Power distribution network operation optimization method and system based on dynamic electricity price

Abstract

The invention relates to the technical field of power distribution network data processing, in particular to a power distribution network operation optimization method and system based on dynamic electricity price. The method comprises the following steps: acquiring the difference between the electricity price and electricity consumption at the moment and the historical electricity price and electricity consumption to obtain the initial acceptance probability at each moment; presetting an initial current solution at a moment, constructing an objective function of the solution at the moment, and randomly selecting a solution from a neighbor solution set of the current solution as a new solution; calculating the acceptance probability of a new solution in each iteration at the moment; under the condition that the new solution objective function value is greater than the current solution objective function value, obtaining a new current solution based on the acceptance probability of the new solution; and iterating to obtain the electricity purchasing quantity at the moment so as to realize the operation optimization of the power distribution network and effectively improve the operation efficiency of the power distribution network based on the dynamic electricity price.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network data processing, and in particular to a method and system for optimizing the operation of a distribution network based on dynamic electricity prices. Background Art

[0002] In order to reduce carbon emissions and optimize the electricity market, a dynamic electricity price mechanism has been proposed. The dynamic electricity price mechanism can reflect the supply and demand situation of the electricity market. Users adjust their electricity purchase amounts according to the electricity price signals, so that the community can still ensure a stable energy supply, reduce unnecessary electricity consumption, and optimize the allocation of electricity resources when the electricity supply fluctuates and the price changes.

[0003] The patent application document with the publication number CN112990600A discloses a microgrid optimal scheduling based on a linearization method of a price discrete matrix. This application is applied to a microgrid optimal scheduling model based on dynamic time-of-use electricity prices. By introducing a price discrete matrix, the problem of multiplying the real variables of electricity purchase and sale prices and electricity quantities is respectively transformed into the problem of multiplying 0-1 variables and real variables, and then the problem is further transformed into a linear programming problem by establishing Big-M constraints. Finally, the mixed-integer nonlinear programming problem is converted into a mixed-integer linear programming problem.

[0004] The above-mentioned existing technology simplifies the microgrid optimal scheduling model through a linearization method. In the scenario where the electricity price fluctuates frequently, the linearization method may not be able to quickly adjust the electricity purchase strategy, and ignores the problem of calculating the electricity purchase amount of users under dynamic electricity prices, taking the electricity purchase amount of users as a known quantity, resulting in the inability to accurately implement the grid scheduling based on dynamic electricity prices and user electricity purchase amounts.

[0005] Based on this, how to accurately optimize the operation of the distribution network based on dynamic electricity prices is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] In order to solve the technical problem of how to accurately optimize the operation of the distribution network based on dynamic electricity prices, the present invention provides a method and system for optimizing the operation of a distribution network based on dynamic electricity prices.

[0007] In the first aspect, the present invention provides a method for optimizing the operation of a distribution network based on dynamic electricity prices, adopting the following technical solution: A method for optimizing the operation of a distribution network based on dynamic electricity prices, comprising the steps of: Obtaining the initial acceptance probability at each moment through the differences between the electricity price and electricity consumption at the acquisition moment and the historical electricity price and electricity consumption; presetting an initial current solution at a moment, constructing an objective function for the solution at that moment, and randomly selecting a solution from the neighborhood solution set of the current solution as a new solution; ; is the acceptance probability of the new solution in the j-th iteration at this moment, is the initial acceptance probability at this moment, is the variance between the current solution in the j-th iteration at this moment and the current solutions in the previous 3 iterations, 、 are the objective function values of the new solution and the current solution respectively in the j-th iteration at this moment, is the linear normalization function, is the exponential function with base e; when the objective function value of the new solution is greater than that of the current solution, a new current solution is obtained based on the acceptance probability of the new solution; the electricity purchase quantity at this moment is obtained through iteration to achieve the optimal operation of the distribution network.

