Dynamic optimization method and system for carrying capacity of power distribution network

Abstract

The invention discloses a dynamic optimization method and system for the carrying capacity of a power distribution network. The method comprises the steps that demand response models of different types of users are established through a piecewise linear function; generating a large amount of load and distributed resource output scenes by using a Monte Carlo simulation method, and selecting a typical scene from the generated large amount of scenes by using a K-means clustering algorithm; dynamically evaluating a bearing capacity evaluation result through a power distribution network bearing capacity index; constructing a multi-objective optimization model according to the economical efficiency objective, the reliability objective and the user satisfaction objective; a random optimization mechanism is adopted, weighted averaging is carried out on the optimal solutions in all the scenes, and a final optimization objective function is obtained; solving an optimization problem in a rolling time domain through a rolling optimization strategy to obtain a real-time optimization result; and designing and implementing a user incentive mechanism to obtain a final solution for dynamically optimizing the carrying capacity of the power distribution network. The bearing capacity of the power distribution network can be remarkably improved, and optimization of the bearing capacity of the power distribution network is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic optimization of distribution networks, and in particular to a method and system for dynamically optimizing the carrying capacity of a distribution network. Background Art

[0002] With the large-scale access of new loads such as distributed energy and electric vehicles, the operation of distribution networks faces greater challenges. Traditional distribution network carrying capacity assessment methods are mainly based on static load models, which are difficult to accurately reflect the dynamic characteristics and controllability of loads. In addition, existing methods often ignore the impact of load demand response on the carrying capacity of distribution networks, resulting in conservative carrying capacity assessment results and failure to fully tap the potential of distribution networks. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for dynamically optimizing the carrying capacity of a distribution network to solve the problem that traditional distribution networks are unable to accurately capture these characteristics when facing dynamically changing loads, resulting in low evaluation accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for dynamically optimizing the carrying capacity of a distribution network, comprising:

[0008] Collect and pre-process historical load data, distributed resource output data and electricity price data, and establish demand response models for different types of users through piecewise linear functions;

[0009] Based on the demand response model, a large number of load and distributed resource output scenarios are generated using the Monte Carlo simulation method, and the K-means clustering algorithm is used to select typical scenarios from the large number of generated scenarios;

[0010] Based on typical scenarios and demand response models, dynamic carrying capacity evaluation results are obtained through distribution network carrying capacity indicators;

[0011] Based on the dynamic carrying capacity evaluation results, a multi-objective optimization model is constructed according to the economic objectives, reliability objectives and user satisfaction objectives;

[0012] Based on the multi-objective optimization model and typical scenarios, a random optimization mechanism is used to perform weighted average of the optimal solutions in each scenario to obtain the final optimization objective function.

[0013] Based on the final objective function, a rolling optimization strategy is used to solve an optimization problem in a rolling time domain and obtain real-time optimization results.

[0014] Based on the real-time optimization results, a user incentive mechanism is designed and implemented to obtain the final solution for dynamically optimizing the distribution network's carrying capacity.

[0015] As a preferred solution of the method for dynamically optimizing the carrying capacity of a distribution network according to the present invention, wherein:

[0016] The demand response models of different types of users are established by piecewise linear functions, including using piecewise linear functions to model the load regulation of residential users and industrial and commercial users;

[0017] The load regulation of the residential user is expressed as:

[0018]

[0019] in, is the load regulation of residential users at time t; α r is the price sensitivity coefficient of residential users; p(t) is the electricity price at time t; p0 is the base electricity price; Maximum load regulation capability for residential users;

[0020] The load regulation of the industrial and commercial users, taking into account the time dependence of the load regulation, is expressed as:

[0021]

[0022] in, is the load regulation of industrial and commercial users at time t; β c is the price sensitivity coefficient of industrial and commercial users; γ c is the time dependence coefficient of load regulation; ΔP c (t-1) is the load adjustment at the previous moment; Maximum load regulation capacity for industrial and commercial users.

[0023] As a preferred solution of the method for dynamically optimizing the carrying capacity of a distribution network according to the present invention, wherein:

[0024] The distribution network carrying capacity index is expressed as:

[0025]

[0026] Among them, P L(t) is the base load at time t; ΔP DR (t) Load regulation contributed to demand response; P DG (t) is the output of distributed resources; S T is the capacity of distribution transformer.

