Energy trading method, device, computer equipment and computer-readable storage medium

Abstract

This application belongs to the technical field of energy trading, and relates to an energy trading method, device, equipment and storage medium. Among them, the energy trading method determines the loss sensitivity coefficient by constructing a power flow model to obtain the bilateral trading weight coefficient, and incorporates it into the idealized first optimization model to obtain a second optimization model considering the physical constraints (security constraints and location constraints) of the distribution network. For the second optimization model, according to the preset clearing algorithm, the bilateral trading strategies among the producers and consumers are obtained. The obtained bilateral trading strategies can effectively encourage or contain some bilateral transactions, so as to ensure the operation reliability of the distribution network while taking into account the social benefit optimality of the bilateral market. At the same time, by solving the bilateral trading strategies of the producers and consumers respectively, the privacy rights of each independent producer and consumer are protected.

Description

Technical Field

[0001] This application belongs to the technical field of energy trading, and particularly relates to an energy trading method, an energy trading device, a computer device, and a computer-readable storage medium. Background Art

[0002] With the increase in decision-making entities in the distribution network and the deepening reform of the electricity sales side power market, the modern electricity market shows a trend of decentralization and flexibility. The next-generation energy management technology P2P (Peer to Peer) trading focuses on building an electricity market centered on prosumers, and encourages each decision-making entity to independently participate in bilateral trading negotiations.

[0003] The traditional Distflow power flow model cannot guarantee the global optimality of P2P market clearing and is difficult to achieve the optimal social benefits. In addition, in a P2P market with a high degree of independence of decision-making entities, how to ensure the safe operation of the distribution network through limited information interaction while ensuring the rationality of bilateral transactions and the privacy of decision-making entities is a technical problem that needs to be solved urgently by those skilled in the art.

[0004] The foregoing description is to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] The technical problem solved by this application is that the prior art cannot guarantee the global optimality of P2P market clearing and is difficult to achieve the optimal social benefits. For this reason, an energy trading method, an energy trading device, a computer device, and a computer-readable storage medium are provided.

[0006] The technical problem of this application is solved by adopting the following technical solutions:

[0007] This application provides an energy trading method, including the following steps: respectively obtaining the cost parameters and utility models of prosumers, and constructing a first optimization model according to the cost parameters and utility models; obtaining the power transfer distribution factor and voltage sensitivity factor, constructing a power flow model according to the power transfer distribution factor and voltage sensitivity factor, and determining the loss sensitivity factor according to the power flow model; obtaining the transaction loss information of prosumers, and inputting the transaction loss information into the power flow model to obtain a bilateral transaction weight coefficient model including the loss sensitivity factor; wherein, the transaction loss information includes location information and distribution network security constraint information; merging the bilateral transaction weight coefficient model with the first optimization model to obtain a second optimization model; clearing the second optimization model according to a preset algorithm to obtain the bilateral transaction strategies between prosumers, and the bilateral transaction strategies are used to determine the transaction price and transaction quantity between prosumers.

[0008] In an alternative embodiment of the present application, obtaining the utility models of the producer and the consumer includes: obtaining the consumer utility parameters and the first active power within a preset market period, and establishing a consumer utility model according to the consumer utility parameters and the first active power; wherein, the consumer utility parameters include a preset primary preference coefficient and a secondary benefit coefficient; obtaining the producer utility parameters and the output power, and establishing a producer utility model according to the producer utility parameters and the output power.

[0009] In an alternative embodiment of the present application, constructing a first optimization model according to the cost parameters and the utility model includes: obtaining the cost parameters, where the cost parameters include: a first trading strategy, a participation cost function, and a second active power; wherein, the first trading strategy is used to indicate the quantity and unit price of purchases between prosumers, the participation cost function is used to calculate the additional cost for prosumers to participate in the trading market, and the second power is used to indicate the active power of the prosumer's renewable energy unit within a preset market period; according to the cost parameters and the utility model, respectively construct the total revenue models of the prosumers; based on the total revenue models, construct a first optimization model, and the first optimization model is an optimization model for obtaining the energy trading strategy that maximizes the social benefits of prosumers under the assumption of ignoring the physical constraints of the distribution network and the bilateral trading network losses.

[0010] In an alternative embodiment of the present application, obtaining the power transfer distribution coefficient and the voltage sensitivity coefficient, constructing a power flow model according to the power transfer distribution coefficient and the voltage sensitivity coefficient, and determining the loss sensitivity coefficient according to the power flow model includes: based on the loss relationship between the power quantities of the nonlinear branch and the linear branch, constructing a first power flow model, and the first power flow model is an ideal model that ignores the branch losses; obtaining the preset power transfer distribution coefficient and voltage sensitivity coefficient and incorporating them into the first power flow model to obtain a second power flow model; obtaining the branch loss parameters, and determining the loss sensitivity coefficient of the corresponding node through the second power flow model based on the branch loss parameters; wherein the branch loss parameters include the resistance and reactance of the branch.

[0011] In an alternative embodiment of the present application, obtaining the trading loss information of the prosumer and inputting the trading loss information into the power flow model to obtain a bilateral trading weight coefficient model including the loss sensitivity coefficient includes: obtaining the distribution network security constraint information through the Lagrangian function of the first optimization model according to the power flow model, where the distribution network security constraint information includes: global voltage constraint information and global line congestion constraint information; substituting the location information into the second power flow model to obtain the loss sensitivity coefficient of the corresponding node; obtaining the bilateral trading weight coefficient model of the corresponding node according to the loss sensitivity coefficient of the corresponding node and the distribution network security constraint information.

[0012] In an alternative embodiment of the present application, merging the bilateral transaction weight coefficient model with the first optimization model to obtain a second optimization model includes: obtaining a preset congestion constraint index function and a voltage constraint index function, and merging the congestion constraint index function, the voltage constraint index function, the bilateral transaction weight coefficient model, and the first optimization model to obtain a second optimization model. The second optimization model is an optimization model for an energy trading strategy that considers the weight of physical constraints of the distribution network to maximize the social benefits of prosumers.

