Optimized scheduling method and system for frequency modulation and output participation of energy storage of new energy station

Abstract

The invention discloses a new energy station energy storage participation frequency modulation and output optimization scheduling method and system. The method comprises the following steps: fitting a frequency difference integral autoregression prediction model according to historical frequency difference data collected by a new energy station common connection point; constructing a day-ahead optimal scheduling model and an intra-day optimal scheduling model; before the day of operation, estimating a current-day primary frequency modulation action amount according to the obtained autoregression prediction model of the frequency difference integral, and optimizing a virtual droop coefficient and an economical output plan of energy storage according to the obtained day-ahead optimization scheduling model; and after the day-ahead optimization result is obtained, performing intra-day optimization scheduling once per hour by using the obtained intra-day optimization scheduling model to obtain an economical output plan correction result of the remaining hours of the day, and updating the state of charge according to an actual primary frequency modulation signal until all intra-day optimization scheduling of the day is completed. According to the invention, the long-term primary frequency modulation capability of the energy storage system can be brought into full play, and the income of peak-valley arbitrage is improved.

Description

Background Art

[0002] When new energy units participate in primary frequency regulation, there are operating condition requirements and it will affect their operating economy. Therefore, it has become a research hotspot in the field of primary frequency regulation of the current power system for energy storage with rapid response and flexible control to undertake the primary frequency regulation task of new energy power stations.

[0003] Currently, most energy storage participates in primary frequency regulation services through virtual droop control, and calculates the primary frequency regulation output command of energy storage through a determined virtual droop coefficient. In the current optimization dispatch methods for energy storage to participate in primary frequency regulation, the change in state of charge caused by the primary frequency regulation output of energy storage is usually simplified or ignored, and most do not consider the uncertainty of the primary frequency regulation signal, resulting in a lack of certain adaptability in the existing optimization dispatch methods and being unable to fully utilize the advantages of energy storage in participating in primary frequency regulation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an optimized scheduling method and system for energy storage in new energy power stations to participate in frequency regulation and output, aiming at the deficiencies in the above-mentioned existing technologies, to solve the technical problem that energy storage in new energy power stations cannot take into account system frequency response, maximization of economic benefits, and safety constraints of the state of charge of energy storage when participating in power grid frequency regulation and output dispatch.

[0005] The present invention adopts the following technical solutions: An optimized scheduling method for energy storage in new energy power stations to participate in frequency regulation and output, comprising the following steps: Construct a model for the output of energy storage assisting primary frequency regulation and economic output in new energy power stations, and fit an autoregressive prediction model of frequency deviation integral based on historical frequency deviation data collected at the common connection point of new energy power stations; Taking the maximization of the overall revenue of new energy power stations as the goal, and taking the virtual droop coefficient of energy storage and the economic output plan as optimization variables, construct a day-ahead optimization dispatch model; Taking the minimum deviation of the economic output optimization result within the day and the minimum probability of state of charge over-limit as the goal, and taking the economic output plan within the day as the optimization variable, construct an intra-day optimization dispatch model; Before operation, estimate the daily primary frequency regulation action amount according to the obtained autoregressive prediction model of frequency deviation integral, and optimize the virtual droop coefficient of energy storage and the economic output plan according to the obtained day-ahead optimization dispatch model; after obtaining the day-ahead optimization result, use the obtained intra-day optimization dispatch model to perform intra-day optimization dispatch once per hour to obtain the corrected result of the economic output plan for the remaining hours of the day, and update the state of charge according to the actual primary frequency regulation signal until all intra-day optimization dispatches of the day are completed.

[0006] Preferably, the autoregressive prediction model of frequency deviation integral is specifically:

[0007] Among them, is instantaneous frequency difference integral prediction; is a Gaussian distribution random variable; is the frequency difference integral measurement quantity; is the autoregressive model fitting parameter.

[0008] Preferably, the objective function of the day-ahead optimal scheduling model is:

[0009] Among them, is the primary frequency regulation revenue coefficient; is the time-of-use electricity price; is the total number of day-ahead and intra-day scheduling intervals, represents its primary frequency regulation revenue, is the penalty term for the change in energy storage output.

[0010] Preferably, the constraint conditions are the state of charge probability constraint constructed based on the frequency difference integral, and the power constraints for the operation of energy storage and new energy power stations.

[0011] Preferably, the state of charge deviation of the energy storage at different times satisfies the following formula:

[0012] Among them, is the state of charge deviation of the energy storage between the time intervals to ; is the state of charge of the energy storage at time k + 1, is the state of charge of the energy storage at time k, is the day-ahead and intra-day scheduling interval, is the active power output obtained through day-ahead and intra-day optimal scheduling, is the virtual droop coefficient of the energy storage participating in frequency regulation, is the integral of the frequency deviation over ; is the rated capacity of the energy storage, is the allowable deviation range of the change in the state of charge of the energy storage; Probability constraint condition for the operation of the energy storage:

[0013] Among them, is the quantile of the Gaussian distribution, is the variance of the frequency deviation integral, is the upper limit of the state of charge of the energy storage; The constraint conditions that the energy storage also needs to satisfy during the optimization process are:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019] Among them, is the maximum virtual droop coefficient; is the rated power of the energy storage; is the maximum frequency deviation of the system; is the maximum ramp power of the energy storage; is the rated power of the power station.

