AC-DC hybrid system energy storage optimization configuration method

Abstract

The invention discloses an AC-DC hybrid system energy storage optimal configuration method, relates to a new energy energy storage configuration method, and aims to solve the problem that the operation reliability of a power grid is low because the operation characteristics of the micro-grid are not considered in the conventional energy storage configuration method. According to the single energy storage optimal configuration method, optimization variables are the number of photovoltaic modules, the number of fans, the maximum capacity of a storage battery, the maximum power of the storage battery and the capacity of a grid-connected converter; optimization variables of the hybrid energy storage optimization configuration method are the number of photovoltaic modules, the number of draught fans, the maximum capacity of a storage battery, the maximum power of the storage battery, the maximum capacity of a super capacitor, the maximum power of the super capacitor and the capacity of a grid-connected converter. The two methods also adopt the improved differential evolution algorithm to solve the DC subsystem comprising the storage battery energy storage system, and the energy storage configuration of the AC / DC hybrid system is optimized according to the solving result. The beneficial effects are that the service life of the storage battery is prolonged, the replacement of the storage battery is reduced, the cost is reduced, and the reliability of power grid operation is improved.

Description

Technical Field

[0001] The invention relates to a new energy storage configuration method. Background Art

[0002] At present, energy has become the main bottleneck restricting the sustainable development of the national economy, especially in remote areas, where the power supply users have weak grid characteristics, large voltage and frequency fluctuations, poor reliability, low operating efficiency, and the power supply quality cannot be fully guaranteed. Because renewable energy is intermittent, its integration into weak grids will further deteriorate the system voltage qualification rate, hindering its application in remote areas; at the same time, the long-distance large grid power supply method has problems such as high construction cost, difficult maintenance, and large losses, which makes it difficult to meet the urgent needs of remote areas for efficient and high-quality power supply; therefore, how to combine the energy structure characteristics of remote areas, introduce AC / DC hybrid technology, and carry out the construction of new power supply systems in remote areas according to local conditions, so as to transform the traditional extensive energy utilization method into a refined, decentralized, and sustainable energy utilization method, is an important topic for improving the comprehensive production level of the power system.

[0003] A distributed energy storage system is connected to the AC / DC hybrid system, and a public energy storage system is added to the power interconnection line. The voltage stability and energy exchange quota of the AC / DC hybrid system are further improved by adding active power units. However, when realizing the energy exchange function between the microgrid and the public energy storage system and between the microgrid, the existing energy storage configuration method does not take into account the operating characteristics of the microgrid, resulting in reduced reliability of grid operation. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the existing energy storage configuration method does not take into account the operating characteristics of the microgrid, resulting in low grid operation reliability, and proposes an AC / DC hybrid system energy storage optimization configuration method.

[0005] The AC / DC hybrid system energy storage optimization configuration method described in the present invention includes a single energy storage optimization configuration method and a hybrid energy storage optimization configuration method;

[0006] The energy storage device of the single energy storage optimization configuration method is a battery; the optimization variables are the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of the battery N3, the maximum power of the battery N4 and the capacity of the grid-connected converter N5, and an improved differential evolution algorithm is used to solve the DC subsystem including the battery energy storage system, and the configuration of the AC / DC hybrid system energy storage is optimized according to the solution results;

[0007] The energy storage devices of the hybrid energy storage optimization configuration method are batteries and supercapacitors; the optimization variables are the number N1 of photovoltaic modules, the number N2 of wind turbines, the maximum capacity N3 of the battery, the maximum power N4 of the battery, the maximum capacity N5 of the supercapacitor, the maximum power N6 of the supercapacitor and the capacity N7 of the grid-connected converter; the improved differential evolution algorithm is also used to solve the DC subsystem including the battery energy storage system, and the configuration of the energy storage of the AC / DC hybrid system is optimized according to the solution results.