[0027] As a preferred solution of the method for dynamically optimizing the carrying capacity of a distribution network according to the present invention, wherein:

[0028] According to the economic goal, reliability goal and user satisfaction goal, a multi-objective optimization model is constructed, wherein the objective function of the multi-objective optimization model is expressed as:

[0029] minF={f1,f2,f3}

[0030] f1=∑[C g (t)*P g (t)+C DR (t)*ΔP DR (t)]

[0031] f2=E[∑P cut (t)]

[0032]

[0033] Among them, f1 is the economic goal, f2 is the reliability goal, f3 is the user satisfaction goal, C g (t) is the power generation cost at time t; P g (t) is the conventional power generation; C DR (t) is the demand response compensation cost, w i is the weight of user i; ΔP i (t) is the actual load adjustment of user i; is the expected load adjustment of user i.

[0034] As a preferred solution of the method for dynamically optimizing the carrying capacity of a distribution network according to the present invention, wherein:

[0035] The method adopts a random optimization mechanism to solve the multi-objective optimization problem and select the optimal compromise solution, including the following steps:

[0036] Monte Carlo simulation is used to generate a large number of scenarios, and the K-means clustering algorithm is used to select typical scenarios from the generated large number of scenarios;

[0037] For each scenario, build an optimization model of the operating status and constraints under the specific scenario;

[0038] For each scenario, solve the corresponding optimization problem and obtain the optimal solution for that scenario;

[0039] Comprehensively consider the solutions in each scenario to obtain the final optimization objective function;

[0040] Verify the performance of the obtained decision-making scheme in actual operation through simulation and make adjustments based on the actual situation;

[0041] The final optimization objective function is expressed as:

[0042] min F=∑π s *F s

[0043] Among them, π s is the probability of scene s; F s is the objective function value under scenario s.

[0044] As a preferred solution of the method for dynamically optimizing the carrying capacity of a distribution network according to the present invention, wherein:

[0045] The rolling optimization strategy is used to update the system state at each time step to obtain real-time optimization results, including the following steps:

[0046] Update system status and forecast information;

[0047] Construct and solve optimization problems to obtain the optimal decision for the current period;

[0048] Execute the decision of the current period and roll over to the next time step to obtain real-time optimization results;

[0049] The construction optimization problem is expressed as:

[0050] min F=∑ τ∈[t，t+T] F(τ)

[0051] st

[0052] g(x(τ),u(τ))=0

[0053] h(x(τ),u(τ))≤0

[0054] x(t)=x t

[0055] Where T is the optimization time domain; x(τ) is the state variable; u(τ) is the control variable; g(·) and h(·) are the equality and inequality constraints, respectively; x t is the initial state at time t.

[0056] As a preferred solution of the method for dynamically optimizing the carrying capacity of a distribution network according to the present invention, wherein:

[0057] The designing and implementing of the user incentive mechanism includes calculating the user's incentive benefits;

[0058] The user's incentive income is expressed as:

[0059]

[0060] Among them, α is the basic subsidy coefficient; β is the performance reward coefficient; γ is the nonlinear adjustment coefficient.

[0061] In a second aspect, the present invention provides a system for dynamically optimizing the carrying capacity of a distribution network, comprising:

[0062] Demand response modeling module, which is used to collect and pre-process historical load data, distributed resource output data and electricity price data, and establish demand response models for different types of users through piecewise linear functions;

[0063] The scenario generation and clustering module is used to generate a large number of load and distributed resource output scenarios based on the demand response model using the Monte Carlo simulation method, and select typical scenarios from the large number of generated scenarios using the K-means clustering algorithm;

[0064] Dynamic carrying capacity assessment module, which is used to dynamically assess the carrying capacity of the distribution network based on typical scenarios and demand response models and the carrying capacity indicators of the distribution network;

[0065] A multi-objective optimization model building module is used to build a multi-objective optimization model based on the dynamic load-bearing capacity evaluation results, economic objectives, reliability objectives, and user satisfaction objectives;

[0066] The stochastic optimization solution module is used to obtain the final optimization objective function by weighted averaging the optimal solutions in each scenario based on the multi-objective optimization model and typical scenarios using a stochastic optimization mechanism;

[0067] The real-time optimization strategy module is used to solve the optimization problem in a rolling time domain based on the final objective function through a rolling optimization strategy to obtain real-time optimization results;

[0068] The user incentive mechanism design module is used to design and implement the user incentive mechanism to obtain the final solution for dynamically optimizing the distribution network carrying capacity.