[0020] Preferably, the objective function of the intraday optimal scheduling model at the th hour is as follows:

[0021] Among them, is the deviation penalty coefficient; and are the weight coefficients; and are the quantiles in the CH and RH state of charge constraints, is the total number of intervals for day-ahead and intraday scheduling.

[0022] Preferably, the constraint conditions of the intraday optimal scheduling model are the state of charge probability constraints constructed based on the integral of frequency deviation, and the power constraints for the operation of the energy storage and new energy power stations.

[0023] Preferably, combined with the day-ahead constraint conditions, variables and are respectively introduced for the two-stage optimization of the first hour and the remaining hours of the intraday, and the following constraints are constructed:

[0024]

[0025]

[0026]

[0027] Among them, is the rated capacity of the energy storage, is the upper safety limit of the state of charge of the energy storage, is the state of charge at time k, is the time interval coefficient, is the economic output power of the energy storage at time k, is the virtual droop coefficient of the energy storage participating in frequency regulation, is the quantile in the state of charge constraint condition of CH, is the variance of the integral of the frequency deviation, is the frequency regulation demand updated within the day, is the lower limit of the state of charge of the energy storage.

[0028] Preferably, after obtaining the day-ahead optimization result the intra-day optimization scheduling is carried out once respectively at ; Suppose the intra-day optimization is run at time, after obtaining the intra-day optimization result the energy storage will operate according to for step lengths, and update the state of charge SOC according to the actual primary frequency regulation action; When the intra-day optimization will continue to run according to obtain the optimization result for hours and run step lengths, repeat the content of hours until the optimization task for one day is completed; In the intra-day optimization, only the optimization result within the first hour coming soon after the optimization moment is actually run by the energy storage, and the optimization results of the remaining hours are not used as the basis for the energy storage operation.

[0029] In a second aspect, an embodiment of the present invention provides an optimized scheduling system for a new energy power station energy storage to participate in frequency regulation and output, including: A fitting module, which constructs a model for the energy storage of the new energy power station to assist primary frequency regulation output and economic output, and fits an autoregressive prediction model of the integral of the frequency difference according to the historical frequency difference data collected at the point of common connection of the new energy power station; A day-ahead module, with the goal of maximizing the overall revenue of the new energy power station, using the virtual droop coefficient of the energy storage and the economic output plan as optimization variables, constructs a day-ahead optimized scheduling model; An intra-day module, with the goal of minimizing the deviation of the economic output optimization result between day-ahead and intra-day and the probability of the state of charge exceeding the limit, using the intra-day economic output plan as the optimization variable, constructs an intra-day optimized scheduling model; The scheduling module, before the day-ahead operation, estimates the primary frequency regulation action amount for the current day according to the obtained autoregressive prediction model of the frequency difference integral, and optimizes the virtual droop coefficient and the economic output plan of the energy storage according to the obtained day-ahead optimal scheduling model; after obtaining the day-ahead optimal result, it performs intraday optimal scheduling once every hour by using the obtained intraday optimal scheduling model to obtain the corrected results of the economic output plan for the remaining hours of the day, and updates the state of charge according to the actual primary frequency regulation signal until all intraday optimal scheduling for the day is completed.

[0030] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned optimal scheduling method for a new energy power station energy storage to participate in frequency regulation and output are implemented.

[0031] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned optimal scheduling method for a new energy power station energy storage to participate in frequency regulation and output are implemented.

[0032] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned optimal scheduling method for a new energy power station energy storage to participate in frequency regulation and output are implemented.

[0033] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, and when the computer program is executed by the electronic device, the steps of the above-mentioned optimal scheduling method for a new energy power station energy storage to participate in frequency regulation and output are implemented.

[0034] Compared with the prior art, the present invention has at least the following beneficial effects: An optimal scheduling method for a new energy power station energy storage to participate in frequency regulation and output constructs the state of charge constraint conditions satisfied by the energy storage operation. In the day-ahead optimization, the droop coefficient for the energy storage to participate in primary frequency regulation and the charge and discharge plan for the next day are obtained according to the predicted frequency regulation demand. The intraday optimization further improves the safety level of the state of charge on the basis of the day-ahead optimization result, thereby ensuring that the state of charge of the energy storage operates within a safe and reliable range, enabling the energy storage to stably provide primary frequency regulation services, improving the long-term operation ability of the energy storage. The economic charge and discharge plan of the energy storage obtained by this optimal scheduling method can better respond to the time-of-use electricity price fluctuation curve, bringing more benefits to the energy storage and achieving the economic optimum of the overall revenue of the power station; compared with the existing optimal scheduling method for the energy storage to participate in primary frequency regulation operation, the present invention considers the uncertainty of primary frequency regulation actions, improves the adaptability and feasibility of the optimal scheduling method, and further ensures the safety of the energy storage state of charge through intraday rolling optimization, improving the reliability of the energy storage operation.

