Hybrid energy storage optimal configuration method and system for treating voltage out-of-limit of low-voltage distribution network

Abstract

The invention provides a hybrid energy storage optimal configuration method and system for solving a voltage out-of-limit problem of a low-voltage power distribution network. The method comprises the following steps: step 1, simulating new energy output characteristics of the low-voltage power distribution network and investigating load characteristics of the power distribution network; 2, establishing a mathematical model including the charge states of the supercapacitor and the storage battery; 3, establishing an economic optimization model of the daily operation cost of the power distribution network system; 4, constraint conditions are set for the models established in the step 2 and the step 3; 5, solving the optimization model established in the step 3 by adopting an optimization algorithm in combination with the constraint conditions in the step 4 by taking the minimum daily operation cost as an optimization target; and step 6, outputting the optimal cost and the input number of the super capacitor and the storage battery. According to the invention, energy is saved, the system voltage is stabilized, by optimizing the capacity of the energy storage system, the voltage stability of the system is ensured, the economic requirement of the system is met, and the method has important significance for improving the voltage stability of the system.

Description

Technical Field

[0001] The present invention belongs to the field of power quality, and more specifically, relates to a method and system for optimizing configuration of a hybrid energy storage system. Background Art

[0002] In recent years, my country's photovoltaic power generation industry has achieved rapid development. In 2020, my country's photovoltaic grid-connected installed capacity reached 253GW, and the annual photovoltaic power generation was 260.5 billion kWh, accounting for about 3.5% of the country's total annual power generation. In the context of high-proportion distributed photovoltaic access, the stable operation of the distribution network has brought greater challenges. The imbalance between high-proportion distributed photovoltaic power and load demand will cause the flow of the distribution network to be reversed, causing the voltage along the line to rise and thus causing the voltage to exceed the upper limit. According to the overvoltage protection requirements of the distributed photovoltaic grid-connected technical specifications, when the grid-connected point voltage exceeds the specified range, the distributed photovoltaic should stop transmitting electricity to the grid within the specified time. The voltage over-limit problem of the distribution network under the background of high-proportion distributed photovoltaic access not only threatens the safe and stable operation of the distribution network, but also damages the economic benefits of photovoltaic investors. The voltage over-limit is an irreversible damage to the user's electrical equipment.

[0003] At present, the main methods used to solve the problem of voltage exceeding the limit in distribution networks include on-load tap-changing transformers, reactive power compensation devices, voltage regulation through distributed photovoltaics, energy storage systems, and coordinated regulation of multiple devices.

[0004] On-load tap-changing transformers can adjust voltage by changing the transformer ratio without interrupting the load power supply, thereby meeting the needs of the power system. However, on-load tap-changing switches have action delays and cannot frequently adjust the tap position, so they cannot quickly respond to voltage over-limit problems caused by frequent fluctuations in the output of distributed power sources. The main function of the reactive power compensation device is to provide or absorb reactive power in the power system, thereby improving the power factor, reducing line losses, and improving voltage quality. However, high-performance reactive power compensation devices are often expensive, which increases the investment cost of the power system. In addition, due to the rapid technological updates, equipment maintenance, upgrades, and replacements may also require additional costs. Distributed photovoltaic voltage regulation requires the reduction of active power output, which will cause active power loss in the photovoltaic process. The energy storage system can flexibly and quickly adjust the active charging and discharging power, realize the peak shaving and valley filling of distributed power generation power and load power consumption, smooth the fluctuation of distributed power output, and improve the power quality of the distribution network, but the high construction cost of the energy storage system is still the main factor limiting the widespread application of energy storage systems in distribution networks.

[0005] Prior art document 1 (CN112260296A) discloses a hybrid energy storage system optimization method for smoothing intermittent loads. The hybrid energy storage system model established in this document does not take into account the full life cycle cost of the hybrid energy storage system.

