Method and device for generating power system optimization scheme, medium and terminal

Abstract

The application discloses a power system optimization scheme generation method and device, medium and terminal, relates to the technical field of power system planning, and mainly aims to improve the problem that the total cost of a planning scheme is relatively high due to high new construction cost. The method comprises the following steps: acquiring historical wind and light load time series data sets, clustering the historical wind and light load time series data sets, obtaining a typical wind and light load scene set and an extreme wind and light load scene set, constructing a minimum target function of an optimization investment total cost, solving the minimum target function based on the typical wind and light load scene set, the extreme wind and light load scene set and a preset investment constraint condition, obtaining an initial power system optimization scheme, performing time series operation simulation operation on the initial power system optimization scheme based on the typical wind and light load scene set and the extreme wind and light load scene set, obtaining simulation operation results, and adjusting the initial power system optimization scheme based on the simulation operation results to generate a final power system optimization scheme.

Description

Technical Field

[0001] This application relates to the field of power system planning technology, and in particular to a method, apparatus, medium, and terminal for generating power system optimization schemes. Background Technology

[0002] With the increasing installed capacity of highly volatile new energy sources such as wind power and photovoltaics in the power grid, renewable energy has become a new force in ensuring my country's power supply. Due to changes in the power source structure, the power system needs to be redesigned to maintain power balance on both the source and load sides.

[0003] Traditional power system planning methods mainly focus on the construction of various types of resources, such as what kind of power plants to build, which routes to plan, and what the energy storage capacity should be.

[0004] Although power system planning schemes based on traditional methods can meet the need to maintain power balance on both the source and load sides of the power system under the condition of large-scale renewable energy feed-in, the total cost of the planning scheme is high due to the high cost of building new power plants, transmission lines and other equipment. Summary of the Invention

[0005] In view of this, this application provides a method, apparatus, medium, and terminal for generating power system optimization schemes, with the main purpose of improving the problem that the total cost of planning schemes is too high due to the high cost of new construction.

[0006] According to one aspect of this application, a method for generating power system optimization schemes is provided, comprising:

[0007] The historical wind, solar and load time series dataset of the target power system region is obtained, and the historical wind, solar and load time series dataset is clustered to obtain multiple typical wind, solar and load scenario sets and multiple extreme wind, solar and load scenario sets.

[0008] The minimum objective function of the optimized total investment cost of the target power system is constructed, and the minimum objective function is solved based on multiple typical wind and solar load scenario sets, multiple extreme wind and solar load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme. The optimized total investment cost is used to characterize the sum of the total cost model of coal-fired power unit retrofit, the cost of new units, and the operating cost of the power system. The total cost model of coal-fired power unit retrofit includes the total cost model of flexibility retrofit of in-service coal-fired power units, as well as the decommissioning cost model, life extension operation cost model, emergency standby cost model, and equal capacity replacement clean coal power cost model of coal-fired power units that have reached the end of their service life.

[0009] Based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, a time-series simulation operation is performed on the initial power system optimization scheme to obtain the simulation operation results of the initial power system optimization scheme. Based on the simulation operation results, the initial power system optimization scheme is adjusted to generate the final power system optimization scheme of the target power system.

[0010] Preferably, the objective function for minimizing the total optimized investment cost of constructing the target power system includes:

[0011] A total cost model for the flexibility retrofitting of in-service coal-fired power units is constructed, expressed by the following formula.

[0012]

[0013] in, This represents the total cost of flexibility retrofitting all in-service coal-fired power units. Indicator variable representing the flexibility retrofit of in-service coal-fired power units. Indicates in-service coal-fired power units The cost of flexible modification Indicator variables representing in-service coal-fired power units, This indicates the number of coal-fired power units in operation;

[0014] A decommissioning cost model for coal-fired power units reaching the end of their service life is constructed, expressed by the following formula.

[0015]

[0016] in, This indicates the decommissioning cost of retired units. This indicates the cost of dismantling retired units. This indicates the recovery revenue from decommissioned generator units;

[0017] A cost model for extending the service life of aging coal-fired power units is constructed, expressed by the following formula.

[0018]

[0019]

[0020]

[0021]

[0022] in, This indicates the extended service life cost of the unit. This indicates the cost of extending the service life of the generating unit. This indicates the extended service life cost of the unit. This indicates the operation and maintenance cost of units with extended service life. This indicates the carbon trading cost of units operating with extended service life. Indicates extended service life of the unit The lower limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The upper limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The rate of ascent, Indicates the time scale of the study. Indicates extended service life of the unit downhill / climbing speed;

[0023] A cost model for switching to emergency standby mode for aging coal-fired power units is constructed, expressed as the following formula.

[0024]

[0025]

[0026] in, This indicates the cost of switching the generator unit to emergency standby mode. This indicates the cost of converting the generator unit to emergency standby mode. This indicates the emergency standby operating cost of the unit. This indicates the fuel cost of switching to emergency standby units. This indicates the maintenance cost of the emergency standby unit. This indicates the carbon trading cost of switching to emergency standby units. This indicates the emergency start-up cost of switching to an emergency standby unit;

[0027] A cost model for replacing aging coal-fired power units with clean coal power of equal capacity is constructed, expressed as the following formula.

[0028]

[0029]

[0030] in, This represents the cost of replacing a unit with an equivalent capacity. This indicates the cost of upgrading to replace an existing unit with one of equal capacity. This represents the equivalent capacity replacement operating cost of the unit. This represents the equivalent capacity replacement operating cost of the unit. This indicates the carbon trading cost of replacing units of equal capacity;

[0031] Based on the total cost model for the flexible retrofitting of in-service coal-fired power units, as well as the decommissioning cost model, life-extending operation cost model, emergency standby cost model, and equal-capacity replacement clean coal power cost model for coal-fired power units reaching the end of their service life, a total cost model for the retrofitting of coal-fired power units is constructed, expressed as the following formula.

[0032]

[0033] in, This indicates the total cost of retrofitting coal-fired power units. Indicates coal-fired power units reaching the end of their service life Life extension operation decision variables, Indicates coal-fired power units reaching the end of their service life The variables of retirement decision, Indicates coal-fired power units reaching the end of their service life The decision variables for emergency backup Indicates coal-fired power units reaching the end of their service life Equal-capacity replacement decision variables, coal-fired power units reaching the end of their service life The cost of extending service life and upgrading operations, Indicates coal-fired power units reaching the end of their service life The cost of retirement coal-fired power units reaching the end of their service life The cost of converting to emergency backup coal-fired power units reaching the end of their service life The cost of replacing clean coal power with equivalent capacity;

[0034] Based on the aforementioned total cost model for coal-fired power unit retrofitting, the cost model for new power units, and the power system operation cost model, an optimized total investment cost model is constructed. Furthermore, based on this optimized total investment cost model, a minimum objective function for optimizing the total investment cost is constructed, where the minimum objective function is expressed as the following formula.

[0035] ,

[0036] in, This indicates optimizing the total investment cost. This indicates the cost of building a new generating unit. This represents the operating cost of the power system.

[0037] Preferably, the step of obtaining the historical wind-solar-load time-series dataset of the target power system region and performing clustering processing on the historical wind-solar-load time-series dataset to obtain multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets includes:

[0038] Obtain the historical wind-solar-load time-series dataset for the region to which the target power system belongs;

[0039] Based on the stacked autoencoder that has completed model training, low-dimensional feature extraction processing is performed on each historical wind and solar load time series data contained in the historical wind and solar load time series dataset to obtain multiple historical wind and solar load feature vectors.

[0040] Based on a preset number of clusters, deep embedding clustering is performed on multiple historical wind and solar load feature vectors to obtain multiple typical wind and solar load feature vector sets.

[0041] Each of the aforementioned typical wind-solar-load feature vector sets is used as the target typical wind-solar-load feature vector set;

[0042] Calculate the divergence parameter between each historical wind and solar load feature vector contained in the target typical wind and solar load feature vector set and the cluster center of the target typical wind and solar load feature vector set;

[0043] Historical wind-solar load feature vectors with divergence parameters greater than a preset divergence threshold are selected and marked as extreme historical wind-solar load feature vectors;

[0044] By integrating the extreme historical wind and solar load feature vectors from the typical wind and solar load feature vector sets of each target, an extreme historical wind and solar load feature vector set is obtained, and multiple extreme wind and solar load feature vector sets are generated based on the extreme historical wind and solar load feature vector set.

[0045] The cluster centers of the multiple typical wind-solar-load feature vector sets are decoded to obtain multiple typical wind-solar-load scene sets, and the cluster centers of the multiple extreme wind-solar-load feature vector sets are decoded to obtain multiple extreme wind-solar-load scene sets.