[0035] Furthermore, the state of charge constraint condition satisfied by the energy storage operation is a probabilistic constraint condition considering the state of charge deviation, and its uncertainty comes from the uncertainty of the primary frequency regulation action, which improves the ability of the energy storage to cope with the randomness of the primary frequency regulation action and ensures the primary frequency regulation effect of the energy storage.

[0036] Furthermore, the economic output of the energy storage can better respond to the time-of-use electricity price mechanism, conforms to the changing trend of the regional power supply demand, is conducive to promoting the energy transformation of the power system, and improving the operation safety of the power grid.

[0037] Furthermore, by using the energy storage to undertake the primary frequency regulation task of the new energy power station, the new energy power generation is fully connected to the grid, reducing the demand for the primary frequency regulation control function of the new energy units, helping to complete the primary frequency regulation assessment of the power grid for the new energy power station, and improving the evaluation level of the primary frequency regulation ability of the new energy power station.

[0038] It can be understood that the beneficial effects of the second to sixth aspects above can be referred to the relevant descriptions in the first aspect above, and will not be repeated here.

[0039] In summary, the present invention is applicable to the energy storage of new energy power stations, has a good optimization effect on the energy storage participating in primary frequency regulation and peak-valley arbitrage through economic output, helps to give full play to the long-term primary frequency regulation ability of the energy storage system, and improves the income of peak-valley arbitrage.

[0040] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0042] Figure 1 It is the overall architecture diagram of the new energy power station with energy storage in the present invention; Figure 2 It is the schematic diagram of the allowable deviation range of the state of charge of the energy storage constructed in the present invention; Figure 3 It is the schematic diagram of the intraday optimal scheduling in the present invention; Figure 4 is the simulation experiment data diagram of the primary frequency regulation optimal scheduling carried out in the embodiment of the present invention. Among them, (a) is the day-ahead optimization result, (b) is the situation where the state of charge exceeds the limit at some moments only through the day-ahead optimal scheduling, and (c) is that the energy storage can stably provide primary frequency regulation output throughout the day after the intraday optimization; Figure 5Schematic diagram of a computer device provided by an embodiment of the present invention; Figure 6 Block diagram of an electronic device provided by an embodiment of the present invention; Figure 7 Flow schematic diagram of the present invention.

[0043] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access storage unit; 6202. Cache storage unit; 6203. Read-only storage unit; 6204. Program / utilities; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0046] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0047] It should be further understood that the term " / and / " used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For example, A and / or B can represent: the case where A exists alone, the case where A and B exist simultaneously, and the case where B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the preceding and following associated objects.

[0048] It should be understood that although terms such as first, second, and third may be used in the embodiments of the present invention to describe preset ranges and the like, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0049] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0050] Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the drawings. These figures are not drawn to scale, where certain details are enlarged for the purpose of clear expression, and certain details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary, and may actually deviate due to manufacturing tolerances or technical limitations. Those skilled in the art can additionally design regions / layers with different shapes, sizes, and relative positions according to actual requirements.

[0051] The present invention provides an optimized scheduling method for energy storage in new energy power stations to participate in frequency regulation and output. First, an autoregressive model is used to fit the estimation model of the frequency difference integral to achieve the prediction of primary frequency regulation actions. Secondly, in day-ahead optimization, the virtual droop coefficient and economic output plan of energy storage are obtained with economy as the goal. Finally, in intra-day, the day-ahead optimization results are fine-tuned through rolling optimization to improve the long-term operation ability of energy storage. Compared with the existing primary frequency regulation optimized scheduling method for energy storage, this method takes into account the uncertainty of primary frequency regulation actions, has better adaptability in real frequency regulation scenarios, and the constructed state of charge constraint conditions of energy storage ensures the operation safety of energy storage and the reliability of primary frequency regulation services. In addition, this method optimizes the economic output of energy storage considering time-of-use electricity prices, which can further improve the economy and overall revenue of energy storage power stations.

[0052] Embodiment 1 Please refer to Figure 7, an optimized scheduling method for a new - energy power station's energy storage to participate in frequency regulation and power output. For the application scenario where the energy storage participates in primary frequency regulation and conducts economic power output according to time - of - use electricity prices, it optimizes the virtual droop coefficient of the energy storage participating in primary frequency regulation and the economic charge - discharge plan. During the optimization process, it fully considers the uncertainty and randomness of the future primary frequency regulation demand of the energy storage, thereby further ensuring the ability of the energy storage to stably provide primary frequency regulation services, and improving the overall revenue of the power station through the economic power output after optimized scheduling. The specific steps are as follows: S1. Construct a model for the auxiliary primary frequency regulation power output and economic power output of the new - energy power station's energy storage, obtain the energy - storage state - of - charge change model, and fit an autoregressive prediction model of the frequency - difference integral based on the historical frequency - difference data collected at the point of common coupling of the new - energy power station. The autoregressive prediction model of the frequency - difference integral is fitted based on historical frequency - difference data, predicts the future frequency - regulation action amount, quantifies the uncertainty and randomness of the frequency - regulation demand, provides input data for the frequency - regulation power - output model and the state - of - charge model, and is the core basis for the energy - storage power distribution and constraint - condition design.

