Power distribution network optimization scheduling method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of optimal scheduling, and particularly discloses a power distribution network optimal scheduling method and system, electronic equipment and a storage medium, and the method comprises the steps: constructing an optimal scheduling model of a power distribution network; wherein the optimization scheduling model takes the maximization of the flexible domain of the power distribution network and the minimization of the network operation cost as targets; and solving the optimal scheduling model by using Monte Carlo simulation and an improved optical microscope optimization algorithm to obtain an optimal scheduling result of the power distribution network. According to the method, accurate optimization scheduling of the power distribution network can be realized, so that each flexible resource of the power distribution network can be effectively scheduled, fluctuation is coped, the photovoltaic acceptance capability is improved, and an effective solution is provided for stable operation of the power distribution network.

Description

Background Art

[0002] In the context of global energy transformation, the importance of distribution network dispatching has become increasingly prominent. As a key link connecting power generation and users in the power system, the distribution network is undergoing a transformation from traditional centralized power supply to diversified, distributed energy grid connection. With the rapid growth of renewable energy (such as wind power and solar energy), the distribution network faces new challenges such as intensified load fluctuations and increased power generation uncertainty.

[0003] In recent years, scholars have made significant progress in the field of distribution network dispatching, mainly focusing on the development and application of intelligent dispatching algorithms. Many studies have adopted a data-driven approach, using machine learning and deep learning techniques to improve the accuracy of load forecasting and thus optimize dispatching strategies. In addition, the research on distributed optimization algorithms has also attracted widespread attention. Such algorithms decompose complex dispatching problems into local problems, enabling various types of distributed energy to optimize autonomously in a local range and improve the efficiency of overall dispatching. Researchers have also explored robust dispatching and uncertainty modeling to cope with the instability of renewable energy, and proposed an intelligent dispatching framework based on digital twins and the Internet of Things to achieve real-time monitoring and rapid response. In general, the research on distribution network dispatching increasingly needs to develop in the direction of intelligence, diversification and systematization, highlighting the urgent need for efficient dispatching in the current complex power environment. This progress is not only a necessary means to cope with renewable energy access and load fluctuations, but also an important guarantee for ensuring the safe and stable operation of future power systems.

[0004] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a distribution network optimization scheduling method, system, electronic device and storage medium.

[0006] In a first aspect, the present invention provides a method for optimizing the dispatching of a distribution network, and the technical solution of the method is as follows:

[0007] Constructing an optimal dispatching model for the distribution network; wherein the optimal dispatching model aims to maximize the flexible domain of the distribution network and minimize the network operation cost;

[0008] The optimization dispatching model is solved by using Monte Carlo simulation and improved optical microscope optimization algorithm to obtain the optimization dispatching result of the distribution network.

[0009] The beneficial effects of a distribution network optimization dispatching method of the present invention are as follows:

[0010] The method of the present invention can achieve accurate optimization and scheduling of the distribution network, so as to effectively schedule various flexibility resources of the distribution network, cope with fluctuations and improve photovoltaic acceptance capacity, and provide an effective solution for the stable operation of the distribution network.

[0011] On the basis of the above scheme, a distribution network optimization dispatching method of the present invention can also be improved as follows.

[0012] In an optional manner, the method further includes:

[0013] The target evaluation index of the distribution network is used to evaluate the optimization scheduling result.

[0014] In an optional manner, the target evaluation indicators are: flexible domain utilization and average power deviation.

[0015] In an optional manner, the method further includes:

[0016] Based on the adaptive differential mutation mechanism, the optical microscope optimization algorithm is improved and optimized to obtain the improved optical microscope optimization algorithm.

[0017] In a second aspect, the present invention provides a distribution network optimization dispatching system, the technical solution of which is as follows:

[0018] Includes: building module and running module;

[0019] The construction module is used to: construct an optimal dispatching model for the distribution network; wherein the optimal dispatching model aims to maximize the flexible domain of the distribution network and minimize the network operation cost;

[0020] The operation module is used to solve the optimization scheduling model by using Monte Carlo simulation and improved optical microscope optimization algorithm to obtain the optimization scheduling result of the distribution network.