[0046] Preferably, before performing deep embedding clustering on multiple historical wind-solar-load feature vectors based on a preset number of clusters to obtain multiple typical wind-solar-load feature vector sets, the method further includes:

[0047] Set the range of values ​​for the preset number of clusters, and randomly select multiple values ​​within the range as the random number of clusters;

[0048] Based on each of the random clustering numbers, deep embedding clustering is performed on multiple historical wind load feature vectors to obtain multiple clustering results, and the silhouette coefficient corresponding to each clustering result is calculated.

[0049] Based on the number of random clusters and the corresponding silhouette coefficients, a curve is constructed showing how the silhouette coefficients change with the number of clusters.

[0050] The number of clusters corresponding to the maximum silhouette coefficient is selected from the curves and used as the preset number of clusters.

[0051] Preferably, the step of performing time-series simulation operations on the initial power system optimization scheme based on multiple sets of typical wind-solar-load scenarios and multiple sets of extreme wind-solar-load scenarios to obtain the simulation operation results of the initial power system optimization scheme includes:

[0052] Based on multiple typical wind-solar-load scenario sets, the conventional generating units in the initial power system optimization scheme are used to perform day-ahead optimization operations to determine the simulated conventional generating unit operation scheme. Based on the preset typical scenario constraints, the unit start-up and shutdown plan is optimized intraday to obtain the fine-tuned simulated conventional generating unit operation scheme.

[0053] Based on multiple extreme wind-solar-load scenario sets and preset extreme scenario constraints, emergency operations are performed using the standby units in the initial power system optimization scheme to determine the simulated standby unit operation scheme.

[0054] Based on the fine-tuned simulated conventional unit operation scheme and the simulated standby unit operation scheme, the conventional unit and the standby unit are controlled to perform time-series operation simulation operations to obtain the simulation operation results of the initial power system optimization scheme.

[0055] Preferably, the preset typical scenario constraints include typical scenario power balance constraints and typical scenario spinning reserve constraints.

[0056] The preset extreme scenario constraints include extreme scenario power balance constraints and extreme scenario rotational backup constraints.

[0057] Preferably, the preset investment constraints include mutually exclusive constraints on the transformation paths of aging coal-fired power units, constraints on the power system's power generation adequacy, and constraints on the total investment cost budget.

[0058] According to another aspect of this application, an apparatus for generating power system optimization schemes is provided, comprising:

[0059] The wind-solar-load scene clustering module is used to obtain the historical wind-solar-load time series dataset of the target power system region, and to perform clustering processing on the historical wind-solar-load time series dataset to obtain multiple typical wind-solar-load scene sets and multiple extreme wind-solar-load scene sets.

[0060] The initial power system optimization scheme generation module is used to construct the minimum objective function of the total optimized investment cost of the target power system, and solve the minimum objective function based on multiple typical wind and solar load scenario sets, multiple extreme wind and solar load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme. The total optimized investment cost is used to characterize the sum of the total cost model of coal-fired power unit retrofit, the cost of new units, and the operating cost of the power system. The total cost model of coal-fired power unit retrofit includes the total cost model of flexibility retrofit of in-service coal-fired power units, as well as the decommissioning cost model, life extension operation cost model, emergency standby cost model, and equal capacity replacement clean coal power cost model of coal-fired power units reaching the end of their service life.

[0061] The initial power system optimization scheme adjustment module is used to perform time-series operation simulation on the initial power system optimization scheme based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, obtain the simulation operation results of the initial power system optimization scheme, and adjust the initial power system optimization scheme based on the simulation operation results to generate the final power system optimization scheme of the target power system.

[0062] Preferably, the initial power system optimization scheme generation module is used for:

[0063] A total cost model for the flexibility retrofitting of in-service coal-fired power units is constructed, expressed by the following formula.

[0064]

[0065] in, This represents the total cost of flexibility retrofitting all in-service coal-fired power units. Indicator variable representing the flexibility retrofit of in-service coal-fired power units. Indicates in-service coal-fired power units The cost of flexible modification Indicator variables representing in-service coal-fired power units, This indicates the number of coal-fired power units in operation;

[0066] A decommissioning cost model for coal-fired power units reaching the end of their service life is constructed, expressed by the following formula.

[0067]

[0068] in, This indicates the decommissioning cost of retired units. This indicates the cost of dismantling retired units. This indicates the recovery revenue from decommissioned generator units;

[0069] A cost model for extending the service life of aging coal-fired power units is constructed, expressed by the following formula.

[0070]

[0071]

[0072]

[0073]

[0074] in, This indicates the extended service life cost of the unit. This indicates the cost of extending the service life of the generating unit. This indicates the extended service life cost of the unit. This indicates the operation and maintenance cost of units with extended service life. This indicates the carbon trading cost of units operating with extended service life. Indicates extended service life of the unit The lower limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The upper limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The rate of ascent, Indicates the time scale of the study. Indicates extended service life of the unit downhill / climbing speed;

[0075] A cost model for switching to emergency standby mode for aging coal-fired power units is constructed, expressed as the following formula.

[0076]

[0077]

[0078] in, This indicates the cost of switching the generator unit to emergency standby mode. This indicates the cost of converting the generator unit to emergency standby mode. This indicates the emergency standby operating cost of the unit. This indicates the fuel cost of switching to emergency standby units. This indicates the maintenance cost of the emergency standby unit. This indicates the carbon trading cost of switching to emergency standby units. This indicates the emergency start-up cost of switching to an emergency standby unit;

[0079] A cost model for replacing aging coal-fired power units with clean coal power of equal capacity is constructed, expressed as the following formula.

[0080]

[0081]

[0082] in, This represents the cost of replacing a unit with an equivalent capacity. This indicates the cost of upgrading to replace an existing unit with one of equal capacity. This represents the equivalent capacity replacement operating cost of the unit. This represents the equivalent capacity replacement operating cost of the unit. This indicates the carbon trading cost of replacing units of equal capacity;

[0083] Based on the total cost model for the flexible retrofitting of in-service coal-fired power units, as well as the decommissioning cost model, life-extending operation cost model, emergency standby cost model, and equal-capacity replacement clean coal power cost model for coal-fired power units reaching the end of their service life, a total cost model for the retrofitting of coal-fired power units is constructed, expressed as the following formula.

[0084]

[0085] in, This indicates the total cost of retrofitting coal-fired power units. Indicates coal-fired power units reaching the end of their service life Life extension operation decision variables, Indicates coal-fired power units reaching the end of their service life The variables of retirement decision, Indicates coal-fired power units reaching the end of their service life The decision variables for emergency backup Indicates coal-fired power units reaching the end of their service life Equal-capacity replacement decision variables, coal-fired power units reaching the end of their service life The cost of extending service life and upgrading operations, Indicates coal-fired power units reaching the end of their service life The cost of retirement coal-fired power units reaching the end of their service life The cost of converting to emergency backup coal-fired power units reaching the end of their service life The cost of replacing clean coal power with equivalent capacity;

[0086] Based on the aforementioned total cost model for coal-fired power unit retrofitting, the cost model for new power units, and the power system operation cost model, an optimized total investment cost model is constructed. Furthermore, based on this optimized total investment cost model, a minimum objective function for optimizing the total investment cost is constructed, where the minimum objective function is expressed as the following formula.

[0087] ,

[0088] in, This indicates optimizing the total investment cost. This indicates the cost of building a new generating unit. This represents the operating cost of the power system.

[0089] Preferably, the wind-solar-flood scene clustering module includes:

[0090] The data acquisition unit is used to acquire historical wind, solar and load time-series datasets for the region to which the target power system belongs;

[0091] The low-dimensional feature extraction unit is used to perform low-dimensional feature extraction processing on each historical wind and solar load time series data contained in the historical wind and solar load time series dataset based on the stacked autoencoder that has completed model training, to obtain multiple historical wind and solar load feature vectors.

[0092] A deep embedding clustering unit is used to perform deep embedding clustering on multiple historical wind and solar load feature vectors based on a preset number of clusters to obtain multiple typical wind and solar load feature vector sets; and each of the typical wind and solar load feature vector sets is used as a target typical wind and solar load feature vector set.

[0093] The divergence calculation unit is used to calculate the divergence parameters between each historical wind and solar load feature vector contained in the target typical wind and solar load feature vector set and the cluster center of the target typical wind and solar load feature vector set.

[0094] The labeling unit is used to filter out historical wind-solar load feature vectors with divergence parameters greater than a preset divergence threshold and label them as extreme historical wind-solar load feature vectors.