[0008] Furthermore, the specific steps of optimizing the configuration of the single energy storage optimization configuration method are as follows:

[0009] Step 1: Enter the basic information of the DC subsystem work location;

[0010] Step 2: Randomly generate an initial population;

[0011] Step 3: Calculate the hourly renewable energy power generation according to the environmental information and the power output model of the renewable energy unit;

[0012] Step 4: Calculate the objective function value of each individual in the initial population after completing the full-year simulation;

[0013] Step 5: For the selected individual, perform mutation, randomly select two other different individuals in the population, add them to the selected individual after weighted difference, and obtain the intermediate individual;

[0014] Step 6: Perform variable crossover between the selected individuals and the mutated individuals to obtain candidate individuals;

[0015] Step 7: Calculate the objective function value of the candidate individuals after the crossover after completing the full-year simulation;

[0016] Step 8: Compare the objective function values ​​of the selected individuals and the candidate individuals after crossover, select individuals with better performance to enter the next generation population, and improve the population performance;

[0017] Step 9: Determine whether the number of iterations has reached the maximum value. If so, the algorithm ends and outputs the final result of the optimized configuration. Otherwise, return to step 5 and continue iterating until the algorithm stop condition is reached.

[0018] Furthermore, the basic information of the DC subsystem work location input in step 1 includes user load information, environmental prediction information, energy storage system information and power grid exchange information;

[0019] The environmental prediction information includes light intensity, ambient temperature and wind speed; the energy storage system information includes initial capacity, state of charge and charging and discharging power constraints; the grid exchange information includes grid power exchange restrictions and electricity purchase and sales prices.

[0020] Furthermore, the specific method for randomly generating the initialization population in step 2 is:

[0021] Among the five optimization variables, namely the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of batteries N3, the maximum power of batteries N4 and the capacity of grid-connected converter N5, 30 individuals are randomly generated within the variable constraints to form an initialized population.

[0022] Furthermore, the specific steps of optimizing the configuration of the hybrid energy storage optimization configuration method are as follows:

[0023] Step 1. Enter the basic information of the DC subsystem work location;

[0024] Step 2: Randomly generate an initial population;

[0025] Step 3: Calculate the hourly renewable energy power generation according to the environmental information and the power output model of the renewable energy unit;

[0026] Step 4, calculate the objective function value of each individual in the initial population after completing the full-year simulation;

[0027] Step 5: For the selected individual, perform mutation, randomly select two other different individuals in the population, add them to the selected individual after weighted difference, and obtain the intermediate individual;

[0028] Step 6: Perform variable crossover between the selected individuals and the mutated individuals to obtain candidate individuals;

[0029] Step 7, calculate the objective function value of the candidate individuals after the crossover after completing the full-year simulation;

[0030] Step 8: Compare the objective function values ​​of the selected individuals with those of the candidate individuals after crossover, select individuals with better performance to enter the next generation population, and improve population performance;

[0031] Step 9: Determine whether the number of iterations reaches the maximum value. If so, the algorithm ends and outputs the final result of the optimized configuration. Otherwise, return to step 5 and continue iterating until the algorithm stop condition is reached.

[0032] Furthermore, the basic information of the DC subsystem work location input in step 1 includes user load information, environmental prediction information, energy storage system information and power grid exchange information;

[0033] The environmental prediction information includes light intensity, ambient temperature and wind speed; the energy storage system information includes initial capacity, state of charge and charging and discharging power constraints; the grid exchange information includes grid power exchange restrictions and electricity purchase and sales prices.

