A Smart Home Optimal Scheduling Method and System for a Microgrid Cluster

Abstract

The present disclosure provides a method and system for optimizing the scheduling of smart homes in a microgrid cluster, obtaining parameter data of smart home devices in the microgrid cluster; obtaining a scheduling strategy for the smart homes in the microgrid cluster according to the obtained parameter data and a preset scheduling model; wherein the preset scheduling model aims to minimize the sum of the electricity consumption costs of all smart home devices; the present disclosure aims to minimize the electricity consumption costs of all smart home devices and takes the home comfort of each smart home device as a constraint condition, and the obtained scheduling strategy takes into account both comfort and electricity consumption costs, improving the accuracy of the optimization scheduling of smart homes.

Description

Technical Field

[0001] The present disclosure relates to the technical field of microgrid group scheduling, and particularly to a method and system for optimizing the scheduling of smart homes in a microgrid group. Background Art

[0002] The statements in this part only provide the background art related to the present disclosure and do not necessarily constitute the prior art.

[0003] In the research of the energy Internet, demand response has become a hot issue that has received extensive attention. As a necessary means to achieve demand response (DR), the Smart Home Management System (SHMS) has become the focus of research.

[0004] The inventors found that the existing microgrid groups simply use grid parameters as feedback quantities for scheduling, lacking comprehensive consideration and scheduling of smart home devices, and not integrating the scheduling of smart home devices with the electricity consumption characteristics and overall cost of each smart home device. As a result, the final scheduling strategy cannot achieve a balance between home comfort and electricity cost. Summary of the Invention

[0005] In order to solve the deficiencies of the existing technology, the present disclosure provides a method and system for optimizing the scheduling of smart homes in a microgrid group. With the goal of minimizing the electricity cost of all smart home devices and using the home comfort of each smart home device as a constraint condition, the obtained scheduling strategy takes into account both comfort and electricity cost, and improves the accuracy of optimizing the scheduling of smart homes.

[0006] In order to achieve the above object, the present disclosure adopts the following technical solutions:

[0007] The first aspect of the present disclosure provides a method for optimizing the scheduling of smart homes in a microgrid group.

[0008] A method for optimizing the scheduling of smart homes in a microgrid group includes the following processes:

[0009] Obtain the parameter data of the smart home devices in the microgrid group;

[0010] According to the obtained parameter data and a preset scheduling model, obtain the scheduling strategy for the smart homes in the microgrid group;

[0011] Among them, the preset scheduling model aims to minimize the sum of the electricity costs of all smart home devices.

[0012] Furthermore, the constraints of the preset scheduling model at least include: the sum of the electricity consumption of each home device within a preset time period is less than or equal to the electricity consumption upper limit within this time period.

[0013] Further, the constraints of the preset scheduling model at least include: the power consumption of the home appliances within the preset time period is equal to their rated power consumption or equal to zero.

[0014] Further, in the preset scheduling model, according to the indoor temperature at the previous moment, the outdoor temperature at the current control moment, the influence coefficient of the outdoor temperature on the room temperature, the influence coefficient of the power consumption per unit time of the air conditioner on the room temperature, and the power consumption of the air conditioner in the current time period, the indoor temperature control model at the current moment is obtained, and the indoor temperature is within the preset control range.

[0015] Further, in the preset scheduling model, according to the water temperature at the previous moment, the temperature of water cooled per time period, and the influence coefficient of the power consumption per unit time of the water heater on the water temperature, the water heater water temperature control model at the current moment is obtained, and the water heater water temperature is within the preset control range.

[0016] Further, in the preset scheduling model, the sum of the total power consumption of the electric vehicle in the current time period and the initial power of the electric vehicle is less than or equal to the rated capacity of the capacitor vehicle battery and greater than or equal to 80% of the rated capacity of the capacitor vehicle battery.

[0017] Further, in the preset scheduling model, the total power consumption of the washing machine in the current time period is equal to the rated power consumption for the washing machine to complete the task.

