Method and device for generating multi-season typical output scene of island microgrid

Abstract

The invention discloses an island micro-grid multi-season typical output scene generation method and device. The method comprises the steps that an original data set of the island micro-grid in multiple seasons is acquired, and the original data set comprises meteorological data, a meteorological-output correlation model, operation conditions of various units and a micro-grid load curve; according to the original data set, constructing and calculating a wind speed matrix, a wave height matrix, a daily accumulated electric quantity matrix and a minimum power value of each season; based on the meteorological data, the minimum power value, and the daily cumulative electric quantity matrix, the wind speed matrix and the wave height matrix of each season, typical output scenes are obtained, and the typical output scenes comprise an extreme output scene and a conventional output scene; performing clustering analysis on the typical output scenes, and calculating probability values of various scenes to obtain an 8760 curve matrix; and correcting the 8760 curve matrix according to the random state value of each unit to obtain an 8760 corrected curve matrix.

Description

Technical Field

[0001] This invention relates to the field of microgrid operation control technology, and more specifically, to a method and apparatus for generating typical multi-seasonal power output scenarios for island microgrids. Background Technology

[0002] To implement the national strategy of building a maritime power and the goal of clean and low-carbon energy transformation, the National Energy Administration and many coastal provinces have successively issued policies to explicitly support the construction of island microgrids. However, the planning, design, and efficient operation of island microgrids heavily rely on the accurate prediction and assessment of their possible future states, i.e., the generation of representative operating scenarios. Currently, although there are many methods for generating scenarios, they still have significant limitations when dealing with the unique island environment. First, existing methods fail to fully consider the distinct seasonal characteristics of island resources and loads. Wind, solar, and tourism loads in island areas all exhibit strong seasonal fluctuations, while current models often use mixed data from the entire year, obscuring the typical patterns and transition rules of different seasons, resulting in planning results that cannot accurately match the actual demand during the summer peak or winter depletion period. Second, the impact of random unit failures is often ignored. As a relatively isolated system, the random outage of wind turbines, photovoltaic inverters, energy storage converters, and even wave energy devices in an island microgrid can trigger a serious power supply crisis. Most existing scenario generation methods are based on the ideal assumption that all equipment is always operating normally, failing to include the forced outage rate of key equipment in the uncertainty scope, thus overestimating the actual power supply reliability of the system. Finally, there is a serious lack of depiction of extreme weather scenarios. Islands are prone to extreme events such as typhoons, severe corrosion, and high salt spray. These events not only cause drastic fluctuations in renewable energy output but may also lead to structural damage and load surges, directly threatening the system's survival.

[0003] Therefore, in order to support scientific decision-making for island microgrids, it is urgent to propose scenario generation and mitigation technologies that take into account seasonal characteristics, random unit failures, extreme weather, and other factors. These technologies can simultaneously and precisely depict multiple uncertainties such as seasonal patterns, random equipment failures, and extreme weather impacts, thereby generating a set of typical scenarios that are more in line with the actual conditions of islands and more robust. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and apparatus for generating typical power output scenarios for island microgrids in multiple seasons.

[0005] According to one aspect of the present invention, a method for generating typical multi-seasonal power output scenarios for island microgrids is provided, comprising:

[0006] Obtain raw datasets of island microgrids for multiple seasons. The raw datasets include meteorological data, meteorological-output correlation models, operating conditions of various generating units, and microgrid load curves.

[0007] Based on the original dataset, construct and calculate the wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season;

[0008] Based on meteorological data, minimum power values, daily cumulative power matrix for each season, wind speed matrix, and wave height matrix, typical power output scenarios are obtained, including extreme power output scenarios and normal power output scenarios.

[0009] Cluster analysis was performed on typical power output scenarios to calculate the probability values ​​of each scenario, resulting in an 8760 curve matrix.

[0010] The 8760 curve matrix is ​​corrected based on the random state values ​​of each unit to obtain the 8760 corrected curve matrix.

[0011] Optionally, based on the original dataset, wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season are constructed and calculated, including:

[0012] Based on meteorological data and microgrid load curves, wind speed and wave height matrices for each season are constructed.

[0013] Based on meteorological data, the comprehensive output of new energy sources is formed according to the meteorological-output correlation model.

[0014] Based on the comprehensive output of new energy sources, the daily output curves of each season are collected from each year to form a matrix of daily cumulative electricity generation for the four seasons;

[0015] Analyze the daily cumulative electricity consumption of the load to determine the minimum power value for each season.

