Power grid dispatching plan and load power supply plan generation method, device and equipment

Abstract

The application discloses a power grid scheduling plan and load power supply plan generation method, device and equipment, the method comprises the following steps: classifying each target user in a target user set according to the power use behavior of each target user, and obtaining a plurality of classification subsets; determining the time-of-use electricity price corresponding to each classification subset according to each classification subset; sending the time-of-use electricity price corresponding to the classification subset to the client of each target user in the classification subset; and generating a power grid scheduling plan and a load power supply plan according to the number of target users accepting each time-of-use electricity price and the time-of-use electricity price. In this way, the power use behavior of each user can be regulated to a certain extent according to the time-of-use electricity price, so as to determine the range of the power consumption of each target user in each time period. Based on this, the power grid scheduling plan and the load power supply plan generated based on the number of target users accepting each time-of-use electricity price and the time-of-use electricity price are relatively accurate, and resource waste is not easily caused.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and more specifically, to a method and apparatus for generating power grid dispatch plans and load power supply plans. Background Technology

[0002] Electricity has become an indispensable part of people's lives and a necessity of modern life. To deliver electricity to people, a power grid is required. Under the current power grid organization model, transmitting electricity through interconnecting lines between different regions is a necessary link in power grid transmission and an important component of power grid economic dispatch. Therefore, power dispatch planning occupies a relatively important position in power grid economics.

[0003] However, existing power grid dispatch plans are generated directly from power grid operation data, such as power grid energy storage data and electromechanical generator data, which are related to power grid performance. However, the power grid dispatch plans generated through this process focus on the maximum power supply of the power grid. When the maximum power supply is provided, the total electricity consumption of all users may be far less than the maximum power supply, resulting in a waste of resources. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus and equipment for generating power grid dispatch plans and load power supply plans, which can reduce the waste of power grid resources.

[0005] To achieve the above objectives, the following solution is proposed:

[0006] A method for generating power grid dispatch plans and load power supply plans includes:

[0007] Based on the electricity usage behavior of each target user in the target user set, each target user is classified into multiple classification subsets, wherein each target user is a power grid user who has confirmed acceptance of the electricity usage behavior call;

[0008] Based on each of the aforementioned subsets, determine the time-of-use electricity price corresponding to each subset.

[0009] The time-of-use electricity price corresponding to the category subset is sent to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price;

[0010] Based on the number of target users accepted by each time-of-use electricity price and the time-of-use electricity price, a power grid dispatch plan and a load power supply plan are generated. The power grid dispatch plan includes a dispatch plan for the reserve capacity in the power grid.

[0011] Optionally, it may also include an electricity model, which includes a classification hierarchy and a time-of-use pricing hierarchy;

[0012] The classification hierarchy is used to classify each target user according to the electricity usage behavior of each target user in the target user set, resulting in multiple classification subsets;

[0013] The time-of-use pricing hierarchy is used to determine the time-of-use price corresponding to each of the aforementioned subsets.

[0014] Optionally, the step of classifying each target user in the target user set according to their electricity usage behavior yields multiple classification subsets, including:

[0015] Using the Canopy-K-means clustering method, each target user in the target user set is classified according to their electricity usage behavior, resulting in multiple subsets.

[0016] Optionally, based on each of the said classification subsets, determine the time-of-use electricity price corresponding to each classification subset, including:

[0017] Based on the electricity usage behavior of each target user in the classification subset, the time-of-use electricity price corresponding to the classification subset is generated;

[0018] Calculate the electricity expenditure satisfaction of each target user in the category subset corresponding to the time-of-use electricity price;

[0019] Calculate the time-of-use electricity price satisfaction of each target user in the category subset corresponding to the time-of-use electricity price;

[0020] Based on the satisfaction with electricity expenditure and the satisfaction with time-of-use pricing, determine the likelihood of each target user in the category subset choosing the time-of-use pricing;

[0021] Based on the aforementioned probability, determine the sum of electricity cost reductions for each target user who accepts the time-of-use pricing corresponding to the subset of categories;

[0022] The revenue corresponding to the time-of-use electricity price is determined based on the sum of the reduced electricity costs.

[0023] A genetic algorithm is used to modify the time-of-use electricity price based on the revenue until the revenue reaches its maximum value. The final time-of-use electricity price is then used as the time-of-use electricity price corresponding to the category subset, so as to obtain the time-of-use electricity price corresponding to each category subset.

[0024] Optionally, generating a power grid dispatch plan and a load supply plan based on the number of target users accepting each time-of-use price and the time-of-use price includes:

[0025] Based on the electricity usage behavior of each target user in each category subset, determine the demand for each type of electrical appliance corresponding to the category subset;

[0026] Based on the electricity usage behavior of each target user in the aforementioned category subset and the demand for each type of appliance, predict the appliance electricity consumption plan for each category subset.

[0027] Based on the electricity consumption plan and time-of-use pricing for each category subset, as well as the number of target users accepted by each time-of-use pricing, a power grid dispatch plan and a load supply plan are generated.

[0028] Optionally, based on the appliance electricity consumption plan and time-of-use pricing corresponding to each category subset, and the number of target users accepting each time-of-use pricing, a power grid dispatch plan and a load supply plan are generated, including:

[0029] At each preset time interval, based on the electricity consumption plan and time-of-use electricity price of each appliance in each category subset, as well as the number of target users for each time-of-use electricity price, the future electricity consumption of each region is predicted;

[0030] Based on the future electricity consumption of each region, a power grid dispatch plan and a load power supply plan are generated.

[0031] Optionally, the power grid dispatch plan includes a power grid dispatch plan within 24 hours, a power grid dispatch plan within 8 hours, and a power grid dispatch plan within 4 hours;

[0032] At preset time intervals, based on the electricity consumption plans and time-of-use pricing for each appliance category subset, and the number of target users accepting each time-of-use pricing, the future electricity consumption for each region is predicted, including:

[0033] After obtaining the number of target users corresponding to each time-of-use electricity price in the first preset time period, the electricity consumption for the next 24 hours is predicted based on the electricity consumption plan and time-of-use electricity price of each category subset, as well as the number of target users accepted by each time-of-use electricity price.

