Offshore wind power hybrid energy storage capacity optimal distribution method based on harmony search algorithm

Abstract

A harmony search algorithm-based offshore wind power hybrid energy storage capacity optimal distribution method comprises the following specific steps of: 1, establishing mathematical models of an offshore wind and light complementary energy storage system, specifically comprising a wind power generation model, a solar module model, a storage battery model and a super capacitor model; step 2, establishing system capacity optimization targets and constraint conditions, wherein the system capacity optimization targets and constraint conditions specifically comprise the lowest investment cost, the highest cost benefit, the minimum wind and light abandoning rate, the minimum load loss rate and the power supply and demand balance constraint, the limitation of the charging state of a storage battery, and the limitation of the power and the number of solar equipment; and step 3, obtaining a related data curve and equipment parameters, operating a harmony search algorithm to obtain an optimal solution of the target function, and analyzing a result. And the model is optimized by taking the lowest investment cost, the highest cost benefit, the minimum wind and light abandoning rate and the minimum load loss rate as targets, and finally the configuration optimization problem is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore microgrid capacity optimization configuration, and in particular to an offshore wind power hybrid energy storage capacity optimization allocation method based on a harmony search algorithm. Background Art

[0002] As the global demand for energy continues to grow, control technologies related to wind power and photovoltaic power generation have developed rapidly, which has alleviated the problem of energy shortage on the one hand and is in line with the concept of sustainable development on the other. However, the uncertainty of solar and wind energy still poses a challenge to the anti-interference ability of the power system, becoming a key issue restricting the development of wind and solar energy technologies.

[0003] Traditional system capacity configuration design often relies on the human experience of designers. This method lacks strict scientificity and often leads to high investment costs, poor economy, poor stability and other problems. In order to solve this problem, relevant scholars have proposed to alleviate the volatility of renewable energy by configuring energy storage systems. Therefore, the capacity configuration of energy storage systems has become a key research area for scholars. To this end, we design an offshore wind power hybrid energy storage capacity optimization allocation method based on the harmony search algorithm. Summary of the invention

[0004] In view of the above technical problems, the present invention provides an optimal allocation method for offshore wind power hybrid energy storage capacity based on a harmony search algorithm. By establishing a mathematical model of an offshore wind-solar complementary energy storage system, and taking the lowest investment cost, the highest cost-effectiveness, the lowest wind and solar abandonment rate, and the lowest load loss rate as the goals, a microgrid capacity configuration plan with the lowest total cost is obtained, thereby solving the problem of optimal configuration of offshore wind power hybrid energy storage capacity.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] The specific steps of the optimal allocation method of offshore wind power hybrid energy storage capacity based on the harmony search algorithm are as follows:

[0007] Step 1. Establish a mathematical model of the offshore wind-solar hybrid energy storage system, including: wind power generation model, solar panel model, battery model, and supercapacitor model;

[0008] Step 2. Establish system capacity optimization objectives and constraints, including: minimum investment cost, maximum cost-effectiveness, minimum wind and solar curtailment rate, minimum load loss rate and power supply and demand balance constraints, battery charge state restrictions, and power and quantity restrictions of solar energy equipment;

[0009] Step 3: Obtain relevant data curves and equipment parameters and run the harmony search algorithm to obtain the optimal solution of the objective function and analyze the results.

[0010] Specifically, the specific contents of establishing the mathematical model of the offshore wind-solar complementary energy storage system in step 1 are as follows:

[0011] The establishment of the wind power generation model: When the wind turbine is turned on or off, the power generation of the wind power generation system is related to the wind speed, and its mathematical model is as shown in Expression 1:

[0012]

[0013] In the formula and Respectively represent the wind speed when the fan is started and stopped; and P e Respectively represent the rated wind speed and the output power at the rated wind speed; Indicates the current wind speed; T d Indicates the working time of the fan.

