Rural household electricity utilization optimization scheduling method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a rural household electricity utilization optimization scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing a flexible load model corresponding to each family user type according to energy consumption preference data corresponding to each family user type of a target rural area; according to the time-of-use electricity price parameters of the target rural area, constructing an electricity utilization cost model; according to the energy consumption preference data corresponding to each family user type, constructing a discomfort index model corresponding to each family user type; combining the power consumption cost model and the flexible load model and the discomfort index model corresponding to each family user type to construct a power consumption scheduling model corresponding to each family user type; and by taking the minimum power consumption cost and the minimum discomfort index as targets, carrying out optimization solution on the power consumption scheduling model corresponding to each household user type to obtain a power consumption optimization scheduling scheme corresponding to each household user type. By adopting the method, a more targeted power utilization optimization scheduling scheme can be obtained.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device, computer equipment, computer-readable storage medium, and computer program product for optimizing and scheduling electricity consumption in rural households. Background Art

[0002] In order to reduce environmental pollution, renewable energy is gradually being used in power generation. However, there are obvious random fluctuations in the supply of renewable energy, which leads to challenges in balancing energy supply and demand. Among them, flexible load regulation is regarded as an important strategy to solve the problem of renewable energy fluctuations. Compared with urban households, rural households are not subject to strict working hours restrictions in terms of energy consumption, and their energy load flexibility is more prominent, showing a higher flexible load potential.

[0003] In traditional flexible load scheduling, people usually only focus on how to utilize users' load characteristics and distributed power sources to reduce power generation costs and improve economic benefits, but they do not pay enough attention to users' comfort, and it is difficult to provide targeted scheduling solutions for different types of users. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for optimizing the scheduling of rural household electricity consumption in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for optimizing the dispatching of rural household electricity consumption, comprising:

[0006] According to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed;

[0007] Constructing an electricity cost model based on the time-of-use electricity price parameters of the target rural area;

[0008] Constructing a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types;

[0009] Combining the electricity cost model and the flexible load model and discomfort index model corresponding to each type of household user, constructing an electricity scheduling model corresponding to each type of household user;

[0010] With the goal of minimizing electricity cost and minimizing discomfort index, the electricity scheduling model corresponding to each type of household user is optimized and solved to obtain an optimized electricity scheduling plan corresponding to each type of household user.

[0011] In one of the embodiments, the electricity scheduling model corresponding to each type of household user is optimized and solved with the goal of minimizing the electricity cost and the discomfort index to obtain the electricity optimization scheduling plan corresponding to each type of household user, including: considering the electricity cost and the discomfort index, using a genetic algorithm to optimize and solve the electricity scheduling model corresponding to each type of household user, and obtaining a Pareto solution set of the electricity scheduling plan corresponding to each type of household user; with the goal of minimizing the electricity cost and the discomfort index, using the superior and inferior solution distance method to determine the electricity optimization scheduling plan corresponding to each type of household user from each Pareto solution set.

[0012] In one of the embodiments, the flexible load model includes a movable load model; the flexible load model corresponding to each household user type in the target rural area is constructed according to the energy usage preference data corresponding to the household user type, including: obtaining the preferred operating time of each movable device for the household user type according to the energy usage preference data corresponding to the household user type; obtaining the rated power and set operating time of each movable device; and constructing the movable load model corresponding to the household user type according to the preferred operating time of each movable device, the rated power and the set operating time.

[0013] In one of the embodiments, the flexible load model includes an interruptible load model; the flexible load model corresponding to each household user type in the target rural area is constructed according to the energy usage preference data corresponding to the household user type, including: obtaining the preferred operating time of each interruptible device for the household user type according to the energy usage preference data corresponding to the household user type; obtaining the rated power and the set operating time of each interruptible device; and constructing the interruptible load model corresponding to the household user type according to the preferred operating time, the rated power, and the set operating time of each interruptible device.

[0014] In one of the embodiments, the flexible load model includes a reducible load model; the flexible load model corresponding to each household user type in the target rural area is constructed according to the energy usage preference data corresponding to the household user type, including: obtaining the preference effect parameters of the household user type for each reducible device according to the energy usage preference data corresponding to the household user type; obtaining the power range, energy efficiency ratio and operating environment parameters of each reducible device; and constructing the reducible load model corresponding to the household user type according to the preference effect parameters, the power range, the energy efficiency ratio and the operating environment parameters of each reducible device.

[0015] In one of the embodiments, the discomfort index model corresponding to each household user type is constructed according to the energy usage preference data corresponding to each household user type, including: if the flexible load corresponding to the household user type includes a movable load, then according to the preferred operating time of the movable device for the household user type contained in the energy usage preference data, a first discomfort sub-model of the household user type is constructed; the first discomfort sub-model is used to determine the first discomfort sub-index according to the deviation between the scheduled operating time of the movable device in the electricity scheduling scheme of the household user type and the preferred operating time; if the flexible load corresponding to the household user type includes an interruptible load, then according to the perceived power outage duration and acceptable power outage duration of the interruptible device for the household user type contained in the energy usage preference data, the first discomfort sub-model is constructed. a second discomfort sub-model for the household user type; the second discomfort sub-model is used to determine the second discomfort sub-index according to the relationship between the continuous power outage duration of the interruptible device in the power scheduling plan and the perceived power outage duration and the acceptable power outage duration; if the flexible load corresponding to the household user type includes curtailable equipment, then according to the preference effect parameters of the household user type for curtailable equipment contained in the energy preference data, a third discomfort sub-model for the household user type is constructed; the third discomfort sub-model is used to determine the third discomfort sub-index according to the parameter relationship between the operating effect parameters corresponding to the curtailable equipment in the power scheduling plan and the preference effect parameters; according to each discomfort sub-model corresponding to the household user type, the discomfort index model corresponding to the household user type is constructed.

[0016] In a second aspect, the present application also provides a rural household electricity optimization scheduling device, comprising:

[0017] The first construction module is used to construct a flexible load model corresponding to each household user type according to the energy preference data corresponding to each household user type in the target rural area;

[0018] A second construction module is used to construct an electricity cost model according to the time-of-use electricity price parameters of the target rural area;

[0019] A third construction module is used to construct a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types;

[0020] A fourth construction module is used to construct an electricity scheduling model corresponding to each type of household user by combining the electricity cost model and the flexible load model and the discomfort index model corresponding to each type of household user;

[0021] The optimization module is used to optimize and solve the electricity scheduling model corresponding to each type of household user with the goal of minimizing electricity cost and minimizing discomfort index, and obtain the electricity optimization scheduling plan corresponding to each type of household user.

[0022] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0023] According to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed;

[0024] Constructing an electricity cost model based on the time-of-use electricity price parameters of the target rural area;

[0025] Constructing a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types;

[0026] Combining the electricity cost model and the flexible load model and discomfort index model corresponding to each type of household user, constructing an electricity scheduling model corresponding to each type of household user;

[0027] With the goal of minimizing electricity cost and minimizing discomfort index, the electricity scheduling model corresponding to each type of household user is optimized and solved to obtain an optimized electricity scheduling plan corresponding to each type of household user.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0029] According to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed;

[0030] Constructing an electricity cost model based on the time-of-use electricity price parameters of the target rural area;

[0031] Constructing a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types;

[0032] Combining the electricity cost model and the flexible load model and discomfort index model corresponding to each type of household user, constructing an electricity scheduling model corresponding to each type of household user;

[0033] With the goal of minimizing electricity cost and minimizing discomfort index, the electricity scheduling model corresponding to each type of household user is optimized and solved to obtain an optimized electricity scheduling plan corresponding to each type of household user.

