Method and device for improving photovoltaic consumption capability of medium and low voltage power distribution network, and storage medium

Abstract

The invention discloses a method and a device for improving photovoltaic consumption capability of a medium and low voltage power distribution network, and a storage medium, and belongs to the technical field of power distribution network optimization. According to the method, a cross-voltage-level collaborative interconnection architecture with a multi-terminal intelligent soft switch as a core is constructed, various resources such as distributed photovoltaic resources, energy storage resources, adjustable loads, on-load voltage regulating transformers and capacitor banks are integrated, and a multi-resource collaborative optimization model is established; constructing an uncertainty set by adopting a distributed robust optimization method to cope with photovoltaic output and load fluctuation, and solving the model through a column and constraint generation algorithm to obtain an optimal equipment configuration and operation strategy; according to the invention, the hierarchical and court barriers of a traditional power distribution network can be effectively broken, the cross-regional flexible transfer and global optimization distribution of photovoltaic power are realized, the distributed photovoltaic consumption capability is significantly improved, and the safe, stable and economic operation of the system is guaranteed at the same time.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network optimization technology, and relates to a method, equipment and storage medium for improving the photovoltaic absorption capacity of medium and low voltage power distribution networks. Background Technology

[0002] With the ongoing transformation of the energy structure, distributed photovoltaic (PV) power, with its clean, flexible, and low-carbon characteristics, has been widely used in medium- and low-voltage distribution networks. In recent years, the installed capacity of distributed PV has experienced explosive growth, driving the evolution of distribution networks from traditional passive networks to active networks. However, traditional medium- and low-voltage distribution networks suffer from structural defects such as "independent levels and fragmented distribution areas." The medium- and low-voltage networks are only connected by conventional transformers for unidirectional voltage transformation, and distribution areas at the same voltage level are connected by traditional tie switches with non-adjustable power, lacking flexible, bidirectional power exchange channels. This results in the inability to effectively transfer surplus power generated by distributed PV between different areas. Furthermore, the output of distributed PV is significantly affected by natural factors such as sunlight and temperature, exhibiting significant intermittency and fluctuation. During periods of abundant sunlight, PV-rich distribution areas often experience curtailment rates as high as 15%-20%; while during peak electricity consumption periods, densely loaded distribution areas still rely on the upstream grid for power supply, creating a contradictory situation of "surplus waste and deficit replenishment." Existing control measures mostly focus on optimizing single devices (such as energy storage and adjustable loads) or intelligent soft switches at both ends, lacking multi-resource coordination mechanisms across voltage levels and distribution areas. This makes it difficult to cope with the uncertainties in distributed photovoltaic (PV) output and load demand, easily leading to safety issues such as voltage exceeding limits and power imbalance, severely restricting the large-scale absorption of PV. Therefore, there is an urgent need for a technical approach that can restructure the distribution network architecture, integrate multiple types of resources, and has the ability to cope with uncertainties, in order to break through the bottleneck of distributed PV absorption and improve the safety and economy of distribution network operation. Summary of the Invention

[0003] The technical solution of this invention is used to solve the problem of how to improve the distributed photovoltaic power absorption rate and the system operation safety.

[0004] The present invention solves the above-mentioned technical problems through the following technical solutions: This invention provides a method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks, comprising the following steps: S1. Construct a flexible interconnection architecture for medium and low voltage distribution networks based on multi-terminal intelligent soft switches to coordinate and control distributed photovoltaic, energy storage, and adjustable load resources; S2. An objective function is defined with the goal of maximizing the absorption of distributed photovoltaic power generation and minimizing the operating cost of medium and low voltage power distribution systems. A multi-resource collaborative optimization model is constructed with constraints from the mathematical model of multi-terminal intelligent soft switching, distributed photovoltaic constraints, energy storage constraints, on-load tap changer constraints, capacitor bank constraints, power flow constraints, and operational safety constraints. The mathematical model constraints of the multi-terminal intelligent soft switching are transformed into standard second-order cone model constraints through second-order cone relaxation. The absolute value terms in the capacitor bank constraints and on-load tap changer operation constraints are linearized by introducing two auxiliary variables, positive and negative change quantities. S3. The multi-resource collaborative optimization model is converted into an uncertain sub-Bruker optimization model. The sub-Bruker optimization model uses discrete equipment states as the first-stage decision variables and continuous variables as the second-stage decision variables, and uses the 1-norm and ∞-norm to constrain the confidence set of the probability distribution. S4. The column and constraint generation algorithm is adopted to transform the model solving problem into an alternating iterative solution of the main problem and sub-problems. The main problem solves the optimal configuration and operation strategy, while the sub-problems find the worst probability distribution until the solution converges to the optimal solution that meets the accuracy requirements.

