Power distribution competitive pricing modeling method and system based on blockchain technology

Abstract

The application belongs to the technical field of electricity, and discloses a power distribution competitive pricing modeling method and system based on a blockchain technology, the method comprising: dividing competitive pricing subjects into power supply side subjects and user side subjects based on parameter characteristics of transaction subjects; determining that the competitive pricing subjects are power supply side subjects, then establishing a first target function with the maximization of total profits of each operator as a target, and optimizing the first target function based on a first constraint condition to obtain an optimization model based on the power supply side subjects. The method of the application solves the problem in the prior art that distributed power generation cannot develop a competitive pricing method according to multiple operating subjects, thereby making the competitive pricing yield of single operators relatively low.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electrical technology, in particular to a power distribution bidding modeling method and system based on blockchain technology. BACKGROUND

[0002] Traditional power supply is mainly based on large grid system, which is powered by thermal power plants or other clean energy power plants, and the grid is responsible for dispatching power. However, with the development of power technology and the improvement of people's demand for power quality, the shortcomings of traditional large grid gradually appear. First, once the traditional large grid fails, the influence is wide and easy to cause huge economic loss; in addition, the traditional power supply method has insufficient dispatching flexibility, and high economic cost is needed to cope with sudden increase and decrease of load demand. Distributed power generation is produced, which is to provide power supply nearby by small generator sets, among which small photovoltaic generator sets are widely used. Distributed power generation has small capacity and more flexible dispatching, and is widely used for peak shaving, and has become an important supplement to traditional large power plant power supply.

[0003] However, the single power supply of distributed power generation is small, the operation subject is too much, and the individual market competitiveness is insufficient. Aggregating a large number of distributed power generation operators into a whole to participate in bidding ignores the income of individual operators, resulting in low bidding income rate of single operator. SUMMARY

[0004] The embodiment of the present application provides a power distribution bidding modeling method and system based on blockchain technology, to at least partially solve the problem that distributed power generation cannot develop bidding methods according to multiple operators, so that the bidding income rate of single operator is low. In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This part is not a general review, nor does it determine the key / important elements or describe the protection scope of these embodiments. The only purpose is to present some concepts in a simple form as a preface to the detailed description below.

[0005] According to a first aspect of the embodiment of the present application, a power distribution bidding modeling method based on blockchain technology is provided, and the method comprises:

[0006] Dividing the bidding subject into power supply side subject and user side subject based on the parameter characteristics of the transaction subject;

[0007] If the bidding subject is the power supply side subject, a first objective function is established with the maximum total profit of each operator as the target, and the first objective function is optimized based on the first constraint condition to obtain an optimization model based on the power supply side subject.

[0008] Further, the method further comprises:

[0009] If the bidding subject is determined to be a user-side subject, a second target function is established with the maximum difference between the utility function and the electricity purchase cost as the target, and the second target function is optimized based on a second constraint condition to obtain an optimization model based on the user-side subject.

[0010] Further, the first target function is:

[0011]

[0012] wherein ρ(t) represents the electricity selling price in the t time period;

[0013] P i represents the electricity selling amount of the operator i;

[0014] N represents the total amount of operators;

[0015] P i (t) represents the electricity selling amount of the operator i in the t time period;

[0016] C i (t) represents the total generation operation cost in the t time period;

[0017] α represents a reduced decision cost proportion coefficient, 0 < α < 1;

[0018] λ i represents the additional cost of the power source i in the traditional case.

[0019] Further, the total generation operation cost C i (t) in the t time period is calculated by the following formula:

[0020]

[0021] wherein, represents the operation cost of the operator i in the t time period under the distribution network;

[0022] represents the operation cost of the traditional power source i in the t time period.

[0023] Further, the operation cost of the operator i in the t time period under the distribution network is calculated by the following formula:

[0024]

[0025] wherein, represents the power generation amount of the operator i in the t time period;

[0026] a and b represent cost coefficients for different power generators.

[0027] Further, the operation cost of the traditional power supply i in the tth time period is calculated by using the following formula

[0028]

[0029] wherein, represents the power generation amount of the traditional power supply i in the t time period;

[0030] c, d and e represent the coefficients of the traditional power generation cost.

