User demand response contract design method and system based on ALO algorithm

Abstract

The invention discloses a user demand response contract design method and system based on an ALO algorithm, and the method comprises the steps: determining a user load structure model, and constructing a positive degree index model of a user participating in a demand response according to the user load structure model; determining a user load power threshold interval, and constructing a user total power consumption expenditure model according to the user load power threshold interval and a preset out-of-limit penalty rule; designing an objective function according to the positive degree index model and the user total power consumption expenditure model, and determining constraint conditions of the objective function; and solving the objective function according to the constraint condition based on an ALO algorithm to obtain the demand response contract. The method can flexibly, efficiently and intelligently generate the user demand response contract, gives consideration to the economic benefits of the load aggregator and the user, improves the performance of the user, and can be widely applied to the field of demand response business.

Description

Technical Field

[0001] The present application relates to the field of demand response business, and in particular to a user demand response contract design method and system based on the ALO algorithm. Background Art

[0002] The ancillary service market, as a supplement to the electric energy trading market, has also been established rapidly, among which demand services are an important tradable product. However, due to the entry barriers to the ancillary service market, most load-side users can only participate in the market through market agents such as load aggregators. These users can be roughly divided into high-energy-consuming enterprise users, commercial users, and residential users according to their nature. Among them, residential users have a huge volume and have great potential for the expansion of demand response business.

[0003] However, judging from the current situation of demand response, the demand response business for residential users has grown slowly, and there is no mature operation model. This is because, on the one hand, although the residential electricity load is huge, the individual units are small in scale, scattered in distribution, and the user development cost is high; on the other hand, the current cooperation model between load aggregators and residential users has problems such as a single response method, unreasonable allocation method, opaque settlement process, and lack of incentives. Residential users have no enthusiasm to participate in demand response, and users who participate in demand response often fail to fulfill their contracts.

[0004] From the perspective of the process of cooperation between load aggregators and residential users in demand response, load aggregators generally reach a cooperation agreement with users in advance to determine the specific cooperation method and profit distribution mechanism; after the load aggregator wins the bid in the ancillary service market, when the power grid initiates a demand response signal, the load aggregator will initiate an invitation to the user, and the users participating in the invitation will reduce or transfer the load to complete the load aggregator's winning bid. Since the cooperation agreement between the load aggregator and the user is pre-placed, and the subsequent invitation interaction process is post-placed and dynamic, there are actually hidden consultation and game processes in the invitation process, and these two stages are disconnected. From the actual situation, the demand response invitation of the aggregator will either result in a serious imbalance between supply and demand, or the users participating in the invitation will have a low degree of compliance, and the timeliness is not strong, which cannot meet the dispatcher's requirements for quickly smoothing out power fluctuations. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a user demand response contract design method and system based on the ALO algorithm, which can generate demand response contracts flexibly, efficiently and intelligently.

[0006] To achieve the above purpose, one aspect of an embodiment of the present application proposes a user demand response contract design method based on the ALO algorithm, comprising the following steps:

[0007] Determine a user load structure model, and construct a user participation demand response activeness index model based on the user load structure model;

[0008] Determine a user load power threshold interval, and construct a user total electricity expenditure model according to the user load power threshold interval and a preset over-limit penalty rule;

[0009] Designing an objective function according to the positivity index model and the user's total electricity expenditure model, and determining constraints of the objective function;

[0010] The objective function is solved according to the constraint conditions based on the ALO algorithm to obtain a demand response contract.

[0011] In some embodiments, the user demand response contract design method further includes:

[0012] Determine the load power value of the user under peak and valley conditions before the user participates in the demand response, and construct a first relationship function between the load power change value before and after the user participates in the demand response and the load power value according to the user load structure model and the load power value;

[0013] The peak-valley electricity price difference is determined, and a second relationship function between the user load transfer rate and the peak-valley electricity price difference is constructed according to the peak-valley electricity price difference.

[0014] In some embodiments, determining the user load structure model and constructing a user participation demand response activeness index model according to the user load structure model specifically includes:

[0015] Determining the user load structure model;

[0016] The user comfort level is calculated, and the electricity cost before and after the user participates in the demand response is determined. Based on the user comfort level and the electricity cost, an indicator model of the degree of enthusiasm of the user in participating in the demand response is constructed.

[0017] In some embodiments, determining the user load power threshold interval specifically includes:

[0018] Calculating a maximum value of the user load transfer rate according to the first relationship function and the second relationship function;

[0019] Obtaining the minimum load power value after the user participates in the demand response according to the maximum value;

[0020] The user load power threshold interval is determined according to the load power minimum value and the load power value under peak and valley conditions before the user participates in demand response.