[0014] The formula for the monthly power generation is as shown in Expression 2:

[0015]

[0016] The establishment of the solar power module model: Solar power generation is related to the average peak sunshine hours of the solar panel, and its modeling is as shown in Expression 3:

[0017] E(σ)=DP b T k P z (Expression 3),

[0018] Where D is the number of days; P b is the power of solar photovoltaic panels; P z is the photovoltaic array power; T k is the average peak sunshine hours of photovoltaic panels in that month,

[0019] The battery model is established as follows: the energy storage E of a battery pack consisting of m batteries b (Mw·h) calculation formula is as shown in Expression 4:

[0020] E b =m·C b ·U b / 10 6 (Expression 4),

[0021] Where U b (V) is the rated voltage of each battery, C b (A·h) is the rated capacity,

[0022] The energy released by the battery is the highest at the maximum discharge depth, so the remaining energy of the battery is E bmin (Mw·h) can be calculated by the following formula:

[0023] E bmin =m·C b ·U b (1-DOD) / 10 6 (Expression 5),

[0024] Assuming that the battery is discharged at a normal C10 time rate, the formula for calculating the rated output power of the battery is:

[0025] P b =m·C b ·U b / 10 7 (Expression 6),

[0026] The supercapacitor model is established, and the total energy stored in the supercapacitor is:

[0027]

[0028] Where U c The terminal voltage of each supercapacitor is Ce, the capacitance value of the capacitor is Ce, and the supercapacitor group consists of n supercapacitors.

[0029] As can be seen from the above formula, the energy stored in the supercapacitor bank is related to the voltage, and the maximum stored energy is:

[0030]

[0031] Where E cmax Indicates the maximum voltage,

[0032] Similarly, the minimum energy storage value is:

[0033]

[0034] Where E cmin is the minimum voltage,

[0035] The maximum output power of the supercapacitor group can be expressed as:

[0036] P cmax =n·U cmax I cmax / 10 6 (Expression 10),

[0037] In the formula, I cmax is the maximum operating current of the supercapacitor.

[0038] Specifically, the specific contents of establishing the system capacity optimization target and constraint conditions in step 2 are as follows:

[0039] The minimum investment cost includes equipment planning, design, manufacturing, installation, use, maintenance and disposal. The total cost of the life cycle can be divided into four parts: procurement, operation, maintenance and disposal costs. Therefore, the energy storage system optimization target model is established as follows:

[0040] minC=C iv +C oc +C mc +C dc (Expression 11),

[0041] Among them, C iv is the equipment purchase cost; C oc is the operating cost, including the cost of experiment, installation, wear and tear, and labor; C mc Includes maintenance costs before and after a failure; C dc is the disposal cost, including scrapping cost and residual value cost,

[0042] The highest cost-effectiveness is because the solar power generation and load vary greatly in different months of each year, so the annual maximum economic benefit is calculated based on the month as the basic unit. Among them, the main reasons affecting the economic benefit include the installed capacity of different types of power sources and the switching power of the connection point. The established economic benefit model is as follows:

[0043]

[0044] Where P Wi (t) represents wind power installed capacity, P PVi (t) represents the photovoltaic installed capacity, E Bi (t) represents the installed capacity of energy storage, P gi (t) represents the grid-connected switch power, c1 and c2 represent the energy storage utilization rate and power conversion coefficient respectively.

[0045] The minimum wind and solar power abandonment rate is due to the uncertainty of wind power, photovoltaic power generation and load, which will inevitably produce wind and solar power abandonment rates, but these values ​​should be reduced as much as possible. The wind and solar power abandonment rate model is established as follows:

[0046]

[0047] where η EWR P represents the system abandonment rate of wind and solar power at time t, g (t) represents the power generation of the system, P UL (t) represents the load power, P PV (t) represents the photovoltaic power generation, P WT (t) represents wind power generation power,

[0048] The minimum load loss rate is an important indicator of power supply reliability and has always been an important indicator of power grid operation. A lower load power shortage rate should also be taken as an optimization target. The following model is established:

[0049]

