Distributed transaction method and system for intelligent power distribution network

Abstract

The invention discloses a distributed transaction method and system for an intelligent power distribution network, and relates to the field of power system optimization scheduling, and the method comprises the steps: classifying loads according to different user load characteristics, and constructing a comprehensive demand response model; calculating the contribution degree of each transaction to the line loss by using the power transmission distribution factor matrix according to the power loss generated in the energy transaction, and obtaining a system network loss model; according to the comprehensive demand response model and the system network loss model, respectively constructing point-to-point market transaction models of the two parties based on the perspective of the energy buyer and the energy seller; and further constructing a distributed transaction model according to the point-to-point market transaction models of the two parties. According to the distributed transaction method, the benefit problem of the energy buyer and the energy seller is considered, the distributed transaction method considering the comprehensive demand response and the system network loss is provided for the distributed transaction problem in the market, and the decentralization and privacy of the distributed transaction are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a distributed trading method and system for an intelligent distribution network. Background Art

[0002] With the large-scale popularization of distributed energy and the tremendous progress in communication and control technology, traditional energy users can be transformed into energy producers and sellers who both generate electricity and use electricity. As the proportion of producers and consumers participating in the market gradually increases, the user-centered market is about to replace the traditional energy market. Under the new market mechanism, participants can independently choose trading objects, which greatly weakens the market centrality and protects the privacy information of all parties under the condition of reasonable energy distribution. This point-to-point transaction method can trade energy without energy intermediaries. At the same time, each producer and consumer can also trade energy with the superior energy market, thereby improving market flexibility.

[0003] Since the transaction process between prosumers and general users is carried out in the existing energy transmission network, the relevant limitations of the transmission network need to be taken into account. The energy loss in energy transportation is unavoidable, and the resulting costs also need to be taken into account in the transaction model. At the same time, with the gradual improvement of the demand response mechanism, the user's enthusiasm for participation will gradually increase, so that the demand response part needs to be considered in the energy transaction process. However, the current distributed trading method does not take into account the losses caused by energy transmission and the costs incurred by users for participating in the demand response plan. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that the current distributed trading method does not take into account both the losses caused by energy transmission and the costs incurred by users due to participating in the demand response program.

[0006] To solve the above technical problems, in a first aspect, the present invention provides the following technical solutions: a distributed trading method for a smart distribution network, comprising: classifying loads and constructing a comprehensive demand response model according to the load characteristics of different users; calculating the contribution of each transaction to the line loss according to the power loss generated in the energy transaction using the power transmission distribution factor matrix to obtain a system network loss model; constructing a point-to-point market trading model for both parties based on the comprehensive demand response model and the system network loss model and from the perspectives of energy buyers and sellers; further constructing a distributed trading model with maximizing the total welfare of both parties as the objective function according to the point-to-point market trading model of the two parties and solving it through the distributed alternating multiplier direction method to obtain a system distributed trading method that considers comprehensive demand response and network loss.

[0007] As a preferred solution of the distributed trading method of the smart distribution network described in the present invention, the classification of the load includes multiple types of transferable loads, curtailable loads and replaceable loads.

[0008] As a preferred solution of the distributed trading method of the smart distribution network of the present invention, the method for constructing the comprehensive demand response model includes:

[0009] Modeling the load curtailment involved in price-based demand response:

[0010]

[0011] In the formula, p t,j represents the elasticity coefficient of the price elasticity matrix, represents the initial load size of the i-th at time t, represents the size of the load that can be reduced at time t. Δe t are the initial energy price and energy price change at time t respectively;

[0012] The reducible load of the i-th type load is further obtained as follows:

[0013]

[0014] In the formula, represents the initial reducible load size of the i-th type of load, The price demand elasticity matrix factor that represents the load that can be reduced, e t Indicates the price of relevant load energy;

[0015] The transferable load model involved in price-based demand response is constructed as follows:

[0016]