[0034] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0035] According to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed;

[0036] Constructing an electricity cost model based on the time-of-use electricity price parameters of the target rural area;

[0037] Constructing a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types;

[0038] Combining the electricity cost model and the flexible load model and discomfort index model corresponding to each type of household user, constructing an electricity scheduling model corresponding to each type of household user;

[0039] With the goal of minimizing electricity cost and minimizing discomfort index, the electricity scheduling model corresponding to each type of household user is optimized and solved to obtain an optimized electricity scheduling plan corresponding to each type of household user.

[0040] The above-mentioned rural household electricity optimization scheduling method, device, computer equipment, computer-readable storage medium and computer program product construct a flexible load model corresponding to each household user type according to the energy preference data corresponding to each household user type in the target rural area, and construct an electricity cost model according to the time-of-use electricity price parameters of the target rural area, and then construct a discomfort index model corresponding to each household user type according to the energy preference data corresponding to each household user type, and then combine the electricity cost model and the flexible load model and discomfort index model corresponding to each household user type to construct an electricity scheduling model corresponding to each household user type, and then optimize and solve the electricity scheduling model corresponding to each household user type with the goal of minimizing the electricity cost and minimizing the discomfort index, and obtain the electricity optimization scheduling plan corresponding to each household user type. This solution can construct the corresponding flexible load model and discomfort index model for household users in the target rural areas according to the energy preferences of each household user type, and construct the electricity cost model according to the time-of-use electricity price parameters of the target rural areas. Therefore, the electricity cost model, flexible load model and discomfort index model can be combined to construct an electricity dispatching model with higher accuracy and applicability, which can accurately reflect the relationship between the energy consumption of the corresponding types of rural household users and the electricity cost and discomfort index. Therefore, with the goal of minimizing the electricity cost and the discomfort index, the electricity dispatching model corresponding to each household user type is optimized and solved respectively, which can effectively tap the flexible load potential of different types of rural households while fully balancing the economy and comfort, and provide more reference-based optimization dispatching solutions for various types of rural households, thereby ensuring the actual implementation effect of the dispatching strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of a rural household electricity optimization scheduling method in one embodiment;

[0043] Figure 2 A schematic diagram of a process of obtaining a Pareto solution set of a power dispatching scheme by using a genetic algorithm in one embodiment;

[0044] Figure 3 A schematic diagram of a process for constructing a flexible load model in one embodiment;

[0045] Figure 4A schematic diagram of a process for constructing a flexible load model in another embodiment;

[0046] Figure 5 A schematic diagram of a process for constructing a flexible load model in another embodiment;

[0047] Figure 6 A schematic diagram of a process for constructing a discomfort index model corresponding to each family user type in an embodiment;

[0048] Figure 7 A schematic diagram of average energy load curves of each household user type in a typical day in summer in an embodiment;

[0049] Figure 8 A schematic diagram of optimization results of power consumption scheduling schemes corresponding to various household user types in one embodiment;

[0050] Fig. 9 A schematic diagram showing a comparison of power consumption scheduling schemes corresponding to each household user type before and after optimization in one embodiment;

[0051] Fig.10 It is a schematic diagram of comparison of electricity cost and discomfort index before and after optimization corresponding to each household user type in one embodiment;

[0052] Fig.11 A structural block diagram of a rural household electricity optimization scheduling device in one embodiment;

[0053] Fig.12 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] In one embodiment, Figure 1 As shown, a rural household electricity optimization scheduling method is provided. This embodiment uses the method applied to a server as an example for illustration. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0056] Step S101 : constructing a flexible load model corresponding to each household user type according to energy preference data corresponding to each household user type in the target rural area.

[0057] The target rural area may be a rural area that needs to be optimized for electricity consumption. There may be multiple types of household users in the area, and the corresponding energy preference data may be obtained according to the household user type. The energy preference data may be data that can reflect the energy preference of household users of the same type, such as but not limited to the types of equipment used by household users, the preferred operating time for each type of equipment, the preferred effect parameters, basic energy load and other data.

[0058] For example, the energy usage of multiple household users in the target rural area can be investigated, and cluster analysis can be performed on data on dimensions such as the demographic structure and energy usage habits of the household users. The household users in the target rural area can be divided into multiple corresponding household user types, and the energy usage preference data corresponding to each household user type can be statistically calculated.

[0059] Based on the energy preference data, a corresponding flexible load model can be constructed for each household user type, wherein the flexible load model can be a mathematical model that can describe the operating status and operating constraints of various flexible load devices used by household users.

[0060] Exemplarily, flexible loads can be divided into different types of loads such as shiftable loads, interruptible loads, and reducible loads, and the flexible loads corresponding to each household type may include one or more of these loads. Among them, for each household type, a corresponding load model can be constructed according to the load type included in the corresponding flexible load, and then the flexible load model corresponding to the household type can be obtained by combining different load models. For example, if the flexible load corresponding to a household type includes a shiftable load and an interruptible load, a shiftable load model and an interruptible load model of the household type can be constructed according to the energy preference data of the household type, and then the flexible load model corresponding to the household type can be obtained by combining the two.

[0061] Step S102: constructing an electricity cost model according to the time-of-use electricity price parameters of the target rural area.

[0062] The time-of-use electricity price parameters may include the electricity prices corresponding to each time period in the target rural area. For example, the rural area may use peak-valley electricity prices, where the time periods of a day may be divided into peak periods, flat periods, and valley periods, and the corresponding electricity prices are reduced in sequence.

[0063] In this step, based on the time-of-use electricity price parameters, an electricity cost model suitable for household users in the area can be constructed. The model can calculate the electricity cost of household users based on the energy load of household users at each time and the electricity price at the corresponding time in the time-of-use electricity price parameters.

[0064] Step S103: construct a discomfort index model corresponding to each household user type according to the energy preference data corresponding to each household user type.

[0065] Among them, according to the energy preference data corresponding to each type of household user, the preferences of each type of household user for various flexible load devices in terms of operating time, operating effect, etc. can be determined. Based on this, the flexible load operation that meets the user's preference can be regarded as a high comfort situation, while the flexible load operation that deviates from the user's preference can be regarded as a low comfort situation.

[0066] In this step, considering the impact of the power dispatching scheme on user comfort, a discomfort index model corresponding to each household user type can be constructed according to the energy preference data corresponding to each household user type. The model can analyze the impact on the user's comfort according to the deviation between the operating time, operating effect and other parameters of each flexible load in the flexible load dispatching scheme and the corresponding data in the energy preference data, and obtain the discomfort index corresponding to the flexible load dispatching scheme. Among them, the larger the discomfort index, the greater the impact on the comfort of household users of this type, and the lower the comfort; and the smaller the discomfort index, the smaller the impact on the comfort of household users of this type, and the higher the comfort.

[0067] Among them, in some embodiments, when there is more than one flexible load corresponding to a household user type, a corresponding discomfort sub-model can be constructed for each flexible load, and then a discomfort index model corresponding to the overall comfort of the household user type can be constructed based on each discomfort sub-model.

[0068] Step S104: construct an electricity scheduling model corresponding to each household user type by combining the electricity cost model and the flexible load model and discomfort index model corresponding to each household user type.

[0069] Among them, combined with the electricity cost model of the target rural area constructed in the above process, and the flexible load model and discomfort index model corresponding to each household user type in the area, the electricity dispatch model corresponding to each household user type in the area can be constructed respectively. Using this electricity dispatch model, the electricity dispatch scheme that meets the flexible load operation constraints of the household user type and its corresponding electricity cost and discomfort index can be obtained.

[0070] Optionally, the power dispatch model may further include optimization constraints for power optimization dispatch. Exemplarily, the optimization constraints may include a balance constraint between user-side power and grid power, and a constraint on the range of variation of the discomfort index before and after optimization.

