Supply-demand interaction game method based on electricity consumption behaviors of user groups, and system

Abstract

The present invention relates to the technical field of electricity applications. Disclosed are a supply-demand interaction game method based on electricity consumption behaviors of user groups, and a system. The method comprises: collecting and preprocessing historical electricity consumption data of smart terminals of user groups; using a clustering analysis method to perform classification on the types of electricity users; by means of the central-limit theorem and Monte-Carlo method, constructing a user electricity consumption behavior analysis model; creating an electricity user group evolutionary game model, so as to analyze long-term evolution patterns of electricity consumption strategies of the user groups; and power supply entities adjusting and formulating, on the basis of the analysis result, an electricity price incentive plan that maximizes benefits, and transmitting, via power lines, the updated plan to smart electricity consumption terminals of the users. By means of using electricity consumption data of user groups collected by a large number of smart terminal devices and combining the data with the evolutionary game theory, the present invention successfully performs mathematical modeling and analysis on electricity consumption behaviors of user groups, thus achieving mutual benefit, win-win outcomes and stable equilibrium between supply-demand parties in long-term supply-demand interaction.

Description

A supply and demand interactive game method and system based on user group electricity consumption behavior Technical Field

[0001] The present invention relates to the technical field of power application, and in particular to a supply and demand interactive game method and system based on the electricity consumption behavior of a user group. Background Art

[0002] With the development of smart grids, a vast number of smart electricity terminals (including smart meters and smart appliances) have been installed in residential homes, providing a solid interactive foundation for user participation in demand-side response (DSR). Capturing user electricity usage data and analyzing their behavior through these terminals is a pressing issue. For power supply entities (such as electricity retailers), understanding the evolution of electricity usage strategies for different user groups over a period of time would be beneficial for designing attractive, attractive pricing incentives. However, in real-world electricity usage scenarios, most users are not completely rational. Given limited information, their behavior is often unpredictable, highly random, and unpredictable. This makes it difficult for traditional game theory to accurately analyze these behaviors. This is because classic game theory requires participants to be completely rational and fully informed, but in reality, varying levels of education and access to information make it difficult for residents to be completely rational. In the DSR process, driven by profit, only high-yield strategies are adopted by more residents. Based on this, evolutionary game theory, due to its dual assumptions of bounded rationality and limited information, can be well applied to the study of long-term supply and demand interactions between different types of user groups and power supply entities. Within the evolutionary game framework, the electricity consumption behavior of group residents is boundedly rational, which is consistent with actual scenarios. Group users will adjust their electricity consumption strategies through continuous summary and improvement, gradually maximizing their benefits in the process of participating in intelligent supply and demand interactions. This process is not achieved overnight, but rather a gradual evolution. Power supply entities can use the evolutionary trends of different types of group users' willingness to participate in supply and demand interactions (i.e., participation, or electricity consumption strategies) during this evolutionary process to adjust their electricity sales strategies. Thus, by formulating reasonable and attractive electricity price incentive schemes while maximizing their own benefits, they can guide different types of group users to gradually participate in the demand-side response process of supply and demand friendly interactions.

[0003] Inspired by this, the present invention discloses a demand-response supply-demand interactive game method based on the electricity consumption behavior of user groups. The method aims to mathematically model the electricity consumption strategies adopted by different types of group users in the process of participating in the supply-demand interaction, and effectively analyze the long-term evolution trend of users' electricity consumption behavior (i.e., electricity consumption strategy) by using evolutionary game theory based on bounded rationality and limited information assumptions. The method is used to guide power supply entities to formulate reasonable electricity price incentive plans, thereby guiding both parties to spontaneously form an evolutionary stable equilibrium state in the long-term participation in the supply-demand interaction on the demand side of the smart grid, and ultimately maximize the benefits of both parties. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to effectively coordinate the supply and demand relationship between the power user group and the power supply entity in the power market environment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a supply and demand interactive game method based on the electricity consumption behavior of user groups, which includes the following steps:

[0007] Collect historical electricity consumption data provided by group user smart terminals, and pre-process and normalize the historical data; perform cluster analysis on the types of electricity users through cluster analysis methods; build a user electricity consumption behavior analysis model through the central limit theorem and Monte Carlo method; build an electricity user group evolutionary game model to analyze the long-term evolution of the electricity consumption strategies of group users; the power supply entity adjusts and updates the evolution trend of the electricity consumption behavior of group users within a certain period of time calculated by the electricity user group evolutionary game model and formulates an electricity price incentive plan that maximizes benefits; the power supply entity will distribute the optimized and updated electricity price incentive plan to the smart electricity terminals of each group user through power lines.

[0008] As a preferred solution of the supply and demand interactive game method based on the electricity consumption behavior of user groups described in the present invention, the cluster analysis of the types of electricity users includes using a fuzzy C-means cluster analysis algorithm to maximize the similarity of electricity consumption behavior between residential users belonging to the same cluster and minimize the similarity of electricity consumption behavior between residents in different clusters to obtain a target function, and using the Lagrange multiplier method to find the minimum value of the target function obtained by cluster analysis.

[0009] As a preferred solution of the supply and demand interactive game method based on the electricity consumption behavior of user groups described in the present invention, the construction of the user electricity consumption behavior analysis model includes selecting representative users from each type of user group and performing normal distribution estimation with the power of household appliances owned by the representative users as the center point.

[0010] A non-sequential Monte Carlo algorithm is used to randomly sample the appliance combination and power of typical users. After obtaining the electricity usage behavior patterns of typical users, the Monte Carlo algorithm is used again to randomly sample the time points of the typical users' electricity usage behavior to obtain the electricity usage behavior status of each user.

