A power retail package making method and system considering user adjustment ability

Abstract

The application discloses a kind of power retail package formulation methods and systems considering user adjustment capacity, it is related to electric power market technical field.The method includes: constructing the power retail package score model considering user adjustment capacity, for giving the corresponding power retail package score of user according to the actual adjustment situation of user;According to the number of times of participating in adjustment, sustainable adjustment time and cumulative power of adjustable user in power retail package, construct the adjustable user power retail package economic model based on LSTM, give user economic reward;And construct the power retail package power model of adjustable user based on LSTM, give user power reward;Construct the power retail package model considering user adjustment capacity, according to economic reward and power reward, adjust original power retail package.The application improves the participation and satisfaction of user by formulating intelligent and personalized power retail package, improves the flexibility of power system.

Description

Technical Field

[0001] This invention relates to the field of electricity market technology, and specifically to a method and system for formulating electricity retail packages that take into account users' adjustment capabilities. Background Technology

[0002] With the growth of global energy demand and the application of renewable energy, power systems are facing increasing pressure. Traditional electricity retail packages, which are typically fixed-pricing methods, may have the following shortcomings:

[0003] 1) Existing packages do not fully consider users' specific electricity consumption behavior and adjustment capabilities, resulting in a discrepancy between the actual benefits of the packages and users' needs;

[0004] 2) The existing electricity pricing mechanism may not be flexible enough to respond in real time to changes in the electricity market and fluctuations in user demand, thereby affecting users' enthusiasm for participating in the electricity retail market;

[0005] 3) Electricity retail packages on the market may be too homogeneous, lacking customized services for different user groups and failing to effectively meet the diverse needs of users;

[0006] 4) Existing packages neglect to improve user participation and fail to effectively utilize users' own adjustment capabilities to optimize electricity consumption patterns;

[0007] 5) Traditional forecasting models may not be advanced enough to accurately predict users' electricity demand and the changing trends of the electricity market.

[0008] Therefore, the approach to developing electricity retail packages that considers users' adjustment capabilities has multiple implications: Customized electricity retail packages allow users to more intuitively experience the thoughtfulness and practicality of the design, thereby increasing their enthusiasm for participating in the electricity retail market; users' electricity consumption behavior can be regulated through electricity price signals, and package design can encourage users to use electricity during periods of lower demand, helping to balance electricity supply and demand and optimize the allocation of electricity resources; flexible adjustments on the user side can reduce the peak-valley difference in the power system, lower system operating costs, and improve the economic efficiency and reliability of the power system. Summary of the Invention

[0009] To address the problems of existing electricity retail packages, such as neglecting user adjustment capabilities, lack of flexibility in pricing mechanisms, lack of customization, failure to enhance user participation, and inaccurate prediction models, this invention proposes a method and system for formulating electricity retail packages that takes into account user adjustment capabilities. Through intelligent analysis methods and optimization mechanisms, it provides a new way to customize electricity retail packages, effectively improving the overall efficiency and flexibility of the power system, while optimizing user experience and the economic benefits of electricity retailers.

[0010] To achieve the above objectives, the present invention employs the following technical solution:

[0011] On one hand, this invention proposes a method for formulating electricity retail packages that takes into account users' adjustment capabilities, the method comprising:

[0012] A points-based model for electricity retail packages that takes into account users' adjustment capabilities is constructed, including a points-based model for the number of times electricity retail packages are adjusted, a points-based model for the sustainable adjustment time of electricity retail packages, and a points-based model for the cumulative electricity consumption of electricity retail packages. This model is used to assign corresponding points to users based on their actual adjustment behavior.

[0013] Based on the number of times adjustable users participate in adjustment, the sustainable adjustment time, and the cumulative electricity consumption within the adjustable user's electricity retail package, an economic model of the adjustable user's electricity retail package based on LSTM is constructed, and economic rewards are given to users. The economic model of the adjustable user's electricity retail package aims to minimize the sum of the adjustment number deviation, the sustainable adjustment time deviation, the cumulative electricity consumption deviation, and the economic reward cost. It is constrained by the adjustment number constraint, the sustainable adjustment time constraint, the cumulative electricity consumption constraint, and the economic reward distribution constraint. The economic reward factor given to the adjustment user is trained based on the LSTM algorithm.

[0014] Based on the number of times adjustable users participate in adjustment, the sustainable adjustment time, and the cumulative electricity consumption within the adjustable user's electricity retail package, an adjustable user electricity retail package electricity model based on LSTM is constructed, and electricity rewards are given to users. The adjustable user electricity retail package electricity model aims to minimize the sum of adjustment number deviation, sustainable adjustment time deviation, cumulative electricity consumption deviation, and electricity reward cost, and uses adjustment number constraint, sustainable adjustment time constraint, cumulative electricity consumption constraint, and power generation reward constraint as constraints. The electricity reward factor given to the adjustment user is trained based on the LSTM algorithm.