[0017] In the formula, represents the initial transferable load size of the i-th type of load, A price demand elasticity matrix factor representing the transferable load;

[0018] According to the characteristics of some heat loads, the electricity-to-heat equipment is used to convert them into electrical loads. The model is expressed as follows:

[0019]

[0020] In the formula denote the replaceable electrical load and thermal load, respectively, eth represents the electric heat substitution coefficient and is calculated as follows:

[0021]

[0022] In the above formula, h e 、h h are the unit calorific values ​​of electricity and thermal energy respectively;

[0023] In summary, the final electric heating load can be expressed as follows:

[0024]

[0025] i∈{electricity;heat}

[0026] Where P i DR represents the final load size of the i-th energy source, P i It represents the base load size of the i-th energy source;

[0027] Comprehensive demand response compensation costs for various loads:

[0028]

[0029] Where R CL , R TL , R FL They represent the compensation costs of load reduction, load transfer and load substitution for demand response, respectively. Respectively represent the compensation price of electrical and thermal loads that can be reduced per unit of electricity, Respectively represent the compensation price of transferable electrical and thermal load per unit of electricity, They respectively represent the compensation price for replacing electrical and thermal loads per unit of electricity.

[0030] As a preferred solution of the distributed trading method of the smart distribution network of the present invention, the method for constructing the system network loss model is as follows:

[0031] The power transfer generated by the energy transaction between the buyer and the seller is:

[0032]

[0033] in represents the power flowing through line l between energy buyer j and energy seller i, represents an element in the power transfer distribution factor matrix (PTDF), which is the contribution of power transfer between seller i and buyer j;

[0034] Network losses caused by power transmission:

[0035]

[0036] In the formula, Represents the total power loss incurred by both parties in the energy transaction process; γ l It represents the ratio of the resistance of line l to the square of the network standard voltage, and is calculated as follows:

[0037]

[0038] In the formula, R l Indicates the equivalent resistance of line l, V b Indicates the network standard voltage;

[0039] During the energy transmission process, buyers and sellers need to pay the network owner a transaction fee of:

[0040]

[0041] In the above formula, JF(P ij ) represents the transaction cost in the energy transaction process, Indicates the line usage fee per unit electrical distance, d ij Indicates the power transmission distance.

[0042] As a preferred solution of the distributed trading method of the smart distribution network of the present invention, wherein: the point-to-point market trading model of the two parties includes a buyer's trading model and a seller's trading model, wherein the buyer's trading model construction method includes:

[0043] From the buyer's perspective, the transaction costs of energy trading are borne by the buyer, while the system network loss costs are only borne by the seller. Therefore, for the buyer, its objective function is constructed as follows:

[0044]

[0045] In the formula, GF j is the total revenue of the energy transaction buyer, U j (P jt ) is the utility function of the energy buyer, P jt represents the total energy demand of buyer j at time t, is the energy price between buyer j and seller i, P jit The amount of energy purchased, represents the energy price in the upper market, represents the energy purchased by the buyer from the upper market, N S represents the total number of energy buyers;

[0046] The constraints include the following:

[0047] The total energy demand of buyer j consists of:

[0048]

[0049] In the formula, is the demand response program that participates in energy buyer j at time t;

[0050] Power balance constraints:

[0051]

[0052] Where P jit Indicates the amount of energy purchased, represents the energy purchased by the buyer from the upper market, represents the energy demand of buyer j, denotes participation in the demand response program of energy buyer j at time t;

[0053] Demand response constraints:

[0054]

[0055] Where η is the percentage of participating energy in demand response, represents the energy demand of buyer j, and T represents the total energy transaction period.

[0056] As a preferred solution of the distributed transaction method of the smart distribution network of the present invention, the method for constructing the seller transaction model includes:

[0057] The seller needs to bear the total loss cost but does not need to bear the transaction cost, so the objective function is as follows:

[0058]

[0059] Where MF is the total profit of energy sellers, C i (P it ) is the cost function of the energy seller, represents the energy loss price, represents the total power loss caused by the transaction between buyer j and seller i, N B represents the total number of energy buyers, P it Represents the amount of energy sold by energy seller i.

