Method and system for optimizing producer-consumer distributed p2p transaction based on negotiated dynamic run envelope

Abstract

This invention belongs to the field of distribution network optimization and dispatching, and discloses a method and system for optimizing producer-consumer distributed peer-to-peer (P2P) transactions based on negotiated dynamic operating envelopes. This invention combines a producer-consumer P2P energy trading model with an optimal dynamic operating envelope optimization model of the distribution system operator (DSO) to construct a two-layer optimization framework model, effectively balancing the privacy and autonomy of producer-consumer transactions while achieving an organic coordination between distribution system security and market participant economics. This invention fully considers the trading preferences and autonomous behavior of producer-consumers, allowing them and the DSO to negotiate and obtain the optimal dynamic operating envelope (DOE). P2P transactions conducted under the negotiation results will not violate system security constraints. Based on the two-layer optimization framework model, this invention can dynamically and specifically adjust inlet and outlet power to guide producer-consumer P2P transactions, achieving a free P2P market while satisfying distribution network security.

Description

Technical Field

[0001] This invention belongs to the field of distribution network optimization and scheduling, specifically involving a producer-consumer distributed P2P transaction optimization method and system based on negotiation dynamic operating envelope. Background Technology

[0002] With the increasing penetration of distributed energy and the advancement of a new round of power system reform, traditional electricity users are transforming into prosumers with both electricity production and consumption capabilities, participating in electricity market transactions as independent stakeholders. Peer-to-peer (P2P) trading, as an emerging energy trading model, allows prosumers to share electricity through bilateral negotiations based on their own energy needs, effectively promoting the local consumption of distributed energy and improving the economic efficiency and flexibility of prosumers. In this context, frequent information transmission and power interaction, along with the increased privacy and autonomy of each participant, place higher demands on the safe operation and economic management of the system. Therefore, research on the optimization problem of prosumers in the P2P energy market is of great significance for achieving a balanced approach between distribution network security and prosumer economics.

[0003] Currently, many scholars have conducted extensive research on the producer-consumer optimization problem in P2P energy markets. Centralized markets achieve a globally optimal solution by introducing a central entity to coordinate energy transactions among various participants, but this raises privacy concerns. In contrast, distributed markets allow producers and consumers to negotiate P2P transactions based on their own trading preferences, but the profit-seeking nature of market participants under incomplete information can easily lead to operational risks in the system after transactions. To address this, some scholars have introduced distribution system operators (DSOs) to supervise and guide producer-consumer trading behavior. Based on energy management methods, these can be divided into two categories: direct control and indirect adjustment. The former utilizes direct regulatory means such as network reconfiguration, on-load tap changers, reactive power management, and ancillary services to optimize power flow and coordinate P2P transactions to meet distribution network constraints, but lacks incentives for producers and consumers, failing to fully mobilize their enthusiasm for participating in grid-friendly interactions. The latter, based on electricity price guidance, increases the additional transaction costs for producers and consumers, making mitigation of system risks a self-incentive, but there may be situations where P2P transaction results lead to an infeasible optimal power flow solution. Summary of the Invention

[0004] The purpose of this invention is to overcome the problem that producer-consumer distributed P2P transactions under incomplete information cannot take into account the constraints of the distribution network, and to provide a producer-consumer distributed P2P transaction optimization method and system based on negotiation dynamic operating envelope.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing producer-consumer distributed P2P transactions based on negotiation-driven dynamic operating envelopes, comprising: Based on the relationship between producers and consumers and power distribution system operators, a two-layer optimization framework model for producers and consumers and power distribution system operators is constructed. The two-layer optimization framework model includes a producer-consumer P2P energy trading model and an optimal dynamic operation envelope optimization model for power distribution system operators. Based on the producer-consumer P2P energy trading model, the optimal dynamic operating envelope of the producer-consumer is obtained; Based on the optimal dynamic operating envelope optimization model of the power distribution system operator, the optimal dynamic operating envelope of the power distribution system operator is obtained; Based on the optimal dynamic operating envelope of the producer and consumer and the optimal dynamic operating envelope of the distribution system operator, the distributed P2P transaction power that meets the security constraints of the distribution network is obtained.