[0031] Further, the first constraint condition specifically comprises:

[0032]

[0033]

[0034] ρ min (t)≤ρ(t)≤ρ max (t)

[0035] P ij (t)≤P l-max

[0036] wherein, P l-max represents the maximum transmission capacity of the line;

[0037] represents the power generation amount of the operator i in the t time period;

[0038] represents the upper limit of the power generation amount of the operator i in the t time period;

[0039] represents the power generation amount of the traditional power supply i in the t time period;

[0040] represents the upper limit of the power generation amount of the traditional power supply i in the t time period;

[0041] ρ min (t) represents the minimum limit electricity price set in the t time period;

[0042] ρ(t) represents the electricity price in the t time period;

[0043] ρ max (t) represents the maximum limit electricity price set in the t time period;

[0044] P ij (t) represents the power amount delivered between the operator i and the user j.

[0045] Further, the second objective function is:

[0046]

[0047] Among them, P j This represents the electricity consumption of user j;

[0048] N t Indicates the total number of time periods;

[0049] w is the utility ratio coefficient;

[0050] P j (t) represents the electricity consumption of user j during the time period t;

[0051] C j (t) represents the user's electricity purchase cost.

[0052] Furthermore, the second constraint includes:

[0053]

[0054] ρ min (t)≤ρ(t)≤ρ max (t)

[0055] Among them, P j This represents the electricity consumption of user j;

[0056] P j (t) represents the electricity consumption of user j during the time period t;

[0057] This represents the maximum amount of electricity that user j can generate during the time period t;

[0058] This represents the lower limit of the amount of electricity that user j can obtain during the time period t;

[0059] ρ min (t) represents the minimum electricity price set for time period t;

[0060] ρ(t) represents the electricity price during time period t;

[0061] ρ max (t) represents the maximum electricity price set for the time period t.

[0062] Furthermore, the second constraint also includes a constraint on user satisfaction with electricity consumption. This satisfaction constraint is further derived from the following formula:

[0063] R j (t)=βln[wP j (t)+1]

[0064] Among them, R j (t) represents the utility of user j during time period t;

[0065] β and w are utility ratio coefficients;

[0066] P j (t) represents the electricity consumption of user j during the time period t.

[0067] According to a second aspect of the present invention, a blockchain-based matchmaking bidding modeling system is provided for implementing the method described above.

[0068] In some embodiments, the system includes:

[0069] The bidding entity segmentation is used to divide the bidding entities into power-side entities and user-side entities based on the parameter characteristics of the trading entities;

[0070] The first modeling and optimization unit is used to determine that the bidding entity is the power supply entity, and then establish a first objective function with the goal of maximizing the total profit of each operator, and optimize the first objective function based on the first constraint condition to obtain an optimization model based on the power supply entity.

[0071] Furthermore, the system also includes:

[0072] The second modeling and optimization unit is used to determine that the bidding subject is a user-side subject, and then establish a second objective function with the objective of maximizing the difference between the utility function and the electricity purchase cost, and optimize the second objective function based on the second constraint condition to obtain an optimization model based on the user-side subject.

[0073] Furthermore, the first objective function is:

[0074]

[0075] Where ρ(t) represents the electricity price during time period t;

[0076] P i This represents the electricity sales volume of operator i;

[0077] N represents the total number of operators;

[0078] P i (t) represents the electricity sales volume of operator i during time period t;

[0079] C i (t) represents the total power generation operating cost within the time period t;

[0080] α represents the reduction in decision-making costs proportionally, 0 < α < 1;

[0081] λ i This represents the additional cost of power supply i under conventional circumstances;

[0082] The second objective function is:

[0083]

[0084] Among them, P j This represents the electricity consumption of user j;

[0085] N t This indicates the total number of time periods;

[0086] w is the utility ratio coefficient;

[0087] P j (t) represents the electricity consumption of user j during the time period t;

[0088] C j (t) represents the user's electricity purchase cost.

[0089] According to a third aspect of the present invention, a computer device is provided.

[0090] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0091] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0092] In some embodiments, the computer storage medium includes one or more program instructions for performing the method described above.

[0093] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0094] The distribution bidding modeling method and system based on blockchain technology provided by this invention divides the bidding entities into power-side entities and user-side entities based on the parameter characteristics of the trading entities. When the bidding entity is determined to be a power-side entity, a first objective function is established with the goal of maximizing the total profit of each operator. The first objective function is then optimized based on a first constraint condition to obtain an optimized model based on the power-side entity. Thus, by using the maximization of the total profit of each operator as the objective function, the bidding revenue of individual operators is improved, solving the problem in existing technologies where distributed generation cannot formulate bidding methods based on multiple operating entities, resulting in low bidding revenue for individual operators.