[0095] An extreme wind and solar load feature vector set generation unit is used to integrate the extreme historical wind and solar load feature vectors in the typical wind and solar load feature vector sets of each target to obtain an extreme historical wind and solar load feature vector set, and generate multiple extreme wind and solar load feature vector sets based on the extreme historical wind and solar load feature vector set.

[0096] The decoding unit is used to decode the cluster centers of multiple typical wind-solar-load feature vector sets to obtain multiple typical wind-solar-load scene sets, and to decode the cluster centers of multiple extreme wind-solar-load feature vector sets to obtain multiple extreme wind-solar-load scene sets.

[0097] Preferably, before the deep embedding clustering unit, the wind-solar-load scene clustering module further includes a clustering quantity determination unit, used for:

[0098] Set the range of values ​​for the preset number of clusters, and randomly select multiple values ​​within the range as the random number of clusters;

[0099] Based on each of the random clustering numbers, deep embedding clustering is performed on multiple historical wind load feature vectors to obtain multiple clustering results, and the silhouette coefficient corresponding to each clustering result is calculated.

[0100] Based on the number of random clusters and the corresponding silhouette coefficients, a curve is constructed showing how the silhouette coefficients change with the number of clusters.

[0101] The number of clusters corresponding to the maximum silhouette coefficient is selected from the curves and used as the preset number of clusters.

[0102] Preferably, the initial power system optimization scheme adjustment module is used for:

[0103] Based on multiple typical wind-solar-load scenario sets, the conventional generating units in the initial power system optimization scheme are used to perform day-ahead optimization operations to determine the simulated conventional generating unit operation scheme. Based on the preset typical scenario constraints, the unit start-up and shutdown plan is optimized intraday to obtain the fine-tuned simulated conventional generating unit operation scheme.

[0104] Based on multiple extreme wind-solar-load scenario sets and preset extreme scenario constraints, emergency operations are performed using the standby units in the initial power system optimization scheme to determine the simulated standby unit operation scheme.

[0105] Based on the fine-tuned simulated conventional unit operation scheme and the simulated standby unit operation scheme, the conventional unit and the standby unit are controlled to perform time-series operation simulation operations to obtain the simulation operation results of the initial power system optimization scheme.

[0106] Preferably, the preset typical scenario constraints include typical scenario power balance constraints and typical scenario spinning reserve constraints.

[0107] The preset extreme scenario constraints include extreme scenario power balance constraints and extreme scenario rotational backup constraints.

[0108] Preferably, the preset investment constraints include mutually exclusive constraints on the transformation paths of aging coal-fired power units, constraints on the power system's power generation adequacy, and constraints on the total investment cost budget.

[0109] According to another aspect of this application, a storage medium is provided, wherein at least one executable instruction is stored therein, the executable instruction causing a processor to perform an operation corresponding to the above-described method for generating power system optimization schemes.

[0110] According to another aspect of this application, a terminal is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0111] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for generating power system optimization schemes.

[0112] By employing the above technical solutions, the technical solutions provided in the embodiments of this application have at least the following advantages:

[0113] This application provides a method, apparatus, medium, and terminal for generating power system optimization schemes. First, it obtains historical wind-solar-load time-series datasets of the target power system's region and performs clustering processing on these datasets to obtain multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets. Second, it constructs a minimum objective function for the total optimized investment cost of the target power system and solves this minimum objective function based on the multiple typical wind-solar-load scenario sets, the multiple extreme wind-solar-load scenario sets, and preset investment constraints to obtain an initial power system optimization scheme. The total optimized investment cost is used to characterize coal-fired power. The total cost model for unit retrofitting, the cost of new unit construction, and the power system operation cost are summed. The diversified transformation of coal-fired power units includes extended service life of coal-fired power units, shutdown of coal-fired power units, conversion of coal-fired power units to emergency standby, and capacity replacement with the construction of clean coal-fired power plants on the original site. Finally, based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, the initial power system optimization scheme is subjected to time-series operation simulation operation to obtain the simulation operation results of the initial power system optimization scheme. Based on the simulation operation results, the initial power system optimization scheme is adjusted to generate the final power system optimization scheme of the target power system. Compared with existing technologies, the embodiments of this application construct a minimum objective function to optimize the total investment cost, solve the minimum objective function based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets to obtain an initial power system optimization scheme, and then conduct simulation operation under typical wind-solar-load scenarios and extreme wind-solar-load scenarios based on the initial power system optimization scheme, and adjust the optimization scheme according to the operation results, so that the optimization scheme can meet the power balance requirements on both the source and load sides of the power system under the condition of large-scale renewable energy feed-in, while minimizing the cost. Furthermore, by introducing decisions such as retirement of aging coal-fired power units, life extension operation, conversion to emergency reserve, and equal capacity replacement of clean coal power into the minimum objective function, the coal-fired power units can be fully utilized, further reducing the cost of the optimization scheme.

[0114] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0115] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0116] Figure 1 A flowchart illustrating a method for generating a power system optimization scheme according to an embodiment of this application is shown;

[0117] Figure 2 A flowchart illustrating another method for generating a power system optimization scheme provided in an embodiment of this application is shown;

[0118] Figure 3 This paper shows a block diagram of a power system optimization scheme generation device according to an embodiment of the present application;

[0119] Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of this application is shown. Detailed Implementation

[0120] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0121] 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.

[0122] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0123] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0124] 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.

[0125] The embodiments of this application can be applied to computer systems / servers that can operate with a wide range of other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations suitable for use with computer systems / 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 that include any of the above systems, etc.

[0126] Computer systems / servers can be described in the general context of computer system executable instructions (such as program modules) executed by the 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 performed by remote processing devices linked through a communication network. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0127] This application provides a method for generating power system optimization schemes, such as... Figure 1 As shown, the method includes:

[0128] 101. Obtain the historical wind, solar and load time series dataset of the target power system region, and perform clustering processing on the historical wind, solar and load time series dataset to obtain multiple typical wind, solar and load scenario sets and multiple extreme wind, solar and load scenario sets.

[0129] Among them, historical wind, solar, and load time-series data are used to characterize the wind power, solar radiation, and electricity load time-series data within a certain period in the past; typical wind, solar, and load scenarios are used to characterize frequently occurring scenarios; extreme wind, solar, and load scenarios are used to characterize rare but dangerous scenarios, such as continuous windless and sunless weather, extremely cold weather, and extremely hot weather. In this embodiment of the application, the current execution terminal can be the planning module of the power system.

[0130] 102. Construct the minimum objective function of the total investment cost of the target power system, and solve the minimum objective function based on multiple typical wind-solar-load scenario sets, multiple extreme wind-solar-load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme.

[0131] Among them, the optimized total investment cost is used to characterize the sum of the total cost model for coal-fired power unit retrofitting, the cost of new units, and the operating cost of the power system. The total cost model for coal-fired power unit retrofitting includes the total cost model for the flexibility retrofitting of in-service coal-fired power units, as well as the decommissioning cost model, life extension operation cost model, emergency standby cost model, and equal capacity replacement clean coal power cost model for coal-fired power units reaching the end of their service life. The initial power system optimization scheme includes, but is not limited to, the selection of the transformation path for each coal-fired power unit reaching the end of its service life, and the type and capacity of new resources.

[0132] In this embodiment, multiple typical wind-solar-load scenario sets are used as probability distributions, and multiple extreme wind-solar-load scenario sets are used as high-risk boundary conditions. Simultaneously, preset investment constraints are applied to solve the minimum objective function in step 101 of the embodiment, making the obtained initial power system optimization scheme more reliable. Furthermore, by incorporating decisions such as decommissioning aging coal-fired power units, extending their service life, switching to emergency reserve, and replacing clean coal power with equivalent capacity into the minimum objective function, aging coal-fired power units are not completely abandoned but can be fully utilized, thereby reducing the cost of the optimization scheme.

[0133] 103. Based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, perform time-series operation simulation on the initial power system optimization scheme to obtain the simulation operation results of the initial power system optimization scheme, and adjust the initial power system optimization scheme based on the simulation operation results to generate the final power system optimization scheme of the target power system.

[0134] In this embodiment, the time-series simulation operation is used to simulate the power system's operation under various typical and extreme scenarios over a future period, based on the hardware determined by the initial power system optimization scheme. This simulation includes parameters such as the hourly output of each power plant and the charging and discharging times of energy storage. Furthermore, the process of adjusting the initial power system optimization scheme based on the simulation results is an iterative feedback optimization process. Specifically, adjustments can be made based on indicators such as wind curtailment rate and reserve capacity. The goal of these adjustments is to minimize daily operating costs while meeting electricity demand and system security, thereby obtaining the final power system optimization scheme.

