CN113779883A - Wind power energy storage system charge-discharge process optimization method based on variant artificial fish school - Google Patents

Wind power energy storage system charge-discharge process optimization method based on variant artificial fish school Download PDF

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CN113779883A
CN113779883A CN202111075348.3A CN202111075348A CN113779883A CN 113779883 A CN113779883 A CN 113779883A CN 202111075348 A CN202111075348 A CN 202111075348A CN 113779883 A CN113779883 A CN 113779883A
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张东
李昊轩
马艳娟
赵琰
姜河
罗金鸣
宋世巍
李昱材
王健
王东来
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Shenyang Institute of Engineering
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Abstract

The invention discloses a method for optimizing a charging and discharging process of a wind power energy storage system 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 the real-time monitoring equipment of the wind power energy storage system; introducing a plurality of pieces of information according to the acquired state information, giving the information to the artificial fish, establishing an artificial fish school and establishing a bulletin board; starting iteration when the artificial fish school generates variation; selecting corresponding behaviors by the artificial fish school based on self perception and environmental feedback; recording the position of each artificial fish, and updating a bulletin board; the system discharges or charges according to whether the sum of the detection point full energy quantity reaches the set highest food concentration or lowest food concentration; when the sum of the energy points full of the system detection points returns to be below the maximum food concentration setting, the normal operation of the system is restored again; and outputting a system operation result. The method introduces the concept of the variation fish, and combines the variation artificial fish swarm algorithm with the fan energy storage system compensation to optimize the wind power.

Description

Wind power energy storage system charge-discharge process optimization method based on variant artificial fish school
Technical Field
The invention relates to a wind power energy storage system, in particular to a charging and discharging process optimization method of the wind power energy storage system based on a variant artificial fish school.
Background
In order to respond to the national call on energy cleaning, China vigorously pushes forward the construction work of wind generating sets, the wind power capacity is continuously improved in the energy use proportion of China, and in the northeast of China, a large amount of clean wind energy naturally exists, so that a large amount of wind generating sets are arranged, and due contribution is made to the cleaning of energy. However, in the northeast, wind seasons and no wind seasons exist, rich wind energy does not cover the whole year, the time period of 9-11 months and 1-5 months per year is the most rich time period of the wind energy, and almost no wind energy exists between 6 months and 8 months, so that an energy storage battery needs to be introduced to complete the consumption of electric energy, when wind is too much, the super-generation electric energy exceeding the upper limit of electric energy conversion grid connection enters the energy storage battery for storage, and when wind is not too much and less, the energy storage battery starts to output the stored electric energy to make up for the shortage of fan output. 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, the fluctuation problem of wind power output power is timely and effectively solved and compensated, the production and consumption of electric energy are decoupled in time, the electric energy is stored in the load valley time, the electric energy is released in the load peak time, and the wind power system is enabled to provide economic and effective clean energy. Meanwhile, the energy storage battery system is superior to thermal power regulation and control in response time and adjustable range, the capacity of the thermal power unit required by the wind power regulation and control is more than 2 times of that of the wind power unit, the energy storage battery system only needs to provide 20% of the total installed capacity of the wind power, a large amount of cost is saved, and therefore the energy storage battery system has practical application value,
there are two field situations based on energy storage battery systems:
case 1: when the wind energy is rich, the over-generated electric energy is charged into the energy storage battery, and after the energy storage battery system is fully charged, the battery needs to be timely stopped from being charged, so that the energy storage battery system is prevented from being damaged or other accidents caused by over-charging of the energy storage battery system are avoided.
Case 2: when the wind speed is too high at a certain moment, the rotating speed of fan blades is too high, 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 large, the energy storage battery system needs to be cut off when the situation happens, and then charging is continued when the charging current returns to a normal range.
Disclosure of Invention
The invention aims to provide a method for optimizing the charging and discharging process of a wind power energy storage system based on a variant artificial fish school. The method introduces a variation artificial fish swarm algorithm, and combines the variation artificial fish swarm algorithm with the fan energy storage system compensation to optimize the wind power.
