Power grid form evolution deduction method based on source-load side form evolution key elements

Abstract

The invention discloses a power grid form evolution deduction method based on a source-load side form evolution key element, and the method comprises the steps: determining a parameter affected by a driving factor in a power system, and obtaining historical data corresponding to the parameter; determining evolution scene boundary conditions and evolution parameters based on historical data corresponding to the parameters; according to the evolution scene boundary conditions and the evolution parameters, evolution model parameters are determined, and an evolution deduction model is constructed; and solving the evolution deduction model to obtain a deduction result. According to the deduction method, a multi-dimensional and dynamically adjustable deduction framework is provided, by considering the influence of multi-dimensional driving factors and quantizing the parameter relevance, the characterization capability of power grid evolution complex driving elements can be remarkably improved, and power grid form evolution evaluation under various scenes is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and more particularly to a method for deducing power grid morphology evolution based on key elements of source-load side morphology evolution. Background Art

[0002] In the current evolution of the new power system, the evolution of the source-load side morphology is affected by the combined influence of multi-dimensional driving factors such as policy regulation, economic development and technological iteration, and its action mechanism shows dynamic coupling characteristics.

[0003] However, traditional power grid planning methods mostly use deterministic models or single-factor sensitivity analysis, which makes it difficult to accurately quantify the interaction between policy orientation adjustments, industrial transformation and upgrading, and the penetration of new technologies. This leads to deviations between the evolutionary path deduction results and the actual development trend, making it difficult to accurately predict the medium- and long-term power grid structure.

[0004] At the same time, existing grid evolution models often simplify driving factors into independent variables when addressing multi-source uncertainty, ignoring the inherent correlations between policy transmission effects, economic indicator volatility, and technological maturity. This fragmented modeling approach can lead to insufficient coordination between investment decisions and operational constraints in evolutionary simulations, making it impossible to effectively assess resource substitution effects across time scales and limiting the scope for economic optimization of low-carbon grid transition paths.

[0005] Furthermore, current methods for evaluating the economic feasibility of grid evolution generally lack the ability to systematically analyze the dynamic parameters of driving factors. In particular, when mapping historical data to future scenarios, traditional probabilistic statistical methods struggle to capture nonlinear characteristics such as policy effectiveness decay and technology diffusion curves. This results in an inadequate match between parameter boundaries in evolutionary models and actual development patterns, limiting the effectiveness of multi-scenario simulation results in supporting grid planning decisions. Summary of the Invention

[0006] In view of this, in order to at least partially solve the above technical problems, the present invention provides a grid morphology evolution deduction method based on key elements of source-load side morphology evolution.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] First, this application provides a method for deducing the evolution of power grid morphology based on key elements of the source-load side morphology evolution, the steps of which include:

[0009] Determining parameters in the power system that are affected by driving factors, and obtaining historical data corresponding to the parameters;

[0010] Determining evolution scenario boundary conditions and evolution parameters based on historical data corresponding to the parameters;

[0011] Determine the evolution model parameters and construct the evolution deduction model based on the evolution scenario boundary conditions and evolution parameters;

[0012] Solve the evolutionary deduction model to obtain deduction results.

[0013] Preferably, determining the evolution scenario boundary conditions based on the historical data corresponding to the parameters includes:

[0014] Obtaining a utility factor of each driving factor based on historical data corresponding to the parameters;

[0015] Normalizing the utility factor and determining the probability density function of the driver factor parameter based on the normalized utility factor;

[0016] Determine the boundary conditions of the evolution scenario based on the probability density function.

[0017] Preferably, when solving the evolutionary deduction model, the objective function is:

[0018]

