A household electricity task scheduling determination method and device and a medium thereof

Abstract

The application discloses a household electricity task scheduling determination method and device and a medium thereof, relates to the technical field of micro-grid scheduling, and is used for optimizing the scheduling strategy of household electricity appliances. In view of the current problems in optimizing household electricity, the application provides a household electricity task scheduling determination method. By taking the expected operation period into the constraint condition of the optimization model, a user can customize the time range expected to be used by each appliance, so that the problem that the operation of the appliance affects the normal life of the user does not occur in the appliance scheduling optimization process, the effect of saving electricity bills and considering user satisfaction is realized, and the needs in the household electricity scene are better met. Meanwhile, the application also solves the established optimization model based on a genetic algorithm based on hybrid coding, so that the appliance scheduling optimization process can have a faster convergence speed, higher universality, is not easy to fall into a local optimum, and the optimization effect is more guaranteed.

Description

Technical Field

[0001] This application relates to the field of microgrid dispatching, and in particular to a method, device and medium for determining household electricity dispatching tasks. Background Technology

[0002] As a crucial carrier of the energy internet, the smart grid's high intelligence, high operational efficiency, and high security have made it a focus of attention in the energy sector. However, in recent years, with the increasing severity of energy problems and the development and deployment of various electrical equipment, some regions are experiencing intermittent power shortages and power resource scarcity. Therefore, some regions have adopted time-of-use pricing and other methods to curb concentrated power consumption during peak hours. Consequently, how to rationally schedule and manage household electricity load based on different time-of-use pricing in various regions to reduce electricity costs and improve the daily load fluctuation of household electricity has become a key concern for both users and the grid.

[0003] The core issue of smart home electricity scheduling is the operation and scheduling of household appliances. While some progress has been made in this area, most scheduling methods only address single-level optimizations such as reducing electricity costs. However, for home users, the response to electricity consumption should be reflected in the operational status of various appliances. Scheduling appliances solely for cost reduction may result in some appliances operating in ways that don't meet user expectations, impacting user experience and satisfaction. Furthermore, current methods for optimizing and scheduling electricity load response at the appliance level, such as particle swarm optimization, linear programming, and game theory, suffer from slow convergence, poor universality, and a tendency to get trapped in local optima, failing to adequately meet the practical needs of home electricity scheduling optimization.

[0004] Therefore, those skilled in the art urgently need a method for determining household electricity usage scheduling to solve the aforementioned problems that arise when optimizing household electricity usage. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus and medium for determining household electricity usage scheduling, in order to solve the problems that currently arise when optimizing household electricity usage.

[0006] To address the aforementioned technical problems, this application provides a method for determining household electricity usage tasks, comprising:

[0007] Obtain time-of-use electricity pricing, as well as the operating power, task information, and expected operating time of each appliance to be optimized;

[0008] Establish corresponding scheduling models for each appliance to be optimized based on task information;

[0009] An optimization model with electricity economy as the objective is established based on time-of-use pricing, the operating power of each appliance to be optimized, and the scheduling model, with the desired operating time as the constraint condition.

[0010] The optimization model is solved by a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each appliance to be optimized.

[0011] Preferably, the task information includes the task type; wherein, the task type is divided into interruptible and continuous.

[0012] Accordingly, the optimization model is solved using a genetic algorithm based on hybrid coding to obtain the scheduling strategies for each appliance to be optimized, including:

[0013] If the task type corresponding to the appliance to be optimized is interruptible, the running status of the appliance to be optimized in each time period is represented by binary encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0014] If the task type corresponding to the appliance to be optimized is continuous, the start-up time of the appliance to be optimized is represented by real number encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0015] Preferred options also include:

[0016] Get the user-defined time interval to be scheduled;

[0017] Correspondingly, the establishment and solution of the scheduling model and the optimization model are carried out within the time interval to be scheduled.

[0018] Preferably, the task information includes the task duration;

[0019] Correspondingly, if the task type corresponding to the scheduling model is interruptible, then the scheduling model consists of the first formula and the first constraint condition.

[0020] The first formula is:

[0021]

[0022] This indicates the on / off state of an interruptible electrical appliance a within a unit time period h, where, This indicates that interruptible electrical appliance a is working. This indicates that interruptible electrical appliance 'a' is not working; 'm' represents the total number of unit time periods 'h' contained within the time interval to be scheduled; 'r'... a This represents the total number of time periods h required for interruptible electrical appliance a to complete the scheduling task, determined by the task duration;

[0023] The first constraint is:

[0024]

[0025]

[0026] s a e represents the lower limit of the working time of interruptible electrical appliance a. a This indicates the maximum operating time of interruptible electrical appliance type a, s. a and e a Determined by task duration; t a This indicates the sequence number of the unit time period in which the interruptible electrical appliance a is first turned on and put into operation;

[0027] If the task type corresponding to the scheduling model is continuous, then the scheduling model consists of the second formula and the second constraint.

[0028] The second formula is:

[0029]

[0030] This represents the on / off state of a continuous electrical appliance b within a unit time period h, where... This indicates that the continuous-type electrical appliance b is working. This indicates that the continuous electrical appliance b is not working; r b This represents the total number of time periods required for continuous electrical appliance b to complete the scheduling task, determined by the task duration;

[0031] The second constraint is:

[0032]

[0033]

[0034] s b e represents the lower limit of the working time of continuous electrical appliance b. b This indicates the upper limit of the working time for a continuous electrical appliance (b), s b and e b Determined by task duration; t b This indicates the sequence number of the unit time period when the continuous electrical appliance b is first turned on.

[0035] Preferably, the optimization model consists of a third formula and a third constraint corresponding to the desired runtime.

[0036] The third formula is:

[0037]

[0038] Cost represents the total electricity cost of the dispatching scheme; p ap represents the operating power of interruptible electrical appliance a. b k represents the operating power of continuous-type electrical appliance b. h This represents the time-of-use electricity price for a unit time period h, determined according to the time-of-use electricity price; i and j represent the number of interruptible electrical appliances a and the number of continuous electrical appliances b, respectively;

[0039] The third constraint is:

[0040] [s a ,e a ]∈[u a ,d a ]

[0041] [s b ,e b ]∈[u b ,d b ]

[0042] [u a ,d a ] and [u b ,d b ] represent the expected operating time segments for appliances a and b, respectively.

