Power distribution network scheduling method and device based on source load uncertainty, equipment and medium

Abstract

The embodiment of the invention provides a power distribution network scheduling method and device based on source load uncertainty, equipment and a medium. The method comprises the following steps: on the basis of a preset membership function and a fuzzy chance constraint model, according to source side data and load side data, constructing a source-load uncertain joint fuzzy model, the fuzzy chance constraint model being used for representing the uncertainty of wind and light output and load demand of a power distribution network; a target function is used as an optimization target, the uncertain source load joint fuzzy model is converted through a target constraint condition, a target optimization model is constructed, and the target function is constructed with the operation cost minimization and the carbon emission minimization of the power distribution network as targets; and outputting a target scheduling plan according to the target optimization model and the current output data and the current load data of the power distribution network. According to the invention, precision and robustness optimization of low-carbon economic operation of the power distribution network can be realized.

Description

Technical Field

[0001] This application relates to the field of power system dispatching technology, and in particular to a method, apparatus, equipment and medium for dispatching distribution networks based on source-load uncertainty. Background Technology

[0002] Driven by the "dual-carbon" strategic goals, my country is gradually shifting towards a new power system model dominated by new energy sources, moving towards low-carbon and clean energy transformation. Against this backdrop, the distribution network will gradually transform from a traditional passive power system with unidirectional energy flow into an active distribution network system with responsive capabilities, incorporating distributed generation resources, energy storage, and demand response users.

[0003] However, the output of distributed wind and solar power units is significantly intermittent and volatile, and the demand response on the load side also has prediction errors and uncertainties in response rate. These factors will affect the accuracy and robustness of distribution network dispatch.

[0004] For example, sudden changes in the output of wind and solar turbines can easily lead to a surge in the cost of curtailment. Furthermore, the difficulty in accurately predicting the demand response rate on the load side can easily cause load-side forecasting errors. Summary of the Invention

[0005] This application provides a distribution network scheduling method, device, equipment, and medium based on source-load uncertainty. By integrating fuzzy logic and chance constraint theory, a coupled modeling framework for source-load uncertainty is constructed. A collaborative solution mechanism of two-stage multi-objective optimization and improved ant colony algorithm is designed to achieve precise and robust optimization of the low-carbon and economical operation of the distribution network.

[0006] In a first aspect, embodiments of this application provide a distribution network scheduling method based on source-load uncertainty. The method includes: constructing a joint fuzzy model of source-load uncertainty based on a preset membership function and a fuzzy chance constraint model, using source-side data and load-side data, wherein the fuzzy chance constraint model is used to characterize the uncertainty of wind and solar power output and load demand in the distribution network; transforming the joint fuzzy model of source-load uncertainty using an objective function as the optimization objective, through objective constraints, to construct a target optimization model, wherein the objective function is constructed with the goal of minimizing the operating cost and carbon emissions of the distribution network; and outputting a target scheduling plan based on the target optimization model, the current power output data of the distribution network, and the current load data.

[0007] In one possible implementation, the aforementioned source-side data includes historical power output data and historical power output forecast data, while the load-side data includes historical load data, historical electricity price data, and historical demand response data. The aforementioned construction of a source-load uncertain joint fuzzy model based on a preset membership function and a fuzzy chance constraint model, according to the source-side data and load-side data, includes: constructing a wind and solar power output fuzzy model based on historical power output data and historical power output forecast data using a preset membership function; constructing a load fuzzy model based on historical load data, historical electricity price data, and historical demand response data using a preset membership function; and fusing the wind and solar power output fuzzy model and the load fuzzy model based on the fuzzy chance constraint model to obtain the source-load uncertain joint fuzzy model.

[0008] In one possible implementation, the above-mentioned construction of a fuzzy model of wind and solar power output based on historical power output data and historical power output prediction data using a preset membership function includes: using the preset membership function to fuzzify the historical power output data to obtain the fuzzy quantity of the historical power output data; and using the deviation between the current scheduling value and the power output prediction value to adjust the membership parameter corresponding to the fuzzy quantity of the historical power output data to obtain the fuzzy model of wind and solar power output.

[0009] In one possible implementation, the above-mentioned construction of a load fuzzy model based on historical load data, historical electricity price data, and historical demand response data, using a preset membership function, includes:

[0010] Historical load data, historical electricity price data, and historical demand response data are analyzed to construct an elasticity relationship model and an error segmentation strategy. The elasticity relationship model is used to indicate the correlation between load response rate and electricity price change rate. Using a preset membership function, the load response rate in the elasticity relationship model is fuzzified to obtain a fuzzy load response rate variable. Based on the error segmentation strategy, the fuzzy load response rate variable is adjusted to determine the adjusted fuzzy load response rate variable. The current predicted load power change value is obtained and fuzzified to obtain the predicted load response fuzzy value. Based on the adjusted fuzzy load response rate variable and the predicted load response fuzzy value, the actual load response fuzzy value is determined. The predicted load response fuzzy value and the actual load response fuzzy value are superimposed to generate a load fuzzy model.

[0011] In one possible implementation, the above-mentioned fusion of the wind and solar power output fuzzy model and the load fuzzy model based on the fuzzy chance constraint model to obtain the source-load uncertainty joint fuzzy model includes: jointly modeling the wind and solar power output fuzzy model and the load fuzzy model to obtain the source-load uncertainty set; and using the source-load uncertainty set as input based on the fuzzy chance constraint model and performing credibility constraint processing to obtain the source-load uncertainty joint fuzzy model.

[0012] In one possible implementation, the objective function includes a first objective function and a second objective function; the objective constraints include power balance constraints and reserve capacity constraints; using the objective function as the optimization objective, the source-load uncertain joint fuzzy model is transformed through the objective constraints to construct an objective optimization model, including: constructing a first objective function with the goal of minimizing the operating cost of the distribution network; constructing a second objective function with the goals of minimizing the total economic cost and carbon emissions of the distribution network, combined with demand response cost, total carbon emissions, energy storage operating cost, and the first objective function; optimizing the source-load uncertain joint fuzzy model based on the first and second objective functions to output a multi-objective fuzzy optimization model; and transforming the multi-objective fuzzy optimization model through power balance constraints and reserve capacity constraints to obtain the objective optimization model.

[0013] In one possible implementation, the above-mentioned output of the target scheduling plan based on the target optimization model, the current output data of the distribution network, and the current load data includes: using the ant colony algorithm to output the target scheduling plan based on the target optimization model, the current output data of the distribution network, and the current load data.

