A method and system for end-to-end producer-consumer transactions based on distribution network operation optimization

Abstract

This invention discloses a producer-consumer end-to-end transaction method and system based on distribution network operation optimization, belonging to the field of power system distribution network operation technology. The transaction method includes: producers and consumers perform electricity load forecasting and power generation forecasting, determine their identities, and upload the data to the distribution network operator; the distribution network operator calculates the electrical distance for each possible transaction based on the producer-consumer identities and distribution network parameters, and distributes the data to the corresponding producers and consumers; based on the electrical distances distributed by the distribution network operator, a producer-consumer operation objective function is constructed and distributed coordination optimization is performed to formulate a power purchase and sale plan and a peer-to-peer (P2P) transaction plan, which is then uploaded to the distribution network operator; the distribution network operator constructs a distribution network operation objective function based on the plans uploaded by the producers and consumers, processes multiple objectives using the NBI method, and then distributes the plans to the producers and consumers for iterative correction until the producers and consumers and the distribution network operator reach an equilibrium solution, and settles various fees based on the equilibrium solution.

Description

Technical Field

[0001] This invention belongs to the field of power system distribution network operation technology, specifically relating to a producer-consumer end-to-end transaction method and system based on distribution network operation optimization. Background Technology

[0002] To build a new power system, distributed wind power and distributed photovoltaic power have been developed on a large scale on the distribution network side. The large-scale grid connection of distributed renewable energy poses challenges to the safe operation of the distribution network. Constrained by grid power flow and voltage, distributed renewable energy generation cannot be fully fed into the grid, resulting in power curtailment. With the introduction of distributed trading policies, end-to-end (P2P) transactions are conducted between producers and consumers within the distribution network area. Changes in node power will alter the power flow of the distribution network and affect node voltage. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a producer-consumer end-to-end transaction method and system based on distribution network operation optimization, thereby solving the problems in existing technologies.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] A producer-consumer end-to-end transaction method based on distribution network operation optimization includes the following steps:

[0006] Prosumers forecast electricity load and power generation, identify themselves as prosumers, and upload the data to the distribution network operator.

[0007] Based on the identities and distribution network parameters uploaded by the prosumers, the distribution network operator calculates the electrical distance for each possible transaction and sends the corresponding information to the prosumers.

[0008] Based on the electrical distance issued by the distribution network operator, the objective function for the operation of producers and consumers is constructed and distributed coordination optimization is performed to formulate the power purchase and sale plan and the P2P transaction plan, which are then uploaded to the distribution network operator.

[0009] The distribution network operator constructs the distribution network operation objective function based on the plans uploaded by the producers and consumers, and uses the NBI method to process multiple objectives. The plan is then distributed to the producers and consumers for iterative correction until the producers, consumers, and distribution network operator reach an equilibrium solution. Various cost settlements are then performed based on the equilibrium solution.

[0010] Furthermore, the formula for determining one's own prosumer status is as follows:

[0011]

[0012]

[0013] In the formula, For the power generation of gas turbines, and These represent the electricity purchased and sold between producer-consumer i and the power grid, respectively. In response power, For the predicted electricity load of consumer i, and These are the output capacities of distributed wind power and photovoltaic power, respectively. and These are 01 state variables. When each takes the value 1, it indicates that producer i is a producer and consumer respectively.

[0014] Furthermore, the objective function for producers and sellers is:

[0015] min Cost i =C ele,i +C gas,i +C DR,i +C P2P,i -F P2P,i

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In the formula, C ele,i The cost of purchasing and selling electricity for producer-consumer i; C gas,i For the gas purchase cost of producer-consumer i; C DR,i The cost of losses incurred in responding to the demand of prosumer i; C P2P,i and F P2P,i These represent the P2P electricity purchase cost and electricity sales revenue for producer-consumer i, respectively. and These are the purchase and sale prices of electricity from the power grid; and These represent the electricity purchased and sold between producer-consumer i and the power grid, respectively. For the power generation of the gas turbine; a i b i and c i c is the gas turbine power generation cost coefficient; dr Cost per unit of response loss; For response power; and These are the electricity purchase price from producer-consumer j and the electricity sales price to producer-consumer j, respectively. and These represent the electricity purchase price of producer-consumer i from producer-consumer j and the electricity sold to producer-consumer j, respectively; c P2P The unit distance transmission cost set for distribution network operators and gas distribution network operators; d ij The electrical distance between two producers and consumers in a P2P transaction.