[0005] Furthermore, the mathematical model expression for the multi-terminal intelligent soft switch described in step S1 is as follows: (1) (2) (3) In the formula, express Time Port active power, express Time Port reactive power, Indicates port Apparent power limitation Indicates port The power loss coefficient, for Time Port The active power loss of intelligent soft switches, This is a collection of all smart soft switches.

[0006] Furthermore, the expression for the objective function in step S2 is as follows: (4) In the formula, for The voltage level during the period is Distribution network nodes The rated active power of the photovoltaic power generation unit connected at the point, for The voltage level during the period is Distribution network nodes The actual active power consumed by the photovoltaic power generation unit connected at the location. for Time-of-day routes Active power loss, express Time Port Active power loss, , and These are respectively a distribution network set at different voltage levels, a photovoltaic power generation system set, and a branch network set. This is the cost coefficient. For level Distribution network lines Active power loss at the location, for Time Port The active power loss of the intelligent soft switch at the location is T, which is the sum of the time intervals.

[0007] Furthermore, the expression for the sub-Bruker optimization model described in step S3 is as follows: (36) In the formula, For the scene The probability of occurrence; The total number of scenes; To optimize the matrices corresponding to the variables in the model constraints; These are the decision variables for the first stage. For the second stage decision variables, Let be the right-hand constant vector of the first-stage inequality constraint, b be the right-hand constant vector of the first-stage equality constraint, c be the right-hand constant vector of the second-stage inequality constraint, d be the right-hand constant vector of the second-stage equality constraint, e be the right-hand constant vector of the cross-stage equality constraint, and g be the right-hand constant vector of the cross-stage inequality constraint.

[0008] Furthermore, the method for constraining the confidence set of the probability distribution using the 1-norm and ∞-norm described in step S3 is as follows: The confidence intervals of the 1-norm and ∞-norm are used to constrain the range of probability distribution fluctuations, and the objective function is modified to the following form: (37) In the formula, p is the probability weight vector of the scene occurrence; The confidence set of the probability distribution can be derived from equation (37) as follows: (38) In the formula, Let be the interval of the probability distribution set of the scene, and let represent the confidence set constrained by the 1-norm and the ∞-norm. Representing a scene The set of positive real numbers in a probability distribution; For the scene The initial or expected probability, Let be the upper limit of the sum of absolute probabilities. This represents the upper limit of the probability of the maximum absolute deviation.

[0009] Furthermore, the main problem described in step S4 is represented as follows: (39) In the formula, Given a threshold; This represents the total number of model iterations; MP stands for Multi-Scenario Robust Optimization. For decision variables to be robust parameters or uncertainty moderating variables in the model, For the scene In the The probability of a set of scenes. For the first Scenes in a set of scenes The second-stage decision variables are obtained by solving the main problem. With the lower bound of the model .

[0010] Furthermore, the sub-problem described in step S4 is represented as follows: (40) In the formula, SP represents the scene probability robust optimization. For the first stage variables The robust objective function value.

[0011] Furthermore, the method described in step S4, which uses a column and constraint generation algorithm to transform the model solving problem into an iterative solution of the main problem and sub-problems, is as follows: 1) Set the number of iterations The iteration termination flag is upper bound value lower bound value and apply the initial probability distribution ; 2) Solve the main problem to obtain its optimal solution. and update the lower bound value. ; 3) Based on the optimal solution of the main problem Solving the subproblems yields the worst-case probability distribution. Optimal solution to the subproblem and objective function and update the upper bound value. ; 4) Comparison Is it less than If so, then the optimal solution is obtained. If not, then update. Continue repeating the above iterative steps.

