Virtual power plant frequency modulation and peak regulation method, device, system, equipment and storage medium

Abstract

The application provides a virtual power plant frequency modulation and peak regulation method, comprising the following steps: acquiring a wind and light load prediction curve, market price prediction information and a power grid next-day peak regulation demand curve, establishing a first planning model to obtain each controllable resource base point power plan and energy storage SOC plan of each preset time period of the next day; after the next day, every preset time period is executed: acquiring short-term wind and light load prediction data, actual operation state of each resource and real-time market price information, establishing a second planning model to correct each controllable resource base point power plan and energy storage SOC plan of the current time period, and determining a frequency modulation parameter of the current time period; according to the corrected each controllable resource base point power plan and energy storage SOC plan of the current time period, instructing corresponding equipment to execute; if a power grid frequency modulation instruction is received, adjusting the output of the frequency modulation resource according to the frequency modulation parameter of the current time period. The method solves the limitation problem of the virtual power plant in frequency modulation and peak regulation.

Description

Technical Field

[0001] This application relates to the field of virtual power plant technology, and in particular to a virtual power plant frequency regulation and peak shaving method, apparatus, system, equipment and storage medium. Background Technology

[0002] As more and more new energy sources (such as photovoltaic and wind power) are connected to the grid, the randomness and volatility of their output may lead to increased fluctuations in grid frequency and load. In order to improve the grid's ability to absorb a high proportion of new energy sources and the stability of grid operation, virtual power plants (VPPs) are usually used to achieve this technical objective.

[0003] A virtual power plant integrates "source-grid-load-storage." "Source" refers to distributed power sources (such as photovoltaics, wind power, and gas turbines), "grid" refers to the distribution network, "load" refers to flexible loads (such as interruptible loads and adjustable loads), and "storage" refers to energy storage systems (such as battery storage). Integration means aggregating and managing these dispersed resources to collaboratively respond to grid demands. Therefore, a virtual power plant can function as a special type of "power plant," participating in grid peak shaving and frequency regulation.

[0004] Existing virtual power plants have certain limitations in frequency regulation and peak shaving, such as mismatch between resource characteristics and service demand, difficulty in balancing the real-time performance of frequency regulation and the economic efficiency of peak shaving, insufficient global economic optimization, and weak ability to cope with uncertainties. Summary of the Invention

[0005] To address the technical limitations of virtual power plants in frequency regulation and peak shaving, this application provides a virtual power plant frequency regulation and peak shaving method, device, system, equipment, and storage medium. This application adopts a three-level optimization structure of day-ahead, intraday, and real-time to handle peak shaving plans, rolling corrections, and frequency regulation control tasks respectively, and solves the limitation problem based on multi-timescale coordination.

[0006] Firstly, this application provides a virtual power plant frequency regulation and peak shaving method, comprising:

[0007] Obtain wind and solar load forecast curves, market price forecast information, and the grid peak-shaving demand curve for the next day, and establish a first planning model to obtain the power plan and energy storage SOC plan for each controllable resource base point in each preset time period of the next day.

[0008] The following steps are performed every preset time period after the next day: acquire short-term wind and solar load forecast data, actual operating status of each resource and real-time market price information, establish a second planning model to correct the base point power plan and energy storage SOC plan of each controllable resource in the current time period, and determine the frequency regulation parameters for the current time period.

[0009] Based on the revised power plans and energy storage SOC plans for each controllable resource base point in the current time period, the corresponding equipment is instructed to execute; if a grid frequency regulation command is received, the output of the frequency regulation resources is adjusted according to the frequency regulation parameters in the current time period.

[0010] Secondly, this application provides a virtual power plant frequency regulation and peak shaving device, comprising:

[0011] The daytime peak shaving planning layer is used to: obtain wind and solar load forecast curves, market price forecast information, and the grid peak shaving demand curve for the next day, and establish the first planning model to obtain the power plan and energy storage SOC plan for each controllable resource base point in each preset time period of the next day.

[0012] The intraday rolling planning layer is used to execute every preset time period after the next day: obtain short-term wind and solar load forecast data, actual operating status of each resource and real-time market price information, establish a second planning model to correct the base point power plan and energy storage SOC plan of each controllable resource in the current time period, and determine the frequency regulation parameters of the current time period.

[0013] The real-time frequency regulation control layer is used to: instruct the corresponding equipment to execute according to the revised power plan and energy storage SOC plan of each controllable resource base point for the current time period; if a grid frequency regulation command is received, adjust the output of the frequency regulation resources according to the frequency regulation parameters for the current time period.

[0014] Thirdly, this application provides a virtual power plant frequency regulation and peak shaving system, including a data acquisition and communication device, a resource proxy control unit, and a virtual power plant frequency regulation and peak shaving device as described in the second aspect.

[0015] Fourthly, this application provides a virtual power plant frequency regulation and peak shaving device, including a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program and implement the virtual power plant frequency regulation and peak shaving method as described in the first aspect when executing the computer program.

[0016] Fifthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the virtual power plant frequency regulation and peak shaving method as described in the first aspect.

