Flexible power distribution system flexibility transaction clearing method based on linear convex approximation

Abstract

The invention relates to a flexible power distribution system flexible transaction clearing method based on linear convex approximation, and the method comprises the steps: building a flexible pricing convex quadratic programming model based on LCA-OPF based on flexible pricing related information and flexible pricing related data at a next moment t + delta t, establishing an LCA-OPF-based model for identifying the worst operation scene of the system under non-optimization regulation and control; updating the flexible pricing convex quadratic programming model based on the LCA-OPF according to the source load operation parameter related information of the worst operation scene of the system; the updated flexible pricing convex quadratic programming model based on the LCA-OPF is solved, and a price clearing scheme of flexible transactions including P2P transactions and P2G transactions at the next moment t + delta t is obtained; the price clearing scheme is solved with acceptable precision and efficiency, the acceptability of the power distribution network is ensured, and the overall operation flexibility benefit is enhanced.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power distribution system interactive regulation, in particular to a flexible power distribution system flexibility transaction clearing method based on linear convex approximation. BACKGROUND

[0002] With high proportion of distributed access of controllable flexible resources such as distributed photovoltaics (PV), energy storage systems (ESS), load aggregators (LA), etc., the power distribution system should have high operational flexibility, and should be transformed in function to a source-grid-load-storage deep integration hub, an electric power transaction and service innovation carrier.

[0003] In order to adapt to the continuous development of source and new demand, the current distribution network is promoting the application of flexible reconstruction of power electronic distribution devices such as intelligent soft open point (SOP) to enhance the fine controllable ability of multi-feed line power flow. The flexible power distribution system is not only an optimized matching platform for electric energy flow, but also a multi-element management platform for energy-information-value integration, which has the bottom foundation to support the realization of source-load flexible interaction demand such as peer to peer (P2P) electric energy transaction.

[0004] At the same time of peer to grid (P2G) electric energy transaction with the distribution system operator (DSO), opening P2P electric energy transaction to source-load-storage flexible resource subjects can make them have more flexible interaction options. The supply and demand relationship of the operational flexibility of the flexible power distribution system can also be matched and adjusted through transaction, guiding resources to dynamically adjust power generation and consumption behavior according to price. It is conducive to promoting the reasonable competition of multiple subjects, improving the operational risk of the flexible power distribution system caused by unstable power generation and consumption of new type of source and load and space-time mismatch, and realizing the maximization of flexibility benefit. At the same time, the public utility attribute of the distribution network is obvious, and the operational flexibility in the distribution network is embodied in the form of power transfer. Opening P2P electric energy transaction between nodes will not only affect the DSO revenue, but also exacerbate the problems of electric energy space-time imbalance and voltage out-of-limit without reasonable guidance. The power distribution side flexibility market transaction matching must consider multi-dimensional physical feasibility and operational state, and it is also very important to ensure transaction efficiency and reasonable fairness.

[0005] Intraday trading can capture near-real-time forecasts and operational data, providing participants with more refined spatiotemporal price signals, which better meets the flexible control requirements of flexible power distribution systems in complex environments. However, the rapid and efficient analysis and decision-making capabilities of intraday clearing face challenges, mainly due to the trade-off between computational accuracy and efficiency. To meet computational efficiency requirements, simplified or relaxed models can be used, but this cannot avoid discrepancies between the calculation results and the actual situation, or even the absence of a feasible solution. Summary of the Invention

[0006] Therefore, it is necessary to provide a flexible power distribution system flexible transaction clearing method, device and computer-readable storage medium based on linear convex approximation to address the above-mentioned technical problems. This method can solve the clearing scheme with high accuracy and efficiency, realize flexible P2P power trading interaction between new sources and loads, ensure the acceptability of the power distribution network and its robustness to operational uncertainties, and enhance the overall operational flexibility benefits of the flexible power distribution system.

[0007] In a first aspect, this application provides a method for clearing flexible distribution system transactions based on linear convex approximation, the method comprising:

[0008] Obtain information related to flexible pricing;

[0009] If the next time step t+Δt is less than the end time T of the trading day, at the current time t, obtain the relevant data on flexible pricing for the next time step t+Δt, where Δt is the trading time interval;

[0010] Based on the aforementioned flexibility pricing information and the flexibility pricing data at the next time step t+Δt, a convex quadratic programming model for flexibility pricing based on LCA-OPF is established.

[0011] Based on the relevant data of flexible pricing at the next time t+Δt and the convex quadratic programming model of flexible pricing based on LCA-OPF, a worst-case operating scenario identification model of the system under non-optimized control based on LCA-OPF is established.

[0012] The LCASE algorithm is used to solve the worst-case operating scenario identification model of the system under no-optimal control based on LCA-OPF, and obtain the source and load operating parameter information of the worst-case operating scenario of the flexible distribution system at the next time t+Δt. The flexible pricing convex quadratic programming model based on LCA-OPF is updated according to the source and load operating parameter information.

[0013] Solving the updated LCA-OPF-based flexible pricing convex quadratic programming model by using the LCASE algorithm to obtain a price clearing scheme of the flexible transaction including P2P transaction and P2G transaction at the next time t+Δt, wherein the price clearing scheme includes node flexible pricing, distribution network flexible support service price of P2P transaction, P2G transaction fee and SOP capacity usage apportionment fee.

[0014] In one of the embodiments, the LCA-OPF-based flexible pricing convex quadratic programming model includes: taking the minimum overall flexible operation total cost of the sum of flexible distribution system operation cost and P2P transaction overall benefit as the objective function, and LCA-based distribution network flexible operation constraint, P2P transaction constraint, distribution network resource SOP operation constraint, source and load uncertainty constraint, linear transformation constraint of the objective function.

[0015] In one of the embodiments, the system worst operation scenario identification model under non-optimization regulation based on LCA-OPF includes: taking the maximum sum of active power fee, reactive power service fee purchased by the flexible distribution system from the upper-level power grid and voltage deviation penalty fee in the flexible distribution system as the objective function, and LCA-based distribution network flexible operation constraint, source and load uncertainty constraint, ESS operation constraint, linear transformation constraint of the objective function.

[0016] In one of the embodiments, the method further includes: issuing the resource operation scheme in the price clearing scheme to the corresponding controllable resource, so that the controllable resource operates according to the corresponding resource operation scheme when the next time t+Δt comes.

[0017] In one of the embodiments, the flexible pricing related information includes: network topology structure and line parameters of the flexible distribution system, upper and lower limits of node voltage and branch current meeting the safe operation of the flexible distribution system, capacity, output range and access position of intelligent soft switch, distributed power supply, load and energy storage, daily operation prediction curve and uncertainty fluctuation parameter of distributed power supply and load, daily industrial and commercial electricity price data, daily DSO and upper-level power grid active power transaction prediction electricity price, reference voltage and reference power of the flexible distribution system, and iteration convergence standard of the LCASE algorithm.

