Power distribution network resource scheduling method and device, equipment, storage medium and computer program product

Abstract

The invention relates to the technical field of power distribution network optimization scheduling, and discloses a power distribution network resource scheduling method and device, equipment, a storage medium and a computer program product, and the method comprises the steps: obtaining a total cost function and an initial constraint condition of a power system; establishing modeling of the total cost function and the initial constraint condition in a Bernstein function space based on a Bernstein polynomial to obtain a target function and a target constraint condition; processing the target function and the target constraint condition through a preset fuzzy information gap decision theory model, and solving to obtain the total cost and the wind power uncertainty radius; and optimizing a current resource scheduling method of the power distribution network according to the total cost and the wind power uncertainty radius to obtain a target resource scheduling strategy. A continuous time model based on a Bernstein polynomial function is adopted, the slope capacity of a natural gas generator and a power demand response plan can be better captured, sudden wind energy change per hour can be dealt with, and the problem of wind power uncertainty in a power system is solved.

Description

Technical Field

[0001] This application relates to the field of power distribution network optimization and scheduling technology, and in particular to a power distribution network resource scheduling method, device, equipment, storage medium and computer program product. Background Technology

[0002] Currently, cooperation between power and natural gas / hydrogen transmission systems is achieved by solving the unit commitment problem, which involves hourly scheduling of a set of NGFGs (Natural Gas Fired Generators) to meet hourly forecasted loads and cover hourly variations in those loads. However, with the increase in WEG (Wind Energy Generation), the sub-hourly variability of the power system increases, and large sub-hourly boost events occur more frequently, making the unit commitment model inadequate. Furthermore, instantaneous boost / deboost within hourly intervals is impossible, therefore the unit commitment model cannot manage the sub-hourly variations of WEGs and the boost capacity of NGFGs within the power system. This leads to a scarcity of boost resources. Summary of the Invention

[0003] The main objective of this application is to provide a distribution network resource scheduling method, apparatus, equipment, storage medium, and computer program product, aiming to solve the technical problems of lacking effective methods in existing research to manage the rapid sub-hourly changes of WEG, the rapid voltage boosting capability of NGFG and EDRP (Electricity Demand Response Program) in power systems, and the accurate modeling of the uncertainty of wind turbine output in power system operation.

[0004] To achieve the above objectives, this application proposes a distribution network resource scheduling method, which includes:

[0005] Obtain the total cost function and initial constraints of the power system; model the total cost function and initial constraints on the Bernstein function space based on Bernstein polynomials to obtain the objective function and objective constraints; process the objective function and objective constraints using a preset fuzzy information gap decision theory model to solve for the total cost and wind power uncertainty radius; optimize the current resource scheduling method of the distribution network based on the total cost and the wind power uncertainty radius to obtain the target resource scheduling strategy.

[0006] Optionally, before the step of processing the objective function and the objective constraints using a preset fuzzy information gap decision theory model to obtain the total cost and the wind power uncertainty radius, the method further includes:

[0007] Assess the randomness and unpredictability of wind power output, and set uncertainty parameters based on the deviation between the wind power output and its predicted value;

[0008] Membership functions are established using fuzzy logic, and the uncertain parameters are mapped to fuzzy sets to form fuzzy information gaps;

[0009] The fuzzy information gap decision theory model is constructed based on the fuzzy information gap and information gap decision theory model.

[0010] Optionally, the step of processing the objective function and objective constraints using a preset fuzzy information gap decision theory model to obtain the total cost and wind power uncertainty radius includes:

[0011] Based on the fuzzy information gap decision theory model, risk aversion strategy and opportunity seeker strategy are formulated, and the basic costs of the risk aversion strategy and the opportunity seeker strategy are specified.

[0012] Based on the aforementioned basic cost, the objective function is characterized using fuzzy sets to obtain the objective membership function;

[0013] The target membership function is input into the fuzzy information gap decision theory model, and the fuzzy optimization algorithm is used to solve the model to obtain the total cost and the corresponding wind power uncertainty radius under different wind power uncertainty levels.

[0014] Optionally, the initial constraints include grid constraints and natural gas constraints;

[0015] The steps of establishing the model of the total cost function and the initial constraints on the Bernstein function space based on Bernstein polynomials to obtain the objective function and objective constraints include:

[0016] The objective function is generated by using the individual cost components in the Bernstein polynomial approximation of the total cost function.

[0017] The natural gas constraint in the initial constraint conditions is transformed into the Bernstein function space to obtain the target natural gas constraint;

[0018] Based on the grid constraints in the initial constraints, the Bernstein polynomial is used to construct the power load balance, power network constraints, and hydrogen power plant model;

[0019] The objective constraint conditions of the objective function are established based on the target natural gas constraint, the power load balance, the power network constraint, and the hydrogen power plant model.

[0020] Optionally, before the step of generating the objective function using the Bernstein polynomial to approximate each cost component in the total cost function, the method further includes:

[0021] The order of the Bernstein polynomial is selected from the order mapping table according to the determined accuracy threshold;

[0022] The time period is divided into multiple intervals based on the length set of the interval, and the Bernstein polynomial operator is used to approximate the intervals to calculate the binomial coefficients and control points in each Bernstein basis polynomial.

[0023] The Bernstein basis polynomial is generated based on the order, the binomial coefficients, and the control points.

[0024] Optionally, the step of obtaining the total cost function and initial constraints of the power system includes:

[0025] Collect cost information from various parts of the power system operation and determine decision variables related to the cost information based on the generator sets;

[0026] Construct the total cost function of the power system based on the cost information and the decision variables;

[0027] The physical and operational constraints of the power system are obtained, and the constraints of the total cost function are established using a continuous-time model.