[0014] Secondly, embodiments of this application provide a distribution network dispatching device based on source-load uncertainty, comprising:

[0015] The acquisition module is used to acquire source-side data and load-side data of the distribution network;

[0016] The module is used to construct a joint fuzzy model of source-load uncertainty based on a preset membership function and a fuzzy chance constraint model, according to source-side data and load-side data. The fuzzy chance constraint model is used to characterize the uncertainty of wind and solar power output and load demand in the distribution network.

[0017] The optimization module is used to transform the source-load uncertain joint fuzzy model with the objective function as the optimization objective and through objective constraints to construct the objective optimization model. The objective function is constructed with the objectives of minimizing the operating cost and carbon emissions of the distribution network.

[0018] The output module is used to output the target scheduling plan based on the target optimization model, the current output data of the distribution network, and the current load data.

[0019] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0020] The memory stores instructions that the computer executes;

[0021] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When executed by a processor, the computer-executable instructions are used to implement the first aspect and / or various possible implementations of the first aspect, or to implement the second aspect and / or various possible implementations of the second aspect.

[0023] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0024] In this embodiment, based on a preset membership function and a fuzzy chance constraint model, a source-load uncertain joint fuzzy model is constructed according to source-side and load-side data. The fuzzy chance constraint model characterizes the uncertainty of wind and solar power output and load demand in the distribution network. Using an objective function as the optimization objective, the source-load uncertain joint fuzzy model is transformed through objective constraints to construct a target optimization model. The objective function is constructed with the goal of minimizing the operating cost and carbon emissions of the distribution network. Based on the target optimization model, the current power output data of the distribution network, and the current load data, a target scheduling plan is output. By employing a fuzzy chance constraint fusion model, combined with triangular / trapezoidal membership functions, the fuzzy characteristics such as the interval error of the load's low-carbon response and the residual prediction error of wind and solar power output are accurately captured. Source-load coupled modeling breaks through the limitations of traditional separate analysis and solves the problem that probabilistic models cannot adequately characterize complex uncertainties. Simultaneously, the clear equivalent transformation technique transforms fuzzy constraints into deterministic conditions, providing a solvable basis for subsequent optimization and making uncertainty modeling more closely aligned with the actual operating scenarios of the distribution network. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0026] Figure 1 A flowchart illustrating a distribution network scheduling method based on source-load uncertainty provided in this application embodiment;

[0027] Figure 2 A schematic diagram of a load response error rate provided in an embodiment of this application;

[0028] Figure 3 A flowchart illustrating another distribution network scheduling method based on source-load uncertainty provided in this application embodiment;

[0029] Figure 4A schematic diagram of the structure of a distribution network dispatching device based on source-load uncertainty is provided for an embodiment of this application;

[0030] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application as detailed in the claims.

[0032] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0033] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0034] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0035] The term "module" refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0036] Driven by the "dual-carbon" strategic goals, my country is gradually shifting towards a new power system model dominated by new energy sources, moving towards low-carbon and clean energy transformation. Against this backdrop, the distribution network will gradually transform from a traditional passive power system with unidirectional energy flow into an active distribution network system with responsive capabilities, incorporating distributed generation resources, energy storage, and demand response users.

[0037] However, the output of distributed wind and solar power units is significantly intermittent and volatile, and the demand response on the load side also has prediction errors and uncertainties in response rate. These factors will affect the accuracy and robustness of distribution network dispatch.

[0038] For example, sudden changes in the output of wind and solar turbines can easily lead to a surge in the cost of curtailment. Furthermore, the difficulty in accurately predicting the demand response rate on the load side can easily cause load-side forecasting errors.

[0039] In view of this, this application provides a distribution network scheduling method based on source-load uncertainty. Based on a preset membership function and a fuzzy chance constraint model, a joint fuzzy model of source-load uncertainty is constructed according to source-side and load-side data. The fuzzy chance constraint model characterizes the uncertainty of wind and solar power output and load demand in the distribution network. Using an objective function as the optimization objective, the joint fuzzy model of source-load uncertainty is transformed through objective constraints to construct a target optimization model. The objective function is constructed with the goal of minimizing the operating cost and carbon emissions of the distribution network. Based on the target optimization model, the current power output data and current load data of the distribution network, a target scheduling plan is output. By employing a fuzzy chance constraint fusion model combined with triangular / trapezoidal membership functions, the fuzzy characteristics such as the interval error of low-carbon load response and the residual prediction error of wind and solar power output are accurately captured. Source-load coupled modeling breaks through the limitations of traditional separate analysis and solves the problem that probabilistic models cannot adequately characterize complex uncertainties. Simultaneously, a clear equivalent transformation technique is used to transform fuzzy constraints into deterministic conditions, providing a solvable basis for subsequent optimization and making uncertainty modeling more consistent with the actual operation scenario of the distribution network.

[0040] This application applies to new power system distribution network scenarios with a high proportion of distributed energy resources (such as wind power and photovoltaics) and flexible loads (such as demand response loads). In this scenario, the distribution network needs to coordinate complex factors such as the volatility of wind and solar power output and the uncertainty of load response errors, while meeting power balance and reserve capacity constraints, to achieve synergistic optimization of minimizing operating costs and carbon emissions. A typical network architecture includes distributed generation (DG), energy storage systems (ESS), flexible loads (such as adjustable load clusters), main grid power purchase interfaces, and a monitoring center, which realizes dynamic interaction and optimized scheduling of source and load data through real-time data acquisition and communication networks.

[0041] The following section, in conjunction with the accompanying drawings and application scenarios, provides a detailed description of the distribution network scheduling method based on source-load uncertainty provided in the embodiments of this application.

[0042] Figure 1 This is a flowchart illustrating a distribution network scheduling method based on source-load uncertainty, provided as an embodiment of this application. Figure 1 As shown, the distribution network dispatching method based on source-load uncertainty may include the following steps:

[0043] S101, acquire source-side data and load-side data of the distribution network.

[0044] Source-side data refers to relevant data from the power generation side of the power grid. Source-side data includes historical power output data and historical power output forecast data for wind and solar turbines. Historical power output data, such as historical power output curves for wind and solar power, reflects actual operational fluctuations and can be used to determine the actual output power of wind and solar power. Historical power output forecast data can be, for example, short-term wind and solar power output forecast curves used to assess forecast errors. Source-side data may also include equipment parameters (installed capacity, ramp rate), electricity price data, and carbon emission factor data.