[0071] Step S105 , with the goal of minimizing the electricity cost and minimizing the discomfort index, the electricity scheduling model corresponding to each household user type is optimized and solved to obtain the electricity optimization scheduling plan corresponding to each household user type.

[0072] Among them, the corresponding objective functions can be constructed for the two goals of minimizing electricity cost and minimizing discomfort index, and then the electricity scheduling model corresponding to each type of household user can be optimized and solved through a multi-objective optimization method to obtain an electricity optimization scheduling scheme that can take into account both electricity cost and user comfort. Among them, the electricity optimization scheduling scheme can include operating state parameters of various flexible load devices of household users at various times (such as operation or non-operation), and operating effect parameters of various flexible load devices with adjustable operating effects (such as the temperature of air conditioners, the brightness of lamps, etc.) and other parameters.

[0073] In the above rural household electricity optimization scheduling method, it is possible to construct the flexible load model and discomfort index model corresponding to each household user type in the target rural area according to the energy preference corresponding to each household user type, and to construct the electricity cost model according to the time-of-use electricity price parameters of the target rural area, so that the electricity scheduling model with higher accuracy and applicability can be constructed by combining the electricity cost model, the flexible load model and the discomfort index model, which can accurately reflect the relationship between the energy consumption of the corresponding type of rural household users and the electricity cost and discomfort index. Therefore, with the goal of minimizing the electricity cost and the discomfort index, the electricity scheduling model corresponding to each household user type is optimized and solved respectively, which can effectively tap the flexible load potential of different types of rural households while fully balancing the economy and comfort, and provide more reference-meaningful optimization scheduling solutions for various types of rural households, thereby ensuring the actual implementation effect of the scheduling strategy.

[0074] In an exemplary embodiment, with the goal of minimizing electricity cost and discomfort index, the electricity scheduling model corresponding to each household user type is optimized and solved to obtain the electricity optimization scheduling plan corresponding to each household user type, which may include: considering electricity cost and discomfort index, using genetic algorithm to optimize the electricity scheduling model corresponding to each household user type, and obtaining the Pareto solution set of the electricity scheduling plan corresponding to each household user type; with the goal of minimizing electricity cost and discomfort index, using the superior and inferior solution distance method to determine the electricity optimization scheduling plan corresponding to each household user type from each Pareto solution set.

[0075] Among them, for each type of household user, we can first construct a first objective function corresponding to the electricity cost and a second objective function corresponding to the discomfort index according to the electricity cost model and the discomfort index model in the electricity scheduling model, and then combine the two objective functions and use a genetic algorithm to iteratively optimize and solve the electricity scheduling model to obtain the Pareto solution set of the electricity scheduling plan corresponding to the household user type.

[0076] For example, the process of iterative optimization solution using genetic algorithm can be as follows: Figure 2 As shown. Among them, the corresponding flexible load model can be set according to the household user type. Exemplarily, the flexible load model can include the operation constraints, preferred operation time, preferred effect parameters, etc. of each flexible load device corresponding to the household user type. Then, based on the operation constraints, the optimization variables can be initialized to obtain the initial population of the genetic algorithm. Among them, the initial population can include multiple individuals, each of which corresponds to a power dispatching plan for the household user type. Among them, as Figure 2 As shown, for a total schedulable period of length N, the values ​​of the optimization variables corresponding to each schedulable period t can be set in chronological order until the values ​​of the optimization variables at all times in the scheduling period are obtained to form a power scheduling plan for the scheduling period. Exemplarily, the optimization variables may include the start and stop states of each flexible load device, and the operating power of the power-adjustable device when it is started. Then, the first objective function (i.e., electricity cost) and the second objective function (i.e., discomfort index) corresponding to each individual in the initial population can be calculated using the first objective function and the second objective function, respectively.

[0077] Among them, in combination with the first objective function value and the second objective function value corresponding to each individual in the initial population, each individual can be non-dominated sorted. Then, based on the non-dominated sorting result, individuals with higher fitness can be selected from the initial population, and then these individuals are subjected to crossover and mutation operations to obtain a new offspring population. Then, the aforementioned processing of the initial population can be repeated on the offspring population until the termination condition (for example, the number of iterations reaches a preset value, or the improvement of multiple generations of populations is not obvious, etc.) is met, and then the Pareto solution set of the power scheduling scheme corresponding to the household user type is output. Among them, the Pareto solution set may include multiple power scheduling schemes corresponding to the household user type that strike a balance between the electricity cost and the discomfort index.

[0078] Then, based on the Pareto solution set obtained by the genetic algorithm, the technique fororder preference by similarity to an ideal solution (TOPSIS) can be used to determine the power optimization scheduling scheme that can achieve the best results in the two goals of minimizing the power cost and minimizing the discomfort index. Among them, the technique fororder preference by similarity to an ideal solution (TOPSIS) can calculate the overall performance index of each power scheduling scheme in the Pareto solution set, and the power scheduling scheme with the largest overall performance index value is used as the power optimization scheduling scheme.

[0079] For example, the overall performance index Y of the power dispatching scheme is TOPSIS The calculation process can be shown as follows:

[0080]

[0081] v ij =w j × ij ,i=1,2,…,m; j=1,2,…,n

[0082]

[0083] In the formula, x ij is the jth objective function value of the ith power dispatching scheme, m is the total number of optional power dispatching schemes, n is the total number of objective functions, r ij For x ij The standardized value; w j is the weight factor corresponding to the jth objective function, v ij is the performance index after weighted normalization; is the positive ideal solution (PIS) of the jth objective function among all power dispatching schemes, that is, the minimum value of the jth objective function. is the Euclidean distance from the ith electricity dispatching scheme to the positive ideal solution; is the negative ideal solution (NIS) of the jth objective function among all power dispatching schemes, that is, the maximum value of the jth objective function. is the Euclidean distance from the i-th electricity dispatching scheme to the negative ideal solution.

[0084] In this embodiment, for the multi-objective optimization solution of the electricity scheduling model, a genetic algorithm is first used to solve the Pareto solution set of the electricity scheduling plan, which can obtain multiple electricity scheduling plans that achieve a balance between economy and comfort indicators. Then, the superiority and inferior solution distance method is used to make decisions, and the most ideal electricity optimization scheduling plan can be selected from them. This process is conducive to improving the optimization efficiency and the accuracy of the optimization results.

[0085] In an exemplary embodiment, the flexible load model may include a translatable load model, such as Figure 3 As shown, according to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed, which may include:

[0086] Step S301, obtaining the preferred operating time of each portable device by the household user type according to the energy preference data corresponding to the household user type.

[0087] Step S302, obtaining the rated power and set operating time of each movable device.

[0088] Step S303: construct a movable load model corresponding to the household user type according to the preferred operating time, rated power and set operating time of each movable device.

[0089] The shiftable load may be a load that can be flexibly adjusted in terms of operating time, for example, the use time of certain non-emergency electrical equipment can be shifted from one time period to another. For example, the shiftable load may include washing machines, dishwashers, dryers and other equipment, whose operating time can be shifted from one time period to another.

[0090] Among them, for the energy preference data corresponding to each type of household users, the preferred operating time of each movable device for each household user type can be obtained. Exemplarily, the preferred operating time may include the user's preferred starting operating time and preferred ending operating time for the device. At the same time, for each movable device of the same household user type, its corresponding rated power, set operating time and other parameters can be obtained respectively. Then, the corresponding movable load model can be constructed in combination with the preferred operating time, rated power and maximum set operating time of each movable device. Among them, the preferred operating time of the movable device may include the preferred starting operating time and the preferred ending operating time, which can be obtained based on the statistics of the time when the household user continuously uses the movable device.