[0018] In one of the embodiments, the flexible pricing related data at the next time t+Δt includes: daily operation prediction curve and uncertainty fluctuation parameter of distributed power supply and load, active power transaction prediction electricity price with the upper-level power grid, and flexible distribution system operation data at the current time t, and whether each distribution node participates in the P2P transaction of the next period, and transaction identity, price and power range information are reported to the transaction platform under the flexible distribution system daily flexible market transaction architecture.

[0019] In a second aspect, based on the same inventive concept, the present application provides a flexible power distribution system flexibility transaction clearing device based on linear convex approximation, the device comprising:

[0020] A first acquisition module is configured to acquire flexibility pricing related information.

[0021] A second acquisition module is configured to acquire flexibility pricing related data at a next time t+Δt in a case where the next time t+Δt is less than an end time T of a transaction day at a current time t, wherein Δt is a transaction time interval.

[0022] A first model establishing module is configured to establish a LCA-OPF based flexibility pricing convex quadratic programming model based on the flexibility pricing related information and the flexibility pricing related data at the next time t+Δt.

[0023] A second model establishing module is configured to establish a LCA-OPF based system worst operating scenario identification model without optimization control based on the flexibility pricing related data at the next time t+Δt and the LCA-OPF based flexibility pricing convex quadratic programming model.

[0024] A model updating module is configured to solve the LCA-OPF based system worst operating scenario identification model without optimization control by using an LCASE algorithm to obtain source and load operating parameter related information of a flexible power distribution system worst operating scenario at the next time t+Δt, and update the LCA-OPF based flexibility pricing convex quadratic programming model based on the source and load operating parameter related information.

[0025] A clearing module is configured to solve the updated LCA-OPF based flexibility pricing convex quadratic programming model by using the LCASE algorithm to obtain a price clearing scheme of flexibility transactions including P2P transactions and P2G transactions at the next time t+Δt, wherein the price clearing scheme comprises node flexibility pricing, power distribution network flexibility support service pricing of P2P transactions, P2G transaction fees, and SOP capacity usage apportionment fees.

[0026] In a third aspect, based on the same inventive concept, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method of the first aspect.

[0027] The flexible power distribution system flexibility transaction clearing method, device and computer readable storage medium based on linear convex approximation consider the new type source and load flexible interaction demand, first construct the flexible power distribution system daily flexibility market transaction framework containing P2P electric energy transaction. In order to guide the friendly transaction behavior of the flexible power distribution system, the transaction matching takes the overall benefit optimization as the target, and the DLMP (Distribution Locational Marginal Pricing, distribution node marginal pricing) is used to price the flexible support service of the distribution network which guarantees the transaction implementation. Then, the flexible pricing convex quadratic programming model (CQPM) based on LCA-OPF (Linear Convex Approximation-Optimal Power Flow, linear convex approximation-optimal power flow) is established, the linear convex approximation constraint is used to approximate the non-convex and nonlinear accurate coupling relationship of voltage / current / loss. Finally, the daily transaction clearing method based on LCASE (LCA Successive Enhancement, LCA iterative enhancement) algorithm is designed, the model parameters are corrected efficiently, the operation uncertainty is considered and the approximation degree to the real state is enhanced. The clearing scheme is solved with acceptable high precision and high efficiency, and the distribution network acceptability is guaranteed, and the overall operation flexibility benefit is enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0029] Figure 1 Fig. 1 is one of the flexible power distribution system flexibility transaction clearing method flowcharts based on linear convex approximation in an embodiment;

[0030] Figure 2 Fig. 2 is another of the flexible power distribution system flexibility transaction clearing method flowcharts based on linear convex approximation in an embodiment;

[0031] Figure 3 Fig. 3 is a daily flexibility market transaction framework schematic diagram of the flexible power distribution system in an embodiment;

[0032] Figure 4 Fig. 4 is a radial distribution network power flow schematic diagram in an embodiment;

[0033] Figure 5This is the third schematic diagram of a flexible power distribution system flexible transaction clearing method based on linear convex approximation in one embodiment;

[0034] Figure 6 This is a schematic diagram of an improved Tianjin Beichen flexible distribution network example structure;

[0035] Figure 7 This is a schematic diagram of the source load operation level curve of one embodiment;

[0036] Figure 8a This is a schematic diagram of an intraday active trading price prediction curve for one embodiment;

[0037] Figure 8b This is a schematic diagram of historical industrial and commercial electricity price curves;

[0038] Figure 9a This is a schematic diagram of active power pricing for Node I in one embodiment;

[0039] Figure 9b This is a schematic diagram of active power pricing at a node in Scenario II of an embodiment;

[0040] Figure 9c This is a schematic diagram of reactive power pricing for Node I in one embodiment;

[0041] Figure 9d This is a schematic diagram of reactive power pricing at node II in one embodiment.

[0042] Figure 10a This is a schematic diagram of the active power operation scheme of an ESS as a P2P participant in one embodiment.

[0043] Figure 10b This is a schematic diagram of the active power operation scheme of DG as a P2P participant in one embodiment.

[0044] Figure 11a This is a schematic diagram of P2P transaction volume at 1:00 in one embodiment;

[0045] Figure 11b This is a schematic diagram of P2G active power trading volume at 1:00 in one embodiment;

[0046] Figure 11c This is a schematic diagram of P2P transaction volume at 10:00 in one embodiment;

[0047] Figure 11d This is a schematic diagram of the distribution network flexibility support service price for P2P transactions at 10:00 in one embodiment;

[0048] Figure 12 This is a block diagram of a flexible power distribution system flexible transaction clearing device based on linear convex approximation, according to one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to examples and drawings. It should be understood that the specific examples described herein are only used to explain the present application and not used to limit the present application.

[0050] The embodiment of the present application constructs a day-ahead flexible market transaction architecture of a flexible power distribution system supporting P2P electric energy transaction. Overall, P2P electric energy transaction matching and P2G electric energy transaction matching jointly form a social benefit optimization problem of the entire flexible power distribution system.

[0051] The embodiment of the present application adopts a centralized P2P transaction clearing architecture to improve transaction efficiency. The DSO is responsible for transaction matching, differential pricing and clearing of the power distribution network flexibility support service of P2P transaction, and makes up for the electric energy loss of the power transmission between the supply and demand sides through the power distribution network and the remaining influence on the operation state of the flexible power distribution system. The price-guided P2P transaction considers multi-dimensional operation influence, ensures transaction efficiency and system flexible operation. To ensure the calculation efficiency, actual effectiveness and feasibility of the day-ahead decision, a day-ahead flexible transaction clearing method based on linear convex approximation (LCA) is further proposed. First, a flexible pricing convex quadratic programming model (CQPM) based on LCA optimal power flow (OPF) is established, LCA flexible operation constraints are established by using overestimation / underestimation states, and dynamic linear approximation is used for accurate coupling relationships such as voltage, current and loss. Then, a day-ahead transaction clearing algorithm based on LCA successive enhancement (LCASE) is constructed, considering operation uncertainty, based on LCA parameter correction, and quickly solving the clearing scheme based on distribution locational marginal pricing (DLMP). The transaction clearing ensures the feasibility of the power distribution network, realizes the temporal and spatial adjustment of the flexible operation level of the flexible power distribution system, and provides a transitional practice idea for more open and flexible distribution side transactions in the future new type of source and load interaction.

