Power system reserve stochastic optimization method, system, device and medium based on typical photovoltaic scenarios

Abstract

The present invention belongs to the technical field of optimal dispatching of power systems, and discloses a power system reserve stochastic optimization method, system, device and medium based on typical photovoltaic scenarios. The method of the present invention includes obtaining predicted photovoltaic power probability data and forming sequences of data corresponding to different quantiles, using linear interpolation to obtain the photovoltaic power cumulative distribution function to calculate the photovoltaic predicted power; using the Gaussian Copula function to construct a multivariate distribution function that satisfies the temporal correlation of photovoltaic power, and obtaining multiple photovoltaic power prediction scenarios through Monte Carlo sampling; based on the K-means++ algorithm, reducing the photovoltaic power prediction scenarios to obtain multiple typical photovoltaic scenarios; constructing an objective function based on operation cost, reserve cost and start-stop cost and obtaining preset reserve stochastic optimization conditions to construct a power system reserve stochastic optimization model under multiple typical photovoltaic scenarios. The present invention not only improves the operation efficiency and economy of the power system, but also provides guarantee for the safe operation dispatching of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimal dispatching of power systems, and particularly relates to a method, system, device and medium for stochastic optimization of power system reserve based on typical photovoltaic scenarios. Background Art

[0002] To continuously optimize the energy structure, vigorously developing new energy sources such as photovoltaic and wind power has become the core support for the construction of a new power system. However, affected by the chaotic nature of the atmospheric system, the output of new energy sources such as wind power shows high uncertainty and strong volatility, resulting in significant power deviation challenges for the power system after high-proportion new energy grid connection. Therefore, it is particularly necessary to configure a standby power source with an appropriate capacity while connecting new energy to the grid. However, traditional standby quantification models have deficiencies in considering the uncertainty of new energy and low computational efficiency, which directly affects the reasonable reservation of standby capacity for conventional units and energy storage devices. Summary of the Invention

[0003] Based on the above-mentioned disadvantages and deficiencies existing in the prior art, one of the objectives of the present invention is to at least solve one or more of the above problems existing in the prior art. In other words, one of the objectives of the present invention is to provide a method, system, device and medium for stochastic optimization of power system reserve based on typical photovoltaic scenarios that meet one or more of the aforementioned requirements, so as to meet the needs of efficiently and accurately considering the uncertainty of new energy and reasonably reserving the standby capacity of conventional units and energy storage devices.

[0004] To achieve the above-mentioned invention objective, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a method for stochastic optimization of power system reserve based on typical photovoltaic scenarios, including the steps of:

[0006] S1. Obtain the predicted photovoltaic power probability data and form a sequence of data corresponding to different quantiles therein, and based on the sequence, use the linear interpolation method to obtain the photovoltaic power cumulative distribution function to calculate the photovoltaic predicted power;

[0007] S2. Based on the photovoltaic predicted power, use the Gaussian Copula function to construct a multivariate distribution function that satisfies the temporal correlation of photovoltaic power, and sample the multivariate distribution function by the Monte Carlo method to obtain a plurality of photovoltaic power prediction scenarios with temporal correlation;

[0008] S3. Based on the K-means++ algorithm, reduce the plurality of photovoltaic power prediction scenarios with temporal correlation to obtain a plurality of typical photovoltaic scenarios;

[0009] S4. Construct an objective function based on the operating cost, reserve cost, and start-stop cost of typical photovoltaic scenarios, obtain the preset reserve stochastic optimization conditions, and build a reserve stochastic optimization model for the power system under multiple typical photovoltaic scenarios to quantify the system reserve under the randomness of photovoltaic output in typical photovoltaic scenarios, thereby achieving optimization.

[0010] As a preferred solution, step S1 is specifically as follows:

[0011] Define the cumulative distribution function of photovoltaic power at a certain moment based on the quantile regression theory as ;

[0012] Define the quantile of the quantile level as , and . In the formula, is the probability operator, represents the photovoltaic power at time ;

[0013] Use the probability prediction method of quantile regression to represent the predicted quantile of photovoltaic power as . In the formula, represents the sequence of predicted quantiles corresponding to the quantile level taking , is the number of quantiles, represents the estimated value of the actual quantile ;

[0014] At time , based on the predicted quantile sequence , use the linear interpolation method to obtain the complete cumulative distribution function of photovoltaic power ;

[0015] Let the sequence of random variables follow the uniform distribution, where represents the duration of the scheduling period. Using the sequence of random variables as the cumulative probability of the random variable, according to the cumulative distribution function of photovoltaic power there is , and thus the predicted photovoltaic power is obtained.

