Power distribution network voltage coordination method and system considering P2P transaction risk

Abstract

The invention belongs to the field of power distribution network optimization scheduling, and provides a power distribution network voltage coordination method and system considering P2P transaction risk, and the method comprises the steps: constructing a producer and consumer P2P energy transaction model and an optimization decision model of a power distribution system operator DSO; constructing a coordination model based on the guidance of the fusion dynamic operation envelope DOE and the risk network crossing cost RC-NUC; the method comprises the following steps: constructing a double-layer coordination optimization framework of multi-agent interaction of a power distribution system operator DSO and a producer and disagger based on a coordination model guided by fusion of a dynamic operation envelope DOE and risk network cost RC-NUC; and solving the double-layer coordination optimization framework of the multi-agent interaction of the operator DSO and the producer and the consumer of the power distribution system to obtain a coordination decision of the multi-agent. The DOE and RC-NUC are utilized to guide the producer and the consumer to adjust the transaction strategy so as to ensure the network security, only price and power information is exchanged among multiple subjects, the privacy and autonomy of the transaction are considered, and the coordinated optimization of the power distribution network security and the economic benefits of market participants is realized.

Description

Technical Field

[0001] The present invention belongs to the field of distribution network optimization and dispatching, and relates to a distribution network voltage coordination method and system taking into account P2P transaction risks. Background Art

[0002] The rapid popularization of distributed energy has promoted new modes of power system operation and market transactions, especially the rise of peer-to-peer (P2P) markets. Producers and consumers with the ability to generate and consume electricity participate in energy trading and sharing as independent entities, which can effectively achieve supply and demand balance and improve energy efficiency. However, the demand for privacy and autonomy from multiple entities has pushed the market towards decentralization. In a free market environment, producers and consumers are driven by profit-seeking and are prone to risks such as voltage over-limit after transactions. Therefore, integrating and tapping into local system flexibility and coordinating the economic efficiency of producers and consumers with the security of distribution networks have become key challenges.

[0003] Existing studies have introduced distribution system operators (DSOs) to supervise and guide prosumer transactions to ensure network security. Some studies use sensitivity analysis to prevent transactions that endanger network security, or optimize power flows to meet network constraints through network reconstruction, on-load tap changers, reactive power management, etc. However, the above direct control methods rely on DSOs to centrally obtain transaction information and lack incentive mechanisms. To this end, indirect guidance methods based on price signals have been proposed, such as designing network usage charges (NUC) based on electrical distance to increase transaction costs, or using distribution locational marginal pricing (DLMP) to reflect power flow distribution, accurately guiding prosumer transactions through price decomposition, and taking into account both network security and transaction privacy. However, when P2P transactions lead to an unsolvable optimal power flow (OPF), DSOs cannot extract Lagrange multipliers to calculate DLMP and NUC, which limits the applicability of this method. Summary of the invention

[0004] The purpose of the present invention is to provide a distribution network voltage coordination method and system taking into account P2P transaction risks, so as to solve the P2P transaction risk problem of producers and consumers in the distribution network.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a distribution network voltage coordination method considering P2P transaction risks, comprising: According to the internal power surplus and shortage of the prosumer, the prosumer can purchase and sell power from the main grid or share power with other prosumers, and build a P2P energy trading model for prosumers; with the goal of minimizing the total operating cost of the distribution system operator DSO, the distribution network is modeled to obtain the optimization decision model of the distribution system operator DSO; Construct a coordination model based on the fusion dynamic operation envelope DOE and risk-based network fee RC-NUC guidance; Based on the coordination model guided by the integration of dynamic operation envelope DOE and risk-based network access fee RC-NUC, a two-layer coordination optimization framework for multi-agent interaction between distribution system operators DSO and prosumers is constructed. Combining the P2P energy trading model of producers and consumers and the optimization decision model of distribution system operators (DSOs), a two-layer coordination optimization framework for the interaction between distribution system operators (DSOs) and producers and consumers is solved to obtain the coordinated decision of multiple parties.

[0006] Optionally, the method of purchasing and selling electricity from the main network or sharing electricity with other prosumers based on the internal electricity surplus or shortage of the prosumer to build a prosumer P2P energy trading model includes: Each prosumer is equipped with photovoltaic power generation, micro gas turbine, energy storage and demand response load. , the optimal operation model of P2P energy trading is expressed as follows: Objective Function (1) (2) (3) (4) (5) (6) In the formula, , and They are the cost coefficients of MT; is the unit discomfort cost of load deviation; is the battery degradation cost coefficient; and For on-grid electricity prices and time-of-use electricity prices; is the original load; It is a collection of transaction objects for producers and consumers. is the decision variable of prosumer k; , , / , / , and represents MT output power, actual power load, BES charging and discharging power, buying and selling power with DSO, P2P transaction power and price; the total objective function is composed of MT power generation cost, DR incompatibility cost, BES degradation cost, P2P and P2G transaction costs; Constraints (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) In the formula, is the maximum output power of MT; The maximum / minimum power demand of the demand response load in time period t is set; and is the charge and discharge efficiency; is the capacity of the energy storage unit at time t; is the charge; and The maximum and minimum limits for energy storage capacity; and is the initial and ending capacity of energy storage; and is the maximum charge and discharge power; is the net injected power; constraint (7) is the power output limit of MT; constraint (8) is the upper and lower bounds of the actual load power; constraint (9) is the relationship between the actual and expected load demand; for BES, constraint (10) is the relationship between the measured SoC and the charging / discharging power; constraints (11) and (12) are the constraints and initial / final conditions of SoC, respectively; constraint (13) is the charging and discharging power; constraint (14) is that the P2P transaction power quantities negotiated between the two prosumers are equal and have opposite signs; constraints (15) and (16) are the active power balance and net power injection equations.

