A non-iterative decentralized clearing method for a p2p energy market

Abstract

The application discloses a non-iterative decentralized clearing method for a P2P energy market, which comprises the following steps: obtaining usage data, wherein the usage data comprises user load, real-time electricity price, power network parameters, operation constraints of each distributed power supply and utility data of each market participant, and sending the obtained usage data to an optimization model; using Stackelberg game to describe the interaction between each producer-consumer buyer and seller, wherein each seller in each micro-grid first publishes a transaction price, and then each buyer determines the purchase power according to the transaction price published by the seller, and the specific model is as follows: using Stackelberg game to constitute double-layer optimization transaction, solving the piecewise constant relationship of the upper and lower optimization variables based on the best response of the energy buyer; and screening the safety constraints corresponding to the piecewise constant relationship.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power market transaction model, and particularly relates to a decentralized clearing method for a non-iterative P2P energy market. BACKGROUND

[0002] In recent years, the rapid development of user-side distributed energy has promoted the transformation of energy consumption mode. On the one hand, the rapid development of photovoltaic (PV) and wind power generation technology provides an opportunity for the power grid to improve local network problems (such as voltage fluctuation) in a flexible manner; on the other hand, energy end users can also reduce the generation cost of renewable energy by sharing the surplus energy of local power generation. The application introduces the concept of energy producer and consumer to describe the end users with energy sharing capability. Each energy producer and consumer needs to reach a transaction agreement with other energy consumers to complete local P2P energy transactions in a decentralized form, and each microgrid also needs to reach a transaction agreement with other microgrids to make up for the power excess and deficiency of its own community. The transaction market with a decentralized structure can protect user privacy and promote efficient transactions, but it may lead to repeated iterations in the market clearing process. Therefore, it has become a research hotspot to establish a non-iterative P2P (peer-to-peer) energy transaction clearing method through model equivalence and scenario reduction. The existing P2P energy market is mostly cleared in a decentralized manner, which requires a decentralized solving algorithm for clearing, and the calculation is difficult, does not guarantee convergence, and takes too long to solve, which cannot meet the time requirement of fast clearing of market transactions. SUMMARY

[0003] In view of the deficiencies of the prior art, the purpose of the application is to provide a decentralized clearing method for a non-iterative P2P energy market.

[0004] The purpose of the application can be achieved by the following technical solution: a decentralized clearing method for a non-iterative P2P energy market, the method comprising the following steps:

[0005] obtaining usage data, the usage data including user load, real-time electricity price, power network parameters, operation constraints of each distributed power supply, and utility data of each market participant, and sending the obtained usage data to an optimization model, the optimization model being used for clearing calculation of the entire market;

[0006] a Stackelberg game model is used to describe the interaction between the buyers and sellers of each producer and consumer. The sellers in each microgrid first publish transaction prices, and then the buyers determine their own purchase power according to the transaction prices published by the sellers:

[0007] The Stackelberg game model is used to constitute a double-layer optimization transaction, and the segmented constant relationship of the upper and lower optimization variables is solved based on the best response of the energy buyer.

[0008] The marginal transaction constant segment is obtained by screening the security constraints corresponding to the segmented constant relationship.

[0009] Preferably, the user load includes the user's annual load data, the real-time electricity price adopts the national unified peak-valley flat three-time electricity price, the power network parameters are the resistance and reactance parameters corresponding to each branch of the network, the transmission capacity of each branch, and the upper and lower limits of the node voltage of each node, the operation constraints of each distributed power source include the output power range of the distributed power source, and the utility data of each market participant includes the parameters of the utility function of different market subjects.

[0010] Preferably, the minimum value of the data acquisition interval that the user meets is 15 minutes.

