Regional thermal resource allocation and selling dynamic modeling method and system

Abstract

The invention discloses a regional thermal resource allocation and selling dynamic modeling method and system, and relates to the technical field of thermal resource management. The method comprises the following steps: acquiring original heat consumption data and reducing the original heat consumption data to calculate resource pricing cost; based on a total revenue maximization target, user heat utilization resources are limited, and incentive compatibility is achieved through display principle constraint and individual rationality constraint; implementing a resource configuration optimization mechanism for the thermal resource price; according to the method, an original thermal resource allocation optimization problem with constraints is established, the maximization of the total income of the effectiveness of a user and the benefits of a thermal resource manufacturer is taken as an optimization target, whether the user succeeds in purchasing or not is judged according to a solving result of the original thermal resource allocation optimization problem, and then a selling plan and time arrangement are confirmed. Configuration and selling of regional thermal resources can be optimized, benefit maximization of buying, selling, supplying and demanding parties is ensured, effective configuration of the thermal resources is promoted, and maximization of total revenue is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of thermal resource sales, and in particular relates to a method and system for dynamic modeling of regional thermal resource configuration and sales. Background Art

[0002] Thermal resource allocation and sales refer to the process of heat production, distribution and sales to users in the heating system. With the commercialization, monetization and marketization of heating, the allocation and sales of thermal resources must also be more flexible and efficient.

[0003] Most of the existing research on the classified heat price system is aimed at user utility or the minimum cost of heat system operation. There are very few studies that take the maximization of total revenue as the optimization goal, which fails to take into account economic benefits, environmental benefits and social equity. In addition, the existing heat resource allocation and sales methods have low decision-making efficiency in practical applications and mainly rely on manual research, which can no longer meet the needs of new market-oriented reforms. In general, the current regional heat resource allocation and sales issues are still in the initial exploration stage. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the prior art, improve the efficiency of thermal resource allocation and sales, maximize the total revenue of the thermal resource allocation and sales process, and provide a regional thermal resource allocation and sales dynamic modeling method and system.

[0005] In a first aspect, the present invention provides a method for dynamic modeling of regional thermal resource allocation and sales, which comprises the following steps:

[0006] S1. Obtain the original heat usage data of the area to be configured for thermal resources and reduce it, and calculate the resource pricing cost based on the reduced heat usage data;

[0007] S2. Limit the user's heat resources based on the goal of maximizing total revenue, and achieve incentive compatibility through display principle constraints and individual rationality constraints;

[0008] S3. Design resource allocation optimization mechanism and implement resource allocation optimization mechanism for thermal resource prices;

[0009] S4. An original thermal resource configuration optimization problem with constraints is established according to the resource configuration optimization mechanism, the display principle constraints, and the individual rationality constraints. The optimization goal is to maximize the total benefit that combines the user's utility and the thermal resource manufacturer's benefits. After solving the original thermal resource configuration optimization problem, the success or failure of the user's purchase is determined by the solution result. If the user's purchase is successful, a sales plan and time arrangement are designed based on the user's purchase data to complete the regional thermal resource configuration and sales. If the user's purchase is unsuccessful, feedback on the unsuccessful purchase is given to the user.

[0010] In order to achieve the above-mentioned invention object, the present invention specifically adopts the following technical solutions:

[0011] As a preferred embodiment of the first aspect, in step S1, the original heat usage data is reduced to the following form:

[0012]

[0013] Among them, Purchase i represents the purchase record set of user i; t i Indicates the submission time of user i's purchase record; deadline ij The latest delivery time of the thermal resource required by user i for the jth time; duration ij represents the total duration of user i's j-th demand for thermal resources; count ij represents the demand for thermal resources per unit time for user i's jth demand; price ij represents the total price of thermal resources purchased by user i for the jth time; [J] represents the set of purchase records of a single user.

[0014] As a preferred embodiment of the first aspect, in step S1, the function form of the resource pricing cost TRP is:

[0015]

[0016] Where TRP is the resource pricing cost, which is used to guarantee the basic income of thermal resource producers; [I] represents the set of all thermal users; b ij Indicates whether the jth purchase of user i is successful. ij =1, it means that user i’s jth purchase is successful. ij =0, it means that the j-th purchase of user i is unsuccessful.