[0045] Load-side data refers to relevant data on the load side of a distribution network. Load-side data can include historical load data, historical electricity price data, and historical demand response data. Historical load data can be time-series load power data, such as daily load curves, reflecting electricity consumption patterns. Historical electricity price data can be historical time-of-use pricing or real-time pricing data. Historical demand response data can include historical demand response events, predicted load shedding, and actual load reduction.

[0046] In some embodiments, power grid data may also be acquired, including network topology, line parameters (resistance, reactance), transformer capacity and loss characteristics, and energy storage device parameters (capacity, efficiency, charge and discharge rate).

[0047] In some examples, source-side and load-side data are obtained from SCADA systems, historical databases, or forecasting platforms. Outlier removal and normalization are then performed on the source-side and load-side data. For instance, statistical patterns are fitted to wind and solar power output data, and peak-valley patterns are identified in load data.

[0048] S102. Based on the preset membership function and fuzzy chance constraint model, and based on source-side data and load-side data, a joint fuzzy model of source-load uncertainty is constructed. The fuzzy chance constraint model is used to characterize the uncertainty of wind and solar power output and load demand in the distribution network.

[0049] Preset membership functions are used to quantify the uncertainty of fuzzy variables. Common forms include triangular membership functions and trapezoidal membership functions. The triangular membership function defines the fuzzy interval using the minimum, most likely, and maximum values, and is suitable for symmetrical uncertainties (such as wind and solar power output errors). The trapezoidal membership function extends the triangular function to cover asymmetrical uncertainties (such as sudden load increases).

[0050] Fuzzy chance constraint models are optimization frameworks that allow constraints to hold with a certain probability (confidence level), handling uncertainty. Fuzzy chance constraint models include a credibility measure (Cr) and a confidence level. The credibility measure combines probability and necessity measures to assess the likelihood of a fuzzy event holding true. The confidence level is a preset threshold, such as α representing the minimum probability of the constraint holding true, and β representing the minimum probability of the goal being achieved.

[0051] Joint fuzzy model of source-load uncertainty: This model integrates the uncertainties of wind and solar power output (source side) and load demand response (load side) into a unified fuzzy set, represented as follows: (Where Ṗ represents the fuzzy quantity).

[0052] In some embodiments, before constructing the joint fuzzy model for source-load uncertainty, a fuzzy chance-constrained optimization model for source-load uncertainty is first constructed. Specifically, considering the stochastic characteristics of intermittent wind and solar turbine output and load fluctuations in the distribution network, a planning method integrating "fuzzy logic + chance constraints" is adopted: decisions are allowed to deviate from the constraints to a certain extent, but the probability of the fuzzy constraints being met is required to be greater than or equal to the pre-set confidence level. This enables optimization modeling under uncertain conditions. Specifically, it includes the following steps:

[0053] (1) Construction of the objective and constraint system of the fuzzy optimization model

[0054] 1) Single-objective fuzzy chance-constrained model

[0055] Define the objective function Fuzzy constraints ( ), build the model:

[0056]

[0057] In the formula: As decision variables, For fuzzy variables, A measure of the credibility of ambiguous events. To constrain confidence levels, Preset a threshold for the target confidence level.

[0058] 2) Multi-objective fuzzy chance constraint model

[0059] When the optimization objectives include carbon emissions, cost, reliability, etc. The model is expanded to include: [Number] dimensions.

[0060]

[0061] In the formula: For the first The confidence level of each objective is determined to achieve a balance constraint of credibility among multiple objectives.

[0062] 3) Fuzzy chance constraint model with priority structure

[0063] Introducing priority parameters Positive / negative deviation weights measure the degree of deviation from the target. , and deviation variables , Model building:

[0064]

[0065] In the formula: For the goal The optimistic baseline value, , The reliability threshold of the deviation is used to achieve hierarchical optimization of multiple objectives by minimizing the deviation.

[0066] (2) Calculation method for the credibility of fuzzy events

[0067] For variables containing fuzzy variables Target / Constraint Calculate the credibility using the following steps. :

[0068] 1) Derivation of Possibility (Pos) and Necessity (Nec)

[0069] possibility:

[0070] Among them, fuzzy variables membership degree The supremum of the product of the indicator function and the event that is true.

[0071] Necessity, derived using duality:

[0072]

[0073] in, It is a probability measure, representing the probability complement of an event being "non-negative".

[0074] 2) Credibility calculate

[0075] Credibility is defined by the mean of probability and necessity:

[0076]

[0077] 3) Solving for the optimistic value of the fuzzy objective

[0078] Generating blurred scenes: uniform sampling ( ),satisfy ( It is the minimum value. (For a sufficient number of samples).

[0079] Constructing the credibility function:

[0080]

[0081] in, For the scene The degree of membership. Because Monotonic, find the maximum using the binary search method satisfy That is, the optimistic target value. .

[0082] (3) Construction and parameter calibration of membership functions

[0083] 1) Membership function of a triangle

[0084]

[0085] in, It is the minimum value; The most likely value (i.e., possible value); This is the maximum value. It is set based on the uncertainty boundaries of source load, such as the fluctuation range of wind and solar power output and the peak-valley range of load.

[0086] 2) Trapezoidal membership function

[0087]

[0088] in, It is the minimum value; The threshold for the rise; The threshold for the decrease; The parameters are calibrated for the maximum value, covering uncertain scenarios such as asymmetric and wide-range load surges and extreme wind and solar power output.

[0089] In some examples, using a preset membership function, a joint fuzzy model of source-load uncertainty can be constructed based on source-side and load-side data, which may include the following steps:

[0090] S201, using a preset membership function, constructs a fuzzy model of wind and solar power output based on historical power output data and historical power output prediction data.

[0091] In some examples, triangular membership functions are used to represent the fuzzy values ​​of the actual output power of wind power and photovoltaic power, with parameters calibrated based on historical prediction errors. The membership parameters are adjusted piecewise according to the deviation between the scheduled and predicted values ​​to improve model adaptability.

[0092] In some examples, using a preset membership function, a fuzzy model of wind and solar power output is constructed based on historical power output data and historical power output prediction data. This may include the following steps:

[0093] S301. Using a preset membership function, the historical output data is fuzzified (i.e., fuzzy representation) to obtain the fuzzy quantity of the historical output data (i.e., the membership parameter is constructed based on the historical output data).

[0094] Historical power output data includes wind power output data, such as the actual output power of wind power generation. Historical power output data also includes photovoltaic (PV) power output data, such as the actual output power of PV power generation.

