Method for improving photovoltaic bearing capacity of multi-voltage-class power distribution system based on differential flexible interconnection

Abstract

The invention discloses a photovoltaic bearing capacity improving method for a multi-voltage-class power distribution system based on differential flexible interconnection, and the method comprises the steps: 1, constructing an asymmetric capacity model and a time-varying topology recombination model of a reconfigurable intelligent soft switch, and determining the port capacity distribution and topology connection mode of the reconfigurable intelligent soft switch; 2, establishing a differentiated flexible interconnection topology of the multi-voltage-level power distribution system, and realizing cross-level power cooperative transmission of medium and low voltage systems; 3, a double-layer optimization model is constructed, the upper layer takes the maximum photovoltaic bearing capacity as a target, and the lower layer minimizes deployment input and system operation consumption of the reconfigurable intelligent soft switch; and 4, solving the model through an optimization algorithm to obtain an optimal arrangement and photovoltaic access scheme of the reconfigurable intelligent soft switch. According to the invention, the limitation of a traditional single voltage grade is broken through, the photovoltaic bearing capacity and the adaptability to uncertainty of the power distribution system are improved, and the safe and stable operation of the system is ensured.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system technology, specifically a method for improving the photovoltaic carrying capacity of multi-voltage level power distribution systems based on differentiated flexible interconnection. Background Technology

[0002] With the large-scale integration of photovoltaic (PV) power, the inherent limitations of traditional distribution networks in terms of power coordination, flexible regulation, and uncertainty response are becoming increasingly apparent, posing a severe challenge to improving PV carrying capacity. PV power output exhibits significant randomness and intermittency; its large-scale grid connection alters the unidirectional power flow distribution characteristics of traditional distribution networks, easily triggering issues such as voltage exceeding limits and power fluctuations, severely restricting the improvement of PV carrying capacity.

[0003] Existing research largely focuses on optimizing the photovoltaic (PV) carrying capacity of single-voltage-level distribution networks. This approach only improves PV carrying capacity by adjusting reactive power compensation equipment within the same voltage level or through limited network reconfiguration, failing to fully explore the coupling characteristics and collaborative carrying potential of multi-voltage-level distribution systems, such as medium and low-voltage systems. Different voltage-level distribution networks exhibit significant differences in structure, load characteristics, and PV distribution. Single-voltage-level optimization struggles to achieve cross-level power complementarity and resource optimization, resulting in a substantial limitation on overall PV carrying capacity. Furthermore, traditional smart soft switches, employing fixed topologies and uniform voltage source converter port capacity designs, struggle to flexibly respond to the differentiated interconnection needs of various regions. When PV output in a region surges while the corresponding smart soft switch port capacity is insufficient, power backlog occurs, preventing external transmission. Conversely, in areas with high load demand, inappropriate smart soft switch port capacity configuration may lead to insufficient power supply, further restricting the improvement of PV carrying capacity. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention proposes a method for enhancing the photovoltaic carrying capacity of multi-voltage-level power distribution systems based on differentiated flexible interconnection. This method aims to break through the limitations of traditional single-voltage-level systems, improve the photovoltaic carrying capacity of power distribution systems and their adaptability to uncertainties, thereby ensuring the safe and stable operation of the system.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: The photovoltaic carrying capacity enhancement method for a multi-voltage level power distribution system based on differentiated flexible interconnection, as described in this invention, is characterized by the following steps: S1: Based on the power transmission requirements of interconnection between different areas in a multi-voltage level power distribution system, the capacity of each port of the reconfigurable intelligent soft switch is allocated according to the corresponding ratio to determine the capacity of each voltage source converter in the reconfigurable intelligent soft switch. S2: Based on the asymmetric capacity and time-varying topology reconfiguration characteristics of reconfigurable intelligent soft switches, establish cross-voltage level power transmission constraints for multi-voltage level power distribution systems to achieve cross-level optimized power transmission between medium and low voltage systems. S3: Construct a two-layer optimization model to enhance photovoltaic carrying capacity. The upper-layer objective of the two-layer optimization model is to maximize the photovoltaic access capacity, while the lower-layer objective is to minimize the deployment investment of reconfigurable smart soft switches and the system operation consumption. S4: Solve the two-layer optimization model to obtain the optimal layout scheme and operation strategy of the reconfigurable smart soft switch, as well as the photovoltaic access capacity and operation strategy.

