CN113779883B - Wind power energy storage system charging and discharging process optimization method based on variant artificial fish school - Google Patents
Wind power energy storage system charging and discharging process optimization method based on variant artificial fish school Download PDFInfo
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Abstract
The invention discloses a wind power energy storage system charging and discharging process optimization method based on a variant artificial fish school, which comprises the following steps: acquiring real-time running state information of each storage battery pack according to real-time monitoring equipment of the wind power energy storage system; according to the acquired state information, introducing a plurality of pieces of artificial fish information, establishing artificial fish shoals and establishing a bulletin board; the artificial fish school generates variation and starts iteration; the artificial fish shoal selects corresponding behaviors based on self perception and environmental feedback; recording the position of each artificial fish and updating the bulletin board; the system performs discharging or charging according to whether the sum of the full energy numbers of the detection points reaches the set maximum food concentration or the set minimum food concentration; when the sum of the full energy points of the detection points of the system is below the maximum food concentration setting, the normal operation of the system is restored; and outputting a system operation result. The invention introduces the concept of variant fish, and optimizes wind power by combining a variant artificial fish swarm algorithm with the compensation of a fan energy storage system.
Description
Technical Field
The invention relates to a wind power energy storage system, in particular to a wind power energy storage system charging and discharging process optimization method based on a variant artificial fish school.
Background
In order to respond to the call of the nation about energy cleaning, the construction work of wind turbine generators is greatly promoted in the nation, the energy utilization proportion of wind power productivity in the nation is continuously improved, and a large amount of clean wind energy naturally exists in the northeast area of the nation, so that a large amount of wind turbine generators are installed, and the due contribution to the cleaning of the energy is made. However, in northeast areas, wind seasons and no wind seasons exist, abundant wind energy does not cover the whole year, the period between 9-11 months and 1-5 months of each year is the most abundant time period of wind energy, and during the period between 6 months and 8 months, almost no wind energy is needed, so that the energy storage battery is required to be introduced to complete the consumption of electric energy, when the wind is more, the electric energy is converted and the super-power generation energy on the upper limit of the net is exceeded, the electric energy enters the energy storage battery to be stored, and when the wind is less, the energy storage battery starts to output the stored electric energy to make up for the deficiency of the output of the fan. Through the combination of the energy storage battery system and the wind power output prediction result, the energy storage battery system is used for participating in the regulation of wind power output, so that the fluctuation problem of wind power output power is effectively and timely smoothed and compensated, the production and consumption of electric energy are decoupled in time, the electric energy is stored in the time of load low-peak, and the electric energy is released in the time of load high-peak, so that the wind power system can provide economic and effective clean energy. Meanwhile, the energy storage battery system is superior to thermal power regulation and control in response time in the adjustable range, the capacity of the thermal power unit required by the wind power regulation and control is more than 2 times that of the wind power unit, and the energy storage battery system only needs to provide 20 percent of the total installed capacity of wind power, so that a large amount of cost is saved, and the energy storage battery system has practical application value,
there are two field conditions based on energy storage battery systems:
case 1: when the energy storage battery system is full, the battery charging is required to be stopped in time, and the damage or other accidents of the energy storage battery system caused by the overcharging of the energy storage battery system are prevented.
Case 2: when the wind speed is too high at a certain moment, the fan blade rotating speed is too high, and at the moment, if the energy storage battery system is charged, the energy storage battery system is damaged due to the fact that the charging current is too high, the energy storage battery system is needed to be cut off to stop charging when the situation occurs, and when the charging current returns to the normal range, the energy charging is continued.
Disclosure of Invention
The invention aims to provide a wind power energy storage system charging and discharging process optimization method based on a variant artificial fish school. According to the method, a variant artificial fish swarm algorithm is introduced, and wind power is optimized by combining the variant artificial fish swarm algorithm with fan energy storage system compensation.