[0052] In some exemplary embodiments, with reference to Figure 1 , a flexible power distribution system flexibility transaction clearing method based on linear convex approximation is provided, which can include the following steps:

[0053] S101, obtaining flexibility pricing related information.

[0054] The step S101 can be step 1).

[0055] In some example embodiments, the flexibility pricing related information includes: network topology of the flexible power distribution system, line parameters; upper limit of node voltage, upper limit of branch current, lower limit of branch current that meet safe operation of the flexible power distribution system; capacity, output range, access location of the intelligent soft switch; capacity, output range, access location of the distributed generation (DG); capacity, output range, access location of the load; capacity, output range, access location of the energy storage; intra-day operation prediction curve, uncertainty fluctuation parameter of the distributed generation; intra-day operation prediction curve, uncertainty fluctuation parameter of the load; day-ahead industrial and commercial electricity price data; active power transaction prediction price of the DSO and the upper grid in the intra-day; reference voltage, reference power of the flexible power distribution system; iteration convergence standard of the LCASE algorithm.

[0056] S102, in the case that the next time t+Δt is less than the end time T of the trading day, obtaining, at the current time t, flexibility pricing related data of the next time t+Δt, wherein Δt is a trading time interval.

[0057] The step S102 can be step 2).

[0058] The start time of the trading day is set as t0, the end time of the trading day is set as T, the current time is set as t, and the trading time interval is set as Δt; from the start time t0 to the end time T is a trading day.

[0059] In some example embodiments, in the case that the next time t+Δt is less than the end time T of the trading day, the flexibility pricing related data of the next time t+Δt can be obtained by the DSO at the current time t.

[0060] In some example embodiments, the flexibility pricing related data of the next time t+Δt includes: intra-day operation prediction curve, uncertainty fluctuation parameter of the distributed generation; intra-day operation prediction curve, uncertainty fluctuation parameter of the load; active power transaction prediction price of the DSO and the upper grid (referring to the upper grid connected to the distribution network); operation data of the flexible power distribution system at the current time t; under the intra-day flexibility market transaction architecture of the flexible power distribution system, each distribution node reports to the transaction platform whether to participate in the P2P transaction at the next time, and in the case of participation, transaction identity information, transaction selling price information, and tradable power range information.

[0061] In some example embodiments, the intra-day flexibility market transaction architecture of the flexible power distribution system is:

[0062] The transaction content is to match the output of the power generation resource node and the power demand of the power consumption node, and to provide a flexible support service for the supply-demand matching transaction of each node in the form of power. The P2P transaction content is limited to active power.

[0063] The transaction mode includes two modes: a) allowing P2P transactions between controllable DGs, ESSs, and LA nodes, and the P2P transaction adopts a seller quantity offer and a buyer quantity only mode. b) If the power cannot be matched by the P2P transaction or is considered to be economic, the power can be traded with the DSO, i.e., a P2G transaction. The P2G transaction adopts a DSO pricing mode.

[0064] The clearing price depends on the power matching between the P2P buyer and seller and the P2G transaction, and the transaction scheme should not violate the physical feasibility of the distribution network to avoid damaging the power supply quality. The price clearing of the P2P transaction depends not only on the reported price of the P2P seller but also on the price of the flexible support service of the distribution network.

[0065] The transaction information reporting requirement is that each P2P participant needs to report whether to participate in the P2P transaction at the next time to the transaction platform in advance. If yes, the participant should report the buyer / seller identity and the tradable power range. The P2P seller should additionally report the P2P transaction selling price information. If no, the participant should only report the allowed transaction power range information, which is consistent with the node participating in the P2G transaction. The information reporting of each node is independent and does not affect each other.

[0066] S103, based on the flexibility pricing related information and the flexibility pricing related data at the next time t+Δt, a convex quadratic programming model of flexibility pricing based on LCA-OPF is established.

[0067] This step S103 can be step 3).

[0068] Based on the flexibility pricing related information obtained in step 1) and the flexibility pricing related data obtained in step 2), a convex quadratic programming model (P) of flexibility pricing based on LCA-OPF is established as a basic model for solving the transaction clearing scheme. In some exemplary embodiments, the model (P) includes: an objective function of minimizing the overall flexible operation total cost with the lowest sum of the flexible power distribution system operation cost and the overall benefit of P2P transaction, and a linear transformation constraint of the LCA-based power distribution network flexible operation constraint, the P2P transaction constraint, the power distribution network resource SOP operation constraint, the source-load uncertainty constraint, and the objective function.

[0069] In some exemplary embodiments, the objective function of minimizing the overall flexible operation total cost with the lowest sum of the flexible power distribution system operation cost and the overall benefit of P2P transaction is:

[0070] The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C t The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C

[0071] The overall flexible power distribution system is flexibly operated with the lowest total cost as the target, as shown in equation (1), and the target function C

[0072]

[0073] In the above expressions, and represent the price of active power interaction between DSO and the upper grid at time t and the price of reactive auxiliary service interaction between DSO and the upper grid; ρ V represents the penalty price of voltage deviation in the flexible power distribution system; ρ SOP is the unit capacity use cost of SOP; z i,t represents the voltage deviation calculation auxiliary variable of node i at time t; represents the LCA overestimation value of active power interaction between DSO and the upper grid at time t; represents the absolute value of reactive auxiliary service interaction between DSO and the upper grid at time t; and are the active output and reactive output of the converter of SOP port k at time t; Ω n is the node set in the flexible power distribution system; Ω SOP is the set of distribution nodes accessed by the SOP port; and are the seller node set and the buyer node set of P2P transaction at time t, respectively; represents the total P2P active transaction amount of P2P seller j at time t; and are the operation cost parameters of P2P seller j; represents the total P2P active transaction amount of P2P buyer i at time t; and is the electricity utility parameter of the buyer i for P2P transaction.

[0074] It should be understood that the numerous variables, parameters, coefficients, etc. mentioned in the embodiments of the present application are all power grid parameters, which can be calculated by the calculation formulas and actual calculation examples listed in the embodiments of the present application.