[0006] The photovoltaic carrying capacity enhancement method for multi-voltage level distribution systems based on differentiated flexible interconnection described in this invention is also characterized in that, in S1, the first reconfigurable intelligent soft switch is obtained using equation (1). The capacity of a voltage source converter : (1) In equation (1), Voltage source converter The percentage of capacity; The total capacity of the reconfigurable intelligent soft switch; It is a collection of voltage source converters.

[0007] Furthermore, S2 includes: S2-1: Time-varying feeder capacity constraints for constructing differentiated flexible interconnected multi-voltage level power distribution systems using equations (2)-(5): (2) (3) (4) (5) In equations (2)-(5), for Time-of-use feeder Active power on the DC side; and for Time-of-use feeder Transmitted active and reactive power; for Time-of-use feeder Active power loss; The loss factor of the reconfigurable intelligent soft switch; for Time-of-use feeder The transmission capacity; for Time-of-use feeder Apparent power; This is a set of feeders that can be selectively connected to the voltage source converter.

[0008] S2-2: Active reconfiguration constraints for constructing differentiated flexible interconnected multi-voltage level distribution systems using equations (6)-(8): (6) (7) (8) In equations (6)-(8), To represent a voltage source converter Is it connected to the feeder? binary variables.

[0009] S2-3: Constructing cross-voltage level power transmission constraints for multi-voltage level power distribution systems using equations (9)-(17): (9) (10) (11) (12) (13) (14) (15) (16) (17) In equations (9)-(17), and They are respectively The voltage level during the period is Nodes in multi-voltage level power distribution systems With nodes The lines between 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 yes Time period nodes The active and reactive power of the load; for Time period nodes With nodes The lines between Active power loss; For voltage level Reference values ​​for power distribution system voltage; for Time period nodes The voltage; For voltage level The power distribution system and voltage level are The transformation ratio between multiple voltage level power distribution systems; , For voltage level Upper and lower limits of node voltage in a multi-voltage level power distribution system; For voltage level Multi-voltage level power distribution system lines exist Transmission power during a given time period; , For voltage level The distribution network lines The upper and lower limits of transmission power, Represents the set of downstream nodes of node i; To sum over downstream nodes j connected to node i; Represents the set of downstream nodes of node i; Sum the values ​​of the upstream node k connected to node i.

[0010] Furthermore, S3 includes: S3-1: The objective function of the two-layer optimization model for improving photovoltaic carrying capacity is constructed using equations (18)-(20): (18) (19) (20) In equations (18)-(20), This represents the upper-level objective function. This represents the lower-level objective function. This represents the total number of candidate locations for photovoltaic projects. The installed capacity of the photovoltaic system at the candidate location numbered k; The installation capacity of the reconfigurable intelligent soft switch for candidate location number n. , These are the overall deployment investment and overall system operation consumption of reconfigurable intelligent soft switches; and The annual fixed deployment investment and annual operation and maintenance losses for reconfigurable intelligent soft switches; This is the conversion factor; The service life of a reconfigurable intelligent soft switch; This represents the total number of reconfigurable intelligent soft switches installed. This serves as a standard for the unit capacity deployment of reconfigurable intelligent soft switches; This indicates the operation and maintenance coefficient of the reconfigurable intelligent soft switch; , and The voltage levels are respectively The losses in the power distribution system, curtailment of solar power, and load interruption consumption; , and These are the consumption coefficients for losses, wasted light, and load interruptions, respectively. for Time period nodes The active power of solar power curtailment; For voltage level power distribution system Time period nodes Interruptible load power at the location; A collection of interruptible loads; A collection of power distribution systems at different voltage levels; S3-2: The constraints of the two-layer optimization model for improving photovoltaic carrying capacity include: asymmetric capacity constraints, time-varying feeder capacity constraints, active reconfiguration constraints, cross-voltage level power transmission constraints, equipment installation constraints, and post-installation operation constraints.

[0011] Furthermore, S3-2-1: Constructing the device installation constraints includes: i) Construct photovoltaic load constraints using equations (21)-(26): (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) In equations (21)-(26): This represents the number of photovoltaic units with a unit capacity to be installed at the candidate location numbered k. The installed capacity of the photovoltaic system at the candidate location numbered k; This indicates whether photovoltaic (PV) installation is required at the candidate location numbered k. , They are respectively The active and reactive power of photovoltaic power at the candidate location with time period number k; , These represent the minimum and maximum active power of the photovoltaic system at candidate location k, respectively. The maximum reactive power of the photovoltaic system at the candidate location numbered k; The power factor angle of the photovoltaic at the candidate location numbered k; Photovoltaic unit installed capacity; , They are respectively Actual light intensity and photovoltaic cell surface temperature during the time period; , These are the light intensity and ambient temperature under standard test conditions; This is the power temperature coefficient.