[0006] A further improvement of this invention lies in the following method for obtaining the optimal dynamic operating envelope of prosumers based on the prosumer-consumer P2P energy trading model: Based on the optimal dynamic operating envelope variables obtained through negotiation between producers and consumers and the distribution system operator, with the goal of minimizing the operating costs of producers and consumers, the scheduling strategy of the current flexible resources of producers and consumers, as well as the trading strategy with other producers and consumers and the power grid, are optimized, and the optimal dynamic operating envelope of producers and consumers is calculated and uploaded for updating.

[0007] A further improvement of this invention lies in the following method for obtaining the optimal dynamic operating envelope of a power distribution system operator based on the optimal dynamic operating envelope optimization model: Obtain network status information from power distribution system operators, as well as the expected import and export power of producers and consumers; Based on the network status information of the power distribution system operator and the expected import and export power of producers and consumers, the optimal dynamic operating envelope is calculated and sent to the power distribution system operator for updating.

[0008] A further improvement of this invention lies in the following method for obtaining the distributed P2P transaction power that meets the security constraints of the distribution network, based on the optimal dynamic operating envelope of the prosumer and the optimal dynamic operating envelope of the distribution system operator: The optimal dynamic operating envelopes of producers and consumers and the optimal dynamic operating envelopes of distribution system operators are sent to the distribution system operators for updates, so that the data in the distribution system operators meets the distribution network security constraints for distributed P2P transaction power.

[0009] A further improvement of this invention is that when sending the optimal dynamic operating envelope of producers and consumers and the optimal dynamic operating envelope of the distribution system operator to the distribution system operator for updating, the analysis target cascading method is adopted.

[0010] A further improvement of this invention is that, when obtaining the optimal dynamic operating envelope of the prosumer based on the prosumer-consumer P2P energy trading model, an adaptive alternating direction multiplier method is adopted.

[0011] Secondly, the present invention provides a producer-consumer distributed P2P transaction optimization system based on negotiation dynamic operating envelope, comprising: The model building module is used to construct a two-layer optimization framework model for producers and consumers and distribution system operators based on the relationship between them. The two-layer optimization framework model includes a producer-consumer P2P energy trading model and an optimal dynamic operating envelope optimization model for distribution system operators. The producer-consumer P2P energy trading model calculation module is used to obtain the optimal dynamic operating envelope of the producer-consumer based on the producer-consumer P2P energy trading model. The optimal dynamic operating envelope optimization model calculation module is used to obtain the optimal dynamic operating envelope of the power distribution system operator based on the optimal dynamic operating envelope optimization model of the power distribution system operator. The data negotiation module is used to obtain the distributed P2P transaction power that meets the security constraints of the distribution network based on the optimal dynamic operating envelope of the producer and consumer and the optimal dynamic operating envelope of the distribution system operator.

[0012] A further improvement of this invention is that the function of the producer-consumer P2P energy trading model calculation module is implemented through the following method: Based on the optimal dynamic operating envelope variables obtained through negotiation between producers and consumers and the distribution system operator, with the goal of minimizing the operating costs of producers and consumers, the scheduling strategy of the current flexible resources of producers and consumers, as well as the trading strategy with other producers and consumers and the power grid, are optimized, and the optimal dynamic operating envelope of producers and consumers is calculated and uploaded for updating.

[0013] A further improvement of this invention is that the function of the optimal dynamic envelope optimization model calculation module is implemented through the following method: Obtain network status information from power distribution system operators, as well as the expected import and export power of producers and consumers; Based on the network status information of the power distribution system operator and the expected import and export power of producers and consumers, the optimal dynamic operating envelope is calculated and sent to the power distribution system operator for updating.

[0014] A further improvement of this invention is that the data negotiation module is implemented through the following method: The optimal dynamic operating envelopes of producers and consumers and the optimal dynamic operating envelopes of distribution system operators are sent to the distribution system operators for updates, so that the data in the distribution system operators meets the distribution network security constraints for distributed P2P transaction power.

[0015] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a producer-consumer distributed P2P transaction optimization method based on negotiation dynamic running envelope.