[0021] In some embodiments, the constructing of the user's total electricity expenditure model according to the user load power threshold interval and the preset over-limit penalty rule specifically includes:

[0022] Calculate the additional expenditure when the user breaches the contract according to the user load power threshold interval and the over-limit penalty rule, and calculate the electricity expenditure when the user fulfills the contract according to the user load power threshold interval;

[0023] Determine the time-of-use electricity price when the user fulfills the contract and the load power is within the user load power threshold range;

[0024] The user's total electricity expenditure model is constructed according to the user's load power threshold range, the additional expenditure, the electricity expenditure and the time-of-use electricity price.

[0025] In some embodiments, the objective function includes a first objective function, a second objective function, and a third objective function. The objective function is designed according to the positivity index model and the user's total electricity expenditure model, and the constraint conditions of the objective function are determined, specifically including:

[0026] Determine the load peak and valley values ​​during the period when the user participates in the demand response, and design the first objective function according to the load peak and valley values, wherein the first objective function is used to minimize the peak-valley difference at the load end;

[0027] Designing the second objective function according to the user's total electricity expenditure model, wherein the second objective function is used to minimize the user's total electricity expenditure;

[0028] Designing the third objective function according to the positivity index model, wherein the third objective function is used to maximize the positivity index;

[0029] Determine the constraints of the objective function, the constraints including load power balance constraints before and after the user participates in demand response, contract constraints after applying the user load power threshold interval and the over-limit penalty rule, load transfer rate constraints of the peak-valley electricity price difference and the user load transfer rate, and time-of-use electricity price constraints.

[0030] In some embodiments, the ALO algorithm is used to solve the objective function according to the constraint conditions to obtain a demand response contract, specifically including:

[0031] Construct ant population parameter matrix;

[0032] Obtaining a first load power change value and a first user load transfer rate based on historical data, and obtaining a second load power change value and a second user load transfer rate based on a questionnaire survey;

[0033] The user load power threshold interval, the additional expenditure and the time-of-use subsidized electricity price are taken as parameters to be determined, and the first load power change value, the first user load transfer rate, the second load power change value, the second user load transfer rate and the time-of-use unsubsidized electricity price are taken as known parameters;

[0034] Substitute the parameters to be determined and the known parameters into the ant population parameter matrix, and then solve the ant population parameter matrix based on the ALO algorithm according to the constraint conditions to obtain the demand response contract.

[0035] To achieve the above purpose, another aspect of the embodiment of the present application proposes a user demand response contract design system based on the ALO algorithm, including:

[0036] The first module is used to determine a user load structure model and construct a user participation demand response activeness index model according to the user load structure model;

[0037] The second module is used to determine the user load power threshold interval, and build a user total electricity expenditure model according to the user load power threshold interval and the preset over-limit penalty rule;

[0038] The third module is used to design an objective function according to the positive degree index model and the user's total electricity expenditure model, and determine the constraint conditions of the objective function;

[0039] The fourth module is used to solve the objective function according to the constraint conditions based on the ALO algorithm to obtain a demand response contract.

[0040] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the user demand response contract design method based on the ALO algorithm as described above is realized.

[0041] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the user demand response contract design method based on the ALO algorithm as described above.

[0042] The beneficial effects of the present invention are as follows: the user demand response contract design method and system based on the ALO algorithm of the present invention first determines the user load structure model, constructs a user participation demand response activeness index model according to the user load structure model, and then determines the user load power threshold interval, and constructs the user total electricity expenditure model according to the user load power threshold interval and the preset over-limit penalty rule, and then designs the objective function according to the activeness index model and the user total electricity expenditure model, and determines the constraint conditions of the objective function, and finally solves the objective function according to the constraint conditions based on the ALO algorithm to obtain the demand response contract. The present invention can improve the user's contract performance by constructing an activeness index model for user participation in demand response, comprehensively weighing user comfort and economic benefits, and constructing a user total electricity expenditure model based on the user load power threshold interval and over-limit penalty rules, and finally using the ALO algorithm to solve the objective function, and can flexibly, efficiently and intelligently generate user demand response contracts, and take into account the economic benefits of load aggregators and users. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flowchart of a method for designing a user demand response contract based on an ALO algorithm provided in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the structure of a user demand response contract design system based on the ALO algorithm provided in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0048] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0049] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0050] The ancillary service market, as a supplement to the electric energy trading market, has also been established rapidly, among which demand services are an important tradable product. However, due to the entry barriers to the ancillary service market, most load-side users can only participate in the market through market agents such as load aggregators. These users can be roughly divided into high-energy-consuming enterprise users, commercial users, and residential users according to their nature. Among them, residential users have a huge volume and have great potential for the expansion of demand response business.