In order to solve the problems existing in the prior art, the invention adopts the following technical scheme:
a wind power energy storage system charging and discharging process optimization method based on variant artificial fish shoals comprises the following steps:
s1) acquiring real-time running state information of each storage battery pack according to real-time monitoring equipment of a wind power energy storage system;
s2) introducing a plurality of artificial fish information according to the acquired state information, establishing artificial fish shoals and establishing bulletin boards;
s3) artificial fish shoals are arranged to generate variation, and variant fish with larger visual field and step length are generated, and iteration is started;
s4) a step of selecting corresponding behaviors of the artificial fish shoals based on self perception and environmental feedback is arranged;
s5) recording the position information of each artificial fish after each iteration to a bulletin board, and outputting bulletin board information in real time after each iteration is finished;
s6) judging whether the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration or not;
s7) if the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration, stopping the system from charging, and only allowing stopping or discharging to occur at the moment;
s8) a step of recovering the normal operation of the system until the total energy after detection of each energy storage battery in the energy storage system is below the maximum food concentration setting;
s9) judging whether the total energy after detection of each energy storage battery in the energy storage system is lower than the minimum food concentration or not;
s10) if the total energy after detection of each energy storage battery in the energy storage system is lower than the minimum food concentration, stopping discharging the system, and only allowing stopping or charging to occur;
s11) a step of recovering the system to a normal running state until the total energy after detection of each energy storage battery in the system exceeds the set minimum food concentration after multiple iterations;
s12) after each iteration, judging whether the value of the current iteration I| exceeds the defined maximum charge and discharge power;
s13) if the value of I exceeds the defined maximum charge and discharge power, stopping the charge and discharge operation of the system;
s14) a step of recovering the charge/discharge operation of the system until the value of i is within the defined maximum charge/discharge power.
S15) setting the end of one iteration process, repeating the processes from S3) to S14), and carrying out the steps of the next iteration process.
S16) a step of outputting a system operation result after each iteration is provided.
Wherein, the artificial fish individuals in the artificial fish shoal carry the number information of the detection points of the energy storage battery, the state information of the energy storage battery, the step length, the field of view, the iteration limit, the fish shoal moving range and the food concentration of the position of the artificial fish in the last iteration.
The method comprises the steps of firstly, carrying out first iteration on a first fish, then carrying out second iteration on the first fish, wherein the mutant fish is randomly generated by adopting a random () function, the number of the mutant fish is n, the mutant fish is recovered to be common fish after one iteration is completed, and the mutant fish is regenerated before the second iteration is started, so that the second iteration is carried out; the quantity of the variant fish accounts for 10-20% of the quantity of the fish shoal.
Sometimes, the artificial fish school undergoes a gathering behavior:
in the range of artificial fish, the center position of the artificial fish shoal is x c The method comprises the steps of carrying out a first treatment on the surface of the Normal artificial fish performs the action of the formula (3), variant artificial fish performs the action of the formula (4), so that the artificial fish moves from the position xi to the next position point, after the action is finished, the bulletin board is updated, and otherwise, other actions are performed;
Sometimes, the artificial fish school generates rear-end collision behavior:
when the food is found in the visual field of the artificial fish, the artificial fish goes to a high food concentration place, other artificial fish follow the previous artificial fish to carry out rear-end collision, and the fish position of the first found food source is x d The other artificial fish moves from xi to the next position after rear-end collisionA dot; normal artificial fish will execute the action of the formula (5), variant artificial fish will execute the action of the formula (6), after the action is completed, the bulletin board is updated, otherwise, other actions are performed;
Sometimes, the artificial fish school undergoes foraging behavior:
the artificial fish can move to the next position point with higher food concentration from xi, the common artificial fish adopts the action of the formula (7) to feed, the variant artificial fish adopts the action of the formula (8) to feed, otherwise, other actions are carried out;
Sometimes, the artificial fish school takes on a suboptimal behavior:
the position of the mutant fish is x f The ordinary fish goes to the next position point from xi according to the step length of the ordinary fish, and after the iterative process, the ordinary fish continues to move to the next position point, and the ordinary fish adopts the behavior of the formula (9):
When the artificial fish cannot perform four behaviors of clustering, rear-end collision, foraging and preferred, selecting to perform random behaviors;
the fish shoal randomly moves to the next position point from the xi according to the step length of the fish shoal, other behaviors are selected through iteration, if the four behaviors of gathering, rear-end collision, foraging and optimizing cannot be carried out, the random behaviors are selected continuously, normal artificial fish adopts the behavior of the formula (10), and variant artificial fish adopts the behavior of the formula (11):
The invention has the advantages and beneficial effects that:
the invention introduces a variant artificial fish swarm algorithm to optimize the charging process, and rated voltage of each storage battery in the wind power energy storage battery system is U a (V) rated capacity of C a (Ah) present electric power storageThe pool group is formed by combining n storage batteries, and the total energy storage energy is E a (MWh) is:
E a ＝n·C a ·U a /10 6 (1)
simultaneously setting the maximum depth of discharge DOD, and when the system reaches the maximum discharge condition, the residual quantity of the storage battery pack is the minimum energy storage quantity E amin The method comprises the following steps:
E am in＝n·C a ·U a ·(1-DOD)/10 6 (2)
the energy storage battery has three running states, namely a charging state, a discharging state and a stopping state in wind power regulation, the charging state and the discharging state cannot occur simultaneously, and the energy storage battery is not in an overcharging and discharging state or is in an overcharging and discharging state for prolonging the service time of the energy storage battery.