[0069] In a third aspect, the present invention provides a computing device, comprising:

[0070] Memory, used to store programs;

[0071] A processor is used to execute the computer executable instructions, which, when executed by the processor, implement the steps of the method for dynamically optimizing the carrying capacity of the distribution network.

[0072] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the method for dynamically optimizing the carrying capacity of a distribution network are implemented.

[0073] Beneficial effects of the invention: The invention aims to accurately model the load demand response characteristics of different types of users to better evaluate and utilize demand-side flexibility, combine load demand response with distributed resource scheduling, dynamically evaluate and optimize the distribution network carrying capacity, and optimize the distribution network carrying capacity while considering multiple objectives such as economy, reliability and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0075] Figure 1 A basic flow chart of a method for dynamically optimizing the carrying capacity of a distribution network provided by an embodiment of the present invention;

[0076] Figure 2 A schematic diagram of a basic model of a method for dynamically optimizing the carrying capacity of a distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0078] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0079] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0080] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0081] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0082] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0083] Example 1

[0084] Reference Figure 1-2 , is an embodiment of the present invention, and provides a method for dynamically optimizing the carrying capacity of a distribution network, such as Figure 1 , as shown in 2, comprising the following steps:

[0085] S1: Collect and pre-process historical load data, distributed resource output data and electricity price data, and establish demand response models for different types of users through piecewise linear functions;

[0086] In the embodiment of the present application, in order to accurately describe the demand response characteristics of different types of users, a piecewise linear function is used to model the load regulation capability of the user. Taking residential users as an example, their demand response model can be expressed as:

[0087]

[0088] in, is the load regulation of residential users at time t; α r is the price sensitivity coefficient of residential users; p(t) is the electricity price at time t; p0 is the base electricity price; Maximum load regulation capability for residential users;

[0089] In the embodiment of the present application, for industrial and commercial users, considering the time dependence of their load regulation, the following model is adopted:

[0090]

[0091] in, is the load regulation of industrial and commercial users at time t; β c is the price sensitivity coefficient of industrial and commercial users; γ c is the time dependence coefficient of load regulation; ΔP c (t-1) is the load adjustment at the previous moment; Maximum load regulation capacity for industrial and commercial users.

[0092] It should be noted that based on the preprocessed data, formulas (1) and (2) are used to establish demand response models for different types of users and calibrate relevant parameters.

[0093] S2: Based on the demand response model, a large number of load and distributed resource output scenarios are generated using the Monte Carlo simulation method, and the K-means clustering algorithm is used to select typical scenarios from the large number of generated scenarios;

[0094] S3: Dynamic carrying capacity evaluation results based on typical scenarios and demand response models through distribution network carrying capacity indicators;

[0095] In the embodiment of the present application, based on the load demand response model and combined with the output of distributed resources, a distribution network dynamic carrying capacity evaluation model is constructed. The distribution network carrying capacity index η(t) is defined as follows:

[0096]

[0097] Among them, P L (t) is the base load at time t; ΔP DR (t) Load regulation contributed to demand response; P DG (t) is the output of distributed resources; S T is the capacity of distribution transformer.

[0098] It should be noted that based on formula (3), combined with the load demand response model and distributed resource output scenario, the dynamic carrying capacity of the distribution network is evaluated.

[0099] S4: Based on the dynamic carrying capacity evaluation results, a multi-objective optimization model is constructed according to the economic objectives, reliability objectives and user satisfaction objectives;

[0100] In the embodiment of the present application, a multi-objective optimization model is established by considering multiple objectives such as economy, reliability and user satisfaction:

[0101] minF={f1,f2,f3} (4)

[0102] Among them, the economic target f1 is expressed as:

[0103] f1=∑[C g (t)*P g (t)+C DR (t)*ΔP DR (t)] (5)

[0104] In the formula, C g (t) is the power generation cost at time t; P g (t) is the conventional power generation; C DR (t) is the cost of demand response compensation.