[0022] Furthermore, the steps of distributed coordination optimization include:

[0023] Step 1, Data initialization;

[0024] Step 2: Modify the objective function of the prosumer using the objective cascade analysis method, and optimize the solution based on the transaction volume information transmitted by other users to obtain its own transaction volume information;

[0025] Step 3: Determine if the transaction volume converges. If it converges, proceed to Step 4. If it does not converge, update the coordination multiplier and return to Step 2 to solve.

[0026] Step 4: Determine if the trading electricity price converges. If it converges, output the optimal trading result; if it does not converge, update the electricity price and return to step 2.

[0027] Furthermore, the modified objective function for the prosumer-consumer model is as follows:

[0028] min COST i =Cost i +C ATC

[0029]

[0030] In the formula, and These are the first-order coordination multipliers for P2P electricity purchase and sale by producer-consumer i; and These are the secondary coordination multipliers for P2P electricity purchase and sale by producer-consumer i.

[0031] Furthermore, the convergence criterion for optimizing P2P transactions between producers and consumers is:

[0032]

[0033] The update method for the coordination multiplier is as follows:

[0034]

[0035] In the formula, z represents the number of iterations; ρ is a constant greater than or equal to 1.

[0036] Furthermore, the convergence criterion for the electricity trading price is:

[0037]

[0038] When the electricity price does not meet the convergence criteria, the electricity price is updated using the following formula:

[0039]

[0040] In the formula, ρ i The sensitivity factor of producer-consumer i to electricity prices.

[0041] Furthermore, the objective function for distribution network operation is:

[0042]

[0043]

[0044]

[0045] In the formula, δ cur For the curtailment rate, and These are 0-1 state variables, representing whether there is distributed wind power and distributed photovoltaic power at node h, respectively; N D Let h be the set of nodes in the distribution network. This represents the maximum power output of the wind turbine. For wind power, This represents the maximum power output of the photovoltaic system. Photovoltaic power; f loss For distribution network losses; hk For the line between node h and node k; P hk and Q hk U represents the active and reactive power flowing from node h to node k; h The voltage at node h; r hk The resistance of the line between node h and node k is denoted by U; ΔU is the voltage deviation of the distribution network. ref This is the reference voltage for the distribution network.

[0046] Furthermore, the NBI method is used to process the objective function of the distribution network operation; the specific steps include:

[0047] 1) The objective function is simplified to:

[0048]

[0049]

[0050] In the formula, x is a vector consisting of decision variables; g(x) and h(x) represent the equality and inequality constraints in the model, respectively; h(x) and These are the upper and lower limits of the inequality constraints in the model, respectively;

[0051] 2) Normalize the objective function as follows:

[0052]

[0053]

[0054]

[0055] It is a hypothetical optimal point, called the utopian point; The hypothetical worst point is called the lowest point; the normalized point... and The endpoints of the Pareto front constitute the plane defined by the Utopian plane;

[0056] 3) Generate points uniformly on the Utopia surface;

[0057] Any point P on the Utopia surface can be obtained from its endpoints. and The linear combination representation, i.e.

[0058]

[0059] In the formula, θ wv V represents the linear combination coefficients; V represents the number of uniform points generated on the utopia surface; H represents the number of segments.

