Superconducting energy storage system and its capacity configuration method applied to photovoltaic-storage-direct-drive-flexible systems

Abstract

This application belongs to the field of microgrid technology, specifically disclosing a superconducting energy storage system and its capacity configuration method applied to a photovoltaic-storage-DC-flexible system. The system includes: a capacitor, a chopper circuit connected in parallel across the capacitor, and a first bidirectional DC-DC converter and a second bidirectional DC-DC converter connected in parallel across the capacitor via their output terminals. The first bidirectional DC-DC converter is connected to a high-voltage DC bus; the second bidirectional DC-DC converter is connected to a low-voltage DC bus. The first bidirectional DC-DC converter controls the charging and discharging state of the capacitor to compensate for power fluctuations generated on the high-voltage DC bus side; the second bidirectional DC-DC converter controls the charging and discharging state of the capacitor to compensate for power fluctuations generated on the low-voltage DC bus side; the chopper circuit uses a superconducting magnet to absorb or release electrical energy into the capacitor to eliminate voltage fluctuations generated across the capacitor. This application can improve the response speed and transmission efficiency of the superconducting energy storage system in a multi-voltage-level photovoltaic-storage-DC-flexible system.

Description

Technical Field

[0001] This application relates to the field of microgrid technology, and in particular to a superconducting energy storage system and its capacity configuration method applied to a photovoltaic-storage-DC-flexible system. Background Technology

[0002] As a form of DC microgrid, the photovoltaic-storage-DC-flexible system can achieve flexible and efficient coordination between the source, grid, load and energy storage, and improve the utilization rate of renewable energy. Therefore, the photovoltaic-storage-DC-flexible system has been widely studied in the field of microgrid technology.

[0003] Currently, in research on hybrid energy storage technologies used in DC microgrids, particularly regarding the application of superconducting energy storage systems in photovoltaic-storage-DC-flexible systems, the superconducting energy storage coils are mostly directly connected to the high-voltage DC bus via choppers. However, in multi-voltage-level photovoltaic-storage-DC-flexible systems, this structure makes the response speed and transmission efficiency of the superconducting energy storage system easily limited by the multiple DC converters installed between the buses of each voltage level, making it difficult to fully realize its transient high-power compensation benefits for the photovoltaic-storage-DC-flexible system.

[0004] Therefore, how to better realize the application of superconducting energy storage systems in multi-voltage level photovoltaic-storage DC-flexible systems has become a technical problem that the industry urgently needs to solve. Summary of the Invention

[0005] Therefore, it is necessary to address the aforementioned technical problems by providing a superconducting energy storage system and its capacity configuration method for application in multi-voltage-level photovoltaic-storage-direct-flexible systems, which can better realize the application of superconducting energy storage systems in photovoltaic-storage-direct-flexible systems.

[0006] In a first aspect, this application provides a superconducting energy storage system applied to a photovoltaic-storage DC-DC-flexible system. The photovoltaic-storage DC-DC-flexible system includes a high-voltage DC bus and a low-voltage DC bus, comprising:

[0007] A capacitor, a chopper circuit connected in parallel across the capacitor, and a first bidirectional DC-DC converter and a second bidirectional DC-DC converter connected in parallel across the capacitor via their output terminals; the input terminal of the first bidirectional DC-DC converter is connected to the high-voltage DC bus; the input terminal of the second bidirectional DC-DC converter is connected to the low-voltage DC bus; the chopper circuit is built based on a superconducting magnet, a bipolar transistor, and a diode;

[0008] The first bidirectional DC-DC converter is used to control the charging and discharging state of the capacitor to compensate for the power fluctuations generated on the high-voltage DC bus side; the second bidirectional DC-DC converter is used to control the charging and discharging state of the capacitor to compensate for the power fluctuations generated on the low-voltage DC bus side; the chopper circuit is used to absorb or release electrical energy to the capacitor using a superconducting magnet to eliminate the voltage fluctuations generated across the capacitor.

[0009] Optionally, the first bidirectional DC-DC converter includes a first bipolar transistor, a second bipolar transistor, a first inductor, a first diode connected in parallel between the emitter and collector of the first bipolar transistor, and a second diode connected in parallel between the emitter and collector of the second bipolar transistor.

[0010] One end of the first inductor is connected to the high-voltage DC bus, and the other end is connected to the emitter of the first bipolar transistor and the collector of the second bipolar transistor. The collector of the first bipolar transistor is connected to the positive terminal of the capacitor, and the emitter of the second bipolar transistor and the negative terminal of the capacitor are connected to ground.

[0011] The second bidirectional DC-DC converter includes a third bipolar transistor, a fourth bipolar transistor, a second inductor, a third diode connected in parallel between the emitter and collector of the third bipolar transistor, and a fourth diode connected in parallel between the emitter and collector of the fourth bipolar transistor.

[0012] One end of the second inductor is connected to the low-voltage DC bus, and the other end is connected to the emitter of the third bipolar transistor and the collector of the fourth bipolar transistor. The collector of the third bipolar transistor is connected to the positive terminal of the capacitor, and the emitter of the fourth bipolar transistor is connected to the negative terminal of the capacitor.

[0013] Optionally, the chopper circuit includes a fifth bipolar transistor, a sixth bipolar transistor, a fifth diode, a sixth diode, and a superconducting magnet;

[0014] One end of the superconducting magnet is connected to the collector of the fifth bipolar transistor and the anode of the fifth diode. The other end of the superconducting magnet is connected to the emitter of the sixth bipolar transistor and the cathode of the sixth diode. The anode of the sixth diode, the emitter of the fifth bipolar transistor, and the cathode of the capacitor are connected together. The collector of the sixth bipolar transistor, the cathode of the fifth diode, and the anode of the capacitor are connected together.

[0015] When both the fifth and sixth bipolar transistors are turned on, the superconducting magnet absorbs electrical energy from the capacitor; when both the fifth and sixth bipolar transistors are turned off, the superconducting magnet releases electrical energy to the capacitor.

[0016] Secondly, this application provides a capacity configuration method for superconducting energy storage systems as described above, comprising:

[0017] Based on the inductance parameters, rated current parameters, and required electrical energy parameters of the superconducting magnet in the superconducting energy storage system, the capacity configuration model of the superconducting energy storage system is determined. The electrical energy parameters are determined based on the power fluctuation information of the photovoltaic-storage DC-flexible system on the high and low voltage DC buses and the output power of the superconducting energy storage system.

[0018] Based on the objective function of the capacity configuration model, the objective fitness function is obtained; the objective function is constructed with the goal of minimizing the investment cost and operating cost of the superconducting energy storage system.

[0019] The target fitness function is optimized by minimizing its fitness value. The target fitness function is solved iteratively to determine the rated capacity of the superconducting magnet in the superconducting energy storage system.

[0020] Optionally, with minimizing the fitness value of the target fitness function as the optimization objective, the target fitness function is iteratively solved to determine the rated capacity of the superconducting magnet in the superconducting energy storage system, including:

[0021] Step S101: Based on the preset range of inductance parameters and rated current parameters of the superconducting magnet in the superconducting energy storage system, initialize each particle of the particle swarm optimization algorithm, the local best position of each particle, and the current global best particle.

[0022] Step S102: Substitute the inductance parameter information and rated current parameter information corresponding to each particle, as well as the electrical energy parameter information required to be purchased by the superconducting energy storage system, into the target fitness function to determine the fitness value of each particle.

[0023] Step S103: With the goal of minimizing the fitness value, update the local best position and the global best particle for each particle in the current iteration.

[0024] Step S104: Determine whether the current iteration count has reached the maximum iteration count or whether the fitness value of the current global best particle has converged; if not, update the position and velocity of each particle and jump to step S103; if yes, execute step S105.