[0012] The present invention also provides an electronic device, including a memory and a processor. The memory is used to store a program that supports the processor in executing the above-described method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks, and the processor is configured to execute the program stored in the memory.

[0013] The present invention also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of the above-described method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks.

[0014] The beneficial effects of this invention are as follows: This invention proposes a flexible interconnection architecture centered on multi-terminal intelligent soft switches. Through the collaboration of multi-port converters and DC buses, it constructs multi-dimensional channels of "medium-voltage-medium-voltage," "medium-voltage-low-voltage," and "low-voltage-low-voltage," enabling bidirectional transmission of active power across regions and independent regulation of reactive power. Structurally, it breaks down power interaction barriers, providing support for the global allocation of surplus photovoltaic power and significantly expanding the absorption capacity. By integrating various resources such as multi-terminal intelligent soft switches, distributed photovoltaics, energy storage, adjustable loads, on-load tap-changing transformers, and capacitor banks, a collaborative strategy is formed. The multi-terminal intelligent soft switches are responsible for precise... By refining power allocation, energy storage charges during periods of high photovoltaic output and discharges during periods of low output. Adjustable loads complement each other through peak-shaving and load-shaving operations, while on-load tap-changing transformers and capacitor banks work together to maintain voltage safety. This multi-resource linkage mode maximizes photovoltaic absorption while reducing system operating costs, balancing efficiency and economy. Based on historical data and real-time weather deviations, a set of uncertainties for the "most unfavorable scenario" is constructed, covering extreme situations such as sudden drops in output and sudden increases in load. This ensures that the dispatching scheme meets safety constraints in any scenario, effectively avoiding curtailment or safety issues caused by uncertainties. Attached Figure Description

[0015] Figure 1 A schematic diagram of a medium- and low-voltage power distribution system based on flexible interconnection of multi-terminal intelligent soft switches; Figure 2 A schematic diagram of a strategy framework aimed at improving photovoltaic power grid integration capacity; Figure 3 This is a schematic diagram of a 155-node test system; Figure 4 A schematic diagram of the simulation results for distributed photovoltaic power consumption; Figure 5 A schematic diagram showing the simulation results of the active power of each multi-terminal intelligent soft switch; Figure 6 A schematic diagram showing the simulation results of reactive power for each multi-terminal intelligent soft switch; Figure 7 A schematic diagram of the simulation results for the active power and state of charge of adjustable load and energy storage; Figure 8 This is a three-dimensional schematic diagram showing the simulation results of voltage in medium-voltage and low-voltage distribution networks. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 1 As shown, this embodiment of the invention provides a method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks based on flexible interconnection of multi-terminal intelligent soft switches, including the following steps: Step 1: Construct a flexible interconnection architecture for medium and low voltage distribution networks based on multi-terminal intelligent soft switches to effectively coordinate and control various resources such as distributed photovoltaics, energy storage, and adjustable loads, and realize flexible energy mutual assistance between different distribution networks.

[0018] like Figure 2 As shown, a flexible interconnection architecture for medium and low voltage distribution networks based on multi-terminal intelligent soft switches is constructed by using multiple VSCs as ports, with each port connected to different power distribution system feeder areas. The converters of different ports are coupled through the DC bus to form a power interaction channel.

[0019] The mathematical model expression for the multi-terminal intelligent soft switch is as follows: (1) (2) (3) In the formula, express Time Port active power, express Time Port reactive power, Indicates port Apparent power limitation Indicates port The power loss coefficient, for Time Port The active power loss of intelligent soft switches, This is a collection of all smart soft switches.