[0016] Optionally, wind speed matrix SPN k Wave height matrix WH k The expression is:

[0017]

[0018]

[0019] In the formula, SPN j,k This is a matrix containing wind speed curves for the k-th season of the j-th year, with 24 rows and N columns. k SPN k This is a matrix containing wind speed curves for the k-th season, with 24 rows and J*N columns. k WH j,k This is a matrix containing wave height curves for the k-th season of the j-th year, with 24 rows and N columns. k WH k This is a matrix containing wave height curves for the k-th season, with 24 rows and J*N columns. k; The wind speed at time t on day i of year j; h i,j,k (t) represents the wave height at time t on day i in year j; J is the number of years, N k Let k be the number of days in the k-th season.

[0020] Optionally, the expression for the comprehensive output of new energy sources is:

[0021]

[0022] In the formula, These represent the wind power output, photovoltaic power output, and wave energy output at time t on day i in the k-th season of year j; The combined output of all new energy sources at time t on day i of year j; N wd N pv N wv These represent the installed capacity of wind power, solar power, and wave power, respectively; i = 1, 2, 3, 4, ..., N k ; j=1,2,...,J; k=1,2,3,4; N k Let k be the number of days in the k-th season; The wind speed at time t on day i of year j; h i,j,k (t) represents the wave height at time t on day i of year j; Let t be the ambient temperature on day i of year j at time t.

[0023] Optionally, the expression for the daily cumulative electricity consumption matrix for the four seasons is:

[0024]

[0025]

[0026] In the formula, The combined output of all new energy sources at time t on day i of year j; PN j,k This is the new energy output matrix for the k-th season of the j-th year, with 24 rows and N columns. k ;PN k This is the new energy output matrix for the k-th season, with 24 rows and J*N columns. k SUMPN k This is a matrix representing the daily cumulative electricity output from new energy sources in the k-th season, with 1 row and (J*N) columns. k SUMPN k (1,n) represents the value in the first row and nth column of the daily cumulative electricity output of new energy sources in the k-th season, PN k (m,n) represents the value in the m-th row and n-th column of the new energy output matrix for the k-th season.

[0027] Alternatively, the expression for the minimum power value is:

[0028]

[0029] MINPL k =min(PL k (m,n))

[0030] In the formula, PL j,k This is the load matrix for the k-th season of the j-th year, with 24 rows and N columns. k ;PL k This is the load matrix for the k-th season, with 24 rows and (J*N) columns. k );MINPL k PL represents the minimum load in the k-th season. k (m,n) represents the value in the m-th row and n-th column of the k-th seasonal load matrix; This represents the load data for day i in year j at time t.

[0031] Optionally, the expression for a typical output scenario is:

[0032]

[0033] EX k =[PN k (:,find(jg k =1,1))PN k (:,find(jg k =1,1))…PN k (:,find(jg k =1,sum(jg k )))]

[0034] NOR k =[PN k (:,find(jg k =0,1))PN k (:,find(jg k =0,1))…PN k (:,find(jg k =0,(J*N k -sum(jg k ))))]

[0035]

[0036] In the formula, These are the determination matrices for identifying whether day n of the k-th season is an extreme scenario day based on wind speed, wave height, and load conditions; SPN k(:,n) represents all wind speed values ​​on day n of the k-th season; WH k (:,n) represents all wave height values ​​on day n of the k-th season; jg k (1,n) is the determination matrix for whether the nth day of the kth season is an extreme scenario day; EX k Let J represent the extreme scenarios of the renewable energy output curve for the k-th season, with 24 rows and sum(jgk) columns; NOR k This is a standard scenario set for the renewable energy output curve in the k-th season, with 24 rows and (J*N) columns. k -sum(jg k find(jg) k =1,sum(jg k The goal is to find the column number where the matrix jgk equals 1, and the column number is the sum(jgk)th occurrence. Let be the probability of an extreme scenario day in the k-th season;

[0037] The expressions for the probability values ​​of various scenarios are as follows:

[0038]

[0039] In the formula, This is a set of extremely typical daily scenes clustered using the K-means method; This represents the probability of an extreme typical day when clustered using the K-means method. The target number of clusters for extreme scenarios; This is a typical daily scene set clustered using the K-means method; The probability corresponding to a typical day when clustered using the K-means method; P represents the target number of clusters in a typical scenario; k The final set of typical daily scenarios for new energy output has 24 rows and [number of columns missing]. PE k This is the probability set corresponding to the typical daily scenario of new energy power output, with 24 rows and [number of columns].