[0017] Based on the above technical solutions, it can be seen that the problem-solving approach of this application is layered and time-divisional, and collaborative optimization. The frequency regulation and peak shaving tasks of the virtual power plant are decomposed into a day-ahead peak shaving plan layer, an intraday rolling optimization layer, and a real-time frequency regulation control layer according to the time scale. Each layer solves decision-making problems with different time scales and different levels of precision, realizing refined division of resources and multi-time scale collaboration. At the same time, closed-loop optimization is achieved through information interaction between upper and lower layers (such as the issuance of planned values ​​and the reporting of actual status). Therefore, this application can achieve the following technical effects: 1) Refined division of labor: Decoupling peak-shaving tasks (long-term, high-power) and frequency regulation tasks (short-term, rapid) in terms of time scale and resource type, so that various resources can give full play to their strengths and avoid their weaknesses, and the overall efficiency is significantly improved; 2) Collaborative optimization: Through a three-layer architecture, the connection from day-ahead macro planning to real-time precise control is realized, effectively coordinating resources with different response speeds and taking into account both economy and reliability; 3) Maximizing economic benefits: Through multi-time scale optimization, market benefits and operating costs are comprehensively considered, and detailed costs such as energy storage life are considered in real-time control, so as to maximize the benefits of the virtual power plant; 4) Strong robustness: Rolling optimization and real-time feedback mechanisms can effectively smooth out the fluctuations caused by the uncertainty of new energy and load, improve the reliability of the virtual power plant, and thus ensure the safety of the power grid. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a virtual power plant frequency regulation and peak shaving method in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of a virtual power plant frequency regulation and peak shaving device in an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of a virtual power plant frequency regulation and peak shaving system in one embodiment of this application;

[0022] Figure 4 This is a schematic diagram of a virtual power plant frequency regulation and peak shaving device in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should also be understood that the terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, or the above-mentioned drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence, and should not be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated.

[0026] It should also be understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations. Furthermore, in the following description, the use of suffixes such as "module," "part," "layer," or "unit" to denote elements is solely for the purpose of illustration and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0027] Existing virtual power plants have certain limitations in frequency regulation and peak shaving. For example:

[0028] First, the mismatch between resource characteristics and service demands leads to resource waste. Peak shaving and frequency regulation have different requirements for resource response speed and duration. For example, energy storage systems have a fast response but limited energy, making them suitable for frequency regulation, while gas turbines have a slow regulation speed but abundant energy, making them suitable for peak shaving. However, existing virtual power plants have failed to make refined divisions of labor based on the technical characteristics of peak shaving and frequency regulation. This can lead to high-quality frequency regulation resources (such as energy storage systems) being used for peak shaving, resulting in resource waste.

[0029] Second, it is difficult to balance the real-time performance of frequency regulation with the economic efficiency of peak shaving. Peak shaving (hour-minute level) and frequency regulation (minute-second level) belong to different time scales, but existing virtual power plants usually adopt a planning model with a single time scale, that is, they only use a planning model with one time scale to meet either peak shaving or frequency regulation. This cannot simultaneously take into account the real-time performance of frequency regulation and the economic efficiency of peak shaving.

[0030] Third, insufficient optimization of overall economic efficiency. When simultaneously participating in the energy market, frequency regulation ancillary service market, and peak shaving ancillary service market, how to coordinate the allocation of aggregated resources to maximize the total revenue of each aggregator in the virtual power plant while meeting grid dispatch requirements is a key concern for users. Existing virtual power plant control strategies often fail to adequately consider market fluctuations and resource costs, resulting in poor actual economic efficiency.

[0031] Fourth, weak ability to cope with uncertainty. The output and load demand of distributed new energy sources (such as photovoltaic and wind power) are highly random and volatile. This uncertainty brings great difficulties to the precise scheduling of virtual power plants, which can easily lead to excessive deviation between expected regulation capacity and actual output. This may not only affect the safety of the power grid, but may even lead to performance evaluation and fines from the power grid.

[0032] To address this, embodiments of this application provide a virtual power plant frequency regulation and peak shaving method, apparatus, system, equipment, and storage medium. The problem-solving approach of these embodiments is layered, time-sharing, and collaborative optimization. The frequency regulation and peak shaving tasks of the virtual power plant are decomposed into a day-ahead peak shaving planning layer, an intraday rolling optimization layer, and a real-time frequency regulation control layer. Each layer addresses decision-making problems at different time scales and with varying levels of precision, achieving refined resource allocation and multi-time-scale collaboration. Simultaneously, closed-loop optimization is achieved through information exchange between upper and lower layers (such as the distribution of planned values ​​and the reporting of actual status).

[0033] The virtual power plant frequency regulation and peak shaving method provided in this application embodiment, such as Figure 1 As shown, it may include steps S10 to S30.

[0034] Step S10: Obtain the wind and solar load forecast curve, market price forecast information, and the grid peak-shaving demand curve for the next day. Establish the first planning model to obtain the power plan of each controllable resource base point and the energy storage SOC (State of Charge, battery state of charge, used to represent the remaining power) plan for each preset time period of the next day.