In order to solve the problems in the prior art, the invention adopts the technical scheme that:
a wind power energy storage system charge-discharge process optimization method based on a variant artificial fish school comprises the following steps:
s1) a step of acquiring real-time running state information of each storage battery pack according to the real-time monitoring equipment of the wind power energy storage system is arranged;
s2) introducing a plurality of fish information according to the acquired state information, giving artificial fish information, establishing artificial fish schools and establishing bulletin boards;
s3) providing a step of generating a variation fish with larger visual field and step length by the variation of the artificial fish school and starting iteration;
s4) selecting corresponding behaviors by the artificial fish school based on self perception and environmental feedback;
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) a step of judging whether the total energy of the energy storage batteries in the energy storage system after detection exceeds the set highest food concentration is provided;
s7) if the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration, the system stops charging, and only the stopping behavior or the discharging behavior is allowed to occur;
s8) a step of restoring the normal operation of the system again until the total energy returns to the value below the highest food concentration setting after the detection of each energy storage battery in the energy storage system is carried out;
s9) a step of judging whether the total energy of the energy storage batteries in the energy storage system after detection is lower than the lowest food concentration is provided;
s10) a step of stopping discharging and only allowing the stopping action or the charging action to occur if the total energy of the energy storage batteries in the energy storage system is lower than the lowest food concentration after detection;
s11) a step of recovering the system to a normal running state until the total energy of the energy storage batteries in the system after multiple iterations exceeds the set minimum food concentration;
s12) after each iteration, a step of judging whether the value of the current iteration i exceeds the limited maximum charge and discharge power is needed;
s13) if the | i | value exceeds the limited maximum charge/discharge power, the system stops the charge/discharge operation;
s14) includes a step of resuming the charge/discharge operation of the system until the | i | value returns to within the limited maximum charge/discharge power.
S15) finishing one iteration process, and repeating the processes from S3) to S14) to carry out the next iteration process.
S16) is provided with a step of outputting the system operation result after each iteration.
The artificial fish in the artificial fish school carries energy storage battery detection point number information, energy storage battery state information, step length, visual field, iteration limitation, fish school moving range and food concentration of the position of the artificial fish in the last iteration.
The number of the variant fishes is n, the variant fishes are recovered to be common fishes after one iteration is completed, and the variant fishes are regenerated before the second iteration is started and are subjected to the second iteration; the number of the variant fishes accounts for 10-20% of the fish school.
Sometimes, the artificial fish herd undergoes herding behavior:
in the range of artificial fish, the center position of the artificial fish group is xc(ii) a The normal artificial fish carries out the action (3), the variant artificial fish carries out the action (4), so that the artificial fish moves from the position xi to the next position point, the bulletin board is updated after the action is finished, and other actions are carried out otherwise;
Figure BDA0003261933910000041
Figure BDA0003261933910000042
Figure BDA0003261933910000043
representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure BDA0003261933910000044
in order to ensure that the relative movement distance required for one fish to get to the coincident point of the position of the other fish in the clustering action between two artificial fishes in the visual field, Rand () represents a random number between 0 and 1 to avoid possible collision, and the action of the two fishes needs to be carried outTo meet the requirements of visual field and step length, V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
Sometimes, the artificial fish school has rear-end collision:
in the visual field range of the artificial fish, when food is found, the artificial fish moves to a high food concentration place, other artificial fishes follow the previous artificial fish to perform rear-end collision, and the fish position of the food source is found to be x at firstdOther artificial fishes move from xi to the next position point when the rear-end collision occurs; the normal artificial fish will perform the action (5), the variant artificial fish will perform the action (6), and the bulletin board is updated after the action is completed, otherwise other actions are performed;
Figure BDA0003261933910000051