[0021] The beneficial effects of a distribution network optimization dispatching system of the present invention are as follows:

[0022] The system of the present invention can realize accurate optimization and dispatching of the distribution network, so as to effectively dispatch various flexibility resources of the distribution network, cope with fluctuations and improve photovoltaic acceptance capacity, and provide an effective solution for the stable operation of the distribution network.

[0023] On the basis of the above scheme, a distribution network optimization dispatching system of the present invention can also be improved as follows.

[0024] In an optional manner, the method further includes: an evaluation module; the evaluation module is used to:

[0025] The target evaluation index of the distribution network is used to evaluate the optimization scheduling result.

[0026] In an optional manner, the target evaluation indicators are: flexible domain utilization and average power deviation.

[0027] In an optional manner, the method further includes: an optimization module; the optimization module is used to:

[0028] Based on the adaptive differential mutation mechanism, the optical microscope optimization algorithm is improved and optimized to obtain the improved optical microscope optimization algorithm.

[0029] In a third aspect, a technical solution of an electronic device of the present invention is as follows:

[0030] The invention comprises a memory, a processor and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the distribution network optimization scheduling method of the present invention are implemented.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0032] Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the distribution network optimization scheduling method of the present invention.

[0033] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0035] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for optimizing and dispatching a distribution network according to the present invention;

[0036] Figure 2 It is the interactive block diagram of the distribution network;

[0037] Figure 3 Flowchart for solving the optimization algorithm for improved optical microscopy;

[0038] Figure 4 A schematic diagram of the structure of an embodiment of a distribution network optimization dispatching system of the present invention;

[0039] Figure 5 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0041] Figure 1 A flow chart of an embodiment of a distribution network optimization scheduling method provided by the present invention is shown, and the distribution network optimization scheduling method can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the distribution network optimization scheduling method by calling computer-readable instructions stored in a memory through a processor. As Figure 1 As shown, the following steps are included:

[0042] S1. Construct an optimal dispatching model for distribution network.

[0043] Among them, the optimization dispatch model aims to maximize the flexibility of the distribution network and minimize the network operation cost.

[0044] In S1, the following two points need to be explained:

[0045] 1) As an important part of the power system, the distribution network is responsible for distributing electricity from the high-voltage transmission network to end users. The core task of the distribution network is to ensure the reliability and stability of power supply. With the rapid development of renewable energy, especially the large-scale application of photovoltaic power generation, the distribution network faces higher regulation needs and challenges. The intermittent and volatile nature of photovoltaic power generation requires the distribution network to have greater flexibility to cope with the uncertainty caused by the imbalance between supply and demand. Therefore, the distribution network needs to continuously improve its flexibility and optimize resource scheduling to adapt to the future changes in the diverse energy structure and ensure the stable integration of renewable energy.

[0046] 2) Energy storage systems play a key role in improving the flexibility of distribution networks and optimizing resource scheduling. Energy storage systems can store excess energy when there is a surplus of electricity, and release it when there is a power shortage or peak demand, helping to balance the supply and demand of electricity. During the peak period of photovoltaic power generation, the energy storage system can store excess electricity and put it into use when photovoltaic power generation is insufficient or when electricity demand peaks. This scheduling function not only improves the reliability of power supply, but also reduces dependence on traditional fossil energy. Through the organic combination with the distribution network, the energy storage system significantly enhances the flexibility and economy of the power system, providing a guarantee for the widespread application of renewable energy.

[0047] 3) In the current research on power system flexibility, the flexibility concepts of the International Energy Agency (IEA) and the North American Electric Reliability Council (NERC) are mainly used as reference. Flexibility refers to the ability of the power system to quickly respond to fluctuations in supply and demand to meet changing load demands. It has become a core feature required by modern power systems in the face of a rapidly changing energy environment. With the increase in photovoltaic power generation and other renewable energy sources, the volatility of power demand has also increased, and system flexibility is particularly important. Figure 2 As shown in Figure 2, the flexibility of the distribution network is reflected in the rapid response to load changes, the adjustment of power generation output and the precise scheduling of energy storage systems. Through intelligent management and optimized scheduling, the synergy between the distribution network, energy storage system and renewable energy is enhanced, laying a solid foundation for the sustainable development of the energy system. S2. The optimization scheduling model is solved by using Monte Carlo simulation and improved optical microscope optimization algorithm to obtain the optimized scheduling result of the distribution network.

