Local area power grid optimization scheduling method based on double-cycle plan and double-mode control

Abstract

The invention discloses a local power grid optimal scheduling method based on double-cycle planning and double-mode control, and belongs to the technical field of power grid scheduling. Comprising the steps of generating a day-ahead plan according to prediction data, energy storage constraint data and time-of-use electricity price policy data, generating an intra-day rolling plan according to ultra-short-term prediction data, and correcting the day-ahead plan by using the intra-day rolling plan to obtain a double-cycle scheduling plan; when the local power grid executes the autonomous consumption mode, executing the double-cycle scheduling plan, and generating an autonomous consumption mode scheduling result; and when the local power grid executes the scheduling centralized control mode, generating a scheduling centralized control mode plan according to the scheduling instruction, executing the scheduling centralized control mode plan, and generating a scheduling result of the scheduling centralized control mode. According to the invention, through dual-cycle cooperative dual-mode adaptation, multi-target balance of high consumption of renewable energy sources, low power consumption cost and stable operation of the system is realized.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a local power grid optimization dispatching method based on dual-cycle planning and dual-mode control. Background Technology

[0002] With the large-scale integration of distributed wind power and photovoltaic power, integrated local power grids combining power generation, grid, load, and storage have become an important form of new power systems. However, existing dispatching methods suffer from three major pain points, making it difficult to adapt to the system's multi-objective requirements:

[0003] The plan has a slow response and poor adaptability: Traditional dispatching relies only on static day-ahead plans (24-hour intervals) and does not combine ultra-short-term forecasts for real-time correction. When wind and solar power output fluctuates (such as cloud cover causing a sudden drop in photovoltaic power) or load changes, the deviation between the plan and the actual operating conditions can reach more than 20%, which can easily lead to voltage fluctuations or an increase in curtailment rate.

[0004] Single control mode and insufficient scenario adaptability: Most scheduling methods only support one of the modes of autonomous consumption or centralized scheduling control. Under the autonomous consumption mode, they cannot respond to grid dispatching instructions, while under the centralized scheduling control mode, they ignore the economic operation target and cannot take into account both grid security and user costs.

[0005] Insufficient coordination among multiple objectives limits benefits: The lack of a linkage mechanism for cycle planning, mode control, and equipment coordination either focuses only on economic costs (such as excessive electricity purchases during off-peak hours) or only pursues the absorption rate (such as excessive discharge of energy storage leading to power shortages during peak hours), making it impossible to balance the three major objectives of renewable energy absorption, electricity costs, and energy storage lifespan. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a local power grid optimization scheduling method based on dual-cycle planning and dual-mode control. Through dual-cycle coordination and dual-mode adaptation, a multi-objective balance of high renewable energy consumption, low electricity cost, and stable system operation can be achieved.

[0007] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0008] On the one hand, this invention provides a local power grid optimization scheduling method based on dual-cycle planning and dual-mode control, including:

[0009] Acquire local power grid data; the local power grid data includes short-term forecast data, ultra-short-term forecast data, energy storage constraint data, and time-of-use pricing policy data;

[0010] Based on short-term forecast data, energy storage constraint data, and time-of-use pricing policy data, a day-ahead plan is generated. Based on ultra-short-term forecast data, an intraday rolling plan is generated. The day-ahead plan is then revised using the intraday rolling plan to obtain a dual-cycle dispatch plan.

[0011] When the local power grid implements the autonomous absorption mode, a two-cycle scheduling plan is executed to generate the autonomous absorption mode scheduling results;

[0012] When the local power grid executes the dispatch and control mode, a dispatch and control mode plan is generated according to the dispatch instructions, the dispatch and control mode plan is executed, and the dispatch and control mode dispatch results are generated.

[0013] Optionally, prior to the plan being generated, the following may also be included:

[0014] The negative power values ​​in the local power grid data are corrected to zero, and the time granularity of the local power grid data is linearly interpolated to obtain standardized local power grid data.