[0035] The wind and solar load forecast curves include wind power output forecast curves (i.e., forecasts of wind power generation in the next 24 hours), solar power output forecast curves (i.e., forecasts of solar power generation in the next 24 hours), and load forecast curves (i.e., forecasts of electricity consumption in the next 24 hours). This data can be predicted based on the historical operating data of virtual power plants, for example, by using AI models with predictive capabilities and inputting historical operating data. In one embodiment, after obtaining the wind and solar load forecast curves, an LSTM (Long Short-Term Memory) network can be used to optimize the wind and solar load forecast curves to improve prediction accuracy. Market price forecast information includes energy market clearing price forecast information (i.e., forecasts of electricity sales prices in the next 24 hours, such as the sales price per kilowatt-hour between 6:00 AM and 8:00 AM the following day) and frequency regulation / peak shaving ancillary service market price forecast information (i.e., forecasts of service prices for participating in frequency regulation and peak shaving in the next 24 hours). This data can be obtained directly from market regulators, for example. The next day's peak-shaving demand curve is the peak-shaving demand curve published by the power grid for the next day (i.e., how virtual power plants participate in peak shaving within the next 24 hours). This type of data can be obtained directly from the power grid. It should be noted that the data obtained in step S10 must include complete data for the next day. For example, if the next 24 hours are divided into 15-minute segments, with each 15-minute segment corresponding to a data set, then the data obtained in step S10 will include 96 sets of predicted data, that is, a total of 96 data points, with each 15-minute segment as a point. It can also be seen that the preset time period can be 15 minutes, or it can be 10 minutes, 20 minutes, or other preset time periods.

[0036] Step S10 corresponds to the day-ahead peak shaving planning layer. Its technical purpose is to formulate the macro-level power plan for peak shaving the next day. It serves peak shaving and is key to completing the peak shaving task in this application embodiment. It also lays the foundation for subsequent steps. It can be understood that step S10 only needs to be executed once a day. Specifically, the data obtained in step S10 is used to establish a first planning model. Through this planning model, the baseline power plan and energy storage SOC plan for each controllable resource at each preset time period of the next day are obtained. For example, every 15 minutes of the next day is a point, and the baseline power plan and energy storage SOC plan for each controllable resource at each point are as follows: such as the SOC plan value of the energy storage system at 6:15, 30, 45 minutes, etc. in the future, as well as the baseline power plan values ​​of controllable resources such as gas turbines and controllable interruptible loads.

[0037] It is understandable that the data obtained through the model can be used to formulate a macro-level power plan for peak shaving the following day. For example, the first planning model can be a mixed integer programming (MIP) model or a linear programming (LP) model, etc.

[0038] In some embodiments, step S10 may include steps S110 to S130.

[0039] Step S110: Set the objective function of the first planning model to maximize revenue, and add power balance constraints, energy storage SOC dynamic constraints, gas turbine ramp rate constraints, gas turbine start-stop constraints, and peak shaving reserve capacity constraints; and set the time scale of the first planning model to 24 hours, the granularity to a preset time period, and the decision variables to each controllable resource.

[0040] Step S120: Input the wind and solar load forecast curve, market price forecast information, and the grid peak-shaving demand curve for the next day into the first planning model so that the first planning model can solve according to the objective function and constraints.

[0041] Step S130: After the solution is completed, the power plan and energy storage SOC plan for each resource base point in each preset time period of the next day are obtained.

[0042] In this embodiment, the first programming model is suitable for handling multivariable, multi-constraint optimization problems, such as the mixed integer programming (MIP) model described above. Based on this, some necessary settings can be made to the model before inputting the data.

[0043] On the one hand, the objective function of the first planning model can be set to maximize revenue, that is, maximize the revenue of the virtual power plant. This will make the resulting plan (i.e., the model output) more economical and improve the overall economic efficiency. Therefore, the objective function can be set as: Max(Energy Market Electricity Sales Revenue + Peak Shaving Ancillary Service Revenue - Electricity Purchase Cost - Resource Operating Cost), where Max means taking the maximum value of the formula within the parentheses. Energy market electricity sales revenue comes from selling electricity to the grid, i.e., how much electricity was sold to the grid. Peak shaving ancillary service revenue comes from providing peak shaving services, i.e., the revenue obtained from participating in peak shaving tasks. Electricity purchase cost is the expenditure of the virtual power plant on purchasing electricity from the grid, i.e., how much electricity was purchased from the grid. Resource operating cost includes energy storage losses, gas turbine fuel costs, etc. At the same time, several constraints can be added, including power balance constraints, energy storage SOC dynamic constraints, gas turbine ramp rate constraints, gas turbine start-stop constraints, and peak shaving reserve capacity constraints. The purpose of adding constraints is to ensure that these constraints are met during the model calculation process, so as to ensure that the resulting plan is feasible and reliable. For example, the purpose of power balance constraints is to balance supply and demand and avoid power deficits / surpluses. This can be expressed as: Photovoltaic power generation + wind power generation + gas turbine power generation + energy storage discharge + power purchase from the grid = load power consumption + interruptible load power consumption + energy storage charging + power sales to the grid. For example, the purpose of dynamic energy storage SOC constraints is to reserve sufficient power for the next day's dispatch. This can be expressed as: energy storage SOC must be within a safe range (e.g., 20%-80%), and the SOC must return to the initial set value at the end of the next day. For example, gas turbine ramp rate constraints represent the maximum output change of the gas turbine per minute, and gas turbine start-stop constraints represent the limit on the number of start-stop cycles of the gas turbine within a day. For example, peak-shaving reserve capacity constraints mean that the total adjustable power of the virtual power plant must meet the peak-shaving demand issued by the grid. For example, if the grid requires 10MW of peak-shaving capacity during a certain period, then the total adjustable resources of the virtual power plant during that period must be ≥10MW. On the other hand, since the purpose of using the first planning model is to formulate a macro-level power plan for peak shaving the following day, the time scale of the first planning model is set to 24 hours, and the granularity is set to a preset time period, such as 15 minutes, so that 96 sets of data can be output. In addition, the decision variables can be set to various controllable resources, such as energy storage, gas turbines, interruptible loads, etc., so that the output parameters of the solved model are the various controllable resources.