[0015] Based on the economic model and the electricity consumption model of the adjustable user electricity retail package, economic and electricity rewards are given to the user's actual adjustment capabilities. An electricity retail package model that takes into account the user's adjustment capabilities is constructed. Based on the economic and electricity rewards, the original electricity retail package is adjusted, including adjustments to the basic fee and the electricity consumption included in the package.

[0016] As a preferred embodiment of the present invention, the integral model for the number of adjustments to the electricity retail package is expressed as follows:

[0017]

[0018] In the formula, F cishuThe integral represents the number of times the electricity retail package is adjusted; N1, N2, and N3 are the adjustment thresholds; k1, k2, and k3 are the integral coefficients corresponding to the adjustment thresholds; a is the unit integral; and n is the number of user adjustments.

[0019] The sustainable adjustment time integral model for the electricity retail package is expressed as follows:

[0020]

[0021] In the formula, F shijian The sustainable adjustment time is the integral of the electricity retail package; T1, T2, and T3 are the sustainable adjustment time thresholds; α1, α2, and α3 are the integral coefficients corresponding to the sustainable adjustment time; t is the user's sustainable adjustment time.

[0022] The cumulative electricity consumption points model for the electricity retail package is expressed as follows:

[0023]

[0024] In the formula, F dianliang Accumulated electricity consumption points for electricity retail packages; Q1, Q2, and Q3 are the accumulated electricity consumption thresholds; β1, β2, and β3 are the point coefficients corresponding to the accumulated electricity consumption; q is the user's accumulated electricity consumption;

[0025] The formulas for calculating the cumulative power consumption thresholds Q1, Q2, and Q3 are as follows:

[0026]

[0027] In the formula, △P(t) represents the user's adjustment capability at time t.

[0028] As a preferred embodiment of the present invention, the electricity retail package points model further includes an additional points reward model, expressed as follows:

[0029] F jiangli =θ·a,n≥N3,t≥T3,q≥Q3;

[0030] In the formula, F jiangli θ represents the bonus points; θ is the bonus point reward coefficient.

[0031] As a preferred embodiment of the present invention, the objective function of the economic model for the adjustable user electricity retail package is expressed as: minF piancp ;

[0032] in,

[0033] F piancp =|N G -n|+|T G -t|+|Q G-q|+P buc ;

[0034] P buc =φ1·F cishu +φ2·F shijian +φ3·F dianliang +φ4·F jiangli ;

[0035] In the formula, F piancp Adjustment deviation for the economic model of adjustable user electricity retail packages; N G T represents the number of adjustments issued, where n is the number of adjustments made by the user; G Q represents the sustainable adjustment time issued, where t is the user's sustainable adjustment time; G P represents the cumulative battery charge issued, where q represents the user's cumulative battery charge; buc Economic rewards for electricity retail package users; φ1, φ2, φ3, and φ4 are user economic reward factors; F cishu Points are awarded for the number of times electricity retail packages can be adjusted, F shijian For sustainable adjustment time points in electricity retail packages, F dianliang To accumulate electricity points for electricity retail packages, F jiangli Additional reward points;

[0036] The specific constraints are as follows:

[0037] Adjustment number constraint: N min <n<N max ;

[0038] Sustainable adjustment time constraint: T min <t<T max ;

[0039] Cumulative power consumption constraint: Q min <q<Q max ;

[0040] Issuing economic incentive constraints: P buc <P XF ;

[0041] In the formula, N min N max These represent the minimum and maximum adjustment times, respectively; T min T max These are the minimum and maximum sustainable adjustment times, respectively; Q min Q max These represent the minimum and maximum cumulative battery levels, respectively; P XF Economic rewards for users of the electricity retail packages issued;

[0042] Economic incentives P for electricity retail package users bucEconomic incentives for electricity retail package users (P) XF Number of adjustments issued (N) G User adjustment count n, and the issued sustainable adjustment time T G User-adjustable time t, and cumulative power consumption Q. G The user's cumulative battery consumption q is the input, and the user's economic reward factor φ is the input. i Assuming i = 1, 2, 3, 4 are the outputs, construct an LSTM-based model for adjusting user economic reward factors. The economic reward factor for adjusting users is trained based on the LSTM algorithm, and its expression is:

[0043]

[0044] In the formula, φ i This represents the economic reward factor for the i-th user.