[0011] Based on the random sampling results, the prediction results of the electricity consumption behavior of the category group users are calculated and expressed as,

[0012] Where M represents the number of categories of users in the region divided after data preprocessing and cluster analysis. is the expected power of the Mth group of users at time t, n M is the total number of users included in the M-th user group, is the electrical information value of the jth user in the Mth group of users, It is equal to the sampling result of the user's household appliance combination multiplied by the sampling result of the power of the user's household appliances. is the probability sampling result of the switch state of household appliances of the jth user in the Mth group of users at time t.

[0013] Based on the prediction results of the user's electricity consumption behavior and the expected power ratio of each user in the M group, the electricity load forecast value of the total user group in the area is predicted, which is expressed as:

[0014] in, is the total expected power value of all categories of users at time t, is the expected power consumption value of the nth user group at time t, where n = 1, 2, …, M;

[0015] Based on the electricity load forecast value of the user group, the initial value of the participation of different categories of users in the supply and demand interaction is calculated.

[0016] As a preferred solution of the supply and demand interactive game method based on the electricity consumption behavior of user groups described in the present invention, the analysis of the long-term evolution law of the electricity consumption strategies of group users includes constructing an electricity user group evolutionary game model based on the user group classification and initial participation value calculated by the user electricity consumption behavior analysis model.

[0017] The electricity user group evolutionary game model divides the user benefits of each type of user group into electricity economic payment and comfort utility benefits.

[0018] If users in the group participate in the supply and demand interaction guided by the power supply entity, the economic payment for electricity consumption is If not participating, it is

[0019] The comfort utility benefit is expressed in quadratic, exponential, and logarithmic function forms, as shown below.

[0020] The quadratic group user comfort utility benefit is expressed as,

[0021] Among them, τ=1,2,…,M represents the τth user group, is the comfort utility benefit of the jth user in the τth user group at time t, is the power consumption of the jth user in the τth user group at time t, μ τ is the utility parameter of the τth user group, which is used to describe the comfort level of the user group, x τ (t) is the proportion of users in the τth user group participating in the supply and demand interaction at time t, α is a preset parameter, and C is a constant preset according to actual conditions.

[0022] The exponential group user comfort utility benefit is expressed as,

[0023] Among them, κ τ,1 The parameter used to describe the comfort level of the τth user group, κ τ,2 is a random number set for the electricity consumption behavior of the τth user group. The random number is obtained from the interval (0,1) and is used to describe the degree to which the electricity consumption comfort of the τth user group is affected by external environmental factors.

[0024] The logarithmic group user comfort utility benefit is expressed as,

[0025] Among them, τ is the proportional coefficient of the utility function used to measure the electricity consumption behavior of the τth user group, is the load consumed by the τth user group at time t.

[0026] Based on the total electricity consumption benefits of the user group, a replicator dynamic model of the electricity consumption behavior strategy of the τth user group at time t is further constructed.

[0027] As a preferred solution of the supply and demand interactive game method based on the electricity consumption behavior of a user group described in the present invention, the replica dynamic model includes discrete and continuous types.

[0028] The replica dynamic model under the continuous type is expressed as,

[0029] in, is the expected benefit obtained by the user in the τth user group when he chooses to participate in the supply and demand interaction strategy at time t, It is the total expected benefit obtained by the τth user group when executing the mixed strategy at time t.

[0030] The replica dynamic model under discrete type is expressed as:

[0031] Discretize the replica dynamic model under discrete type and express it as:

[0032] Where σ is the number of iterations, i.e., the evolution time of the electricity consumption strategy of the τth user group at time t, x τ (σ) is the proportion of users in the τth user group who choose to participate in the supply-demand interaction strategy at the σth iteration, which is in the interval [0, 1]. and Then, they are the expected benefits of the individual user group in the τth user group when they choose to participate in the supply and demand interaction strategy at the σth iteration and the total expected benefits of the group, respectively. στ is the iteration step size set for the τth user group at the σth iteration.

[0033] Determine whether the replicator dynamic model meets the expected convergence accuracy requirements.

[0034] As a preferred solution of the supply and demand interactive game method based on the electricity consumption behavior of a user group described in the present invention, the judgment of whether the replica dynamic model has achieved the expected convergence accuracy requirement includes judging according to the algorithm formula for controlling the replica dynamic model, including if all individuals in the τth user group have internally selected the proportion of users participating in the supply and demand interactive strategy after σ weeks of iteration. τ (σ) reaches the first threshold in [0, 1], indicating that all users in the τth user group will choose to participate in the supply-demand interaction strategy. τ If (σ) reaches the second threshold in [0, 1], it means that all users are unwilling to participate in the supply-demand interaction.

[0035] As a preferred solution of the supply and demand interactive game method based on the electricity consumption behavior of a user group described in the present invention, the algorithm formula for controlling the replica dynamic model is expressed as follows:

[0036] Among them, τ Represents a positive number.

[0037] Another object of the present invention is to provide a supply and demand interactive game system based on the electricity consumption behavior of user groups. The system can intelligently analyze user electricity consumption data, accurately simulate electricity consumption behavior, and build an efficient supply and demand interactive game model on this basis, thereby solving the problems in the existing electricity market where user electricity consumption behavior is difficult to accurately predict, supply and demand relationship adjustment is inflexible, and electricity price incentive scheme design is unreasonable.

[0038] To solve the above technical problems, the present invention provides the following technical solutions: a supply and demand interactive game system based on the electricity consumption behavior of user groups, including a data acquisition and preprocessing module, a user type clustering analysis module and a user electricity consumption behavior analysis model construction module.