[0060] 4) By uniformly selecting V points on the utopia surface and solving for the intersection of each point along the quasi-normal direction with the boundary of the corresponding feasible region of the objective function space, the multi-objective optimization problem is transformed into V single-objective optimization problems:

[0061]

[0062] In the formula, D v Given a uniformly distributed point P selected on the utopian surface within the feasible region, the distance reached along the quasi-normal direction is maximized by D. v This allows us to determine the points below the Pareto front; g2(x,D) v The distance D between point P on the Utopia surface and the quasi-normal direction after applying the NBI method is... v The expression for the three coordinates of a point in three-dimensional space.

[0063] A producer-consumer end-to-end trading system based on distribution network operation optimization includes:

[0064] Identity verification module: Prosumers perform electricity load forecasting and power generation forecasting, and determine their own prosumer identity, which is then uploaded to the distribution network operator;

[0065] Electrical distance calculation module: Based on the identity and distribution network parameters uploaded by the prosumer, the distribution network operator calculates the electrical distance for each possible transaction and sends the corresponding information to the prosumer.

[0066] Planning module: Based on the electrical distance issued by the distribution network operator, construct the objective function for the operation of producers and consumers and perform distributed coordination optimization to formulate the power purchase and sale plan and P2P transaction plan, and then upload them to the distribution network operator;

[0067] In addition, the cost calculation module: The distribution network operator constructs the distribution network operation objective function based on the plan uploaded by the producer and consumer, and uses the NBI method to process multiple objectives. Then, the plan is sent to the producer and consumer for iterative correction until the producer and consumer and the distribution network operator reach an equilibrium solution, and various cost settlements are carried out based on the equilibrium solution.

[0068] The beneficial effects of this invention are:

[0069] 1. By organizing P2P transactions between producers and consumers within the distribution network area, distribution network operators can promote the consumption of distributed renewable energy, reduce the curtailment rate, and improve social welfare.

[0070] 2. By organizing P2P transactions between producers and consumers, the power flow distribution of the distribution network can be improved and the system network loss can be reduced.

[0071] 3. P2P transactions between producers and consumers can improve node voltage and reduce distribution network voltage deviation while improving power flow distribution.

[0072] 4. The NBI method is used to solve multi-objective optimization problems, enabling the generation of uniformly distributed Pareto front solution sets for multiple optimization objectives with different dimensions. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a diagram of the distribution network organization producer-consumer P2P transaction operation system architecture of the present invention;

[0075] Figure 2 This is a flowchart of the producer-consumer P2P transaction collaboration optimization process of the present invention;

[0076] Figure 3 This is a flowchart of the distribution network organization producer-consumer P2P transaction method of the present invention. Detailed Implementation

[0077] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] like Figure 1 As shown, a producer-consumer end-to-end trading system based on distribution network operation optimization includes: a producer-consumer control terminal, a producer-consumer smart trading device, and a distribution network P2P trading system; the producer-consumer smart trading device system predicts power generation and power consumption based on data collected by the control terminal, and reports the producer-consumer identity and power purchase and sale plan to the distribution network P2P trading system; the distribution network P2P trading system organizes P2P transactions according to the distribution network optimization operation target, and sends the plan to the producer-consumer smart trading device, and then sends control commands to the control terminal;

[0079] like Figure 3 As shown, the producer-consumer end-to-end transaction method based on distribution network operation optimization includes the following steps:

[0080] S1, Producers and consumers conduct electricity load forecasting and power generation forecasting;

[0081] The collected data is used to predict power generation and electricity load. Power generation prediction requires data including the previous day's wind / solar power generation and the predicted ambient temperature for the next 24 hours; electricity load prediction requires data including the previous day's user electricity consumption and the predicted ambient temperature for the next 24 hours. The prediction method uses a relatively mature neural network, where the collected data is input into a pre-trained neural network to obtain the predicted electricity load and power generation for the next day.