In order to timely separate the system from the overcharging or overdischarging, a variant artificial fish swarm intelligent algorithm is introduced, and the overcharging starting and stopping processes of the energy storage battery are controlled through the algorithm.
To handle both existing cases simultaneously, a complex concept is introduced. In the initialization process of the artificial fish shoal, the artificial fish carries the detected energy storage information of each battery in the battery pack, the state of the current detection point storage battery is marked as 1 when the state of the current detection point storage battery is full of energy, the state of the current detection point storage battery reaches the maximum discharging condition, the state of the current detection point storage battery is marked as i, and the information detected by the mark-i is used as the information carried by the artificial fish when the current detection point storage battery is in a discharging state. The method comprises the steps of setting the maximum food concentration, namely the maximum energy charging energy, as a limit, stopping the energy charging action of an energy storage system when the fish school reaches the position of the maximum food concentration and is equal to or larger than the set maximum food concentration, discarding redundant wind energy, setting the minimum food concentration as the lower limit of energy storage, namely the electric energy storage capacity when the maximum depth of discharge is reached, and re-charging when the maximum food concentration value reached after the iteration of the fish school is smaller than the set minimum food concentration, so that the energy storage system is ensured to be not excessively charged, but not excessively discharged, and the service life of the system is prolonged. Meanwhile, in order to prevent the damage of the energy storage system caused by the too high energy charging speed due to excessive wind energy surplus, aiming at the i value, when the i value is smaller than the maximum charge and discharge power limit, the system is charged and discharged normally, when the i value exceeds the maximum charge and discharge power limit, the charging is stopped, the surplus wind energy is discarded, and when the food concentration change rate is lower than the maximum food concentration change rate again, the energy storage battery system is restarted.
The invention provides a variant artificial fish swarm algorithm, introduces a concept of variant fish, introduces a plurality of information carried by the artificial fish, satisfies the state detection of the fish swarm on the food concentration through a constant in the carried information, and optimizes wind power by combining the variant artificial fish swarm algorithm with the compensation of a fan energy storage system through the state detection of the plurality of the carried information on the food concentration change rate of the fish swarm.
Drawings
The invention is further described in detail below with reference to the attached drawing figures:
FIG. 1 is a block diagram of a wind power generation and energy storage system;
FIG. 2 is a graph of wind power output power over 48 hours;
FIG. 3 is a graph of generated power over 48 hours in a wind farm;
FIG. 4 is a schematic diagram of the energy storage system compensation without algorithm optimization;
FIG. 5 is a schematic diagram of the optimization of the energy storage system compensation by the variant artificial fish swarm algorithm;
FIG. 6 is a graph of power output to a load after 48 hours integration of a wind farm;
FIG. 7 is a flowchart of a method for optimizing the charge and discharge processes of a wind power energy storage system based on a variant artificial fish school.
Detailed Description
The present invention will be described in further detail with reference to the following examples, but the scope of the present invention is not limited to the examples, and the claims should be construed. In addition, any modification or variation which can be easily realized by those skilled in the art without departing from the technical scheme of the present invention falls within the scope of the claims of the present invention.
As shown in FIG. 7, the method for optimizing the charge and discharge process of the wind power energy storage system based on the variant artificial fish school, disclosed by the invention, comprises the following steps of:
s1) detecting and acquiring the energy storage state of each battery in the storage battery pack according to the battery state of the wind power energy storage system;
s2) introducing a plurality of pieces of information according to the acquired state information, giving artificial fish information, establishing artificial fish shoals and establishing bulletin boards:
setting the full energy state of the detection point as 1, setting the detection point as 0 when reaching the maximum discharge condition, setting the monitoring point as i when being in the charge state, setting the detection point as-i when being in the discharge state, and setting the detection point as 0i when the discharge is in the stop state. At this time, the artificial fish has the following information carrying states:
(1) 1+i: the detection point is full of energy and continues to be full of energy;
(2) 1+0: the detection point is full of energy and no charge-discharge action exists;
(3) 1-i: the detection point is full of energy and is discharging;
(4) 0+i: the detection point is energy-free and in a charging state;
(5) 0+0i: the detection point has no energy and no charge and discharge actions;
(6) 0-i: the detection point is energy-free and in a discharge action.