Figure BDA0003261933910000052
Figure BDA0003261933910000053
representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure BDA0003261933910000054
in order to ensure that one fish between two artificial fishes in the visual field has a rear-end collision behavior and the distance of relative movement required by the coincidence point of the position of the other fish is reached, Rand () represents a random number between 0 and 1, and the behavior of the two fishes needs to meet the requirements of the visual field, the step length and V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
Sometimes, the artificial fish population undergoes foraging:
the artificial fish can move from xi to the next position point with higher food concentration, the ordinary artificial fish forages by adopting the behavior of the formula (7), the variant artificial fish forages by adopting the behavior of the formula (8), and otherwise, other behaviors are carried out;
Figure BDA0003261933910000055
Figure BDA0003261933910000056
Figure BDA0003261933910000057
representing the position information of the artificial fish after t +1 iterations, xi representing the initial position of the artificial fish after the fish swarm initialization,
Figure BDA0003261933910000058
in order to move the artificial fish to the highest position of food concentration in the visual field by the required relative movement distance, Rand () represents a random number between 0 and 1, and the foraging behavior of the fish needs to meet the requirements of the visual field and the step length, V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
Sometimes, the artificial fish shoal occurs from the superior behavior:
the position of the variant fish is xfThe common fish moves to the next position point from xi according to the self step length, and continues to move to the next position point after the iteration process, and the common fish can adopt the behavior of the formula (9):
Figure BDA0003261933910000061
Figure BDA0003261933910000062
representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure BDA0003261933910000063
the Rand () represents a random number between 0 and 1 for the relative distance between the ordinary fish and the variant fish in the visual field, the ordinary fish follows the variant fish by the position change distance, and the action of the ordinary fish needs to meet the requirements of the visual field and the step length, V1Is the maximum visual field range of normal artificial fish, S1The maximum step size of normal artificial fish, di,jIs the maximum distance between two artificial fish.
When the artificial fish cannot perform four actions of clustering, rear-end collision, foraging and optimization, selecting to perform a random action;
the fish school randomly moves to the next position point from xi according to the self step length, other behaviors are selected through iteration, if the four behaviors of clustering, rear-end collision, foraging and optimization cannot be carried out, the random behavior is continuously selected, normal artificial fish adopts the behavior of the formula (10), and the variant artificial fish adopts the behavior of the formula (11):
Figure BDA0003261933910000067
Figure BDA0003261933910000064
Figure BDA0003261933910000065
representing the position information of the artificial fish after t +1 iterations,
Figure BDA0003261933910000066
representing the position of the artificial fish after t iterations, Rand () representing a random number between 0 and 1, following the variant fish by the change distance of the position, and meeting the requirements of the visual field and the step length when the behavior of the ordinary fish occurs; s1、S2The maximum step length of the normal artificial fish and the maximum step length of the variant artificial fish are respectively.
The invention has the advantages and beneficial effects that:
the invention introduces a variation artificial fish swarm algorithm to optimize the energy charging process of the wind power energy storage battery system, and the rated voltage of each storage battery in the wind power energy storage battery system is Ua(V) rated capacity Ca(Ah) when the existing storage battery pack is formed by combining n storage batteries, the total energy storage energy is Ea(MWh) is:
Ea＝n·Ca·Ua/106 (1)
setting the maximum depth of discharge DOD, and when the system reaches the maximum discharge condition, the minimum residual energy E of the storage battery packaminComprises the following steps:
Eamin＝n·Ca·Ua·(1-DOD)/106 (2)
the energy storage battery has three operation states in wind power regulation, namely an energy charging state, a discharging state and a stopping state, wherein the energy charging state and the discharging state cannot occur simultaneously, and the energy storage battery is not in or is in an overcharging discharging state for a short time in order to prolong the service life of the energy storage battery.
In order to timely enable the system to be timely separated from the overcharging state or the overdischarging state, a variant artificial fish swarm intelligent algorithm is introduced, and the overcharge starting and stopping processes of the energy storage battery are controlled through the algorithm.