[0039] The data acquisition and preprocessing module is responsible for collecting historical electricity consumption data of group user smart terminals and preprocessing and normalizing the data.

[0040] The user type cluster analysis module is responsible for classifying and analyzing the types of power users using a cluster analysis method.

[0041] The user electricity consumption behavior analysis model building module is responsible for building a user electricity consumption behavior analysis model using the central limit theorem and the Monte Carlo method.

[0042] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of the supply and demand interactive game method based on the electricity consumption behavior of a user group as described above.

[0043] 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 supply and demand interactive game method based on the electricity consumption behavior of a user group are implemented as described above.

[0044] Beneficial effects of the present invention: The present invention utilizes electricity consumption data of group users collected by a large number of smart terminal devices. Through these data, combined with evolutionary game theory, the present invention successfully conducts in-depth mathematical modeling and analysis of the electricity consumption behavior of group users under conditions of bounded rationality and limited information. This method not only takes into account the non-completely rational behavior of users, but also adapts to the evolution of their behavior over time and changes in market incentives. Therefore, power supply entities can optimize and adjust their electricity price incentive strategies based on this evolutionary trend, which can not only ensure their own economic interests, but also effectively guide users to participate in the demand response of smart grids, thereby promoting the efficient operation of the power market and achieving mutual benefit and stable equilibrium between the supply and demand sides in the long-term supply and demand interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] FIG1 is an overall flow chart of a supply-demand interactive game method based on the electricity consumption behavior of a user group provided by the first embodiment of the present invention.

[0047] FIG2 is a detailed schematic diagram of the principle of a supply-demand interactive game method based on the electricity consumption behavior of a user group provided by the first embodiment of the present invention.

[0048] FIG3 is an analysis flow chart of a supply-demand interactive game method based on the electricity consumption behavior of a user group provided in the first embodiment of the present invention.

[0049] FIG4 is a flowchart of the game analysis of the evolution of electricity consumption strategies of different categories of users in a supply-demand interactive game method based on the electricity consumption behavior of user groups provided by the first embodiment of the present invention.

[0050] FIG5 is an overall framework diagram of a supply-demand interactive game system based on the electricity consumption behavior of a user group provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0052] Example 1

[0053] 1 to 4 , an embodiment of the present invention provides a supply and demand interactive game method based on the electricity consumption behavior of a user group, which is characterized by:

[0054] S1: Collect historical electricity consumption data provided by group users’ smart terminals, and perform preprocessing and normalization operations on the historical data.

[0055] S2: Cluster analysis is performed on the types of electricity users through cluster analysis method.

[0056] Furthermore, cluster analysis of the types of electricity users includes using a fuzzy C-means cluster analysis algorithm to maximize the similarity of electricity usage behaviors between residential users belonging to the same cluster and minimize the similarity of electricity usage behaviors between residents in different clusters to obtain the objective function, and using the Lagrange multiplier method to minimize the objective function obtained through cluster analysis.

[0057] S3: Construct a user electricity consumption behavior analysis model using the central limit theorem and Monte Carlo method.

[0058] Furthermore, constructing a user electricity consumption behavior analysis model includes selecting representative users from each user group and performing normal distribution estimation with the power of household appliances owned by the representative users as the center point.

[0059] A non-sequential Monte Carlo algorithm is used to randomly sample the appliance combination and power of typical users. After obtaining the electricity usage behavior patterns of typical users, the Monte Carlo algorithm is used again to randomly sample the time points of the typical users' electricity usage behavior to obtain the electricity usage behavior status of each user.

[0060] Based on the random sampling results, the prediction results of the electricity consumption behavior of the category group users are calculated and expressed as,

[0061] Where M represents the number of categories of users in the region divided after data preprocessing and cluster analysis. is the expected power of the Mth group of users at time t, n M is the total number of users included in the M-th user group, is the electrical information value of the jth user in the Mth group of users, It is equal to the sampling result of the user's household appliance combination multiplied by the sampling result of the power of the user's household appliances. is the probability sampling result of the switch state of household appliances of the jth user in the Mth group of users at time t.

[0062] Based on the prediction results of the user's electricity consumption behavior and the expected power ratio of each user in the M group, the electricity load forecast value of the total user group in the area is predicted, which is expressed as:

[0063] in, is the total expected power value of all categories of users at time t, is the expected power value of the nth user group at time t, where n = 1, 2, …, M.

[0064] Based on the electricity load forecast value of the user group, the initial value of the participation of different categories of users in the supply and demand interaction is calculated.

[0065] FIG2 further illustrates the detailed principle of a demand response supply-demand interactive game method based on user group electricity usage behavior. The left frame shows the detailed internal structure of the group user electricity usage behavior analysis model 111 in FIG1 . The right frame shows the detailed internal structure of the power user group evolution game model 112 in FIG1 .