[0012] ii) Construct reconfigurable smart soft switch layout constraints using equations (27)-(28): (27) (28) In equations (27)-(28), The number of unit capacity reconfigurable smart soft switches installed on the feeder connected to the candidate location numbered n; The total capacity of the reconfigurable intelligent soft switch at candidate position number n; Indicates whether the feeder connected to the candidate position numbered n is equipped with a reconfigurable intelligent soft switch; The unit installation capacity for reconfigurable intelligent soft switches.

[0013] S3-2-2: The post-installation runtime constraints include: i) Construct photovoltaic operation constraints using equations (29)-(30): (29) (30) In equations (29)-(30), for In the scene The reactive power of curtailed photovoltaic power at the candidate location with time period number k; ii) Construct interruptible load constraints using equations (31)-(32): (31) (32) In equations (31)-(32), for In the scene Time period nodes The active power of the load can be interrupted at this point; for In the scene Time period nodes The reactive power of the load can be interrupted at this point; For nodes The rated power factor of the load.

[0014] Considering the impact of photovoltaic and load uncertainties and load growth, the power flow constraints in equations (11) and (12) are modified using equations (33) and (34): (33) (34) In equations (33)-(34), For nodes Forecasted growth rate of load.

[0015] Furthermore, in S4, the column and constraint generation algorithm is used to solve the two-layer optimization model to obtain the optimal layout scheme and operation strategy of the reconfigurable smart soft switch, as well as the photovoltaic access capacity and operation strategy.

[0016] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program supporting the processor in performing the method described therein, and the processor is configured to execute the program stored in the memory.

[0017] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to perform the steps of the method described thereon.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention designs a distribution system topology based on reconfigurable intelligent soft switches and differentiated flexible interconnection. Through asymmetric capacity allocation and dynamic topology reconfiguration, it achieves precise power matching in different regions. Furthermore, based on this differentiated flexible interconnection topology, a two-layer photovoltaic (PV) carrying capacity enhancement model is established with maximizing PV access capacity as its core objective. This achieves an optimal balance between maximizing PV carrying capacity and minimizing investment in reconfigurable intelligent soft switches, thus achieving the optimal match between PV carrying capacity enhancement and investment costs.

[0019] 2. This invention explores the coupling characteristics of multi-voltage level power distribution systems and investigates the collaborative carrying potential of photovoltaics in power distribution systems at different voltage levels, thereby enhancing the overall adaptability and reliability of the system and improving its carrying capacity for photovoltaics.

[0020] 3. This invention constructs a sub-Blu-rod optimization model and uses a column constraint generation algorithm to solve the problem. Through iteration, it effectively addresses the uncertainties of photovoltaics, ensuring the safe operation of the power distribution system while improving the comprehensive load-bearing capacity and adaptability of multi-voltage level power distribution systems to photovoltaics. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of a reconfigurable intelligent soft switch topology; Figure 2 This is a schematic diagram of a multi-voltage-level differentiated interconnected power distribution system; Figure 3 This is a schematic diagram of a medium- and low-voltage interconnected power distribution system; Figure 4 This is a schematic diagram of a medium- and low-voltage interconnected power distribution system that incorporates photovoltaics and reconfigurable smart soft switches; Figure 5 This is a schematic diagram of the photovoltaic (PV) grid connection locations and capacities: (a) Capacity of PV at each node; (b) Active power of PV at each node; Reactive power of PV at each node; Figure 6 This is a schematic diagram of the system topology in Case 3. Detailed Implementation

[0022] The invention will now be further described with reference to the accompanying drawings.