[0015] Optionally, the daily plan is revised based on the intraday rolling plan to obtain a two-cycle scheduling plan, including:

[0016] Calculate the forecast deviation value based on the intraday rolling plan;

[0017] If the prediction deviation is positive, the planned purchase volume is reduced or the planned abandonment volume is increased. If the prediction deviation is negative, the planned purchase volume is increased or the planned abandonment volume is reduced. If the current SOC value is lower than the preset percentage of the planned SOC value for the same period, the planned off-peak charging volume is increased or the planned peak discharging volume is reduced, thus obtaining a dual-cycle scheduling plan.

[0018] Optionally, the dual-cycle scheduling plan includes:

[0019] During off-peak hours, the surplus electricity from wind and solar power is used to charge the energy storage to obtain the off-peak charging amount. If the off-peak charging amount is less than the peak discharge demand, electricity is purchased from the grid according to the off-peak electricity price to obtain the off-peak electricity purchase amount.

[0020] During peak hours, the energy storage is discharged according to the peak discharge demand to obtain the peak discharge amount. If the energy storage discharge amount is less than the peak discharge demand, the electricity is purchased from the grid according to the peak electricity price to obtain the peak purchase amount.

[0021] During normal periods, maintain the energy storage SOC within a safe range.

[0022] Optionally, the amount of charging during off-peak hours in the dual-cycle scheduling plan is represented as follows:

[0023] ;

[0024] ;

[0025] in, , , These represent the maximum rechargeable power, the remaining power, and the maximum charging power of the energy storage, respectively. Indicates the upper limit of SOC; Indicates the current SOC value; Indicates the rated capacity; Indicates the duration of the valley period; Indicates the charging amount during off-peak hours; This indicates taking the minimum value.

[0026] Optionally, the peak discharge amount in the dual-cycle scheduling plan is expressed as:

[0027] ;

[0028] ;

[0029] in, , , These represent the maximum dischargeable power, the power outage power, and the maximum discharge power of the stored energy, respectively. Indicates the current SOC value; Indicates the lower limit of SOC; Indicates the rated capacity; Indicates the duration of the peak period; Indicates the discharge amount during the peak period; This indicates taking the minimum value.

[0030] Optionally, the centralized control and scheduling mode plan includes:

[0031] During off-peak hours, the ratio of energy storage charging to off-peak electricity purchase is allocated according to dispatch instructions to obtain the off-peak charging amount and off-peak electricity purchase amount;

[0032] During peak hours, the ratio of energy storage discharge to peak-hour purchase volume is allocated according to dispatch instructions to obtain peak-hour discharge volume and peak-hour purchase volume.

[0033] During normal periods, maintain the energy storage SOC within a safe range.

[0034] Optionally, it also includes performing consistency verification on the autonomous absorption mode scheduling results to generate qualified autonomous absorption mode scheduling results:

[0035] Calculate the planned wind power, planned photovoltaic power, and total curtailed power based on the autonomous power consumption mode dispatch results;

[0036] The autonomous consumption mode dispatch results are considered qualified if the deviations between the planned wind power and its preset power value, the planned photovoltaic power and its preset power value, and the total curtailed power and its preset power value are all less than the preset thresholds.

[0037] Secondly, the present invention provides a local power grid optimization scheduling system based on dual-cycle planning and dual-mode control, comprising:

[0038] The data acquisition module is used to: acquire local power grid data; the local power grid data includes short-term forecast data, ultra-short-term forecast data, energy storage constraint data, and time-of-use pricing policy data;

[0039] The dual-cycle planning module is used to: generate a day-ahead plan based on short-term forecast data, energy storage constraint data, and time-of-use pricing policy data; generate an intraday rolling plan based on ultra-short-term forecast data; and revise the day-ahead plan based on the intraday rolling plan to obtain a dual-cycle dispatch plan.

[0040] The dual-mode control module is used to: execute a two-cycle scheduling plan and generate the autonomous consumption mode scheduling results when the local power grid is in autonomous consumption mode.