[0006] Prior art document 2 (CN107947231A) discloses a hybrid energy storage system control method for optimizing the operation of a distribution network. This document does not optimize the capacity of the hybrid energy storage system. Summary of the invention

[0007] In view of the above-mentioned defects of the prior art, the present invention proposes to use a hybrid energy storage system to absorb excess renewable energy electricity and supplement the electricity required by the system when the voltage is too low. And proposes a hybrid energy storage system optimization configuration method for managing voltage over-limit in low-voltage distribution networks. The method uses a hybrid energy storage system composed of supercapacitors and batteries to absorb the electricity generated by distributed power sources when the output of renewable energy such as photovoltaics is large and the load is small, and releases electricity when the output of renewable energy is small and the load is large, while solving the problem of grid voltage over-limit caused by the failure of renewable energy output to be absorbed by the system load in time. And with the lowest grid operation cost as the optimization goal, the required energy storage system is optimally configured in capacity.

[0008] In order to achieve the above-mentioned object, the first aspect of the present invention discloses a hybrid energy storage system optimization configuration method for managing voltage over-limit of a low-voltage distribution network, comprising:

[0009] Step 1: simulate the new energy output characteristics of the low-voltage distribution network and investigate the load characteristics of the distribution network;

[0010] Step 2, establishing a mathematical model including the state of charge of the supercapacitor and the battery;

[0011] Step 3, establishing an economic optimization model for the daily operating cost of the distribution network system;

[0012] Step 4, setting constraints on the models established in steps 2 and 3;

[0013] Step 5, taking the lowest daily operating cost as the optimization goal, combining the constraints in step 4, taking the output of new energy and load characteristics as parameter inputs, and using the optimization algorithm to solve the economic optimization model established in step 3;

[0014] Step 6: Output the optimal cost and the number of supercapacitors and batteries invested. According to the number of supercapacitors and batteries invested, calculate the total capacity of the supercapacitors and batteries, and then determine the optimal configuration of the energy storage system capacity.

[0015] Preferably, the mathematical model established in step 2 is as follows:

[0016]

[0017]

[0018] In the formula, is the SOC value of the supercapacitor at time t, is the battery SOC value at time t, T is the scheduling time period, and are the charging and discharging states of the supercapacitor, respectively. and are the charging and discharging states of the battery, respectively. and are the charging and discharging power of the supercapacitor, and are the charging and discharging power of the battery respectively, and are the charge states of the supercapacitor and battery at the initial scheduling moment, E Ba and E Sc is the total energy of the battery and supercapacitor, t is a certain moment in the scheduling time period, η SCcha and η SCdis is the charging and discharging efficiency of the supercapacitor connected to the grid through the converter, η Bacha and η Badis The charging and discharging efficiency of the battery connected to the grid through a converter.

[0019] Preferably, the charging efficiency η of the supercapacitor connected to the grid through the converter is SCcha and discharge efficiency η SCdis It can be expressed by the following formula:

[0020]

[0021] In the formula, and is the charging and discharging efficiency of the supercapacitor itself, η DC / DC Indicates the efficiency of the DC / DC converter, η DC / AC Indicates the efficiency of the DC / AC converter.

[0022] Preferably, the battery is connected to the charging station of the power grid via a converter. Bacha Efficiency and discharge efficiency η Badis It can be expressed by the following formula:

[0023]

[0024] Where η DC / DC Indicates the efficiency of the DC / DC converter, η DC / AC Indicates the efficiency of the DC / AC converter, and are the charging and discharging efficiencies of the battery itself, respectively.

[0025] Preferably, in step 3, an economic optimization model for the daily operation cost of the distribution network system is established with the lowest total daily operation cost of the distribution network system as the optimization goal, and the objective function is shown in formula (7):

[0026] minC total =C1+C2+C3+C4+C5+C6 (7)

[0027] In the formula, C1 is the electricity purchase cost; C2 is the daily investment cost of the battery; C3 is the daily operation and maintenance cost of the battery; C4 is the daily investment cost of the supercapacitor; C5 is the daily operation and maintenance cost of the supercapacitor; C6 is the average daily investment cost of the converter.