[0007] Optionally, the distribution network is modeled with the goal of minimizing the total operating cost of the DSO to obtain an optimization decision model of the distribution system operator DSO, including: The distribution network is modeled using the DistFlow model, and the radial distribution network is represented by a tree diagram. , the node and branch sets are and ;The root node is connected to the main network and is numbered 1, and the remaining nodes are connected to their parent nodes and a collection of child nodes ; The node arrive The branch number is j; the specific model of DSO is as follows: Objective Function (17) In the formula, is the node marginal electricity price; is the active power injection for the root node; the objective function represents the minimization of the total operating cost of the DSO; the first term represents the cost of purchasing electricity from the wholesale market; the last two terms give the income from P2G transactions; Constraints (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) In the formula, and Respectively represent the square of the voltage amplitude of node i and its parent node; Represents the square of the current amplitude of branch j; parameter and denote the resistance and reactance of branch j respectively; and Respectively represent the active and reactive loads of node i; and is the actual and maximum output power of generator m; is the net injected power of the prosumer connected at node i; and They represent the active and reactive power flows of branch i in period t respectively; and denote the lower and upper bounds of the square of the voltage amplitude at node i, respectively; constraints (19) and (20) are active and reactive power balance; constraints (21) and (22) are power limits at the receiving and sending nodes of each line; constraint (23) is the voltage drop of the distribution line; branch current constraint (24) is formulated in standard second-order cone form; constraint (25) limits the node voltage amplitude. The generator constraint is given by (26).

[0008] Optionally, the construction of a coordination model based on a fusion dynamic operation envelope DOE includes: The DSO calculates the DOE and sets a specific security domain for the net injected power of each prosumer. The calculation is carried out in two steps: In the first step, the DSO checks the network security of the prosumer's expected import and export power. If there is no violation of the voltage constraint, the expected transaction power is approved; if there is a violation, the DOE is calculated in the second step, and the DOE result is directly passed to the prosumer; The objective function is designed to minimize the expected net injected power of the prosumer With DOE defined value The sum of the squares of the differences between them is as follows: (27) (28) (29) (30) Optionally, the risk-based network fee RC-NUC guided coordination model includes: NUC is generated by calculating the DLMP difference between nodes, which serves as a price signal to guide users' electricity consumption behavior and reflects the risk of node voltage exceeding the limit.

[0009] The NUC is improved by the risk factor RC related to voltage safety to form RC-NUC, which is used to guide prosumers to conduct "grid-friendly" transactions and set a safety margin value. ,definition is the absolute value of the node voltage deviation, when When the value exceeds the limit, the node is considered to be at risk. The larger the value exceeds, the higher the potential risk of voltage limit. definition: (31) (32) In the formula, is the risk coefficient of prosumers i and j; is the set of nodes that are most affected when producers and consumers i and j trade. From formula (31), we can see that when a node is at risk, the risk coefficient The value of is greater than 1; by calculating the VSC between two nodes, we get: (33) In the formula, is a 0-1 variable of the node that is most affected when a transaction occurs between prosumer i and prosumer j. If its value is 1, it means that the voltage of the node is greatly affected by the transaction and is included in middle; is the voltage change of node k caused by the transaction between prosumer i and prosumer j, The voltage amplitude at node k is the VSC with active injection power at node i; It is an adjustable risk control parameter; The expression for RC-NUC is set as: (34) Where: is the DLMP corresponding to node i.

[0010] Optionally, based on the coordination model guided by the fusion of dynamic operation envelope DOE and risk-based network access fee RC-NUC, a two-layer coordination optimization framework for multi-agent interaction between distribution system operators DSO and prosumers is constructed, including: The lower-level producers and consumers in the two-layer coordination optimization framework optimize the scheduling resources, negotiate P2P transactions based on demand and transaction preferences, or trade with DSO based on time-of-use electricity prices and grid-connected electricity prices. The upper-level DSO performs safety checks based on network information and the expected import and export power of producers and consumers. If the safety constraints are violated, the DOE is calculated and issued. The DSO then calculates the DLMP and risk transmission fee to guide producers and consumers to adjust their trading decisions.