[0013] In an alternative embodiment of the present application, obtaining the bilateral trading strategies among prosumers includes: iteratively adjusting the parameters of the bilateral transaction weight coefficient model in the second optimization model based on the subgradient iteration method until the bilateral transaction weight coefficient model meets the preset convergence condition, and determining the bilateral transaction weight coefficients of the prosumers; inputting the bilateral transaction weight coefficients into the second optimization model, and distributively clearing the second optimization model according to the alternating direction multiplier method to obtain the bilateral trading strategies.

[0014] The present application also provides an energy trading device, including: a first optimization model construction module, which is used to respectively obtain the cost parameters and utility models of prosumers, and construct a first optimization model according to the cost parameters and utility models; a power flow model construction module, which is used to obtain the power transfer distribution coefficient and voltage sensitivity coefficient, construct a power flow model according to the power transfer distribution coefficient and voltage sensitivity coefficient, and determine the loss sensitivity coefficient according to the power flow model; a bilateral transaction weight coefficient model construction module, which is used to obtain the trading loss information of prosumers, input the trading loss information into the power flow model to obtain a bilateral transaction weight coefficient model including the loss sensitivity coefficient; wherein the trading loss information includes location information and distribution network security constraint information; a second optimization model construction module, which is used to merge the bilateral transaction weight coefficient model with the first optimization model to obtain a second optimization model; a trading strategy determination module, which is used to clear the second optimization model according to a preset algorithm to obtain the bilateral trading strategies among prosumers, and the bilateral trading strategies are used to determine the trading price and trading quantity among prosumers.

[0015] The present application also provides a computer device, including a processor and a memory: the processor is used to execute the computer program stored in the memory to implement the method as described above.

[0016] The present application also provides a computer-readable storage medium, storing a computer program, which when executed by a processor, implements the steps as described above.

[0017] Adopting the embodiments of the present application has the following beneficial effects:

[0018] This application determines the loss sensitivity coefficient by constructing a power flow model to obtain the bilateral transaction weight coefficient, and incorporates it into the idealized first optimization model to obtain a second optimization model that considers the physical constraints of the distribution network (safety constraints and location constraints). The second optimization model obtains the bilateral transaction strategy between each producer and consumer according to the preset clearing algorithm. The obtained bilateral transaction strategy can effectively stimulate or curb some bilateral transactions, thereby ensuring the reliability of the distribution network operation while taking into account the optimality of the social benefits of the bilateral market. At the same time, the bilateral transaction strategy of each producer and consumer is adopted to ensure the privacy rights of each independent subject of the producer and consumer. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 A schematic diagram of the energy trading method provided in Example 1 of the present application;

[0021] Figure 2 A schematic diagram of the energy trading method provided in Example 2 of the present application;

[0022] Figure 3 A schematic diagram of an energy trading application scenario provided in Example 2 of this application;

[0023] Figure 4 A schematic diagram of a day-ahead forecast curve of a controllable load of a consumer provided in Example 2 of the present application;

[0024] Figure 5 A schematic diagram of a day-ahead forecast curve of a producer's renewable energy unit provided in Example 2 of the present application;

[0025] Figure 6 A schematic diagram of a user voltage curve at a distribution network node provided in Embodiment 2 of the present application;

[0026] Figure 7 A schematic diagram of an active power curve of a power supply branch of a distribution network provided in Embodiment 2 of the present application;

[0027] Figure 8 A schematic diagram of the decomposition of the clearing electricity selling price of producer 5 provided in Example 2 of the present application;

[0028] Figure 9 A schematic diagram of the decomposition of the electricity purchase price of consumer 3 provided in Example 2 of the present application;

[0029] Figure 10 Schematic diagram of the cleared power of the total market cycle consumer 5 provided in the second embodiment of the present application;

[0030] Figure 11 Schematic diagram of the cleared power of the total market cycle producer 1 provided in the second embodiment of the present application;

[0031] Figure 12 Schematic diagram of the evolution curve method of consumer 1 on the "consumer side" provided in the second embodiment of the present application;

[0032] Figure 13 Schematic diagram of the evolution curve of consumer 1 on the "producer side" provided in the second embodiment of the present application;

[0033] Figure 14 For the second embodiment of the present application, different adjustment parameters μ l and μ v First schematic diagram of the convergence curve of the global error δ;

[0034] Figure 15 For the second embodiment of the present application, different adjustment parameters μ l and μ v First schematic diagram of the convergence curve of the global error δ;

[0035] Figure 16 Schematic diagram of the structure of the energy trading device provided in the third embodiment of the present application;

[0036] Figure 17 Schematic diagram of the structure of the computer device provided in the fourth embodiment of the present application. Detailed implementation manners

[0037] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0038] The following further describes the embodiments of the present application with reference to the accompanying drawings.

[0039] Embodiment 1

[0040] Figure 1 Schematic diagram of the energy trading method flow provided in the first embodiment of the present application. To clearly describe the energy trading method provided in the first embodiment of the present application, please refer to Figure 1 .

[0041] The next-generation energy management technology has focused on building a prosumer-centric P2P energy trading market to encourage decision-making entities to independently participate in bilateral trading negotiations. Among them, the traditional Distflow power flow model in the existing technology cannot guarantee the global optimality of P2P market clearing. Therefore, it is necessary to perform reasonable linearization on the power flow model. At the same time, how to ensure the safe operation of the energy transmission network, the rationality of bilateral transactions, and the privacy of decision-making entities through limited information interaction is the focus of current research. Based on this, this application proposes an energy trading method, including steps S110 to S150. Among them, the energy trading method proposed in this application can be applied to, including but not limited to, thermal energy, electrical energy, natural gas, petroleum, etc., and can also be energy that may appear in the future but has not been detected or utilized yet. In this embodiment, for the convenience of understanding, power trading is used as an example for illustration. The distribution network operator sets trading weight coefficients based on the prosumer's geographical location and the state of the distribution network to encourage or curb some bilateral transactions.

[0042] Step S110: Obtain the cost parameters and utility models of prosumers respectively, and construct a first optimization model according to the cost parameters and utility models.