[0025] Step S105: Obtain the global optimal particle of the particle swarm optimization algorithm, and determine the rated capacity of the superconducting magnet in the superconducting energy storage system based on the inductance parameter information and rated current parameter information corresponding to the global optimal particle.

[0026] Optionally, before determining the capacity configuration model of the superconducting energy storage system based on the inductance parameters, rated current parameters, and required electrical energy parameters of the superconducting magnet in the superconducting energy storage system, the method further includes:

[0027] Acquire power fluctuation information of the high and low voltage DC buses of the photovoltaic-storage-DC-flexible system at various sampling times within the target time period;

[0028] Based on the power fluctuation information at each sampling time and the maximum output power of the superconducting energy storage system at each sampling time, the electrical energy parameters required to be purchased for the superconducting energy storage system are determined.

[0029] Optionally, based on the inductance parameters, rated current parameters, and required electrical energy parameters of the superconducting magnet in the superconducting energy storage system, a capacity configuration model for the superconducting energy storage system is determined, including:

[0030] Based on the inductance parameter information and rated current parameter information of the superconducting energy storage system, the investment cost function of the superconducting energy storage system is determined, and based on the rated current parameter information and the electrical energy parameter information required to purchase the superconducting energy storage system, the operating cost function of the superconducting energy storage system is determined.

[0031] With the goal of minimizing the investment cost function and operating cost function of the superconducting energy storage system, an objective function for the capacity configuration model is constructed, and the objective constraints are determined based on the respective value ranges of the inductance parameter information and rated current parameter information of the superconducting energy storage system.

[0032] A capacity configuration model for a superconducting energy storage system is established based on the objective function and objective constraints.

[0033] Thirdly, this application provides a capacity configuration device for any of the aforementioned superconducting energy storage systems, comprising:

[0034] The first processing module is used to determine the capacity configuration model of the superconducting energy storage system based on the inductance parameter information, rated current parameter information, and electrical energy parameter information required to be purchased by the superconducting energy storage system. The electrical energy parameter information is determined based on the power fluctuation information of the photovoltaic-storage DC-flexible system on the high and low voltage DC buses and the output power of the superconducting energy storage system.

[0035] The second processing module is used to obtain the target fitness function based on the objective function of the capacity configuration model; the objective function is constructed with the goal of minimizing the investment cost and operating cost of the superconducting energy storage system.

[0036] The capacity configuration module is used to optimize the target fitness function by iteratively solving the target fitness function to determine the rated capacity of the superconducting magnet in the superconducting energy storage system.

[0037] Fourthly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0038] Fifthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0039] In a sixth aspect, this application provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0040] The aforementioned superconducting energy storage system and its capacity configuration method applied to a photovoltaic-storage-DC-flexible system include a capacitor and chopper circuits, a first bidirectional DC-DC converter, and a second bidirectional DC-DC converter connected in parallel across the capacitor. The input terminal of the first bidirectional DC-DC converter is connected to the high-voltage DC bus, and the input terminal of the second bidirectional DC-DC converter is connected to the low-voltage DC bus. This connects the superconducting energy storage system between the high-voltage and low-voltage DC buses of the photovoltaic-storage-DC-flexible system. On the one hand, a single bidirectional DC-DC converter can serve as a direct bridge for power compensation at various bus levels, allowing the superconducting energy storage system to perform power compensation on the high- and low-voltage DC bus systems with only one converter, resulting in high power transmission efficiency. On the other hand, the voltage difference between the high- and low-voltage DC bus systems can effectively improve the power response speed of the two bidirectional DC-DC converters, reduce Joule losses caused by current, and further improve the response speed and transmission efficiency of the superconducting energy storage system in a multi-voltage-level photovoltaic-storage-DC-flexible system. This effectively enhances the transient high-power compensation benefit of the superconducting energy storage system for the photovoltaic-storage-DC-flexible system. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is one of the structural schematic diagrams of a superconducting energy storage system applied to a photovoltaic-storage direct-drive flexible system provided in the embodiments of this application;

[0043] Figure 2 This is the second schematic diagram of the structure of the superconducting energy storage system applied to the photovoltaic-storage direct-drive flexible system provided in the embodiments of this application;

[0044] Figure 3 This is a flowchart illustrating the capacity configuration method of the superconducting energy storage system provided in the embodiments of this application;

[0045] Figure 4 This is a schematic flowchart of the method for purchasing electrical energy required by the system in the capacity configuration method provided in this application embodiment;

[0046] Figure 5This is a flowchart illustrating the capacity configuration method for a superconducting energy storage system based on the PSO algorithm provided in an embodiment of this application.

[0047] Figure 6 This is a schematic diagram of the superconducting energy storage system provided in this application embodiment applied to a dual-busbar structure photovoltaic-energy storage DC-flexible system;

[0048] Figure 7 This is a schematic diagram of the capacity configuration device of the superconducting energy storage system provided in the embodiments of this application;

[0049] Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of the objects. For example, "first bidirectional DC-DC converter" and "second bidirectional DC-DC converter" are used to distinguish different bidirectional DC-DC converters, not to describe a specific order of the bidirectional DC-DC converters.

[0052] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0053] First, the technical terms involved in the embodiments of this application will be introduced.

[0054] (1) DC microgrid

[0055] DC microgrids, composed of direct current (DC), are an important component of future smart power distribution systems and are of great significance for promoting energy conservation, emission reduction, and achieving sustainable energy development. Compared to AC microgrids, DC microgrids can more efficiently and reliably accommodate distributed renewable energy generation systems such as wind and solar power, energy storage units, electric vehicles, and other DC loads.

[0056] (2) Photovoltaic-storage-direct-flexible system

[0057] As a form of DC microgrid, the Photovoltaic-Storage-Direct-Flexible (PEDF) system is an abbreviation for the application of four technologies in the building sector: solar photovoltaic, energy storage, direct current, and flexibility. It is an important pillar for the development of zero-carbon energy and is conducive to the direct absorption of wind and solar power.

[0058] Among them, "light" refers to solar photovoltaic technology. Solar photovoltaic power generation is one of the main renewable power sources in the future, and the huge building exterior surfaces are a spatial resource for developing distributed photovoltaics.

[0059] "Storage" refers to energy storage technology. Energy storage will be an indispensable component of future power systems.

[0060] "DC" refers to direct current technology. Compared with alternating current (AC), DC has the advantages of being simpler, easier to control, and having higher transmission efficiency. DC power supply systems are widely used in specialized systems such as aviation, communications, and ships.

[0061] "Flexibility" refers to flexible power technology. Flexibility means the ability to proactively change the power output a building draws from the municipal power grid.

[0062] (3) Superconducting energy storage system

[0063] Superconducting magnetic energy storage (SMES) systems utilize superconducting coil magnets to directly store electromagnetic energy, which is then returned to the power grid (including DC microgrids) or other loads when needed. SMES systems offer advantages such as fast response and high conversion efficiency. They can not only reduce or even eliminate low-frequency power oscillations in the power grid but also regulate reactive and active power, playing a significant role in improving power quality and enhancing the dynamic stability of the power grid.

[0064] It's important to note that, unlike AC power grids, DC microgrids do not experience reactive power fluctuations; the sole indicator of power quality is the DC bus voltage. Therefore, a key technology in a photovoltaic-storage-DC-flexible system lies in maintaining a stable DC bus voltage, i.e., maintaining a dynamic balance between the power supplied by the power source and the power absorbed by the load. Furthermore, the energy supply in a microgrid is temporally random, often necessitating peak shaving and valley filling by energy storage systems.