[0082] S2, based on electricity load forecast and power generation forecast, determines its own producer-consumer identity and uploads it to the distribution network operator;

[0083] The expression for verifying the identity of a prosumer is:

[0084]

[0085]

[0086] In the formula, For the power generation of gas turbines, and These represent the electricity purchased and sold between producer-consumer i and the power grid, respectively. In response power, For the predicted electricity load of consumer i, and These are the output capacities of distributed wind power and photovoltaic power, respectively. and These are 01 state variables. When each takes the value 1, it indicates that producer i is a producer and consumer respectively.

[0087] S3, the distribution network operator calculates the electrical distance for each possible transaction based on the identity and distribution network parameters uploaded by the prosumer and consumer, and distributes the corresponding information to the relevant prosumer and consumer. The electrical distance is calculated using the Thevenin impedance distance method, where the Thevenin impedance distance between distribution network node h and node k is... The following can be calculated:

[0088]

[0089] In the formula Z kk Z hh Z hk and Z kh is the corresponding element in the node impedance matrix Z.

[0090] S4, based on the electrical distance issued by the distribution network operator, constructs the objective function for the operation of producers and consumers and performs distributed coordination optimization to formulate the power purchase and sale plan and P2P transaction plan, and then uploads them to the distribution network operator;

[0091] With the goal of minimizing operating costs, the objective function for prosumer operations is constructed as follows:

[0092] min Cost i =C ele,i +C gas,i +C DR,i +C P2P,i -F P2P,i

[0093] In the formula, C ele,i The cost of purchasing and selling electricity for producer-consumer i; C gas,i For the gas purchase cost of producer-consumer i; C DR,i The cost of losses incurred in responding to the demand of prosumer i; C P2P,i and F P2P,i These represent the P2P electricity purchase cost and electricity sales revenue for producer-consumer i, respectively.

[0094] Among them, the electricity purchase and sale cost C for producer-consumer i ele,i The calculation expression is:

[0095]

[0096] In the formula, and These are the purchase and sale prices of electricity from the power grid; and These represent the power purchased and sold between producer-consumer i and the power grid, respectively.

[0097] For producer-consumer i, the gas purchase cost C gas,i The calculation expression is:

[0098]

[0099] In the formula, For the power generation of the gas turbine; a i b i and c i This represents the cost coefficient for gas turbine power generation.

[0100] The loss cost C incurred by producer-consumer i in responding to demand. DR,i The calculation expression is:

[0101]

[0102] In the formula, c dr Cost per unit of response loss; For response power.

[0103] For producer-consumer i, the P2P electricity purchase cost C P2P,i and electricity sales revenue F P2P,i The calculation expressions are as follows:

[0104]

[0105]

[0106] In the formula, and These are the electricity purchase price from producer-consumer j and the electricity sales price to producer-consumer j, respectively. and These represent the electricity purchase price of producer-consumer i from producer-consumer j and the electricity sold to producer-consumer j, respectively; c P2P The unit distance transmission cost set for distribution network operators and gas distribution network operators; d ij The electrical distance between two producers and consumers in a P2P transaction.

[0107] The constraints on the objective function of prosumers include: power balance constraints, prosumer identity verification, gas turbine output constraints, distributed wind and solar power output constraints, demand response constraints, P2P transaction constraints, and grid power purchase and sale constraints.

[0108] 1) Power balance constraints;

[0109]

[0110] In the formula, For the predicted electricity load of consumer i, and These are the output capacities of distributed wind power and photovoltaic power, respectively.

[0111] 2) Verification of producer-consumer identity;

[0112]

[0113]

[0114] In the formula, and These are 01 state variables, where each value is 1, representing producer and consumer respectively.