[0028] Preferably, the calculation formula of the electricity purchase cost C1 is as shown in formula (8):

[0029]

[0030] In formula (8), is the power purchased during period t, C price is the grid electricity price;

[0031] The calculation formula of battery daily investment cost C2 is shown in formula (10):

[0032]

[0033] In formula (10), r is the discount rate, L Ba is the service life of the battery, C 2_inv Fixed investment cost for batteries;

[0034] The daily operation and maintenance cost of the battery C3 is shown in formula (11):

[0035]

[0036] In formula (11), C Ba_op is the operation and maintenance cost of the battery per unit of charge and discharge energy, and are the charging and discharging power of the battery respectively;

[0037] The daily investment cost of supercapacitor C4 is shown in formula (13):

[0038]

[0039] In formula (11), r is the discount rate, L SC is the service life of the supercapacitor, C 4_inv is the fixed investment cost of the supercapacitor;

[0040] The daily operation and maintenance cost C5 of the supercapacitor is shown in formula (14):

[0041]

[0042] In formula (12), C SC_op is the operation and maintenance cost of supercapacitor per unit charge and discharge energy, and are the charging and discharging power of the supercapacitor respectively;

[0043] The average daily investment cost C6 of the converter is shown in formula (15):

[0044]

[0045] In formula (13), r is the discount rate, L converter is the service life of the converter, C converter is the fixed investment cost of the converter.

[0046] Preferably, the battery fixed investment cost C 2_inv As shown in formula (9):

[0047] C 2_inv =m·C Ba_unit (9)

[0048] In formula (9), C 2_inv is the fixed investment cost of the battery; m is the number of battery units invested; C Ba_unit is the battery unit price;

[0049] Fixed investment cost of supercapacitor C 4_inv As shown in formula (10):

[0050] C 4_inv =n·C SC_unit (12)

[0051] In formula (10), n is the number of supercapacitor units put into use; C SC_unit is the supercapacitor unit price.

[0052] Preferably, the constraints in step 4 are as follows:

[0053] The electric power balance constraint is shown in formula (16):

[0054]

[0055] In formula (16): Contribute to new energy, is the system load, is the power purchased during period t, and are the charging and discharging power of the supercapacitor, and are the charging and discharging power of the battery respectively;

[0056] The constraints of the supercapacitor are shown in equation (17):

[0057]

[0058] In formula (17), is the SOC value of the supercapacitor at time t, and are the lower and upper limits of the SOC of the supercapacitor, and are the state of charge at the initial and final time of supercapacitor scheduling, respectively. and are the charging and discharging states of the supercapacitor, respectively. and are the charging and discharging power of the supercapacitor, and are the maximum values ​​of the charging and discharging power of the supercapacitor, respectively;

[0059] The constraints of the battery are shown in formula (18):

[0060] Similar to supercapacitors, batteries also have constraints on SOC, charge and discharge state, and charge and discharge power, as shown in equation (18).

[0061]

[0062] In formula (18), is the battery SOC value at time t, and are the lower and upper limits of the battery SOC, and are the state of charge of the battery at the initial time of scheduling and the state of charge at the final time of scheduling, and are the charging and discharging states of the battery respectively; and are the charging and discharging power of the battery respectively; and are the maximum values ​​of the charging and discharging power of the battery respectively;

[0063] The system voltage constraint is shown in equation (19):

[0064]

[0065] In formula (19), is the system voltage, is the minimum value of the national standard voltage, It is the maximum value of national standard voltage.

[0066] A second aspect of the present invention provides a hybrid energy storage optimization configuration system for managing voltage over-limit in a low-voltage distribution network, comprising:

[0067] A new energy output and load characteristics simulation module is used to simulate the new energy output characteristics of the low-voltage distribution network and investigate the load characteristics of the distribution network to obtain the new energy output and load characteristics of the distribution network;

[0068] A hybrid energy storage system mathematical model building module is used to build a mathematical model of the charge state of supercapacitors and batteries;

[0069] An economic optimization model building module is used to build an economic optimization model for the total daily operating cost of the distribution network system;

[0070] A constraint condition establishment module is used to establish the constraint conditions of the mathematical model and economic optimization model of the hybrid energy storage system;

[0071] The model solving module is used to calculate the total capacity of supercapacitors and batteries, and then determine the optimal configuration of the energy storage system capacity.