[0017] As a preferred embodiment of the first aspect, in step S2, the display principle constraint is specifically to use the VCG bidding mechanism, so that the optimal strategy of the user is to truly express the user's own value assessment of the thermal resources;

[0018] The functional form of the VCG bidding mechanism is as follows:

[0019] VCG i =max∑ j≠i socialwelfare(l)-max∑ j≠i socialwelfare * ).

[0020] As a preferred embodiment of the first aspect, in step S2, the individual rationality constraint is that the user's payment will not exceed the user's own personal valuation, and the functional form of the individual rationality constraint is specifically:

[0021] VCG i ≤price ij

[0022] Among them, VCG i represents the cost that user i needs to pay to obtain thermal resources under the optimal allocation; max∑ j≠ i socialwelfare(l) is the maximum value that all other participants can obtain when user i does not participate; max∑ j≠ i socialwelfare * ) is the maximum value that all other participants can obtain when user i participates; l represents the distribution plan when user i participates; l * Represents the allocation plan when user i does not participate.

[0023] As a preferred embodiment of the first aspect, in step S3, the function form of the resource allocation optimization mechanism is specifically:

[0024]

[0025] Among them, Inspirit t Represents the resource allocation optimization mechanism at time t; avaerage t represents the average thermal resource allocation rate up to time t; utilization i Indicates the proportion of the resources purchased by user i to the total available selling time; total T It indicates the total duration of thermal resources available for configuration and sale; maxProduction indicates the maximum output per unit time of the thermal resource selling factory.

[0026] As a preferred embodiment of the first aspect, in step S4, the function form of the original thermal resource configuration optimization problem is as follows:

[0027] Max: Utility sys =Utility user +Utility seller

[0028]

[0029] payment i =VCG i +Inspirit t *TRP

[0030] Among them, Utility user Indicates the user's utility; Utility seller Represents the benefits of thermal resource manufacturers; Utility sys It represents the total revenue created by the regional thermal resource allocation and sales system corresponding to the original thermal resource allocation optimization problem; payment i represents the final price paid by user i.

[0031] As a preferred embodiment of the first aspect, in step S4, the conditional constraints of the original thermal resource allocation optimization problem are effective purchase constraints, continuous time constraints, allocation resource constraints, and total production constraints;

[0032] The functional form of the effective purchase constraint is as follows:

[0033]

[0034] The functional form of the continuous time constraint is as follows:

[0035]

[0036] starttime ijd ≤allacated it

[0037]

[0038] The functional form of the resource allocation constraint is as follows:

[0039]

[0040] The functional form of the total production constraint is as follows:

[0041]

[0042] Where [T] represents the time slot set of thermal resources available for configuration and sale; starttime ijd is a binary variable representing whether to select time d as the starting service time of user i’s jth purchase, starttime ijd =1 means that time d is selected as the start time of heating for the jth purchase of user i, starttime ijd =0 means that time d is not selected as the starting time of heating for the jth purchase of user i; t i Submission time of the purchase record of user i; allacated itis a binary variable indicating whether to arrange a production and sales plan for user i at time t, allacated it =1 means that thermal resources have been provided to users at time t, allacated it =0 means that no thermal resources are provided to the user at time t.

[0043] As a preferred embodiment of the first aspect, in step S4, the specific process of solving the original thermal resource configuration optimization problem is as follows:

[0044] S41. The original thermal resource allocation optimization problem is transformed into a Lagrangian dual problem in the following form:

[0045]

[0046] The constraints corresponding to the Lagrangian dual problem are as follows:

[0047]

[0048] loss t ≤TRP

[0049] Among them, δ i Represents the decision variable for heat resource allocation and sale; edge t represents the shadow price of thermal resources at time t; arrangement represents the possible thermal resource allocation and sales plan; loss t It represents the profit loss caused by the dynamic adjustment of thermal resources;

[0050] 9. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 8, characterized in that in step S4, the specific process of solving the original thermal resource allocation optimization problem is as follows:

[0051] S41. The original thermal resource allocation optimization problem is transformed into a Lagrangian dual problem in the following form:

[0052]

[0053] The constraints corresponding to the Lagrangian dual problem are as follows:

[0054]

[0055] loss t ≤TRP

[0056] Among them, δ i Represents the decision variable for heat resource allocation and sale; edge trepresents the shadow price of thermal resources at time t; arrangement represents the possible thermal resource allocation and sales plan; loss t It represents the profit loss caused by the dynamic adjustment of thermal resources;