[0018] Further, an adaptive particle swarm optimization algorithm is used to solve the scheduling model, including:

[0019] In the fitness formula of the particle swarm optimization algorithm, a decay constant is introduced, and the individual optimal value and the global optimal value decay at a preset rate. If the fitness at the current position is higher than the decayed fitness, it will replace the previous fitness. The decay constants of all particles are the same, and the update frequencies of each particle are different. The particles frequently update the optimal value until the iteration times are reached.

[0020] The second aspect of the present disclosure provides a microgrid group smart home optimal scheduling system.

[0021] A microgrid group smart home optimal scheduling system includes:

[0022] A data acquisition module, configured to: acquire the parameter data of the microgrid group smart home appliances;

[0023] A home scheduling module, configured to: obtain the microgrid group smart home scheduling strategy according to the acquired parameter data and the preset scheduling model;

[0024] Wherein, the preset scheduling model aims to minimize the sum of the electricity costs of all smart home appliances.

[0025] The third aspect of the present disclosure provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, the steps in the intelligent home optimal scheduling method for a microgrid group as described in the first aspect of the present disclosure are implemented.

[0026] The fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, the steps in the intelligent home optimal scheduling method for a microgrid group as described in the first aspect of the present disclosure are implemented.

[0027] Compared with the prior art, the beneficial effects of the present disclosure are as follows:

[0028] 1. For the method, system, medium or electronic device described in the present disclosure, with the goal of minimizing the electricity cost of all intelligent home devices and the home comfort of each intelligent home device as a constraint condition, the obtained scheduling strategy takes into account both comfort and electricity cost, and improves the accuracy of intelligent home optimal scheduling.

[0029] 2. For the method, system, medium or electronic device described in the present disclosure, a decay constant is introduced into the fitness value formula of the particle swarm algorithm. The individual optimal value and the global optimal value decay at a preset rate. If the fitness at the current position is higher than the decayed fitness, it will replace the previous fitness. The decay constants of all particles are the same, and the update frequencies of each particle are different. The particles frequently update the optimal value until the iteration times are reached, which greatly improves the generation speed and accuracy of the scheduling strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The specification drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The schematic embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0031] Figure 1 It is a schematic flow chart of the intelligent home optimal scheduling method for a microgrid group provided in Embodiment 1 of the present disclosure.

[0032] Figure 2 It is a schematic structural diagram of the intelligent home management system provided in Embodiment 1 of the present disclosure.

[0033] Figure 3 It is a schematic diagram of load parameters and user settings provided in Embodiment 1 of the present disclosure.

[0034] Figure 4 It is the operation situation of basic lighting and entertainment loads provided in Embodiment 1 of the present disclosure.

[0035] Figure 5 It is a schematic diagram of the outdoor temperature curve provided in Embodiment 1 of the present disclosure.

[0036] Figure 6 Schematic diagram of the operation of the air conditioner before optimization provided in Embodiment 1 of the present disclosure.

[0037] Figure 7 Schematic diagram of the operation of the air conditioner after optimization provided in Embodiment 1 of the present disclosure.

[0038] Figure 8 Schematic diagram of the change in room temperature after optimization provided in Embodiment 1 of the present disclosure.

[0039] Figure 9 Schematic diagram of the operation of the water heater before optimization provided in Embodiment 1 of the present disclosure.

[0040] Figure 10 Schematic diagram of the operation of the water heater after optimization provided in Embodiment 1 of the present disclosure.

[0041] Figure 11 Schematic diagram of the change in water temperature after optimization provided in Embodiment 1 of the present disclosure.

[0042] Figure 12 Schematic diagram of the charging situation of the electric vehicle before optimization provided in Embodiment 1 of the present disclosure.

[0043] Figure 13 Schematic diagram of the charging situation of the electric vehicle after optimization provided in Embodiment 1 of the present disclosure.

[0044] Figure 14 Schematic diagram of the operation of the washing machine before and after optimization provided in Embodiment 1 of the present disclosure.