[0048] Among them, the Monte Carlo simulation obtains the fluctuation range and probability distribution of the upper and lower bounds of the flexible domain corresponding to the optimization scheduling model by simulating a large amount of data, and then determines the upper and lower bounds and provides a clear optimization space for the optimization scheduling model (that is, the Monte Carlo simulation is used to simplify the expression of the optimization scheduling model). The optical microscope optimization algorithm is inspired by the magnification ability of the optical microscope on the target object. The naked eye is used for initial observation, and the magnification process is simulated by the objective lens and the eyepiece. Based on the adaptive differential mutation mechanism, the optical microscope optimization algorithm is improved and optimized to obtain an improved optical microscope optimization algorithm. The adaptive differential mutation mechanism dynamically adjusts the mutation operation according to the distribution of the current population, which can not only enhance the diversity of the population, but also more effectively avoid falling into the local optimal solution. The optimization scheduling results include: the optimization scheduling results of the flexibility resources of the distribution network.

[0049] Preferably, it also includes:

[0050] The target evaluation index of the distribution network is used to evaluate the optimization scheduling result.

[0051] Among them, the target evaluation indicators are: flexible domain utilization and average power deviation. The flexible domain utilization and average power deviation are used to evaluate the effectiveness of the distribution network's flexibility resources in the flexible domain and analyze their impact on the flexible domain, thereby improving the photovoltaic acceptance capacity and the reliability of the distribution network.

[0052] The following examples are used to illustrate this scheme:

[0053] S10. Construct a flexibility resource model corresponding to the distribution network.

[0054] The flexibility resource model includes: photovoltaic power generation model, generator model, electric-hydrogen hybrid energy storage model and demand response model. Specifically:

[0055] 1) Photovoltaic power generation model is:

[0056] P pv =AG t η ref (1-β(T amb +γG t -T ref )

[0057] Among them, P pv represents the output power of the photovoltaic panel, A represents the effective area of ​​the photovoltaic panel, G t represents the solar irradiance, η ref Reference temperature η ref PV module efficiency under T amb is the ambient temperature, β and γ are constants related to the photovoltaic system.

[0058] 2) The generator model is:

[0059]

[0060] Among them, P m is the mechanical input power, P e is the electrical output power, δ is the power angle, ω is the rotor angular velocity, ω s is the synchronous angular velocity, M is the moment of inertia. V is the generator terminal voltage, E is the generator terminal potential, X s is the synchronous reactance at the generator end, and I is the current at the generator end.

[0061] 3) In the electric-hydrogen hybrid energy storage model, hydrogen is a clean energy with no carbon emissions, which helps achieve low-carbon goals. The electric-hydrogen energy storage model converts electrical energy into hydrogen storage, and then generates electricity through fuel cells to improve system flexibility. Since it is difficult to achieve minute-level power regulation in alkaline electrolyzers, a proton exchange membrane electrolyzer model is used to achieve efficient power regulation. The proton exchange membrane electrolyzer model is:

[0062]

[0063] Among them, η char (T) is the charging efficiency at temperature T, η disc (T) is the discharge efficiency at temperature T, P char is the charging power, P disc is the discharge power.

[0064] The hydrogen energy storage model is:

[0065]

[0066] Among them, N b is the amount of hydrogen in and out, N b,max The maximum capacity for storing hydrogen.

[0067] 4) Demand response models include:

[0068] ① Interruptible load. Interruptible load refers to the power load that can be temporarily interrupted or reduced under certain conditions during the operation of the power system after negotiation and agreement with the user. The user agrees to reduce or interrupt his power consumption at the request of the power company during peak grid demand or emergency situations, and receive corresponding compensation in the process. The expression of interruptible load is:

[0069]

[0070] This expression is the compensation cost per unit power at time t.

[0071] At time t, the total power supply power P t Must meet load demand D t and interruptible load P IL,t Relationship:

[0072] P t =D t -P IL,t

[0073] ②The expression of time-of-use electricity price is:

[0074]

[0075] Among them, P i represents the electricity price in the ith period (￥ / kWh), E i represents the electricity consumption in the ith time period (￥ / kWh), and n represents the total number of time periods in a day.