[0011] Optionally, the P2P energy trading model of prosumers and the optimization decision model of the distribution system operator DSO are combined to solve the two-layer coordination optimization framework of the interaction between the distribution system operator DSO and the prosumers to obtain the coordination decision of the multiple subjects, including: The objective function and constraints of the updated prosumer P2P energy trading model are as follows: (35) (36) (37) In the formula, represents the DOE limit issued by the DSO to the prosumer in time period t; Positive means there are import restrictions; A negative value indicates export restrictions; The ADMM algorithm is used to iteratively solve the P2P transaction problem. First, auxiliary variables are introduced to decouple the consistency constraints (14): (38) In the formula, represents the auxiliary variable introduced, which is regarded as the estimated value of the P2P transaction volume of producer and consumer k; is the Lagrange multiplier, representing the shadow price of transaction power, defined as the P2P price; Then, the augmented Lagrangian function is established, and the P2P transaction problem is further decomposed into a single sub-problem that each producer and consumer k can solve independently: (39) In the formula, is a positive penalty factor; when the optimal solution of the lower layer is obtained, the sum of the P2P transaction costs of all producers and consumers is zero; Let v be the number of iterations of the ADMM algorithm, and the Lagrange multipliers and auxiliary variables are updated as follows: (40) (41) Raw residual and the dual residual The convergence criteria are as follows: (42) (43) In the formula, and They are the error accuracy of the settings; The penalty factor is adaptively updated to speed up the iterative convergence: (44) In the formula, is a constant that determines the relationship between the primal and dual residuals; and are the multiples of the penalty factor expansion and reduction respectively; Let z be the number of iterations of the upper and lower layers; after the lower layer loop converges, DSO updates DLMP and RC-NUC according to the OPF results; when the variables of DSO and prosumers satisfy equations (45) and (46), the decisions of both parties will no longer change, and the benefits are both optimal; (45) (46).

[0012] In a second aspect, the present invention provides a distribution network voltage coordination system considering P2P transaction risks, comprising: The trading model and decision model construction module is used to build a P2P energy trading model for producers and consumers, based on the internal power surplus or shortage of producers and consumers, to purchase and sell power from the main grid or share power with other producers and consumers; with the goal of minimizing the total operating cost of the distribution system operator DSO, the distribution network is modeled to obtain the optimization decision model of the distribution system operator DSO; Coordination model building module, used to build a coordination model guided by fused dynamic operation envelope DOE and risk-based network fee RC-NUC; A two-layer coordination optimization framework building module is used to build a two-layer coordination optimization framework for multi-agent interaction between distribution system operators (DSOs) and prosumers based on a coordination model guided by the fusion of dynamic operation envelope DOE and risk-based network charges RC-NUC; The output solution module is used to combine the P2P energy trading model of producers and consumers and the optimization decision model of the distribution system operator DSO to solve the two-layer coordination optimization framework of the interaction between the distribution system operator DSO and the producers and consumers, and obtain the coordinated decision of multiple parties.

[0013] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the distribution network voltage coordination method taking into account P2P transaction risks when executing the computer program.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the distribution network voltage coordination method considering P2P transaction risks.

[0015] Compared with the prior art, the present invention has the following technical effects: The present invention discloses a distribution network voltage management method considering P2P transaction risks, constructs a DSO-prosumer double-layer optimization model based on DOE and RC-NUC, and designs an improved distributed algorithm solution, thereby realizing the coordinated optimization of distribution network security and prosumer economy in the P2P energy market. The DOE constraint is used to ensure that the power flow can be solved, and RC-NUC is used to further guide prosumers to participate in friendly interaction with the power grid, effectively taking into account market benefits and system security, and improving the reliability of P2P transaction risk management.

[0016] Compared with the traditional method, the present invention: 1) A DSO-prosumer two-layer optimization framework that takes into account both system security and market benefits is proposed. The upper-layer DSO is guided by system security and guides prosumer transactions through DOE and RC-NUC methods; the lower layer builds a decentralized P2P market, where prosumers participate in transactions with the goal of maximizing economic benefits, achieving multi-party privacy protection and synergistic improvement of benefits; 2) The RC-NUC method is proposed to quantify the impact of decentralized P2P transactions on the power flow of the distribution network through price signals, accurately reflect the impact of each transaction on the node voltage risk, effectively guide prosumers to optimize their trading behavior, and improve the reliability of system risk management in P2P trading scenarios.

[0017] 3) A DOE calculation method suitable for P2P transactions is proposed to ensure that the upper-level power flow has a solution while allowing producers and consumers to freely determine the transaction power and price under DOE constraints. The lower-level P2P transaction problem is solved using the adaptive alternating direction method of multipliers (ADMM), which accelerates convergence by dynamically adjusting parameters, achieves independent optimization under limited information sharing, and takes into account the privacy and autonomy requirements of transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The DSO-prosumer coordination optimization framework of the present invention; Figure 2 It is a diagram of the steps of solving the problem of the present invention.