[0105] The reliability target f2 can be expressed as the expected value of the load shedding amount:

[0106] f2=E[∑P cut (t)] (6)

[0107] The user satisfaction target f3 can be expressed as the weighted sum of demand response deviations:

[0108]

[0109] In the formula, w i is the weight of user i; ΔP i (t) is the actual load adjustment of user i; is the expected load adjustment of user i.

[0110] It should be noted that a multi-objective optimization model considering economy, reliability and user satisfaction is constructed according to formulas (4)-(7).

[0111] S5: Based on the multi-objective optimization model and typical scenarios, a random optimization mechanism is used to perform weighted averaging of the optimal solutions in each scenario to obtain the final optimization objective function;

[0112] In the embodiment of the present application, a scenario-based stochastic optimization method is used to handle the uncertainty of load demand and distributed resource output. This method captures various possibilities of uncertainty by simulating different future scenarios and makes decisions accordingly, so that better performance can be achieved in various possible future scenarios.

[0113] In the embodiment of the present application, the scenario-based random optimization method mainly includes the following steps:

[0114] Scenario generation: First, a series of possible future scenarios are generated based on historical data and other relevant information. These scenarios should be able to represent the main sources of uncertainty and cover a variety of possible situations.

[0115] Scenario reduction: Sometimes the number of generated scenarios may be very large and difficult to handle. In this case, scenario reduction techniques (such as cluster analysis) can be used to reduce the number of scenarios and retain the most representative ones.

[0116] Model construction: For each scenario, an optimization model is constructed. The model should be able to reflect the operating status and constraints of the system in a specific scenario. Common models include linear programming, mixed integer programming, etc.

[0117] Solve optimization problems: For each scenario, solve the corresponding optimization problem and obtain the optimal solution for that scenario.

[0118] Comprehensive decision: Consider the solutions in various scenarios and get a comprehensive decision that performs well in multiple scenarios. This usually involves weighted averaging or other integration methods.

[0119] Verification and adjustment: Finally, the performance of the obtained decision-making scheme in actual operation is verified through simulation, and adjustments are made according to the actual situation.

[0120] In the embodiment of the present application, a large number of scenarios are first generated using Monte Carlo simulation, and then a K-means clustering algorithm is used to select typical scenarios. The final optimization objective function can be expressed as:

[0121] minF=∑π s *F s (8)

[0122] Among them, π s is the probability of scene s; F s is the objective function value under scenario s.

[0123] In the embodiment of the present application, the scenario-based stochastic optimization method shown in formula (8) is used to solve the multi-objective optimization problem. The Pareto optimal solution set can be obtained using a multi-objective optimization algorithm such as NSGA-II.

[0124] In the embodiment of the present application, a fuzzy decision method is used to select the optimal compromise solution from the Pareto optimal solution set.

[0125] S6: Based on the final objective function, a rolling optimization strategy is used to solve an optimization problem in a rolling time domain and obtain real-time optimization results;

[0126] In the embodiment of the present application, a real-time optimization strategy based on a rolling time domain is designed to dynamically adjust the demand response and resource scheduling scheme. At each time step t, the following steps are performed:

[0127] (1) Update system status and prediction information.

[0128] (2) Create and solve optimization problems to obtain the optimal decision for the current period.

[0129] (3) Execute the decision of the current period and scroll to the next time step to obtain the real-time optimization result. In the embodiment of the present application, the optimization problem is created as:

[0130] min F=∑ τ∈[t，t+T] F(τ) (9)

[0131] st

[0132] g(x(τ),u(τ))=0 (10)

[0133] h(x(τ),u(τ))≤0 (11)

[0134] x(t)=x t (12)

[0135] Where T is the optimization time domain; x(τ) is the state variable; u(τ) is the control variable; g(·) and h(·) are the equality and inequality constraints, respectively; x t is the initial state at time t.

[0136] In the embodiment of the present application, according to the rolling optimization strategy shown in formulas (9)-(12), the system state is updated at each time step, and steps (4)-(7) are repeatedly executed to obtain real-time optimization results.