[0060] The constraints consist of the following:

[0061] Constraints on sales capacity composition:

[0062]

[0063] Where P it It represents the amount of energy sold by energy seller i, which is composed of the energy purchased by the buyer and the system network loss;

[0064] Constraints on energy sales:

[0065]

[0066] In the formula, They respectively represent the upper and lower limits of energy sales for energy sellers.

[0067] As a preferred solution of the distributed transaction method of the smart distribution network of the present invention, the method for constructing the distributed transaction model includes:

[0068] The overall goal of the problem is to maximize the benefits of all buyers and sellers in the local market. First, the problem can be modeled as a single subject, and its objective function is as follows:

[0069]

[0070] In the formula, MF i GF j Respectively represent the total revenue of buyers and sellers of energy transactions;

[0071] In addition to the above constraints, coupling constraints are added to ensure power balance during the transaction process, as follows:

[0072]

[0073] In the formula, is a dual variable, which means the energy price that buyer j buys energy from seller i. The above constraints can be used as a market clearing condition, which stipulates that at each time interval, the energy purchased by each buyer must be equal to the energy sold by each seller;

[0074] The Lagrangian of the above optimization problem is as follows:

[0075]

[0076] Where μ is the Lagrangian coefficient, which represents the penalty coefficient and is used to increase the range of the Lagrangian function. From this, we can derive the sub-problems of energy buyers and sellers:

[0077] Energy sellers:

[0078]

[0079] Where P it represents the sum of the energy sold by energy seller i to all energy buyers at time t, are the upper and lower limits of energy sold, P ijt It represents the energy of the transaction between seller i and buyer j at time t;

[0080] Energy buyers:

[0081]

[0082] In the formula, represents the penalty term in the augmented Lagrangian function, P jt represents the total demand of energy buyer j at time t. All the main variables in the sub-problems of buyers and sellers can be calculated according to the following formula:

[0083]

[0084] In the formula are the sizes of the main variables calculated by the energy buyer and seller from the corresponding problems using the incremental gradient method, and the iteration stop condition is given by the following formula:

[0085]

[0086] In the formula, α represents a very small positive number;

[0087] In order to calculate the satisfaction of market participants and market fairness, a mathematical model of the market fairness index is established through the following formula:

[0088]

[0089] In the formula, GP represents the fairness index, N represents the number of market participants, MJ Represents the category of market participants, including buyers and sellers of energy.

[0090] In a second aspect, the present invention further provides a distributed trading system for a smart distribution network, which is applicable to the above-mentioned distributed trading method for a smart distribution network.

[0091] In a third aspect, the present invention further provides a computer device, which includes a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, the above-mentioned smart distribution network distributed trading method is implemented.

[0092] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned distributed trading method of the smart distribution network is implemented.

[0093] The beneficial effects of the present invention are:

[0094] 1. The present invention establishes a comprehensive demand response model in the system according to different load characteristics, which further helps to realize the concreteness of demand response participating in market transactions.

[0095] 2. The present invention uses the power transmission distribution factor matrix extracted from the system network structure characteristics to construct a system network loss model, providing a model support for considering system network losses in market transactions.

[0096] 3. In order to solve the problem of different interests between energy buyers and sellers, the present invention constructs an optimal energy trading model for buyers and sellers based on the perspectives of both parties, further providing assistance for maximizing profits for both parties.

[0097] 4. The present invention takes into account the interests of both energy buyers and sellers, and proposes a distributed trading method that takes into account comprehensive demand response and system network loss to address distributed trading issues in the market, thereby further improving the decentralization and privacy of distributed transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0099] Figure 1 A flow chart of the steps of a distributed trading method for a smart distribution network.