[0095] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0096] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0097] Figure 1 A flowchart illustrating a specific implementation of the matching esports pricing modeling method provided by the present invention;

[0098] Figure 2 This is an architectural diagram of the electricity trading platform provided by the present invention;

[0099] Figure 3 This is a structural block diagram of a specific embodiment of the matching esports pricing modeling system provided by the present invention;

[0100] Figure 4 This is a structural diagram of the computer device provided by the present invention. Detailed Implementation

[0101] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0102] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings. They are used solely for the convenience of describing the document and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements; they can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0103] In this document, unless otherwise stated, the term "multiple" means two or more.

[0104] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0105] In this article, the term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0106] For distribution systems with a high proportion of distributed generation, the method provided by this invention introduces blockchain technology, integrating the advantages of blockchain—openness, transparency, and data reliability—into a distribution market with a large number of distributed power sources. This ensures the openness and transparency of distribution market transactions, reliable transaction information, and real-time accessibility. By establishing a bidding model centered on distributed operators and users, the method comprehensively considers the individual interests of operators and the welfare of users, quantitatively analyzes the benefits that blockchain technology brings to the distribution network, thereby effectively improving the marketization of distributed power resource transactions, reducing costs, and mobilizing users' enthusiasm for participating in distributed power market transactions.

[0107] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a specific implementation of the matching esports pricing modeling method provided by the present invention.

[0108] In one specific embodiment, the blockchain-based allocation bidding modeling method provided by the present invention includes the following steps:

[0109] S1: Based on the parameter characteristics of the trading entities, the bidding entities are divided into power supply side entities and user side entities.

[0110] The main participants in the distributed electricity market under the blockchain architecture include distributed energy consumers, various distributed energy operators, distribution system operators (DSOs), and the main power grid. Each participant has a different role within the architecture, and analyzing this division of labor is beneficial for the architecture's development.

[0111] Distributed electricity consumers refer to users who choose to participate in the blockchain distributed electricity market to obtain cheaper electricity. Consumers are also divided into different types, including ordinary consumers who only buy electricity from the grid, and consumers who have distributed generation capabilities and sell additional electricity on the blockchain trading platform.

[0112] A distribution system operator (DSO) is an entity that can participate independently in the operation of the blockchain-based electricity market, publishing specific demands on the chain and engaging in real-time transactions. Similar to traditional markets, DSOs can also provide distribution ancillary services and charge other entities a percentage of the revenue. More importantly, the blockchain market for distributed generation cannot operate entirely in a decentralized manner; it requires DSOs for regulation, access review, and transaction approval.

[0113] If a blockchain architecture only contains distributed energy, it can easily lead to system instability. The existence of a large power grid can ensure the stable operation of the system, acquire surplus clean energy, sell the energy that users lack, and maintain stable operation.

[0114] S2: If the bidding entity is determined to be the power supply entity, then a first objective function is established with the goal of maximizing the total profit of each operator, and the first objective function is optimized based on the first constraint to obtain an optimization model based on the power supply entity.

[0115] S3: If the bidding entity is determined to be the user-side entity, then a second objective function is established with the objective of maximizing the difference between the utility function and the electricity purchase cost. The second objective function is then optimized based on the second constraint condition to obtain an optimization model based on the user-side entity.

[0116] The existing distributed generation electricity market involves numerous different trading entities, making the trading process highly complex. Furthermore, it exhibits significant centralization, with a lack of complete trust between third-party trading platforms and other market participants. The presence of untrustworthy traders can disrupt the market and cause losses for other stakeholders. Moreover, current trading methods lack transparency and require substantial costs for information collection and analysis; improper information access by users increases transaction costs. The application of blockchain architecture, however, enables real-time uploading of distributed power trading information, ensuring transparency and openness; encryption algorithms protect the privacy of each individual. The application of blockchain architecture effectively reduces the participation costs for distributed entities and improves user welfare.