[0115] 3) Gas turbine output constraints;

[0116]

[0117] In the formula, and These represent the minimum and maximum output of the gas turbine, respectively; u i,t These are the state variables of the gas turbine;

[0118] 4) Output constraints of distributed wind and solar power;

[0119]

[0120]

[0121] In the formula, and These are the output capacities of distributed wind power and photovoltaic power, respectively. and These are the maximum power generation capacity;

[0122] 5) Demand response constraints;

[0123]

[0124] In the formula, The maximum allowable response power;

[0125] 6) P2P transaction constraints;

[0126]

[0127]

[0128]

[0129] 7) Power purchase and sale constraints in the power grid;

[0130]

[0131] To ensure user privacy during P2P transactions, a distributed coordination optimization approach is used to calculate transaction volume, such as... Figure 2 As shown; the steps of distributed coordination optimization include:

[0132] Step 1, data initialization, i.e., initial values ​​of coupling variables, coordination multipliers, convergence accuracy, maximum number of iterations, and number of iterations z = 1;

[0133] Step 2: Modify the producer-consumer objective function using the objective cascade analysis method, and optimize the solution based on the transaction volume information transmitted by other users to obtain the producer's own transaction volume information;

[0134] The objective function of the prosumer is modified using the Objective Cascade Analysis (ATC) method by adding a coordination multiplier; that is:

[0135] minCOST i =Cost i +C ATC

[0136]

[0137] In the formula, and These are the first-order coordination multipliers for P2P electricity purchase and sale by producer-consumer i; and These are the secondary coordination multipliers for P2P electricity purchase and sale by producer-consumer i, respectively.

[0138] Step 3: Determine if the transaction volume converges. If it converges, proceed to Step 4. If it does not converge, update the coordination multiplier and return to Step 2 to solve.

[0139] Convergence criterion for optimizing peer-to-peer (P2P) transactions between producers and consumers:

[0140]

[0141] The update method for the coordination multiplier is as follows

[0142]

[0143] In the formula, z represents the number of iterations; ρ is a constant greater than or equal to 1.

[0144] Step 4: Determine if the trading electricity price converges. If it converges, output the optimal trading result; if it does not converge, update the electricity price and return to step 2.

[0145] To demonstrate the control effect of the transaction price on the optimization result before and after the two iterations, since there is a price difference between the two entities in a P2P transaction... Therefore, given the relationship between the producers and consumers, we only need to consider the electricity purchase price for the producers and consumers. The electricity price in the two iterations should satisfy the following:

[0146]

[0147] In the above formula, ε1 and ε2 are the corresponding convergence errors, respectively;

[0148] When the electricity price does not meet the convergence criteria, the electricity price is updated using the following formula:

[0149]

[0150] In the formula, ρ i The sensitivity factor of producer-consumer i to electricity prices.

[0151] S5. The distribution network operator constructs the distribution network operation objective function based on the plan uploaded by the producer and consumer, and uses the NBI method to process multiple objectives. Then, the plan is sent to the producer and consumer for iterative correction until the producer and consumer and the distribution network operator reach an equilibrium solution, and various fees are settled based on the equilibrium solution.

[0152] The distribution network promotes the consumption of distributed generation resources, reduces network losses, and improves voltage levels by organizing producer-consumer transactions within its jurisdiction. Therefore, the distribution network involves multi-objective optimization, and its operational objective function can be specifically expressed as:

[0153] 1) The rate of curtailment of renewable energy is the lowest;

[0154] P2P transactions can promote the absorption of wind and solar power curtailment within the distribution network area, maximizing social welfare, with the goal of minimizing the curtailment rate within the distribution network area.

[0155]

[0156] In the formula, δ cur For the curtailment rate, and These are 0-1 state variables, representing whether there is distributed wind power and distributed photovoltaic power at node h, respectively; N D Let h be the set of nodes in the distribution network. This represents the maximum power output of the wind turbine. For wind power, This represents the maximum power output of the photovoltaic system. Photovoltaic power.

[0157] 2) Minimizes distribution network losses;

[0158] Organizing peer-to-peer (P2P) transactions between producers and consumers within the distribution network area can impact the transmission power on the lines, thereby reducing active power losses in the network and minimizing distribution network losses as the optimization objective.