[0023] Reference Figure 1 and Figure 6 As shown, a method for enhancing the photovoltaic carrying capacity of a multi-voltage-level distribution system based on differentiated flexible interconnection is presented. This method overcomes the limitations of a single voltage level, achieves coordinated carrying capacity across multiple voltage levels, and adapts to the differentiated needs of different regions, thus effectively improving photovoltaic carrying capacity and ensuring the safe and stable operation of the distribution network. Specifically, the method includes the following steps: S1: Based on the power transmission requirements of interconnection between different areas in a multi-voltage level power distribution system, the capacity of each port of the reconfigurable intelligent soft switch is allocated according to the corresponding ratio to determine the capacity of each voltage source converter in the reconfigurable intelligent soft switch. In specific implementation, the first reconfigurable intelligent soft switch is obtained using equation (1). The capacity of a voltage source converter : (1) In equation (1), Voltage source converter The percentage of capacity; The total capacity of the reconfigurable intelligent soft switch; It is a collection of voltage source converters.

[0024] S2: Based on such Figure 1 The asymmetric capacity and time-varying topology reconfiguration characteristics of the reconfigurable intelligent soft switch are shown to establish cross-voltage level power transmission constraints in multi-voltage level distribution systems, in order to achieve... Figure 2 Cross-level power optimization transmission between multi-voltage-level differentiated interconnected power distribution systems is shown in the diagram. S2-1: Time-varying feeder capacity constraints for constructing differentiated flexible interconnected multi-voltage level power distribution systems using equations (2)-(5): (2) (3) (4) (5) In equations (2)-(5), for Time-of-use feeder Active power on the DC side; and for Time-of-use feeder Transmitted active and reactive power; for Time-of-use feeder Active power loss; The loss factor of the reconfigurable intelligent soft switch; for Time-of-use feeder The transmission capacity; for Time-of-use feeder Apparent power; This is a set of feeders that can be selectively connected to the voltage source converter.

[0025] S2-2: Active reconfiguration constraints for constructing differentiated flexible interconnected multi-voltage level distribution systems using equations (6)-(8): (6) (7) (8) In equations (6)-(8), To represent a voltage source converter Is it connected to the feeder? binary variables.

[0026] S2-3: Constructing cross-voltage level power transmission constraints for multi-voltage level power distribution systems using equations (9)-(17): (9) (10) (11) (12) (13) (14) (15) (16) (17) In equations (9)-(17), and They are respectively The voltage level during the period is Nodes in multi-voltage level power distribution systems With nodes The lines between 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 yes Time period nodes The active and reactive power of the load; for Time period nodes With nodes The lines between Active power loss; For voltage level Reference values ​​for power distribution system voltage; for Time period nodes The voltage; For voltage level The power distribution system and voltage level are The transformation ratio between multiple voltage level power distribution systems; , For voltage level Upper and lower limits of node voltage in a multi-voltage level power distribution system; For voltage level Multi-voltage level power distribution system lines exist Transmission power during a given time period; , For voltage level The distribution network lines The upper and lower limits of transmission power, To sum the values ​​of the downstream nodes connected to node i; This is to sum the values ​​of the upstream nodes connected to node i.

[0027] S3: Construct a two-layer optimization model to enhance photovoltaic carrying capacity. The upper-layer objective of the two-layer optimization model is to maximize the photovoltaic access capacity, while the lower-layer objective is to minimize the deployment investment of reconfigurable smart soft switches and the system operation consumption. S3-1: The objective function of the two-layer optimization model for improving photovoltaic carrying capacity is constructed using equations (18)-(20): (18) (19) (20) In equations (18)-(20), This represents the upper-level objective function. This represents the lower-level objective function. The number of candidate locations for photovoltaic projects; The installed capacity of the photovoltaic system at the candidate location numbered k; The installation capacity of the reconfigurable intelligent soft switch for candidate location number n. , These are the overall deployment investment and overall system operation consumption of reconfigurable intelligent soft switches; and The annual fixed deployment investment and annual operation and maintenance losses for reconfigurable intelligent soft switches; This is the conversion factor; The service life of a reconfigurable intelligent soft switch; The number of reconfigurable intelligent soft switches to be installed; This serves as a standard for the unit capacity deployment of reconfigurable intelligent soft switches; This indicates the operation and maintenance coefficient of the reconfigurable intelligent soft switch; , and The voltage levels are respectively The losses in the power distribution system, curtailment of solar power, and load interruption consumption; , and These are the consumption coefficients for losses, wasted light, and load interruptions, respectively. for Time period nodes The active power of solar power curtailment; For voltage level power distribution system Time period nodes Interruptible load power at the location; A collection of interruptible loads; A collection of power distribution systems at different voltage levels.