[0016] As a preferred solution, step S2 includes the following steps:

[0017] S21. Use the multivariate Gaussian Copula function to construct a multivariate distribution function , let , then

[0018] ;

[0019] S22. Generate a set of random numbers that are uniformly distributed ;

[0020] S23. Let to obtain the value of the solution random variable , let the value of the marginal cumulative distribution function of the random variable be equal to , and use the formula to obtain the value of the random variable , and based on the formula to obtain the value of the random variable ;

[0021] S24. Repeat the calculation processes of steps S22 and S23 times to obtain sets of sampling results of the multivariate random variable. In the th sampling, the multivariate random variable is ;

[0022] S25. According to , solve from to obtain the photovoltaic power of the time-correlated scenario . Traverse to obtain probability-prediction-based time-correlated photovoltaic power prediction scenarios.

[0023] As a preferred solution, step S3 includes the steps:

[0024] S31. Randomly select from as the first clustering center;

[0025] S32. For of the photovoltaic power prediction scenarios, calculate the shortest distance between each of them and the current existing clustering center , and sum the squares of all the shortest distances to obtain . The calculation formula for the shortest distance is

[0026] ;

[0027] S33. Calculate the probability that each of the photovoltaic power prediction scenarios is selected as the next cluster center , and the probability is calculated by the formula

[0028] ;

[0029] S34. Take a random number between the interval [0, 1] , and use to subtract . If the result is greater than 0, continue to subtract until the result is not greater than 0, and record the photovoltaic power prediction scenario corresponding to the probability at this time as the second cluster center;

[0030] S35. Repeat steps S32 to S34 to obtain initial cluster centers;

[0031] S36. Based on the initial cluster centers, use the K - means clustering algorithm to obtain photovoltaic typical scenarios and their corresponding probabilities , and the probability satisfies .

[0032] As a preferred solution, step S4 is specifically as follows:

[0033] Based on the photovoltaic typical scenarios, denote the photovoltaic typical scenarios as a set . Select any one from the set as the base scenario and denote the remaining ones as , and thus construct a stochastic optimization model for power system reserve under multiple photovoltaic typical scenarios.

[0034] As a preferred solution, the objective function is:

[0035] ,

[0036] In the formula, is the set of conventional units in the power system, represents the set of energy storage devices in the power system, and are the start - up cost coefficient and shut - down cost coefficient of the conventional unit respectively, and respectively represent a conventional unit provide a positive reserve cost coefficient and a negative reserve cost coefficient and respectively are the positive reserve cost coefficient and the negative reserve cost coefficient provided by the energy storage device represent a conventional unit operating cost coefficient and respectively represent the discharging cost coefficient and the charging cost coefficient of the energy storage device and respectively represent at time the on - state and off - state of the conventional unit, and are binary variables, and the two cannot be at the same time. When it represents that the conventional unit is in the starting state. When it represents that the conventional unit is in the shutdown state and respectively represent at time the positive reserve capacity and the negative reserve capacity provided by the conventional unit and respectively are at the positive reserve capacity and the negative reserve capacity provided by the energy storage device at time represents the scenario under which the active power of the conventional unit at time and respectively represent under the scenario the discharging power and the charging power of the energy storage device at time

[0037] As a preferred solution, the standby stochastic optimization conditions include power balance constraints, power system power flow constraints, minimum start - stop time constraints, unit output constraints, unit ramp - rate constraints, unit reserve capacity constraints, energy storage device constraints, standby constraints of the energy storage device, and photovoltaic output constraints;

[0038] The expression of the power balance constraint is

[0039] ,

[0040] In the formula, and respectively represent the photovoltaic set and the load set represents the scenario The actual output of the th photovoltaic at time is the load value of the th load at time in the scenario;

[0041] The expression of the power flow constraint of the power system is

[0042] ,

[0043] wherein, is the set of nodes; , , and respectively represent the sets of conventional units, energy storage devices, photovoltaics and loads under the node , represents the set of power system lines, is the upper limit of the transmission power of the line , is the transfer factor of the node to the line ;

[0044] The expression of the minimum start-stop time constraint includes

[0045] ,

[0046] ,

[0047] ,

[0048] ,

[0049] wherein, is the state of the conventional unit at time , which is a binary variable. When , it means that the conventional unit is in the on state at time . When , it means that the conventional unit is in the off state at time , and respectively represent the minimum start-up and shutdown times of the conventional unit ;