[0065] Battery energy storage boasts advantages such as high energy density, ease of expansion, and maintenance, making it one of the most widely used energy storage methods in microgrids. However, applying batteries to photovoltaic-storage-DC-flexible systems presents two main problems. First, the power response speed of batteries is insufficient to meet the rapidly changing load demands of such systems. While increasing battery capacity can reduce the impact of load on the bus voltage, the added capacity remains idle for extended periods, significantly increasing system construction costs and wasting resources. Second, the power deficit in photovoltaic-storage-DC-flexible systems fluctuates repeatedly between positive and negative values, leading to a high number of charge-discharge cycles for batteries, which greatly reduces their lifespan. To address these issues, research has proposed solutions using hybrid energy storage technologies in DC microgrids.

[0066] Existing hybrid energy storage technologies, such as battery-supercapacitor and battery-superconducting energy storage, leverage the high energy density of batteries and the high power density of other energy storage methods. Through rational power control strategies, they simultaneously compensate for low-frequency and high-frequency power deficits in DC microgrids, addressing voltage fluctuations and mitigating the lifespan reduction caused by frequent battery charging and discharging. Compared to other energy storage methods, superconducting energy storage offers advantages such as high conversion efficiency, fast power response, and long lifespan. The superconducting magnet is the core of the superconducting energy storage system; the superconducting coil has a very high current density and exhibits no Joule losses when powered by direct current.

[0067] However, in existing research on the application of superconducting energy storage systems in photovoltaic-storage DC-flexible systems, the superconducting energy storage coils are mostly directly connected to the high-voltage DC bus via a chopper. In multi-voltage-level photovoltaic-storage DC-flexible systems, this structure makes it difficult to fully utilize the transient high-power compensation benefits of the system. To address the aforementioned shortcomings of the existing technology, this application provides a superconducting energy storage system applicable to photovoltaic-storage DC-flexible systems.

[0068] The embodiments of this application are described below with reference to the accompanying drawings.

[0069] Figure 1 This is one of the structural schematic diagrams of a superconducting energy storage system applied to a photovoltaic-storage DC-DC-flexible system provided in the embodiments of this application. The photovoltaic-storage DC-DC-flexible system includes a high-voltage DC bus and a low-voltage DC bus, such as... Figure 1 As shown, the superconducting energy storage system includes:

[0070] The system includes a capacitor C, a chopper circuit 1 connected in parallel across the capacitor, and a first bidirectional DC-DC converter 2 and a second bidirectional DC-DC converter 3 connected in parallel across the capacitor C via their respective output terminals. The input terminal of the first bidirectional DC-DC converter 2 is connected to the high-voltage DC bus, and the input terminal of the second bidirectional DC-DC converter 3 is connected to the low-voltage DC bus. The chopper circuit 1 is built based on a superconducting magnet, a bipolar transistor, and a diode.

[0071] The first bidirectional DC-DC converter 2 is used to control the charging and discharging state of the capacitor to compensate for the power fluctuations generated on the high-voltage DC bus side; the second bidirectional DC-DC converter 3 is used to control the charging and discharging state of the capacitor to compensate for the power fluctuations generated on the low-voltage DC bus side; the chopper circuit 1 is used to absorb or release electrical energy to the capacitor C using a superconducting magnet to eliminate the voltage fluctuations generated across the capacitor C.

[0072] Specifically, in the embodiments of this application, the superconducting energy storage system consists of a capacitor C, a chopper circuit 1 connected in parallel across the capacitor, and a first bidirectional DC / DC converter 2 and a second bidirectional DC / DC converter 3 connected in parallel across the capacitor C via their output terminals. The first bidirectional DC converter 2 is connected to the high-voltage DC bus of the photovoltaic-energy storage-flexible system, and the second bidirectional DC converter 3 is connected to the low-voltage DC bus of the photovoltaic-energy storage-flexible system. Thus, by using a single bidirectional DC converter as a direct bridge for power compensation at each voltage level, the superconducting energy storage system is connected between the two voltage level buses of the photovoltaic-energy storage-flexible system. This allows for power compensation of the high and low voltage systems to be performed using only one bidirectional DC converter, avoiding the limitation on response speed and transmission efficiency of traditional superconducting energy storage systems in multi-voltage photovoltaic-energy storage-flexible systems, which are easily restricted by multiple DC converters connected between the buses at each voltage level.

[0073] In the embodiments of this application, the charging and discharging state of capacitor C is controlled by controlling the state of the first bidirectional DC converter 2 to compensate for the power fluctuations generated on the high-voltage DC bus side; or the operating state of the second bidirectional DC converter 3 is controlled to control the charging and discharging state of capacitor C to compensate for the power fluctuations generated on the low-voltage DC bus side.

[0074] Meanwhile, the chopper circuit 1 is built based on a superconducting magnet, a bipolar transistor, and a diode, with the superconducting magnet using an inductor L. SC Instead, the chopper circuit 1 uses a superconducting magnet to absorb or release electrical energy into capacitor C, thereby eliminating voltage fluctuations across capacitor C caused by power fluctuations on the bus side and maintaining a constant voltage U across capacitor C.

[0075] It should be noted that the bipolar transistors described in the embodiments of this application generally refer to bipolar junction transistors (BJTs).

[0076] Understandably, the power commands of the controllers of the two bidirectional DC-DC converters are obtained based on the voltage of their respective connected DC buses, while the control signal of chopper circuit 1 is obtained based on the voltage across capacitor C.

[0077] For example, taking the high-voltage direct current (HVDC) bus side as an example, when a source-load imbalance occurs in the high-voltage DC system, the voltage of the HVDC bus will rise or fall, resulting in power fluctuations. By controlling the duty cycle of the first bidirectional DC-DC converter 2, the charging and discharging state of capacitor C is controlled to compensate for the power deficit caused by power fluctuations on the HVDC bus side. At the same time, the chopper circuit 1 uses a superconducting magnet to absorb or release electrical energy to capacitor C to eliminate the voltage fluctuations generated across capacitor C at this time.

[0078] This application describes a superconducting energy storage system applied to a photovoltaic-storage DC-DC-flexible system. The superconducting energy storage system includes a capacitor and chopper circuits, a first bidirectional DC-DC converter, and a second bidirectional DC-DC converter connected in parallel across the capacitor. The input terminal of the first bidirectional DC-DC converter is connected to the high-voltage DC bus, and the input terminal of the second bidirectional DC-DC converter is connected to the low-voltage DC bus. This connects the superconducting energy storage system between the high-voltage and low-voltage DC buses of the photovoltaic-storage DC-DC-flexible system. On one hand, a single bidirectional DC-DC converter can serve as a direct bridge for power compensation at various bus levels, allowing the superconducting energy storage system to perform power compensation on the high- and low-voltage DC bus systems with only one converter, resulting in high power transmission efficiency. On the other hand, the voltage difference between the high- and low-voltage DC bus systems can effectively improve the power response speed of the two bidirectional DC-DC converters, reduce Joule losses caused by current, and further improve the response speed and transmission efficiency of the superconducting energy storage system in a multi-voltage-level photovoltaic-storage DC-DC-flexible system. This effectively enhances the transient high-power compensation benefit of the superconducting energy storage system for the photovoltaic-storage DC-DC-flexible system.

[0079] Figure 2 This is the second structural schematic diagram of a superconducting energy storage system applied to a photovoltaic-storage-flexible system, as provided in the embodiments of this application. Figure 2 As shown, in an embodiment of this application, the first bidirectional DC-DC converter 2 includes a first bipolar transistor T1, a second bipolar transistor T2, a first inductor L1, a first diode D1 connected in parallel between the emitter and collector of the first bipolar transistor T1, and a second diode D2 connected in parallel between the emitter and collector of the second bipolar transistor T2.