[0028] S3-2: The constraints of the two-layer optimization model for improving photovoltaic carrying capacity include: asymmetric capacity constraints, time-varying feeder capacity constraints, active reconfiguration constraints, cross-voltage level power transmission constraints, equipment installation constraints, and post-installation operational constraints. S3-2-1: Constructing the equipment installation constraints includes: i) Construct photovoltaic load constraints using equations (21)-(26): (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) In equations (21)-(26): The number of photovoltaic units with a unit capacity to be installed at the candidate location numbered k; This indicates the total number of candidate locations for photovoltaic projects; The installed capacity of the photovoltaic system at the candidate location numbered k; This indicates whether photovoltaic (PV) installation is required at the candidate location numbered k. , They are respectively The active and reactive power of photovoltaic power at the candidate location with time period number k; , These represent the minimum and maximum active power of the photovoltaic system at candidate location k, respectively. The maximum reactive power of the photovoltaic system at the candidate location numbered k; The power factor angle of the photovoltaic at the candidate location numbered k; Photovoltaic unit installed capacity; , They are respectively Actual light intensity and photovoltaic cell surface temperature during the time period; , These are the light intensity and ambient temperature under standard test conditions; This is the power temperature coefficient.

[0029] ii) Construct reconfigurable intelligent soft switch layout constraints using equations (27)-(28) (27) (28) In equations (27)-(28), The number of unit capacity reconfigurable smart soft switches installed on the feeder connected to the candidate location numbered n; The total capacity of the reconfigurable intelligent soft switch at candidate position number n; Indicates whether the feeder connected to the candidate position numbered n is equipped with a reconfigurable intelligent soft switch; This represents the total number of reconfigurable intelligent soft switches installed. The unit installation capacity for reconfigurable intelligent soft switches.

[0030] S3-2-2: The post-installation runtime constraints include: i) Construct photovoltaic operation constraints using equations (29)-(30): (29) (30) In equations (29)-(30), for In the scene The reactive power of curtailed photovoltaic power at the candidate location with time period number k.

[0031] ii) Construct interruptible load constraints using equations (31)-(32): (31) (32) In equations (31)-(32), for In the scene Time period nodes The active power of the load can be interrupted at this point; for In the scene Time period nodes The reactive power of the load can be interrupted at this point; For nodes The rated power factor of the load.

[0032] Considering the impact of photovoltaic and load uncertainties and load growth, the power flow constraints in equations (11) and (12) are modified using equations (33) and (34): (33) (34) In equations (33)-(34), For nodes Forecasted growth rate of load.

[0033] S4: Solve the two-layer optimization model to obtain the optimal layout scheme and operation strategy of the reconfigurable smart soft switch, as well as the photovoltaic access capacity and operation strategy; The optimal arrangement scheme of the reconfigurable intelligent soft switch is obtained by solving the two-level optimization model using a column and constraint generation algorithm. , and operating strategies Photovoltaic grid connection capacity and operating strategies .

[0034] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0035] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0036] I. Example Description and Simulation Result Analysis; To verify the effectiveness of this invention, Figure 3 Taking the medium- and low-voltage interconnected power distribution system as an example, the effectiveness of the method for improving the carrying capacity of distributed photovoltaic power is calculated and verified. The reference voltages of the medium-voltage and low-voltage power distribution systems are 10kV and 0.4kV, respectively, and the voltage safety deviation is taken as... The transformers allow power backfeed, with maximum allowable transmission capacities of 14.2 MVA and 0.65 MVA for the two types of transformers, respectively. The system includes four types of loads: residential, commercial, industrial, and agricultural, with proportions of 0.45, 0.15, 0.35, and 0.10, respectively. Nodes 2 through 33 can all be connected to distributed photovoltaic systems; their actual connection capacity is a variable to be optimized. A 3-port, 4-feeder reconfigurable smart soft switch is used, with the capacity ratio of each port determined using the predefined golden ratio allocation method. Relevant parameters for the photovoltaic system and the reconfigurable smart soft switch are shown in Table 1.