To deal with two existing situations at the same time, a complex concept is introduced. In the process of initializing the artificial fish school, the artificial fish carries detected energy storage information of each battery in the battery pack, the state of the storage battery at the current detection point is marked as 1 when the storage battery is fully charged, the state of the storage battery at the current detection point is marked as 0 when the battery state at the current detection point reaches the maximum discharge condition, the storage battery at the current detection point is marked as i when the storage battery at the current detection point is in the charge state, and the information detected by the storage battery at the current detection point is marked as-i when the storage battery at the current detection point is in the discharge state and is used as the information carried by the artificial fish. The food concentration is the total amount of points in a charging state, the maximum food concentration, namely the maximum charging energy is set as a limit, when the fish school reaches the position where the maximum food concentration is equal to or greater than the set maximum food concentration, the charging of the energy storage system is stopped, redundant wind energy is abandoned, the minimum food concentration is set as the lower limit of energy storage, namely the electric energy storage amount when the maximum discharge depth is reached, and when the maximum food concentration value reached after the fish school iteration is less than the set minimum food concentration, the energy storage system is recharged again, so that the energy storage system is ensured not to be overcharged, but not discharged, and the service life of the system is prolonged. Meanwhile, in order to prevent the energy storage system from being damaged due to the fact that the energy charging speed is too high due to excessive surplus of wind energy, aiming at the value i, when the value | i | is smaller than the maximum charging and discharging power limit, the system is charged and discharged normally, when the value | i | exceeds the maximum charging and discharging power limit, the energy charging is required to be stopped, surplus wind energy is abandoned, 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 the 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 constants in the carried information, satisfies the state detection of the change rate of the food concentration of the fish swarm through the plurality of the carried information, and optimizes wind power by combining the variant artificial fish swarm algorithm with the fan energy storage system compensation.
Drawings
The invention is described in further detail below with reference to the accompanying drawings:
FIG. 1 is a schematic diagram of a wind power generation and energy storage system;
FIG. 2 is a wind power output curve over 48 hours;
FIG. 3 is a graph of the generated power over 48 hours of the wind farm;
FIG. 4 is a schematic diagram of the compensation condition of the energy storage system without algorithm optimization;
FIG. 5 is a schematic diagram of the compensation situation of the variant artificial fish swarm algorithm for optimizing the energy storage system;
FIG. 6 is a graph of the power output to the load after 48 hours integration of the wind farm;
fig. 7 is a flow chart of a method for optimizing the charging and discharging processes of the wind power energy storage system based on the variant artificial fish school.
Detailed Description
The present invention is further described in detail with reference to the following specific examples, but the scope of the present invention is not limited by the specific examples, which are defined by the claims. In addition, any modification or change that can be easily made by a person having ordinary skill in the art without departing from the technical solution of the present invention will fall within the scope of the claims of the present invention.
As shown in fig. 7, the method for optimizing the charging and discharging process of the wind power energy storage system based on the variant artificial fish school, disclosed by the invention, has the structure as shown in fig. 1, and comprises the following steps:
s1) a step of acquiring the energy storage state of each battery in the storage battery pack according to the battery state detection of the wind power energy storage system is provided;
s2) according to the acquired state information, introducing a plurality of fish information, giving artificial fish information, establishing artificial fish schools and establishing bulletin boards:
setting the full energy state of the detection point as 1, the detection point as 0 when reaching the maximum discharge condition, the monitoring point as i when in the energy charging state, the detection point as-i when in the discharge state, and the detection point as 0i when in the stop state. At this time, the artificial fish has the following states of carrying information:
(1)1+ i: the detection point is fully charged with energy and continues to be charged with energy;
(2)1+0: the detection point is full of energy and has no charge-discharge action;
(3) 1-i: the detection point is full of energy and is discharging;
(4)0+ i: the detection point is in a charging state without energy;
(5)0+0 i: the detection point has no energy and no charging and discharging action;
(6) 0-i: the detection point is not energized and is in discharge operation.
The artificial fish carries energy storage battery detection point number information, energy storage battery state information, step length, visual field, iteration limitation, fish school moving range and food concentration of the position of the artificial fish in the last iteration.
