CN103368193B - Method for distribution real-time power of battery energy storage power station for tracking planned output - Google Patents

Abstract

The present invention proposes a method and system for real-time power distribution of battery energy storage power stations for tracking planned output. The method includes the following steps: read relevant data of battery energy storage power stations in real time, and store and manage the above data; calculate the current The total power demand command value of the battery energy storage power station; calculate the power command value of each energy storage unit in the battery energy storage power station; summarize the power command values of each energy storage unit and output to the external monitoring platform. The system includes a communication module, a data storage and management module, a tracking plan control module, a genetic algorithm control module and an external monitoring platform to complete the real-time power distribution method of the battery energy storage power station for tracking the planned output of the present invention. Realize the application of battery energy storage power stations to support the function of tracking plan curves, and realize the effective control and distribution of real-time power of megawatt lithium battery energy storage power stations.

Description

Real-time power allocation method for battery energy storage power station for tracking planned output

technical field

The invention belongs to the technical field of smart grid and energy storage and conversion, and specifically relates to a real-time power control method based on a large-scale lithium battery energy storage power station, especially suitable for the energy storage power station when the megawatt-level battery energy storage power station participates in the tracking plan output. Real-time power distribution of energy storage units in the station and energy management of energy storage power stations.

Background technique

With the continuous development of batteries and their integration technologies, it has gradually become a feasible solution to apply large-scale battery energy storage power stations to participate in tracking plan output. The battery energy storage unit in the battery energy storage power station has the advantages of fast response and short start-stop time compared with the traditional generator set, and will play an important role in the coordinated control of the distribution network system and smart grid. Several types of large-capacity energy storage batteries commonly used in battery energy storage systems include sodium-sulfur batteries, flow batteries, and lithium batteries.

From the perspective of battery energy storage, overcharging and overdischarging will affect the life of the battery. Therefore, it is necessary to monitor the state of charge of the battery, reasonably allocate the active power demand within the energy storage power station, and control the state of charge of the battery within a certain range.

One of the ways for battery energy storage power stations to participate in tracking planned output is to compensate in real time the difference between the actual power of wind and wind combined power generation and the wind and wind power generation plan, so that new energy power generation equipment such as wind power and solar power can follow the planned curve in advance. generate electricity.

At present, there are few patents, documents, and technical reports on the tracking of wind and wind power generation plans based on megawatt-scale large-scale battery energy storage power stations, and in-depth research and exploration are needed.

Contents of the invention

In view of the above problems, one of the objectives of the present invention is to provide a safe, stable, and easy-to-operate method for real-time power distribution of battery energy storage power stations for tracking planned output.

Method of the present invention is realized by following technical scheme:

A method for real-time power allocation of a battery energy storage power station for tracking planned output, comprising the following steps:

A. Read the relevant data of the battery energy storage power station in real time, and store and manage the above data;

B. Calculate the total power demand command value of the current battery energy storage power station;

C. Calculate the power command value of each energy storage unit in the battery energy storage power station;

D. Summarize the power command values of each energy storage unit and output them to the external monitoring platform.

Further, in step A, the relevant data of the battery energy storage power station includes: the wind and wind power generation plan curve issued by the external monitoring platform, the total power value of wind power generation and photovoltaic power generation, and the total power value of each storage power station in the battery energy storage power station The controllable signal of the energy unit, the state of charge value, the maximum allowable discharge power, the maximum allowable charge power and the rated power, etc.

Further, in step B, the method for calculating the total power demand of the battery energy storage power station includes:

First, according to the wind and wind power generation planning curve read in step A, determine the wind and wind power generation planning value at each time scale;

Secondly, according to the planned value of wind power generation under each time scale, determine the current total power value of wind power storage combined power generation;

Finally, subtract the read total power value of wind power generation and total power value of photovoltaic power generation from the total power value of wind-solar-storage combined power generation to obtain the total power demand of the current battery energy storage power station.