[0076] S20, constructing an optimal dispatching model for the distribution network. The optimal dispatching model in this embodiment includes an objective function and constraints, specifically:

[0077] 1) The objective function is:

[0078]

[0079] Among them, F(α + ,α - ) is the upper bound α of the distribution network flexibility domain Ω + , the lower bound α - function.

[0080]

[0081] in, is the upward flexibility boundary of photovoltaics, The upward flexibility boundary for powering the microgrid, is the upward flexibility boundary of the load, is the downward flexibility boundary of photovoltaics, The downward flexibility boundary for powering the microgrid, is the downward flexibility boundary of the load, σ1, σ2, σ3 and σ4 are all weight coefficients.

[0082]

[0083] Among them, C total is the total cost function of the distribution network. op,i,t is the operating cost of the generator, C maint,i,t is the maintenance cost of the generator, C PV,p,t is the photovoltaic power generation cost, is the cost of abandoned light, is the load shedding cost, C HESS,t is the operating cost of the hybrid energy storage system, C MD,i,t It is the communication loss cost between microgrid and distribution network.

[0084] To simplify the calculation, the objective function is expressed in matrix form as:

[0085]

[0086] in, is the flexibility resource weight matrix, and is the cost matrix, x is all the power-related variables in the flexible domain, and y is all the voltage and current-related variables in the flexible domain.

[0087] 2) Constraints include: flexibility resource constraints, line flow and balanced power constraints, and flexibility domain constraints. Specifically:

[0088] 1. Flexibility resource constraints. Flexibility resource constraints include: generator constraints, electric-hydrogen hybrid energy storage constraints and interruptible load constraints.

[0089] (1) Generator constraints include: output constraints and ramp constraints.

[0090] ①The output constraints are:

[0091] P g,min ≤P g,t ≤P g,max

[0092] Among them, P g,min represents the minimum output power of the g-th generator, P g,t represents the output power of the g-th generator at time t, P g,max represents the maximum output power of the g-th generator.

[0093] ② The climbing constraint is:

[0094]

[0095] Among them, P g,t represents the output power of the g-th generator at time t, P g,t-1 represents the output power of the gth generator at time t-1, R up represents the maximum ramp power of the g-th generator, R down Represents the maximum ramp-down power of the g-th generator.

[0096] (2) Electric-hydrogen hybrid energy storage constraints include: hybrid energy storage capacity constraints, charging and discharging power constraints, state of charge constraints and hydrogen energy storage constraints.

[0097] ① Hybrid energy storage capacity constraints are:

[0098]

[0099] in, represents the maximum capacity of the hybrid energy storage system, Represents the minimum capacity of the hybrid energy storage system. HESS,t Represents the capacity of the hybrid energy storage system at time t.

[0100] ②Charge and discharge power constraints are:

[0101]

[0102] in, Indicates the rated power of the battery output. Indicates the rated power of the supercapacitor output. Indicates the battery charging power when the hybrid energy storage system is working, Indicates the battery discharge power when the hybrid energy storage system is working. Indicates the supercapacitor charging power when the hybrid energy storage system is working, Indicates the supercapacitor discharge power when the hybrid energy storage system is working, Indicates the maximum operating power of the proton exchange membrane electrolyzer, Represents the minimum operating power of the proton exchange membrane electrolyzer, P PEMEC,t represents the operating power of the electrolyzer at time t, Indicates the upper limit of charge and discharge of proton exchange membrane fuel cells, Indicates the lower limit of charge and discharge of proton exchange membrane fuel cells.

[0103] ③ The state of charge constraint is:

[0104]

[0105] Among them, SOC t It represents the state of charge of the hybrid energy storage system at time t, SOC t-1 represents the charge state of the hybrid energy storage system at time t-1, represents the charging of the hybrid energy storage system at time t, represents the discharge of the hybrid energy storage system at time t, η represents the charging and discharging efficiency of the hybrid energy storage system, and Δt represents the charging and discharging time interval.

[0106] ④ Hydrogen energy storage constraints are:

[0107]

[0108] SOH min ≤SOH t ≤SOH max

[0109] Among them, SOH t Indicates the hydrogen storage state of the hydrogen storage tank at time t, SOH t-1 Indicates the hydrogen storage state of the hydrogen storage tank at time t-1; Indicates the amount of hydrogen entering the hydrogen storage tank. Indicates the amount of hydrogen gas discharged from the hydrogen storage tank, V max The maximum capacity of the hydrogen storage tank.