[0050] The expression of the unit output constraint is

[0051] ,

[0052] In the formula, and respectively represent the upper and lower limits of the active power output of the conventional unit ;

[0053] The expression of the ramp constraint of the unit includes

[0054] ,

[0055] ,

[0056] In the formula, and respectively represent the start-up and shut-down ramp rates of the conventional unit , and are respectively the up-ramp rate and down-ramp rate of the conventional unit ;

[0057] The expression of the reserve capacity constraint of the unit includes

[0058]

[0059]

[0060]

[0061]

[0062] In the formula, represents the active power of the conventional unit at time in the base scenario, and are respectively the positive reserve upper limit and negative reserve upper limit provided by the conventional unit ;

[0063] The expression of the energy storage device constraint includes

[0064] ,

[0065] ,

[0066] ,

[0067] ,

[0068] ,

[0069] ,

[0070] In the formula, and respectively represent the minimum and maximum charge-discharge powers of the energy storage device , and are respectively the charge-discharge states of the energy storage device at time under scenario . They are binary variables and cannot be 1 at the same time. When , it means that the energy storage device is in the charging state at time under scenario . When , it means that the energy storage device is in the discharging state at time under scenario . represents the energy of the energy storage device at time under scenario . and respectively represent the charging and discharging efficiencies of the energy storage device . and are respectively the minimum and maximum energies of the energy storage device ;

[0071] The expression of the reserve constraint of the energy storage device includes

[0072] ,

[0073] ,

[0074] ,

[0075] ,

[0076] In the formula, and respectively represent the active powers of the energy storage device at time under the base scenario;

[0077] The expression of the PV output constraint is

[0078] ,

[0079] In the formula, represents the predicted PV output value of the th PV at time under scenario .

[0080] In a second aspect, the present invention provides a power system reserve stochastic optimization system based on typical photovoltaic scenarios, which is used to implement the power system reserve stochastic optimization method as described in the first aspect.

[0081] In a third aspect, the present invention provides an electronic device, which includes a memory, a processor, and a computer program. When the computer program is executed by the processor, it implements the power system reserve stochastic optimization method as described in the first aspect.

[0082] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, it implements the power system reserve stochastic optimization method as described in the first aspect.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] The present invention first uses the quantile regression method for the probability prediction of photovoltaic power, effectively avoiding the calculation errors that may be introduced by parametric assumptions, and thus more realistically and accurately characterizing the cumulative distribution function of photovoltaic power. Then, using the multivariate Gaussian Copula function, a set of photovoltaic power prediction scenarios with time correlation is constructed, providing rich data support for the subsequent operation scheduling of the power system. To further improve the calculation efficiency of the model, the present invention introduces the photovoltaic power prediction scenario reduction of the K-means++ algorithm, effectively reducing the number of scenarios, and at the same time ensuring that the typical scenarios can fully represent the main characteristics of the original scenarios, improving the calculation efficiency of the model. On this basis, the present invention establishes a power system reserve stochastic optimization model under multiple scenarios, which can comprehensively quantify the impact of photovoltaic output uncertainty on system reserves, providing a scientific basis for the optimal allocation of reserves. Compared with the traditional power system reserve quantification method, the power system reserve stochastic optimization method based on typical photovoltaic scenarios proposed by the present invention shows significant advantages in terms of calculation efficiency, prediction accuracy, and application value. This method not only improves the operation efficiency and economy of the power system, but also provides a strong guarantee for the safe operation scheduling of the power system, and is of great significance for promoting the optimization of the energy structure and achieving the goals of carbon peak and carbon neutrality.

[0085] Further or more detailed beneficial effects will be described in combination with specific embodiments in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0087] Figure 1 It is a schematic flowchart of the power system reserve stochastic optimization method provided by the embodiment of the present invention.

[0088] Figure 2 It is a structural diagram of the electronic device provided by the embodiment of the present invention.

[0089] Reference numerals in the drawings:

[0090] 200, electronic device;

[0091] 201, processor; 202, communication bus; 203, user interface; 204, network interface; 205, memory. Detailed implementation manners

[0092] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention.

[0093] In the following description, multiple embodiments of the present invention are provided. Different embodiments can be replaced or combined. Therefore, the present invention can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing all other possible combinations of A, B, C, and D, although such embodiments may not be explicitly described in the following content.