[0016] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of a producer-consumer distributed P2P transaction optimization method based on negotiation dynamic running envelope.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention combines a producer-consumer (P2P) energy trading model with an optimal dynamic operating envelope (DOE) optimization model from a distribution system operator (DSO) to construct a two-layer optimization framework model. This effectively balances the privacy and autonomy of producer-consumers' transactions while achieving a harmonious balance between distribution system security and market participant economics. The invention fully considers the trading preferences and autonomous behavior of producer-consumers, allowing them and the DSO to negotiate and obtain the optimal DOE. P2P transactions conducted under this negotiation will not violate system security constraints. Based on the two-layer optimization framework model, this invention can dynamically and specifically adjust inlet and outlet power to guide producer-consumer P2P transactions, effectively balancing the interests of market participants. While ensuring distribution network security, it also achieves a free P2P market. This method establishes an information-sharing mechanism through the optimization model, reducing the incomplete information problems that producer-consumers may face during transactions. The DSO can provide information about network status and constraints through the optimal DOE model, helping producer-consumers make more rational trading decisions. This invention, by optimizing trading power and dynamically adjusting trading strategies, can effectively balance load fluctuations across different time periods and geographical locations, reducing system pressure caused by uneven load distribution or peak electricity demand, and further enhancing the load regulation capacity of the distribution system. In summary, this invention not only solves the distribution network constraint problem under incomplete information conditions but also improves the overall efficiency, stability, and sustainability of system operation.

[0018] Furthermore, the nested distributed algorithm used in this invention has the characteristics of high privacy protection and complete distribution, which can ensure the independence and dynamic optimization of the decision-making process of different participating entities, while meeting the privacy and autonomy requirements of prosumers in transactions. Attached Figure Description

[0019] Figure 1 This is a flowchart of the present invention; Figure 2 This is a system diagram of the present invention; Figure 3 This is a schematic diagram of the two-layer optimization framework model for producers and consumers and power distribution system operators in this invention; Figure 4 This is a schematic diagram illustrating the negotiation of the optimal dynamic operating envelope in this invention; Figure 5 This is a system diagram of Example 5. Detailed Implementation

[0020] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0021] See Figure 1 A producer-consumer distributed P2P transaction optimization method based on negotiation-driven dynamic envelope includes: S1. Based on the relationship between producers and consumers and distribution system operators, a two-layer optimization framework model for producers and consumers and distribution system operators is constructed. The two-layer optimization framework model includes a producer-consumer P2P energy trading model and an optimal dynamic operating envelope (DOE) optimization model for distribution system operators.

[0022] S2, based on the producer-consumer P2P energy trading model, obtains the optimal dynamic operating envelope for producers and consumers.

[0023] S3. Based on the optimal dynamic operating envelope optimization model of the power distribution system operator, the optimal dynamic operating envelope of the power distribution system operator is obtained.

[0024] S4. Based on the optimal dynamic operating envelope of the producer and consumer and the optimal dynamic operating envelope of the distribution system operator, the distributed P2P transaction power that meets the security constraints of the distribution network is obtained.

[0025] See Figure 2 A producer-consumer distributed P2P transaction optimization system based on negotiation-driven dynamic envelope includes: The model building module is used to construct a two-layer optimization framework model for producers and consumers and distribution system operators based on the relationship between them. The two-layer optimization framework model includes a producer-consumer P2P energy trading model and an optimal dynamic operating envelope optimization model for distribution system operators. The producer-consumer P2P energy trading model calculation module is used to obtain the optimal dynamic operating envelope of the producer-consumer based on the producer-consumer P2P energy trading model. The optimal dynamic operating envelope optimization model calculation module is used to obtain the optimal dynamic operating envelope of the power distribution system operator based on the optimal dynamic operating envelope optimization model of the power distribution system operator. The data negotiation module is used to obtain the distributed P2P transaction power that meets the security constraints of the distribution network based on the optimal dynamic operating envelope of the producer and consumer and the optimal dynamic operating envelope of the distribution system operator.

[0026] Example 1: This embodiment first decomposes the problem of optimizing the security of producer-consumer P2P transactions within the distribution network, where a two-layer optimization framework is as follows: Figure 3 As shown, lower-level prosumers minimize operating costs by optimizing their internal flexible resource scheduling strategies and their trading strategies with other prosumers and the power grid. The upper-level Distribution System (DSO) performs security checks based on network status information and the expected import / export power submitted by prosumers. When a violation of distribution network security constraints occurs, it calculates and issues a Designation of Effect (DOE). After receiving the DOE, prosumers adjust their P2P trading power through further optimization of their own strategies, upload the new import / export power to the DSO, and ultimately reach a DOE consensus through multiple rounds of negotiation with the DSO. The DOE negotiation between the upper-level DSO and lower-level prosumers is implemented using the analytical target cascading (ATC) algorithm, while the distributed P2P trading negotiation between lower-level prosumers is implemented using the alternating direction method of multipliers (ADMM). The resulting nested distributed algorithm consists of the analytical target cascading (ATC) algorithm and the alternating direction method of multipliers (ADMM).