[0057] S42. Based on the optimization goal of maximizing total revenue and satisfying the Carlo Demand-Kuhn-Tucker condition, the Lagrangian dual problem is solved by the selling decision method, and the output result of the selling decision method is used as the solution result of the original thermal resource allocation optimization problem. like The user purchases successfully if δ i ≤0, the user's purchase was unsuccessful;

[0058] The specific process of solving the problem by the sales decision method is as follows:

[0059] S421: First, for the jth purchase of user i, the sales decision function plans a possible starting service time t start , the starting service time satisfies the submission time t of the purchase record of user i i The latest delivery time deadline of the thermal resource required by user i for the jth time ij Between, that is, t start ∈[t i ,deadline ij ];

[0060] S422, then select a continuous time period l its And satisfy l its =[t start ,t start +duration ij -1], determine whether the remaining thermal resources in the continuous time period meet the following quantity constraints:

[0061] maxProduction-selled t ≥ count ij

[0062]

[0063] If the quantity constraint is not met, the user's purchase is unsuccessful;

[0064] If the quantity constraint is met, the shadow price edge of the thermal resource at time t is calculated. t :

[0065]

[0066] In the formula, sold trepresents the quantity of thermal resources sold at time t; ∈ and ε are both intermediate variables;

[0067] S423. Finally, the maximum decision variable corresponding to all purchases of user i is calculated as follows: And it is used as the solution result of the original thermal resource allocation optimization problem:

[0068]

[0069] Wherein, θ and μ are intermediate variables.

[0070] In a second aspect, the present invention provides a regional thermal resource allocation and sales dynamic modeling system, which includes:

[0071] An initialization module is used to obtain the original heat consumption data of the area to be configured with thermal resources and reduce it;

[0072] Resource pricing module, used to calculate resource pricing costs based on reduced heat usage data;

[0073] The resource allocation optimization module is used to restrict the user's use of thermal resources based on the goal of maximizing total revenue, achieve incentive compatibility through display principle constraints and individual rationality constraints, and design a resource allocation optimization mechanism to implement the resource allocation optimization mechanism for thermal resource prices;

[0074] The sales decision and online transaction module is used to establish the original thermal resource configuration optimization problem with constraints according to the resource allocation optimization mechanism, the display principle constraints, and the individual rationality constraints, and to maximize the total benefit that combines the user's utility and the thermal resource manufacturer's benefits. After solving the original thermal resource configuration optimization problem, the success of the user's purchase is judged by the solution result. If the user's purchase is successful, the sales plan and time arrangement are designed based on the user's purchase data to complete the regional thermal resource configuration and sales; if the user's purchase is unsuccessful, the unsuccessful purchase is fed back to the user.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] 1. The regional thermal resource allocation and sales dynamic modeling method provided by the present invention adds the total profit maximization goal to the thermal resource allocation and sales process for the first time, while considering both the user's utility and the resource producer's profit, designs a distribution mechanism with the maximum total profit, achieves incentive compatibility, and improves the people's livelihood in the thermal resource allocation and sales process (such as reducing the manpower and material costs of thermal resource allocation).

[0077] 2. The present invention determines the resource pricing cost based on the real original heat usage data, provides theoretical support for the selling pricing of thermal resources, and is both authentic and efficient.

[0078] 4. The present invention establishes the original thermal resource configuration optimization problem with constraints based on the resource allocation optimization mechanism and display principle constraints and individual rationality constraints, and adopts a new generation of efficient large-scale mathematical programming optimizer to solve it. Based on real-time user thermal resource demand information, the regional thermal resource configuration and sales plan that maximizes the total profit is planned, providing a scientific, real and win-win decision-making and scheduling method for the configuration and sale of thermal resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 is a flow chart of the steps of the method of the present invention;

[0080] Figure 2 It is a structural schematic diagram of the regional thermal resource configuration and sales dynamic modeling system of the present invention;

[0081] Figure 3 It is a schematic diagram of the architecture of the regional thermal resource configuration and sales dynamic modeling platform in an embodiment of the present invention;

[0082] Figure 4 A diagram showing the excitation compatibility properties of the present invention;

[0083] Figure 5 This is a schematic diagram of changes in the total revenue that can be obtained when the number of users and the number of purchases by users change in an embodiment of the present invention;

[0084] Figure 6 This is a schematic diagram of the change of the running time when the number of users and the number of purchases by users change in an embodiment of the present invention;

[0085] Figure 7 A schematic diagram of the total revenue results that can be obtained by the method of the present invention and other methods;

[0086] Figure 8 Schematic diagram of the running time results of the method of the present invention and other methods. DETAILED DESCRIPTION

[0087] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0088] like Figure 1As shown, in a preferred implementation of the present invention, the above-mentioned method for dynamic modeling of regional thermal resource configuration and sales includes the following steps S1 to S4. The specific implementation process is described in detail below.