[0019] Figure 3 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described below in conjunction with the accompanying drawings: Example 1, please refer to Figure 3 The present invention provides a distribution network voltage coordination method considering P2P transaction risks, including: According to the internal power surplus and shortage of the prosumer, the prosumer can purchase and sell power from the main grid or share power with other prosumers, and build a P2P energy trading model for prosumers; with the goal of minimizing the total operating cost of the distribution system operator DSO, the distribution network is modeled to obtain the optimization decision model of the distribution system operator DSO; Construct a coordination model based on the fusion dynamic operation envelope DOE and risk-based network fee RC-NUC guidance; Based on the coordination model guided by the integration of dynamic operation envelope DOE and risk-based network access fee RC-NUC, a two-layer coordination optimization framework for multi-agent interaction between distribution system operators DSO and prosumers is constructed. Combining the P2P energy trading model of producers and consumers and the optimization decision model of distribution system operators (DSOs), a two-layer coordination optimization framework for the interaction between distribution system operators (DSOs) and producers and consumers is solved to obtain the coordinated decision of multiple parties.

[0021] The present invention proposes a distribution network voltage management method considering P2P transaction risks, and constructs a DSO-prosumer coordination optimization method guided by DOE and RC-NUC in a P2P market transaction environment. The proposed method takes into account network constraints, uses DOE and RC-NUC to guide prosumers to adjust transaction strategies to ensure network security, and only exchanges price and power information between multiple subjects, taking into account the privacy and autonomy of transactions, and successfully achieves the coordinated optimization of distribution network security and the economic benefits of market participants. The method proposed in the present invention has the following advantages: 1) The proposed RC-NUC fully considers the impact of P2P transactions on the power flow of the distribution network, accurately guides producers and consumers to adjust their trading behaviors, and effectively improves the system's risk response capabilities; 2) The proposed DOE method dynamically optimizes import and export power, allowing prosumers to autonomously determine trading power and prices within constraints, while ensuring that the optimal power flow problem of the distribution network is solved; 3) The adaptive ADMM algorithm proposed in the present invention improves the convergence performance and ensures the independence and privacy of the transaction process between producers and consumers.

[0022] Embodiment 2, the present invention provides a distribution network voltage coordination method considering P2P transaction risks, comprising: A DSO-producer two-level optimization model based on DOE and RC-NUC is constructed, and an improved distributed algorithm is designed to solve it, achieving the coordinated optimization of distribution network security and producer economy in the P2P energy market.

[0023] The specific implementation steps are as follows: Step 1: Construct a two-layer coordination optimization framework for multi-agent interaction between DSO and prosumers.

[0024] Firstly, the interactive relationship between DSO and multiple prosumers in the distribution network is explained, and the two-layer coordination optimization framework is constructed as follows: Figure 1 As shown. The lower-level prosumers optimize the dispatch resources, negotiate P2P transactions based on demand and transaction preferences, and can also trade with DSO based on time-of-use electricity prices and grid-connected electricity prices. All transactions are autonomous behaviors of prosumers, ensuring their autonomy and privacy. The upper-level DSO performs security checks based on network information and the expected import and export power of prosumers. If the security constraints are violated, the DOE is calculated and issued. Then the DSO calculates the DLMP and risk transmission fee to guide prosumers to adjust their transaction decisions and reduce the risks of P2P transactions to the distribution network.

[0025] Step 2: Construct mathematical models of prosumers and DSOs.

[0026] Step 2.1: Prosumer P2P energy trading model.

[0027] Prosumers are active participants in the P2P market. It is assumed that each prosumer is equipped with flexible resources such as photovoltaic power generation (PV), micro-turbines (MT), battery energy storage (BES) and demand response load (DR). At any time, prosumers can purchase and sell electricity from the main grid or share electricity with other prosumers based on the internal power surplus or shortage. ,The optimal operation model of P2P energy trading is expressed as follows.

[0028] (1) Objective function (1) (2) (3) (4) (5) (6) In the formula, , and They are the cost coefficients of MT; is the unit discomfort cost of load deviation; is the battery degradation cost coefficient; and For on-grid electricity prices and time-of-use electricity prices; is the original load; It is a collection of transaction objects for producers and consumers. is the decision variable of prosumer k. , , / , / , and represents MT output power, actual power load, BES charging and discharging power, buying and selling power with DSO, P2P transaction power and price. The total objective function (1) consists of MT power generation cost, DR incompatibility cost, BES degradation cost, P2P and P2G transaction costs. The specific formula of each cost function is expressed as (2)-(6).

[0029] (2) Constraints (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) In the formula, is the maximum output power of MT; Set the maximum / minimum power demand of the demand response load in time period t; and is the charge and discharge efficiency; is the capacity of the energy storage unit at time t; is the charge; and The maximum and minimum limits for energy storage capacity; and is the initial and ending capacity of the energy storage; and is the maximum charge and discharge power; is the net injected power. Constraint (7) is the power output limit of MT. Constraint (8) is the upper and lower bounds of the actual load power. Constraint (9) is the relationship between the actual and expected load demand. For BES, constraint (10) is the relationship between the measured SoC and the charging / discharging power. Constraints (11) and (12) are the constraints and initial / final conditions of SoC, respectively. Constraint (13) is the charging and discharging power. Constraint (14) is that the P2P transaction power negotiated between the two prosumers is equal in quantity but opposite in sign. Constraints (15) and (16) are the active power balance and net power injection equations.