[0011] Preferably, the Stackelberg game model is used to describe the interaction between each producer and consumer buyer and seller, and the process in which each seller in the microgrid first publishes a transaction price, and then each buyer determines its own purchase power according to the transaction price published by the seller includes the following steps:

[0012] The energy seller directly sells electricity to the energy buyer within the same microgrid, and sells the remaining electricity to the upper grid, so the transaction model of the energy seller is established as follows:

[0013]

[0014]

[0015]

[0016]

[0017] In the formula, is the energy transaction price of seller i sold to buyer j, is the energy transaction power of seller i sold to buyer j, is the on-grid price of the energy seller sold to the upper grid, is the total power generation of the energy seller i, and Δt is the time step of the transaction, j∈J s represents the energy buyer corresponding to the energy seller i, t∈T is the transaction period of the entire electricity market, is the time-of-use electricity price of the entire grid, represents the grid operation constraint in the microgrid;

[0018] The energy buyers first satisfy their own electricity demand by P2P trading within the microgrids, and then purchase the remaining electricity from the upper grid. Therefore, the energy buyers are modeled as follows:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] where, is the total load value of the user, is the base value of the time-of-use electricity price, k t is the rate of change of the time-of-use electricity price, is the active power at the end of the line, the active power at the end of the line is affected by the physical constraints;

[0025] After the completion of the transaction within each microgrid, each microgrid manager conducts a continuous double auction to make up for the excess and deficiency of energy in the internal transaction. The double auction is modeled as follows:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] where, denotes the set of microgrids of the energy sellers, denotes the set of microgrids of the energy buyers, denotes the profit function of all energy sellers, denotes the profit function of all energy buyers, is the total power output of the energy seller s, is the transaction electricity of the energy seller s to the buyer b, is the total electricity load of the energy buyer b, are the power on the microgrid tie line at the power generation and electricity consumption ends, respectively, and the tie line power satisfies the network power flow constraint total power consumption benefit for energy buyer b, clearing price for bilateral continuous auction, total power generation cost for energy seller s;

[0033] After each energy producer and consumer in each micro-grid completes the transaction, each micro-grid needs to bid to the market operator according to its power excess and deficiency, and when the lowest price received by the energy seller is lower than the highest price offered by the energy buyer, the transaction is established.

[0034] Preferably, the total load value of the user is directly proportional to the base value of the time-of-use electricity price.

[0035] Preferably, the two-layer optimization transaction formed by using the Stackelberg game model includes the following steps based on the best response of the energy buyer to solve the piecewise constant relationship of the upper and lower optimization variables:

[0036] The original energy buyer model is reconstructed into the following matrix form:

[0037]

[0038] Ap≤b:γ

[0039] p≥0

[0040] In the formula, λ and θ are corresponding price and constant coefficient parameters, p is the electricity quantity of the transaction, A and b are constant matrices of inequality constraints, and γ is the corresponding Lagrange dual variable.

[0041] The reconstructed matrix form is taken as a dual as follows:

[0042]

[0043]

[0044] γ≤0

[0045] The dual form is written in a unified form as follows:

[0046]

[0047]

[0048]

[0049] The optimal solution can only be obtained at the extreme point of the feasible region, and the invention takes the constraints forming the extreme point as equations and the remaining constraints as inequalities, and when the optimal solution is unchanged.

[0050] ​Preferably, the process of screening the security constraints on the corresponding piecewise constant relationship comprises the following steps:

[0051] Linearize the operation constraints of the entire power network:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] In the formula, represents the transaction in the marginal section in effect, is the upper limit of the transaction power of the transaction section s, is the upper limit of the reactive power of the transaction section s, is the power generation of node k, is the load power of node k, is the transaction power in the upper limit, is the generated reactive power of node k, is the reactive load power of node k, is the reactive transaction power in the upper limit, N is the linear section corresponding to the transaction power, and n is the section number corresponding to the linear section, is the upper limit of the transaction power, is the conductance between the buyer and the seller k and l, respectively represent the node voltage amplitude of the buyer and the seller k and l, is the susceptance between the buyer and the seller k and l, respectively represent the node voltage phase angle of the buyer and the seller k and l, respectively represent the lower limit and the upper limit of the square of the voltage amplitude of node k;