[0008] The present invention obtains the electricity purchase quantity at the current moment through the stochastic hill climbing algorithm, enabling the distribution network to accurately allocate electricity based on the electricity purchase quantity. In this process, the present invention takes into account that the accuracy of the acceptance probability of the new solution set in the iterative process of the stochastic hill climbing algorithm is relatively low and is easily affected by data quality; based on this, the present invention analyzes the differences between the electricity price and electricity consumption at the current moment and the historical collection moments to obtain the initial acceptance probability, making the initial acceptance probability depend on the actual data situation and improving the accuracy of the initial acceptance probability, thereby effectively improving the operation efficiency of the distribution network. On this basis, the present invention also takes into account that relying only on the actual data situation may result in some new solutions with poor quality being accepted as the new current solution; based on this, the present invention corrects the initial acceptance probability by analyzing the difference in the objective function values of the current solution and the new solution in the iterative process, and can accurately obtain the acceptance probability of the new solution in each iteration, thereby accurately obtaining the electricity purchase quantity at the current moment and effectively improving the operation efficiency of the distribution network.

[0009] According to a method for optimizing the operation of a distribution network based on dynamic electricity price provided by the present invention, before obtaining the initial acceptance probability of each moment through the differences between the electricity price and electricity consumption at the collection moment and the historical electricity price and electricity consumption, it further includes: obtaining the electricity price data and electricity consumption data of each collection moment, and obtaining the electricity price and electricity consumption of each collection moment after preprocessing; obtaining the electricity purchase moment and the electricity purchase quantity of each moment.

[0010] The present invention takes into account that there may be situations such as data missing and large differences in data values of different types in the original collected data, so the data is preprocessed to facilitate subsequent data analysis.

[0011] According to a method for optimizing the operation of a distribution network based on dynamic electricity price provided by the present invention, obtaining the initial acceptance probability of each moment through the differences between the electricity price and electricity consumption at the collection moment and the historical electricity price and electricity consumption includes: ; is the initial acceptance probability at a moment, is the absolute value of the difference between the electricity price at this moment and the updated electricity price last time, is the absolute value of the difference between the electricity price at this moment and the average value of the electricity prices updated 5 times before this moment, is the absolute value of the difference in electricity consumption between the two collection moments before this moment, is the average value of the electricity consumption of the 5 collection moments before the previous collection moment of this moment, is a hyperparameter, is a linear normalization function.

[0012] By analyzing the differences between the electricity price and electricity consumption at the current moment and the historical electricity prices and electricity consumptions, the present invention obtains the prominence of the electricity price and electricity consumption at the current moment. The higher the prominence, the higher the difficulty of predicting the electricity purchase quantity at the current moment. Therefore, the corresponding initial acceptance probability should also be higher to accurately obtain the global optimal solution at the current moment.

[0013] According to an operation optimization method for a distribution network based on dynamic electricity price provided by the present invention, constructing the objective function of the solution at this moment includes: ; is the objective function value of the solution at this moment, is the electricity purchase quantity corresponding to the solution, is the electricity price at this moment, is the electricity purchase quantity at the historical moment when the electricity storage quantity and electricity price are the same as those at this moment, is a linear normalization function.

[0014] By analyzing the electricity purchase cost of the solution at the current moment, the present invention can accurately obtain the objective function value corresponding to each solution at the current moment. The objective function value is negatively correlated with the preference degree of the solution. When selecting a new current solution in the subsequent steps, it can be evaluated based on the preference degree of the solution, where the solution can include a new solution and the current solution.

[0015] According to an operation optimization method for a distribution network based on dynamic electricity price provided by the present invention, the method for obtaining the neighbor solution set of the current solution includes: presetting the fluctuation range of the current solution, and forming the neighbor solution set of the current solution with all integers within the fluctuation range of the current solution.

[0016] According to an operation optimization method for a distribution network based on dynamic electricity price provided by the present invention, when the objective function value of the new solution is greater than the objective function value of the current solution, obtaining a new current solution based on the acceptance probability of the new solution further includes: when the objective function value of the new solution is not greater than the objective function value of the current solution, taking the new solution as the new current solution.

[0017] A method for optimizing the operation of a distribution network based on dynamic electricity prices provided by the present invention, wherein the iterative process for obtaining the electricity purchase quantity at this moment includes: presetting the number of iterations; and using the electricity purchase quantity corresponding to the new current solution obtained in the last iteration as the electricity purchase quantity at this moment.

[0018] A method for optimizing the operation of a distribution network based on dynamic electricity prices provided by the present invention, wherein the iterative process for obtaining the electricity purchase quantity at this moment to achieve the optimization of the distribution network operation includes: the distribution network allocating corresponding electricity quantities based on the electricity purchase quantity at this moment.