[0043] Preferably, the optimization model is solved using a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each appliance to be optimized, including:

[0044] For appliances to be optimized with interruptible task type, the on / off state of each appliance in all time periods is represented by a binary code set; for appliances to be optimized with continuous task type, the on / off time period of each appliance is represented by a real number code set.

[0045] The binary encoding set is as follows:

[0046]

[0047] Binary encoding set There are i sets of binary codes, each containing m binary characters, representing the on / off state of interruptible electrical appliance a during time intervals 1 to m.

[0048] The set of real number encodings is:

[0049]

[0050] Chrom, a set of real number encodings b Includes j positive integers, representing the sequence number of the unit time period when the j consecutive electrical appliances b are first turned on;

[0051] An initial running plan for the optimized model is generated based on each binary encoding set and real number encoding set;

[0052] The initial running scheme is iterated using a genetic algorithm to obtain the scheduling strategy.

[0053] Preferably, the duration of a unit time period is the minimum operating time required to turn on each of the interruptible electrical appliances to be optimized once.

[0054] To address the aforementioned technical problems, this application also provides a household electricity task scheduling and determination device, comprising:

[0055] The information acquisition module is used to acquire time-of-use electricity prices, as well as the operating power, task information, and expected operating time of each appliance to be optimized;

[0056] The first module is used to establish a corresponding scheduling model for each appliance to be optimized based on the task information.

[0057] The second module is used to establish an optimization model with the goal of electricity economy based on time-of-use pricing, the operating power of each appliance to be optimized, and the scheduling model. The optimization model uses the desired operating time as a constraint.

[0058] The strategy determination module is used to solve the optimization model using a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each appliance to be optimized.

[0059] In a preferred embodiment, the task information includes a task type; wherein the task type is divided into interruptible and continuous types; correspondingly, the strategy determination module is specifically used for:

[0060] If the task type corresponding to the appliance to be optimized is interruptible, the running status of the appliance to be optimized in each time period is represented by binary encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0061] If the task type corresponding to the appliance to be optimized is continuous, the start-up time of the appliance to be optimized is represented by real number encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0062] In a preferred embodiment, the above-mentioned household electricity task scheduling and determination device further includes:

[0063] The interval determination module is used to obtain the user-defined time interval to be scheduled; correspondingly, the execution of the first establishment module, the second establishment module, and the strategy determination module is carried out within the time interval to be scheduled.

[0064] In a preferred embodiment, the task information includes the task duration; correspondingly, if the task type corresponding to the scheduling model is interruptible, the scheduling model determined by the first establishment module consists of the first formula and the first constraint condition.

[0065] The first formula is:

[0066]

[0067] This indicates the on / off state of an interruptible electrical appliance a within a unit time period h, where, This indicates that interruptible electrical appliance a is working. This indicates that interruptible electrical appliance 'a' is not working; 'm' represents the total number of unit time periods 'h' contained within the time interval to be scheduled; 'r'... a This represents the total number of time periods h required for interruptible electrical appliance a to complete the scheduling task, determined by the task duration;

[0068] The first constraint is:

[0069]

[0070]

[0071] s a e represents the lower limit of the working time of interruptible electrical appliance a. a This indicates the maximum operating time of interruptible electrical appliance type a, s. a and e a Determined by task duration; t a This indicates the sequence number of the unit time period in which the interruptible electrical appliance a is first turned on and put into operation;

[0072] If the task type corresponding to the scheduling model is continuous, then the scheduling model determined by the first establishment module consists of the second formula and the second constraint conditions.

[0073] The second formula is:

[0074]

[0075] This represents the on / off state of a continuous electrical appliance b within a unit time period h, where... This indicates that the continuous-type electrical appliance b is working. This indicates that the continuous electrical appliance b is not working; r b This represents the total number of time periods required for continuous electrical appliance b to complete the scheduling task, determined by the task duration;

[0076] The second constraint is:

[0077]

[0078]

[0079] s b e represents the lower limit of the working time of continuous electrical appliance b.b This indicates the upper limit of the working time for a continuous electrical appliance (b), s b and e b Determined by task duration; t b This indicates the sequence number of the unit time period when the continuous electrical appliance b is first turned on.

[0080] In a preferred embodiment, the optimization model determined by the second establishment module consists of a third formula and a third constraint condition corresponding to the desired runtime period;

[0081] The third formula is:

[0082]

[0083] Cost represents the total electricity cost of the dispatching scheme; p a p represents the operating power of interruptible electrical appliance a. b k represents the operating power of continuous-type electrical appliance b. h This represents the time-of-use electricity price for a unit time period h, determined according to the time-of-use electricity price; i and j represent the number of interruptible electrical appliances a and the number of continuous electrical appliances b, respectively;

[0084] The third constraint is:

[0085] [s a ,e a ]∈[u a ,d a ]

[0086] [s b ,e b ]∈[u b ,d b ]

[0087] [u a ,d a ] and [u b ,d b ] represent the expected operating time segments for appliances a and b, respectively.

[0088] In a preferred embodiment, the strategy determination module is specifically used for:

[0089] For appliances to be optimized with interruptible task type, the on / off state of each appliance in all time periods is represented by a binary code set; for appliances to be optimized with continuous task type, the on / off time period of each appliance is represented by a real number code set.

[0090] The binary encoding set is as follows:

[0091]

[0092] Binary encoding set There are i sets of binary codes, each containing m binary characters, representing the on / off state of interruptible electrical appliance a during time intervals 1 to m.

[0093] The set of real number encodings is:

[0094]

[0095] Chrom, a set of real number encodings b Includes j positive integers, representing the sequence number of the unit time period when the j consecutive electrical appliances b are first turned on;

[0096] An initial running plan for the optimized model is generated based on each binary encoding set and real number encoding set;

[0097] The initial running scheme is iterated using a genetic algorithm to obtain the scheduling strategy.

[0098] In a preferred embodiment, the duration of the unit time period is the minimum operating time required for each interruptible appliance to be optimized to be turned on once.

[0099] To address the aforementioned technical problems, this application also provides a household electricity task scheduling and determination device, comprising:

[0100] Memory, used to store computer programs;

[0101] A processor is used to execute computer programs to implement the steps of the household electricity task scheduling determination method described above.

[0102] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the household electricity task scheduling and determination method described above.