[0095] For example, by using a preset membership function, the actual output power of wind power generation is fuzzy-represented to obtain the fuzzy quantity of the actual output power of wind power generation (i.e., based on the actual output power of wind power generation, the membership parameter of the actual output power of wind power generation is constructed).

[0096] The preset membership function can be a triangular membership function.

[0097] For example, the membership function of a triangle is:

[0098]

[0099] in, It is the minimum value; The most likely value (i.e., possible value); This is the maximum value.

[0100] For example, the actual output power of wind power generation is fuzzy-represented to obtain the fuzzy quantity of the actual output power of wind power generation, which is the parameterized expression of the triangular membership function of the actual output power of wind power generation. The fuzzy quantity of the actual output power of wind power generation is expressed as:

[0101] ,

[0102] Its triangle membership parameter satisfies the following formula:

[0103]

[0104] in, This is the lower limit of output, that is, the lower limit of the actual output power of wind power generation. This is the predicted output value corresponding to the lower limit of output. ; The most likely output power, that is, the most likely value of the actual output power of wind power generation. The predicted output value corresponds to the most likely output. ; This refers to the upper limit of output power, that is, the upper limit of the actual output power of wind power generation. This is the predicted output value corresponding to the upper limit of output. .

[0105] For example, the actual output power of photovoltaic power generation is fuzzy-represented to obtain the fuzzy quantity of the actual output power of photovoltaic power generation, which is the parameterized expression of the triangular membership function of the actual output power of photovoltaic power generation (i.e., constructing the membership parameters of the actual output power of photovoltaic power generation based on the actual output power of photovoltaic power generation). The fuzzy quantity of the actual output power of photovoltaic power generation is expressed as:

[0106] ,

[0107] Its triangle membership parameter satisfies the following formula:

[0108]

[0109] in, This is the lower limit of output power, that is, the lower limit of the actual output power of photovoltaic power generation. This is the predicted output value corresponding to the lower limit of output. ; The most likely output power, that is, the most likely value of the actual output power of photovoltaic power generation. The predicted output value corresponds to the most likely output. ; This refers to the upper limit of output power, that is, the upper limit of the actual output power of photovoltaic power generation. This is the predicted output value corresponding to the upper limit of output. .

[0110] It should be noted that, , , , , , The membership coefficient is determined by fitting historical output data and can reflect the statistical regularity of the prediction error of the force data.

[0111] S302, by utilizing the deviation between the current scheduling value and the predicted output value, the membership parameter corresponding to the fuzzy quantity of historical output data is adjusted to obtain the fuzzy model of wind and solar power output.

[0112] Among them, the source-side data of the distribution network can include historical power output data of wind and solar turbines, historical power output prediction data, and dispatch data (such as dispatch power), and there is a corresponding relationship among the three.

[0113] In some examples, taking photovoltaic power generation as an example, as shown in S401, the scheduling value during time period t is... The triangular membership parameter of the predicted output value is The triangular membership parameter of the scheduling value is .

[0114] The adjustment rule for the membership parameter is a segmented adjustment rule, as follows:

[0115] like (i.e., the dispatch value ≤ the lower limit of the predicted output), then the original prediction parameters (i.e., the triangular membership parameters of the predicted output value) are maintained. ;

[0116] like (i.e., the scheduled value is within the prediction interval), then , , ;

[0117] like (i.e., the scheduled value ≥ the predicted median), then , , ;

[0118] like (i.e., the scheduled value > the predicted output limit), then the membership coefficient of the excess part is set to 0, that is... .

[0119] The adjustment of the membership parameter of the dispatch value for wind power generation is similar to that for photovoltaic power generation, and will not be elaborated here.

[0120] It should be noted that the fuzzy model of wind and solar power output is obtained by adjusting the membership parameter.

[0121] By adjusting in segments, the prediction error and scheduling decision are dynamically coupled, thereby improving the model's adaptability to actual working conditions.

[0122] S202, using a preset membership function, constructs a load fuzzy model based on historical load data, historical electricity price data, and historical demand response data.

[0123] In some examples, using a preset membership function, a load fuzzy model can be constructed based on historical load data, historical electricity price data, and historical demand response data. This can include the following steps:

[0124] S401 analyzes historical load data, historical electricity price data, and historical demand response data to construct a resilience relationship model and an error segmentation strategy (i.e., error segmentation rules).

[0125] In other words, we analyze the correlation characteristics and error evolution law of electricity price response rate of load low-carbon demand response, and determine the elasticity relationship model and error segmentation strategy.

[0126] Load-side data can include historical load data, historical electricity price data, and historical demand response data. Historical load data can be time-series load power data. Historical electricity price data can be historical time-of-use electricity price data or real-time electricity price data. Historical demand response data can include historical demand response events, predicted load shedding, and actual load reduction.

[0127] The elasticity relationship model is used to represent the elastic correlation between load response rate and electricity price change rate.

[0128] An exemplary elastic correlation model between load response rate and electricity price change rate can be represented as follows:

[0129]

[0130] in, Indicates the load response rate. Indicates the rate of change in electricity prices. This represents the elasticity coefficient, also known as the self-elasticity coefficient, which is used to characterize the flexibility of a load.

[0131] Specifically, when constructing the elasticity relationship model, regression analysis is performed on historical load data and historical electricity price data to obtain elasticity coefficients. Based on these elasticity coefficients, an elasticity correlation model between load response rate and electricity price change rate is constructed.

[0132] Error segmentation strategy is used to represent the maximum error level of the predicted load response rate under different electricity price change rates, that is, to represent the maximum error level of the predicted load response rate (i.e., the predicted response rate). The magnitude of the rate of change of electricity price (i.e.) The changing pattern of ).

[0133] For example, the error segmentation strategy is as follows:

[0134] when hour, ;

[0135] when hour, ;

[0136] in, This indicates the actual rate of change in electricity prices; This indicates the maximum error level of the predicted load response rate (i.e., the predicted response rate); This represents the slope of the error before the inflection point, which varies with the magnitude of the load response rate (i.e., Linear growth; This indicates the error slope after the inflection point, after which the rate of increase in the error slope slows down and then begins to decay. The threshold value representing the inflection point of the rate of change in electricity prices; This represents the inflection point threshold and inflection point limit of the rate of change in electricity prices, where, The response rate at the inflection point enables a piecewise nonlinear characterization of the error model.