[0044] After setting up the first planning model, the wind and solar load forecast curves, market price forecasts, and the grid's peak-shaving demand curve for the next day can be input into the first planning model. This allows the first planning model to solve the problem based on the objective function and constraints. For example, when the first planning model uses a mixed integer programming algorithm, the solution variables are the baseline power and energy storage SOC of each controllable resource. The branch and bound method is used for the solution. It should be noted that this solution is existing technology and will not be described in detail here. Understandably, after the solution is completed, the model's output will be the baseline power plan for each resource in each preset time period for the next day (e.g., the output value of the gas turbine every 15 minutes, the energy storage charging and discharging power, and the interruptible load adjustment amount) and the energy storage SOC plan (e.g., the target SOC value every 15 minutes).

[0045] Step S20: Perform the following for each preset time period: acquire short-term wind and solar load forecast data, actual operating status of each resource, and real-time market price information; establish a second planning model to correct the baseline power plan and energy storage SOC plan of each controllable resource in the current time period; and determine the frequency regulation parameters for the current time period.

[0046] The short-term (e.g., within the next 1-4 hours) wind and solar load forecast data includes short-term wind power output forecast curves, short-term photovoltaic power output forecast curves, and short-term load forecast curves. This short-term wind and solar load forecast data is the latest forecast data, similar to the wind and solar load forecast curve in step S10, but with significantly higher accuracy. It also includes the actual operating status of various resources, such as the current actual SOC of energy storage and the current output of gas turbines. Real-time market price information represents real-time market prices, including energy market clearing price information and frequency regulation / peak shaving ancillary service market price information. It should be noted that, for accuracy, the data obtained in step S20 may only include short-term data, such as forecast data for the next 4 hours. Alternatively, the next 4 hours can be divided into 15-minute intervals, with each 15-minute interval corresponding to a set of data. In this case, the data obtained in step S20 would include 16 sets of forecast data, that is, 16 data points in 15-minute intervals. It is also known that the preset time period can be 15 minutes.

[0047] As can be seen from the implementation of step S10, the forecast data obtained by the day-ahead peak-shaving planning layer has a relatively long time period, which may often result in errors compared to the actual situation. This could lead to partial errors in the plan formulated in step S10. For example, the forecast data might indicate a photovoltaic output of 15MW at 10:00 AM the next day, but due to actual shading, the actual photovoltaic output at that time might be only 12MW, thus introducing errors into the plan. Therefore, step S20 corresponds to the intraday rolling planning layer, whose technical purpose is to correct the deviation between the day-ahead plan and the actual situation (e.g., deviations caused by inaccurate forecasts of new energy output). It is understandable that no correction is needed if there is no deviation. In this way, through rolling correction, the virtual power plant's ability to cope with uncertainties can be improved. Furthermore, the intraday rolling planning layer also allocates resources for real-time frequency regulation to ensure that the virtual power plant can complete the frequency regulation task, thus balancing the real-time performance of frequency regulation with the economic efficiency of peak shaving.

[0048] Based on this, the daytime peak shaving planning layer will send the output results to the intraday rolling planning layer, which will send the power plans for each controllable resource base point and the energy storage SOC plan for each preset time period of the next day. The intraday rolling planning layer will then make rolling adjustments. It can be understood that the intraday rolling planning layer must execute once every preset time period, making rolling adjustments, for example, once every 15 minutes.

[0049] In each correction, similar to establishing the first planning model in step S10, the data acquired during each correction is used to establish the second planning model. However, the difference is that the data acquired at this time is short-term data, which has higher accuracy and requires less data and computation. Therefore, the controllable resource base point power plan and energy storage SOC plan for the current time period obtained through the second planning model have higher accuracy and shorter result waiting time, and can therefore be used for correction to obtain the corrected result. At the same time, based on the corrected result, the frequency regulation parameters for the current time period also need to be determined during each correction, that is, resources are allocated for real-time frequency regulation to meet the real-time frequency regulation needs that the power grid may have. For example, the second planning model can also be a mixed integer programming (MIP) model or a linear programming (LP) model, etc.

[0050] In some embodiments, step S20, "acquiring short-term wind and solar load forecast data, actual operating status of each resource, and real-time market price information, and establishing a second planning model to revise the baseline power plan and energy storage SOC plan of each controllable resource for the current time period", may include steps S210 to S230.

[0051] Step S210: Set the objective function of the second planning model to maximize revenue, and add power balance constraints, energy storage SOC dynamic constraints, gas turbine ramp rate constraints, gas turbine start-stop constraints, and peak-shaving reserve capacity constraints; and set the time scale of the second planning model to 4 hours, the granularity to a preset time period, and the decision variables to each controllable resource.

[0052] Step S220: Input short-term wind and solar load forecast data, actual operating status of each resource, and real-time market price information into the second planning model so that the second planning model can solve according to the objective function and constraints.

[0053] Step S230: After the solution is completed, the output results for the current time period are obtained, and the power plans and energy storage SOC plans for each controllable resource base point are corrected based on the output results for the current time period.