[0075] In some exemplary embodiments, the flexible operation constraint of the power distribution network based on LCA-OPF is:

[0076]

[0077]

[0078] In the above expression, and is the LCA overestimation and underestimation value vector of the node voltage amplitude square term of the flexible power distribution system at time t; and is the LCA overestimation and underestimation value vector of the branch current amplitude square term of the flexible power distribution system at time t; and is the LCA overestimation and underestimation value vector of the branch active power of the flexible power distribution system at time t; and is the LCA overestimation and underestimation value vector of the branch reactive power of the flexible power distribution system at time t; and is the node net active and reactive power vector of the flexible power distribution system at time t; P ij,t , Q ij,t and v j,t represent the true values of the active power, the reactive power and the terminal node j voltage amplitude square term flowing through the branch ij at time t; is the LCA overestimation and underestimation value of the branch current amplitude square term at time t; Ω b represents the branch set of the power distribution network; and V are the upper and lower limits of the voltage safety allowed operation of the flexible power distribution system; 1 N is an N-dimensional column vector with all elements being 1, and N represents the number of nodes in the flexible power distribution system; and are the dual vectors of the node voltage LCA overestimation / underestimation value equation constraint at time t; vectors W1, D R,1 and D X,1 are the first column of the matrix W, D R and D X ; is the LCA overestimation value of the reactive power of the DSO interacting with the upper-level power grid at time t; LCA overestimation value of DSO's active power interaction with the upper grid at time t; and Dual vector of the equality constraint of DSO's active and reactive LCA overestimation value with the upper grid at time t; Vector of square value of upper limit of branch current; and Dual vector of the equality constraint of branch active LCA overestimation / underestimation value at time t; and Dual vector of the equality constraint of branch reactive LCA overestimation / underestimation value at time t; Vector of power flow reference value related to branch ij at time t, including and Active power reference value flowing through branch ij at time t; Reactive power reference value flowing through branch ij at time t; Node voltage square term reference value of end node j of branch ij at time t; Current square term reference value of branch ij at time t; vector δ ij,t Distance of operating state relative to reference state at time t, δ ij,t and Jacobian matrix J ij,t Expression as formula (9), J ij+,t and J ij-,t respectively represent the matrix composed of non-negative values and non-positive values in J ij,t , T represents inversion; similarly,

[0079] In some exemplary embodiments, the flexible power distribution system matrix M p , M q , H, W, D R and D X may be determined by flexible power distribution network topology connection and line admittance parameter calculation:

[0080]

[0081] In the above expression, the branch resistance per unit value The branch reactance per unit value The square term of branch impedance per unit value The association matrix of nodes and branches 1 if there is a connection relationship, otherwise 0; A: = [0 N I N ]M-I N , zero vector I N N b ×N b unit matrix, then W: = (IN -A) -1 ; N b N is the number of branches in the flexible power distribution system; N0 is the number of voltage source nodes in the flexible power distribution system; r ij and x ij are the resistance and reactance per unit of branch ij.

[0082] In some example embodiments, the P2P transaction constraints are:

[0083] It is considered that the internal operation uncertainty of P2P participants is self-solved, and 100% of the decision scheme can be executed. P2P participants can also trade active and reactive power with DSO while conducting P2P active transactions.

[0084] For P2P sellers

[0085]

[0086] In the above expressions, and are the upper and lower limits of P2P active transactions of P2P transaction seller j at time t; represents the set of all possible P2P matching nodes of P2P transaction seller j at time t; p ω,t is the active transaction amount of P2P transaction ω at time t; represents the total amount of P2P active transactions of P2P transaction seller j at time t.

[0087] For P2P buyers

[0088]

[0089] In the above expressions, represents the total amount of P2P active transactions of P2P transaction buyer i at time t; represents the set of all possible P2P matching nodes of P2P transaction buyer i at time t; p ω,t is the active transaction amount of P2P transaction ω at time t; and are the upper and lower limits of P2P active transactions of P2P transaction buyer i at time t.

[0090] In some example embodiments, the power distribution network resource SOP operation constraints are:

[0091]

[0092] In the above expressions, Ω SOP is the set of power distribution nodes accessed by the SOP port; Let t be the active power output of the SOP port connected to node i at time t; Let t be the reactive power output of the SOP port connected to node i at time t; The maximum active power output at the SOP port of node i; This represents the maximum reactive power output of the SOP port at node i.

[0093] In some exemplary embodiments, the source load uncertainty constraint is:

[0094] Given the relatively small scale of distributed loads, and considering that their power uncertainty is beyond their control, they are only traded with DSOs. Trading decisions need to take into account the impact of such operational uncertainties in distributed loads.

[0095] The source load operation constraints with uncertain fluctuations are expressed as follows:

[0096]

[0097] In the above expression, and This represents the uncertainty fluctuation coefficient of DG and load at node i at time t; and This represents the upper limit of the uncertainty fluctuation coefficient for DG and load; Let be the predicted active power output of DG at node i at time t; Let be the predicted active power demand of the load at node i at time t; Let be the predicted reactive power demand of the load at node i at time t; The active power output of the DG at node i at time t is uncertain; The active power demand of the load at node i at time t is uncertain; Let t represent the uncertain reactive power demand at node i.

[0098] It is assumed that the upper limit of the source load uncertainty fluctuation relative to the predicted value follows a normal distribution N(0,σ). 2 The uncertainty fluctuation of source load power in the predicted value The confidence level within is η, expressed as:

[0099]

[0100] In the above expression, Φ -1 (·) represents the inverse cumulative distribution function; standard deviation Let be the predicted active power output of DG at node i at time t; Let be the predicted active power demand of the load at node i at time t; Let be the predicted reactive power demand of the load at node i at time t; is the DG active power output with uncertainty at node i at time t; is the load active demand with uncertainty at node i at time t; is the load reactive demand with uncertainty at node i at time t.

[0101] In some example embodiments, the linear transformation constraint of the objective function is:

[0102] The linear transformation constraint of the node voltage deviation penalty cost is expressed as:

[0103]

[0104] In the above expression, z i,t is the voltage deviation calculation auxiliary variable of node i at time t; is the predicted value of the load active demand at node i at time t; and is the LCA overestimation and underestimation value of the square term of the voltage amplitude of node i at time t; and V is the upper and lower limit of the voltage safety allowed operation of the flexible power distribution system; and V f lx is the upper and lower limit of the ideal operation range of the voltage of the flexible power distribution system.

[0105] The linear transformation constraint of the cost of the reactive power auxiliary service purchased by the distribution network from the upper-level power grid is expressed as:

[0106]

[0107] In the above expression, is the absolute value of the reactive power auxiliary service exchanged between the DSO and the upper-level power grid at time t; is the LCA overestimation value of the reactive power exchanged between the DSO and the upper-level power grid at time t.

[0108] S104, according to the flexibility pricing related data of the next time t+Δt and the LCA-OPF based flexibility pricing convex quadratic programming model, an LCA-OPF based system worst operating scenario identification model without optimization regulation is established.