[0137] Specifically, when constructing the error segmentation strategy (i.e., error segmentation rules), error analysis is performed on historical demand response data to determine the error slope parameter. This parameter is a statistical characteristic parameter used to characterize the changing pattern of prediction errors. The error slope parameter includes the error slope before the inflection point and the error slope after the inflection point. Historical electricity price data is analyzed to determine the inflection point threshold and limit value of the electricity price change rate. Based on the external parameters (i.e., the error slope before the inflection point, the error slope after the inflection point, and the inflection point threshold and limit value of the electricity price change rate), the error segmentation strategy is constructed.

[0138] S402, using a preset membership function, fuzzy characterizes the load response rate in the elastic relationship model to obtain the fuzzy load response rate variable.

[0139] The preset membership function can be a triangular membership function.

[0140] For example, the membership function of a triangle is:

[0141]

[0142] in, It is the minimum value; The most likely value (i.e., possible value); This is the maximum value.

[0143] For example, the load response rate in the elasticity relationship model is represented by fuzzy representation, resulting in a fuzzy load response rate variable (i.e., a triangular membership parameter), expressed as follows: The membership parameters of its triangle satisfy the following formula:

[0144]

[0145] in, This is the lower limit of the response rate; The most likely value; This represents the upper limit of the response rate. Used to define the error range.

[0146] S403, based on the error segmentation strategy, adjusts the fuzzy load response rate variable and determines the adjusted fuzzy load response rate variable.

[0147] Specifically, will Substituting the piecewise expression (i.e., the error segmentation strategy) into the above triangle membership parameters, we derive the piecewise model of the fuzzy response rate.

[0148] The piecewise model for the fuzzy response rate is as follows:

[0149] when hour, ;

[0150] when hour,

[0151] in, The response rate at the inflection point enables a piecewise nonlinear characterization of the error model.

[0152] For example, a fuzzy response rate is defined based on the elasticity of electricity prices. The parameters after error segmentation correction are as follows: Figure 2 As shown.

[0153] S404: Obtain the current predicted load power change value (i.e., predicted load response value), and perform fuzzy characterization on the current predicted load power change value to obtain the fuzzy value of the predicted load response.

[0154] The load-side data also includes historical load forecast data. Based on this data, the predicted load power change can be determined. The predicted load power change is then fuzzified to obtain the predicted load response fuzzy value.

[0155] For example, the predicted fuzzy value of the load response can be expressed as: Among them, the triangular membership model is used to characterize the prediction uncertainty. This is the lower limit for predicting load power changes. To predict the most probable value of the load power change, This represents the upper limit for predicting changes in load power.

[0156] S405, determine the actual load response fuzzy value based on the adjusted fuzzy load response rate variable and the predicted load response fuzzy value.

[0157] For example, the fuzzy value of the actual load response is determined by the following formula.

[0158]

[0159] in, This represents the fuzzy value of the actual load response; This represents the adjusted fuzzy load response rate variable; This represents the fuzzy value of the predicted load response; This represents the elasticity coefficient.

[0160] By using fuzzy multiplication, the fuzziness of the response rate and the fuzziness of the predicted value are integrated to characterize the uncertainty of the actual response.

[0161] S406, superimpose the predicted load response fuzzy value and the actual load response fuzzy value to obtain the load fuzzy model (i.e. the post-response load fuzzy model).

[0162] In other words, the predicted load response fuzzy value and the actual load response fuzzy value are superimposed, and the load modulus variable is used.

[0163] For example, the load fuzzy model can be determined by the following formula.

[0164]

[0165] in, This represents a fuzzy load model.

[0166] The final integrated load model fully characterizes the uncertainty of low-carbon demand response on the load side, providing accurate input for subsequent distribution network optimization.

[0167] S203, based on the fuzzy chance constraint model, integrates the fuzzy model of wind and solar power output and the fuzzy model of load to obtain a source-load uncertain joint fuzzy model.

[0168] In some examples, the fuzzy models of wind and solar power output and load are unified by fuzzy chance constraints, such as setting confidence constraints for wind and solar power output and load, to obtain a joint fuzzy model of source-load uncertainty.

[0169] For example, based on a fuzzy chance-constrained model, fusing the fuzzy model of wind and solar power output and the fuzzy model of load to obtain a joint fuzzy model of source-load uncertainty can include the following steps:

[0170] S501 combines the dynamically adjusted historical power output data fuzzy quantity (i.e., wind and solar power output fuzzy model) and the load modulus variable (load fuzzy model) to form a source-load uncertainty set.

[0171] The set of uncertainties in the source load can be expressed as follows:

[0172]

[0173] in, Represents the set of uncertainties in the source load; This represents a load fuzzy model (i.e., a load fuzzy model containing uncertainty in low-carbon demand response). This represents the fuzzy model of wind power generation in the fuzzy model of wind and solar power output; This represents the fuzzy model of photovoltaic power generation in the fuzzy model of wind and solar power output.

[0174] S502, based on the fuzzy chance constraint model, takes the source-load uncertainty set as input and performs credibility constraint processing to obtain a joint fuzzy model of source-load uncertainty (i.e., a unified source-load uncertainty set). The fuzzy chance constraint model can be the constraint condition of the two-stage optimization model of the distribution network, including wind and solar power output constraints and load constraints.

[0175] Specifically: Wind and solar power output constraints:

[0176]

[0177] Load constraints:

[0178]

[0179] in, , , The confidence level for source-load uncertainty reflects the system's tolerance to uncertainty and needs to be set according to the power grid's risk appetite. It should be noted that the confidence level for source-load uncertainty is determined based on the credibility calculation method for fuzzy events described earlier.

[0180] In this embodiment, the interval characteristics of source load uncertainty can be accurately captured, overcoming the shortcomings of probabilistic models in representing complex errors. Through fuzzy coupling modeling, the model's adaptability to actual fluctuations is improved, providing constraints that better fit the input for optimization.

[0181] S103 uses the objective function as the optimization objective and transforms the source-load uncertain joint fuzzy model through objective constraints to construct an objective optimization model. The objective function is constructed with the goal of minimizing the operating cost and carbon emissions of the distribution network.

[0182] In some examples, the objective function includes a first objective function and a second objective function. Using the objective function as the optimization objective, and through objective constraints, transforming the source-load uncertain joint fuzzy model to construct the objective optimization model can include the following steps:

[0183] S601 aims to minimize the operating cost of the distribution network and constructs the first objective function, that is, in the first stage, constructs the economic dispatch model.

[0184] Specifically, S601 may include:

[0185] S6011, based on a source-load uncertain joint fuzzy model, determines the fuzzy values ​​of wind and solar power output, and determines the cost of curtailing wind and solar power based on these fuzzy values. In other words, it determines the fuzzy values ​​of wind and solar power output based on the fuzzy model, and then determines the cost of curtailing wind and solar power based on these fuzzy values.

