Dispatching operation method for new energy and storage combined participation in frequency modulation auxiliary service market

Abstract

The application relates to the field of power systems, and particularly discloses a dispatching operation method for new energy power stations with energy storage participating in a frequency modulation auxiliary service market, which comprises the following steps: inputting the output values of each new energy power station with energy storage in the days to be aggregated into a trained day-ahead output prediction model, generating the predicted output values of each new energy power station in each day, reducing the day scenarios by using a probability reduction method, and obtaining the occurrence probabilities of multiple typical days; based on the occurrence probabilities of the multiple typical days and the predicted output values of each new energy power station in the multiple typical days, using a particle swarm algorithm, taking the dispatching operation revenue of the alliance structure as the fitness value, optimizing the initialized multiple alliance structures, obtaining a target alliance structure, and outputting the capacity of each new energy power station in each alliance in the target alliance structure allocated to the frequency modulation auxiliary service market and the electric energy market in each typical day. The application can not only improve the energy storage utilization rate of new energy, but also improve the power supply quality of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, and particularly relates to a dispatching operation method for new energy storage combined participation in frequency modulation auxiliary service market. BACKGROUND

[0002] With the development of new energy technology, the installed capacity of new energy power plants has rapidly grown. However, the output of new energy (such as wind power and photovoltaic power) is greatly affected by weather conditions, and has the characteristics of intermittency and volatility, which brings great challenges to the safe and stable operation of the power system. Therefore, China proposes to build a system-friendly new energy power station equipped with new energy storage, that is, to equip new energy power plants with energy storage devices, so as to effectively smooth the fluctuations of new energy power generation and improve the flexibility and regulation capacity of the power system, thereby improving the consumption capacity of new energy.

[0003] However, at present, most energy storage services a single subject, and the equivalent utilization coefficient of new energy equipped with energy storage devices, the calling frequency and utilization rate of energy storage devices are low. SUMMARY

[0004] Therefore, the present application provides a dispatching operation method for new energy storage combined participation in frequency modulation auxiliary service market, in an attempt to solve or at least alleviate the above problems.

[0005] According to one aspect of the present application, a new energy storage joint participation in frequency modulation auxiliary service market scheduling operation method is provided, comprising: inputting the output value of each new energy power station in each day for a predetermined number of days to be aggregated into a trained day-ahead output prediction model to generate the predicted output value of each new energy power station in each day, each new energy power station being equipped with energy storage; based on the predicted output value of each new energy power station in each day, using the probability reduction method to reduce the day scene to obtain the occurrence probability of multiple typical days and the predicted output value of each new energy power station in multiple typical days; based on the occurrence probability of multiple typical days and the predicted output value of each new energy power station in multiple typical days, using the particle swarm algorithm to take the scheduling operation revenue of the alliance structure as the fitness value, and optimizing the initialized multiple alliance structures to obtain the target alliance structure, and outputting the scheduling operation scheme of each new energy storage in each typical day in each alliance in the target alliance structure, the scheduling operation scheme including the capacity of each new energy storage allocated to the frequency modulation auxiliary service market and the energy market in each typical day, and any alliance including at least one new energy power station equipped with energy storage; wherein the scheduling operation revenue of any alliance structure is obtained by: considering the new energy output deviation penalty, taking the maximum scheduling operation revenue of each alliance in the alliance structure as the target, and constructing a scheduling operation model of each alliance participating in the frequency modulation auxiliary service market; based on the occurrence probability of multiple typical days and the predicted output value of each new energy power station in multiple typical days, solving the constructed scheduling operation model of each alliance participating in the frequency modulation auxiliary service market to obtain the scheduling operation scheme of each new energy storage in each typical day in each alliance and the scheduling operation revenue of each alliance under the corresponding scheduling operation scheme; based on the obtained scheduling operation revenue of each alliance, obtaining the scheduling operation revenue of the alliance structure.

[0006] Optionally, in the new energy storage joint participation in frequency modulation auxiliary service market scheduling operation method according to the present application, for each alliance, the scheduling operation model thereof includes an objective function, the objective function including:

[0007]

[0008] wherein R year represents the scheduling operation revenue of the alliance, p k represents the occurrence probability of the typical day k, represents the secondary frequency modulation income of the alliance in the t period of the typical day k, represents the on-grid electricity sale income of the alliance in the t period of the typical day k, represents the output deviation penalty of the alliance in the t period of the typical day k, represents the energy storage operation cost of the alliance in the t period of the typical day k, K represents the total number of typical days, and T represents the total number of periods.

[0009] Optionally, in the dispatch operation method for new energy power storage joint participation in frequency modulation auxiliary service market according to the application, the particle swarm algorithm is used to optimize the initialized multiple alliance structures by taking the dispatch operation income of the alliance structure as the fitness value, and the target alliance structure is obtained, including: the number of initialized alliance structures, the speed and position of each alliance structure, and the iteration number; based on the current position of each alliance structure, the fitness value of each alliance structure is obtained; according to the obtained fitness value of each alliance structure, the optimal individual position of each alliance structure and the optimal group position of the alliance structure group are determined, and it is detected whether the initialized iteration number is reached or all new energy power stations become a union; if the iteration number is not reached and all new energy power stations do not become a union, the current iteration number is increased by one, the speed and position of each alliance structure are updated, and the above steps of obtaining the fitness value, determining the optimal individual position and the optimal group position, and detecting whether the current iteration number and the alliance meet the requirements are repeatedly executed until the initialized iteration number is reached or all new energy power stations become a union, the current optimal group position is taken as the final position of the alliance structure, and the target alliance structure is obtained.

[0010] Optionally, in the dispatch operation method for new energy power storage joint participation in frequency modulation auxiliary service market according to the application, the position of each alliance structure is composed of the number of alliances under the alliance structure and the number of new energy power stations participating in the alliance.

[0011] Optionally, in the dispatch operation method for new energy power storage joint participation in frequency modulation auxiliary service market according to the application, the day-ahead output prediction model adopts an Elman neural network model.

[0012] Optionally, in the dispatch operation method for new energy power storage joint participation in frequency modulation auxiliary service market according to the application, the day-ahead output prediction model is obtained by training based on the following method: the real output value of a predetermined number of days in the training set is input into the pre-trained day-ahead output prediction model to obtain the predicted output value of the prediction day, and the prediction day is any day in the training set; the parameters of the day-ahead output prediction model are updated based on the loss value between the real output value and the predicted output value of the prediction day until the loss value meets the predetermined condition, the training is ended, and the trained day-ahead output prediction model is obtained.

[0013] Optionally, in the dispatch operation method for new energy power storage joint participation in frequency modulation auxiliary service market according to the application, for each alliance, the dispatch operation model further includes a constraint condition, and the constraint condition includes the constraint of the declared power and the discharged power of each new energy power station in the alliance, the charge and discharge capacity constraint of each energy storage in the alliance, the state of charge constraint of each energy storage in the alliance, and the capacity constraint of each new energy power station participating in the electricity market in the alliance.

[0014] Optionally, in the dispatch operation method of the new energy storage combined participation in the frequency modulation auxiliary service market according to the present application, the output deviation penalty of the alliance in the t period of the typical day k is obtained by the following formula:

[0015]

[0016] wherein, represents the deviation penalty ratio of participating in the electric energy market, represents the unit online power price of the t period of the typical day k, represents the electric energy deviation amount of the energy storage i in the t period of the typical day k, or the electric energy deviation amount of the main body composed of the energy storage i and the new energy power station to which the energy storage i belongs in the t period of the typical day k, and I represents the total number of energy storages.

[0017] According to still another aspect of the present application, a computing device is provided, comprising at least one processor; and a memory storing program instructions configured to be executed by the at least one processor, the program instructions comprising instructions for executing the dispatch operation method of the new energy storage combined participation in the frequency modulation auxiliary service market according to the present application.

[0018] According to still another aspect of the present application, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to execute the dispatch operation method of the new energy storage combined participation in the frequency modulation auxiliary service market according to the present application.