[0109] This step S104 can be step 4).

[0110] According to the flexibility pricing related data obtained in step 2) and the model (P) in step 3), a system worst operation scenario identification model (W) based on LCA-OPF without optimization control is established to meet the robustness requirement of real-time operation of the flexible power distribution system. It is considered that the operation of DG and load of non-P2P transaction participants is uncertain, the output of ESS is variable within its power range, and the power injection / demand of P2P transaction participants is controlled by P2P transaction participants and is considered to have no uncertainty. The model (W) includes: the sum of active power cost, reactive power service cost and voltage deviation penalty cost of the flexible power distribution system purchased from the upper-level power grid as the objective function, and the linear transformation constraint of the LCA-based distribution network flexible operation constraint, the source and load uncertainty constraint, the ESS operation constraint, and the target function.

[0111] In some example embodiments, the system worst operation scenario identification model (W) without optimization control is:

[0112]

[0113]

[0114] In the above expressions, and are the net active power injection and the net reactive power injection of node i at time t; is the active power output prediction value of the controllable DG at node i at time t; and are the active and reactive demand prediction values of the LA at node i at time t; is the active power output of the DG with uncertainty at node i at time t; and are the active and reactive demand of the load with uncertainty at node i at time t; is the active demand prediction value of the load at node i at time t; represents the absolute value of the reactive power auxiliary service interaction between the DSO and the upper-level power grid at time t; is the LCA overestimation value of the reactive power interaction between the DSO and the upper-level power grid at time t; represents the LCA overestimation value of the active power interaction between the DSO and the upper-level power grid at time t; is the linearization auxiliary variable of the reactive power auxiliary service constraint at time t; and are the LCA overestimation and underestimation values of the voltage amplitude squared term at node i at time t; and V f lx are the upper and lower limits of the ideal voltage operation range of the flexible power distribution system; and The voltage deviation target function at time t Linearization auxiliary variable; And DSO and the price of the upper grid interactive active power and the price of the DSO and the upper grid interactive reactive auxiliary service at time t; ρ V The penalty price of the node voltage deviation in the flexible power distribution system; The active power injected by the ESS at node i into the power distribution network at time t; The active power output upper limit of the ESS at node i; M is a constant coefficient of the big M method, which is set to 10e3. Ω n The node set in the flexible power distribution system. And V The upper and lower limits of the voltage safety allowed operation of the flexible power distribution system.

[0115] S105, using the LCASE algorithm, solving the LCA-OPF-based system worst operating scenario identification model without optimization control, obtaining the source and load operating parameter related information of the worst operating scenario of the flexible power distribution system at the next time t+Δt, and updating the LCA-OPF-based flexibility pricing convex quadratic programming model according to the source and load operating parameter related information.

[0116] This step S105 can be step 5).

[0117] S106, using the LCASE algorithm, solving the updated LCA-OPF-based flexibility pricing convex quadratic programming model, obtaining the price clearing scheme of the flexibility transaction including P2P transaction and P2G transaction at the next time t+Δt, wherein the price clearing scheme includes node flexibility pricing, P2P transaction power distribution network flexibility support service price, P2G transaction cost and SOP capacity use apportionment cost.

[0118] This step S106 can be step 6).

[0119] Step 5), updating the LCA-OPF-based flexibility pricing model (P) according to the data of step 2), the model (P) of step 3) and the results obtained by solving the model (W) of step 4), including: taking the minimum overall flexible operation total cost as the target function, which is the sum of the minimum flexible power distribution system operation cost and the overall P2P transaction benefit, and the LCA-based power distribution network flexible operation constraint, P2P transaction constraint, power distribution network resource SOP operation constraint, source and load uncertainty constraint, linear transformation constraint of the target function. Exemplary as follows:

[0120]

[0121]

[0122] In the above expression, C t is the target function at time t; is the flexible power distribution system operation cost at time t; is the overall benefit of P2P transactions at time t.

[0123] Step 6), to find the worst-case scenario of system operation, first solve the model (W) in step 3) based on the LCASE algorithm to obtain the source and load operation parameters and other information of the worst-case scenario of system operation at the next time, and then solve the model (P) in step 4) based on the LCASE algorithm to obtain the price clearing and power clearing scheme of flexible transactions including P2P transactions and P2G transactions at the next time, the price clearing scheme including: node flexibility pricing, P2P transaction power distribution network flexibility support service price, P2G transaction cost, SOP capacity usage apportionment cost. Specifically as follows:

[0124] In some example embodiments, the LCASE algorithm is as follows:

[0125] According to the relative error of the target function values calculated by the previous and subsequent iterations As the iteration convergence judgment standard, the system power flow data at time t is used as the reference value initial value, and in subsequent iteration solving, the latest power flow data or power flow calculation is used to continuously update the reference state point and related LCA constraint parameters. Until the iteration converges.

[0126] In some example embodiments, the node flexibility pricing is as follows:

[0127]

[0128] In the above expression, and respectively represent the flexibility price vectors of the active and reactive power of the node at time t; and are positive, and the node power injection can obtain a benefit. and are the dual vectors of the overestimation / underestimation value equation constraints of the node voltage LCA at time t; and are the dual vectors of the overestimation / underestimation value equation constraints of the branch active LCA at time t; and are the dual vectors of the overestimation / underestimation value equation constraints of the branch reactive LCA at time t; W, M p and M q are system matrices that can be determined through the power distribution network topology connection and line admittance parameter calculation.

[0129] The node flexibility pricing based on the LCA DLMP is formed by a linear combination of the dual variables related to the flexibility constraints of the distribution network LCA and the corresponding coefficients of the node power in the constraints, and quantifies the comprehensive value of the services provided / required by each node in the form of power regulation. The price includes the price component of the power interaction with the upper-level grid, and the impact price of each node on voltage fluctuation, branch load, network loss, etc.

[0130] In some example embodiments, the distribution network flexibility support service price of the P2P transaction is:

[0131] For the P2P transaction p ω,t , ω:j→i, the clearing scheme is as follows:

[0132] The P2P buyer i pays: The P2P seller j earns: Where Δt represents the transaction time interval; the distribution network operation flexibility support service fee of the P2P transaction p ω,t at time t is: The price is obtained by subtracting the flexibility pricing of the distribution nodes where the buyer and seller are located, and the calculation formula is formula (21). The price is affected by the operation state of the flexible distribution system and has the characteristics of space-time variation, and reflects the value of the power p ω,t transmitted through the distribution network, the impact on the overall system operation such as voltage fluctuation, branch load, network loss, etc. According to the principle of “who benefits, who bears”, it is considered that the P2P buyer and seller equally benefit from the transaction, and the distribution network operation flexibility support service fee is equally borne.

[0133]

[0134] In the above expressions, represents the active power flexibility pricing of the node of the P2P transaction ω seller j at time t; represents the active power flexibility pricing of the node of the P2P transaction ω buyer i at time t.