[0072] The present invention has the following advantages:

[0073] 1. The energy storage system realizes energy transfer, saves wasted energy, and stabilizes the system voltage;

[0074] 2. The high power density characteristics of supercapacitors and the high energy density characteristics of batteries in the hybrid energy storage system complement each other, which can absorb both short-term peak power in the output of new energy and long-term high-energy output of new energy, and have more flexibility in adjusting the system voltage;

[0075] 3. The present invention optimizes the capacity of the energy storage system, thereby ensuring the voltage stability of the system and satisfying the economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is the energy management flow chart of energy storage system;

[0077] Figure 2 Flowchart for optimization solution. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0079] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0080] As the instruction manual Figure 2 As shown, the first embodiment of the present invention discloses a hybrid energy storage configuration method for managing voltage over-limit in a low-voltage distribution network, specifically comprising:

[0081] Step 1: Simulate the new energy output characteristics of the low-voltage distribution network and investigate the load characteristics of the distribution network to obtain the new energy output and load characteristics of the distribution network;

[0082] Step 2: Establish a mathematical model of the hybrid energy storage system considering investment and operation and maintenance costs;

[0083] The mathematical model of the hybrid energy storage system is as follows:

[0084] The state of charge (SOC) of a supercapacitor and a battery is shown in equation (1) and equation (2), where SOC represents the ratio of the remaining capacity to the total capacity.

[0085]

[0086] Where T is the scheduling time period, and are the charging and discharging states of the supercapacitor, respectively. and are the charging and discharging states of the battery, respectively. and are the charging and discharging power of the supercapacitor, and are the charging and discharging power of the battery respectively, and are the charge states of the supercapacitor and battery at the initial scheduling moment, E B x and E Sc is the total energy of the battery and supercapacitor, t is a certain moment in the scheduling time period, η SCcha and η SCdis is the charging and discharging efficiency of the supercapacitor connected to the grid through the converter, as shown in equations (3) and (4):

[0087]

[0088] In the formula, and is the charging and discharging efficiency of the supercapacitor itself, η DC / DC Indicates the efficiency of the DC / DC converter, η DC / AC Indicates the efficiency of the DC / AC converter.

[0089] Similarly, η Bacha and η Badis is the charging and discharging efficiency of the battery connected to the grid through the converter, as shown in equations (5) and (6):

[0090]

[0091] In the formula, and are the charging and discharging efficiencies of the battery itself, respectively.

[0092] Step 3: Establish an economic optimization model for the daily operating cost of the distribution network system;

[0093] The optimization goal is to minimize the total daily operating cost of the distribution network system. The objective function is shown in formula (7):

[0094] minC total =C1+C2+C3+C4+C5+C6 (7)

[0095] In the formula, C1 is the electricity purchase cost; C2 is the daily investment cost of the battery; C3 is the daily operation and maintenance cost of the battery; C4 is the daily investment cost of the supercapacitor; C5 is the daily operation and maintenance cost of the supercapacitor; C6 is the average daily investment cost of the converter.

[0096] The cost of purchasing electricity from the power grid is:

[0097]

[0098] In formula (8), is the power purchased during period t, C price The grid electricity price.

[0099] The investment cost of the battery is related to the number of unit batteries invested, as shown in formula (9):

[0100] C 2_inv =m·C Ba_unit (9)

[0101] In formula (9), C 2_inv is the fixed investment cost of the battery; m is the number of battery units invested; C Ba_unit is the battery unit price.

[0102] Taking into account the cost over the entire life cycle, the uniform annual value method is used to convert the investment cost into daily terms.

[0103]

[0104] In formula (10), r is the discount rate, L Ba is the service life of the battery, C 2_inv Fixed investment cost for batteries.

[0105] The operation and maintenance cost of the battery is related to its charging and discharging power and charging and discharging time, as shown in formula (11):

[0106]

[0107] In formula (11), C Ba_op is the operation and maintenance cost of the battery per unit of charge and discharge energy, and

[0108] are the charging and discharging power of the battery respectively.

[0109] Similarly, the investment cost of supercapacitors is related to the number of unit supercapacitors invested, as shown in formula (12):

[0110] C 4_inv =n·C SC_unit (12)

[0111] In formula (12), C 4_inv is the fixed investment cost of the supercapacitor; n is the number of supercapacitor units invested; C SC_unit is the supercapacitor unit price.