[0051] However, judging from the current situation of demand response, the demand response business for residential users has grown slowly, and there is no mature operation model. This is because, on the one hand, although the residential electricity load is huge, the individual units are small in scale, scattered in distribution, and the user development cost is high; on the other hand, the current cooperation model between load aggregators and residential users has problems such as a single response method, unreasonable allocation method, opaque settlement process, and lack of incentives. Residential users have no enthusiasm to participate in demand response, and users who participate in demand response often fail to fulfill their contracts.

[0052] From the perspective of the process of cooperation between load aggregators and residential users in demand response, load aggregators generally reach a cooperation agreement with users in advance to determine the specific cooperation method and profit distribution mechanism; after the load aggregator wins the bid in the ancillary service market, when the power grid initiates a demand response signal, the load aggregator will initiate an invitation to the user, and the users participating in the invitation will reduce or transfer the load to complete the load aggregator's winning bid. Since the cooperation agreement between the load aggregator and the user is pre-placed, and the subsequent invitation interaction process is post-placed and dynamic, there are actually hidden consultation and game processes in the invitation process, and these two stages are disconnected. From the actual situation, the demand response invitation of the aggregator will either result in a serious imbalance between supply and demand, or the users participating in the invitation will have a low degree of compliance, and the timeliness is not strong, which cannot meet the dispatcher's requirements for quickly smoothing out power fluctuations.

[0053] To this end, an embodiment of the present invention proposes a user demand response contract design method based on the ALO algorithm. First, the user load structure model is determined, and a user participation demand response activeness index model is constructed according to the user load structure model, and then the user load power threshold interval is determined. According to the user load power threshold interval and the preset over-limit penalty rule, the user total electricity expenditure model is constructed, and then the objective function is designed according to the activeness index model and the user total electricity expenditure model, and the constraint conditions of the objective function are determined. Finally, the objective function is solved according to the constraint conditions based on the ALO algorithm to obtain the demand response contract. The present invention can improve the user's contract compliance by constructing an activeness index model for user participation in demand response, comprehensively weighing user comfort and economic benefits, and constructing a user total electricity expenditure model based on the user load power threshold interval and the over-limit penalty rule. Finally, the ALO algorithm is used to solve the objective function, and the user demand response contract can be generated flexibly, efficiently and intelligently, and the economic benefits of the load aggregator and the user are taken into account.

[0054] Reference Figure 1 , Figure 1 The present invention provides a flowchart of a method for designing a user demand response contract based on an ALO algorithm. The present invention provides a method for designing a user demand response contract based on an ALO algorithm. The method includes steps S101 to S104:

[0055] S101, determining a user load structure model, and constructing a user participation demand response activeness index model according to the user load structure model;

[0056] As an optional implementation, the step of determining the user load structure model and constructing a user participation demand response activeness index model according to the user load structure model can be specifically divided into the following steps S1011 to S1012:

[0057] S1011. Determine a user load structure model;

[0058] In some optional embodiments, as the degree of home intelligence increases, various load types that can be flexibly adjusted appear in the loads of residential users. According to the nature of the loads, the loads of residential users can be divided into the following three types:

[0059] ① Rigid load: refers to equipment that must be powered continuously, such as refrigerators, water dispensers, etc.;

[0060] ②Transferable load: equipment whose use can be postponed, such as air conditioners, washing machines, etc.;

[0061] ③Reducible load: load that can directly reduce power, such as energy-saving equipment.

[0062] Therefore, the user load structure model can be constructed as shown below:

[0063]

[0064] Among them, l t represents the total load of users in period t, represents the load that cannot be adjusted by the user during period t (i.e., rigid load), It indicates the load that can be adjusted by the user in period t (including transferable load and curtailable load). represents the proportion of adjustable load that can be transferred, Indicates the ratio of adjustable load that can be reduced.

[0065] As an optional implementation, the user demand response contract design method further includes the following steps A101 and A102:

[0066] A101. Determine the load power value of the user under peak and valley conditions before the user participates in the demand response, and construct a first relationship function between the load power change value and the load power value before and after the user participates in the demand response according to the user load structure model and the load power value;

[0067] Specifically, after obtaining the user load structure model, considering the user's power consumption value in peak and valley conditions, the relationship between the load power change value and the power consumption value before and after participating in demand response is expressed by the first relationship function of the following formula:

[0068]

[0069] in, It represents the maximum value of user load power after participating in demand response. It indicates the maximum load power of the user during the peak hours before participating in demand response. It represents the minimum value of user load power after participating in demand response. It indicates the minimum load power of the user at the off-peak hour before participating in demand response.