[0103] This application provides a method for determining household electricity task scheduling. By incorporating the expected runtime into the constraints of the optimization model, users can customize the expected usage time range of each appliance. This ensures that appliance operation does not disrupt users' normal lives during appliance scheduling optimization, improving user experience and satisfaction. It achieves the effect of saving electricity costs while simultaneously improving user satisfaction, better meeting the needs of the household electricity application scenario. Furthermore, this method uses a genetic algorithm based on hybrid coding to solve the optimization model to obtain an optimized scheduling model. On the one hand, the genetic algorithm achieves faster convergence speed, higher universality, and is less prone to getting trapped in local optima; on the other hand, the hybrid coding separately represents two task types with different characteristics to achieve higher solution efficiency, ensuring a more reliable optimization effect.

[0104] The household electricity task scheduling and determination device and computer-readable storage medium provided in this application correspond to the above method and have the same effect. Attached Figure Description

[0105] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0106] Figure 1 A flowchart of a method for determining household electricity usage scheduling provided by the present invention;

[0107] Figure 2 A flowchart of another method for determining household electricity usage scheduling provided by the present invention;

[0108] Figure 3 A schematic diagram illustrating the iterative process of solving the proposed model using a hybrid coding-based genetic algorithm, as provided in this invention.

[0109] Figure 4 The diagram shows the power distribution of appliances before and after optimizing the scheduling strategy using the household electricity task scheduling determination method provided by this invention.

[0110] Figure 5 A structural diagram of a household electricity task scheduling and determination device provided by the present invention;

[0111] Figure 6 A structural diagram of another household electricity task scheduling and determination device provided by the present invention. Detailed Implementation

[0112] 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 of ordinary skill in the art without creative effort are within the protection scope of this application.

[0113] The core of this application is to provide a method, device, and medium for determining household electricity task scheduling.

[0114] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0115] The performance of commonly used appliance scheduling methods in the application scenario of household electricity consumption is unsatisfactory. This is because there are many types of appliances involved in household electricity consumption, and the time of electricity consumption is not fixed. Even for the same user, the time of use for different individuals with different needs for the same type of appliance may not be the same. Therefore, in the current scheduling optimization aimed at saving electricity bills, it is difficult to take into account the user experience.

[0116] To address the aforementioned problems in household electricity usage scenarios, this application provides a method for determining household electricity task scheduling, such as... Figure 1 As shown, it includes:

[0117] S11: Obtain the time-of-use electricity price, as well as the operating power, task information, and expected operating time of each appliance to be optimized.

[0118] S12: Establish a corresponding scheduling model for each appliance to be optimized based on the task information.

[0119] S13: Based on the time-of-use electricity price, the operating power of each appliance to be optimized, and the scheduling model, establish an optimization model with the goal of electricity economy, and use the desired operating time as a constraint condition for the optimization model.

[0120] S14: Solve the optimization model using a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each appliance to be optimized.

[0121] The time-of-use pricing mentioned above refers to the different electricity prices offered in different time periods in areas where dispatch optimization is required. Generally, time-of-use pricing consists of four tiers, from low to high: off-peak price, flat-period price, peak price, and peak-hour price.

[0122] In the context of household electricity consumption, the appliances to be optimized are those used in the user's home. It should be noted that the appliances to be optimized can be customized and do not have to be all the appliances in the user's home. Some appliances that are not in the scheduling needs (such as electric heaters, which may not be used for a long time, or refrigerators, which need to be kept on at all times) may not be included in this scheduling optimization.

[0123] Operating power is the power of the electrical appliance to be optimized during normal operation, reflecting the amount of electrical energy consumed by the appliance per unit time.

[0124] Task information may include, but is not limited to: task type and task duration.

[0125] Task types can be divided into two categories: interruptible and continuous. Interruptible means that the appliance to be optimized can be interrupted while it is working, and this scheduling task can be divided into multiple time periods, such as air conditioners and various chargers (mobile phones, electric vehicles), etc. Continuous means that once the appliance starts running, it cannot be interrupted until the scheduling task is completed.

[0126] The task duration is the running time required for the current electrical appliance to complete the current scheduling task. Interruptible electrical appliances can complete the current scheduling task through multiple separate time periods with a total duration equal to the task duration, while continuous electrical appliances must complete the task duration required for the current scheduling task through a complete running segment.

[0127] The desired operating time period is the time period set by the user for each type of appliance (which can be further subdivided into different individuals of the same appliance; in the method provided in this application, different desired operating times for the same appliance are considered as different individual appliances to be optimized). The desired operating time period reflects user needs, so power scheduling optimization under the constraint of the desired operating time period can meet user needs as much as possible.

[0128] Furthermore, after obtaining the above information, a scheduling model for each appliance to be optimized can be established to represent the operating status of each appliance at any given time (to simplify calculations, it is usually represented by the on / off state of the appliance within a unit time period, the duration of which can be freely set). For most appliances, their operating status can be simply divided into on and off. For some appliances, more specific operating status control is usually controlled by the user or requires manual control (the appliance is not connected to the network or does not have network functionality). Therefore, the scheduling of the above appliances in this application is the scheduling of whether the appliance uses electricity, that is, scheduling the on / off state of the appliance. In the process of determining the scheduling decision, "1" and "0" can be used to represent the on / off state of the appliance, respectively.

[0129] As mentioned above, electrical appliances can be categorized into two types based on their task types: interruptible and continuous. Completing their scheduling tasks requires the appliance to run for a specified duration. Interruptible appliances can enter the running state multiple times to reach the required task duration, while continuous appliances must run continuously for the specified task duration after entering the on state to complete the scheduling task. Therefore, corresponding constraints can be added to the scheduling model representing appliances in various time periods to indicate whether an appliance is interruptible or continuous. Specifically, the characteristics of the two types of appliances are: interruptible appliances can have discontinuous on states, and their total duration is equal to the task duration (generally, the duration of a unit time period needs to consider the task duration of each appliance; ideally, the task duration of each appliance can be represented by an integer multiple of the unit time period); while continuous appliances must have continuous on states and run for the specified task duration. These characteristics allow us to determine the corresponding constraints to constrain the scheduling model and distinguish between appliances of different task types.

[0130] Once the scheduling model is established, a global optimization model can be built. Based on the scheduling models of all electrical appliances and their corresponding operating power, as well as the time-of-use electricity price in the current region, a model with electricity cost as the optimization objective can be established. By scheduling the operating status of each electrical appliance at different times, the total electricity cost under the current decision can be obtained through the optimization model. Then, with electricity economy as the objective, a more ideal scheduling decision can be obtained.