[0112] The cost over the entire life cycle is converted into daily investment costs using the unified annual value method. The daily investment cost of supercapacitors is shown in (13):

[0113]

[0114] In formula (13), r is the discount rate, L SC is the service life of the supercapacitor.

[0115] The operation and maintenance cost of a supercapacitor is related to its charging and discharging power and charging and discharging time, as shown in formula (14):

[0116]

[0117] In formula (14), and are the charging and discharging power of the supercapacitor, C SC_opIt is the operation and maintenance cost of supercapacitor per unit charge and discharge energy.

[0118] The average daily investment cost of the converter is shown in formula (15):

[0119]

[0120] In formula (15), r is the discount rate, L converter is the service life of the converter, C converter is the fixed investment cost of the converter.

[0121] As a prominent substantive feature of the present invention and one of the significant advances brought to the prior art, the present invention establishes a full life cycle cost model for a hybrid energy storage system. Therefore, the present invention can solve the voltage over-limit problem with a lower hybrid energy storage system investment cost.

[0122] Step 4: Establish the constraints of the hybrid energy storage system mathematical model and economic optimization model, as follows:

[0123] 1) Electric power balance constraints.

[0124]

[0125] In formula (16): Contribute to new energy, is the system load.

[0126] 2) Constraints of supercapacitors.

[0127]

[0128] In formula (17), is the SOC value of the supercapacitor at time t, and are the lower and upper limits of the SOC of the supercapacitor, and are the charge states of the supercapacitor at the initial and final scheduling moments, respectively. and are the charging and discharging states of the supercapacitor, respectively. and are the maximum values ​​of the charging and discharging power of the supercapacitor, respectively.

[0129] In order to prevent the supercapacitor from overcharging or overdischarging, its SOC is constrained. In order to facilitate scheduling during the entire working process, the supercapacitor's initial SOC must be equal to the final SOC. The formula also includes the constraint that the supercapacitor can only exist in one working state of charging or discharging at time t and the supercapacitor's charging and discharging power constraints.

[0130] 3) Battery constraints.

[0131] Similar to supercapacitors, batteries also have constraints on SOC, charge and discharge state, and charge and discharge power, as shown in equation (18).

[0132]

[0133] In formula (18), and are the lower and upper limits of the battery SOC, and are the charge states of the battery at the initial and final scheduling moments, respectively. and They are and are the charging and discharging states of the battery respectively.

[0134] 4) System voltage constraints.

[0135]

[0136] In formula (19), is the system voltage, is the minimum value of the national standard voltage, It is the maximum value of national standard voltage.

[0137] Step 5: Taking the lowest cost as the optimization goal, input the new energy output power, system load, energy storage system power, capacity cost, SOC range and electricity price information and other parameters, solve the daily total operation cost function of the distribution network system established in step 3, and optimize the required energy storage system capacity configuration;

[0138] Step 6: Output the optimal cost and the number of supercapacitors and batteries invested. According to the number of supercapacitors and batteries invested, calculate the total capacity of supercapacitors and batteries, and then determine the optimal configuration of the energy storage system capacity.

[0139] As a prominent substantive feature of the present invention and one of the significant advances brought to the prior art, the present invention optimizes the configuration of the capacities of supercapacitors and batteries in the hybrid energy storage system. Therefore, the optimized configuration result of the hybrid energy storage system obtained by the present invention is more excellent.

[0140] A second embodiment of the present invention provides a hybrid energy storage optimization configuration system for managing voltage over-limit in a low-voltage distribution network, comprising:

[0141] A new energy output and load characteristics simulation module is used to simulate the new energy output characteristics of the low-voltage distribution network and investigate the load characteristics of the distribution network to obtain the new energy output and load characteristics of the distribution network;

[0142] A hybrid energy storage system mathematical model building module is used to build a mathematical model of the charge state of supercapacitors and batteries;

[0143] An economic optimization model building module is used to build an economic optimization model for the total daily operating cost of the distribution network system;

[0144] A constraint condition establishment module is used to establish the constraint conditions of the mathematical model and economic optimization model of the hybrid energy storage system;

[0145] The model solving module is used to calculate the total capacity of supercapacitors and batteries, and then determine the optimal configuration of the energy storage system capacity.