[0040] Optionally, the expression for the 8760 correction curve matrix is:

[0041]

[0042] in,

[0043]

[0044]

[0045] In the formula, The sampled 8760 curve matrix has 24 rows and 365 columns; These are typical daily sampling curves for each of the four seasons, taking into account seasonal characteristics and the probability of extreme scenarios. USA Nnew These are the availability state matrices for photovoltaic, wind power, wave power, and new energy units, with N rows respectively. pv N wd N wv 、(N pv +N wd +N wv The number of columns is 365; the status value is 1 for available and 0 for unavailable. The 8760-correction curve matrix, which takes into account random unit failures and maintenance time sampling, has 24 rows and 365 columns.

[0046] According to another aspect of the present invention, a device for generating typical output scenarios of island microgrids in multiple seasons is provided, comprising:

[0047] The acquisition module is used to acquire raw datasets of the island microgrid for multiple seasons. The raw datasets include meteorological data, meteorological-output correlation models, operating conditions of various generating units, and microgrid load curves.

[0048] The building module is used to construct and calculate the wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season based on the original dataset;

[0049] The module is used to obtain typical power output scenarios based on meteorological data, minimum power values, daily cumulative power matrix for each season, wind speed matrix, and wave height matrix. The typical power output scenarios include extreme power output scenarios and normal power output scenarios.

[0050] The calculation module is used to perform cluster analysis on typical power output scenarios, calculate the probability values ​​of each scenario, and obtain an 8760 curve matrix.

[0051] The correction module is used to correct the 8760 curve matrix based on the random state values ​​of each unit, so as to obtain the 8760 corrected curve matrix.

[0052] Optionally, the building module includes:

[0053] Based on meteorological data and microgrid load curves, wind speed and wave height matrices for each season are constructed.

[0054] Based on meteorological data, the comprehensive output of new energy sources is formed according to the meteorological-output correlation model.

[0055] Based on the comprehensive output of new energy sources, the daily output curves of each season are collected from each year to form a matrix of daily cumulative electricity generation for the four seasons;

[0056] Analyze the daily cumulative electricity consumption of the load to determine the minimum power value for each season.

[0057] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0058] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0059] Therefore, this invention proposes a scenario generation and reduction technology that takes into account seasonal characteristics, random unit failures, extreme weather, and other factors. It can simultaneously and accurately depict multiple uncertainties such as seasonal patterns, random equipment failures, failure durations, and extreme weather impacts, thereby forming a set of typical scenarios that are more in line with the actual conditions of islands and more robust, which is conducive to improving the reliability of island microgrid operation simulation. Attached Figure Description

[0060] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0061] Figure 1 This is a flowchart illustrating a method for generating typical multi-season power output scenarios for island microgrids, provided in an exemplary embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram of the structure of a device for generating typical power output scenarios of island microgrids in multiple seasons, provided in an exemplary embodiment of the present invention.

[0063] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0064] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0065] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0066] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0067] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0068] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0069] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0070] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0071] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0072] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0073] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0074] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0075] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0076] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0077] Exemplary methods

[0078] Figure 1 This is a flowchart illustrating a method for generating typical multi-season power output scenarios for island microgrids according to an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the method for generating typical power output scenarios for island microgrids in multiple seasons includes the following steps:

[0079] Step 101: Obtain the original dataset of the island microgrid for multiple seasons. The original dataset includes meteorological data, meteorological-output correlation model, operating conditions of various units, and microgrid load curves.

[0080] Step 102: Based on the original dataset, construct and calculate the wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season;

[0081] Step 103: Based on meteorological data, minimum power value, daily cumulative power matrix for each season, wind speed matrix and wave height matrix, typical power output scenarios are obtained, including extreme power output scenarios and normal power output scenarios.

[0082] Step 104: Perform cluster analysis on typical power output scenarios, calculate the probability values ​​of each scenario, and obtain an 8760 curve matrix;

[0083] Step 105: Correct the 8760 curve matrix according to the random state values ​​of each unit to obtain the 8760 corrected curve matrix.