[0043] In one embodiment, in step S110: Obtain the utility models of producers and consumers, including: obtaining consumer utility parameters and the first active power within a preset market period, and establishing a consumer utility model according to the consumer utility parameters and the first active power; among them, the consumer utility parameters include a preset primary preference coefficient and a secondary benefit coefficient; obtain producer utility parameters and the output power of the output, and establish a producer utility model according to the producer utility parameters and the output power of the output.

[0044] In one embodiment, in step S110: Construct a first optimization model according to the cost parameters and utility models, including: obtaining cost parameters, where the cost parameters include: the first trading strategy, the participation cost function, and the second active power; among them, the first trading strategy is used to indicate the quantity and unit price purchased between prosumers, the participation cost function is used to calculate the additional cost for prosumers to participate in the trading market, and the second power is used to indicate the active power of the prosumer's renewable energy unit within a preset market period; according to the cost parameters and utility models, respectively construct the total revenue models of prosumers; based on the total revenue models, construct a first optimization model, and the first optimization model is an optimization model for obtaining an energy trading strategy that maximizes the social benefits of prosumers under the assumption of ignoring the physical constraints of the distribution network and the network losses of bilateral transactions.

[0045] In one embodiment, establishing a consumer utility model according to the consumer utility parameters and the first active power can be expressed as:

[0046]

[0047] Among them, λ j is the first preference coefficient of consumer j, θ j is the secondary benefit coefficient, which is a predetermined constant. is the first active power of consumer j of the controllable load within the t market cycle, and are the lower limit and upper limit of the controllable load power respectively. Further, assuming that there are no physical constraints in the market, since each bilateral electricity transaction has a different transaction electricity price, by obtaining the cost parameters of consumer j, the total revenue model of the consumer can be determined and established, which can be expressed as:

[0048]

[0049] Among them, is the set of producers; the first trading strategy is used to specify the quantity and unit price of purchases between prosumers, specifically and and are the electricity purchase price and the transaction electricity quantity of consumer j from producer i respectively; ζ is the additional cost function for consumer j to participate in the P2P market, that is, the participation cost function; is the active power of the renewable energy unit of consumer j within the t market cycle, that is, the second power of consumer j.

[0050] Similarly, the producer utility model can be obtained and expressed as:

[0051]

[0052] Among them, is the output power of the gas turbine unit, that is, the output power of producer i, and are the lower limit and upper limit of the output of the gas turbine unit respectively; a i and b i are the cost function parameters of the gas turbine unit, that is, the utility parameters of producer i. Assuming that the heterogeneous preferences of consumer j and the energy attributes of producer i are ignored, the total revenue model of producer i can be considered as:

[0053]

[0054] Electric energy and the selling electricity price, that is, the information contained in the first trading strategy. ζ is the additional cost function for producer i to participate in the P2P market, that is, the participation cost function; is the active power of the renewable energy unit of producer i within the t market cycle, that is, the second power of producer i.

[0055] Through the total revenue model of prosumers, the first optimization model can be constructed. The first optimization model is a P2P electricity market optimization model that maximizes the social benefits of prosumers under the assumption of ignoring the physical constraints of the distribution network and the losses of bilateral transactions. Specifically, it can be expressed as:

[0056]

[0057] Specifically, considering the requirement of the supply-demand balance constraint that the electricity purchase and sale volumes of producer i and consumer j are equal in bilateral transactions, that is:

[0058]

[0059] Among them, is the dual variable of the supply-demand balance constraint and can be regarded as the clearing price of bilateral transactions between producers and consumers. Assuming that the controllable load and the power constraint of the gas turbine unit are ignored, the corresponding Lagrangian function is:

[0060]

[0061] It can be understood that the first optimization model is an ideal model established by ignoring the physical constraints of the distribution network. Based on this, the clearing strategy and bidding strategy of each prosumer under the optimal social benefit model can be preliminarily determined. Assuming that the physical constraints of the distribution network and the losses of bilateral transactions are ignored, the supply-demand balance constraint in the bilateral market is as follows:

[0062]

[0063] When the P2P market is in an equilibrium state, the electricity selling prices of producers are the same, that is, it satisfies Among them, π* is the clearing price of the P2P market and can be expressed as:

[0064]

[0065] Furthermore, the optimal transaction clearing volumes and of generator i and consumer j can be further obtained and are respectively:

[0066]

[0067]

[0068] Among them, G nThe active power of the renewable energy unit of the producer i or the consumer j in the t market cycle. It should be noted that since the first optimization model is an ideal model, and it can be seen from formulas (9) to (11) that the parameters therein are preset values, that is, the finally obtained trading strategy is a fixed value. The purpose of establishing the first optimization model is to lay the foundation and assist in understanding for obtaining the second optimization model and the dual-standard trading strategy later.

[0069] Step S120: Obtain the power transfer distribution coefficient and the voltage sensitivity coefficient, construct a power flow model according to the power transfer distribution coefficient and the voltage sensitivity coefficient, and determine the loss sensitivity coefficient according to the power flow model.

[0070] In an embodiment, in step S120: Obtain the power transfer distribution coefficient and the voltage sensitivity coefficient, construct a power flow model according to the power transfer distribution coefficient and the voltage sensitivity coefficient, and determine the loss sensitivity coefficient according to the power flow model, including: Based on the loss relationship between the power of the nonlinear branch and the linear branch, construct a first power flow model, and the first power flow model is an ideal model that ignores the branch loss; Obtain the preset power transfer distribution coefficient and voltage sensitivity coefficient and incorporate them into the first power flow model to obtain a second power flow model; Obtain the branch loss parameters, and determine the loss sensitivity coefficient of the corresponding node through the second power flow model based on the branch loss parameters; where the branch loss parameters include the resistance and reactance of the branch.

[0071] In an embodiment, for the power flow model mentioned in this embodiment, preferably, it can be the Distflow power flow model. Considering that the nonlinear branch loss in the Distflow power flow model is much smaller than the power of the linear branch, that is, based on the loss relationship between the power of the nonlinear branch and the linear branch, construct a first power flow model, and the first power flow model is an ideal model that ignores the branch loss. It is to ignore the branch loss term and rewrite the Distflow power flow model to obtain:

[0072]

[0073] Among them, is the active power flowing through the distribution network line l, is the reactive power flowing through the distribution network line l. δ(l) is the end node of the distribution network line l compared with the slack node, pa(i) is the set of parent nodes of node i, and m is a node in the distribution network. and are respectively the active power and reactive power flowing into the end node of the distribution network line l compared with the slack node. r mi and r mi are respectively the resistance and reactance of node m. u i and u m are respectively the voltage matrices of the producer i and node m.