[0027] Example 2: Assuming each prosumer is equipped with flexible resources such as photovoltaic (PV) power generation, micro-turbines (MT), energy storage systems (ESS), and demand response loads (DR), each prosumer, as an independent autonomous entity, is connected to the distribution network via a tie line, and can buy and sell electricity from the main grid or share electricity with other prosumers. For a given prosumer... , The optimal scheduling model for its P2P energy trading is as follows.

[0028] (1) Micro gas turbine MT: Micro gas turbines (MTs) offer a degree of flexibility as controllable distributed power sources, and their power response is faster than hourly-level dispatching; therefore, ramp rate constraints are not considered. (1) (2) In the formula, , and These are the coefficients of the quadratic cost function of the micro gas turbine (MT). and Indicates the micro gas turbine MT int Actual and maximum permissible output power for the time period.

[0029] (2) Demand Response Load (DR): Considering load scenarios with shiftable demand response, the electricity consumption plan for part of the load can be moved forward or delayed from one time period to another. The relevant electricity consumption characteristic constraints can be expressed as follows: (3) (4) In the formula, and express t Actual and expected loads for different time periods For demand response load in t The ratio of maximum to minimum electricity demand during a given time period.

[0030] Changes to electricity plans will inevitably affect user comfort. t The undesirable costs arising from time-based load adjustments can be expressed as a quadratic function: (5) In the formula, This represents the unit unsuitable cost of the load deviation.

[0031] (3) Energy Storage System (ESS): The operating cost of energy storage mainly considers its depreciation cost, which can be expressed as: (6) In the formula, This is the unit charge / discharge cost coefficient. and These represent the energy storage charging and discharging power, respectively.

[0032] The constraints that energy storage units must meet during operation include: (7) (8) (9) (10) (11) In the formula, and These represent the maximum and minimum values ​​of the energy storage capacity; and This indicates the maximum and minimum values ​​of the energy storage charging and discharging power; and Indicates the charging and discharging efficiency of energy storage; For energy storage units tTime capacity; This represents the initial capacity of the energy storage unit.

[0033] (4) Peer-to-grid (P2G) energy trading: When a producer's internal power generation cannot meet its load demand, it can purchase electricity from the grid; conversely, a producer can sell surplus electricity back to the grid to generate revenue. The power interaction cost between the producer / consumer and the distribution network can be expressed as: (12) In the formula, and Power purchased and sold from the power grid; and Time-of-use tariffs and grid connection tariffs are for transactions between producers and consumers and the distribution network.

[0034] (5) P2P energy trading: Each prosumer completes a peer-to-peer (P2P) energy transaction with other prosumers through information exchange. Without considering transmission losses, prosumers... k and j The transaction power between them must meet the following constraints: (13) In the formula, Indicates the time period t Producers and consumers k and j The power of P2P transactions between them. For positive representation of producers and consumers k Towards j Purchase power, A negative value indicates a consumer. k Towards j Selling power.

[0035] Producers and consumers k During the period t The cost of P2P transactions is: (14) In the formula: Indicates the time period t Producers and consumers k and j A P2P transaction price was agreed upon through negotiation.

[0036] (6) Active power balance constraint: (15) Each producer-consumer k The decision variables are The overall objective function is shown in (16), and the constraints to be satisfied include equations (2)-(4), (7)-(11), (13), and (15): (16) Without considering network constraints, the producer-consumer can be obtained by solving equation (16). k The scheduling and trading strategies of the power sources allow them to upload their net injected power and complete delivery through transmission lines. Prosumers k During the period t The net injected power is divided into two parts: P2G transaction power and P2P transaction power.

[0037] (17) In the formula, This represents the expected net injection power without considering network constraints. A positive value indicates imported electrical energy; A negative value indicates exported electrical energy.

[0038] To ensure that the expected net power injection does not affect the safe operation of the distribution network, prosumers must also comply with DOE (Design of Energy) limits to participate in P2P energy trading. Because prosumers... k exist t The transaction involves either buying or selling electricity during a specific time period, and therefore is subject to only one type of transaction restriction: import or export. DOE constraints can be expressed as: (18) (19) In the formula, This indicates that the DSO is issued to the producer and consumer. k Time period t DOE restrictions.