[0084] Specifically, taking into account seasonal characteristics, random unit failures, and extreme weather, a typical operating scenario more closely aligned with the realities of island microgrids is proposed to ensure their safe and stable operation. Based on historical meteorological data combined with the operating characteristics of offshore wind power, photovoltaic power, and wave energy, a scenario generation method that practically considers both operational characteristics and meteorological factors is proposed to clarify the combined unit output characteristics of island microgrids under various complex weather conditions and random operating conditions. The specific steps include:

[0085] (1) Data acquisition: acquire meteorological data, meteorological-output correlation models of various types of units, operating conditions of various types of units, microgrid load curves and other data, and generate output data of various power sources and various types of units.

[0086] 1) Obtain meteorological data, such as wind speed, for the i-th day at time t in the k-th season of the j-th year. wind direction Ambient temperature Light intensity Wave height h i,j,k (t), Wave period H i,j.k (t); extreme weather parameters V that require shutdown of various generating units EX h EX Number of various types of generating units N pv N wd N wv Load data of a typical microgrid on day t of year j. This forms the wind speed matrix and wave height matrix for each season.

[0087]

[0088] Among them, SPN j,k This is a matrix containing wind speed curves for the k-th season of the j-th year, with 24 rows and N columns. k SPN k This is a matrix containing wind speed curves for the k-th season, with 24 rows and (J*N) columns. k WH j,k This is a matrix containing wave height curves for the k-th season of the j-th year, with 24 rows and N columns. k WH k This is a matrix containing wave height curves for the k-th season, with 24 rows and (J*N) columns. k ).

[0089] 2) Combining the meteorological-power output correlation model, the individual power outputs of various power generation units are generated, resulting in the total power output curve of new energy sources:

[0090]

[0091] in, These represent the wind power output, photovoltaic power output, and wave power output at time t on day i in the k-th season of year j. The combined output of all new energy power sources at time t on day i of year j. wd N pv N wv These represent the installed capacity of wind power, solar power, and wave power, respectively. i = 1, 2, 3, 4, ..., N k ;j=1,2,...,J;k=1,2,3,4. N k Let k be the number of days in the k-th season.

[0092] 3) Collect the sunrise power curves for each season from each year to form a sunrise power curve library matrix PN for four seasons. k .

[0093]

[0094] Among them, PN j,k This is a matrix containing the renewable energy output curves for the k-th season of year j, with 24 rows and N columns. k PN k This is a matrix containing the new energy output curves for the k-th season, with 24 rows and (J*N) columns. k SUMPNk is the daily cumulative electricity output matrix of new energy sources in the k-th season, with 1 row and (J*N) columns. k SUMPN k (1,n) represents the value in the first row and nth column of the daily cumulative electricity output of new energy sources in the k-th season, PN k (m,n) represents the value in the m-th row and n-th column of the new energy output curve library matrix for the k-th season.

[0095] 4) Analyze the daily cumulative power consumption of the load and select the minimum power value for each season.

[0096]

[0097] MINPL k =min(PL k (m,n))

[0098] Among them, PL j,k PL is a matrix containing the load curves for the k-th season of the j-th year, with 24 rows and Nk columns. k This is the load curve library matrix for the k-th season, with 24 rows and (J*N) columns. k ). MINPLk This represents the minimum load value for the k-th season. PL k (m,n) represents the value in the m-th row and n-th column of the k-th seasonal load curve library matrix.

[0099] (2) Based on the minimum load demand of each season, consider whether the minimum safe operating power of the load can be met to form extreme and normal scenarios for each season.

[0100] 1) Screening and statistics of extreme typical days, taking the whole day load electricity as the unit, and analyzing the probability of each scenario.

[0101]

[0102] EX k =[PN k (:,find(jg k =1,1))PN k (:,find(jg k =1,1))…PN k (:,find(jg k =1,sum(jg k )))]

[0103] NOR k =[PN k (:,find(jg k =0,1))PN k (:,find(jg k =0,1))…PN k (:,find(jg k =0,(J*N k -sum(jg k ))))]

[0104]

[0105] in, These are the determination matrices for identifying whether day n of season k is an extreme scenario day based on wind speed, wave height, and load conditions. SPN k (:,n) represents all wind speed values ​​on day n of the k-th season. WH k (:,n) represents all wave height values ​​on day n of the k-th season. k (1,n) is the determination matrix for whether the nth day of the kth season is an extreme scenario day. EX k This is the set of extreme scenarios for the renewable energy output curve in the k-th season, with 24 rows and sum(jgk) columns. NOR kThis is a set of typical scenarios for the renewable energy output curve in the k-th season, with 24 rows and (J*Nk-sum(jgk)). The function `find(jgk)` is used to find (jgkk) the output curve in the k-th season. k =1,sum(jg k The expression is used to find the column number in which the matrix jgk equals 1 and the sum(jgk)th occurrence of jgk. Let be the probability of an extreme scenario day in the k-th season.