[0019] The dispatch operation method of the new energy storage combined participation in the frequency modulation auxiliary service market according to the present application aggregates the energy storages configured by the new energy to participate in the electric energy market and the frequency modulation auxiliary service market, considers the new energy deviation penalty cost when constructing the dispatch operation model, and further proposes the alliance structure dominant theory through the construction of the evolutionary cooperation game solution algorithm based on the particle swarm algorithm, so as to obtain the stable game structure of the cooperation game. Therefore, the present application not only can improve the energy storage utilization rate of the new energy, but also can effectively participate in the frequency modulation auxiliary, so as to improve the power supply quality of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0020] To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative of various ways in which the principles disclosed herein can be practiced and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above- and other advantages of the present disclosure, as defined solely by the claims, will become more fully apparent from the detailed description given herein below and the accompanying drawings, wherein like elements are referred to by like reference numerals. Such description is given for the sake of

[0021] Figure 1A structural block diagram of the computing device 100 according to one embodiment of the present application is shown;

[0022] Figure 2 A flow chart of a dispatch operation method 200 of new energy and storage joint participation in frequency modulation auxiliary service market according to one embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram of a dispatch operation framework of new energy and storage joint participation in frequency modulation auxiliary service market according to one embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of a day-ahead output prediction model based on an Elman neural network model according to one embodiment of the present application is shown;

[0025] Figure 5 A schematic diagram of a flow chart of a dispatch operation method of new energy and storage joint participation in frequency modulation auxiliary service market according to another embodiment of the present application is shown;

[0026] Figure 6 A schematic diagram of an electricity market price, a frequency modulation capacity price and a frequency modulation mileage price according to one embodiment of the present application is shown;

[0027] Figure 7 A schematic diagram of a five-minute frequency modulation calling ratio according to one embodiment of the present application is shown;

[0028] Figure 8 A schematic diagram of a new energy prediction error scenario according to one embodiment of the present application is shown;

[0029] Figure 9 A schematic diagram of different new energy power plant benefits and new energy consumption rates under different schemes according to one embodiment of the present application is shown;

[0030] Figure 10 A schematic diagram of an alliance structure iteration result according to one embodiment of the present application is shown;

[0031] Figure 11 A schematic diagram of a result obtained after a cooperation game is allocated according to a shapley value according to one embodiment of the present application is shown;

[0032] Figure 12 A schematic diagram of capacity allocation results of storage under different typical days according to one embodiment of the present application is shown;

[0033] Figure 13 A schematic diagram of a result comparison under different electricity deviation penalty rates according to one embodiment of the present application is shown;

[0034] Figure 14A schematic diagram of alliance benefits under different matching storage ratios according to one embodiment of the present application is shown.

[0035] Figure 15 A schematic diagram of predicted error absorption rates under different matching storage ratios according to one embodiment of the present application is shown.

[0036] Figure 16 A schematic diagram of result comparison under different new energy change rate limits according to one embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0038] Building a system-friendly new energy power station with new energy storage is an important guarantee for improving the ability of green electricity to access the system and building a new power system. However, a large number of system-friendly new energy power stations generally have low utilization rates of matching storage (i.e., storage devices configured by new energy power stations / plants) and ineffective flexible adjustment capabilities.

[0039] In view of this, the present application proposes that new energy matching storage can be used to participate in frequency modulation to improve the utilization rate of storage configured in new energy, which can also improve the stability of the power system, that is, new energy matching storage participates in both the electricity market and the frequency modulation auxiliary service market. On this basis, the present application further proposes that multiple new energy matching storages can be aggregated into a new energy matching storage cluster to participate in the electricity market and the frequency modulation auxiliary service market (i.e., multiple new energy matching storages jointly participate in the electricity market and the frequency modulation auxiliary service market), which can further improve the utilization rate of new energy storage and the stability of the power system.

[0040] Based on this, how to aggregate new energy matching storage and how to balance the capacity of new energy matching storage allocated to the electricity market and the frequency modulation auxiliary service market have become a major problem. To this end, the present application provides a dispatching and operating method for new energy matching storage jointly participating in the frequency modulation auxiliary service market.

[0041] The dispatching and operating method for new energy matching storage jointly participating in the frequency modulation auxiliary service market of the present application can be executed in a computing device. Figure 1A block diagram of physical components (i.e., hardware) of a computing device 100 is shown. In a basic configuration, computing device 100 includes at least one processing unit 102 and system memory 104. According to an aspect, depending on the configuration and type of computing device, processing unit 102 can be implemented as a processor. System memory 104 includes, but is not limited to, volatile (e.g., random access memory (RAM)), non-volatile (e.g., read-only memory (ROM)), flash memory, or any combination. According to an aspect, system memory 104 includes operating system 105 and program module 106, which includes joint operation module 120 configured to perform the dispatch operation method of new energy storage joint participating in frequency modulation auxiliary service market 200 of the present application.

[0042] According to an aspect, operating system 105 is suitable for controlling the operation of computing device 100, for example. Furthermore, examples are practiced in conjunction with a graphics library, other operating systems, or any other application program, and are not limited to any particular application or system. In Figure 1 This basic configuration is illustrated in FIG. 1 by those components within dashed line 108. According to an aspect, computing device 100 has additional features or functionality. For example, according to an aspect, computing device 100 includes additional data storage devices (removable and / or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 1 by removable storage 109 and non-removable storage 110. Figure 1 According to an aspect, removable storage 109 includes a computer-readable storage medium having stored thereon computer executable instructions (e.g., software) that, when executed by, for example, processing unit 102, cause the computing device 100 to perform desired functions. According to an aspect, non-removable storage 110 includes a computer-readable storage medium having stored thereon computer executable instructions (e.g., software) that, when executed by, for example, processing unit 102, cause the computing device 100 to perform desired functions.

[0043] As stated above, according to an aspect, program module is stored in system memory 104. According to an aspect, program modules can include one or more applications. The application is not limited by the type of application program, for example, the application program can include an email and contacts application, a word processing application, a spreadsheet application, a database application, a slide presentation application, a drawing or computer-aided application, a web browser application, etc.

[0044] According to an aspect, examples can be practiced with circuitry at the component level, in a bulk integrated circuit, a modular integrated circuit, and / or with discrete components. For example, examples can be practiced via a system-on-a-chip (SoC). Figure 1Each or many of the components illustrated in the FIGURE can be practiced on a system on a chip (SOC) integrated on a single integrated circuit to practice examples. According to one aspect, such a SOC device can include one or more processing units, graphics units, communications units, system virtualization units, and various application functionality all of which are integrated (or "burned") onto the chip substrate according to one aspect. When operating via the SOC, the functionality described herein can be operated upon via application specific logic integrated with other components of the computing device 100 on the single integrated circuit (chip). Embodiments of the application can also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the application can be practiced within a general computer device, or in any other circuits or systems.

[0045] According to one aspect, the computing device 100 can also have one or more input device(s) 112 such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output device(s) 114 such as a display, speakers, a printer, etc. can also be included. The aforementioned devices are examples and others can be used. The computing device 100 can include one or more communication connections 116 allowing communications with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver, and / or transceiver circuitry; universal serial bus (USB), parallel, and / or serial ports.

[0046] The term computer readable media as used herein includes computer storage media. Computer storage media can include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, or program modules. The system memory 104, the removable storage 109, and the non-removable storage 110 are all computer storage media examples (i.e., memory storage.) Computer storage media can include Random Access Memory (RAM), Read Only Memory (ROM), Electronically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 100. According to one aspect, any such computer storage media can be part of the computing device 100. Computer storage media does not include a modulated data signal or other propagated data signal.

[0047] According to an aspect, a communication medium is embodied by a computer readable instruction, a data structure, a program module, or other data embodied in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. According to an aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0048] Figure 2 A flowchart of a dispatch operation method 200 of a new energy storage combined participation in a frequency modulation auxiliary service market according to an embodiment of the application is shown, and the method 200 is suitable for being executed in a computing device (for example Figure 1 The computing device 100 shown).

[0049] Here, the operation framework of the new energy storage combined participation in the frequency modulation auxiliary service market (or the operation framework of the new energy storage cluster participating in the frequency modulation auxiliary service market) of the application is first described.