[0091] For example, if the translatable load corresponding to a certain household user type includes a washing machine, the corresponding preferred washing machine start running time and preferred washing machine end running time can be obtained according to the energy preference data of the household user type, and the rated power and set running time of the washing machine can be obtained. Then, a translatable load model as shown in the following formula can be constructed:

[0092] P w,t =P w I w,t

[0093]

[0094] Among them, P w,t is the operating power of the washing machine in the tth time period, in kW; P w is the rated power of the washing machine, in kW; I w,t is a binary variable that controls the start and stop of the washing machine in the tth time period, which is 1 when running and 0 otherwise; α w The preferred washing machine start run time for the household user type; The starting running time of the washing machine; The washing machine stops running time; w is the preferred end time of the washing machine for this household user type; L is the upper limit of the washing machine's operating time; d wis the set running time of the washing machine, in hours. and Corresponding to the time when the household user actually starts and ends using the washing machine, it can fall into the statistically obtained α w and β w Between, the washing machine and Continuous operation between.

[0095] It is understandable that when a certain type of household user uses multiple translatable devices, a sub-model corresponding to each translatable device can be established separately, and then the multiple sub-models can be combined to obtain a translatable load model corresponding to the household user type.

[0096] In this embodiment, by constructing a movable load model, for the movable load, a movable load model is established based on the user's preferred operating time for the movable load equipment and the rated power and set operating time of the movable load equipment itself. Therefore, a mathematical model can be used to quantitatively describe the user's preferred usage of the movable load, which is conducive to improving the accuracy and effectiveness of the subsequent scheduling optimization process.

[0097] In an exemplary embodiment, the flexible load model may include an interruptible load model, such as Figure 4 As shown, according to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed, which may include:

[0098] Step S401, obtaining the preferred operating time of each interruptible device of the household user type according to the energy preference data corresponding to the household user type.

[0099] Step S402, obtaining the rated power and set operating time of each interruptible device.

[0100] Step S403: construct an interruptible load model corresponding to the household user type according to the preferred operating time, rated power, and set operating time of each interruptible device.

[0101] The interruptible load may be a load that can be temporarily interrupted, for example, it can be temporarily interrupted during operation and resume operation after a period of time. For example, the interruptible load may include electric bicycle charging equipment, electric vehicle charging equipment, etc. During the operation of these equipment, even if the operation time does not reach the set operation time, the operation process can be interrupted and the operation of the equipment can be resumed after a period of time.

[0102] Among them, for the energy preference data corresponding to each type of household user, the preferred operating time of each interruptible device for each household user type can be obtained. At the same time, for each interruptible device of the same household user type, its corresponding rated power, set operating time and other parameters can be obtained respectively. Then, the corresponding interruptible load model can be constructed by combining the parameters such as the preferred operating time, rated power and set operating time of each interruptible device. Among them, the preferred operating time of the interruptible device may include the preferred starting operating time and the preferred operating end time, which can be obtained based on the time statistics of the household user using the interruptible device once or more in a day.

[0103] For example, if the interruptible load corresponding to a certain household user type includes an electric bicycle charging device, the corresponding preferred charging start time and preferred charging end time can be obtained according to the energy preference data of the household user type, and the rated power and set operating time of the electric bicycle charging device can be obtained. Then, the interruptible load model shown in the following formula can be constructed:

[0104] P b,t =P b I b,t

[0105] 1≤α b ≤t≤β b ≤T

[0106] d b ≤β b -α b +1

[0107] I b,t ={0,1},t∈[α b ,,β b ]

[0108] I b,t =0,

[0109] Among them, P b,t is the operating power of the electric bicycle charging equipment in the tth time period, in kW; P b is the rated power of the electric bicycle charging equipment, in kW; I b,t is a binary variable that controls whether the electric bicycle is charged, 1 if charged, and 0 otherwise; α b Preferred charging start time for household user types; β b is the preferred charging termination time of the household user type; T is the upper limit of the operating time of the electric bicycle charging equipment; d b is the set operating time of the electric bicycle charging device, in hours. The electric bicycle charging device can beb and β b The time period between the two is based on the set running time d b Continuous operation; it can also be in α b and β b In the period between d and d, the battery charger runs for a period of time, then stops running, and then runs again after a period of time. The total running time of the battery charger is equal to d b .

[0110] It is understandable that when a certain type of household user uses multiple interruptible devices, a sub-model corresponding to each interruptible device can be established respectively, and then the multiple sub-models can be combined to obtain the interruptible load model corresponding to the household user type.

[0111] In this embodiment, for the interruptible load, an interruptible load model is established based on the user's preferred operating time for the interruptible load device and the rated power and set operating time of the interruptible load device itself, so that a mathematical model can be used to quantitatively describe the user's preferred usage of the interruptible load, which is conducive to improving the accuracy and effectiveness of the subsequent scheduling optimization process.

[0112] In an exemplary embodiment, the flexible load model may include a curtailable load model, such as Figure 5 As shown, according to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed, which may include:

[0113] Step S501: obtaining the preference effect parameters of the household user type for each reducible device according to the energy preference data corresponding to the household user type.

[0114] Step S502: Obtain the power range, energy efficiency ratio and operating environment parameters of each reducible device.

[0115] Step S503: construct a reducible load model corresponding to the household user type according to the preference effect parameters, power range, energy efficiency ratio and operating environment parameters of each reducible device.

[0116] Among them, the reducible load can be a load that can reduce the power demand under specific circumstances. For example, the power demand of the equipment can be reduced by reducing the operating effect requirements of the reducible equipment. Exemplarily, the reducible load may include equipment such as air conditioners and lamps. Optionally, when the reducible load is an air conditioner, its corresponding preference effect parameter may include the air conditioner temperature set by the user, and its corresponding operating environment parameter may include parameters such as the ambient temperature; when the reducible load is a lamp device, its corresponding preference effect parameter may include the lamp brightness set by the user, and its corresponding operating environment parameter may include parameters such as the ambient brightness.

[0117] Among them, for the energy preference data corresponding to each type of household user, the preference effect parameters of each household user type for each reducible device can be obtained. At the same time, for each reducible device of the same household user type, its corresponding power range, energy efficiency ratio, operating environment parameters and other parameters can be obtained respectively. Then, the corresponding reducible load model can be constructed by combining the preference effect parameters, power range, energy efficiency ratio, operating environment parameters and other parameters of each reducible device.

[0118] For example, if the reducible load corresponding to a certain household user type includes air conditioning, the corresponding preferred setting temperature range can be obtained according to the energy preference data of the household user type, and the power range, energy efficiency ratio and operating environment parameters of the air conditioning equipment can be obtained. Among them, the operating environment parameters may include outdoor temperature, room equivalent thermal resistance, room heat capacity, etc. Then, a reducible load model as shown below can be constructed:

[0119]

[0120] In the formula, is the indoor temperature in the tth period, in °C; is the outdoor temperature in the tth period, in °C; R AC is the equivalent thermal resistance of the room, in ℃ / kW; δ AC is the energy efficiency ratio of the air conditioner; is the operating power of the air conditioner in the tth period, in kW; C is a binary variable that controls the start and stop of the air conditioner. It is 1 when it is running and 0 when it is not running, indicating the start and stop status of the air conditioner. AC is the room heat capacity, in kJ / ℃; The minimum air conditioning temperature set by the user's preference, in °C; The maximum air conditioning temperature set by the user, in °C; P min is the minimum operating power of the air conditioner, in kW; P max is the maximum operating power of the air conditioner, in kW; Q t is the cooling load of the air-conditioned room in the tth period, in kW.

[0121] It is understandable that when a certain type of household user uses multiple reducible devices, a sub-model corresponding to each reducible device can be established respectively, and then the reducible load model corresponding to the household user type can be obtained by combining the multiple sub-models.