[0034] Every second preset time period, based on the electricity consumption plan and time-of-use electricity price of each appliance in each category subset, as well as the number of target users accepted by each time-of-use electricity price, the electricity consumption in the next 8 hours is predicted;

[0035] Every third preset time period, based on the electricity consumption plan and time-of-use price of the appliances corresponding to each category subset, as well as the number of target users accepted by each time-of-use price, the electricity consumption in the next 4 hours is predicted.

[0036] Optionally, the load power supply plan includes a power supply plan for Class A loads, a power supply plan for Class B loads, a power supply plan for Class C loads, and a power supply plan for Class D loads. Class A loads are loads whose total amount remains unchanged within 8 hours but can be transferred; Class B loads are loads whose total amount remains unchanged within 4 hours but can be transferred; Class C loads are loads whose total amount remains unchanged within 2 hours but can be transferred; and Class D loads are loads that can be reduced or interrupted.

[0037] The process of generating power grid dispatch plans and load power supply plans based on the future electricity consumption of each region includes:

[0038] Every preset first time period, a 24-hour power grid dispatch plan and a power supply plan for Class A loads are generated based on the electricity consumption of each region in the next 24 hours.

[0039] Every second preset time period, based on the electricity consumption of each region in the next 8 hours, a power grid dispatch plan and a power supply plan for Class B loads are generated for the next 8 hours.

[0040] Every third preset time period, based on the electricity consumption of each region in the next 4 hours, a power grid dispatch plan, a power supply plan for Class C loads, and a power supply plan for Class D loads are generated for the next 4 hours.

[0041] A power grid dispatching plan and load power supply plan generation device, comprising:

[0042] The classification unit is used to classify each target user according to the power usage behavior of each target user in the target user set, and obtain multiple classification subsets, wherein each target user is a power grid user who has confirmed that it accepts the power usage behavior call;

[0043] The determining unit is used to determine the time-of-use electricity price corresponding to each of the classification subsets;

[0044] The sending unit is used to send the time-of-use electricity price corresponding to the category subset to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price;

[0045] The generation unit is used to generate a power grid dispatch plan and a load power supply plan based on the number of target users accepting each time-of-use electricity price and the time-of-use electricity price. The power grid dispatch plan includes a dispatch plan for the reserve capacity in the power grid.

[0046] A device for generating power grid dispatch plans and load power supply plans includes a memory and a processor;

[0047] The memory is used to store programs;

[0048] The processor is used to execute the program to implement each step of the above-described method for generating power grid dispatching plans and load power supply plans.

[0049] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for generating power grid dispatching plans and load power supply plans.

[0050] As can be seen from the above technical solution, the power grid dispatch plan and load power supply plan generation method provided in this application classifies each target user in the target user set according to the power usage behavior of each target user, obtaining multiple classification subsets, wherein each target user is a power grid user who has confirmed acceptance of the power usage behavior call; determines the time-of-use electricity price corresponding to each classification subset; sends the time-of-use electricity price corresponding to each classification subset to the client of each target user in the classification subset, so that each target user can choose whether to accept the time-of-use electricity price; and generates a power grid dispatch plan and a load power supply plan according to the number of target users accepting each time-of-use electricity price and the time-of-use electricity price. The power grid dispatch plan includes reserve capacity. Thus, this application can regulate the electricity consumption behavior of each user to a certain extent based on time-of-use pricing, so as to determine the range of electricity consumption of each target user in each time period. Therefore, after obtaining the number of target users accepting each time-of-use pricing and the time-of-use pricing, the range of the sum of electricity consumption of each target user can be obtained. Based on this, the power grid dispatch plan and load power supply plan generated by this application based on the number of target users accepting each time-of-use pricing and the time-of-use pricing are more accurate, less likely to lead to waste of resources, and can reduce the reserve capacity of the power grid. The reduction of reserve capacity can improve the stability and security of the power grid and maintain the safe and stable operation of the power grid. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating a method for generating power grid dispatch plans and load power supply plans, as disclosed in an embodiment of this application.

[0053] Figure 2 This is a structural block diagram of a power grid dispatching plan and load power supply plan generation device disclosed in an embodiment of this application;

[0054] Figure 3 This is a hardware structure block diagram of a power grid dispatching plan and load power supply plan generation device disclosed in an embodiment of this application. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] The power grid dispatching plan and load power supply plan generation method provided in this application can be applied to a wide range of general-purpose or special-purpose computing device environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, and distributed computing environments including any of the above devices.

[0057] Next, combine Figure 1 The present application provides a detailed description of the methods for generating power grid dispatch plans and load power supply plans, including the following steps:

[0058] Step S1: Based on the electricity usage behavior of each target user in the target user set, classify each target user to obtain multiple classification subsets.

[0059] Specifically, the target user set includes the user identifier of each target user and the electricity usage behavior corresponding to each user identifier.

[0060] The electricity usage behavior of each target user can include electricity-related behaviors such as daily load factor, peak-to-valley load difference, total electricity consumption, and daily load curve.

[0061] The target users are those who accept the invocation of electricity usage behavior and those who accept the classification of grid users.

[0062] Clustering methods can be used to classify each target user in the target user set according to their electricity usage behavior, resulting in multiple subsets of categories.

[0063] Each category subset also contains user identifiers for multiple target users, as well as the electricity usage behavior corresponding to those user identifiers.

[0064] Step S2: Determine the time-of-use electricity price corresponding to each category subset.

[0065] Specifically, time-of-use (TOU) pricing for each target user within a subset can be determined based on their electricity usage behavior. For example, by analyzing the electricity usage behavior of each target user within a subset, peak and off-peak electricity consumption periods can be identified. More favorable pricing can be offered for peak periods, while higher pricing can be offered for off-peak periods. This aims to minimize electricity consumption during off-peak periods and maximize consumption during peak periods, thereby enabling the prediction of electricity consumption curves for each target user accepting TOU pricing.

[0066] Step S3: Send the time-of-use electricity price corresponding to the category subset to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price.

[0067] Specifically, each time-of-use electricity price can be sent to the client of the corresponding target user. When the target user selects to accept through their client, electricity bills can be collected according to the time-of-use electricity price. This application can also predict the daily electricity consumption curve based on the time-of-use electricity price.

[0068] Step S4: Generate a power grid dispatch plan and a load power supply plan based on the number of target users accepted by each time-of-use price and the time-of-use price.