[0089] S1. Obtain the original heat usage data of the area to be configured for thermal resources and reduce it, and calculate the resource pricing cost based on the reduced heat usage data.

[0090] It should be noted that in step S1, the original heat usage data is reduced to the following form:

[0091] Purchase i ={t i ,{deadline ij ,duration ij ,count ij ,price ij} j∈[J]}

[0092] Among them, Purchase i represents the purchase record set of user i; t i Indicates the submission time of user i's purchase record; deadline ij The latest delivery time of the thermal resource required by user i for the jth time; duration ij represents the total duration of user i's j-th demand for thermal resources; count ij represents the demand for thermal resources per unit time for user i's jth demand; price ij represents the total price of thermal resources purchased by user i for the jth time; [J] represents the set of purchase records of a single user.

[0093] It should be noted that in step S1, the function form of the resource pricing cost TRP is:

[0094]

[0095] Where TRP is the resource pricing cost, which is used to guarantee the basic income of thermal resource producers; [I] represents the set of all thermal users; b ij Indicates whether the jth purchase of user i is successful. ij =1, it means that user i’s jth purchase is successful; when b ij =0, it means that the j-th purchase of user i is unsuccessful.

[0096] S2. Based on the goal of maximizing total revenue, users’ thermal resources are restricted, and incentive compatibility is achieved through display principle constraints and individual rationality constraints to meet the basic economic properties and prerequisites for maximizing total revenue.

[0097] It should be noted that in step S2, incentive compatibility refers to the consistency between the behavior of individuals in the pursuit of maximizing their own interests and the effective allocation of social resources. By designing an appropriate bidding mechanism, users can report their own valuations truthfully, thereby achieving efficient allocation of resources. Maximizing total revenue depends on complete and symmetric information to ensure the effective allocation of resources. The incentive compatibility mechanism helps to achieve this target condition.

[0098] It should be noted that in step S2, the display principle constraint is specifically to use the VCG bidding mechanism, so that the user's optimal strategy is to truly express the user's own value assessment of thermal resources. The basic principle of the VCG bidding mechanism ensures that for any individual participant, participating in the VCG bidding mechanism is always non-negative, and encourages each participant to bid according to their own true valuation.

[0099] In this embodiment, the functional form of the VCG bidding mechanism is specifically:

[0100]

[0101] Among them, VCG i represents the cost that user i needs to pay to obtain thermal resources under the optimal allocation; max∑ j≠ i socialwelfare(l) is the maximum value that all other participants can obtain when user i does not participate; max∑ j≠ i socialwelfare * ) is the maximum value that all other participants can obtain when user i participates; l represents the distribution plan when user i participates; l * Represents the allocation plan when user i does not participate.

[0102] In this embodiment, under the VCG bidding mechanism, the total revenue created by low bids, high bids, and true bids is as follows: Figure 4 As shown. Among them, y(z) represents the distribution result, v represents the real value, b represents the quotation, z represents the valuation distribution, the blue area represents the expected value, the orange area represents the expected deduction, and the green area represents the total income, and finally the incentive compatibility is achieved.

[0103] It should be noted that in step S2, the individual rationality constraint is that the user's payment will not exceed the user's own personal valuation, that is, the interests of each user will not be harmed by participating in the sales activity.

[0104] In this embodiment, the functional form of the individual rationality constraint is specifically:

[0105] VCGi ≤price ij

[0106] S3. Design a resource allocation optimization mechanism and implement it on thermal resource prices to improve resource allocation rate and make full use of thermal resources.

[0107] It should be noted that in step S3, the function form of the resource allocation optimization mechanism is specifically:

[0108]

[0109]

[0110] Among them, Inspirit t Represents the resource allocation optimization mechanism at time t; avaerage t represents the average thermal resource allocation rate up to time t; utilization i Indicates the proportion of the resources purchased by user i to the total available selling time; total T It indicates the total duration of thermal resources available for configuration and sale; maxProduction indicates the maximum output per unit time of the thermal resource selling factory.