[0110] (3) The interruptible load constraint is:

[0111] 0≤P IL,t ≤P IL,max

[0112] Among them, P IL,t is the power of the interruptible load, P IL,max It is the maximum value of interruptible load power.

[0113]

[0114] Among them, t off,i is the interruption time, t on,i To cancel the interruption time, T IL,max is the maximum interruption time of the interruptible load.

[0115] 2. Line power flow and balanced power constraints include: line DC power flow and balanced power constraints, line transmission capacity constraints and tie line constraints.

[0116] (1) The line DC power flow and balanced power constraints are:

[0117]

[0118]

[0119] in, represents the power balance of node k at time t, represents the power flow of the line starting from node k, represents the power flow of the line into node k, represents the power generation of all generators at node k, represents the installed load power of all loads at node k.

[0120] (2) The line transmission capacity constraint is:

[0121] S line ≤S line,max

[0122] Among them, S line,max The maximum capacity of the line transmission.

[0123] (3) The tie line constraints are:

[0124] |P micro,t -P grid,t |≤P max

[0125] 3. Flexible domain constraints. Flexible domain constraints include: flexible domain upper and lower bound constraints, flexible domain permission constraints, and flexibility climbing constraints.

[0126] (1) The upper and lower bounds of the flexible domain are:

[0127] α - ≤α≤α +

[0128] (2) Beyond the flexible domain license constraints:

[0129]

[0130] (3) The flexibility climbing constraint is:

[0131]

[0132] Among them, P t =[P g,t ,P e,t ,P pv,t ,P m,t ] is the output of each flexible resource at time t, P t =[P g,t ,P e,t ,P pv,t ,P m,t ] is the output of each flexible resource at time t-1, U max ,D max They represent the maximum up and down powers coupled by the flexibility resources, respectively.

[0133] S30, using Monte Carlo simulation and improved optical microscope optimization algorithm to solve the optimization scheduling model and obtain the optimization scheduling result of the distribution network. Specifically:

[0134] 1) Monte Carlo simulation.

[0135] In this embodiment, Monte Carlo simulation is used to simulate a large amount of data to obtain the fluctuation range and probability distribution of the upper and lower bounds of the flexible domain, and then determine the upper and lower bounds to provide a clear optimization space. First, the output of each flexible resource in each period is weighted and summed to obtain the fuzzy curve of the flexible domain. Suppose the output of the flexible domain in period t is P t , the upper and lower bounds of the flexible domain are denoted as α up ,α down , initialize the upper and lower bounds to infinity. Then, use the Monte Carlo simulation method to perform 1000 simulations to generate the output distribution data set. Using the confidence interval formula:

[0136]

[0137] in, is the sample mean, Z is the critical value of the standard normal distribution, σ is the sample standard deviation, and n is the sample size. In order to determine the confidence interval of the flexible domain, the extreme values ​​of 1.5% above and below are ignored, that is, the simulation results are arranged in ascending order, and the Nth data point is taken as the lower bound. The 1000-Nth data point is used as the upper bound Thus, we can get the upper and lower bounds of the confidence interval. Downward volatility bias Solve separately Z,σ, ensures that the fluctuation satisfies the confidence interval. Although the output of the flexible resources in the flexible domain is not linearly additive, a range can be found to ensure that the upper and lower bounds meet this range and can be optimized using algorithms. As the minimum value of the upper bound, take As the maximum value of the upper bound; take As the minimum value of the lower bound, take As the maximum value of the upper bound. The upper and lower bounds are scaled from infinity to the visible range, recorded as:

[0138]

[0139] Therefore, the optimal scheduling model can be transformed into:

[0140]

[0141] Gx+Hy≤z,

[0142]

[0143] Δα - ≤α≤Δα +

[0144] The optimization scheduling model is simplified by using Monte Carlo simulation. The simplified optimization scheduling model has the conditions for iterative solution through optimization algorithm, and the solution process is relatively simple.

[0145] 2) Improved optical microscope optimization algorithm.