[0019] A method for optimizing the operation of a distribution network based on dynamic electricity prices provided by the present invention, after achieving the optimization of the distribution network operation, further includes: generating a user report based on each moment and the corresponding electricity purchase quantity.

[0020] The present invention takes into account that optimizing the operation of the distribution network depends not only on the electricity purchase quantity at the current moment but also on the actual electricity consumption of the target object. Therefore, by generating a user report to analyze the electricity consumption of the target object, it is convenient to optimize the operation of the distribution network based on the electricity consumption of the target object in the future.

[0021] In a second aspect, the present invention provides a system for optimizing the operation of a distribution network based on dynamic electricity prices, adopting the following technical solution: A system for optimizing the operation of a distribution network based on dynamic electricity prices includes: a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned method for optimizing the operation of a distribution network based on dynamic electricity prices is implemented.

[0022] By adopting the above technical solution, the above-mentioned method for optimizing the operation of a distribution network based on dynamic electricity prices is generated into a computer program and stored in the memory, so as to be loaded and executed by the processor, and thus a terminal device is manufactured according to the memory and the processor, which is convenient for use.

[0023] The present invention has the following technical effects: Based on the above technical solution, a method and system for optimizing the operation of a distribution network based on dynamic electricity prices provided by the present invention obtain the electricity purchase quantity at the current moment through a stochastic hill climbing algorithm during the operation of the distribution network, enabling the distribution network to accurately allocate electricity based on the electricity purchase quantity. During this process, the present invention takes into account that the acceptance probability of the new solution set during the iteration of the stochastic hill climbing algorithm is relatively low and is susceptible to the influence of data quality; based on this, the present invention analyzes the differences between the electricity prices and electricity consumption at the current moment and the historical collection moments to obtain the initial acceptance probability, making the initial acceptance probability depend on the actual data situation, improving the accuracy of the initial acceptance probability, and thus effectively improving the operation efficiency of the distribution network. On this basis, the present invention also considers that relying solely on the actual data situation may result in some new solutions with poor quality being accepted as the new current solution; based on this, the present invention analyzes the difference in the objective function values of the current solution and the new solution during the iteration process to correct the initial acceptance probability, and can accurately obtain the acceptance probability of the new solution for each iteration, thereby accurately obtaining the electricity purchase quantity at the current moment and effectively improving the operation efficiency of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 FIG. is a schematic flowchart of a method for optimizing the operation of a distribution network based on dynamic electricity prices provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0026] When accurately implementing the distribution of the target object, the distribution network based on dynamic electricity prices can allocate electricity based on the electricity purchase quantity of the target object.

[0027] Based on this, an embodiment of the present invention discloses a method for optimizing the operation of a distribution network based on dynamic electricity prices. This method analyzes the historical electricity consumption data and the dynamic changes in electricity prices of the target object to accurately obtain the electricity purchase quantity of the target object at the current moment, and allocates power resources based on the electricity purchase quantity of the target object at the current moment, which can reduce power consumption and resource waste and effectively improve the operation efficiency of the distribution network.

[0028] Specifically, refer to Figure 1 as shown in Figure 1 FIG., which is a schematic flowchart of a method for optimizing the operation of a distribution network based on dynamic electricity prices provided by an embodiment of the present invention. The method specifically includes the following steps.

[0029] S1: Obtain the electricity price, electricity consumption, and electricity purchase quantity at each collection moment.

[0030] It should be noted that based on the preset collection frequency, the electricity price at each collection moment and the power consumption of the target object can be obtained. Taking the electricity price and power consumption corresponding to each collection moment as a data point, a set of time series can be obtained. However, the electricity purchase behavior of the target object is usually random, and there may not be corresponding user electricity purchase volume data at each collection moment. Therefore, in the embodiments of the present invention, the electricity purchase volume data of the user is analyzed as a separate individual to obtain each moment when the user purchases electricity and the corresponding electricity purchase volume.

[0031] Exemplarily, in the embodiments of the present invention, obtaining the electricity price, power consumption, and electricity purchase volume at each collection moment includes: obtaining the electricity price data and power consumption data at each collection moment, and obtaining the electricity price and power consumption at each collection moment after preprocessing; obtaining the electricity purchase moment and the electricity purchase volume at each moment.

[0032] Among them, the preprocessing can be missing data processing, data normalization processing, etc., and can be specifically set according to actual needs. The embodiments of the present invention do not limit this too much here.