[0137] S7: Based on the real-time optimization results, design and implement the user incentive mechanism to obtain the final solution for dynamically optimizing the distribution network carrying capacity.

[0138] In the embodiment of the present application, in order to improve the enthusiasm of users to participate in demand response, a performance-based incentive mechanism is designed. The user's incentive income R i It can be expressed as:

[0139]

[0140] Among them, α is the basic subsidy coefficient; β is the performance reward coefficient; γ is the nonlinear adjustment coefficient. This mechanism takes into account the actual contribution of users ΔP i , and motivate users to fully exploit their regulatory potential

[0141] In the embodiment of the present application, the user's incentive income is calculated according to formula (13), and subsidies are issued in a timely manner to increase the user's enthusiasm for participation.

[0142] In the embodiment of the present application, the optimization results are regularly evaluated, the effect of improving the carrying capacity of the distribution network is analyzed, and the model parameters and optimization strategies are adjusted according to the evaluation results.

[0143] This embodiment also provides a distribution network carrying capacity dynamic optimization system, including:

[0144] Demand response modeling module, which is used to collect and pre-process historical load data, distributed resource output data and electricity price data, and establish demand response models for different types of users through piecewise linear functions;

[0145] The scenario generation and clustering module is used to generate a large number of load and distributed resource output scenarios based on the demand response model using the Monte Carlo simulation method, and select typical scenarios from the large number of generated scenarios using the K-means clustering algorithm;

[0146] Dynamic carrying capacity assessment module, which is used to dynamically assess the carrying capacity of the distribution network based on typical scenarios and demand response models and the carrying capacity indicators of the distribution network;

[0147] A multi-objective optimization model building module is used to build a multi-objective optimization model based on the dynamic load-bearing capacity evaluation results, economic objectives, reliability objectives, and user satisfaction objectives;

[0148] The stochastic optimization solution module is used to obtain the final optimization objective function by weighted averaging the optimal solutions in each scenario based on the multi-objective optimization model and typical scenarios using a stochastic optimization mechanism;

[0149] The real-time optimization strategy module is used to solve the optimization problem in a rolling time domain based on the final objective function through a rolling optimization strategy to obtain real-time optimization results;

[0150] The user incentive mechanism design module is used to design and implement the user incentive mechanism to obtain the final solution for dynamically optimizing the distribution network carrying capacity.

[0151] Furthermore, it also includes:

[0152] Memory, used to store programs;

[0153] A processor is used to load the program to execute the method for dynamically optimizing the carrying capacity of the distribution network.

[0154] This embodiment also provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the method for dynamically optimizing the carrying capacity of a distribution network.

[0155] The storage medium proposed in this embodiment and the method for dynamically optimizing the distribution network carrying capacity proposed in the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0156] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0157] Example 2

[0158] This is an embodiment of the present invention, which provides a distribution network carrying capacity dynamic optimization system, including a demand response modeling module, a scenario generation and clustering module, a dynamic carrying capacity evaluation module, a multi-objective optimization model building module, a random optimization solution module, a real-time optimization strategy module and a user incentive mechanism design module;

[0159] In the embodiment of the present application, the demand response modeling module includes collecting and preprocessing historical load data, distributed resource output data and electricity price data, preparing high-quality data, and providing a basis for subsequent modeling and optimization. Demand response models for different types of users are established through piecewise linear functions, and a behavioral model of user demand response is established to provide a basis for subsequent scenario generation and optimization.

[0160] In the embodiment of the present application, the scenario generation and clustering module includes generating a large number of load and distributed resource output scenarios based on the demand response model using the Monte Carlo simulation method, generating enough scenarios to cover various possible situations and ensure that the model can handle future uncertainties. The K-means clustering algorithm is used to select typical scenarios from the large number of generated scenarios, generate and select representative scenarios, reduce the number of scenarios, retain the most representative scenarios, reduce computational complexity, and improve optimization efficiency.

[0161] In an embodiment of the present application, the dynamic carrying capacity evaluation module includes a dynamic carrying capacity evaluation result based on typical scenarios and demand response models through distribution network carrying capacity indicators; this evaluation provides the necessary input information for constructing a multi-objective optimization model.