[0094] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the content of the present invention. Various processes or components can be appropriately omitted, substituted, or added to each example. For example, the described methods can be executed in a different order from the described order, and various steps can be added, omitted, or combined. In addition, the features described in some examples can be combined into other examples.

[0095] To facilitate a better understanding of the embodiments of the present invention, before explaining the detailed implementation manners of the present invention in detail, its application scenarios will be described first.

[0096] The power system reserve stochastic optimization method described in the embodiments of this specification is applied to scenarios of power supply and management. In these scenarios, the application of the power system reserve stochastic optimization method aims to make the power supply more stable and reliable, thereby significantly improving the operating efficiency and security of the entire power system.

[0097] The following provides a brief explanation of the typical photovoltaic scenarios, quantile regression method, linear interpolation method, Gaussian Copula function, K-means++ algorithm, and Monte Carlo method involved in multiple embodiments of this specification:

[0098] Typical photovoltaic scenarios refer to several situations with representative or universal power output characteristics during the process of photovoltaic power generation, which are caused by various factors such as weather, seasons, and geographical locations. Through the research and analysis of these typical scenarios, the behavior of photovoltaic power generation can be better understood and predicted, and then the reserve power configuration of the power system can be optimized.

[0099] The quantile regression method is a statistical method used to estimate the conditional quantiles of the response variable. In the stochastic optimization of power system reserves, the quantile regression method can be used to predict the fluctuation range of power demand and configure appropriate reserve power based on these prediction results. This method can capture the uncertainty of power demand and improve the accuracy and flexibility of reserve power configuration.

[0100] The linear interpolation method is a simple mathematical method used to estimate the value of an unknown data point between two known data points. In the stochastic optimization of power system reserves, the linear interpolation method can be used to fill or smooth missing values in historical data, or to predict the trend of future power demand. Although this method is simple, it can provide effective approximate results in some cases.

[0101] The Gaussian Copula function is a tool for modeling the dependence relationship between multi-dimensional random variables. In the stochastic optimization of power system reserves, the Gaussian Copula function can be used to capture the correlation between different power demands or photovoltaic power generation scenarios, so as to more accurately evaluate the required amount and risk of reserve power.

[0102] The K-means++ algorithm is an improved K-means clustering algorithm used to divide a data set into K clusters. In the stochastic optimization of power system reserves, the K-means++ algorithm can be used to perform clustering analysis on historical power demand data, identify different demand patterns or scenarios, and configure appropriate reserve power for each scenario.

[0103] The Monte Carlo method is a statistical method based on random sampling, used to estimate the behavior or performance of complex systems. In the stochastic optimization of power system reserves, the Monte Carlo method can be used to simulate the operation of the power system under different scenarios, evaluate the effect of reserve power configuration, and find the optimal reserve power configuration plan. This method can take into account the influence of various uncertainties and random factors in the power system and provide relatively accurate evaluation results.

[0104] Embodiment 1:

[0105] As Figure 1 shown, this embodiment provides a method for stochastic optimization of power system reserves based on typical photovoltaic scenarios, including the steps:

[0106] S1. Obtain the predicted photovoltaic power probability data and form a sequence of data corresponding to different quantiles therein. Based on the sequence, use the linear interpolation method to obtain the photovoltaic power cumulative distribution function to calculate the photovoltaic predicted power;

[0107] S2. Based on the photovoltaic predicted power, use the Gaussian Copula function to construct a multivariate distribution function that satisfies the temporal correlation of photovoltaic power, and sample the multivariate distribution function through the Monte Carlo method to obtain multiple photovoltaic power prediction scenarios with temporal correlation;

[0108] S3. Based on the K-means++ algorithm, reduce the multiple photovoltaic power prediction scenarios with temporal correlation to obtain multiple typical photovoltaic scenarios;

[0109] S4. Based on the operating cost, reserve cost, and start-stop cost of the typical photovoltaic scenario, construct an objective function, obtain the preset stochastic optimization conditions for reserves, to construct a stochastic optimization model for power system reserves under multiple typical photovoltaic scenarios, and quantify the system reserves under the randomness of photovoltaic output in the typical photovoltaic scenario so as to be optimized.