[0069] Specifically, based on the number of target users accepting each time-of-use (TOU) price and the TOU price itself, the electricity consumption curves of each target user accepting the TOU price are predicted. Based on this, the electricity consumption curves of each grid user are predicted, and based on these electricity consumption curves, a grid dispatch plan and a load power supply plan are generated.

[0070] The power grid dispatch plan includes the dispatch plan for the reserve capacity in the power grid.

[0071] A load power supply plan is the amount of load supplied to electrical appliances.

[0072] As can be seen from the above technical solutions, the embodiments of this application provide a method for generating power grid dispatch plans and load power supply plans. The above-mentioned method for generating power grid dispatch plans and load power supply plans can classify each target user based on the power usage behavior of the target user, and generate time-of-use electricity prices based on the classification results. Using the time-of-use electricity prices, the electricity consumption curves of each user receiving the time-of-use electricity prices can be predicted more accurately, thereby generating power grid dispatch plans and load power supply plans more accurately, avoiding waste of resources, and further reducing reserve capacity. The reduction of reserve capacity can reduce the operating costs of the power grid and help the power grid operate smoothly.

[0073] In some embodiments of the present application, considering that a power model can be pre-trained, by inputting the set of target users into the power model, the time-of-use electricity price corresponding to each target user can be obtained, further improving the efficiency of the present application.

[0074] Based on this, the power model may include a classification hierarchy and a time-of-use electricity price determination hierarchy.

[0075] The classification hierarchy can be used to classify each target user in the set of target users according to the power usage behavior of each target user, and obtain multiple classification subsets.

[0076] The time-of-use electricity price determination hierarchy can be used to determine the time-of-use electricity price corresponding to each classification subset according to each of the classification subsets.

[0077] Specifically, input the set of target users into the power model, use the classification hierarchy to cluster each target user in the target users, and obtain multiple classification subsets. Use the time-of-use electricity price determination hierarchy of the power model to determine the time-of-use electricity price corresponding to each classification subset. For the target users in the same classification subset, their time-of-use electricity prices are the same, and for the target users in different subsets, different time-of-use electricity prices are corresponding.

[0078] From the above technical solutions, it can be seen that this embodiment provides an optional way to determine the time-of-use electricity price corresponding to each target user. Through the above method, the time-of-use electricity price of each target user can be generated more efficiently, thereby improving the efficiency and reliability of the present application.

[0079] In some embodiments of the present application, the process of step S1, classifying each target user in the set of target users according to the power usage behavior of each target user to obtain multiple classification subsets, is described in detail as follows:

[0080] S10. Use the Canopy-K-means clustering method to classify each target user in the set of target users according to the power usage behavior of each target user, and obtain multiple classification subsets.

[0081] Specifically, the initial distance thresholds D1 and D2 can be determined, and D1 < D2. Among them, D1 and D2 can be first predicted according to the power usage behavior of each target user, and then revised by means of cross-validation.

[0082] Randomly select a target user in the set of target users as the first Canopy centroid, and generate the first Canopy subset according to this target user, and delete this target user from the set of target users.

[0083] Next, randomly select the current target user from the target user set. Based on the power usage behavior of the Canopy centroid and the power usage behavior of the current target user, calculate the distance L between the current target user and the first Canopy centroid. Based on this distance L, determine whether the target user should be included in the first Canopy subset.

[0084] If L≤D1, then the current target user is strongly marked and placed into the first Canopy subset;

[0085] If L≤D2, then the current target user is weakly labeled and placed into the first Canopy subset;

[0086] If L>D2, a new Canopy subset is generated, with the current target user as the centroid of the new Canopy subset.

[0087] Return to the previous step and randomly select the current target user from the target user set until the target user set is empty.

[0088] The centroid of each Canopy subset is used as the cluster center for the K-means algorithm, and the value of K is determined at the same time.

[0089] Based on the power usage behavior of the cluster centers and the power usage behavior of each target user, calculate the Euclidean distance from each target user to each cluster center, and reallocate the samples to the nearest Canopy subset based on the Euclidean distance from each target user to each cluster center.

[0090] Repeat the steps of using the centroid of each Canopy subset as the cluster center of the K-means algorithm while determining the value of K, until the cluster centers no longer change. Then, use the resulting multiple Canopy subsets as multiple classification subsets.

[0091] K represents the number of Canopy subsets and can be used to determine the number of clusters for the K-means clustering method.

[0092] As can be seen from the above technical solution, this embodiment provides an optional method for clustering target users in a target user set. The above method can accurately cluster each target user in the target user set, thereby determining the time-of-use electricity price based on the clustered subset, further improving the matching between the time-of-use electricity price of this application and the corresponding target users.

[0093] In some embodiments of this application, the process of determining the time-of-use electricity price corresponding to each category subset is described in detail, and the steps are as follows:

[0094] S20. Generate the time-of-use electricity price corresponding to the category subset based on the electricity usage behavior of each target user in the category subset.

[0095] Specifically, based on the electricity usage behavior of each target user in the classification subset, the electricity consumption curve of each target user in that subset can be determined. At the peak of the electricity consumption curve, the electricity price is reduced by a certain amount, and at the trough of the electricity consumption curve, the electricity price is increased by a certain amount, thus obtaining a time-of-use electricity price that matches the electricity consumption curve. The magnitude of the reduction and increase can be determined based on the peak-to-peak value.

[0096] S21. Calculate the electricity expenditure satisfaction of each target user in the category subset corresponding to the time-of-use electricity price.

[0097] Specifically, it can be determined how satisfied each target user is with their electricity bill if the time-of-use electricity price is provided to each target user in the subset of categories at this time.

[0098] The formula for calculating satisfaction with electricity expenses is as follows:

[0099]

[0100] Among them, F′ ui For the daily electricity bill expenses of target user i when the time-of-use electricity price is not accepted, F uij For target user i, the daily electricity cost after accepting the time-of-use pricing, b j To accept the time-of-use pricing, the discount factor for the electricity price during time period t is f. t For the electricity price during peak, valley and normal periods, η ij,t Q represents the percentage of electricity consumption by target user i during time period t after applying the time-of-use pricing at this time. i Let T represent the daily electricity consumption of target user i after applying the time-of-use pricing at this time. T represents the various time periods of the time-of-use pricing.

[0101] S22. Calculate the time-of-use electricity price satisfaction of each target user in the category subset corresponding to the time-of-use electricity price.