[0117] It should be noted that, in order to solve the above problems, the method provided by this invention is based on a blockchain-based electricity trading platform, such as... Figure 2 As shown, this blockchain-based electricity trading platform consists of four layers: an information encryption layer, a smart contract layer, a consensus layer, and a data layer. The encryption layer uses hash algorithms to encrypt newly generated data, including various electricity quantities, prices, and private information of participating entities, ensuring information security and protecting participant privacy, which enhances market competitiveness. The data layer is responsible for recording every transaction occurring on the current power distribution network, adding new data to new blocks. It verifies the transaction information generated by nodes and the electronic signatures of each node. Verified electricity transactions are temporarily stored, and a consensus mechanism is used to elect the node that records the transaction. The consensus mechanism used in this invention employs Delegated Proof-of-Stake (DPoS), which, compared to traditional Proof-of-Work (PoW), reduces energy waste and increases the speed of blockchain block generation. The smart contract layer uploads the determined transaction model for automatic trading. Traditional power companies only need to supervise execution, reducing transaction costs.

[0118] When users and distributed power operators want to participate in the blockchain-based electricity trading, they only need to register, submit their information for review, and once approved, they can participate in the blockchain platform's electricity trading, ensuring equal rights for all participants. Approved participants will receive their own virtual address. Participants in this market need to install smart meters to automatically record data to ensure accuracy and prevent data tampering. Participants can upload transaction requests to the blockchain platform, informing the entire network of their location, electricity quantity, and price. The smart contracts on the blockchain, using the game theory model described in this paper, can automatically optimize and match transactions. Transactions can proceed after regulatory agencies confirm they are problem-free. Simultaneously, participants who falsify their information will be penalized in the smart contract, and the penalty information will be broadcast on the blockchain to the entire network, directly linked to the participant's reputation. This architecture records transaction data in real time, allowing all participants to view it and make subsequent decisions, resulting in high efficiency. It also ensures data integrity, giving each participant equal rights to compete in the market, ensuring fair and just transactions. Excess electricity can be traded with the main grid, which will purchase the excess electricity from distributed systems and provide the shortfall to users.

[0119] In practical applications, maximizing the benefits on the power generation side is achieved by adjusting the output of distributed generation and the main grid. The game objective should be to maximize the total profit of each operator. Therefore, the first objective function for the power generation side is:

[0120]

[0121] Where ρ(t) represents the electricity price during time period t;

[0122] P i This represents the electricity sales volume of operator i;

[0123] N represents the total number of operators;

[0124] P i (t) represents the electricity sales volume of operator i during the time period t;

[0125] C i (t) represents the total power generation operating cost within the time period t;

[0126] α represents the reduction in decision-making costs proportionally, 0 < α < 1;

[0127] λ i This represents the additional cost of power supply i under conventional circumstances.

[0128] Cost analysis prioritizes the consumption of clean energy produced by distributed generation units. The market, due to the involvement of blockchain, is a perfectly competitive market where market participants are entirely rational. In cost analysis, the introduction of blockchain should first be considered in terms of market activation and cost reduction, which is beneficial for quantitative analysis of the benefits to the power distribution market. Beyond operating costs, the lack of transparency and the existence of third-party platforms profiting from price differences during bidding among various operators increase decision-making costs and other additional costs in the bidding process. The introduction of blockchain architecture into transactions can effectively reduce these costs, as shown in equation (2).

[0129] C M,i =(1-α)λ i (2)

[0130] Among them, C M,i Let λ represent the additional cost of power source i, and α represent the proportion of decision-making costs that can be reduced with the development and penetration of blockchain technology. The invention employs an innovative method to fit this parameter, where 0 < α < 1. i This represents the additional cost of power supply i under conventional circumstances.

[0131] The cost calculation methods for distributed power operators differ from those for traditional thermal power operators. The most significant costs for distributed power operators come from the fixed costs of purchasing and maintaining equipment, as well as the costs associated with generating electricity. For clarity and explanation, this embodiment uses superscripts x and y to distinguish between distributed and traditional power sources.

[0132] Specifically, the total power generation operating cost C during the time period t is calculated using the following formula. i (t):

[0133]

[0134] in, This represents the operating cost of operator i in the distribution network during the t-th time period;

[0135] This represents the operating cost of the traditional power supply i in the t-th time period.

[0136] The following formula can be used to calculate the operating cost of operator i in the distribution network during the t-th time period.

[0137]

[0138] in, This represents the amount of electricity generated by operator i during the time period t;

[0139] a and b represent cost coefficients for different generator sets, depicting the fixed cost and the rate at which the cost changes with varying power generation.