[0028] Furthermore, to achieve the above objectives, this application also proposes a power distribution network resource scheduling device, which includes:

[0029] The function acquisition module is used to obtain the total cost function and initial constraints of the power system.

[0030] The model building module is used to model the total cost function and the initial constraints in the function space based on Bernstein polynomials, and to obtain the objective function and objective constraints.

[0031] The cost calculation module is used to process the objective function and the objective constraints through a preset fuzzy information gap decision theory model, and solve for the total cost and the wind power uncertainty radius.

[0032] The scheduling optimization module is used to optimize the current resource scheduling method of the distribution network based on the total cost and the wind power uncertainty radius to obtain the target resource scheduling strategy.

[0033] In addition, to achieve the above objectives, this application also proposes a power distribution network resource scheduling device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the power distribution network resource scheduling method as described above.

[0034] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the power distribution network resource scheduling method described above.

[0035] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the power distribution network resource scheduling method described above.

[0036] This application discloses a method for obtaining the total cost function and initial constraints of a power system; establishing a model of the total cost function and initial constraints on the Bernstein function space based on Bernstein polynomials to obtain the objective function and objective constraints; processing the objective function and objective constraints through a pre-defined fuzzy information gap decision theory model to solve for the total cost and wind power uncertainty radius; and optimizing the current resource scheduling method of the distribution network based on the total cost and wind power uncertainty radius to obtain the target resource scheduling strategy. The continuous-time model based on Bernstein polynomial functions is employed, which can better capture the ramp capacity of natural gas generators and electricity demand response plans, cope with hourly wind energy fluctuations, and solve the wind power uncertainty problem in the power system. Attached Figure Description

[0037] 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.

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating the first embodiment of the power distribution network resource scheduling method of this application;

[0040] Figure 2 This is a flowchart illustrating the second embodiment of the power distribution network resource scheduling method of this application;

[0041] Figure 3This is a flowchart illustrating the third embodiment of the power distribution network resource scheduling method of this application;

[0042] Figure 4 Bernstein coefficient diagram for the distribution network resource scheduling method in this application;

[0043] Figure 5 This is a model diagram of a hydrogen power plant for the power distribution network resource scheduling method of this application;

[0044] Figure 6 This is a schematic diagram of the module structure of the power distribution network resource scheduling device according to an embodiment of this application;

[0045] Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the power distribution network resource scheduling method in this application embodiment.

[0046] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0048] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solution of this application embodiment is as follows: obtaining the total cost function and initial constraints of the power system; establishing a model of the total cost function and the initial constraints on the Bernstein function space based on Bernstein polynomials to obtain the objective function and objective constraints; processing the objective function and the objective constraints through a preset fuzzy information gap decision theory model to solve for the total cost and the wind power uncertainty radius; optimizing the current resource scheduling method of the distribution network based on the total cost and the wind power uncertainty radius to obtain the target resource scheduling strategy.

[0050] Wind power generation is a crucial resource for power system operation, playing a key role in power generation. However, a major challenge for many system operators is operating the power system under conditions of rapid hourly variations and uncertainties in wind turbine generators. To mitigate the uncertainty and rapid variations of wind turbine generators in the power system, generator sets with rapid start-up and voltage boosting capabilities are used to address these challenges. Natural gas (NGFG) and hydrogen (EDRP) generator sets can serve as rapid start-up and voltage boosting units for this purpose. Because NGFG and hydrogen generator sets are essential components for providing flexible ramp capacity in the power system, they play a crucial role in mitigating the rapid variations and uncertainties in wind turbine generator output (WEG). Therefore, the availability of natural gas supply directly impacts the operation of the power system in terms of cost, dispatch, and WEG integration. In this context, demand response planning can mitigate hourly variations and WEG uncertainties by increasing the rapid voltage boosting and bucking capabilities in the power system. However, existing research lacks effective methods for managing sub-hourly rapid variations in WEG, as well as for accurately modeling the rapid voltage boosting capabilities of NGFG and EDRP in the power system and the uncertainty of wind turbine output during power system operation.

[0051] Therefore, this application provides a coordinated optimization method for an electricity-gas-hydrogen joint system considering wind power uncertainties. It employs a CTM (Continuous Time Model) based on Bernstein polynomial functions, which better captures the ramping capabilities of NGFG and EDRP because it more accurately reflects hourly ramp demand to address hourly WEG mutations. Furthermore, applying CTM to the proposed problem can alter the cooperation between the electricity and gas systems, coordinating NGFG and EDRP to better handle real-time WEG and load mutations. A novel IGDT (Information Gap Decision Theory) method based on a fuzzy model, namely the Fuzzy IGDT (F-IGDT) model, is proposed to solve the WEG uncertainty problem in the power system.

[0052] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following uses a power distribution network system as an example to describe this embodiment and the subsequent embodiments.

[0053] Based on this, embodiments of this application provide a distribution network resource scheduling method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the power distribution network resource scheduling method of this application.

[0054] In this embodiment, the power distribution network resource scheduling method includes:

[0055] Step S10: Obtain the total cost function and initial constraints of the power system.

[0056] It should be noted that the total cost function refers to the calculation of the total cost of the power system during the dispatch period, under constraints, including the continuous-time generation cost and start-up cost of the generating units. Constraints are various restrictions on the operating state or parameters of the power system to ensure the safe and stable operation of the system and meet user needs; they often appear in the form of inequalities or equations.