[0033] Specifically, based on the preset collection frequency, the electricity price and power consumption of the target object are collected at each collection moment, and each moment when the target object purchases electricity and the corresponding electricity purchase volume are obtained.

[0034] Among them, the collection frequency can be set to once per minute, and can be specifically set according to actual needs.

[0035] Among them, the target object can be a community, and can be specifically set according to actual needs.

[0036] It can be understood that the collection moment mentioned in the embodiments of the present invention hereinafter is the collection moment of the electricity price and / or power consumption, and the moment is the electricity purchase moment of the target object.

[0037] The random hill climbing algorithm, also known as the randomized hill climbing method, is a heuristic search algorithm used to iteratively find the global optimal solution in the solution space. During the iteration process, in order to avoid falling into the local optimal solution, the algorithm randomly selects a solution from the neighboring solution set of the current solution as the new solution, and obtains the final current solution based on the acceptance probability of the new solution.

[0038] Based on this, in the embodiments of the present invention, the optimal solution is iteratively obtained based on the acceptance probability of the solution in the neighboring solution set at the current moment through the random hill climbing algorithm, and finally the electricity purchase volume corresponding to the optimal solution is obtained, that is, the following steps are executed.

[0039] S2: Obtain the initial acceptance probability at each moment through the difference between the electricity price and power consumption at the collection moment and the historical electricity price and power consumption.

[0040] It should be noted that the random hill climbing algorithm sets the acceptance probability for the new solution randomly selected from the neighborhood solution set. If the evaluation function of the new solution is greater than that of the current solution, the new solution is selected as the new current solution; otherwise, the new solution needs to be accepted as the new current solution based on its acceptance probability. The conventional way to set the acceptance probability is to compare the quality of the current solution with that of its neighborhood solution set. This method has relatively high requirements for the quality of the neighborhood solution set. When the quality of the neighborhood solution is high, the difference between the neighborhood solution and the current solution can accurately reflect the optimization direction of the problem, and the acceptance probability set based on the difference can guide the algorithm to approach the global optimal solution; if the quality of the neighborhood solution is low and there is a lot of noise or misleading information, it may cause the algorithm to deviate from the correct direction during the search process.

[0041] Based on this, in the process of iteratively determining the electricity purchase quantity corresponding to the global optimal solution at the current moment in the embodiments of the present invention, by analyzing the electricity price fluctuation situation and the historical electricity consumption situation of the target object, the initial acceptance probability at this moment is obtained, so that the initial acceptance probability of the new solution at the current moment depends on the actual data change, thereby accurately obtaining the value of the initial acceptance probability.

[0042] Exemplarily, in the embodiments of the present invention, the initial acceptance probability at each moment is obtained through the difference between the electricity price and electricity consumption at the acquisition moment and the historical electricity price and electricity consumption. Specifically, the following relational expression can be referred to: ; is the initial acceptance probability at a moment, is the absolute value of the difference between the electricity price at this moment and the electricity price after the previous update, is the absolute value of the difference between the electricity price at this moment and the average value of the electricity prices after the previous 5 updates, is the absolute value of the difference between the electricity consumption at this moment and the electricity consumption at the previous 2 acquisition moments, is the average value of the electricity consumption at the previous 5 acquisition moments before the previous acquisition moment at this moment, is a hyperparameter, is a linear normalization function.

[0043] Among them, the hyperparameter in is to avoid the situation that the electricity price at the current moment is the same as the electricity price after the previous update, resulting in the formula being meaningless, that is, the electricity price has not changed newly from the previous update to the current moment; the hyperparameter in is to avoid the situation that the absolute value of the difference between the electricity price at the current moment and the average value of the electricity prices after the previous 5 updates is the same, that is, the electricity price at the current moment has not changed compared with the previous 5 updates. The value of the hyperparameter can be set to 0.01, and the value of the hyperparameter can be specifically set according to actual needs. The embodiments of the present invention do not limit this too much here.

[0044] It is understandable that the electricity price is affected by various factors. After the electricity price is updated, it may be the same as the original electricity price or may change. Therefore, if the current time is the same as the electricity price after the previous 5 updates, then is 0.

[0045] In the above formula, represents the prominence of the electricity price at the current time. The greater the prominence of the electricity price, the greater the degree of fluctuation of the electricity price, and the greater the difficulty of predicting the electricity consumption of the target object at the current time. In order to accurately obtain the global optimal solution, it is necessary to increase the acceptance probability of the new solution, and the corresponding initial acceptance probability is also greater.