[0131] As mentioned above, the scheduling model can represent the on / off state of electrical appliances in various time periods, which can be represented by binary numbers "0" and "1". The scheduling decision sought by the optimization model is the specific operating scheme of scheduling each electrical appliance to be closed in some time periods and open in some time periods. Therefore, the above operating scheme can be simplified and represented by a set of binary characters. The set should include the same number of binary characters as the number of unit time periods, thereby representing the on / off state of the current electrical appliance in all time periods. That is, the scheduling scheme of each electrical appliance is represented by binary encoding, thereby solving for the optimal scheme that meets the optimization objective and constraints.

[0132] However, furthermore, since continuous electrical appliances have the characteristic that they need to complete a full scheduling task once they are turned on, that is, the on-time period of continuous electrical appliances is continuous, the on-off state of the continuous electrical appliances in all time periods can be determined by the on-time period of the continuous electrical appliances and their task duration. Therefore, in order to simplify the coding and obtain better solution efficiency, this embodiment also provides a preferred implementation scheme, and the above step S14 is specifically as follows:

[0133] S141: If the task type corresponding to the appliance to be optimized is interruptible, the operating status of the appliance to be optimized in each time period is represented by binary encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0134] S142: If the task type corresponding to the appliance to be optimized is continuous, the start-up time of the appliance to be optimized is represented by real number encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0135] In other words, for continuous electrical appliances, the encoding of the on / off state of an appliance in all time periods is simplified from a string of binary codes (the number of characters in the code is the same as the number of unit time periods) to a one-bit real number code. The value of the real number code can be the sequence number of the first time period when the appliance is turned on, which is a positive integer, so as to simplify the calculation and improve efficiency.

[0136] It should also be noted that the use of electrical appliances is a continuous process with a long time span. Therefore, when optimizing the scheduling of electrical appliances, it is usually necessary to set a certain time range. The scheduling optimization process only optimizes the scheduling of electrical appliances within this time range to obtain more economical scheduling decisions. Therefore, to further improve user satisfaction with the optimization of appliance scheduling, this embodiment provides a preferred implementation scheme. The above method also includes:

[0137] S15: Get the user-defined time interval to be scheduled.

[0138] Correspondingly, the establishment of subsequent scheduling models and optimization models, as well as the solution of optimization models and the determination of scheduling strategies, should all be carried out within the time interval to be scheduled obtained in step S15. Therefore, the execution order of step S15 must be before steps S12-S14, while there is no order restriction for steps S11.

[0139] Just as the duration of the unit time period and the task duration of each appliance need to be compatible, for ease of calculation, the duration of the unit time period also needs to be compatible with the total duration of the time interval to be scheduled defined by the user. Ideally, the time interval to be scheduled should still be represented by unit time periods that are integer multiples of each other.

[0140] Furthermore, this embodiment also provides another preferred scheme for determining the duration of a unit time period:

[0141] The duration of a unit time period is the minimum operating time required to turn on each interruptible electrical appliance to be optimized once.

[0142] The minimum operating time required for an interruptible appliance to be turned on once (for all interruptible appliances) is the smallest scheduling time unit in this scheduling task. Therefore, determining the unit time period based on it can ensure the flexibility of appliance scheduling without reducing efficiency due to excessively fine scheduling time granularity, thus achieving good performance in both electricity economy and scheduling decision-making efficiency.

[0143] This embodiment provides a method for determining household electricity task scheduling. It incorporates user-defined desired runtime periods into the constraints of the optimization model, ensuring that the scheduling decisions obtained from the optimization model meet users' needs for appliance usage periods. This optimizes electricity economy while maintaining household electricity comfort, effectively addressing the complex and diverse needs of household electricity use and improving user satisfaction with appliance scheduling optimization. In a preferred embodiment, the method further enhances the flexibility of scheduling decisions by allowing users to define their own optimization time periods, satisfying even more diverse user needs and further improving user satisfaction. The method also uses a genetic algorithm based on hybrid coding to solve the optimization model, resulting in an optimized scheduling model. On one hand, the genetic algorithm achieves faster convergence, higher universality, and is less prone to getting trapped in local optima; on the other hand, the hybrid coding approach separately represents two task types with different characteristics, achieving higher solution efficiency and ensuring more efficient household electricity scheduling decisions to meet the ever-increasing demands of practical applications.

[0144] To more clearly illustrate the household electricity task scheduling and determination method provided in the above embodiments, this embodiment further explains the method with reference to examples:

[0145] like Figure 2 As shown, the method's steps include:

[0146] S21: Obtain the current time-of-use electricity price for the region, the operating power and task information of each appliance to be optimized, as well as the user-defined desired operating time segment and the time interval to be scheduled.

[0147] The task information includes the task type and task duration. The task type is specifically divided into interruptible and continuous types.

[0148] For example, the scheduling time interval in this embodiment is one day, and the time-of-use electricity price is as follows:

[0149] Off-peak hours: 23:00-7:00 (divided into two periods on a daily basis: 23:00-0:00 and 0:00-7:00), the electricity price is 0.3583 yuan / kWh;

[0150] During off-peak hours (7:00-11:00, 12:00-14:00, and 21:00-23:00), the electricity price is 0.5283 yuan / kWh.

[0151] Peak hours: 11:00-12:00, 14:00-15:00, and 17:00-21:00, the electricity price is 0.6283 yuan / kWh;

[0152] Peak hours: 15:00-17:00, electricity price is 0.8380 yuan / kWh.

[0153] The parameters for each appliance to be optimized are provided in Tables 1 and 2 below, depending on the task type:

[0154] Table 1 Interruptible Electrical Appliances

[0155] Air conditioner 1 5:00—9:00 3 1.8 Air conditioner 2 5:00—8:30 2.5 1.3 Air Conditioner 3 20:30—24:00 3 1.3 Disinfection cabinet 1 0:00—24:00 3 0.6 Electric vehicle 1 0:00—24:00 6 2 Electric vehicle 2 0:00—24:00 6 2 air purifier 0:00—24:00 12 0.06

[0156] Table 2 Continuous Electric Appliances

[0157] washing machine 0:00—7:30 1 0.38 Electric water heater 1 0:00—7:00 0.5 2 Electric water heater 2 7:00—20:30 0.5 2 electric kettle 0:00—7:00 0.5 1.2 Vacuum cleaner 6:00—20:30 1 1.4 TV set 17:00—22:30 2 0.1 desktop computer 9:00—22:30 8 0.4 Automatic massage bathtub 20:30—23:00 0.5 1.3 Rice cooker 1 5:00—10:00 0.5 0.85 Rice cooker 2 10:30—15:00 0.5 0.85 Rice cooker 3 16:00—20:30 0.5 0.85 Induction cooker 1 10:30—15:00 0.5 2 Induction cooker 2 16:00—20:30 0.5 2

[0158] In this embodiment, four types of interruptible electrical appliances are selected, and there are seven interruptible power consumption tasks, which are treated as different devices each time, as shown in Table 1; nine types of continuous electrical appliances are selected, and there are thirteen continuous power consumption tasks, which are treated as different devices each time, as shown in Table 2.