[0050] The wind and light new energy output has the characteristics of volatility, intermittence, uncertainty and the like, and separate grid connection often causes certain risks to power grid dispatch operation, and therefore the power side energy storage can be used to track the day-ahead output plan of the new energy to generate power, so as to increase the quality of the new energy output. However, at present, the power side energy storage has the problem of low resource utilization rate, and in view of this, the new energy output can be aggregated and the energy storage resources can be shared to provide frequency modulation resources for the power grid, so that not only additional benefits can be brought to the power side and the investment return period is shortened, but also the power grid margin is improved and the safety of the power grid dispatch operation is increased.

[0051] Therefore, the application provides a power side energy storage sharing mode. The power side energy storage at least includes new energy storage, and specifically, the new energy storage can include photovoltaic power generation storage and wind power generation storage, and of course in some embodiments, other storage can also be included, and the application does not limit this.

[0052] Next, the power side energy storage sharing mode of the application will be described in combination with Figure 3The operation framework of the new energy storage combined participation in the frequency modulation auxiliary service market constructed by the application is described. The photovoltaic power storage and wind power storage give priority to meeting the output of photovoltaic power plants and wind power plants-renewable energy power plants (REPP) in the day-ahead plan when the self-provided storage cannot meet the output of the new energy power station-storage plan. When the self-provided storage cannot meet the output of the new energy power station-storage plan, capacity can be purchased from other storage to avoid causing an assessment deviation. In addition, the new energy self-provided power station can form an alliance with independent storage, and the remaining capacity can jointly participate in the frequency modulation auxiliary service to increase income. Among them, regarding the alliance, it is described here. In the application, a union includes not only a new energy power station equipped with storage, but also independent storage. That is, a union includes at least one new energy power station equipped with storage composed of a new energy power station and a storage.

[0053] The above is the operation framework of the new energy storage combined participation in the frequency modulation auxiliary service market of the application. Next, the dispatching operation method of the new energy storage combined participation in the frequency modulation auxiliary service market of the application is described. As shown in Figure 2 the method 200 starts at 210.

[0054] In 210, the output value of the predetermined number of days of each new energy power station to be aggregated is input to the trained day-ahead output prediction model to generate the predicted output value of each new energy power station to be aggregated each day, and each new energy power station to be aggregated is equipped with storage.

[0055] Among them, the new energy day-ahead output prediction is divided into physical and statistical methods. The physical method predicts the weather by solving fluid mechanics and thermodynamics equations, combined with unit characteristics modeling, which is suitable for short-term to medium-term prediction, but requires complex data and high-precision weather forecast. The statistical method is based on historical data, simplified processing, suitable for short-term or ultra-short-term prediction, but the long-term prediction error is larger. Based on this, combined with the consideration of the characteristics of Elman neural network in memorizing historical data, the application proposes to predict the new energy day-ahead output based on Elman neural network, which can improve the accuracy, efficiency and stability of the new energy day-ahead output prediction. Therefore, according to an embodiment of the application, the day-ahead output prediction model adopts an Elman neural network model, and the structure diagram can be seen from Figure 4 .

[0056] In addition, considering that the output of the new energy power station has a certain seasonality, the output data of the new energy power station (specifically, the new energy power station equipped with energy storage) in three months can be selected as the training set (i.e., the training sample) of each season according to the seasonal principle, so as to train the day-ahead output prediction model of each season (i.e., one day-ahead output prediction model corresponds to each season). Further, in some embodiments, the day-ahead output prediction model of any season can be trained in the following manner.

[0057] The real output value of the new energy power station in the prediction day of the training set is input into the pre-trained day-ahead output prediction model to obtain the predicted output value of the new energy power station in the prediction day. Wherein, the prediction day is any day in the training set, and further, there are a predetermined number of days before any day in the training set. Then, based on the loss value between the real output value and the predicted output value of the new energy power station in the prediction day, the parameters of the day-ahead output prediction model are updated (i.e., the parameters of the day-ahead output prediction model are adjusted). Repeat the above steps until the loss value meets the predetermined condition, the training is completed, and the trained day-ahead output prediction model of the season is obtained. In this way, for any new energy power station and any day, the predicted output value of the day can be obtained by inputting the output value of the day-ahead predetermined number of days into the trained day-ahead output prediction model of the season to which the day belongs.

[0058] Wherein, in some embodiments, the predetermined number can be three, of course, this is only an example, and the present application is not limited thereto. Below, the learning process is further described in combination with the day-ahead output prediction model based on the Elman neural network model shown in the following. Figure 4

[0059] The mathematical expression between each two layers of the day-ahead output prediction model is specifically as follows.

[0060] p d =g(ω 3 , {x, b2})

[0061] x=f(ω 1 x c +ω 2 {p d-1 ,p d-2 ,p d-3 ,b1})

[0062] x c (k)=x(k-1)

[0063] In the formula, p d ​is a T-dimensional output vector, represents the new energy output prediction value of day d, T represents that day d is divided into T time periods, x is an n-dimensional intermediate layer vector, x(k) and x(k-1) represent the kth component and the (k-1)th component of x respectively, b2 is a constant term from the intermediate layer to the output layer, {p d-1 ,p d-2 ,p d-3 is a (3*T+1)-dimensional input vector, p d-1 , p d-2 , p d-3 represent the new energy output of (d-1), (d-2), (d-3) days respectively, b1 is a constant term from the input layer to the intermediate layer, x c is an m-dimensional hidden layer state vector, x c (k) represents the kth component of x c , ω 3 is a hidden layer to output layer connection weight, ω 2 is an input layer to intermediate layer connection weight, ω 1 is a hidden layer to intermediate layer connection weight, g(*) is a transfer function of the output neuron, and is a linear combination of the intermediate layer output, that is, g(ω 3 , {x, b2}) = ω 3 ·{x, b2}, ω 3 is an n+1-dimensional vector, {x, b2} is an n+1-dimensional vector, f(*) is a transfer function of the intermediate layer neuron, and the Sigmoid function is used in the embodiment.

[0064] The learning index function can use the sum of squared errors function, as follows. In some embodiments, the gradient descent method can be used for solving.

[0065]

[0066] , p d (t) and p represent the predicted output value and the actual output value of the tth time period of the new energy d day respectively, and E(ω) is the sum of squared errors function, which is used to evaluate the error between the predicted output value and the actual output value.

[0067] After the above training, the trained day-ahead output prediction model is obtained, and then it can be used to generate the predicted output values of each day of each new energy power station to be aggregated. Specifically, in some embodiments, the trained day-ahead output prediction model can be used to generate the predicted output values of each day of each new energy power station in a year to be aggregated. The predicted output values of each day can specifically include the predicted output values of each time period of the day.

[0068] At this point, the predicted output values of each new energy power station on each day of the year to be polymerized are obtained. Among them, considering that wind and light power generation is greatly affected by environmental factors, its output has a certain uncertainty, but the number of daily output scenarios for a whole year is too large, therefore, in some embodiments, a probability quick reduction method can be further used to reduce the scenarios, specifically as follows.

[0069] In 220, based on the predicted output values of each new energy power station on each day, the daily scenarios are reduced by using the probability reduction method to obtain the occurrence probability of multiple typical days and the predicted output values of multiple typical days of each new energy power station. Among them, according to an embodiment of the present application, the daily scenarios are reduced by using the probability reduction method, which can specifically include the following steps.

[0070] Firstly, the Euclidean distance of each two scenarios is calculated, which can be calculated by the following formula. It is explained here that in this embodiment, the daily output is taken as a scenario, and it is assumed that the probability of occurrence of each scenario is the same.

[0071]

[0072] In the formula, d u,v represents the Euclidean distance of scenario u and scenario v, g u (t) and g v (t) represent the predicted output values of scenario u and scenario v at the t period (i.e. the predicted output values of u day and v day at the t period). Among them, about g u (t), g v (t), it is explained here. In some embodiments, g u (t) and g v (t) can be obtained by combining the predicted output values of all new energy power station scenarios u and scenario v at the t period. Further, the specific combination method can be summation, that is, the predicted output values of all new energy power station scenarios u at the t period are summed up as g u (t), and the predicted output values of all new energy power station scenarios v at the t period are summed up as g v (r). Of course, this is only an example, and the present application is not limited thereto, and in specific embodiments, those skilled in the art can set it according to actual needs.