[0039] DOE optimization model for upper-level DSO: Consider a radial distribution network represented by a tree diagram. ,in Represents a set of nodes. This represents the set of branches. The root node is connected to the main network and is numbered 1. Except for the root node, each node has a unique parent node. and a series of The set of child nodes represented. (From node) arrive The branch roads are simplified and numbered as j The specific model of DSO is shown below: (1) Objective function The primary objective of DSO (Distribution System Operation) is to minimize network losses while ensuring the safety of the distribution network. Simultaneously, to avoid excessively reducing producer-consumer trading power, the expected node trading power should be... and DOE defined by DSO The difference should be as small as possible.

[0040] (20) In the formula, express t Unit network loss cost per time period This serves as a penalty factor. The objective function differs from the common DOE formula. The difference between DOE and the expected node transaction power is not the primary objective, but rather a soft penalty function. The principle can be understood as follows: without losing the original definition of DOE, the DSO, as an independent entity, is more concerned with distribution network losses when making decisions; while the power difference, related to the interests of prosumers, serves only as an incentive objective, encouraging the DSO to minimize power reduction by prosumers as much as possible.

[0041] (2) Network constraints: (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) (27) (28) In the formula, and express t Time period nodes j and its parent node The square of the voltage amplitude; express t Time-of-day branch j The square of the current amplitude; and Indicates a branch j Resistance and reactance; and express t Time period nodes j Injected active and reactive power; and express t Time-of-day branch i The trend of achieving merit without achieving merit; and Represents a node i The upper and lower limits of the square of the voltage amplitude. Equations (22) and (23) are the active and reactive power balance constraints of the node; Equations (24) and (25) are the power flow constraints at the beginning and end of the branch; Equation (26) represents the voltage drop of the branch; Equation (27) is the relationship between the power flow of the branch and the node voltage, expressed in the form of a standard second-order cone; Equation (28) is the upper and lower limit constraints of the voltage.

[0042] Thus, the upper-level DSO model is expressed as a second-order cone programming (SOCP) problem. The overall objective function of the DSO is shown in (20), and the constraints to be satisfied are equations (21)-(28). The decision variables are... Obtain the optimal solution. The guidelines were then distributed to various producers and consumers to ensure that P2P energy transactions met the constraints of the power distribution network.

[0043] Negotiated Dynamic Operating Envelope (NDOE) method: Due to the autonomous behavior and self-optimization process of prosumers, prosumers do not want the DOE they receive to excessively restrict their transaction amounts. They can choose to adjust their scheduling and transaction strategies to seek higher import and export power licenses, that is, to request a higher expected net injection power from the DSO than the DOE they receive. The DSO then chooses to accept or reject the new import and export power request. If it chooses to reject, it recalculates the DOE based on the new request.

[0044] set up For the first n Middle-class consumers in rounds of negotiations k The net injection power applied for, For the first n The DOE calculated by the DSO during rounds of negotiation is included in the proposed P2P transaction framework as follows: 1) For any producer-consumer k First, negotiate with other producers and consumers to reach a consensus on P2P transactions, and then calculate the net injected power and upload it to the DSO through formula (16); 2) The DSO receives the initial net injected power. Then, calculate the DOE power. And distributed to producers and consumers; 3) The process of adjusting prosumers and DSO strategies, as follows: Figure 4 As shown.

[0045] Assumption Indicates producer-consumer k During the period t Export power, using electricity sales as an example, illustrates the DOE negotiation process. In the... n In this negotiation, the prosumer and consumer based on the received... The net injected power is recalculated through rescheduling. ,have DSO received Then, considering the current system security constraints, a new round of DOE calculations is performed, resulting in: This process is repeated in cycles, with each round of negotiation... and Gradually converging to a consensus; when and When the values ​​no longer change and are equal, it means that the producer-consumer and the DSO have finally reached an agreement, and the outcome of the negotiation is optimal for the interests of both parties.

[0046] In the proposed NDOE method, Instead of being a constant directly issued by the DSO, prosumers will reach a DOE consensus through multiple rounds of negotiation with the DSO by adjusting the P2P transaction power. This will effectively guarantee the autonomy of prosumers and the flexibility of bilateral transactions, and achieve the optimal interests of all parties involved.