[0066] In the left frame of Figure 2, by collecting data samples installed on the user's smart electricity terminals, and performing data preprocessing and cluster analysis, different categories of user groups are obtained, and the smart electricity terminals installed in these user groups are also divided into corresponding categories, namely user category 1-smart electricity terminal 10 and user category 2-smart electricity terminal 11, up to user category N-smart electricity terminal 12 shown in Figure 2; the historical electricity consumption data collected by these different categories of smart electricity terminals are respectively adopted by the corresponding built-in electricity consumption behavior analysis modules 13, 14 and 15 to realize the analysis of the electricity consumption behavior of a single user and the electricity consumption behavior of the entire user group, and obtain the proportion of users in each category of user groups who participate in the demand-side response supply and demand interaction process guided by the power supply entity, that is, the initial value of user participation; the initial value is transmitted by the electricity consumption behavior analysis modules (13, 14 and 15) to the power consumption behavior analysis modules in Figure 2 The comfort utility calculation modules (16, 17 and 18) shown above are used to obtain the comfort utility benefits of individuals in each user group who choose to "participate in supply and demand interaction" according to the above-mentioned comfort utility benefit calculation method, and combined with the above-mentioned electricity economic expenditure function, the overall benefit calculation modules (19, 20 and 21) finally obtain the overall benefit of each user in each user group when executing each strategy in the strategy set; driven by the overall benefit, the calculated user overall benefit will be continuously adjusted and updated as the user chooses whether to participate in supply and demand interaction and the selected electricity price incentive plan, so the participation of each corresponding user group changes in real time within a certain number of years, and is statistically obtained through the participation calculation modules (22, 23 and 24), uploaded to the participation data acquisition and control system module 26, and further transmitted to the power user group evolution game model shown in the right box diagram.

[0067] Specifically, in the right frame diagram shown in Figure 2, based on the participation statistics of each user group transmitted by module 25, the group user supply and demand interaction participation replica dynamic calculation module calculates the replica dynamic model (27, 28 and 29) of each type of user group choosing to execute the "participation in supply and demand interaction" strategy, thereby immediately obtaining the total replica dynamic system equation group of all user groups, that is, the N (N≥2)-sided M (M≥2) strategic symmetric and asymmetric evolutionary game model 30 of all types of user groups participating in long-term supply and demand interaction. Through this model, the evolution data and evolution path of group users' participation in intelligent supply and demand interaction can be calculated within a certain number of years (usually with weeks as the basic unit, that is, all individual users and power supply entities adjust and update their respective power consumption strategies and power sales strategies once a week). This type of decision data is transmitted by the data aggregation module 31 to the smart power consumption terminals (32, 33 and 34) of various user groups, and is finally uploaded to the data acquisition system 35 of the upper-level power supply entity through the power line. The power supply entity analyzes and judges the data in its data acquisition system 35 and adjusts and formulates a new electricity price incentive plan, which is issued to different types of user groups through the smart power consumption terminals (32, 33 and 34). The user groups make certain adjustments and updates to their power consumption strategies based on their own interests (mainly including the aforementioned economic expenditure on electricity consumption and comfort utility benefits).

[0068] S4: Construct an evolutionary game model of power user groups to analyze the long-term evolution of group users’ electricity consumption strategies.

[0069] Furthermore, the long-term evolution of the electricity consumption strategies of group users is analyzed, including constructing an electricity user group evolution game model based on the user group classification and initial participation value calculated by the user electricity consumption behavior analysis model.

[0070] The electricity user group evolutionary game model divides the user benefits of each user group into economic payment for electricity consumption and comfort utility benefits.

[0071] If users in the group participate in the supply and demand interaction guided by the power supply entity, the economic payment for electricity consumption is If not participating, it is

[0072] It should be further explained that The incentive electricity price formulated for the power supply entity can be called the agreed electricity price reached by the power supply entity and the group of users for participating in the supply and demand interaction. Its value is higher than the fixed electricity price issued by the power supply entity under the normal mode. It should be lower, but not lower than the electricity selling cost of the power supply entity, that is,

[0073] Comfort utility benefits are mainly used to describe the changes in comfort (i.e., the actual electricity utility enjoyed due to the improvement in comfort) and psychological satisfaction of user groups due to their participation in supply and demand interactions, which can be converted into comfort utility benefits.

[0074] The comfort utility benefit is expressed in quadratic, exponential, and logarithmic function forms, as shown below.

[0075] The quadratic group user comfort utility benefit is expressed as,

[0076] Among them, τ=1,2,…,M represents the τth user group (obtained through cluster analysis), P τ j (t) is the comfort utility benefit of the jth user in the τth user group at time t, is the electricity consumption of the jth user in the τth user group at time t (predicted by the user electricity consumption behavior analysis model), μ τ is the utility parameter of the τth user group, which is used to describe the comfort level of the user group, x τ (t) is the proportion of users in the τth user group participating in the supply and demand interaction at time t, α is a preset parameter, and C is a constant preset according to actual conditions.

[0077] The exponential group user comfort utility benefit is expressed as,

[0078] Among them, κ τ,1 The parameter used to describe the comfort level of the τth user group, κ τ,2 is a random number set for the electricity consumption behavior of the τth user group. The random number is obtained from the interval (0,1) and is used to describe the degree to which the electricity consumption comfort of the τth user group is affected by external environmental factors.

[0079] The logarithmic group user comfort utility benefit is expressed as,

[0080] Among them, τ is the proportional coefficient of the utility function used to measure the electricity consumption behavior of the τth user group, is the load consumed by the τth user group at time t.

[0081] Based on the total electricity consumption benefits of the user group, a replicator dynamic model of the electricity consumption behavior strategy of the τth user group at time t is further constructed.

[0082] Replicator dynamic models include discrete and continuous types.

[0083] The replica dynamic model under the continuous type is expressed as,

[0084] in, is the expected benefit (the sum of the total electricity economic payment and comfort utility benefit) obtained by the user in the τth user group when he chooses to participate in the supply and demand interaction strategy at time t, It is the total expected benefit obtained by the τth user group when executing the mixed strategy at time t.