[0159]

[0160] In the formula, f loss For distribution network losses; hk For the line between node h and node k; P hk and Q hk U represents the active and reactive power flowing from node h to node k; h The voltage at node h; r hk Let be the resistance of the line between node h and node k.

[0161] 3) The voltage deviation of the distribution network is minimal;

[0162] The distribution network can improve the voltage level of some nodes by organizing peer-to-peer (P2P) transactions between producers and consumers at different nodes to change the power flow. The optimization goal is to minimize the voltage deviation of the distribution network.

[0163]

[0164] In the formula, ΔU is the voltage deviation of the distribution network, U ref This is the reference voltage for the distribution network.

[0165] The constraints of the objective function for distribution network operation include: power flow constraints, voltage constraints, line transmission power constraints, and P2P transaction electrical distance constraints.

[0166] 1) Current constraints:

[0167]

[0168]

[0169] In the formula, x hk and r hk P represents the impedance and reactance of the line between nodes h and k, respectively; k and Q k Let represent the active power and reactive power flowing into node k, respectively; u(k) is the set of nodes to which power flows into node k; and v(k) is the set of nodes to which power flows from node k. k P is the node voltage. hk and Q hk These represent the active power and reactive power flowing from node h to node k, respectively.

[0170] 2) Voltage constraint;

[0171]

[0172] In the formula, and These are the lower and upper limits of the voltage amplitude at node i, respectively.

[0173] 3) Line transmission power constraints;

[0174]

[0175] In the formula, For line l hk Maximum transmission power.

[0176] 4) Electrical distance constraints for P2P transactions;

[0177] Using the Thevenin impedance distance The expression for estimating the electrical distance between the seller and buyer in a P2P transaction is as follows:

[0178]

[0179] In the formula Z kk Z hh Z hk and Z kh is the corresponding element in the node impedance matrix Z.

[0180] The objective function for power distribution network operation is multi-objective optimization. The traditional normal boundary intersection (NBI) method is used to process these multi-objectives. Specific steps include:

[0181] S51, the objective function of multi-objective optimization is simplified to:

[0182]

[0183]

[0184] In the formula, x is a vector consisting of decision variables; g(x) and h(x) represent the equality and inequality constraints in the model, respectively; h(x) and These are the upper and lower limits of the inequality constraints in the model, respectively;

[0185] S52, by optimizing each objective in the objective function individually, the corresponding optimal solution x can be obtained. 1* x 2* and x 3* , corresponding to point F1[δ] in the target space coordinate systemcur (x 1* ),f loss (x 1* ),ΔU(x 1* )]、F2[δ cur (x 2* ),f loss (x 2* ),ΔU(x 2* )] and F3[δ cur (x 3* ),f loss (x 3* ),ΔU(x 3* )];

[0186] The optimization solution steps include:

[0187] S521, Normalize the objective function;

[0188] The three optimization objectives of this invention have different dimensions and physical meanings, so normalization is required to match the three objectives. The normalization process is as follows:

[0189]

[0190]

[0191]

[0192] In the formula, the superscripts max and min represent the maximum and minimum values ​​of the objective value during single-objective optimization, respectively.

[0193] It is a hypothetical optimal point, called the utopian point; This is the hypothetical worst point, called the lowest point; it can be seen that the normalized Utopia point is located at the origin, and the normalized point... and The endpoints of the Pareto front constitute the plane defined by the Pareto front, which is the Utopian plane.

[0194] S522, generate points uniformly on the Utopia surface;

[0195] In a three-dimensional coordinate system, any point can be represented by a linear combination of three base points. Therefore, any point P on the Utopian surface can be represented by its endpoints. and The linear combination representation, i.e.