[0045] As a preferred embodiment of the present invention, the objective function of the adjustable user electricity retail package electricity model is expressed as: minF piancq ;

[0046] in,

[0047] F piancq =|N G -n|+|T G -t|+|Q G -q|+Q buc ;

[0048] Q buc =ψ1·F cishu +ψ2·F shijian +ψ3·F dianliang +ψ4·F jiangli ;

[0049] In the formula, F piancq The adjustment deviation of the adjustable user electricity retail package electricity consumption model; N G T represents the number of adjustments issued, where n is the number of adjustments made by the user; G Q represents the sustainable adjustment time issued, where t is the user's sustainable adjustment time; G Q represents the cumulative battery charge issued, where q represents the user's cumulative battery charge. buc Electricity retail package users receive electricity rewards; ψ1, ψ2, ψ3, and ψ4 are the user's electricity reward factors; F cishu Points are awarded for the number of times electricity retail packages can be adjusted, F shijian For sustainable adjustment time points in electricity retail packages, F dianliang To accumulate electricity points for electricity retail packages, F jiangli Additional reward points;

[0050] The specific model constraints are as follows:

[0051] Adjustment number constraint: N min <n<N max ;

[0052] Sustainable adjustment time constraint: T min <t<T max ;

[0053] Cumulative power consumption constraint: Q min <q<Q max ;

[0054] Lower power generation incentive constraint: Q buc XF ;

[0055] In the formula, N min N max These represent the minimum and maximum adjustment times, respectively; T min T max These are the minimum and maximum sustainable adjustment times, respectively; Q min Q max These represent the minimum and maximum cumulative battery charge, respectively; Q XF Electricity rewards for users of the electricity retail packages issued;

[0056] Electricity retail package users' electricity consumption reward Q buc Electricity retail package user electricity reward Q XF Number of adjustments issued (N) G User adjustment count n, and the issued sustainable adjustment time T G User-adjustable time t, and cumulative power consumption Q. G The user's cumulative battery level q is the input, and the user's battery reward factor ψ is the input. j The outputs are j = 1, 2, 3, 4. An LSTM-based model for adjusting user electricity reward factors is constructed. The power reward factor for adjusting users is trained based on the LSTM algorithm, and its expression is:

[0057]

[0058] In the formula, ψ j This represents the battery reward factor for the j-th user.

[0059] As a preferred embodiment of the present invention, the electricity retail package model includes a basic fee adjustment model and a package-included electricity volume adjustment model;

[0060] The basic cost adjustment model is expressed as follows:

[0061] P chenb ​=P taoc +P buc ;

[0062] The package includes a power adjustment model, which is represented as follows:

[0063] Q chenb =Q taoc +Q buc ;

[0064] In the formula, P chenb The adjusted base fee for the electricity retail package; P taoc For the basic fee of the original electricity retail package, P buc Economic incentives for electricity retail package users based on their adjustment capabilities; Q chenb The adjusted electricity retail package includes the amount of electricity; Q taoc For the electricity included in the original retail electricity package, Q buc Electricity rewards are given to users of electricity retail packages based on their ability to regulate power consumption.

[0065] On the other hand, the present invention proposes a system for formulating electricity retail packages that takes into account users' adjustment capabilities. Based on the electricity retail package formulating method that takes into account users' adjustment capabilities as described above, the system includes:

[0066] The integral model construction module is used to construct an integral model for electricity retail packages that takes into account the user's adjustment capabilities, including an integral model for the number of adjustments of electricity retail packages, an integral model for the sustainable adjustment time of electricity retail packages, and an integral model for the cumulative electricity consumption of electricity retail packages.

[0067] The economic model building module is used to construct an LSTM-based economic model for adjustable user electricity retail packages based on the number of times adjustable users participate in adjustment, the sustainable adjustment time, and the cumulative electricity consumption, and to provide economic rewards to users.

[0068] The power model construction module is used to build an adjustable user power retail package power model based on LSTM based on the number of times the adjustable user participates in adjustment, the sustainable adjustment time and the cumulative power consumption within the adjustable user's power retail package, and to give the user power consumption rewards.

[0069] The package model construction module is used to provide economic and electricity rewards to users based on the adjustable user electricity retail package economic model and the adjustable user electricity retail package electricity volume model, and to construct an electricity retail package model that takes into account the user's adjustment capabilities.

[0070] The package adjustment module is used to adjust the original electricity retail package according to the electricity retail package model, including the adjustment of the basic fee and the adjustment of the electricity included in the package.

[0071] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for formulating an electricity retail package that takes into account the user's adjustability, as described above.