[0085] The replica dynamic model under discrete type is expressed as:

[0086] It should be further explained that, based on the above two forms of replica dynamic models, the present invention further designs a convergent iterative algorithm for solving the long-term evolution stable equilibrium state of the above replica dynamic models. Specifically, the replica dynamic model under the discrete type is discretized and expressed as:

[0087] Where σ is the number of iterations, i.e., the evolution time of the electricity consumption strategy of the τth user group at time t, x τ (σ) is the proportion of users in the τth user group who choose to participate in the supply-demand interaction strategy at the σth iteration, which is in the interval [0, 1]. and Then, they are the expected benefits of the individual user group in the τth user group when they choose to participate in the supply and demand interaction strategy at the σth iteration and the total expected benefits of the group, respectively. σ,τ is the iteration step size set for the τth user group at the σth iteration; it can usually be set to a value close to 10 -3 , so as to ensure that the iteration time limit (i.e. the evolution time length of the user's electricity consumption strategy) and the value in the iteration process are in the interval [0,1].

[0088] Determine whether the replicator dynamic model meets the expected convergence accuracy requirements.

[0089] The judgment of whether the replicator dynamic model has achieved the expected convergence accuracy requirements includes judging according to the algorithm formula that controls the replicator dynamic model, including if all individuals in the τth user group choose to execute the user proportion x participating in the supply and demand interaction strategy after σ weeks of iteration τ (σ) reaches the first threshold in [0, 1], indicating that all users in the τth user group will choose to participate in the supply-demand interaction strategy. τ If (σ) reaches the second threshold within [0, 1], it means that all users are unwilling to participate in the supply and demand interaction, or only users are willing to choose to participate in the supply and demand interaction.

[0090] Further explanation of the first threshold and the second threshold is given. In order to ensure that the evolutionary stable equilibrium point of the replica dynamic model can be found, the present invention proposes to set a very small positive number. τ To determine whether the above iterative process has reached the expected convergence accuracy requirements, and once the expected accuracy requirements are reached, the iteration can be terminated. At this time, the calculated x τ (σ) is the proportion of all individuals in the τth user group who ultimately choose to implement the "participate in supply and demand interaction" strategy after σ weeks of supply and demand interaction strategy selection. If this proportion value is close to 1 within the above precision, it means that all users in the τth user group will choose to implement the "participate in supply and demand interaction" strategy, that is, all users will choose to participate in the supply and demand interaction on the demand response side of the smart grid guided by the power supply entity; conversely, if this proportion value is close to 0, it means that almost all users are unwilling to participate in supply and demand interaction, or only very few users are willing to choose to participate in supply and demand interaction.

[0091] The algorithm formula for controlling the replica dynamic model is expressed as,

[0092] Among them, τ Represents a positive number.

[0093] It should be further noted that, as shown in Figure 1, if the total benefits obtained by individual users from different types of user groups when choosing the same electricity usage strategy are the same, then the payoff distribution matrix of the evolutionary game formed by these different types of user groups participating in the supply-demand interaction process will be symmetrical, that is, the corresponding evolutionary game is a symmetric game. Conversely, if the total benefits obtained by choosing the same strategy are different (due to coming from different user group classifications), then the type of evolutionary game formed is asymmetric. Furthermore, if there are only two types of user groups in the region, and each group has only two electricity usage strategies (i.e., "participating in supply-demand interaction" strategy S1 and "not participating in supply-demand interaction" strategy S2, which are a pair of opposing strategies), then two game scenarios can be formed: a two-group, two-strategy symmetric evolutionary game and a two-group, two-strategy asymmetric evolutionary game.

[0094] Among them, the symmetric evolutionary game of two groups and two strategies can be constructed as follows: if user group 1 and user group 2 simultaneously execute strategy S1 and strategy S2 respectively, the total benefits are and The total benefits of user group 1 and user group 2 when they execute strategy S1 and strategy S2 respectively are The total benefits of user group 1 and user group 2 when they execute strategy S2 and strategy S1 respectively are In addition, assuming that the proportion of user group 1 who chooses to implement strategy S1 is x, and the proportion who chooses to implement strategy S2 is (1-x), and assuming that the proportion of user group 2 who chooses to implement strategy S1 is y, and the proportion who chooses to implement strategy S2 is (1-y), where. Based on this, the present invention designs a symmetric evolutionary game model in which the two user groups participate in the supply and demand interaction process guided by the power supply entity for a long time, that is, a replica dynamic model of the evolution of each group's electricity consumption strategy (i.e., "participation in supply and demand interaction" strategy S1 and "participation in supply and demand interaction" strategy S2), as shown below,

[0095] Among them, in the above formula: and The expected total benefits of individuals in user groups 1 and 2 when they choose to execute the "participate in supply and demand interaction" strategy S1, and The total expected return of user groups 1 and 2 when they implement the mixed strategy (for user group 1, that is, x users choose to implement the "participate in supply and demand interaction" strategy S1, and 1-x users choose to implement the "not participate in supply and demand interaction" strategy S2; for user group 2, that is, y users choose to implement the "participate in supply and demand interaction" strategy S1, and 1-y users choose to implement the "not participate in supply and demand interaction" strategy S2). Where:

[0096] For user group 1, the two expected benefits are:

[0097] in, The expected total benefit of individuals in user group 1 when they choose to implement strategy S2 of "not participating in supply and demand interaction"; is the expected benefit of user group 1.

[0098] For user group 2, the two expected benefits are:

[0099] in, The expected total benefit of individuals in user group 2 when they choose to implement strategy S2 of "not participating in supply and demand interaction"; is the expected return of group 2.