[0054] The implementation of establishing the second planning model is similar to that of the first planning model, but the second planning model requires less input data with higher accuracy. Furthermore, the second planning model has a 4-hour timescale and a granularity of a preset time period (e.g., 15 minutes), resulting in faster solution speeds and more accurate output results. Therefore, after obtaining the output results for the current time period, it can be compared with the controllable resource baseline power plan and energy storage SOC plan issued by the day-ahead peak-shaving planning level for the current time period. If there are discrepancies, corrections are made. For example, assuming the day-ahead peak-shaving planning level issues a photovoltaic output of 10MW and a gas turbine output of 5MW for the current time period, but the actual photovoltaic output is 8MW and the actual load demand is 1MW lower than the predicted data, then assuming the output of the second planning model is a controllable resource gas turbine output of 6MW, the output of the controllable resource gas turbine for the current time period will be corrected to 6MW to ensure that the load and peak-shaving demands are met.

[0055] In some embodiments, "determining the frequency modulation parameters for the current time period" in step S20 may include steps S240 to S250.

[0056] Step S240: Determine the upper limit of frequency regulation power of the energy storage system within the current time period.

[0057] Step S250: Determine the first frequency regulation parameter of the energy storage system based on the upper limit of the frequency regulation power, thereby determining the second frequency regulation parameter of the gas turbine. The sum of the first and second frequency regulation parameters is one.

[0058] In general, both energy storage systems and gas turbines are common participants in frequency regulation, but energy storage systems are a better frequency regulation resource. Therefore, the embodiments of this application mainly determine the allocation parameters based on the adjustable frequency capacity of the energy storage system in the current time period. This can ensure the matching of resource characteristics with service demand and save resources.

[0059] In one embodiment, step S240 may include: if the current SOC of the energy storage system is less than the revised energy storage SOC plan for the current time period, then the average value of the current SOC of the energy storage system and the revised energy storage SOC plan for the current time period is taken, and then the frequency regulation power upper limit is determined based on the average value and a preset energy storage SOC lower limit; if the current SOC of the energy storage system is greater than or equal to the revised energy storage SOC plan for the current time period, then the frequency regulation power upper limit is determined based on the revised energy storage SOC plan for the current time period and the preset energy storage SOC lower limit.

[0060] If the current SOC of the energy storage system is less than the revised planned SOC for the current time period, it indicates that the energy storage system needs to be charged, for example, from the current 55% SOC to 60% SOC within the next 15 minutes. Since frequency regulation is a sudden event, and the energy storage system cannot be charged to the planned SOC value immediately, it is necessary to ensure the smooth implementation of frequency regulation when determining the upper limit of frequency regulation power. Therefore, this embodiment takes the average of the current SOC and the planned SOC value of the energy storage system, and uses this average value to determine the upper limit of frequency regulation power. For example, if the average value is 55%, and the preset lower limit of SOC (which can be determined according to the dynamic constraints of energy storage SOC described above) is 45%, then 10% of the energy storage system's capacity is the upper limit of frequency regulation power.

[0061] If the current SOC of the energy storage system is greater than the revised planned SOC for the current time period, it indicates that the energy storage system needs to discharge, for example, from the current 70% SOC to 60% SOC within the next 15 minutes. Although frequency regulation is an emergency, the energy storage system in discharge mode needs to supply power to the load. Therefore, when determining the upper limit of frequency regulation power, it is necessary to ensure reliable power supply to the load. Therefore, this embodiment uses the planned SOC value to determine the upper limit of frequency regulation power. For example, if the planned SOC value is 60% and the preset lower limit of SOC is 45%, then 15% of the energy storage system's capacity is the upper limit of frequency regulation power. If the current SOC of the energy storage system is equal to the revised planned SOC for the current time period, it indicates that the energy storage system does not need to charge or discharge, and the planned SOC value can be used when determining the upper limit of frequency regulation power.

[0062] In one embodiment, step S250, "determining the first frequency regulation parameter of the energy storage system based on the upper limit of the frequency regulation power," may include: determining the first frequency regulation parameter of the energy storage system based on the upper limit of the frequency regulation power and a preset mapping relationship. That is, the first frequency regulation parameter can be determined by querying a preset mapping relationship, wherein this mapping relationship can be predetermined based on the historical frequency regulation experience of the virtual power plant, i.e., based on historical frequency regulation experience, a correspondence between the upper limit of the frequency regulation power and the first frequency regulation parameter is preset. For example, assuming the mapping relationship records that when the upper limit of the frequency regulation power is 5 ± 0.5 MW, the first frequency regulation parameter is 0.8, and when the upper limit of the frequency regulation power is 4 ± 0.5 MW, the first frequency regulation parameter is 0.7, then if the upper limit of the frequency regulation power is 5 MW, then according to this mapping relationship, the first frequency regulation parameter is 0.8. It can be understood that after determining the first frequency regulation parameter of the energy storage system, the second frequency regulation parameter of the gas turbine can be determined, for example, 0.2, etc.

[0063] In some embodiments, after "determining the first frequency regulation parameter of the energy storage system" in step S250, the frequency regulation and peak shaving method of the virtual power plant may further include: if the current SOC of the energy storage system exceeds a preset threshold, then adding a preset fine-tuning amount to the first frequency regulation parameter of the energy storage system.