[0019] Where, F n represents the total cost in year n; i B / N bus ,i H / N H ,i S / N S ,i W / N W ,i C / N C ,i G / N G ,i ACinv / n ACinv ,i DCinv / n DCinv ,i Cinv / n Cinv ,i Ginv / n Ginv ,i ESinv / n ESinv is the index / number of nodes, hydropower units, photovoltaic units, wind turbines, coal-fired units, gas-fired units, selected AC lines, selected DC lines, selected coal-fired units, selected gas-fired units, and selected energy storage devices; and are the candidate AC line i in year n affected by the driving factors ACinv 、Candidate DC line i DCinv 、Coal-fired power units to be prepared Cinv 、Selected gas-fired generator set i Ginv and selected energy storage devices i ESinv investment costs; and They are the candidate AC line i in year n. ACinv 、Selected DC line i DCinv 、Coal-fired power units to be prepared Cinv 、Selected gas-fired generator set i Ginv and selected energy storage devices i ESinv A Boolean variable indicating whether to invest in construction; and are the coal-fired power unit i in the nth year affected by the driving factors C Heqi generator set G Unit startup costs; and are the coal-fired power unit i in the nth year affected by the driving factors C Heqi generator set G Unit operating cost; δ Shed and δ Cur is the unit penalty cost of load shedding and renewable energy curtailment; is the node i in year n B The amount of load shedding in the jth period of a typical day m; and is the photovoltaic unit i in year n S and wind turbines W The amount of electricity abandoned in the jth period of a typical day m; is the hydropower unit i in year n H The amount of electricity wasted in a typical day; and It is the coal-fired power unit i in the nth year C Heqi generator set G A Boolean variable representing the startup status in the jth period of a typical day m; and The coal-fired power unit i in year n is C Heqi generator set G The power generation in the jth period of a typical day m; p m,n is the corresponding probability of a typical day m in year n.

[0020] Second, the present application provides a grid morphology evolution deduction system based on key elements of source-load side morphology evolution. The system applies any of the above-described grid morphology evolution deduction methods based on key elements of source-load side morphology evolution, including:

[0021] Data collection module: used to determine the parameters affected by driving factors in the power system and obtain historical data corresponding to the parameters;

[0022] Data processing module: used for determining the evolution scenario boundary conditions and evolution parameters based on the historical data corresponding to the parameters;

[0023] Model construction module: used to determine the evolution model parameters and build the evolution deduction model according to the evolution scenario boundary conditions and evolution parameters;

[0024] Model solving module: used to solve the evolutionary deduction model to obtain deduction results.

[0025] Third, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the method for deducing the evolution of a power grid based on the key elements of the evolution of the source and load sides as described in any one of the above items are implemented.

[0026] Fourth, the present application provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps in the method for deducing the evolution of a power grid based on the key elements of the source-load side evolution as described in any one of the above items are implemented.

[0027] As can be seen from the above technical solutions, the present invention discloses a method for deducing the evolution of power grid morphology based on the key elements of the source-load side morphology evolution. Compared with the existing technology, this application has the following advantages:

[0028] 1. By constructing a deductive model of multi-dimensional driving factors and quantifying parameter correlations, the ability to characterize the complex driving factors of power grid evolution is improved. This breaks through the limitations of traditional single-factor linear modeling and can more comprehensively reflect the dynamic coupling impact of policy guidance, industrial upgrading, and technological penetration on power grid morphology, providing multi-dimensional evolution scenario support for medium- and long-term planning.

[0029] 2. The grid morphology evolution deduction model established in this application realizes the coordinated optimization of the grid evolution path by dynamically coupling the source-load side morphology evolution and the system economic goals, providing a multi-dimensional, dynamically adjustable deduction framework for the planning and optimization of the evolution path of the new power system, and supporting the grid morphology evolution evaluation under various scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0031] Figure 1This is a flow chart of the grid morphology evolution deduction method based on the key elements of the source-load side morphology evolution of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] The following description sets forth many specific details to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Example 1:

[0035] The embodiment of the present invention discloses a method for deducing the evolution of power grid morphology based on the key elements of the source-load side morphology evolution. Figure 1 , the specific steps include:

[0036] S1. Determine parameters in the power system that are affected by driving factors, and obtain historical data corresponding to the parameters;

[0037] S2. Determining evolution scenario boundary conditions and evolution parameters based on historical data corresponding to the parameters;

[0038] S3. Determine the evolution model parameters and construct the evolution deduction model according to the evolution scenario boundary conditions and evolution parameters;

[0039] S4. Solve the evolutionary deduction model to obtain deduction results.