[0146] The optical microscope optimization algorithm is inspired by the magnification ability of an optical microscope on the target object, using the naked eye for initial observation and simulating the magnification process through the objective lens and eyepiece. The specific optimization steps of the optical microscope optimization algorithm include:

[0147] ① Naked eye observation: First, the object is properly mounted on the specimen stage. The observer observes the object with the naked eye to obtain preliminary information about its size, shape, and characteristics.

[0148] ②Objective: Observation of the initial target object continues with the objective. Magnification (MP) is a factor used to determine the image magnification in this step and is a fixed variable set by the specifications of the specific objective optical element used.

[0149] ③ Eyepiece: The eyepiece further magnifies the objective and its magnification (MP). The total magnification of a particular microscope is calculated by multiplying the magnification values ​​of the objective and eyepiece.

[0150] ④ Best target: Compare the results of each amplification cycle to obtain the magnification value of the target.

[0151] For the improved optical microscope optimization algorithm, specifically:

[0152] ① Exploration stage. The exploration stage is mainly the optimization stage of the objective lens. The magnification of the target object follows the magnification principle of the compound microscope. The formula of the eyepiece is:

[0153] M total =M O *M E

[0154]

[0155] M i,new =M i +MP O *M best

[0156] Among them, M total Represents the total visual magnification of the microscope, M O is the magnification value of the objective lens, M E is the magnification value of the eyepiece, L is the length of the microscope tube, f0 is the focal length of the objective lens, M i represents the initial target object, MP O Indicates the magnification of the objective lens, M best The best target object.

[0157] ② Utilization stage. The utilization stage mainly involves the optimization of the eyepiece, and the relevant formula is:

[0158]

[0159] M i,new =M i +MP E *space

[0160] Where D is the minimum viewing distance, f is the focal length of the eyepiece, MP E Represents the magnification of the eyepiece, space represents the local search space of the magnification, and the microscope parameters can be set according to the actual situation. By using the formulas and steps of the stage, the optical microscope optimization algorithm can effectively simulate the magnification process of the microscope, thereby achieving the best observation effect of the target object.

[0161] ③Improved optical microscope optimization algorithm.

[0162] In order to improve the performance of the optical microscope optimization algorithm (OMA), this embodiment proposes an adaptive differential mutation mechanism based on the traditional differential evolution algorithm (DE). The adaptive differential mutation mechanism dynamically adjusts the mutation operation according to the distribution of the current population, which can not only enhance the diversity of the population, but also more effectively avoid falling into the local optimal solution.

[0163] In traditional differential evolution, the mutation operation is usually expressed as:

[0164] v i =x r1 +F·(x r2 -x r3 )

[0165] Among them, v i is the mutation vector of the i-th target object, x r1 、x r2 、x r3 is the optimal target object among all candidates, and F is the variation factor.

[0166] To further optimize the mutation strategy, an adaptive factor is introduced in this embodiment to dynamically adjust the mutation factor according to the diversity and evolutionary generations of the target object. Specifically, the adaptive mutation factor is defined as:

[0167]

[0168] Among them, F min is the minimum value of the variation factor, F max is the maximum value of the mutation factor, gen is the current generation, gen max is the maximum algebra.

[0169] In addition to the traditional differential mutation strategy, an additional differential term is introduced to further enhance the exploration ability of the population. The improved mutation operation formula is:

[0170] v i =x r1 +F i ·(x r2 -x r3 )+λ·(M best -x r4 )

[0171] Among them, M best is the optimal target object in the current optimal solution, x r4 is an individual randomly selected from the population, λ is a weight factor, and λ is used to balance the influence of the double difference term.

[0172] Through the above improvements, the new differential mutation strategy (improved optical microscope optimization algorithm) not only enhances the diversity of the population, but also improves the global search capability and local search accuracy of OMA, thereby significantly improving the effect of OMA optimization. This adaptive double differential mutation strategy has shown better optimization effect and convergence speed in practical applications, verifying its effectiveness and superiority in OMA optimization.