[0064] Since energy storage systems are superior frequency regulation resources, they should be used more extensively for frequency regulation tasks when their power capacity is sufficient. Therefore, if the current State of Charge (SOC) of the energy storage system exceeds a preset threshold, such as 80%, the first frequency regulation parameter can be slightly increased, for example, by 0.05 or 0.1. This allows the energy storage system to undertake more frequency regulation tasks, improving the match between resource characteristics and service demands, and also enhancing economic efficiency. In some implementations, the preset threshold range can be 70%-90%, and the preset fine-tuning range can be 0.01-0.1.

[0065] Step S30: Based on the revised power plans and energy storage SOC plans for each controllable resource base point in the current time period, instruct the corresponding equipment to execute. Simultaneously, if a grid frequency regulation command is received, adjust the output of the frequency regulation resources according to the frequency regulation parameters for the current time period.

[0066] After the intraday rolling planning layer is implemented, the revised plan for the current time period can be obtained. Therefore, it is necessary to instruct the corresponding equipment to execute according to these revised plans. At the same time, adjustments may also be needed according to the power grid frequency regulation command. For this reason, step S30 corresponds to the real-time frequency regulation control layer. In addition, as can be seen from the implementation of step S20, the real-time frequency regulation control layer will feed back the actual operating status of each resource to the intraday rolling planning layer in real time, helping the intraday rolling planning layer to achieve rolling optimization.

[0067] Specifically, the intraday rolling planning layer sends the revised baseline power plans and energy storage SOC plans for each controllable resource point in the current time period, along with the frequency regulation parameters for the current time period, to the real-time frequency regulation control layer. In this way, the real-time frequency regulation control layer can instruct the corresponding equipment to execute actions based on the revised baseline power plans and energy storage SOC plans for the current time period, such as instructing the energy storage system controller to execute actions, thus completing the peak-shaving task. For example, if the baseline power of the gas turbine in the revised current time period (e.g., the current time period of the next 15 minutes) is 8MW, and assuming the current gas turbine output is 6MW, then the gas turbine output will increase to 8MW within the next 15 minutes. For example, if the SOC of the energy storage system in the revised current time period (e.g., the current time period of the next 15 minutes) is 60%, and assuming the current SOC of the energy storage system is 50%, then the energy storage system will be charged to 60% within the next 15 minutes. On the other hand, if a real-time frequency regulation command is received from the Automatic Generation Control (AGC) system, the output of frequency regulation resources is adjusted according to the frequency regulation parameters of the current time period, such as increasing the output of the energy storage system, thus completing the frequency regulation task. Since the intraday rolling planning layer has already determined the frequency regulation scheme, the real-time frequency regulation control layer can quickly respond to the grid frequency regulation command, ensuring grid frequency stability and achieving the technical objective of real-time precise control. That is, because the frequency regulation strategy is preset, a response time to the frequency regulation command can be achieved within seconds. It should also be noted that in the actual operation scenario of the power grid, frequency regulation is mainly used to supplement the power deficit (i.e., mainly to generate more power). Therefore, in this embodiment, the frequency regulation command indicates supplementing the power deficit. If the frequency regulation command indicates absorbing excess power, adjustments can be made according to the actual situation, such as charging the energy storage system or increasing the load.

[0068] In some implementations, step S30, "if a grid frequency regulation command is received, adjust the output of the frequency regulation resource according to the frequency regulation parameters of the current time period," may include: if a grid frequency regulation command is received, determining the frequency regulation power; adjusting the output of the energy storage system according to the product of the first frequency regulation parameter and the frequency regulation power; and adjusting the output of the gas turbine according to the product of the second frequency regulation parameter and the frequency regulation power.

[0069] Under normal circumstances, the power grid frequency regulation command will explicitly indicate the frequency regulation power, so the frequency regulation power can be determined directly. P_AGC can be allocated based on two frequency regulation parameters. On one hand, the output of the energy storage system can be adjusted based on the product of the first frequency regulation parameter and the frequency regulation power. P_batt=K_batt* P_AGC, which means increasing the output of the energy storage system. P_batt, for example, increases by 3MW. On the other hand, the output of the gas turbine is adjusted based on the product of the second frequency regulation parameter and the frequency regulation power, i.e. P_GT=K_GT* P_AGC, which means increasing the output of the gas turbine. P_GT, for example, increases by 1MW. Here, K_batt and K_GT are the first and second frequency modulation parameters, respectively, and their sum equals one.

[0070] As discussed above, through the layered and time-division technical means of this application embodiment, the following are achieved: 1) Refined division of labor: Decoupling peak-shaving tasks (long-term, high-power) and frequency regulation tasks (short-term, rapid) in terms of time scale and resource type, enabling various resources to leverage their strengths and avoid their weaknesses, resulting in a significant improvement in overall efficiency; 2) Collaborative optimization: A three-layer architecture enables the connection from day-ahead macro-planning to real-time precise control, effectively coordinating resources with different response speeds and balancing economy and reliability; 3) Maximizing economy: Through multi-time-scale optimization, market revenue and operating costs are comprehensively considered, and detailed costs such as energy storage lifespan are considered in real-time control, maximizing the revenue of the virtual power plant; 4) Strong robustness: Rolling optimization and real-time feedback mechanisms can effectively smooth out fluctuations caused by the uncertainty of new energy sources and loads, improve the reliability of the virtual power plant, and thus ensure grid security.