[0030] The P2P transaction power and price can be determined by solving the above model. The DSO receives the net power injection information submitted by the prosumer and executes OPF to minimize the operating cost and ensure that the distribution network can operate within the constraints.

[0031] Step 2.2: Optimization decision model of DSO.

[0032] The DistFlow model is used to model the distribution network. The radial distribution network is represented by a tree diagram , the node and branch sets are and The root node is connected to the main network and is numbered 1. The remaining nodes are connected to their parent nodes. and a collection of child nodes . arrive The branch number is j. The specific model of DSO is as follows: (1) Objective function (17) In the formula, is the node marginal electricity price. is the root node active power injection. The objective function (17) represents the minimization of the total operating cost of the DSO. The first term represents the cost of purchasing electricity from the wholesale market. The last two terms give the income from P2G transactions.

[0033] (2) Constraints (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) In the formula, and Respectively represent the square of the voltage amplitude of node i and its parent node; Represents the square of the current amplitude of branch j; parameter and denote the resistance and reactance of branch j respectively; and Respectively represent the active and reactive loads of node i; and is the actual and maximum output power of generator m; is the net injected power of the prosumer connected at node i; and They represent the active and reactive power flows of branch i in period t respectively; and denote the lower and upper bounds of the squared voltage amplitude at node i, respectively. Constraints (19) and (20) are active and reactive power balances. Constraints (21) and (22) are power limits at the receiving and sending nodes of each line. Constraint (23) is the voltage drop across the distribution line. The branch current constraint (24) is formulated in standard second-order cone form. Constraint (25) limits the node voltage amplitude. The generator constraint is given by (26).

[0034] DSO can only calculate OPF based on the net injected power submitted by the prosumer, but cannot directly control the P2P transaction power and price, which may lead to voltage over-limit problems. Therefore, it is necessary to coordinate the relationship between DSO and prosumers to achieve a comprehensive balance between the security of the distribution network and the privacy and economy of prosumers.

[0035] Step 3: Construct a coordination model based on DOE and RC-NUC guidance.

[0036] Step 3.1: DOE calculation model.

[0037] To reduce the risk of P2P transactions to the distribution network, the DSO first calculates the DOE and sets a specific security domain for the net injected power of each prosumer. The calculation is carried out in two steps: in the first step, the DSO checks the network security of the prosumer's expected import and export power. If the voltage constraints are not violated, the expected transaction power is approved; if there is a violation, the DOE is calculated in the second step. The DOE results are directly passed to the prosumer, allowing it to manage its assets autonomously while protecting privacy and ensuring that network constraints are not violated.

[0038] In order to improve transaction fairness and avoid excessive power reduction of prosumers far from the root node, the objective function is designed to minimize the expected net injected power of prosumers. With DOE defined value The sum of the squares of the differences between them is as follows: (27) st(18), (20)-(25); (28) (29) (30) Given that the DSO defines the DOE for prosumers and has no ability to directly control their internal assets and energy transactions, certain principles need to be considered: (1) If prosumers wish to import electricity, they cannot be forced to export electricity or import more electricity than expected. (2) If prosumers wish to export electricity, their exports can be reduced to zero, but they cannot be forced to import electricity or export more electricity than expected. The corresponding constraints are expressed in (28)-(29). In addition, the generation and load of node i remain unchanged before and after the DOE calculation. Therefore, the active power balance constraint of node i based on Equation (19) is reformulated as Equation (30).

[0039] Step 3.2: Definition and model of RC-NUC bootstrap method.

[0040] NUC generates by calculating the DLMP difference between nodes as a price signal to guide users' electricity consumption behavior and reflect the risk of node voltage exceeding the limit. However, the traditional NUC method is not sensitive enough to voltage constraints. Especially when the voltage is close to the safety boundary, it may cause the system to change from a safe state to an over-limit risk due to the uncertainty of new energy output. In this case, it is difficult for traditional NUC to accurately reflect the network status and effectively guide prosumers to adjust their trading strategies to alleviate local voltage problems.

[0041] To address the above shortcomings, a risk coefficient (RC) related to voltage safety is proposed to improve NUC, forming RC-NUC, which is used to guide prosumers to conduct "grid-friendly" transactions and improve the voltage safety of the power grid in an uncertain environment. The core role of RC-NUC is to reduce the risks that may be brought about by P2P transactions and uncertain factors. In order to identify risks, a safety margin value is set. ,definition is the absolute value of the node voltage deviation, when When the value exceeds the limit, it is considered that the node is at risk. The larger the value exceeds, the higher the potential risk of voltage limit. RC can fully reflect the risk of any transaction on the node voltage, which can be expressed by the part exceeding the safety margin value. The larger the RC is, the higher the system risk that may be caused by the transaction. Therefore, the value of RC should be a positive feedback to RC-NUC, and can characterize the impact of each transaction on the node voltage. Definition: (31) (32) In the formula, is the risk coefficient of prosumers i and j; is the set of nodes that are most affected when producers and consumers i and j trade. From formula (31), we can see that when a node has a risk, the risk coefficient The value of is greater than 1. Considering that the voltage sensitivity coefficient (VSC) represents the change in node voltage amplitude caused by the power change of a node in the power system, it can be used as a basis to describe the impact of each transaction on the voltage of each node. Therefore, the VSC between two nodes can be calculated to obtain: (33) In the formula, is a 0-1 variable of the node that is most affected when a transaction occurs between prosumer i and prosumer j. If its value is 1, it means that the voltage of the node is greatly affected by the transaction and can be included in middle; is the voltage change of node k caused by the transaction between prosumer i and prosumer j, The voltage amplitude at node k is the VSC with active injection power at node i; It is an adjustable risk control parameter.