[0080] One end of the first inductor L1 is connected to the high-voltage DC bus, and the other end is connected to the emitter of the first bipolar transistor T1 and the collector of the second bipolar transistor T2. The collector of the first bipolar transistor T1 is connected to the positive terminal of the capacitor C, and the emitter of the second bipolar transistor T2 and the negative terminal of the capacitor C are connected to ground.

[0081] The second bidirectional DC-DC converter 3 includes a third bipolar transistor T3, a fourth bipolar transistor T4, a second inductor L2, a third diode D3 connected in parallel between the emitter and collector of the third bipolar transistor T3, and a fourth diode D4 connected in parallel between the emitter and collector of the fourth bipolar transistor T4.

[0082] One end of the second inductor L2 is connected to the low-voltage DC bus, and the other end is connected to the emitter of the third bipolar transistor T3 and the collector of the fourth bipolar transistor T4. The collector of the third bipolar transistor T3 is connected to the positive terminal of the capacitor C, and the emitter of the fourth bipolar transistor T4 is connected to the negative terminal of the capacitor C.

[0083] Specifically, in the embodiments of this application, the first inductor L1 on the side of the first bidirectional DC-DC converter 2 and the second inductor L2 on the side of the second bidirectional DC-DC converter 3 are respectively the freewheeling inductors of the two bidirectional DC-DC converters. They can not only limit the rate of current change and smooth the current waveform, but also store and release energy, protect other devices from the effects of sudden changes in voltage and current, and ensure the stability and efficiency of the system circuit.

[0084] In addition, capacitor C is connected to the two buses through two bidirectional DC-DC converters, thereby realizing the power flow between the superconducting magnet, capacitor and bus.

[0085] For example, when a source-load imbalance occurs in the high-voltage side DC system, the high-voltage bus voltage will rise or fall. To reduce this voltage fluctuation, the charging and discharging state of capacitor C can be controlled by controlling the duty cycle of the fully controlled device of the first bidirectional DC converter 2, namely the first bipolar transistor T1 or the second bipolar transistor T2, to compensate for this power deficit.

[0086] Similarly, when a source-load imbalance occurs in the low-voltage side DC system, the low-voltage bus voltage will rise or fall. To reduce this voltage fluctuation, the charging and discharging state of capacitor C can be controlled by controlling the duty cycle of the fully controlled device of the second bidirectional DC converter 3, namely the third bipolar transistor T3 or the fourth bipolar transistor T4, to compensate for this power deficit.

[0087] The system of this application embodiment constructs a bidirectional DC-DC converter by utilizing two bipolar transistors, an inductor, and two diodes connected in parallel between the emitter and collector of the corresponding bipolar transistors. This enables bidirectional transmission of DC power, improves circuit flexibility and adaptability, and is beneficial for improving the response speed and transmission efficiency of the superconducting energy storage system. At the same time, its cost is relatively low, which can effectively control the cost of the entire converter.

[0088] Continue to refer to Figure 2In the embodiments of this application, the chopper circuit 1 includes a fifth bipolar transistor T5, a sixth bipolar transistor T6, a fifth diode D5, a sixth diode D6, and a superconducting magnet L. SC ;

[0089] Superconducting magnet L SC One end is connected to the collector of the fifth bipolar transistor T5 and the positive terminal of the fifth diode D5, and the superconducting magnet L SC The other end is connected to the emitter of the sixth bipolar transistor T6 and the cathode of the sixth diode D6. The anode of the sixth diode D6, the emitter of the fifth bipolar transistor T5, and the cathode of the capacitor C are connected together. The collector of the sixth bipolar transistor T6, the cathode of the fifth diode D5, and the anode of the capacitor C are connected together.

[0090] When the fifth bipolar transistor T5 and the sixth bipolar transistor T6 are simultaneously turned on, the superconducting magnet L SC Energy is absorbed from capacitor C; when the fifth bipolar transistor T5 and the sixth bipolar transistor T6 are simultaneously turned off, the superconducting magnet L... SC Electrical energy is released into capacitor C.

[0091] Specifically, in the embodiments of this application, the chopper circuit 1 may consist of a fifth bipolar transistor T5, a sixth bipolar transistor T6, a fifth diode D5, a sixth diode D6, and a superconducting magnet L. SC composition.

[0092] like Figure 2 As shown, I represents the current flowing through the superconducting magnet L. SC The current. When the fully controlled devices, namely the fifth bipolar transistor T5 and the sixth bipolar transistor T6, are simultaneously turned on, the superconducting magnet L... SC Energy is absorbed from capacitor C; when the fifth bipolar transistor T5 and the sixth bipolar transistor T6 are simultaneously turned off, the superconducting magnet L... SC Electrical energy is released to capacitor C. However, when only one of the fifth bipolar transistor T5 and the sixth bipolar transistor T6 is turned on, the superconducting magnet L... SC The current forms a circuit through the sixth diode D6 or the fifth diode D5. At this time, the superconducting magnet L SC It neither absorbs nor releases electrical energy.

[0093] The system of this application embodiment constructs a chopper circuit using two bipolar transistors, two diodes, and a superconducting magnet. Since the superconducting magnet has extremely low resistance, energy loss in the chopper circuit can be greatly reduced, improving the efficiency of system operation transmission and power conversion. At the same time, the noise level of the superconducting magnet and semiconductor devices is relatively low, which can greatly reduce the noise output of the chopper circuit, thereby further improving the reliability and stability of the system circuit.

[0094] In existing technologies, the design and manufacturing of superconducting energy storage systems are complex, and the equipment of cooling systems and expensive materials lead to relatively high costs. Therefore, economical capacity configuration methods are crucial for the large-scale application and promotion of superconducting energy storage.

[0095] To address the deficiencies in the prior art, this application also provides a capacity configuration method for the aforementioned superconducting energy storage system.

[0096] Figure 3 This is a flowchart illustrating the capacity configuration method for a superconducting energy storage system provided in this application embodiment. It can be applied to any of the superconducting energy storage systems described in the foregoing embodiments, such as... Figure 3 As shown, the method includes:

[0097] Step S10: Based on the inductance parameter information, rated current parameter information, and electrical energy parameter information required to be purchased by the superconducting magnet in the superconducting energy storage system, determine the capacity configuration model of the superconducting energy storage system; the electrical energy parameter information is determined based on the power fluctuation information of the photovoltaic-storage DC-flexible system on the high and low voltage DC buses and the output power of the superconducting energy storage system.

[0098] Step S20: Based on the objective function of the capacity configuration model, obtain the objective fitness function; the objective function is constructed with the goal of minimizing the investment cost and operating cost of the superconducting energy storage system.

[0099] Step S30: With minimizing the fitness value of the target fitness function as the optimization objective, the target fitness function is iteratively solved to determine the rated capacity of the superconducting magnet in the superconducting energy storage system.

[0100] Specifically, the inductance parameter information described in the embodiments of this application refers to the superconducting magnet L in the superconducting energy storage system. SC The inductance parameter L and its value range information are required. The value range of the inductance parameter L can be determined based on the type of superconducting magnet used.

[0101] The rated current parameter information described in the embodiments of this application refers to the superconducting magnet L in the superconducting energy storage system. SC Rated current parameter I N And its value range information. Similarly, the rated current parameter I N The range of values ​​can also be determined based on the model of the superconducting magnet used.

[0102] The power parameter information described in this application refers to the quantitative information on the power required by the superconducting energy storage system to purchase power under power fluctuations generated by the high-voltage DC bus and / or low-voltage DC bus. Specifically, it can be determined based on the power fluctuation information of the photovoltaic-storage DC-flexible system on the high and low voltage DC buses and the output power of the superconducting energy storage system.