[0173] ④The solution process of the improved optical microscope optimization algorithm is as follows Figure 3 As shown, specifically:

[0174] Step 1: Read the initial data of various flexibility resources, lines, nodes, etc. in the distribution network, and import the inherent data of OMA;

[0175] Step 2: Initialize and set parameters such as the number of target objects and the maximum number of iterations, and enter the visual search stage;

[0176] Step 3: Calculate the fitness value of the target object;

[0177] Step 4: Start the exploration phase, i.e. the objective search phase, generate the best candidate solution and leave other feasible solutions;

[0178] Step 5: Perform fitness evaluation on other feasible solutions and keep the better solutions;

[0179] Step 6: Start the utilization phase, i.e. the eyepiece search phase, and update the optimal solution;

[0180] Step 7: Use the adaptive mutation operator of the target object to perform mutation operation on it;

[0181] Step 8: Constraint judgment. Evaluate whether the constraint conditions are currently met. If yes, proceed to the next step. If not, return to step 4.

[0182] Step 9: Termination judgment. If the maximum number of iterations has been reached, output the result, otherwise return to step 3.

[0183] S40. Evaluate the optimization dispatching results using the target evaluation indicators of the distribution network.

[0184] The target evaluation indicators include: flexible domain utilization and average power deviation. Specifically:

[0185] 1) Flexible domain utilization. In order to evaluate the performance of the flexible domain, the flexible domain utilization is introduced as an evaluation indicator. When optimizing the scheduling of flexible resources, it is necessary to ensure that each flexible resource reaches the optimal output rather than the maximum output. Therefore, the flexible domain utilization is used to indicate the index of the optimal degree of flexibility resources. The calculation formula of the flexible domain utilization is:

[0186]

[0187] Where η represents the utilization rate of the flexible domain, P op is the current optimal output of the flexible resource, P op Maximize the output of flexible resources.

[0188] 2) Average power deviation. To further evaluate the performance of the flexible domain, the average power deviation is introduced as another evaluation indicator. The average power deviation reflects the smoothness of the dispatching flexibility resources, that is, there is no large output change in a short period of time. When the flexibility deviation is large, the average power deviation indicator can also reflect the current situation and assist the dispatching system to make adjustments to maintain the stability of the system. The calculation formula for the average power deviation is:

[0189]

[0190] Where ΔP avg represents the average power deviation, T is the total time period, P t is the power output at time t, P op The current optimal output of flexible resources.

[0191] This embodiment proposes the concept of a flexible domain of a distribution network, which is defined as a set of flexible resources that can provide regulation and response capabilities when the distribution network responds to uncertain factors such as large-scale photovoltaic access and load fluctuations. Based on this, a boundary definition method for the flexible domain is constructed, and the characteristics of different flexible resources in the distribution network are combined to ensure that the uncertainty in the operation of the distribution network can be effectively dealt with when scheduling resources. In addition, the present invention designs an optimization model with the goal of maximizing the flexible domain and minimizing the network operation cost, which provides guidance on the economy and scheduling efficiency of resource allocation in the distribution network. In order to achieve efficient solution, the Monte Carlo simulation method (MCS) and the improved optical microscope optimization algorithm (OMA) are used to optimize the model. Further, in order to quantify the effect of flexible resources, this embodiment proposes the flexible domain utilization rate and average power deviation as evaluation indicators, evaluates the effectiveness of flexible resources in the flexible domain, and analyzes its impact on the flexible domain, thereby improving the photovoltaic acceptance capacity and the reliability of the distribution network, and providing an effective solution for the stable operation of the distribution network.

[0192] The technical solution of this embodiment can achieve accurate optimization and scheduling of the distribution network, so as to effectively schedule various flexibility resources of the distribution network, cope with fluctuations and improve photovoltaic acceptance capacity, providing an effective solution for the stable operation of the distribution network.

[0193] Figure 4 FIG. 2 shows a schematic diagram of a structure of an embodiment of a distribution network optimization dispatching system 200 provided by the present invention. Figure 4As shown, the system 200 includes: a construction module 210 and an operation module 220;

[0194] The construction module 210 is used to: construct an optimal dispatching model for the distribution network; wherein the optimal dispatching model aims to maximize the flexible domain of the distribution network and minimize the network operation cost;

[0195] The operation module 220 is used to solve the optimization scheduling model by using Monte Carlo simulation and improved optical microscope optimization algorithm to obtain the optimization scheduling result of the distribution network.