S3) setting artificial fish school to generate variation fish with larger visual field and step length, and starting iteration:
introducing the variant fish, randomly generating by using Rand (), wherein the variant fish only has a larger moving step length and a larger sensing range compared with the common fish, recovering the variant fish to be the common fish after completing one iteration, regenerating the variant fish before starting the second iteration, and performing the second iteration. The common artificial fish moves within a set maximum Visual field (Visual) V1 within a set Step length (Step) S1, the crowding degree is set to be delta, and the concentration caused by the action of the whole fish shoal needs to meet the requirement that the set crowding degree is not exceeded. The maximum distance between artificial fishes is di,j＝||xi-xjI, the randomly generated variant fish has V2(2V 1)>V2>V1), and has a field of view of S2(2S 1)>S2>S1), due to the larger visual field and the larger step length, the sensitivity to local data change is easily lost, the difference between the step length and the visual field after the change and the step length and the visual field of the common fish is limited, and the loss of the application reliability is avoided. In addition, the generation rate of the variation is strictly limited, the defect that the artificial fish swarm algorithm falls into local optimum is not meaningful to improve, and the defect that the artificial fish swarm algorithm falls into the local optimum is meaningless, and the defect that the artificial fish swarm algorithm falls into the local optimum is overcome, so that the optimization meaning is lost due to the fact that all the fish move to an optimum point or a few optimum points due to the fact that the ratio of the number of the varied fish in the fish swarm is 10% -20%, the effect of preventing the local optimum can be achieved, and the data sensitivity can be prevented from being lost.
S4) based on the above steps, iteration starts, and the artificial fish school selects corresponding foraging, herding, rear-end collision, priority and random behaviors based on self-perception and environmental feedback.
Artificial fish selection occurs clustering behavior: in the range of artificial fish, the center position of the artificial fish group is xc. The normal artificial fish executes the action (3), and the variant artificial fish executes the action (4), so that the position of the artificial fish is moved and the artificial fish is moved from the positionAnd (4) moving the set xi to the next position point, updating the bulletin board after the action is finished, otherwise executing other actions, and simulating fish shoals to finish clustering through the actions (3) and (4) so as to avoid dangerous actions, wherein in the clustering action, the set crowding degree is ensured to be met.
Figure BDA0003261933910000101
Figure BDA0003261933910000102
Figure BDA0003261933910000111
Representing the position information of the artificial fish after t +1 iterations, xi representing the initial position of the artificial fish after the fish swarm initialization,
Figure BDA0003261933910000112
in order to avoid possible collision by using a random number between 0 and 1 as a relative movement distance required for a fish to reach a coincident point of the position of another fish when a clustering behavior occurs between two artificial fishes in a visual field, V1 and V2 are the maximum visual field ranges of a normal artificial fish and a variant artificial fish respectively, S1 and S2 are the maximum step lengths of the normal artificial fish and the variant artificial fish respectively, and d is the maximum step length of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
Selecting the artificial fish to have rear-end collision: in the visual field range of the artificial fish, when food is found, the artificial fish moves to a high food concentration place, other artificial fishes follow the previous artificial fish to perform rear-end collision, and the fish position of the food source is found to be x at firstdAnd other artificial fishes are moved from xi to the next position point by the action of rear-end collision. Normal artificial fish will perform behavior (5) and variant artificial fish will perform behavior (6), and the bulletin board is updated after the actions are completed, otherwise other behaviors are performed.
Figure BDA0003261933910000113
Figure BDA0003261933910000114
Figure BDA0003261933910000115
Representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure BDA0003261933910000116
in order to ensure that one fish between two artificial fishes in the visual field has a rear-end collision behavior and moves to a relative movement distance required by a coincident point of the position of the other fish, Rand () represents a random number between 0 and 1, and the behavior of the two fishes needs to meet the requirements of the visual field, the step length and the like, V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length of the normal artificial fish and the maximum step length of the variant artificial fish are respectively.
The artificial fish is selected to have foraging behavior: the artificial fish senses the highest position x of the food concentration in the visual fieldeAnd 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 ordinary artificial fish adopts (7) behavior to forage, the variant artificial fish adopts (8) behavior to forage, and otherwise, other behaviors are performed.