[0034] Furthermore, the specific method for randomly generating the initialization population in step 2 is:

[0035] Among the seven optimization variables, namely the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of batteries N3, the maximum power of batteries N4, the maximum capacity of supercapacitors N5, the maximum power of supercapacitors N6 and the capacity of grid-connected converters N7, 30 individuals are randomly generated within the variable constraints to form an initialized population.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] In the AC / DC hybrid system energy storage optimization configuration method of the present invention, the supercapacitor mainly bears the energy storage demand P hess The frequent power fluctuations in the battery fully utilize its advantages of high power density, fast response speed and long charge and discharge cycle life, avoiding the defect of small capacity; while the battery bears the unbalanced power P hess The basic power in the microgrid is to avoid frequent charging and discharging that affects the service life, give full play to its large capacity advantage, and improve the overall economy of the energy storage system; since the battery can be cycled a few times, too frequent charging and discharging and fluctuations in charging and discharging power will affect its service life, so the power allocation in the hybrid energy storage system is particularly important. Supercapacitors can quickly respond to power changes in the microgrid and meet most of the power requirements of the microgrid; the battery uses a segmented constant current method to charge and discharge according to the state of the DC bus and system instructions, and only works at the rated power P batmax and 0.5P batmax Based on the two-stage constant power charging and discharging strategy, the present invention divides the battery segmented working process into four stages of charging and discharging. batmax , 0.25P batmax , 0.5P batmax , 0.75P batmax Under the four power states, the charging and discharging are closer to the actual situation, further optimizing the working process of the energy storage system; thus, the battery life is extended, the replacement of batteries is reduced, the cost is reduced, and the reliability of the grid operation is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the topological structure of a three-port converter in a specific implementation mode 1;

[0039] Figure 2 It is a topological circuit diagram of the interconnected converter of the DC hybrid system in the first specific implementation mode;

[0040] Figure 3 It is a flowchart of the single energy storage optimization configuration method in the second specific implementation mode;

[0041] Figure 4Schematic diagram of the working condition of a single energy storage system in the second specific implementation mode; (a) is a schematic diagram of the change of the charging and discharging power of the battery; (b) is a schematic diagram of the change of the state of charge SOC of the battery;

[0042] Figure 5 It is a flowchart of the hybrid energy storage optimization configuration method in the fifth specific implementation mode;

[0043] Figure 6 Schematic diagram of the working condition of the energy storage system in the hybrid energy storage in the fifth specific implementation mode; wherein, (a) is a schematic diagram of the charging and discharging power change of the battery; (b) is a schematic diagram of the charging and discharging power change of the supercapacitor; (c) is a schematic diagram of the state of charge SOC change of the battery; (d) is a schematic diagram of the state of charge SOC change of the supercapacitor. DETAILED DESCRIPTION

[0044] Specific implementation method 1. Combination Figure 1 to Figure 2 The present embodiment is described. The present embodiment describes an AC / DC hybrid system energy storage optimization configuration method, including a single energy storage optimization configuration method and a hybrid energy storage optimization configuration method;

[0045] The energy storage device of the single energy storage optimization configuration method is a battery; the optimization variables are the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of the battery N3, the maximum power of the battery N4 and the capacity of the grid-connected converter N5, and an improved differential evolution algorithm is used to solve the DC subsystem including the battery energy storage system, and the configuration of the AC / DC hybrid system energy storage is optimized according to the solution results;

[0046] The energy storage devices of the hybrid energy storage optimization configuration method are batteries and supercapacitors; the optimization variables are the number N1 of photovoltaic modules, the number N2 of wind turbines, the maximum capacity N3 of the battery, the maximum power N4 of the battery, the maximum capacity N5 of the supercapacitor, the maximum power N6 of the supercapacitor and the capacity N7 of the grid-connected converter; the improved differential evolution algorithm is also used to solve the DC subsystem including the battery energy storage system, and the configuration of the energy storage of the AC / DC hybrid system is optimized according to the solution results.

[0047] In this embodiment, since the three-port converter has great potential and advantages in the field of new energy, the three-port converter topology is classified as follows: Figure 1 As shown in the figure, these topologies are divided into three types according to the electrical isolation type of the multi-port converter: non-isolated, semi-isolated and fully isolated; the non-isolated topology is small in size, high in power density and simple to control; the isolated topology combines different power units in the form of magnetic coupling (MCI) for power transmission and distribution, with flexible voltage levels. At the same time, soft switching technology brings higher efficiency.