[0196] Preferably, the system further comprises: an evaluation module; the evaluation module is used to:

[0197] The target evaluation index of the distribution network is used to evaluate the optimization scheduling result.

[0198] Preferably, the target evaluation indicators are: flexible domain utilization and average power deviation.

[0199] Preferably, the system further comprises: an optimization module; the optimization module is used to:

[0200] Based on the adaptive differential mutation mechanism, the optical microscope optimization algorithm is improved and optimized to obtain the improved optical microscope optimization algorithm.

[0201] It should be noted that the beneficial effects of the distribution network optimization and dispatching system provided by the above-mentioned embodiment are the same as the beneficial effects of the distribution network optimization and dispatching method described above, which will not be described in detail here. In addition, when the system provided by the above-mentioned embodiment realizes its functions, it only takes the division of the above-mentioned functional modules as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided by the above-mentioned embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described in detail here.

[0202] Among them, the distribution network optimization and dispatching system of the present invention can be a computer program (including program code) running in a computer device. For example, the distribution network optimization and dispatching system of the present invention is an application software that can be used to execute the corresponding steps in the distribution network optimization and dispatching method of the present invention.

[0203] In some embodiments, the distribution network optimization and dispatching system of the present invention can be implemented by a combination of software and hardware. As an example, the distribution network optimization and dispatching system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the distribution network optimization and dispatching method of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.

[0204] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The name of a module does not limit the module itself in some cases.

[0205] An electronic device according to an embodiment of the present invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any one of the above-mentioned distribution network optimization and scheduling methods is implemented. That is to say, an electronic device according to an embodiment of the present invention may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the distribution network optimization and scheduling method shown in any embodiment of the present invention by calling a computer program.

[0206] In an alternative embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0207] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0208] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0209] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0210] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0211] Among them, the electronic device can also be a terminal device, and the terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0212] It should be noted that Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0213] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, any of the above-mentioned distribution network optimization scheduling methods is implemented.

[0214] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0215] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the above-mentioned distribution network optimization scheduling method.

[0216] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0217] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0218] The computer-readable storage medium provided by the embodiment of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or component.

[0219] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0220] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

[0221] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0222] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0223] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A distribution network optimization dispatching method, characterized in that: include: Constructing an optimal dispatching model for the distribution network; wherein the optimal dispatching model aims to maximize the flexible domain of the distribution network and minimize the network operation cost; The optimization dispatching model is solved by using Monte Carlo simulation and improved optical microscope optimization algorithm to obtain the optimization dispatching result of the distribution network.

2. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: Also includes: The target evaluation index of the distribution network is used to evaluate the optimization scheduling result.

3. The distribution network optimization dispatching method according to claim 2, characterized in that: The target evaluation indicators are: flexible domain utilization and average power deviation.

4. The distribution network optimization dispatching method according to any one of claims 1 to 3, characterized in that: Also includes: Based on the adaptive differential mutation mechanism, the optical microscope optimization algorithm is improved and optimized to obtain the improved optical microscope optimization algorithm.

5. A distribution network optimization dispatching system, characterized in that: include: Building modules and running modules; The construction module is used to: construct an optimal dispatching model for the distribution network; wherein the optimal dispatching model aims to maximize the flexible domain of the distribution network and minimize the network operation cost; The operation module is used to solve the optimization scheduling model by using Monte Carlo simulation and improved optical microscope optimization algorithm to obtain the optimization scheduling result of the distribution network.

6. The distribution network optimization dispatching system according to claim 5, characterized in that: Also includes: Evaluation module; the evaluation module is used to: The target evaluation index of the distribution network is used to evaluate the optimization scheduling result.

7. The distribution network optimization dispatching system according to claim 6, characterized in that: The target evaluation indicators are: flexible domain utilization and average power deviation.

8. The distribution network optimization dispatching system according to any one of claims 5 to 7, characterized in that: Also includes: Optimization module; The optimization module is used to: Based on the adaptive differential mutation mechanism, the optical microscope optimization algorithm is improved and optimized to obtain the improved optical microscope optimization algorithm.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the distribution network optimization scheduling method as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor so that the computer-readable storage medium implements the distribution network optimization scheduling method according to any one of claims 1 to 4.

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