Figure BDA0003261933910000121
Figure BDA0003261933910000122
Figure BDA0003261933910000123
Representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure BDA0003261933910000124
in order to move the artificial fish to the highest position of food concentration in the visual field by the required relative movement distance, Rand () represents a random number between 0 and 1, and the foraging behavior of the fish needs to meet the requirements of the visual field, step length and the like, V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
The selection of the artificial fish takes place from the excellent behavior: the weak individuals in the fish school are used to follow stronger individuals, when the variant fish exists in the fish school, the variant fish has the characteristics of large visual field and large step length to attract normal fish to follow, and the position of the variant fish is xfWhen the common fish is not at the optimal point, in order to find a position with higher food concentration in a wider range, the common fish can follow the action of the variant fish with special characteristics, the common fish moves to the next position point from the xi according to the self step length, and after the iteration process, the common fish continuously moves to the next position point, and the common fish can adopt the action (9).
Figure BDA0003261933910000125
Figure BDA0003261933910000126
Representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure BDA0003261933910000127
the Rand () represents a random number between 0 and 1 as the relative distance between the normal fish and the variant fish in the visual fieldThe position change distance follows the variant fish, and the behavior of the common fish needs to meet the requirements of visual field, step length and the like, V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
And when the artificial fish cannot generate the four behaviors, selecting the random behavior to generate, wherein the random behavior is the natural behavior of the fish school, changing the position of the artificial fish by random action, randomly moving the artificial fish from xi to the next position point according to the self step length, iteratively selecting other behaviors, and if the behaviors cannot be completed, continuing selecting the random behavior, wherein the normal artificial fish adopts the behavior (10) and the variant artificial fish adopts the behavior (11).
Figure BDA0003261933910000131
Figure BDA0003261933910000132
Figure BDA0003261933910000133
Representing the position information of the artificial fish after t +1 iterations,
Figure BDA0003261933910000134
representing the position of the artificial fish after t iterations, Rand () representing a random number between 0 and 1, following the variant fish by the change distance of the position, meeting the requirements of visual field, step length and the like when the behavior of the ordinary fish occurs, and V1Is the maximum visual field range of normal artificial fish, S1Is the maximum step size of a normal artificial fish.
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) includes a step of determining whether or not the total energy detected by the energy storage cells in the energy storage system exceeds a set maximum food concentration, the food concentration being the total energy detected by the energy storage cells.
S7) if the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration, the system stops charging, and only the stopping behavior or the discharging behavior is allowed to occur;
s8) a step of restoring the normal operation of the system again until the total energy returns to the value below the highest food concentration setting after the detection of each energy storage battery in the energy storage system is carried out;
s9) a step of judging whether the total energy of the energy storage batteries in the energy storage system after detection is lower than the lowest food concentration is provided;
s10) a step of stopping discharging and only allowing the stopping action or the charging action to occur if the total energy of the energy storage batteries in the energy storage system is lower than the lowest food concentration after detection;
s11) a step of recovering the system to a normal running state until the total energy of the energy storage batteries in the system after multiple iterations exceeds the set minimum food concentration;
s12) after each iteration, a step of judging whether the value of the current iteration i exceeds the limited maximum charge and discharge power is needed;
s13) if the | i | value exceeds the limited maximum charge/discharge power, the system stops the charge/discharge operation;
s14) includes a step of resuming the charge/discharge operation of the system until the | i | value returns to within the limited maximum charge/discharge power.
S15) finishing one iteration process, and repeating the steps S3) to S14) to carry out the next iteration process;
s16) is provided with a step of outputting the system operation result after each iteration.
Example 1:
in the existing 60MW wind farm project, the local load level is about 40MW, the inverter efficiency is 0.95, and the power curve of the load in 48 hours is shown in FIG. 2 and the power curve of the fan in 48 hours is shown in FIG. 3. And combining the relevant parameters of the storage battery to optimize the power effect of the target in the time. The energy storage system adopts 50000 storage batteries as elements of the wind power energy storage system, and the storage batteries have the following relevant parameters: the rated voltage is 12V, the rated capacity is 100Ah, the discharge depth is 0.6, the charge efficiency is 0.7, and the discharge efficiency is 0.8.