[0048] Combining the advantages and disadvantages of the above existing three-port topologies, Figure 2 The AC / DC hybrid system interconnected converter topology designed for this embodiment is composed of a switch tube multiplexing type three-port DC-DC converter and a full-bridge DC-DC converter cascaded, and the secondary output port 2' of the three-port converter is used as the input of the full-bridge DC-DC converter. The three-port converter integrates the Buck-Boost circuit in a multiplexing manner on the secondary side of the DAB through two staggered parallel inductors, and the DAB circuit and the Buck-Boost circuit reuse switch tubes S5~S8. The three-port converter topology adds an input and output port on the basis of not adding an additional number of switch tubes, and has the advantages of simple structure, small size and low cost. The primary side is the high voltage side, and the secondary side is the low voltage side. The power transmission between the high and low voltage sides is realized through a high-frequency transformer, so that there is no direct electrical connection between the primary and secondary sides of the converter. Port 3 of the converter integrates a bidirectional Buck-Boost circuit through staggered parallel inductors, so that port 3 can transmit power with port 2' through multiplexing switch tubes. The secondary output (i.e., port 2') voltage of the three-port converter is always kept at a constant voltage reference value, which serves as the input of the subsequent full-bridge DC-DC converter. The full-bridge DC-DC converter works in voltage control mode, adopts unipolar modulation, outputs bipolar dynamically adjustable voltage, and can achieve four-quadrant operation. Among them, S1~S4 are the primary main switch tubes of the three-port converter, S5~S8 are the secondary main switch tubes, S9~S 12 is the main switch of the full-bridge DC-DC converter. V3 is the voltage of converter port 1, V lk is the voltage at port 2' of the three-port converter and is also the input voltage of the full-bridge DC-DC converter, V s is the output voltage of the full-bridge DC-DC converter, i3, i b and i LS are the currents flowing through port 1, port 2, and port 3, respectively, i Lk is the power flowing through the power transfer inductor L k The current of C1, C b , C s The input voltage stabilizing capacitors of each port of the converter, C lk is the filter capacitor of port 2', and N1 and N2 represent the number of transformer turns.

[0049] During the operation of the subsystem, the charging and discharging power of the hybrid energy storage system has the characteristics of large fluctuation and high peak power, and the supercapacitor mainly bears the energy storage demand P hess The frequent power fluctuations in the battery fully utilize its advantages of high power density, fast response speed and long charge and discharge cycle life, avoiding the defect of small capacity; while the battery bears the unbalanced power P hessThe basic power in the medium range can avoid frequent charging and discharging that affects the service life, give full play to its large capacity advantage, and improve the overall economy of the energy storage system.

[0050] Since the number of cycles of batteries is limited, too frequent charging and discharging and fluctuations in charging and discharging power will affect their service life, so power allocation in hybrid energy storage systems is particularly important. Supercapacitors can quickly respond to power changes in microgrids and meet most of the power requirements of microgrids; batteries use segmented constant current to charge and discharge according to the state of the DC bus and system instructions, and only work at the rated power P batmax and 0.5P batmax Based on the two-stage constant power charging and discharging strategy, the present invention divides the battery segmented working process into four stages of charging and discharging. batmax , 0.25P batmax , 0.5P batmax , 0.75P batmax Under the four power states, its charging and discharging are closer to the actual situation, further optimizing the working process of the energy storage system.

[0051] When the total power generation of photovoltaic and wind turbines is greater than the load demand, the hybrid energy storage system is charged first, and the charging power P hess =P pv +P wt -P load , the battery and supercapacitor are allocated according to Table 1; when the total power generation of photovoltaic and wind turbines is insufficient to meet the load demand, the power is purchased from the large power grid first. If there is still a shortage, the hybrid energy storage system discharges, and the discharge power P hess =P pv +P wt -P load -P con , the power distribution is the same as the charging process, where: E bat , P bat Respectively represent the capacity and power of the energy storage unit battery pack, P con is the grid-connected converter power, P pv is the output power of the photovoltaic unit, P wt is the output power of the wind power unit, P load is the load power.