[0162] In an embodiment of the present application, a multi-objective optimization model construction module includes building a multi-objective optimization model based on the dynamic carrying capacity evaluation results, according to economic goals, reliability goals and user satisfaction goals, evaluating the carrying capacity of the distribution network under different scenarios, and providing input data for the optimization model.

[0163] In the embodiment of the present application, the random optimization solution module includes a multi-objective optimization model and a typical scenario, and adopts a random optimization mechanism to perform weighted average of the optimal solutions in each scenario to obtain the final optimization objective function, define the multi-objective optimization problem, and ensure that the optimization model can comprehensively consider multiple optimization objectives. The multi-objective optimization problem is solved by a random optimization method to obtain the optimal solution that comprehensively considers multiple scenarios.

[0164] In the embodiment of the present application, the stochastic optimization solution module further includes solving the multi-objective optimization model constructed above using algorithms such as NSGA-II to find a series of Pareto optimal solutions, and selecting a most suitable compromise solution from the Pareto optimal solution set as the final optimization solution.

[0165] In an embodiment of the present application, the real-time optimization strategy module includes solving an optimization problem in a rolling time domain based on the final objective function through a rolling optimization strategy, obtaining real-time optimization results, implementing a real-time optimization strategy, dynamically adjusting demand response and resource scheduling plans, and ensuring that the system is in the optimal state within each time step.

[0166] In the embodiment of the present application, the user incentive mechanism design module includes designing and implementing the user incentive mechanism to obtain the final solution of dynamically optimizing the distribution network carrying capacity. The incentive mechanism is used to increase the enthusiasm of users to participate in demand response and ensure the effective implementation of the optimization plan.

[0167] In the embodiment of the present application, each module is interconnected and complementary, and together constitutes a complete dynamic optimization framework for the distribution network's carrying capacity.

[0168] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for dynamically optimizing the carrying capacity of a distribution network, characterized in that: include: Collect and pre-process historical load data, distributed resource output data and electricity price data, and establish demand response models for different types of users through piecewise linear functions; Based on the demand response model, a large number of load and distributed resource output scenarios are generated using the Monte Carlo simulation method, and the K-means clustering algorithm is used to select typical scenarios from the large number of generated scenarios; Based on typical scenarios and demand response models, dynamic carrying capacity evaluation results are obtained through distribution network carrying capacity indicators; Based on the dynamic carrying capacity evaluation results, a multi-objective optimization model is constructed according to the economic objectives, reliability objectives and user satisfaction objectives; Based on the multi-objective optimization model and typical scenarios, a random optimization mechanism is used to perform weighted average of the optimal solutions in each scenario to obtain the final optimization objective function. Based on the final objective function, a rolling optimization strategy is used to solve an optimization problem in a rolling time domain and obtain real-time optimization results. Based on the real-time optimization results, a user incentive mechanism is designed and implemented to obtain the final solution for dynamically optimizing the distribution network's carrying capacity.

2. The method for dynamically optimizing the carrying capacity of a distribution network according to claim 1, characterized in that: The demand response models of different types of users are established by piecewise linear functions, including using piecewise linear functions to model the load regulation of residential users and industrial and commercial users; The load regulation of the residential user is expressed as: in, is the load regulation of residential users at time t; α r is the price sensitivity coefficient of residential users; p(t) is the electricity price at time t; p0 is the base electricity price; Maximum load regulation capability for residential users; The load regulation of the industrial and commercial users, taking into account the time dependence of the load regulation, is expressed as: in, is the load regulation of industrial and commercial users at time t; β c is the price sensitivity coefficient of industrial and commercial users; γ c is the time dependence coefficient of load regulation; ΔP c (t-1) is the load adjustment at the previous moment; Maximum load regulation capacity for industrial and commercial users.

3. The method for dynamically optimizing the carrying capacity of a distribution network according to claim 1 or 2, characterized in that: The distribution network carrying capacity index is expressed as: Among them, P L (t) is the base load at time t; ΔP DR (t) Load regulation contributed to demand response; P DG (t) is the output of distributed resources; S T is the capacity of distribution transformer.