[0061] The operation constraints are modeled using the big M method in the following form:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] 0 < α n ≤ M(1 - t n ), n ∈ [1, N]

[0072] 0 < β k,- ≤ M(1 - t k,- ), 0 < β k,+ ≤ M(1 - t k,+ )

[0073] 0 < γ p,- ≤ M(1 - s p,- ), 0 < γ p,+ ≤ M(1 - s p,+ )

[0074] 0 < η q,- ≤ M(1 - s q,- ), 0 < η q,+ ≤ M(1 - s q,+ )

[0075]

[0076]

[0077]

[0078]

[0079] t n , t k,- and t k,+ are auxiliary Boolean variables introduced, s p,- , s p,+ , s q,- and s q,+ are auxiliary Boolean variables introduced, M is a positive number, used for linearization of the original constraints;

[0080] According to the auxiliary Boolean variable and the auxiliary type Boolean variable introduced above, the marginal transaction section and the corresponding security constraint are modeled as follows:

[0081]

[0082] In the formula, the corresponding flag bit of each transaction section is maximized, and when the target function ψ m When the calculated target function value is 0, the marginal transaction section and the security constraint of the entire market optimization model are identified, and until the calculated target function value is 0, the marginal transaction section and the security constraint in the entire clearing model are identified.

[0083] Preferably, the auxiliary Boolean variable is used to represent whether the nth section of the complex power constraint, the upper limit constraint of the voltage amplitude of the kth node, and the lower limit constraint of the voltage amplitude of the kth node are effective, and the auxiliary type Boolean variable is used to represent the lower limit, the upper limit of the active transaction power section, and the lower limit, the upper limit of the reactive transaction power section.

[0084] An apparatus comprising:

[0085] one or more processors;

[0086] a memory for storing one or more programs;

[0087] When the one or more programs are executed by the one or more processors, the one or more processors implement the non-iterative decentralized clearing method of the P2P energy market as described above.

[0088] A storage medium containing computer executable instructions for executing the non-iterative decentralized clearing method of the P2P energy market as described above when executed by a computer processor.

[0089] Advantages of the present application:

[0090] The present application uses the characteristics of the optimal response model of the energy consumers in each microgrid in Stackelberg game to establish a segmented constant transaction power model, and uses the big M method to identify the effective security constraints and the corresponding transaction power sections, which ensures the decentralized degree of user participation in P2P transaction, improves the efficiency and accuracy of the optimization algorithm, and has strong use value. BRIEF DESCRIPTION OF DRAWINGS

[0091] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below, and obviously, other drawings can also be obtained by those skilled in the art without creative labor.

[0092] Figure 1 is a schematic diagram of the decentralized P2P multi-level transaction market corresponding to the present application;

[0093] Figure 2 is a schematic diagram of the influence of the internal transaction price segmentation of the micro-grid on the entire P2P multi-level transaction market corresponding to the embodiment of the present application;

[0094] Figure 3 is a flowchart of the problem required to be pre-solved by the asymmetric algorithm of the embodiment of the present application. DETAILED DESCRIPTION

[0095] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0096] As shown in Figure 1 , a non-iterative P2P energy market clearing method is provided, and the method comprises the following steps:

[0097] (1) Obtain user load, real-time electricity price, power network parameters, operation constraints of each distributed power supply, and utility data of each market participant, and transmit the collected data into an optimization model;

[0098] Further, the user load data comprises annual load data of the user, and the data acquisition interval is at least 15 minutes;

[0099] Further, the real-time electricity price adopts a unified peak-valley-flat three-time electricity price of the country;

[0100] Further, the power network parameters are resistance and reactance parameters corresponding to each branch of the network, transmission capacity of each branch, and upper and lower limits of node voltage of each node;

[0101] Further, the operation constraints of each distributed power supply comprise an output power range of the distributed power supply;

[0102] Further, the utility data of each market participant comprises parameters of utility functions of different market subjects.