[0102] Specifically, it can be determined how satisfied each target user is with the time-of-use electricity price if it is provided to each target user in the subset of categories at this time.

[0103] The formula for calculating satisfaction with time-of-use electricity pricing is as follows:

[0104]

[0105] Where, η′ i,t r represents the percentage of electricity consumption by target user i during time period t when i does not accept the time-of-use pricing. i and q iThe correlation coefficient of target user i's acceptance of time-of-use pricing is related to the classification subset; for different classification subsets, r... i and q i It may differ, by adjusting r i and q i It can simulate the satisfaction of different types of target users with their electricity usage methods. 1 indicates that the original electricity satisfaction of target user i without selecting a package is 100%, so as to compare the satisfaction after selecting time-of-use pricing with the original satisfaction.

[0106] S23. Based on the satisfaction with electricity expenditure and the satisfaction with time-of-use pricing, determine the probability that each target user in the category subset will choose the time-of-use pricing.

[0107] Specifically, the weights of the probability of satisfaction with electricity expenditure and the probability of satisfaction with time-of-use pricing can be determined, and based on the weights of the electricity expenditure satisfaction, the weights of the time-of-use pricing satisfaction, the electricity expenditure satisfaction, and the time-of-use pricing satisfaction, the probability of each target user in the classification subset choosing the time-of-use pricing can be determined.

[0108] Based on the satisfaction with electricity expenses and the satisfaction with time-of-use pricing, the calculation formula for determining the probability of each target user in the category subset choosing the time-of-use pricing is as follows:

[0109] M ij =β i M ij,a +(1-β i M ij,b β i ∈[0,1]

[0110] Where, β i This represents the degree of importance that different types of target users place on electricity costs and electricity usage patterns. Those who are sensitive to electricity prices will have a larger value, while those who are sensitive to load adjustments will have a smaller value.

[0111] S24. Based on the aforementioned probability, determine the sum of the electricity cost reductions for each target user who accepts the time-of-use pricing corresponding to the subset of categories.

[0112] Specifically, the electricity cost reduction for the target user after accepting the time-of-use pricing is calculated, and the reduced electricity cost is multiplied by the probability corresponding to the target user to obtain the electricity cost reduction for the target user. The electricity cost reductions for each target user in the subset of categories corresponding to the time-of-use pricing are added together to obtain the sum of the electricity cost reductions corresponding to the time-of-use pricing at this time.

[0113] The formula for calculating the sum of reduced electricity costs is shown below:

[0114]

[0115] This represents the total daily electricity cost for each target user within this subset who did not accept the time-of-use pricing. The total daily electricity cost for each target user in this subset after accepting the time-of-use pricing. The total daily electricity consumption of each target user in this subset after accepting the time-of-use pricing.

[0116] S25. Determine the revenue corresponding to the time-of-use electricity price based on the sum of the reduced electricity costs.

[0117] Specifically, the cost D for promoting and managing the time-of-use electricity prices corresponding to each of the aforementioned subsets can be determined. t The reduced fault costs, resulting from the generation of the grid dispatch plan and load power supply plan, are also considered. These reduced fault costs are due to the decrease in reserve capacity and the enhanced stability and security of the grid side after adopting the time-of-use pricing.

[0118] The formula for calculating the reduced failure cost is shown below:

[0119]

[0120] Among them, A p k1 represents the average unit cost of transmission lines and base stations; k2 represents the reserve capacity ratio; k3 represents the transmission and distribution network loss coefficient; ΔP represents the potential peak load reduction; A VOLL and p LOLP For power load loss value and power system load loss probability; A SMP A represents the marginal cost of electricity; ΔQ represents the potential response of the target user to grid demand during peak hours. f This refers to the electricity sales price on the power grid.

[0121] The sum of reduced electricity costs, reduced failure costs, and advertising and management costs will be included. t Add them together to get the revenue corresponding to the time-of-use electricity price.

[0122] The profit calculation formula is as follows:

[0123] S = B p -D u -D t

[0124] S26. Using a genetic algorithm, the time-of-use electricity price is modified according to the revenue until the revenue reaches its maximum value. The final time-of-use electricity price is then used as the final time-of-use electricity price corresponding to the category subset, so as to obtain the time-of-use electricity price corresponding to each category subset.

[0125] Specifically, a genetic algorithm is used to adjust the time-of-use electricity price corresponding to the category subset according to the size of the revenue, until the revenue reaches the maximum value that the category subset can achieve. Each category subset can generate its corresponding time-of-use electricity price using the above process.

[0126] As can be seen from the above technical solution, this embodiment provides an optional method for generating time-of-use electricity prices corresponding to each category subset. Through the above method, time-of-use electricity prices that maximize the benefits to the power grid can be generated for each category subset, thereby improving the economic efficiency of this application. Furthermore, this application takes into account the reduced fault costs that can be achieved by adopting the time-of-use electricity price when determining the time-of-use electricity price. The fault costs are the costs reduced by the decrease in reserve capacity and the enhancement of grid stability and security after adopting the time-of-use electricity price. Therefore, this embodiment can take into account the security of the power grid when formulating the time-of-use electricity price, and further maintain the stable operation of the power grid.

[0127] In some embodiments of this application, the process of generating a power grid dispatch plan and a load supply plan based on the number of target users accepting each time-of-use price and the time-of-use price, wherein the power grid dispatch plan includes reserve capacity, is described in detail as follows:

[0128] S40. Based on the electricity usage behavior of each target user in each category subset, determine the demand for each type of electrical appliance corresponding to the category subset.

[0129] Specifically, electricity usage behavior can also include the usage time of various types of electrical appliances. Therefore, the demand for each type of electrical appliance corresponding to each category subset can be determined based on the usage time of each type of electrical appliance of each target user in each category subset.

[0130] The longer the usage time, the higher the demand. The demand for different types of appliances may vary at different times.

[0131] Demand is generally related to the comfort level of the target user. For air conditioners or heaters, the difference between the current temperature and the temperature at which the human body is most comfortable can be used, and the electricity usage behavior of the target user can be taken into account to determine their demand.

[0132] Similarly, for some target users who are highly concerned about electricity expenses, electricity expenses are also a key factor affecting the demand for appliances. Therefore, based on the time-of-use electricity price and the electricity usage behavior of each target user, the electricity cost of each type of appliance in each time period after adopting the time-of-use electricity price can be calculated.