[0041] When the local power grid executes the dispatch and control mode, a dispatch and control mode plan is generated according to the dispatch instructions, the dispatch and control mode plan is executed, and the dispatch and control mode dispatch results are generated.

[0042] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the local power grid optimization scheduling method based on dual-cycle planning and dual-mode control described in the first aspect.

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0044] This invention achieves a multi-objective balance of high renewable energy consumption, low electricity costs, and stable system operation by combining a dual-cycle coordination of daily planning to determine direction and intraday planning to compensate for deviations, along with a dual-mode adaptation of autonomous consumption to ensure economy and centralized dispatch and control to ensure safety. It improves the renewable energy consumption rate and reduces electricity costs within the region, and is applicable to various integrated local power grids of source, grid, load and storage, providing core support for the stable, economical and efficient operation of the system. Attached Figure Description

[0045] Figure 1 The diagram shown is a flowchart of one embodiment of the local power grid optimization scheduling method based on dual-cycle planning and dual-mode control of the present invention.

[0046] Figure 2 The diagram shown is a schematic representation of the local power grid optimization scheduling system based on dual-cycle planning and dual-mode control in one embodiment of the present invention. Detailed Implementation

[0047] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0048] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0049] Example 1

[0050] like Figure 1 As shown in the figure, this embodiment introduces a local power grid optimization scheduling method based on dual-cycle planning and dual-mode control, including the following steps:

[0051] Step 1: Obtain local area network data, specifically:

[0052] Local power grid data includes forecast data, energy storage constraint data, and time-of-use pricing policy data;

[0053] Short-term forecast data (15-minute interval, 96 points / day, used for day-ahead planning) is read from the short-term forecast result table in the database. Ultra-short-term forecast data (15-minute interval, 16 points / 4 hours, used for intraday planning) is read from the ultra-short-term forecast result table in the database. Energy storage constraint data is read from the configuration database or configuration file (configuration method can be flexibly selected), including the current State of Charge (SOC) value, upper limit SOC value, lower limit SOC value, rated capacity, maximum charging power of energy storage, and maximum discharging power of energy storage. Time-of-use electricity price policy data is obtained by parsing the configuration file, including peak hours, valley hours, normal hours and corresponding electricity prices by month and season. The mode pressure plate status is read from the real-time database to determine whether to implement the autonomous consumption mode or the dispatch centralized control mode.

[0054] Short-term forecast data is collected once a day (for day-ahead planning), ultra-short-term forecast data is collected every 5 minutes (for intraday planning), energy storage SOC data collection interval is ≤1 second, and time-of-use electricity price policy data is updated once a month according to local policies (based on configuration file updates) to ensure data timeliness supports the coordination of dual-cycle plans.

[0055] The upper limit of SOC is ≥80% of the rated capacity, the lower limit of SOC is ≤30% of the rated capacity, the maximum charging power is ≤1.2 times the rated energy storage power, and the maximum discharging power is ≤1.2 times the rated energy storage power. When planning to generate energy, the charging and discharging power must be limited to the constraints.

[0056] Step 2: Generate a two-cycle plan, specifically as follows:

[0057] The daily plan sets the framework, while the intraday plan corrects for discrepancies. The two are coordinated through data preprocessing and SOC (System-Oriented Computing) integration. Whether it's a daily or intraday plan, data standardization is essential first.

[0058] First, perform abnormal data cleanup, correcting negative power values ​​in the forecast data (such as invalid data caused by equipment failure) to zero to avoid abnormal data affecting the accuracy of the plan;

[0059] Then, slope linear interpolation is performed to convert the 15-minute interval prediction data into 5-minute interval prediction data, ensuring that the time granularity of the prediction data meets the scheduling accuracy requirements.

[0060] Triggered daily at 8:00 AM (time is configurable), it generates a 288-point (5-minute interval) day-ahead plan for the next day based on forecast data, energy storage constraint data, and time-of-use electricity pricing policy data, providing a basic framework for the intraday plan.