[0100] Figure 2 Comparison between the upper market energy price and transaction price under scenario 1.

[0101] Figure 3 Comparison of energy buyer’s load and transaction energy in scenario 1.

[0102] Figure 4 It is the ratio of energy transaction prices under scenario 1 and scenario 2.

[0103] Figure 5 is the ratio of line power in scenario 1 to that in scenario 2. DETAILED DESCRIPTION

[0104] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0105] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0106] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0107] Example 1

[0108] Reference Figure 1 , which is the first embodiment of the present invention, and which provides a distributed trading method for a smart distribution network, the distributed trading method for a smart distribution network comprising:

[0109] S1: Classify the loads according to the load characteristics of different users and build a comprehensive demand response model.

[0110] Specifically, the classification of loads includes multiple types of transferable loads, curtailable loads, and replaceable loads. The methods for building a comprehensive demand response model include:

[0111] 1) First, a model is constructed for the load that can be reduced in price-based demand response:

[0112]

[0113] In the formula, p t,j represents the elasticity coefficient of the price elasticity matrix, represents the initial load size of the i-th at time t, represents the size of the load that can be reduced at time t. Δe t are the initial energy price and energy price change at time t respectively.

[0114] The reducible load of the i-th type load is further obtained as follows:

[0115]

[0116] In the formula, represents the initial reducible load size of the i-th type of load, The price demand elasticity matrix factor that represents the load that can be reduced, e t Indicates the size of the relevant load energy price.

[0117] The transferable load model involved in price-based demand response is constructed as follows:

[0118]

[0119] In the formula, represents the initial transferable load size of the i-th type of load, Represents the price demand elasticity matrix factors of transferable load.

[0120] 2) According to the characteristics of some heat loads, electricity-to-heat related equipment can be used to convert them into electrical loads. The model is expressed as follows:

[0121]

[0122] In the formula denote the replaceable electrical load and thermal load, respectively, eth represents the electric heat substitution coefficient and is calculated as follows:

[0123]

[0124] In the above formula, h e 、h h are the unit calorific values ​​of electricity and thermal energy respectively;

[0125] In summary, the final electric heating load can be expressed as follows:

[0126]

[0127] i∈{electricity;heat}

[0128] Where P i DR represents the final load size of the i-th energy source, P i Represents the base load size of the i-th energy source.

[0129] 3) Comprehensive demand response and various load compensation costs:

[0130]

[0131] Where R CL , R TL , R FL They represent the compensation costs of load reduction, load transfer and load substitution in response to demand, respectively. Respectively represent the compensation price of electrical and thermal loads that can be reduced per unit of electricity, Respectively represent the compensation price of transferable electrical and thermal load per unit of electricity, They respectively represent the compensation price for replacing electrical and thermal loads per unit of electricity.

[0132] S2: According to the power loss generated in energy transactions, the power transmission distribution factor matrix is ​​used to calculate the contribution of each transaction to the line loss, and the system network loss model is obtained.

[0133] Specifically, the network loss model used to calculate the system power loss is as follows:

[0134] 1) Power transmission generated by energy transactions between buyers and sellers

[0135]

[0136] in represents the power flowing through line l between energy buyer j and energy seller i, It represents an element in the power transfer distribution factor matrix (PTDF), which is the contribution of power transfer between seller i and buyer j. The transfer distribution factor matrix is ​​only related to the physical structure of the power grid and is therefore a constant matrix.

[0137] 2) Network loss caused by power transmission:

[0138]

[0139] In the formula, Represents the total power loss incurred by both parties in the energy transaction process; γ l It represents the ratio of the resistance of line l to the square of the network standard voltage, and is calculated as follows:

[0140]

[0141] Where R l Indicates the equivalent resistance of line l, V b Indicates the network standard voltage.

[0142] 3) Transaction fees for peer-to-peer energy transactions

[0143] During the energy transmission process, buyers and sellers need to pay transaction fees to the network owner.