[0042] Therefore, in order to reflect the impact of regional resources on the safe operation of the distribution network from the spatial and temporal dimensions and to evaluate the severity of each transaction on the voltage risk of different nodes, the expression of RC-NUC is set as: (34) Where: is the DLMP corresponding to node i, which can be obtained from the relevant dual variables in step 2.2.

[0043] In any transaction, prosumer i and prosumer j will receive the corresponding RC-NUC fairly without price discrimination. RC is dynamically adjusted according to voltage changes. When the grid voltage approaches the safety boundary, RC-NUC increases, prompting prosumers to adjust their trading behaviors in response to changes in grid conditions, thereby enhancing the adaptability and flexibility of the system. At the same time, the existence of RC will not affect the physical and economic significance of the original NUC. Since RC-NUC brings additional transaction costs, prosumers may appropriately reduce transaction power in order to reduce energy costs. Therefore, the introduction of RC-NUC effectively guides prosumers to autonomously adjust their trading behaviors and ensure that transactions follow network constraints.

[0044] Step 4: Solution process of the two-layer coordinated optimization framework.

[0045] According to the decision-making model of each subject constructed in step 2 and the safety guidance method in step 3, prosumers minimize energy costs by participating in the P2P market, while DSO guides prosumers through DOE and RC-NUC to ensure network security and maximize economic benefits. Both parties influence each other and adjust their own decisions according to each other's strategies.

[0046] When the prosumer accepts the guidance of the RC-NUC price signal, he needs to pay an additional risk operation fee. Since the buyer and seller share equal profits in P2P transactions, the fee is shared by both parties. To this end, the prosumer model in step 2.1 needs to be modified, and the objective function and constraints are updated as follows: (35) st(2)-(16); (36) (37) In the formula, It represents the DOE limit issued by the DSO to the prosumer k in period t. Positive means there are import restrictions; A negative value indicates export restrictions.

[0047] The ADMM algorithm is used to iteratively solve the P2P transaction problem. First, auxiliary variables are introduced to decouple the consistency constraints (14): (38) In the formula, It represents the introduced auxiliary variable, which can be regarded as the estimated value of the P2P transaction volume of producer and consumer k. is the Lagrange multiplier representing the shadow price of transaction power, which is defined as the P2P price.

[0048] Then, the augmented Lagrangian function is established, and the P2P transaction problem is further decomposed into a single sub-problem that each producer and consumer k can solve independently: (39) st (2)-(4), (6)-(13), (15)-(16), (36)-(37).

[0049] In the formula, is a positive penalty factor. When the optimal solution of the lower layer is obtained, the sum of the P2P transaction costs of all producers and consumers is zero, so the objective function (39) of the decomposed subproblem does not include the P2P transaction cost.

[0050] Let v be the number of iterations of the ADMM algorithm, and the Lagrange multipliers and auxiliary variables are updated as follows: (40) (41) Raw residual and the dual residual The convergence criteria are as follows: (42) (43) In the formula, and are the error precisions set respectively.

[0051] Since the penalty factor has a significant impact on the convergence performance of ADMM, using a fixed step size may waste computing resources and is highly dependent on the choice of initial values. Therefore, the penalty factor adaptive update method is used to speed up the iterative convergence speed: (44) In the formula, is a constant that determines the relationship between the primal and dual residuals; and are the multiples of the penalty factor expansion and reduction respectively.

[0052] Let z be the number of iterations of the upper and lower layers. After the lower loop converges, DSO updates DLMP and RC-NUC according to the OPF results. When the variables of DSO and prosumers satisfy equations (45) and (46), the decisions of both parties will no longer change, the benefits are optimal, the system risk is successfully eliminated, and the benefits of market entities are improved and the system operates safely in the market environment.

[0053] (45) (46) The solution steps of the DSO-prosumer two-level optimization model based on DOE and RC-NUC under any operating boundary are as follows: Figure 2 shown.