[0037] Table 1 Installation parameters for photovoltaic and reconfigurable smart soft switches

[0038] Table 2 shows the optimal arrangement of distributed photovoltaic (PV) loads and reconfigurable smart soft switches. The topology of the medium- and low-voltage differentiated interconnected distribution system and the distributed PV installation capacity at each node are shown in Table 2. Figure 4 and Figure 5. Figure 5 (a) shows the photovoltaic capacity of each node. Figure 5 (b) shows the active power of the photovoltaic system at each node. Figure 5 (c) shows the reactive power of the photovoltaic system at each node. In the medium-voltage distribution system, photovoltaic systems are connected at nodes 2, 5, 6, 7, 8, 14, 16, 17, 18, 19, 20, 22, 24, 25, 27, 28, 29, 31, 32, and 33, with a total capacity of 18.0 MVA. In the low-voltage distribution system, photovoltaic systems are installed at nodes 35, 36, 37, 38, 41, 42, 44, 46, 48, and 51, with a capacity of 4.3 MVA. Three reconfigurable smart soft switches are connected, with capacities of 4.5, 4.8, and 3.1 MVA, respectively. This result is consistent with the model's objective of considering different regional load characteristics, maximizing the photovoltaic carrying capacity, and achieving the optimal arrangement of reconfigurable smart soft switches. In areas where distributed photovoltaic power is concentrated and the load is relatively small, such as some nodes (5~7, 19, 20, 22, etc.) of medium-voltage power distribution systems, reconfigurable smart soft switches with larger connection capacity ensure the smooth transmission of photovoltaic power. In areas with large loads but limited photovoltaic power, reconfigurable smart soft switches with appropriate capacity are also configured to meet load requirements, effectively improving the carrying capacity of distributed photovoltaic power in different areas.

[0039] Table 2. Layout Results of Photovoltaic Power and Reconfigurable Smart Soft Switch

[0040] To fully demonstrate the effectiveness of the proposed method, three other photovoltaic carrying capacity enhancement schemes were set up to compare and analyze the carrying capacity of the power distribution system for distributed photovoltaics under different schemes.

[0041] Case 1: A method to improve the load capacity by only considering the reactive power output optimization and adjustment of distributed photovoltaic inverters, i.e., no interconnection between medium and low voltage and no flexible equipment; Case 2: Considering methods to improve the load-bearing capacity of distributed photovoltaic inverters through reactive power output optimization and low- and medium-voltage interconnection; Case 3: Building upon the method for enhancing the load-bearing capacity of flexible interconnection based on fixed topology intelligent soft switches, multi-voltage level interconnection is added; Case 4: The load-bearing capacity enhancement method proposed in this paper, which considers the differentiated flexible interconnection of multiple voltage levels.

[0042] The results for the four cases are shown in Table 3.

[0043] Table 3 Comparison of results from different schemes

[0044] Case 1 has a photovoltaic (PV) grid connection capacity of only 12.60 MVA, while Case 2 reaches 16.70 MVA. Case 2 considers medium- and low-voltage interconnection, breaking the limitations of a single voltage level and enabling distribution systems at different voltage levels to cooperate with each other. When there is excess distributed PV power in the medium-voltage distribution system, the excess power can be transmitted to areas in the low-voltage distribution system with demand through interconnection channels. This achieves optimized allocation of PV resources across different voltage levels, significantly increasing the PV grid connection capacity and demonstrating the advantages of multi-voltage level interconnection in tapping the potential of PV. Furthermore, multi-voltage level interconnection makes the system more flexible in power allocation, reducing curtailment and load interruptions caused by power imbalances in local areas, effectively reducing operating costs, and enhancing system stability and reliability.

[0045] Compared to Case 2, Case 3 introduces intelligent soft switching for multi-voltage level flexible interconnected devices with a fixed topology. Its optimized topology structure is as follows: Figure 6 As shown, intelligent soft switches possess flexible power regulation capabilities, adjusting power transmission and reactive power support according to the real-time system status. When distributed photovoltaic (PV) output fluctuates or load changes, intelligent soft switches can respond quickly and optimize power allocation, thereby improving the system's capacity to support distributed PV and highlighting the significant value of flexible interconnection devices in optimizing system operation.