[0186] For example, the fuzzy values ​​of wind and solar power output are calculated using fuzzy multiplication. These fuzzy values ​​are obtained by fuzzy summation of wind power and solar curtailment / solar power outputs from the fuzzy wind power output model.

[0187] The blur value of the wind and light output can be expressed as:

[0188]

[0189] in, node The ambiguity of wind abandonment at time t For nodes The amount of light blur at time t.

[0190] S6012 aims to minimize the operating cost of the distribution network (i.e., to minimize the total cost within the distribution network operating cycle), and constructs the first objective function by combining the costs of electricity purchase, network loss, distributed generation (DG), and wind and solar curtailment.

[0191] The first objective function can be expressed as:

[0192]

[0193] in, To determine the cost of electricity purchase, a fuzzy quantity of the power purchased from the main grid is associated with it; The cost of network loss is calculated based on the fuzzy value of network loss. The operating cost of distributed power generation depends on the fuzzy variables of wind and solar turbine output; To avoid the cost of wind and solar power, the output of wind and solar power is blurred.

[0194] It should be noted that due to the uncertainty of the fuzzy quantity, a pessimistic value for the cost of wind and solar power curtailment is introduced. Through credibility measure constraints ( , To establish a confidence level, the cost estimate is made conservative. It should be noted that the confidence measure constraint is determined based on the confidence calculation method for fuzzy events described earlier.

[0195] It should be noted that the fuzzy quantities used in cost calculations The fuzzy model of wind and solar power output mentioned above ensures that the economic assessment covers the risk of fluctuations in wind and solar power output.

[0196] S602 aims to minimize the total economic cost and carbon emissions of the distribution network. It combines demand response cost, total carbon emissions, energy storage operation cost, and the first objective function to construct a second objective function, which is the second stage of the multi-objective collaborative low-carbon scheduling model.

[0197] Specifically, S602 may include:

[0198] S6021, based on a source-load uncertain joint fuzzy model, determines the demand response cost and, based on the power flow of the distribution network, correlates the fuzzy load values ​​of each node to determine the total carbon emissions. In other words, it determines the demand response cost based on a load fuzzy model and, based on the power flow of the distribution network, correlates the fuzzy load values ​​of each node to determine the carbon emission parameters.

[0199] S6022 aims to minimize the total economic cost and carbon emissions of the distribution network (i.e., the dual objectives are minimum total economic cost and minimum carbon emissions), and constructs a second objective function by combining demand response cost, total carbon emissions, and energy storage operation cost.

[0200] The second objective function (multi-objective collaborative low-carbon scheduling model) is expressed as:

[0201]

[0202] in, Cost of responding to demand; For energy storage operating costs; This represents the total carbon emissions.

[0203] Demand response cost: Ambiguity of low-carbon response related to load side It is calculated using fuzzy multiplication. The demand response cost can be expressed as:

[0204]

[0205] Total carbon emissions: Based on power flow in the distribution network, correlated with fuzzy load values ​​at each node. We obtain the total carbon emissions, which can be expressed as:

[0206]

[0207] in, Let be the carbon emission factor at time t.

[0208] S603 provides a clear equivalent transformation of the objective constraints.

[0209] (1) Construct target constraints.

[0210] The objective constraints ensure that the optimization scheme meets the physical operation limitations of the distribution network, and the fuzzy chance constraints handle the uncertainty of source and load, thereby improving the robustness of the scheduling scheme.

[0211] The target constraints include power balance constraints and reserve capacity constraints.

[0212] 1) Power balance constraints

[0213] Based on fuzzy simulation theory, power balance is transformed into a reliability constraint, which is then correlated with wind power ( ), photovoltaic ( ),load( ), energy storage ( ) and network loss ( The fuzzy quantity of ) and the power balance constraint expression are as follows:

[0214]

[0215] in, For the confidence level of power balance, DG is in operation. The main network purchase power fuzzy quantity.

[0216] 2) Reserve capacity constraints

[0217] To ensure power supply reliability under uncertainty, and to constrain reserve capacity to meet source-load fluctuation requirements, the expression is:

[0218]

[0219] in, The confidence level for the backup constraint. The main grid backup power is used to characterize the probability that the backup capacity will cover the source load fluctuations through fuzzy chance constraints.

[0220] It should be noted that the objective constraint is based on the fuzzy chance constraint model constructed above (the objective and constraint system construction of the fuzzy optimization model), and can be called fuzzy chance constraint.

[0221] (2) Perform a clear equivalent transformation of the objective constraints (a clear equivalent transformation of fuzzy opportunity constraints).

[0222] Since fuzzy constraints cannot be solved directly, the clear equivalence method is used to transform fuzzy chance constraints into deterministic constraints, which are then processed in three categories:

[0223] 1) Equivalent conversion of the pessimistic value of abandoning wind and solar power costs

[0224] For pessimistic value constraints, using trapezoidal / triangular membership parameters, the equivalence formula is derived:

[0225]

[0226] in, The load change is piecewise corrected using membership parameters to match the nonlinear characteristics of source load uncertainty. It should be noted that the demand response cost is calculated using the fuzzy response quantity on the load side.

[0227] 2) Equivalent transformation of power balance constraints

[0228] Combined with the direction of load change ( (Positive and negative), deriving a clear equivalence for power balance:

[0229]

[0230] in, , For network loss in different scenarios, , For energy storage power in different scenarios, fuzzy boundaries are distinguished by membership parameters.

[0231] 3) Equivalent transformation of standby capacity constraints

[0232] Similarly, for the spare constraint, a clear equivalent expression is derived:

[0233]

[0234] By confidence level By coupling with the membership parameter, the transformation from fuzzy constraints to deterministic inequalities is achieved.

[0235] It should be noted that the conversion method directly applies the credibility calculation rules mentioned above (i.e., the credibility calculation method for fuzzy events).

[0236] In other words, the objective function and cleared constraints are integrated into a unified two-stage multi-objective deterministic optimization model (including a dual-objective optimization model for economic cost and carbon emissions (i.e., an objective optimization model)), and all...

[0237] In other words, a first objective function is constructed with the goal of minimizing the operating cost of the distribution network; a second objective function is constructed by combining the demand response cost, total carbon emissions, energy storage operating cost, and the first objective function with the goals of minimizing the total economic cost and carbon emissions of the distribution network; based on the first and second objective functions, the source-load uncertain joint fuzzy model is optimized to output a multi-objective fuzzy optimization model; through power balance constraints and reserve capacity constraints, the multi-objective fuzzy optimization model is transformed to obtain the target optimization model.