[0135] Compared with existing technologies, the embodiments of this application construct a minimum objective function to optimize the total investment cost, solve the minimum objective function based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets to obtain an initial power system optimization scheme, and then conduct simulation operation under typical wind-solar-load scenarios and extreme wind-solar-load scenarios based on the initial power system optimization scheme, and adjust the optimization scheme according to the operation results, so that the optimization scheme can meet the power balance requirements on both the source and load sides of the power system under the condition of large-scale renewable energy feed-in, while minimizing the cost. Furthermore, by introducing decisions such as retirement of aging coal-fired power units, life extension operation, conversion to emergency reserve, and equal capacity replacement of clean coal power into the minimum objective function, the coal-fired power units can be fully utilized, further reducing the cost of the optimization scheme.

[0136] This application provides another method for generating power system optimization schemes, such as... Figure 2 As shown, the method includes:

[0137] 201. Determine the preset number of clusters based on the silhouette coefficient.

[0138] Accordingly, step 201 of the embodiment specifically includes: setting a preset range of values ​​for the number of clusters, and randomly selecting multiple values ​​within the range as the random number of clusters; performing deep embedding clustering on multiple historical wind load feature vectors based on each random number of clusters to obtain multiple clustering results, and calculating the silhouette coefficient corresponding to each clustering result; constructing a curve of the silhouette coefficient changing with the number of clusters based on each random number of clusters and the corresponding silhouette coefficient; and selecting the number of clusters corresponding to the maximum silhouette coefficient from the curve as the preset number of clusters.

[0139] The silhouette coefficient, used to evaluate the clustering effect, can be expressed by the following formula.

[0140]

[0141] The value ranges from [-1, 1], with values ​​closer to 1 indicating a more favorable curve. The more reasonable the clustering results, the better; conversely, the less reasonable the clustering results, the better. The cluster separation degree is represented by the curve. The average distance from all curves in other scene types is the value of the curve; the larger the value, the better. The less it belongs to other types of scene concentration; The curve represents the cluster cohesion. The smaller the average distance to other curves in the same scenario, the better the curve. The more likely they are to be clustered into this type of scenario group; The outline coefficient is represented by N; N represents the total number of days in the dataset.

[0142] 202. Obtain the historical wind, solar and load time series dataset of the target power system region, and perform clustering processing on the historical wind, solar and load time series dataset to obtain multiple typical wind, solar and load scenario sets and multiple extreme wind, solar and load scenario sets.

[0143] Accordingly, step 202 of the embodiment specifically includes: obtaining the historical wind, solar, and load time-series dataset of the region to which the target power system belongs; performing low-dimensional feature extraction processing on each historical wind, solar, and load time-series data contained in the historical wind, solar, and load time-series dataset based on the stacked autoencoder that has completed model training, to obtain multiple historical wind, solar, and load feature vectors; performing deep embedding clustering processing on the multiple historical wind, solar, and load feature vectors based on a preset number of clusters, to obtain multiple typical wind, solar, and load feature vector sets; using each typical wind, solar, and load feature vector set as the target typical wind, solar, and load feature vector set; and calculating the relationship between each historical wind, solar, and load feature vector contained in the target typical wind, solar, and load feature vector set and the target typical wind, solar, and load feature vector set. The divergence parameters between the cluster centers of the typical wind and solar load feature vector sets are defined; historical wind and solar load feature vectors with divergence parameters greater than a preset divergence threshold are selected and marked as extreme historical wind and solar load feature vectors; extreme historical wind and solar load feature vectors from each target typical wind and solar load feature vector set are integrated to obtain an extreme historical wind and solar load feature vector set, and multiple extreme wind and solar load feature vector sets are generated based on the extreme historical wind and solar load feature vector set; the cluster centers of the multiple typical wind and solar load feature vector sets are decoded to obtain multiple typical wind and solar load scene sets, and the cluster centers of the multiple extreme wind and solar load feature vector sets are decoded to obtain multiple extreme wind and solar load scene sets.

[0144] In this embodiment of the application, preferably, the historical wind-solar-load time-series dataset can be preprocessed, including but not limited to outlier removal (for outliers that have a large difference compared to the rate of change of the previous time, or are much higher or lower than the statistical range, Gaussian filtering can be used for removal), missing value completion (spline interpolation can be used for completion), and normalization processing (based on formulas). ,in, This represents the normalized data. This represents the data before normalization. This represents the maximum value in the data. (This refers to the minimum value in the data). Further, low-dimensional features of historical wind and solar load time-series data are extracted to obtain historical wind and solar load feature vectors. Specifically, this extraction can be performed using a pre-trained stacked autoencoder, where MSE can be used as the loss function during stacked autoencoder training. Further, deep embedding clustering is performed based on the preset number of clusters determined in step 201 of the embodiment to obtain multiple typical wind and solar load feature vector sets. Further, for each typical wind and solar load feature vector set, the divergence parameter between each vector and the cluster center is calculated, and vectors with divergence parameters exceeding a threshold are marked as extreme historical wind and solar load feature vectors. All extreme historical wind and solar load feature vectors are integrated, and multiple extreme wind and solar load feature vector sets can be obtained again through clustering. Finally, the cluster centers of multiple typical wind and solar load feature vector sets are decoded to obtain multiple typical wind and solar load scene sets, and the cluster centers of multiple extreme wind and solar load feature vector sets are decoded to obtain multiple extreme wind and solar load scene sets.

[0145] 203. Construct the minimum objective function of the total investment cost of the target power system, and solve the minimum objective function based on multiple typical wind-solar-load scenario sets, multiple extreme wind-solar-load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme.

[0146] Accordingly, step 203 of the embodiment, which involves constructing the minimum objective function for optimizing the total investment cost of the target power system, specifically includes: constructing a total cost model for the flexibility retrofitting of in-service coal-fired power units, expressed as the following formula.

[0147]

[0148] in, This represents the total cost of flexibility retrofitting all in-service coal-fired power units. Indicator variable representing the flexibility retrofit of in-service coal-fired power units. Indicates in-service coal-fired power units The cost of flexible modification Indicator variables representing in-service coal-fired power units, This indicates the number of coal-fired power units in operation;

[0149] A decommissioning cost model for coal-fired power units reaching the end of their service life is constructed, expressed by the following formula.

[0150]

[0151] in, This indicates the decommissioning cost of retired units. This indicates the cost of dismantling retired units. This indicates the recovery revenue from decommissioned generator units;

[0152] A cost model for extending the service life of aging coal-fired power units is constructed, expressed by the following formula.

[0153]

[0154]

[0155]

[0156]

[0157] in, This indicates the extended service life cost of the unit. This indicates the cost of extending the service life of the generating unit. This indicates the extended service life cost of the unit. This indicates the operation and maintenance cost of units with extended service life. This indicates the carbon trading cost of units operating with extended service life. Indicates extended service life of the unit The lower limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The upper limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The rate of ascent, Indicates the time scale of the study. Indicates extended service life of the unit downhill / climbing speed;

[0158] A cost model for switching to emergency standby mode for aging coal-fired power units is constructed, expressed as the following formula.

[0159]

[0160]

[0161] in, This indicates the cost of switching the generator unit to emergency standby mode. This indicates the cost of converting the generator unit to emergency standby mode. This indicates the emergency standby operating cost of the unit. This indicates the fuel cost of switching to emergency standby units. This indicates the maintenance cost of the emergency standby unit. This indicates the carbon trading cost of switching to emergency standby units. This indicates the emergency start-up cost of switching to an emergency standby unit;

[0162] A cost model for replacing aging coal-fired power units with clean coal power of equal capacity is constructed, expressed as the following formula.

[0163]

[0164]

[0165] in, This represents the cost of replacing a unit with an equivalent capacity. This indicates the cost of upgrading to replace an existing unit with one of equal capacity. This represents the equivalent capacity replacement operating cost of the unit. This represents the equivalent capacity replacement operating cost of the unit. This indicates the carbon trading cost of replacing units of equal capacity;

[0166] Based on the total cost model for the flexible retrofitting of in-service coal-fired power units, as well as the decommissioning cost model, life-extending operation cost model, emergency standby cost model, and equal-capacity replacement clean coal power cost model for coal-fired power units reaching the end of their service life, a total cost model for the retrofitting of coal-fired power units is constructed, expressed as the following formula.