[0045] Figure 15 Schematic diagram of the operation of the overall load before optimization provided in Embodiment 1 of the present disclosure.

[0046] Figure 16 Schematic diagram of the operation of the overall load after optimization provided in Embodiment 1 of the present disclosure. Detailed implementation manners

[0047] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further descriptions of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present disclosure belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0050] In the case of no conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other.

[0051] Embodiment 1:

[0052] As Figure 1 shown, Embodiment 1 of the present disclosure provides a method for optimizing the scheduling of smart homes in a microgrid group, including the following processes:

[0053] Obtain the parameter data of the smart home devices in the microgrid group;

[0054] According to the obtained parameter data and the preset scheduling model, obtain the scheduling strategy of the smart homes in the microgrid group;

[0055] Among them, the preset scheduling model aims to minimize the sum of the electricity costs of all smart home devices.

[0056] Specifically, it includes the following content:

[0057] S1: Design of the basic structure of the SHMS (Smart Home Management System)

[0058] As Figure 2 shown, the SHMS includes a smart meter, a smart human-computer interaction terminal, a controller, a load, etc. The system communicates internally through wireless or broadband Internet, and the load is controlled by the user or the controller according to different classifications.

[0059] The load is divided into three categories: basic load, temperature-controlled load, and transferable load. Among them, the basic load belongs to the uncontrollable load, including electric lights, computers, televisions, refrigerators, etc. The temperature-controlled load is controlled by temperature setting constraints, such as air conditioners, water heaters, etc. The transferable load is completely controllable and can be controlled within the optimized time, such as electric vehicles, washing machines, etc. The smart meter, as the intermediary between the SHMS and the smart grid, realizes the two-way interaction of energy and information. On the one hand, the electric energy of the power grid is measured and transmitted to the home through the smart meter; on the other hand, the electricity price information and demand response strategy of the power grid are also sent to the users through the smart meter, and the electricity consumption status and plan of the users are also uploaded to the power grid. The controller integrates information such as electricity price, electric energy, user settings, and real-time data (such as temperature) obtained from the Internet, formulates an optimized load plan for future time periods, and sends control commands to the load. On the basis of using electricity according to the optimized plan, real-time monitoring is carried out to achieve dynamic feedback control. The human-computer interaction terminal (computer, mobile phone, etc.) can provide users with information such as the historical data, current status, and future optimization plan of the SHMS. Users can flexibly set the load optimization duration, operation tasks, room temperature, and water temperature range, etc. according to their preferences.

[0060] S2: Microgrid Group Smart Home Optimal Scheduling Model

[0061] According to the optimized time H hours set by the user, each hour is divided into l time periods, so the optimizable time is L = l·H time periods. Suppose there are n loads in the SHMS. The objective function of the minimum electricity cost, the overall load electricity consumption constraint, the constraint that the load operates at the rated power, and other constraint conditions are reflected in the user settings and load characteristics.

[0062]

[0063]

[0064] e a，t = e a，nomal or ea,t = 0

[0065] Among them: C is the total electricity cost; E max is the upper limit of the total electricity consumption in the time period; p(t) is the electricity price in the time period; e a，t is the electricity consumption of load a in time period t; e a，normal is the rated electricity consumption of load a.

[0066] S2.1: Air Conditioner

[0067] In this embodiment, the operating power of the air conditioner model is the rated power. Generally, the indoor temperature change of a family is related to the house structure, building materials, initial room temperature value, outdoor temperature change, heating or cooling performance of the air conditioner, etc. Therefore, the room temperature model is established as:

[0068] T in(t) = T in (t - 1)+α[T out (t)-T in (t - 1)]+β·e l，t

[0069] T low ≤T in (t)≤T high ,

[0070] Where: T in (t), T out (t) are the indoor and outdoor temperatures in the t-th period respectively; α is the influence coefficient of outdoor temperature on room temperature; β is the influence coefficient of electricity consumption per unit period of air conditioner operation on room temperature. In this embodiment, the summer model is mainly considered, and the air conditioner is cooling, so β is less than 0, and vice versa in winter; T high , T low are the upper and lower limits of room temperature set by the user.