[0133] In summary, the demand for each type of appliance can be determined based on the electricity cost of using each type of appliance in different time periods, the usage time of each type of appliance in electricity usage behavior, and the comfort level of the target users after adopting each type of appliance.

[0134] S41. Based on the electricity usage behavior of each target user in the classification subset and the demand for each type of electrical appliance, predict the electrical appliance electricity consumption plan corresponding to each classification subset.

[0135] Specifically, the electricity consumption plan for each category subset can be determined based on the usage time of each type of appliance by each target user in the category subset and the demand for each type of appliance.

[0136] The electrical appliance electricity plan can include the usage duration and likelihood of using various types of electrical appliances at different times.

[0137] S42. Generate a power grid dispatch plan and a load power supply plan based on the electrical appliance power consumption plan and time-of-use electricity price corresponding to each category subset, as well as the number of target users accepted by each time-of-use electricity price.

[0138] Specifically, based on the appliance electricity consumption plan, time-of-use electricity price, and the number of target users accepting each time-of-use electricity price corresponding to each category subset, the total electricity consumption of each target user accepting the time-of-use electricity price can be predicted. Based on this, the total electricity consumption of each grid user can be predicted, and a grid dispatch plan and a load power supply plan can be generated based on the total electricity consumption.

[0139] As can be seen from the above technical solution, this embodiment provides an optional method for generating a power grid dispatch plan and a load power supply plan based on the number of target users accepting each time-of-use electricity price and the time-of-use electricity price. Through the above method, the accuracy of predicting total electricity consumption can be further improved, thereby further reducing the size of the reserve capacity and further improving the reliability and economy of this application.

[0140] In some embodiments of this application, the process of generating a power grid dispatch plan and a load supply plan based on the electricity consumption plan and time-of-use pricing corresponding to each category subset, and the number of target users accepted by each time-of-use pricing, is described in detail below:

[0141] S420. At each preset time interval, based on the electricity consumption plan and time-of-use price of the appliances corresponding to each category subset, and the number of target users accepted by each time-of-use price, predict the future electricity consumption of each region.

[0142] Specifically, in order to ensure the reliability of the power grid dispatch plan, this application can collect the electricity usage behavior of each target user at regular intervals, and use the electricity usage behavior to update the electrical appliance electricity consumption plan. Using each time-of-use electricity price and the number of target users corresponding to it, as well as the updated electrical appliance electricity consumption plan, the predicted future electricity consumption is updated.

[0143] Future electricity consumption refers to the total electricity consumption of all grid users over a future period.

[0144] The interval can be set according to actual needs; when higher accuracy is required, the interval can be shorter.

[0145] S421. Generate a power grid dispatch plan and a load power supply plan based on the future electricity consumption of each region.

[0146] Specifically, the grid dispatch plan and load power supply plan are updated using the updated future electricity consumption. If the updated future electricity consumption is higher than the original forecast, the grid dispatch plan and load power supply plan need to increase the power supply to meet the load power supply plan.

[0147] As can be seen from the above technical solution, this embodiment provides an optional method for generating a power grid dispatch plan and a load power supply plan based on the electrical appliance power consumption plan and time-of-use electricity price corresponding to each category subset, as well as the number of target users accepted by each time-of-use electricity price. Through the above method, the power grid dispatch plan and the load power supply plan can be updated in a timely manner, thereby further improving the accuracy of the power grid dispatch plan and the load power supply plan of this application.

[0148] In some embodiments of this application, considering that different types of electrical appliances have different power consumption and on / off times at different times, timely updates are necessary. For example, the daily on / off times for household lights are not the same and may change with the time of sunset. Therefore, timely updates to power usage behavior and grid dispatch plans can improve the reliability of the grid dispatch plan. To ensure the reliability of the grid dispatch plan while reducing computational load, the grid dispatch plan generally includes a 24-hour grid dispatch plan, an 8-hour grid dispatch plan, and a 4-hour grid dispatch plan.

[0149] Based on this, step S420, which involves predicting the future electricity consumption of each region at preset time intervals based on the electricity consumption plan and time-of-use pricing for each category subset, as well as the number of target users eligible for each time-of-use pricing, is explained in detail below:

[0150] S4200: After obtaining the number of target users corresponding to each time-of-use electricity price after each preset first time period, predict the electricity consumption for the next 24 hours based on the electricity consumption plan and time-of-use electricity price of each category subset, as well as the number of target users accepted by each time-of-use electricity price.

[0151] Specifically, the first time period can be 24 hours.

[0152] Every 24 hours, a new set of target users can be identified, thereby updating the time-of-use electricity price and appliance electricity consumption plan, and predicting the total electricity consumption for the next 24 hours based on the time-of-use electricity price, the number of target users who accept it, and the appliance electricity consumption plan of the target users.

[0153] S4201. Every second preset time period, based on the electricity consumption plan and time-of-use price of the appliances corresponding to each category subset, and the number of target users accepted by each time-of-use price, predict the electricity consumption within the next 8 hours.

[0154] Specifically, the second time period can be 8 hours.

[0155] Every 8 hours, the electricity usage behavior of each target user is updated, and the electricity consumption plan of each appliance in each category subset is updated. Based on the electricity consumption plan of each appliance in each category subset, the number of target users who accept each time-of-use price, and the time-of-use price, the total electricity consumption of grid users in the next 8 hours is predicted.

[0156] The total electricity consumption of grid users in the next 8 hours is used to update the total electricity consumption of the corresponding time period for the next 24 hours generated in step S4200.

[0157] S4202. Every third preset time period, based on the electricity consumption plan and time-of-use price of the appliances corresponding to each category subset, and the number of target users accepted by each time-of-use price, predict the electricity consumption within the next 4 hours.

[0158] Specifically, the third time period can be 4 hours. It should be noted that the first, second, and third time periods in this application refer only to time periods of different lengths and do not imply any sequential relationship between them.

[0159] Every 4 hours, update the electricity usage behavior of each target user and update the electricity consumption plan of each appliance in each category subset. Based on the electricity consumption plan of each appliance in each category subset, the number of target users who accept each time-of-use price, and the time-of-use price, predict the total electricity consumption of grid users in the next 4 hours.

[0160] The total electricity consumption of grid users in the next 4 hours is used to update the total electricity consumption of the corresponding time period for the next 24 hours generated in step S4200.