[0061] Triggered every 5 minutes, an intraday rolling plan is generated based on ultra-short-term forecast data. The intraday rolling plan is used to correct the 48-point data for the next 4 hours in the daily plan, reducing the deviation between the plan and real-time operating conditions, and obtaining a dual-cycle daily plan to determine the charging amount, purchasing amount, discharging amount, and purchasing amount during the off-peak hours.

[0062] Only the overlapping data for the next 4 hours in the day-ahead plan is replaced with the intraday rolling plan, while the non-overlapping data is retained from the day-ahead plan. At the same time, based on the current real-time SOC and the SOC target value of the plan, with the core focus on tracking ultra-short-term forecast deviations and connecting the day-ahead SOC target, the correction steps are as follows:

[0063] First, calculate the forecast deviation value based on the intraday rolling plan. Forecast deviation value = ultra-short-term forecast power - day-ahead plan forecast power (positive deviation indicates an increase in wind and solar power output, and negative deviation indicates a decrease in solar power output).

[0064] Then, the charging / discharging / electricity purchase plan is revised. If the forecast deviation is positive, the planned electricity purchase amount is reduced or the planned abandoned amount is increased. If the forecast deviation is negative, the planned electricity purchase amount is increased or the planned abandoned amount is decreased.

[0065] Finally, SOC connection is performed. The current SOC value is read. If the current SOC value is lower than the preset percentage of the SOC value of the same period of the previous day plan, the charging amount during the valley period in the previous day plan is increased or the discharging amount during the peak period in the previous day plan is decreased to ensure that the SOC returns to the target value of the previous day plan at the end of the day plan, thus obtaining the dual-cycle previous day plan. In this embodiment, the preset percentage is 5%.

[0066] With the goal of minimizing economic costs and balancing energy storage SOC, strategies are formulated according to different time periods:

[0067] During off-peak hours, surplus wind and solar power is prioritized for charging energy storage, resulting in off-peak charging capacity. The charging power does not exceed the maximum charging power and SOC limit of the energy storage. If peak-hour discharge capacity needs to be stored and the off-peak charging capacity is less than the peak-hour discharge demand, electricity is purchased at the off-peak electricity price to supplement charging, resulting in off-peak purchased electricity. The off-peak charging capacity in the dual-cycle dispatch plan is expressed as:

[0068] ;

[0069] ;

[0070] in, , , These represent the maximum rechargeable power, the remaining power, and the maximum charging power of the energy storage, respectively. Indicates the upper limit of SOC; Indicates the current SOC value; Indicates the rated capacity; Indicates the duration of the valley period; Indicates the charging amount during off-peak hours; This indicates taking the minimum value;

[0071] During peak hours, energy storage discharge is prioritized to meet peak discharge demand, resulting in the peak discharge volume. The discharge power does not exceed the lower limit of the maximum discharge power (SOC) of the energy storage. If the energy storage discharge volume is less than the peak discharge demand, electricity is purchased at the peak electricity price, resulting in the peak purchase volume. The peak discharge volume in the dual-cycle dispatch plan is expressed as follows:

[0072] ;

[0073] ;

[0074] in, , , These represent the maximum dischargeable power, the power outage power, and the maximum discharge power of the stored energy, respectively. Indicates the lower limit of SOC; Indicates the duration of the peak period; Indicates the discharge amount during the peak period;

[0075] During normal periods, maintain the energy storage SOC within a reasonable range to reserve sufficient margin for flexible charging and discharging; if If <40%, charge using the remaining power; if >70%, discharge or abandon power, maintain Between 40% and 70% (the SOC security range is configurable).

[0076] Step 3: Dual-mode control executes the scheduling plan, specifically as follows:

[0077] By flexibly switching modes to adapt to different operating scenarios, the mode control board status is monitored in real time, and the mode switch is completed within 1 second after the status changes, ensuring rapid adaptation to scheduling instructions or operating requirements. The two modes share the basic data of the dual-cycle plan, and the only difference is in the strategy execution stage.