[0072] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a multi-dimensional integral model (number of adjustments, sustainable adjustment time, and cumulative electricity consumption), combined with an economic and electricity consumption model based on the LSTM algorithm, a comprehensive analysis and optimization of user adjustment capabilities is achieved, realizing accurate prediction of user adjustment behavior and reward allocation. Through the integral model and LSTM algorithm, user adjustment capabilities are accurately assessed and predicted; economic and electricity consumption reward mechanisms are optimized to ensure a reasonable balance between reward costs and adjustment effects; the flexibility of the power system is improved, supporting wider access to renewable energy and power dispatch; and the cost and included electricity consumption of electricity retail packages are dynamically adjusted to optimize power resource allocation. Attached Figure Description

[0073] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] in:

[0075] Figure 1 This is a flowchart of the method of the present invention;

[0076] Figure 2 This is a schematic diagram of the modular structure of the system of the present invention. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0078] like Figure 1 As shown, this is an embodiment of the present invention, which provides a method for formulating electricity retail packages that take into account users' adaptability, including the following steps:

[0079] S1: Construct an electricity retail package points model that takes into account users' adjustment capabilities, so as to give users corresponding electricity retail package points based on their actual adjustment situation, thereby increasing the enthusiasm of adjustable users to participate in electricity retail packages;

[0080] The electricity retail package points model includes the electricity retail package adjustment frequency points model, the electricity retail package sustainable adjustment time points model, and the electricity retail package cumulative electricity consumption points model.

[0081] In one embodiment, the integral model for the number of adjustments to the electricity retail package is expressed as follows:

[0082]

[0083] In the formula, F cishu The integral represents the number of times the electricity retail package is adjusted; N1, N2, and N3 are the adjustment thresholds; k1, k2, and k3 are the integral coefficients corresponding to the adjustment thresholds; a is the unit integral; and n is the number of user adjustments.

[0084] The sustainable adjustment time integral model for electricity retail packages is expressed as follows:

[0085]

[0086] In the formula, F shijian The sustainable adjustment time is the integral of the electricity retail package; T1, T2, and T3 are the sustainable adjustment time thresholds; α1, α2, and α3 are the integral coefficients corresponding to the sustainable adjustment time; t is the user's sustainable adjustment time.

[0087] The cumulative electricity consumption points model for electricity retail packages is represented as follows:

[0088]

[0089] In the formula, F dianliang Accumulated electricity consumption points for electricity retail packages; Q1, Q2, and Q3 are the accumulated electricity consumption thresholds; β1, β2, and β3 are the point coefficients corresponding to the accumulated electricity consumption; q is the user's accumulated electricity consumption;

[0090] Among them, the cumulative power thresholds Q1, Q2, and Q3 are mainly calculated based on the adjustment time. Different cumulative adjustment times will generate different power levels. The calculation formula is as follows:

[0091]

[0092] In the formula, △P(t) represents the user's adjustment capability at time t.

[0093] Furthermore, the electricity retail package points model also includes an additional points reward model, which provides extra rewards to users who adjust their electricity usage, have a longer duration of adjustment, and accumulate more electricity than N3, T3, and Q3, respectively. This is expressed as:

[0094] F jiangli =θ·a,n≥N3,t≥T3,q≥Q3;

[0095] In the formula, F jiangli θ represents the bonus points; θ is the bonus point reward coefficient.

[0096] S2: Based on the number of times adjustable users participate in adjustment within the electricity retail package, the sustainable adjustment time, and the cumulative electricity consumption, construct an economic model for adjustable user electricity retail packages based on LSTM (Long Short-Term Memory) network, and provide users with certain economic rewards to incentivize them to better participate in electricity retail package activities;

[0097] The economic model for adjustable user electricity retail packages aims to minimize the sum of adjustment frequency deviation, sustainable adjustment time deviation, cumulative electricity deviation, and economic reward cost. It uses constraints such as adjustment frequency constraint, sustainable adjustment time constraint, cumulative electricity constraint, and economic reward distribution constraint as conditions. Based on the LSTM algorithm, it trains the economic reward factor for adjustment users to ensure that users are given appropriate economic rewards.

[0098] In a specific embodiment, the objective function of the economic model for adjustable user electricity retail packages is expressed as: minF piancp ;

[0099] in,

[0100] F piancp =|N G -n|+|T G -t|+|Q G -q|+P buc ;

[0101] P buc =φ1·F cishu +φ2·F shijian +φ3·F dianliang +φ4·F jiangli ;

[0102] In the formula, F piancp Adjustment deviation for the economic model of adjustable user electricity retail packages; N G T represents the number of adjustments issued. G For the timeframe for sustainable adjustment; Q G This refers to the cumulative electricity consumption recorded; P bucEconomic rewards are provided to users of electricity retail packages; φ1, φ2, φ3, and φ4 are the user's economic reward factors; economic rewards are given based on the user's actual number of adjustments, actual sustainable adjustment time, actual cumulative electricity consumption, and performance.