[0122] In this embodiment, according to the operating characteristics of the reducible load, a reducible load model is established based on the user's preference effect parameters for the reducible load equipment and parameters such as the power range, energy efficiency ratio and operating environment parameters of the reducible load equipment. Therefore, a mathematical model can be used to quantitatively describe the user's preference for the use of the reducible load, which is conducive to improving the accuracy and effectiveness of the subsequent scheduling optimization process.

[0123] It is understandable that in the optimization scheduling of household electricity consumption, the operation adjustment of various types of flexible loads of household users in the electricity scheduling plan can affect the comfort of users. Based on this, for various flexible loads corresponding to household user types, discomfort sub-models can be constructed to measure the impact of their corresponding comfort, and then the overall discomfort index model of household user types for flexible loads can be obtained by combining various discomfort sub-models.

[0124] In an exemplary embodiment, Figure 6 As shown, according to the energy preference data corresponding to each household user type, a discomfort index model corresponding to each household user type is constructed, which may include:

[0125] Step S601: If the flexible load corresponding to the household user type includes a movable load, a first unsuitable sub-model of the household user type is constructed according to the preferred operating time of the household user type for the movable equipment contained in the energy preference data.

[0126] In the case where the flexible load corresponding to the household user type includes a movable load, a first discomfort sub-model corresponding to the movable load can be constructed according to the operation preference time of the movable load of the household user type. The model can analyze and count the deviation between the scheduled operation time and the preferred operation time of the movable equipment in the power scheduling scheme to obtain the first discomfort sub-index corresponding to the movable load. The larger the first discomfort sub-index, the greater the impact on the comfort of the movable load and the lower the comfort; the smaller the first discomfort sub-index, the smaller the impact on the comfort of the movable load and the higher the comfort.

[0127] For example, for a portable device, the baseline of the device operation can be expressed by the following formula according to the preferred operation time corresponding to the home user type:

[0128]

[0129] In the formula, B i,t is a binary variable, indicating the usage status of device i in the tth period, B i,t =1 means the device is running, otherwise 0; L i,b is the start time of the user’s preference for device i in the baseline case; U i,bis the end running time of the user's preference for device i in the baseline case; N is the total number of schedulable time periods; T i is the device operation time, in hours. Optionally, if the total schedulable time period is 24 hours, the value of N may be 24, where the length of each schedulable time period t is 1 hour.

[0130] Similarly, the operation of the shiftable load in the power dispatching plan can be expressed by the following formula:

[0131]

[0132] In the formula, S i,t is a binary variable, indicating the usage status of device i in the tth period, S i,t =1 means the device is running, otherwise 0; L i,s is the start time of equipment i in the power scheduling plan; U i,s is the end operation time of equipment i in the power dispatch plan; T i It is the device operation time, in hours.

[0133] Then, the Euclidean distance method can be used to construct the calculation expression of the first discomfort sub-index corresponding to the translatable load:

[0134]

[0135] In the formula, γ D,s is the first discomfort sub-index corresponding to the translatable load, is the discomfort sub-index caused by the start time of device i in the power scheduling plan being ahead of the user's preferred start time; is the discomfort sub-index caused by the delay of the start time of device i in the power scheduling scheme to the user's preferred start time, where and Not less than 0.

[0136] Based on this, the first discomfort sub-index corresponding to the operating time adjustment of the shiftable load in the total dispatchable time period can be calculated.

[0137] Step S602: If the flexible load corresponding to the household user type includes an interruptible load, a second discomfort sub-model of the household user type is constructed according to the perceived power outage duration and acceptable power outage duration of the interruptible device of the household user type contained in the energy preference data.

[0138] Among them, when the flexible load corresponding to the household user type includes an interruptible load, each power outage of the interruptible load in the power scheduling plan may affect the user's comfort. Based on this, the perceptible power outage duration and acceptable power outage duration of the household user of this type for the interruptible device can be obtained from the energy preference data of the household user type, and a second discomfort sub-model corresponding to the interruptible load can be constructed. The model can analyze and count the duration relationship between the continuous power outage duration of the interruptible device in the power scheduling plan and the perceptible power outage duration and acceptable power outage duration of the user to obtain the second discomfort sub-index corresponding to the interruptible load. Among them, the larger the second discomfort sub-index, the greater the impact on the comfort of the interruptible load, and the lower the comfort; the smaller the second discomfort sub-index, the smaller the impact on the comfort of the interruptible load, and the higher the comfort.

[0139] The calculation expression of the second discomfort sub-index can be shown as follows:

[0140]

[0141] In the formula, γ Dl,z is the second unsuitable sub-index, N is the total number of schedulable time periods, u i,t is the discomfort sub-index caused by interruptible device i in the tth period; t1 is the continuous power outage duration of interruptible device i in the tth period, in h; T off,max is the acceptable power outage duration, in h; T off,best It is the perceived power outage duration, in hours.

[0142] As shown in the above formula, if the continuous power outage duration of the interruptible device i in the tth time period is less than the perceptible power outage duration, the corresponding discomfort sub-index is the minimum value; when the continuous power outage duration is between the perceptible power outage duration and the acceptable power outage duration, the corresponding discomfort sub-index increases with the increase of the continuous power outage duration; and when the continuous power outage duration is greater than the acceptable power outage duration, the corresponding discomfort sub-index is the maximum value. Based on this, the impact of the power outage of the interruptible device on the user's comfort in each schedulable time period can be effectively measured, and finally the second discomfort sub-index corresponding to the adjustment of the operating time of the interruptible load in the entire schedulable total time period can be calculated.

[0143] Step S603: if the flexible load corresponding to the household user type includes a curtailable load, a third unsuitable sub-model of the household user type is constructed according to the preference effect parameters of the household user type for curtailable equipment included in the energy preference data.

[0144] Among them, when the flexible load corresponding to the household user type includes a reducible load, when there is a deviation between the operating effect of the reducible equipment in the power scheduling plan and the user's preferred operating effect, it can affect the user's comfort. Based on this, a third discomfort sub-model corresponding to the reducible load can be constructed according to the preference effect parameters of the reducible equipment for the household user type. This model can analyze and statistically analyze the parameter relationship between the operating effect parameters of the reducible load in the power scheduling plan and the user's preference effect parameters to obtain the third discomfort sub-index corresponding to the reducible load. Among them, the larger the third discomfort sub-index, the greater the impact on the comfort of the reducible load, and the lower the comfort; the smaller the third discomfort sub-index, the smaller the impact on the comfort of the reducible load, and the higher the comfort.

[0145] For example, taking the reducible equipment as an air conditioner as an example, the calculation expression of the corresponding third discomfort sub-index can be as follows:

[0146]

[0147] In the formula, γ DI,c is the third discomfort sub-index, d i,t is the discomfort sub-index of device i that can be reduced in the tth period; is the indoor temperature in the tth period, in °C, The minimum air conditioning temperature set by the user in °C. It is the highest air-conditioning temperature set by the user's preference, in °C; ΔT1 is the upper dead zone temperature, in °C; ΔT2 is the lower dead zone temperature, in °C.

[0148] As shown in the above formula, the user's acceptable temperature range can be determined based on the air conditioning temperature range set by the user's preference (i.e., the range of the preferred operating effect parameters) and the upper and lower dead zone temperatures. When the set temperature (i.e., the operating effect parameter) of the air conditioning equipment in the schedulable period is within the acceptable temperature range, its corresponding discomfort sub-index is the minimum value, and when the set temperature is lower or higher than the acceptable temperature range, its corresponding discomfort sub-index increases with the increase of the deviation between the set temperature and the preferred air conditioning temperature range. Based on this, the impact of the change in the operating effect of the reducible equipment on the user's comfort in each schedulable period can be effectively measured, and finally the third discomfort sub-index corresponding to the adjustment of the operating effect of the reducible load in the entire schedulable total period can be calculated.