[0078] When the mode control pressure plate status is 0, the local power grid executes the autonomous consumption mode. Under the autonomous consumption mode, time-of-use electricity price policy data and energy storage constraint data are the core, and the dual-cycle day-ahead plan is executed to generate autonomous consumption mode dispatch results. It is applicable to scenarios where the power grid has no mandatory dispatch instructions, ensuring that the user side prioritizes economic benefits.

[0079] In one specific embodiment, during a certain off-peak period (00:00-04:00), the remaining power is 5MW, the maximum charging power of energy storage is 10MW, the current SOC value is 50%, the rated capacity is 20MWh, the upper limit of SOC is 80%, and the duration of the off-peak period is 4 hours.

[0080] Maximum rechargeable capacity = (80% - 50%) × 20 = 6MWh, rechargeable power = 6 / 4 = 1.5MW;

[0081] Therefore, the charging power = min(5MW, 10MW, 1.5MW) = 1.5MW, no need to purchase electricity, and the SOC rises to 80% after 4 hours;

[0082] When the mode control switch is in state 1, the local power grid executes the dispatch and centralized control mode. In this mode, dispatch commands are the core, and a dispatch and centralized control mode plan is generated, executed, and dispatch and centralized control mode dispatch results are generated based on these commands. This mode is suitable for scenarios where the power grid issues mandatory dispatch commands (such as when the power grid is short of power and energy storage discharge is needed to support it), ensuring the safety of the power grid side is prioritized. The centralized control dispatch mode plan includes:

[0083] During off-peak hours, the ratio of energy storage charging to off-peak electricity purchase is allocated according to dispatch instructions to obtain the off-peak charging amount and off-peak electricity purchase amount;

[0084] During peak hours, the ratio of energy storage discharge to peak-hour purchase volume is allocated according to dispatch instructions to obtain peak-hour discharge volume and peak-hour purchase volume.

[0085] During normal periods, maintain the energy storage SOC within a safe range.

[0086] In one specific embodiment, during a certain off-peak period (00:00-04:00), the dispatching instruction requires the energy storage to discharge 2MW to support the grid. Although it is an off-peak period and there is a surplus of 3MW, the discharge power of 2MW is still executed according to the instruction. The purchased power = power shortage + discharge power = (-3MW) + 2MW = -1MW (that is, the surplus power is 1MW, which can be abandoned or the output of other power sources can be reduced).

[0087] Manual switching is supported, and the switching process is uninterrupted: the current plan execution status is recorded before switching, and the new mode strategy is continued to be executed based on the current status after switching, avoiding voltage fluctuations caused by plan gaps.

[0088] Step four, generate the optimized scheduling results, specifically:

[0089] The autonomous consumption mode dispatch results generated by merging the intraday rolling plan and the day-ahead plan are used to calculate the wind power planned power = wind power predicted power - wind power curtailment power, the photovoltaic planned power = photovoltaic power predicted power - photovoltaic curtailment power, and the total curtailment power = wind power curtailment + photovoltaic curtailment.

[0090] When the deviations between the planned wind power and its preset power value, the planned photovoltaic power and its preset power value, and the total curtailed power and its preset power value are all less than the preset threshold, the plan is deemed qualified. After the verification is passed, the qualified plan result is stored in the database result table, and the operation log is recorded. In this embodiment, the preset threshold is 0.001MW.

[0091] Example 2

[0092] Based on Example 1, this example introduces an experimental example of a local power grid optimization scheduling method based on dual-cycle planning and dual-mode control:

[0093] Taking the integrated local area system of power generation, grid, load and storage as an example, the system includes 10MW of wind power, 5MW of photovoltaic power, 20MWh / 10MW of energy storage, and 15MW of load. The electricity pricing policy is configured for the windy season (peak hours are 06:00-08:00 / 18:00-22:00, with an electricity price of 600 yuan / MWh; normal hours are 04:00-06:00 / 08:00-11:00 / 16:00-18:00 / 22:00-24:00, with an electricity price of 400 yuan / MWh; and off-peak hours are 00:00-04:00 / 11:00-16:00, with an electricity price of 200 yuan / MWh). The priority configuration for wind and solar power is wind priority.