[0073] Secondly, for each current scenario, the sum of the Euclidean distances with other scenarios is obtained, and the scenario with the smallest sum of the Euclidean distances with other scenarios is determined as the candidate scenario, which can be expressed as the following formula.

[0074]

[0075] In the formula, sd(u) represents the sum of the Euclidean distances between scene u and all remaining scenes, V represents the total number of remaining scenes, and u * The candidate scenario is the scenario whose sum of Euclidean distances to all other scenarios is minimized.

[0076] The third step is to find the scene with the smallest Euclidean distance to the candidate scene from all current scenes, which can be expressed as the following formula.

[0077]

[0078] In the formula, d u*,v Representing candidate scenario u * The Euclidean distance between v and scene v, v * This represents the scene with the smallest Euclidean distance to the candidate scenes.

[0079] The fourth step is to add the probability of the scenario with the smallest Euclidean distance to the candidate scenario to the probability of the candidate scenario, and then delete the scenario with the smallest Euclidean distance to the candidate scenario. That is, remove the candidate scenario u. * The probability of occurrence is updated to candidate scenario u * The probability of occurrence and scenario v * The sum of probabilities, and scene v * delete.

[0080] The fifth step is to check whether the current number of remaining scenes has reached the preset number of remaining scenes. For example, in some embodiments, the preset number of remaining scenes is NRE, so the check will determine whether the current number of remaining scenes has reached NRE.

[0081] Step 6: If the current number of remaining scenarios has not reached the pre-set number of remaining scenarios, repeat steps 1 to 5 until the current number of remaining scenarios reaches the pre-set number of remaining scenarios, obtaining multiple typical scenarios and the probability of occurrence for each typical scenario. Taking the above example, this yields NRE typical scenarios and the probability of occurrence for each of the NRE typical scenarios.

[0082] In section 210 above, the predicted power output values ​​for each new energy power station to be aggregated for each day of the year have been obtained. Therefore, the predicted power output values ​​for each typical day of each new energy power station can be directly obtained. Thus, the probability of occurrence of multiple typical days and the predicted power output values ​​for multiple typical days of each new energy power station have been obtained.

[0083] Subsequently, entering 230, based on the occurrence probability of the plurality of typical days and the predicted output values of the plurality of typical days of each new energy power station, the scheduling operation revenue of the alliance structure is used as the fitness value, and the initialized plurality of alliance structures are optimized by using the particle swarm algorithm to obtain the target alliance structure, and the scheduling operation scheme of each new energy storage in each typical day in the target alliance structure is output.

[0084] It is first noted that any alliance structure includes at least one alliance. Any alliance includes at least one new energy power station equipped with energy storage. The scheduling operation scheme of each new energy storage in each typical day includes the capacity of each new energy storage allocated to the frequency modulation auxiliary service market and the electric energy market in each typical day, and in some embodiments, specifically, the capacity of each new energy storage allocated to the frequency modulation auxiliary service market and the capacity of each new energy storage allocated to the electric energy market in each period of each typical day.

[0085] Next, the method for obtaining the scheduling operation revenue of any alliance structure is described as follows.

[0086] First, considering the new energy output deviation penalty, a scheduling operation model of each alliance participating in the frequency modulation auxiliary service market is constructed with the maximum scheduling operation revenue of each alliance in the alliance structure as the target.

[0087] Considering the limited energy storage resources, in order to balance the capacity reserved for REPP and the capacity participating in the frequency modulation auxiliary service market, and minimize the operation cost of REPP-energy storage, according to an embodiment of the present application, for each alliance, a scheduling operation model (or referred to as a day-ahead scheduling operation model) of the alliance can be constructed with the maximum scheduling operation revenue of the alliance as the target.

[0088] Specifically, the scheduling operation model of the alliance includes the following objective function.

[0089]

[0090] Wherein, R year represents the scheduling operation revenue of the alliance, p k represents the occurrence probability of the typical day k, represents the secondary frequency modulation income of the alliance in the t period of the typical day k, represents the on-grid electricity sales income of the alliance in the t period of the typical day k, represents the output deviation penalty of the alliance in the t period of the typical day k, represents the energy storage operation cost of the alliance in the t period of the typical day k, K represents the total number of typical days, and T represents the total number of periods.

[0091] The secondary frequency modulation income, the on-grid electricity sales income (also referred to as the income participating in the electric energy market), the energy storage operation cost, and the output deviation penalty cost are described as follows.

[0092] 1) Secondary frequency modulation income

[0093] According to the market rules, the compensation fee obtained by the winning AGC unit for the frequency modulation service includes frequency modulation capacity compensation and frequency modulation mileage compensation. Since the frequency modulation mileage needs to be known in the intraday stage to obtain the specific clearing amount, the historical capacity-mileage ratio is used to estimate the possible clearing amount of the frequency modulation mileage in the day-ahead stage in this embodiment. Based on this, in some embodiments, the secondary frequency modulation income can be specifically obtained by the following formula.

[0094]

[0095] wherein, represents the unit capacity compensation price of the alliance participating in the secondary frequency modulation in the t period of the typical day k (i.e., the unit capacity compensation price of the energy storage in the alliance participating in the secondary frequency modulation in the t period of the typical day k), represents the capacity of the i th energy storage (which can be a storage or an independent energy storage) in the alliance participating in the secondary frequency modulation in the t period of the typical day k, represents the unit mileage compensation price of the alliance participating in the secondary frequency modulation in the t period of the typical day k, represents the frequency modulation mileage calling rate, and I represents the total number of energy storages in the alliance, specifically, the total number of storage and independent energy storages in the alliance.

[0096] 2) On-grid electricity sale income

[0097] The on-grid electricity sale income can also be referred to as the income of the alliance participating in the electricity market or the benefit of the on-grid electricity quantity. Considering the total on-grid power of the alliance, the on-grid electricity sale income can be obtained by the following formula.

[0098]

[0099] wherein, represents the unit on-grid electricity quantity price in the t period of the typical day k, g j,k,t represents the actual on-grid electricity quantity (i.e., the actual declared on-grid quantity) of the j th new energy power plant REPP in the alliance in the t period of the typical day k, and J represents the total number of new energy power plants REPP in the alliance.

[0100] 3) Energy storage operation cost

[0101] In order to maximize the operation income of the REPP-energy storage alliance, the energy storage with low cost can be preferentially called to meet the tracking plan and frequency modulation capacity of the REPP, and therefore the energy storage operation cost is included in the objective function in this embodiment. The energy storage operation cost can be specifically obtained by the following formula.

[0102]

[0103] wherein, represents the unit capacity operation and maintenance cost of the i-th energy storage in the alliance, represents the capacity of the i-th energy storage in the alliance participating in secondary frequency modulation at the t time period of the typical day k, represents the capacity of the i-th energy storage in the alliance reserved for tracking the output of the REPP plan at the t time period of the typical day k.

[0104] 4) Output deviation penalty cost

[0105] To balance the balance between capacity allocation income and deviation penalty and maximize the income, the deviation penalty cost needs to be measured, at this time, not only the corresponding income is obtained, but also the corresponding penalty is deducted, so the deviation penalty can be obtained by the following formula.

[0106]

[0107] wherein, represents the deviation penalty ratio of participating in the electricity market, represents the electricity deviation amount of the i-th energy storage in the alliance at the t time period of the typical day k, the electricity deviation amount is the deviation between the actual output of the cluster and the declared on-grid amount, and the energy storage can reserve capacity to track the new energy output to make up for the deviation.

[0108] Further, the dispatching operation model of the alliance also includes constraint conditions corresponding to the above objective function, specifically including the declared electricity amount and discharge amount of each new energy power station in the alliance, the charge and discharge capacity constraint of each energy storage in the alliance, the state of charge constraint of each energy storage in the alliance, and the capacity constraint of each new energy power station participating in the electricity market in the alliance, which will be specifically described below.

