus1 of the station;

[0032] Step 3.4: The scheduling subsystem calculates Site_out - (surplus0 - surplus1) / Delta_interval to obtain the power generation Moment_dep per unit time within Delta_interval of the site, where Site_out is the power generation value required to be connected to the grid per unit time of the site;

[0033] Step 3.5: The scheduling subsystem determines whether Min_dep is greater than Moment_dep. If it is, Moment_dep is assigned to ref_dep. If not, Min_dep is assigned to ref_dep. In step 1, assuming that method 1 is used, the Min_dep value is calculated additionally according to method 2.

[0034] Step 3.6: The scheduling subsystem calculates the supplementary time Supplement_time = (Total_storage - surplus1) / ref_dep, and calculates the amount of electricity supplementation required for the under-energy site to complete energy storage reconstruction.

[0035] Supplement_site = Supplement_time * Site_out, where Total_storage is the total power storage capacity of the site;

[0036] Step 3.7: The scheduling subsystem sorts the terminal devices from high to low energy storage efficiency based on their energy storage capacity. The first N terminal devices are scheduled to perform self-supply of power according to the power requirements of the site per unit time (after the N terminal devices are depleted of their own energy storage, they automatically switch to non-self-supply mode to provide power service). The total energy of the N terminal devices must be greater than or equal to Supplement_site. If N terminal devices that meet the requirement of being greater than or equal to Supplement_site cannot be selected, the process jumps to step 3.9.

[0037] Step 3.8: The scheduling subsystem schedules the under-energy distributed grid-connected sites to stop grid connection output and enter the energy storage reconstruction phase.

[0038] Step 3.9: The scheduling subsystem calculates the total power supply of First_site and all sites in ListA, and then selects the traditional power station with the smallest power generation and a power generation greater than the total power supply from the traditional power stations, and schedules the traditional power station to be put into service.

[0039] Step 3.10: Stop scheduling.

[0040] Better still, in step 3.8, after scheduling each grid station to enter energy storage reconstruction, the scheduling subsystem starts a timer_site for each station and periodically queries the energy storage reconstruction status. If reconstruction is completed before the timer_site timeout is less than Supplement_time, the station is scheduled to enter normal service, and the terminal equipment is scheduled to stop self-supply. If it is determined that the timer_site timeout is greater than Supplement_time and the station is still in a state of underpowerment, the station is shut down, a backup conventional power station is started to provide power supplementation, and the self-supply of the N terminal equipment is stopped.

[0041] The beneficial effects of this invention are as follows:

[0042] Compared with existing technologies, this invention has the following advantages and beneficial effects: By employing the method of this invention, addressing the uncertainty of source-grid load, and based on the priority principle of heavy-duty energy storage efficiency, it dynamically schedules the energy storage capacity of widely distributed but grid-incompatible end-point devices for end-point self-supply, thereby improving the overall grid's reserve capacity. Furthermore, it uses the reserve capacity to offset the power supply to under-capacity distributed grid-connected sites, and then schedules these sites to gradually integrate, achieving the goal of energy storage reconstruction at these sites. This effectively addresses the problem of large fluctuations in green power generation capacity affecting stable power supply. The method of this invention significantly lowers the entry threshold for stable green grid-connected capacity, effectively promoting the construction and development of green power grids. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a dynamic power grid dispatching method for addressing uncertainties in source and grid loads.

[0044] Figure 2 This is a schematic diagram of a dynamic power grid dispatching system for addressing the uncertainty of source and grid loads. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] A dynamic power grid scheduling method for addressing uncertainties in source and grid loads, such as Figure 1 As shown, the specific steps include:

[0047] Step 1: The grid connection subsystem reports the duration of stable power connection at each distributed grid connection point;

[0048] Step 2: The end-point management subsystem evaluates the recharge efficiency of end-point devices based on IoT information;

[0049] Step 3: The scheduling subsystem detects the presence of underpowered distributed grid points, selects the top N terminal devices based on energy storage efficiency to self-supply electricity using terminal energy storage, and schedules the underpowered distributed grid points to reduce their power supply accordingly.

[0050] In step 1, the information on the reporting of the connection and maintenance duration is actively reported by the distributed connection points in the grid connection subsystem to the end management subsystem and the scheduling subsystem after the power stable connection and maintenance duration is less than the preset threshold value Threshold1. The preset threshold value Threshold1 is preferably the response time from the discovery of the power shortage of the site to the realization of the power supply switch of the traditional site.