[0053] Driven by the predicted frequency - regulation demand, the frequency - regulation and economic power - output model determines the virtual droop coefficient and economic power - output plan of the energy storage, coordinates the frequency - regulation service and the charge - discharge benefits under time - of - use electricity prices through the power - distribution strategy, and directly affects the real - time power action of the energy storage.

[0054] The state - of - charge change model is a safety - constraint carrier. Based on the superposition effect of the frequency - regulation power output and the economic power output, it dynamically updates the energy - storage SOC, and ensures that the energy storage operates within the safety boundary through probability constraints; at the same time, the real - time state of the SOC is fed back to the power - output model to form a closed - loop optimization and prevent the risk of over - limit.

[0055] The autoregressive prediction model provides frequency - regulation demand prediction → the frequency - regulation and economic power - output model optimizes power distribution according to the predicted value → the state - of - charge model tracks the power action and updates the SOC → the SOC constraint reversely restricts the optimization space of the power - output model → finally realizes the dynamic balance of frequency - regulation response ability, economic benefits and energy - storage safety.

[0056] Please refer to Figure 1 , which is the overall architecture of the new - energy power station with energy storage. is the total power output of the new - energy power station. is the power output of the battery energy storage. Let \(P_{total}\) be the total output of the new - energy power plant units. The new - energy power plant mainly consists of three parts: new - energy generating units, battery energy storage, and the dispatching center of the new - energy power plant. Among them, the grid dispatching center is mainly responsible for communicating with the new - energy power plant, issuing and receiving grid - side dispatching instructions, etc. The total output of the new - energy power plant is integrated into the power grid through the Grid Coupling Point (GCP). The new - energy generating units are composed of several wind turbines or photovoltaic power generation devices. Since the new - energy generating units do not participate in primary frequency regulation, it is considered that the new - energy generating units operate in the maximum power tracking mode, and their output power is decoupled from the system frequency and is fully fed into the grid.

[0057] It is considered that the primary frequency regulation service of the new - energy power plant is borne by the energy storage, and the primary frequency regulation output of the energy storage is denoted as The economic output of the energy storage using the remaining capacity for arbitrage through time - of - use electricity price is denoted as , will be obtained through the day - ahead and intra - day optimal scheduling results. Therefore, the energy storage output is composed as follows:

[0058] The dispatching center of the new - energy power plant is responsible for measuring and collecting the operation data of the power station and maintaining communication and data exchange with the power grid. At the same time, after the dispatching center runs the day - ahead and intra - day optimal algorithms, it will transmit the dispatching instructions to the battery energy storage. Before running the day - ahead optimal scheduling, the power plant dispatching center will collect the time - of - use electricity price policy of the power grid, obtain the prediction of primary frequency regulation demand, the initial capacity of the energy storage, and the prediction of new - energy output. Through day - ahead optimal scheduling, the virtual droop coefficient and charge - discharge plan of the energy storage under the goal of maximizing revenue can be obtained. Intra - day optimal scheduling, on the basis of the day - ahead optimal result, adjusts the day - ahead charge - discharge plan according to the latest prediction of primary frequency regulation demand and the real - time SOC of the energy storage, so as to ensure the long - term reliable operation of the energy storage. The day - ahead and intra - day optimal scheduling results of the energy storage in the new - energy power plant will also be transmitted to the grid dispatching side in time to facilitate the power grid to carry out higher - level power system operation planning.

[0059] The calculation method of the energy storage participating in primary frequency regulation through virtual droop control is as follows:

[0060] where, is the primary frequency regulation output of the energy storage; is the virtual droop coefficient; is the frequency deviation; is the rated frequency; is the rated power of the new - energy power plant.

[0061] Considering the change of the state of charge of the energy storage as a discrete process, let the output of the energy storage at the th moment be , and the capacity of the energy storage is , the change in the state of charge of the energy storage satisfies:

[0062] where is the scheduling interval between day-ahead and intra-day.

[0063] The output of the energy storage consists of primary frequency regulation output and economic output. The economic output is the determined output obtained by the optimal scheduling method; the primary frequency regulation output is a random variable within the scheduling interval and is related to the real-time frequency deviation of the system. To obtain the change in the energy storage SOC caused by primary frequency regulation within a certain scheduling interval, it is calculated by the method of integration, specifically:

[0064] Specifically, the autoregressive prediction model for constructing the frequency difference integral is as follows:

[0065] where is the prediction of the frequency difference integral at time ; is a Gaussian distributed random variable; is the measured value of the frequency difference integral; is the fitting parameter of the autoregressive model.

[0066] S2. Construct a day-ahead optimal scheduling model, where the objective function is to maximize the overall revenue of the new energy power station, the optimization variables are the virtual droop coefficient of the energy storage and the economic output plan, and the constraint conditions are the state of charge probability constraint constructed based on the frequency difference integral and the power constraints for the operation of the energy storage and the new energy power station; The goal of day-ahead optimal scheduling is to maximize the overall revenue of the new energy power station and obtain the virtual droop coefficient of the energy storage operation and the daily charge and discharge plan

[0067] where is the primary frequency regulation revenue coefficient; is the time-of-use electricity price; is the total number of scheduling intervals between day-ahead and intra-day. The overall revenue of the new energy power station is mainly composed of the primary frequency regulation revenue of the energy storage and the electricity price revenue. The virtual droop coefficient of the energy storage represents the magnitude of its primary frequency regulation ability. Furthermore, is used to represent its primary frequency regulation revenue, is used as a penalty term for the change in the energy storage output to prevent sudden changes in the energy storage output and make the energy storage output smoother.