[0110] Specifically, this embodiment provides a preferred implementation manner. Step S1 is specifically:

[0111] Based on the quantile regression theory, define the cumulative distribution function of photovoltaic power at a certain moment as ;

[0112] Define the quantile of the quantile level as , and , where, is the probability operator, represents the photovoltaic power at time ;

[0113] A probability prediction method using quantile regression represents the prediction quantiles of photovoltaic power as , where represents the time The quantile level takes The sequence composed of the corresponding prediction quantiles when is the number of quantiles represents the estimated value of the actual quantile ;

[0114] At time Based on the prediction quantile sequence , the complete cumulative distribution function of photovoltaic power is obtained by using linear interpolation ;

[0115] Let the sequence of random variables follow uniform distribution, where represents the duration of the scheduling period. Taking the sequence of random variables as the cumulative probability of the random variable, according to the cumulative distribution function of photovoltaic power there is , and thus the predicted photovoltaic power is obtained.

[0116] Specifically, this embodiment provides a preferred implementation manner, and step S2 includes the steps:

[0117] S21. Use the multivariate Gaussian Copula function to construct a multivariate distribution function that satisfies the temporal correlation of photovoltaic power. Let , then

[0118] ;

[0119] S22. Generate a set of random numbers that satisfy uniform distribution;

[0120] S23. Let to obtain the value of the solution random variable . Let the value of the marginal cumulative distribution function of the random variable be equal to . Use the formula to obtain the value of the random variable . Based on the formula to obtain the value of the random variable ;

[0121] S24. Repeat the calculation processes of steps S22 and S23 for a certain number of times to obtain multiple sets of sampling results of the multivariate random variable. In the th sampling, the multivariate random variable is ;

[0122] S25. According to , solve to obtain the photovoltaic power of the scenario with time correlation . Traverse to obtain probability-predicted photovoltaic power prediction scenarios with time correlation. Specifically, this embodiment provides a preferred implementation manner. Step S3 includes the steps:

[0123] S31. Randomly select

[0124] from the photovoltaic power prediction scenarios as the first clustering center;

[0125] S32. For the photovoltaic power prediction scenarios, calculate the shortest distance between each of them and the current existing clustering center , and sum the squares of all the shortest distances to obtain . The calculation formula for the shortest distance is

[0126] ;

[0127] S33. Calculate the probability that each photovoltaic power prediction scenario is selected as the next clustering center. The calculation formula for the probability is

[0128] ;

[0129] S34. Take a random number in the interval [0, 1], subtract from it. If the result is greater than 0, continue to subtract until the result is not greater than 0, and record the photovoltaic power prediction scenario corresponding to the probability at this time as the second clustering center;

[0130] S35. Repeat steps S32 to S34 to obtain initial clustering centers;

[0131] S36. Based on the initial cluster centers, use the K-means clustering algorithm to obtain photovoltaic typical scenarios and their corresponding probabilities , and the probabilities satisfy .

[0132] Specifically, this embodiment provides a preferred implementation manner, and step S4 is specifically as follows:

[0133] Based on the photovoltaic typical scenarios, the photovoltaic typical scenarios are denoted as set , select any one from set as the base scenario and denote the remaining ones as

[0134] Specifically, this embodiment provides a preferred implementation manner, and the objective function is:

[0135] ,

[0136] In the formula, is the set of conventional units in the power system, represents the set of energy storage devices in the power system, and are respectively the start-up cost coefficient and shutdown cost coefficient of the conventional unit and respectively represent the positive reserve cost coefficient and negative reserve cost coefficient provided by the conventional unit , and are respectively the positive reserve cost coefficient and negative reserve cost coefficient provided by the energy storage device represents the operating cost coefficient of the conventional unit and are respectively the discharge cost coefficient and charge cost coefficient of the energy storage device and respectively represent the operating state and shutdown state of the conventional unit at time , and are binary variables, and the two cannot be at the same time. When it means that the conventional unit When it indicates a conventional unit is in the off state, and respectively represent the positive reserve capacity and negative reserve capacity provided by the conventional unit at time ; and respectively represent the positive reserve capacity and negative reserve capacity provided by the energy storage device at time; represents the active power of the conventional unit under scenario at time, and respectively represent the discharge power and charge power of the energy storage device under scenario at time.