[0052] Table 1 Power distribution of hybrid energy storage system during charging process

[0053]

[0054]

[0055] Specific implementation method 2: Combination Figure 3 to Figure 4This embodiment is described. This embodiment further limits the AC / DC hybrid system energy storage optimization configuration method described in the specific embodiment 1. In this embodiment, the specific steps of the optimization configuration of the single energy storage optimization configuration method are as follows:

[0056] Step 1: Enter the basic information of the DC subsystem work location;

[0057] Step 2: Randomly generate an initial population;

[0058] Step 3: Calculate the hourly renewable energy power generation according to the environmental information and the power output model of the renewable energy unit;

[0059] Step 4: Calculate the objective function value of each individual in the initial population after completing the full-year simulation;

[0060] Step 5: For the selected individual, perform mutation, randomly select two other different individuals in the population, add them to the selected individual after weighted difference, and obtain the intermediate individual;

[0061] Step 6: Perform variable crossover between the selected individuals and the mutated individuals to obtain candidate individuals;

[0062] Step 7: Calculate the objective function value of the candidate individuals after the crossover after completing the full-year simulation;

[0063] Step 8: Compare the objective function values ​​of the selected individuals and the candidate individuals after crossover, select individuals with better performance to enter the next generation population, and improve the population performance;

[0064] Step 9: Determine whether the number of iterations has reached the maximum value. If so, the algorithm ends and outputs the final result of the optimized configuration. Otherwise, return to step 5 and continue iterating until the algorithm stop condition is reached.

[0065] In this embodiment, in step 4, according to Figure 3 Calculate the objective function value of each individual in the initial population after completing a full year (8760 hours) of simulation;

[0066] In the case of single energy storage, the simulation results of the battery operation status in the 1-8760h period throughout the year, the charging and discharging power and the state of charge SOC changes are shown as follows Figure 4 shown.

[0067] Specific implementation method 3: This implementation method further limits the energy storage optimization configuration method of the AC / DC hybrid system described in specific implementation method 2. In this implementation method, the basic information of the DC subsystem working location input in step 1 includes user load information, environmental prediction information, energy storage system information and power grid exchange information;

[0068] The environmental prediction information includes light intensity, ambient temperature and wind speed; the energy storage system information includes initial capacity, state of charge and charging and discharging power constraints; the grid exchange information includes grid power exchange restrictions and electricity purchase and sales prices.

[0069] Specific implementation method 4: This implementation method further limits the energy storage optimization configuration method of the AC / DC hybrid system described in specific implementation method 2. In this implementation method, the specific method of randomly generating the initialization population in step 2 is:

[0070] Among the five optimization variables, namely the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of batteries N3, the maximum power of batteries N4 and the capacity of grid-connected converter N5, 30 individuals are randomly generated within the variable constraints to form an initialized population.

[0071] In this embodiment, the specific formula for initializing the population is:

[0072] X i =[N i1 ,N i2 ,N i3 ,N i4 ,N i5 ]

[0073] Based on this, the formula for calculating the hourly renewable energy power generation in step 3 is:

[0074]

[0075] Specific implementation method V. Combination Figures 5 and 6 This embodiment is described. This embodiment further limits the AC / DC hybrid system energy storage optimization configuration method described in the specific embodiment 1. In this embodiment, the specific steps of the hybrid energy storage optimization configuration method are as follows:

[0076] Step 1. Enter the basic information of the DC subsystem work location;

[0077] Step 2: Randomly generate an initial population;

[0078] Step 3: Calculate the hourly renewable energy power generation according to the environmental information and the power output model of the renewable energy unit;

[0079] Step 4, calculate the objective function value of each individual in the initial population after completing the full-year simulation;

[0080] Step 5: For the selected individual, perform mutation, randomly select two other different individuals in the population, add them to the selected individual after weighted difference, and obtain the intermediate individual;