[0051] In step 1, the method for calculating the duration of stable grid connection maintenance includes:

[0052] Method 1: Divide the remaining power storage of the distributed grid-connected power station by the grid-connected power output requirement per unit time of the grid-connected power station to obtain the stable grid connection maintenance time of the grid-connected power station;

[0053] or,

[0054] Method 2: Subtract the minimum power generation Min_dep per unit time within the preset time period from the grid-connected output requirement of the distributed grid-connected site to obtain Delta_out2in. Then, divide the remaining power storage of the grid-connected site by Delta_out2in to obtain the stable grid connection maintenance time.

[0055] The method for calculating the preset time period includes:

[0056] Case A: The preset time period refers to the period from the current time T_current to T_last (where T_last is equal to T_current minus Rang1, and Rang1 is set). For example, if the current time is 2022-12-21, 20:00, and Rang1 is set to 48 hours, then the preset time period is from 2022-12-19, 20:00 to 2022-12-21, 20:00.

[0057] or,

[0058] Case B: The time period from the current time point T_current to T_last includes X1 time periods P1. Then, based on the X1 time points RefPoint(i) = T_current - i * P1 (where i takes values ​​of 0, ..., X1-1), the union of X1 time periods [RefPoint(i) - Rang2, RefPoint(i)] is obtained. For example, if the current time T_current is 2022-12-21, 20:00, and Rang1 is set to 48 hours and the period P1 is set to 24 hours, then the result of X1 calculation is floor(48 / 24) = 2 time periods P1, thus obtaining X1 time periods, that is, the two time periods are:

[0059] RefPoint(0) = T_current - 0 * P1 = T_current, meaning RefPoint(0) is 2022-12-21, 20:00;

[0060] RefPoint(1) = T_current-1 * P1 = T_current-24 hours, that is, RefPoint(1) is 2022-12-20, 20:00.

[0061] Therefore, the preset time period is the union of the times of [RefPoint(0)-Rang2,RefPoint(0)] and [RefPoint(1)-Rang2,RefPoint(1)]. Assuming that Rang2 is set to 1 hour, the preset time period is "2022-12-21, 19:00 to 2022-12-21, 20:00" and "2022-12-20, 19:00 to 2022-12-20, 20:00", which are two hours.

[0062] In step 2, the end-point management subsystem evaluates the recharge efficiency of the end-point device based on IoT information. The end-point management subsystem obtains IoT information and evaluates the recharge efficiency of the end-point device after detecting the occurrence of a low-energy distributed grid point.

[0063] In step 2, the end-user management subsystem evaluates the recharge efficiency of the end-user devices based on IoT information. Specifically, the process is as follows:

[0064] Step 2.1: The end-point management subsystem reads the sensor data and power generation equivalent area of ​​each end device;

[0065] Step 2.2: The end-point management subsystem obtains the power generation per unit area per unit time by looking up a table based on the sensor data.

[0066] Step 2.3: The end-point management subsystem calculates the recharge efficiency of the end-point device by multiplying the "equivalent area" by the "power generation per unit equivalent area per unit time".

[0067] In step 3, the method for determining the occurrence of the underpowered distributed grid site is that the stable grid connection time is less than a preset threshold value Threshold1. Preferably, the preset threshold value Threshold1 is the response time from the discovery of the underpowered site to the switching of power supply from the traditional site.

[0068] In step 3, the scheduling subsystem detects the presence of under-powered distributed grid connection points, selects the top N end devices based on energy storage efficiency to utilize end-point energy storage for self-power supply, and schedules the under-powered distributed grid connection points to reduce their power supply accordingly. The specific steps are as follows:

[0069] Step 3.1: Investigate the under-powered distributed sites according to their energy storage capacity from low to high, and store them in ListA;

[0070] Step 3.2: Determine if ListA is empty. If it is, proceed to step 3.10. If not, retrieve the first site First_site from ListA and delete it from ListA.

[0071] Step 3.3: The scheduling subsystem continuously reads the remaining power storage of the underpowered distributed station at Delta_interval intervals to obtain the remaining power values ​​Surplus0 and surplus1 of the station;

[0072] Step 3.4: The scheduling subsystem calculates Site_out - (surplus0 - surplus1) / Delta_interval to obtain the power generation Moment_dep per unit time within Delta_interval of the site, where Site_out is the power generation value required to be connected to the grid per unit time of the site;

[0073] Step 3.5: The scheduling subsystem determines whether Min_dep is greater than Moment_dep. If it is, Moment_dep is assigned to ref_dep. If not, Min_dep is assigned to ref_dep. In step 1, assuming that method 1 is used, the Min_dep value is calculated additionally according to method 2.