[0100] Similarly, the two-group two-strategy asymmetric evolutionary game can be constructed as follows: Assume that the total benefits of user group 1 and user group 2 when they simultaneously execute strategy S1 and strategy S2 are and The total benefits of user group 1 and user group 2 when they execute strategy S1 and strategy S2 respectively are The total benefits of user group 1 and user group 2 when they execute strategy S2 and strategy S1 respectively are in and At least one of the four inequality conditions is satisfied. In addition, it is still assumed that the proportion of user group 1 that chooses to implement strategy S1 is x, and the proportion that chooses to implement strategy S2 is (1-x), and it is assumed that the proportion of user group 2 that chooses to implement strategy S1 is y, and the proportion that chooses to implement strategy S2 is (1-y), where. Based on this, the present invention designs an asymmetric evolutionary game model in which the above two user groups participate in the supply and demand interaction process guided by the power supply entity for a long time, that is, a replica dynamic model of the evolution of each group's electricity consumption strategy (that is, the "participation in supply and demand interaction" strategy S1 and the "participation in supply and demand interaction" strategy S2), as shown below.

[0101] For user group 1, the two expected benefits are:

[0102] For user group 2, the two expected benefits are:

[0103] In addition, in the embodiment shown in FIG1 , when there are two or more user groups and each group's electricity consumption strategy set contains two or more strategies, a multi-group and multi-strategy supply and demand interactive evolutionary game model can be constructed.

[0104] S5: The power supply entity adjusts and updates the evolution trend of the electricity consumption behavior of a group of users within a certain period of time calculated by the electricity user group evolution game model and formulates an electricity price incentive plan that maximizes benefits.

[0105] The present invention is further supplemented by the fact that FIG3 is an analysis flow chart of a demand response supply-demand interactive game method based on the electricity consumption behavior of a user group.

[0106] Step S01: After preprocessing, clustering analysis, and Monte Carlo simulation (random sampling) of the historical electricity consumption data of all users in the region, the user group categories are determined. On this basis, a multi-party, multi-strategy evolutionary game model is established in which the user groups participate in the long-term demand-side supply and demand interactive response guided by power supply entities (such as electricity retailers, power grid companies, etc.). If the benefit (payment) distribution matrix in the model is symmetrical, that is, the overall benefits (or payments) obtained by users from different categories who choose the same electricity consumption strategy are the same, then the aforementioned symmetric evolutionary game model 110 (as shown in Equations (11)-(13)) is adopted, and the process proceeds to Step S04 for the next step of analysis.

[0107] Step S02: If the benefit (payment) distribution matrix of different types of user groups participating in the long-term supply and demand interaction is asymmetric, the aforementioned symmetric evolutionary game model 111 (as shown in equations (14)-(15)) is adopted, and the process proceeds to step S04 for the next step of analysis.

[0108] Step S03: If the benefit (payment) distribution matrix of different types of user groups participating in the long-term supply and demand interaction is a complex form based on multiple parties and multiple strategies (that is, each user group's strategy set contains multiple electricity consumption strategies, such as the power supply entity can provide electricity sales strategies with 30%, 50%, 80% and 100% participation for users to choose), then a multi-party multi-strategy asymmetric evolutionary game model 112 is constructed according to the aforementioned asymmetric evolutionary game modeling idea, and the process goes to step S04 for the next step of analysis.

[0109] Step S04: In this step, regardless of whether the symmetric or asymmetric evolutionary game model is finally adopted, it is necessary to first calculate the expected benefits of each user group under the pure strategy of long-term participation in supply and demand interaction (such as the two pure strategies of "participating in supply and demand interaction" and "not participating in supply and demand interaction") through the replica dynamic equation theory in evolutionary game theory (as shown in formulas (12)-(15)); then, on this basis, calculate the expected benefits of each user group under the mixed strategy (that is, the number of users who choose each pure strategy for electricity consumption only accounts for a certain proportion, and the proportion of all pure strategies for electricity consumption selected is 1 / 3 of the total). and add up to 1), that is, the total average expected return of the group (as shown in Equations (12)-(15)); then, by calculation, a set of replicator dynamic equations for different types of user groups participating in the long-term supply and demand interaction is constructed (as shown in Equation (11)); finally, all the internal equilibrium points and the corresponding Jacobian matrices of the established replicator dynamic equations are solved, and the asymptotic stability of the Jacobian matrix at each internal equilibrium point is analyzed based on the Lyapunov stability law, that is, the long-term evolutionary stable equilibrium characteristics of different types of user groups participating in the supply and demand interaction process.

[0110] Step S05: After calculating the long-term evolutionary stable equilibrium characteristics under the initial user participation scenario, further analyze the evolutionary trends of different types of user groups in participating in the supply and demand interaction over a certain period of time under different electricity price incentive packages provided by the power supply entity, that is, the long-term evolutionary stable equilibrium characteristics of each power consumption strategy. Ultimately, the power consumption strategies of each type of user group and the power sales strategy of the power supply entity that can achieve an evolutionary stable equilibrium state are obtained.

[0111] Step S06: Within a sufficiently long period of time (usually using weeks as the benchmark unit to continuously update the evolutionary paths of different types of user groups participating in the supply and demand interaction, that is, the user groups implement the electricity purchasing strategy once a week, and the power supply entity releases the electricity selling strategy once a week), further study and analyze the long-term evolutionary stable equilibrium state and its evolutionary path between different types of user groups and the power supply entity, so as to ultimately determine whether both parties can form a long-term friendly intelligent demand-side response model.

[0112] S6: The power supply entity will distribute the optimized and updated electricity price incentive plan to the smart electricity terminals of various groups of users through power lines.

[0113] The present invention further supplements that FIG4 shows the process of the evolutionary game analysis of electricity consumption strategies of different categories of user groups.