[0071] S2.2: Water heater

[0072] As a temperature-controlled load, the characteristics of the water heater are similar to those of the air conditioner. Generally, the room temperature is lower than the water temperature inside the water heater, so the water temperature will gradually decrease over time. For simplicity, in this embodiment, it is assumed that the temperature drop of water cooling is proportional to time. The change of hot water temperature is affected by factors such as the heat insulation performance, heating performance, and initial water temperature of the water heater. Considering the above factors, the water temperature model is:

[0073] T water (t) = T water (t - 1)-T cooling +γ·e 2，t

[0074] T water，l ≤T water (t)≤T water，h

[0075] Where: T water (t) is the water temperature in the t-th period; T cooling is the temperature drop of water cooling per period; γ is the influence coefficient of electricity consumption per unit period of the water heater on water temperature. Since the water heater is heating, γ is greater than 0, T water，h , T water，l are the set upper and lower limits of water temperature.

[0076] S2.3: Electric vehicle

[0077] The user comfort of an electric vehicle is mainly reflected in whether the state of charge (SOC) of the electric vehicle reaches the lower limit of the SOC set by the user after the planned optimization period, so as to facilitate the user's travel after the optimization. When the user sets the SOC to reach at least 80% of the rated battery capacity after charging, according to the charging characteristics of the electric vehicle, the electric vehicle charging model in this embodiment is as follows:

[0078]

[0079] Among them, E 3，full is the rated capacity of the electric vehicle battery; E 3，start is the initial battery charge of the electric vehicle.

[0080] S2.4: Washing machine

[0081] The washing machine belongs to the transferable load. Generally speaking, the washing machine needs to run continuously until the task is completed. Similar to the above, the washing machine runs at a constant power during the operation period. When the washing machine needs to run continuously for 4 time periods to complete the task, the operation model is:

[0082]

[0083] 1≤t s ≤L - 3

[0084] Among them: E 4，normal is the rated power consumption of the washing machine to complete the task; t s is the time period when the washing machine starts to run.

[0085] S3: Adaptive particle swarm algorithm

[0086] The particle swarm optimization algorithm (PSO) uses a group of random particles to search in the solution space. Each particle has a random velocity vector, and the quality of the particle position is evaluated through the optimization objective function. The objective function takes the particle position as a parameter, calculates the objective function value, and obtains the historical best solution Pi and the global best solution Pg of each particle. The algorithm can be expressed by the following formula:

[0087]

[0088]

[0089] Among them: ω is the inertia weight coefficient, which is used to maintain the velocity during the iteration process; c 1 is used to maintain the learning of the particle itself and is called the individual acceleration coefficient; c 2It is used to maintain the learning for all particles and is called the global acceleration coefficient; μ and η are random numbers ranging from 0 to 1; ρ is used to refresh the positions of the particles, and its value is usually set to 1, which is called the constraint factor.

[0090] Based on the basic PSO, this embodiment proposes a new optimal fitness update mechanism. The update formula of the adaptive PSO algorithm is as follows:

[0091]

[0092] Introduce the decay constant T, T ∈ [0, 1]. The individual optimal value and the global optimal value will decay at a certain rate. If the fitness of the current position is higher than the decayed fitness, it will replace the previous fitness. The decay constants of all particles are the same, but the update frequencies of each particle are different. As f(X) increases, f(P) becomes smaller and smaller, and the particles frequently update the optimal value until the iteration times are reached.

[0093] S5: Case analysis

[0094] This case divides each hour into 4 time periods and optimizes the 24 hours (96 time periods) starting from 6 am. The load parameters, user settings, and the operating conditions of the basic lighting and entertainment loads are respectively as Figure 3 and Figure 4 shown. There are three electricity prices for time-of-use electricity, namely peak-time, normal-time, and valley-time electricity prices, which also serve as a signal for demand response. The outdoor temperature curve in this case is as Figure 5 shown, with large fluctuations, which is conducive to testing the stability of the model algorithm. For simplicity, the operating power of the load is set to the rated power. According to the user's living habits, the basic load curve has slight fluctuations in the early morning, noon, and evening, and is relatively stable during the user's working hours outside and at night.