[0111] S4. An original thermal resource configuration optimization problem with constraints is established according to the resource configuration optimization mechanism, the display principle constraints, and the individual rationality constraints. The optimization goal is to maximize the total benefit that combines the user's utility and the thermal resource manufacturer's benefits. After solving the original thermal resource configuration optimization problem, the success or failure of the user's purchase is determined by the solution result. If the user's purchase is successful, a sales plan and time arrangement are designed based on the user's purchase data to complete the regional thermal resource configuration and sales. If the user's purchase is unsuccessful, feedback on the unsuccessful purchase is given to the user.

[0112] It should be noted that in step S4, the function form of the original thermal resource configuration optimization problem is as follows:

[0113] Max: Utility sys =Utility user +Utility seller

[0114]

[0115] payment i =VCG i +Inspirit t *TRP

[0116] Among them, Utility userIndicates the user's utility; Utility seller Represents the benefits of thermal resource manufacturers; Utility sys It represents the total revenue created by the regional thermal resource allocation and sales system corresponding to the original thermal resource allocation optimization problem; payment i represents the final price paid by user i.

[0117] It should be noted that in step S4, the constraints of the original thermal resource allocation optimization problem are as follows:

[0118] a) Effective purchase constraints

[0119] The effective purchase constraint means that each user's multiple purchases at the same time can only be successful once at most, and its function form is as follows:

[0120]

[0121] b) Continuous time constraints

[0122] The continuous time constraint means that the usage time of the thermal resources allocated to each user is continuous, and its function form is as follows:

[0123]

[0124] starttime ijd ≤allacated it

[0125]

[0126] Where [T] represents the time slot set of thermal resources available for configuration and sale; starttime ijd is a binary variable representing whether to select time d as the starting service time of user i’s jth purchase, starttime ijd =1 means that time d is selected as the start time of heating for the jth purchase of user i, starttime ijd =0 means that time d is not selected as the starting time of heating for the jth purchase of user i; t i Submission time of the purchase record of user i; allacated it is a binary variable indicating whether to arrange a production and sales plan for user i at time t, allacated it =1 means that thermal resources have been provided to users at time t, allacated it =0 means that no thermal resources are provided to the user at time t.

[0127] c) Allocation resource constraints

[0128] The allocation resource constraint refers to the thermal resources allocated to each user to meet the customer's needs. Its function form is as follows:

[0129]

[0130] d) Total production constraints

[0131] The total production constraint means that the total amount of thermal resources sold by the sales plan at the same time does not exceed the maximum production. Its function form is as follows:

[0132]

[0133] It should be noted that in step S4, the specific process of solving the original thermal resource configuration optimization problem is as follows:

[0134] S41. First, the original thermal resource allocation optimization problem is transformed into a Lagrangian dual problem in the following form:

[0135]

[0136] Among them, δ i Represents the decision variable for heat resource allocation and sale; edge t Represents the shadow price of thermal resources at time t.

[0137] In this embodiment, when δ i When >0, it means that all constraints are currently met and the total benefit is maximized, and the user's purchase is accepted; otherwise, the user's purchase is rejected.

[0138] The constraints corresponding to the above Lagrangian dual problem are as follows:

[0139]

[0140] loss t ≤TRP

[0141] Among them, δ i Represents the decision variable for heat resource allocation and sale; edge t represents the shadow price of thermal resources at time t; arrangement represents the possible thermal resource allocation and sales plan; loss t Represents the profit loss caused by the dynamic adjustment of thermal resources.

[0142] In this embodiment, the actual meaning of the original thermal resource configuration optimization problem is: to obtain the highest total value under the premise of limited thermal resources. Correspondingly, the actual meaning of the Lagrangian dual problem can be interpreted as: to obtain the optimal time arrangement under the premise of limited thermal resources so that the cost of both parties is minimized.