[0196]

[0197] In the formula, θ wvis the linear combination coefficient; V is the number of uniform points generated on the utopia surface; H is the number of segments, the larger H is, the more uniform points are generated;

[0198] S523, find the Pareto optimal solution;

[0199] By uniformly selecting V points on the utopian surface and solving for the intersection of each point along the quasi-normal direction with the boundary of the corresponding feasible region in the objective function space, the multi-objective optimization problem is transformed into V single-objective optimization problems:

[0200]

[0201] In the formula, D v Given a uniformly distributed point P selected on the utopian surface within the feasible region, the distance reached along the quasi-normal direction is maximized by D. v This allows us to determine the points below the Pareto front; g2(x,D) v The distance D between point P on the Utopia surface and the quasi-normal direction after applying the NBI method is... v The expression for the three coordinates of a point in three-dimensional space.

[0202] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0203] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A producer-consumer end-to-end transaction method based on distribution network operation optimization, characterized in that, Includes the following steps: Prosumers forecast electricity load and power generation, identify themselves as prosumers, and upload the data to the distribution network operator. Based on the identities and distribution network parameters uploaded by the prosumers, the distribution network operator calculates the electrical distance for each possible transaction and sends the corresponding information to the prosumers. Based on the electrical distance issued by the distribution network operator, a producer-consumer operation objective function is constructed and distributed coordination optimization is performed to formulate power purchase and sale plans and P2P transaction plans, which are then uploaded to the distribution network operator. The distribution network operator constructs the distribution network operation objective function based on the plans uploaded by the producers and consumers, and uses the NBI method to process multiple objectives. The plan is then distributed to the producers and consumers for iterative correction until the producers, consumers and the distribution network operator reach an equilibrium solution, and various fee settlements are carried out based on the equilibrium solution. The objective function for the prosumer is: In the formula, For producers and consumers Electricity purchase and sales costs; For producers and consumers The cost of purchasing gas; For producers and consumers The costs and losses incurred in responding to demand; and Producers and consumers respectively The cost of purchasing electricity and the revenue from selling electricity through P2P platforms; and These are the purchase and sale prices of electricity from the power grid; and Producers and consumers respectively Power purchased and sold with the power grid; For the power generation of gas turbines; , and This represents the cost coefficient for gas turbine power generation. Cost per unit of response loss; For response power; and Producers and consumers respectively From producers and consumers Electricity purchase price and the price paid to producers and consumers The electricity sales price; and Producers and consumers respectively From producers and consumers The power purchased by the consumer and the electricity supplied to the consumer Electricity sales capacity; The unit distance transmission cost set for distribution network operators and gas distribution network operators; The electrical distance between two producers and consumers in a P2P transaction; The steps of distributed coordination optimization include: Step 1, Data initialization; Step 2: Modify the objective function of the prosumer using the objective cascade analysis method, and optimize the solution based on the transaction volume information transmitted by other users to obtain its own transaction volume information; Step 3: Determine if the transaction volume converges. If it converges, proceed to Step 4. If it does not converge, update the coordination multiplier and return to Step 2 to solve. Step 4: Determine if the trading electricity price converges. If it converges, output the optimal trading result; if it does not converge, update the electricity price and return to step 2.

2. The producer-consumer end-to-end transaction method based on distribution network operation optimization according to claim 1, characterized in that, The formula for determining one's prosumer status is: In the formula, For the power generation of gas turbines, and Producers and consumers respectively Power purchased and sold with the power grid In response power, For producers and consumers Predicted electricity load, and These are the output capacities of distributed wind power and photovoltaic power, respectively. and These are 01 state variables; when each takes the value 1, they represent producer and consumer, respectively. For producers and consumers.

3. The producer-consumer end-to-end transaction method based on distribution network operation optimization according to claim 1, characterized in that, The modified objective function for the prosumer-consumer model is as follows: In the formula, and Producers and consumers respectively A primary coordination multiplier during P2P electricity purchase and sale; and Producers and consumers respectively The secondary term coordination multiplier when conducting P2P electricity purchase and sale.