[0095] S5.1: Analysis of the optimization results of the temperature control load

[0096] S5.1.1: Analysis of the optimization results of air conditioners

[0097] When α takes 0.025 and β takes -1.8, the operations of the air conditioner before and after optimization are respectively as Figure 6 and Figure 7 shown. Obviously, before optimization, the operating time periods of the air conditioner are relatively concentrated, mainly in the noon and evening when users are at home, exactly during the high-temperature period and the electricity consumption peak, with a relatively high electricity price. The air conditioner operates in a "strong-weak" state, consuming a large amount of electric energy. After optimization, the operating time periods of the air conditioner are dispersed and reasonably distributed. It cools down in advance during the low-electricity-price periods to reduce electricity consumption during the high-temperature period and the electricity consumption peak. The operating power also remains at the optimal cooling power, avoiding waste of electric energy.

[0098] The change in room temperature after optimization is as Figure 8As shown. In the morning, the temperature is lower than the room temperature. Under the dual effects of air conditioning refrigeration and the temperature difference between indoor and outdoor, the room temperature drops. After eight o'clock, the temperature rises and the room temperature continues to rise. Every time it is about to exceed the temperature limit set by the user, the air conditioning refrigeration is adjusted. At night, the temperature drops below the room temperature, and the room temperature drops accordingly, and the air conditioner does not need to run. During the whole process, the air conditioner avoids unnecessary operation with the "cooperation" of the outdoor temperature and keeps the room temperature within the user-set range.

[0099] S5.1.2: Analysis of water heater optimization results

[0100] The operation of the water heater before and after optimization is as follows Figure 9 and Figure 10 Before optimization, users were used to running the water heater before use, so the water heater was concentrated in the period with higher electricity prices. After optimization, the operation of the water heater was obviously dispersed to the period with lower electricity prices, and the operation time was reduced.

[0101] After optimization, the water temperature changes as follows Figure 11 As shown in the figure, the hot water is constantly cooling, while the water heater operates during the period of low electricity prices to raise the water temperature and keep it within the range set by the user. In addition, the average water temperature at night is slightly lower than that during the day, which is in line with the user's living habits and also saves electricity.

[0102] S5.2: Analysis of transferable load optimization results

[0103] The charging status of electric vehicles before and after optimization Figure 12 and Figure 13 As shown in the figure, before the optimization, electric vehicles were charged during the high electricity price period after the rush hour. After the optimization, charging was transferred to non-peak hours such as night and morning, when the electricity price is lower and does not delay daytime users' travel.

[0104] The operating conditions of the washing machine before and after optimization are as follows Figure 14 As shown in the figure, similar to electric car charging, the optimized washing machine operation time period is also shifted to the low electricity price period, and it completes 4 consecutive periods of operation in the morning, which is convenient for users.

[0105] S5.3: Overall load optimization results analysis

[0106] The total load operation before and after optimization is as follows Figure 15 and Figure 16As shown in the figure. Before optimization, the total load was mostly concentrated during periods when users were at home, such as at noon and in the evening, which was exactly the peak electricity consumption period. The electricity price was high and it was contrary to the demand-side response advocated by the power grid. In addition, users consumed electricity according to their living habits, and the predictability and quick response of their control were not strong, which easily caused untimely operation control and waste of electric energy. After optimization, the total load generally decreased and was reasonably distributed, significantly avoiding the high electricity price during the peak electricity consumption period, transferring the load to low electricity price periods such as in the morning and at night, forming a demand-side response and achieving the purpose of peak shaving and valley filling. Before optimization, the total electricity consumption of the user load was 36.34 kwh, and the total electricity cost of the load was 39.368 yuan. After optimization, the electricity consumption was 25.3 kwh, and the electricity cost was 15.02 yuan, saving nearly 30.4% of the electricity consumption and reducing the electricity bill by about 62%. The effect of electricity saving and cost saving of the scheme is obvious.