[0137] Specifically, this embodiment provides a preferred implementation manner. The spare random optimization conditions include power balance constraint, power system power flow constraint, minimum start-stop time constraint, unit output constraint, unit ramp constraint, unit reserve capacity constraint, energy storage device constraint, spare constraint of the energy storage device, and photovoltaic output constraint;

[0138] The expression of the power balance constraint is

[0139] ,

[0140] wherein, and respectively represent the photovoltaic set and the load set, represents the actual output of the th photovoltaic at time under scenario , is the load value of the th load at time

[0141] The expression of the power system power flow constraint is

[0142] ,

[0143] wherein, is the node set; , , and respectively represent the sets of conventional units, energy storage devices, photovoltaics, and loads under node , Represents the set of power system lines, is the line transmission power limit, is the node for the line transfer factor;

[0144] The expression of the minimum start-stop time constraint includes

[0145] ,

[0146] ,

[0147] ,

[0148] ,

[0149] In the formula, is the state of the conventional unit at moment, which is a binary variable. When , it means that the conventional unit is in the on state at moment. When , it means that the conventional unit is in the off state at moment, and respectively represent the minimum start-up and shutdown times of the conventional unit ;

[0150] The expression of the unit output constraint is

[0151] ,

[0152] In the formula, and respectively represent the upper and lower limits of the active power output of the conventional unit ;

[0153] The expression of the unit ramp rate constraint includes

[0154] ,

[0155] ,

[0156] In the formula, and respectively represent the start-up and shutdown ramp rates of the conventional unit , and respectively are the up-ramp rate and down-ramp rate of the conventional unit ;

[0157] The expression of the reserve capacity constraint of the unit group includes

[0158]

[0159]

[0160]

[0161]

[0162] In the formula, represents the active power of the conventional unit at time in the base scenario, and respectively provide the positive reserve upper limit and the negative reserve upper limit for the conventional unit ;

[0163] The expression of the energy storage device constraint includes

[0164] ,

[0165] ,

[0166] ,

[0167] ,

[0168] ,

[0169] ,

[0170] In the formula, and respectively represent the minimum and maximum charge-discharge powers of the energy storage device ; and respectively represent the charge-discharge states of the energy storage device under scenario at time , which are binary variables, and the two cannot be 1 at the same time. When , it means that the energy storage device under scenario is in the charging state at time . When , it means that the energy storage device under scenario is in the discharging state at time ; represents the energy storage device under scenario ​ At time energy, and respectively represent the charging and discharging efficiencies of the energy storage device ; and are respectively the minimum and maximum values of the energy of the energy storage device ;

[0171] The expression of the reserve constraint of the energy storage device includes

[0172] ,

[0173] ,

[0174] ,

[0175] ,

[0176] In the formula, and respectively represent the active power of the energy storage device at time in the base scenario;

[0177] The expression of the photovoltaic output constraint is

[0178] ,

[0179] In the formula, represents the predicted value of the output of the th photovoltaic at time in scenario .

[0180] Example 2:

[0181] This example provides a power system reserve stochastic optimization system based on typical photovoltaic scenarios, which is used to implement the power system reserve stochastic optimization method described in Example 1.

[0182] Example 3:

[0183] As Figure 2 shown, this example provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0184] Among them, the communication bus can be used to realize the connection and communication of the above components.

[0185] Among them, the user interface may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0186] Among them, the network interface can but is not limited to include a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0187] Among them, the processor may include one or more processing cores. The processor connects various parts within the entire electronic device using various interfaces and circuits, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory, as well as by calling data stored in the memory, it executes various functions of the electronic device and processes data. Optionally, the processor may be implemented in at least one of the hardware forms of DSP, FPGA, and PLA. The processor may integrate one or a combination of several of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may also not be integrated into the processor and may be implemented separately by a single chip.

[0188] Among them, the memory may include RAM and may also include ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory may also be at least one storage device located far from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module, and an optimization application program. The processor may be used to call the optimization application program stored in the memory and execute the steps of the power system standby stochastic optimization method mentioned in the foregoing embodiments.

[0189] Embodiment 4:

[0190] This embodiment provides a computer-readable storage medium, in which instructions are stored. When it runs on a computer or a processor, it causes the computer or the processor to execute one or more of the steps in the above Figure 1 illustrated embodiments. If the various component modules of the above-mentioned electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0191] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this specification are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a Digital Versatile Disc (DVD)), or a semiconductor medium (for example, a Solid State Disk (SSD)), etc.

[0192] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned first embodiment of the method can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned various methods. The foregoing storage media include: various media such as ROM, RAM, magnetic disks, or optical discs that can store program codes. Without conflict, the technical features in this embodiment and the implementation solutions can be combined arbitrarily.

[0193] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0194] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0195] The above are only exemplary embodiments of the present invention and should not be used to limit the scope of the present invention. That is, any equivalent changes and modifications made in accordance with the teachings of the present invention still fall within the scope covered by the present invention. Those skilled in the art will readily think of other embodiments of the present invention after considering the specification and practicing the disclosure herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not recorded in the present invention. The specification and examples are only regarded as exemplary, and the scope and spirit of the present invention are defined by the claims.