[0081] Step 6: Perform variable crossover between the selected individuals and the mutated individuals to obtain candidate individuals;

[0082] Step 7, calculate the objective function value of the candidate individuals after the crossover after completing the full-year simulation;

[0083] Step 8: Compare the objective function values ​​of the selected individuals with those of the candidate individuals after crossover, select individuals with better performance to enter the next generation population, and improve population performance;

[0084] Step 9: Determine whether the number of iterations reaches the maximum value. If so, the algorithm ends and outputs the final result of the optimized configuration. Otherwise, return to step 5 and continue iterating until the algorithm stop condition is reached.

[0085] In this embodiment, in step 4, according to Figure 5 Calculate the objective function value of each individual in the initial population after completing a full year (8760 hours) of simulation;

[0086] In the case of hybrid energy storage, the simulation results of the energy storage system's operation status from 1 to 8760 hours throughout the year, the charging and discharging power and state of charge SOC changes of the battery and supercapacitor are shown in the figure below. Figure 6 shown.

[0087] The maximum charging and discharging power and energy storage capacity provided by hybrid energy storage technology are better than those of a single energy storage system. It will have better energy storage performance during operation. At the same time, it can extend the service life of the battery, reduce battery replacement, and reduce costs. It has advantages and is the choice of future subsystem energy storage systems.

[0088] Specific implementation method 6. This implementation method further limits the energy storage optimization configuration method of the AC / DC hybrid system described in specific implementation method 5. In this implementation method, the basic information of the DC subsystem working location input in step 1 includes user load information, environmental prediction information, energy storage system information and power grid exchange information;

[0089] The environmental prediction information includes light intensity, ambient temperature and wind speed; the energy storage system information includes initial capacity, state of charge and charging and discharging power constraints; the grid exchange information includes grid power exchange restrictions and electricity purchase and sales prices.

[0090] Specific implementation method VII: This implementation method further limits the AC / DC hybrid system energy storage optimization configuration method described in specific implementation method V. In this implementation method, the specific method of randomly generating the initialization population in step 2 is:

[0091] Among the seven optimization variables, namely the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of batteries N3, the maximum power of batteries N4, the maximum capacity of supercapacitors N5, the maximum power of supercapacitors N6 and the capacity of grid-connected converters N7, 30 individuals are randomly generated within the variable constraints to form an initialized population.

[0092] In this embodiment, the specific formula for initializing the population is:

[0093] X i =[N i1 ,N i2 ,N i3 ,N i4 ,N i5 ,N i6 ,N i7 ]

[0094] Based on this, the formula for calculating the hourly renewable energy generation power in step 3 is:

[0095]

[0096] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing energy storage configuration of an AC / DC hybrid system, characterized in that: The optimization configuration method includes a single energy storage optimization configuration method and a hybrid energy storage optimization configuration method; The energy storage device of the single energy storage optimization configuration method is a battery; the optimization variables are the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of the battery N3, the maximum power of the battery N4 and the capacity of the grid-connected converter N5, and an improved differential evolution algorithm is used to solve the DC subsystem including the battery energy storage system, and the configuration of the AC / DC hybrid system energy storage is optimized according to the solution results; The energy storage devices of the hybrid energy storage optimization configuration method are batteries and supercapacitors; the optimization variables are the number N1 of photovoltaic modules, the number N2 of wind turbines, the maximum capacity N3 of the battery, the maximum power N4 of the battery, the maximum capacity N5 of the supercapacitor, the maximum power N6 of the supercapacitor and the capacity N7 of the grid-connected converter; the improved differential evolution algorithm is also used to solve the DC subsystem including the battery energy storage system, and the configuration of the energy storage of the AC / DC hybrid system is optimized according to the solution results.