[0107] Example 2:

[0108] Embodiment 2 of the present disclosure provides a microgrid group smart home optimization scheduling system, including:

[0109] A data acquisition module, configured to: acquire parameter data of smart home devices in the microgrid group;

[0110] A home scheduling module, configured to: obtain a microgrid group smart home scheduling strategy according to the acquired parameter data and a preset scheduling model;

[0111] Among them, the preset scheduling model aims to minimize the sum of the electricity costs of all smart home devices.

[0112] The working method of the system is the same as the microgrid group smart home optimization scheduling method provided in Embodiment 1, and will not be elaborated here.

[0113] Example 3:

[0114] Embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, it implements the steps in the microgrid group smart home optimization scheduling method as described in Embodiment 1 of the present disclosure.

[0115] Example 4:

[0116] Embodiment 4 of the present disclosure provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the microgrid group smart home optimization scheduling method as described in Embodiment 1 of the present disclosure.

[0117] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, a system, or a computer program product. Therefore, the present disclosure can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0118] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0119] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0121] Those of ordinary skill in the art can understand that all or part of the processes of implementing the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0122] The above are only the preferred embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, various modifications and variations can be made to the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for optimizing the scheduling of smart homes in a microgrid cluster, characterized in that: It includes the following processes: Obtain the parameter data of the smart home devices in the microgrid cluster; According to the obtained parameter data and the preset scheduling model, obtain the smart home scheduling strategy for the microgrid cluster; Among them, the preset scheduling model aims to minimize the sum of the electricity consumption costs of all smart home devices; The constraints of the preset scheduling model at least include: the sum of the electricity consumption of each home device within the preset time period is less than or equal to the electricity consumption upper limit within this time period; In the preset scheduling model, according to the indoor temperature at the previous moment, the outdoor temperature at the current control moment, the influence coefficient of the outdoor temperature on the room temperature, the influence coefficient of the air conditioner's unit-time electricity consumption on the room temperature, and the air conditioner's electricity consumption in the current time period, obtain the indoor temperature control model at the current moment, and the indoor temperature is within the preset control range; In the preset scheduling model, according to the water temperature at the previous moment, the temperature cooled by water per time period, and the influence coefficient of the water heater's unit-time electricity consumption on the water temperature, obtain the water heater water temperature control model at the current moment, and the water heater water temperature is within the preset control range; The preset scheduling model divides each hour into l time periods according to the optimized time H hours set by the user, then the optimizable time is L = l·H time periods; assume that the smart home management system has a total of n loads; the objective function of the minimum electricity consumption cost, the overall load electricity consumption constraint, the constraint that the load operates at the rated power, and other constraint conditions are reflected in the user settings and load characteristics; e a，t = e a,nomal or e a,t = 0 Where: C is the total electricity cost; E max is the upper limit of the total electricity consumption in a time period; p(t) is the time-of-use electricity price; e a,t is the electricity consumption of load a at time t; e a，normal is the rated electricity consumption of load a; The operating power of the air conditioner model is the rated power; the change of the indoor temperature of the family is related to the house structure, building materials, initial room temperature value, outdoor temperature change, and the heating or cooling performance of the air conditioner. Therefore, the room temperature model is established as: T in (t) = T in (t - 1) + α[T out (t) - T in (t - 1)] + β·e 1，t T low ≤T in (t)≤T high , Where: T in (t), T out (t) are the indoor and outdoor temperatures in the t-th period respectively; α is the influence coefficient of outdoor temperature on room temperature; β is the influence coefficient of electricity consumption per unit period of air conditioner operation on room temperature. In the summer model, the air conditioner cools, so β is less than 0, and vice versa in winter; T high , T low are the upper and lower limits of the room temperature set by the user; The water heater is a temperature-controlled load. The room temperature is lower than the water temperature inside the water heater, so the water temperature will gradually decrease over time; assume that the temperature drop of the water cooling is proportional to the time; the change of the hot water temperature is affected by factors such as the heat insulation performance, heating performance, and initial water temperature of the water heater; considering the above factors, the water temperature model is: T water (t) = T water (t - 1) - T cooling + γ·e 2，t T water，1 ≤T water (t)≤T water，h Where: T water (t) is the water temperature at the t-th time period; T cooling is the temperature of water cooling per time period; γ is the influence coefficient of the electricity consumption per unit time period of the water heater on the water temperature. Since the water heater is heating, γ is greater than 0, T water,h 、T water，1 are the upper and lower limits of the set water temperature; The user comfort of the electric vehicle is mainly reflected in whether the state of charge of the electric vehicle reaches the lower limit of the SOC set by the user after the planned optimization period to facilitate the user's travel after the optimization; when the user sets the SOC to at least reach 80% of the battery's rated capacity after charging, according to the charging characteristics of the electric vehicle, the electric vehicle charging model is as follows: Among them, E 3 , full is the rated capacity of the electric vehicle battery; E 3 , start is the initial battery charge of the electric vehicle; The washing machine belongs to the shiftable load. The washing machine needs to run continuously until the task is completed; the washing machine runs at a constant power during the running period; when the washing machine needs to run continuously for 4 time periods to complete the task, the running model is: 1 ≤ t s ≤ L - 3 Where: E 4,normal is the rated power consumption for the washing machine to complete the task; t s is the time period when the washing machine starts to run; There are three electricity prices for time-of-use electricity, namely peak time, normal time, and valley time, which are used as a signal for demand response when optimizing the above preset scheduling model; An adaptive particle swarm algorithm is used to solve the above preset scheduling model, including: Introduce a decay constant into the fitness formula of the particle swarm algorithm. The individual optimal value and the global optimal value decay at a preset rate. If the fitness at the current position is higher than the decayed fitness, it will replace the previous fitness. The decay constants of all particles are the same, and the update frequencies of each particle are different. The particles frequently update the optimal value until the iteration times are reached.