[0103] (2) Considering the flexible generation and consumption behavior of each energy producer and consumer in the micro-grid, the present application uses a Stackelberg game to describe the interaction between the buyers and sellers of each producer and consumer, and each seller in the micro-grid first publishes a transaction price, and then the buyers determine their own purchase power according to the transaction price published by the sellers, and the specific model is as follows:

[0104] (21) Energy sellers need to sell energy to the same energy buyers within the microgrid directly, and the rest of the energy needs to be sold to the upper grid, so its transaction model can be established as follows:

[0105]

[0106]

[0107]

[0108]

[0109] where, is the energy transaction price of seller i to buyer j, is the energy transaction capacity of seller i to buyer j, is the on-grid price of energy seller to the upper grid, is the total generation capacity of energy seller i, and Δt is the time step of transaction, j ∈ J s represents the energy buyer corresponding to energy seller i, t ∈ T is the transaction period of the entire electricity market, is the time-of-use price of the entire grid, represents the grid operation constraints in the microgrid. The objective function is to maximize the selling revenue of each seller, the first constraint indicates that all the sold energy does not exceed the total generation capacity of the seller, the second constraint indicates that the transaction price is between the on-grid price and the time-of-use price, to ensure that users can benefit from participating in the P2P market transaction, and the third constraint indicates that the transaction energy is subject to network constraints.

[0110] (22) Energy buyers first meet their own electricity demand through P2P transactions within the microgrid, and then purchase the remaining energy from the upper grid, so the buyer can be modeled as follows:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] where, is the total load value of the user, is the base value of the time-of-use price, the higher the load, the higher the time-of-use price, k t is the rate of change of the time-of-use price, Pd is the active power at the end of the line, which is also affected by physical constraints. The remaining variables in this model have the same meaning as their counterparts in (21). The objective of the model is to minimize the total cost of the energy buyers. The first constraint indicates that the total amount of transactions of the energy buyers is less than their total load. The second constraint indicates that the amount of electricity purchased by the energy buyers is non-negative. The third constraint indicates the way the time-of-use price is calculated for the entire grid. The fourth constraint indicates that the amount of electricity traded is subject to network constraints.

[0117] (23) After the completion of transactions within each microgrid, each microgrid manager can conduct a continuous double auction to make up for the excess and deficiency of energy in the internal transactions. The modeling of the double auction is as follows:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] where, denotes the set of microgrids of energy sellers, denotes the set of microgrids of energy buyers, denotes the profit function of all energy sellers, denotes the profit function of all energy buyers, is the total power generation of energy seller s, is the amount of electricity traded by energy seller s to buyer b, is the total electricity load of energy buyer b, is the power on the microgrid tie line at the generation and consumption ends, respectively. The tie line power satisfies the network power flow constraint is the total electricity benefit of energy buyer b, is the clearing price of the double continuous auction, is the total electricity generation cost of energy seller s.

[0125] (24) After each energy producer and consumer within a microgrid completes transactions with each other, each microgrid needs to bid to the market operator according to its excess and deficiency of power. When the lowest price that the seller is willing to accept is lower than the highest price that the buyer is willing to pay, the transaction is established.

[0126] (3) Because each energy buyer needs to decide the transaction power according to the price published by the seller in the Stackelberg game, the whole transaction constitutes a double-layer optimization form, and the present application directly solves the piecewise constant relationship of the upper and lower optimization variables based on the optimal response of the energy buyer to simplify the calculation of the whole market clearing.

[0127] (31) In order to facilitate the analysis of the transaction results of each energy buyer, the original energy buyer model is reconstructed into the following matrix form in the present application:

[0128]

[0129] Ap≤b:γ

[0130] p≥0

[0131] In the formula, λ and θ are corresponding price and constant coefficient parameters respectively, p is the transaction power, A and b are constant matrices of inequality constraints respectively, and γ is the corresponding Lagrange dual variable.