[0109] 1) The declared electricity amount and discharge amount of each new energy power station in the alliance: that is, the electricity market declared amount and discharge amount constraint, which is specifically as follows.

[0110]

[0111] wherein, g j,k,t represents the actual on-grid electricity amount of the j-th new energy power station REPP in the alliance at the t time period of the typical day k; represents the actual discharge amount of the j-th new energy power station REPP in the alliance at the t time period of the typical day k; represents the tracking output of the i-th energy storage in the alliance (that is, the energy storage equipped by the j-th new energy power station) to new energy at the t time period of the typical day k when participating in the electricity market, when the value is positive, the actual output of the new energy is less than the predicted value, at this time, the discharge power of the energy storage is the charging power is 0, when the value is negative, the actual output of the new energy is greater than the predicted value, at this time, the charging power of the energy storage is The discharging power is 0, so and The relationship can be specifically expressed as follows.

[0112]

[0113]

[0114] wherein, Pi, k, t and Pdi, k, t respectively represent the charging power and the discharging power of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t and Pdi, k, t respectively represent the charging power and the discharging power of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t and Pdi, k, t respectively represent the charging power and the discharging power of the i-th energy storage in the alliance at the t time period of the typical day k,

[0115] 2) The charging and discharging capacity constraints of each energy storage in the alliance: considering the safe operation of the power grid, the capacity should meet the physical constraints when the energy storage is declared, which is specifically as follows.

[0116]

[0117] wherein, Pi, k, t represents the charging capacity of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t represents the down-regulation frequency modulation capacity of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t represents the down-regulation new energy output capacity of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t represents the maximum charging capacity of the i-th energy storage in the alliance, Pi, k, t represents the discharging capacity of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t represents the up-regulation frequency modulation capacity of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t represents the up-regulation new energy output capacity of the i-th energy storage in the alliance at the t time period of the typical day k, Pi, k, t represents the maximum discharging capacity of the i-th energy storage in the alliance.

[0118] 3) The state of charge constraints of each energy storage in the alliance: in actual operation, the state of charge of the energy storage specifically meets the following constraints.

[0119]

[0120] SOC i,min ≤SOC i,k,t ≤SOC i,max

[0121] wherein, SOC i,k,t , SOC i,k,t-1 represent the state of charge of the i-th energy storage in the alliance at the t time period and the (t-1) time period of the typical day k, Pi, k, t and Pdi, k, t respectively represent the charging efficiency and the discharging efficiency of the i-th energy storage in the alliance, denotes the frequency modulation mileage of the i-th energy storage in the alliance at the t time period of the typical day k, E i,max denotes the capacity of the i-th energy storage in the alliance, Δt denotes the length of a time period, SOC i,min , SOC i,max denote the minimum and maximum state of charge of the i-th energy storage in the alliance, respectively.

[0122] 4) Capacity constraint of each new energy power plant in the alliance participating in the electricity market: also known as the capacity constraint of the new energy output tracking plan, wherein the standby capacity of the energy storage for the REPP output uncertainty should meet the predicted error range, and the adjustment of the common output of the REPP- energy storage includes downward adjustment and upward adjustment, the downward adjustment means that when the real output of the REPP is higher than the predicted output, the energy storage performs charging action, the upward adjustment means that when the real output of the REPP is lower than the predicted output, the energy storage performs discharging action, and it is too complex to allocate the capacity of the energy storage according to the time period, therefore, the capacity of the new energy output tracking plan can be set as a certain proportion of the predicted output of the new energy, which can be seen in the following formula.

[0123]

[0124]

[0125] wherein, denotes the predicted power generation (i.e. predicted output value) of the j-th new energy power plant REPP in the alliance at the t time period of the typical day k, and denote the upward capacity allocation coefficient and the downward capacity allocation coefficient of the j-th new energy power plant REPP in the alliance, respectively, denote the upward new energy output capacity and the downward new energy output capacity of the i-th energy storage (i.e. the energy storage equipped by the j-th new energy power plant) in the alliance at the t time period of the typical day k.

[0126] So far, for each alliance in the alliance structure, the dispatching operation model of the alliance participating in the frequency modulation auxiliary service market has been constructed, and the second step is entered.

[0127] Secondly, based on the occurrence probability of multiple typical days and the predicted output values of multiple typical days of each new energy power plant, the dispatching operation model of each alliance participating in the frequency modulation auxiliary service market is solved to obtain the dispatching operation scheme of each new energy storage in each typical day and the dispatching operation income of each alliance under the corresponding dispatching operation scheme.

[0128] According to one embodiment of the present application, when solving the dispatch operation model of each constructed alliance participating in the frequency auxiliary service market, the actual online output value (also referred to as actual online electricity quantity) of the new energy power station in multiple typical days is input into the model. Considering that some of the predicted output values of the new energy power station in multiple typical days may not meet the technical regulation of wind power plant access to power system, the actual declared electricity quantity of the new energy power station is further adjusted in this embodiment.

[0129] Specifically, the technical regulation of wind power plant access to power system and the technical regulation of photovoltaic power station access to power system regulate the active power variation limit of the new energy power station. Based on this, in some embodiments, a new energy power station actual declared online quantity decision model can be constructed to make a decision on the actual declared online quantity of the new energy power station, with the goal of minimizing the distance between the online output and the predicted output.

[0130] According to one embodiment of the present application, the new energy power station actual declared online quantity decision model includes the following objective function.

[0131]

[0132] Wherein, f represents the distance between the online output and the predicted output, g j,k,t represents the actual online electricity quantity of the jth new energy power station REPP in the t period of the typical day k, represents the predicted electricity generation quantity of the jth new energy power station REPP in the t period of the typical day k.

[0133] Further, the new energy power station actual declared online quantity decision model further includes constraint conditions corresponding to the objective function, specifically including the new energy power station output variation rate constraint and the daily online quantity and daily predicted quantity constraint, specifically as follows.

[0134] New energy power station output variation rate constraint: the output variation rate of the new energy power station cannot exceed the limit value, specifically as follows.

[0135]

[0136] Wherein, g j,k,t+10min represents the actual online electricity quantity of the jth new energy power station REPP in the (t+10min) period of the typical day k, represents the installed capacity of the jth new energy power station REPP.

[0137] Daily online quantity and daily predicted quantity constraint: in order to make the best use of new energy, the online quantity and the predicted quantity in a day can be set to be the same, specifically as follows.

[0138]

[0139] By solving the above-mentioned new energy power station actual declaration on-grid quantity decision model, the actual on-grid power quantity of the new energy power station can be obtained. Subsequently, by solving the dispatching operation model of each alliance participating in the frequency modulation auxiliary service market, the dispatching operation scheme of each new energy power storage in each alliance on each typical day and the dispatching operation income of each alliance under the corresponding dispatching operation scheme can be obtained. As for the solution of the model, a related solver can be used to complete it, which will not be described here.

[0140] Thirdly, based on the obtained dispatching operation income of each alliance, the dispatching operation income of the alliance structure is obtained. Specifically, the dispatching operation incomes of each alliance are added, and the sum is the dispatching operation income of the alliance structure.

[0141] The above is the acquisition method of the dispatching operation income of any alliance structure. Next, how to optimize the alliance structure by using the particle swarm algorithm to obtain the target alliance structure (i.e. the most stable alliance structure) is described as follows.

[0142] Step one: initialize the number of alliance structures, the speed and position of each alliance structure, and the iteration number. In some embodiments, the initial inertia weight, the inertia weight when the iteration reaches the maximum number of iterations are also set.

[0143] The position of each alliance structure can be composed of the number of alliances under the alliance structure and the number of alliances participated by each new energy power station. Further, the position of each alliance structure can be represented as a vector, and the elements in the vector are the number of alliances under the alliance structure and the number of alliances participated by each new energy power station in turn.

[0144] Specifically, the number of new energy power stations to be aggregated is M, and the position of the nth alliance structure particle x n is represented by a (M+1) dimensional vector, wherein the first element x n,t (1) represents the number of alliances under the nth alliance structure in the tth iteration, and the subsequent elements x n,t (2), x n,t (3)…x n,t (M+1) represent the number of alliances participated by each new energy power station.