[0074] Step 3.6: The scheduling subsystem calculates the supplementary time Supplement_time = (Total_storage - surplus1) / ref_dep, and calculates the amount of electricity supplementation required for the under-energy site to complete energy storage reconstruction.

[0075] Supplement_site = Supplement_time * Site_out, where Total_storage is the total power storage capacity of the site;

[0076] Step 3.7: The scheduling subsystem sorts the terminal devices from high to low energy storage efficiency based on their energy storage capacity. The first N terminal devices are scheduled to perform self-supply of power according to the power requirements of the site per unit time (after the N terminal devices are depleted of their own energy storage, they automatically switch to non-self-supply mode to provide power service). The total energy of the N terminal devices must be greater than or equal to Supplement_site. If N terminal devices that meet the requirement of being greater than or equal to Supplement_site cannot be selected, the process jumps to step 3.9.

[0077] Step 3.8: The scheduling subsystem schedules the under-energy distributed grid-connected sites to stop grid connection output and enter the energy storage reconstruction phase.

[0078] Step 3.9: The scheduling subsystem calculates the total power supply of First_site and all sites in ListA, and then selects the traditional power station with the smallest power generation and a power generation greater than the total power supply from the traditional power stations, and schedules the traditional power station to be put into service.

[0079] Step 3.10: Stop scheduling.

[0080] In step 3.8, after scheduling each grid station to enter energy storage reconstruction, the scheduling subsystem starts a timer_site for each station and periodically queries the energy storage reconstruction status. If reconstruction is completed before the timer_site timeout is less than Supplement_time, the station is scheduled to enter normal service, and the terminal equipment is scheduled to stop self-supply. If it is determined that the timer_site timeout is greater than Supplement_time and the station is still in a state of underpowerment, the station is shut down, a backup conventional power station is started to provide power supplementation, and the self-supply of the N terminal equipment is stopped.

[0081] Example, hereby using Figure 2 Taking an example, this invention proposes a dynamic power grid dispatching system for addressing uncertainties in source and grid loads. The system includes: a grid connection subsystem, an end-point management subsystem, and a dispatching subsystem. The functions of each subsystem are described below:

[0082] The grid connection subsystem is responsible for reporting information on the duration of stable power connection at each distributed grid connection site.

[0083] The end-point management subsystem is responsible for evaluating the recharge efficiency of end-point devices based on IoT information.

[0084] The scheduling subsystem is responsible for selecting the top N end devices based on their energy storage efficiency to supply power to themselves when a low-energy distributed grid is detected, and scheduling the low-energy distributed grid to reduce its connection accordingly.

[0085] The following specific examples describe a detailed implementation of a dynamic power grid dispatching system for addressing uncertainties in source and grid loads:

[0086] Example: This example includes three distributed clean energy grid sites, namely site 1, site 2, and site 3. Site 1 has a stable grid connection requirement of 100 units per unit time and an energy storage capacity of 2000 units. Site 2 has a stable grid connection requirement of 200 units per unit time and an energy storage capacity of 4000 units. Site 3 has a stable grid connection requirement of 300 units per unit time and an energy storage capacity of 6000 units. In this example, the preset threshold value Threshold1 is 10 units of time.

[0087] Each site is constantly monitoring the duration of its stable integration.

[0088] At time T0, station 1 detects that its local energy storage is only 950. Since the stable connection requirement of station 1 is 100 per unit time, this embodiment uses method 1 to calculate the stable connection maintenance time of the connection station. Therefore, the stable connection maintenance time of station 1 is 950 / 100 = 9.5. Since Threshold1 is set to 10 units of time, station 1 reports the event to the end management subsystem and the scheduling subsystem.

[0089] Next, the end-point management subsystem evaluates the re-storage efficiency of the end-point devices according to steps 2.1 to 2.3: First, according to step 2.1, the end-point management subsystem reads the sensor data and power generation equivalent area of ​​each end-point device; then, according to step 2.2, the end-point management subsystem obtains the power generation per unit equivalent area per unit time by looking up a table based on the sensor data; finally, according to step 2.3, the end-point management subsystem obtains the re-storage efficiency of the end-point devices by multiplying the "equivalent area" by the "power generation per unit equivalent area per unit time". In this embodiment, it is assumed that there are 60 end-point devices with energy storage function, of which the first 30 end-point devices have an "equivalent area" of 10, a "power generation per unit equivalent area per unit time" of 5, and a storage capacity of 100, and the last 30 end-point devices have an "equivalent area" of 1, a "power generation per unit equivalent area per unit time" of 1, and a storage capacity of 60. The power of each end-point device has been saturated.