[0074] Further, obtain the preset power transfer distribution coefficient and voltage sensitivity coefficient and incorporate them into the first power flow model. It can be known that the second power flow model is:

[0075] P f = MP in , Q f = MQ in (13)

[0076]

[0077] Among them, P f is the active power matrix of the distribution network line, Q f is the reactive power matrix of the distribution network line, u is the node voltage matrix of the distribution network, u base is the quasi-voltage matrix of the distribution network node, M is the power transfer distribution coefficient matrix, R and X are the resistance matrix and reactance matrix respectively, ψ is the voltage sensitivity coefficient matrix, and is the sensitivity coefficient between the net load of node i and the voltage of node j. In addition, the elements and need to meet the following conditions:

[0078]

[0079] Among them, Ψ(0, i) is the set of distribution network lines that determine the optimal path from the slack node to node i according to the graph search algorithm, and r j is the resistance of the distribution network line with node j as the end node.

[0080] Obtain the branch loss parameters, and based on the branch loss parameters, determine the loss sensitivity coefficient of the corresponding node through the second power flow model, that is, determine the relationship between the net load Pin, Qin of the PQ node and the distribution network loss according to the loss sensitivity coefficient matrix. Specifically:

[0081]

[0082] Among them, ΔP in is the net load difference matrix, φ p is the active power loss sensitivity coefficient matrix of the distribution network, φ q is the reactive power loss sensitivity coefficient matrix of the distribution network, P loss is the active power loss corresponding to the PQ node, and q loss is the reactive power loss corresponding to the PQ node. Therefore, the loss sensitivity coefficient can be determined according to the power flow model. Specifically:

[0083]

[0084]

[0085] Among them, is the overall active power loss P of the distribution network loss The sensitivity coefficient corresponding to the net active load of node i; is the overall reactive power loss Q of the distribution network loss The sensitivity coefficient corresponding to the net active load of node i. r l is the resistance of branch l, x l is the reactance of branch l. and are the clearing values of the optimal power flow of the distribution network in the non-P2P trading mode, and are set as the initial values of the linearization of the loss sensitivity coefficient.

[0086] Step S130: Obtain the transaction loss information of prosumers, and input the transaction loss information into the power flow model to obtain a bilateral transaction weight coefficient model including loss sensitivity coefficients; among them, the transaction loss information includes location information and distribution network security constraint information.

[0087] In one embodiment, in step S130: Obtain the transaction loss information of prosumers, and input the transaction loss information into the power flow model to obtain a bilateral transaction weight coefficient model including loss sensitivity coefficients, including: Obtain the distribution network security constraint information through the Lagrangian function of the first optimization model according to the power flow model, and the distribution network security constraint information includes: global voltage constraint information and global line congestion constraint information; Obtain the loss sensitivity coefficient of the corresponding node according to the location information substituting into the second power flow model; Obtain the bilateral transaction weight coefficient model of the corresponding node according to the loss sensitivity coefficient of the corresponding node and the distribution network security constraint information.

[0088] In one embodiment, obtain the distribution network security constraint information through the Lagrangian function of the first optimization model according to the power flow model. Specifically, the bilateral transaction loss is included in the optimization objective through the Distflow power flow linear power flow model obtained in step S120. According to the global voltage constraint and the global line congestion constraint, the Lagrangian function of the first optimization model can be expressed as:

[0089]

[0090] Among them, π0 is the retail electricity price of the main grid, is the active power loss in the P2P trading mode of the distribution network. α is the dual variable of the global power flow constraint, and β is the dual variable of the global voltage constraint. It is not a fixed value, but a value to be obtained, and can be expressed as: and In addition, is based on the bilateral transaction sold electricity quantity x of producer i i and the bilateral transaction purchased electricity quantity y of consumer j j Determine the active power quantity of distribution network line l; It is based on the bilateral transaction power sales volume x of producer i i and the bilateral transaction power purchase volume y of consumer j j to determine the voltage of node m, which can be expressed as:

[0091]

[0092]

[0093] where is the initial active power value of line l without bilateral transactions, is the initial voltage value of node m without bilateral transactions. The location information is reflected by the location of the node purchase. Finally, according to the loss sensitivity coefficient of the corresponding node determined in step 120 and the distribution network security constraint information of the corresponding node obtained in step S130, the bilateral transaction weight coefficient model of the corresponding node is obtained. That is, according to the KKT conditions of the relaxation problems of producer i and consumer j, the bilateral transaction weight coefficient τ of the corresponding node can be finally determined ij The mathematical model, that is, the bilateral transaction weight coefficient model, can be expressed as:

[0094]

[0095] The sensitivity coefficient of the bilateral transaction between i and j to the voltage of node m. The calculation method can follow the following formula:

[0096]

[0097]

[0098]

[0099] The calculation method for the above coefficients has been described in the previous text and will not be repeated here. Therefore, through step S130, the bilateral transaction weight coefficient model can be obtained. The bilateral transaction weight coefficient model also takes into account the physical constraints of the distribution network such as the geographical locations of producers and consumers and the distribution network security constraints. And it should be noted that the bilateral transaction weight coefficient model obtained in step S130 is divided into the power sales weight coefficient from producer i to consumer j and the power purchase weight coefficient

[0100] Step S140: Combine the bilateral transaction weight coefficient model with the first optimization model to obtain the second optimization model.

[0101] In one embodiment, in Step S140: Combine the bilateral transaction weight coefficient model with the first optimization model to obtain the second optimization model, including: Obtain the preset congestion constraint index function and voltage constraint index function, and combine the congestion constraint index function, voltage constraint index function, bilateral transaction weight coefficient model with the first optimization model to obtain the second optimization model. The second optimization model is an optimization model for an energy trading strategy that considers the physical constraint weight of the distribution network to maximize the social benefits of prosumers.

