CN117713177B - Method, device, equipment and medium for optimizing and configuring battery capacity of wind farm - Google Patents
Method, device, equipment and medium for optimizing and configuring battery capacity of wind farm Download PDFInfo
- Publication number
- CN117713177B CN117713177B CN202410168456.2A CN202410168456A CN117713177B CN 117713177 B CN117713177 B CN 117713177B CN 202410168456 A CN202410168456 A CN 202410168456A CN 117713177 B CN117713177 B CN 117713177B
- Authority
- CN
- China
- Prior art keywords
- power
- energy storage
- frequency modulation
- battery
- discharge
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 69
- 238000004146 energy storage Methods 0.000 claims abstract description 206
- 238000005457 optimization Methods 0.000 claims abstract description 53
- 239000002245 particle Substances 0.000 claims abstract description 35
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 230000002441 reversible effect Effects 0.000 claims abstract description 19
- 230000000087 stabilizing effect Effects 0.000 claims abstract description 19
- 238000011217 control strategy Methods 0.000 claims abstract description 18
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 18
- 208000028659 discharge Diseases 0.000 claims description 110
- 230000005611 electricity Effects 0.000 claims description 28
- 238000003860 storage Methods 0.000 claims description 22
- 230000006870 function Effects 0.000 claims description 16
- 238000012423 maintenance Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000009467 reduction Effects 0.000 claims description 14
- 230000001629 suppression Effects 0.000 claims description 14
- 230000000694 effects Effects 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 8
- 230000007812 deficiency Effects 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 8
- 238000009826 distribution Methods 0.000 claims description 5
- 238000013459 approach Methods 0.000 claims description 4
- 239000002131 composite material Substances 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 238000011084 recovery Methods 0.000 claims description 4
- 239000013589 supplement Substances 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 11
- 238000010586 diagram Methods 0.000 description 7
- 230000009977 dual effect Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000002829 reductive effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Abstract
The invention relates to the technical field of energy storage optimal configuration, in particular to a method, a device, equipment and a medium for optimizing the capacity of a wind power plant battery, which comprise the following steps: combining energy storage stabilizing power fluctuation and primary frequency modulation working condition requirements, and constructing a planning layer model considering energy storage cost according to a net present value method; an operation layer model considering the energy storage cooperative frequency modulation cost of the fan is constructed by taking optimal allocation of frequency modulation power of the fan and the energy storage as a target; the method comprises the steps of combining a planning layer model and an operation layer model, constructing a capacity optimization configuration model, and embedding a battery grouping control strategy and a variation modal decomposition method to update grid-connected optimization reference power; and adopting an improved particle swarm algorithm combining reverse learning early-stage optimization with variant cross later-stage optimization, and solving a battery capacity optimal configuration scheme by using a nested mathematical programming optimizer. According to the invention, the fluctuation demand is stabilized, and the residual energy storage power is used for the frequency modulation standby of the wind power plant, so that the battery utilization rate and the energy storage benefit can be effectively improved.
Description
Technical Field
The invention relates to the technical field of energy storage optimal configuration, in particular to a method, a device, equipment and a medium for optimizing the capacity of a wind power plant battery.
Background
Along with large-scale grid connection of wind power, the uncertain characteristic of output of the wind power is a great challenge to safe and stable operation of a power grid, and in order to improve the safe and stable performance, the new standard requires that the wind power grid connection technology is changed from 'passive adaptation' to 'active support' and 'autonomous operation', and the capability of the wind power plant for participating in frequency modulation adjustment is gradually improved. The energy storage equipment can be assembled and a reasonable operation strategy is formulated, so that the power output fluctuation of the wind power plant can be effectively smoothed, primary frequency modulation can be assisted, and the operation safety and stability of the power grid are enhanced. In consideration of the overall economy of the system, the energy storage configuration capacity is as small as possible on the premise of meeting the requirements, so that the optimal battery energy storage configuration scheme in the wind storage system has important research significance.
In order to enable the wind farm to have the capacity of rapid frequency adjustment, two modes of reserved backup and additionally arranged energy storage are mainly provided. The traditional standby mode has high cost and large air discarding quantity, so that the development of the wind-storage combined system is a more economical and practical scheme. In the prior art, most of research on the configuration problem of the energy storage participating in a single working condition is carried out, and the effect of the energy storage is not fully utilized. In addition, the grid-connected reference power under the wave suppression working condition in the existing research generally adopts methods such as sectional average, filtering, decomposition and the like to stabilize the high-frequency output of wind power under the condition of considering the fluctuation index, but an actual wind power station may also need to participate in primary frequency modulation, and the traditional method can not update the grid-connected reference power in real time according to the actual condition of the system.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for optimizing the configuration of the battery capacity of a wind farm, thereby effectively solving the problems in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a wind farm battery capacity optimizing configuration method comprises the following steps:
combining energy storage stabilizing power fluctuation and primary frequency modulation working condition requirements, and constructing a planning layer model considering energy storage cost according to a net present value method;
An operation layer model considering the energy storage cooperative frequency modulation cost of the fan is constructed by taking optimal allocation of frequency modulation power of the fan and the energy storage as a target;
The planning layer model and the operation layer model are combined to construct a capacity optimization configuration model, and a battery grouping control strategy and a variation modal decomposition method are embedded to update grid-connected optimization reference power;
and adopting an improved particle swarm algorithm combining reverse learning early-stage optimization with variant cross later-stage optimization, and solving a battery capacity optimal configuration scheme by using a nested mathematical programming optimizer.