[0090] Next, the scheduling subsystem completes the supplementary scheduling according to steps 3.1 to 3.10. First, according to step 3.1, it checks the under-powered distributed sites according to their energy storage capacity from low to high and stores them in ListA. Then, according to step 3.2, it determines that ListA is not empty (at this point, only site 1 reports an event where the stable connection time is less than the threshold), so it retrieves the first site First_site from ListA and deletes it from ListA. Then, according to step 3.3, the scheduling subsystem continuously reads the remaining energy storage of the under-powered distributed sites at Delta_interval intervals to obtain the remaining energy values ​​Surplus0 and Surplus1 of the sites. Then, according to step 3.4, the scheduling... The subsystem calculates Site_out - (surplus0 - surplus1) / Delta_interval to obtain the power generation Moment_dep per unit time within Delta_interval of the site. Site_out is the power generation value required for grid connection of the site per unit time. In this embodiment, it is assumed that the calculated result Moment_dep is 50. Then, according to step 3.5, in this embodiment, it is assumed that Min_dep is 30. The scheduling subsystem determines that Min_dep is less than Moment_dep, so it assigns 30 to ref_dep. Then, according to step 3.6, the scheduling subsystem calculates the supplementary time Supplement_tim. e = (Total_storage - surplus1) / ref_dep = (2000 - 930) / 30 = 36. Then, calculate the required supplementary power supply (Supplement_site = Supplement_time * Site_out = 36 * 100 = 3600) needed for the under-energy site to complete energy storage reconstruction. Next, according to step 3.7, the scheduling subsystem sorts the terminal devices by their recharge efficiency from highest to lowest based on their energy storage capacity. The first N terminal devices are then scheduled to perform self-supply of connected power according to the site's unit-time power requirements (the N scheduled terminal devices automatically switch to non-self-supply mode to provide power service after their own energy storage is depleted). The total power of the N terminal devices is greater than or equal to Supplement_site. According to the strategy of this invention, the first 30 terminal devices and the 31st to 42nd terminal devices are selected to cooperate in providing self-supply of backup power (the total backup power is 30*100+12*50=3600). The self-supply of backup power is based on the power requirement of the site per unit time (in this embodiment, the power requirement of site 1 is 100 per unit time). The scheduling subsystem schedules each terminal device to connect according to time, maintaining a continuous output of 100 per unit time, thereby achieving a stable replacement for the connection of site 1. Then, according to step 3.8, the scheduling subsystem schedules the under-powered distributed grid connection site to stop grid connection output and enter the energy storage reconstruction.In this embodiment, in step 3.8, after the scheduling subsystem schedules each connected site to enter energy storage reconstruction, it starts a timer_site for each site. Before Supplement_time, site 1 completes energy storage reconstruction. The preset scheduling subsystem schedules the site to enter normal service and terminates the self-powering of the terminal devices in advance. In this embodiment, if at this moment the first 30 terminal devices have just completed power supply and the remaining 12 terminal devices have not yet started power supply, then at this moment, the energy storage of the first 30 terminal devices is exhausted, while the energy storage of the remaining 30 terminal devices is still saturated.

[0091] Next, at time T1 (T1 is 38 time units after T0), station 2 detected that its local energy storage was only 1800. Since the stable grid connection requirement for station 2 is 200 per unit time, this embodiment uses method 1 to calculate the stable grid connection maintenance time. Therefore, the stable grid connection maintenance time for station 2 is 1800 / 200 = 9. Since Threshold1 is set to 10 units of time, station 2 reports this event to the end management subsystem and the scheduling subsystem.