2. The method for optimizing energy storage configuration of an AC / DC hybrid system according to claim 1, characterized in that: The specific steps of optimizing the configuration of the single energy storage optimization configuration method are as follows: Step 1: Enter the basic information of the DC subsystem work location; Step 2: Randomly generate an initial population; Step 3: Calculate the hourly renewable energy power generation according to the environmental information and the power output model of the renewable energy unit; Step 4: Calculate the objective function value of each individual in the initial population after completing the full-year simulation; Step 5: For the selected individual, perform mutation, randomly select two other different individuals in the population, add them to the selected individual after weighted difference, and obtain the intermediate individual; Step 6: Perform variable crossover between the selected individuals and the mutated individuals to obtain candidate individuals; Step 7: Calculate the objective function value of the candidate individuals after the crossover after completing the full-year simulation; Step 8: Compare the objective function values ​​of the selected individuals and the candidate individuals after crossover, select individuals with better performance to enter the next generation population, and improve the population performance; Step 9: Determine whether the number of iterations has reached the maximum value. If so, the algorithm ends and outputs the final result of the optimized configuration. Otherwise, return to step 5 and continue iterating until the algorithm stop condition is reached.

3. The method for optimizing energy storage configuration of an AC / DC hybrid system according to claim 2, characterized in that: The basic information of the DC subsystem work location input in step 1 includes user load information, environmental prediction information, energy storage system information and power grid exchange information; The environmental prediction information includes light intensity, ambient temperature and wind speed; the energy storage system information includes initial capacity, state of charge and charging and discharging power constraints; the grid exchange information includes grid power exchange restrictions and electricity purchase and sales prices.

4. The method for optimizing energy storage configuration of an AC / DC hybrid system according to claim 2, characterized in that: The specific method for randomly generating the initialization population in step 2 is: Among the five optimization variables, namely the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of batteries N3, the maximum power of batteries N4 and the capacity of grid-connected converter N5, 30 individuals are randomly generated within the variable constraints to form an initialized population.

5. The method for optimizing energy storage configuration of an AC / DC hybrid system according to claim 1, characterized in that: The specific steps of optimizing configuration of the hybrid energy storage optimization configuration method are as follows: Step 1. Enter the basic information of the DC subsystem work location; Step 2: Randomly generate an initial population; Step 3: Calculate the hourly renewable energy power generation according to the environmental information and the power output model of the renewable energy unit; Step 4, calculate the objective function value of each individual in the initial population after completing the full-year simulation; Step 5: For the selected individual, perform mutation, randomly select two other different individuals in the population, add them to the selected individual after weighted difference, and obtain the intermediate individual; Step 6: Perform variable crossover between the selected individuals and the mutated individuals to obtain candidate individuals; Step 7, calculate the objective function value of the candidate individuals after the crossover after completing the full-year simulation; Step 8: Compare the objective function values ​​of the selected individuals with those of the candidate individuals after crossover, select individuals with better performance to enter the next generation population, and improve population performance; Step 9: Determine whether the number of iterations reaches the maximum value. If so, the algorithm ends and outputs the final result of the optimized configuration. Otherwise, return to step 5 and continue iterating until the algorithm stop condition is reached.

6. The method for optimizing energy storage configuration of an AC / DC hybrid system according to claim 5, characterized in that: The basic information of the DC subsystem work location input in step 1 includes user load information, environmental prediction information, energy storage system information and power grid exchange information; The environmental prediction information includes light intensity, ambient temperature and wind speed; the energy storage system information includes initial capacity, state of charge and charging and discharging power constraints; the grid exchange information includes grid power exchange restrictions and electricity purchase and sales prices.

7. The method for optimizing energy storage configuration of an AC / DC hybrid system according to claim 5, characterized in that: The specific method for randomly generating the initialization population in step 2 is: Among the seven optimization variables, namely the number of photovoltaic modules N1, the number of wind turbines N2, the maximum capacity of batteries N3, the maximum power of batteries N4, the maximum capacity of supercapacitors N5, the maximum power of supercapacitors N6 and the capacity of grid-connected converters N7, 30 individuals are randomly generated within the variable constraints to form an initialized population.

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