[0135] In some example embodiments, the P2G transaction fee is:

[0136] For the active power and the reactive power of the P2G transaction node, the clearing scheme is as follows:

[0137] At time t, the node i pays the P2G transaction fee to the DSO:

[0138] For the node directly transacted with the DSO, and the corresponding price is formed by the node flexibility pricing based on the DLMP. and In addition to the purchase price of the DSO to the upper grid, the operation flexibility compensation / punishment of the state influence of each node voltage and branch load is also included.

[0139] In some example embodiments, the SOP capacity usage apportioned cost is:

[0140] The final settlement cost of each node also includes the SOP capacity usage apportioned cost The capacity usage cost of the SOP port is apportioned according to the feeder where the node is located:

[0141]

[0142] In the above expression, ρ SOP is the unit capacity usage cost of the SOP; and is the active power and reactive power of the converter of the SOP port k at time t; N n,k is the number of nodes in the distribution network region connected to the SOP port k; and Δt represents the transaction time interval.

[0143] In some example embodiments, referring to Figure 2 The flexible distribution system flexibility transaction dispatching method based on linear convex approximation further includes the following steps S207-S208.

[0144] S207, the resource operation scheme in the price dispatching scheme is issued to the corresponding controllable resource, so that at the next time t+Δt, the controllable resource operates according to the corresponding resource operation scheme.

[0145] This step S207 can be step 7).

[0146] Step 7), the resource operation scheme in the price dispatching scheme obtained in step 6) is issued to each resource, and at the next time, the controllable resources such as PV, ESS, LA, and SOP operate according to the operation scheme.

[0147] S208, as step 8), update t to t=t+Δt, and judge whether it is the final time of the transaction day, if not, return to step 2), and repeatedly perform the transaction dispatching in turn until the end of the transaction day.

[0148] The above flexible distribution system flexibility market transaction dispatching method based on linear convex approximation provided by the embodiments of the present application realizes the solution of the flexible distribution system intra-day flexibility market transaction dispatching scheme.

[0149] As an example, for the instance of the present application, combined with Figures 3 to 6, first input the improved line parameters in Tianjin Beichen flexible power distribution system, the capacity, output range, and access location of intelligent soft switch, the capacity, output range, and access location of distributed generation (DG), the capacity, output range, and access location of load, the capacity, output range, and access location of energy storage, and the network topology connection relationship, the example structure is shown in Figure 6 , the detailed parameters are shown in Table 1 (for listing the load benchmark power of Tianjin Beichen flexible power distribution network example) and Table 2 (for listing the line parameters of Tianjin Beichen demonstration area power distribution network example); the DG parameters are shown in Figure 6 . The ESS installation parameters, SOP converter capacity, and LA power demand parameters that can participate in P2P transaction are listed in Table 3 (for listing the SOP and ESS installation parameter table) and Table 4 (for listing the power demand parameters of LA node), respectively. The source and load uncertainty fluctuation parameters are set to η = 95% and σ = 0.102, the intra-day prediction operation curve and uncertainty fluctuation coping range of DG and load are shown in Figure 7 . The price prediction of active interaction with the upper-level power grid is shown in Figure 8a , the single price of reactive auxiliary service purchased by the upper-level is considered to be 0.1 times of the active price, and the reference active of ESS is determined according to the historical industrial and commercial power price as shown in Figure 8b . The relative threshold value for the iterative convergence of LCASE algorithm is 0.05. The voltage expectation range is [0.97, 1.03] (p.u.). The P2P seller bidding parameter is set to 51.4 USD / MWh, and the bidding does not affect the transaction game equilibrium solution of the method. The intra-day flexibility market transaction time scale of flexible power distribution system is 15 minutes.

[0150] Three scenarios are used for comparative analysis, respectively:

[0151] Scenario I: the worst running scenario of the system under random fluctuation of source and load, without considering P2P transaction, and without optimization control of DSO.

[0152] Scenario II: for scenario I, the transaction scheme including P2P transaction is cleared by using the proposed method.

[0153] Scenario III: considering the access of SOP, but not considering P2P transaction, the DSO optimizes the decision for scenario I.

[0154] The test is carried out in MATLAB R2019b, and YALMIP optimization toolkit is called for solution by GUROBI 12.10. The computer hardware environment for executing optimization calculation is Intel Xeon CPU E5-1620@3.70GHz, and the memory is 40GB; the software environment is Windows 10 operating system.

[0155] The comparison of the node pricing results of scenarios I and II is shown in FIG. 9 (including Figure 9a , Figure 9b , Figure 9c , Figure 9d ). The price is positive, indicating that the node net power consumption needs to pay a fee. The change of the price indicates that the influence of the power operation scheme at different nodes in the flexible power distribution system on the system operation flexibility exists in space-time difference. In the worst operation scenario I without optimization control, the node active power price varies significantly at different nodes and time, and the node reactive power price fluctuates greatly, indicating that there is an urgent need for reactive power compensation and system operation flexibility adjustment in scenario I. By using the proposed method, the supply and demand matching in the system is significantly improved, and the price difference between nodes is also significantly reduced.

[0156] In scenario II, the operation scheme of ESS and DG as P2P participants within a trading day is shown in FIG. 10 (including Figure 10a , Figure 10b ), and the power injection into the power distribution network is taken as the positive direction. Considering the new energy consumption demand, the PV output is 100% consumed. For active power, the node price decreases significantly with time before 10:00, which is due to the increase of PV output in the power distribution system, the power supply in the system is excessive, and the ESS gradually changes from selling electricity to buying electricity. After 18:00, the load demand increases and the PV output tends to 0, the system supply tends to be tight, and the price is at a high level, and the ESS changes to sell electricity.

[0157] Taking the power distribution flexibility market transaction clearing results at 1:00 and 10:00 as examples, the specific situation of P2P transaction is described, as shown in FIG. 11 (including Figure 11a , Figure 11b , Figure 11c , Figure 11d ). In Figure 11a , the arrow indicates the power flow direction. In Figure 11b , the arrow indicates the power flow direction. In Figure 11c , the sellers are sorted according to the node number where the DG is located, and the buyers 1-6 are LAs, and the buyers 7-8 are ESSs. In Figure 11d , the sellers are sorted according to the node number where the DG is located, and the buyers 1-6 are LAs, and the buyers 7-8 are ESSs. For the power distribution network operation flexibility support service fee of P2P transaction, if the price is positive, it means that the transaction parties of the P2P power matching should pay the fee to the DSO; for P2G transaction, the node pricing is positive, indicating that the power consumption of the node needs to pay the fee to the DSO; and the pricing is negative, the power consumption can obtain the payment from the DSO.