[0103] In the embodiments of this application, before determining the capacity configuration model of the superconducting energy storage system in step S10, based on the inductance parameter information, rated current parameter information, and electrical energy parameter information required to be purchased by the superconducting magnet in the superconducting energy storage system, the method further includes:

[0104] Acquire power fluctuation information of the high and low voltage DC buses of the photovoltaic-storage-DC-flexible system at various sampling times within the target time period;

[0105] Based on the power fluctuation information at each sampling time and the maximum output power of the superconducting energy storage system at each sampling time, the electrical energy parameters required to be purchased for the superconducting energy storage system are determined.

[0106] Specifically, the target time period described in the embodiments of this application refers to the duration for which the superconducting energy storage system needs to emit compensation power, which can be denoted as T.

[0107] Furthermore, in the embodiments of this application, the power fluctuation information of the high- and low-voltage DC buses of the photovoltaic-storage DC-DC-flexible system is obtained at each sampling time within the target time period. This information can characterize the total power fluctuation of the high- and low-voltage DC buses and can be denoted as ΔP. i Where i represents each sampling time, and its value is 0, 1, 2, 3, ..., n. For example... Figure 4 As shown, it is necessary to initialize variables in advance to determine ΔP0=0 and the initial capacity of the superconducting energy storage system. .

[0108] Next, the calculation time step is set to Δt, then the number of sampling times is n = T / Δt.

[0109] Meanwhile, the maximum output power of the superconducting energy storage system at each sampling time It decreases as the depth of discharge α increases, which can be expressed as:

[0110] ;

[0111] in, This represents the discharge depth of the superconducting energy storage system at the i-th sampling time. This indicates the rated capacity of the superconducting energy storage system.

[0112] Among them, the discharge depth of the guided energy storage system at the i-th sampling time .

[0113] Wherein, the capacity of the superconducting energy storage system at the i-th sampling time .

[0114] It should be noted that the real-time maximum output power of the superconducting magnet... It can also be derived from the formula It is determined that U is the voltage across capacitor C, which remains constant under control, while I determines the state of charge (SOC) of the superconducting energy storage system. Therefore, the lower the SOC, the less power the magnet can emit or absorb, and the worse the high-power compensation capability of the microgrid.

[0115] To ensure that the superconducting energy storage system can meet the power demand of the microgrid at any given time, its output power must be guaranteed. Established. Here, ΔP represents the unbalanced power caused by a short-term reduction in photovoltaic power generation or the start-up of high-power loads (such as motor loads) in the photovoltaic-storage system, and the superscripts 1 and 2 represent the high-voltage and low-voltage subsystems, respectively.

[0116] Therefore, further in the embodiments of this application, it is necessary to determine ΔP at each sampling time. i Is it greater than This indicates whether the total power fluctuation of the high- and low-voltage DC buses exceeds the current maximum output power of the superconducting energy storage system. If so, the amount of electricity required by the superconducting energy storage system at the current sampling time can be expressed as: If not, then .

[0117] Therefore, the electrical energy parameters required for the superconducting energy storage system to be purchased during the target time period T. It can be represented as: .

[0118] The method in this application embodiment, by exploring the intrinsic relationship between DC bus power fluctuations and superconducting energy storage system output power over a time scale, calculates the amount of electricity required for the superconducting energy storage system to be purchased at different sampling times. This ensures the accuracy and reliability of the calculated parameters for the required electricity to be purchased by the superconducting energy storage system, which is beneficial to improving the accuracy of subsequent superconducting energy storage system capacity configuration results.

[0119] Based on the above embodiments, as an optional embodiment, step S10, based on the inductance parameter information, rated current parameter information, and electrical energy parameter information required to be purchased by the superconducting magnet in the superconducting energy storage system, determines the capacity configuration model of the superconducting energy storage system, including:

[0120] Based on the inductance parameter information and rated current parameter information of the superconducting energy storage system, the investment cost function of the superconducting energy storage system is determined, and based on the rated current parameter information and the electrical energy parameter information required to purchase the superconducting energy storage system, the operating cost function of the superconducting energy storage system is determined.

[0121] With the goal of minimizing the investment cost function and operating cost function of the superconducting energy storage system, an objective function for the capacity configuration model is constructed, and the objective constraints are determined based on the respective value ranges of the inductance parameter information and rated current parameter information of the superconducting energy storage system.

[0122] A capacity configuration model for a superconducting energy storage system is established based on the objective function and objective constraints.

[0123] Specifically, in the embodiments of this application, based on the inductance parameter information and rated current parameter information of the superconducting energy storage system, the rated capacity S of the superconducting magnet is... SMES It can be represented as It can be seen that the superconducting magnet L... SC The inductance L and rated current I N These are the two most intuitive economic parameters. Considering the previously calculated electrical energy purchase parameter ΔE from the AC system, the total cost of the superconducting energy storage system capacity configuration scheme can be expressed as: F = F 投资 +YF 运行 = F(L,I N, ΔE).

[0124] Among them, F 投资 and F 运行 Let S represent the investment cost and operating cost of the superconducting energy storage system, respectively, and Y represent the service life. The investment cost of a superconducting energy storage system is related to the length and unit price of its superconducting tape, cooling equipment, etc., and can be considered as being related to the capacity S of the superconducting energy storage system. SMES Proportional to LI N 2 Proportional; operating costs mainly include transmission losses and electrical energy F. 传输 The cost of the refrigeration equipment and the electrical energy consumed by the refrigeration equipment. 制冷 The costs, such as the cost of the electricity required to purchase ΔE, etc.

[0125] Among them, F 传输 =365(1-η)E 交互 In the formula, η is the power transmission efficiency of the converter, E 交互 For the daily interactive electrical energy of the superconducting energy storage system, therefore F 传输 Determined by the system's daily power compensation requirements and the converter; F 制冷 Generally, F is positively correlated with the power of the superconducting energy storage system. Since the terminal voltage U is constant in this system, therefore, 制冷 Can be regarded as I N Proportional. Therefore, based on the inductance and rated current parameters of the superconducting energy storage system, the investment cost function of the superconducting energy storage system can be determined, which can be expressed as:

[0126] F 投资 =k1LIN 2 ;

[0127] In the formula, k1 represents the cost coefficient for investing in a superconducting energy storage system.

[0128] Simultaneously, based on the rated current parameters of the superconducting energy storage system and the electrical energy parameters required for its purchase, the operating cost function of the superconducting energy storage system can be determined, which can be expressed as:

[0129] F 运行 = k2I N + k3ΔE+ k4;

[0130] In the formula, k2, k3, and k4 represent the cost coefficients of the electrical energy consumed by the cooling equipment during the operation of the superconducting energy storage system, the cost coefficients of the purchased electrical energy, and the cost coefficients of the electrical energy lost during transmission, respectively.

[0131] Furthermore, based on the aforementioned investment cost function and operating cost function of the superconducting energy storage system, the objective function of the capacity configuration model can be constructed by minimizing these functions. This objective function can be expressed as: min F(L, I) N ,ΔE)= k1LI N 2 + k2I N + k3ΔE+ k4;

[0132] Among them, k1, k2, k3, and k4 represent cost coefficients. In practice, they are closely related to factors such as real-time electricity prices, system operating status, and seasons, and can be estimated empirically based on actual conditions.

[0133] In the embodiments of this application, considering the superconducting magnet L in the superconducting energy storage system SC The inductance L and rated current I N The range of values ​​for is set as follows:

[0134] ;

[0135] In the formula, L max L min They represent superconducting magnet L SC The inductance L can be set to a maximum and a minimum value; They represent superconducting magnet L SC Rated current I N The maximum and minimum values ​​can be set.