[0146] like Figure 1 As shown, the third embodiment of the present invention uses the hybrid energy storage system of the present invention to manage the voltage over-limit of the low-voltage distribution network. The entire process is as follows: detect whether the line voltage of the system exceeds the limit. The judgment of over-limit includes but is not limited to judging whether the voltage is within the national standard range. If the voltage exceeds the upper limit, it means that there is surplus energy after the new energy output meets the system load demand, and the hybrid energy storage system should be started to charge; first detect the charge state of the battery to determine whether the battery can absorb energy again. If the charge state is less than the set maximum value, the battery starts charging. If the maximum value is reached, the charge state of the supercapacitor is determined. If the charge state of the supercapacitor is less than the set maximum value, the supercapacitor charging is started. When the battery is working at the maximum charging power, if the new energy output still has surplus after meeting the system load and the maximum charging power of the battery, the supercapacitor charging is started at the same time.

[0147] If the system voltage exceeds the lower limit, it means that the system load is too large and more energy is required. At this time, the hybrid energy storage system starts to discharge. First, determine the state of charge of the supercapacitor. If the state of charge is greater than the set minimum value, the supercapacitor discharge is started. If it reaches the minimum value, determine the battery state of charge. If the battery state of charge is greater than the set minimum value, the battery discharges. If the supercapacitor works at the maximum discharge power and the system voltage is still less than the national standard, the battery discharge is started.

[0148] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network, characterized in that: Step 1, simulating the new energy output characteristics of the low-voltage distribution network and the load characteristics of the distribution network; Step 2, establishing a mathematical model of a hybrid energy storage system including the state of charge of a supercapacitor and a battery; Step 3, establishing an economic optimization model for the daily operating cost of the distribution network system; Step 4, setting constraints on the models established in steps 2 and 3; Step 5, taking the lowest daily operating cost as the optimization goal, combining the constraints in step 4, taking the output of new energy and load characteristics as parameter inputs, and using the optimization algorithm to solve the economic optimization model established in step 3; Step 6: Output the optimal cost and the number of supercapacitors and batteries invested. According to the number of supercapacitors and batteries invested, calculate the total capacity of the supercapacitors and batteries, and then determine the optimal configuration of the energy storage system capacity.

2. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 1, characterized in that: The supercapacitor charge state mathematical model established in step 2 is shown in formula (1): In the formula, is the SOC value of the supercapacitor at time t, T is the scheduling time period, and are the charging and discharging states of the supercapacitor, respectively. and are the charging and discharging power of the supercapacitor, is the state of charge of the supercapacitor at the initial time of scheduling, E Sc is the total energy of the supercapacitor, t is a certain moment in the scheduling time period, η SCcha and η SCdis The charging and discharging efficiency of the supercapacitor connected to the grid through the converter.

3. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 1, characterized in that: The battery state of charge mathematical model established in step 2 is shown in formula (2): is the battery SOC value at time t, T is the scheduling time period, and are the charging and discharging states of the battery, respectively. and are the charging and discharging power of the battery respectively, is the state of charge of the battery at the initial time of scheduling, E Ba is the total energy of the battery, t is a certain moment in the scheduling time period, η Bacha and η Badis The charging and discharging efficiency of the battery connected to the grid through a converter.

4. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 2, characterized in that: Charging efficiency of supercapacitor connected to the grid through converter η SCcha and discharge efficiency η SCdis As shown in formula (3) and formula (4): In the formula, and is the charging and discharging efficiency of the supercapacitor itself, η DC / DC Indicates the efficiency of the DC / DC converter, η DC / AC Indicates the efficiency of the DC / AC converter.

5. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 3, characterized in that: The charging efficiency of the battery connected to the grid through the converter is η Bacha and discharge efficiency η Badis , as shown in formula (5) and formula (6): Where η DC / DC Indicates the efficiency of the DC / DC converter, η DC / AC Indicates the efficiency of the DC / AC converter, and are the charging and discharging efficiencies of the battery itself, respectively.

6. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 1, characterized in that: In step 3, an economic optimization model for the daily operation cost of the distribution network system is established with the lowest total daily operation cost of the distribution network system as the optimization goal. The objective function is shown in formula (7): minC total =C1+C2+C3+C4+C5+C6 (7) In the formula, C1 is the electricity purchase cost; C2 is the daily investment cost of the battery; C3 is the daily operation and maintenance cost of the battery; C4 is the daily investment cost of the supercapacitor; C5 is the daily operation and maintenance cost of the supercapacitor; C6 is the average daily investment cost of the converter.

7. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 6, characterized in that: The calculation formula of electricity purchase cost C1 is shown in formula (8): In formula (8), is the power purchased during period t, C price is the grid electricity price; The calculation formula of battery daily investment cost C2 is shown in formula (10): In formula (10), r is the discount rate, L Ba is the service life of the battery, C 2_inv Fixed investment cost for batteries; The daily operation and maintenance cost of the battery C3 is shown in formula (11): In formula (11), C Ba_op is the operation and maintenance cost of the battery per unit of charge and discharge energy, and are the charging and discharging power of the battery respectively; The daily investment cost of supercapacitor C4 is shown in formula (13): In formula (13), r is the discount rate, L SC is the service life of the supercapacitor, C 4_inv is the fixed investment cost of the supercapacitor; The daily operation and maintenance cost C5 of the supercapacitor is shown in formula (14): In formula (14), C SC_op is the operation and maintenance cost of supercapacitor per unit charge and discharge energy, and are the charging and discharging power of the supercapacitor respectively; The average daily investment cost C6 of the converter is shown in formula (15): In formula (15), r is the discount rate, L converter is the service life of the converter, C converter is the fixed investment cost of the converter.

8. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 7, characterized in that: Battery fixed investment cost C 2_inv As shown in formula (9): C 2_inv =m·C Ba_unit (9) In formula (9), C 2_inv is the fixed investment cost of the battery; m is the number of battery units invested; C Ba_unit is the battery unit price; Fixed investment cost of supercapacitor C 4_inv As shown in formula (10): C 4_inv =n·C SC_unit (12) In formula (10), n is the number of supercapacitor units put into use; C SC_unit is the supercapacitor unit price.

9. A hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in claim 8, characterized in that: The constraints in step 4 are as follows: The electric power balance constraint is shown in formula (16): In formula (16): Contribute to new energy, is the system load, is the power purchased during period t, and are the charging and discharging power of the supercapacitor, and are the charging and discharging power of the battery respectively; The constraints of the supercapacitor are shown in equation (17): In formula (17), is the SOC value of the supercapacitor at time t, and are the lower and upper limits of the SOC of the supercapacitor, and are the charge states of the supercapacitor at the initial and final time of scheduling, and are the charging and discharging states of the supercapacitor, respectively. and are the charging and discharging power of the supercapacitor, and are the maximum values ​​of the charging and discharging power of the supercapacitor, respectively; The constraints of the battery are shown in formula (18): In formula (18), is the battery SOC value at time t, and are the lower and upper limits of the battery SOC, and are the state of charge of the battery at the initial and final time of scheduling, and are the charging and discharging states of the battery respectively; and are the charging and discharging power of the battery respectively; and are the maximum values ​​of the charging and discharging power of the battery respectively; The system voltage constraint is shown in equation (19): In formula (19), is the system voltage, is the minimum value of the national standard voltage, It is the maximum value of national standard voltage.

10. A hybrid energy storage optimization configuration system for managing voltage over-limit in a low-voltage distribution network, running a hybrid energy storage optimization configuration method for managing voltage over-limit in a low-voltage distribution network as claimed in any one of claims 1 to 9, characterized in that: A simulation module is used to simulate the output characteristics of new energy sources in a low-voltage distribution network and investigate the load characteristics of the distribution network to obtain the output and load characteristics of new energy sources in the distribution network; Model building module, used to build the mathematical model of the state of charge of supercapacitors and batteries and the economic optimization model of the total daily operating cost of the distribution network system; Constraint condition establishment module, used to establish the constraint conditions of the system mathematical model and economic optimization model; The model solving module is used to calculate the total capacity of the supercapacitor and the electrical demand, and then determine the optimal configuration of the energy storage system capacity.

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