[0103] The specific constraints are as follows:

[0104] Adjustment number constraint: N min <n<N max ;

[0105] Sustainable adjustment time constraint: T min <t<T max ;

[0106] Cumulative power consumption constraint: Q min <q<Q max ;

[0107] Issuing economic incentive constraints: P buc <P XF ;

[0108] In the formula, N min N max These represent the minimum and maximum adjustment times, respectively; T min T max These are the minimum and maximum sustainable adjustment times, respectively; Q min Q max These represent the minimum and maximum cumulative battery levels, respectively; P XF Economic rewards for users of the electricity retail packages issued; the economic rewards given shall not exceed the issued economic reward value;

[0109] Economic incentives P for electricity retail package users buc Economic incentives for electricity retail package users (P) XF Number of adjustments issued (N) G User adjustment count n, and the issued sustainable adjustment time T G User-adjustable time t, and cumulative power consumption Q. G The user's cumulative battery consumption q is the input, and the user's economic reward factor φ is the input. i Assuming i = 1, 2, 3, 4 are the outputs, construct an LSTM-based model for adjusting user economic reward factors. The economic reward factor for adjusting users is trained based on the LSTM algorithm, and its expression is:

[0110]

[0111] In the formula, φ i This represents the economic reward factor for the i-th user.

[0112] S3: Based on the number of times adjustable users participate in adjustment within the electricity retail package, the sustainable adjustment time, and the cumulative electricity consumption, construct an adjustable user electricity retail package electricity model based on LSTM, and give users electricity rewards;

[0113] The adjustable user electricity retail package electricity model aims to minimize the sum of adjustment frequency deviation, sustainable adjustment time deviation, cumulative electricity deviation, and electricity reward cost. It uses adjustment frequency constraints, sustainable adjustment time constraints, cumulative electricity constraints, and power generation reward constraints as constraints. Based on the LSTM algorithm, it trains the electricity reward factor for the adjustable user to ensure that the user is given an appropriate electricity reward.

[0114] Specifically, the objective function of the adjustable user electricity retail package electricity consumption model is expressed as: minF piancq ;

[0115] in,

[0116] F piancq =|N G -n|+|T G -t|+|Q G -q|+Q buc ;

[0117] Q buc =ψ1·F cishu +ψ2·F shijian +ψ3·F dianliang +ψ4·F jiangli ;

[0118] In the formula, F piancq The adjustment deviation of the adjustable user electricity retail package electricity consumption model; Q buc Electricity retail package users receive electricity rewards; ψ1, ψ2, ψ3, and ψ4 are user electricity reward factors; rewards are given based on the user's actual number of adjustments, actual sustainable adjustment time, actual cumulative electricity consumption, and performance.

[0119] The specific model constraints are as follows:

[0120] Adjustment number constraint: N min <n<N max ;

[0121] Sustainable adjustment time constraint: T min <t<T max ;

[0122] Cumulative power consumption constraint: Q min <q<Q max ;

[0123] Lower power generation incentive constraint: Q buc <QXF ;

[0124] In the formula, Q XF This refers to the electricity reward given to users of the electricity retail package; the electricity reward given cannot exceed the issued electricity reward value.

[0125] Electricity retail package users' electricity consumption reward Q buc Electricity retail package user electricity reward Q XF Number of adjustments issued (N) G User adjustment count n, and the issued sustainable adjustment time T G User-adjustable time t, and cumulative power consumption Q. G The user's cumulative battery level q is the input, and the user's battery reward factor ψ is the input. j The outputs are j = 1, 2, 3, 4. An LSTM-based model for adjusting user electricity reward factors is constructed. The power reward factor for adjusting users is trained based on the LSTM algorithm, and its expression is:

[0126]

[0127] In the formula, ψ j This represents the battery reward factor for the j-th user.

[0128] S4: Based on the economic model of the adjustable user electricity retail package and the electricity volume model of the adjustable user electricity retail package, give certain economic and electricity rewards to the user's actual adjustment capabilities, and construct an electricity retail package model that takes into account the user's adjustment capabilities. Based on the economic and electricity rewards, adjust the original electricity retail package, including the adjustment of the basic fee and the adjustment of the electricity volume included in the package.