[0114] Step S01: First, collect data on the initial participation of different types of user groups in supply and demand interaction and upload it to the smart electricity terminals of each user group;

[0115] Step S02: The power supply entity analyzes the evolution trend and evolution path of the electricity consumption strategies of each user group within a certain period of time, and provides users with different levels of electricity sales plans (i.e., different forms of electricity consumption strategies implemented by corresponding users) for participating in intelligent supply and demand interaction. These plans are then distributed to each user's smart electricity consumption terminal through power lines, thereby forming an electricity consumption strategy set for each user. This strategy set contains a total of m electricity consumption strategies. Driven by profit and comfort requirements, users can choose the electricity consumption strategy that suits them.

[0116] Step S03: Based on the diverse electricity price incentive packages provided by the power supply entity, different types of user groups update the proportion of users participating in the supply and demand interaction, i.e., user participation data;

[0117] Step S04: recalculating the overall benefit of each user based on the updated user participation of different types of user groups, including the economic expenditure on electricity consumption and the utility benefit of electricity comfort;

[0118] Step S05: Based on the calculated overall user benefits, the replica dynamic equation theory in evolutionary game theory is used to construct the evolutionary game replica dynamic equations of each type of user group after strategy update;

[0119] Step S06: using Lyapunov's stability law to analyze the evolutionary trend and evolutionary path of the long-term participation of different types of user groups in the "user-power supplier" supply and demand interaction (i.e., the evolutionary trend of the proportion of users of each type of user group who implement the electricity consumption strategy) over a certain period of time;

[0120] Step S07: Determine the respective strategies of the "user-power supplier" when they reach a long-term evolutionary stable equilibrium state, including the electricity consumption strategies (i.e., electricity consumption behaviors) adopted by various types of user groups and the electricity sales strategies adopted by power supply entities (such as electricity retailers, power grid companies, etc.). The above strategies can maximize the interests of both parties, that is, form a strict and refined Nash equilibrium state.

[0121] Example 2

[0122] Referring to Figure 5, an embodiment of the present invention provides a system for a supply and demand interactive game method based on the electricity consumption behavior of a user group. The supply and demand interactive game system based on the electricity consumption behavior of a user group includes a data acquisition and preprocessing module, a user type clustering analysis module, and a user electricity consumption behavior analysis model construction module.

[0123] The data acquisition and preprocessing module is responsible for collecting historical electricity consumption data from group users' smart terminals and preprocessing and normalizing the data.

[0124] The user type cluster analysis module is responsible for classifying and analyzing the types of electricity users using cluster analysis methods.

[0125] The user electricity consumption behavior analysis model construction module is responsible for constructing the user electricity consumption behavior analysis model using the central limit theorem and Monte Carlo method.

[0126] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0127] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0128] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0129] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0130] Example 3

[0131] In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment aims to demonstrate the superiority of the supply and demand interactive game method based on the electricity consumption behavior of user groups (hereinafter referred to as the "new method") compared to the traditional electricity user behavior analysis method (hereinafter referred to as the "traditional method"). The new method adopts advanced cluster analysis, central limit theorem and Monte Carlo simulation, combined with evolutionary game theory, based on a large amount of historical electricity consumption data collected by smart terminals, to more accurately analyze and predict the user's electricity consumption behavior, and adjust the electricity price incentive plan accordingly. In order to verify the effectiveness of the new method, we applied the new method and the traditional method under the same conditions, and recorded the key performance indicators, as shown in Table 1.

[0132] Table 1 Experimental effect comparison chart

[0133] The above data demonstrates that the new method significantly outperforms traditional methods in multiple areas, including forecast accuracy, user response rate, grid load balance, satisfaction with the electricity price incentive scheme, economic benefit growth, and system stability. This demonstrates that the new method can more effectively analyze and guide user electricity usage behavior, optimize power resource allocation, and improve grid operational efficiency, while also delivering higher economic returns and user satisfaction.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A supply and demand interactive game method based on the electricity consumption behavior of user groups, characterized in that: include: Collect historical electricity consumption data provided by group users' smart terminals, and perform preprocessing and normalization operations on the historical data; Cluster analysis is conducted on the types of electricity users through cluster analysis method; Construct a user electricity consumption behavior analysis model through the central limit theorem and Monte Carlo method; Construct an evolutionary game model of power user groups to analyze the long-term evolution of group users' power consumption strategies; The power supply entity adjusts and updates the electricity price incentive plan to maximize benefits based on the evolution trend of the electricity consumption behavior of the group of users within a certain period of time calculated by the evolutionary game model of the power user group; The power supply entity will optimize and update the electricity price incentive plan and distribute it to the smart electricity terminals of various groups of users through power lines.

2. A supply and demand interactive game method based on the electricity consumption behavior of a user group as claimed in claim 1, characterized in that: The cluster analysis of the types of electricity users includes: using a fuzzy C-means cluster analysis algorithm to maximize the similarity of electricity usage behaviors between residential users belonging to the same cluster, and minimize the similarity of electricity usage behaviors between residents in different clusters to obtain an objective function, and using a Lagrange multiplier method to minimize the objective function obtained by cluster analysis.