[0057] It should be understood that obtaining the total cost function and initial constraints of the power system can be based on predictions of historical electric power data, or it can be based on the actual situation of the power system, classifying various costs according to their nature and establishing corresponding continuous-time models.

[0058] Of course, in order to accurately collect cost information, comprehensively understand the various costs in power system operation, and more accurately reflect the actual physical and operational constraints of the power system through the constraints established by the continuous-time model, step S10 may include:

[0059] Cost information of each part of the power system operation is collected, and decision variables related to the cost information are determined based on the generator sets; the total cost function of the power system is constructed according to the cost information and the decision variables; the physical and operational constraints of the power system are obtained, and the constraints of the total cost function are established through a continuous-time model.

[0060] It should be noted that decision variables are variables used to represent different decision schemes in optimization problems. In constructing the total cost function of a power system, decision variables may include the output power of generator units, their on / off states, and the charging / discharging states of energy storage devices. A continuous-time model is a mathematical model used to describe the continuous changes of a system over time. In power systems, continuous-time models can be used to describe the dynamic behavior of the power system, such as load fluctuations and changes in the output power of generator units. Through continuous-time models, the physical and operational constraints of the power system can be transformed into constraints for the total cost function.

[0061] In one example, the initial optimization problem is a continuous-time cooperation problem for an electric and natural gas system, namely, minimizing the total cost (TC) of the electric system under constraints during the dispatch period. Here, TC includes: (i) the continuous-time generation and start-up costs of generator units (GUs) (the first and second terms); and (ii) the continuous-time cost of the EDRP (Electric Power Responsibility System). As follows:

[0062]

[0063] The first part concerns the cost of the generator set, c g G is the power generation cost coefficient of generator set g. g t is the amount of electricity generated by generator set g at time t. It is the starting cost coefficient of generator set g, y g t represents the starting state of generator set g at time t (usually a binary variable indicating whether the generator set is starting). The integral represents the total cost of generator set g within the time interval T.

[0064] Part Two concerns the cost of demand response, c n It is the cost coefficient of demand response n. and These represent the positive (increased load) and negative (decreased load) adjustments in the demand response n at time t, respectively. The integral represents the total cost of the demand response over the time interval T.

[0065] The following discussion covers continuous-time formulas for electricity, hydrogen power plants, and natural gas constraints.

[0066] 1. Power grid constraints

[0067] The constraints of the power grid are simulated by the following continuous-time equations, including the current of each transmission line, the maximum transmission line power flow, the ramp constraint for flexible demand, and the continuous-time rise / fall limit for flexible demand.

[0068]

[0069] Among them, G g I gt and G is the continuous time power generation of GU gt Lower limit and upper limit and Indicates the limits of uphill and downhill routes over a continuous period of time.

[0070]

[0071] I gt Let z represent the power generation of generator set g at time t, where UTg and DTg represent the upper and lower limit constraints of generator set g, respectively. gt y represents the state of generator set g at time t. gt -z gt =I gt -I gt-1 This indicates the relationship between the state transitions of a generator set and changes in power generation.

[0072]

[0073] f kt This represents the power flow through line k at time t. (b) nm θ is the admittance of line k, a complex number representing the degree to which the line impedes power flow, including the effects of resistance and reactance. nt and θ mt These are the voltage phase angles of nodes n and m at time t, respectively. It is the rated or maximum value of the power flow of line k.

[0074]

[0075] Among them, D nt This represents the rate of change of power in the demand response n. This represents the initial value of the demand response, ΔΦ. ± This indicates that there is an upper limit to the amount of demand response adjustments. ΔE represents the lower and upper limits of the rate of change of power in the demand response n. n Due to energy limitations.

[0076]

[0077] This represents the sum of the power generated by all generator sets g at node n at time t. This represents the sum of the power generated by all wind turbines w at node n at time t. This represents the sum of power flows through all lines k from node n to node m at time t. D represents the sum of power flows through all lines k from node m to node n at time t. nt This represents the total load demand at time node n (t). This represents the amount of electricity generated by generator set g at the initial time t=0. This represents the load demand of node n at the initial time t=0. and It is a vector with constant initial values.

[0078] 2. Natural Gas Constraints

[0079] The continuous-time equation for the natural gas system is as follows:

[0080]

[0081] in, ρ i , This represents the lower and upper limits of the squared natural gas pressure at the demand node.

[0082]

[0083] in, and These are parameters related to power system components. For pipelines The natural gas flow rate, Θ p It is a parameter that depends on the minimum / maximum constraints imposed on the segmented linear pipe sections by the pipeline type, ρ it ,ρ jt The pipeline represents the square of the natural gas pressure at node ij.

[0084]

[0085] in, f represents the minimum and maximum flow rates of natural gas in the pipeline. pt This represents the total flow rate of natural gas on the pipeline.

[0086]

[0087] Among them, g i , For gas supply amount g it The lower and upper limits, For non-electrical loads, For electrical load, L it For natural gas load.

[0088] The state of charge (SOC) of a natural gas storage unit (NGSU) is controlled by a continuous-time difference equation, as follows:

[0089]

[0090] Among them, E s , The gas storage capacity E of each interruptible gas delivery system NGSU st The lower and upper limits, For natural gas inflow / Outflow The lower and upper limits, Inflow / Outflow The lower and upper limits of the boost path, and the minimum and maximum limits of the path are represented by underlined and overlined constant terms, respectively.