Further, the planning layer model includes: constructing an objective function of energy storage participation double scenes:
；
Wherein f ou is the annual average effect of energy storage, S flu is the effect of energy storage to stabilize wind power fluctuation, B fre is the effect of energy storage to assist primary frequency modulation, and C life is the annual cost of energy storage and the like;
Wherein: ;
；
Wherein S 1 is punishment and cost reduction; s 2 is increasing the power utility of surfing the Internet; p W (t) is the original output of the fan at the moment t; p G (t) is the actual grid-connected power of the wind farm at the moment t; c qf is unit wind abandon punishment cost; c qd units of electricity deficiency punishment cost; Δt is the sampling period; t annual run time; c w is the unit online electricity price of the wind power plant, and when the annual increased electricity generation amount of the wind power plant is positive after energy storage is configured, the utility is obtained; otherwise, the cost is born as negative;
；
；
Wherein, B 1 is the utility of the FM service; b 2 energy storage and electricity purchase cost; b 3 energy storage frequency modulation charge-discharge loss cost; k tp is the FM service charge; p bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; a buy、asell is the unit price of energy storage electricity purchase and electricity selling respectively; η c and η d are respectively the charge and discharge efficiencies of the stored energy;
；
；
Wherein, C inve is the energy storage construction investment cost; c main is the energy storage operation maintenance cost; l Y is a present value coefficient, represents a change coefficient of energy storage maintenance cost along with service life, and C reco is recovery cost; e total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; c rec is the rejection cost ratio; b is the discount rate; y is the service life of the energy storage battery;
The service life of the battery is the ratio of the total discharge capacity of the battery under the service life period at the rated discharge depth to the annual discharge capacity converted into the rated discharge depth:
；
wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e (i) is the actual discharge capacity of the battery in the ith switching stage; n r is the number of battery cycles at rated depth of discharge; i is the discharge phase count; n (i) is the number of battery cycles at the actual depth of discharge for the ith discharge stage.
Further, the planning layer model further includes:
Wind power grid-connected power fluctuation constraint conditions:
；
Wherein, P G is the actual output of the wind turbine; p WN is the installed capacity of the wind farm; a b is a grid-connected fluctuation rate limit value, which indicates that the power change rate of the output of the wind power plant in a fixed time interval should not exceed a certain proportion of the installed capacity of the wind power plant;
Energy storage constraint conditions:
；
；
；
Wherein S min is the lower limit of the remaining battery power; s (t) is the residual capacity of the battery at the moment t, and E r is the rated capacity of the battery; p min is the stabilizing power required to make the wind power fluctuation meet the defined standard; p r is the maximum rated charge-discharge power of the energy storage battery pack; and ψ max、Ψmin is the maximum and minimum values of the battery SOC.
Further, the run layer model includes:
consider the objective function of the annual primary frequency modulation composite cost:
；
；
Wherein f in is the annual primary frequency modulation comprehensive cost; w 1 is wind power load reduction standby cost, and W 2 is wind power frequency modulation cost; b 2 is the energy storage frequency modulation electricity purchasing cost; b 3 is the energy storage frequency modulation charge-discharge loss cost; b 4 is the punishment cost of insufficient frequency modulation, and c w is the unit online price of the wind power plant; p gref (t) is the theoretical grid-connected initial reference power of the wind power plant at the t moment; p wdown (t) is the downward frequency modulation power of the wind turbine generator; a pe is punishment unit price of insufficient frequency modulation of the system; p p2 (t) is energy storage primary frequency modulation power, P de (t) is optimal load shedding active output of the wind farm, and the expression is as follows;
；
Wherein P act (t) is the actual total frequency modulation power provided by the wind power storage system; p ins (t) is wind farm frequency modulation required power, and the expression is shown as follows;
；
；
D opt (t) is the optimal load shedding rate of the wind power plant; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; f (t) is the actual sampling frequency of the system at the moment t; f N is the system nominal frequency; r t is a difference adjustment coefficient.