[0102] In one embodiment, starting from establishing the ideal first optimization model in Step S110, and then to the bilateral transaction weight coefficient model with physical constraint significance determined in Step S120 and Step S130, the second optimization model for determining the prosumer trading strategy in the P2P market can be determined. That is, obtain the preset congestion constraint index function and voltage constraint index function, and combine the congestion constraint index function, voltage constraint index function, bilateral transaction weight coefficient model with the first optimization model to obtain the second optimization model. The second optimization model is an optimization model for an energy trading strategy that considers the physical constraint weight of the distribution network to maximize the social benefits of prosumers, ensuring the strong convexity of the model and the optimality of market clearing. Specifically, it can be expressed as:

[0103]

[0104] Among them, is the preset congestion constraint index function, is the preset voltage constraint index function. The two are equivalent to the complementary slackness theorem, that is, when the distribution network security operation constraints are met, the bilateral transaction weights will not further reduce the social benefits. Further, by introducing the auxiliary variable x ij rewrite the bilateral coupling constraint. Specifically, it can be:

[0105]

[0106] The coefficient can be understood as the intended selling price from producer i to consumer j and the intended purchase price from consumer j to producer i. Similar to the bilateral transaction weight model, at this time both are not specific values, and in the final result, the two should be equal. Therefore, they need to be determined through calculation, and they are also the information that the double-label trading strategy needs to clarify.

[0107] Step S150: Settle the second optimization model according to the preset algorithm to obtain the bilateral trading strategies among prosumers. The bilateral trading strategies are used to determine the trading price and trading quantity among prosumers.

[0108] In one embodiment, in step S150: Obtain the bilateral trading strategies among various prosumers, including: iteratively adjusting the parameters of the bilateral trading weight coefficient model in the second optimization model based on the subgradient iteration method until the bilateral trading weight coefficient model meets the preset convergence condition, and determining the bilateral trading weight coefficients of the prosumers; input the bilateral trading weight coefficients into the second optimization model, and distributively clear the second optimization model according to the alternating direction method of multipliers to obtain the bilateral trading strategies.

[0109] In one embodiment, considering the protection of prosumer privacy and the scalability of the P2P market, in this embodiment, it is preferably to clear the second optimization model by the alternating direction method of multipliers. Each prosumer solves the strongly convex second-order objective function in parallel, and each prosumer can be divided into its own sub-problem for solution. Among them, the sub-problem of producer i is:

[0110]

[0111] Similarly, the mathematical expression of the sub-problem of any consumer j is:

[0112]

[0113] Among them, ф is the preset penalty term coefficient, and the value of ф will affect the convergence speed. k represents the number of iterative calculations. Further, according to the optimal solution [x ij,k+1 , y ij,k+1 determined by the Lagrangian function in step S130, according to the alternating direction method of multipliers, the expression of the Lagrangian term is updated as:

[0114] x ij.k+1 =(x ij，k+1 +y ij.k+1 ) / 2 (31)

[0115]

[0116]

[0117] According to the complementary slackness theory, update the dual variables α and β along the negative direction of the gradient of the objective function to maximize the reduction of the objective function. The update expression is:

[0118]

[0119]

[0120]

[0121]

[0122] Among them, []+ is an operator mapped to the non-negative domain, and ensuring the non-negativity of the dual coefficient is denoted as [x] + = max(0, x). μ l and μ v are the adjustment step size parameters of the global coupling constraint. To ensure the convergence of the alternating direction multiplier method and increase the convergence speed, the trading weight coefficient The update expression can be:

[0123]

[0124] Among them, and are the excessive weight coefficients determined according to the updated dual variables α k+1 and β k+1 respectively. Therefore, it can be seen that the bilateral weight coefficient, the final clearing price, and the clearing volume are all gradually converged and determined in the continuous iterative parameter adjustment process. Therefore, to judge whether the market clearing converges, the corresponding algorithm error and convergence condition can be:

[0125]

[0126] Among them, ε k is the global convergence error at the k-th iteration, δ k.se and δ k.te correspond to the 2-norm values of the original residual and the dual residual of the prosumer clearing volume respectively, and δ k.α corresponds to the 2-norm value between the dual coefficients α k+1 and α k representing the power flow constraint boundary, and δ k.β corresponds to the 2-norm value between the dual coefficients β k+1 and β k representing the voltage constraint boundary. Therefore, according to the energy trading method provided by the present application, at the end, the bilateral trading weight coefficient, the clearing volume, and the clearing price are simultaneously cleared, and the clearing is carried out from the perspective of the prosumer respectively. The distributed clearing mechanism based on the alternating direction multiplier method ensures the global optimality while only sharing the prosumer clearing power information, protects the privacy of independent entities, and finally obtains the bilateral trading strategy between prosumers.

[0127] This application can determine the loss sensitivity coefficient through constructing a power flow model to obtain the bilateral transaction weight coefficient, and incorporate it into an ideal first optimization model to obtain a second optimization model considering the physical constraints of the distribution network (security constraints and location constraints). By using a preset clearing algorithm for the second optimization model, bilateral transaction strategies among prosumers can be obtained. The obtained bilateral transaction strategies can effectively incentivize or curb some bilateral transactions, thereby ensuring the operational reliability of the distribution network while taking into account the optimal social benefits of the bilateral market. At the same time, by solving the bilateral transaction strategies of prosumers separately, the privacy rights of each prosumer as an independent entity are protected. In addition, further, the energy trading method provided by an embodiment of this application can also incorporate the physical constraints of the distribution network into the distributed clearing step in the form of bilateral transaction weight coefficients, incentivize some bilateral transactions that maintain the stability of the distribution network and curb bilateral transactions that cause power flow congestion and voltage crossing, taking into account the operational reliability of the distribution network and the optimality of market clearing. And it is proposed that the bilateral transaction weight coefficient is based on the sensitivity coefficient and the Distflow linear power flow model, ensuring the strong convexity of the P2P market optimization model and the optimality of the market clearing result. Moreover, the distributed clearing mechanism based on the alternating direction multiplier method ensures the global optimality while only sharing the prosumer clearing power information, protecting the privacy rights of independent entities. It reduces the operations of users, increases the convenience of users, and improves the user experience.