[0070] A102. Determine the peak-valley electricity price difference, and construct a second relationship function between the user load transfer rate and the peak-valley electricity price difference based on the peak-valley electricity price difference;

[0071] Specifically, by introducing the price factor, the price difference between peak and valley electricity consumption can be regarded as a behavioral stimulus signal to stimulate users to transfer loads. Therefore, a second relationship function between the user load transfer rate and the peak-valley electricity price difference can be constructed. The second relationship function is a piecewise function, as shown in the following formula:

[0072]

[0073] Among them, p-v It represents the ratio between the load that users are willing to transfer after obtaining the peak-valley electricity price difference and the load that can be transferred, Δ p-v It represents the peak-valley electricity price difference, which can be specifically expressed as shown in the following formula:

[0074] Δ p-v =ρ p -ρ v

[0075] Among them, ρ p and ρ v The time-of-use electricity prices represent the peak and valley electricity consumption respectively. Indicates that p-v When the value is 0, Δ p-v The value of α p-v is Δ p-v The slope of To make p-v When reaching the extreme value Δ p-v The value of .

[0076] Furthermore, the meaning of the second relationship function is: when the peak-valley electricity price difference is lower than the threshold When the peak-valley electricity price difference exceeds the threshold When the load that users are willing to transfer is linearly positively correlated with the peak-valley price difference; however, when the peak-valley price difference exceeds the threshold After that, since the load that users can transfer has reached its limit, the transfer rate no longer increases. At this time, the incentive effect of the peak-to-valley price difference on users reaches saturation.

[0077] S1012. Calculate user comfort and determine the electricity cost before and after the user participates in demand response. According to the user comfort and electricity cost, construct an indicator model of the degree of user participation in demand response.

[0078] Specifically, when users participate in demand response, they will not only consider the difference between peak and valley electricity prices, but also consider their own electricity comfort and the economic benefits that can be obtained after adjusting the load. First, it is necessary to calculate the user comfort. Considering that demand response is actually the user's reallocation of the original electricity plan across time periods, the full-time user comfort λ ​​in hours can be expressed as follows:

[0079]

[0080] Where t represents the time period during which the user participates in demand response, T represents the length of the time period during which the original electricity consumption level is postponed forward or backward after the user participates in demand response, represents the load level in time period t after the user participates in demand response, Represents the user utility function related to the load level, i.e., electricity consumption.

[0081] Furthermore, in addition to user comfort, users are also concerned about whether they can get economic compensation after participating in demand response. From the perspective of revenue and expenditure, this compensation can reduce the user's electricity cost. Therefore, the following positive degree index model can be constructed to represent the factors that users weigh comfort and economic benefits when participating in demand response:

[0082]

[0083] Among them, θ represents an indicator that can comprehensively measure the degree of users' enthusiasm for participating in demand response. and is the weight coefficient, c0 represents the electricity cost of users who do not participate in demand response, c dr Represents the electricity cost after users participate in demand response.

[0084] S102, determining a user load power threshold interval, and constructing a user total electricity expenditure model according to the user load power threshold interval and a preset over-limit penalty rule;

[0085] As an optional implementation, the step of determining the user load power threshold interval may be specifically divided into the following steps S1021 to S1023:

[0086] S1021. Calculate a maximum value of a user load transfer rate according to the first relationship function and the second relationship function;

[0087] S1022. Obtaining a minimum load power value after the user participates in demand response according to the maximum value;

[0088] S1023. Determine a user load power threshold range according to the minimum load power value and the user's load power value under peak and valley conditions before the user participates in demand response.

[0089] Specifically, the first relationship function and the second relationship function are combined, assuming that after a period of cooperation, the maximum value of the peak-to-valley load transfer rate of the user is p-v It can be obtained that the minimum load power after the user transfers the maximum load (Maximum load power ) can be expressed as follows:

[0090]

[0091] According to the above formula, the contract can adjust the upper and lower limits of the user load power threshold range, as shown in the following formula:

[0092]

[0093] Furthermore, based on the user data, the second relationship function can provide the user's reaction degree to the peak-valley electricity price difference as a basis for determining the subsidy level.