[0167]

[0168] in, This indicates the total cost of retrofitting coal-fired power units. Indicates coal-fired power units reaching the end of their service life Life extension operation decision variables, Indicates coal-fired power units reaching the end of their service life The variables of retirement decision, Indicates coal-fired power units reaching the end of their service life The decision variables for emergency backup Indicates coal-fired power units reaching the end of their service life Equal-capacity replacement decision variables, coal-fired power units reaching the end of their service life The cost of extending service life and upgrading operations, Indicates coal-fired power units reaching the end of their service life The cost of retirement coal-fired power units reaching the end of their service life The cost of converting to emergency backup coal-fired power units reaching the end of their service life The cost of replacing clean coal power with equivalent capacity;

[0169] Based on the total cost model for coal-fired power unit retrofitting, the cost model for new power units, and the power system operation cost model, an optimized total investment cost model is constructed. Based on this optimized total investment cost model, a minimum objective function for optimizing the total investment cost is constructed, where the minimum objective function is expressed as the following formula.

[0170] ,

[0171] in, This indicates optimizing the total investment cost. This indicates the cost of building a new generating unit. This represents the operating cost of the power system.

[0172] Furthermore, a solver can be used to solve the aforementioned minimum objective function. The preset investment constraints include mutually exclusive constraints on the transition paths of aging coal-fired power units, constraints on the power system's generation adequacy, and constraints on the total investment cost budget. Specifically...

[0173] The mutually exclusive constraint conditions for the conversion paths of aging coal-fired power units can be expressed by the following formula.

[0174] ,

[0175] The power generation adequacy constraint of a power system can be expressed by the following formula:

[0176]

[0177] in, This represents the maximum output limit of all coal-fired power units. (All coal-fired power units mentioned here) This includes existing and newly added coal-fired power units, extended service life units, coal-fired power units undergoing capacity replacement, and pumped storage units. , This represents the credibility coefficient of wind power. This represents the credibility coefficient of photovoltaics. Indicates wind farm The installed capacity, Indicates photovoltaic power station The installed capacity, This indicates the system's power generation resource adequacy requirement (i.e., total reserve factor). This represents the maximum load demand in the set of nodes. It should be noted that the power system generation adequacy constraint is not differentiated by scenario or time.

[0178] 204. Based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, time-series operation simulation operation is performed on the initial power system optimization scheme to obtain the simulation operation results of the initial power system optimization scheme.

[0179] Accordingly, step 204 of the embodiment specifically includes: based on multiple typical wind and solar load scenario sets, performing day-ahead optimization operations using conventional units in the initial power system optimization scheme to determine the simulated conventional unit operation scheme, and performing intraday optimization operations on the unit start-up and shutdown plan based on preset typical scenario constraints to obtain a fine-tuned simulated conventional unit operation scheme; based on multiple extreme wind and solar load scenario sets and preset extreme scenario constraints, performing emergency operations using standby units in the initial power system optimization scheme to determine the simulated standby unit operation scheme; and controlling the conventional units and standby units to perform time-series operation simulation operations according to the fine-tuned simulated conventional unit operation scheme and the simulated standby unit operation scheme to obtain the simulation operation results of the initial power system optimization scheme.

[0180] The preset typical scenario constraints include typical scenario power balance constraints and typical scenario spinning reserve constraints. Specifically...

[0181] The power balance constraint in a typical scenario can be expressed by the following formula.

[0182]

[0183] in, Indicator variables representing the scene, G This refers to the collection of existing conventional coal-fired power units (including coal-fired power units that have undergone flexibility retrofitting). This represents the set of newly added coal-fired power units affected by decision variables. This represents the set of aging coal-fired power units that have undergone extended service life operation or have been replaced with units of equal capacity. This represents a collection of pumped-storage units. Indicates a collection of photovoltaic power plants. Indicates a collection of wind farms. This represents a new type of energy storage system. Indicates load node combination, Represents the set of demand-side response nodes. This refers to the in-service coal-fired power units (including existing and newly added units) in the scenario. time The effort put in This refers to coal-fired power units that have reached the end of their service life and are being replaced with units of equal capacity. In the scene time The effort put in Indicates pumped storage unit In the scene time The power generation capacity of the power plant is as follows: Indicates pumped storage unit In the scene time The pumping power of the water is as follows: Indicates photovoltaic power station In the scene time The power generation capacity of the power plant is as follows: Indicates photovoltaic power station In the scene time The power curtailment, Indicates wind farm In the scene time The power generation capacity of the power plant is as follows: Indicates wind farm In the scene time The power curtailment, Indicating new types of energy storage In the scene time The discharge power at that time, Indicating new types of energy storage In the scene time The charging power at the following levels Indicates load node In the scene time The load size, Indicates load node In the scene time The magnitude of the shear load, Indicates the demand-side response node n In the scene time t The amount of load interruption, Indicates the demand-side response node n In the scene time t Load interruption status variables;

[0184] The typical scenario's rotational backup constraint can be expressed by the following formula.

[0185]

[0186] in, This represents the maximum output limit of all operating coal-fired power units. All coal-fired power units listed here... This includes existing and newly added coal-fired power units, as well as coal-fired power units that have reached the end of their service life and those undergoing equivalent capacity replacement. , Indicates the corresponding coal-fired power unit time The actual output power, Indicating new types of energy storage Rated discharge power, Indicating new types of energy storage At any moment The discharge power at that time, Indicating new types of energy storage At any moment The charging power at the following levels Indicates wind farm time The power curtailment, Indicates photovoltaic power station time The power curtailment, Indicates load node time The load size, The formula represents the spinning reserve factor. This formula indicates that, in the embodiments of this application, in addition to considering traditional coal-fired power units, the power of new energy off-load and energy storage are also included as part of the spinning reserve, expressed as the power of abandoned energy.

[0187] The preset extreme scenario constraints include extreme scenario power balance constraints and extreme scenario spin-off reserve constraints. Specifically,

[0188] The power balance constraint in extreme scenarios can be expressed by the following formula:

[0189]

[0190] in, This indicates the assembly of aging generating units that are being transferred to emergency standby. Indicates emergency standby unit In the scene time The term "output" indicates that the emergency standby unit starts up in extreme scenarios, providing additional regulation capacity to the system. Other formula symbols are consistent with the power balance constraints of typical scenarios.

[0191] The extreme scenario rotational backup constraint can be expressed as the following formula.

[0192]

[0193] in, This indicates the maximum output limit of all operating coal-fired power units. This includes existing and newly added coal-fired power units, as well as coal-fired power units that have reached the end of their service life, are undergoing capacity extension, are being replaced with equivalent capacity units, or are being used as emergency backups. Furthermore, it is noted that new energy storage technologies can consider high-rate discharge in extreme scenarios, therefore Indicating new types of energy storage The rated rate discharge power, and other symbols are consistent with the extreme scenario rotating standby constraint conditions.

[0194] In this embodiment, firstly, under typical wind and solar load scenarios, day-ahead optimization operations are simulated for conventional generating units in the initial power system optimization scheme to formulate simulated conventional generating unit operation schemes, such as start-up and shutdown plans. Further, output is fine-tuned through intraday optimization operations to meet preset typical scenario constraints, such as power leveling and spinning reserve constraints, thereby obtaining a fine-tuned simulated conventional generating unit operation scheme. Further, under extreme wind and solar load scenarios, i.e., when a power deficit is detected, standby generating units in the initial power system optimization scheme are activated to simulate emergency operations to formulate simulated standby generating unit operation schemes, such as the amount of additional output and reserve capacity provided. Finally, conventional generating units are controlled to perform time-series operation simulations according to the fine-tuned simulated conventional generating unit operation scheme, and standby generating units are controlled to perform time-series operation simulations according to the simulated standby generating unit operation scheme, obtaining the simulation operation results of the initial power system optimization scheme.

[0195] 205. Based on the simulation results, adjust the initial power system optimization scheme to generate the final power system optimization scheme for the target power system.

[0196] Specifically, the initial power system optimization scheme can be adjusted based on indicators such as wind curtailment rate and reserve power. Then, the time-series simulation operation can be carried out again according to the adjusted optimization scheme until the preset iteration stop condition is reached to obtain the final power system optimization scheme.