[0091]

[0092] Where p(i,j) is the pipeline node from i to j, and α,β,γ are the adjustment parameters and the amount of natural gas required for each natural gas generator NGFGs. gtDepends on its continuous time generation dispatch The natural gas flow rate at the injecting node is equal to the natural gas flow rate at the outflowing node. f is the constant initial value for each decision variable. ij ,f ji This represents the power flow from node i to node j and from node j to node i at time t. it L represents the power generation load at time node i, t. lt This represents the total load demand at time node i (t).

[0093] Step S20: Based on Bernstein polynomials, establish the model of the total cost function and the initial constraints on the Bernstein function space to obtain the objective function and objective constraints.

[0094] It should be understood that Bernstein polynomials are a basis for polynomial spaces, possessing nonnegativity and unit partitioning. The Bernstein polynomial (BP) method can be chosen to construct continuous-time models of functions or datasets. This is because smoothing conditions can be more easily applied using them, not only at discontinuities but also within the intervals of interest, simply by processing the coefficients of the Bernstein spline expansion.

[0095] Understandably, in practical economic or engineering problems, the total cost function is usually a complex nonlinear function involving multiple variables and parameters. Using Bernstein polynomials, the total cost function can be approximated as a polynomial function, which exhibits good approximation properties within the Bernstein function space. By choosing an appropriate degree *n* of the Bernstein polynomial and determining its coefficients by solving a system of linear equations based on known cost data points, an approximate expression for the total cost function can be obtained.

[0096] Step S30: The objective function and the objective constraints are processed by a preset fuzzy information gap decision theory model to obtain the total cost and the wind power uncertainty radius.

[0097] It should be noted that fuzzy information gap decision theory is a decision analysis framework used to handle complex decision problems containing uncertainty and fuzziness. It combines fuzzy set theory and information gap theory to quantify decision-makers' tolerance for information incompleteness and help them make optimal decisions under conditions of incomplete information. The wind power uncertainty radius is an indicator that quantifies the uncertainty of wind power output, describing the range of fluctuation in wind power output at a certain confidence level.

[0098] Understandably, in decision-making problems, objective functions are typically used to quantify the merits of different decision options. By using a pre-defined FIGDT model, uncertainties in the objective function (such as the uncertainty of wind power output) can be fuzzified, and the impact of incomplete information on the objective function can be considered to reflect the decision-maker's tolerance for uncertainty and risk preference.

[0099] Step S40: Optimize the current resource scheduling method of the distribution network based on the total cost and the wind power uncertainty radius to obtain the target resource scheduling strategy.

[0100] It should be understood that optimizing the current resource scheduling method of the distribution network based on the total cost and the wind power uncertainty radius to obtain the target resource scheduling strategy can be achieved using linear programming (LP) to minimize the total cost while satisfying a series of linear constraints. Alternatively, it can involve determining how to manage the risks brought about by wind power uncertainty, selecting risk avoidance, risk neutrality, or risk seeking strategies, and designing specific resource scheduling strategies, including generator start-up and shutdown plans, load allocation, and demand response measures.

[0101] In this embodiment, the total cost function and initial constraints of the power system are obtained; a model of the total cost function and initial constraints on the Bernstein function space is established based on Bernstein polynomials to obtain the objective function and objective constraints; the objective function and objective constraints are processed through a pre-defined fuzzy information gap decision theory model to solve for the total cost and wind power uncertainty radius; the current resource scheduling method of the distribution network is optimized based on the total cost and wind power uncertainty radius to obtain the target resource scheduling strategy. The continuous-time model based on Bernstein polynomial functions is adopted, which can better capture the ramp capacity of natural gas generators and power demand response plans, cope with hourly wind energy fluctuations, and solve the wind power uncertainty problem in the power system.

[0102] Reference Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the distribution network resource scheduling method of this application. Based on the first embodiment described above, a second embodiment of the distribution network resource scheduling method of this application is proposed.

[0103] In the second embodiment, before step S30, the method further includes:

[0104] Step S301: Evaluate the randomness and unpredictability of wind power output, and set uncertainty parameters based on the deviation between the wind power output and its predicted value.

[0105] It should be noted that the randomness and unpredictability of wind power output refer to the fact that the unpredictable and irregular nature of wind power output mainly stems from the random fluctuations in wind speed and various uncertainties in the wind speed-wind power conversion process, such as the timing, type, and location of equipment failures. Uncertainty parameters are used to describe the deviation between wind power output and its predicted value. These parameters reflect the uncertainty of wind power forecasting and provide a basis for power system dispatching and decision-making.

[0106] It should be understood that the randomness and unpredictability of wind power output can be predicted by analyzing historical data or calculated based on wind power output at specific times and locations.

[0107] Step S302: Establish a membership function through fuzzy logic, and map the uncertain parameters to a fuzzy set to form fuzzy information gaps.

[0108] It's important to understand that fuzzy logic is a mathematical tool for handling uncertainty and fuzziness, allowing variables to take values ​​within a continuous range between true and false. A membership function describes the degree to which an element belongs to a fuzzy set; it's typically a function taking values ​​in the interval [0,1], where 0 represents not belonging to the set at all, 1 represents completely belonging to the set, and values ​​between 0 and 1 represent different degrees of membership. Fuzzy sets are the fundamental objects in fuzzy logic, allowing elements to belong to sets with a certain degree of membership. Fuzzy information gaps refer to the situations where information is missing or incomplete due to the fuzziness and uncertainty of information when processing fuzzy information.

[0109] Understandably, the uncertainty and unpredictability of wind power output pose significant challenges to resource scheduling in power systems. Fuzzy logic and fuzzy sets can more accurately describe the uncertainty of wind power output and formulate corresponding resource scheduling strategies. Mapping uncertainty parameters to fuzzy sets allows for effective modeling and representation of uncertainty.