[0144]

[0145] In the above formula, JF(P ij ) represents the transaction cost in the energy transaction process, Indicates the line usage fee per unit electrical distance, d ij Represents the power transmission distance, calculated by the power transfer distribution factor, and represents the number of network lines required for a point-to-point transaction between two peer entities.

[0146] S3: Based on the comprehensive demand response model and system network loss model and based on the perspectives of energy buyers and sellers, a point-to-point market transaction model is constructed for both parties.

[0147] Specifically, the peer-to-peer market transaction model from the perspectives of both the buyer and seller of energy transactions is constructed as follows:

[0148] 1) Energy trading buyer perspective:

[0149] From the buyer's perspective, the transaction costs of energy trading are borne by the buyer, while the system network loss costs are only borne by the seller. Therefore, for the buyer, its objective function is constructed as follows:

[0150]

[0151] In the formula, GF j is the total revenue of the energy transaction buyer, U j (P jt ) is the utility function of the energy buyer, P jt represents the total energy demand of buyer j at time t, is the energy price between buyer j and seller i, P jit The amount of energy purchased, represents the energy price in the upper market, represents the energy purchased by the buyer from the upper market, N S Represents the total number of energy buyers.

[0152] The constraints include the following:

[0153] (1) Composition of total energy demand of buyer j:

[0154]

[0155] In the formula, is the demand response program that participates in energy buyer j at time t.

[0156] (2) Power balance constraints:

[0157]

[0158] Where P jit Indicates the amount of energy purchased, represents the energy purchased by the buyer from the upper market, represents the energy demand of buyer j, denotes participation in the demand response program of energy buyer j at time t.

[0159] (3) Demand response constraints:

[0160]

[0161] Where η is the percentage of participating energy in demand response, represents the energy demand of buyer j, and T represents the total energy transaction period.

[0162] 2) Energy trading seller perspective:

[0163] The seller needs to bear the total loss cost but does not need to bear the transaction cost, so the objective function is as follows:

[0164]

[0165] Where MF is the total profit of energy sellers, C i (P it ) is the cost function of the energy seller, represents the energy loss price, represents the total power loss caused by the transaction between buyer j and seller i, N B represents the total number of energy buyers, P it Represents the amount of energy sold by energy seller i.

[0166] The constraints consist of the following:

[0167] (1) Constraints on sales capacity composition:

[0168]

[0169] Where P it It represents the amount of energy sold by energy seller i, which is composed of the energy purchased by the buyer and the system network loss;

[0170] (2): Constraints on energy sales:

[0171]

[0172] In the formula, They respectively represent the upper and lower limits of energy sales for energy sellers.

[0173] S4: The distributed trading model with the profit maximization of all energy buyers and sellers in the system as the objective function is constructed as follows:

[0174] The overall goal of the problem is to maximize the benefits of all buyers and sellers in the local market. First, the problem can be modeled as a single subject, and its objective function is as follows:

[0175]

[0176] In the formula MF i GF j Respectively represent the total revenue of buyers and sellers of energy transactions.

[0177] In addition to the above constraints, coupling constraints are added to ensure power balance during the transaction process; as follows:

[0178]

[0179] In the formula, is a dual variable, which means the energy price at which buyer j purchases energy from seller i. The above constraints can be used as a market clearing condition, which stipulates that at each time interval, the energy purchased by each buyer must be equal to the energy sold by each seller.

[0180] Considering that centralized optimization may lead to the violation of the privacy of market participants, a distributed algorithm is adopted to solve the problem, such as the alternating direction multiplier algorithm. In the presence of coupling constraints, the initial optimization problem needs to be decomposed into several sub-problems according to the dual decomposition principle, and then solved separately.