[0040] The deduction model constructed in this embodiment quantifies parameter correlations and improves the ability to characterize the complex driving factors of power grid evolution. It breaks through the limitations of traditional single-factor linear modeling and can more comprehensively reflect the dynamic coupling impact of policy guidance, industrial upgrading and technological penetration on power grid morphology, providing multi-dimensional evolution scenario support for medium- and long-term planning.

[0041] In a specific embodiment, the driving factors in step S1 include policy driving factors, economic driving factors and technology driving factors.

[0042] For example, policy drivers include:

[0043] (1) Quota-type policy factors (such as new energy installed capacity targets and grid construction plans); (2) Subsidy-type policy factors (such as renewable energy subsidies and demand-side management incentives).

[0044] Economic driving factors are manifested as the driving effect of the increase in industrial added value on load scale, electricity consumption and system demand, which is represented by the national / regional gross domestic product.

[0045] Technology drivers include:

[0046] (1) Technological innovation factors (research and development or improvement of innovative technologies for power systems); (2) Technical support factors (promotion and market application of innovative technologies).

[0047] In a specific embodiment, after obtaining historical data corresponding to parameters affected by driving factors, first determining the evolution scenario boundary conditions based on the historical data, the steps include:

[0048] S21. Obtaining a utility factor of each driving factor based on historical data corresponding to the parameters;

[0049] This application aims at the evolution assessment of a specific power system, taking into account the dynamic changes of various driving factors in different stages of system evolution. By collecting historical change data of parameters affected by driving factors, the policy, economic, and technical utility factors of the corresponding period are quantified. That is, this application quantifies the historical impact of driving factors through data collection, which involves measuring and analyzing data such as policy changes, economic output value data, technical data, resource investment and operation and maintenance costs of each link of the power system source, grid, load and storage in the historical period, and then the utility factors of each driving factor.

[0050] In this embodiment, the driver parameter vector is defined as a vector composed of several driver utility factors. The driver utility factor is a quantitative indicator of the effect of a single driver on a specific model parameter in a specified period. The modeling scheme of each typical driver utility factor is expressed as follows:

[0051]

[0052] In the formula, The v-dimensional component of the driving factor parameter vector represents the quota policy utility factor, represents the quota value for the development of a specific goal corresponding to dimension v, Indicates the current development value of the project corresponding to the vth dimension, Indicates the remaining years of the policy target corresponding to the vth dimension from the current time node; The v-dimensional component of the driving factor parameter vector represents the subsidy policy utility factor, represents the unit subsidy amount corresponding to the vth dimension, represents the unit operation and maintenance cost corresponding to the vth dimension, represents the subsidy coverage rate corresponding to the vth dimension; The v-dimensional component economic utility factor representing the driving factor parameter vector, represents the current year's value of the industry added value corresponding to the vth dimension, γ v represents the discount rate corresponding to the vth dimension; N represents the number of years; The v-dimensional component of the driving factor parameter vector represents the technological innovation utility factor, represents the new technical indicator corresponding to the vth dimension, Represents the current technical indicator corresponding to the vth dimension, represents the unit cost of the new technology corresponding to the vth dimension, represents the current technology unit cost corresponding to the vth dimension, It represents the quantitative restriction coefficient of the application of new technology from successful R&D to mature promotion corresponding to the vth dimension; The v-dimensional component of the driving factor parameter vector represents the technology promotion utility factor, λ v C Indicates the current application ratio of the technology corresponding to the vth dimension, Represents the proportion of technology application potential corresponding to the vth dimension.

[0053] S22. normalizing the utility factor, and determining a probability density function of the driving factor parameter based on the normalized utility factor;

[0054] In order to compare and analyze different driving factors on a unified scale, the historical data of utility factors need to be normalized. The normalized data can more clearly show the relative change trend of each factor over time.

[0055] Furthermore, based on the normalized utility factor data, the probability density function of the driving factor parameters is calculated to evaluate the changing process of each driving factor in the future evolution of the system.

[0056] S23. Determine the boundary conditions of the evolution scenario according to the probability density function.

[0057] Through normalization processing and probability density analysis, this application can evaluate the linkage effects of factors such as policy adjustments, economic growth, and technological iteration in a unified dimension, support the flexible setting of parameter thresholds at different development stages, and provide an adjustable deduction framework for dealing with uncertainties in the evolution of new power systems.