[0159] S22: The duration of a unit time period is the minimum operating time required for an interruptible electrical appliance to be turned on once among the electrical appliances to be dispatched.

[0160] For example, in this embodiment, the minimum value t is determined based on the minimum operating time required for each interruptible electrical appliance to be turned on once. min =15min, so the time interval to be scheduled (one day) is divided into 96 unit time periods, each unit time period is 15min long, and the time of one day from 0:00 to 24:00 corresponds to time period 1 to time period 96 respectively.

[0161] S23: Determine the scheduling model for each appliance for different task types.

[0162] Specifically, for interruptible appliance a, the scheduling model consists of the first formula and the first constraint condition:

[0163] The first formula is:

[0164]

[0165] This indicates the on / off state of an interruptible electrical appliance a within a unit time period h, where, This indicates that interruptible electrical appliance a is working. This indicates that interruptible electrical appliance 'a' is not working; 'm' represents the total number of unit time periods 'h' contained within the time interval to be scheduled; 'r'... a This represents the total number of time periods h required for interruptible electrical appliance a to complete the scheduling task, determined by the task duration;

[0166] The first constraint is:

[0167]

[0168]

[0169] s a e represents the lower limit of the working time of interruptible electrical appliance a. a This indicates the maximum operating time of interruptible electrical appliance type a, s. a and e a Determined by task duration; t a This indicates the sequence number of the unit time period in which the interruptible electrical appliance a is first turned on and put into operation;

[0170] Accordingly, for continuous electrical appliance b, the scheduling model consists of the second formula and the second constraint condition:

[0171] The second formula is:

[0172]

[0173] This represents the on / off state of a continuous electrical appliance b within a unit time period h, where... This indicates that the continuous-type electrical appliance b is working. This indicates that the continuous electrical appliance b is not working; r b This represents the total number of time periods required for continuous electrical appliance b to complete the scheduling task, determined by the task duration;

[0174] The second constraint is:

[0175]

[0176]

[0177] s b e represents the lower limit of the working time of continuous electrical appliance b. b This indicates the upper limit of the working time for a continuous electrical appliance (b), s b and e b Determined by task duration; t b This indicates the sequence number of the unit time period when the continuous electrical appliance b is first turned on.

[0178] That is, the first constraint condition restricts the scheduling model to work within the desired runtime period and allows discontinuous operation; while the second constraint condition restricts the scheduling model to work within the desired runtime period and requires continuous operation.

[0179] S24: Establish an optimization model with electricity economy as the optimization objective and the desired operating time as the constraint condition.

[0180] Specifically, the optimization model consists of the following third formula and third constraint conditions:

[0181] The third formula is:

[0182]

[0183] Cost represents the total electricity cost of the dispatching scheme; p a p represents the operating power of interruptible electrical appliance a. b k represents the operating power of continuous-type electrical appliance b. h This represents the time-of-use electricity price for a unit time period h, determined according to the time-of-use electricity price; i and j represent the number of interruptible electrical appliances a and the number of continuous electrical appliances b, respectively;

[0184] The third constraint is:

[0185] [s a ,e a ]∈[u a ,d a ]

[0186] [s b ,e b ]∈[u b ,d b ]

[0187] [u a ,d a ] and [u b ,d b ] represent the expected operating time segments for appliances a and b, respectively.

[0188] S25: Solve the optimization model using a genetic algorithm based on hybrid coding to obtain the corresponding scheduling strategy.

[0189] For an interruptible electrical appliance 'a', its operating state in each time period can be represented by a binary code set. Specifically, "0" represents off and "1" represents on. Therefore, the operating scheme (i.e., the operating state in each time period) corresponding to each interruptible electrical appliance 'a' can be represented as:

[0190]

[0191] Among them, binary encoding set There are i strings, corresponding to i interruptible electrical appliances a; each string of binary codes includes m binary characters, which represent the on / off state of the interruptible electrical appliance a in unit time period 1 to unit time period m. In this embodiment, m is 96, so the binary code set of each interruptible electrical appliance a includes 96 binary codes.

[0192] For a continuous electrical appliance b, the on-time periods of all continuous electrical appliances b can be represented by a string of real numbers, thus characterizing its on / off state in all time periods. The corresponding real number code is:

[0193]

[0194] Chrom, a set of real number encodings b It includes j positive integers, representing the sequence number of the unit time period when the j consecutive electrical appliances b are first turned on.

[0195] After representing the operation schemes of the interruptible appliance a and the continuous appliance b using a hybrid encoding based on binary and real number encoding, combining these schemes yields the initial population used for the genetic algorithm iteration:

[0196]

[0197] Wherein, initialpop is an appliance operation scheme randomly generated based on various constraints and appliance types, with a size of i*(m+j).

[0198] Once the initial population is obtained, a genetic algorithm can be used to iterate through the initial population to find the optimal solution under constraints, with the goal of minimizing electricity economy (total electricity cost), which is the final scheduling strategy.

[0199] Based on the example data provided in this embodiment, software simulation calculations show that the total electricity cost before scheduling optimization (i.e., when users randomly complete the aforementioned electricity consumption tasks) is 27.8562 yuan, and the cost after optimization is 21.3073 yuan. The algorithm iteration process of the scheduling optimization method provided in this embodiment is as follows: Figure 3 As shown, the algorithm found the optimal solution outside the initial population in about 80 generations, indicating that the algorithm has relatively ideal convergence.