Further, in step C, the method for calculating the power command value of each energy storage unit includes:

When the total power demand of the battery energy storage power station is zero, directly set the power command value of each energy storage unit to zero; Power or the maximum allowable charging power to calculate the decision variables of each energy storage unit, and then obtain the power command value of each energy storage unit participating in the tracking plan output; and then judge whether each energy storage unit meets the maximum allowable discharge power constraints or the maximum allowable Charging power constraints, if there is an energy storage unit that violates the corresponding constraints, recalculate the power command value of the energy storage unit through the maximum allowable discharge power or maximum allowable charging power; otherwise, end the judgment.

Further, in step D, the power command value of each energy storage unit calculated in step C is stored and then output to the external monitoring platform, so as to perform power control on the battery energy storage power station, and at the same time realize the participation of the battery energy storage power station in tracking Real-time power control function when planning output.

Another object of the present invention is to propose a real-time power distribution system for battery energy storage power stations for tracking planned output, the system includes:

The communication module is used to receive the relevant data of the battery energy storage power station issued by the external monitoring platform;

The data storage and management module is used to store and manage the relevant data of the battery energy storage power station, and assign the calculated power command value of each energy storage unit to the corresponding interface variable;

A tracking plan control module for determining the current total power demand of the battery energy storage plant in real time; and

The genetic algorithm control module is used to calculate the power command value of each energy storage unit in real time.

Compared with prior art, the beneficial effect that the present invention reaches is:

The real-time power distribution method and system of the battery energy storage power station for tracking the planned output of the present invention are easy to realize and master in practical engineering applications, and the battery energy storage power station controlled by the method and system is safer and more stable, and can simultaneously meet the needs of large-scale batteries. The energy storage power station tracks the active power demand of the planned output and the real-time regulatory requirements for the storage energy of the large-capacity battery energy storage power station. This method mainly combines the allowable charge and discharge capabilities that can represent the real-time power characteristics of lithium battery energy storage units (ie: the maximum allowable discharge power and maximum allowable charging power of each lithium battery energy storage unit, etc.) The state of charge SOC of energy characteristics, based on the tracking plan control module and the genetic algorithm control module, conducts online real-time distribution of the total power demand for the tracking plan output of the battery energy storage power station, thus realizing the real-time allocation of the tracking plan output of the lithium battery energy storage power station. At the same time as the total power demand, it also realizes the energy management and real-time power control purpose of tracking the planned megawatt-level battery energy storage power station.

Description of drawings

Fig. 1 is a system schematic diagram of a megawatt-level lithium battery energy storage power station of the present invention;

Fig. 2 is a structural block diagram of an embodiment of a real-time power distribution system for a battery energy storage power station according to the present invention for tracking planned output;

Fig. 3 is an implementation block diagram of an embodiment of a method for real-time power distribution of a battery energy storage power station according to the present invention for tracking planned output;

Detailed ways

The method and system of the present invention will be further described in detail below by taking a lithium battery energy storage unit as an example and in conjunction with the accompanying drawings.

As shown in Figure 1, the lithium battery energy storage power station includes lithium battery energy storage units connected in parallel, and each energy storage unit includes a bidirectional converter and a plurality of lithium ion battery packs arranged in parallel. The converter can perform functions such as switching control of the corresponding lithium-ion battery pack and charging and discharging power commands.

Fig. 2 is an implementation block diagram of a real-time power distribution control method for a lithium battery energy storage power station for tracking planned output. As shown in FIG. 2 , the present invention is realized through a communication module 10 , a data storage and management module 20 , a tracking plan control module 30 , and a genetic algorithm control module 40 arranged in the industrial computer.

The communication module 10 is responsible for receiving the relevant data of the battery energy storage power station, and sending the power command value of each energy storage unit to the external monitoring platform. The monitoring platform is set on the left side of the communication module, and communicates with the communication module to realize monitoring and control of the communication module. role;

The data storage and management module 20 is used to store and manage the relevant data of the battery energy storage power station; and is responsible for assigning the calculated power command value of each lithium battery energy storage unit to the relevant interface variable according to the pre-set protocol for external monitoring platform call;

The tracking plan control module 30 is used to calculate the current total power demand command value of the battery energy storage power station in real time;

The genetic algorithm control module 40 is used to calculate the power command value of each battery energy storage unit in real time.