[0128] Embodiment 2

[0129] Figure 2 It is a schematic flowchart of the energy trading method provided by Embodiment 2 of this application. To clearly describe the energy trading method provided by Embodiment 2 of this application, please refer to Figures 2 to 15 .

[0130] In an implementation manner, to facilitate understanding of the energy trading method provided in this embodiment, reference can be made to Figure 3 for understanding. Figure 3 It is a schematic diagram of the energy trading application scenario provided by Embodiment 2 of this application. Specifically, Figure 3 the shown application scenario diagram is actually an improved IEEE-69 node distribution network topology diagram, where producers 1 to 5 and consumers 1 to 5 are respectively set on different nodes. Among them, the line between nodes 3 and 4 is a power flow congestion line, and nodes 27 and 65 are voltage lower limit nodes. The energy trading method provided by Embodiment 2 of this application includes steps S210 to S280.

[0131] Step S210: Obtain the cost parameters and utility models of prosumers respectively, and construct a first optimization model according to the cost parameters and utility models.

[0132] Step S220: Obtain the power transfer distribution coefficient and the voltage sensitivity coefficient, construct a power flow model based on the power transfer distribution coefficient and the voltage sensitivity coefficient, and determine the loss sensitivity coefficient according to the power flow model.

[0133] Step S230: Obtain the transaction loss information of prosumers, and input the transaction loss information into the power flow model to obtain a bilateral transaction weight coefficient model including the loss sensitivity coefficient; wherein, the transaction loss information includes location information and distribution network security constraint information.

[0134] Step S240: Merge the bilateral transaction weight coefficient model with the first optimization model to obtain a second optimization model.

[0135] In an embodiment, for steps S210 to S240, a detailed description has been given in Embodiment 1, and specific reference can be made to the foregoing. Further, for the convenience of understanding the calculation processes of steps S210 to S240 and formulas (1) to (39), configurations are respectively made for producers and consumers, and the configuration parameters can be referred to Table 1:

[0136]

[0137]

[0138] Table 1 Producer and consumer configuration parameters

[0139] The day-ahead prediction curves of the renewable energy units and controllable load units of each prosumer are as Figure 4 、 Figure 5 shown, Figure 4 which is a schematic diagram of the day-ahead prediction curve of the controllable load of the consumer provided in Embodiment 2 of the present application; Figure 5 which is a schematic diagram of the day-ahead prediction curve of the renewable energy unit of the producer provided in Embodiment 2 of the present application.

[0140] Step S250: Based on the subgradient iteration method, perform iterative parameter adjustment on the bilateral transaction weight coefficient model to obtain the bilateral transaction weight coefficient.

[0141] Step S260: Input the bilateral weight coefficient into the second optimization model, and distributively clear the second optimization model according to the alternating direction multiplier method.

[0142] Step S270: Determine whether the preset convergence condition is satisfied;

[0143] If the preset convergence condition is not satisfied, return to step S250 to perform the next round of iterative parameter adjustment; if the preset convergence condition is satisfied, execute step S280: Determine the bilateral transaction strategy and output it.

[0144] In one embodiment, the calculation and judgment processes in steps S250 to S270 have been described in detail in the previous Embodiment 1, and will not be elaborated here. Specifically, the solution mentioned in this embodiment focuses on reflecting the influence of the bilateral transaction weight coefficient on the determined bilateral transaction strategy. It can be understood that the bilateral transaction weight coefficient model obtained in step S240 is not a determined value, and needs to be determined by an iterative parameter adjustment through a preset algorithm. Therefore, the iterative method in steps S250 to S270 needs to be executed. Further, for the energy trading method provided in this embodiment, in the process of determining the bilateral transaction strategy, the execution order of steps S250 to S260 may not be followed, that is, the bilateral transaction weight coefficient, the clearing price, and the clearing volume can be cleared simultaneously. When the above parameters are equal for both the producer and the consumer, it can be considered that the bilateral transaction strategy is determined. At the same time, to ensure the optimal market clearing, the above results need to meet the preset convergence conditions, that is, the verification process in step S270, to finally obtain the bilateral transaction strategy.

[0145] In one embodiment, as described above, the first optimization model is an ideal model that does not consider the physical constraints of the distribution network, while the final second optimization model is a calculation model that takes into account the physical constraints of the distribution network. The specific implementation process is to consider the influence of the bilateral transaction weight coefficient, and the specific influence effect can be referred to Figure 6 and Figure 7 , Figure 6 is the schematic diagram of the voltage curve of the distribution network node users provided in the second embodiment of the present application; Figure 7 is the schematic diagram of the active power curve of the power supply branch of the distribution network provided in the second embodiment of the present application. As shown in the figure, for the second optimization model considering the bilateral transaction weight coefficient, both the node voltage and the branch power are more stable than those ignoring the bilateral transaction weight coefficient.

[0146] In one embodiment, for the bilateral transaction loss weight coefficient τ loss , the line congestion weight coefficient τ l and the voltage weight coefficient τ v among the producers and consumers in this embodiment are shown in Tables 2 to 4 respectively. The distribution network operator restricts or encourages some bilateral transactions among the producers and consumers according to the weight coefficient τ, and finally affects the clearing power and the transaction price of each producer and consumer at market equilibrium, that is, it has an impact on the bilateral transaction strategy.

[0147]

[0148]

[0149] Table 2 Transaction loss weight τ among producers and consumers loss / (yuan / kWh)

[0150]

[0151] Table 3 Line congestion weight coefficient τ between prosumers l / (yuan / kWh)

[0152]

[0153] Table 4 Voltage weight coefficient τ between prosumers v / (yuan / kWh)

[0154] Furthermore, it can be understood that whether to consider the prosumer trading weight coefficient will affect the clearing volume. The clearing volumes obtained by ignoring and considering the weight coefficient are shown in Table 5 below.