2. The intelligent home optimal scheduling method for a microgrid group as described in claim 1, characterized in that: The constraints of the preset scheduling model at least include: the electricity consumption of home appliances within a preset time period is equal to their rated electricity consumption or equal to zero.

3. An intelligent home optimal scheduling system for a microgrid group, based on the intelligent home optimal scheduling method for a microgrid group as described in any one of claims 1-2, characterized in that: It includes the following processes: A data acquisition module, configured to: acquire the parameter data of the intelligent home appliances in the microgrid group; A home scheduling module, configured to: obtain the intelligent home scheduling strategy for the microgrid group according to the acquired parameter data and the preset scheduling model; wherein, the preset scheduling model aims to minimize the sum of the electricity costs of all intelligent home appliances; The constraints of the preset scheduling model at least include: the sum of the electricity consumption of each home appliance within a preset time period is less than or equal to the electricity consumption upper limit during this time period; In the preset scheduling model, according to the indoor temperature at the previous moment, the outdoor temperature at the current control moment, the influence coefficient of the outdoor temperature on the room temperature, the influence coefficient of the electricity consumption per unit time of the air conditioner on the room temperature, and the electricity consumption of the air conditioner during the current time period, a current moment indoor temperature control model is obtained, and the indoor temperature is within the preset control range; In the preset scheduling model, according to the water temperature at the previous moment, the temperature cooled by water per time period, and the influence coefficient of the electricity consumption per unit time of the water heater on the water temperature, a current moment water heater water temperature control model is obtained, and the water heater water temperature is within the preset control range; There are three electricity prices for time-of-use electricity price, namely peak time, normal time, and valley time, as a signal for demand response.

4. A computer-readable storage medium, on which a program is stored, characterized in that, When the program is executed by a processor, it implements the steps in the intelligent home optimal scheduling method for a microgrid group as described in any one of claims 1-2.

5. An electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements an intelligent home optimal scheduling method for a microgrid group as described in any one of claims 1-2.

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