[0129] In a preferred embodiment, the electricity retail package model includes a basic fee adjustment model and a package-included electricity volume adjustment model;

[0130] The basic cost adjustment model is expressed as follows:

[0131] P chenb =P taoc +P buc ;

[0132] The package includes a power adjustment model, which is represented as follows:

[0133] Q chenb =Q taoc +Q buc ;

[0134] In the formula, P chenb The adjusted base fee for the electricity retail package; P taoc For the basic fee of the original electricity retail package, Pbuc Economic incentives for electricity retail package users based on their adjustment capabilities; Q chenb The adjusted electricity retail package includes the amount of electricity; Q taoc For the electricity included in the original retail electricity package, Q buc Electricity rewards are given to users of electricity retail packages based on their ability to regulate power consumption.

[0135] After the above steps, reasonable electricity retail packages can be formulated for adjustable users. Based on the user's actual adjustment capacity, certain economic and electricity rewards can be given to enhance the user's enthusiasm for participating in electricity retail package activities.

[0136] like Figure 2 As shown, another embodiment of the present invention provides a system for formulating electricity retail packages that takes into account users' adaptability. This system is based on the electricity retail package formulating method that considers users' adaptability as described above, and includes:

[0137] The integral model construction module is used to construct an integral model for electricity retail packages that takes into account the user's adjustment capabilities, including an integral model for the number of adjustments of electricity retail packages, an integral model for the sustainable adjustment time of electricity retail packages, and an integral model for the cumulative electricity consumption of electricity retail packages.

[0138] The economic model building module is used to construct an LSTM-based economic model for adjustable user electricity retail packages based on the number of times adjustable users participate in adjustment, the sustainable adjustment time, and the cumulative electricity consumption, and to provide economic rewards to users.

[0139] The power model construction module is used to build an adjustable user power retail package power model based on LSTM based on the number of times the adjustable user participates in adjustment, the sustainable adjustment time and the cumulative power consumption within the adjustable user's power retail package, and to give the user power consumption rewards.

[0140] The package model construction module is used to provide economic and electricity rewards to users based on the economic model and the electricity volume model of adjustable user electricity retail packages, and to construct an electricity retail package model that takes into account the user's adjustment capabilities.

[0141] The package adjustment module is used to dynamically adjust the electricity retail package according to the electricity retail package model, including adjustments to the basic fee and the electricity included in the package.

[0142] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any other combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0143] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0144] Therefore, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for formulating an electricity retail package that takes into account the user's adjustability as described above.

[0145] In summary, this invention, by introducing a multi-dimensional electricity retail package integration model (integration model of adjustment frequency, integration model of sustainable adjustment time, and integration model of cumulative electricity consumption), can comprehensively and accurately assess a user's adjustment capability within an electricity retail package. The integration models of adjustment frequency, sustainable adjustment time, and cumulative electricity consumption can refine the adjustment behavior of each user, thereby providing electricity retailers with quantitative user adjustment data and accurately assessing user adjustment capabilities. Through a comprehensive evaluation of user adjustment behavior, electricity retailers can understand the adjustment capability of each user in advance, optimize load dispatch, avoid over-reliance on the adjustment behavior of a particular type of user, and thus improve the flexibility and stability of the entire power system.

[0146] LSTM (Laser-Based Memory) models can capture long-term patterns in user regulation behavior through time series forecasting, thereby optimizing the distribution of economic rewards. By training LSTM models, electricity retailers can accurately predict users' future regulation behavior based on historical data and distribute economic rewards accordingly, avoiding excessive or insufficient reward expenditures and improving the accuracy of rewards. By using regulation frequency deviation, sustainable regulation time deviation, and cumulative electricity volume deviation as constraints, the economic incentive mechanism can be optimized to ensure its sustainability and rationality. Electricity retailers can flexibly adjust economic rewards based on different users' regulation patterns, thereby maximizing regulation effectiveness and balancing reward costs and regulation benefits.

[0147] By using an LSTM-based power model, future power demand can be predicted based on user regulation behavior, and corresponding power rewards can be distributed according to their regulation capabilities and actions. This ensures the rationality of rewards, avoids excessive rewards, and encourages active user participation in power regulation. Accurate power prediction and regulation can effectively reduce power supply fluctuations, especially during peak load periods. By encouraging user participation in load regulation, power waste is reduced, power supply is stabilized, and the overall stability and efficiency of the power system are improved.

[0148] By analyzing the results of economic and electricity-based incentives, electricity retailers can dynamically adjust users' electricity packages to better align with their actual needs and regulatory behaviors. For example, adjusting base fees and included electricity allowances ensures users receive appropriate compensation when participating in regulatory adjustments, thus increasing their motivation to participate. Package adjustments can effectively help electricity retailers balance their revenue and costs, improving their market competitiveness and profitability through reasonable pricing and flexible electricity adjustment strategies, while simultaneously protecting users' interests.