[0132] (32) In order to separate the decision variables of the buyer and the seller, the present application takes the dual of the problem as follows:

[0133]

[0134]

[0135] γ≤0

[0136] In the formula, the meanings of the variables are the same as those in (31).

[0137] (33) The constraints of the dual problem in (32) are written in a unified form as follows:

[0138]

[0139]

[0140]

[0141] Because the dual problem is a convex linear problem, the optimal solution can only be obtained at the extreme point of the feasible region. The present application writes the constraints constituting the extreme point as equations and the remaining constraints as inequalities. As long as the optimal solution will not change, and therefore the optimal point of the whole problem will not move, and the transaction power purchased by the buyer is Figure 2 the transaction price piecewise constant function shown in the formula.

[0142] (4) After the segmentation of the transaction power, the security constraints corresponding to the segmented section are screened so that only the power network constraints that affect the optimization results are counted in the transaction results in the entire solving process;

[0143] (41) The present application first linearizes the operation constraints of the entire power network, and the linearized mathematical model is as follows:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152] In the formula, denotes the marginal section of the transaction in effect, is the upper limit of the transaction power of the transaction section s, is the upper limit of the reactive power of the transaction section s, is the generated power of node k, is the load power of node k, is the transaction power in the upper limit, is the generated reactive power of node k, is the reactive load power of node k, is the reactive transaction power in the upper limit, N is the linear section corresponding to the transaction power, and n is the section number corresponding to the linear section, is the upper limit of the transaction power, is the conductance between the buyer and the seller k and l, denote the node voltage amplitudes of the buyer and the seller k and l respectively, is the susceptance between the buyer and the seller k and l, denote the node voltage phase angles of the buyer and the seller k and l respectively, denote the lower limit and the upper limit of the square of the voltage amplitude of node k respectively.

[0153] (42) In order to screen out the marginal section power in effect and the corresponding security constraints, the original constraints are modeled into the following form by using the big M method:

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163] 0≤α n ≤M(1-t n ),n∈[1,N]

[0164] 0≤β k,- ≤M(1-t k,- ),0≤β k,+ ≤M(1-t k,+ )

[0165] 0≤γ p,- ≤M(1-s p,- ),0≤γ p,+ ≤M(1-s p,+ )

[0166] 0≤η q,- ≤M(1-s q,- ),0≤η q,+ ≤M(1-s q,+ )

[0167]

[0168]

[0169]

[0170]

[0171] wherein the meanings of the variables are the same as the meanings of the corresponding variables in (41), t n , t k,- and t k,+respectively are introduced auxiliary Boolean variables to represent whether the nth segment of complex power constraint, the upper limit constraint of the voltage amplitude of the kth node, the lower limit constraint of the voltage amplitude of the kth node is effective, s p,- p,+ q,- q,+ respectively are introduced auxiliary Boolean variables to represent the lower limit, the upper limit of the active trading power segment, the lower limit, the upper limit of the reactive trading power segment, M is a large enough positive number for linearization of the original constraint.

[0172] (43) According to the above seven flag bits, the marginal trading segment and the corresponding security constraint can be modeled as follows:

[0173]

[0174] In the formula, the corresponding flag bit of each trading segment is maximized, and when the objective function ψ m =0, the marginal trading segment and the effective security constraint of the entire market clearing model are identified, so the present application first optimizes the model in a loop until the calculated objective function value is 0, and the marginal trading segment and the security constraint in the entire clearing model are identified.

[0175] (5) As shown in Figure 3 , before the non-iterative trading clearing is carried out, the problem needs to be pre-solved first. Therefore, the present application optimizes the Boolean optimization model corresponding to the (43) model, identifies the marginal trading segment inside each micro-grid, and then clears the decentralized P2P trading inside the micro-grid, and then solves the power excess and deficiency of each micro-grid. After obtaining the power excess and deficiency of each micro-grid, the micro-grid manager will bid for the power demand and the power sold, and then the market manager will carry out bilateral matching clearing.