[0161] As can be seen from the above technical solution, this embodiment provides an optional method for predicting future electricity consumption, which can further ensure the accuracy and efficiency of this application.

[0162] In some embodiments of this application, considering that the update speed of different types of electrical appliances can be reduced to further improve the efficiency of this application, the prediction interval of the load power supply plan for appliances with long usage time can be appropriately increased. In order to facilitate the determination of the prediction frequency of each type of appliance, each type of appliance can be divided into Class A loads, Class B loads, Class C loads, and Class D loads. Class A loads can be appliances such as refrigerators and lights whose total power consumption remains unchanged during an 8-hour period, but whose power consumption can be transferred. Class B loads can be appliances such as air conditioners whose total power consumption remains unchanged during a 4-hour period, but whose power consumption can be transferred. Class C loads can be appliances such as washing machines whose total power consumption remains unchanged during a 2-hour period, but whose power consumption can be transferred. Class D loads can be interruptible loads such as irons whose power consumption can be reduced.

[0163] Based on this, the process of generating a power grid dispatch plan and a load power supply plan according to the future electricity consumption of each region in step S421 is described in detail, and the steps are as follows:

[0164] S4210. Every preset first time period, generate a 24-hour power grid dispatch plan and a power supply plan for Class A loads based on the electricity consumption of each region in the next 24 hours.

[0165] Specifically, a 24-hour power grid dispatch plan and a Class A load power supply plan can be generated every 24 hours.

[0166] S4211. Every second preset time period, based on the electricity consumption of each region in the next 8 hours, generate a power grid dispatch plan and a power supply plan for Class B loads within 8 hours.

[0167] Specifically, every 8 hours, a power grid dispatch plan and a Class B load power supply plan for the 8-hour period can be generated, and the power grid dispatch plan for the corresponding time period in the 24-hour power grid dispatch plan can be updated using the power grid dispatch plan for the 8-hour period.

[0168] S4212. Every third preset time period, based on the electricity consumption of each region in the next 4 hours, generate a power grid dispatch plan, a power supply plan for Class C loads, and a power supply plan for Class D loads within 4 hours.

[0169] Specifically, every 4 hours, a power grid dispatch plan, a Class C load power supply plan, and a Class D load power supply plan can be generated for the 4-hour period, and the corresponding time period in the 24-hour power grid dispatch plan can be updated using the power grid dispatch plan for the 4-hour period.

[0170] As can be seen from the above technical solution, this embodiment provides an optional method for updating the power grid dispatch plan and the load power supply plan. In this embodiment, the update frequency is different for different types of loads, which further ensures the high efficiency and accuracy of this application.

[0171] See Figure 2 , Figure 2 This is a schematic diagram of a power grid dispatching plan and load power supply plan generation device disclosed in an embodiment of this application.

[0172] like Figure 2 As shown, the power grid dispatching plan and load power supply plan generation device may include:

[0173] Classification unit 1 is used to classify each target user according to the power usage behavior of each target user in the target user set, and obtain multiple classification subsets, wherein each target user is a power grid user who has confirmed that it accepts the power usage behavior call;

[0174] Determining unit 2 is used to determine the time-of-use electricity price corresponding to each of the classification subsets;

[0175] Sending unit 3 is used to send the time-of-use electricity price corresponding to the category subset to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price;

[0176] The generation unit 4 is used to generate a power grid dispatch plan and a load power supply plan based on the number of target users accepting each time-of-use electricity price and the time-of-use electricity price. The power grid dispatch plan includes reserve capacity.

[0177] Optionally, the above-mentioned power grid dispatching plan and load power supply plan generation device may further include:

[0178] A model storage unit is used to store power models, which include classification levels and time-of-use pricing levels.

[0179] The classification hierarchy is used to classify each target user according to the electricity usage behavior of each target user in the target user set, resulting in multiple classification subsets;

[0180] The time-of-use pricing hierarchy is used to determine the time-of-use price corresponding to each of the aforementioned subsets.

[0181] Optionally, the classification units of this application may include:

[0182] The user clustering subunit is used to classify each target user in the target user set according to the electricity usage behavior of each target user using the Canopy-K-means clustering method, resulting in multiple classification subsets.

[0183] Optionally, the determining element of this application may include:

[0184] The first determining subunit is used to generate the time-of-use electricity price corresponding to the classification subset based on the electricity usage behavior of each target user in the classification subset;

[0185] The second determining subunit is used to calculate the electricity expenditure satisfaction of each target user in the category subset corresponding to the time-of-use electricity price;

[0186] The third determining subunit is used to calculate the time-of-use electricity price satisfaction of each target user in the category subset corresponding to the time-of-use electricity price;

[0187] The fourth determining subunit is used to determine the probability that each target user in the classification subset will choose the time-of-use electricity price based on the electricity expenditure satisfaction and the time-of-use electricity price satisfaction.

[0188] The fifth determining subunit is used to determine, based on the probability, the sum of the electricity cost reductions for each target user who accepts the time-of-use electricity price corresponding to the category subset;

[0189] The sixth determining subunit is used to determine the revenue corresponding to the time-of-use electricity price based on the sum of the reduced electricity costs;

[0190] The seventh subunit is used to modify the time-of-use electricity price according to the revenue using a genetic algorithm until the revenue reaches its maximum value. The final time-of-use electricity price is then used as the final time-of-use electricity price corresponding to the classification subset, so as to obtain the time-of-use electricity price corresponding to each classification subset.

[0191] Optionally, the generating unit of this application may include:

[0192] The appliance demand determination subunit is used to determine the demand for each type of appliance corresponding to each category subset based on the electricity usage behavior of each target user in each category subset.

[0193] The electricity consumption planning prediction subunit is used to predict the electricity consumption plan of each appliance in each category subset based on the electricity usage behavior of each target user in the category subset and the demand of each type of appliance.

[0194] The electricity consumption planning sub-unit is used to generate a power grid dispatch plan and a load power supply plan based on the electricity consumption plan and time-of-use pricing of the appliances corresponding to each category subset, as well as the number of target users that can accept each time-of-use pricing.

[0195] Optionally, the electricity consumption plan utilization sub-unit of this application may include:

[0196] The electricity consumption forecasting subunit is used to predict the future electricity consumption of each region at preset time intervals, based on the electricity consumption plan and time-of-use price of each appliance in each category subset, as well as the number of target users for each time-of-use price.