[0047] Example 3: The solution method for P2P energy trading negotiation based on ADMM is as follows: The distributed algorithm based on ADMM decomposes the centralized optimization problem into multiple sub-problems that can be solved independently under limited information exchange. In the P2P energy trading model, producers and consumers only need to exchange P2P information, and other decision variables can be optimized independently. To this end, auxiliary variables are first introduced to decouple the consistency constraints (13), as shown in the following expression: (29) In the formula, The introduced auxiliary variable can be viewed as a producer-consumer. k Estimated P2P transaction volume; For Lagrange multipliers, from an economic perspective, it represents the shadow price of transaction power.

[0048] Then, the augmented Lagrangian function for the lower-level optimization problem is constructed. The P2P transaction problem is further decomposed into each prosumer-consumer problem. k A single subproblem that can be solved independently can be specifically represented as: (30) In the formula, The penalty factor is positive. When the optimal solution of the lower level is obtained, the sum of the P2P transaction costs of all producers and consumers is zero. Therefore, the objective function (30) of the decomposed subproblem does not include P2P transaction costs.

[0049] The DOE negotiation solution method based on ATC is as follows: By solving problem (30), the producer-consumer can be obtained. k During the periodt Net injection power In the negotiation, it is defined as a response DOE variable. The target DOE variable of the upper-level DSO ( ) and the response DOE variables of lower-level prosumers ( These are considered as a set of coupled variables. The DOE consistency constraints corresponding to the upper and lower layers are expressed as follows: (31) For the proposed two-layer model with parallel coupling structure, traditional centralized algorithms can cause privacy issues among the agents. The ATC algorithm, however, has advantages such as good convergence and high computational efficiency when solving multi-level optimization models. This algorithm maintains the optimal solution for each agent while obtaining the optimal solution that satisfies the consistency constraints between different levels through iterative iteration. Therefore, the ATC algorithm is used to decouple the two-layer model, implementing the DOE negotiation process in a decentralized manner, while protecting the privacy and decision independence of the DSO and producers / consumers. Similarly, by introducing Lagrange relaxation and penalty functions to relax the consistency constraints (31), the decoupled independent optimization model between the upper and lower layers can be expressed as follows: (32) (33) In the formula, and Let Lagrange multipliers and penalty factors represent the penalty functions for the linear and quadratic terms of ATC.

[0050] According to the solution process of the standard ATC algorithm, the lower-level prosumers will Treating it as a constant, ADMM is called to solve it. Each prosumer obtains a new response DOE variable by adjusting its energy trading strategy. Then, the upper-level DSO receives the expected trading requests from the prosumers. If there is a violation of the distribution network constraints, a new target DOE variable is solved and distributed. Unlike the traditional centralized DOE calculation method, the prosumers adjust their P2P energy trading strategies to comply with the physical network constraints in each round of negotiation, and finally the DOE consistency coupling constraint (31) is satisfied.

[0051] The nested distributed algorithm solution method is as follows: set up z and v These represent the iteration counts of the ATC algorithm and the ADMM algorithm, respectively. The ADMM algorithm is used to solve the P2P transaction problem involving multiple prosumers at the lower level. In the... v In this iteration, prosumers k According to auxiliary variables and Lagrange multipliers and target DOE Update the local decision variables. The Lagrange multipliers and auxiliary variables are updated as follows.

[0052] (34) (35) The ADMM algorithm stops updating after both the original residual and the dual residual converge simultaneously, and then calculates and uploads the DOE response variables. The specific convergence criteria are as follows: (36) (37) In the formula, and Represents the primary and dual residuals of the ADMM algorithm; and These are the set error precision.

[0053] Since the penalty factor has a significant impact on the convergence performance of ADMM, using a fixed step size may waste computational resources and is highly dependent on the choice of initial values. Therefore, an adaptive ADMM algorithm is adopted to accelerate the iterative convergence speed. (38) In the formula, A constant used to determine the relationship between the original and dual residuals; and These represent the factors that increase and decrease the penalty factor, respectively.

[0054] The upper and lower layers achieve decision consistency between the DSO and the producer-consumer based on the ATC algorithm. In the first... z In this iteration, prosumers k Treat the target DOE as a known variable and solve for the response. DSO is based on DOE response Solve the target DOE The Lagrange multipliers and penalty factors are updated as follows.

[0055] (39) (40) In the formula, This ensures that the penalty parameter sequence is non-decreasing and converges to the optimal solution under the convex assumption.