4. The method for dynamically optimizing the carrying capacity of a distribution network according to claim 3, characterized in that: According to the economic goal, reliability goal and user satisfaction goal, a multi-objective optimization model is constructed, wherein the objective function of the multi-objective optimization model is expressed as: min F = {f1, f2, f3} f1=∑[C g (t)*P g (t)+C DR (t)*ΔP DR (t)] f2=E[∑P cut (t)] Among them, f1 is the economic goal, f2 is the reliability goal, f3 is the user satisfaction goal, C g (t) is the power generation cost at time t; P g (t) is the conventional power generation; C DR (t) is the demand response compensation cost, w i is the weight of user i; ΔP i (t) is the actual load adjustment of user i; is the expected load adjustment of user i.

5. The method for dynamically optimizing the carrying capacity of a distribution network according to claim 4, characterized in that: The method adopts a random optimization mechanism to solve the multi-objective optimization problem and select the optimal compromise solution, including the following steps: Monte Carlo simulation is used to generate a large number of scenarios, and the K-means clustering algorithm is used to select typical scenarios from the generated large number of scenarios; For each scenario, build an optimization model of the operating status and constraints under the specific scenario; For each scenario, solve the corresponding optimization problem and obtain the optimal solution for that scenario; Comprehensively consider the solutions in each scenario to obtain the final optimization objective function; Verify the performance of the obtained decision-making scheme in actual operation through simulation and make adjustments based on the actual situation; The final optimization objective function is expressed as: min F=∑π s *F s Among them, π s is the probability of scene s; F s is the objective function value under scenario s.

6. The method for dynamically optimizing the carrying capacity of a distribution network according to claim 5, characterized in that: The rolling optimization strategy is used to update the system state at each time step to obtain real-time optimization results, including the following steps: Update system status and forecast information; Construct and solve optimization problems to obtain the optimal decision for the current period; Execute the decision of the current period and roll over to the next time step to obtain real-time optimization results; The construction optimization problem is expressed as: min F=∑ τ∈[t，t+T] F(t) st g(x(τ),u(τ))=0 h(x(τ),u(τ))≤0 x(t)=x t Where T is the optimization time domain; x(τ) is the state variable; u(τ) is the control variable; g(·) and h(·) are the equality and inequality constraints respectively; x t is the initial state at time t.

7. The method for dynamically optimizing the carrying capacity of a distribution network according to claim 6, characterized in that: The designing and implementing of the user incentive mechanism includes calculating the user's incentive benefits; The user's incentive income is expressed as: Among them, α is the basic subsidy coefficient; β is the performance reward coefficient; γ is the nonlinear adjustment coefficient.

8. A system based on the method for dynamically optimizing the carrying capacity of a distribution network according to claim 1, characterized in that: Demand response modeling module, which is used to collect and pre-process historical load data, distributed resource output data and electricity price data, and establish demand response models for different types of users through piecewise linear functions; The scenario generation and clustering module is used to generate a large number of load and distributed resource output scenarios based on the demand response model using the Monte Carlo simulation method, and select typical scenarios from the large number of generated scenarios using the K-means clustering algorithm; Dynamic carrying capacity assessment module, which is used to dynamically assess the carrying capacity of the distribution network based on typical scenarios and demand response models and the carrying capacity indicators of the distribution network; A multi-objective optimization model building module is used to build a multi-objective optimization model based on the dynamic load-bearing capacity evaluation results, economic objectives, reliability objectives, and user satisfaction objectives; The stochastic optimization solution module is used to obtain the final optimization objective function by weighted averaging the optimal solutions in each scenario based on the multi-objective optimization model and typical scenarios using a stochastic optimization mechanism; The real-time optimization strategy module is used to solve the optimization problem in a rolling time domain based on the final objective function through a rolling optimization strategy to obtain real-time optimization results; The user incentive mechanism design module is used to design and implement the user incentive mechanism to obtain the final solution for dynamically optimizing the distribution network carrying capacity.

9. An electronic device, characterized in that: include: Memory, used to store programs; A processor, used to load the program to execute the steps of the method for dynamically optimizing the carrying capacity of a distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the steps of the method for dynamically optimizing the carrying capacity of a distribution network as described in any one of claims 1 to 7 are implemented.

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