[0200] Furthermore, Figure 4 This refers to the operating power of appliances corresponding to each time-of-use pricing period in the appliance dispatching scheme optimized according to the method of this embodiment, wherein the time-of-use pricing can be obtained from the above-mentioned time-of-use pricing:

[0201] Low-frequency periods: 0:00-7:00 (period 1), 23:00-0:00 (period 9);

[0202] Regular hours: 7:00-11:00 (segment 2), 12:00-14:00 (segment 4), 21:00-23:00 (segment 8);

[0203] Peak hours: 11:00-12:00 (segment 3), 14:00-15:00 (segment 5), 17:00-21:00 (segment 7);

[0204] Peak hours: 15:00-17:00 (segment 6).

[0205] Based on the above time-of-use pricing periods, it can be found that... Figure 4 As can be seen, the scheduling scheme optimized by the method of this embodiment has most of its power concentrated in the periods of lower electricity price, namely periods 1, 2, 8, and 9. In contrast, the unoptimized scheduling strategy has a more even power distribution, resulting in higher electricity costs. Compared with the unoptimized scheduling strategy, the method of this embodiment can save about 24% of electricity costs when completing the same electricity task.

[0206] In the above embodiments, a method for determining household electricity usage tasks has been described in detail. This application also provides an embodiment corresponding to a household electricity usage task scheduling and determination device. It should be noted that this application describes the device embodiment from two perspectives: one based on functional modules and the other based on hardware.

[0207] From the perspective of functional modules, such as Figure 5 As shown, this embodiment provides a household electricity task scheduling and determination device, including:

[0208] The information acquisition module 31 is used to acquire the time-of-use electricity price, as well as the operating power, task information and expected operating time of each appliance to be optimized;

[0209] The first module 32 is used to establish a corresponding scheduling model for each appliance to be optimized based on the task information.

[0210] The second module 33 is used to establish an optimization model with the goal of electricity economy based on the time-of-use electricity price, the operating power of each appliance to be optimized, and the scheduling model, and the optimization model uses the desired operating time as a constraint condition.

[0211] The strategy determination module 34 is used to solve the optimization model through a genetic algorithm based on hybrid coding to obtain the scheduling strategy of each appliance to be optimized.

[0212] In a preferred embodiment, the task information includes a task type; wherein the task type is divided into interruptible and continuous types; correspondingly, the strategy determination module is specifically used for:

[0213] If the task type corresponding to the appliance to be optimized is interruptible, the running status of the appliance to be optimized in each time period is represented by binary encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0214] If the task type corresponding to the appliance to be optimized is continuous, the start-up time of the appliance to be optimized is represented by real number encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

[0215] In a preferred embodiment, the above-mentioned household electricity task scheduling and determination device further includes:

[0216] The interval determination module is used to obtain the user-defined time interval to be scheduled; correspondingly, the execution of the first establishment module, the second establishment module, and the strategy determination module is carried out within the time interval to be scheduled.

[0217] In a preferred embodiment, the task information includes the task duration; correspondingly, if the task type corresponding to the scheduling model is interruptible, the scheduling model determined by the first establishment module consists of the first formula and the first constraint condition.

[0218] The first formula is:

[0219]

[0220] This indicates the on / off state of an interruptible electrical appliance a within a unit time period h, where, This indicates that interruptible electrical appliance a is working. This indicates that interruptible electrical appliance 'a' is not working; 'm' represents the total number of unit time periods 'h' contained within the time interval to be scheduled; 'r'... a This represents the total number of time periods h required for interruptible electrical appliance a to complete the scheduling task, determined by the task duration;

[0221] The first constraint is:

[0222]

[0223]

[0224] s a e represents the lower limit of the working time of interruptible electrical appliance a. a This indicates the maximum operating time of interruptible electrical appliance type a, s. a and e a Determined by task duration; t a This indicates the sequence number of the unit time period in which the interruptible electrical appliance a is first turned on and put into operation;

[0225] If the task type corresponding to the scheduling model is continuous, then the scheduling model determined by the first establishment module consists of the second formula and the second constraint conditions.

[0226] The second formula is:

[0227]

[0228] This represents the on / off state of a continuous electrical appliance b within a unit time period h, where... This indicates that the continuous-type electrical appliance b is working. This indicates that the continuous electrical appliance b is not working; r b This represents the total number of time periods required for continuous electrical appliance b to complete the scheduling task, determined by the task duration;

[0229] The second constraint is:

[0230]

[0231]

[0232] s b e represents the lower limit of the working time of continuous electrical appliance b. b This indicates the upper limit of the working time for a continuous electrical appliance (b), s b and e b Determined by task duration; t b This indicates the sequence number of the unit time period when the continuous electrical appliance b is first turned on.

[0233] In a preferred embodiment, the optimization model determined by the second establishment module consists of a third formula and a third constraint condition corresponding to the desired runtime period;

[0234] The third formula is:

[0235]

[0236] Cost represents the total electricity cost of the dispatching scheme; p a p represents the operating power of interruptible electrical appliance a. b k represents the operating power of continuous-type electrical appliance b. h This represents the time-of-use electricity price for a unit time period h, determined according to the time-of-use electricity price; i and j represent the number of interruptible electrical appliances a and the number of continuous electrical appliances b, respectively;

[0237] The third constraint is:

[0238] [s a ,e a ]∈[u a ,d a ]

[0239] [s b ,e b ]∈[u b ,d b ]

[0240] [u a ,d a ] and [u b ,d b ] represent the expected operating time segments for appliances a and b, respectively.

[0241] In a preferred embodiment, the strategy determination module is specifically used for:

[0242] For appliances to be optimized with interruptible task type, the on / off state of each appliance in all time periods is represented by a binary code set; for appliances to be optimized with continuous task type, the on / off time period of each appliance is represented by a real number code set.

[0243] The binary encoding set is as follows:

[0244]

[0245] Binary encoding set There are i sets of binary codes, each containing m binary characters, representing the on / off state of interruptible electrical appliance a during time intervals 1 to m.

[0246] The set of real number encodings is:

[0247]

[0248] Chrom, a set of real number encodings b Includes j positive integers, representing the sequence number of the unit time period when the j consecutive electrical appliances b are first turned on;

[0249] An initial running plan for the optimized model is generated based on each binary encoding set and real number encoding set;

[0250] The initial running scheme is iterated using a genetic algorithm to obtain the scheduling strategy.

[0251] In a preferred embodiment, the duration of the unit time period is the minimum operating time required for each interruptible appliance to be optimized to be turned on once.