[0140] The operating cost of a traditional power plant can be expressed as a quadratic function, which is consistent with reality. The following formula can be used to calculate the operating cost of a traditional power source i in the t-th time period.

[0141]

[0142] in, This represents the amount of electricity generated by the conventional power source i at time t;

[0143] c, d, and e represent coefficients for the cost of traditional power generation.

[0144] Furthermore, the bidding for electricity generation on the power generation side needs to meet output constraints, electricity price constraints set by the DSO, and line constraints. Therefore, the first constraint specifically includes:

[0145]

[0146]

[0147] ρ min (t)≤ρ(t)≤ρ max (t) (8)

[0148] P ij (t)≤P l-max (9)

[0149] Among them, P l-max Indicates the maximum transmission capacity of the line;

[0150] This represents the amount of electricity generated by operator i during the time period t;

[0151] This represents the maximum amount of electricity generated by operator i during the time period t;

[0152] This represents the amount of electricity generated by the traditional power source i during the time period t.

[0153] This represents the upper limit of the power generation of traditional power source i during the time period t;

[0154] ρ min (t) represents the minimum electricity price set for time period t;

[0155] ρ(t) represents the electricity price during time period t;

[0156] ρ max (t) represents the maximum electricity price set for the time period t;

[0157] P ij (t) represents the amount of electricity transmitted between operator i and user j.

[0158] The game between users can be represented by user welfare, which can be expressed as the difference between the user's utility function and the user's electricity purchase cost. Therefore, the second objective function is:

[0159]

[0160] Among them, P j This represents the electricity consumption of user j;

[0161] N t This indicates the total number of time periods;

[0162] w is the utility ratio coefficient;

[0163] P j (t) represents the electricity consumption of user j during the time period t;

[0164] C j (t) represents the user's electricity purchase cost.

[0165] The load side also considers the limitations of users' electricity purchasing capacity, aiming to control the load between maximum and minimum, while simultaneously ensuring that the electricity price meets the price limits set by the DSO. Therefore, the second constraint includes:

[0166]

[0167] ρ min (t)≤ρ(t)≤ρ max (t) (12)

[0168] Among them, Pj (t) represents the electricity consumption of user j during the time period t;

[0169] This represents the maximum amount of electricity that user j can generate during the time period t;

[0170] This represents the lower limit of the amount of electricity that user j can obtain during the time period t;

[0171] ρ min (t) represents the minimum electricity price set for time period t;

[0172] ρ(t) represents the electricity price during time period t;

[0173] ρ max (t) represents the maximum electricity price set for the time period t.

[0174] Furthermore, the second constraint also includes a constraint on the user's satisfaction with the consumed electricity. This invention uses a user-side utility function to describe the user's benefits. A utility function is a mathematically quantifiable expression of the degree of satisfaction a user derives from the combination of different goods when purchasing a product. Here, this function represents the user's satisfaction with the consumed electricity.

[0175] That is, the satisfaction constraint is obtained according to the following formula:

[0176] R j (t)=βln[wP j [(t)+1] (13)

[0177] Among them, R j (t) represents the utility of user j during time period t;

[0178] β and w are utility ratio coefficients, which represent the rate at which user utility increases.

[0179] P j (t) represents the electricity consumption of user j during the time period t.

[0180] The user's electricity purchase cost is the product of the electricity consumption and the electricity price during time period t, as shown in equation (14).

[0181] C j (t)=P j (t)ρ(t) (14)

[0182] Blockchain technology shares a similar architecture with the distributed electricity trading market. Firstly, distributed electricity operators are numerous, involving diverse types of entities. The decentralized nature of blockchain technology is well-suited for fair trading among multiple stakeholders. Secondly, the distributed electricity market is prone to dishonesty and data tampering, disrupting the market. Blockchain technology, through consensus mechanisms and cryptographic algorithms, ensures information reliability, effectively preventing such behaviors and guaranteeing fair transactions. Furthermore, the decentralization of blockchain technology helps reduce intermediaries, increasing the enthusiasm of distributed entities and users. In summary, blockchain technology is highly suitable for market-based trading in distribution networks with a large number of distributed power sources. However, determining the appropriate architecture to integrate blockchain with the distributed generation market, and optimizing mathematical algorithms within the blockchain architecture to maximize returns, remain challenges that need to be addressed.