[0197] This application provides a method for generating power system optimization schemes. First, it obtains historical wind-solar-load time-series datasets of the target power system's region and performs clustering processing on these datasets to obtain multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets. Second, it constructs a minimum objective function for the total optimization investment cost of the target power system and solves the minimum objective function based on the multiple typical wind-solar-load scenario sets, the multiple extreme wind-solar-load scenario sets, and preset investment constraints to obtain an initial power system optimization scheme. The total optimization investment cost is used to characterize the retrofitting of coal-fired power units. The total cost model, the cost of new generating units, and the operating cost of the power system are summed. The diversified transformation of coal-fired power units includes extending the service life of coal-fired power units, phasing out and shutting down coal-fired power units, converting coal-fired power units to emergency standby, and replacing capacity with the construction of clean coal-fired power plants on the original site. Finally, based on multiple typical wind and solar load scenarios and multiple extreme wind and solar load scenarios, the initial power system optimization scheme is simulated in a time series to obtain the simulation results of the initial power system optimization scheme. Based on the simulation results, the initial power system optimization scheme is adjusted to generate the final power system optimization scheme for the target power system. Compared with existing technologies, the embodiments of this application construct a minimum objective function to optimize the total investment cost, solve the minimum objective function based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets to obtain an initial power system optimization scheme, and then conduct simulation operation under typical wind-solar-load scenarios and extreme wind-solar-load scenarios based on the initial power system optimization scheme, and adjust the optimization scheme according to the operation results, so that the optimization scheme can meet the power balance requirements on both the source and load sides of the power system under the condition of large-scale renewable energy feed-in, while minimizing the cost. Furthermore, by introducing decisions such as retirement of aging coal-fired power units, life extension operation, conversion to emergency reserve, and equal capacity replacement of clean coal power into the minimum objective function, the coal-fired power units can be fully utilized, further reducing the cost of the optimization scheme.

[0198] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this application provides a power system optimization scheme generation apparatus, such as... Figure 3 As shown, the device includes:

[0199] Wind-solar-flood scene clustering module 31, initial power system optimization scheme generation module 32, initial power system optimization scheme adjustment module 33;

[0200] The wind-solar-load scene clustering module 31 is used to obtain the historical wind-solar-load time series dataset of the region to which the target power system belongs, and to perform clustering processing on the historical wind-solar-load time series dataset to obtain multiple typical wind-solar-load scene sets and multiple extreme wind-solar-load scene sets.

[0201] The initial power system optimization scheme generation module 32 is used to construct the minimum objective function of the total optimized investment cost of the target power system, and solve the minimum objective function based on multiple typical wind and solar load scenario sets, multiple extreme wind and solar load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme. The total optimized investment cost is used to characterize the sum of the total cost model of coal-fired power unit retrofit, the cost of new units, and the operating cost of the power system. The total cost model of coal-fired power unit retrofit includes the total cost model of flexibility retrofit of in-service coal-fired power units, as well as the decommissioning cost model, life extension operation cost model, emergency standby cost model, and equal capacity replacement clean coal power cost model of coal-fired power units reaching the end of their service life.

[0202] The initial power system optimization scheme adjustment module 33 is used to perform time-series operation simulation on the initial power system optimization scheme based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, obtain the simulation operation results of the initial power system optimization scheme, and adjust the initial power system optimization scheme based on the simulation operation results to generate the final power system optimization scheme of the target power system.

[0203] Preferably, the initial power system optimization scheme generation module is used for:

[0204] A total cost model for the flexibility retrofitting of in-service coal-fired power units is constructed, expressed by the following formula.

[0205]

[0206] in, This represents the total cost of flexibility retrofitting all in-service coal-fired power units. Indicator variable representing the flexibility retrofit of in-service coal-fired power units. Indicates in-service coal-fired power units The cost of flexible modification Indicator variables representing in-service coal-fired power units, This indicates the number of coal-fired power units in operation;

[0207] A decommissioning cost model for coal-fired power units reaching the end of their service life is constructed, expressed by the following formula.

[0208]

[0209] in, This indicates the decommissioning cost of retired units. This indicates the cost of dismantling retired units. This indicates the recovery revenue from decommissioned generator units;

[0210] A cost model for extending the service life of aging coal-fired power units is constructed, expressed by the following formula.

[0211]

[0212]

[0213]

[0214]

[0215] in, This indicates the extended service life cost of the unit. This indicates the cost of extending the service life of the generating unit. This indicates the extended service life cost of the unit. This indicates the operation and maintenance cost of units with extended service life. This indicates the carbon trading cost of units operating with extended service life. Indicates extended service life of the unit The lower limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The upper limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The rate of ascent, Indicates the time scale of the study. Indicates extended service life of the unit downhill / climbing speed;

[0216] A cost model for switching to emergency standby mode for aging coal-fired power units is constructed, expressed as the following formula.

[0217]

[0218]

[0219] in, This indicates the cost of switching the generator unit to emergency standby mode. This indicates the cost of converting the generator unit to emergency standby mode. This indicates the emergency standby operating cost of the unit. This indicates the fuel cost of switching to emergency standby units. This indicates the maintenance cost of the emergency standby unit. This indicates the carbon trading cost of switching to emergency standby units. This indicates the emergency start-up cost of switching to an emergency standby unit;

[0220] A cost model for replacing aging coal-fired power units with clean coal power of equal capacity is constructed, expressed as the following formula.

[0221]

[0222]

[0223] in, This represents the cost of replacing a unit with an equivalent capacity. This indicates the cost of upgrading to replace an existing unit with one of equal capacity. This represents the equivalent capacity replacement operating cost of the unit. This represents the equivalent capacity replacement operating cost of the unit. This indicates the carbon trading cost of replacing units of equal capacity;

[0224] Based on the total cost model for the flexible retrofitting of in-service coal-fired power units, as well as the decommissioning cost model, life-extending operation cost model, emergency standby cost model, and equal-capacity replacement clean coal power cost model for coal-fired power units reaching the end of their service life, a total cost model for the retrofitting of coal-fired power units is constructed, expressed as the following formula.

[0225]

[0226] in, This indicates the total cost of retrofitting coal-fired power units. Indicates coal-fired power units reaching the end of their service life Life extension operation decision variables, Indicates coal-fired power units reaching the end of their service life The variables of retirement decision, Indicates coal-fired power units reaching the end of their service life The decision variables for emergency backup Indicates coal-fired power units reaching the end of their service life Equal-capacity replacement decision variables, coal-fired power units reaching the end of their service life The cost of extending service life and upgrading operations, Indicates coal-fired power units reaching the end of their service life The cost of retirement coal-fired power units reaching the end of their service life The cost of converting to emergency backup coal-fired power units reaching the end of their service life The cost of replacing clean coal power with equivalent capacity;

[0227] Based on the aforementioned total cost model for coal-fired power unit retrofitting, the cost model for new power units, and the power system operation cost model, an optimized total investment cost model is constructed. Furthermore, based on this optimized total investment cost model, a minimum objective function for optimizing the total investment cost is constructed, where the minimum objective function is expressed as the following formula.

[0228] ,

[0229] in, This indicates optimizing the total investment cost. This indicates the cost of building a new generating unit. This represents the operating cost of the power system.

[0230] Preferably, the wind-solar-flood scene clustering module includes:

[0231] The data acquisition unit is used to acquire historical wind, solar and load time-series datasets for the region to which the target power system belongs;

[0232] The low-dimensional feature extraction unit is used to perform low-dimensional feature extraction processing on each historical wind and solar load time series data contained in the historical wind and solar load time series dataset based on the stacked autoencoder that has completed model training, to obtain multiple historical wind and solar load feature vectors.

[0233] A deep embedding clustering unit is used to perform deep embedding clustering on multiple historical wind and solar load feature vectors based on a preset number of clusters to obtain multiple typical wind and solar load feature vector sets; and each of the typical wind and solar load feature vector sets is used as a target typical wind and solar load feature vector set.

[0234] The divergence calculation unit is used to calculate the divergence parameters between each historical wind and solar load feature vector contained in the target typical wind and solar load feature vector set and the cluster center of the target typical wind and solar load feature vector set.

[0235] The labeling unit is used to filter out historical wind-solar load feature vectors with divergence parameters greater than a preset divergence threshold and label them as extreme historical wind-solar load feature vectors.

[0236] An extreme wind and solar load feature vector set generation unit is used to integrate the extreme historical wind and solar load feature vectors in the typical wind and solar load feature vector sets of each target to obtain an extreme historical wind and solar load feature vector set, and generate multiple extreme wind and solar load feature vector sets based on the extreme historical wind and solar load feature vector set.

[0237] The decoding unit is used to decode the cluster centers of multiple typical wind-solar-load feature vector sets to obtain multiple typical wind-solar-load scene sets, and to decode the cluster centers of multiple extreme wind-solar-load feature vector sets to obtain multiple extreme wind-solar-load scene sets.