[0197] The electricity consumption utilization subunit is used to generate power grid dispatch plans and load power supply plans based on the future electricity consumption of each region.

[0198] Optionally, the electricity consumption prediction subunit of this application may include:

[0199] The first time period prediction subunit is used to predict the electricity consumption for the next 24 hours after obtaining the number of target users corresponding to each updated time-of-use electricity price in each preset first time period, based on the electricity consumption plan and time-of-use electricity price of each category subset, as well as the number of target users accepted by each time-of-use electricity price.

[0200] The second time period prediction subunit is used to predict the electricity consumption within the next 8 hours every preset second time period, based on the electricity consumption plan and time-of-use price of the appliances corresponding to each category subset, as well as the number of target users accepted by each time-of-use price.

[0201] The third time period prediction subunit is used to predict the electricity consumption within the next 4 hours every preset third time period, based on the electricity consumption plan and time-of-use price of the appliances corresponding to each category subset, as well as the number of target users accepted by each time-of-use price.

[0202] Optionally, the power consumption utilization subunit of this application may include:

[0203] The first time period generation sub-unit is used to generate a 24-hour power grid dispatch plan and a Class A load power supply plan every preset first time period, based on the electricity consumption of each region in the next 24 hours.

[0204] The second time period generation sub-unit is used to generate a power grid dispatch plan and a Class B load power supply plan within 8 hours based on the electricity consumption of each region in the next 8 hours, every preset second time period.

[0205] The third time period generation sub-unit is used to generate a power grid dispatch plan, a Class C load power supply plan, and a Class D load power supply plan within 4 hours based on the electricity consumption of each region in the next 4 hours, every preset third time period.

[0206] The power grid dispatching plan and load power supply plan generation device provided in this application embodiment can be applied to power grid dispatching plan and load power supply plan generation equipment, such as PC terminals, cloud platforms, servers, and server clusters. Optionally, Figure 3 The hardware structure block diagram of the power grid dispatching plan and load power supply plan generation equipment is shown. (Refer to...) Figure 3 The hardware structure of the power grid dispatching plan and load power supply plan generation equipment may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

[0207] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0208] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0209] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0210] The memory stores a program, which the processor can call. The program is used for:

[0211] Based on the electricity usage behavior of each target user in the target user set, each target user is classified into multiple classification subsets, wherein each target user is a power grid user who has confirmed acceptance of the electricity usage behavior call;

[0212] Based on each of the aforementioned subsets, determine the time-of-use electricity price corresponding to each subset.

[0213] The time-of-use electricity price corresponding to the category subset is sent to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price;

[0214] Based on the number of target users accepted by each time-of-use electricity price and the time-of-use electricity price, a power grid dispatch plan and a load power supply plan are generated, wherein the power grid dispatch plan includes reserve capacity.

[0215] Optionally, the refined and extended functions of the program can be referred to the above description.

[0216] This application embodiment also provides a readable storage medium that can store a program suitable for execution by a processor, the program being used for:

[0217] Based on the electricity usage behavior of each target user in the target user set, each target user is classified into multiple classification subsets, wherein each target user is a power grid user who has confirmed acceptance of the electricity usage behavior call;

[0218] Based on each of the aforementioned subsets, determine the time-of-use electricity price corresponding to each subset.

[0219] The time-of-use electricity price corresponding to the category subset is sent to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price;

[0220] Based on the number of target users accepted by each time-of-use electricity price and the time-of-use electricity price, a power grid dispatch plan and a load power supply plan are generated, wherein the power grid dispatch plan includes reserve capacity.

[0221] Optionally, the refined and extended functions of the program can be referred to the above description.

[0222] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0223] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0224] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments of this application can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating power grid dispatch plans and load power supply plans, characterized in that, include: Based on the electricity usage behavior of each target user in the target user set, each target user is classified into multiple classification subsets, wherein each target user is a power grid user who has confirmed that their electricity usage behavior has been invoked. The process of determining the time-of-use electricity price for each category subset includes: generating a time-of-use electricity price for each category subset based on the electricity usage behavior of each target user within the category subset; calculating the electricity cost satisfaction of each target user within the category subset corresponding to the time-of-use electricity price; calculating the time-of-use electricity price satisfaction of each target user within the category subset corresponding to the time-of-use electricity price; determining the probability that each target user in the category subset will choose the time-of-use electricity price based on the electricity cost satisfaction and the time-of-use electricity price satisfaction; determining the sum of electricity cost reductions for each target user who accepts the time-of-use electricity price corresponding to the category subset based on the probability; adding the sum of electricity cost reductions, the reduction in fault costs, and the advertising and management costs to determine the revenue corresponding to the time-of-use electricity price; and using a genetic algorithm to modify the time-of-use electricity price based on the revenue until the revenue reaches its maximum value, and using the final time-of-use electricity price as the final time-of-use electricity price corresponding to the category subset to obtain the time-of-use electricity price corresponding to each category subset. The revenue calculation formula corresponding to the time-of-use electricity price is as follows: In the formula, S represents the revenue corresponding to the time-of-use electricity price; To promote management costs; D u The sum of the reduced electricity costs; B p To reduce failure costs; A p k1 represents the average unit cost of transmission lines and base stations; k2 represents the reserve capacity ratio; k3 represents the transmission and distribution network loss coefficient; ΔP represents the potential peak load reduction; A VOLL and p LOLP For power load loss value and power system load loss probability; A SMP A represents the marginal cost of electricity; ΔQ represents the potential response of the target user to grid demand during peak hours. f The price of electricity sold by the power grid; The time-of-use electricity price corresponding to the category subset is sent to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price; Based on the number of target users accepted by each time-of-use electricity price and the time-of-use electricity price, a power grid dispatch plan and a load power supply plan are generated. The power grid dispatch plan includes a dispatch plan for the reserve capacity in the power grid.

2. The method for generating power grid dispatching plans and load power supply plans according to claim 1, characterized in that, It also includes an electricity model, which includes a classification hierarchy and a time-of-use pricing hierarchy; The classification hierarchy is used to classify each target user according to the electricity usage behavior of each target user in the target user set, resulting in multiple classification subsets; The time-of-use pricing hierarchy is used to determine the time-of-use price corresponding to each of the aforementioned subsets.