[0058] Among them, the parameter thresholds in the development stage include parameter prediction values, maximum offsets, uncertainties, etc.

[0059] The predicted values and maximum offsets for model parameters determine the highest or lowest possible values of a specific parameter during its evolution. Furthermore, the uncertainty setting provides an estimate of the range of future parameter variations. By evaluating different scenarios that may arise in the future through changes in parameter settings, this approach supports relevant decision-making.

[0060] Furthermore, determining the evolution parameter based on the historical data corresponding to the parameter includes:

[0061] Obtaining a utility factor of each driving factor based on historical data corresponding to the parameters;

[0062] The covariance matrix of the evolution parameters is determined according to the utility factor as follows:

[0063]

[0064]

[0065] Where Γ represents the covariance matrix of driving factor parameters; σ ij represents the i-row and j-column element of the covariance matrix of the driving factor parameters; ξ i and ξ j denote a utility factor i and a utility factor j respectively; Cov(·) denotes the covariance function.

[0066] This application constructs a covariance matrix based on historical data and quantifies uncertainty, enhancing the adaptability of evolutionary deduction. Specifically, the calculation of the covariance matrix is to quantify the interrelationships between different driving factors. In the real world, driving factors often do not exist in isolation. The interactions between them may strengthen or weaken the impact of individual factors. For example, economic growth may accelerate technological development, while policy changes may affect economic trends. The covariance matrix can reveal the inherent connections between these interactions and provide a mathematical basis for comprehensively considering the joint impact of various factors in system evolution.

[0067] In a specific embodiment, the evolution model parameters are determined based on the evolution scenario boundary conditions and evolution parameters, and an evolution deduction model is constructed. Considering that the evolution driving factors do not change in isolation, the correlation between the uncertain parameters of the driving factors needs to be incorporated into the modeling process, and an ellipsoid uncertainty set is used to describe the evolution model parameters affected by the driving factors. Therefore, based on the type analysis of the evolution driving factors of the new power system, this application characterizes the evolution driving factors in the form of an uncertainty set and describes the evolution model parameters affected by the driving factors. The calculation formula of the evolution model parameters is:

[0068]

[0069] In the formula, a nis the evolution model parameter in the nth year; a' n is the predicted value of the evolution model parameter in the nth year; ξ n is the parameter vector of driving factors in the nth year; is the maximum offset vector of model parameters in the nth year; is the expected or predicted value vector of the driving factor parameters in the nth year; Γ is the covariance matrix of the evolution parameters; Ω n is the uncertainty in year n.

[0070] In a specific embodiment, when constructing a grid morphology evolution deduction model, it is necessary to comprehensively consider the dynamic coupling relationship between key elements of the source-load side morphology evolution and their economic impact on the long-term evolution of the grid. Therefore, the objective function of the grid morphology evolution deduction model in the nth year in this embodiment is expressed as:

[0071]

[0072] Where, F n represents the total cost in year n; i B / N bus ,i H / N H ,i S / N S ,i W / N W ,i C / N C ,i G / N G ,i ACinv / n ACinv ,i DCinv / n DCinv ,i Cinv / n Cinv ,i Ginv / n Ginv ,i ESinv / n ESinv is the index / number of nodes, hydropower units, photovoltaic units, wind turbines, coal-fired units, gas-fired units, selected AC lines, selected DC lines, selected coal-fired units, selected gas-fired units, and selected energy storage devices; and are the candidate AC line i in year n affected by the driving factors ACinv 、Selected DC line i DCinv 、Coal-fired power units to be prepared Cinv 、Selected gas-fired generator set i Ginv and selected energy storage equipment i ESinv investment costs; and They are the candidate AC line i in year n. ACinv 、Selected DC line iDCinv 、Coal-fired power units to be prepared Cinv 、Selected gas-fired generator set i Ginv and selected energy storage devices i ESinv A Boolean variable indicating whether to invest in construction; and are the coal-fired power unit i in the nth year affected by the driving factors C Heqi generator set G Unit startup costs; and are the coal-fired power unit i in the nth year affected by the driving factors C Heqi generator set G Unit operating cost; δ Shed and δ Cur is the unit penalty cost of load shedding and renewable energy curtailment; is the node i in year n B The amount of load shedding in the jth period of a typical day m; and is the photovoltaic unit i in year n S and wind turbines W The amount of electricity abandoned in the jth period of a typical day m; is the hydropower unit i in year n H The amount of electricity wasted in a typical day; and It is the coal-fired power unit i in the nth year C Heqi generator set G A Boolean variable representing the startup status in the jth period of a typical day m; and The coal-fired power unit i in year n is C Heqi generator set G The power generation in the jth period of a typical day m; p m,n is the corresponding probability of a typical day m in year n.