[0068] The deviation of the state of charge of the energy storage at different times satisfies the following formula:

[0069]

[0070] Such as Figure 2 For two cases of the change in the state of charge that satisfy the above constraints, where from changing to and respectively correspond to the two cases of the state of charge (SOC) rising or falling. When the state of charge of the energy storage at a certain moment is within the yellow area or the red area, it indicates that the state of charge of the energy storage is safe.

[0071] Since the predicted by the autoregressive model is a random variable subject to a Gaussian distribution, therefore is also a random variable subject to a Gaussian distribution, and its randomness comes from the uncertainty of.

[0072] Combined with the above safe operating range of the energy storage state of charge, the following probabilistic constraint conditions are set for the energy storage operation:

[0073] Among them, is the confidence level, and the above formula can be deduced into the following form according to the knowledge of probability theory:

[0074] Among them, is the quantile of the Gaussian distribution, is the predicted variance of.

[0075] During the optimization process, the constraint conditions that the energy storage needs to satisfy are:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Among them, is the maximum virtual droop coefficient; is the rated power of the energy storage; is the maximum frequency deviation of the system; is the maximum ramp power of the energy storage; is the rated power of the power station.

[0082] S3. Build an intraday optimal scheduling model, where the objective function is to minimize the deviation of the economic output optimization results between day-ahead and intraday and the probability of the state of charge (SOC) exceeding the limit, the optimization variable is the intraday economic output plan, and the constraint conditions are the SOC probability constraint constructed based on the frequency difference integral and the power constraints for the operation of energy storage and new energy power stations; The objective function of the intraday optimal scheduling for the th hour is as follows:

[0083] where, is the deviation penalty coefficient; and are the weight coefficients; and are the quantiles in the intraday SOC constraint conditions. The larger the value of this objective function, the smaller the deviation between the day-ahead and intraday results, and thus the smaller the deviation penalty; in addition, increases, the probability of meeting the SOC constraint increases, and the corresponding safety level of the energy storage operation increases.

[0084] Combined with the day-ahead constraint conditions, variables and are respectively introduced for the two-stage optimization of the first hour and the remaining hours of the intraday, and the following constraints are constructed:

[0085]

[0086]

[0087]

[0088] S4. Before operation, first estimate the primary frequency regulation action amount of the day according to the autoregressive prediction model of the frequency difference integral, and optimize the virtual droop coefficient and economic output plan of the energy storage according to the day-ahead optimal scheduling model. After obtaining the day-ahead optimization results, the intraday optimal scheduling is carried out once per hour to obtain the corrected results of the economic output plan for the remaining hours of the day. The energy storage only executes the economic output of the first hour in each intraday optimization result and updates the SOC according to the actual primary frequency regulation signal until all intraday optimal scheduling for the day is completed.

[0089] Please refer to Figure 3 for the schematic diagram of day-ahead and intraday optimal scheduling, represents the number of hours in a day; represents the number of scheduling intervals included in one hour. After obtaining the day-ahead optimization results , the intraday optimal scheduling is carried out at Once for each time.

[0090] Set at Run the intraday optimization at a certain moment. After obtaining the intraday optimization results The energy storage will, according to Run a number of steps, and update the state of charge (SOC) according to the actual primary frequency regulation action; when At this time, the intraday optimization will, according to Continue to run and obtain the optimization results for a certain number of hours and run a number of steps, repeating the content for a certain number of hours until the optimization task for one day is completed. In the intraday optimization, only the optimization results within the first hour immediately following the optimization moment are actually run by the energy storage, and the optimization results for the remaining hours are not used as the basis for the energy storage operation.

[0091] Those skilled in the art of the present technology can understand that various aspects of the present invention can be implemented as a system, method, or program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to here as "circuit", "module", or "platform".

[0092] Embodiment 2 The present invention provides an optimized dispatching system for a new energy power station energy storage to participate in frequency regulation and output. This system can be used to implement the optimized dispatching method for the new energy power station energy storage to participate in frequency regulation and output. Specifically, the optimized dispatching system for the new energy power station energy storage to participate in frequency regulation and output includes a fitting module, a day-ahead module, an intraday module, and a dispatching module.