[0094] As an optional implementation, the step of constructing a user's total electricity expenditure model according to the user's load power threshold interval and the preset over-limit penalty rule can be specifically divided into the following steps S1024 to S1026:

[0095] S1024. Calculate the additional expenditure when the user breaches the contract based on the user load power threshold interval and the over-limit penalty rule, and calculate the electricity expenditure when the user fulfills the contract based on the user load power threshold interval;

[0096] S1025. Determine the time-of-use subsidy electricity price when the user fulfills the contract and the load power is within the user load power threshold range;

[0097] S1026. Construct a user's total electricity expenditure model based on the user's load power threshold range, additional expenditure, electricity expenditure, and time-of-use subsidy electricity price.

[0098] Specifically, in response to the aforementioned problem of residential users participating in demand response invitations but not fulfilling their obligations, the embodiments of the present invention add penalty measures in the contract design to increase the cost of user default. The penalty measures can be implemented by setting user load power threshold intervals and over-limit penalty rules. Specifically, the load aggregator reaches an agreement with the users who participate in the invitation and stipulates the following matters to the users:

[0099] ① During the demand response period, the user's load power should be within a pre-agreed user load power threshold range. Inside;

[0100] ②When the user's load power is within the user's load power threshold range When the user defaults, the load aggregator gives the user an electricity price subsidy based on the electricity price during the demand response period; when the user defaults, the load exceeds the user load power threshold interval When a user makes a deposit, they will need to pay an additional fee, similar to a security deposit deduction.

[0101] Through the above settings, the user's total electricity expenditure model can be constructed as shown below:

[0102]

[0103] In the above formula, C syn represents the total electricity expenditure of the user after the demand response trigger period after adding the user load power threshold interval and the over-limit penalty rule, C dr represents the electricity expenditure during the demand response period when the user fulfills the contract, C ob It represents the additional expenditure during the demand response period when the user defaults. It represents the time-of-use electricity price paid by users after the demand response subsidy, that is, the time-of-use subsidy electricity price.

[0104] Among them, the additional expenditure C ob It can be expressed as the following formula:

[0105]

[0106] Among them, ξ down Indicates that the user load does not reach the lower limit of the user load power threshold range When the additional electricity price is up Indicates that the user load exceeds the upper limit of the user load power threshold range The additional electricity price should be paid when

[0107] S103, designing an objective function according to the activeness index model and the user's total electricity expenditure model, and determining constraints of the objective function;

[0108] As an optional implementation, the objective function includes a first objective function, a second objective function and a third objective function. The objective function is designed according to the positive degree index model and the user's total electricity expenditure model, and the step of determining the constraint conditions of the objective function can be specifically divided into the following steps S1031 to S1034:

[0109] S1031, determining the peak and valley values ​​of the load during the period when the user participates in the demand response, and designing a first objective function according to the peak and valley values ​​of the load, wherein the first objective function is used to minimize the peak and valley difference at the load end;

[0110] S1032. Design a second objective function according to the user's total electricity expenditure model, where the second objective function is used to minimize the user's total electricity expenditure;

[0111] S1033. Design a third objective function according to the positivity index model, where the third objective function is used to maximize the positivity index;

[0112] Specifically, according to the aforementioned positive degree index model and user total electricity expenditure model, the objectives of the contract design include minimizing the peak-to-valley difference at the load end, minimizing the user's total electricity expenditure, and maximizing the user's comprehensive balance between comfort and economic benefits. The objective function is as follows:

[0113]

[0114] Among them, F1 represents the first objective function, F2 represents the second objective function, and F3 represents the third objective function. and They respectively represent the load peak and valley values ​​during the period when users participate in demand response.

[0115] S1034. Determine the constraints of the objective function, which include load power balance constraints before and after the user participates in demand response, contract constraints after applying user load power threshold range and over-limit penalty rules, load transfer rate constraints of peak-valley electricity price difference and user load transfer rate, and time-of-use electricity price constraints.

[0116] In some optional implementations, the constraints of the objective function include:

[0117] ① Load power balance constraint conditions before and after users participate in demand response, specifically as follows:

[0118]

[0119] in, represents the load of users participating in demand response in period t, l t Represents the load of users who do not participate in demand response during period t.

[0120] ② The contract constraint conditions after applying the user load power threshold interval and over-limit penalty rules are as follows:

[0121]

[0122] ③ The load transfer rate constraint condition of the peak-valley electricity price difference and the user load transfer rate, which can be given by the second relationship function.