Claims

1. A stochastic optimization method for power system reserve based on typical photovoltaic scenarios, characterized in that, Including the steps: S1. Obtain the predicted photovoltaic power probability data, form sequences with the data corresponding to different quantiles therein, and use the linear interpolation method based on the sequences to obtain the photovoltaic power cumulative distribution function for calculating the photovoltaic predicted power; S2. Based on the photovoltaic predicted power, use the Gaussian Copula function to construct a multivariate distribution function that satisfies the time-series correlation of photovoltaic power, and sample the multivariate distribution function through the Monte Carlo method to obtain multiple photovoltaic power prediction scenarios with time-series correlation; S3. Based on the K-means++ algorithm, reduce the multiple photovoltaic power prediction scenarios with time-series correlation to obtain multiple photovoltaic typical scenarios; S4. Construct an objective function based on the operating cost, reserve cost, and start-stop cost of the photovoltaic typical scenarios, obtain the preset reserve stochastic optimization conditions, and construct a reserve stochastic optimization model of the power system under multiple photovoltaic typical scenarios to quantify the system reserve under the randomness of photovoltaic output in the photovoltaic typical scenarios for optimization; The reserve stochastic optimization conditions include power balance constraint, power system power flow constraint, minimum start-stop time constraint, unit output constraint, unit ramp constraint, unit reserve capacity constraint, energy storage device constraint, reserve constraint of the energy storage device, and photovoltaic output constraint; The expression of the power balance constraint is ， In the formula, and respectively represent the photovoltaic set and the load set, represents the scenario the active power of the conventional unit at time, and respectively represent the discharge power and the charge power of the energy storage device at time, the actual output of the th photovoltaic at time is ; is the scenario the th load value of the load at time The expression of the power system power flow constraint is ， In the formula, is the set of nodes; , , and respectively represent the sets of conventional units, energy storage devices, photovoltaic, and loads under node ; represents the set of power system lines; is the upper limit of the transmission power of line ; is the transfer factor of node to line ; The expression of the minimum start-stop time constraint includes ， ， ， ， In the formula, is the set of conventional units in the power system, is the conventional unit at time, which is a binary variable. When , it means that the conventional unit is in the on state at time. When , it means that the conventional unit is in the off state at time, and respectively represent the on state and off state of the conventional unit at time , and they are binary variables and cannot be both . When , it means that the conventional unit is in the starting state. When , it means that the conventional unit is in the shutdown state, and respectively represent the minimum start-up and shutdown times of the conventional unit ; The expression of the unit output constraint is ， In the formula, and respectively represent the upper and lower limits of the active power output of the conventional unit ; The expression of the unit ramp constraint includes ， ， In the formula, is a set of typical photovoltaic scenarios, and respectively represent the start-up and shut-down ramp rates of a conventional unit , and are respectively the up-ramp rate and down-ramp rate of the conventional unit . The expression of the unit reserve capacity constraint includes In the formula, the set Select any one as the base scenario and denote the remaining ones as , represents the active power of the conventional unit at time . and are respectively the positive reserve capacity and the negative reserve capacity provided by the energy storage device at time, and are respectively the positive reserve upper limit and the negative reserve upper limit provided by the conventional unit . The expression of the energy storage device constraint includes ， ， ， ， ， ， Wherein, represents the set of energy storage devices in the power system, and respectively represent the minimum and maximum charge-discharge powers of the energy storage device ; and are respectively the charge-discharge states of the energy storage device at time in scenario , which are binary variables and cannot be 1 at the same time. When , it means that the energy storage device is in the charging state at time in scenario . When , it means that the energy storage device is in the discharging state at time in scenario ; represents the energy of the energy storage device at time in scenario ; and respectively represent the charging and discharging efficiencies of the energy storage device ; and are respectively the minimum and maximum values of the energy of the energy storage device ; The expression of the reserve constraint of the energy storage device includes ， ， ， ， In the formula, and respectively represent the active power of the energy storage device at the moment ; The expression of the photovoltaic output constraint is ， In the formula, represents the scenario at the th photovoltaic power output prediction value at time .