Claims

1. A distribution network voltage coordination method considering P2P transaction risks, characterized in that: include: According to the internal power surplus and shortage of the prosumer, the prosumer can purchase and sell power from the main grid or share power with other prosumers, and build a P2P energy trading model for prosumers; with the goal of minimizing the total operating cost of the distribution system operator DSO, the distribution network is modeled to obtain the optimization decision model of the distribution system operator DSO; Construct a coordination model based on the fusion dynamic operation envelope DOE and risk-based network fee RC-NUC guidance; Based on the coordination model guided by the integration of dynamic operation envelope DOE and risk-based network access fee RC-NUC, a two-layer coordination optimization framework for multi-agent interaction between distribution system operators DSO and prosumers is constructed. Combining the P2P energy trading model of producers and consumers and the optimization decision model of distribution system operators (DSOs), a two-layer coordination optimization framework for the interaction between distribution system operators (DSOs) and producers and consumers is solved to obtain the coordinated decision of multiple parties.

2. The distribution network voltage coordination method considering P2P transaction risks according to claim 1 is characterized in that: According to the internal power surplus and shortage of the prosumer, the prosumer purchases and sells power to the main network or shares power with other prosumers to build a prosumer P2P energy trading model, including: Each prosumer is equipped with photovoltaic power generation, micro gas turbine, energy storage and demand response load. , the optimal operation model of P2P energy trading is expressed as follows: Objective Function (1) (2) (3) (4) (5) (6) In the formula, , and They are the cost coefficients of MT; is the unit discomfort cost of load deviation; is the battery degradation cost coefficient; and For on-grid electricity prices and time-of-use electricity prices; is the original load; It is a collection of trading objects for producers and consumers; is the decision variable of prosumer k; , , / , / , and represents MT output power, actual power load, BES charging and discharging power, buying and selling power with DSO, P2P transaction power and price; the total objective function is composed of MT power generation cost, DR incompatibility cost, BES degradation cost, P2P and P2G transaction costs; Constraints (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) In the formula, is the maximum output power of MT; The maximum / minimum power demand of the demand response load in time period t is set; and is the charge and discharge efficiency; is the capacity of the energy storage unit at time t; is the charge; and The maximum and minimum limits for energy storage capacity; and is the initial and ending capacity of energy storage; and is the maximum charge and discharge power; is the net injected power; constraint (7) is the power output limit of MT; constraint (8) is the upper and lower bounds of the actual load power; constraint (9) is the relationship between the actual and expected load demand; for BES, constraint (10) is the relationship between the measured SoC and the charging / discharging power; constraints (11) and (12) are the constraints and initial / final conditions of SoC, respectively; constraint (13) is the charging and discharging power; constraint (14) is that the P2P transaction power quantities negotiated between the two prosumers are equal and have opposite signs; constraints (15) and (16) are the active power balance and net power injection equations.

3. The distribution network voltage coordination method considering P2P transaction risks according to claim 1 is characterized in that: The distribution network is modeled with the goal of minimizing the total operating cost of the DSO, and an optimization decision model of the distribution system operator DSO is obtained, including: The distribution network is modeled using the DistFlow model, and the radial distribution network is represented by a tree diagram. , the node and branch sets are and ;The root node is connected to the main network and is numbered 1, and the remaining nodes are connected to their parent nodes and a collection of child nodes ; The node arrive The branch number is j; the specific model of DSO is as follows: Objective Function (17) In the formula, is the node marginal electricity price; is the active power injection for the root node; the objective function represents the minimization of the total operating cost of the DSO; the first term represents the cost of purchasing electricity from the wholesale market; the last two terms give the income from P2G transactions; Constraints (18) (19) (20) (21) (22) (23) (24) (25) (26) In the formula, and Respectively represent the square of the voltage amplitude of node i and its parent node; Represents the square of the current amplitude of branch j; parameter and denote the resistance and reactance of branch j respectively; and Respectively represent the active and reactive loads of node i; and is the actual and maximum output power of generator m; is the net injected power of the prosumer connected at node i; and They represent the active and reactive power flows of branch i in period t respectively; and denote the upper and lower bounds of the square of the voltage amplitude at node i, respectively; constraints (19) and (20) are active and reactive power balance; constraints (21) and (22) are power limits at the receiving and sending nodes of each line; constraint (23) is the voltage drop of the distribution line; branch current constraint (24) is formulated in standard second-order cone form; constraint (25) limits the node voltage amplitude; and the generator constraint is given by (26).