[0046] represents the credibility of the prominence of the electricity price at the current time. The larger this value, the higher the credibility of the greater prominence of the electricity price, and the greater the corresponding initial acceptance probability.

[0047] represents the prominence of the electricity consumption at the current time. The larger this value, the greater the difference in electricity consumption between the previous collection time of the current time and its previous collection time, and the greater the difficulty of predicting the electricity consumption of the target object at the current time. In order to accurately obtain the global optimal solution, it is necessary to increase the acceptance probability of the new solution, and the corresponding initial acceptance probability is also greater.

[0048] represents the credibility of the prominence of the electricity consumption at the current time. The larger this value, the higher the credibility of the greater prominence of the electricity consumption at the current time, and the greater the corresponding initial acceptance probability.

[0049] The following is an example to illustrate some parameters in the above formula: The collection times are from collection time 1 to collection time 9. If the current time is between collection time 8 and collection time 9, at this time, the previous collection time of the current time is collection time 8; the previous 2 collection times of the current time are collection time 8 and collection time 7; the previous 5 collection times before the previous collection time of the current time are collection times 3 to 7. Based on the electricity consumption at each collection time, the and at the current time can be obtained.

[0050] If the current time is the same as collection time 8, that is, the current time and collection time 8 are the same moment, then the previous collection time of the current time is collection time 7; the previous 2 collection times of the current time are collection time 7 and collection time 6; the previous 5 collection times before the previous collection time of the current time are collection times 2 to 6.

[0051] Exemplarily, when calculating the initial acceptance probability at the current moment, if the number of collection moments before the current moment is insufficient, the calculation is performed based on the actual number of collection moments obtained, which can be specifically set according to actual needs, and the embodiments of the present invention do not impose too many restrictions here.

[0052] It should be noted that based on the above method, the initial acceptance probability at the current moment can be obtained. The greater the initial acceptance probability, the higher the possibility that the current solution accepts the new solution, making the randomness of the algorithm higher and the possibility of falling into the local optimal solution lower. However, the above method for calculating the initial acceptance probability only considers the changes in electricity price and electricity consumption in the historical period before the current moment, ignoring the actual changes during the iteration process. If the initial acceptance probability is too large, it may lead to the acceptance of a new solution with poor quality as the new current solution, affecting the accuracy of the final obtained electricity purchase quantity.

[0053] Based on this, the embodiments of the present invention can obtain the difference in the evaluation function between the current solution and the new solution at the current moment during the iteration process, and correct the initial acceptance probability at the current moment, so as to accurately obtain the acceptance probability of the new solution at the current moment, that is, continue to execute the following steps.

[0054] S3: Preset a current solution at the initial moment, construct the objective function of the solution at this moment, randomly select a solution from the neighbor solution set of the current solution as the new solution, and calculate the acceptance probability of the new solution.

[0055] Among them, the current solution at the initial moment can be set to 100,000, with the unit of kilowatt-hour; the value of the initial current solution can be specifically set according to actual needs, and the embodiments of the present invention do not impose too many restrictions here.

[0056] It should be noted that the objective function of the solution constructed in the embodiments of the present invention is the evaluation function of the solution, and the initial acceptance probability can be corrected through the difference in the objective functions of the current solution and the new solution.

[0057] Exemplarily, in the embodiments of the present invention, the method for obtaining the neighbor solution set of the current solution includes: presetting the fluctuation range of the current solution, and forming the neighbor solution set of the current solution with all integers within the fluctuation range of the current solution.

[0058] Among them, the fluctuation range of the current solution can be set to the current solution , with the unit of kilowatt-hour; the fluctuation range can be specifically set according to actual needs, and the embodiments of the present invention do not impose too many restrictions here.

[0059] For example: If the current solution is 100,000 kWh, then its corresponding fluctuation range is from 95,000 kWh to 105,000 kWh. All integers between 95,000 kWh and 105,000 kWh in the fluctuation range are used as the neighbor solution set of the current solution, and the neighbor solution set obtained includes 95,000 kWh and 105,000 kWh.

[0060] After obtaining the neighbor solution set of the current solution based on the above steps, an objective function can be constructed for each solution.