[0020] Step 2: Define the objective function with the goal of maximizing the absorption of distributed photovoltaic power generation and minimizing the operating cost of medium and low voltage distribution systems. Construct a multi-resource collaborative optimization model with constraints such as the mathematical model of multi-terminal intelligent soft switching, distributed photovoltaic constraints, energy storage constraints, on-load tap changer constraints, capacitor bank constraints, power flow constraints, and operational safety constraints. Optimize various resources in the medium and low voltage distribution network to achieve "maximum absorption, minimum cost, and controllable safety".

[0021] The expression for the objective function is as follows: (4) In the formula, for The voltage level during the period is Distribution network nodes The rated active power of the photovoltaic power generation unit connected at the point, for The voltage level during the period is Distribution network nodes The actual active power consumed by the photovoltaic power generation unit connected at the location. for Time-of-day routes Active power loss, express Time Port Active power loss, , and These are respectively a distribution network set at different voltage levels, a photovoltaic power generation system set, and a branch network set. This is a cost coefficient (a weighting coefficient used to balance the cost of curtailment and the cost of loss). For level Distribution network lines Active power loss at the location, for Time Port The active power loss of the intelligent soft switch at the location is T, which is the sum of the time intervals.

[0022] The expression for the distributed photovoltaic constraint is as follows: (5) (6) (7) In the formula, for Time period nodes The rated reactive power of the photovoltaic system is connected at the location; for Time period nodes Photovoltaic capacity was connected at the location; for The voltage level during the period is Distribution network nodes The actual reactive power consumed by the photovoltaic power generation unit connected at the location.

[0023] The expression for the energy storage constraint is as follows: (8) (9) (10) (11) (12) In the formula, and They are respectively Energy storage at all times The charging and discharging power; and Energy storage The maximum value of charging and discharging power; and They are respectively Energy storage at all times Discharge and charge state variables; for Energy storage at all times The state of charge, for Energy storage at all times The state of charge; and Energy storage SOC upper and lower limits; For the collection of all energy storage, To improve energy storage charging efficiency; For energy storage discharge efficiency; This refers to the rated capacity of the energy storage.

[0024] The expression for the on-load regulating transformer constraint is as follows: (13) (14) (15) (16) In the formula, The introduced auxiliary voltage variable represents the voltage on the secondary side of the on-load regulating transformer; for Timetable The tap position of the on-load regulating transformer. for Timetable The tap position of the on-load regulating transformer; for Timetable The voltage regulation rate of the on-load regulating transformer; For the line The initial voltage regulation rate of the on-load tap changer; This represents the change in voltage regulation between adjacent tap positions; A set of integer variables; The limit on the total number of times the tap position of an on-load regulating transformer changes within a day; For the line The maximum value of the tap position of the on-load regulating transformer; This refers to the voltage on the primary side of the on-load regulating transformer.

[0025] The expression for the capacitor bank constraint is as follows: (17) (18) (19) In the formula, for Time Node The number of capacitor banks put into operation at the location. for Time Node The number of capacitor banks connected at the location; For nodes The reactive power per unit number of capacitor banks; The limit on the total number of operations of the capacitor bank per day; For nodes The total number of capacitor banks; for Time Node The total reactive power input to the capacitor bank.

[0026] The expression for the power flow constraint is as follows: (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) In the formula, and They are respectively The voltage level during the period is Distribution network lines Transmitted active and reactive power; and They are respectively Time period nodes The active and reactive power of the photovoltaic system connected to the photovoltaic system; and It is a node The active and reactive power of the load; For voltage level Reference values ​​for distribution network voltage; for Time Level nodes in the distribution network The voltage; for The voltage level during the period is In the distribution network, there are nodes Transmit to node active power, for The voltage level during the period is nodes in the distribution network Injected active power, for The voltage level during the period is In the distribution network, there are nodes Transmit to node reactive power, for The voltage level during the period is nodes in the distribution network Inject reactive power, for The voltage level during the period is nodes in the distribution network The active power injected by the intelligent soft switch for The voltage level during the period is nodes in the distribution network The active power regulation of the adjustable load. for The voltage level during the period is nodes in the distribution network The reactive power injected by the intelligent soft switch for The voltage level during the period is nodes in the distribution network The reactive power regulation of the adjustable load, for The voltage level during the period is nodes in the distribution network The total reactive power input to the capacitor bank is [missing information]. for Time period level Distribution network lines Active power loss at the location, For the line The resistance value, For the line The reactance value.