[0071] Consistent with the above, embodiments of this application also provide a virtual power plant frequency regulation and peak shaving device, such as... Figure 2 As shown, it includes a day-ahead peak shaving planning layer, an intraday rolling optimization layer, and a real-time frequency modulation control layer. Exemplarily, the day-ahead peak shaving planning layer, the intraday rolling optimization layer, and the real-time frequency modulation control layer can be housed in the same device. Exemplarily, the day-ahead peak shaving planning layer, the intraday rolling optimization layer, and the real-time frequency modulation control layer can be housed in different devices, in which case each of them can be equipped with corresponding communication units to fully realize the data transmission required in the embodiments of this application.

[0072] The day-ahead peak shaving planning layer is used to: obtain wind and solar load forecast curves, market price forecast information, and the grid's peak shaving demand curve for the next day; establish a first planning model to obtain the power plans for each controllable resource base point and the energy storage SOC plan for each preset time period of the next day. It can be understood that the day-ahead peak shaving planning layer will distribute the power plans for each controllable resource base point and the energy storage SOC plan for each preset time period of the next day to the intraday rolling planning layer.

[0073] The intraday rolling planning layer is used to execute every preset time period after the next day: it acquires short-term wind and solar load forecast data, the actual operating status of each resource, and real-time market price information; it establishes a second planning model to revise the baseline power plan and energy storage SOC plan for each controllable resource in the current time period; and it determines the frequency regulation parameters for the current time period. It can be understood that each execution of the intraday rolling planning layer will send the revised baseline power plan and energy storage SOC plan for each controllable resource in the current time period, as well as the frequency regulation parameters for the current time period, to the real-time frequency regulation control layer.

[0074] The real-time frequency regulation control layer is used to: instruct corresponding equipment to execute based on the revised power plans and energy storage SOC plans for each controllable resource base point in the current time period; and if a grid frequency regulation command is received, adjust the output of the frequency regulation resources according to the frequency regulation parameters for the current time period. It can be understood that the real-time frequency regulation control layer is connected to each resource control device, instructing the resources to execute according to the revised plan; furthermore, it can be understood that the real-time frequency regulation control layer collects the actual operating status of each resource and feeds it back to the intraday rolling planning layer.

[0075] It should be noted that the virtual power plant frequency regulation and peak shaving device corresponds one-to-one with the virtual power plant frequency regulation and peak shaving method. Therefore, the specific implementation can be found in the previous discussion and will not be repeated here.

[0076] This application also provides a virtual power plant frequency regulation and peak shaving system, such as Figure 3 As shown, the system includes a data acquisition and communication device, a resource proxy control unit, and the virtual power plant frequency regulation and peak shaving device described above. The data acquisition and communication unit collects grid dispatch instructions, market information, and real-time operating data (power, energy storage SOC, etc.) of various distributed resources, and completes data transmission between the virtual power plant frequency regulation and peak shaving device and each resource controller. The resource proxy control unit is deployed on the side of each controllable resource; for example, one resource proxy control unit is set up for each controllable resource. The resource proxy control unit receives instructions from the virtual power plant frequency regulation and peak shaving device and converts them into specific equipment execution actions. The specific implementation of the virtual power plant frequency regulation and peak shaving device is as described above and will not be repeated here.

[0077] This application also provides a virtual power plant frequency regulation and peak shaving device, such as... Figure 4 As shown, it includes a processor and a memory. The memory is used to store computer programs; the processor is used to execute the computer programs and, when executing the computer programs, implement any of the virtual power plant frequency regulation and peak shaving methods provided in the embodiments of this application.

[0078] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0079] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements any of the virtual power plant frequency regulation and peak shaving methods provided in this application.

[0080] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer-readable storage media (or non-transitory media) and communication media (or transient media).

[0081] As is known to those skilled in the art, the term computer-readable storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0082] For example, the computer-readable storage medium may be an internal storage unit of the virtual power plant frequency regulation and peak shaving equipment described in the foregoing embodiments, such as the hard disk or memory of the virtual power plant frequency regulation and peak shaving equipment. The computer-readable storage medium may also be an external storage device of the virtual power plant frequency regulation and peak shaving equipment, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard equipped on the virtual power plant frequency regulation and peak shaving equipment.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this invention, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A virtual power plant frequency regulation and peak shaving method, characterized in that, include: Obtain wind and solar load forecast curves, market price forecast information, and the grid peak-shaving demand curve for the next day, and establish a first planning model to obtain the power plan and energy storage SOC plan for each controllable resource base point in each preset time period of the next day. The following steps are performed every preset time period after the next day: acquire short-term wind and solar load forecast data, actual operating status of each resource and real-time market price information, establish a second planning model to correct the base point power plan and energy storage SOC plan of each controllable resource in the current time period, and determine the frequency regulation parameters for the current time period. Based on the revised controllable resource baseline power plan and energy storage SOC plan for the current time period, instruct the corresponding equipment to execute; If a frequency regulation command is received from the power grid, the output of the frequency regulation resources will be adjusted according to the frequency regulation parameters for the current time period. Determining the frequency modulation parameters for the current time period includes: If the current SOC of the energy storage system is less than the revised energy storage SOC plan for the current time period, then the average value of the current SOC of the energy storage system and the revised energy storage SOC plan for the current time period is taken, and then the upper limit of frequency regulation power is determined based on the average value and the preset lower limit of energy storage SOC. If the current SOC of the energy storage system is greater than or equal to the revised energy storage SOC plan for the current time period, then the upper limit of the frequency regulation power is determined based on the revised energy storage SOC plan for the current time period and the preset lower limit of energy storage SOC. Based on the upper limit of the frequency regulation power, the first frequency regulation parameter of the energy storage system is determined, thereby determining the second frequency regulation parameter of the gas turbine; the sum of the first frequency regulation parameter and the second frequency regulation parameter is one. If the current SOC of the energy storage system exceeds a preset threshold, then the first frequency regulation parameter of the energy storage system is increased by a preset fine-tuning amount.