[0183] The optimization model considers both the generation and user sides separately, aiming to improve generation revenue and user welfare. The model fully considers the cost reduction that blockchain integration will bring, and performs cost analysis on both sides accordingly. The model can analyze trading plans in real time, which is beneficial for electricity trading clearing, effectively improving operator revenue and user welfare, and promoting the marketization of distribution network trading. The model can be used in the smart contract layer of a blockchain architecture to improve the level of transaction automation.

[0184] The distribution bidding modeling method and system based on blockchain technology provided by this invention divides the bidding entities into power-side entities and user-side entities based on the parameter characteristics of the trading entities. When the bidding entity is determined to be a power-side entity, a first objective function is established with the goal of maximizing the total profit of each operator. The first objective function is then optimized based on a first constraint condition to obtain an optimized model based on the power-side entity. Thus, by using the maximization of the total profit of each operator as the objective function, the bidding revenue of individual operators is improved, solving the problem in existing technologies where distributed generation cannot formulate bidding methods based on multiple operating entities, resulting in low bidding revenue for individual operators.

[0185] According to a second aspect of the present invention, a blockchain-based matchmaking bidding modeling system is provided for implementing the method described above.

[0186] In some embodiments, such as Figure 3 As shown, the system includes:

[0187] The bidding entity is divided into power supply 100, which is used to divide the bidding entities into power supply side entities and user side entities based on the parameter characteristics of the trading entities;

[0188] The first modeling and optimization unit 200 is used to determine that the bidding entity is the power supply entity, and then establish a first objective function with the goal of maximizing the total profit of each operator, and optimize the first objective function based on the first constraint condition to obtain an optimization model based on the power supply entity.

[0189] Furthermore, the system also includes:

[0190] The second modeling and optimization unit 300 is used to determine that the bidding subject is a user-side subject, and then establish a second objective function with the objective of maximizing the difference between the utility function and the electricity purchase cost, and optimize the second objective function based on the second constraint condition to obtain an optimization model based on the user-side subject.

[0191] Furthermore, the first objective function is:

[0192]

[0193] Where ρ(t) represents the electricity price during time period t;

[0194] P i This represents the electricity sales volume of operator i;

[0195] N represents the total number of operators;

[0196] P i (t) represents the electricity sales volume of operator i during time period t;

[0197] C i (t) represents the total power generation operating cost within the time period t;

[0198] α represents the reduction in decision-making costs proportionally, 0 < α < 1;

[0199] λ i This represents the additional cost of power supply i under conventional circumstances;

[0200] The second objective function is:

[0201]

[0202] Among them, P j This represents the electricity consumption of user j;

[0203] N t This indicates the total number of time periods;

[0204] w is the utility ratio coefficient;

[0205] P j (t) represents the electricity consumption of user j during the time period t;

[0206] C j(t) represents the user's electricity purchase cost.

[0207] The blockchain-based power generation bidding modeling system provided by this invention divides bidding entities into power-side entities and user-side entities based on the parameter characteristics of the trading entities. When a bidding entity is determined to be a power-side entity, a first objective function is established with the goal of maximizing the total profit of each operator. This first objective function is then optimized based on a first constraint condition to obtain an optimized model based on the power-side entity. By using the maximization of each operator's total profit as the objective function, the bidding revenue of individual operators is improved, solving the problem in existing technologies where distributed generation cannot formulate bidding methods based on multiple operating entities, resulting in low bidding revenue for individual operators.

[0208] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and model predictions. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The model predictions of the computer device store static and dynamic information data. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0209] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0210] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0211] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the method embodiments described above.