[0155]

[0156]

[0157] Table 5 Prosumer clearing electricity quantity and market benefit when ignoring the weight coefficient (τ = 0) and considering the weight coefficient (τ ≠ 0)

[0158] Based on this, during the t = 12h market cycle, the breakdown of the transaction electricity price for Producer 5 and Consumer 3 is as shown in Figure 8 、 Figure 9 shown, Figure 8 which is the schematic diagram of the breakdown of the clearing electricity selling price for Producer 5 provided in the second embodiment of this application; Figure 9 which is the schematic diagram of the breakdown of the clearing electricity purchasing price for Consumer 3 provided in the second embodiment of this application. When the renewable energy units and load powers within the prosumers respectively correspond to the Figure 3 and Figure 4 day-ahead prediction curves during the total market cycle T = {1,..., T}, the bilateral transaction electricity quantities between Producer 1 and Consumer 5 during the total market cycle are as shown in Figure 10 and Figure 11 shown, Figure 10 which is the schematic diagram of the clearing electricity quantity for Consumer 5 during the total market cycle provided in the second embodiment of this application; Figure 11 which is the schematic diagram of the clearing electricity quantity for Producer 1 during the total market cycle provided in the second embodiment of this application.

[0159] In one embodiment, from the perspective of Consumer 1, through the energy trading method provided in this embodiment, it can be obtained that both producers and consumers can reach convergence in each bilateral transaction. The specific change process of the bilateral transaction electric energy evolution curve between Consumer 1 and each producer can refer to Figure 12 and Figure 13 , Figure 12 which is the schematic diagram of the evolution curve method for "consumer side" Consumer 1 provided in the second embodiment of this application; Figure 13Schematic diagram of the evolution curve of the "producer-side" consumer 1 provided in the second embodiment of the present application.

[0160] In one embodiment, it should be noted that in steps S250 to S260, especially in formulas (34) to (37), according to the complementary slackness theory, along the negative direction of the objective function gradient during the process of updating the dual variables α and β. μ l and μ v are the adjustment step size parameters for the global coupling constraint, which will affect the global error δ. Specifically, reference can be made to Figure 14 and Figure 15 , Figure 14 is the first schematic diagram of the convergence curve of the global error δ under different adjustment parameters μ l and μ v provided in the second embodiment of the present application; Figure 15 is the first schematic diagram of the convergence curve of the global error δ under different adjustment parameters μ l and μ v provided in the second embodiment of the present application.

[0161] In one embodiment, the IEEE-69 distribution network example of 5 producers and 5 consumers provided by this embodiment shows that the bilateral transactions between different prosumers are motivated / deterred according to the corresponding weight coefficients, and the clearing electricity prices of each prosumer consist of three parts: marginal cost / utility, capacity-limited electricity price, and weight coefficient. The distributed clearing mechanism based on the alternating direction multiplier method ensures global optimality while only sharing the prosumer clearing electricity quantity information, protecting the privacy rights of independent entities.

[0162] Therefore, the present application can determine the loss sensitivity coefficient by constructing a power flow model to obtain the bilateral transaction weight coefficient, and incorporate it into the idealized first optimization model to obtain a second optimization model that considers the physical constraints of the distribution network (safety constraints and location constraints). The second optimization model is obtained by obtaining the bilateral transaction strategy between each producer and consumer according to the preset clearing algorithm. The obtained bilateral transaction strategy can effectively stimulate or curb some bilateral transactions, thereby ensuring the reliability of the distribution network operation while taking into account the optimality of the social benefits of the bilateral market. At the same time, the bilateral transaction strategy of each producer and consumer is adopted to solve the privacy rights of the independent subjects of each producer and consumer. In addition, further, the energy trading method provided by an embodiment of the present application can also incorporate the physical constraints of the distribution network into the distributed clearing step with the bilateral transaction weight coefficient, stimulate some bilateral transactions that maintain the stability of the distribution network, and curb bilateral transactions that cause power congestion and voltage crossing, taking into account the reliability of the distribution network operation and the optimality of market clearing. And it is proposed that the bilateral transaction weight coefficient is based on the sensitivity coefficient and the Distflow linear power flow model to ensure the strong convexity of the P2P market optimization model and the optimality of the market clearing results. In addition, the distributed clearing mechanism based on the alternating direction multiplier method ensures global optimality while only sharing the clearing power information of producers and consumers, thus protecting the privacy of independent entities. This reduces user operations, increases user convenience, and improves user experience.

[0163] Embodiment 3

[0164] Figure 16 A schematic diagram of the structure of an energy trading device provided in Example 3 of the present application.

[0165] The present application also provides an energy trading device including: a first optimization model construction module, a flow model construction module, a bilateral transaction weight coefficient model construction module, a second optimization model construction module, and a trading strategy determination module.

[0166] In one implementation, the implementation process of the energy trading device refers to the description of the energy trading method, which will not be repeated here.

[0167] Embodiment 4

[0168] Figure 17 Schematic diagram of the structure of the computer device provided in the fourth embodiment of the present application. The computer device may be a terminal or a server. Figure 17As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the method for detecting a dirty reaction cup. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can execute the method for detecting a dirty reaction cup. Those skilled in the art can understand that Figure 7 The structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0169] In one embodiment, a computer device 40 is proposed, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the method described in Embodiment 1 or Embodiment 2.

[0170] In one embodiment, the present application also proposes a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the processor executes the steps of the method described in Embodiment 1 or Embodiment 2.

[0171] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0172] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