Fig. 3 is a block diagram of the power allocation control algorithm of the battery energy storage power station participating in the tracking plan output of the present invention. The following describes the implementation in detail in combination with specific implementation steps. As shown in Figure 3, the real-time power allocation method of the battery energy storage power station used to track the planned output in this example includes the following steps:

Step A: Read the relevant data of the battery energy storage power station through the communication module 10, and then transmit the data to the data storage and management module 20 for storage and management; wherein, the relevant data of the battery energy storage power station is read by the communication module from the external monitoring platform Directly issued, including: wind power generation plan curve, total power value of wind power generation, total power value of photovoltaic power generation, controllable signal of each energy storage unit in the battery energy storage power station, state of charge (SOC), maximum allowable discharge power , the maximum allowable charging power and rated power, etc.

Step B: Calculate the total power demand of the current battery energy storage power station in real time based on the tracking plan control module 30 .

Step C: Based on the genetic algorithm control module 40, calculate the power command value of each lithium battery energy storage unit in the battery energy storage power station in real time.

Step D: After summarizing the power command values of each energy storage unit calculated in step C in the data storage and management module 20 , output through the communication module 10 .

In step B, the calculation method of the total power demand command value of the energy storage power station is as follows:

First, based on the wind and wind power generation planning curve read in step A, determine the wind and wind power generation planning value at each time scale;

Then, based on the planned value of wind-solar power generation under each time scale, the current total power value P of wind-solar-storage combined _{power generation} is determined;

According to specific requirements, the selected time scale for tracking the output plan value is desirable, such as 5 minutes or 15 minutes. For example, when the selected time scale for tracking the planned value of output is 5 minutes, the calculation formula for the total power value of the current wind-storage-storage combined power generation is as follows:

The T _{time scale} is the selected time scale for tracking the output planning value, and the unit is second.

Finally, based on the calculated total power requirements of wind-solar-storage combined power generation, the total power requirements of the current battery energy storage power station are determined based on the following formula (3):

Among the above formulas, and are the planned values of wind and wind power generation at the current and next time scales (that is, after 5 minutes), and the above-mentioned planned values are updated in real time every other time interval (that is, 5 minutes); Δt is the control period, for example, it can be set to 2 seconds ; P _{wind power} and P _{photovoltaic power} are the total power values of wind power and photovoltaic power generation respectively.

In step C, the calculation method of the power command value of each battery energy storage unit is as follows:

Step C1. When tracking the total power demand of the planned battery energy storage power station When it is a positive value, it means that the battery energy storage power station will be in the discharge state, then based on the state of charge (State of Charge: SOC) of each energy storage unit and the maximum allowable discharge power value, the power command of each energy storage unit is calculated by the following steps Value P _i :

C11) Calculate the decision variable x _i of each energy storage unit based on the genetic algorithm control module 40:

(11a) Determine the number N of individuals (chromosomes) in the population, and the number of genes in each chromosome is the number L of energy storage units. Each individual is binary coded (coded into a vector, that is, a chromosome, and each element of the vector is a gene, and the corresponding gene value will correspond to the decision value x _i (i=1, . .., L)), randomly generate N individuals as the initial population, and obtain the 0, 1 combination mode of the gene string in each chromosome; and make the evolutionary algebraic counter value G=0;

(11b) Determine whether the evolution algebra counter value G is less than or equal to the maximum evolution algebra counter value G ^max , and whether each individual satisfies the following constraints: if the above two judgment conditions are satisfied, perform step 11c, otherwise, jump to step 11f;

(11c) Calculate the fitness value S _k corresponding to each individual k based on the following formula, and evaluate its fitness according to the size of S _k ;

(k=1,...,N)(5)