[0149] Step S604: construct a discomfort index model corresponding to the family user type according to each discomfort sub-model corresponding to the family user type.

[0150] For each family user type, a corresponding discomfort index model can be constructed according to each discomfort sub-model corresponding to the family user type. Using the discomfort index model, the discomfort index of the family user type as a whole can be calculated according to each discomfort sub-indices obtained from each discomfort sub-model.

[0151] Exemplarily, the discomfort index model can calculate the discomfort index corresponding to the household user type according to the following formula:

[0152] γ DI =(ω1·γ DI,s +ω2·γ DI,z +ω3·γ DI,c )

[0153] In the formula, γ DI is the discomfort index, γ DI,s is the first discomfort sub-index corresponding to the translatable load, ω1 is the weight of the first discomfort sub-index, γ DI,z is the second discomfort sub-index corresponding to the interruptible load, ω2 is the weight of the second discomfort sub-index, γ DI,c is the third discomfort sub-index corresponding to the reducible load, and ω3 is the weight of the third discomfort sub-index.

[0154] It can be understood that when the flexible load corresponding to the home user type includes a shiftable load, an interruptible load, and a curtailable load, the weight value corresponding to each load can be set to adjust the contribution of the discomfort sub-index corresponding to each load to the discomfort index. For example, ω1=ω2=ω3 can be set to achieve a balance among the three. When the flexible load corresponding to the home user type includes only one or two of the shiftable load, the interruptible load, and the curtailable load, the weight value corresponding to the missing load can be set to 0, and then the weight values ​​of other loads can be set.

[0155] In this embodiment, according to the flexible load type corresponding to the household user type, discomfort sub-models corresponding to various flexible load types are respectively established, so that according to the comfort characteristics of various flexible load types, the corresponding discomfort sub-models can be used to evaluate the comfort impact corresponding to the flexible load type, and then combined with each discomfort sub-indices calculated by each discomfort sub-model, a discomfort index that can accurately evaluate the impact on the overall comfort of the household user type can be obtained.

[0156] In order to further illustrate the rural household electricity optimization scheduling method of the present application, it is described below through detailed embodiments.

[0157] Specifically, in this embodiment, by analyzing the household electricity consumption in the target rural area, household users in the area are divided into four typical types, and washing machines, electric bicycle charging equipment, and air conditioners are used as typical equipment corresponding to shiftable loads, interruptible loads, and reducible loads, respectively.

[0158] The average energy load curve of each household user type in a typical day in summer is as follows: Figure 7 As shown. Among them, the load of the first type of households (accounting for 34%) shows a typical midday and evening double peak feature, mainly single-living elderly households with few energy-consuming equipment; the load of the second type of households (accounting for 18%) maintains a higher load at night, which is common in three-generation families with a large population, many energy-consuming equipment and a late work and rest schedule; the load peak of the third type of households (accounting for 16%) is high and the air conditioner is turned on continuously in the afternoon, which is common in young families who are unemployed and raising infants at home and have high requirements for comfort; the fourth type of households (accounting for 32%) have the characteristics of turning on the air conditioner briefly at night and turning it off before falling asleep, which is common in young migrant workers who go out to work every day and use relatively less energy. Since local residents mainly use loose coal for heating in winter and generally use air conditioners for cooling in summer, the electricity load in summer has greater adjustment potential because it is significantly higher than that in winter. Therefore, in this embodiment, various types of flexible loads are optimized and scheduled for typical days in summer.

[0159] Among them, the basic operating parameters of various flexible load equipment are shown in Table 1:

[0160] Table 1

[0161] Flexible load Flexible load type Average rated power / kW Average running time / h washing machine Translable load 0.2 2 Electric bicycle charging equipment Interruptible load 0.2 8 air conditioner Load reduction possible 1.4 ——

[0162] The preferred setting temperature of air-conditioning equipment for each household user type involving the use of air-conditioning equipment is shown in Table 2:

[0163] Table 2

[0164] Family Type Air conditioning temperature / ℃ Category II 24-26 Category 3 24-26 Category 4 22-24

[0165] At the same time, the time-of-use electricity price parameters of the target rural areas can also be obtained, as shown in Table 3:

[0166] Table 3

[0167]

[0168] As shown in Table 3, the target rural areas implement a dual-track system of flat electricity price and peak-valley electricity price. In the peak-valley electricity price type, the intraday time period is divided into peak period, flat period and valley period, and the corresponding electricity prices decrease in sequence. Among them, household users in the target rural areas can choose to use flat electricity price or peak-valley electricity price. In this embodiment, the flat electricity price is used as the time-of-use electricity price parameter before optimization, and the peak-valley electricity price is used as the time-of-use electricity price parameter used in scheduling optimization.

[0169] Based on this, the energy preference data corresponding to each household user type can be determined respectively, and the parameters of the power dispatch model can be set as follows:

[0170] Table 4

[0171]

[0172] Among them, based on the aforementioned model parameters, the energy preference data of each household user type for each flexible load device, and the time-of-use electricity price parameters before and after optimization in the target rural area, the electricity scheduling model corresponding to each household user type can be constructed respectively.

[0173] Specifically, for each user family type, a corresponding flexible load model and discomfort index model may be constructed according to the load types included in the flexible load corresponding to the family type.

[0174] For example, the flexible load corresponding to the first type of household includes shiftable load and interruptible load, but does not include curtailable load. When constructing the flexible load model corresponding to this household user type, the corresponding shiftable load model and interruptible load model can be constructed first, and then the two models can be combined to obtain the flexible load model. Similarly, when constructing the discomfort index model corresponding to this household user type, the corresponding first discomfort sub-model and second discomfort sub-model can be constructed first, and then the two discomfort sub-models can be combined to obtain the corresponding discomfort index model.

[0175] For another example, for the second, third, and fourth types of households whose flexible loads include shiftable loads, interruptable loads, and reducible loads, their corresponding shiftable load models, interruptable load models, and reducible load models can be constructed respectively, and then these models can be combined to obtain the flexible load models corresponding to each household user type. Similarly, when constructing the discomfort index models corresponding to these household user types, the first discomfort sub-model, the second discomfort sub-model, and the third discomfort sub-model corresponding to the shiftable load and the interruptable load can be constructed first, and then the three discomfort sub-models can be combined to obtain the corresponding discomfort index model.

[0176] At the same time, for each household user type, a corresponding electricity cost model can be constructed according to the time-of-use electricity price parameters. For example, the electricity cost can be expressed as:

[0177] In the formula, is the amount of electricity purchased in period t, in kWh; is the electricity purchase price in the tth period, in yuan / kWh; Δt is the time step, in h; is the base load power in the tth period, in kW, P w,t is the operating power of the washing machine in the tth period, P b,t is the operating power of the electric bicycle charging equipment in the tth period, is the operating power of the air conditioning equipment in the tth period, is the base load power in the tth period.

[0178] At the same time, the power consumption scheduling model of each household user type may also include optimization constraints for optimizing power consumption scheduling. Exemplarily, the optimization constraints may include power constraints and comfort constraints.

[0179] The power constraints may include the balance constraint between the user-side power and the grid power, and the range constraint of the grid power:

[0180]

[0181] Where P g,t is the power supply power of the grid side in the tth period, P w,t is the operating power of the washing machine in the tth period, P b,t is the operating power of the electric bicycle charging equipment in the tth period, is the operating power of the air conditioning equipment in the tth period, is the base load power in the tth period, is the minimum power supply of the grid, The maximum power supply of the power grid.