At this time, if there is no algorithmic compensation, the compensation condition of the energy storage system is as shown in fig. 4, the change of the charging process and the discharging process is large, and the influence on the stability of the system is large, so that a variant artificial fish swarm algorithm needs to be introduced to optimize the charging and discharging process, and the compensation condition after optimization is as shown in fig. 5.
After the charging and discharging processes of the energy storage system are optimized through the variation of the artificial fish school, the power curve from output to load is formed after the wind field output power coupling is integrated, and as shown in fig. 6, 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 illustrated and described, it will be appreciated by those skilled in the art that various modifications and additions may be made to the specific embodiments described and illustrated, and such modifications and additions are intended to be covered by the scope of the present invention.

Claims (9)

1. A wind power energy storage system charge-discharge process optimization method based on a variant artificial fish school is characterized by comprising the following steps:
s1) a step of acquiring real-time running state information of each storage battery pack according to the real-time monitoring equipment of the wind power energy storage system is arranged;
s2) introducing a plurality of fish information according to the acquired state information, giving artificial fish information, establishing artificial fish schools and establishing bulletin boards;
s3) providing a step of generating a variation fish with larger visual field and step length by the variation of the artificial fish school and starting iteration;
s4) selecting corresponding behaviors by the artificial fish school based on self perception and environmental feedback;
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) a step of judging whether the total energy of the energy storage batteries in the energy storage system after detection exceeds the set highest food concentration is provided;
s7) if the total energy detected by each energy storage battery in the energy storage system exceeds the set highest food concentration, the system stops charging, and only the stopping behavior or the discharging behavior is allowed to occur;
s8) a step of restoring the normal operation of the system again until the total energy returns to the value below the highest food concentration setting after the detection of each energy storage battery in the energy storage system is carried out;
s9) a step of judging whether the total energy of the energy storage batteries in the energy storage system after detection is lower than the lowest food concentration is provided;
s10) a step of stopping discharging and only allowing the stopping action or the charging action to occur if the total energy of the energy storage batteries in the energy storage system is lower than the lowest food concentration after detection;
s11) a step of recovering the system to a normal running state until the total energy of the energy storage batteries in the system after multiple iterations exceeds the set minimum food concentration;
s12) after each iteration, a step of judging whether the value of the current iteration i exceeds the limited maximum charge and discharge power is needed;
s13) if the | i | value exceeds the limited maximum charge/discharge power, the system stops the charge/discharge operation;
s14) a step of resuming the charge/discharge operation of the system until the | i | value returns to within the limited maximum charge/discharge power;
s15) finishing one iteration process, and repeating the steps S3) to S14) to carry out the next iteration process;
s16) is provided with a step of outputting the system operation result after each iteration.
2. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 1, wherein the method comprises the following steps: the artificial fish in the artificial fish school individually carries energy storage battery detection point number information, energy storage battery state information, step length, visual field, iteration limitation, fish school moving range and food concentration of the position of the artificial fish in the last iteration.
3. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 1, wherein the method comprises the following steps: the number of the variant fishes is n, the variant fishes are randomly generated by a Rand () random generation function, the variant fishes are recovered to be common fishes after one iteration is completed, and the variant fishes are regenerated before the second iteration is started to perform the second iteration.
4. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 3, wherein the varied fish number accounts for 10-20% of the fish school.
5. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 1, wherein the artificial fish school performs a clustering behavior:
in the range of artificial fish, the center position of the artificial fish group is xc(ii) a The normal artificial fish carries out the action (3), the variant artificial fish carries out the action (4), so that the artificial fish moves from the position xi to the next position point, the bulletin board is updated after the action is finished, and other actions are carried out otherwise;
Figure FDA0003261933900000031
Figure FDA0003261933900000032
Figure FDA0003261933900000033
represents a passing throughPosition information, x, of the artificial fish after t +1 iterationsiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure FDA0003261933900000034
in order to obtain the distance of relative movement required by a fish between two artificial fishes in a visual field when a clustering behavior of the fish reaches a coincident point of the position of the other fish, Rand () represents a random number between 0 and 1 to avoid possible collision, and the behavior of the two fishes needs to meet the requirements of the visual field and the step length, V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
6. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 1, wherein the artificial fish school has a rear-end collision behavior:
in the visual field range of the artificial fish, when food is found, the artificial fish moves to a high food concentration place, other artificial fishes follow the previous artificial fish to perform rear-end collision, and the fish position of the food source is found to be x at firstdOther artificial fishes move from xi to the next position point when the rear-end collision occurs; normal artificial fish will perform the action of (5); the variant artificial fish will perform the action (6) and update the bulletin board after completing the action, otherwise perform other actions;
Figure FDA0003261933900000035
Figure FDA0003261933900000036
Figure FDA0003261933900000041
representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure FDA0003261933900000042
in order to ensure that one fish between two artificial fishes in the visual field has a rear-end collision behavior and the distance of relative movement required by the coincidence point of the position of the other fish is reached, Rand () represents a random number between 0 and 1, and the behavior of the two fishes needs to meet the requirements of the visual field, the step length and V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
7. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 1, wherein the artificial fish school has foraging behavior:
the artificial fish can move from xi to the next position point with higher food concentration, the ordinary artificial fish forages by adopting the behavior of the formula (7), the variant artificial fish forages by adopting the behavior of the formula (8), and otherwise, other behaviors are carried out;
Figure FDA0003261933900000043
Figure FDA0003261933900000044
Figure FDA0003261933900000045
representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure FDA0003261933900000046
in order to move the artificial fish to the highest position of food concentration in the visual field by the required relative movement distance, Rand () represents a random number between 0 and 1, and the foraging behavior of the fish needs to meet the requirements of the visual field and the step length, V1、V2The maximum visual field ranges of the normal artificial fish and the variant artificial fish, S1、S2The maximum step length, d, of the normal artificial fish and the variant artificial fish respectivelyi,jIs the maximum distance between two artificial fish.
8. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 1, wherein the artificial fish school is optimized:
the position of the variant fish is xfThe common fishes go to the next position point from xi according to the self step length, and continue to move to the next position point after the iteration process, and the common fishes adopt the behavior of the formula (9):
Figure FDA0003261933900000051
Figure FDA0003261933900000052
representing the position information of the artificial fish after t +1 iterations, xiRepresenting the initial position of the artificial fish after the fish school is initialized,
Figure FDA0003261933900000053
the Rand () represents a random number between 0 and 1 for the relative distance between the ordinary fish and the variant fish in the visual field, the ordinary fish follows the variant fish by the position change distance, and the action of the ordinary fish needs to meet the requirements of the visual field and the step length, V1Is the maximum visual field range of normal artificial fish, S1The maximum step size of normal artificial fish, di,jIs the maximum distance between two artificial fish.
9. The method for optimizing the charging and discharging process of the wind power energy storage system based on the varied artificial fish school according to claim 1, wherein random behavior is selected to occur when the artificial fish can not have four behaviors of gathering, rear-end collision, foraging and following optimization;
the fish school randomly moves to the next position point from xi according to the self step length, other behaviors are selected through iteration, if the four behaviors of clustering, rear-end collision, foraging and optimization cannot be carried out, the random behavior is continuously selected, normal artificial fish adopts the behavior of the formula (10), and the variant artificial fish adopts the behavior of the formula (11):
Figure FDA0003261933900000054
Figure FDA0003261933900000055
Figure FDA0003261933900000056
representing the position information of the artificial fish after t +1 iterations,
Figure FDA0003261933900000057
representing the position of the artificial fish after t iterations, Rand () representing a random number between 0 and 1, following the variant fish by the change distance of the position, and meeting the requirements of the visual field and the step length when the behavior of the ordinary fish occurs; s1、S2The maximum step length of the normal artificial fish and the maximum step length of the variant artificial fish are respectively.
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