(11d) Based on the fitness value calculated in step 11c, the selection operation is carried out according to the principle of survival of the fittest. For example, the winning individual can be selected by using the roulette selection method. In this method, the selection probability of the individual will be compared with the fitness value proportional. Then, based on the crossover probability and mutation probability, recombination and mutation operations are performed respectively to obtain offspring;

(11e) Select the optimal offspring based on the following objective function (1), and reinsert it into the population according to a certain insertion probability to perform the replacement operation; then make G=G+1, and return to step 11b;

(11f) Calculate the optimal solution that satisfies the objective function (I), and decode the individual corresponding to the optimal solution to obtain the arrangement and combination of its gene strings, and each gene value is the decision variable value of the energy storage unit i corresponding to it xi(i=1,...,L); C12) Calculate the power command value of each energy storage unit i participating in the output of the tracking plan:

C13) Determine whether the power command value P _i of each energy storage unit i obtained in step C12 satisfies the following constraints on the maximum allowable discharge power of the active power of the energy storage unit:

C14) If there is an energy storage unit that violates the above-mentioned maximum allowable discharge power constraints, then perform step C15, otherwise end the judgment;

C15) Re-determine the power command value P _i of each energy storage unit based on the following formula;

Step C2, when tracking the total power demand of the planned battery energy storage power station When it is a negative value, it means that the battery energy storage power station will be in the charging state. Then, based on the discharge state of each energy storage unit and the maximum allowable charging power value, the power command value P _i of each energy storage unit is calculated by the following steps:

C21) Calculate the decision variable x _i of each energy storage unit based on the genetic algorithm control module:

(21a) Determine the number N of individuals (chromosomes) in the population, and the number of genes in each chromosome is the number L of energy storage units. Each individual is binary coded (coded into a vector, that is, a chromosome, and each element of the vector is a gene, and the corresponding gene value will correspond to the decision value x _i (i=1, . .., L)), randomly generate N individuals as the initial population, and obtain the 0, 1 combination mode of the gene string in each chromosome; and make the evolutionary algebraic counter value G=0;

(21b) Determine whether the evolution algebra counter value G is less than or equal to the maximum evolution algebra counter value G ^max , and whether each individual satisfies the following constraints: if the above two judgment conditions are satisfied, perform step 21c, otherwise, jump to step 21f;

(21c) Calculate the fitness value S _k corresponding to each individual k based on the following formula, and evaluate its fitness according to the size of S _k ;

(k=1,...,N)(10)

(21d) Based on the fitness value calculated in step 21c, the selection operation is carried out according to the principle of survival of the fittest. For example, the winning individual can be selected by using the roulette selection method. In this method, the selection probability of the individual will be compared with the fitness value proportional. Then, based on the crossover probability and mutation probability, recombination and mutation operations are performed respectively to obtain offspring;

(21e) Select the optimal offspring based on the following objective function (II), and reinsert it into the population according to a certain insertion probability for replacement; then set G=G+1 and return to step 21b;

(21f) Calculate the optimal solution that satisfies the objective function (II), and decode the individual corresponding to the optimal solution to obtain the arrangement and combination of gene strings, and each gene value is the decision variable value of the corresponding energy storage unit i x _i (i=1, . . . , L).

C22) Calculate the power command value P _i of each energy storage unit i participating in the output tracking plan;

SOD _i =1-SOC _i (12)

C23) Judging whether the power command value _Pi of each energy storage unit i obtained in step C22 satisfies the following constraints on the maximum allowable charging power of the active power of the energy storage unit:

|P _i |≤|P _i ^{maximum allowable charge} | (13)

C24) If there is an energy storage unit that violates the above-mentioned maximum allowable charging power constraints, then perform the following step C25, otherwise end the judgment;

C25) Re-determine the power command value P _i of each energy storage unit based on the following formula;

Step C3, when the total power demand of the battery energy storage power station When is zero, set the power command value P _i of each energy storage unit to zero;

In formulas (4)-(14), u _i is the controllable signal of energy storage unit i, which is read through step A. When the energy storage unit i is controllable, the controllable signal value is 1, and other The situation value is 0; x _i is a 0-1 decision variable. When x _i = ₁ , it means that the energy storage unit i will participate in the power allocation calculation, and when x _i =0, it means that it will not participate in this power allocation; SOD _i is the discharge state of energy storage unit i; L is the number of battery energy storage units; ^{the maximum allowable discharge} of P _i is the maximum ^allowable discharge power value of energy storage unit _i ; ^Charging is the maximum allowable charging power value of energy storage unit i.