[0145] In addition, the position of each alliance structure satisfies the following two constraints.

[0146]

[0147] 1≤x n,t (2),x n,t (3),…,x n,t (M+1)≤x n,t (1)

[0148] The speed of the alliance structure is:

[0149] v n,t+1 =v n,t +c1r1[p n,best -x n,t ]+c2r2[p best -x n,t ]

[0150] Among them, v n,t+1 Let c1 be the velocity of the nth particle at iteration (t+1), r1 and r2 be uniformly distributed random numbers in the range [0,1], and c2 be the group learning factor. n,best p represents the optimal individual extreme value of the nth particle. best Let x represent the global optimal extremum of the particle swarm. n,t This represents the position of the nth particle in the t-th iteration.

[0151] Step 2: Based on the current position of each alliance structure, obtain the fitness value of each alliance structure.

[0152] The above has explained how to obtain the scheduling and operation benefits of each alliance structure. In this embodiment, the scheduling and operation benefits of the alliance structure are used as the fitness value. Therefore, after obtaining the predicted output values ​​of each new energy power station to be aggregated on multiple typical days and the occurrence probability of each typical day, the fitness value of each alliance structure can be obtained based on the current position of each alliance structure.

[0153] Step 3: Based on the obtained fitness values ​​of each alliance structure, determine the optimal individual position of each alliance structure and the optimal group position of the alliance structure group, and check whether the initial iteration number has been reached or whether all the new energy power plants to be aggregated have become an alliance.

[0154] Specifically, for each alliance structure, the optimal individual position of each alliance structure is determined by comparing its current fitness value with its historical best fitness value, and then the optimal group position is determined based on the last individual position.

[0155] If the number of iterations required for initialization has not been reached and all the new energy power plants to be aggregated have not formed an alliance, then proceed to step four.

[0156] Step four: Increment the current iteration count by one, update the speed and position of each alliance structure, and continue to execute steps two through four until the initial iteration count is reached or all the new energy power plants to be aggregated become an alliance. Stop the iteration, take the current optimal group position as the final position of the alliance structure, and obtain the target alliance structure.

[0157] Wherein, about the speed and position of the alliance structure, update according to the particle speed update formula and particle position update formula of the particle swarm algorithm, about this, not repeated here. In addition, when obtaining the fitness value of each alliance structure, the scheduling operation scheme of each new energy storage in the typical day in each alliance in the alliance structure has been obtained, so after obtaining the target alliance structure, the scheduling operation scheme of each new energy storage in each typical day in each alliance in the target alliance structure can be obtained.

[0158] So far, it can be seen that the scheduling operation optimization of the new energy storage joint participation in the frequency modulation auxiliary service market is divided into two stages, the first stage needs to consider the stability of the alliance to optimize the alliance structure, and the second stage aims to further optimize the allocation of the energy storage capacity on the basis of the corresponding alliance structure, to determine the required energy storage capacity for error tracking of the new energy and the capacity participating in the frequency modulation auxiliary service, so as to maximize the alliance benefit. In order to better understand, the present application further provides an embodiment of a scheduling operation method of a new energy storage joint participation in a frequency modulation auxiliary service market, which is described below in combination with Figure 5 the specific process.

[0159] (1) In data processing:

[0160] 1) Use the trained day-ahead output prediction model to predict the day-ahead output value of renewable energy;

[0161] 2) Use the probability reduction method to generate annual typical scenarios and their occurrence probabilities.

[0162] (2) In data input:

[0163] Input the set alliance data, typical day wind and light output and prediction data into the solution model. Among them, the alliance data includes the exogenous parameters related to the optimal operation of the alliance, such as energy storage capacity, new energy installed capacity, etc.

[0164] (3) Solve the two-stage model:

[0165] To solve such a two-stage model, the present embodiment sets an evolutionary cooperation game solution algorithm based on the particle swarm algorithm, which is as follows.

[0166] 1) Initialize the speed and position of the alliance structure particles.

[0167] First, set the required parameters of the algorithm, including the number of iterations G, the number of particles N l , the initial inertia weight ω int , the inertia weight ω end when the iteration reaches the maximum number of generations, and the number of subjects M.

[0168] The position x l of the nth(n∈N n,twhich can be expressed as a (M+1) dimensional vector, where x n,t (1) represents the number of coalitions under this coalition structure, [x n,t (2), x n,t (3), …, x n,t (M+1)] represents the number of each agent participating in the coalition. The position of the particle needs to satisfy the following two constraints.

[0169]

[0170] 1≤x n,t (2), x n,t (3), …, x n,t (M+1)≤x n,t (1)

[0171] The position of the nth(n∈N l ) coalition structure particle v n can also be expressed as a (M+1) dimensional vector.

[0172] 2) After defining the position and velocity of the particle, the second stage of the optimal solution of the coalition operation scheduling needs to be entered. After solving by using the related solver, the optimal income of the corresponding coalition structure particle is obtained. Then the fitness of the coalition structure particle is calculated where R n (i) represents the income of the ith coalition in the coalition structure. In order to prove the rationality of this fitness function, the following describes the dominance of the coalition under different income conditions.

[0173] When , there must be at least one agent in the m coalition structure whose allocation is less than in the n coalition structure.

[0174] ① When only one agent A is allocated less, and other agents obtain no change in the benefits of the two coalition structures, A is more inclined to choose the n coalition structure, so n>m.

[0175] ② When a part of the agents B in the m coalition structure are allocated less than n, and another part C in the m coalition structure are allocated more than n, sum(|R - (B)|) > sum(R + (C)), that is, the less part is more than the more part, B will have more motivation to disband the coalition m and form the coalition n. Therefore, n>m.

[0176] 3) Calculate the individual optimal and group optimal, and update the position and velocity of the particle.

[0177] 4) Stop iteration when the coalition reaches the maximum number of iterations or all agents become a coalition, otherwise continue 2)-4).

[0178] The above is the specific process of the operation method of the new energy storage combined participation in frequency modulation and electricity market of the embodiment. In order to facilitate further understanding, the following takes four subjects of wind power plant 1, photovoltaic power plant 1, wind power plant 2 and photovoltaic power plant 2 equipped with energy storage as an example. The initial alliance structure particle only contains [2, 1, 2, 1, 2] (indicating two alliances, the first subject in the first alliance, the second subject in the second alliance, the third subject in the first alliance, and the fourth subject in the second alliance) and [3, 3, 2, 3, 1] two alliance structures. After optimizing the alliance decision respectively, the first and second alliance income of the first alliance structure is 10 and 20 respectively, and the income of the first, second and third alliance of the second alliance structure is 2, 3 and 5 respectively. The alliance structure fitness is 30 and 10 respectively, and the first alliance is more stable. At this time, the [2, 1, 2, 1, 2] particle shares its position, that is, the offspring particle tends to be closer to [2, 1, 2, 1, 2]. After multiple iterations, the solution tends to be stable, and the alliance structure can be output.

[0179] In addition, the embodiment also verifies the effectiveness of the proposed operation strategy through simulation examples of the obtained five typical day-ahead 24h periods, as follows.

[0180] 1. Basic parameters

[0181] The electricity market price, frequency modulation capacity price and mileage market price are respectively referred to the PJM market price, as shown in Figure 6

[0182] The frequency modulation mileage calling rate adopts the related PJM market open auxiliary service market operation data, wherein, the frequency modulation calling of every five minutes is randomly sampled to obtain the frequency modulation calling ratio of every five minutes, see Figure 7 , the value of which is negative when indicating downward frequency modulation, and the value of which is positive when indicating upward frequency modulation, and the calling ratio multiplied by the absolute value of the frequency modulation capacity is accumulated to obtain the frequency modulation mileage calling rate of this hour.