The artificial fish carries the number information of the detection point of the energy storage battery, the state information of the energy storage battery, the step length, the visual field, the iteration limit, the movable range of the fish shoal and the food concentration of the position of the artificial fish in the last iteration.
S3) artificial fish shoals are arranged to generate variation, and the step of generating variation fish with larger visual field and step length is started to iterate:
introducing variant fish, randomly generating n variant fish by Rand (), wherein the variant fish has larger moving step length and larger perception range than the common fish, recovering the variant fish into the common fish after one iteration is completed, regenerating the variant fish before the second iteration is started, and performing the second iteration. The common artificial fish is arranged in the set maximum Visual field (Visual) V1The fixed Step (Step) S1 is moved within a range where the crowding degree is set to be δ, and the crowding degree caused by the overall behavior of the fish shoal needs to be satisfied so as not to exceed the set crowding degree. The distance between the maximum artificial fish is d i,j ＝||x i -x j The randomly generated variant fish has V2 (2V 1)>V2>V1) and has a visual field of S2 (2S 1)>S2>S1), the sensitivity to local data change is easily lost due to the large visual field and the large step, and the difference between the step and the visual field after mutation and the step and the visual field of common fish is limited, so that the reliability of application of the fish is prevented from being lost. In addition, the mutation generation rate is strictly limited, the too small ratio of the mutated fish is meaningless for improving the defect of sinking into local optimum of the artificial fish swarm algorithm, and the too large ratio of the mutated fish can lead all the fish to move towards one optimum point or less optimum points, so that the optimizing significance is lost, the quantity of the mutated fish is optimal when the quantity of the mutated fish accounts for 10-20% of the quantity of the fish swarm, the effect of preventing local optimum can be achieved, and the loss of sensitivity to data can be prevented.
S4) based on the method, starting iteration, selecting corresponding foraging, clustering, rear-end collision, optimizing and random behaviors by the artificial fish shoal based on self perception and environmental feedback.
Artificial fish selection occurs with aggregation behavior: in the range of artificial fish, the center position of the artificial fish shoal is x c . Normal artificial fish performs (3) action, variant artificial fish performs (4) action, so that the artificial fish moves from position xi to the next position point, after the action is completed, the bulletin board is updated, otherwise, other actions are performed, and the artificial fish simulates the shoal to complete the shoal gathering through (3) (4), thereby avoiding dangerous actions, and ensuring that the set crowding degree is met in the shoal gathering action.
The artificial fish is selected to have rear-end collision behavior: when the food is found in the visual field of the artificial fish, the artificial fish goes to a high food concentration place, other artificial fish follow the previous artificial fish to carry out rear-end collision, and the fish position of the first found food source is x d Other artificial fish move from xi to the next location point in a rear-end collision. Normal artificial fish will execute (5) action, variant artificial fish will execute (6) action, update bulletin board after completing action, otherwise do other actions.
Artificial fish selection takes place foraging behavior: artificial fish passing through sensing highest position x of food concentration in visual field e The artificial fish can move to a position with higher food concentration, the artificial fish can move to the next position point with higher food concentration from xi, the common artificial fish adopts the (7) action to feed, the variant artificial fish adopts the (8) action to feed, and otherwise, other actions are carried out.
Artificial fish selection occurs from the optimal behavior: weak and small individual habit in the fish swarm follows more powerful individuals, when variant fish exist in the fish swarm, the characteristics of large visual field and large step length of the variant fish attract normal fish to follow, and the position of the variant fish is x f When the ordinary fish is not at the optimal point, in order to find the position with higher food concentration in a wider range, the ordinary fish can follow the action of the variant fish with special characteristics, the ordinary fish moves from the xi position to the next position point according to the step length of the ordinary fish, and after the iterative process, the ordinary fish continues to move to the next position point, and the ordinary fish can adopt the action of (9).