[0182] Among them, the comfort constraint can be used to limit the range of change of the discomfort index before and after optimization, which can be shown as follows:

[0183]

[0184] In the formula, is the optimized discomfort index, γ DI,s is the first discomfort sub-index corresponding to the translatable load before optimization, ω1 is the weight of the first discomfort sub-index, γ DI , z is the second discomfort sub-index corresponding to the interruptible load before optimization, ω2 is the weight of the second discomfort sub-index, γ DI,cis the third discomfort sub-index corresponding to the load that can be reduced before optimization, ω3 is the weight of the third discomfort sub-index, and δ is the discomfort index constraint parameter after optimization. For example, the value of δ can be 1.5, so that the increase ratio of the discomfort index after the optimized scheduling can be constrained not to exceed 50%.

[0185] Then, for the optimization goals of minimizing the electricity cost and minimizing the discomfort index in the scheduling optimization, the corresponding first objective function and second objective function can be constructed respectively.

[0186] For example, for the optimization goal of minimizing electricity cost, a first objective function may be constructed:

[0187]

[0188] In the formula, is the amount of electricity purchased in period t, in kWh; is the electricity purchase price in the tth period, in yuan / kWh; Δt is the time step, in h; is the base load power in period t, in kW.

[0189] Exemplarily, for the optimization goal of minimizing the discomfort index, a second objective function can be constructed:

[0190]

[0191] In the formula, is the first discomfort sub-index corresponding to the translatable load after optimization, ω1 is the weight of the first discomfort sub-index, is the optimized second discomfort sub-index corresponding to the interruptible load, ω2 is the weight of the second discomfort sub-index, is the third discomfort sub-index corresponding to the load that can be reduced after optimization, and ω3 is the weight of the third discomfort sub-index.

[0192] Among them, the average power consumption time of each household user type for each type of flexible load is used as the power consumption scheduling plan before optimization corresponding to each household user type. The genetic algorithm can be used as follows: Figure 2 The process shown in the figure optimizes and solves the electricity dispatch model corresponding to each household user type to obtain the Pareto solution set of the electricity dispatch plan corresponding to each household user type. Then, the superiority and inferiority solution distance method can be used to determine the corresponding approximate ideal solution from the Pareto solution set corresponding to each household user type, and use it as the electricity optimization dispatch plan corresponding to each household user type. Among them, the Pareto frontier and the approximate ideal solution of the electricity dispatch plan corresponding to each household user type are as follows: Figure 8 shown.

[0193] Among them, please refer to Fig. 9, which shows the comparison of the power scheduling schemes corresponding to each household user type before and after optimization. Among them, for the portable washing machine, the use time of the washing machine of the first, second and third types of households after optimization scheduling is advanced to 06:00-07:00. The reason is that the original use time of the washing machine of these three types of households is in the peak period of electricity price. In order to reduce electricity cost, it is transferred to the valley period of electricity price; for the fourth type of households, their original washing machine use time is in the valley period of electricity price, so the use time remains unchanged. Therefore, the first, second and third types of households have greater potential for load regulation. For the interruptible electric bicycle charging equipment, the four types of households interrupted charging for 6, 5, 4 and 4 hours respectively. The charging period is interrupted in the peak period of electricity price, and charging starts at 23:00 in the valley period of electricity price. Therefore, the first type of households has greater potential for load regulation. For the air conditioners that can be cut, the load reduction of the second type of households is the largest, and the load reduction potential is the greatest; the peak time of the energy load of the third and fourth types of households is transferred from the original 19:00 to 18:00; the load reduction of the fourth type of households has not changed significantly, mainly because this type of household turns on the air conditioner for a short time, and has higher requirements for comfort in a short period of time than saving electricity costs. Therefore, the second type of households has greater potential for regulating the cuttable load. Among them, the optimization scheduling results of the four types of household users for the three types of typical flexible loads can be summarized as shown in Table 5:

[0194] Table 5

[0195]

[0196] Among them, please refer to Fig.10 , which shows the comparison of electricity cost and discomfort index before and after optimization for each household user type. Fig.10 As shown in (a), the electricity costs of the four types of household users are reduced to varying degrees after optimization, with the reduction percentages being 15.1%, 19.5%, 16.3%, and 12.3% respectively; Fig.10As shown in (b), the discomfort index of the four types of household users increased to varying degrees after optimization, with the percentage increases being 34.8%, 46.3%, 21.2%, and 17.8%, respectively. Among them, the electricity cost reduction ratio and discomfort index increase ratio of the second type of households are the largest, which is related to the fact that this type of household has many types of energy-using equipment, long energy use time, no strict restrictions on energy use time, and is more willing to pursue economy than comfort. The electricity cost reduction ratio and discomfort index increase ratio of the fourth type of households are the smallest, which is related to the fact that the flexible load energy use time of this type of household is relatively concentrated, and family members pay more attention to comfort. Based on this, the flexible regulation potential of each household user type in the target rural area can be ranked from large to small as follows: the second type of household> the third type of household> the first type of household> the fourth type of household. Therefore, in the process of implementing the overall optimization scheduling of flexible loads in the target rural area, pilot work can be carried out first from the second and third types of households, and then extended from point to surface to other types of household users.

[0197] In this embodiment, rural household users are classified according to the energy consumption habits and load characteristics of different household users in the target rural areas, and a classification result that can reflect the diversity of rural household users is obtained. Then, for each type of household user, a corresponding power dispatching model is established according to its corresponding energy preference data, and the power dispatching scheme of the household user type is optimized by considering both economic and comfort goals, so that the user's comfort can be maintained or improved to a certain extent while effectively reducing the user's electricity cost, and a more balanced dispatching optimization result can be obtained. This result can effectively improve the enthusiasm of rural household users to participate in dispatching, thereby better ensuring the stability and sustainability of the rural power grid system.

[0198] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0199] Based on the same inventive concept, the embodiment of the present application also provides a rural household electricity optimization scheduling device for implementing the rural household electricity optimization scheduling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more rural household electricity optimization scheduling device embodiments provided below can refer to the limitations of the rural household electricity optimization scheduling method above, and will not be repeated here.

[0200] In an exemplary embodiment, Fig.11 As shown, a rural household electricity optimization scheduling device 1100 is provided, comprising:

[0201] The first construction module 1101 is used to construct a flexible load model corresponding to each household user type according to the energy preference data corresponding to each household user type in the target rural area;

[0202] The second construction module 1102 is used to construct an electricity cost model according to the time-of-use electricity price parameters of the target rural area;

[0203] The third construction module 1103 is used to construct a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types;

[0204] The fourth construction module 1104 is used to construct an electricity scheduling model corresponding to each of the household user types by combining the electricity cost model and the flexible load model and the discomfort index model corresponding to each of the household user types;

[0205] The optimization module 1105 is used to optimize and solve the electricity scheduling model corresponding to each type of household user with the goal of minimizing electricity cost and minimizing discomfort index, and obtain an optimized electricity scheduling plan corresponding to each type of household user.

[0206] In an exemplary embodiment, the optimization module 1105 is also used to: consider the electricity cost and the discomfort index, use a genetic algorithm to optimize and solve the electricity scheduling model corresponding to each household user type, and obtain a Pareto solution set of the electricity scheduling plan corresponding to each household user type; with the goal of minimizing the electricity cost and minimizing the discomfort index, use the superior and inferior solution distance method to determine the electricity optimization scheduling plan corresponding to each household user type from each Pareto solution set.

[0207] In an exemplary embodiment, the flexible load model includes a translatable load model; the first construction module 1101 is also used to: obtain the preferred operating time of each translatable device for the household user type according to the energy preference data corresponding to the household user type; obtain the rated power and maximum operating time of each of the translatable devices; and construct the translatable load model corresponding to the household user type according to the preferred operating time, the rated power and the maximum operating time of each of the translatable devices.