[0183] ​According to the data of power supply side energy storage installed capacity in a certain province, the power supply side energy storage participating in aggregation in this embodiment includes one lithium iron phosphate battery, two compressed air energy storages, and one full-bridge flow energy storage. The capacities of the four energy storages are 510, 300, 110, and 120 MWh, respectively, and the maximum charge and discharge powers are 255, 150, 55, and 60 MW, respectively. The new energy output is referenced to the wind and light output of the PJM in the past year. The annual utilization hours of a certain province are used, the annual utilization hours of wind power are 2500 hours, and the annual utilization hours of photovoltaic are 1588.5. The installed capacity is calculated, and the new energy output is scaled up in proportion by using the energy storage installed capacity ratio. Finally, two wind power plants and two photovoltaic power plants are set up as new energy stations, with installed capacities of 1700, 1000, 200, and 400 MW, respectively. The storage capacity and installed capacity are shown in Table 1.

[0184] Table 1

[0185]

[0186] After predicting the annual wind and light output using the ELMAN NN neural network, the daily prediction error is reduced based on the distance fast reduction method to obtain five typical day scenarios, with scenario occurrence probabilities of 0.003, 0.622, 0.2202, 0.1518, and 0.003, respectively. The data are shown in Table 3. Figure 8 Figure 8 The times 1 to 24, 25 to 48, 49 to 72, and 73 to 96 are the errors of wind power plant 1, photovoltaic power plant 1, wind power plant 2, and photovoltaic power plant 2 in this scenario.

[0187] 2. Analysis of the effectiveness of the scheme

[0188] In order to verify that the scheme proposed in this embodiment can increase the revenue of new energy storage while not causing instability of the system, the scheme comparison shown in Table 2 is set up.

[0189] Table 2

[0190]

[0191]

[0192] The revenue comparison of the above schemes is shown in Table 4. Figure 9 From the figure, it can be seen that the revenue of scheme 2 relative to scheme 1 has a certain improvement, which is because the prediction errors of different new energy power plants have a certain time complementarity. Scheme 3 has a certain improvement relative to scheme 2 because the energy storage reduces the deviation penalty caused by the error. Scheme 4 has a large improvement relative to scheme 3, indicating that the new energy power plant with storage participating in the electricity market and the frequency modulation auxiliary service market has a good economic performance.

[0193] ​Figure 9 The red line is the error accommodation rate under different schemes, wherein the new energy accommodation rate represents the proportion of the predicted error reduced by the scheme in the total error. As can be seen from the figure, the accommodation rate of scheme 2 is improved, which is because the prediction errors of different new energy power plants have a certain time complementarity. The error accommodation rate of scheme 3 is greatly improved due to the addition of energy storage. The error accommodation rate does not decrease significantly under the condition of the income increase of scheme 4, because the alliance will use the idle time of the energy storage to participate in the frequency modulation auxiliary service, such as not participating in the electricity market during the night period of photovoltaic. The energy storage will not allocate the corresponding capacity to deal with the prediction deviation of new energy, and at this time, the capacity will be used to participate in the frequency modulation auxiliary service market in scheme four.

[0194] 3. Result analysis

[0195] 1) Alliance structure stability analysis and benefit distribution

[0196] Figure 10 The iteration result of the alliance structure can be seen from the figure. After 20 iterations, the result is stable, and the stable alliance structure is {wind 1, light 1, wind 2, light 2}, that is, all the main bodies are in the alliance. In order to verify the stability of the alliance, the possible benefits of all alliances are calculated by the exhaustive method as shown in Table 3.

[0197] Table 3

[0198]

[0199] After calculating the above data, it can be known that the alliance {wind 1, light 1, wind 2, light 2} meets the condition of stability of distribution strategy. The alliance constructed in this embodiment has stability and can guarantee the existence of the alliance. The reason for forming this alliance is that there is a certain time complementarity between the prediction errors of different power plants, and the aggregation of energy storage can guarantee that the energy storage meets the demand of new energy and has more capacity to participate in frequency modulation auxiliary service.

[0200] The related results obtained after the shapley value is used to distribute the cooperative game are shown in Table 4. Figure 11 As can be seen from the blue line in the figure, with the increase of the installed capacity of the alliance members, the benefits also increase. The unit energy storage installed power benefit represents the unit installed power benefit of different subjects excluding the new energy power generation benefit. The Min-Max normalized result is shown in the figure. It can be seen that the unit energy storage installed power benefit is inversely proportional to the new energy error under the unit energy storage installed. This indicates that when the model considers the penalty of electricity energy deviation, the alliance will allocate more energy storage capacity to avoid the penalty of deviation, and at the same time, the energy storage cannot participate in the corresponding frequency modulation auxiliary service. However, when the model does not consider the deviation penalty, the distribution stage should be considered, otherwise it will cause unfairness.

[0201] 2) Capacity allocation result analysis

[0202] The capacity of the energy storage system is limited. How to meet the demand of new energy unit output tracking while using the remaining capacity to participate in the frequency modulation market, Figure 12 Capacity allocation results of energy storage in different typical days. Under this scheme, the consumption rate of new energy reaches 94.67%, and the capacity can still participate in the frequency modulation auxiliary service market, which shows that new energy sharing energy storage participating in the frequency modulation auxiliary service market is feasible. This is because: (1) New energy jointly participates in the electricity market, and the prediction error will offset each other to a certain extent, which avoids the simultaneous charging and discharging of different energy storages. (2) The output of renewable energy is low in some period, and the required energy storage capacity is also low. At this time, the energy storage capacity can be used to participate in the frequency modulation auxiliary service market. For example, the photovoltaic unit does not generate electricity at night, and there will be no corresponding output deviation. At this time, the energy storage can be used to participate in the frequency modulation market. (3) The prediction of new energy output by neural network is mostly accurate, and only in some scenarios the prediction is not accurate, and more energy storage is needed to track the output of new energy. For example, in the typical day 4.

[0203] 4, Sensitivity analysis

[0204] 1) Sensitivity analysis of electricity deviation penalty rate

[0205] Because the electricity deviation penalty rate has a certain influence on the allocation of energy storage capacity, when the deviation penalty rate is too low, it will lead to the allocation of too small capacity to new energy, and a large part of the capacity of energy storage will participate in the frequency modulation auxiliary service, which will affect the safe and stable operation of the power system. When the deviation penalty rate is too high, the market subject's income will decrease significantly, but the improvement of error consumption is not obvious. Therefore, the sensitivity analysis of the electricity deviation penalty rate is carried out to select the appropriate range of the electricity deviation penalty rate. Figure 13 Sensitivity analysis of electricity deviation penalty rate. From the figure, it can be seen that when the electricity deviation penalty rate changes from 0.2 to 1.2, the new energy consumption rate increases significantly, but the alliance income does not decrease obviously. When the electricity deviation penalty rate changes from 1.2 to 1.4, the new energy consumption rate does not increase obviously, so the electricity deviation penalty rate should be selected in the range of 0.4-1.2.

[0206] 2) Sensitivity analysis of new energy power plant storage ratio

[0207] Figure 14 and Figure 15 The alliance income and prediction error consumption rate under different storage ratios are shown in Figure 14 and Figure 15It can be seen that when the matching storage ratio increases from a small position to a certain amount, the alliance income and the prediction error absorption rate increase synchronously, which shows that the increase of the alliance income mainly depends on the reduction of the penalty cost caused by the prediction error; when the matching storage ratio increases to a certain proportion, its change rate is basically stable, and it can be seen that the change rate of the alliance income and the prediction error absorption rate with the change of the matching storage power proportion of the installed capacity is greater than that with the change of the energy storage time, which shows that the frequency modulation and the new energy matching storage both need power type storage rather than capacity type storage.

[0208] 3) Sensitivity analysis of new energy change rate

[0209] Since the new energy change rate limit has a certain influence on the allocation of energy storage capacity, when the new energy change rate limit is low, it is difficult for part of the new energy to be absorbed even if the specified capacity of the storage is equipped; when the new energy change rate limit is too high, the change fluctuation of the new energy participating in the electric energy market is too large, which will affect the safe and stable operation of the power system, therefore, the sensitivity analysis of the new energy change rate limit is carried out in this embodiment, which aims to select a suitable new energy change rate limit. Figure 16 For the sensitivity analysis of the new energy change rate limit, it can be seen from the figure that as the new energy change rate limit increases, the alliance income shows an upward trend. This shows that under a higher new energy change rate limit, most of the new energy output in the alliance can meet its limit value requirement with the help of energy storage, so as to more effectively utilize new energy and bring higher economic benefits. It can be seen that when the fluctuation limit is 40, the storage equipped according to the regulation can basically realize the new energy consumption, but the establishment of this policy not only needs to consider the new energy consumption, but also needs to consider the power system consumption capacity under different new energy installed capacity proportions.