[0212] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, model prediction, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0213] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for modeling allocation bidding based on blockchain technology, characterized in that, The blockchain-based power trading platform consists of four layers: an information encryption layer, a smart contract layer, a consensus layer, and a data layer. The encryption layer uses a hash algorithm to encrypt the newly generated data, including various electricity consumption, electricity prices, and private information of participating entities. The data layer is responsible for recording every transaction that occurs in the current power distribution network and recording new data in the new block; the consensus layer verifies the transaction information generated by the nodes and the electronic signature of each node, temporarily stores the verified power transactions, and elects the node that records the transaction through the consensus mechanism. The smart contract layer uploads the defined transaction model and enables automatic transactions. The method includes: Based on the parameter characteristics of the trading entities, the bidding entities are divided into power supply-side entities and user-side entities; If the bidding entity is determined to be the power supply entity, then a first objective function is established with the goal of maximizing the total profit of each operator, and the first objective function is optimized based on the first constraint condition to obtain an optimization model based on the power supply entity. If the bidding entity is determined to be the user-side entity, then a second objective function is established with the objective of maximizing the difference between the utility function and the electricity purchase cost. The second objective function is then optimized based on the second constraint condition to obtain an optimization model based on the user-side entity. The first objective function is: Where ρ(t) represents the electricity price during time period t; P i This represents the electricity sales volume of operator i; N represents the total number of operators; P i (t) represents the electricity sales volume of operator i during the time period t; C i (t) represents the total power generation operating cost within the time period t; α represents the reduction in decision-making costs proportionally, 0 < α < 1; λ i This represents the additional cost of power supply i under conventional circumstances; The second objective function is: Among them, P j This represents the electricity consumption of user j; N t Indicates the total number of time periods; w is the utility ratio coefficient; P j (t) represents the electricity consumption of user j during the time period t; C j (t) represents the user's electricity purchase cost.

2. The matching game pricing modeling method according to claim 1, characterized in that, Calculate the total power generation operating cost C over time period t using the following formula. i (t): in, This represents the operating cost of operator i in the distribution network during the t-th time period; This represents the operating cost of the traditional power supply i in the t-th time period.

3. The matching game pricing modeling method according to claim 2, characterized in that, The following formula can be used to calculate the operating cost of operator i in the distribution network during the t-th time period. Among them, P i x (t) represents the amount of electricity generated by operator i during time period t; a and b represent cost coefficients for different generator sets.

4. The matching game pricing modeling method according to claim 2, characterized in that, Calculate the operating cost of conventional power supply i in the t-th time period using the following formula. Among them, P i y (t) represents the amount of electricity generated by the traditional power source i during the time period t; c, d, and e represent coefficients for the cost of traditional power generation.

5. The matching game pricing modeling method according to claim 4, characterized in that, The first constraint specifically includes: ρ min (t)≤ρ(t)≤ρ max (t) P ij (t)≤P l-max Among them, P l-max Indicates the maximum transmission capacity of the line; P i x (t) represents the amount of electricity generated by operator i during time period t; This represents the maximum amount of electricity generated by operator i during the time period t; P i y (t) represents the amount of electricity generated by the traditional power source i during the time period t; This represents the upper limit of the power generation of traditional power source i during the time period t; ρ min (t) represents the minimum electricity price set for time period t; ρ(t) represents the electricity price during time period t; ρ max (t) represents the maximum electricity price set for the time period t; P ij (t) represents the amount of electricity transmitted between operator i and user j.

6. The matching game pricing modeling method according to claim 5, characterized in that, The second constraint includes: P j min (t)≤P j (t)≤P j max (t) ρ min (t)≤ρ(t)≤ρ max (t) Among them, P j This represents the electricity consumption of user j; P j (t) represents the electricity consumption of user j during the time period t; P j max (t) represents the maximum amount of electricity that user j can obtain during the time period t; P j min (t) represents the lower limit of the amount of electricity that user j can obtain during the time period t; ρ min (t) represents the minimum electricity price set for time period t; ρ(t) represents the electricity price during time period t; ρ max (t) represents the maximum electricity price set for the time period t.

7. The matching game pricing modeling method according to claim 1, characterized in that, The second constraint also includes a constraint on user satisfaction with electricity consumption.

8. The matching game pricing modeling method according to claim 7, characterized in that, The satisfaction level constraint is obtained based on the following formula: R j (t)=βln[wP j (t)+1] Among them, R j (t) represents the utility of user j during time period t; β and w are utility ratio coefficients; P j (t) represents the electricity consumption of user j during the time period t.