[0110] Step S303: Construct the fuzzy information gap decision theory model based on the fuzzy information gap and the information gap decision theory model.

[0111] It should be noted that the information gap decision theory model is an optimization method for handling the impact of uncertainties. In this model, "information" refers to the data information affecting the uncertainty of the system's objective, while "gap" reflects the difference between the predicted and actual values ​​of uncertainties—that is, the gap state between known and unknown information. The fuzzy information gap decision theory model combines fuzzy information gaps with information gap decision theory. It utilizes fuzzy logic and fuzzy sets to model and represent fuzzy information gaps, while also drawing on methods from information gap decision theory to handle the impact of uncertainties.

[0112] Specifically, it is necessary to clarify the decision problem and objectives, utilize fuzzy logic and fuzzy sets to model and represent the uncertain parameters, and analyze the impact of uncertain factors on the decision objectives within the framework of information gap decision theory. This includes steps such as determining the fluctuation range of uncertain parameters, constructing robust models, and solving optimization problems. Fuzzy logic and fuzzy sets are used to model and represent the uncertain parameters.

[0113] In the second embodiment, step S30 includes:

[0114] Step S304: Based on the fuzzy information gap decision theory model, formulate risk aversion strategy and opportunity seeker strategy, and specify the basic costs of the risk aversion strategy and the opportunity seeker strategy.

[0115] It's important to note that risk aversion (RA) strategies involve taking conservative measures to reduce potential losses when facing uncertainty or risk. Opportunity seeker (OA) strategies, on the other hand, emphasize finding and utilizing potential opportunities amidst uncertainty. In this strategy, the decision-maker (DM) uses uncertainty to reduce total cost. Compared to RA strategies, WEG uncertainty is the most favored event in this strategy, and it is associated with higher-than-predicted WEG.

[0116] In one example, the optimization framework for RA and OS strategies can be formulated as follows:

[0117]

[0118] In these strategies, DM first specifies the basic costs of the two strategies. Then, the objective function is maximized to maximize the WEG uncertainty radius. For radius W wt It may deviate from its predicted value, where λ is the deviation coefficient. In Ω∈{r,o}, r and o represent the RA and OS policies, respectively. σ ΩThe positive parameter is determined by DM. It's important to note that for operating cost values ​​at a specified level, assuming no uncertainty in the model, the radius of WEG uncertainty will become 0, and the hourly WEG will be the same as its predicted value. The basic total cost is Θ. b Then based on the parameter σ Ω (1±σ Ω Due to the uncertainty of the WEG radius, the greediness of the basic total cost value will be further reduced (increased).

[0119] Step S305: Based on the basic cost, use a fuzzy set to characterize the objective function and obtain the objective membership function.

[0120] Understandably, in the optimization process of the IGDT method, TC is fixed at the upper limit of the inequality constraint, i.e., the cost threshold. In this model, the decision-maker cannot obtain the optimal value of TC. However, the IGDT model based on fuzzy methods is used to achieve the optimal values ​​of TC and WEG uncertainty radius.

[0121] In one example, consider the TC and WEG uncertainty radii (i.e., ±λ). To simultaneously optimize these objective functions, each should be characterized by a fuzzy set, typically represented by a membership function μ with lower and upper bounds, and strictly monotonically decreasing and continuous functions for different objective functions. The membership functions for the RA and OS strategies can be defined as follows:

[0122]

[0123] The above are the membership functions μ for calculating the RA and OS policies, respectively. γ (λ) and μ o (λ), the membership functions μ of the two strategies OF (Θ) is calculated as follows:

[0124]

[0125] In the above equation, λ max / Θ max and λ min / Θ min These are the maximum and minimum values ​​of the objective function, i.e., the values ​​of λ and Θ are obtained through single-objective optimization.

[0126] The F-IGDT models for RA and OS strategies are as follows:

[0127]

[0128] Where, β r and β o β represents satisfaction with RA and OS, respectively. r / oThis maximizes the overall level of satisfaction, i.e., all member functions.

[0129] Step S306: Input the target membership function into the fuzzy information gap decision theory model, use the fuzzy optimization algorithm to solve the model, and obtain the total cost and the corresponding wind power uncertainty radius under different wind power uncertainty levels.

[0130] It should be noted that fuzzy optimization algorithms are a mathematical method for handling fuzzy optimization problems. Based on fuzzy mathematics and optimization theory, they seek the optimal (maximized or minimized) solution of the objective function under fuzzy constraints through iterative search and other methods.

[0131] Understandably, based on the characteristics of the problem and the solution requirements, appropriate fuzzy optimization algorithms can be selected for solving it, including fuzzy linear programming, fuzzy nonlinear programming, and fuzzy dynamic programming. During the solution process, the algorithm iteratively searches for the optimal solution, considering the impact of fuzziness and uncertainty on the decision outcome. By continuously adjusting the values ​​of the decision variables, it ultimately finds the solution that optimizes the objective function. After the solution is completed, based on the optimal solution and relevant information in the model, the total cost and the wind power uncertainty radius are calculated.

[0132] In this embodiment, based on the fuzzy information gap decision theory model, a risk aversion strategy and an opportunity seeker strategy are formulated, and the basic costs of the risk aversion strategy and the opportunity seeker strategy are specified. Based on the basic costs, a fuzzy set is used to characterize the objective function, obtaining the objective membership function. The objective membership function is input into the fuzzy information gap decision theory model, and a fuzzy optimization algorithm is used to solve the model, obtaining the total cost and the corresponding wind power uncertainty radius under different levels of wind power uncertainty. Formulating the risk aversion strategy and the opportunity seeker strategy allows the model to strike a balance between risk aversion and seeking opportunities to reduce costs. This enables flexible responses to different levels of wind power uncertainty.