[0176] An apparatus comprising:

[0177] one or more processors;

[0178] a memory to store one or more programs;

[0179] when the one or more programs are executed by the one or more processors, so that the one or more processors implement a non-iterative decentralized clearing method of a P2P energy market as described above.

[0180] A storage medium containing computer executable instructions for executing a non-iterative decentralized clearing method of a P2P energy market as described above when executed by a computer processor.

[0181] ​​​The application is suitable for a power distribution network level P2P transaction market formed by multiple microgrids with high renewable energy penetration, analyzes the optimal response of each producer and consumer in the microgrid to the transaction from the perspective of energy consumers, optimizes the continuous double-sided matching scheme of the P2P transaction of each microgrid from the perspective of the total system, provides theoretical guidance for saving energy, reducing carbon emissions and improving market clearing calculation efficiency, and effectively promotes the application of the P2P transaction in the power distribution network system.

[0182] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0183] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only illustrative of the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A non-iterative, decentralized clearing method for the P2P energy market, characterized in that, The method includes the following steps: The system acquires usage data, including user load, real-time electricity price, power network parameters, operating constraints of each distributed power source, and utility data of each market participant. The acquired usage data is then sent to the optimization model, which is used to perform clearing calculations for the entire market. The Stackelberg game model is used to describe the interaction between buyers and sellers in each microgrid. Sellers first publish transaction prices, and then buyers determine their electricity purchase volume based on the transaction prices published by the sellers. The Stackelberg game model is used to construct a two-level optimal transaction, and the piecewise constant relationship between the optimization variables of the upper and lower levels is solved based on the best response of the energy buyer. The process of constructing a two-layer optimized transaction using the Stackelberg game model and solving the piecewise constant relationship between the upper and lower optimization variables based on the energy buyer's optimal response includes the following steps: The original energy buyer's model is reconstructed into the following matrix form: In the formula, These are the corresponding price and constant coefficient parameters, For the electricity traded, These are the constant matrices of the inequality constraints. For the corresponding Lagrange dual variable; The dual of the reconstructed matrix form is as follows: The dualized form can be written in a unified form as follows: The optimal solution can only be obtained at the extreme points in the feasible region. The constraints constituting these extreme points are treated as equality equations, and the remaining constraints as inequalities. The optimal solution remains unchanged; By filtering the security constraints on the corresponding segmented constant relationships, the marginal transaction constant segments are obtained; The process of filtering the security constraints on the corresponding piecewise constant relationships includes the following steps: Linearization of operational constraints for the entire power network: In the formula, This indicates the marginal segment where the transaction is in effect. For the transaction segment s The maximum transaction volume. For the transaction segment s The corresponding maximum reactive power capacity, For nodes k Power generation capacity, For nodes k The load power, For the transaction volume that is at its maximum limit, For nodes k Reactive power generation For nodes k reactive load power, For reactive power trading volume that is at its maximum limit, Linear segmentation of the corresponding traded electricity volume, The paragraph number corresponding to the linear segmentation. This is the upper limit for the amount of electricity that can be traded. For both buyers and sellers k and l The electrical conductance between them Representing the buyer and seller respectively k and l The node voltage amplitude, For both buyers and sellers k and l The susceptance between them Representing the buyer and seller respectively k and l The node voltage phase angle, Representing nodes respectively k The lower and upper limits of the square of the voltage amplitude; The operational constraints are modeled using the Big M method as follows: , and These are the introduced auxiliary Boolean variables, , , and These are the introduced auxiliary Boolean variables, A positive number is used to linearize the original constraints; Based on the auxiliary Boolean variables and auxiliary Boolean variables introduced above, the marginal transaction segment and its corresponding security constraints are modeled as follows: The formula maximizes the flag bits corresponding to each transaction segment, when the objective function At that time, the marginal transaction segments and the safety constraints that are in effect in the entire market optimization model are identified until the calculated objective function value is 0. At that time, the marginal transaction segments and safety constraints in the entire clearing model are all identified. The auxiliary Boolean variable is used to represent the first... n Segment complex power constraint, the first k Upper limit constraint on the voltage amplitude of the node, the first k Whether the lower limit constraint of the voltage amplitude of each node is effective, the auxiliary Boolean variable is used to represent the lower limit and upper limit of the active power trading volume segment, and the lower limit and upper limit of the reactive power trading volume segment.