2. The virtual power plant frequency regulation and peak shaving method according to claim 1, characterized in that, The process involves acquiring wind and solar load forecast curves, market price forecasts, and the grid's peak-shaving demand curve for the following day, and establishing a first planning model to obtain the power plan and energy storage SOC plan for each controllable resource base point in each preset time period for the following day, including: The objective function of the first planning model is set to maximize revenue, and power balance constraints, energy storage SOC dynamic constraints, gas turbine ramp rate constraints, gas turbine start-stop constraints, and peak-shaving reserve capacity constraints are added; and the time scale of the first planning model is set to 24 hours, the granularity is the preset time period, and the decision variables are each controllable resource. The wind and solar load forecast curve, market price forecast information, and the grid peak-shaving demand curve for the next day are input into the first planning model so that the first planning model can solve the problem according to the objective function and constraints. Once the solution is completed, the power plan and energy storage SOC plan for each resource base point in each preset time period of the next day will be obtained; And / or, The process of acquiring short-term wind and solar load forecast data, actual operating status of various resources, and real-time market price information, and establishing a second planning model to revise the baseline power plan and energy storage SOC plan for each controllable resource in the current time period, includes: The objective function of the second planning model is set to maximize revenue, and power balance constraints, energy storage SOC dynamic constraints, gas turbine ramp rate constraints, gas turbine start-stop constraints, and peak-shaving reserve capacity constraints are added; and the time scale of the second planning model is set to 4 hours, the granularity is the preset time period, and the decision variables are each controllable resource. The short-term wind and solar load forecast data, the actual operating status of each resource, and real-time market price information are input into the second planning model so that the second planning model can solve the problem according to the objective function and constraints. After the solution is completed, the output results for that time period are obtained, and the power plans and energy storage SOC plans for each controllable resource base point are revised according to the output results for the current time period.

3. The virtual power plant frequency regulation and peak shaving method according to claim 1, characterized in that, If a power grid frequency regulation command is received, the output of the frequency regulation resources is adjusted according to the frequency regulation parameters for the current time period, including: If a frequency regulation command is received from the power grid, determine the frequency regulation power; The output of the energy storage system is adjusted according to the product of the first frequency regulation parameter and the frequency regulation power; and the output of the gas turbine is adjusted according to the product of the second frequency regulation parameter and the frequency regulation power.

4. A virtual power plant frequency regulation and peak shaving device, characterized in that, include: The daytime peak shaving planning layer is used to: obtain wind and solar load forecast curves, market price forecast information, and the grid peak shaving demand curve for the next day, and establish the first planning model to obtain the power plan and energy storage SOC plan for each controllable resource base point in each preset time period of the next day. The intraday rolling planning layer is used to execute every preset time period after the next day: obtain short-term wind and solar load forecast data, actual operating status of each resource and real-time market price information, establish a second planning model to correct the base point power plan and energy storage SOC plan of each controllable resource in the current time period, and determine the frequency regulation parameters of the current time period. The real-time frequency regulation control layer is used to instruct the corresponding equipment to execute based on the revised controllable resource base point power plan and energy storage SOC plan for the current time period. If a frequency regulation command is received from the power grid, the output of the frequency regulation resources will be adjusted according to the frequency regulation parameters for the current time period. Determining the frequency modulation parameters for the current time period includes: If the current SOC of the energy storage system is less than the revised energy storage SOC plan for the current time period, then the average value of the current SOC of the energy storage system and the revised energy storage SOC plan for the current time period is taken, and then the upper limit of frequency regulation power is determined based on the average value and the preset lower limit of energy storage SOC. If the current SOC of the energy storage system is greater than or equal to the revised energy storage SOC plan for the current time period, then the upper limit of the frequency regulation power is determined based on the revised energy storage SOC plan for the current time period and the preset lower limit of energy storage SOC. Based on the upper limit of the frequency regulation power, the first frequency regulation parameter of the energy storage system is determined, thereby determining the second frequency regulation parameter of the gas turbine; the sum of the first frequency regulation parameter and the second frequency regulation parameter is one. If the current SOC of the energy storage system exceeds a preset threshold, then the first frequency regulation parameter of the energy storage system is increased by a preset fine-tuning amount.

5. A virtual power plant frequency regulation and peak shaving system, characterized in that, It includes a data acquisition and communication device, a resource proxy control unit, and a virtual power plant frequency regulation and peak shaving device as described in claim 4.

6. A virtual power plant frequency regulation and peak shaving device, characterized in that, Including processor and memory; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the virtual power plant frequency regulation and peak shaving method as described in any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the virtual power plant frequency regulation and peak shaving method as described in any one of claims 1 to 3.

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