[0133] Reference Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the distribution network resource scheduling method of this application. Based on the second embodiment described above, the third embodiment of the distribution network resource scheduling method of this application is proposed.

[0134] In the second embodiment, step S20 includes:

[0135] Step S201: Generate the objective function using the individual cost components in the Bernstein polynomial approximation of the total cost function.

[0136] Understandably, the proposed continuous-time problem is an optimization problem with an infinite-dimensional decision space, which is computationally difficult to solve. To address this problem, a function-space-based solution method can be proposed. This method reduces the dimensionality of continuous-time decisions and parameter trajectories by modeling the continuous-time decisions and parameter trajectories within the finite-order function space spanned by the Bernstein polynomial method.

[0137] Of course, by selecting an appropriate Bernstein polynomial order, the model can adaptively adjust its complexity according to the desired accuracy threshold, capturing changes in time series data more precisely. Prior to step S201, the following steps are also included:

[0138] The order of the Bernstein polynomial is selected from the order mapping table according to the determined accuracy threshold; the time period is divided into multiple intervals based on the length set of the interval, and the Bernstein polynomial operator is used to approximate the intervals to calculate the binomial coefficients and control points in each Bernstein basis polynomial; the Bernstein basis polynomial is generated based on the order, the binomial coefficients and the control points.

[0139] In one example, reference Figure 4 , Figure 4 This is a Bernstein coefficient diagram for the power distribution network resource scheduling method in this application. The Bernstein polynomial interpolation-fitted power curve can be represented as a weighted sum of a series of Bernstein basis functions, where the weights are the Bernstein coefficients. These coefficients can be calculated from the power curve to be fitted and then used to optimize the power system scheduling plan. The diagram shows the variation of the Bernstein coefficients within a certain interval and how to adjust these coefficients to meet specific constraints, such as maximum and minimum value limits. First, the Q+1 Bernstein basis polynomial is defined as:

[0140]

[0141] in These are binomial coefficients. To simulate the function in a continuous-time model, the following steps are required:

[0142] (i) Divide the time period T into M intervals. The length of each interval is T. m =t m+1 -t m .

[0143] (ii) BP operator In the function χ t interval [t] m ,t m+1 ) is executed on ) and mapped to a Q-degree polynomial.

[0144]

[0145] Among them, coefficient These are called Bernstein coefficients or control points. The equation is represented in matrix form, i.e., m = 1, ..., M; q = 0, ..., Q. The product is as follows:

[0146]

[0147] Once Q m order As the value increases, the approximate error decreases, i.e., it is limited to... The derivative is written as two lower-order polynomials (Q-1). m The sum of.

[0148]

[0149] (iii) The convexity of the property leads to and The coefficients are limited to between the maximum and minimum coefficients.

[0150]

[0151] In order to preserve the function x t The continuity between the starting and ending points must be enforced by requiring control points to match at the starting and ending points.

[0152]

[0153] vector These are the definite integral and the constant parameter of the definite integral, i.e. Given Q m In the case of starting from the initial position t m To the final position t m+1 for It is a Q m A 1×1 dimensional unit vector.

[0154] The continuous-time form of the objective function obtained using the BP approximation is as follows:

[0155]

[0156] in, Substitution Bernstein stated.

[0157] Step S202: Transform the natural gas constraint in the initial constraint conditions into the Bernstein function space to obtain the target natural gas constraint.

[0158] Understandably, the transformation equation for natural gas is similar to the transformation of the objective function described above. In one example, the transformation of some natural gas-constrained equation terms to the Bernstein function space is as follows:

[0159]

[0160] Similarly, the converted natural gas constraint equations are as follows:

[0161]

[0162] Step S203: Based on the grid constraints in the initial constraints, construct the power load balance, power network constraints, and hydrogen power plant model using the Bernstein polynomial.

[0163] It should be understood that constructing power load balancing, power network constraints, and hydrogen power plant models using the Bernstein polynomial based on the grid constraints in the initial constraints requires consideration of other factors, such as electricity market pricing mechanisms, environmental policy requirements, and the integration of renewable energy. Since the accuracy and computational efficiency of the Bernstein polynomial approximation depend on the order of the polynomial and the choice of the approximation point, appropriate parameter adjustments and optimizations are necessary in practical applications.

[0164] For ease of understanding, the following examples are provided, but they do not limit this application. In one example, refer to... Figure 5 , Figure 5 This is a model diagram of a hydrogen power plant based on the power grid resource scheduling method proposed in this application. The left side of the diagram shows a dam that converts the potential energy of the water flow into electrical energy. This is achieved through a turbine and generator, representing the utilization of renewable energy. The electricity is transmitted through the power grid to an electrolyzer. Here, the electricity is used to decompose water into hydrogen and oxygen. This process is called electrolysis. The produced hydrogen is stored in a dedicated hydrogen storage tank. When electricity is needed, the stored hydrogen can be converted back into electrical energy through a hydrogen fuel cell or a hydrogen turbine.