[0027] The expression for the operational safety constraint is as follows: (26) (27) In the formula, For voltage level The distribution network and voltage level are The transformation ratio between distribution networks for The voltage level during the period is nodes in the distribution network Voltage at that point for The voltage level during the period is nodes in the distribution network Voltage at that point and The voltage levels are respectively The upper and lower limits of node voltage within the distribution network.

[0028] To simplify the calculation, the mathematical model constraints of the multi-terminal intelligent soft switch need to be transformed into standard second-order cone model constraints, and the absolute value terms in the capacitor bank constraints and on-load regulating transformer operation constraints need to be linearized.

[0029] Since the mathematical model constraint of the multi-terminal intelligent soft switch in formula (3) is a quadratic constraint, it needs to be transformed into a standard second-order cone model constraint. The transformation formula is as follows: (28) in, For nodes The loss coefficient of the intelligent soft switch.

[0030] By transforming formula (28), the mathematical model constraints of the multi-terminal intelligent soft switch in formula (3) are relaxed from equality constraints to inequality constraints, thereby expanding the feasible domain of the mathematical model constraints of the multi-terminal intelligent soft switch.

[0031] To ensure the accuracy of the solution, after obtaining the optimization results, it is necessary to verify the accuracy of the constraint relaxation. The verification formula is as follows: (29) In the formula, for The maximum value of the SOP loss constraint relaxation gap at time.

[0032] For the absolute value terms in capacitor bank constraints and on-load tapping transformer operation constraints, absolute value linearization is performed by introducing two auxiliary variables, positive and negative changes, as shown in the following formula: (30) (31) (32) (33) (34) (35) In the formula, and respectively Time Node The increase and decrease in the number of capacitor banks switched on and off; and respectively Timetable The increase and decrease in the tap position of the on-load regulating transformer; for Time Node The number of capacitor banks put into operation at the location. for Time Node The number of capacitor banks put into operation at the location. for Timetable The tap position of the on-load regulating transformer. for Timetable The tap position of the on-load regulating transformer.

[0033] Step 3: Convert the multi-resource collaborative optimization model into an uncertain bibliometric optimization model.

[0034] (1) Transformation of uncertain forms Based on historical data on distributed photovoltaic (PV) output and load demand, the operating states of discrete on-load tap changers (OTTCs), capacitor banks, and energy storage are set as the first-stage decision variables. Other continuous variables (continuous distribution network operation control variables: power variables such as the actual active / reactive power absorbed by distributed PVs, the active / reactive power of energy storage charging and discharging, the active / reactive power regulation of smart soft switches (SOPs), and the active / reactive power adjustment of demand response; voltage variables such as the voltage amplitude at each node of the distribution network and the node voltage after voltage regulation by on-load tap changers (OLTCs); and loss variables such as line active / reactive losses and SOP active power losses) are used as the second-stage decision variables. This transforms the multi-resource collaborative optimization model into a distributed bar optimization model with the following uncertainties: (36) In the formula, For the scene The probability of occurrence; The total number of scenes; To optimize the matrices corresponding to the variables in the model constraints; These are the decision variables for the first stage. For the second stage decision variables, denoted as , b is the right-hand constant vector of the first-stage inequality constraint, c is the right-hand constant vector of the first-stage equality constraint (scenario-dependent), d is the right-hand constant vector of the second-stage equality constraint (scenario-dependent), e is the right-hand constant vector of the cross-stage equality constraint (scenario-dependent), and g is the right-hand constant vector of the cross-stage inequality constraint (scenario-dependent).

[0035] (2) Transformation of a two-stage optimization problem The confidence intervals of the 1-norm and ∞-norm are used to constrain the range of probability distribution fluctuations, and the objective function is modified to the following form: (37) In the formula, p is the probability weight vector of the scene occurrence.