[0106] 2) Typical day processing: K-means clustering is used to cluster typical days in each season and analyze the corresponding probability of each scenario.

[0107]

[0108] in, This is a set of extremely typical daily scenes clustered using the K-means method; This represents the probability of an extreme typical day when clustering is performed using the K-means method. The target number of clusters for extreme scenarios. This is a typical daily scene set clustered using the K-means method; This represents the probability of a typical day clustered using the K-means method. P represents the target number of clusters in a typical scenario. k The final set of typical daily scenarios for new energy output has 24 rows and [number of columns missing]. PE k This is the probability set corresponding to the typical daily scenario of new energy power output, with 24 rows and [number of columns].

[0109] (3) Based on the typical day scene set and the typical day scene probability set, Monte Carlo sampling is used to generate 24*365 8760 curves.

[0110]

[0111] in, The sampled 8760 curve matrix has 24 rows and 365 columns. These are typical daily sampling curves for each of the four seasons, taking into account seasonal characteristics and the probability of extreme scenarios.

[0112] (4) Sampling of random faults in each unit and adjusting the capacity of each type of unit and the overall power output.

[0113] The available state matrix for each unit is calculated using the state duration modeling method over 365 days.

[0114]

[0115] in, USA Nnew These are availability matrices for photovoltaic, wind power, wave power, and new energy units, respectively. The number of rows are Npv, Nwd, Nwv, and (Npv+Nwd+Nwv), and the number of columns is 365 for each. A status value of 1 indicates availability, and 0 indicates unavailability.

[0116] Correcting the typical 8760 output curve by incorporating random state values

[0117]

[0118] in, The 8760-correction curve matrix, which takes into account random unit failures and maintenance time sampling, has 24 rows and 365 columns.

[0119] Therefore, this invention proposes a scenario generation and reduction technology that takes into account seasonal characteristics, random unit failures, extreme weather, and other factors. It can simultaneously and accurately depict multiple uncertainties such as seasonal patterns, random equipment failures, failure durations, and extreme weather impacts, thereby forming a set of typical scenarios that are more in line with the actual conditions of islands and more robust, which is conducive to improving the reliability of island microgrid operation simulation.

[0120] Exemplary device

[0121] Figure 2 This is a schematic diagram of the structure of a device for generating typical multi-season power output scenarios for island microgrids, provided in an exemplary embodiment of the present invention. (See diagram below.) Figure 2 As shown, the device 200 includes:

[0122] The acquisition module 210 is used to acquire the raw dataset of the island microgrid for multiple seasons. The raw dataset includes meteorological data, meteorological-output correlation model, operating conditions of various units, and microgrid load curves.

[0123] Module 220 is used to construct and calculate the wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season based on the original dataset.

[0124] Module 230 is used to obtain typical power output scenarios based on meteorological data, minimum power value, daily cumulative power matrix of each season, wind speed matrix and wave height matrix. The typical power output scenarios include extreme power output scenarios and normal power output scenarios.

[0125] The calculation module 240 is used to perform cluster analysis on typical power output scenarios, calculate the probability values ​​of each type of scenario, and obtain an 8760 curve matrix.

[0126] The correction module 250 is used to correct the 8760 curve matrix according to the random state values ​​of each unit, so as to obtain the 8760 corrected curve matrix.

[0127] Optionally, module 220 includes:

[0128] Based on meteorological data and microgrid load curves, wind speed and wave height matrices for each season are constructed.

[0129] Based on meteorological data, the comprehensive output of new energy sources is formed according to the meteorological-output correlation model.

[0130] Based on the comprehensive output of new energy sources, the daily output curves of each season are collected from each year to form a matrix of daily cumulative electricity generation for the four seasons;

[0131] Analyze the daily cumulative electricity consumption of the load to determine the minimum power value for each season.