[0050] Where P L is the load power failure rate; E y is the total annual load energy consumption; Represents the battery charging rate,

[0051] In summary, the objective function model is as follows:

[0052]

[0053] min C=C iv +C oc +C mc +C dc

[0054]

[0055] The power supply and demand balance constraint is that the energy distribution of the solar energy storage system must meet both the load requirements and the energy balance criteria of the system. At this time, the total solar power generation of the system should be equal to the load of the system. The total power generation is calculated by the following formula:

[0056] E(Z)=E(σ)+E(σ) (Expression 16),

[0057] The charging state of the battery is restricted. Since the battery should avoid overcharging or overdischarging, the charging state of the battery should be restricted:

[0058]

[0059] S OC,min ≤S OC (t)≤S OC,max (Expression 17),

[0060] Where S OC,min is the lower limit of safe operation, S OC,max is the upper limit of safe operation.

[0061] The power and quantity restrictions of the solar energy equipment are due to the fact that wind power generation must meet a certain range and the number of installed equipment must be limited. The constraints are as follows:

[0062]

[0063]

[0064] Where PPV,min , P PV,max , P WT,min , P WT,max Respectively represent the minimum and maximum output power of photovoltaic power generation system and wind turbine generator set, N PV,min 、N PV,max 、N WT,min 、N WT,max Represent the maximum installed capacity of photovoltaic power generation and wind power generation respectively.

[0065] Specifically, the principle of the harmony search algorithm in step 3 includes the following contents:

[0066] The unconstrained optimization problem can be defined as:

[0067] min f(X),X={x1,x2,…,x n}∈R n (Expression 19),

[0068] The main parameters of the harmony search algorithm are harmony search (HMS), pitch fine tuning probability (PAR), pitch memory value probability (HMCR) and pitch fine tuning bandwidth (BW). The harmonic variable is usually used to represent the variable of the solution space: X 1 , X 2 , …X HMS , put all potential energy solutions and the corresponding f(X) into the harmony library, which can be expressed as:

[0069]

[0070] Traditional new harmony generation requires comparing a random number r1 between [0, 1] and HMCR:

[0071] x new =x old r1<HMCR

[0072] x new ∈(0,1)r1>=HMCR(Expression 21),

[0073] where x new and x newi is the i-th component of the new solution before and after the perturbation,

[0074] Since the harmony variables of the classic harmony algorithm are randomly selected from the harmony memory, in order to improve the optimization ability of the algorithm, a new harmony generation algorithm is adopted, and the formula is as follows:

[0075]

[0076] where η1 and η2 are random parameters in [0, 1], u is the bandwidth, u is a random number in [0, 1], when η2 > PAR and < HMCR, a new harmony is randomly selected from the harmony library,

[0077] when η2 ≤ PAR and η1 ≤ HMCR, a new harmony is selected from the old harmonics and the newly generated harmonics, making full use of the newly generated information. In other cases, a new harmony is generated by combining the new harmony library information with the bandwidth information u.

[0078] Before updating the harmony repository, the newly obtained harmony x new needs to be evaluated using the objective function, that is, u is evaluated. Only when the newly generated function value is better than the worst function value in the harmony library, that is, f(x new ) < f(x worst ) is it necessary to use the worst function value x worst in HM to replace the harmony x new . In other cases, no modification is required. The above steps are repeatedly calculated until the number of iterations reaches Tmax.

[0079] Specifically, the specific content of obtaining relevant data curves and equipment parameters in step 3 and running the harmony search algorithm to obtain the optimal solution of the objective function and analyzing the results is as follows:

[0080] 1) Obtain relevant parameter data of the energy storage power generation system;

[0081] 2) Obtain data on the offshore sunshine intensity, sunshine duration, wind speed, monthly average solar power generation, wind power generation, and monthly average load power consumption;

[0082] 3) Obtain relevant parameters of wind energy and solar energy equipment;

[0083] 4) Analyze the capacity optimization effect of the obtained parameters.