[0181] Therefore, the Lagrangian of the above optimization problem is as follows:

[0182]

[0183] Where μ is the Lagrangian coefficient and γ is the penalty coefficient, which is used to increase the range of the Lagrangian function. From this, we can derive the sub-problems of energy buyers and sellers:

[0184] 1) Energy seller:

[0185]

[0186] P ijt ≥0

[0187] Where P it represents the sum of the energy sold by energy seller i to all energy buyers at time t, are the upper and lower limits of energy sold, P ijt It represents the energy of the transaction between seller i and buyer j at time t.

[0188] 2) Energy buyers:

[0189]

[0190] In the formula, represents the penalty term in the augmented Lagrangian function, P jt represents the total demand of energy buyer j at time t. All the main variables in the sub-problems of buyers and sellers can be calculated according to the following formula:

[0191]

[0192] In the formula are the sizes of the main variables calculated by the energy buyer and seller from the corresponding problems using the incremental gradient method, and the iteration stop condition is given by the following formula:

[0193]

[0194] In the formula, α represents a small positive number.

[0195] In order to calculate the satisfaction of market participants and market fairness, a mathematical model of the market fairness index is established through the following formula:

[0196]

[0197] In the formula, GP represents the fairness index, N represents the number of market participants, MJ Represents the category of market participants, including buyers and sellers of energy.

[0198] Following the above steps, we finally get a mixed integer linear programming problem. By iteratively solving it on the MATLAB platform, we finally get the optimal distributed trading solution for the system.

[0199] Example 2

[0200] Reference Figure 2 to Figure 5 , which is the second embodiment of the present invention, and this embodiment is based on the previous embodiment. This embodiment considers the following two different scenarios, which are used to test the feasibility of the method.

[0201] Scenario 1: When both parties conduct energy transactions, the energy user’s demand response plan is not considered, and the additional costs caused by system network losses are not considered.

[0202] Scenario 2: In the energy trading process, the participation of multiple energy users in the demand response plan is taken into account, as well as the costs caused by system network losses and the transaction costs paid to the grid during energy transmission.

[0203] In this embodiment, the parameters of the buyer and seller are shown in Table 1. In order to calculate the network loss and transaction cost, the network structure and line information in the 9-node IEEE network are used. The demand response participation ratio is set to 20%, and the loss cost price generated in the transaction process is set to the upper-level network energy price.

[0204] Table 1 Relevant parameters of energy buyers and sellers

[0205]

[0206] Figure 1 For the comparison between the price of the peer-to-peer market and the price of the parent market, Figure 2It is the load size of energy buyers in a day and the amount of energy traded throughout the day under scenario 1. It can be seen that in the initial stage, the market price is lower than the transaction price, so buyers give priority to purchasing energy from the upstream market. In the period from 9:00 to 18:00, the transaction price is significantly lower than the market price, and the proportion of energy buyers purchasing energy from energy sellers gradually increases. Finally, in the period from 19:00 to 24:00, the market price is lower than the transaction price, and energy buyers increase the proportion of energy purchased from the upstream market. It can be seen that the size of transactions between energy buyers and energy sellers in scenario 1 is related to the ratio of market price to transaction price.

[0207] Figure 3 The figure shows the comparison of transaction prices under scenario 1 and scenario 2. It can be seen that considering the system network loss and comprehensive demand response, buyers and sellers are encouraged to trade energy, thereby reducing the price of point-to-point transactions. Figure 4 It can be seen that after considering the energy loss, transaction fees, and penalty fees caused by energy loss, the transaction volume with longer lines between the two parties will be greatly reduced.

[0208] Table 2 Market Participant Fairness Index

[0209] parameter Seller Buyer Scenario 1 64.1 101.7 Scenario 2 63.9 99.8

[0210] Table 2 shows that even after taking into account system network losses and demand response costs, market participants’ satisfaction with the market remains relatively stable.

[0211] Example 3

[0212] This is the third embodiment of the present invention, which is based on the first two embodiments. This embodiment also provides a computer device, including: a memory and a processor and a computer program stored in the memory, when the computer program is executed on the processor, the above-mentioned smart distribution network distributed trading method is implemented.