[0136] Furthermore, based on the aforementioned objective function and objective constraints, a capacity configuration model for the superconducting energy storage system can be established, which can be specifically expressed as:

[0137] minF(L,I) N ,ΔE)=min(k1LI N 2 + k2I N + k3ΔE+ k4),

[0138] .

[0139] The method in this application embodiment, by comprehensively considering the inherent relationship between the inductance and rated current parameters of the superconducting magnet in the superconducting energy storage system and its investment and operating costs, and combining the value range of these two types of electrical parameters of the superconducting magnet, aims to minimize the investment and operating costs of the superconducting energy storage system, and establishes a capacity configuration model for the superconducting energy storage system. This helps to ensure the reliability and economy of the model construction, so as to effectively obtain the optimal capacity configuration scheme of the superconducting energy storage system through subsequent model optimization analysis.

[0140] Furthermore, in the embodiments of this application, in step S20, the target fitness function can be obtained based on the objective function of the capacity allocation model, that is, the target fitness function can be expressed as:

[0141] fit = F(L, I) N ,ΔE)= k1LI N 2 + k2I N + k3ΔE+ k4.

[0142] Furthermore, in the embodiments of this application, in step S30, an optimal solution search algorithm, such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), can be used to minimize the fitness value of the target fitness function as the optimization objective. The target fitness function is iteratively solved, and the optimal solution searched by the algorithm can determine the superconducting magnet L in the superconducting energy storage system. SC The required rated capacity.

[0143] The capacity configuration method of the superconducting energy storage system in this application fully explores the correlation between the electrical parameter characteristics of the superconducting magnet in the superconducting energy storage system and its investment and operating costs. It uses an iterative optimization algorithm to obtain the optimal configuration of the superconducting energy storage system. Under the premise of ensuring short-term high-power compensation requirements, it can effectively reduce the total investment and operating costs of the superconducting energy storage system and achieve the optimal economic efficiency of superconducting energy storage system commissioning.

[0144] Based on the above embodiments, as an optional embodiment, step S30, with minimizing the fitness value of the target fitness function as the optimization objective, iteratively solves the target fitness function to determine the rated capacity of the superconducting magnet in the superconducting energy storage system, including:

[0145] Step S101: Based on the preset range of inductance parameters and rated current parameters of the superconducting magnet in the superconducting energy storage system, initialize each particle of the particle swarm optimization algorithm, the local best position of each particle, and the current global best particle.

[0146] Step S102: Substitute the inductance parameter information and rated current parameter information corresponding to each particle, as well as the electrical energy parameter information required to be purchased by the superconducting energy storage system, into the target fitness function to determine the fitness value of each particle.

[0147] Step S103: With the goal of minimizing the fitness value, update the local best position and the global best particle for each particle in the current iteration.

[0148] Step S104: Determine whether the current iteration count has reached the maximum iteration count or whether the fitness value of the current global best particle has converged; if not, update the position and velocity of each particle and jump to step S103; if yes, execute step S105.

[0149] Step S105: Obtain the global optimal particle of the particle swarm optimization algorithm, and determine the rated capacity of the superconducting magnet in the superconducting energy storage system based on the inductance parameter information and rated current parameter information corresponding to the global optimal particle.

[0150] Specifically, in the embodiments of this application, taking the PSO algorithm as an example, we will introduce a specific implementation method for determining the rated capacity of the superconducting magnet in the superconducting energy storage system by iteratively solving the target fitness function with the optimization objective of minimizing the fitness value of the target fitness function.

[0151] Figure 5 This is a flowchart illustrating the capacity configuration method for a superconducting energy storage system based on the PSO algorithm provided in this application embodiment, as shown below. Figure 5 As shown in the embodiments of this application, in step S101, parameter initialization is performed. Specifically, in the superconducting magnet L in the superconducting energy storage system... SC Inductance parameter L and rated current parameter I N The preset value range is used to randomly initialize each particle of the PSO algorithm and the local optimal position of each particle.

[0152] It is understandable that each particle carries a randomly initialized inductance parameter L and rated current parameter I. N The data.

[0153] Further, in step S102, the fitness value *fit* of each particle is calculated to evaluate the individual particle. Specifically, the electrical energy parameters required for the superconducting energy storage system are determined according to the aforementioned embodiments. This electrical energy parameter information The inductance parameter L and rated current parameter I carried by each particle N The data, along with various cost coefficients, are substituted into the aforementioned target fitness function fit = F(L, I) N In ΔE, the fitness value of each particle can be calculated.

[0154] Then, in step S103, the globally optimal particles are initialized. Based on the previously initialized individual particles and the local optimal positions of each particle, the globally optimal particles are initialized.

[0155] Next, in step S104, the velocity and position of each particle are updated. Specifically, with the goal of minimizing the fitness value, the minimum fitness value *fit* is searched to update the velocity and position of each particle in the current iteration.

[0156] Next, in step S105, the value of pBest, the local best position of each particle, is determined and updated by evaluating the fitness of each particle.

[0157] Furthermore, in step S106, the fitness value gBest of the globally best particle in the current iteration is determined and updated from the local best position of each particle. The smallest fitness value among all fitness values ​​pBest is gBest.

[0158] Further, in step S107, it is determined whether the current iteration count has reached the maximum iteration count, or whether the fitness value gBest of the current global best particle has converged. If not, the process jumps to step S104 to estimate the state of each particle, adaptively control the algorithm parameters, and execute the elite learning strategy to update the velocity and position of each particle in the new round of iterations, until the aforementioned step S107 is repeated.

[0159] Furthermore, in step S107, it is determined whether the current iteration count has reached the maximum iteration count or whether the fitness value gBest of the current globally optimal particle has converged. If so, it indicates that the algorithm iteration termination condition is met, and step S108 is executed.

[0160] Finally, in step S108, the optimal solution can be obtained, which is the global optimal particle of the current PSO algorithm. Based on the fitness value gBest of this global optimal particle, its corresponding inductance parameter L and rated current parameter I can be determined. NThe data can ultimately be used to obtain the superconducting magnet L in the superconducting energy storage system. SC Rated capacity S SMES .

[0161] The method in this application embodiment, by combining the aforementioned optimal economic design of the superconducting energy storage system and using the PSO algorithm to iteratively solve the target fitness function constructed based on the electrical parameter characteristics of the superconducting magnet in the superconducting energy storage system, can further greatly improve the accuracy and efficiency of the capacity optimization configuration of the superconducting energy storage system.

[0162] Figure 6 This is a schematic diagram of the superconducting energy storage system provided in this application embodiment applied to a dual-busbar structure photovoltaic-storage-DC-flexible system, as shown below. Figure 6 As shown, the system adopts a unipolar connection and includes two voltage-level subsystems. The high-voltage subsystem includes a photovoltaic system, a battery energy storage system, a constant power load, an electric vehicle load, and a motor-type load; the low-voltage subsystem includes the battery energy storage system, a constant power load, and a motor-type load. The external AC grid is connected to the high-voltage subsystem via an AC / DC converter (VSC), and the superconducting energy storage system is connected to both the high-voltage and low-voltage subsystems. Simulation fitting is used to determine the power absorbed by the constant power load within a certain time period (P...). L_min P L_max The power output varies continuously and randomly between these periods, during which motor-type loads start up and absorb a significant amount of power from the microgrid. The steps for configuring the superconducting energy storage system capacity are as follows: First, determine the power deficit that the superconducting energy storage should compensate for during this period based on the battery's response power and the power generated by the photovoltaic system. Within the target constraints, determine a group of systems, and obtain the economically optimal solution through the steps of the aforementioned PSO algorithm.