[0094] Deploy the optimized scheduling module on the application server, confirm that it can obtain data from the short-term forecast table and the ultra-short-term forecast table, and configure the relevant time-series electricity price configuration file.

[0095] Configure energy storage constraint data, including SOC upper limit of 80%, SOC lower limit of 30%, rated capacity of 20MWh, maximum charging power of 10MW, and maximum discharging power of 10MW;

[0096] Configure dual-cycle plan parameters, including the daily plan trigger time of 8:00 and the intraday plan trigger interval of 5 minutes;

[0097] Configure mode switching parameters, including the mode switching pressure plate status;

[0098] Short-term forecast data for the next day is collected at 7:00 every day, ultra-short-term forecast data for the next 4 hours is collected every 5 minutes, and energy storage SOC is collected in real time (at 1-second intervals).

[0099] The dual-cycle plan was generated and implemented (taking December 5th as an example):

[0100] First, generate the day-ahead plan (December 4th, 8:00 AM), specifically as follows:

[0101] Read the short-term forecast data for December 5th, correct outliers, and interpolate to 288 points at 5-minute intervals;

[0102] Strategy calculation during off-peak hours (11:00-16:00):

[0103] At 11:00, the predicted load was 10MW, wind power was 8MW, and solar power was 3MW. =10-(8+3)=-1MW (1MW of surplus power);

[0104] =50%, Maximum rechargeable capacity = (80%-50%)×20=6MWh, =5h、 = 6 / 5 = 1.2MW;

[0105] = min(1MW, 10MW, 1.2MW) = 1MW, no need to purchase electricity, cumulative charging from 11:00 to 16:00 is 5MW × 5h = 25MWh (correction here: 5-minute interval, 12 points in 1 hour, 60 points in 5 hours, charging power 1MW, cumulative charging 1MW × 5h = 5MWh, SOC rises to 50% + 5 / 20 = 75%)

[0106] Strategy calculation during peak hours (18:00-22:00):

[0107] At 19:00, the load forecast was 15MW, the wind power forecast was 5MW, and the photovoltaic forecast was 0MW. =15-5=10MW (10MW power shortage);

[0108] =75%, Maximum discharge capacity = (75%-30%)×20=9MWh, =4h、 = 9 / 4 = 2.25MW;

[0109] = min(10MW, 10MW, 2.25MW) = 2.25MW, purchased power = 10 - 2.25 = 7.75MW, purchased at 600 yuan / MWh.

[0110] Then, the intraday plan is revised (December 5th, 14:00), as follows:

[0111] Reading the ultra-short-term forecast data from 14:00 to 18:00, it was found that the wind power forecast at 15:00 was revised from the planned 7MW to 9MW (positive deviation of 2MW).

[0112] At 15:00, =11-(9+2)=0MW (no surplus power / power shortage), the planned charging power at 15:00 is 1MW, which is revised to 0MW, to reduce power purchase (if any) or abandon power;

[0113] =72% (as planned at 15:00) =73%), deviation of 1% < 5%, no additional adjustment is needed, maintain the intraday planned SOC target consistent with the previous day.

[0114] Finally, dual-mode control will be implemented (December 5th, 10:00 AM), specifically as follows:

[0115] Operating in self-consumption mode (00:00-10:00), the mode switch is in self-consumption mode, executing according to the daily plan, charging during off-peak hours until... =75%, maintained during normal periods At 70%-75%, the cumulative electricity purchase cost is 23,000 yuan;

[0116] Mode switching (10:00): Receive dispatch instructions, switch mode control panel status to 1 (dispatch control), requiring 3MW of energy storage to discharge to support the grid from 10:00 to 12:00;

[0117] The system operates in centralized dispatch control mode (10:00-12:00). At 10:00, the predicted load is 12MW, wind power is 6MW, and photovoltaic power is 4MW. =12-(6+4)=2MW (power shortage of 2MW); Discharge 3MW according to dispatch instructions, actual power shortage = 2MW+3MW=5MW (power needs to be purchased 5MW); Discharge power = 3MW (according to instructions), power purchased = 5MW. Although it is a normal period (400 yuan / MWh), the dispatch instructions are given priority.