[0158] Under the transaction clearing scheme of scenario II, the impact of the node supply-demand matching system on the system operation at 1:00 is small, the flexibility state of the system operation is relatively good, the difference between the node prices is small, the active power price of the interaction with the upper grid is approximate, and the unit price of the distribution network operation flexibility support service is also small. At 10:00, the difference between the node power injection / consumption increases due to the increase of PV consumption and load demand, as well as the transaction benefit driving, the unit price of the distribution network operation flexibility support service increases and the difference is significant. The high injection amount of 2MWp distributed power in nodes 27 and 29 affects the supply-demand state of the system operation flexibility, reduces the node price and is lower than that of the remaining nodes, and the absolute value of the distribution network operation flexibility support service of P2P transaction is also relatively high. It can be seen that the distribution network operation flexibility support service pricing based on DLMP reflects the value of the impact of P2P active transaction on the flexible operation of the distribution network.

[0159] Table 5 (for listing the operation cost comparison of scenarios I, II and III) is the detailed operation cost of scenarios I, II and III, and Table 6 (for listing the FDN operation state comparison of scenarios I, II and III) is the specific operation state comparison of scenarios I, II and III. From the comparison of Table 5 and Table 6 of the overall operation results in a day, it can be seen that after the FDN operation flexibility market transaction clearing of scenario II, the active power purchase amount and the reactive power service purchase amount of the DSO to the upper grid, as well as the system voltage deviation penalty cost, are reduced by 19.30%, 79.18% and 99.74% respectively, and the total system operation cost is reduced by 46.05%. In addition, after the regulation of scenario II using market means, the network loss, voltage distribution, maximum branch load and dependence on the upper grid are obviously improved, the voltage distribution is in a relatively ideal interval, and the branch capacity is also better utilized, avoiding the problem of branch congestion. Compared with scenario II considering P2P transaction and adopting the proposed method, and scenario III not considering P2P transaction and adopting the system global optimization regulation method, the overall effect of the system operation is similar, which is because the proposed clearing method matches the transaction based on the principle of overall benefit optimization.

[0160] Table 1

[0161]

[0162]

[0163] Table 2

[0164]

[0165]

[0166] Table 3

[0167]

[0168] Table 4

[0169]

[0170] Table 5

[0171] Scenario C Ps (USD) C Qs (USD) C V (USD) C SOP (USD) Total (USD) I 5005.23 567.66 2338.30 - 7911.18 II 4039.01 118.19 6.06 105.00 4268.26 III 4264.27 119.08 3.68 88.66 4465.66

[0172] Table 6

[0173]

[0174] It should be understood that, although each step in the flowchart involved in the embodiments described above is shown in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0175] Based on the same inventive concept, the embodiments of the present application also provide a linear convex approximation-based flexible power distribution system flexibility transaction clearing device for implementing the above-mentioned linear convex approximation-based flexible power distribution system flexibility transaction clearing method. The problem-solving implementation scheme provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more linear convex approximation-based flexible power distribution system flexibility transaction clearing device embodiments provided below can refer to the limitations of the linear convex approximation-based flexible power distribution system flexibility transaction clearing method described above, and will not be repeated here.

[0176] In one exemplary embodiment, as shown in FIG. 12, a linear convex approximation-based flexible power distribution system flexibility transaction clearing device is provided, comprising: Figure 12

[0177] A first acquisition module 1210 is configured to acquire flexibility pricing related information;

[0178] A second acquisition module 1220 is configured to acquire, at a current time t, flexibility pricing related data of a next time t+Δt, in a case where the next time t+Δt is less than an end time T of a transaction day, wherein Δt is a transaction time interval;

[0179] ​The first model establishing module 1230 is configured to establish a LCA-OPF-based flexible pricing convex quadratic programming model based on the flexibility pricing related information and the flexibility pricing related data at the next time t+Δt.

[0180] The second model establishing module 1240 is configured to establish a LCA-OPF-based system worst operating scenario identification model without optimization control based on the flexibility pricing related data at the next time t+Δt and the LCA-OPF-based flexible pricing convex quadratic programming model.

[0181] The model updating module 1250 is configured to solve the LCA-OPF-based system worst operating scenario identification model without optimization control by using the LCASE algorithm to obtain source and load operating parameter related information of the flexible power distribution system worst operating scenario at the next time t+Δt, and update the LCA-OPF-based flexible pricing convex quadratic programming model based on the source and load operating parameter related information.

[0182] The clearing module 1260 is configured to solve the updated LCA-OPF-based flexible pricing convex quadratic programming model by using the LCASE algorithm to obtain a price clearing scheme of the flexibility transaction including P2P transaction and P2G transaction at the next time t+Δt, wherein the price clearing scheme includes node flexible pricing, power distribution network flexible support service price of P2P transaction, P2G transaction cost and SOP capacity use apportionment cost.

[0183] The above-mentioned modules in the flexible power distribution system flexibility transaction clearing device based on linear convex approximation can be realized by software, hardware and combinations thereof, wholly or partially. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0184] In an exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above-mentioned method embodiments when executing the computer program.

[0185] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above-mentioned method embodiments when executed by a processor.

[0186] In an exemplary embodiment, a computer program product is provided, including a computer program, and the computer program implementing the steps in the above-mentioned method embodiments when executed by a processor.

[0187] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0188] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0189] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for clearing flexible distribution system transactions based on linear convex approximation, characterized in that, The method includes: Obtain information related to flexible pricing; If the next time step t+Δt is less than the end time T of the trading day, at the current time t, obtain the relevant data on flexible pricing for the next time step t+Δt, where Δt is the trading time interval; Based on the aforementioned flexible pricing information and the flexible pricing data at the next time step t+Δt, a convex quadratic programming model for flexible pricing based on LCA-OPF is established. Based on the relevant data of flexible pricing at the next time t+Δt and the convex quadratic programming model of flexible pricing based on LCA-OPF, a worst-case operating scenario identification model of the system under non-optimized control based on LCA-OPF is established. The LCASE algorithm is used to solve the worst-case operating scenario identification model of the system under no-optimal control based on LCA-OPF, and obtain the source and load operating parameter information of the worst-case operating scenario of the flexible distribution system at the next time t+Δt. The flexible pricing convex quadratic programming model based on LCA-OPF is updated according to the source and load operating parameter information. Using the LCASE algorithm, the updated convex quadratic programming model for flexibility pricing based on LCA-OPF is solved to obtain the price clearing scheme for flexibility transactions, including P2P and P2G transactions, at the next time step t+Δt. The price clearing scheme includes node flexibility pricing, distribution network flexibility support service price for P2P transactions, P2G transaction fees, and SOP capacity usage amortization fees.

2. The method according to claim 1, characterized in that, The LCA-OPF-based flexible pricing convex quadratic programming model includes: the objective function being the minimum overall flexible operation cost that minimizes the sum of the flexible distribution system operating cost and the overall benefits of P2P transactions; and LCA-based constraints on flexible distribution network operation, P2P transactions, distribution network resource SOP operation, source-load uncertainty, and linear transformation of the objective function.