[0132] Optionally, wind speed matrix SPN k Wave height matrix WH k The expression is:

[0133]

[0134] In the formula, SPN j,k This is a matrix containing wind speed curves for the k-th season of the j-th year, with 24 rows and N columns. k SPN k This is a matrix containing wind speed curves for the k-th season, with 24 rows and J*N columns. k WH j,k This is a matrix containing wave height curves for the k-th season of the j-th year, with 24 rows and N columns. k WH k This is a matrix containing wave height curves for the k-th season, with 24 rows and J*N columns. k ; The wind speed at time t on day i of year j; h i,j,k (t) represents the wave height at time t on day i in year j; J is the number of years, N k Let k be the number of days in the k-th season.

[0135] Optionally, the expression for the comprehensive output of new energy sources is:

[0136]

[0137] In the formula, These represent the wind power output, photovoltaic power output, and wave energy output at time t on day i in the k-th season of year j; The combined output of all new energy sources at time t on day i of year j; N wd Npv N wv These represent the installed capacity of wind power, solar power, and wave power, respectively; i = 1, 2, 3, 4, ..., N k ; j=1,2,...,J; k=1,2,3,4; N k Let k be the number of days in the k-th season; The wind speed at time t on day i of year j; h i,j,k (t) represents the wave height at time t on day i of year j; Let t be the ambient temperature on day i of year j at time t.

[0138] Optionally, the expression for the daily cumulative electricity consumption matrix for the four seasons is:

[0139]

[0140] In the formula, The combined output of all new energy sources at time t on day i of year j; PN j,k This is the new energy output matrix for the k-th season of the j-th year, with 24 rows and N columns. k ;PN k This is the new energy output matrix for the k-th season, with 24 rows and J*N columns. k SUMPN k This is a matrix representing the daily cumulative electricity output from new energy sources in the k-th season, with 1 row and (J*N) columns. k SUMPN k (1,n) represents the value in the first row and nth column of the daily cumulative electricity output of new energy sources in the k-th season, PN k (m,n) represents the value in the m-th row and n-th column of the new energy output matrix for the k-th season.

[0141] Alternatively, the expression for the minimum power value is:

[0142]

[0143] MINPL k =min(PL k (m,n))

[0144] In the formula, PL j,k This is the load matrix for the k-th season of the j-th year, with 24 rows and N columns. k ;PL k This is the load matrix for the k-th season, with 24 rows and (J*N) columns. k );MINPL k PL represents the minimum load in the k-th season. k (m,n) represents the value in the m-th row and n-th column of the k-th seasonal load matrix; This represents the load data for day i in year j at time t.

[0145] Optionally, the expression for a typical output scenario is:

[0146]

[0147]

[0148] EX k =[PN k (:,find(jg k =1,1))PN k (:,find(jg k =1,1))…PN k (:,find(jg k =1,sum(jg k )))]

[0149] NOR k =[PN k (:,find(jg k =0,1))PN k (:,find(jg k =0,1))…PN k (:,find(jg k =0,(J*N k -sum(jg k ))))]

[0150]

[0151] In the formula, These are the determination matrices for identifying whether day n of the k-th season is an extreme scenario day based on wind speed, wave height, and load conditions; SPN k (:,n) represents all wind speed values ​​on day n of the k-th season; WH k (:,n) represents all wave height values ​​on day n of the k-th season; jg k (1,n) is the determination matrix for whether the nth day of the kth season is an extreme scenario day; EX k Let J represent the extreme scenarios of the renewable energy output curve for the k-th season, with 24 rows and sum(jgk) columns; NOR k This is a standard scenario set for the renewable energy output curve in the k-th season, with 24 rows and (J*N) columns. k -sum(jg k find(jg) k =1,sum(jg kThe goal is to find the column number where the matrix jgk equals 1, and the column number is the sum(jgk)th occurrence. Let be the probability of an extreme scenario day in the k-th season;

[0152] The expressions for the probability values ​​of various scenarios are as follows:

[0153]

[0154] In the formula, This is a set of extremely typical daily scenes clustered using the K-means method; This represents the probability of an extreme typical day when clustered using the K-means method. The target number of clusters for extreme scenarios; This is a typical daily scene set clustered using the K-means method; The probability corresponding to a typical day when clustered using the K-means method; P represents the target number of clusters in a typical scenario; k The final set of typical daily scenarios for new energy output has 24 rows and [number of columns missing]. PE k This is the probability set corresponding to the typical daily scenario of new energy power output, with 24 rows and [number of columns].