[0046] Case 4 has a photovoltaic (PV) grid connection capacity of 22.40 MVA, slightly higher than Case 3's 21.80 MVA, but its installation loss of the reconfigurable smart soft switch is 1568.95, lower than Case 3's 1824.55. Based on the differentiated interconnection architecture of the reconfigurable smart soft switch, the asymmetric allocation of port capacity allows for more precise matching of load characteristics and distributed energy distribution in different areas. Increasing port capacity in concentrated PV areas ensures power transmission; in areas with high loads, capacity is rationally configured to meet demand, achieving a balance between increased carrying capacity and loss control. Furthermore, the VSC port topology of the reconfigurable smart soft switch can be flexibly changed and reconfigured, and feeders can select switches based on regional power transmission needs, providing more precise power flow regulation capabilities, thus effectively reducing network loss costs and voltage deviation. Compared to the fixed topology flexible interconnection of Case 3, Case 4 is more flexible and efficient in dealing with distributed PV output fluctuations and load changes, fully exploring the collaborative carrying potential of multi-voltage level distribution systems and improving the overall system adaptability.

Claims

1. A method for enhancing the photovoltaic carrying capacity of a multi-voltage level power distribution system based on differentiated flexible interconnection, characterized in that, Includes the following steps: S1: Based on the power transmission requirements of interconnection between different areas in a multi-voltage level power distribution system, the capacity of each port of the reconfigurable intelligent soft switch is allocated according to the corresponding ratio to determine the capacity of each voltage source converter in the reconfigurable intelligent soft switch. S2: Based on the asymmetric capacity and time-varying topology reconfiguration characteristics of reconfigurable intelligent soft switches, establish cross-voltage level power transmission constraints for multi-voltage level power distribution systems to achieve cross-level optimized power transmission between medium and low voltage systems. S3: Construct a two-layer optimization model to enhance photovoltaic carrying capacity. The upper-layer objective of the two-layer optimization model is to maximize the photovoltaic access capacity, while the lower-layer objective is to minimize the deployment investment of reconfigurable smart soft switches and the system operation consumption. S4: Solve the two-layer optimization model to obtain the optimal layout scheme and operation strategy of the reconfigurable smart soft switch, as well as the photovoltaic access capacity and operation strategy.

2. The method for enhancing the photovoltaic carrying capacity of a multi-voltage level distribution system based on differentiated flexible interconnection as described in claim 1, characterized in that, S1 is obtained using equation (1) in the reconfigurable intelligent soft switch. The capacity of a voltage source converter : (1) In equation (1), Voltage source converter The percentage of capacity; The total capacity of the reconfigurable intelligent soft switch; It is a collection of voltage source converters.

3. The method for enhancing the photovoltaic carrying capacity of a multi-voltage level distribution system based on differentiated flexible interconnection as described in claim 2, characterized in that, S2 include: S2-1: Time-varying feeder capacity constraints for constructing differentiated flexible interconnected multi-voltage level power distribution systems using equations (2)-(5): (2) (3) (4) (5) In equations (2)-(5), for Time-of-use feeder Active power on the DC side; and for Time-of-use feeder Transmitted active and reactive power; for Time-of-use feeder Active power loss; The loss factor of the reconfigurable intelligent soft switch; for Time-of-use feeder The transmission capacity; for Time-of-use feeder Apparent power; A set of feeders that can be selectively connected to the voltage source converter; S2-2: Active reconfiguration constraints for constructing differentiated flexible interconnected multi-voltage level distribution systems using equations (6)-(8): (6) (7) (8) In equations (6)-(8), To represent a voltage source converter Is it connected to the feeder? binary variables; S2-3: Constructing cross-voltage level power transmission constraints for multi-voltage level power distribution systems using equations (9)-(17): (9) (10) (11) (12) (13) (14) (15) (16) (17) In equations (9)-(17), and They are respectively The voltage level during the period is Nodes in multi-voltage level power distribution systems With nodes The lines between 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 yes Time period nodes The active and reactive power of the load; for Time period nodes With nodes The lines between Active power loss; For voltage level Reference values ​​for power distribution system voltage; for Time period nodes The voltage; For voltage level The power distribution system and voltage level are The transformation ratio between multiple voltage level power distribution systems; , For voltage level Upper and lower limits of node voltage in a multi-voltage level power distribution system; For voltage level Multi-voltage level power distribution system lines exist Transmission power during a given time period; , For voltage level The distribution network lines The upper and lower limits of transmission power, Represents the set of downstream nodes of node i; To sum over downstream nodes j connected to node i; Represents the set of downstream nodes of node i; Sum the values ​​of the upstream node k connected to node i.