[0036] The confidence set of the probability distribution can be derived from equation (37) as follows: (38) In the formula, Let be the interval of the probability distribution set of the scene, and let represent the confidence set constrained by the 1-norm and the ∞-norm. Representing a scene The set of positive real numbers in a probability distribution; For the scene The initial or expected probability, Let be the upper limit of the sum of absolute probabilities. This represents the upper limit of the probability of the maximum absolute deviation.

[0037] Step 4: Use the column and constraint generation algorithm to divide the model solution problem into the main problem and sub-problems for solution, so as to obtain the optimal solution that satisfies the constraints.

[0038] The main problem is given the probability distribution of the scenario. Given the constraints, the main problem is to find the optimal solution that satisfies the given conditions, as follows: (39) In the formula, Given a threshold; This represents the total number of model iterations; MP is an abbreviation for Multi-scenario Robust Programming. Decision variables are robust parameters or uncertainty modifiers in the model (their function is to characterize the range of uncertainty in the model, such as the deviation of the probability distribution, the fluctuation range of parameters, etc.); they participate in decision-making as optimization variables, helping the model find the most robust solution to uncertainty within the multi-scenario robust optimization (MP) framework, ensuring that the decision is made within the bounds of the model's capabilities. (It exhibits stability within the defined range of uncertainty). For the scene In the The probability of a set of scenes. For the first Scenes in a set of scenes The second-stage decision variables are obtained by solving the main problem. With the lower bound of the model .

[0039] The subproblem is solved after the first-stage variables are given, to find the worst probability distribution under real-time operation, and returns it to the main problem, providing an upper bound for equation (39). The subproblem is expressed as follows: (40) In the formula, SP is the abbreviation for scene probability robust optimization. For the first stage variables The robust objective function value.

[0040] The subproblem is then solved in two steps: first, the inner minimum value problem is solved, and then the outer maximum value problem is solved.

[0041] The specific solution process is as follows: 1) Set the number of iterations The iteration termination flag is upper bound value lower bound value and apply the initial probability distribution ; 2) Solve the main problem to obtain its optimal solution. and update the lower bound value. ; 3) Based on the optimal solution of the main problem Solving the subproblems yields the worst-case probability distribution. Optimal solution to the subproblem and objective function and update the upper bound value. ; 4) Comparison Is it less than If so, then the optimal solution is obtained. If not, then update. Continue repeating the above iterative steps.

[0042] Case Analysis The test case is a medium- and low-voltage interconnected distribution network based on multi-terminal intelligent soft switches and flexible interconnection, with the topology as follows: Figure 3 As shown, the reference voltage for medium-voltage distribution networks is 10kV, and the reference voltage for low-voltage distribution networks is 0.4kV. Voltage safety deviations are both taken as... The medium-voltage distribution network has one 2.5MW 5-terminal SOP (SOP1); the low-voltage distribution network has three 1.2MW 4-terminal SOPs (SOP2 to SOP4). The medium-voltage distribution network contains 11 photovoltaic nodes, and the low-voltage distribution network contains 6 photovoltaic nodes. The installed PV capacity of each node is shown in Table 1, and other parameter settings are shown in Table 2. This indicates the tap adjustment step size of the on-load tap changer (OLTC). This indicates the maximum permissible number of daily operations for an on-load tap changer (OLTC).

[0043] Table 1. Capacity of PV installed at each node

[0044] Table 2 Parameter Settings

[0045] The method of this invention is verified by a general simulation example. The simulation results are shown in [link to simulation results]. Figures 4 to 8 Through flexible power transmission, multi-resource coordinated regulation, and voltage safety control, the distributed photovoltaic (PV) grid has significantly improved its absorption capacity and absorption rate, while ensuring the safe and economical operation of the distribution network. This provides an effective technical solution for distribution networks with high-penetration distributed PV grid access.