[0084] Advantages of the present invention:

[0085] By establishing mathematical models of key equipment such as photovoltaic, fan, supercapacitor, and battery in the wind-solar complementary energy storage system, modeling the wind-solar complementary system, and then taking low investment cost, high economic benefits, low wind and light abandonment rates, and low load power loss rates as optimization objectives to establish a system capacity optimization model, and finally using an improved harmony search algorithm to solve the optimization problem, the present invention can obtain high power supply reliability with less investment. Brief Description of the Drawings

[0086] Figure 1 It is a schematic structural diagram of an offshore wind-solar-storage microgrid system;

[0087] Figure 2This is a schematic diagram of the change of monthly average sunshine intensity at sea;

[0088] Figure 3 This is a schematic diagram of the changes in the average monthly sunshine hours at sea;

[0089] Figure 4 This is a schematic diagram of the change of the monthly average wind speed at sea;

[0090] Figure 5 Schematic diagram of average monthly offshore solar and wind power generation;

[0091] Figure 6 It is a schematic diagram of the average monthly power consumption of the load;

[0092] Figure 7 A schematic diagram comparing the optimization results of equipment quantity and cost;

[0093] Figure 8 A comparative diagram of power outage rate optimization results. DETAILED DESCRIPTION

[0094] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] Example 1

[0096] The specific steps of the optimal allocation method of offshore wind power hybrid energy storage capacity based on the harmony search algorithm are as follows:

[0097] Step 1. Establish a mathematical model of the offshore wind-solar hybrid energy storage system, including: wind power generation model, solar panel model, battery model, and supercapacitor model;

[0098] Step 2. Establish system capacity optimization objectives and constraints, including: minimum investment cost, maximum cost-effectiveness, minimum wind and solar curtailment rate, minimum load loss rate and power supply and demand balance constraints, battery charge state restrictions, and power and quantity restrictions of solar energy equipment;

[0099] Step 3. Obtain relevant data curves and equipment parameters and run the harmony search algorithm to obtain the optimal solution of the objective function and analyze the results.

[0100] Specifically, the specific contents of establishing the mathematical model of the offshore wind-solar hybrid energy storage system in step 1 are as follows:

[0101] Establishment of wind power generation model: When the wind turbine is turned on or off, the power generation of the wind power generation system is related to the wind speed. Its mathematical model is shown in Expression 1:

[0102]

[0103] In the formula and Respectively represent the wind speed when the fan is started and stopped; and P e Respectively represent the rated wind speed and the output power at the rated wind speed; Indicates the current wind speed; T d Indicates the fan working time.

[0104] The formula for the monthly power generation is as shown in Expression 2:

[0105]

[0106] Establishment of solar power module model: Solar power generation is related to the average peak sunshine hours of solar panels, and its modeling is as shown in Expression 3:

[0107] E(σ)=DP b T k P z (Expression 3),

[0108] Where D is the number of days; P b is the power of solar photovoltaic panels; P z is the photovoltaic array power; T k It is the average peak sunshine hours of photovoltaic panels in that month.

[0109] Battery model establishment: The energy storage E of a battery pack consisting of m cells b (Mw·h) calculation formula is as shown in Expression 4:

[0110] E b =m·C b ·U b / 10 6 (Expression 4),

[0111] Where U b (V) is the rated voltage of each battery, C b (A·h) is the rated capacity.

[0112] The energy released by the battery is the highest at the maximum discharge depth, so the remaining energy of the battery is E bmin (Mw·h) can be calculated by the following formula:

[0113] E bmin =m·C b ·U b(1-DOD) / 10 6 (Expression 5),

[0114] Assuming that the battery is discharged at a normal C10 time rate, the formula for calculating the rated output power of the battery is:

[0115] P b =m·C b ·U b / 10 7 (Expression 6),

[0116] The supercapacitor model is established, and the total energy stored in the supercapacitor is:

[0117]

[0118] Where U c The terminal voltage of each supercapacitor is Ce, the capacitance value of the capacitor is Ce, and the supercapacitor group consists of n supercapacitors.