3. The method for generating power grid dispatching plans and load power supply plans according to claim 1 or 2, characterized in that, The process involves classifying each target user in the target user set based on their electricity usage behavior, resulting in multiple subsets of categories, including: Using the Canopy-K-means clustering method, each target user in the target user set is classified according to their electricity usage behavior, resulting in multiple subsets.

4. The method for generating power grid dispatching plans and load power supply plans according to claim 1, characterized in that, The process of generating a power grid dispatch plan and a load supply plan based on the number of target users accepting each time-of-use price and that time-of-use price includes: Based on the electricity usage behavior of each target user in each category subset, determine the demand for each type of electrical appliance corresponding to the category subset; Based on the electricity usage behavior of each target user in the aforementioned category subset and the demand for each type of appliance, predict the appliance electricity consumption plan for each category subset. Based on the electricity consumption plan and time-of-use pricing for each appliance in each category subset, and the number of target users for each time-of-use pricing, a power grid dispatch plan and a load supply plan are generated.

5. The method for generating power grid dispatching plans and load power supply plans according to claim 4, characterized in that, The process of generating a power grid dispatch plan and a load supply plan based on the electricity consumption plan and time-of-use pricing for each category subset, and the number of target users accepting each time-of-use pricing, includes: At each preset time interval, based on the electricity consumption plan and time-of-use electricity price of the appliances corresponding to each category subset, as well as the number of target users accepted by each time-of-use electricity price, the future electricity consumption of each region is predicted; Based on the future electricity consumption of each region, a power grid dispatch plan and a load power supply plan are generated.

6. The method for generating power grid dispatching plans and load power supply plans according to claim 5, characterized in that, The power grid dispatch plan includes a power grid dispatch plan within 24 hours, a power grid dispatch plan within 8 hours, and a power grid dispatch plan within 4 hours. At preset time intervals, based on the electricity consumption plans and time-of-use pricing for each appliance category subset, and the number of target users accepting each time-of-use pricing, the future electricity consumption for each region is predicted, including: After obtaining the number of target users corresponding to each time-of-use electricity price in the first preset time period, the electricity consumption for the next 24 hours is predicted based on the electricity consumption plan and time-of-use electricity price of each category subset, as well as the number of target users accepted by each time-of-use electricity price. Every second preset time period, based on the electricity consumption plan and time-of-use electricity price of each appliance in each category subset, as well as the number of target users accepted by each time-of-use electricity price, the electricity consumption in the next 8 hours is predicted; Every third preset time period, based on the electricity consumption plan and time-of-use price of the appliances corresponding to each category subset, as well as the number of target users accepted by each time-of-use price, the electricity consumption in the next 4 hours is predicted.

7. The method for generating power grid dispatching plans and load power supply plans according to claim 6, characterized in that, The load power supply plan includes power supply plans for Class A loads, Class B loads, Class C loads, and Class D loads. Class A loads are loads whose total amount remains unchanged within 8 hours but can be transferred; Class B loads are loads whose total amount remains unchanged within 4 hours but can be transferred; Class C loads are loads whose total amount remains unchanged within 2 hours but can be transferred; and Class D loads are loads that can be reduced or interrupted. The process of generating power grid dispatch plans and load power supply plans based on the future electricity consumption of each region includes: Every preset first time period, a 24-hour power grid dispatch plan and a power supply plan for Class A loads are generated based on the electricity consumption of each region in the next 24 hours. Every second preset time period, based on the electricity consumption of each region in the next 8 hours, a power grid dispatch plan and a power supply plan for Class B loads are generated for the next 8 hours. Every third preset time period, based on the electricity consumption of each region in the next 4 hours, a power grid dispatch plan, a power supply plan for Class C loads, and a power supply plan for Class D loads are generated for the next 4 hours.

8. A device for generating power grid dispatch plans and load power supply plans, characterized in that, include: The classification unit is used to classify each target user according to the electricity usage behavior of each target user in the target user set, and obtain multiple classification subsets, wherein each target user is a power grid user who has confirmed that their electricity usage behavior has been invoked; A determining unit is configured to determine the time-of-use electricity price corresponding to each of the classification subsets, including: generating the time-of-use electricity price corresponding to each classification subset based on the electricity usage behavior of each target user in the classification subset; calculating the electricity expenditure satisfaction of each target user in the classification subset corresponding to the time-of-use electricity price; calculating the time-of-use electricity price satisfaction of each target user in the classification subset corresponding to the time-of-use electricity price; determining the probability that each target user in the classification subset will choose the time-of-use electricity price based on the electricity expenditure satisfaction and the time-of-use electricity price satisfaction; determining the sum of electricity cost reductions for each target user who accepts the time-of-use electricity price corresponding to the classification subset based on the probability; adding the sum of electricity cost reductions, the reduction in fault costs, and the publicity and management costs to determine the revenue corresponding to the time-of-use electricity price; and using a genetic algorithm to modify the time-of-use electricity price according to the revenue until the revenue reaches its maximum value, and using the final time-of-use electricity price as the final time-of-use electricity price corresponding to the classification subset, so as to obtain the time-of-use electricity price corresponding to each classification subset. The revenue calculation formula corresponding to the time-of-use electricity price is as follows: In the formula, S represents the revenue corresponding to the time-of-use electricity price; To promote management costs; D u The sum of the reduced electricity costs; B p To reduce failure costs; A p k1 represents the average unit cost of transmission lines and base stations; k2 represents the reserve capacity ratio; k3 represents the transmission and distribution network loss coefficient; ΔP represents the potential peak load reduction; A VOLL and p LOLP For power load loss value and power system load loss probability; A SMP A represents the marginal cost of electricity; ΔQ represents the potential response of the target user to grid demand during peak hours. f The price of electricity sold by the power grid; The sending unit is used to send the time-of-use electricity price corresponding to the category subset to the client of each target user in the category subset, so that each target user can choose whether to accept the time-of-use electricity price; The generation unit is used to generate a power grid dispatch plan and a load power supply plan based on the number of target users accepting each time-of-use electricity price and the time-of-use electricity price. The power grid dispatch plan includes a dispatch plan for the reserve capacity in the power grid.

9. A device for generating power grid dispatch plans and load power supply plans, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the power grid dispatch plan and load power supply plan generation method as described in any one of claims 1-7.

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