[0155] Optionally, the expression for the 8760 correction curve matrix is:

[0156]

[0157] in,

[0158]

[0159] In the formula, The sampled 8760 curve matrix has 24 rows and 365 columns; These are typical daily sampling curves for each of the four seasons, taking into account seasonal characteristics and the probability of extreme scenarios. USA Nnew These are the availability state matrices for photovoltaic, wind power, wave power, and new energy units, with N rows respectively. pv N wd N wv 、(N pv +N wd +N wv The number of columns is 365; the status value is 1 for available and 0 for unavailable. The 8760-correction curve matrix, which takes into account random unit failures and maintenance time sampling, has 24 rows and 365 columns.

[0160] Exemplary electronic devices

[0161] Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 3 As shown, the electronic device 30 includes one or more processors 31 and memory 32.

[0162] The processor 31 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0163] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 33 and an output device 34, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0164] In addition, the input device 33 may also include, for example, a keyboard, a mouse, etc.

[0165] The output device 34 can output various information to the outside. The output device 34 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0166] Of course, for the sake of simplicity, Figure 3 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0167] Exemplary computer program products and computer-readable storage media

[0168] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0169] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0170] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0171] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0172] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0173] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0174] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0175] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0176] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0177] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for generating typical multi-seasonal power output scenarios for island microgrids, characterized in that, include: Obtain raw datasets of island microgrids for multiple seasons, including meteorological data, meteorological-output correlation models, operating conditions of various generating units, and microgrid load curves; Based on the original dataset, construct and calculate the wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season; Based on the meteorological data, the minimum power value, the daily cumulative power matrix for each season, the wind speed matrix, and the wave height matrix, typical power output scenarios are obtained, wherein the typical power output scenarios include extreme power output scenarios and normal power output scenarios. Cluster analysis was performed on the typical power output scenarios to calculate the probability values ​​of each scenario, resulting in an 8760 curve matrix. The 8760 curve matrix is ​​corrected based on the random state values ​​of each unit to obtain the 8760 corrected curve matrix.

2. The method according to claim 1, characterized in that, Based on the original dataset, construct and calculate the wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season, including: Based on the meteorological data and microgrid load curves, wind speed and wave height matrices for each season are constructed. Based on the meteorological data, the comprehensive output of new energy sources is formed according to the meteorological-output correlation model. Based on the comprehensive output of the new energy sources, the daily output curves for each season are collected from each year to form a daily cumulative power matrix for the four seasons. Analyze the daily cumulative electricity consumption of the load to determine the minimum power value for each season.

3. The method according to claim 2, characterized in that, The wind speed matrix SPN k and the wave height matrix WH k The expression is: In the formula, SPN j,k This is a matrix containing wind speed curves for the k-th season of the j-th year, with 24 rows and N columns. k SPN k This is a matrix containing wind speed curves for the k-th season, with 24 rows and J*N columns. k WH j,k This is a matrix containing wave height curves for the k-th season of the j-th year, with 24 rows and N columns. k WH k This is a matrix containing wave height curves for the k-th season, with 24 rows and J*N columns. k ; The wind speed at time t on day i of year j; h i,j,k (t) represents the wave height at time t on day i in year j; J is the number of years, N k Let k be the number of days in the k-th season.

4. The method according to claim 2, characterized in that, The expression for the comprehensive output of the new energy source is: In the formula, These represent the wind power output, photovoltaic power output, and wave energy output at time t on day i in the k-th season of year j; The combined output of all new energy sources at time t on day i of year j; N wd N pv N wv These represent the installed capacity of wind power, solar power, and wave power, respectively; i = 1, 2, 3, 4, ..., N k ; j=1,2,...,J; k=1,2,3,4; N k Let k be the number of days in the k-th season; The wind speed at time t on day i of year j; h i,j,k (t) represents the wave height at time t on day i of year j; Let t be the ambient temperature on day i of year j at time t.

5. The method according to claim 2, characterized in that, The expression for the daily cumulative electricity consumption matrix for the four seasons is as follows: In the formula, The combined output of all new energy sources at time t on day i of year j; PN j,k This is the new energy output matrix for the k-th season of the j-th year, with 24 rows and N columns. k ;PN k This is the new energy output matrix for the k-th season, with 24 rows and J*N columns. k SUMPN k This is a matrix representing the daily cumulative electricity output from new energy sources in the k-th season, with 1 row and (J*N) columns. k SUMPN k (1,n) represents the value in the first row and nth column of the daily cumulative electricity output of new energy sources in the k-th season, PN k (m,n) represents the value in the m-th row and n-th column of the new energy output matrix for the k-th season.