[0213] The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The computer device may include but is not limited to a processor and a memory.

[0214] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0215] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the computer device.

[0216] Furthermore, the memory may include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0217] Example 4

[0218] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the distributed trading method of the smart distribution network as described above is implemented.

[0219] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0220] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A distributed trading method for a smart distribution network, characterized in that: include, According to the load characteristics of different users, the load is classified and a comprehensive demand response model is constructed; According to the power loss generated in energy transactions, the power transmission distribution factor matrix is ​​used to calculate the contribution of each transaction to the line loss, and the system network loss model is obtained; According to the comprehensive demand response model and the system network loss model and based on the perspectives of energy buyers and sellers, a point-to-point market transaction model is constructed for both parties; Based on the point-to-point market transaction model of the two parties, a distributed transaction model with maximizing the total welfare of both parties as the objective function is further constructed and solved by the distributed alternating multiplier direction method to obtain a system distributed transaction method that takes into account comprehensive demand response and network loss.

2. The distributed trading method for smart distribution network according to claim 1, characterized in that: The classification of loads includes multiple types of transferable loads, curtailable loads, and replaceable loads.

3. The distributed trading method for smart distribution network according to claim 2, characterized in that: The method for constructing the comprehensive demand response model includes: Modeling the load curtailment involved in price-based demand response: In the formula, p t,j represents the elasticity coefficient of the price elasticity matrix, represents the initial load size of the i-th at time t, represents the size of the load that can be reduced at time t. Δe t are the initial energy price and energy price change at time t respectively; The reducible load of the i-th type load is further obtained as follows: In the formula, represents the initial reducible load size of the i-th type of load, The price demand elasticity matrix factor that represents the load that can be reduced, e t Indicates the price of relevant load energy; The transferable load model involved in price-based demand response is constructed as follows: In the formula, represents the initial transferable load size of the i-th type of load, A price demand elasticity matrix factor representing the transferable load; According to the characteristics of some heat loads, the electricity-to-heat equipment is used to convert them into electrical loads. The model is expressed as follows: In the formula denote the replaceable electrical load and thermal load, respectively, eth represents the electric heat substitution coefficient and is calculated as follows: In the above formula, h e 、h h are the unit calorific values ​​of electricity and thermal energy respectively; In summary, the final electric heating load can be expressed as follows: i∈{electricity;heat} Where P i DR represents the final load size of the i-th energy source, P i It represents the base load size of the i-th energy source; Comprehensive demand response compensation costs for various loads: Where R CL , R TL , R FL They represent the compensation costs of load reduction, load transfer and load substitution for demand response, respectively. Respectively represent the compensation price of electrical and thermal loads that can be reduced per unit of electricity, Respectively represent the compensation price of transferable electrical and thermal load per unit of electricity, They respectively represent the compensation price for replacing electrical and thermal loads per unit of electricity.

4. The distributed trading method for smart distribution network according to claim 3, characterized in that: The construction method of the system network loss model is as follows: The power transfer generated by the energy transaction between the buyer and the seller is: in represents the power flowing through line l between energy buyer j and energy seller i, represents an element in the power transfer distribution factor matrix (PTDF), which is the contribution of power transfer between seller i and buyer j; Network losses caused by power transmission: In the formula, Represents the total power loss incurred by both parties in the energy transaction process; γ l It represents the ratio of the resistance of line l to the square of the network standard voltage, and is calculated as follows: In the formula, R l Indicates the equivalent resistance of line l, V b Indicates the network standard voltage; During the energy transmission process, buyers and sellers need to pay the network owner a transaction fee of: In the above formula, JF(P ij ) represents the transaction cost in the energy transaction process, Indicates the line usage fee per unit electrical distance, d ij Indicates the power transmission distance.