In step D, the data storage and management module sends the power command value of each energy storage unit calculated in step C to the communication module 10, and then the communication module outputs to the external monitoring platform to execute the power command value of the lithium battery energy storage power station. At the same time, it realizes the real-time power control function when the battery energy storage power station participates in the tracking plan output.

By adopting the above-mentioned technical scheme, the present invention has the function of real-time distribution of the total power demand for the tracking plan output of the lithium battery energy storage power station, thereby realizing the convenient and effective realization of the real-time power control and energy management functions of the lithium battery energy storage power station participating in the tracking plan output . The real-time power control method and system when the megawatt-level battery energy storage power station participates in the tracking plan output can simultaneously meet the active power requirements of the large-scale battery energy storage power station tracking plan output and the real-time supervision requirements for the storage energy of the large-capacity battery energy storage power station . In addition, the present invention first selects the energy storage units participating in this power distribution through the genetic algorithm, and then performs power distribution on these units, which greatly improves the work efficiency, thereby realizing convenient and effective real-time power distribution of the battery energy storage power station. control function.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. The present invention has been described in detail in conjunction with the above embodiments, and those of ordinary skill in the art should understand that: Modifications or equivalent replacements can be made to the specific embodiments of the present invention, but these modifications or changes are within the protection scope of the pending claims.

Claims (6)

1., for following the tracks of a battery energy storage power station realtime power distribution method of planning to exert oneself, it is characterized in that, the method comprises the following steps:

The related data of A, in real time reading battery energy storage power station, and store and management is carried out to above-mentioned data;

The overall power requirement bid value of B, calculating present battery energy-accumulating power station;

Each energy storage unit power command value in C, calculating battery energy storage power station;

D, each energy storage unit power command value is gathered after export outer monitoring platform to;

In step C, the method calculating each energy storage unit power command value comprises:

When the overall power requirement of battery energy storage power station is zero, directly each energy storage unit power command value is set to zero; When the overall power requirement non-zero of battery energy storage power station, select according to its symbol the decision variable being calculated each energy storage unit by maximum permission discharge power or maximum permission charge power, and then ask for the power command value participating in each energy storage unit that tracking plan is exerted oneself; Then judge whether each energy storage unit meets maximum permission discharge power constraints or maximum permission charge power constraints, if against anti-phase energy storage unit of answering constraints, then recalculated the power command value of this energy storage unit by maximum permission discharge power or maximum permission charge power; Otherwise, terminate to judge;

Described step C comprises the steps: further

Step C1, overall power requirement when battery energy storage power station for on the occasion of time, represent that this battery energy storage power station will be in discharge condition, then calculate each energy storage unit power command value P by following step _i:

C11) the decision variable x of each energy storage unit is calculated by genetic algorithm _i, pass through x _idetermine the assembled state of the energy storage unit participating in this power division;

C12) each energy storage unit i power command value participating in tracking plan and exert oneself is calculated:

C13) each energy storage unit i power command value P of drawing of determining step C12 _iwhether meet the maximum permission discharge power constraints of following energy storage unit active power:

C14) if there is the energy storage unit violating maximum permission discharge power constraints, then each energy storage unit power command value P is redefined by following formula _i: otherwise terminate to judge;

Step C2, overall power requirement when battery energy storage power station during for negative value, represent that this battery energy storage power station will be in charged state, then calculate each energy storage unit power command value P by following step _i:

C21) the decision variable x of each energy storage unit is calculated by genetic algorithm _i, pass through x _idetermine the assembled state of the energy storage unit participating in this power division;