[0123] ④ According to the TOU electricity price rules, the TOU electricity price after demand response in the model is subject to the following constraints:

[0124]

[0125] Among them, ρ vIt indicates the time-of-use electricity price during the low-consumption period before participating in demand response. It indicates the time-of-use electricity price during the off-peak period after participating in demand response. represents the time-of-use electricity price during normal period after demand response, ρ p It indicates the time-of-use electricity price during the peak hours before the demand response is triggered. It indicates the time-of-use electricity price during peak hours after demand response.

[0126] S104. Solve the objective function according to the constraints based on the ALO algorithm to obtain a demand response contract.

[0127] As an optional implementation, the objective function is solved according to the constraints based on the ALO algorithm to obtain the step of the demand response contract, which can be specifically divided into the following steps S1041 to S1044:

[0128] S1041, constructing an ant population parameter matrix;

[0129] It should be noted that since the previous discussions were all based on the investigation of a single user, when the scope of discussion was expanded to all cooperative user groups, the model changed from multi-objective optimization of a single object to multi-objective optimization of a group object, which is not easy to solve using traditional multi-objective planning methods. Therefore, the embodiment of the present invention uses the ant lion optimization algorithm ALO to solve the objective function.

[0130] Specifically, first initialize the ant population parameter matrix as shown below:

[0131]

[0132] Among them, x ij The element represents the initial position of the ant, m represents the size of the ant population, which can be understood as the total number of variables to be solved for the objective function, and n represents the total number of objects in the objective function, which can be understood as the total number of users in the embodiment of the present invention.

[0133] S1042, obtaining a first load power change value and a first user load transfer rate obtained based on historical data, and obtaining a second load power change value and a second user load transfer rate obtained based on a questionnaire survey;

[0134] S1043, taking the user load power threshold interval, additional expenditure and time-of-use subsidized electricity price as the parameters to be determined, and taking the first load power change value, the first user load transfer rate, the second load power change value, the second user load transfer rate and the time-of-use unsubsidized electricity price as the known parameters;

[0135] S1044. Substitute the parameters to be determined and the known parameters into the ant population parameter matrix, and then solve the ant population parameter matrix according to the constraints based on the ALO algorithm to obtain the demand response contract.

[0136] Specifically, after initializing the ant population parameter matrix, an antlion is matched for each ant, and the antlion population parameter matrix can be expressed as follows:

[0137]

[0138] Among them, y kl The element represents the initial position of the ant lion. The update rule of the ant's position change is as follows:

[0139] X(t)=[(0,sum(2γ(t1)-1),…sum(2γ(t) Γ )-1)]

[0140] Where sum(·) represents the cumulative sum, t represents the iteration step, Γ represents the maximum iteration step length, [t1, t2, …t Γ ] represents the sequence related to the step length in the random walk, γ(·) is a function determined by a random number, and its specific expression is as follows:

[0141]

[0142] Among them, rand represents a random number uniformly generated by the machine in the interval [0.1]. According to the position matrix X of the ant population, its fitness matrix Θ can be expressed as follows:

[0143]

[0144] Among them, X i represents the position sequence of the i-th ant, that is, the i-th row of the matrix X, f(X i ) represents the fitness function of the i-th ant.

[0145] Accordingly, the fitness matrix Φ of the antlion can be constructed as follows:

[0146]

[0147] Among them, Y k represents the position sequence of the k-th ant lion, that is, the k-th row of the matrix Y, g(Y k ) represents the fitness function of the kth antlion.

[0148] In order to maintain the random walk of the search space, the ant position change update rule formula is normalized to obtain the following formula:

[0149]

[0150] in, represents the normalized variable of the random walk position of the i-th ant, α i represents the minimum value in the original random walk process, d i represents the maximum value in the original random walk process, represents the minimum value of the random walk variable of the i-th ant at the t-th iteration, represents the maximum value of the random walk variable of the i-th ant at the t-th iteration, θ i It represents the drift added by the i-th ant's wandering.

[0151] Considering the influence of antlion traps, the parameters in the above formula can be updated as follows:

[0152]

[0153] in, represents the position of the kth antlion selected at the tth iteration, c t Represents the column vector recording the minimum value of the random walk variable of all ants in the tth iteration, d t Represents a column vector recording the maximum value of the random walk variables of all ants in the t-th iteration.

[0154] Afterwards, the ants move towards the trap, and the process can be described as:

[0155]

[0156] Among them, I is a parameter representing the ratio, and its relationship with the iteration step is:

[0157]

[0158] Where T represents the maximum iteration step, Is a constant set based on the current iteration number.