[0093] Among them, the fitting module constructs a model for the primary frequency regulation output and economic output of the new energy power station energy storage, and fits an autoregressive prediction model of the frequency difference integral based on the historical frequency difference data collected at the common connection point of the new energy power station; The day-ahead module constructs a day-ahead optimized dispatching model with the goal of maximizing the overall benefit of the new energy power station, using the virtual droop coefficient of the energy storage and the economic output plan as optimization variables; The intraday module constructs an intraday optimized dispatching model with the goals of minimizing the deviation of the economic output optimization results between day-ahead and intraday and minimizing the probability of the state of charge exceeding the limit, using the intraday economic output plan as the optimization variable; The scheduling module, before the day-ahead operation, estimates the primary frequency regulation action amount for the current day according to the obtained autoregressive prediction model of the frequency difference integral, and optimizes the virtual droop coefficient and the economic output plan of the energy storage according to the obtained day-ahead optimal scheduling model; after obtaining the day-ahead optimization result, it performs intraday optimal scheduling once per hour using the obtained intraday optimal scheduling model to obtain the correction result of the economic output plan for the remaining hours of the day, and updates the state of charge according to the actual primary frequency regulation signal until all intraday optimal scheduling for the day is completed.

[0094] Embodiment 3 The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the optimization scheduling method for the energy storage in the new energy power station to participate in frequency regulation and output, including: Build a model for the energy storage of new energy power stations to assist in primary frequency regulation output and economic output. According to the historical frequency difference data collected at the common connection point of new energy power stations, fit an autoregressive prediction model for the integral of frequency difference. With the goal of maximizing the overall revenue of new energy power stations, and using the virtual droop coefficient of energy storage and the economic output plan as optimization variables, build a day-ahead optimization scheduling model. With the goal of minimizing the deviation of the economic output optimization result within the day-ahead and the probability of the state of charge exceeding the limit, and using the economic output plan within the day as the optimization variable, build an intra-day optimization scheduling model. Before operation, estimate the primary frequency regulation action amount for the current day according to the obtained autoregressive prediction model of the integral of frequency difference, and optimize the virtual droop coefficient of energy storage and the economic output plan according to the obtained day-ahead optimization scheduling model. After obtaining the day-ahead optimization result, use the obtained intra-day optimization scheduling model to perform intra-day optimization scheduling once per hour to obtain the corrected result of the economic output plan for the remaining hours of the day, and update the state of charge according to the actual primary frequency regulation signal until all intra-day optimization scheduling for the day is completed.

[0095] Please refer to Figure 5 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the optimization scheduling method for the energy storage of new energy power stations to participate in frequency regulation and output in the embodiment. To avoid repetition, it will not be elaborated here one by one. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the optimization scheduling system for the energy storage of new energy power stations to participate in frequency regulation and output in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0096] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation on the computer device 60. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0097] The so-called processor 61 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), 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. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0098] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0099] Furthermore, the memory 62 may also include both the internal storage unit of the computer device 60 and the external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store the data that has been output or will be output.

[0100] Please refer to Figure 6 , the terminal device is an electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0101] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method part of this specification. For example, the processing unit 610 may execute the steps as shown in Figure 7 .

[0102] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0103] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0104] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0105] The electronic device 600 may also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication may be carried out through the input / output interface 650. Moreover, the electronic device 600 may also communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0106] Embodiment 4 The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the expandable storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by a processor are also stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0107] The computer-readable storage medium also includes data signals propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component. The program code contained on the readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0108] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages - such as Java, C++, etc., and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0109] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the optimization scheduling method for energy storage participation in frequency regulation and output in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: Build a model for the primary frequency regulation output and economic output of energy storage in a new energy power station, and fit an autoregressive prediction model of the frequency difference integral based on the historical frequency difference data collected at the common connection point of the new energy power station; with the goal of maximizing the overall revenue of the new energy power station, and using the virtual droop coefficient of the energy storage and the economic output plan as optimization variables, build a day-ahead optimization scheduling model; with the goal of minimizing the deviation of the economic output optimization result during the day-ahead and the probability of the state of charge exceeding the limit, and using the economic output plan during the day as the optimization variable, build an intra-day optimization scheduling model; before operation, estimate the primary frequency regulation action amount of the day according to the obtained autoregressive prediction model of the frequency difference integral, and optimize the virtual droop coefficient and economic output plan of the energy storage according to the obtained day-ahead optimization scheduling model; after obtaining the day-ahead optimization result, use the obtained intra-day optimization scheduling model to perform intra-day optimization scheduling once per hour to obtain the corrected results of the economic output plan for the remaining hours of the day, and update the state of charge according to the actual primary frequency regulation signal until all intra-day optimization scheduling for the day is completed.

[0110] The databases involved in the embodiments provided in the present application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. The processors involved in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0111] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0112] 1. Simulation experiment objectives Verify the accuracy of the prediction model: the prediction ability of the autoregressive model for the frequency difference integral.

[0113] Evaluate the effect of hierarchical optimization: the synergy, economic benefits, and SOC stability between day-ahead optimization and intra-day optimization.

[0114] Compare the benchmark scenarios: the differences among scenarios without energy storage participation, without optimized scheduling, and only day-ahead optimization.

[0115] 2. Simulation Experiment Design Basic Parameter Settings

[0116] Experimental Scenarios Scenario 1: Only day-ahead optimization (without intra-day correction).

[0117] Scenario 2: Two-level optimization of day-ahead + intra-day (the proposed method).

[0118] Scenario 3: No energy storage participating in frequency regulation (benchmark comparison).