4. The producer-consumer end-to-end transaction method based on distribution network operation optimization according to claim 3, characterized in that, The convergence criterion for optimizing peer-to-peer (P2P) transactions between producers and consumers is: The update method for the coordination multiplier is as follows: In the formula, Indicates the number of iterations; It is a constant greater than or equal to 1.

5. The producer-consumer end-to-end transaction method based on distribution network operation optimization according to claim 4, characterized in that, The convergence criterion for the electricity trading price is: When the electricity price does not meet the convergence criteria, the electricity price is updated using the following formula: In the formula, For producers and consumers Sensitivity factor to electricity prices.

6. The producer-consumer end-to-end transaction method based on distribution network operation optimization according to claim 1, characterized in that, The objective function for distribution network operation is: In the formula, For the curtailment rate, and These are 0 and 1 state variables, representing the state at the node. Does the area have distributed wind power and distributed photovoltaic power? Let h be the set of nodes in the distribution network. This represents the maximum power output of the wind turbine. For wind power, This represents the maximum power output of the photovoltaic system. Photovoltaic power; For distribution network losses; For nodes and nodes The route between; and For nodes Flow to Node The active and reactive power; For nodes The voltage; For nodes and nodes The resistance of the lines between them; For distribution network voltage deviation, This is the reference voltage for the distribution network.

7. A producer-consumer end-to-end transaction method based on distribution network operation optimization according to claim 6, characterized in that, The NBI method is used to process the objective function of the distribution network operation; the specific steps include: 1) The objective function is simplified to: In the formula, A vector consisting of decision variables; and These represent the equality and inequality constraints in the model, respectively. and These are the upper and lower limits of the inequality constraints in the model, respectively; 2) Normalize the objective function as follows: It is a hypothetical optimal point, called the utopian point; The hypothetical worst point is called the lowest point; the normalized point... , and The endpoints of the Pareto front constitute the plane defined by the Utopian plane; 3) Generate points uniformly on the Utopia surface; Any on the surface of utopia A point can be formed by endpoints , and The linear combination representation, i.e. In the formula, These are the coefficients of the linear combination; The number of uniform points generated on the Utopia surface; The number of segments; 4) By uniformly selecting on the Utopia surface The problem is transformed into finding the intersection of each point along the quasi-normal direction with the boundary of the corresponding feasible region in the objective function space, thus transforming the multi-objective optimization problem into finding the intersection of each point along the quasi-normal direction with the boundary of the corresponding feasible region in the objective function space. Single-objective optimization problem: In the formula, Points uniformly distributed on the utopia surface within the feasible region The distance reached along the quasi-normal direction, maximizing This allows us to determine the points below the Pareto front; To find the points on the Utopia surface along the quasi-normal direction after applying the NBI method. Distance is The expression for the three coordinates of a point in three-dimensional space.

8. A producer-consumer end-to-end transaction system based on distribution network operation optimization, comprising the method described in any one of claims 1-7, characterized in that, include: Identity verification module: Prosumers perform electricity load forecasting and power generation forecasting, and determine their own prosumer identity, which is then uploaded to the distribution network operator; Electrical distance calculation module: Based on the identity and distribution network parameters uploaded by the prosumer, the distribution network operator calculates the electrical distance for each possible transaction and sends the corresponding information to the prosumer. Planning module: Based on the electrical distance issued by the distribution network operator, construct the producer-consumer operation objective function and perform distributed coordination optimization to formulate the power purchase and sale plan and P2P transaction plan, and then upload them to the distribution network operator; In addition, the cost calculation module: The distribution network operator constructs the distribution network operation objective function based on the plan uploaded by the producer and consumer, and uses the NBI method to process multiple objectives. Then, the plan is sent to the producer and consumer for iterative correction until the producer and consumer and the distribution network operator reach an equilibrium solution, and various cost settlements are carried out based on the equilibrium solution.

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