[0159] In order to catch the ant, the ant lion needs to update its position to the ant's position, so its position update method can be expressed as follows:

[0160]

[0161] During the iteration process, the ant lion population produces elite individuals, and the ant population is affected by them and also produces elite ants. The corresponding elite strategy can be expressed as follows:

[0162]

[0163] in, represents the position update result of the elite ant, which can be understood as the optimal solution given by the model at iteration step t. represents the random walk position around the antlion selected by roulette at iteration step t, Represents the random walk position around the elite ant lion at iteration step t.

[0164] Therefore, in the user's total electricity expenditure model constructed above, when the user's load is within the user's load power threshold range when fulfilling the contract, the load aggregator should determine the time-of-use subsidy price When the user's load exceeds the upper limit of the user load power threshold range The additional electricity price to be paid up , when the user's load does not reach the lower limit of the user load power threshold range The additional electricity price to be paid down , User load power threshold range As the parameters to be determined, the first load power change value and the first user load transfer rate obtained according to the historical data, the second load power change value and the second user load transfer rate obtained according to the questionnaire survey, and the time-of-use electricity price before subsidy by the load aggregator (i.e., the time-of-use unsubsidized electricity price) are taken as known parameters. These variables to be determined and the known parameters are substituted into the ant population parameter matrix of the ALO algorithm, and the optimal solution of the objective function, i.e., the demand response contract, is obtained by iterative solution.

[0165] Furthermore, through the above rule setting and objective function solving process, the machine can automatically formulate an invitation contract with the cooperating user and automatically execute the contract under the set trigger conditions.

[0166] The above is an explanation of the user demand response contract design method based on the ALO algorithm of the embodiment of the present invention. It can be recognized that, compared with the demand response contract design method in the prior art, the embodiment of the present invention first analyzes the load composition of the user, and establishes the relationship between the user load power change value and the incentive level through the relationship between the user load transfer rate and the peak-valley electricity price difference, and then based on the utility mechanism of user participation in demand response, establishes a positive degree index model based on user comfort and economic benefits, and on the basis of this model, considering the problem that the user's participation invitation has no default cost, resulting in low user compliance, the load power threshold interval and over-limit penalty rules are proposed, and on the basis of these settings, the objective function is constructed, and finally the ALO algorithm is used to solve the objective function, which can flexibly, efficiently and intelligently generate user demand response contracts, and take into account the economic benefits of load aggregators and users, and provide technical support for load aggregators or power grid managers to formulate smart contracts for residential users, and can further improve user compliance.

[0167] Reference Figure 2The embodiment of the present invention also provides a user demand response contract design system based on the ALO algorithm, including:

[0168] The first module is used to determine the user load structure model and build a user participation demand response activeness index model based on the user load structure model;

[0169] The second module is used to determine the user load power threshold interval, and build a user total electricity expenditure model based on the user load power threshold interval and the preset over-limit penalty rule;

[0170] The third module is used to design the objective function according to the positive degree index model and the user's total electricity expenditure model, and determine the constraint conditions of the objective function;

[0171] The fourth module is used to solve the objective function according to the constraints based on the ALO algorithm to obtain the demand response contract.

[0172] The contents of the above-mentioned user demand response contract design method embodiment based on the ALO algorithm are all applicable to the present user demand response contract design system embodiment based on the ALO algorithm. The functions specifically implemented by the present user demand response contract design system embodiment based on the ALO algorithm are the same as those in the above-mentioned user demand response contract design method embodiment based on the ALO algorithm, and the beneficial effects achieved are also the same as those achieved by the above-mentioned user demand response contract design method embodiment based on the ALO algorithm.

[0173] The embodiment of the present invention also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and when the program is executed by the processor, the user demand response contract design method based on the ALO algorithm is realized. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0174] like Figure 3 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention, referring to FIG. Figure 3 , an embodiment of the present invention provides an electronic device, including:

[0175] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0176] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the user demand response contract design method based on the ALO algorithm of the embodiment of the present invention;

[0177] Input / output interface 1003, used to implement information input and output;

[0178] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0179] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0180] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0181] An embodiment of the present invention also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned user demand response contract design method based on the ALO algorithm.

[0182] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0183] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0184] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0185] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0186] If the above 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, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0187] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0188] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0189] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0190] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A user demand response contract design method based on the ALO algorithm, characterized in that: The following steps are involved: Determine a user load structure model, and construct a user participation demand response activeness index model based on the user load structure model; Determine a user load power threshold interval, and construct a user total electricity expenditure model according to the user load power threshold interval and a preset over-limit penalty rule; Designing an objective function according to the positivity index model and the user's total electricity expenditure model, and determining constraints of the objective function; The objective function is solved according to the constraint conditions based on the ALO algorithm to obtain a demand response contract.