[0149] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A power retail package making method considering user adjustment ability, characterized by, The method comprises: A power retail package adjustment frequency integral model, a power retail package sustainable adjustment time integral model, and a power retail package cumulative power integral model are constructed to give the user corresponding power retail package integrals according to the actual adjustment of the user; An LSTM-based power retail package economic model of the adjustable user is constructed according to the participation adjustment frequency, the sustainable adjustment time, and the cumulative power of the adjustable user in the power retail package, is used to train an economic reward factor of the adjustable user based on an LSTM algorithm, and is combined with the integral value of the power retail package integral model to calculate the economic reward of the user; wherein the LSTM-based power retail package economic model of the adjustable user takes the sum of the adjustment frequency deviation, the sustainable adjustment time deviation, the cumulative power deviation, and the economic reward cost as the target, and takes the adjustment frequency constraint, the sustainable adjustment time constraint, the cumulative power constraint, and the economic reward constraint as the constraint condition; An LSTM-based power retail package power model of the adjustable user is constructed according to the participation adjustment frequency, the sustainable adjustment time, and the cumulative power of the adjustable user in the power retail package, is used to train a power reward factor of the adjustable user based on an LSTM algorithm, and is combined with the integral value of the power retail package integral model to calculate the power reward of the user; wherein the LSTM-based power retail package power model of the adjustable user takes the sum of the adjustment frequency deviation, the sustainable adjustment time deviation, the cumulative power deviation, and the power reward cost as the target, and takes the adjustment frequency constraint, the sustainable adjustment time constraint, the cumulative power constraint, and the power reward constraint as the constraint condition; According to the LSTM-based power retail package economic model of the adjustable user and the LSTM-based power retail package power model of the adjustable user, the actual adjustment ability of the user is given economic reward and power reward, and a power retail package model considering the adjustment ability of the user is constructed, and the original power retail package is adjusted according to the economic reward and the power reward, including the adjustment of the basic fee and the adjustment of the package included power.

2. The method of claim 1, wherein the method further comprises: The power retail package adjustment frequency integral model is represented as: In the formula, F cishu is the power retail package adjustment times integral; N1, N2, N3 are respectively adjustment times threshold values; k1, k2, k3 are respectively integral coefficients corresponding to the adjustment times threshold values; a is a unit integral; n is the user adjustment times; The power retail package sustainable adjustment time integral model is represented as: In the formula, F shijian is the sustainable adjustment time integral of the electricity retail package; T1, T2, and T3 are respectively sustainable adjustment time thresholds; α1, α2, and α3 are respectively integral coefficients corresponding to the sustainable adjustment time; and t is the sustainable adjustment time of the user. The power retail package cumulative power integral model is represented as: In the formula, F dianliang is the cumulative electricity integral of the electricity retail package; Q1, Q2, and Q3 are cumulative electricity threshold values; β1, β2, and β3 are integral coefficients corresponding to the cumulative electricity; and q is the cumulative electricity of the user. The calculation formulae of the cumulative power thresholds Q1, Q2, and Q3 are as follows: In the formula, △P(t) is the adjustment ability of the user at time t.

3. The method of claim 2, wherein the method further comprises: The power retail package integral model further comprises an additional integral reward model, represented as: F jiangli = θ · a,n≥ N3,t≥ T3,q≥ Q3; In the formula, F jiangli is the bonus points; and θ is the bonus points coefficient.

4. The method of claim 1, wherein the method further comprises: The objective function of the adjustable user power retail package economic model is expressed as: min F piancp ; In the formula, F piancp =|N G -n|+|T G -t|+|Q G -q|+P buc ; P buc = φ1 · F cishu + φ2 · F shijian + φ3 · F dianliang + φ4 · F jiangli ; In the formula, F piancp is the adjustment deviation of the adjustable user electricity retail package economic model; N G is the issued adjustment number, and n is the user adjustment number; T G is the issued sustainable adjustment time, and t is the user sustainable adjustment time; Q G is the issued cumulative electricity quantity, and q is the user cumulative electricity quantity; P buc is the user economic reward of the electricity retail package; φ1, φ2, φ3, and φ4 are user economic reward factors, respectively; F cishu is the electricity retail package adjustment number integral; F shijian is the electricity retail package sustainable adjustment time integral; F dianliang is the electricity retail package cumulative electricity quantity integral; and F jiangli is the additional reward integral. The constraint condition is specifically as follows: Adjustment number of times constraint: N min <n < N max ; Sustainable regulation of time constraints: T min <t < T max ; Cumulative energy constraint: Q min <q < Q max ; Issuing economic reward constraint: P buc <P XF ; In the formula, N min , N max are minimum and maximum adjustment times, respectively; T min , T max are minimum and maximum sustainable adjustment times, respectively; Q min , Q max are minimum and maximum cumulative electric quantities, respectively; and P XF is an economic reward for a power retail package user. P buc , the economic reward P of the power retail package user XF , the number of issued adjustments N G , the user adjustment number n, the issued sustainable adjustment time T G , the user sustainable adjustment time t, the issued cumulative power Q G and the user cumulative power q are inputs, the user economic reward factor φ i , i = 1, 2, 3, 4 are outputs, and the adjustment user economic reward factor model based on LSTM is constructed The economic reward factor given to the adjustment user is trained based on the LSTM algorithm, and the expression is: In the formula, φ i represents the i-th user economic reward factor.