4. The method for enhancing the photovoltaic carrying capacity of a multi-voltage level distribution system based on differentiated flexible interconnection as described in claim 3, characterized in that, S3 include: S3-1: The objective function of the two-layer optimization model for improving photovoltaic carrying capacity is constructed using equations (18)-(20): (18) (19) (20) In equations (18)-(20), This represents the upper-level objective function. This represents the lower-level objective function. This represents the total number of candidate locations for photovoltaic projects. The installed capacity of the photovoltaic system at the candidate location numbered k; The installation capacity of the reconfigurable intelligent soft switch for candidate location number n. , These are the overall deployment investment and overall system operation consumption of reconfigurable intelligent soft switches; and The annual fixed deployment investment and annual operation and maintenance losses for reconfigurable intelligent soft switches; This is the conversion factor; The service life of a reconfigurable intelligent soft switch; This represents the total number of reconfigurable intelligent soft switches installed. This serves as a standard for the unit capacity deployment of reconfigurable intelligent soft switches; This indicates the operation and maintenance coefficient of the reconfigurable intelligent soft switch; , and The voltage levels are respectively The losses in the power distribution system, curtailment of solar power, and load interruption consumption; , and These are the consumption coefficients for losses, wasted light, and load interruptions, respectively. for Time period nodes The active power of solar power curtailment; For voltage level power distribution system Time period nodes Interruptible load power at the location; A collection of interruptible loads; A collection of power distribution systems at different voltage levels; S3-2: The constraints of the two-layer optimization model for improving photovoltaic carrying capacity include: asymmetric capacity constraints, time-varying feeder capacity constraints, active reconfiguration constraints, cross-voltage level power transmission constraints, equipment installation constraints, and post-installation operation constraints.

5. The method for enhancing the photovoltaic carrying capacity of a multi-voltage level distribution system based on differentiated flexible interconnection as described in claim 4, characterized in that, S3-2 includes: S3-2-1: Constructing the equipment installation constraints includes: i) Construct photovoltaic load constraints using equations (21)-(26): (21) (22) (23) (24) (25) (26) In equations (21)-(26): This represents the number of photovoltaic units with a unit capacity to be installed at the candidate location numbered k. The installed capacity of the photovoltaic system at the candidate location numbered k; This indicates whether photovoltaic (PV) installation is required at the candidate location numbered k. , They are respectively The active and reactive power of photovoltaic power at the candidate location with time period number k; , These represent the minimum and maximum active power of the photovoltaic system at candidate location k, respectively. The maximum reactive power of the photovoltaic system at the candidate location numbered k; The power factor angle of the photovoltaic at the candidate location numbered k; Photovoltaic unit installed capacity; , They are respectively Actual light intensity and photovoltaic cell surface temperature during the time period; , These are the light intensity and ambient temperature under standard test conditions; The power temperature coefficient; ii) Construct reconfigurable smart soft switch layout constraints using equations (27)-(28): (27) (28) In equations (27)-(28), The number of unit capacity reconfigurable smart soft switches installed on the feeder connected to the candidate location numbered n; The total capacity of the reconfigurable intelligent soft switch at candidate position number n; Indicates whether the feeder connected to the candidate position numbered n is equipped with a reconfigurable intelligent soft switch; Unit installation capacity for reconfigurable intelligent soft switches; S3-2-2: The post-installation runtime constraints include: i) Construct photovoltaic operation constraints using equations (29)-(30): (29) (30) In equations (29)-(30), for In the scene The reactive power of curtailed photovoltaic power at the candidate location with time period number k; ii) Construct interruptible load constraints using equations (31)-(32): (31) (32) In equations (31)-(32), for In the scene Time period nodes The active power of the load can be interrupted at this point; for In the scene Time period nodes The reactive power of the load can be interrupted at this point; For nodes The rated power factor of the load; Considering the impact of photovoltaic and load uncertainties and load growth, the power flow constraints in equations (11) and (12) are modified using equations (33) and (34): (33) (34) In equations (33)-(34), For nodes Forecasted growth rate of load.

6. The method for enhancing the photovoltaic carrying capacity of a multi-voltage level distribution system based on differentiated flexible interconnection as described in claim 1, characterized in that, In S4, the column and constraint generation algorithm is used to solve the two-layer optimization model to obtain the optimal layout scheme and operation strategy of the reconfigurable smart soft switch, as well as the photovoltaic access capacity and operation strategy.

7. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports a processor in executing the method of any one of claims 1-6, the processor being configured to execute the program stored in the memory.

8. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by a processor to perform the steps of the method according to any one of claims 1-6.

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