[0061] Exemplarily, in the embodiment of the present invention, to construct the objective function of the solution at this moment, the following relational expression can be specifically referred to: ; is the objective function value of the solution at this moment, is the purchased electricity quantity corresponding to the solution, is the electricity price at this moment, is the purchased electricity quantity at the historical moment when the stored electricity quantity and the electricity price are the same as those at this moment, is the linear normalization function, is the absolute value symbol.

[0062] In the above formula, represents the purchased electricity cost corresponding to a solution at the current moment. The larger this value is, the greater the purchased electricity cost corresponding to this solution, so the lower the preference degree of this solution, and the smaller the corresponding objective function value.

[0063] is the difference between the purchased electricity quantity of a solution at the current moment and the purchased electricity quantity at the historical moment when the stored electricity quantity and the electricity price are the same as those at the current moment. The stored electricity quantity and the purchased electricity quantity play a complementary role in the community distribution network. The energy storage device can provide power support during power shortage or peak electricity price, while the purchased electricity quantity ensures the stability of power supply under normal circumstances. Therefore, the larger this value is, the greater the difference between the purchased electricity quantity of the solution at the current moment and the purchased electricity quantity at the historical moment under the same conditions, the greater the difference between this solution and the optimal solution, and the greater the corresponding objective function value, and the lower the preference degree.

[0064] Exemplarily, if there is more than 1 historical moment when the stored electricity quantity and the electricity price are the same as those at the current moment, the historical moment closest to the current moment is used as the historical moment when the stored electricity quantity and the electricity price are the same as those at the current moment; if there is no historical moment when the stored electricity quantity and the electricity price are the same as those at the current moment, the stored electricity quantity and the electricity price at a moment within the preset error range can be selected as the historical moment.

[0065] Among them, the preset error range of the stored electricity quantity can be of the stored electricity quantity at the current moment, with the unit of kWh, and the preset error range of the electricity price can be , in yuan per kilowatt-hour; it can be specifically set according to actual needs, and the embodiments of the present invention do not limit it too much here. If there is no corresponding historical moment within the error range at the current moment, then can be set to 1.

[0066] It can be understood that the larger the objective function value of the solution, the smaller the probability that the solution is the global optimal solution, and the lower the corresponding preference level.

[0067] Exemplarily, in the embodiments of the present invention, to determine the acceptance probability of the new solution at each iteration for each moment, the following relational expression can be specifically referred to: ; is the acceptance probability of the new solution at the j-th iteration at this moment, is the initial acceptance probability at this moment, is the variance between the current solution at the j-th iteration at this moment and the current solutions in the previous 3 iterations, is the objective function value of the new solution at the j-th iteration at this moment, is the objective function value of the current solution at the j-th iteration at this moment, is the linear normalization function, is the exponential function with e as the base.

[0068] It can be understood that the current solutions in the previous 3 iterations at the j-th iteration at the current moment are respectively the current solutions at the -th, -th and -th iterations at the current moment.

[0069] In the above formula, the smaller the variance between the current solution at the j-th iteration at the current moment and the current solutions in the previous 3 iterations, the greater the probability that the algorithm falls into the local optimal solution. At this time, a larger acceptance probability needs to be given to the new solution so that the algorithm can jump out of the local optimal solution and has higher flexibility.

[0070] represents the difference between the objective function value of the new solution and the objective function value of the current solution at the j-th iteration at the current moment. The larger this value is, the lower the preference level of the new solution at the j-th iteration at the current moment compared to the current solution, and the lower the quality of the new solution. The corresponding acceptance probability should also be smaller. By constraining , the possibility of taking a new solution with poor quality as the new current solution can be reduced, and the accuracy of the acceptance probability of the new solution in this iteration can be improved.

[0071] Illustrate some parameters in the above formula by way of example: If , it is the 6th iteration at the current moment. The previous 3 current solutions of the current solution in the 6th iteration are the current solution after the 5th iteration, the current solution after the 4th iteration, and the current solution after the 3rd iteration at the current moment. is the variance of the current solutions after the 6th, 5th, 4th, and 3rd iterations at the current moment. If the number of iterations at the current moment is not greater than 3 times, then can be set to 1.

[0072] After obtaining the acceptance probability of the new solution in each iteration process at the current moment based on the above steps, then the new current solution after each iteration can be obtained based on the acceptance probability of the new solution during the iteration process, and finally the electricity purchase quantity corresponding to the global optimal solution at the current moment can be obtained, that is, the following steps are executed.