[0143] S42. Based on the optimization goal of maximizing total revenue and satisfying the Carlo Demand-Kuhn-Tucker condition, the Lagrangian dual problem is solved by the selling decision method, and the output result of the selling decision method is used as the solution result of the original thermal resource allocation optimization problem. like The user purchases successfully if δ i ≤0, the user's purchase was unsuccessful;

[0144] The specific process of solving the problem by the sales decision method is as follows:

[0145] S421: First, for the jth purchase of user i, the sales decision function plans a possible starting service time t start , the starting service time satisfies the submission time t of the purchase record of user i i The latest delivery time deadline of the thermal resource required by user i for the jth time ij Between, that is, t start ∈[t i ,deadline ij ];

[0146] S422, then select a continuous time period l its And satisfy l its =[t start ,t start +duration ij -1], determine whether the remaining thermal resources in the continuous time period meet the following quantity constraints:

[0147] maxProduction-selled t ≥ count ij

[0148]

[0149] If the quantity constraint is not met, the user's purchase is unsuccessful;

[0150] If the quantity constraint is met, the shadow price edge of the thermal resource at time t is calculated. t :

[0151]

[0152] In the formula, sold t represents the quantity of thermal resources sold at time t; ∈ and ε are both intermediate variables;

[0153] S423. Finally, the maximum decision variable corresponding to all purchases of user i is calculated as follows: And it is used as the solution result of the original thermal resource allocation optimization problem:

[0154]

[0155] Wherein, θ and μ are intermediate variables.

[0156] In this embodiment, the shadow price edge of the thermal resource at time t is determined based on the input data of the selling decision method. t and profit loss t For user i’s jth purchase, select a duration that satisfies the user’s needs ij Continuous time period, solve the decision variable δ i If the user's purchase is accepted, a regional thermal resource allocation and sales plan that maximizes total revenue is generated, and payment and production information is determined. Otherwise, service communication is carried out between the user and the resource producer to inform them that the purchase is rejected.

[0157] The present invention will now use a specific example to demonstrate the application effect of a regional thermal resource configuration and sales dynamic modeling method described in S1 to S4 in the above embodiments on a specific data set, so as to facilitate understanding of the essence of the present invention.

[0158] Example

[0159] The specific implementation process of a regional thermal resource allocation and sales dynamic modeling method used in this embodiment is as described above and will not be repeated here.

[0160] This embodiment simulates the method of the present invention on a computer. The simulation results show that as the number of users and the number of purchases by users increase, the total revenue that can be obtained by applying the method of the present invention also increases. Figure 5 At the same time, as the number of users and the number of user purchases increase, the time spent on processing user purchases by the method of the present invention increases, but it always remains below 1 second, as shown in FIG. Figure 6 shown.

[0161] In addition, this embodiment also selects a greedy algorithm and a first-come, first-served algorithm to compare with the method of the present invention. The comparison results are as follows: Figure 7 and Figure 8 As shown, it can be seen that the method proposed by the present invention can create a greater total profit compared with the greedy algorithm and the first-come-first-served algorithm, and the running time is controlled within 1 second.

[0162] In summary, the present invention provides a configuration and selling method for thermal resource configuration that is oriented toward maximizing total revenue and has both operational efficiency.

[0163] The above greedy algorithm and first-come-first-served algorithm are both prior art. The greedy algorithm is derived from the prior art literature: Dong Z, Liu N, Rojas-Cessa R. Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers [J]. Journal of Cloud Computing, 2015, 4: 1-14; the first-come-first-served algorithm is derived from the prior art literature: Feldman M, Gravin N, Lucier B. Combinatorial auctions via posted prices [C] / / Proceedings of the 26 annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 2014: 123-135.

[0164] It should also be noted that the method for dynamic modeling of regional thermal resource configuration and sales in the above embodiment can essentially be executed by a computer program or module. Therefore, similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides a regional thermal resource configuration and sales dynamic modeling system corresponding to the method for dynamic modeling of regional thermal resource configuration and sales provided in the above embodiment, such as Figure 2 As shown, it includes:

[0165] An initialization module is used to obtain the original heat consumption data of the area to be configured with thermal resources and reduce it;

[0166] Resource pricing module, used to calculate resource pricing costs based on reduced heat usage data;

[0167] The resource allocation optimization module is used to restrict the user's use of thermal resources based on the goal of maximizing total revenue, achieve incentive compatibility through display principle constraints and individual rationality constraints, and design a resource allocation optimization mechanism to implement the resource allocation optimization mechanism for thermal resource prices;

[0168] The sales decision and online transaction module is used to establish the original thermal resource configuration optimization problem with constraints according to the resource allocation optimization mechanism, the display principle constraints, and the individual rationality constraints, and to maximize the total benefit that combines the user's utility and the thermal resource manufacturer's benefits. After solving the original thermal resource configuration optimization problem, the success of the user's purchase is judged by the solution result. If the user's purchase is successful, the sales plan and time arrangement are designed based on the user's purchase data to complete the regional thermal resource configuration and sales; if the user's purchase is unsuccessful, the unsuccessful purchase is fed back to the user.