5. The distributed trading method for smart distribution network according to claim 4, characterized in that: The peer-to-peer market transaction model of the two parties includes a buyer transaction model and a seller transaction model, wherein the buyer transaction model construction method includes: From the buyer's perspective, the transaction costs of energy trading are borne by the buyer, while the system network loss costs are only borne by the seller. Therefore, for the buyer, its objective function is constructed as follows: In the formula, GF j is the total revenue of the energy transaction buyer, U j (P jt ) is the utility function of the energy buyer, P jt represents the total energy demand of buyer j at time t, is the energy price between buyer j and seller i, P jit The amount of energy purchased, represents the energy price in the upper market, represents the energy purchased by the buyer from the upper market, N S represents the total number of energy buyers; The constraints include the following: The total energy demand of buyer j consists of: In the formula, is the demand response program that participates in energy buyer j at time t; Power balance constraints: Where P jit Indicates the amount of energy purchased, represents the energy purchased by the buyer from the upper market, represents the energy demand of buyer j, denotes participation in the demand response program of energy buyer j at time t; Demand response constraints: Where η is the percentage of participating energy in demand response, represents the energy demand of buyer j, and T represents the total energy transaction period.

6. The distributed trading method for smart distribution network according to claim 5, characterized in that: The method for constructing the seller transaction model includes: The seller needs to bear the total loss cost but does not need to bear the transaction cost, so the objective function is as follows: Where MF is the total profit of energy sellers, C i (P it ) is the cost function of the energy seller, represents the energy loss price, represents the total power loss caused by the transaction between buyer j and seller i, N B represents the total number of energy buyers, P it Represents the amount of energy sold by energy seller i. The constraints consist of the following: Constraints on sales capacity composition: Where P it It represents the amount of energy sold by energy seller i, which is composed of the energy purchased by the buyer and the system network loss; Constraints on energy sales: In the formula, They respectively represent the upper and lower limits of energy sales for energy sellers.

7. The distributed trading method for smart distribution network according to claim 6, characterized in that: The method for constructing the distributed transaction model includes: The overall goal of the problem is to maximize the benefits of all buyers and sellers in the local market. First, the problem can be modeled as a single subject, and its objective function is as follows: In the formula, MF i GF j Respectively represent the total revenue of buyers and sellers of energy transactions; In addition to the above constraints, coupling constraints are added to ensure power balance during the transaction process, as follows: In the formula, is a dual variable, which means the energy price that buyer j buys energy from seller i. The above constraints can be used as a market clearing condition, which stipulates that at each time interval, the energy purchased by each buyer must be equal to the energy sold by each seller; The Lagrangian of the above optimization problem is as follows: Where μ is the Lagrangian coefficient, which represents the penalty coefficient and is used to increase the range of the Lagrangian function. From this, we can derive the sub-problems of energy buyers and sellers: Energy sellers: P ijt ≥0 Where P it represents the sum of the energy sold by energy seller i to all energy buyers at time t, are the upper and lower limits of energy sold, P ijt It represents the energy of the transaction between seller i and buyer j at time t; Energy buyers: In the formula, represents the penalty term in the augmented Lagrangian function, P jt represents the total demand of energy buyer j at time t. All the main variables in the sub-problems of buyers and sellers can be calculated according to the following formula: In the formula are the sizes of the main variables calculated by the energy buyer and seller from the corresponding problems using the incremental gradient method, and the iteration stop condition is given by the following formula: In the formula, α represents a very small positive number; In order to calculate the satisfaction of market participants and market fairness, a mathematical model of the market fairness index is established through the following formula: Where GP represents the fairness index, N represents the number of market participants, and MJ represents the category of market participants, including energy buyers and sellers.

8. A distributed trading system for a smart distribution network, characterized in that: Applicable to the distributed trading method for smart distribution network as described in any one of claims 1 to 7.

9. A computer device, characterized in that: The invention comprises a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, the distributed trading method of the smart distribution network as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the distributed trading method for the smart distribution network is implemented as described in any one of claims 1 to 7.

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