[0252] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0253] This embodiment provides a household electricity task scheduling determination device. It prepares the data for electricity scheduling by acquiring user-defined desired operating periods, time-of-use electricity prices, appliance operating power, and task information through an information acquisition module. Then, a first establishment module establishes a scheduling model corresponding to each appliance based on the above information, facilitating subsequent optimization decision-making. Next, a second establishment module establishes an optimization model with user-defined desired operating periods as constraints and electricity economy as the optimization objective. Finally, a strategy determination module solves the optimization model using a genetic algorithm with better convergence, stronger universality, and less susceptibility to local optima, to determine the scheduling strategy. Furthermore, a hybrid encoding method is used to express the scheduling model, further simplifying calculations and improving the optimization efficiency of the scheduling strategy. This device incorporates user needs into the optimization process, considering the user-defined desired operating periods of each appliance as constraints in the optimization model solution. This ensures that the optimized operating scheme maximizes user needs and provides higher user satisfaction.

[0254] Figure 6 A structural diagram of a household electricity task scheduling and determination device provided in another embodiment of this application is shown below. Figure 6 As shown, a household electricity task scheduling and determination device includes: a memory 40 for storing computer programs;

[0255] The processor 41 is used to execute a computer program to implement the steps of a household electricity task scheduling and determination method as described in the above embodiment.

[0256] The household electricity task scheduling and determination device provided in this embodiment may include, but is not limited to, smart meters, smartphones, tablets, laptops, or desktop computers.

[0257] The processor 41 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 41 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 41 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 41 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 41 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0258] The memory 40 may include one or more computer-readable storage media, which may be non-transitory. The memory 40 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 40 is used to store at least the following computer program 401, which, after being loaded and executed by the processor 41, is capable of implementing the relevant steps of a household electricity task scheduling determination method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 40 may also include an operating system 402 and data 403, etc., and the storage method may be temporary storage or permanent storage. The operating system 402 may include Windows, Unix, Linux, etc. The data 403 may include, but is not limited to, a household electricity task scheduling determination method.

[0259] In some embodiments, a household electricity task scheduling and determination device may further include a display screen 42, an input / output interface 43, a communication interface 44, a power supply 45, and a communication bus 46.

[0260] Those skilled in the art will understand that Figure 6 The structure shown does not constitute a limitation on a household electricity task scheduling and determination device, and may include more or fewer components than shown.

[0261] This application provides a household electricity task scheduling and determination device, which includes a memory and a processor. When the processor executes a program stored in the memory, it can implement the following method: a household electricity task scheduling and determination method.

[0262] This embodiment provides a household electricity task scheduling and determination device. Through a processor executing a computer program stored in memory, it incorporates user-defined desired runtime segments reflecting user needs into the appliance scheduling optimization process. This ensures the entire optimization process is based on constraints that meet user requirements, and the optimized scheduling results effectively guarantee user satisfaction. Furthermore, this device uses a genetic algorithm for solving the optimization model, which has faster convergence speed, stronger universality, and is less prone to getting trapped in local optima. It also uses hybrid encoding to represent appliances with different task types to simplify the complexity of scheduling decisions, thereby improving efficiency.

[0263] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0264] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0265] This embodiment provides a computer-readable storage medium. When the computer program stored therein is executed, it can incorporate user-defined desired runtime segments reflecting user needs into the appliance scheduling optimization process. This ensures that the entire optimization process is based on constraints that meet user needs, and the optimized scheduling results can effectively guarantee user satisfaction. Furthermore, this device uses a genetic algorithm with faster convergence speed, stronger universality, and less susceptibility to local optima when solving the optimization model. It also uses hybrid encoding to represent appliances with different task types to simplify the complexity of scheduling decision-making, thereby improving efficiency.

[0266] The foregoing has provided a detailed description of a method, apparatus, and medium for determining household electricity usage scheduling, as provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0267] It should also be noted that, in this specification, 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.

Claims

1. A method for determining household electricity usage scheduling, characterized in that, include: Obtain time-of-use electricity pricing, as well as the operating power, task information, and expected operating time of each appliance to be optimized; The task information includes task type and task duration; wherein, the task type is divided into interruptible type and continuous type; Based on the task information, establish a corresponding scheduling model for each of the electrical appliances to be optimized; An optimization model with electricity economy as the objective is established based on the time-of-use electricity price, the operating power of each appliance to be optimized, and the scheduling model, and the optimization model uses the desired operating period as a constraint condition. The optimization model is solved by a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each of the electrical appliances to be optimized; Wherein, if the task type corresponding to the scheduling model is interruptible, the scheduling model is composed of a first formula and a first constraint condition; The first formula is: Indicates interruptible electrical appliances a During the unit time period h The open and closed states within, where... Indicates interruptible electrical appliances a Work, Indicates interruptible electrical appliances a Not working; m Indicates the unit time period contained within the time interval to be scheduled. h The total number; Indicates interruptible electrical appliances a Unit time period required to complete the scheduling task h The total number is determined by the task duration; The first constraint is: Indicates interruptible electrical appliances a The lower limit of the working hours, Indicates interruptible electrical appliances a Maximum working hours and Determined by the task duration; Indicates interruptible electrical appliances a The sequence number of the unit time period when it is first started; If the task type corresponding to the scheduling model is continuous, then the scheduling model consists of the second formula and the second constraint condition; The second formula is: Indicates continuous electrical appliances b During the unit time period h The open and closed states within, where... Indicates continuous electrical appliances b Work, Indicates continuous electrical appliances b Not working; Indicates continuous electrical appliances b The total number of time periods required to complete the scheduling task is determined by the task duration. The second constraint is: Indicates continuous electrical appliances b The lower limit of the working hours, Indicates continuous electrical appliances b Maximum working hours and Determined by the task duration; Indicates continuous electrical appliances b The sequence number of the first time period to be opened; The optimization model consists of a third formula and a third constraint condition corresponding to the desired runtime segment; The third formula is as follows: Cost represents the total electricity cost of the scheduling plan; Indicates interruptible electrical appliances a Operating power Indicates continuous electrical appliances b Operating power; This indicates the unit time period determined according to the time-of-use electricity price. h The corresponding time-of-use electricity price; i and j These represent interruptible electrical appliances. a The number of continuous electrical appliances b The number of; The third constraint is: and They represent electrical appliances. a , b The corresponding expected runtime segment.