[0165] Currently, natural gas is widely used in power plants. Hydrogen turbines consume hydrogen as fuel to generate electricity in hydrogen generator sets. H2 generator set g∈G H On day d, period t, scenario ω, The formula for power generation is as follows:

[0166]

[0167] in Linear power generation per unit g, expressed in MWh / H2-kg; Let g be the H2 usage on day d, in time period t, and in scenario ω. Considering the scheduleability of H2 units, assume that this technology can participate in providing upper and lower reserve capacity. and This represents the total power generation. Reserve capacity can be viewed as a backup energy generation capacity that can be allocated upwards and downwards when there are unexpected changes in power generation or consumption.

[0168] The H2 consumed by the power unit that burns H2 can be obtained by electrolyzing water, which involves decomposing water into H2 and O2. Let... Let represent the number of kilograms of hydrogen produced by electrolyzer e on day d, period t, and scenario ω. Then, the power consumption of the electrolyzer is... The calculation is as follows:

[0169]

[0170] In the formula The power consumption of the electrolyzer is expressed in MWh / H2-kg. The electrolyzer can provide upper and lower standby capacities, respectively... and express. For total hydrogen power generation, upper reserves can be provided by reducing H2 production, while lower reserves can be provided by increasing consumption to generate more H2. Constraints stipulate that the electricity consumption plus the supply of lower reserve capacity must be less than the installed capacity. It is mandatory that the electricity consumption minus the supply of upper reserve capacity must be a positive value.

[0171]

[0172] Currently, hydrogen produced by electrolyzers is typically stored in tanks for future use. Therefore, this study considers H2 to be stored in thick steel tanks. Let J be the set of tank groups that can be installed. Then, for tank group j∈J, on day d, period t, and scenario ω, the H2 balance formula is as follows:

[0173]

[0174] In the formula, represents the amount of hydrogen measured in kg in the storage tank for group j, day d, period t, and scenario ω. represents the minimum coefficient used to adjust or limit the values ​​of other variables.

[0175] The maximum and minimum hydrogen storage capacities for each group of gas storage tanks, cycle, and scenario are respectively used as... and It means that, among them,

[0176]

[0177] The maximum amount of H2 stored is generated when the electrolyzer arranges downstream reserves by increasing H2 production (i.e., increasing its electricity demand), while the H2 power unit provides downstream reserves by reducing its electricity production and H2 consumption.

[0178] Step S204: Establish the target constraint conditions of the objective function based on the target natural gas constraint, the power load balance, the power network constraint, and the hydrogen power plant model.

[0179] In this embodiment, the objective function is generated by approximating the various cost components in the total cost function using Bernstein polynomials. The natural gas constraint in the initial constraints is transformed into the Bernstein function space to obtain the target natural gas constraint. Based on the grid constraint in the initial constraints, the Bernstein polynomials are used to construct power load balancing, power network constraints, and a hydrogen power plant model. The target constraints of the objective function are established based on the target natural gas constraint, the power load balancing, the power network constraints, and the hydrogen power plant model. Using Bernstein polynomials to construct the power load balancing, power network constraints, and hydrogen power plant model improves the simulation accuracy of the model. It also reduces the dimensionality of continuous-time decisions and parameter trajectories, facilitating subsequent calculations.

[0180] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the power distribution network resource scheduling method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0181] This application also provides a power distribution network resource scheduling device; please refer to [reference needed]. Figure 6 The power distribution network resource scheduling device includes:

[0182] Function acquisition module 10 is used to acquire the total cost function and initial constraints of the power system;

[0183] Model building module 20 is used to model the total cost function and the initial constraints in the function space based on Bernstein polynomials, and to obtain the objective function and objective constraints.

[0184] The cost calculation module 30 is used to process the objective function and the objective constraints through a preset fuzzy information gap decision theory model, and solve for the total cost and the wind power uncertainty radius.

[0185] The scheduling optimization module 40 is used to optimize the current resource scheduling method of the distribution network based on the total cost and the wind power uncertainty radius to obtain the target resource scheduling strategy.

[0186] The distribution network resource scheduling device provided in this application, employing the distribution network resource scheduling method in the above embodiments, can solve the technical problems of lacking effective methods in existing research to manage the rapid sub-hourly changes of WEG, the rapid voltage boosting capabilities of NGFG and EDRP in power systems, and the accurate modeling of uncertainties in the output of wind turbine generators during power system operation. Compared with the prior art, the beneficial effects of the distribution network resource scheduling device provided in this application are the same as those of the distribution network resource scheduling method provided in the above embodiments, and other technical features in the distribution network resource scheduling device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0187] This application provides a power distribution network resource scheduling device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the power distribution network resource scheduling method in the above embodiment 1.

[0188] The following is for reference. Figure 7 This document illustrates a structural diagram of a power distribution network resource scheduling device suitable for implementing embodiments of this application. The power distribution network resource scheduling device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle-mounted terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The power distribution network resource scheduling equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0189] like Figure 7As shown, the power distribution network resource scheduling equipment may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the power distribution network resource scheduling equipment. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the distribution network resource dispatching equipment to communicate wirelessly or wiredly with other equipment to exchange data. Although the figure shows distribution network resource dispatching equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0190] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0191] The distribution network resource scheduling equipment provided in this application, employing the distribution network resource scheduling method described in the above embodiments, can solve the technical problems in existing research regarding the lack of effective methods for managing the rapid sub-hourly changes of WEG, the rapid voltage boosting capabilities of NGFG and EDRP in power systems, and the accurate modeling of uncertainties in the output of wind turbine generators during power system operation. Compared with the prior art, the beneficial effects of the distribution network resource scheduling equipment provided in this application are the same as those of the distribution network resource scheduling method provided in the above embodiments, and other technical features of this distribution network resource scheduling equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0192] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0193] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0194] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the power distribution network resource scheduling method in the above embodiments.