[0118] Mode switchback (12:00), scheduling command released, mode pressure plate status switched back to 0, based on =68% (after discharge), increase the charging power to 1.2MW during off-peak hours (11:00-16:00) to ensure charging at 16:00. The planned target for the return date is 80%.

[0119] Example 3

[0120] Based on Example 1, such as Figure 2 As shown in the figure, this embodiment introduces a local power grid optimization scheduling system based on dual-cycle planning and dual-mode control, including:

[0121] The data acquisition module is used to: acquire local power grid data; the local power grid data includes short-term forecast data, ultra-short-term forecast data, energy storage constraint data, and time-of-use pricing policy data;

[0122] The dual-cycle planning module is used to: generate a day-ahead plan based on forecast data, energy storage constraint data, and time-of-use pricing policy data; generate an intraday rolling plan based on ultra-short-term forecast data; and revise the day-ahead plan based on the intraday rolling plan to obtain a dual-cycle dispatch plan.

[0123] The dual-mode control module is used to: execute a two-cycle scheduling plan and generate the autonomous consumption mode scheduling results when the local power grid is in autonomous consumption mode.

[0124] When the local power grid executes the dispatch and control mode, a dispatch and control mode plan is generated according to the dispatch instructions, the dispatch and control mode plan is executed, and the dispatch and control mode dispatch results are generated.

[0125] The result verification module is used to: perform consistency verification on the autonomous absorption mode scheduling results and generate qualified autonomous absorption mode scheduling results.

[0126] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0127] Example 4

[0128] This embodiment introduces a computer-readable storage medium storing a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the steps of the local power grid optimization scheduling method based on dual-cycle planning and dual-mode control described in Embodiment 1.

[0129] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0131] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A local power grid optimization scheduling method based on dual-cycle planning and dual-mode control, characterized in that, include: Acquire local power grid data; the local power grid data includes short-term forecast data, ultra-short-term forecast data, energy storage constraint data, and time-of-use pricing policy data; Based on short-term forecast data, energy storage constraint data, and time-of-use pricing policy data, a day-ahead plan is generated. Based on ultra-short-term forecast data, an intraday rolling plan is generated. The day-ahead plan is then revised using the intraday rolling plan to obtain a dual-cycle dispatch plan. When the local power grid implements the autonomous absorption mode, a two-cycle scheduling plan is executed to generate the autonomous absorption mode scheduling results; When the local power grid executes the dispatch and control mode, a dispatch and control mode plan is generated according to the dispatch instructions, the dispatch and control mode plan is executed, and the dispatch and control mode dispatch results are generated.

2. The local power grid optimization scheduling method based on dual-cycle planning and dual-mode control according to claim 1, characterized in that, Prior to the planned date of generation, it also includes: The negative power values ​​in the local power grid data are corrected to zero, and the time granularity of the local power grid data is linearly interpolated to obtain standardized local power grid data.

3. The local power grid optimization scheduling method based on dual-cycle planning and dual-mode control according to claim 1, characterized in that, The daily rolling plan is used to revise the daily plan, resulting in a two-cycle scheduling plan, including: Calculate the forecast deviation value based on the intraday rolling plan; If the prediction deviation is positive, the planned purchase volume is reduced or the planned abandonment volume is increased. If the prediction deviation is negative, the planned purchase volume is increased or the planned abandonment volume is reduced. If the current SOC value is lower than the preset percentage of the planned SOC value for the same period, the planned off-peak charging volume is increased or the planned peak discharging volume is reduced, thus obtaining a dual-cycle scheduling plan.