When the artificial fish cannot generate the four behaviors, selecting to generate random behaviors, wherein the random behaviors are natural behaviors of the fish shoal, changing the position of the artificial fish through the random behaviors, randomly moving from the xi position to the next position point according to the step length of the artificial fish, and iteratively selecting other behaviors, if the behaviors still cannot be completed, continuing to select the random behaviors, wherein the normal artificial fish adopts the (10) behaviors, and the variant artificial fish adopts the (11) behaviors.
S5) recording the position information of each artificial fish after each iteration to a bulletin board, and outputting bulletin board information in real time after each iteration is finished;
s6) judging whether the total energy after detection of each energy storage battery in the energy storage system exceeds the set highest food concentration or not, wherein the food concentration is the total energy after detection according to each energy storage battery.
S7) if the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration, stopping the system from charging, and only allowing stopping or discharging to occur at the moment;
s8) a step of recovering the normal operation of the system until the total energy after detection of each energy storage battery in the energy storage system is below the maximum food concentration setting;
s9) judging whether the total energy after detection of each energy storage battery in the energy storage system is lower than the minimum food concentration or not;
s10) if the total energy after detection of each energy storage battery in the energy storage system is lower than the minimum food concentration, stopping discharging the system, and only allowing stopping or charging to occur;
s11) a step of recovering the system to a normal running state until the total energy after detection of each energy storage battery in the system exceeds the set minimum food concentration after multiple iterations;
s12) after each iteration, judging whether the value of the current iteration I| exceeds the defined maximum charge and discharge power;
s13) if the value of I exceeds the defined maximum charge and discharge power, stopping the charge and discharge operation of the system;
s14) a step of recovering the charge/discharge operation of the system until the value of i is within the defined maximum charge/discharge power.
S15) setting the end of one iteration process, repeating the steps S3) to S14), and carrying out the step of the next iteration process;
s16) a step of outputting a system operation result after each iteration is provided.
Example 1:
in a conventional wind farm engineering with an engineering scale of 60MW, a local load level is about 40MW, inverter efficiency is 0.95, a power curve in 48 hours of a known load is shown in fig. 2, and a power curve generated by a fan in 48 hours is shown in fig. 3. And the power effect of the target in the period of time is optimized by combining the related parameters of the storage battery. The energy storage system adopts 50000 storage batteries as elements of the wind power energy storage system, and related parameters of the storage batteries are as follows: the rated voltage was 12V, rated capacity was 100Ah, depth of discharge was 0.6, charging efficiency was 0.7, and discharging efficiency was 0.8.
If no algorithm compensation exists, the compensation situation of the energy storage system is shown in fig. 4, the charging process and the discharging process are changed greatly, and the influence on the stability of the system is great, so that a variation artificial fish swarm algorithm is required to be introduced to optimize the charging and discharging process, and the compensation situation after optimization is shown in fig. 5.
After the charge and discharge processes of the energy storage system are optimized through variation of artificial fish shoal, after the output power coupling of the wind field is integrated, a power curve output to a load is formed as shown in fig. 6, and compared with the direct output of fig. 2, the change rate of the output power is smaller, the system is more stable, and the long-term good operation of the system can be promoted.
While the preferred embodiments of the present invention have been described above, those skilled in the art may make various modifications and additions to the specific embodiments described above, and such modifications and additions should also be considered as being within the scope of the present invention.
Claims (9)
1. A wind power energy storage system charging and discharging process optimization method based on a variant artificial fish school is characterized by comprising the following steps:
s1) acquiring real-time running state information of each storage battery pack according to real-time monitoring equipment of a wind power energy storage system;
s2) introducing a plurality of artificial fish information according to the acquired state information, establishing artificial fish shoals and establishing bulletin boards;
s3) artificial fish shoals are arranged to generate variation, and variant fish with larger visual field and step length are generated, and iteration is started;
s4) a step of selecting corresponding behaviors of the artificial fish shoals based on self perception and environmental feedback is arranged;
s5) recording the position information of each artificial fish after each iteration to a bulletin board, and outputting bulletin board information in real time after each iteration is finished;
s6) judging whether the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration or not;
s7) if the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration, stopping the system from charging, and only allowing stopping or discharging to occur at the moment;
s8) a step of recovering the normal operation of the system until the total energy after detection of each energy storage battery in the energy storage system is below the maximum food concentration setting;
s9) judging whether the total energy after detection of each energy storage battery in the energy storage system is lower than the minimum food concentration or not;
s10) if the total energy after detection of each energy storage battery in the energy storage system is lower than the minimum food concentration, stopping discharging the system, and only allowing stopping or charging to occur;
s11) a step of recovering the system to a normal running state until the total energy after detection of each energy storage battery in the system exceeds the set minimum food concentration after multiple iterations;
s12) after each iteration, judging whether the value of the current iteration I| exceeds the defined maximum charge and discharge power;
s13) if the value of I exceeds the defined maximum charge and discharge power, stopping the charge and discharge operation of the system;
s14) a step of recovering the charge and discharge operation of the system until the value of I is within the limit of the maximum charge and discharge power;
s15) setting the end of one iteration process, repeating the steps S3) to S14), and carrying out the step of the next iteration process;
s16) a step of outputting a system operation result after each iteration is provided.