C22) each energy storage unit i power command value participating in tracking plan and exert oneself is calculated:

C23) each energy storage unit i power command value P of drawing of determining step C22 _iwhether meet the maximum permission charge power constraints of following energy storage unit active power:

C24) if there is the energy storage unit violating maximum permission charge power constraints, then each energy storage unit power command value P is redefined by following formula _i: otherwise terminate to judge;

Step C3, overall power requirement when battery energy storage power station when being zero, then by each energy storage unit power command value P _ibe set to zero;

Above-mentioned various in, u _ifor the controllable signal of i energy storage unit; x _ifor 0-1 decision variable; SOC _i, SOD _ibe respectively charged, the discharge condition of i energy storage unit, SOD _i=1-SOC _i; L is battery energy storage unit number; be respectively i energy storage unit maximumly allow to put, charge power value.

2. the method for claim 1, it is characterized in that, in step, the related data of described battery energy storage power station comprises: the wind light generation Plan Curve issued by outer monitoring platform, controllable signal, SOC, maximum permission discharge power, the maximum permission charge power of each energy storage unit in wind power generation total power value and photovoltaic generation total power value and battery energy storage power station.

3. method as claimed in claim 2, is characterized in that, in stepb, the method calculating the overall power requirement of battery energy storage power station comprises:

First, according to the wind light generation Plan Curve that steps A reads, the wind light generation planned value of each time scale is determined;

Secondly, according to the wind light generation planned value under each time scale, determine current wind-solar-storage joint generating total power value;

Finally, from wind-solar-storage joint generating total power value, deduct read wind power generation total power value and photovoltaic generation total power value, obtain the overall power requirement of present battery energy-accumulating power station.

4. method as claimed in claim 3, is characterized in that, determines current wind-solar-storage joint generating total power value by following formula:

Above-mentioned various in, P _{wind-light storage}for current wind-solar-storage joint generating total power value; be respectively current, the wind light generation planned value of future time scale; Δ t is control cycle; T _{time scale}for following the tracks of scale access time of planned value of exerting oneself, unit is second.

5. the method for claim 1, is characterized in that,

Calculated the decision variable x of each energy storage unit by genetic algorithm in described step C11 _imethod comprise:

(11a) the individual number N in colony is determined, gene number in each individuality is energy storage unit number L, binary coding is carried out to each individuality, stochastic generation individuality is as initial population, obtain 0 of gene string in each individuality, 1 compound mode, and make evolutionary generation Counter Value G=0;

(11b) judge whether evolutionary generation Counter Value G is less than or equal to maximum evolutionary generation Counter Value G ^max, and whether each individuality meets the constraints of following formula: if above-mentioned two Rule of judgment are all satisfied, then perform step 11c; Otherwise, jump to step 11f;

(11c) the fitness value S corresponding to each individual k is calculated based on following formula _k;

(11d) based on the fitness value that step 11c calculates, carry out selection operation according to survival of the fittest principle, after then carrying out restructuring and mutation operation respectively based on crossover probability and mutation probability, obtain filial generation;

(11e) select optimum filial generation based on following target function (I), and by its according to insertion probability reinsert in population carry out substitute operation; Then make G=G+1, jump to step 11b;

(11f) calculate the optimal solution meeting target function (I), draw the permutation and combination method of its gene string to the individuality corresponding to optimal solution after decoding, each genic value is the decision variable value x of energy storage unit i corresponding with it _i, wherein i=1 ..., L;

Calculated the decision variable x of each energy storage unit by genetic algorithm in described step C21 _imethod comprise:

(21a) the individual number N in colony is determined, gene number in each individuality is energy storage unit number L, binary coding is carried out to each individuality, stochastic generation individuality is as initial population, obtain 0 of gene string in each individuality, 1 compound mode, and make evolutionary generation Counter Value G=0;