4. The local power grid optimization scheduling method based on dual-cycle planning and dual-mode control according to claim 1, characterized in that, The dual-cycle scheduling plan includes: During off-peak hours, the surplus electricity from wind and solar power is used to charge the energy storage to obtain the off-peak charging amount. If the off-peak charging amount is less than the peak discharge demand, electricity is purchased from the grid according to the off-peak electricity price to obtain the off-peak electricity purchase amount. During peak hours, the energy storage is discharged according to the peak discharge demand to obtain the peak discharge amount. If the energy storage discharge amount is less than the peak discharge demand, the electricity is purchased from the grid according to the peak electricity price to obtain the peak purchase amount. During normal periods, maintain the energy storage SOC within a safe range.

5. The local power grid optimization scheduling method based on dual-cycle planning and dual-mode control according to claim 4, characterized in that, The off-peak charging amount in the dual-cycle scheduling plan is expressed as follows: ； ； in, , , These represent the maximum rechargeable power, the remaining power, and the maximum charging power of the energy storage, respectively. Indicates the upper limit of SOC; Indicates the current SOC value; Indicates the rated capacity; Indicates the duration of the valley period; Indicates the charging amount during off-peak hours; This indicates taking the minimum value.

6. The local power grid optimization scheduling method based on dual-cycle planning and dual-mode control according to claim 4, characterized in that, The peak discharge amount in the dual-cycle scheduling plan is expressed as follows: ； ； in, , , These represent the maximum dischargeable power, the power outage power, and the maximum discharge power of the stored energy, respectively. Indicates the current SOC value; Indicates the lower limit of SOC; Indicates the rated capacity; Indicates the duration of the peak period; Indicates the discharge amount during the peak period; This indicates taking the minimum value.

7. The local power grid optimization scheduling method based on dual-cycle planning and dual-mode control according to claim 1, characterized in that, The centralized control and scheduling mode plan includes: During off-peak hours, the ratio of energy storage charging to off-peak electricity purchase is allocated according to dispatch instructions to obtain the off-peak charging amount and off-peak electricity purchase amount; During peak hours, the ratio of energy storage discharge to peak-hour purchase volume is allocated according to dispatch instructions to obtain peak-hour discharge volume and peak-hour purchase volume. During normal periods, maintain the energy storage SOC within a safe range.

8. The local power grid optimization scheduling method based on dual-cycle planning and dual-mode control according to claim 1, characterized in that, It also includes consistency verification of the autonomous absorption mode scheduling results to generate qualified autonomous absorption mode scheduling results: Calculate the planned wind power, planned photovoltaic power, and total curtailed power based on the autonomous power consumption mode dispatch results; The autonomous consumption mode dispatch results are considered qualified if the deviations between the planned wind power and its preset power value, the planned photovoltaic power and its preset power value, and the total curtailed power and its preset power value are all less than the preset thresholds.

9. A local power grid optimization dispatching system based on dual-cycle planning and dual-mode control, characterized in that, include: The data acquisition module is used to: acquire local power grid data; the local power grid data includes short-term forecast data, ultra-short-term forecast data, energy storage constraint data, and time-of-use pricing policy data; The dual-cycle planning module is used to: generate a day-ahead plan based on short-term forecast data, energy storage constraint data, and time-of-use electricity pricing policy data; generate an intraday rolling plan based on ultra-short-term forecast data; and revise the day-ahead plan based on the intraday rolling plan to obtain a dual-cycle dispatch plan. The dual-mode control module is used to: execute a two-cycle scheduling plan and generate the autonomous consumption mode scheduling results when the local power grid is in autonomous consumption mode. When the local power grid executes the dispatch and control mode, a dispatch and control mode plan is generated according to the dispatch instructions, the dispatch and control mode plan is executed, and the dispatch and control mode dispatch results are generated.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the local power grid optimization scheduling method based on dual-cycle planning and dual-mode control as described in any one of claims 1-8.

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