[0073] Preferably, the constraints of the power grid morphology evolution deduction model include constraints of coal-fired power units, constraints of gas-fired power units, constraints of hydropower units, constraints of new energy power generation units, power balance constraints and transmission constraints.

[0074] For example, the constraints of the coal-fired power units in the system are expressed as:

[0075]

[0076]

[0077] in, and Is a coal-fired power unit C The minimum and maximum output; and It is the coal-fired power unit i in the nth year C Boolean variables for the running state and shutdown state in the jth period of a typical day m; and Is a coal-fired power unit C Minimum startup and shutdown times.

[0078] The constraints of the gas-generator units in the system are expressed as:

[0079]

[0080] in, and Is a gas-generator unit G The minimum and maximum output; and It is the gas generator set of the nth year. G Boolean variables for the running state and shutdown state in the jth period of a typical day m; and Is a gas-generator unit G Minimum startup and shutdown times; is the gas-fired generator i in year n G power generation limit.

[0081] The constraints of the hydropower units in the system are expressed as follows:

[0082]

[0083]

[0084] in, is the hydropower unit i in year n H The power generated in the jth period of a typical day m; It is a hydropower unit H installed capacity; and The hydropower unit i in year n is H The minimum and maximum power generation limits for the month corresponding to the typical day m; is the hydropower unit i in year n H The average power generated during a typical day.

[0085] The constraints of the new energy generator set in the system are expressed as:

[0086]

[0087] in, and They are wind turbine i in year n W and photovoltaic units S The power generated in the jth period of a typical day m; and They are wind turbine i in year n W and photovoltaic units S The maximum generated power in the jth period of a typical day m.

[0088] The power balance and transmission constraints at the system level are expressed as:

[0089]

[0090]

[0091] in, is the node i in year n B Load demand in the jth period of typical day m; i B,C Represents node i B The coal-fired power unit index at the same naming rules as i B,X (X represents the resource type) also applies to node i B other resources of the Department; Represents node i in year n B The voltage phase angle in the jth period of a typical day m; Indicates the nth year and node i B There is an AC line connecting the nodes i' B The voltage phase angle in the jth period of a typical day m; X iB,AC It is line i B,AC reactance; is the candidate AC line i in year n ACinv The power transmitted during the jth period of a typical day m; is the AC line i in year n AC The power transmitted during the jth period of a typical day m; is the AC line i AC The transmission power limit; is the candidate DC line i DCinv The power transmission limit of is the candidate DC line i in year n DCinv The transmission power in the jth period of a typical day m; i DCex is the index of the built DC line; is the built DC line i DCexThe transmission power limit; is the built DC line i in year n DCex The transmission power level during the jth time period of a typical day m.

[0092] The model embeds driving factor parameters into unit investment decisions, operating constraints and other aspects. On the premise of meeting the requirements of new energy absorption and system reliability, it can quantitatively evaluate the comprehensive costs under different evolutionary paths, providing the most economically optimal planning and decision-making basis for the low-carbon transformation of the power grid.

[0093] Example 2:

[0094] The present application provides a power grid morphology evolution deduction system based on key elements of source-load side morphology evolution. The system applies any of the above-described power grid morphology evolution deduction methods based on key elements of source-load side morphology evolution, including:

[0095] Data collection module: used to determine the parameters affected by driving factors in the power system and obtain historical data corresponding to the parameters;

[0096] Data processing module: used for determining the evolution scenario boundary conditions and evolution parameters based on the historical data corresponding to the parameters;

[0097] Model construction module: used to determine the evolution model parameters and build the evolution deduction model according to the evolution scenario boundary conditions and evolution parameters;

[0098] Model solving module: used to solve the evolutionary deduction model to obtain deduction results.