[0208] In an exemplary embodiment, the flexible load model includes an interruptible load model; the first construction module 1101 is also used to: obtain the preferred operating time of each interruptible device of the household user type according to the energy preference data corresponding to the household user type; obtain the rated power, upper limit of operating time and set operating time of each interruptible device; and construct the interruptible load model corresponding to the household user type according to the preferred operating time, the rated power, the upper limit of operating time and the set operating time of each interruptible device.

[0209] In an exemplary embodiment, the flexible load model includes a reducible load model; the first construction module 1101 is also used to: obtain the preference effect parameters of the household user type for each reducible device according to the energy preference data corresponding to the household user type; obtain the power range, energy efficiency ratio and operating environment parameters of each reducible device; and construct the reducible load model corresponding to the household user type according to the preference effect parameters, the power range, the energy efficiency ratio and the operating environment parameters of each reducible device.

[0210] In an exemplary embodiment, the third construction module 1103 is further used to: if the flexible load corresponding to the household user type includes a movable load, then according to the preferred operating time of the household user type for the movable device contained in the energy preference data, construct a first discomfort sub-model of the household user type; the first discomfort sub-model is used to determine a first discomfort sub-index according to the deviation between the scheduled operating time of the movable device in the electricity scheduling scheme of the household user type and the preferred operating time; if the flexible load corresponding to the household user type includes an interruptible load, then according to the perceived power outage duration and acceptable power outage duration of the household user type for the interruptible device contained in the energy preference data, construct a second discomfort sub-model of the household user type; The second discomfort sub-model is used to determine the second discomfort sub-index according to the relationship between the continuous power outage duration of the interruptible device in the power scheduling plan and the perceived power outage duration and the acceptable power outage duration; if the flexible load corresponding to the household user type includes reducible equipment, then the third discomfort sub-model of the household user type is constructed according to the preference effect parameters of the household user type for the reducible equipment contained in the energy preference data; the third discomfort sub-model is used to determine the third discomfort sub-index according to the parameter relationship between the operating effect parameters corresponding to the reducible equipment in the power scheduling plan and the preference effect parameters; according to each discomfort sub-model corresponding to the household user type, the discomfort index model corresponding to the household user type is constructed.

[0211] Each module in the above rural household electricity optimization scheduling device can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0212] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.12As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store energy preference data, time-of-use electricity price parameters and other data corresponding to each type of household user in the target rural area. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing and scheduling rural household electricity consumption is implemented.

[0213] Those skilled in the art will understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0214] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0215] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0216] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0217] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0218] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0219] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0220] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A rural household electricity optimization scheduling method, characterized in that: The method comprises: According to the energy preference data corresponding to each household user type in the target rural area, a flexible load model corresponding to each household user type is constructed; Constructing an electricity cost model based on the time-of-use electricity price parameters of the target rural area; Constructing a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types; Combining the electricity cost model and the flexible load model and discomfort index model corresponding to each type of household user, constructing an electricity scheduling model corresponding to each type of household user; With the goal of minimizing electricity cost and minimizing discomfort index, the electricity scheduling model corresponding to each type of household user is optimized and solved to obtain an optimized electricity scheduling plan corresponding to each type of household user.

2. The method according to claim 1, characterized in that The goal of minimizing the electricity cost and minimizing the discomfort index is to optimize and solve the electricity scheduling model corresponding to each type of household user to obtain the electricity optimization scheduling plan corresponding to each type of household user, including: Considering the electricity cost and the discomfort index, using a genetic algorithm to optimize and solve the electricity scheduling model corresponding to each of the household user types, and obtaining a Pareto solution set of the electricity scheduling scheme corresponding to each of the household user types; With the goal of minimizing the electricity cost and minimizing the discomfort index, the electricity optimization scheduling scheme corresponding to each household user type is determined from each Pareto solution set using the superior-inferior solution distance method.

3. The method according to claim 1, characterized in that The flexible load model includes a translatable load model; The step of constructing a flexible load model corresponding to each household user type according to the energy preference data corresponding to each household user type in the target rural area includes: According to the energy preference data corresponding to the household user type, obtaining the preferred operating time of each translatable device by the household user type; Obtaining the rated power and set operating time of each of the translatably movable devices; The movable load model corresponding to the household user type is constructed according to the preferred operating time, the rated power and the set operating time of each movable device.

4. The method according to claim 1, characterized in that The flexible load model includes an interruptible load model; The step of constructing a flexible load model corresponding to each household user type according to the energy preference data corresponding to each household user type in the target rural area includes: Obtaining, according to the energy usage preference data corresponding to the household user type, a preferred operating time of each interruptible device by the household user type; Obtaining the rated power and set operating time of each of the interruptible devices; The interruptible load model corresponding to the household user type is constructed according to the preferred operating time, the rated power, and the set operating time of each interruptible device.

5. The method according to claim 1, characterized in that The flexible load model includes a curtailable load model; The step of constructing a flexible load model corresponding to each household user type according to the energy preference data corresponding to each household user type in the target rural area includes: According to the energy preference data corresponding to the household user type, obtaining the preference effect parameter of the household user type for each reducible device; Obtaining the power range, energy efficiency ratio and operating environment parameters of each of the reducible devices; The reducible load model corresponding to the household user type is constructed according to the preference effect parameter, the power range, the energy efficiency ratio and the operating environment parameter of each reducible device.

6. The method according to any one of claims 1 to 5, characterized in that: The constructing a discomfort index model corresponding to each household user type according to the energy preference data corresponding to each household user type includes: If the flexible load corresponding to the household user type includes a movable load, a first discomfort sub-model of the household user type is constructed according to the preferred operating time of the movable device of the household user type contained in the energy preference data; the first discomfort sub-model is used to determine a first discomfort sub-index according to the deviation between the scheduled operating time of the movable device in the electricity scheduling plan of the household user type and the preferred operating time; If the flexible load corresponding to the household user type includes an interruptible load, a second discomfort sub-model of the household user type is constructed according to the perceived power outage duration and acceptable power outage duration of the interruptible device of the household user type contained in the energy preference data; the second discomfort sub-model is used to determine a second discomfort sub-index according to the duration relationship between the continuous power outage duration of the interruptible device in the power scheduling plan and the perceived power outage duration and the acceptable power outage duration; If the flexible load corresponding to the household user type includes curtailable equipment, a third discomfort sub-model of the household user type is constructed according to the preference effect parameter of the household user type for curtailable equipment contained in the energy preference data; the third discomfort sub-model is used to determine a third discomfort sub-index according to a parameter relationship between an operation effect parameter corresponding to the curtailable equipment in the electricity scheduling plan and the preference effect parameter; The discomfort index model corresponding to the family user type is constructed according to each discomfort sub-model corresponding to the family user type.

7. A rural household electricity optimization scheduling device, characterized in that: The device comprises: The first construction module is used to construct a flexible load model corresponding to each household user type according to the energy preference data corresponding to each household user type in the target rural area; A second construction module is used to construct an electricity cost model according to the time-of-use electricity price parameters of the target rural area; A third construction module is used to construct a discomfort index model corresponding to each of the household user types according to the energy preference data corresponding to each of the household user types; A fourth construction module is used to construct an electricity scheduling model corresponding to each type of household user by combining the electricity cost model and the flexible load model and the discomfort index model corresponding to each type of household user; The optimization module is used to optimize and solve the electricity scheduling model corresponding to each type of household user with the goal of minimizing electricity cost and minimizing discomfort index, and obtain the electricity optimization scheduling plan corresponding to each type of household user.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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