[0163] To more clearly illustrate the optimized configuration of superconducting energy storage capacity in this application, another specific embodiment is described in detail below. In this embodiment, considering the power loss in the converter transmission and the short-term fluctuations in the photovoltaic system due to weather or other factors, the steps for configuring the superconducting energy storage system capacity are as follows: First, the power ΔP that the superconducting energy storage system needs to generate is predicted, and the specific steps are as follows:

[0164] Suppose that the optical-storage-direct-drive-flexible system is operating under high load at a certain moment. If power transmission loss is ignored, then: P PV +P BESS= P LOAD The power generated by the photovoltaic cells and the storage batteries is P, respectively. PV and P BESS P LOADThis represents the total power absorbed by all loads within the system. If at this moment, a sudden change in light intensity or other reasons causes a change in P over a period of time T... PV The power consumption decreases as the battery energy storage system struggles to compensate for the unbalanced power during this period due to the high load operation of the system. This power consumption will then be borne by the superconducting energy storage system.

[0165] By analyzing the long-term photovoltaic power generation curves of this region, the power fluctuation ΔP with the largest possible decrease within time T can be predicted. PV Then the power required by the superconducting energy storage system is ΔP = ΔP PV The aforementioned power is then corrected by the charging and discharging efficiency η of each converter, as the actual power output of the photovoltaic system fluctuates. The power output that the superconducting energy storage system should emit was calculated. Next, as mentioned above... Figure 5 The illustrated implementation steps involve determining a population within the target constraints, calculating the fitness (fit) of each particle in the population to determine the initial global optimum, then performing individual and social learning on the initial population to obtain a descendant population, evaluating the descendant population and updating the global optimum particle, repeating this process until the maximum number of iterations is reached, and finally obtaining the economically optimal solution to determine the superconducting magnet L in the superconducting energy storage system. SC Rated capacity S SMES The optimal configuration.

[0166] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0167] Based on the same inventive concept, this application also provides a capacity configuration apparatus for implementing the capacity configuration method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more capacity configuration apparatus embodiments provided below can be found in the limitations of the capacity configuration method described above, and will not be repeated here.

[0168] Figure 7This is a schematic diagram of the capacity configuration device for a superconducting energy storage system provided in this application embodiment, which can be applied to any of the aforementioned superconducting energy storage systems, such as... Figure 7 As shown, the capacity configuration device includes:

[0169] The first processing module 100 is used to determine the capacity configuration model of the superconducting energy storage system based on the inductance parameter information, rated current parameter information, and electrical energy parameter information required to be purchased by the superconducting energy storage system; the electrical energy parameter information is determined based on the power fluctuation information of the photovoltaic-storage DC-flexible system on the high and low voltage DC buses and the output power of the superconducting energy storage system.

[0170] The second processing module 200 is used to obtain the target fitness function based on the objective function of the capacity configuration model; the objective function is constructed with the goal of minimizing the investment cost and operating cost of the superconducting energy storage system.

[0171] The capacity configuration module 300 is used to iteratively solve the target fitness function with the optimization objective of minimizing the fitness value of the target fitness function, and determine the rated capacity of the superconducting magnet in the superconducting energy storage system.

[0172] In one embodiment, the capacity configuration module 300 is configured to perform the following steps:

[0173] Step S101: Based on the preset range of inductance parameters and rated current parameters of the superconducting magnet in the superconducting energy storage system, initialize each particle of the particle swarm optimization algorithm, the local best position of each particle, and the current global best particle.

[0174] Step S102: Substitute the inductance parameter information and rated current parameter information corresponding to each particle, as well as the electrical energy parameter information required to be purchased by the superconducting energy storage system, into the target fitness function to determine the fitness value of each particle.

[0175] Step S103: With the goal of minimizing the fitness value, update the local best position and the global best particle for each particle in the current iteration.

[0176] Step S104: Determine whether the current iteration count has reached the maximum iteration count or whether the fitness value of the current global best particle has converged; if not, update the position and velocity of each particle and jump to step S103; if yes, execute step S105.

[0177] Step S105: Obtain the global optimal particle of the particle swarm optimization algorithm, and determine the rated capacity of the superconducting magnet in the superconducting energy storage system based on the inductance parameter information and rated current parameter information corresponding to the global optimal particle.

[0178] In one embodiment, the capacity configuration device further includes:

[0179] The fluctuation information acquisition module is used to acquire the power fluctuation information of the high and low voltage DC buses of the photovoltaic-storage DC-flexible system at various sampling times within the target time period;

[0180] The power parameter information determination module is used to determine the power parameter information that the superconducting energy storage system needs to purchase, based on the power fluctuation information at each sampling time and the maximum output power of the superconducting energy storage system at each sampling time.

[0181] In one embodiment, the first processing module is specifically used to determine the investment cost function of the superconducting energy storage system based on the inductance parameter information and rated current parameter information of the superconducting energy storage system, and to determine the operating cost function of the superconducting energy storage system based on the rated current parameter information and the electrical energy parameters required to be purchased by the superconducting energy storage system; to construct the objective function of the capacity configuration model with the goal of minimizing the investment cost function and operating cost function of the superconducting energy storage system, and to determine the target constraints based on the respective value ranges of the inductance parameter information and rated current parameter information of the superconducting energy storage system; and to establish the capacity configuration model of the superconducting energy storage system based on the objective function and the target constraints.

[0182] The capacity configuration device for the superconducting energy storage system in this application fully explores the correlation between the electrical parameter characteristics of the superconducting magnet in the superconducting energy storage system and its investment and operating costs. By using an iterative optimization algorithm, the optimized configuration of the superconducting energy storage system is obtained. Under the premise of ensuring short-term high-power compensation requirements, the total investment and operating costs of the superconducting energy storage system are effectively reduced, and the optimal economic efficiency of the superconducting energy storage system is achieved.

[0183] Each module in the aforementioned capacity configuration device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the operations corresponding to each module.

[0184] Based on the methods in the above embodiments, this application provides an electronic device, such as... Figure 8 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute the methods in the above embodiments.

[0185] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0186] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0187] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0188] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0189] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0190] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0191] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0192] It should be understood that expressions such as "comprising" and "may include" used in this application indicate the existence of the disclosed functions, operations, or constituent elements, and do not limit one or more additional functions, operations, and constituent elements. In this application, terms such as "comprising" and / or "having" are to be interpreted as indicating a particular characteristic, number, operation, constituent element, component, or combination thereof, but not to exclude the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0193] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A capacity configuration method, characterized in that, include: Based on the inductance parameters, rated current parameters, and required electrical energy parameters of the superconducting magnet in the superconducting energy storage system applied to the photovoltaic-storage-DC-flexible system, a capacity configuration model for the superconducting energy storage system is determined. The electrical energy parameters are determined based on the power fluctuation information of the photovoltaic-storage-DC-flexible system on the high and low voltage DC buses and the output power of the superconducting energy storage system. Based on the objective function of the capacity configuration model, a target fitness function is obtained; the objective function is constructed with the goal of minimizing the investment cost and operating cost of the superconducting energy storage system. The objective is to minimize the fitness value of the target fitness function. The target fitness function is iteratively solved to determine the rated capacity of the superconducting magnet in the superconducting energy storage system. The step of iteratively solving the target fitness function to determine the rated capacity of the superconducting magnet in the superconducting energy storage system, with the optimization objective of minimizing the fitness value of the target fitness function, includes: Step S101: Based on the preset range of inductance parameters and rated current parameters of the superconducting magnet in the superconducting energy storage system, initialize each particle of the particle swarm optimization algorithm, the local best position of each particle, and the current global best particle. Step S102: Substitute the inductance parameter information and rated current parameter information corresponding to each particle, as well as the electrical energy parameter information required to be purchased by the superconducting energy storage system, into the target fitness function to determine the fitness value of each particle. Step S103: With the goal of minimizing the fitness value, update the local best position of each particle in the current iteration and the global best particle; Step S104: Determine whether the current iteration count has reached the maximum iteration count or whether the fitness value of the current global best particle has converged; if not, update the position and velocity of each particle and jump to step S103; if yes, execute step S105. Step S105: Obtain the global optimal particle of the particle swarm optimization algorithm, and determine the rated capacity of the superconducting magnet in the superconducting energy storage system based on the inductance parameter information and rated current parameter information corresponding to the global optimal particle.