(21b) judge whether evolutionary generation Counter Value G is less than or equal to maximum evolutionary generation Counter Value G ^max, and whether each individuality meets the constraints of following formula: if above-mentioned two Rule of judgment are all satisfied, then perform step 21c; Otherwise, jump to step 21f;

(21c) the fitness value S corresponding to each individual k is calculated based on following formula _k;

(21d) based on the fitness value that step 21c calculates, carry out selection operation according to survival of the fittest principle, after then carrying out restructuring and mutation operation respectively based on crossover probability and mutation probability, obtain filial generation;

(21e) select optimum filial generation based on following target function (II), and by its according to insertion probability reinsert in population carry out substitute operation; Then make G=G+1, jump to step 21b;

(21f) calculate the optimal solution meeting target function (II), the individuality corresponding to optimal solution draws the permutation and combination method of its gene string after decoding, and each genic value is the decision variable value x of energy storage unit i corresponding with it _i, wherein i=1 ..., L.

6., for following the tracks of the battery energy storage power station realtime power distribution system planning to exert oneself, it is characterized in that, this system comprises:

Communication module, for receiving the related data of battery energy storage power station;

Data storage and management module, for the related data of store and management battery energy storage power station, and by each energy storage unit power command value assignment of calculating to the corresponding interface variable;

Follow the tracks of plan control module, for determining the current total power demand of battery energy storage power station in real time; With

Genetic algorithm control module, for calculating each energy storage unit power command value in real time;

Described genetic algorithm control module, for when the overall power requirement of battery energy storage power station is zero, is directly set to zero by each energy storage unit power command value; When the overall power requirement non-zero of battery energy storage power station, select according to its symbol the decision variable being calculated each energy storage unit by maximum permission discharge power or maximum permission charge power, and then ask for the power command value participating in each energy storage unit that tracking plan is exerted oneself; Then judge whether each energy storage unit meets maximum permission discharge power constraints or maximum permission charge power constraints, if against anti-phase energy storage unit of answering constraints, then recalculated the power command value of this energy storage unit by maximum permission discharge power or maximum permission charge power; Otherwise, terminate to judge;

Described genetic algorithm control module, also for the overall power requirement when battery energy storage power station for on the occasion of time, represent that this battery energy storage power station will be in discharge condition, then calculate each energy storage unit power command value P by following step _i:

C11) the decision variable x of each energy storage unit is calculated by genetic algorithm _i, pass through x _idetermine the assembled state of the energy storage unit participating in this power division;

C12) each energy storage unit i power command value participating in tracking plan and exert oneself is calculated:

C13) each energy storage unit i power command value P of drawing of determining step C12 _iwhether meet the maximum permission discharge power constraints of following energy storage unit active power:

C14) if there is the energy storage unit violating maximum permission discharge power constraints, then each energy storage unit power command value P is redefined by following formula _i: otherwise terminate to judge;

Described genetic algorithm control module, also for the overall power requirement when battery energy storage power station during for negative value, represent that this battery energy storage power station will be in charged state, then calculate each energy storage unit power command value P by following step _i:

C21) the decision variable x of each energy storage unit is calculated by genetic algorithm _i, pass through x _idetermine the assembled state of the energy storage unit participating in this power division;

C22) each energy storage unit i power command value participating in tracking plan and exert oneself is calculated:

C23) each energy storage unit i power command value P of drawing of determining step C22 _iwhether meet the maximum permission charge power constraints of following energy storage unit active power:

C24) if there is the energy storage unit violating maximum permission charge power constraints, then each energy storage unit power command value P is redefined by following formula _i: otherwise terminate to judge;

Described genetic algorithm control module, also for the overall power requirement when battery energy storage power station when being zero, then by each energy storage unit power command value P _ibe set to zero;

Above-mentioned various in, u _ifor the controllable signal of i energy storage unit; x _ifor 0-1 decision variable; SOC _i, SOD _ibe respectively charged, the discharge condition of i energy storage unit, SOD _i=1-SOC _i; L is battery energy storage unit number; be respectively i energy storage unit maximumly allow to put, charge power value.