Claims

1. An energy trading method, characterized in that, It includes the following steps: Obtain the cost parameters and utility models of prosumers respectively, and construct a first optimization model according to the cost parameters and the utility models, including: obtaining cost parameters, where the cost parameters include: a first trading strategy, a participation cost function, and a second active power; wherein, the first trading strategy is used to indicate the quantity and unit price of purchases between prosumers, the participation cost function is used to calculate the additional cost of the prosumers participating in the trading market, and the second active power is used to indicate the active power of the prosumers' renewable energy units in a preset market period; construct the total revenue models of the prosumers respectively according to the cost parameters and the utility models; construct the first optimization model based on the total revenue models, and the first optimization model is an optimization model for obtaining an energy trading strategy that maximizes the social benefits of prosumers under the assumption of ignoring the physical constraints of the distribution network and the bilateral trading network losses; Obtain the power transfer distribution factor and the voltage sensitivity factor, construct a power flow model according to the power transfer distribution factor and the voltage sensitivity factor, and determine the loss sensitivity factor according to the power flow model; Obtain the trading loss information of the prosumers, and input the trading loss information into the power flow model to obtain a bilateral trading weight coefficient model including the loss sensitivity factor, including: obtaining the distribution network security constraint information through the Lagrangian function of the first optimization model according to the power flow model, where the distribution network security constraint information includes: global voltage constraint information and global line congestion constraint information; substituting the location information into the power flow model to obtain the loss sensitivity factor of the corresponding node; obtaining the bilateral trading weight coefficient model of the corresponding node according to the loss sensitivity factor of the corresponding node and the distribution network security constraint information; wherein, the trading loss information includes location information and distribution network security constraint information; Merge the bilateral trading weight coefficient model with the first optimization model to obtain a second optimization model, including: obtaining a preset congestion constraint index function and a voltage constraint index function, and merging the congestion constraint index function, the voltage constraint index function, the bilateral trading weight coefficient model with the first optimization model to obtain the second optimization model, and the second optimization model is an optimization model for obtaining an energy trading strategy that maximizes the social benefits of prosumers considering the weight of the physical constraints of the distribution network; Settle the second optimization model according to a preset algorithm to obtain the bilateral trading strategies between the prosumers, and the bilateral trading strategies are used to determine the trading price and trading quantity between the prosumers.

2. The energy trading method according to claim 1, characterized in that, Obtain the utility models of producers and consumers, including: Obtain the consumer utility parameters and the first active power in a preset market period, and establish a consumer utility model according to the consumer utility parameters and the first active power; wherein, the consumer utility parameters include a preset primary preference coefficient and a secondary benefit coefficient; Obtain the producer utility parameters and the output power, and establish a producer utility model according to the producer utility parameters and the output power.

3. The energy trading method according to claim 1, characterized in that, Obtaining the power transfer distribution coefficient and the voltage sensitivity coefficient, constructing a power flow model according to the power transfer distribution coefficient and the voltage sensitivity coefficient, and determining the loss sensitivity coefficient according to the power flow model, includes: Based on the loss relationship between the power quantities of the nonlinear branch and the linear branch, a first power flow model is constructed, and the first power flow model is an ideal model that ignores the branch loss; Obtain the preset power transfer distribution coefficient and the voltage sensitivity coefficient and incorporate them into the first power flow model to obtain a second power flow model; Obtain the branch loss parameters, and determine the loss sensitivity coefficient of the corresponding node through the second power flow model based on the branch loss parameters; wherein the branch loss parameters include the resistance and reactance of the branch.

4. The energy trading method according to claim 1, characterized in that, The merging of the bilateral transaction weight coefficient model and the first optimization model to obtain a second optimization model includes: Obtain the preset congestion constraint index function and the voltage constraint index function, and merge the congestion constraint index function, the voltage constraint index function, the bilateral transaction weight coefficient model and the first optimization model to obtain the second optimization model. The second optimization model is an optimization model for an energy trading strategy that considers the physical constraint weight of the distribution network to maximize the social benefits of prosumers.

5. The energy trading method according to claim 1, characterized in that, Obtaining the bilateral transaction strategies between the prosumers includes: Based on the subgradient iteration method, iteratively adjust the parameters of the bilateral transaction weight coefficient model in the second optimization model until the bilateral transaction weight coefficient model meets the preset convergence condition, and determine the bilateral transaction weight coefficient of the prosumers; Input the bilateral transaction weight coefficient into the second optimization model, and distribute and clear the second optimization model according to the alternating direction multiplier method to obtain the bilateral transaction strategy.

6. An energy trading device, characterized in that, Including: A first optimization model construction module, which is used to respectively obtain the cost parameters and utility models of prosumers, and construct a first optimization model according to the cost parameters and the utility models, including: obtaining cost parameters, where the cost parameters include: a first trading strategy, a participation cost function, and a second active power; wherein, the first trading strategy is used to indicate the quantity and unit price of purchases between prosumers, the participation cost function is used to calculate the additional cost for the prosumers to participate in the trading market, and the second active power is used to indicate the active power of the prosumers' renewable energy units in a preset market period; according to the cost parameters and the utility models, respectively construct the total revenue models of the prosumers; based on the total revenue models, construct the first optimization model, and the first optimization model is an optimization model for an energy trading strategy that maximizes the social benefits of prosumers on the assumption of ignoring the physical constraints of the distribution network and the bilateral transaction network loss; A power flow model construction module, which is used to obtain the power transfer distribution coefficient and the voltage sensitivity coefficient, construct a power flow model according to the power transfer distribution coefficient and the voltage sensitivity coefficient, and determine the loss sensitivity coefficient according to the power flow model; Bilateral transaction weight coefficient model construction module, which is used to obtain the transaction loss information of the prosumer and input the transaction loss information into the power flow model to obtain a bilateral transaction weight coefficient model including the loss sensitivity coefficient, including: obtaining the distribution network security constraint information through the Lagrangian function of the first optimization model according to the power flow model, where the distribution network security constraint information includes: global voltage constraint information and global line congestion constraint information; substituting the location information into the power flow model to obtain the loss sensitivity coefficient of the corresponding node; obtaining the bilateral transaction weight coefficient model of the corresponding node according to the loss sensitivity coefficient of the corresponding node and the distribution network security constraint information; where the transaction loss information includes location information and distribution network security constraint information; Second optimization model construction module, which is used to merge the bilateral transaction weight coefficient model with the first optimization model to obtain a second optimization model, including: obtaining a preset congestion constraint index function and a voltage constraint index function, and merging the congestion constraint index function, the voltage constraint index function, the bilateral transaction weight coefficient model with the first optimization model to obtain the second optimization model, where the second optimization model is an optimization model for an energy trading strategy that considers the weight of the physical constraints of the distribution network to maximize the social benefits of the prosumer; Transaction strategy determination module, which is used to clear the second optimization model according to a preset algorithm to obtain the bilateral transaction strategy between the prosumers, and the bilateral transaction strategy is used to determine the transaction price and transaction quantity between the prosumers.

7. A computer device, characterized in that, Including a processor and a memory: The processor is used to execute the computer program stored in the memory to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1 to 5.

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