[0238] S104 outputs the target scheduling plan based on the target optimization model, the current output data of the distribution network, and the current load data.

[0239] In some embodiments, the ant colony algorithm is used to output a target scheduling plan based on the target optimization model, the current output data of the distribution network, and the current load data.

[0240] An improved ant colony optimization algorithm is designed to solve the two-stage multi-objective fuzzy optimization model (i.e., the objective optimization model) of the power distribution network constructed in the preceding steps. Efficient optimization is achieved through dynamic path selection, pheromone management mechanisms, and multi-objective decision-making (entropy weight method), ensuring a balanced solution among multiple objectives such as economic cost and carbon emissions. The solution process includes four core stages: algorithm initialization, iterative search, elite solution set maintenance, and decision output. Each stage is interconnected, ultimately outputting the comprehensive optimal solution. The following is a detailed breakdown of steps 51-56, illustrating the data flow and logical connections between the steps.

[0241] In some examples, the ant colony algorithm is used to output a target scheduling plan based on the target optimization model, the current output data of the distribution network, and the current load data. This may include the following steps:

[0242] (1) Improved design of multi-objective ant colony algorithm

[0243] 1) The dynamic path selection strategy uses a combination of determinism and randomness to dynamically adjust the state transition probability:

[0244] set up The random numbers are uniformly distributed in the range [0,1]. For the preset threshold (∈[0,1]), the path selection rules are as follows:

[0245]

[0246] when When, choose the path with the highest pheromone concentration; when At that time, new paths are explored according to random rules.

[0247] 2) Partial pheromone update rules

[0248] A collaborative mechanism of partial update + global update is adopted:

[0249] Local update: Only update the pheromone of the current best path in the best iteration, as shown in the following formula:

[0250]

[0251]

[0252] in, This is the length of the shortest path in the current loop. The pheromone evaporation coefficient enhances the positive feedback effect.

[0253] b. Global Update: Update all path pheromones after the iteration ends.

[0254] 3) Pheromone interval rules

[0255] Set pheromone concentration range To avoid oversaturation or oversparseness of pheromone concentration, the pheromone interval rule is expressed as follows:

[0256]

[0257] in, This is the length of the longest path in the current loop. , To adjust the constant, the ability to "explore (new paths)" and "utilize (existing paths)" is balanced by dynamically constraining the pheromone concentration, thus adapting to the solution space complexity of uncertain scheduling.

[0258] (2) Multi-objective decision-making mechanism

[0259] For the multi-objective elite solution set output by the improved ant colony algorithm, which includes objectives such as economic cost and carbon emissions, the entropy weight method is used for decision-making, with the following steps:

[0260] 1) Standardization of the target matrix

[0261] Let the objective matrix of the elite solution set be... ( For the target number, (where is the number of non-dominated solutions), standardized using the maxmin range, as shown below:

[0262]

[0263] Normalization yields the standard matrix :

[0264]

[0265] 2) Information entropy calculation

[0266] Calculate the first Information entropy of a target :

[0267]

[0268] Among them, the smaller the entropy value, the higher the decision-making differentiation of the target and the greater its contribution.

[0269] 3) Entropy weight allocation

[0270] Derivation of the target based on information entropy weight :

[0271]

[0272] We can obtain the weights and Positive correlation reflects the degree of influence of the objective on the decision outcome.

[0273] 4) Generation of compromise solutions

[0274] The first in the elite solution set Given a solution, calculate the linearly weighted compromise value:

[0275]

[0276] choose The solution that is the minimum or maximum, depending on the direction of the objective, is taken as the optimal solution for the multi-objective objective.

[0277] (3) Complete solution process

[0278] like Figure 3 As shown, the complete solution process is as follows:

[0279] 1) Perform the first-stage dispatch of the distribution network to obtain the output scheme of distributed units and energy storage, and calculate the carbon potential of each node accordingly.

[0280] 2) Assign an initial position to each ant and calculate its corresponding multi-objective value.

[0281] 3) Define the ant's search strategy in a multi-objective space, including pheromone update rules and current solution selection mechanism.

[0282] 4) The ant updates its position according to the rules defined in step 3), simulating its search process in the solution space.

[0283] 5) Evaluate the updated location of each ant and calculate its total second-stage cost and carbon emissions.

[0284] 6) Determine if the current ant's solution is a non-dominated solution. If so, add it to the elite solution set. If it dominates other solutions in the elite set, remove the dominated solution; if it does not dominate other solutions, proceed to step 7).

[0285] 7) If the algorithm reaches the maximum number of iterations, output the elite solution set. If it does not reach the maximum number of iterations, return to step 3) and continue iterating.

[0286] 8) Standardize all solutions in the elite solution set.

[0287] 9) Calculate the information entropy value of each objective function in the elite solution set.

[0288] 10) Calculate the weights of each objective based on information entropy, and select the comprehensive optimal solution from the elite solution set through a weighted sum method.

[0289] In this embodiment, a fuzzy chance constraint fusion model is employed, combined with triangular / trapezoidal membership functions, to accurately capture fuzzy characteristics such as the interval error of load low-carbon response and the residual prediction error of wind and solar power output. Source-load coupling modeling breaks through the limitations of traditional separate analysis, solving the problem that probabilistic models struggle to represent complex uncertainties. Simultaneously, a clear equivalent transformation technique converts fuzzy constraints into deterministic conditions, providing a solvable basis for subsequent optimization and making uncertainty modeling more closely aligned with the actual operation of the distribution network.

[0290] Furthermore, a two-stage optimization framework for economic feasibility and low-carbon optimization is designed to overcome the shortcomings of fragmented single-stage optimization objectives and achieve synergistic regulation of low-carbon and economic goals. For the multi-objective high-dimensional solution space, the ant colony algorithm is improved by introducing dynamic path selection and refined pheromone update strategies, overcoming the local convergence bottleneck of traditional algorithms. Combined with the objective decision-making mechanism of the entropy weight method, the contribution of objectives is quantified based on information entropy, and weights are derived to replace subjective assignment, solving the decision bias problem of multi-objective Pareto solutions and significantly improving optimization efficiency and solution reliability.