[0119] As can be seen from the above formula, the energy stored in the supercapacitor bank is related to the voltage, and the maximum stored energy is:

[0120]

[0121] Where E cmax Indicates the maximum voltage.

[0122] Similarly, the minimum energy storage value is:

[0123]

[0124] Where E cmin is the minimum voltage.

[0125] The maximum output power of the supercapacitor group can be expressed as:

[0126] P cmax =n·U cmax I cmax / 10 6 (Expression 10),

[0127] In the formula, I cmax is the maximum operating current of the supercapacitor.

[0128] Specifically, the specific contents of establishing the system capacity optimization objectives and constraints in step 2 are as follows:

[0129] The minimum investment cost includes the planning, design, manufacturing, installation, use, maintenance and disposal of equipment. The total cost of the life cycle can be divided into four parts: procurement, operation, maintenance and disposal costs. Therefore, the optimization target model of the energy storage system is established as follows:

[0130] minC=C iv +C oc +C mc +C dc (Expression 11),

[0131] Among them, C iv is the equipment purchase cost; C oc is the operating cost, including the cost of experiment, installation, wear and tear, and labor; C mc Includes maintenance costs before and after a failure; C dc It is the disposal cost, including scrapping cost and residual value cost.

[0132] The highest cost-effectiveness. The solar power generation and load vary greatly in different months of each year. Therefore, the annual maximum economic benefit is calculated based on the month as the basic unit. The main reasons affecting the economic benefit include the installed capacity of different types of power sources and the switching power of the connection point. The economic benefit model that can be established is as follows:

[0133]

[0134] Where P Wi (t) represents wind power installed capacity, P PVi (t) represents the photovoltaic installed capacity, E Bi (t) represents the installed capacity of energy storage, P gi (t) represents the grid-connected switch power, c1 and c2 represent the energy storage utilization rate and power conversion coefficient, respectively.

[0135] Minimum wind and solar power abandonment rate. Due to the uncertainty of wind power, photovoltaic power generation and load, there will be inevitable wind and solar power abandonment rates, but these values ​​should be reduced as much as possible. The wind and solar power abandonment rate model is established as follows:

[0136]

[0137] where η EWR P represents the system abandonment rate of wind and solar power at time t, g (t) represents the power generation of the system, P UL (t) represents the load power, P PV (t) represents the photovoltaic power generation, P WT (t) represents wind power generation power.

[0138] Minimum load loss rate and power supply reliability have always been important indicators of power grid operation. A lower load power shortage rate should also be taken as an optimization goal. The following model is established:

[0139]

[0140] Where P L is the load power failure rate; Ey is the total annual load energy consumption; Represents the battery charge rate.

[0141] In summary, the objective function model is as follows:

[0142]

[0143] min C=C iv +C oc +C mc +C dc

[0144]

[0145]

[0146] The power supply and demand balance constraint, the energy distribution of the solar energy storage system must meet both the load requirements and the energy balance criteria of the system. At this time, the total solar power generation of the system should be equal to the system load, and the total power generation is calculated by the following formula:

[0147] E(Z)=E(σ)+E(σ) (Expression 16).

[0148] The battery's charging state is limited. The battery should avoid overcharging or over-discharging. Therefore, the battery's charging state should be restricted:

[0149]

[0150] S OC,min ≤S OC (t)≤S OC,max (Expression 17),

[0151] Where S OC,min is the lower limit of safe operation, S OC,max The upper limit for safe operation.

[0152] The power and quantity of solar energy equipment are limited. Wind power generation must meet certain ranges and the number of installed equipment must be limited. The constraints are as follows:

[0153]

[0154] Where P PV,min , P PV,max , P WT,min , P WT,max Respectively represent the minimum and maximum output power of photovoltaic power generation system and wind turbine generator set, N PV,min 、N PV,max 、N WT,min 、N WT,maxrespectively represent the maximum installed capacity of photovoltaic power generation and wind power generation.