[0056] Convergence criteria include optimality criteria and consistency criteria. The optimality criterion limits the error of the optimal solution during the iteration process, i.e., the difference in the total cost of the upper and lower layers obtained in two iterations; while the consistency criterion limits the consistency error between two layers of problems, i.e., the error in the first iteration. zThe absolute error of the DOE between the upper and lower layers during the next iteration. The specific expression is: (41) (42) In the formula, and The first z The total cost of the upper-level distribution network and the lower-level producer-consumer model in the next iteration; and The convergence criteria for optimality and consistency error are used. If convergence conditions (41)-(42) are satisfied simultaneously, the ATC iteration terminates.

[0057] The specific update steps of the nested algorithm are as follows: 1) Initialize parameters. Let... z and v Set the iteration counts for the ATC and Adaptive ADMM algorithms, respectively; set the initial values ​​for the target and response DOE variables, Lagrange multipliers, and penalty factors.

[0058] 2) Prosumers k Based on known variables , , , , and Solve problem (32) to obtain the response DOE variables .

[0059] 3) When the convergence conditions (36)-(37) are met, the adaptive ADMM iteration terminates, and the response DOE variable is calculated according to equation (15). Then proceed to step (5); otherwise proceed to step (4).

[0060] 4) v = v + 1, update the Lagrange multipliers according to equations (34)-(35) and (38). Auxiliary variables and penalty factor Then proceed to step (3).

[0061] 5) DSO based on known variables , and Solving problem (33) to obtain the target DOE variable .

[0062] 6) Update the ATC Lagrange multipliers according to equations (39)-(40). and penalty factor .

[0063] 7) The ATC iteration terminates when convergence conditions (41)-(42) are met; otherwise, z = z +1, let v = 1 and jump to step (2).

[0064] Based on the characteristics of the distributed peer-to-peer (P2P) transaction security optimization problem within a power distribution network, this invention decomposes the problem into two negotiation sub-problems: DOE negotiation and P2P transaction negotiation. A nested distributed algorithm composed of ATC and adaptive ADMM is employed to solve the problem, coordinating the operation scheduling of the DSO and prosumers in a decentralized manner. This ensures the independence and dynamic optimization of the decision-making processes of different participants, while simultaneously satisfying the privacy and autonomy requirements of prosumers.

[0065] This invention is a producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope. It fully considers the transaction preferences and autonomous behavior of producers and consumers, and allows producers and consumers to negotiate with DSO to obtain the optimal import and export power envelope under limited information. It effectively takes into account the interests of market participants while ensuring that P2P transactions do not violate system security constraints.

[0066] Example 4; Please see Figure 5 As shown, the present invention also provides an electronic device 100 for a producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0067] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0068] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0069] The memory 101 in the electronic device 100 stores multiple instructions to implement a producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope, and the processor 102 can execute the multiple instructions to achieve the following: Based on the relationship between producers and consumers and power distribution system operators, a two-layer optimization framework model for producers and consumers and power distribution system operators is constructed. The two-layer optimization framework model includes a producer-consumer P2P energy trading model and an optimal dynamic operation envelope optimization model for power distribution system operators. Based on the producer-consumer P2P energy trading model, the optimal dynamic operating envelope of the producer-consumer is obtained; Based on the optimal dynamic operating envelope optimization model of the power distribution system operator, the optimal dynamic operating envelope of the power distribution system operator is obtained; Based on the optimal dynamic operating envelope of the producer and consumer and the optimal dynamic operating envelope of the distribution system operator, the distributed P2P transaction power that meets the security constraints of the distribution network is obtained.

[0070] Example 5: If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope, characterized in that, include: Based on the relationship between producers and consumers and power distribution system operators, a two-layer optimization framework model for producers and consumers and power distribution system operators is constructed. The two-layer optimization framework model includes a producer-consumer P2P energy trading model and an optimal dynamic operating envelope optimization model for power distribution system operators. Based on the producer-consumer P2P energy trading model, the optimal dynamic operating envelope of the producer-consumer is obtained; Based on the optimal dynamic operating envelope optimization model of the power distribution system operator, the optimal dynamic operating envelope of the power distribution system operator is obtained; Based on the optimal dynamic operating envelope of the producer and consumer and the optimal dynamic operating envelope of the distribution system operator, the distributed P2P transaction power that meets the security constraints of the distribution network is obtained.