[0195] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0196] The aforementioned computer-readable storage medium may be included in the power distribution network resource dispatching equipment; or it may exist independently and not be assembled into the power distribution network resource dispatching equipment.

[0197] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the distribution network resource scheduling equipment, cause the distribution network resource scheduling equipment to perform the distribution network resource scheduling method described above.

[0198] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0199] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0200] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0201] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described distribution network resource scheduling method. This addresses the technical problems in existing research regarding the lack of effective methods for managing the rapid sub-hourly variations of WEG, the rapid voltage boosting capabilities of NGFG and EDRP in power systems, and the accurate modeling of uncertainties in wind turbine output during power system operation. Compared to existing technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the distribution network resource scheduling method provided in the above embodiments, and will not be elaborated upon here.

[0202] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the power distribution network resource scheduling method described above.

[0203] The computer program product provided in this application can solve the technical problems of lacking effective methods in existing research to manage the rapid sub-hourly changes of WEG, the rapid voltage boosting capabilities of NGFG and EDRP in power systems, and the accurate modeling of uncertainties in the output of wind turbine generators in power system operation. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the distribution network resource scheduling method provided in the above embodiments, and will not be repeated here.

[0204] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for resource scheduling in a power distribution network, characterized in that, The power distribution network resource scheduling method includes: Obtain the total cost function and initial constraints of the power system; Based on Bernstein polynomials, the total cost function and the initial constraints are modeled on the Bernstein function space to obtain the objective function and objective constraints. The objective function and the objective constraints are processed by a pre-set fuzzy information gap decision theory model to obtain the total cost and the wind power uncertainty radius. Based on the total cost and the wind power uncertainty radius, optimize the current resource scheduling method of the distribution network to obtain the target resource scheduling strategy.

2. The distribution network resource scheduling method as described in claim 1, characterized in that, Before the step of processing the objective function and the objective constraints using a preset fuzzy information gap decision theory model to obtain the total cost and the wind power uncertainty radius, the method further includes: The randomness and unpredictability of wind power output are assessed, and uncertainty parameters are set based on the deviation between the wind power output and its predicted value. Membership functions are established using fuzzy logic, and the uncertain parameters are mapped to fuzzy sets to form fuzzy information gaps; The fuzzy information gap decision theory model is constructed based on the fuzzy information gap and information gap decision theory model.

3. The distribution network resource scheduling method as described in claim 2, characterized in that, The step of processing the objective function and objective constraints using a preset fuzzy information gap decision theory model to obtain the total cost and wind power uncertainty radius includes: Based on the fuzzy information gap decision theory model, risk aversion strategy and opportunity seeker strategy are formulated, and the basic costs of the risk aversion strategy and the opportunity seeker strategy are specified. Based on the aforementioned basic cost, a fuzzy set is used to characterize the objective function, thereby obtaining the objective membership function; The target membership function is input into the fuzzy information gap decision theory model, and the fuzzy optimization algorithm is used to solve the model to obtain the total cost and the corresponding wind power uncertainty radius under different wind power uncertainty levels.

4. The distribution network resource scheduling method as described in claim 1, characterized in that, The initial constraints include power grid constraints and natural gas constraints; The steps of establishing the model of the total cost function and the initial constraints on the Bernstein function space based on Bernstein polynomials to obtain the objective function and objective constraints include: The objective function is generated by using the individual cost components in the Bernstein polynomial approximation of the total cost function. The natural gas constraint in the initial constraint conditions is transformed into the Bernstein function space to obtain the target natural gas constraint; Based on the grid constraints in the initial constraints, the Bernstein polynomial is used to construct the power load balance, power network constraints, and hydrogen power plant models. The objective constraint conditions of the objective function are established based on the target natural gas constraint, the power load balance, the power network constraint, and the hydrogen power plant model.

5. The distribution network resource scheduling method as described in claim 4, characterized in that, Before the step of generating the objective function using the individual cost components in the Bernstein polynomial approximation of the total cost function, the method further includes: The order of the Bernstein polynomial is selected from the order mapping table according to the determined accuracy threshold; The time period is divided into multiple intervals based on the length set of the interval, and the Bernstein polynomial operator is used to approximate the intervals to calculate the binomial coefficients and control points in each Bernstein basis polynomial. The Bernstein basis polynomial is generated based on the order, the binomial coefficients, and the control points.

6. The distribution network resource scheduling method as described in claim 1, characterized in that, The steps for obtaining the total cost function and initial constraints of the power system include: Collect cost information from various parts of the power system operation and determine decision variables related to the cost information based on the generator sets; Construct the total cost function of the power system based on the cost information and the decision variables; The physical and operational constraints of the power system are obtained, and the constraints of the total cost function are established using a continuous-time model.

7. A power distribution network resource dispatching device, characterized in that, The device includes: The function acquisition module is used to obtain the total cost function and initial constraints of the power system. The model building module is used to model the total cost function and the initial constraints in the function space based on Bernstein polynomials, and to obtain the objective function and objective constraints. The cost calculation module is used to process the objective function and the objective constraints through a preset fuzzy information gap decision theory model, and solve for the total cost and the wind power uncertainty radius. The scheduling optimization module is used to optimize the current resource scheduling method of the distribution network based on the total cost and the wind power uncertainty radius to obtain the target resource scheduling strategy.

8. A power distribution network resource dispatching device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the power distribution network resource scheduling method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the power distribution network resource scheduling method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the power distribution network resource scheduling method as described in any one of claims 1 to 6.

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