[0155] Specifically, the principle of the harmony search algorithm described in step 3 includes the following content:

[0156] The unconstrained optimization problem can be defined as:

[0157] min f(X), X = {x1, x2, …, x n} ∈ R n (Expression 19),

[0158] The main parameters of the harmony search algorithm are harmony memory size (HMS), pitch adjustment rate (PAR), harmony memory considering rate (HMCR), and bandwidth (BW). The harmony variables are usually used to represent the variables in the solution space: X 1 , X 2 , … X HMS Put all potential solutions and the corresponding f(X) into the harmony memory. The form of the harmony memory can be expressed as:

[0159]

[0160] Traditional new harmony generation requires comparing the random number r1 between [0, 1] and HMCR:

[0161] x new = x old r1 < HMCR

[0162] x new ∈(0, 1) r1 >= HMCR (Expression 21),

[0163] where x new and x newi are the i-th components of the new solution before and after perturbation,

[0164] Since the harmony variables of the classical harmony algorithm are randomly selected from the harmony memory, in order to improve the optimization ability of the algorithm, a new harmony generation algorithm is adopted, and the formula is as follows:

[0165]

[0166] where η1 and η2 are random parameters in [0, 1], u is the bandwidth, u is a random number in [0, 1]. When η2 > PAR and < HMCR, a new harmony is randomly selected from the harmony memory,

[0167] When η2 ≤ PAR and η1 ≤ HMCR, a new harmony is selected from the old harmonies and the newly generated harmonies, making full use of the newly generated information. In other cases, a new harmony is generated by combining the new harmony memory information with the bandwidth information u,

[0168] Before updating the harmony repository, the newly obtained harmony x needs to be calibrated using the objective function. new Evaluation is performed, that is, evaluation is performed on u. Only when the newly generated function value is better than the worst function value in the harmony library, that is, f(x new )<f(x worst ), then we need to use the worst function value x in HM worst Replace harmony x new , In other cases, no modification is required and the above steps are repeated until the number of iterations reaches Tmax.

[0169] Specifically, the specific contents of obtaining relevant data curves and equipment parameters and running the harmony search algorithm to obtain the optimal solution of the objective function and analyzing the results in step 3 are as follows:

[0170] 1) Obtain relevant parameter data of energy storage power generation system;

[0171] 2) Obtain data on offshore sunshine intensity, sunshine hours, wind speed, monthly average solar power generation, wind power generation, and monthly average load power consumption, such as Figure 2 , 3 , 4, 5, 6;

[0172] 3) Obtain relevant parameters of wind and solar energy equipment;

[0173] 4) Analyze the capacity optimization effect of the obtained parameters, such as Figure 7 and 8 shown.

[0174] The wind-solar complementary energy storage capacity optimization strategy proposed in the present invention can reduce the system power shortage rate while reducing the number of equipment and investment amount, thereby rationally configuring system equipment and providing ideas for the wind-solar complementary energy storage capacity configuration.

[0175] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The method for optimizing the allocation of offshore wind power hybrid energy storage capacity based on the harmony search algorithm has the following specific steps: Step 1. Establish a mathematical model of the offshore wind-solar hybrid energy storage system, including: Wind power generation model, solar panel model, battery model, supercapacitor model; Step 2. Establish system capacity optimization objectives and constraints, including: minimum investment cost, maximum cost-effectiveness, minimum wind and solar curtailment rate, minimum load loss rate and power supply and demand balance constraints, battery charge state restrictions, and power and quantity restrictions of solar energy equipment; Step 3: Obtain relevant data curves and equipment parameters and run the harmony search algorithm to obtain the optimal solution of the objective function and analyze the results.