2. The non-iterative P2P energy market decentralized clearing method according to claim 1, characterized in that, The user load includes the user's load data for the whole year. The real-time electricity price adopts the national unified peak-valley-flat-hour electricity price. The power network parameters are the resistance and reactance parameters of each branch of the network, the transmission capacity of each branch, and the upper and lower limits of the node voltage of each node. The operating constraints of each distributed power source include the output power range of the distributed power source. The utility data of each market participant includes the parameters of the utility function of different market entities.

3. The non-iterative P2P energy market decentralized clearing method according to claim 2, characterized in that, The minimum data collection interval for the user load is 15 minutes.

4. The non-iterative P2P energy market decentralized clearing method according to claim 1, characterized in that, The Stackelberg game model is used to describe the interaction between buyers and sellers in each microgrid. Within each microgrid, sellers first publish transaction prices, and then buyers determine their electricity purchase volume based on these prices. The process includes the following steps: Energy sellers sell electricity directly to energy buyers within the same microgrid, and sell any surplus electricity to the upper-level grid. Therefore, the energy seller's trading model is established as follows: In the formula, For sellers i Sold to buyers j Energy trading prices For sellers i Sold to buyers j Electricity traded in the energy sector This refers to the grid connection price for energy sellers to sell their electricity to the higher-level power grid. For energy sellers i Total power generation, For the time step of the transaction, Indicates energy seller i The corresponding energy buyers, For the entire electricity market trading period, The time-of-use electricity price for the entire power grid. This represents the grid operation constraints in a microgrid; Energy buyers within the microgrid first meet their own electricity needs through peer-to-peer (P2P) transactions, and then purchase surplus electricity from the upper-level grid. Therefore, the energy buyer model is as follows: In the formula, The total load value for users. This is the base value for time-of-use electricity pricing. The rate of change of time-of-use electricity price, This refers to the active power at the end of the line. Influenced by physical constraints; After each transaction within a microgrid is completed, each microgrid manager conducts continuous bilateral auctions to compensate for energy surpluses and deficits in the internal transactions. The bilateral auction model is as follows: In the formula, This represents a collection of microgrids belonging to energy sellers. This represents a collection of microgrids belonging to energy buyers. This represents the revenue function for all energy sellers. This represents the revenue function for all energy buyers. For energy sellers s Total power generation output, For energy sellers s To buyers b The amount of electricity traded. For energy buyers b Total electrical load The power of the microgrid interconnects at the generation and consumption ends are respectively, and the interconnect power satisfies the network power flow constraints. , For energy buyers b Total electricity consumption benefits The clearing price for a bilateral consecutive auction. For energy sellers s The total cost of electricity generation; After energy producers and consumers within each microgrid complete their transactions, each microgrid needs to bid to the market operator based on its own power surplus and deficit. The transaction is established when the lowest price accepted by the energy seller is lower than the highest price offered by the energy buyer.

5. A non-iterative, decentralized clearing method for a P2P energy market according to claim 4, characterized in that, The user's total load value Base value of time-of-use electricity pricing Proportional.

6. A device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a non-iterative decentralized clearing method for a P2P energy market as described in any one of claims 1-5.

7. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform a non-iterative, decentralized clearing method for a P2P energy market as described in any one of claims 1-5.

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