3. The method according to claim 1, characterized in that, The worst-case operation scenario identification model for the system under no-optimal control based on LCA-OPF includes: taking the maximum sum of active power cost, reactive power service fee purchased by the flexible distribution system from the upper-level power grid as a whole, and voltage deviation penalty fee within the flexible distribution system as the objective function, and LCA-based constraints on flexible operation of the distribution network, source-load uncertainty constraints, ESS operation constraints, and linear transformation constraints of the objective function.

4. The method according to claim 2 or 3, characterized in that, The LCA-based flexible operation constraints for distribution networks include the following expressions: in, and Let be the LCA overestimation and underestimation vectors of the squared terms of the node voltage amplitude of the flexible distribution system at time t; and Let be the LCA overestimation and underestimation vector of the squared term of the branch current amplitude of the flexible distribution system at time t; and Let be the LCA overestimation and underestimation vectors of the active power of the flexible distribution system branch at time t; and Let LCA overestimation and underestimation vectors be the reactive power of the branch in the flexible distribution system at time t. and Let P be the net active and reactive power vectors of the nodes in the flexible distribution system at time t; ij,t Q ij,t and v j,t Let be the true values ​​of the active power, reactive power, and squared voltage amplitude of the terminal node j flowing through branch ij at time t; and The LCA overestimation and underestimation are the squared terms of the branch current amplitude at time t; Ω b For distribution network branch collection; and V The upper and lower limits of the system voltage safety allowable operation; 1 N It is an N-dimensional column vector with all elements being 1, where N is the number of nodes in the system; and Let W1 and D be the dual vectors of the equation constraint for the overestimation and underestimation of the node voltage LCA at time t; R,1 and D X,1 Let W and D be matrices. R and D X The first column; The LCA overestimation of the reactive power interaction between the DSO and the upstream power grid at time t; The LCA overestimation of the active power interaction between the DSO and the upstream grid at time t; and Let be the dual vector of the equation constraint between DSO and the upper-level power grid at time t, which represents the active and reactive power LCA overestimation. This is a vector of the squared values ​​of the upper limit of the branch current. and Let be the dual vector of the equation constraint for the overestimation and underestimation of the active power LCA of the branch at time t; and Let be the dual vector of the equation constraint for the overestimation and underestimation of the branch reactive power LCA at time t; Let be the reference value of the active power flowing through branch ij at time t; Let t be the reference value of reactive power flowing through branch ij at time t; Let δ be the reference value of the squared term of the node voltage at the end node j of branch ij at time t; vector δ ij,t J represents the distance between the running state and the reference state at time t. ij+,t and J ij-,t For Jacobi array J ij,t A matrix consisting of non-negative and non-positive values; 5. The method according to claim 4, characterized in that, M p M q H, W, D R and D X The expressions are as follows: D R :=(I N -A) -1 AR D X :=(I N -A) -1 AX Mp ： =2W T RW M q :=2W T XW H:=(W -1 ) T [2(RD R +XD X )+Z 2 ] Among them, the per-unit value of branch resistance Branch reactance per unit value Branch impedance per unit square term Node-branch association matrix The value is 1 if a connection exists, and 0 if no connection exists; N b N0 represents the number of branches within the flexible power distribution system; N0 represents the number of voltage source nodes within the flexible power distribution system; r ij and x ij Let be the per-unit values ​​of resistance and reactance of branch ij; A: =[0 N I N MI N , I N For N b ×N b Identity matrix, W:=(I N -A) -1 .

6. The method according to claim 1, characterized in that, The node flexibility pricing includes the following expression: in, and Let be the flexibility price vector of active and reactive power at node t; and Let be the dual vector of the equation constraint for the overestimation and underestimation of the node voltage LCA at time t; and Let be the dual vector of the equation constraint for the overestimation and underestimation of the active power LCA of the branch at time t; and Let W and M be the dual vectors of the LCA overestimation and underestimation equations for the reactive power of the branch at time t. p and M q This is the system matrix.

7. The method according to claim 1, characterized in that, The method further includes: The resource operation plan in the price clearing scheme is distributed to the corresponding controllable resources so that when the next time t+Δt arrives, the controllable resources operate according to the corresponding resource operation plan.

8. The method according to claim 1, characterized in that, The information related to flexible pricing includes: the network topology and line parameters of the flexible distribution system; the upper and lower limits of node voltage and branch current to ensure the safe operation of the flexible distribution system; the capacity, output range and access location of smart soft switches, distributed power sources, loads, and energy storage; the intraday operation prediction curves and uncertainty fluctuation parameters of distributed power sources and loads; the day-ahead industrial and commercial electricity price data; the intraday active power transaction prediction price of DSO and the upper-level grid; the reference voltage and reference power of the flexible distribution system; and the iterative convergence criteria of the LCASE algorithm. The flexibility pricing-related data for the next time period t+Δt includes: intraday operation forecast curves and uncertainty fluctuation parameters of distributed power sources and loads, active power trading forecast prices with the upper-level grid, and flexible distribution system operation data at the current time t. Simultaneously, under the intraday flexibility market trading framework of the flexible distribution system, each distribution node reports to the trading platform whether it will participate in the P2P trading of the next time period, as well as its trading identity, price, and power range information.

9. A flexible distribution system flexible transaction clearing device based on linear convex approximation, characterized in that, The device includes: The first acquisition module is used to acquire information related to flexible pricing. The second acquisition module is used to acquire flexible pricing-related data for the next time t+Δt at the current time t, when the next time t+Δt is less than the end time T of the trading day, where Δt is the trading time interval. The first model building module is used to build a convex quadratic programming model for flexibility pricing based on LCA-OPF, based on the flexibility pricing-related information and the flexibility pricing-related data at the next time step t+Δt. The second model building module is used to establish a worst-case operating scenario identification model of the system under no-optimization control based on the flexibility pricing related data at the next time t+Δt and the flexibility pricing convex quadratic programming model based on LCA-OPF. The model update module is used to solve the worst operating scenario identification model of the system under no-optimization control based on LCA-OPF using the LCASE algorithm, obtain the source and load operating parameter information of the worst operating scenario of the flexible distribution system at the next time t+Δt, and update the flexible pricing convex quadratic programming model based on LCA-OPF according to the source and load operating parameter information. The clearing module is used to solve the updated convex quadratic programming model for flexibility pricing based on LCA-OPF using the LCASE algorithm, and obtain the price clearing scheme for flexibility transactions including P2P transactions and P2G transactions at the next time t+Δt. The price clearing scheme includes node flexibility pricing, distribution network flexibility support service price for P2P transactions, P2G transaction fees, and SOP capacity usage amortization fees.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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