[0092] Next, Site 2 calculates the required supplemental power according to the calculation method of Site 1. Assuming that the ref_dep calculation result of Site 2 in this embodiment is 98, then according to step 3.6, the scheduling subsystem calculates the supplemental time Supplement_time = (Total_storage - surplus1) / ref_dep = (4000 - 1800) / 98 = 23, and then calculates the supplemental power required for the under-energy site to complete energy storage reconstruction Supplement_site = Supplement_ti me*Site_out=23*200=4600; Since the supply period of site 1 is 36 time units, and site 2 occurs in the 38th time unit, there are 2 time units of charging time during this period. Therefore, the first 30 end devices have fully charged batteries due to their high energy storage efficiency, and the last 30 devices are also fully charged since they have not been used yet. Therefore, the scheduling subsystem schedules the first 3 end devices, plus the 31st to the 46th end devices, to continue self-supply, and schedules site 2 for energy storage reconstruction. As can be seen from this embodiment, if the end devices are not used for self-supply, this embodiment has already triggered two traditional power start-up supplementary supplies. If the heavy storage efficiency assessment is not introduced, and the end devices are only randomly selected for connection (for example, in this embodiment, site 1 selects the last 30 end devices, plus the last 18 end devices, to achieve 3600 power supply supplement, then when site 2 issues a warning, because the heavy storage efficiency of the last 30 end devices is low, they can only charge 2 units of electricity in 2 units of time, so when site 2 issues a warning, the total number of power supplies that the 60 end devices can provide is 100*30+12*60+18*2=3756, which cannot meet the connection requirements of the energy storage reconstruction period of site 2), then site 2 will trigger one traditional power start-up supplementary supply. This invention, on the one hand, departs from the design concept of a fully self-balancing grid-connected subsystem. By constructing a third-party regulating factor (end-device control and regulation), it further offsets and neutralizes the grid fluctuation levels constructed by existing technologies, thereby improving the stable output of production capacity and lowering the entry threshold for stable output of green grids. On the other hand, by introducing heavy storage efficiency assessment and dynamically scheduling end-devices for continuous self-power supply based on the assessment results, it enhances the recovery capability of the third-party regulating factor (end-device self-power supply), greatly improving the overall grid's reserve capacity. This provides a wide range of rapidly reconfigurable hedging technologies for various clean grid-connected subsystems, achieving stable entry thresholds for grid-connected subsystems, reducing the proportion of traditional power source restarts, and realizing the ability for continuous green power generation.

[0093] The method of this invention addresses the uncertainty of source-grid load by dynamically scheduling the energy storage capacity of widely distributed but grid-incompatible end-point devices for end-point self-supply based on the principle of prioritizing energy storage efficiency. This enhances the overall grid's reserve capacity and offsets the power supply to under-capacitated distributed grid-connected sites using the reserve capacity. Furthermore, it schedules limited-time, reduced-volume grid-connection of under-capacitated distributed grid-connected sites to achieve energy storage reconstruction, effectively addressing the problem of large fluctuations in green power generation capacity affecting stable power supply. This method significantly lowers the entry threshold for stable green grid-connected capacity, effectively promoting the construction and development of green power grids.

Claims

1. A power grid dispatching method for combating grid instability, characterized in that... Includes the following steps: Step 1: The grid connection subsystem reports the duration of stable power connection at each distributed grid connection point; Step 2: The end-point management subsystem evaluates the recharge efficiency of end-point devices based on IoT information; Step 3: The scheduling subsystem detects the presence of underpowered distributed grid points, selects the top N terminal devices based on energy storage efficiency to self-supply electricity using terminal energy storage, and schedules the underpowered distributed grid points to reduce their power supply accordingly. The terminal device is a power storage device with reusable energy storage capability but which cannot be connected to the grid; In step 1, the method for calculating the duration of stable grid connection maintenance includes: Method 1: Divide the remaining power storage of the distributed grid-connected power station by the grid-connected power output requirement per unit time of the grid-connected power station to obtain the stable grid connection maintenance time of the grid-connected power station; or, Method 2: Subtract the minimum power generation Min_dep per unit time within the preset time period from the grid-connected output requirement of the distributed grid-connected site to obtain Delta_out2in. Then, divide the remaining power storage of the grid-connected site by Delta_out2in to obtain the stable grid connection maintenance time of the grid-connected site. In step 3, the method for determining the occurrence of the underpowered distributed grid site is that the stable grid connection time is less than a preset threshold value Threshold1, where the preset threshold value Threshold1 is the response time from the discovery of the underpowered site to the switching of power supply from the traditional site. In step 2, the end-point management subsystem evaluates the recharge efficiency of the end-point device based on IoT information. The end-point management subsystem obtains IoT information and evaluates the recharge efficiency of the end-point device after detecting the occurrence of a low-energy distributed grid point. In step 2, the end-user management subsystem evaluates the recharge efficiency of the end-user devices based on IoT information. Specifically, the process is as follows: Step 2.1: The end-point management subsystem reads the sensor data and power generation equivalent area of ​​each end device; Step 2.2: The end-point management subsystem obtains the power generation per unit area per unit time by looking up a table based on the sensor data. Step 2.3: The end-point management subsystem calculates the recharge efficiency of the end-point device by multiplying the "equivalent area" by the "power generation per unit equivalent area per unit time".