9. A blockchain-based allocation bidding modeling system, used to implement the method as described in any one of claims 1 to 8, characterized in that, The blockchain-based power trading platform consists of four layers: an information encryption layer, a smart contract layer, a consensus layer, and a data layer. The encryption layer uses a hash algorithm to encrypt the newly generated data, including various electricity consumption, electricity prices, and private information of participating entities. The data layer is responsible for recording every transaction that occurs in the current power distribution network and recording new data in the new block; the consensus layer verifies the transaction information generated by the nodes and the electronic signature of each node, temporarily stores the verified power transactions, and elects the node that records the transaction through the consensus mechanism. The smart contract layer uploads the defined transaction model and enables automatic transactions. The system includes: The bidding entity segmentation is used to divide the bidding entities into power-side entities and user-side entities based on the parameter characteristics of the trading entities; The first modeling and optimization unit is used to determine that the bidding entity is the power supply entity, and then establish a first objective function with the goal of maximizing the total profit of each operator, and optimize the first objective function based on the first constraint condition to obtain an optimization model based on the power supply entity. The second modeling and optimization unit is used to determine that the bidding subject is a user-side subject, and then establish a second objective function with the objective of maximizing the difference between the utility function and the electricity purchase cost, and optimize the second objective function based on the second constraint condition to obtain an optimization model based on the user-side subject; The first objective function is: Where ρ(t) represents the electricity price during time period t; P i This represents the electricity sales volume of operator i; N represents the total number of operators; P i (t) represents the electricity sales volume of operator i during the time period t; C i (t) represents the total power generation operating cost within the time period t; α represents the reduction in decision-making costs proportionally, 0 < α < 1; λ i This represents the additional cost of power supply i under conventional circumstances; The second objective function is: Among them, P j This represents the electricity consumption of user j; N t Indicates the total number of time periods; w is the utility ratio coefficient; P j (t) represents the electricity consumption of user j during the time period t; C j (t) represents the user's electricity purchase cost.

10. The matchmaking bidding modeling system according to claim 9, characterized in that, Calculate the total power generation operating cost C over time period t using the following formula. i (t): in, This represents the operating cost of operator i in the distribution network during the t-th time period; This represents the operating cost of the traditional power supply i in the t-th time period.

11. The matchmaking bidding modeling system according to claim 10, characterized in that, The following formula can be used to calculate the operating cost of operator i in the distribution network during the t-th time period. Among them, P i x (t) represents the amount of electricity generated by operator i during time period t; a and b represent cost coefficients for different generator sets.

12. The matchmaking bidding modeling system according to claim 10, characterized in that, Calculate the operating cost of conventional power supply i in the t-th time period using the following formula. Among them, P i y (t) represents the amount of electricity generated by the traditional power source i during the time period t; c, d, and e represent coefficients for the cost of traditional power generation.

13. The matchmaking bidding modeling system according to claim 12, characterized in that, The first constraint specifically includes: ρ min (t)≤ρ(t)≤ρ max (t) P ij (t)≤P l-max Among them, P l-max Indicates the maximum transmission capacity of the line; P i x (t) represents the amount of electricity generated by operator i during time period t; This represents the maximum amount of electricity generated by operator i during the time period t; P i y (t) represents the amount of electricity generated by the traditional power source i during the time period t; This represents the upper limit of the power generation of traditional power source i during the time period t; ρ min (t) represents the minimum electricity price set for time period t; ρ(t) represents the electricity price during time period t; ρ max (t) represents the maximum electricity price set for the time period t; P ij (t) represents the amount of electricity transmitted between operator i and user j.

14. The matchmaking bidding modeling system according to claim 13, characterized in that, The second constraint includes: P j min (t)≤P j (t)≤P j max (t) ρ min (t)≤ρ(t)≤ρ max (t) Among them, P j This represents the electricity consumption of user j; P j (t) represents the electricity consumption of user j during the time period t; P j max (t) represents the maximum amount of electricity that user j can obtain during the time period t; P j min (t) represents the lower limit of the amount of electricity that user j can obtain during the time period t; ρ min (t) represents the minimum electricity price set for time period t; ρ(t) represents the electricity price during time period t; ρ max (t) represents the maximum electricity price set for the time period t.

15. The matchmaking bidding modeling system according to claim 9, characterized in that, The second constraint also includes a constraint on user satisfaction with electricity consumption.

16. The matchmaking bidding modeling system according to claim 15, characterized in that, The satisfaction level constraint is obtained based on the following formula: R j (t)=βln[wP j (t)+1] Among them, R j (t) represents the utility of user j during time period t; β and w are utility ratio coefficients; P j (t) represents the electricity consumption of user j during the time period t.

17. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that, The computer storage medium contains one or more program instructions for performing the steps of the method as described in any one of claims 1 to 8.

Images