2. A power system reserve stochastic optimization method based on typical photovoltaic scenarios according to claim 1, characterized in that Step S1 is specifically as follows: Define the cumulative distribution function of photovoltaic power at a certain moment based on the quantile regression theory as ; Define the quantile level The quantile is and wherein is the probability operator represents the photovoltaic power at time ; A probability prediction method using quantile regression expresses the prediction quantiles of photovoltaic power as , where represents the sequence of prediction quantiles corresponding to the quantile level taking at time , where is the number of quantiles, and represents the estimated value of the actual quantile At the moment Based on the predicted quantile sequence ,the complete cumulative distribution function of photovoltaic power is obtained by using the linear interpolation method ; Let the sequence of random variables follow the uniform distribution, where represents the duration of the scheduling period. Taking the sequence of random variables as the cumulative probability of the random variable, according to the cumulative distribution function of photovoltaic power we have , from which the predicted photovoltaic power is obtained.

3. A power system reserve stochastic optimization method based on photovoltaic typical scenarios according to claim 2, characterized in that, Step S2 includes the steps: S21. Use the multivariate Gaussian Copula function to construct a multivariate distribution function that satisfies the temporal correlation of photovoltaic power , let , then ； S22. Generate a set of random numbers that satisfy a uniform distribution ; S23. Let obtain the solution random variable value , let the marginal cumulative distribution function value of the random variable be equal to , and use the formula to obtain the value of the random variable value . Based on the formula obtain the value of the random variable value ; S24. Repeat the calculation processes of steps S22 and S23 for times to obtain groups of sampling results of the multivariate random variable. In the th sampling, the multivariate random variable is S25. According to , from Solve to obtain the photovoltaic power of the time-correlated scenario . Traverse to obtain probability-prediction-based time-correlated photovoltaic power prediction scenarios.

4. A power system reserve stochastic optimization method based on typical photovoltaic scenarios according to claim 3, characterized in that, Step S3 includes the steps: S31. Select randomly from the photovoltaic power prediction scenarios as the first clustering center; S32. For each of the photovoltaic power prediction scenarios, calculate the shortest distance between each of them and the currently existing clustering centers and sum the squares of all the shortest distances to obtain , where the calculation formula for the shortest distance is as follows ； S33. Calculate the probability that each of the photovoltaic power prediction scenarios is selected as the next clustering center , where the probability is calculated by the formula ； S34. Take a random number between the interval [0, 1] , subtract it from . If the result is greater than 0, continue to subtract until the result is not greater than 0, and record the photovoltaic power prediction scenario corresponding to the probability at this time as the second cluster center; ​ S35. Repeat steps S32 to S34 to obtain initial cluster centers; S36. Based on the initial cluster centers, use the K-means clustering algorithm to obtain photovoltaic typical scenarios and their corresponding probabilities , and the probabilities satisfy .

5. A power system reserve stochastic optimization method based on typical photovoltaic scenarios according to claim 4, characterized in that Step S4 is specifically as follows: Based on the above photovoltaic typical scenarios, said photovoltaic typical scenarios are denoted as a set , and from the set any one is selected and denoted as the basic scenario, and the remaining ones are denoted as , and thus a power system reserve stochastic optimization model under multiple photovoltaic typical scenarios is constructed.

6. The power system reserve stochastic optimization method based on typical photovoltaic scenarios according to claim 5, wherein, The objective function is: ， In the formula, is the set of conventional units in the power system, represents the set of energy storage devices in the power system, and are the start-up cost coefficient and shut-down cost coefficient of the conventional unit respectively, and represent the positive reserve cost coefficient and negative reserve cost coefficient provided by the conventional unit respectively, and are the positive reserve cost coefficient and negative reserve cost coefficient provided by the energy storage device respectively, represents the operating cost coefficient of the conventional unit respectively, and represent the discharge cost coefficient and charge cost coefficient of the energy storage device respectively, and represent the start-up state and shut-down state of the conventional unit at time respectively, and are binary variables, and the two cannot be at the same time. When , it means that the conventional unit is in the starting state. When , it means that the conventional unit is in the shutdown state. and represent the positive reserve capacity and negative reserve capacity provided by the conventional unit at time respectively, and are the positive reserve capacity and negative reserve capacity provided by the energy storage device at time respectively, represents the active power of the conventional unit under scenario at time, and represent the discharge power and charge power of the energy storage device under scenario at time respectively.

7. A power system reserve stochastic optimization system based on typical photovoltaic scenarios, characterized in that For implementing the reserve stochastic optimization method of the power system according to any one of claims 1 to 6.

8. A computer device, the computer device comprising a memory, a processor, and a computer program, characterized in that, When the computer program is executed by a processor, it implements the reserve stochastic optimization method of the power system according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the reserve stochastic optimization method of the power system according to any one of claims 1 to 6.

Images