2. According to claim 1, a user demand response contract design method based on the ALO algorithm is characterized in that: The user demand response contract design method also includes: Determine the load power value of the user under peak and valley conditions before the user participates in the demand response, and construct a first relationship function between the load power change value before and after the user participates in the demand response and the load power value according to the user load structure model and the load power value; The peak-valley electricity price difference is determined, and a second relationship function between the user load transfer rate and the peak-valley electricity price difference is constructed according to the peak-valley electricity price difference.

3. A user demand response contract design method based on ALO algorithm according to claim 1, characterized in that: The determining of the user load structure model and constructing a user participation demand response activeness index model according to the user load structure model specifically includes: Determining the user load structure model; The user comfort level is calculated, and the electricity cost before and after the user participates in the demand response is determined. Based on the user comfort level and the electricity cost, an indicator model of the degree of enthusiasm of the user in participating in the demand response is constructed.

4. A user demand response contract design method based on ALO algorithm according to claim 2, characterized in that: The determining of the user load power threshold interval specifically includes: Calculating a maximum value of the user load transfer rate according to the first relationship function and the second relationship function; Obtaining the minimum load power value after the user participates in the demand response according to the maximum value; The user load power threshold interval is determined according to the load power minimum value and the load power value under peak and valley conditions before the user participates in demand response.

5. According to claim 1, a user demand response contract design method based on the ALO algorithm is characterized in that: The constructing of the user's total electricity expenditure model according to the user load power threshold interval and the preset over-limit penalty rule specifically includes: Calculate the additional expenditure when the user breaches the contract according to the user load power threshold interval and the over-limit penalty rule, and calculate the electricity expenditure when the user fulfills the contract according to the user load power threshold interval; Determine the time-of-use subsidy electricity price when the user fulfills the contract and the load power is within the user load power threshold range; The user's total electricity expenditure model is constructed according to the user's load power threshold range, the additional expenditure, the electricity expenditure and the time-of-use subsidy electricity price.

6. A user demand response contract design method based on ALO algorithm according to claim 1, characterized in that: The objective function includes a first objective function, a second objective function and a third objective function. The objective function is designed according to the positive degree index model and the user's total electricity expenditure model, and the constraint conditions of the objective function are determined, which specifically include: Determine the load peak and valley values ​​during the period when the user participates in the demand response, and design the first objective function according to the load peak and valley values, wherein the first objective function is used to minimize the peak-valley difference at the load end; Designing the second objective function according to the user's total electricity expenditure model, wherein the second objective function is used to minimize the user's total electricity expenditure; Designing the third objective function according to the positivity index model, wherein the third objective function is used to maximize the positivity index; Determine the constraints of the objective function, the constraints including load power balance constraints before and after the user participates in demand response, contract constraints after applying the user load power threshold interval and the over-limit penalty rule, load transfer rate constraints of the peak-valley electricity price difference and the user load transfer rate, and time-of-use electricity price constraints.

7. The user demand response contract design method based on the ALO algorithm according to claim 5 is characterized in that: The ALO algorithm is used to solve the objective function according to the constraint conditions to obtain the demand response contract, which specifically includes: Construct ant population parameter matrix; Obtaining a first load power change value and a first user load transfer rate based on historical data, and obtaining a second load power change value and a second user load transfer rate based on a questionnaire survey; The user load power threshold interval, the additional expenditure and the time-of-use subsidized electricity price are taken as parameters to be determined, and the first load power change value, the first user load transfer rate, the second load power change value, the second user load transfer rate and the time-of-use unsubsidized electricity price are taken as known parameters; Substitute the parameters to be determined and the known parameters into the ant population parameter matrix, and then solve the ant population parameter matrix based on the ALO algorithm according to the constraint conditions to obtain the demand response contract.

8. A user demand response contract design system based on the ALO algorithm, characterized in that: include: The first module is used to determine a user load structure model and construct a user participation demand response activeness index model according to the user load structure model; The second module is used to determine the user load power threshold interval, and build a user total electricity expenditure model according to the user load power threshold interval and the preset over-limit penalty rule; The third module is used to design an objective function according to the positive degree index model and the user's total electricity expenditure model, and determine the constraint conditions of the objective function; The fourth module is used to solve the objective function according to the constraint conditions based on the ALO algorithm to obtain a demand response contract.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the user demand response contract design method based on the ALO algorithm as described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the user demand response contract design method based on the ALO algorithm as described in any one of claims 1 to 7.

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