2. The power grid dispatching method for combating grid instability according to claim 1, characterized in that: In step 1, the reported grid connection maintenance duration information is actively reported by the distributed grid connection points in the grid connection subsystem to the end management subsystem and the scheduling subsystem after the stable grid connection maintenance duration is less than the preset threshold value Threshold1.

3. The power grid dispatching method for combating grid instability according to claim 1, characterized in that: The method for calculating the preset time period includes: The preset time period refers to the time period between the current time point T_current and T_last.

4. A power grid dispatching method for combating grid instability according to claim 3, characterized in that: The X1 time periods P1 included in the time period between the current time point T_current and T_last are then used as references based on the X1 time points RefPoint(i) = T_current - i * P1, where i takes the values ​​0, ..., X1-1, to obtain the union of the X1 time periods [RefPoint(i) - Rang2, RefPoint(i)].

5. A power grid dispatching method for combating grid instability according to claim 1, characterized in that: In step 3, the scheduling subsystem detects the presence of under-powered distributed grid connection points, selects the top N end devices based on energy storage efficiency to utilize end-point energy storage for self-power supply, and schedules the under-powered distributed grid connection points to reduce their power supply accordingly. The specific steps are as follows: Step 3.1: Investigate under-powered distributed grid sites according to their energy storage capacity from low to high, and store them in ListA; Step 3.2: Determine if ListA is empty. If it is, proceed to step 3.

10. If not, retrieve the first site First_site from ListA and delete it from ListA. Step 3.3: The scheduling subsystem continuously reads the remaining power storage capacity of the underpowered distributed grid sites at Delta_interval intervals to obtain the remaining power capacity values ​​Surplus0 and surplus1 of the sites; Step 3.4: The scheduling subsystem calculates Site_out - (surplus0 - surplus1) / Delta_interval to obtain the power generation Moment_dep per unit time within Delta_interval of the site, where Site_out is the power generation value required to be connected to the grid per unit time of the site; Step 3.5: The scheduling subsystem determines whether Min_dep is greater than Moment_dep. If it is, Moment_dep is assigned to ref_dep. If not, Min_dep is assigned to ref_dep. In step 1, assuming that method 1 is used, the Min_dep value is calculated additionally according to method 2. Step 3.6: The scheduling subsystem calculates the replenishment time Supplement_time = (Total_storge - surplus1) / ref_dep, and calculates the amount of electricity replenishment required for the under-energy distributed grid site to complete energy storage reconstruction Supplement_site = Supplement_time * Site_out, where Total_storge is the total energy storage capacity of the site; Step 3.7: The scheduling subsystem sorts the terminal devices from high to low energy storage efficiency based on their energy storage capacity. The first N terminal devices are scheduled to perform self-supply of power according to the power requirements of the site per unit time. After the N terminal devices are depleted of their own energy storage, they automatically switch to non-self-supply mode to provide power service. The total energy of the N terminal devices is greater than or equal to Supplement_site. If it is not possible to select N terminal devices that meet the requirement of being greater than or equal to Supplement_site, then proceed to step 3.

9. Step 3.8: The scheduling subsystem schedules the under-energy distributed grid-connected power stations to stop grid connection output and enter the energy storage reconstruction phase; Step 3.9: The scheduling subsystem calculates the total power supply of First_site and all sites in ListA, and then selects the traditional power station with the smallest power generation and a power generation greater than the total power supply from the traditional power stations, and schedules the traditional power station to be put into service. Step 3.10: Stop scheduling.

6. A power grid dispatching method for combating grid instability according to claim 5, characterized in that: In step 3.8, after scheduling each grid station to enter energy storage reconstruction, the scheduling subsystem starts a timer_site for each station and periodically queries the energy storage reconstruction status. If reconstruction is completed before the timer_site timeout is less than Supplement_time, the station is scheduled to enter normal service, and the terminal equipment is scheduled to stop self-supply. If it is determined that the timer_site timeout is greater than Supplement_time and the station is still in a state of underpowerment, the station is shut down, a backup conventional power station is started to provide power supplementation, and the self-supply of the N terminal equipment is stopped.

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