2. The optimization method for the charging and discharging process of the wind power energy storage system based on the variant artificial fish school according to claim 1, which is characterized by comprising the following steps: the artificial fish individuals in the artificial fish shoal carry the number information of the detection points of the energy storage battery, the state information of the energy storage battery, the step length, the visual field, the iteration limit, the fish shoal moving range and the food concentration of the position of the artificial fish in the last iteration.
3. The optimization method for the charging and discharging process of the wind power energy storage system based on the variant artificial fish school according to claim 1, which is characterized by comprising the following steps: the variant fish is randomly generated by adopting a random () generating function, the number of the variant fish is n, the variant fish is recovered to be common fish after one iteration is completed, and the variant fish is regenerated before the second iteration is started, so that the second iteration is carried out.
4. The method for optimizing the charge and discharge process of the wind power energy storage system based on the variant artificial fish school according to claim 3, wherein the number of the variant fish accounts for 10% -20% of the number of the fish school.
5. The optimization method for the charging and discharging process of the wind power energy storage system based on the variant artificial fish school according to claim 1, wherein the artificial fish school performs a gathering behavior:
in the range of artificial fish, the center position of the artificial fish shoal is x c The method comprises the steps of carrying out a first treatment on the surface of the Normal artificial fish performs the action of the formula (3), variant artificial fish performs the action of the formula (4), so that the artificial fish moves from the position xi to the next position point, after the action is finished, the bulletin board is updated, and otherwise, other actions are performed;
6. The optimization method for the charging and discharging process of the wind power energy storage system based on the variant artificial fish school according to claim 1, wherein the artificial fish school is subjected to rear-end collision:
when the food is found in the visual field of the artificial fish, the artificial fish goes to a high food concentration place, other artificial fish follow the previous artificial fish to carry out rear-end collision, and the fish position of the first found food source is x d Other artificial fish move from xi to the next position point when rear-end collision happens; normal artificial fish will perform the action of formula (5); the variant artificial fish will execute the action of the formula (6), after the action is completed, the bulletin board is updated, otherwise, other actions are carried out;
7. The optimization method for the charging and discharging process of the wind power energy storage system based on the variant artificial fish school according to claim 1, wherein the artificial fish school has foraging behaviors:
the artificial fish can move to the next position point with higher food concentration from xi, the common artificial fish adopts the action of the formula (7) to feed, the variant artificial fish adopts the action of the formula (8) to feed, otherwise, other actions are carried out;
8. The optimization method for the charging and discharging process of the wind power energy storage system based on the variant artificial fish school according to claim 1, wherein the artificial fish school performs the following optimization:
the position of the mutant fish is x f The common fish goes to the next position point from xi according to the step length of the common fish, and after the iterative process, the common fish continues to move to the next position point, and the common fish adopts the behavior of the formula (9):
9. The method for optimizing the charge and discharge process of the wind power energy storage system based on the variant artificial fish school according to claim 1, wherein when the artificial fish cannot perform four behaviors of clustering, rear-end collision, foraging and optimizing, random behaviors are selected to occur;
the fish shoal randomly moves to the next position point from the xi according to the step length of the fish shoal, other behaviors are selected through iteration, if the four behaviors of gathering, rear-end collision, foraging and optimizing cannot be carried out, the random behaviors are selected continuously, normal artificial fish adopts the behavior of the formula (10), and variant artificial fish adopts the behavior of the formula (11):
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