4. The distribution network voltage coordination method considering P2P transaction risks according to claim 1, characterized in that: The construction is based on a coordination model of fused dynamic operation envelope DOE, including: The DSO calculates the DOE and sets a specific security domain for the net injected power of each prosumer. The calculation is carried out in two steps: In the first step, the DSO checks the network security of the prosumer's expected import and export power. If there is no violation of the voltage constraint, the expected transaction power is approved; if there is a violation, the DOE is calculated in the second step, and the DOE result is directly passed to the prosumer; The objective function is designed to minimize the expected net injected power of the prosumer With DOE defined value The sum of the squares of the differences between them is as follows: (27) (28) (29) (30)。 5. The distribution network voltage coordination method considering P2P transaction risks according to claim 4 is characterized in that: The RC-NUC-guided coordination model for the risk-based network fee includes: NUC is generated by calculating the DLMP difference between nodes, which serves as a price signal to guide users' electricity consumption behavior and reflects the risk of node voltage exceeding the limit; The NUC is improved by the risk factor RC related to voltage safety to form RC-NUC, which is used to guide prosumers to conduct "grid-friendly" transactions and set a safety margin value. ,definition is the absolute value of the node voltage deviation, when When the value exceeds the limit, the node is considered to be at risk. The larger the value exceeds, the higher the potential risk of voltage limit. definition: (31) (32) In the formula, is the risk coefficient of prosumers i and j; is the set of nodes that are most affected when producers and consumers i and j trade. From formula (31), we can see that when a node is at risk, the risk coefficient The value of is greater than 1; by calculating the VSC between two nodes, we get: (33) In the formula, is a 0-1 variable of the node that is most affected when a transaction occurs between prosumer i and prosumer j. If its value is 1, it means that the voltage of the node is greatly affected by the transaction and is included in middle; is the voltage change of node k caused by the transaction between prosumer i and prosumer j, The voltage amplitude at node k is the VSC with active injection power at node i; It is an adjustable risk control parameter; The expression for RC-NUC is set as: (34) Where: is the DLMP corresponding to node i.

6. The distribution network voltage coordination method considering P2P transaction risks according to claim 1, characterized in that: Based on the coordination model guided by the integration of dynamic operation envelope DOE and risk-based network access fee RC-NUC, a two-layer coordination optimization framework for multi-agent interaction between distribution system operators DSO and prosumers is constructed, including: The lower-level producers and consumers in the two-layer coordination optimization framework optimize the scheduling resources, negotiate P2P transactions based on demand and transaction preferences, or trade with DSO based on time-of-use electricity prices and grid-connected electricity prices. The upper-level DSO performs safety checks based on network information and the expected import and export power of producers and consumers. If the safety constraints are violated, the DOE is calculated and issued. The DSO then calculates the DLMP and risk transmission fee to guide producers and consumers to adjust their trading decisions.

7. The distribution network voltage coordination method considering P2P transaction risks according to claim 2, characterized in that: The proposed method combines the prosumer P2P energy trading model with the optimization decision model of the distribution system operator DSO to solve the two-layer coordination optimization framework of the interaction between the distribution system operator DSO and the prosumer multi-agents, and obtains the coordination decision of the multi-agents, including: The objective function and constraints of the updated prosumer P2P energy trading model are as follows: (35) (36) (37) In the formula, represents the DOE limit issued by the DSO to the prosumer in time period t; Positive means there are import restrictions; A negative value indicates export restrictions; The ADMM algorithm is used to iteratively solve the P2P transaction problem. First, auxiliary variables are introduced to decouple the consistency constraints (14): (38) In the formula, represents the auxiliary variable introduced, which is regarded as the estimated value of the P2P transaction volume of producer and consumer k; is the Lagrange multiplier, representing the shadow price of transaction power, defined as the P2P price; Then, the augmented Lagrangian function is established, and the P2P transaction problem is further decomposed into a single sub-problem that each producer and consumer k can solve independently: (39) In the formula, is a positive penalty factor; when the optimal solution of the lower layer is obtained, the sum of the P2P transaction costs of all producers and consumers is zero; Let v be the number of iterations of the ADMM algorithm, and the Lagrange multipliers and auxiliary variables are updated as follows: (40) (41) Raw residual and the dual residual The convergence criteria are as follows: (42) (43) In the formula, and They are the error accuracy of the settings; The penalty factor is adaptively updated to speed up the iterative convergence: (44) In the formula, is a constant that determines the relationship between the primal and dual residuals; and are the multiples of the penalty factor expansion and reduction respectively; Let z be the number of iterations of the upper and lower layers; after the lower layer loop converges, DSO updates DLMP and RC-NUC according to the OPF results; when the variables of DSO and prosumers satisfy equations (45) and (46), the decisions of both parties will no longer change, and the benefits are both optimal; (45) (46)。 8. A distribution network voltage coordination system considering P2P transaction risks, characterized in that: include: The trading model and decision model construction module is used to build a P2P energy trading model for producers and consumers, based on the internal power surplus or shortage of producers and consumers, to purchase and sell power from the main grid or share power with other producers and consumers; with the goal of minimizing the total operating cost of the distribution system operator DSO, the distribution network is modeled to obtain the optimization decision model of the distribution system operator DSO; Coordination model building module, used to build a coordination model guided by fused dynamic operation envelope DOE and risk-based network fee RC-NUC; A two-layer coordination optimization framework building module is used to build a two-layer coordination optimization framework for multi-agent interaction between distribution system operators (DSOs) and prosumers based on a coordination model guided by the fusion of dynamic operation envelope DOE and risk-based network charges RC-NUC; The output solution module is used to combine the P2P energy trading model of producers and consumers and the optimization decision model of the distribution system operator DSO to solve the two-layer coordination optimization framework of the interaction between the distribution system operator DSO and the producers and consumers, and obtain the coordinated decision of multiple parties.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the distribution network voltage coordination method considering P2P transaction risks as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the distribution network voltage coordination method considering P2P transaction risks as described in any one of claims 1 to 7 are implemented.

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