[0238] Preferably, before the deep embedding clustering unit, the wind-solar-load scene clustering module further includes a clustering quantity determination unit, used for:

[0239] Set the range of values ​​for the preset number of clusters, and randomly select multiple values ​​within the range as the random number of clusters;

[0240] Based on each of the random clustering numbers, deep embedding clustering is performed on multiple historical wind load feature vectors to obtain multiple clustering results, and the silhouette coefficient corresponding to each clustering result is calculated.

[0241] Based on the number of random clusters and the corresponding silhouette coefficients, a curve is constructed showing how the silhouette coefficients change with the number of clusters.

[0242] The number of clusters corresponding to the maximum silhouette coefficient is selected from the curves and used as the preset number of clusters.

[0243] Preferably, the initial power system optimization scheme adjustment module is used for:

[0244] Based on multiple typical wind-solar-load scenario sets, the conventional generating units in the initial power system optimization scheme are used to perform day-ahead optimization operations to determine the simulated conventional generating unit operation scheme. Based on the preset typical scenario constraints, the unit start-up and shutdown plan is optimized intraday to obtain the fine-tuned simulated conventional generating unit operation scheme.

[0245] Based on multiple extreme wind-solar-load scenario sets and preset extreme scenario constraints, emergency operations are performed using the standby units in the initial power system optimization scheme to determine the simulated standby unit operation scheme.

[0246] Based on the fine-tuned simulated conventional unit operation scheme and the simulated standby unit operation scheme, the conventional unit and the standby unit are controlled to perform time-series operation simulation operations to obtain the simulation operation results of the initial power system optimization scheme.

[0247] Preferably, the preset typical scenario constraints include typical scenario power balance constraints and typical scenario spinning reserve constraints.

[0248] The preset extreme scenario constraints include extreme scenario power balance constraints and extreme scenario rotational backup constraints.

[0249] Preferably, the preset investment constraints include mutually exclusive constraints on the transformation paths of aging coal-fired power units, constraints on the power system's power generation adequacy, and constraints on the total investment cost budget.

[0250] This application provides a device for generating power system optimization schemes. First, it acquires a historical wind-solar-load time-series dataset of the region to which the target power system belongs, and performs clustering processing on the historical wind-solar-load time-series dataset to obtain multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets. Second, it constructs a minimum objective function for the total optimized investment cost of the target power system, and solves the minimum objective function based on the multiple typical wind-solar-load scenario sets, the multiple extreme wind-solar-load scenario sets, and preset investment constraints to obtain an initial power system optimization scheme. The total optimized investment cost is used to characterize the retrofitting of coal-fired power units. The total cost model, the cost of new generating units, and the operating cost of the power system are summed. The diversified transformation of coal-fired power units includes extending the service life of coal-fired power units, phasing out and shutting down coal-fired power units, converting coal-fired power units to emergency standby, and replacing capacity with the construction of clean coal-fired power plants on the original site. Finally, based on multiple typical wind and solar load scenarios and multiple extreme wind and solar load scenarios, the initial power system optimization scheme is simulated in a time series to obtain the simulation results of the initial power system optimization scheme. Based on the simulation results, the initial power system optimization scheme is adjusted to generate the final power system optimization scheme for the target power system. Compared with existing technologies, the embodiments of this application construct a minimum objective function to optimize the total investment cost, solve the minimum objective function based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets to obtain an initial power system optimization scheme, and then conduct simulation operation under typical wind-solar-load scenarios and extreme wind-solar-load scenarios based on the initial power system optimization scheme, and adjust the optimization scheme according to the operation results, so that the optimization scheme can meet the power balance requirements on both the source and load sides of the power system under the condition of large-scale renewable energy feed-in, while minimizing the cost. Furthermore, by introducing decisions such as retirement of aging coal-fired power units, life extension operation, conversion to emergency reserve, and equal capacity replacement of clean coal power into the minimum objective function, the coal-fired power units can be fully utilized, further reducing the cost of the optimization scheme.

[0251] According to one embodiment of this application, a storage medium is provided, the storage medium storing at least one executable instruction, which can execute the method for generating power system optimization schemes in any of the above method embodiments.

[0252] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive) and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.

[0253] Figure 4The diagram shows a structural schematic of a terminal according to one embodiment of the present application. The specific embodiments of the present application do not limit the specific implementation of the terminal.

[0254] like Figure 4 As shown, the terminal may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0255] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0256] Communication interface 404 is used to communicate with other network elements such as clients or other servers.

[0257] The processor 402 is used to execute program 410, specifically to execute the relevant steps in the above-described embodiment of the power system optimization scheme generation method.

[0258] Specifically, program 410 may include program code that includes computer operation instructions.

[0259] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The computer device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0260] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0261] Specifically, program 410 can be used to cause processor 402 to perform the following operations:

[0262] The historical wind, solar and load time series dataset of the target power system region is obtained, and the historical wind, solar and load time series dataset is clustered to obtain multiple typical wind, solar and load scenario sets and multiple extreme wind, solar and load scenario sets.

[0263] The minimum objective function of the optimized total investment cost of the target power system is constructed, and the minimum objective function is solved based on multiple typical wind and solar load scenario sets, multiple extreme wind and solar load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme. The optimized total investment cost is used to characterize the sum of the total cost model of coal-fired power unit retrofit, the cost of new units, and the operating cost of the power system. The total cost model of coal-fired power unit retrofit includes the total cost model of flexibility retrofit of in-service coal-fired power units, as well as the decommissioning cost model, life extension operation cost model, emergency standby cost model, and equal capacity replacement clean coal power cost model of coal-fired power units that have reached the end of their service life.

[0264] Based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, a time-series simulation operation is performed on the initial power system optimization scheme to obtain the simulation operation results of the initial power system optimization scheme. Based on the simulation operation results, the initial power system optimization scheme is adjusted to generate the final power system optimization scheme of the target power system.

[0265] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device used to generate the aforementioned power system optimization scheme, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0266] 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.

[0267] The methods and systems of this application 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 this application are not limited to the order specifically described above, unless otherwise specifically stated. Furthermore, in some embodiments, this application may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this application. Thus, this application also covers recording media storing programs for performing the methods according to this application.

[0268] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0269] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method of generating an optimization scheme for an electric power system, characterized by, include: The historical wind, solar and load time series dataset of the target power system region is obtained, and the historical wind, solar and load time series dataset is clustered to obtain multiple typical wind, solar and load scenario sets and multiple extreme wind, solar and load scenario sets. The minimum objective function of the optimized total investment cost of the target power system is constructed, and the minimum objective function is solved based on multiple typical wind and solar load scenario sets, multiple extreme wind and solar load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme. The optimized total investment cost is used to characterize the sum of the total cost model of coal-fired power unit retrofit, the cost of new units, and the operating cost of the power system. The total cost model of coal-fired power unit retrofit includes the total cost model of flexibility retrofit of in-service coal-fired power units, as well as the decommissioning cost model, life extension operation cost model, emergency standby cost model, and equal capacity replacement clean coal power cost model of coal-fired power units that have reached the end of their service life. Based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets, the initial power system optimization scheme is subjected to time-series simulation operation to obtain the simulation operation results of the initial power system optimization scheme. Based on the simulation operation results, the initial power system optimization scheme is adjusted to generate the final power system optimization scheme of the target power system. The initial power system optimization scheme is simulated using time-series operation based on multiple typical wind-solar-load scenario sets and multiple extreme wind-solar-load scenario sets to obtain the simulation operation results of the initial power system optimization scheme, including: Based on multiple typical wind-solar-load scenario sets, the conventional generating units in the initial power system optimization scheme are used to perform day-ahead optimization operations to determine the simulated conventional generating unit operation scheme. Based on the preset typical scenario constraints, the unit start-up and shutdown plan is optimized intraday to obtain the fine-tuned simulated conventional generating unit operation scheme. Based on multiple extreme wind-solar-load scenario sets and preset extreme scenario constraints, emergency operations are performed using the standby units in the initial power system optimization scheme to determine the simulated standby unit operation scheme. Based on the fine-tuned simulated conventional unit operation scheme and the simulated standby unit operation scheme, the conventional unit and the standby unit are controlled to perform time-series operation simulation operations to obtain the simulation operation results of the initial power system optimization scheme.