[0073] S4: When the objective function value of the new solution is greater than the objective function value of the current solution, obtain the new current solution based on the acceptance probability of the new solution.

[0074] It should be noted that when obtaining the new current solution based on the acceptance probability of the new solution when the objective function value of the new solution is greater than the objective function value of the current solution, the new current solution may be the current solution or the new solution. The objective function values of both the current solution and the new solution can be obtained through the objective function constructed by the above steps.

[0075] Exemplarily, when obtaining the new current solution based on the acceptance probability of the new solution, if the new current solution is the current solution, then continue to construct the neighborhood solution set based on the current solution; if the new current solution is the new solution, then continue to construct the neighborhood solution set based on the new solution.

[0076] Exemplarily, in the embodiment of the present invention, when obtaining the new current solution based on the acceptance probability of the new solution when the objective function value of the new solution is greater than the objective function value of the current solution, it further includes: when the objective function value of the new solution is not greater than the objective function value of the current solution, taking the new solution as the new current solution.

[0077] It can be understood that when the stochastic hill climbing algorithm obtains the electricity purchase quantity corresponding to the current moment, it iteratively obtains the solution with a higher evaluation function as the current solution. The smaller the objective function value of the solution constructed based on the above steps, the higher the preference degree of the solution, and the higher the evaluation function. Therefore, if the objective function value of the new solution is not greater than the objective function value of the current solution, it indicates that the preference degree of the new solution is greater than that of the current solution. At this time, the new solution can be taken as the new current solution.

[0078] After obtaining the new current solution in the iteration at the current moment based on the above steps, continue to execute the following steps.

[0079] S5: Iteratively obtain the electricity purchase quantity at this moment to achieve the optimal operation of the distribution network.

[0080] Exemplarily, in the embodiment of the present invention, taking the new solution as the current solution and iteratively obtaining the electricity purchase quantity at this moment includes: presetting the number of iterations; taking the electricity purchase quantity corresponding to the new current solution obtained in the last iteration as the electricity purchase quantity at this moment.

[0081] Among them, the number of iterations can be preset to 50 times; specifically, the number of iterations can be set according to actual needs, and the embodiments of the present invention do not limit this too much here.

[0082] Exemplarily, in the embodiment of the present invention, taking the new solution as the current solution and iteratively obtaining the electricity purchase quantity at this moment to achieve the optimization of the operation of the distribution network includes: the distribution network allocates the corresponding electricity quantity based on the electricity purchase quantity at this moment.

[0083] It can be understood that after the target object, the distribution network, purchases the electricity purchase quantity, it will allocate the electricity purchase quantity based on factors such as the user's electricity load, electricity consumption time, and electricity consumption location. Specifically, it can be set according to actual needs, and the embodiments of the present invention do not limit this too much here.

[0084] Exemplarily, in the embodiment of the present invention, after achieving the optimization of the operation of the distribution network, it further includes: generating a user report based on each moment and the corresponding electricity purchase quantity.

[0085] In this way, by generating a user report, the electricity purchase quantity of the user at different moments can be analyzed, and the peak and trough periods of electricity consumption can be identified, so as to take corresponding measures to optimize the electricity consumption behavior.

[0086] It can be seen that in the embodiment of the present invention, when optimizing the operation of the distribution network based on dynamic electricity prices, the initial acceptance probability at each moment can be obtained by the difference between the electricity price and electricity consumption at the collected moment and the historical electricity price and electricity consumption; preset an initial current solution at a moment, construct the objective function of the solution at this moment, and randomly select a solution from the neighbor solution set of the current solution as the new solution; ; is the acceptance probability of the new solution in the j-th iteration at this moment, is the initial acceptance probability at this moment, is the variance between the current solution in the j-th iteration at this moment and the current solutions in the previous 3 iterations, 、 are the objective function values of the new solution and the current solution in the j-th iteration at this moment respectively, is the linear normalization function, is the exponential function with e as the base; in the case where the objective function value of the new solution is greater than the objective function value of the current solution, a new current solution is obtained based on the acceptance probability of the new solution; iteratively obtaining the electricity purchase quantity at this moment to achieve the optimization of the operation of the distribution network, effectively improving the operation efficiency of the distribution network based on dynamic electricity prices.