6. The method according to claim 2, characterized in that, The expression for the minimum power value is: MINPL k =min(PL k (m,n)) In the formula, PL j,k This is the load matrix for the k-th season of the j-th year, with 24 rows and N columns. k ;PL k This is the load matrix for the k-th season, with 24 rows and (J*N) columns. k );MINPL k PL represents the minimum load in the k-th season. k (m,n) represents the value in the m-th row and n-th column of the k-th seasonal load matrix; This represents the load data for day i in year j at time t.

7. The method according to claim 1, characterized in that, The expression for the typical output scenario is: EX k =[PN k (:,find(jg k =1,1))PN k (:,find(jg k =1,1))…PN k (:,find(jg k =1,sum(jg k )))] NOR k =[PN k (:,find(jg k =0,1))PN k (:,find(jg k =0,1))…PN k (:,find(jg k =0,(J*N k -sum(jg k ))))] In the formula, These are the determination matrices for identifying whether day n of the k-th season is an extreme scenario day based on wind speed, wave height, and load conditions; SPN k (:,n) represents all wind speed values ​​on day n of the k-th season; WH k (:,n) represents all wave height values ​​on day n of the k-th season; jg k (1,n) is the determination matrix for whether the nth day of the kth season is an extreme scenario day; EX k Let J represent the extreme scenarios of the renewable energy output curve for the k-th season, with 24 rows and sum(jgk) columns; NOR k This is a standard scenario set for the renewable energy output curve in the k-th season, with 24 rows and (J*N) columns. k -sum(jg k find(jg) k =1,sum(jg k The goal is to find the column number where the matrix jgk equals 1, and the column number is the sum(jgk)th occurrence. Let be the probability of an extreme scenario day in the k-th season; The expressions for the probability values ​​of various scenarios are as follows: In the formula, This is a set of extremely typical daily scenes clustered using the K-means method; This represents the probability of an extreme typical day when clustered using the K-means method. The target number of clusters for extreme scenarios; This is a typical daily scene set clustered using the K-means method; The probability corresponding to a typical day when clustered using the K-means method; P represents the target number of clusters in a typical scenario; k The final set of typical daily scenarios for new energy output has 24 rows and [number of columns missing]. PE k This is the probability set corresponding to the typical daily scenario of new energy power output, with 24 rows and [number of columns].

8. The method according to claim 1, characterized in that, The expression for the 8760 correction curve matrix is: in, In the formula, The sampled 8760 curve matrix has 24 rows and 365 columns; These are typical daily sampling curves for each of the four seasons, taking into account seasonal characteristics and the probability of extreme scenarios. USA Nnew These are the availability state matrices for photovoltaic, wind power, wave power, and new energy units, with N rows respectively. pv N wd N wv 、(N pv +N wd +N wv The number of columns is 365; the status value is 1 for available and 0 for unavailable. The 8760-correction curve matrix, which takes into account random unit failures and maintenance time sampling, has 24 rows and 365 columns.

9. A device for generating typical output scenarios of island microgrids in multiple seasons, characterized in that, include: The acquisition module is used to acquire raw datasets of the island microgrid for multiple seasons. The raw datasets include meteorological data, meteorological-output correlation models, operating conditions of various generating units, and microgrid load curves. The construction module is used to construct and calculate the wind speed matrix, wave height matrix, daily cumulative power matrix, and minimum power value for each season based on the original dataset. The module is used to obtain typical power output scenarios based on the meteorological data, the minimum power value, the daily cumulative power matrix for each season, the wind speed matrix, and the wave height matrix, wherein the typical power output scenarios include extreme power output scenarios and normal power output scenarios. The calculation module is used to perform cluster analysis on the typical power output scenarios, calculate the probability values ​​of each type of scenario, and obtain an 8760 curve matrix. The correction module is used to correct the 8760 curve matrix according to the random state values ​​of each unit, so as to obtain the 8760 corrected curve matrix.

10. The apparatus according to claim 9, characterized in that, Build modules, including: Based on the meteorological data and microgrid load curves, wind speed and wave height matrices for each season are constructed. Based on the meteorological data, the comprehensive output of new energy sources is formed according to the meteorological-output correlation model. Based on the comprehensive output of the new energy sources, the daily output curves for each season are collected from each year to form a daily cumulative power matrix for the four seasons. Analyze the daily cumulative electricity consumption of the load to determine the minimum power value for each season.

11. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-8.

12. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-8.

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