[0099] Example 3:

[0100] The present application provides an electronic device, including: a processor, a memory, a communication bus, and a communication interface.

[0101] in,

[0102] The processor, memory and communication interface communicate with each other through a communication bus.

[0103] Communication interface, used to communicate with other electronic devices or servers.

[0104] The processor is used to execute the program, and specifically can execute the steps of any one of the above-mentioned embodiments of the method for deducing the evolution of the power grid based on the key elements of the source-load side evolution.

[0105] Specifically, the program may include program codes including computer operation instructions.

[0106] The processor 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 the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or different types of processors, such as one or more CPUs and one or more ASICs.

[0107] Memory is used to store programs. The memory may include high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk storage.

[0108] The program can be specifically used to enable the processor to execute to implement any of the steps of the method for deducing the evolution of the power grid morphology based on the key elements of the source-load side morphology evolution described in the embodiment. The specific implementation of each step in the program can refer to the corresponding descriptions in the steps and units executed by any of the above-mentioned methods for deducing the evolution of the power grid morphology based on the key elements of the source-load side morphology evolution, and will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiment.

[0109] Example 4:

[0110] The present application provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the methods of various embodiments of the present application.

[0111] The method according to the embodiment of the present invention described above can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded via a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown here.

[0112] Thus far, specific embodiments of the present invention have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Additionally, the processes depicted in the accompanying drawings do not necessarily require the specific order shown, or sequential order, to achieve the desired results.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0114] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for deducing the evolution of power grid morphology based on key elements of the source-load side morphology evolution, characterized by: Determining parameters in the power system that are affected by driving factors, and obtaining historical data corresponding to the parameters; Determining evolution scenario boundary conditions and evolution parameters based on historical data corresponding to the parameters; Determine the evolution model parameters and construct the evolution deduction model based on the evolution scenario boundary conditions and evolution parameters; Solve the evolutionary deduction model to obtain deduction results.

2. The deduction method according to claim 1, characterized in that: Determining evolution scenario boundary conditions based on historical data corresponding to the parameters includes: Obtaining a utility factor of each driving factor based on historical data corresponding to the parameters; Normalizing the utility factor and determining the probability density function of the driver factor parameter based on the normalized utility factor; Determine the boundary conditions of the evolution scenario based on the probability density function.

3. The deduction method according to claim 2, characterized in that: Driving factors include policy driving factors, economic driving factors and technological driving factors; the utility factor of each driving factor is calculated as follows: In the formula, The v-dimensional component of the driving factor parameter vector represents the quota policy utility factor, represents the quota value for the development of a specific goal corresponding to dimension v, Indicates the current development value of the target corresponding to the vth dimension, Indicates the remaining years of the policy target corresponding to the vth dimension from the current time node; The v-dimensional component of the driving factor parameter vector represents the subsidy policy utility factor, represents the unit subsidy amount corresponding to the vth dimension, represents the unit operation and maintenance cost corresponding to the vth dimension, represents the subsidy coverage rate corresponding to the vth dimension; The v-dimensional component economic utility factor representing the driving factor parameter vector, represents the current year's value of the industry added value corresponding to the vth dimension, γ v represents the discount rate corresponding to the vth dimension; N represents the number of years; The v-dimensional component of the driving factor parameter vector represents the technological innovation utility factor, represents the new technical indicator corresponding to the vth dimension, Represents the current technical indicator corresponding to the vth dimension, represents the unit cost of the new technology corresponding to the vth dimension, represents the current technology unit cost corresponding to the vth dimension, It represents the quantitative restriction coefficient of the application of new technology from successful R&D to mature promotion corresponding to the vth dimension; The v-dimensional component of the driving factor parameter vector represents the technology promotion utility factor, represents the current application ratio of the technology corresponding to the vth dimension, λ v P Represents the proportion of technology application potential corresponding to the vth dimension.