[0119] 3. Key Simulation Data and Results Performance of the Autoregressive Prediction Model

[0120] The model can effectively predict the trend of the integral of frequency deviation and meet the input requirements of day-ahead optimization.

[0121] Results of Day-ahead Optimized Scheduling

[0122] Optimizing the virtual droop coefficient can improve the frequency regulation response ability and maximize the benefits by combining with electricity price fluctuations.

[0123] Comparison of Intra-day Optimization Effects

[0124] Intra-day optimization significantly reduces the risk of output deviation and SOC over-limit and improves the frequency regulation tracking accuracy.

[0125] Overall Economic Comparison

[0126] The comprehensive benefits of the method of the present invention are increased by 179% (compared with no energy storage), and the SOC security is optimal.

[0127] Please refer to Figure 4. This embodiment considers the scenario where energy storage assists a new energy power station with several wind turbines to participate in primary frequency regulation and conduct peak-valley arbitrage through economic output. As shown in the figure, it presents the day-ahead and intra-day optimal scheduling results of the operation of the energy storage within a day. It can be seen from Figure 4(a) that the day-ahead optimal result is basically consistent with the trend of the time-of-use electricity price fluctuation, and the intra-day optimal result is fine-tuned during the day to ensure the long-term operation of the energy storage. It can be seen from Figure 4(b) that the state of charge (SOC) of the energy storage only through day-ahead optimal scheduling exceeds the limit at some moments, which will cause the energy storage to be unable to provide primary frequency regulation services. However, the SOC of the energy storage after intra-day rolling optimization and adjustment does not exceed the limit. Combining Figure 4(c), it can be seen that the energy storage after intra-day optimization can stably provide primary frequency regulation output throughout the day, ensuring the provision of primary frequency regulation services for the new energy power station.

[0128] Through the day-ahead and intra-day two-layer optimal scheduling strategy, the uncertainty of the primary frequency regulation signal and the time-of-use electricity price mechanism are considered in the day-ahead optimization to optimize the virtual droop coefficient and economic output plan of the energy storage, and the economic output plan is fine-tuned through rolling optimization during the day to ensure the stable participation of the energy storage in primary frequency regulation services. On the basis of primary frequency regulation services, the overall revenue of the power station is increased through the economic output plan at the same time, achieving the optimal economy.

[0129] In summary, for an optimal scheduling method and system for energy storage in a new energy power station to participate in frequency regulation and output, the uncertainty of primary frequency regulation actions is fitted through an autoregressive model, and the safety operation constraint conditions satisfied by the state of charge of the energy storage are constructed. After day-ahead and intra-day rolling optimization, the long-term operation ability of the energy storage is ensured, enabling the energy storage to provide reliable primary frequency regulation services. In addition, the optimized economic output can improve the economy of the energy storage and the overall revenue of the power station. Compared with the existing primary frequency regulation optimal scheduling methods, this method can consider the randomness of primary frequency regulation actions, improve the adaptability of the algorithm, and enhance the robustness of the algorithm through intra-day rolling optimization.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0131] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0133] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0136] If the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it may also be completed by instructing relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0137] This application is described with reference to the flowcharts and / or block diagrams of methods, devices, and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0138] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions in the process Figure 1One or more processes and / or blocks Figure 1 The functions specified in one or more blocks.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 One or more processes and / or blocks Figure 1 The steps of the functions specified in one or more blocks.

[0140] The above content is only to illustrate the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention fall within the protection scope of the claims of the present invention.

Claims

1. An optimal dispatching method for energy storage in new energy power stations to participate in frequency regulation and output, characterized in that, It includes the following steps: Construct a model for the energy storage-assisted primary frequency regulation output and economic output of a new energy power station, and fit an autoregressive prediction model of the frequency difference integral based on the historical frequency difference data collected at the common connection point of the new energy power station; With the goal of maximizing the overall revenue of the new energy power station, and using the virtual droop coefficient of the energy storage and the economic output plan as optimization variables, construct a day-ahead optimal scheduling model; With the goal of minimizing the deviation of the economic output optimization results between day-ahead and within-day and the probability of the state of charge exceeding the limit, and using the within-day economic output plan as the optimization variable, construct a within-day optimal scheduling model; Before operation, estimate the primary frequency regulation action amount of the day according to the autoregressive prediction model of the frequency difference integral obtained, and optimize the virtual droop coefficient of the energy storage and the economic output plan according to the obtained day-ahead optimal scheduling model; After obtaining the day-ahead optimization results, use the obtained within-day optimal scheduling model to perform within-day optimal scheduling once per hour to obtain the corrected results of the economic output plan for the remaining hours of the day, and update the state of charge according to the actual primary frequency regulation signal until all within-day optimal scheduling for the day is completed.

2. The optimized scheduling method for energy storage in new energy power stations to participate in frequency regulation and output according to claim 1, characterized in that, The autoregressive prediction model of the frequency difference integral is specifically: Among them, is instantaneous frequency difference integral prediction; is a Gaussian distribution random variable; is the frequency difference integral measurement quantity; is the autoregressive model fitting parameter.