[0169] Based on the above regional thermal resource configuration and sales dynamic modeling system, in the embodiment of the present invention, a regional thermal resource configuration and sales dynamic modeling platform is also designed, and its architecture diagram is as follows: Figure 3 The platform consists of a buyer agent module representing the thermal resource purchaser, a regional thermal resource configuration and sales dynamic modeling system, and a seller agent module representing the thermal resource producer. Both the buyer and seller interact with the regional thermal resource configuration and sales dynamic modeling system through their corresponding agent modules, and each agent module is responsible for returning purchase and sales information to both parties.

[0170] It should also be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the various embodiments provided in this application, the division of steps or modules in the system and method is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or steps can be combined or integrated together, and a module or step can also be split.

[0171] The above-described embodiment is only a preferred solution of the present invention, but it is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for dynamic modeling of regional thermal resource allocation and sales, characterized in that: The following steps are involved: S1. Obtain the original heat usage data of the area to be configured for thermal resources and reduce it, and calculate the resource pricing cost based on the reduced heat usage data; S2. Limit the user's heat resources based on the goal of maximizing total revenue, and achieve incentive compatibility through display principle constraints and individual rationality constraints; S3. Design resource allocation optimization mechanism and implement resource allocation optimization mechanism for thermal resource prices; S4. An original thermal resource configuration optimization problem with constraints is established according to the resource configuration optimization mechanism, the display principle constraints, and the individual rationality constraints. The optimization goal is to maximize the total benefit that combines the user's utility and the thermal resource manufacturer's benefits. After solving the original thermal resource configuration optimization problem, the success or failure of the user's purchase is determined by the solution result. If the user's purchase is successful, a sales plan and time arrangement are designed based on the user's purchase data to complete the regional thermal resource configuration and sales. If the user's purchase is unsuccessful, feedback on the unsuccessful purchase is given to the user.

2. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 1, characterized in that: In step S1, the original heat data is reduced to the following form: Purchase i ={t i ,{deadline ij ,duration ij ,count ij ,price ij } j∈[J] } Among them, Purchase i represents the purchase record set of user i; t i Indicates the submission time of user i's purchase record; deadline ij The latest delivery time of the thermal resource required by user i for the jth time; duration ij represents the total duration of user i's j-th demand for thermal resources; count ij represents the demand for thermal resources per unit time for user i's jth demand; price ij represents the total price of thermal resources purchased by user i for the jth time; [J] represents the set of purchase records of a single user.

3. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 2, characterized in that: In step S1, the function form of resource pricing cost TRP is: Where TRP is the resource pricing cost, which is used to guarantee the basic income of thermal resource producers; [I] represents the set of all thermal users; b ij Indicates whether the jth purchase of user i is successful. ij =1, it means that user i’s jth purchase is successful. ij =0, it means that the j-th purchase of user i is unsuccessful.

4. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 3, characterized in that: In step S2, the display principle constraint is specifically to use the VCG bidding mechanism so that the user's optimal strategy is to truly express the user's own value assessment of thermal resources; The functional form of the VCG bidding mechanism is as follows: VCG i =max∑ j≠i socialwelfare(l)-max∑ j≠i social welfare (l * )。 5. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 4, characterized in that: In step S2, the individual rationality constraint is that the user's payment will not exceed the user's own personal valuation. The functional form of the individual rationality constraint is: VCG i ≤price ij Among them, VCG i represents the cost that user i needs to pay to obtain thermal resources under the optimal allocation; max∑ j≠ i socialwelfare(l) is the maximum value that all other participants can obtain when user i does not participate; max∑ j≠ i socialwelfare * ) is the maximum value that all other participants can obtain when user i participates; l represents the distribution plan when user i participates; l * Represents the allocation plan when user i does not participate.

6. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 5, characterized in that: In step S3, the function form of the resource allocation optimization mechanism is specifically: Among them, Inspirit t Represents the resource allocation optimization mechanism at time t; avaerage t represents the average thermal resource allocation rate up to time t; utilization i Indicates the proportion of the resources purchased by user i to the total available selling time; total T It indicates the total duration of thermal resources available for configuration and sale; maxProduction indicates the maximum output per unit time of the thermal resource selling factory.

7. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 6, characterized in that: In step S4, the function form of the original thermal resource configuration optimization problem is as follows: Max:Utility sys =Utility user +Utility seller payment i =VCG i +Inspirit t *TRP Among them, Utility user Indicates the user's utility; Utility seller Represents the benefits of thermal resource manufacturers; Utility sys It represents the total revenue created by the regional thermal resource allocation and sales system corresponding to the original thermal resource allocation optimization problem; payment i represents the final price paid by user i.

8. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 7, characterized in that: In step S4, the conditional constraints of the original thermal resource allocation optimization problem are effective purchase constraints, continuous time constraints, allocation resource constraints, and total production constraints; The functional form of the effective purchase constraint is as follows: The functional form of the continuous time constraint is as follows: start time ijd ≤allocated it The functional form of the resource allocation constraint is as follows: The functional form of the total production constraint is as follows: Where [T] represents the time slot set of thermal resources available for configuration and sale; starttime ijd is a binary variable representing whether to select time d as the starting service time of user i’s jth purchase, starttime ijd =1 means that time d is selected as the start time of heating for the jth purchase of user i, starttime ijd =0 means that time d is not selected as the starting time of heating for the jth purchase of user i; t i Submission time of the purchase record of user i; allacated it is a binary variable indicating whether to arrange a production and sales plan for user i at time t, allacated it =1 means that thermal resources have been provided to users at time t, allacated it =0 means that no thermal resources are provided to the user at time t.

9. A method for dynamic modeling of regional thermal resource allocation and sales as claimed in claim 8, characterized in that: In step S4, the specific process of solving the original thermal resource configuration optimization problem is as follows: S41. The original thermal resource allocation optimization problem is transformed into a Lagrangian dual problem in the following form: The constraints corresponding to the Lagrangian dual problem are as follows: loss t ≤TRP Among them, δ i Represents the decision variable for heat resource allocation and sale; edge t represents the shadow price of thermal resources at time t; arrangement represents the possible thermal resource allocation and sales plan; loss t It represents the profit loss caused by the dynamic adjustment of thermal resources; S42. Based on the optimization goal of maximizing total revenue and satisfying the Carlo Demand-Kuhn-Tucker condition, the Lagrangian dual problem is solved by the selling decision method, and the output result of the selling decision method is used as the solution result of the original thermal resource allocation optimization problem. like The user purchases successfully if δ i ≤0, the user's purchase was unsuccessful; The specific process of solving the problem by the sales decision method is as follows: S421: First, for the jth purchase of user i, the sales decision function plans a possible starting service time t start , the starting service time satisfies the submission time t of the purchase record of user i i The latest delivery time deadline of the thermal resource required by user i for the jth time ij between; S422, then select a continuous time period l its And satisfy l its =[t start ,t start +duration ij -1], determine whether the remaining thermal resources in the continuous time period meet the following quantity constraints: maxProduction-selled t ≥count ij If the quantity constraint is not met, the user's purchase is unsuccessful; If the quantity constraint is met, the shadow price edge of the thermal resource at time t is calculated. t : In the formula, sold t represents the quantity of thermal resources sold at time t; ∈ and ε are both intermediate variables; S423. Finally, the maximum decision variable corresponding to all purchases of user i is calculated as follows: And it is used as the solution result of the original thermal resource allocation optimization problem: Wherein, θ and μ are intermediate variables.

10. A regional thermal resource allocation and sales dynamic modeling system, characterized in that: include: An initialization module is used to obtain the original heat consumption data of the area to be configured with thermal resources and reduce it; Resource pricing module, used to calculate resource pricing costs based on reduced heat usage data; The resource allocation optimization module is used to restrict the user's thermal resources based on the goal of maximizing total revenue, achieve incentive compatibility through display principle constraints and individual rationality constraints, and design a resource allocation optimization mechanism to implement the resource allocation optimization mechanism for thermal resource prices; The sales decision and online transaction module is used to establish the original thermal resource configuration optimization problem with constraints according to the resource allocation optimization mechanism, the display principle constraints, and the individual rationality constraints, and to maximize the total benefit that combines the user's utility and the thermal resource manufacturer's benefits. After solving the original thermal resource configuration optimization problem, the success of the user's purchase is judged by the solution result. If the user's purchase is successful, the sales plan and time arrangement are designed based on the user's purchase data to complete the regional thermal resource configuration and sales; if the user's purchase is unsuccessful, the unsuccessful purchase is fed back to the user.

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