2. The producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope as described in claim 1, characterized in that, Based on the producer-consumer P2P energy trading model, the specific method for obtaining the optimal dynamic operating envelope for producer-consumers is as follows: Based on the optimal dynamic operating envelope variables obtained through negotiation between producers and consumers and the distribution system operator, with the goal of minimizing the operating costs of producers and consumers, the scheduling strategy of the current flexible resources of producers and consumers, as well as the trading strategy with other producers and consumers and the power grid, are optimized, and the optimal dynamic operating envelope of producers and consumers is calculated and uploaded for updating.

3. The producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope as described in claim 1, characterized in that, Based on the optimal dynamic operating envelope optimization model for power distribution system operators, the specific method for obtaining the optimal dynamic operating envelope of power distribution system operators is as follows: Obtain network status information from power distribution system operators, as well as the expected import and export power of producers and consumers; Based on the network status information of the power distribution system operator and the expected import and export power of producers and consumers, the optimal dynamic operating envelope is calculated and sent to the power distribution system operator for updating.

4. The producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope as described in claim 1, characterized in that, Based on the optimal dynamic operating envelopes of prosumers and distribution system operators, the specific method for obtaining the distributed P2P transaction power that meets the security constraints of the distribution network is as follows: The optimal dynamic operating envelopes of producers and consumers and the optimal dynamic operating envelopes of distribution system operators are sent to the distribution system operators for updates, so that the data in the distribution system operators meets the distribution network security constraints for distributed P2P transaction power.

5. The producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope as described in claim 4, characterized in that, When sending the optimal dynamic operating envelopes of producers and consumers and the optimal dynamic operating envelopes of distribution system operators to the distribution system operators for updates, the analytical target cascading method is used.

6. The producer-consumer distributed P2P transaction optimization method based on negotiation dynamic operating envelope as described in claim 1, characterized in that, Based on the producer-consumer P2P energy trading model, when obtaining the optimal dynamic operating envelope of the producer-consumer, the adaptive alternating direction multiplier method is adopted.

7. A producer-consumer distributed P2P transaction optimization system based on negotiation dynamic operating envelope, characterized in that, include: The model building module is used to construct a two-layer optimization framework model for producers and consumers and distribution system operators based on the relationship between them. The two-layer optimization framework model includes a producer-consumer P2P energy trading model and an optimal dynamic operating envelope optimization model for distribution system operators. The producer-consumer P2P energy trading model calculation module is used to obtain the optimal dynamic operating envelope of the producer-consumer based on the producer-consumer P2P energy trading model. The optimal dynamic operating envelope optimization model calculation module is used to obtain the optimal dynamic operating envelope of the power distribution system operator based on the optimal dynamic operating envelope optimization model of the power distribution system operator. The data negotiation module is used to obtain the distributed P2P transaction power that meets the security constraints of the distribution network based on the optimal dynamic operating envelope of the producer and consumer and the optimal dynamic operating envelope of the distribution system operator.

8. The producer-consumer distributed P2P transaction optimization system based on negotiation dynamic operating envelope as described in claim 7, characterized in that, The functionality of the producer-consumer P2P energy trading model calculation module is achieved through the following methods: Based on the optimal dynamic operating envelope variables obtained through negotiation between producers and consumers and the distribution system operator, with the goal of minimizing the operating costs of producers and consumers, the scheduling strategy of the current flexible resources of producers and consumers, as well as the trading strategy with other producers and consumers and the power grid, are optimized, and the optimal dynamic operating envelope of producers and consumers is calculated and uploaded for updating.

9. The producer-consumer distributed P2P transaction optimization system based on negotiation dynamic operating envelope as described in claim 7, characterized in that, The functionality of the optimal dynamic envelope optimization model calculation module is achieved through the following methods: Obtain network status information from power distribution system operators, as well as the expected import and export power of producers and consumers; Based on the network status information of the power distribution system operator and the expected import and export power of producers and consumers, the optimal dynamic operating envelope is calculated and sent to the power distribution system operator for updating.

10. The producer-consumer distributed P2P transaction optimization system based on negotiation dynamic operating envelope as described in claim 7, characterized in that, The data negotiation module is implemented through the following methods: The optimal dynamic operating envelopes of producers and consumers and the optimal dynamic operating envelopes of distribution system operators are sent to the distribution system operators for updates, so that the data in the distribution system operators meets the distribution network security constraints for distributed P2P transaction power.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the producer-consumer distributed P2P transaction optimization method based on negotiation dynamic running envelope as described in any one of claims 1 to 6.

12. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the producer-consumer distributed P2P transaction optimization method based on negotiation dynamic running envelope as described in any one of claims 1 to 6.

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