2. The offshore wind power hybrid energy storage capacity optimization allocation method based on the harmony search algorithm according to claim 1 is characterized in that: The specific contents of establishing the mathematical model of the offshore wind-solar hybrid energy storage system in step 1 are as follows: The establishment of the wind power generation model: When the wind turbine is turned on or off, the power generation of the wind power generation system is related to the wind speed, and its mathematical model is as shown in Expression 1: In the formula and Respectively represent the wind speed when the fan is started and stopped; and P e Respectively represent the rated wind speed and the output power at the rated wind speed; Indicates the current wind speed; T d Indicates the working time of the fan. The formula for power generation in the current month is as shown in Expression 2: The solar power module model is established: Solar power generation is related to the average peak sunshine hours of the solar panel, and its modeling is as shown in Expression 3: E(σ)=DP b T k P z (Expression 3), Where D is the number of days; P b is the power of solar photovoltaic panels; P z is the photovoltaic array power; T k is the average peak sunshine hours of photovoltaic panels in that month, The battery model is established as follows: the energy storage E of a battery pack consisting of m batteries b (Mw·h) calculation formula is as shown in Expression 4: E b =m·C b ·U b / 10 6 (Expression 4), Where U b (V) is the rated voltage of each battery, C b (A·h) is the rated capacity, The energy released by the battery is the highest at the maximum discharge depth, so the remaining energy of the battery is E bmin (Mw·h) can be calculated by the following formula: E bmin =m·C b ·U b (1-DOD) / 10 6 (Expression 5), Assuming that the battery is discharged at a normal C10 time rate, the formula for calculating the rated output power of the battery is: P b =m·C b ·U b / 10 7 (Expression 6), The supercapacitor model is established, and the total energy stored in the supercapacitor is: Where U c The terminal voltage of each supercapacitor is Ce, the capacitance value of the capacitor is Ce, and the supercapacitor group consists of n supercapacitors. As can be seen from the above formula, the energy stored in the supercapacitor bank is related to the voltage, and the maximum stored energy is: Where E cmax Indicates the maximum voltage, Similarly, the minimum energy storage value is: Where E cmin is the minimum voltage, The maximum output power of the supercapacitor group can be expressed as: P cmax =n·U cmax I cmax / 10 6 (Expression 10), In the formula, I cmax is the maximum operating current of the supercapacitor.

3. The offshore wind power hybrid energy storage capacity optimization allocation method based on the harmony search algorithm according to claim 1 is characterized in that: The specific contents of establishing the system capacity optimization objectives and constraints in step 2 are as follows: The minimum investment cost includes equipment planning, design, manufacturing, installation, use, maintenance and disposal. The total cost of the life cycle can be divided into four parts: procurement, operation, maintenance and disposal costs. Therefore, the energy storage system optimization target model is established as follows: minC=C iv +C oc +C mc +C dc (Expression 11), Among them, C iv is the equipment purchase cost; C oc is the operating cost, including the cost of experiment, installation, wear and tear, and labor; C mc Includes maintenance costs before and after a failure; C dc is the disposal cost, including scrapping cost and residual value cost, The highest cost-effectiveness is because the solar power generation and load vary greatly in different months of each year, so the annual maximum economic benefit is calculated based on the month as the basic unit. Among them, the main reasons affecting the economic benefit include the installed capacity of different types of power sources and the switching power of the connection point. The established economic benefit model is as follows: Where P Wi (t) represents wind power installed capacity, P PVi (t) represents the photovoltaic installed capacity, E Bi (t) represents the installed capacity of energy storage, P gi (t) represents the grid-connected switch power, c1 and c2 represent the energy storage utilization rate and power conversion coefficient respectively. The minimum wind and solar power abandonment rate is due to the uncertainty of wind power, photovoltaic power generation and load, which will inevitably produce wind and solar power abandonment rates, but these values ​​should be reduced as much as possible. The wind and solar power abandonment rate model is established as follows: where η EWR P represents the system abandonment rate of wind and solar power at time t, g (t) represents the power generation of the system, P UL (t) represents the load power, P PV (t) represents the photovoltaic power generation, P WT (t) represents wind power generation power, The minimum load loss rate is an important indicator of power supply reliability and has always been an important indicator of power grid operation. A lower load power shortage rate should also be taken as an optimization target. The following model is established: Where P L is the load power failure rate; E y is the total annual load energy consumption; Represents the battery charging rate, In summary, the objective function model is as follows: my C=C iv +C oc +C mc +C dc The power supply and demand balance constraint is that the energy distribution of the solar energy storage system must meet both the load requirements and the energy balance criteria of the system. At this time, the total solar power generation of the system should be equal to the load of the system. The total power generation is calculated by the following formula: E(Z)=E(σ)+E(σ) (Expression 16), The charging state of the battery is restricted. Since the battery should avoid overcharging or overdischarging, the charging state of the battery should be restricted: S OC,min ≤S OC (t)≤S OC,max (Expression 17), Where S OC,min is the lower limit of safe operation, S OC,max is the upper limit of safe operation. The power and quantity restrictions of the solar energy equipment are due to the fact that wind power generation must meet a certain range and the number of installed equipment must be limited. The constraints are as follows: Where P PV,min , P PV,max , P WT,min , P WT,max Respectively represent the minimum and maximum output power of photovoltaic power generation system and wind turbine generator set, N PV,min 、N PV,max 、N WT,min 、N WT,max Represent the maximum installed capacity of photovoltaic power generation and wind power generation respectively.