3. The optimized scheduling method for the energy storage of a new energy power station to participate in frequency regulation and output according to claim 1, wherein The objective function of the day-ahead optimal scheduling model is: Among them, is the primary frequency regulation revenue coefficient; is the time-of-use electricity price; is the total interval number of day-ahead and intraday scheduling, represents its primary frequency regulation revenue, is the penalty term for the change in energy storage output.

4. The optimized dispatching method for the energy storage of a new energy power station to participate in frequency regulation and power output according to claim 3, wherein, The constraint conditions are the state of charge probability constraint constructed according to the frequency difference integral, and the power constraints for the operation of the energy storage and the new energy power station.

5. The optimized dispatching method for the energy storage of a new energy power station to participate in frequency regulation and output according to claim 4, characterized in that, The state of charge offset of the energy storage at different times satisfies the following formula: Among them, is the state of charge deviation of the energy storage between the time intervals and ; is the state of charge of the energy storage at the (k + 1)-th moment, is the state of charge of the energy storage at the k-th moment, is the day-ahead and intra-day scheduling interval, is the active power output obtained through day-ahead and intra-day optimal scheduling, is the virtual droop coefficient of the energy storage participating in frequency regulation, is the integral of the frequency deviation over ; is the rated capacity of the energy storage, is the allowable deviation range of the change in the state of charge of the energy storage; The probability constraint conditions for the operation of the energy storage: wherein, is the quantile of the Gaussian distribution, is the variance of the frequency deviation integral, is the upper limit of the energy storage state of charge; The constraint conditions that the energy storage also needs to satisfy during the optimization process are: Among them, is the maximum virtual droop coefficient; is the rated power of the energy storage; is the maximum frequency deviation of the system; is the maximum ramp power of the energy storage; is the rated power of the power station.

6. The optimized scheduling method for energy storage in new energy power stations to participate in frequency regulation and power output according to claim 1, characterized in that The objective function of the in-day optimization scheduling model at the -th hour is as follows: Among them, is the deviation penalty coefficient; and are the weight coefficients; and are the quantiles in the charge state constraints of CH and RH, is the total interval number of day-ahead and intra-day scheduling.

7. The optimized dispatching method for energy storage in new energy power stations to participate in frequency regulation and output according to claim 6, characterized in that, The constraint conditions of the within-day optimal scheduling model are the state of charge probability constraint constructed according to the frequency difference integral, and the power constraints for the operation of the energy storage and the new energy power station.

8. The optimized scheduling method for energy storage in new energy power stations to participate in frequency regulation and output according to claim 7, characterized in that, Combined with the current-day constraint conditions, variables and are respectively introduced for the two-stage optimization of the first hour and the remaining hours within the day, and the following constraints are constructed: Among them, is the rated capacity of energy storage, is the safety upper limit of the state of charge of energy storage, is the state of charge at time k, is the time interval coefficient, is the economic output power of energy storage at time k, is the virtual droop coefficient for energy storage to participate in frequency regulation, is the quantile in the state of charge constraint condition of CH, is the variance of the integral of frequency deviation, is the frequency regulation demand updated within a day, is the lower limit of the state of charge of energy storage.

9. The optimized scheduling method for the energy storage of a new energy power station to participate in frequency regulation and power output according to claim 1, wherein Optimize the result before the acquisition date After that, the intraday optimization scheduling is carried out once at respectively; Suppose to run the intraday optimization at moment, and after obtaining the intraday optimization result the energy storage will be based on Run step lengths, and update the state of charge SOC according to the actual primary frequency regulation action; When occurs, the intraday optimization will continue to run according to to obtain the optimization results for hours and run step lengths, repeating the content for hours until the one-day optimization task is completed; During within-day optimization, only the optimization results within the first hour immediately following the optimization moment are actually used for the operation of the energy storage, and the optimization results for the remaining hours are not used as the basis for the operation of the energy storage.

10. An optimized dispatching system for energy storage in a new energy power station to participate in frequency regulation and output, characterized in that, It includes: A fitting module that constructs a model for the energy storage-assisted primary frequency regulation output and economic output of a new energy power station, and fits an autoregressive prediction model of the frequency difference integral based on the historical frequency difference data collected at the common connection point of the new energy power station; A day-ahead module that, with the goal of maximizing the overall revenue of the new energy power station, and using the virtual droop coefficient of the energy storage and the economic output plan as optimization variables, constructs a day-ahead optimal scheduling model; A within-day module that, with the goal of minimizing the deviation of the economic output optimization results between day-ahead and within-day and the probability of the state of charge exceeding the limit, and using the within-day economic output plan as the optimization variable, constructs a within-day optimal scheduling model; A scheduling module that, before operation, estimates the primary frequency regulation action amount of the day according to the autoregressive prediction model of the frequency difference integral obtained, and optimizes the virtual droop coefficient of the energy storage and the economic output plan according to the obtained day-ahead optimal scheduling model; After obtaining the day-ahead optimization results, use the obtained within-day optimal scheduling model to perform within-day optimal scheduling once per hour to obtain the corrected results of the economic output plan for the remaining hours of the day, and update the state of charge according to the actual primary frequency regulation signal until all within-day optimal scheduling for the day is completed.

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