[0046] To verify the effectiveness of the method of the present invention, four comparative cases were set up: Case 1 without interconnection, Case 2 with transformer interconnection only, Case 3 with medium-voltage multi-terminal intelligent soft switch interconnection, and Case 4 with the method of the present invention. Simulation was performed under the same source load scenario, and the optimization results of the four cases are shown in Table 3 below.

[0047] Table 3 Optimization results of the four cases

[0048] In Case 1, optimized calculations showed that the distributed photovoltaic (PV) reduction was 15.1678 MWh, with a grid integration rate of only 47.68%, network losses of 2.2087 MWh, and a voltage deviation of 7.3327 pu. Both PV grid integration capacity and operational stability were at a low level. In Case 2, under the basic interconnection architecture, the distributed PV reduction decreased to 6.6850 MWh, and the grid integration rate increased to 76.94%, representing a 61.36% improvement in grid integration capacity compared to Case 1. Network losses and voltage deviation also decreased to 1.5745 MWh and 6.6721 pu, respectively. However, due to the lack of flexible adjustment between subgrids, some PV waste and operational losses still existed. Case 3 further reduced the distributed photovoltaic reduction to 2.1622 MWh through flexible power transmission at the medium-voltage level, increasing the absorption rate to 92.54%, which is 94.07% higher than Case 1. Network loss and voltage deviation were further optimized to 0.9684 MWh and 5.8945 pu, respectively. However, the isolated operation of the low-voltage level still constrained the overall absorption potential. Case 4 achieved comprehensive flexible coordination across voltage levels, reducing distributed photovoltaic (PV) load to 0 MWh and achieving a 100% absorption rate. This represents a 109.71% improvement in absorption capacity compared to Case 1, while network losses were only 0.3477 MWh and voltage deviation as low as 4.7196 pu. This verifies the necessity of flexible interconnection across all levels of medium and low voltage distribution networks. By leveraging the cross-regional and cross-voltage level regulation capabilities of multi-terminal intelligent soft switches, combined with the multi-resource coordination of energy storage, adjustable loads, on-load tap changers, and capacitor banks, the coupling characteristics of different levels of distribution networks were fully released, achieving comprehensive optimization and improvement of distributed PV absorption capacity and system operation economy and safety.

[0049] Example 2 An electronic device includes a memory and a processor, the memory being used to store a program that supports the processor in executing the method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks as described in Embodiment 1, and the processor being configured to execute the program stored in the memory.

[0050] Example 3 A storage medium storing a computer program, wherein the computer program is executed by a processor to perform the steps of the method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks in Embodiment 1.

[0051] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks, characterized in that, Includes the following steps: S1. Construct a flexible interconnection architecture for medium and low voltage distribution networks based on multi-terminal intelligent soft switches to coordinate and control distributed photovoltaic, energy storage, and adjustable load resources; S2. An objective function is defined with the goal of maximizing the absorption of distributed photovoltaic power generation and minimizing the operating cost of medium and low voltage power distribution systems. A multi-resource collaborative optimization model is constructed with constraints from the mathematical model of multi-terminal intelligent soft switching, distributed photovoltaic constraints, energy storage constraints, on-load tap changer constraints, capacitor bank constraints, power flow constraints, and operational safety constraints. The mathematical model constraints of the multi-terminal intelligent soft switching are transformed into standard second-order cone model constraints through second-order cone relaxation. The absolute value terms in the capacitor bank constraints and on-load tap changer operation constraints are linearized by introducing two auxiliary variables, positive and negative change quantities. S3. The multi-resource collaborative optimization model is converted into an uncertain sub-Bruker optimization model. The sub-Bruker optimization model uses discrete equipment states as the first-stage decision variables and continuous variables as the second-stage decision variables, and uses the 1-norm and ∞-norm to constrain the confidence set of the probability distribution. S4. The column and constraint generation algorithm is adopted to transform the model solving problem into an alternating iterative solution of the main problem and sub-problems. The main problem solves the optimal configuration and operation strategy, while the sub-problems find the worst probability distribution until the solution converges to the optimal solution that meets the accuracy requirements.