2. The capacity configuration method according to claim 1, characterized in that, Before determining the capacity configuration model of the superconducting energy storage system based on the inductance parameter information, rated current parameter information, and electrical energy parameter information required to be purchased by the superconducting energy storage system, the method further includes: Acquire the power fluctuation information of the high and low voltage DC buses of the photovoltaic-storage DC-flexible system at each sampling time within the target time period; Based on the power fluctuation information at each sampling time and the maximum output power of the superconducting energy storage system at each sampling time, the electrical energy parameters required for the superconducting energy storage system to be purchased are determined.

3. The capacity configuration method according to claim 1 or 2, characterized in that, The capacity configuration model of the superconducting energy storage system is determined based on the inductance parameters, rated current parameters, and required energy parameters of the superconducting magnet in the system. This includes: Based on the inductance parameter information and rated current parameter information of the superconducting energy storage system, the investment cost function of the superconducting energy storage system is determined, and based on the rated current parameter information and the electrical energy parameter information required to be purchased by the superconducting energy storage system, the operating cost function of the superconducting energy storage system is determined. With the goal of minimizing the investment cost function and the operating cost function of the superconducting energy storage system, an objective function for the capacity configuration model is constructed, and the target constraints are determined based on the respective value ranges of the inductance parameter information and the rated current parameter information of the superconducting energy storage system. Based on the objective function and the objective constraints, a capacity configuration model for the superconducting energy storage system is established.

4. A capacity configuration device, characterized in that, include: The first processing module is used to determine the capacity configuration model of the superconducting energy storage system based on the inductance parameter information, rated current parameter information, and electrical energy parameter information required to be purchased by the superconducting energy storage system in the photovoltaic-storage-DC-flexible system; the electrical energy parameter information is determined based on the power fluctuation information of the photovoltaic-storage-DC-flexible system on the high and low voltage DC buses and the output power of the superconducting energy storage system. The second processing module is used to obtain a target fitness function based on the objective function of the capacity configuration model; the objective function is constructed with the goal of minimizing the investment cost and operating cost of the superconducting energy storage system. The capacity configuration module is used to iteratively solve the target fitness function with the optimization objective of minimizing the fitness value of the target fitness function, and determine the rated capacity of the superconducting magnet in the superconducting energy storage system. The capacity configuration module is used to perform the following steps: Step S101: Based on the preset range of inductance parameters and rated current parameters of the superconducting magnet in the superconducting energy storage system, initialize each particle of the particle swarm optimization algorithm, the local best position of each particle, and the current global best particle. Step S102: Substitute the inductance parameter information and rated current parameter information corresponding to each particle, as well as the electrical energy parameter information required to be purchased by the superconducting energy storage system, into the target fitness function to determine the fitness value of each particle. Step S103: With the goal of minimizing the fitness value, update the local best position of each particle in the current iteration and the global best particle; Step S104: Determine whether the current iteration count has reached the maximum iteration count or whether the fitness value of the current global best particle has converged; if not, update the position and velocity of each particle and jump to step S103; if yes, execute step S105. Step S105: Obtain the global optimal particle of the particle swarm optimization algorithm, and determine the rated capacity of the superconducting magnet in the superconducting energy storage system based on the inductance parameter information and rated current parameter information corresponding to the global optimal particle.

5. A superconducting energy storage system applied to a photovoltaic-storage-flexible system, characterized in that, Capacity configuration is performed using the capacity configuration method according to any one of claims 1 to 3, wherein the photovoltaic-storage DC-DC-flexible system includes a high-voltage DC bus and a low-voltage DC bus, and the superconducting energy storage system includes: The system includes a capacitor, a chopper circuit connected in parallel across the capacitor, and a first bidirectional DC-DC converter and a second bidirectional DC-DC converter connected in parallel across the capacitor via their respective output terminals. The input terminal of the first bidirectional DC-DC converter is connected to the high-voltage DC bus, and the input terminal of the second bidirectional DC-DC converter is connected to the low-voltage DC bus. The chopper circuit is constructed based on a superconducting magnet, a bipolar transistor, and a diode. The first bidirectional DC-DC converter is used to control the charging and discharging state of the capacitor to compensate for the power fluctuations generated on the high-voltage DC bus side; the second bidirectional DC-DC converter is used to control the charging and discharging state of the capacitor to compensate for the power fluctuations generated on the low-voltage DC bus side; the chopper circuit is used to absorb or release electrical energy to the capacitor using the superconducting magnet to eliminate the voltage fluctuations generated across the capacitor.

6. The superconducting energy storage system applied to a photovoltaic-storage-flexible system according to claim 5, characterized in that, The first bidirectional DC-DC converter includes a first bipolar transistor, a second bipolar transistor, a first inductor, a first diode connected in parallel between the emitter and collector of the first bipolar transistor, and a second diode connected in parallel between the emitter and collector of the second bipolar transistor. One end of the first inductor is connected to the high-voltage DC bus, and the other end is connected to the emitter of the first bipolar transistor and the collector of the second bipolar transistor; the collector of the first bipolar transistor is connected to the positive terminal of the capacitor, and the emitter of the second bipolar transistor is connected to ground along with the negative terminal of the capacitor. The second bidirectional DC-DC converter includes a third bipolar transistor, a fourth bipolar transistor, a second inductor, a third diode connected in parallel between the emitter and collector of the third bipolar transistor, and a fourth diode connected in parallel between the emitter and collector of the fourth bipolar transistor. One end of the second inductor is connected to the low-voltage DC bus, and the other end is connected to the emitter of the third bipolar transistor and the collector of the fourth bipolar transistor; the collector of the third bipolar transistor is connected to the positive terminal of the capacitor, and the emitter of the fourth bipolar transistor is connected to the negative terminal of the capacitor.

7. The superconducting energy storage system applied to a photovoltaic-storage-flexible system according to claim 6, characterized in that, The chopper circuit includes a fifth bipolar transistor, a sixth bipolar transistor, a fifth diode, a sixth diode, and a superconducting magnet; One end of the superconducting magnet is connected to the collector of the fifth bipolar transistor and the anode of the fifth diode. The other end of the superconducting magnet is connected to the emitter of the sixth bipolar transistor and the cathode of the sixth diode. The anode of the sixth diode and the emitter of the fifth bipolar transistor are connected to the cathode of the capacitor. The collector of the sixth bipolar transistor, the cathode of the fifth diode, and the anode of the capacitor are connected to the positive terminal of the capacitor. When both the fifth and sixth bipolar transistors are turned on, the superconducting magnet absorbs electrical energy from the capacitor; when both the fifth and sixth bipolar transistors are turned off, the superconducting magnet releases electrical energy to the capacitor.

8. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1 to 3.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run on the processor, it causes the processor to perform the method as described in any one of claims 1 to 3.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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