2. The method for determining household electricity usage scheduling according to claim 1, characterized in that, The step of solving the optimization model using a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each of the appliances to be optimized includes: If the task type corresponding to the appliance to be optimized is interruptible, the operating status of the appliance to be optimized in each time period is represented by binary encoding, and then the optimization model is solved by a genetic algorithm to obtain the scheduling strategy. If the task type corresponding to the appliance to be optimized is continuous, the start-up time of the appliance to be optimized is represented by real number encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

3. The method for determining household electricity usage scheduling according to claim 1, characterized in that, Also includes: Get the user-defined time interval to be scheduled; Accordingly, the establishment of the scheduling model and the establishment and solution of the optimization model are carried out within the time interval to be scheduled.

4. The method for determining household electricity usage scheduling according to claim 2, characterized in that, The step of solving the optimization model using a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each of the appliances to be optimized includes: For the appliances to be optimized with the task type being interruptible, the on / off state of each appliance in all time periods is represented by a binary code set; for the appliances to be optimized with the task type being continuous, the on / off time period of each appliance is represented by a real number code set. The binary encoding set is as follows: The binary encoding set Total i Each of the binary encoding sets includes , ... m Each binary character represents an interruptible electrical appliance. a During unit time period 1 - unit time period m The open and closed states; The set of real number codes is: The real number encoding set include j 2 positive integers, respectively representing j A continuous type of electrical appliance b The sequence number of the first time period to be opened; An initial running scheme for the optimization model is generated based on each of the binary encoding sets and the real number encoding sets; The initial running scheme is iterated using a genetic algorithm to obtain the scheduling strategy.

5. The method for determining household electricity usage scheduling according to claim 4, characterized in that, The duration of the unit time period is the minimum operating time required for each of the interruptible electrical appliances to be optimized to be turned on once.

6. A household electricity task scheduling and determination device, characterized in that, include: The information acquisition module is used to acquire time-of-use electricity prices, as well as the operating power, task information, and expected operating time of each appliance to be optimized; The task information includes task type and task duration; wherein, the task type is divided into interruptible type and continuous type; The first module is used to establish a corresponding scheduling model for each of the electrical appliances to be optimized based on the task information. The second establishment module is used to establish an optimization model with electricity consumption economy as the objective based on the time-of-use electricity price, the operating power of each of the appliances to be optimized, and the scheduling model, and the optimization model uses the desired operating period as a constraint condition. The strategy determination module is used to solve the optimization model using a genetic algorithm based on hybrid coding to obtain the scheduling strategy for each of the electrical appliances to be optimized. Wherein, if the task type corresponding to the scheduling model is interruptible, the scheduling model determined by the first establishment module is composed of a first formula and a first constraint condition; The first formula is: Indicates interruptible electrical appliances a During the unit time period h The open and closed states within, where... Indicates interruptible electrical appliances a Work, Indicates interruptible electrical appliances a Not working; m Indicates the unit time period contained within the time interval to be scheduled. h The total number; Indicates interruptible electrical appliances a Unit time period required to complete the scheduling task h The total number is determined by the task duration; The first constraint is: Indicates interruptible electrical appliances a The lower limit of the working hours, Indicates interruptible electrical appliances a Maximum working hours and Determined by the task duration; Indicates interruptible electrical appliances a The sequence number of the unit time period when it is first started; If the task type corresponding to the scheduling model is continuous, then the scheduling model determined by the first establishment module consists of the second formula and the second constraint condition; The second formula is: Indicates continuous electrical appliances b During the unit time period h The open and closed states within, where... Indicates continuous electrical appliances b Work, Indicates continuous electrical appliances b Not working; Indicates continuous electrical appliances b The total number of time periods required to complete the scheduling task is determined by the task duration. The second constraint is: Indicates continuous electrical appliances b The lower limit of the working hours, Indicates continuous electrical appliances b Maximum working hours and Determined by the task duration; Indicates continuous electrical appliances b The sequence number of the first time period to be opened; The optimization model determined by the second establishment module consists of a third formula and a third constraint condition corresponding to the desired runtime segment; The third formula is as follows: Cost represents the total electricity cost of the dispatching scheme; Indicates interruptible electrical appliances a Operating power Indicates continuous electrical appliances b Operating power; This indicates the unit time period determined according to the time-of-use electricity price. h The corresponding time-of-use electricity price; i and j These represent interruptible electrical appliances. a The number of continuous electrical appliances b The number of; The third constraint is: and They represent electrical appliances. a , b The corresponding expected runtime segment.

7. The household electricity task scheduling and determination device according to claim 6, characterized in that, The strategy determination module is specifically used for: If the task type corresponding to the appliance to be optimized is interruptible, the operating status of the appliance to be optimized in each time period is represented by binary encoding, and then the optimization model is solved by a genetic algorithm to obtain the scheduling strategy. If the task type corresponding to the appliance to be optimized is continuous, the start-up time of the appliance to be optimized is represented by real number encoding, and then the optimization model is solved by genetic algorithm to obtain the scheduling strategy.

8. The household electricity task scheduling and determination device according to claim 6, characterized in that, Also includes: The interval determination module is used to obtain the time interval to be scheduled defined by the user; correspondingly, the execution of the first establishment module, the second establishment module and the strategy determination module is carried out within the time interval to be scheduled.

9. The household electricity task scheduling and determination device according to claim 7, characterized in that, The strategy determination module is specifically used for: For the appliances to be optimized with the task type being interruptible, the on / off state of each appliance in all time periods is represented by a binary code set; for the appliances to be optimized with the task type being continuous, the on / off time period of each appliance is represented by a real number code set. The binary encoding set is as follows: The binary encoding set Total i Each of the binary encoding sets includes , ... m Each binary character represents an interruptible electrical appliance. a During unit time period 1 - unit time period m The open and closed states; The set of real number codes is: The real number encoding set include j 2 positive integers, respectively representing j A continuous type of electrical appliance b The sequence number of the first time period to be opened; An initial running scheme for the optimization model is generated based on each of the binary encoding sets and the real number encoding sets; The initial running scheme is iterated using a genetic algorithm to obtain the scheduling strategy.

10. The household electricity task scheduling and determination device according to claim 9, characterized in that, The duration of the unit time period is the minimum operating time required for each of the interruptible electrical appliances to be optimized to be turned on once.

11. A household electricity task scheduling and determination device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the household electricity task scheduling determination method as described in any one of claims 1 to 5 when executing the computer program.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the household electricity task scheduling determination method as described in any one of claims 1 to 5.

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