2. The method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to claim 1, characterized in that, The mathematical model expression for the multi-terminal intelligent soft switch mentioned in step S1 is as follows: （1） （2） （3） In the formula, express Time Port active power, express Time Port reactive power, Indicates port Apparent power limitation Indicates port The power loss coefficient, for Time Port The active power loss of intelligent soft switches, This is a collection of all smart soft switches.

3. The method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to claim 1, characterized in that, The expression for the objective function in step S2 is as follows: （4） In the formula, for The voltage level during the period is Distribution network nodes The rated active power of the photovoltaic power generation unit connected at the point, for The voltage level during the period is Distribution network nodes The actual active power consumed by the photovoltaic power generation unit connected at the location. for Time-of-day routes Active power loss, express Time Port Active power loss, , and These are respectively a distribution network set at different voltage levels, a photovoltaic power generation system set, and a branch network set. This is the cost coefficient. For level Distribution network lines Active power loss at the location, for Time Port The active power loss of the intelligent soft switch at the location is T, which is the sum of the time intervals.

4. The method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to claim 1, characterized in that, The expression for the sub-Bruker optimization model described in step S3 is as follows: （36） In the formula, For the scene The probability of occurrence; The total number of scenes; To optimize the matrices corresponding to the variables in the model constraints; These are the decision variables for the first stage. For the second stage decision variables, Let be the right-hand constant vector of the first-stage inequality constraint, b be the right-hand constant vector of the first-stage equality constraint, c be the right-hand constant vector of the second-stage inequality constraint, d be the right-hand constant vector of the second-stage equality constraint, e be the right-hand constant vector of the cross-stage equality constraint, and g be the right-hand constant vector of the cross-stage inequality constraint.

5. The method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to claim 4, characterized in that, The method for constraining the confidence set of the probability distribution using the 1-norm and ∞-norm in step S3 is as follows: The confidence intervals of the 1-norm and ∞-norm are used to constrain the range of probability distribution fluctuations, and the objective function is modified to the following form: （37） In the formula, p is the probability weight vector of the scene occurrence; The confidence set of the probability distribution can be derived from equation (37) as follows: （38） In the formula, Let be the interval of the probability distribution set of the scene, and let represent the confidence set constrained by the 1-norm and the ∞-norm. Representing a scene The set of positive real numbers in a probability distribution; For the scene The initial or expected probability, Let be the upper limit of the sum of absolute probabilities. This represents the upper limit of the probability of the maximum absolute deviation.

6. The method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to claim 1, characterized in that, The main problem described in step S4 is as follows: （39） In the formula, Given a threshold; This represents the total number of model iterations; MP stands for Multi-Scenario Robust Optimization. For decision variables to be robust parameters or uncertainty moderating variables in the model, For the scene In the The probability of a set of scenes. For the first Scenes in a set of scenes The second-stage decision variables; The variables are obtained by solving the main problem. With the lower bound of the model .

7. The method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to claim 6, characterized in that, The sub-problem described in step S4 is represented as follows: （40） In the formula, SP represents the robust optimization of scene probabilities. First-stage variables The robust objective function value.

8. The method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to claim 7, characterized in that, The method described in step S4, which uses a column and constraint generation algorithm to transform the model solving problem into an iterative solution of the main problem and sub-problems, is as follows: 1) Set the number of iterations The iteration termination flag is upper bound value lower bound value and apply the initial probability distribution ; 2) Solve the main problem to obtain its optimal solution. and update the lower bound value. ; 3) Based on the optimal solution of the main problem Solving the subproblems yields the worst-case probability distribution. Optimal solution to the subproblem and objective function and update the upper bound value. ; 4) Comparison Is it less than If so, then the optimal solution is obtained. If not, then update. Continue repeating the above iterative steps.

9. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store programs that support the processor in executing the method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks according to any one of claims 1 to 8, and the processor is configured to execute the programs stored in the memory.

10. A storage medium storing a computer program, characterized in that, When a computer program is run by a processor, it executes the steps of the method for improving the photovoltaic absorption capacity of medium and low voltage distribution networks as described in any one of claims 1 to 8.

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