[0291] Furthermore, a complete technology chain is formed, from uncertainty modeling and two-stage optimization to intelligent algorithm solution and objective decision-making, overcoming the limitations of fragmented traditional technologies. Deep coupling between each stage ensures the continuity of the technical solution from theoretical design to actual operation, enabling direct adaptation to power grid load fluctuation scenarios. This provides full-process support for low-carbon operation, from bottom-level modeling to top-level decision-making, enhancing the technology's applicability and adaptability in engineering scenarios.

[0292] This application also provides a distribution network dispatching device based on source-load uncertainty. For example... Figure 4 As shown, the distribution network dispatching device 400 based on source-load uncertainty includes an acquisition module 401, a construction module 402, an optimization module 403, and an output module 404. The acquisition module 401 acquires source-side and load-side data of the distribution network. The construction module 402 constructs a joint fuzzy model of source-load uncertainty based on a preset membership function and a fuzzy chance constraint model, using the source-side and load-side data. The fuzzy chance constraint model characterizes the uncertainty of wind and solar power output and load demand in the distribution network. The optimization module 403 transforms the joint fuzzy model of source-load uncertainty using an objective function as the optimization objective and through objective constraints to construct a target optimization model. The objective function is constructed with the goal of minimizing the operating cost and carbon emissions of the distribution network. The output module 404 outputs a target dispatching plan based on the target optimization model, the current power output data of the distribution network, and the current load data.

[0293] The electronic device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0294] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0295] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0296] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0297] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0298] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0299] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0300] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0301] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0302] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0303] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0304] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0305] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0306] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0307] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0308] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0309] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A power distribution network dispatching method based on source load uncertainty, characterized in that, The method includes: Acquire source-side and load-side data of the distribution network; Based on a preset membership function and a fuzzy chance constraint model, a joint fuzzy model of source-load uncertainty is constructed according to the source-side data and the load-side data. The fuzzy chance constraint model is used to characterize the uncertainty of wind and solar power output and load demand of the distribution network. Using the objective function as the optimization objective, and through objective constraints, the source-load uncertain joint fuzzy model is transformed to construct an objective optimization model, wherein the objective function is constructed with the objectives of minimizing the operating cost and carbon emissions of the distribution network. Based on the target optimization model, the current output data and current load data of the distribution network, the target scheduling plan is output.

2. The method of claim 1, wherein, The source-side data includes historical power output data and historical power output forecast data, and the load-side data includes historical load data, historical electricity price data, and historical demand response data; the construction of a joint fuzzy model of source-load uncertainty based on a preset membership function and a fuzzy chance constraint model, according to the source-side data and the load-side data, includes: Using the preset membership function, a fuzzy model of wind and solar power output is constructed based on the historical power output data and the historical power output prediction data; Using the preset membership function, a load fuzzy model is constructed based on the historical load data, the historical electricity price data, and the historical demand response data; Based on the fuzzy chance constraint model, the wind and solar power output fuzzy model and the load fuzzy model are fused to obtain the source-load uncertain joint fuzzy model.

3. The method of claim 2, wherein, The step of constructing a fuzzy model of wind and solar power output based on the preset membership function, historical power output data, and historical power output prediction data includes: The historical output data is fuzzified using the preset membership function to obtain the fuzzy quantity of the historical output data; By utilizing the deviation between the current scheduling value and the predicted output value, the membership parameter corresponding to the fuzzy amount of the historical output data is adjusted to obtain the fuzzy model of wind and solar power output.

4. The method of claim 2, wherein, The step of constructing a load fuzzy model based on the preset membership function, the historical load data, the historical electricity price data, and the historical demand response data includes: The historical load data, historical electricity price data, and historical demand response data are analyzed to construct an elasticity relationship model and an error segmentation strategy. The elasticity relationship model is used to indicate the correlation between the load response rate and the electricity price change rate. Using the preset membership function, the load response rate in the elastic relationship model is fuzzy-represented to obtain the fuzzy load response rate variable; Based on the error segmentation strategy, the fuzzy load response rate variable is adjusted to determine the adjusted fuzzy load response rate variable. Obtain the current predicted load power change value, and perform fuzzy characterization on the predicted load power change value to obtain the predicted load response fuzzy value; The actual load response fuzzy value is determined based on the adjusted fuzzy load response rate variable and the predicted load response fuzzy value. The predicted load response fuzzy value and the actual load response fuzzy value are superimposed to generate the load fuzzy model.

5. The method of claim 2, wherein, The method involves fusing the wind and solar power output fuzzy model and the load fuzzy model based on the fuzzy chance constraint model to obtain the source-load uncertain joint fuzzy model, including: By jointly modeling the wind and solar power output fuzzy model and the load fuzzy model, a set of source-load uncertainties is obtained; Based on the fuzzy chance constraint model, the source load uncertainty set is taken as input and subjected to credibility constraint processing to obtain the source load uncertainty joint fuzzy model.

6. The method of claim 1, wherein, The objective function includes a first objective function and a second objective function; the objective constraints include power balance constraints and reserve capacity constraints; the process of transforming the source-load uncertain joint fuzzy model to construct an objective optimization model by using the objective function as the optimization objective and through the objective constraints includes: The first objective function is constructed with the goal of minimizing the operating cost of the power distribution network; With the objectives of minimizing the total economic cost and carbon emissions of the power distribution network, a second objective function is constructed by combining demand response cost, total carbon emissions, energy storage operation cost, and the first objective function. Based on the first objective function and the second objective function, the joint fuzzy model with uncertain source and load is optimized to output a multi-objective fuzzy optimization model; The multi-objective fuzzy optimization model is transformed by the power balance constraint and the reserve capacity constraint to obtain the objective optimization model.

7. The method of claim 1, wherein, The step of outputting a target scheduling plan based on the target optimization model, the current output data of the distribution network, and the current load data includes: Using the ant colony algorithm, the target scheduling plan is output based on the target optimization model, the current output data of the distribution network, and the current load data.

8. A distribution network dispatching device based on source-load uncertainty, characterized in that, include: The acquisition module is used to acquire source-side data and load-side data of the distribution network; The construction module is used to construct a source-load uncertainty joint fuzzy model based on a preset membership function and a fuzzy chance constraint model, according to the source-side data and the load-side data. The fuzzy chance constraint model is used to characterize the uncertainty of wind and solar power output and load demand of the distribution network. The optimization module is used to transform the source-load uncertain joint fuzzy model with the objective function as the optimization objective and through objective constraints to construct an objective optimization model, wherein the objective function is constructed with the objectives of minimizing the operating cost and carbon emissions of the distribution network. The output module is used to output the target scheduling plan based on the target optimization model, the current output data and current load data of the distribution network.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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