2. The method of claim 1, wherein, The objective function for minimizing the total investment cost of constructing the target power system includes: A total cost model for the flexibility retrofitting of in-service coal-fired power units is constructed, expressed by the following formula. in, This represents the total cost of flexibility retrofitting all in-service coal-fired power units. Indicator variable representing the flexibility retrofit of in-service coal-fired power units. Indicates in-service coal-fired power units The cost of flexible modification Indicator variables representing in-service coal-fired power units, This indicates the number of coal-fired power units in operation; A decommissioning cost model for coal-fired power units reaching the end of their service life is constructed, expressed by the following formula. wherein, represents the decommissioning cost of the decommissioned unit, represents the dismantling cost of the decommissioned unit, represents the recycling revenue of the decommissioned unit; A cost model for extending the service life of aging coal-fired power units is constructed, expressed by the following formula. in, This indicates the extended service life cost of the unit. This indicates the cost of extending the service life of the generating unit. This indicates the extended service life cost of the unit. This indicates the operation and maintenance cost of units with extended service life. This indicates the carbon trading cost of units operating with extended service life. Indicates extended service life of the unit The lower limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The upper limit of technical output, Indicates extended service life of the unit exist Technology output at all times Indicates extended service life of the unit The rate of ascent, Indicates the time scale of the study. Indicates extended service life of the unit downhill / climbing speed; A cost model for switching to emergency standby mode for aging coal-fired power units is constructed, expressed as the following formula. in, This indicates the cost of switching the generator unit to emergency standby mode. This indicates the cost of converting the generator unit to emergency standby mode. This indicates the emergency standby operating cost of the unit. This indicates the fuel cost of switching to emergency standby units. This indicates the maintenance cost of the emergency standby unit. This indicates the carbon trading cost of switching to emergency standby units. This indicates the emergency start-up cost of switching to an emergency standby unit; A cost model for replacing aging coal-fired power units with clean coal-fired power of equal capacity is constructed, expressed as the following formula. in, This represents the cost of replacing a unit with an equivalent capacity. This indicates the cost of upgrading to replace an existing unit with one of equal capacity. This represents the equivalent capacity replacement operating cost of the unit. This represents the equivalent capacity replacement operating cost of the unit. This indicates the carbon trading cost of replacing units of equal capacity; Based on the total cost model for the flexible retrofitting of in-service coal-fired power units, as well as the decommissioning cost model, life-extending operation cost model, emergency standby cost model, and equal-capacity replacement clean coal power cost model for coal-fired power units reaching the end of their service life, a total cost model for the retrofitting of coal-fired power units is constructed, expressed as the following formula. in, This indicates the total cost of retrofitting coal-fired power units. Indicates coal-fired power units reaching the end of their service life Life extension operation decision variables, Indicates coal-fired power units reaching the end of their service life The variables of retirement decision, Indicates coal-fired power units reaching the end of their service life The decision variables for emergency backup Indicates coal-fired power units reaching the end of their service life Equal-capacity replacement decision variables, coal-fired power units reaching the end of their service life The cost of extending service life and upgrading operations, Indicates coal-fired power units reaching the end of their service life The cost of retirement coal-fired power units reaching the end of their service life The cost of converting to emergency backup coal-fired power units reaching the end of their service life The cost of replacing clean coal power with equivalent capacity; Based on the aforementioned total cost model for coal-fired power unit retrofitting, the cost model for new power units, and the power system operation cost model, an optimized total investment cost model is constructed. Furthermore, based on this optimized total investment cost model, a minimum objective function for optimizing the total investment cost is constructed, where the minimum objective function is expressed as the following formula. ， wherein, represents the total cost of investment optimization, represents the cost of new unit construction, represents the cost of power system operation.

3. The method of claim 1, wherein, The process involves acquiring historical wind, solar, and load time-series datasets for the target power system region, and clustering these datasets to obtain multiple typical wind, solar, and load scenario sets and multiple extreme wind, solar, and load scenario sets, including: Obtain the historical wind-solar-load time-series dataset for the region to which the target power system belongs; Based on the stacked autoencoder that has completed model training, low-dimensional feature extraction processing is performed on each historical wind and solar load time series data contained in the historical wind and solar load time series dataset to obtain multiple historical wind and solar load feature vectors. Based on a preset number of clusters, deep embedding clustering is performed on multiple historical wind and solar load feature vectors to obtain multiple typical wind and solar load feature vector sets. Each of the aforementioned typical wind-solar-load feature vector sets is used as the target typical wind-solar-load feature vector set; Calculate the divergence parameter between each historical wind and solar load feature vector contained in the target typical wind and solar load feature vector set and the cluster center of the target typical wind and solar load feature vector set; Historical wind-solar load feature vectors with divergence parameters greater than a preset divergence threshold are selected and marked as extreme historical wind-solar load feature vectors; By integrating the extreme historical wind and solar load feature vectors from the typical wind and solar load feature vector sets of each target, an extreme historical wind and solar load feature vector set is obtained, and multiple extreme wind and solar load feature vector sets are generated based on the extreme historical wind and solar load feature vector set. The cluster centers of the multiple typical wind-solar-load feature vector sets are decoded to obtain multiple typical wind-solar-load scene sets, and the cluster centers of the multiple extreme wind-solar-load feature vector sets are decoded to obtain multiple extreme wind-solar-load scene sets.

4. The method of claim 3, wherein, Before performing deep embedding clustering on multiple historical wind-solar-load feature vectors based on a preset number of clusters to obtain multiple typical wind-solar-load feature vector sets, the method further includes: Set the range of values ​​for the preset number of clusters, and randomly select multiple values ​​within the range as the random number of clusters; Based on each of the random clustering numbers, deep embedding clustering is performed on multiple historical wind load feature vectors to obtain multiple clustering results, and the silhouette coefficient corresponding to each clustering result is calculated. Based on the number of random clusters and the corresponding silhouette coefficients, a curve is constructed showing how the silhouette coefficients change with the number of clusters. The number of clusters corresponding to the largest silhouette coefficient is selected from the curves and used as the preset number of clusters.

5. The method of claim 1, wherein, The preset typical scenario constraints include typical scenario power balance constraints and typical scenario spinning reserve constraints. The preset extreme scenario constraints include extreme scenario power balance constraints and extreme scenario rotational backup constraints.

6. The method of claim 1, wherein, The preset investment constraints include mutually exclusive constraints on the transformation paths of aging coal-fired power units, constraints on the power system's power generation adequacy, and constraints on the total investment cost budget.

7. A device for generating power system optimization schemes, characterized in that, include: The wind-solar-load scene clustering module is used to obtain the historical wind-solar-load time series dataset of the target power system region, and to perform clustering processing on the historical wind-solar-load time series dataset to obtain multiple typical wind-solar-load scene sets and multiple extreme wind-solar-load scene sets. The initial power system optimization scheme generation module is used to construct the minimum objective function of the total optimized investment cost of the target power system, and solve the minimum objective function based on multiple typical wind and solar load scenario sets, multiple extreme wind and solar load scenario sets, and preset investment constraints to obtain the initial power system optimization scheme. The total optimized investment cost is used to characterize the sum of the total cost model of coal-fired power unit retrofit, the cost of new units, and the operating cost of the power system. The total cost model of coal-fired power unit retrofit includes the total cost model of flexibility retrofit of in-service coal-fired power units, as well as the decommissioning cost model, life extension operation cost model, emergency standby cost model, and equal capacity replacement clean coal power cost model of coal-fired power units reaching the end of their service life. The initial power system optimization scheme adjustment module is used to perform time-series operation simulation operation on the initial power system optimization scheme based on multiple typical wind and solar load scenario sets and multiple extreme wind and solar load scenario sets, obtain the simulation operation results of the initial power system optimization scheme, and adjust the initial power system optimization scheme based on the simulation operation results to generate the final power system optimization scheme of the target power system. The initial power system optimization scheme adjustment module is used to perform day-ahead optimization operations on conventional generating units in the initial power system optimization scheme based on multiple typical wind and solar load scenario sets to determine a simulated conventional generating unit operation scheme, and to perform intraday optimization operations on the unit start-up and shutdown plans based on preset typical scenario constraints to obtain a fine-tuned simulated conventional generating unit operation scheme; based on multiple extreme wind and solar load scenario sets and preset extreme scenario constraints, to perform emergency operations on standby generating units in the initial power system optimization scheme to determine a simulated standby generating unit operation scheme; and to control the conventional generating units and the standby generating units to perform time-series operation simulation operations according to the fine-tuned simulated conventional generating unit operation scheme and the simulated standby generating unit operation scheme to obtain the simulation operation results of the initial power system optimization scheme.

8. A storage medium having stored therein at least one executable instruction, characterized in that, The executable instructions cause the processor to perform the operations corresponding to the power system optimization scheme generation method as described in any one of claims 1-6.

9. A terminal comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is configured to store at least one executable instruction, and the executable instruction is configured to enable the processor to perform operations corresponding to the method for generating an optimization scheme of a power system according to any one of claims 1-6.

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