3. A supply and demand interactive game method based on the electricity consumption behavior of a user group as claimed in claim 2, characterized in that: The construction of the user electricity consumption behavior analysis model includes selecting representative users in each user group and performing normal distribution estimation with the power of household electrical appliances owned by the representative users as the center point; A non-sequential Monte Carlo algorithm is used to randomly sample the electrical appliance combination and power of a typical user. After obtaining the electricity consumption behavior pattern of a typical user, the Monte Carlo algorithm is used again to randomly sample the time points of the typical user's electricity consumption behavior to obtain the electricity consumption behavior status of each user. Based on the random sampling results, the prediction results of the electricity consumption behavior of the category group users are calculated, which is expressed as: Where M represents the number of categories of users in the region divided after data preprocessing and cluster analysis. is the expected power of the Mth group of users at time t, n M is the total number of users included in the Mth user group, is the electrical information value of the jth user in the Mth group of users, It is equal to the sampling result of the user's household appliance combination multiplied by the sampling result of the power of the user's household appliances. is the probability sampling result of the switch state of household appliances of the jth user in the Mth group of users at time t; Based on the prediction results of the user's electricity consumption behavior and the expected power proportion of each user in the M group of users, the power load forecast value of the total user group in the area is predicted, which is expressed as: in, is the total expected power value of all categories of users at time t, is the expected power consumption value of the nth user group at time t, where n = 1, 2, …, M; Based on the predicted electricity load values ​​of user groups, the initial values ​​of participation of users of different categories in the supply-demand interaction are calculated.

4. A supply and demand interactive game method based on the electricity consumption behavior of a user group as claimed in claim 3, characterized in that: The analysis of the long-term evolution law of the power consumption strategy of the group users includes building a power user group evolution game model based on the user group classification and the initial value of participation calculated by the user power consumption behavior analysis model; The power user group evolutionary game model divides the user benefits of each user group into economic payment for electricity consumption and comfort utility benefits; If users in the group participate in the supply-demand interaction guided by the power supply entity, the economic payment for electricity consumption is If not participating, it is indicated as The comfort utility benefits are expressed in quadratic, exponential and logarithmic function forms, as shown below: The quadratic group user comfort utility benefit is expressed as, Among them, τ=1,2,…,M represents the τth user group, is the comfort utility benefit of the jth user in the τth user group at time t, is the power consumption of the jth user in the τth user group at time t, μ τ is the utility parameter of the τth user group, which is used to describe the comfort level of the user group, x τ (t) is the proportion of users in the τth user group participating in the supply-demand interaction at time t, α is a preset parameter, and C is a constant preset according to the actual situation; The exponential group user comfort utility benefit is expressed as: Among them, κ τ,1 The parameter used to describe the comfort level of the τth user group, κ τ,2 For the τth A random number set for the electricity consumption behavior of the τth user group, which is obtained from the interval (0,1) and is used to describe the degree to which the electricity consumption comfort of the τth user group is affected by external environmental factors; The logarithmic group user comfort utility benefit is expressed as, Among them, τ is the proportional coefficient of the utility function used to measure the electricity consumption behavior of the τth user group, is the load consumed by the τth user group at time t; Based on the total electricity consumption benefits of the user groups, a replica dynamic model of the electricity consumption behavior strategy of the τth user group at time t is further constructed.

5. A supply and demand interactive game method based on the electricity consumption behavior of a user group as claimed in claim 4, characterized in that: The replicator dynamic model includes discrete type and continuous type; The replicator dynamic model under the continuous mode is expressed as: in, is the expected benefit obtained by the user in the τth user group when he chooses to participate in the supply-demand interaction strategy at time t, Then it is the total expected benefit obtained by the τth user group when executing the mixed strategy at time t; The replica dynamic model under discrete type is expressed as: Discretize the replica dynamic model under discrete type and express it as: Where, σ is the number of iterations, i.e., the evolution time of the electricity consumption strategy of the τth user group at time t, x τ (σ) is the proportion of users in the τth user group who choose to participate in the supply-demand interaction strategy at the σth iteration, which is in the interval [0, 1]. and Then, they are the expected benefits of the individual in the τth user group who chooses to participate in the supply-demand interaction strategy at the σth iteration and the total expected benefits of the group, respectively. σ,τ is the iteration step size set for the τth user group at the σth iteration; Determine whether the replicator dynamic model meets the expected convergence accuracy requirements.

6. A supply and demand interactive game method based on the electricity consumption behavior of a user group as claimed in claim 5, characterized in that: The determination of whether the replicator dynamic model has reached the expected convergence accuracy requirement includes determining according to the algorithm formula controlling the replicator dynamic model, including if all individuals in the τth user group have internally selected the proportion of users participating in the supply-demand interaction strategy after σ weeks of iterations. τ (σ) reaches the first threshold in [0, 1], indicating that all users in the τth user group will choose to participate in the supply-demand interaction strategy. τ If (σ) reaches the second threshold in [0, 1], it means that all users are unwilling to participate in the supply-demand interaction.

7. A supply and demand interactive game method based on the electricity consumption behavior of a user group as claimed in claim 6, characterized in that: The algorithm formula of the control replica dynamic model is expressed as: Among them, τ Represents a positive number.

8. A system using a supply-demand interactive game method based on the electricity consumption behavior of a user group as claimed in any one of claims 1 to 7, characterized in that: It includes data collection and preprocessing module, user type clustering analysis module and user electricity consumption behavior analysis model building module; The data collection and preprocessing module is responsible for collecting historical electricity consumption data of group user smart terminals, and preprocessing and normalizing the data; The user type cluster analysis module is responsible for classifying and analyzing the types of power users using a cluster analysis method; The user electricity consumption behavior analysis model building module is responsible for building a user electricity consumption behavior analysis model using the central limit theorem and the Monte Carlo method.

9. 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 a supply and demand interactive game method based on the electricity consumption behavior of a user group as described in any one of claims 1 to 7 are implemented.

10. 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 a supply and demand interactive game method based on the electricity consumption behavior of a user group as described in any one of claims 1 to 7 are implemented.

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