[0087] An embodiment of the present invention also discloses a distribution network operation optimization system based on dynamic electricity prices, which includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a distribution network operation optimization method provided by the present invention is implemented.

[0088] The above system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0089] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device.

[0090] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing the operation of a distribution network based on dynamic electricity prices, characterized in that, including: obtaining the initial acceptance probability at each moment based on the differences between the electricity price and electricity consumption at the collection moment and the historical electricity price and electricity consumption; presetting an initial current solution at a moment, constructing an objective function for the solution at this moment, and randomly selecting a solution from the neighborhood solution set of the current solution as the new solution; ； is the acceptance probability of the new solution in the j-th iteration at this moment, is the initial acceptance probability at this moment, is the variance of the current solution in the j-th iteration at this moment and the current solutions in the previous 3 iterations, and are the objective function values of the new solution and the current solution in the j-th iteration at this moment respectively, is the linear normalization function, is the exponential function with base e; when the objective function value of the new solution is greater than that of the current solution, obtaining a new current solution based on the acceptance probability of the new solution; iteratively obtaining the electricity purchase quantity at this moment to achieve the optimal operation of the distribution network.

2. The operation optimization method of a distribution network based on dynamic electricity price according to claim 1, wherein, Before the step of obtaining the initial acceptance probability at each moment based on the differences between the electricity price and electricity consumption at the collection moment and the historical electricity price and electricity consumption, it further includes: obtaining the electricity price data and electricity consumption data at each collection moment, and obtaining the electricity price and electricity consumption at each collection moment after preprocessing; obtaining the electricity purchase moment and the electricity purchase quantity at each moment.

3. The operation optimization method of a distribution network based on dynamic electricity price according to claim 1, characterized in that The step of obtaining the initial acceptance probability at each moment based on the differences between the electricity price and electricity consumption at the collection moment and the historical electricity price and electricity consumption includes: ； is the initial acceptance probability at a moment, is the absolute value of the difference between the electricity price at this moment and the updated electricity price last time, is the absolute value of the difference between the electricity price at this moment and the average value of the electricity prices updated 5 times before this moment, is the absolute value of the difference in electricity consumption between the two collection moments before this moment, is the average value of the electricity consumption in the 5 collection moments before the previous collection moment of this moment, is a hyperparameter, is a linear normalization function.

4. A method for optimizing the operation of a distribution network based on dynamic electricity prices according to claim 2, characterized in that The step of constructing an objective function for the solution at this moment includes: ； is the objective function value of the solution at this moment, is the electricity purchase quantity corresponding to the solution, is the electricity price at this moment, is the electricity purchase quantity at the historical moment when the electricity storage quantity and the electricity price are the same as those at this moment, is the linear normalization function.

5. A method for optimizing the operation of a distribution network based on dynamic electricity prices according to claim 1, characterized in that, The method for obtaining the neighborhood solution set of the current solution includes: presetting the fluctuation range of the current solution, and forming the neighborhood solution set of the current solution by all integers within the fluctuation range of the current solution.

6. The operation optimization method of a distribution network based on dynamic electricity price according to claim 1, characterized in that When the objective function value of the new solution is greater than that of the current solution, the step of obtaining a new current solution based on the acceptance probability of the new solution further includes: when the objective function value of the new solution is not greater than that of the current solution, taking the new solution as the new current solution.

7. A method for optimizing the operation of a distribution network based on dynamic electricity prices according to claim 1, characterized in that, The step of iteratively obtaining the electricity purchase quantity at this moment includes: presetting the number of iterations; taking the electricity purchase quantity corresponding to the new current solution obtained in the last iteration as the electricity purchase quantity at this moment.

8. A method for optimizing the operation of a distribution network based on dynamic electricity prices according to claim 1, characterized in that, The step of iteratively obtaining the electricity purchase quantity at this moment to achieve the optimal operation of the distribution network includes: The distribution network allocates the corresponding electricity quantity based on the electricity purchase quantity at this moment.

9. A method for optimizing the operation of a distribution network based on dynamic electricity prices according to claim 1, characterized in that After the step of achieving the optimal operation of the distribution network, it further includes: generating a user report based on each moment and the corresponding electricity purchase quantity.

10. A distribution network operation optimization system based on dynamic electricity prices, characterized in that, including: a processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, it implements a method for optimizing the operation of a distribution network based on dynamic electricity prices according to any one of claims 1-9.

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