4. The offshore wind power hybrid energy storage capacity optimization allocation method based on the harmony search algorithm according to claim 1 is characterized in that: The principle of the harmony search algorithm described in step 3 includes the following content: The unconstrained optimization problem can be defined as: minf(X),X={x1,x2,…,x n }∈R n (Expression 19), The main parameters of the harmony search algorithm are harmony search (HMS), pitch fine tuning probability (PAR), pitch memory value probability (HMCR) and pitch fine tuning bandwidth (BW). The harmonic variable is usually used to represent the variable of the solution space: X 1 , X 2 , …X HMS , put all potential energy solutions and the corresponding f(X) into the harmony library, which can be expressed as: Traditional new harmony generation requires comparing the random number r1 between [0, 1] and HMCR: x new =x old r1<HMCR x new ∈(0,1)r1>=HMCR (Expression 21), where x new and x newi is the i-th component of the new solution before and after the perturbation, Since the harmony variables of the classical harmony algorithm are randomly selected from the harmony memory, in order to improve the optimization ability of the algorithm, a new harmony generation algorithm is adopted, and the formula is as follows: Where η1 and η2 are random parameters in [0, 1], u is the bandwidth, u is a random number in [0, 1], when η2 > PAR and < HMCR, a new harmony is randomly selected from the harmony library, When η2 ≤ PAR and η1 ≤ HMCR, a new harmony is selected from the old harmonics and the newly generated harmonics, making full use of the newly generated information. In other cases, a new harmony is generated by combining the new harmony library information with the bandwidth information u, Before updating the harmony repository, the newly obtained harmony x needs to be calibrated using the objective function. new Evaluation is performed, that is, evaluation is performed on u. Only when the newly generated function value is better than the worst function value in the harmony library, that is, f(x new )<f(x worst ), then we need to use the worst function value x in HM worst Replace harmony x new , In other cases, no modification is required and the above steps are repeated until the number of iterations reaches Tmax.

5. The offshore wind power hybrid energy storage capacity optimization allocation method based on the harmony search algorithm according to claim 1 is characterized in that: The specific content of obtaining the relevant data curve and equipment parameters in step 3, running the harmony search algorithm to obtain the optimal solution of the objective function and analyzing the results is as follows: 1) Obtain the relevant parameter data of the energy storage power generation system; 2) Obtain the data of the offshore sunshine intensity, sunshine duration, wind speed, monthly average solar power generation, wind power generation, and monthly average load power consumption; 3) Obtain the relevant parameters of the wind energy and solar energy equipment; 4) Analyze the capacity optimization effect of the obtained parameters.

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