[0210] In summary, the present application proposes a cluster mode of polymer-friendly new energy power station energy storage, constructs a two-stage dispatching optimization model of cluster frequency modulation auxiliary service, and fully plays the flexible adjustment role of energy storage resources. First, a dispatching framework of new energy and energy storage cluster jointly participating in the frequency modulation auxiliary service market is constructed; secondly, considering the constraints such as the upper limit of energy storage capacity and the fluctuation limit of new energy output, a dispatching model of new energy and energy storage participating in the electric energy and auxiliary service market is established under the deviation examination with the maximum cluster income as the target; finally, an evolutionary cooperation game solving algorithm of particle swarm algorithm is proposed based on the alliance structure fitness value to obtain a stable alliance structure. The present application not only can improve the utilization rate of new energy storage, but also can effectively participate in frequency modulation auxiliary, and improves the quality of power supply.

[0211] The various techniques described herein can be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and apparatus of the present application, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard disks, USB flash drives, floppy diskettes, CD-ROMs, and any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the subject application.

[0212] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to not obscure the understanding of this description.

[0213] It is to be understood that the embodiments of the application described herein are merely exemplary of the application of the principles of the present application. For example, although specific configurations and features are referenced in this disclosure, one of ordinary skill in the art will recognize that embodiments of the present application can be implemented with any number of variations and modifications. Such variations and modifications can include, but are not limited to, combinations of features from different embodiments, combinations of features from the same embodiment with other features not expressly mentioned herein, and combinations of features with other features not expressly mentioned herein. Accordingly, the scope of the present application is not intended to be limited to the described embodiments but is instead defined by the appended claims.

[0214] In addition, unless otherwise indicated, use of the ordinal adjectives such as "first", "second", "third", etc. to describe a common object is merely intended to distinguish that particular object from another common object whose name is not being modified by the ordinal adjective.

[0215] While the application has been described in terms of several embodiments, those skilled in the art will recognize that the application can be practiced with modifications and alterations limited only by the spirit and scope of the inventiveness. Furthermore, the purpose of the description is to enable any person skilled in the art to practice the application as described in the claims. The descriptions herein are not intended to be exhaustive or to limit the application to the precise forms disclosed. Many modifications and variations are possible in light of this disclosure. It is intended that the scope of the application and the protection granted be governed by the following claims and not by the description above, which has been presented only by way of example.

Claims

1. A dispatch operation method for new energy and storage participating in frequency modulation auxiliary service market, the method comprising: inputting the output values of each new energy power station in each day for a predetermined number of days into a trained day-ahead output prediction model to generate predicted output values of each new energy power station in each day, the new energy power station being equipped with energy storage; based on the predicted output values of each new energy power station in each day, using a probability reduction method to reduce day scenarios to obtain occurrence probabilities of multiple typical days and predicted output values of each new energy power station in the multiple typical days; based on the occurrence probabilities of multiple typical days and the predicted output values of each new energy power station in the multiple typical days, using a particle swarm algorithm to optimize multiple initialized alliance structures with dispatch operation revenue of the alliance structure as fitness value to obtain a target alliance structure, and outputting dispatch operation schemes of each new energy and storage in each alliance in each typical day in the target alliance structure, the dispatch operation scheme including capacities of each new energy and storage allocated to frequency modulation auxiliary service market and energy market in each typical day, and any alliance including at least one new energy power station equipped with energy storage; wherein the dispatch operation revenue of any alliance structure is obtained by: considering new energy output deviation penalty, constructing a dispatch operation model of each alliance participating in frequency modulation auxiliary service market with maximum dispatch operation revenue of each alliance in the alliance structure as the target; based on the occurrence probabilities of multiple typical days and the predicted output values of each new energy power station in the multiple typical days, solving the constructed dispatch operation model of each alliance participating in frequency modulation auxiliary service market to obtain dispatch operation schemes of each new energy and storage in each alliance in each typical day and dispatch operation revenues of each alliance under the corresponding dispatch operation scheme; based on the obtained dispatch operation revenues of each alliance, obtaining the dispatch operation revenue of the alliance structure; wherein for each alliance, the dispatch operation model thereof includes an objective function, the objective function including: where R year denotes the dispatching operation revenue of the alliance, p k denotes the occurrence probability of the typical day k, denotes the quadratic frequency modulation income of the alliance in the t period of the typical day k, denotes the income of selling electricity on the grid of the alliance in the t period of the typical day k, denotes the power output deviation penalty of the alliance in the t period of the typical day k, denotes the energy storage operation cost of the alliance in the t period of the typical day k, K denotes the total number of typical days, and T denotes the total number of periods. wherein the output deviation penalty of the alliance in the t period of the typical day k is obtained by: wherein, represents the deviation penalty ratio of participating in the electricity market, represents the unit on-grid electricity price of the t period of the typical day k, represents the electricity energy deviation amount of the energy storage i in the t period of the typical day k, I represents the total number of energy storages; wherein the secondary frequency modulation income of the alliance in the t period of the typical day k is obtained by: wherein, denotes the unit capacity compensation price of the alliance participating in secondary frequency modulation at time period t of typical day k, denotes the capacity of the i-th energy storage in the alliance participating in secondary frequency modulation at time period t of typical day k, denotes the unit mileage compensation price of the alliance participating in secondary frequency modulation at time period t of typical day k, denotes the frequency modulation mileage calling rate, and I denotes the total number of energy storages.

2. The method of claim 1, wherein, the use of the particle swarm algorithm to optimize the multiple initialized alliance structures with the dispatch operation revenue of the alliance structure as the fitness value to obtain the target alliance structure includes: initializing the number of alliance structures, the speed and position of each alliance structure, and the iteration number; based on the current position of each alliance structure, obtaining the fitness value of each alliance structure; determining the optimal individual position of each alliance structure and the optimal group position of the alliance structure group according to the obtained fitness value of each alliance structure, and detecting whether the initialized iteration number is reached or all new energy power stations become a alliance; If the current iteration number is not reached and all new energy power stations are not a union, the current iteration number is increased by one, the speed and position of each union structure are updated, and the above steps of obtaining the fitness value, determining the optimal individual position and the optimal group position, and detecting the current iteration number and whether the union meets the requirements are repeated until the current iteration number reaches the initial iteration number or all new energy power stations become a union. The optimal group position is taken as the final position of the union structure, and the target union structure is obtained.

3. The method of claim 2, wherein, The position of each union structure is composed of the number of unions under the union structure and the number of new energy power stations participating in the union.

4. The method of any one of claims 1-3, wherein, The day-ahead output prediction model adopts an Elman neural network model.

5. The method of any one of claims 1-3, wherein, The day-ahead output prediction model is obtained based on the following method: The real output value of the predicted day-ahead scheduled number of days in the training set is input into the pre-trained day-ahead output prediction model to obtain the predicted output value of the predicted day, and the predicted day is any day in the training set; Based on the loss value between the real output value and the predicted output value of the predicted day, the parameters of the day-ahead output prediction model are updated until the loss value meets the predetermined condition, the training is completed, and the trained day-ahead output prediction model is obtained.

6. The method of claim 1, wherein, For each union, the dispatching operation model further includes constraint conditions, and the constraint conditions include the constraints of the declared power and discharged power of each new energy power station in the union, the charge and discharge capacity constraints of each energy storage in the union, the state of charge constraints of each energy storage in the union, and the capacity constraints of each new energy power station participating in the electricity market in the union.

7. A computing device comprising: at least one processor; and a memory storing program instructions configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method of any one of claims 1-6.

8. A readable storage medium storing program instructions, which, when read and executed by a computing device, cause the computing device to perform the method of any one of claims 1-6.

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