4. The deduction method according to claim 1, characterized in that: Determining the evolution parameter based on historical data corresponding to the parameter includes: Obtaining a utility factor of each driving factor based on historical data corresponding to the parameters; The covariance matrix of the evolution parameters is determined according to the utility factor as follows: Where Γ represents the covariance matrix of driving factor parameters; σ ij represents the i-row and j-column element of the covariance matrix of the driving factor parameters; ξ i and ξ j denote a utility factor i and a utility factor j respectively; Cov(·) denotes the covariance function.

5. The deduction method according to claim 1, characterized in that: The evolution model parameters are determined according to the evolution scenario boundary conditions and evolution parameters. The calculation formula is: In the formula, a n is the evolution model parameter in the nth year; a' n is the predicted value of the evolution model parameter in the nth year; ξ n is the parameter vector of driving factors in the nth year; is the maximum offset vector of model parameters in the nth year; is the expected or predicted value vector of the driving factor parameters in the nth year; Γ is the covariance matrix of the evolution parameters; Ω n is the uncertainty in year n.

6. The deduction method according to claim 1, characterized in that: The objective function for solving the evolutionary deduction model is: Where, F n represents the total cost in year n; i B / N bus ,i H / N H ,i S / N S ,i W / N W ,i C / N C ,i G / N G ,i ACinv / n ACinv ,i DCinv / n DCinv ,i Cinv / n Cinv ,i Ginv / n Ginv ,i ESinv / n ESinv is the index / number of nodes, hydropower units, photovoltaic units, wind turbines, coal-fired units, gas-fired units, selected AC lines, selected DC lines, selected coal-fired units, selected gas-fired units, and selected energy storage devices; and are the candidate AC line i in year n affected by the driving factors ACinv 、Selected DC line i DCinv 、Coal-fired power units to be prepared Cinv 、Selected gas-fired generator set i Ginv and selected energy storage equipment i ESinv investment costs; and They are the candidate AC line i in year n. ACinv 、Selected DC line i DCinv 、Coal-fired power units to be prepared Cinv 、Selected gas-fired generator set i Ginv and selected energy storage equipment i ESinv A Boolean variable indicating whether to invest in construction; and are the coal-fired power unit i in the nth year affected by the driving factors C Heqi generator set G Unit startup costs; and are the coal-fired power unit i in the nth year affected by the driving factors C Heqi generator set G Unit operating cost; δ Shed and δ Cur is the unit penalty cost of load shedding and renewable energy curtailment; is the node i in year n B The amount of load shedding in the jth period of a typical day m; and is the photovoltaic unit i in year n S and wind turbines W The amount of electricity abandoned in the jth period of a typical day m; is the hydropower unit i in year n H The amount of electricity wasted in a typical day; and It is the coal-fired power unit i in the nth year C Heqi generator set G A Boolean variable representing the startup status in the jth period of a typical day m; and The coal-fired power unit i in year n is C Heqi generator set G The power generation in the jth period of typical day m; pm,n is the corresponding probability of typical day m in year n.

7. The deduction method according to claim 1, characterized in that: The constraints of the evolutionary deduction model include constraints of coal-fired power units, constraints of gas-fired power units, constraints of hydropower units, constraints of new energy power units, power balance constraints and transmission constraints.

8. A power grid morphology evolution deduction system based on key elements of source-load side morphology evolution, characterized by: The method for deducing the evolution of a power grid based on key elements of the evolution of the source-load side morphology as described in any one of claims 1 to 7 comprises: Data collection module: used to determine the parameters affected by driving factors in the power system and obtain historical data corresponding to the parameters; Data processing module: used for determining the evolution scenario boundary conditions and evolution parameters based on the historical data corresponding to the parameters; Model construction module: used to determine the evolution model parameters and build the evolution deduction model according to the evolution scenario boundary conditions and evolution parameters; Model solving module: used to solve the evolutionary deduction model to obtain deduction results.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for deducing the evolution of a power grid based on key elements of the evolution of source and load sides are implemented as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that The computer storage medium stores a computer program, which, when executed by a processor, implements the steps in the method for deducing the evolution of a power grid based on key elements of the source-load side evolution as described in any one of claims 1 to 7.

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