5. The method of claim 1, wherein the method further comprises: The objective function of the adjustable user power retail package electricity model is expressed as: min F piancq ; In the formula, F piancq =|N G -n|+|T G -t|+|Q G -q|+Q buc ; Q buc = ψ1 · F cishu + ψ2 · F shijian + ψ3 · F dianliang + ψ4 · F jiangli ; In the formula, F piancq is the adjustment deviation of the adjustable user electricity retail package power model; N G is the issued adjustment number, and n is the user adjustment number; T G is the issued sustainable adjustment time, and t is the user sustainable adjustment time; Q G is the issued cumulative power, and q is the user cumulative power; Q buc is the electricity retail package user power reward; ψ1, ψ2, ψ3, and ψ4 are user power reward factors; F cishu is the electricity retail package adjustment number integral; F shijian is the electricity retail package sustainable adjustment time integral; F dianliang is the electricity retail package cumulative power integral; and F jiangli is the additional reward integral. The model constraint condition is specifically as follows: Adjustment number of times constraint: N min <n < N max ; Sustainable regulation of time constraints: T min t < T max ; Cumulative energy constraint: Q min <q < Q max ; Electricity generation reward constraint: Q buc Q XF ; In the formula, N min , N max are minimum and maximum adjustment times, respectively; T min , T max are minimum and maximum sustainable adjustment times, respectively; Q min , Q max are minimum and maximum cumulative electric quantities, respectively; and Q XF is an electric quantity reward of a power retail package user. The power retail package user electricity reward Q buc The power retail package user electricity reward Q XF The number of adjustments N G The user adjustment number n, the sustainable adjustment time T G The user sustainable adjustment time t, the cumulative electricity Q G And the user cumulative electricity q are inputs, and the user electricity reward factor ψ j , j = 1, 2, 3, 4 are outputs, and a regulation user electricity reward factor model based on LSTM is constructed The electricity reward factor given to the regulation user is trained based on the LSTM algorithm, and the expression is: In the formula, ψ j represents the jth user power reward factor.

6. The method of claim 1, wherein the method further comprises: The power retail package model comprises a basic fee adjustment model and a package included power adjustment model. The basic fee adjustment model is represented as: P chenb = P taoc + P buc ; The package included power adjustment model is represented as: Q chenb = Q taoc + Q buc ; In the formula, P chenb is the adjusted basic cost of the electricity retail package; P taoc is the original basic cost of the electricity retail package, P buc is the economic reward given to the user of the electricity retail package according to the user's adjustment capacity; Q chenb is the adjusted electricity quantity included in the electricity retail package; Q taoc is the original electricity quantity included in the electricity retail package, Q buc is the electricity quantity reward given to the user of the electricity retail package according to the user's adjustment capacity.

7. A power retail package making system considering user adjustment ability based on the power retail package making method considering user adjustment ability according to any one of claims 1 to 6, characterized by, The system comprises: An integral model construction module is configured to construct a power retail package integral model considering the adjustment ability of the user, comprising a power retail package adjustment frequency integral model, a power retail package sustainable adjustment time integral model, and a power retail package cumulative power integral model. An economic model construction module is configured to construct an LSTM-based economic model of the power retail package for the adjustable user according to the number of participation adjustments, the sustainable adjustment time and the cumulative power consumption of the adjustable user in the power retail package, and give an economic reward to the user; A power consumption model construction module is configured to construct an LSTM-based power consumption model of the power retail package for the adjustable user according to the number of participation adjustments, the sustainable adjustment time and the cumulative power consumption of the adjustable user in the power retail package, and give a power consumption reward to the user; A package model construction module is configured to give an economic reward and a power consumption reward to the actual adjustment capacity of the user according to the economic model of the power retail package for the adjustable user and the power consumption model of the power retail package for the adjustable user, and construct a power retail package model considering the adjustment capacity of the user; A package adjustment module is configured to adjust the original power retail package according to the power retail package model, including adjustment of the basic fee and adjustment of the included power consumption of the package.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the method for formulating a power retail package considering the adjustment capacity of the user according to any one of claims 1 to 6.

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