Further, the run layer model further includes:
Energy storage frequency modulation power constraint condition:
；
；
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable of upward and downward frequency modulation, when 1 is used for indicating participation in system frequency modulation, when 0 is used for indicating non-participation in system frequency modulation, P up_max、Pdown_max is a declaration upper limit which energy storage upward and downward frequency modulation power cannot exceed, and the expression is as follows:
；
Wherein P imax、Pomax is the set maximum total charge and discharge power of the stored energy; delta in、δout is the distribution coefficient of the energy storage frequency modulation charge and discharge power; p s (t) is a wave suppression power action domain, and the calculation formula is as follows:
；
Wind farm load shedding power constraint conditions:
；
Wherein d max is the maximum load shedding rate; p de_max (t) is the maximum load shedding power of the wind farm;
Fan frequency modulation power constraint condition:
；
Wherein P gref (t) is the theoretical output of the wind farm; p de (t) is the optimal derate power; p de_max (t) is the maximum off-load power;
Frequency modulation capability constraint:
The probability that the wind power storage system meets the frequency modulation requirement is required to be larger than the set confidence coefficient beta 1, and for the ith frequency modulation, a variable m (i) of 0-1 is set:
；
if the wind storage system meets the frequency modulation requirement, m (i) is 1, otherwise 0 is 0, the probability is replaced by the frequency, and the frequency modulation capacity constraint is as follows:
。
further, the updating the grid-connected optimized reference power comprises the following steps:
After the original wind power is decomposed by adopting a variational mode decomposition method, high-frequency, medium-frequency and low-frequency components are distinguished based on the central frequency, a fluctuation index is regulated according to a wind power plant access power system, the medium-frequency and low-frequency components are taken as theoretical grid-connected initial reference power P gref (t), and the variational model is as follows:
；
wherein { u k } is the IMF component; { ω k } is the center frequency; delta is a pulse function; f is the original input signal;
The theoretical grid-connected initial reference power is transmitted into an embedded battery grouping control strategy and a variation modal decomposition method, the up-down frequency modulation quantity and the dynamic load reduction quantity which are required to be executed by a wind power plant are obtained through optimization calculation, the grid-connected initial reference power is updated to obtain grid-connected optimized reference power P gopt (t), and then the optimal charge and discharge power P p1 (t) required by energy storage stabilizing fluctuation is obtained; when the stabilized power is positive, the battery is required to discharge to supplement the deficiency; when the stabilized power is negative, the battery is charged to absorb the balance; the calculation formula of the grid-connected optimization reference power P gopt (t) and the energy storage optimal wave suppression power P p1 (t) is as follows:
；
；
Wherein, P de (t) wind farm optimally subtracts the active output; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; p W (t) is the original output of the fan;
The inner layer model also calculates and obtains primary frequency modulation power P p2 (t) required to be provided by energy storage; when the system frequency is too high, the energy storage is downwards modulated to absorb power; when the system frequency is too low, the energy storage is upwards modulated, and then power is emitted, so that the energy storage frequency modulation power expression is:
；
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable for energy storage up and down frequency modulation;
the total charge-discharge power P sum (t) of the energy storage tracking comprises energy storage wave suppression power P p1 (t) and energy storage frequency modulation power P p2 (t), and the whole charge-discharge power expression is as follows:
；
；
Wherein P ch(t)、Pdis (t) is the total charge power and discharge power of the stored energy at time t;
Embedding a grouping charge-discharge battery control strategy: the BESS1 absorbs power from a power grid at the initial moment and is used as a rechargeable battery pack; the BESS2 outputs power to the power grid, and is a discharge battery pack; the expression is as follows:
；
If the discharge amount of the BESS2 is large, and reaches the SOC lower limit first, the BESS2 immediately exchanges charge and discharge roles with the BESS1, namely, the BESS1 serves as a discharge battery pack, and the BESS2 serves as a charge battery pack, and the expression is as follows:
；
Wherein P b1(t)、Pb2 (t) is the output of BESS1 and BESS2 at time t.
Further, the improved particle swarm algorithm comprises the following steps:
the particle update process in the particle swarm algorithm is as follows:
；
Wherein w is an inertial weight; c 1、c2 is a learning factor; r 1、r2 is a random number in interval [0,1 ]; Position and velocity for the ith particle, the d-th dimension, the kth iteration; the/> is an individual extremum and a global extremum;
After the individual updating and the group optimal updating are completed, reverse learning is performed in the early stage, and meanwhile, the solutions in the current direction and the opposite direction are considered to approach the global optimal more quickly, wherein the formula is as follows:
；
if the particles are out of range, reinitializing:
；
is a new solution of the current population optimal individuals obtained by a reverse learning strategy; the/> and/> values are the maximum and minimum boundary values; k is a random number in interval [0,1 ];
the later period adopts the variation of differential evolution and a crossing strategy to carry out improved optimization, and the variation process is as follows:
；
the crossover operation is as follows:
；
Wherein z i,d is a variation vector; f is a scaling factor; m 1、m2 is a random number in the interval [1, N ], N is the total size of the population; u i,d is the d-th dimension of heuristic vector u i; c is the crossover probability; n i is a random integer in the interval [1, D ], D is the total dimension of the particle swarm.
The invention also comprises a device for optimizing and configuring the battery capacity of the wind power plant, which comprises the following steps:
The planning model building unit is used for combining the working condition requirements of energy storage stabilizing power fluctuation and primary frequency modulation and building a planning layer model considering energy storage cost according to a net present value method;
The operation model building unit is used for building an operation layer model considering the energy storage cooperative frequency modulation cost of the fan by taking optimal allocation of frequency modulation power of the fan and energy storage as a target;
The double-layer model unit is used for combining the planning layer model and the operation layer model, constructing a capacity optimization configuration model, and embedding a battery grouping control strategy and a variation modal decomposition method to update grid-connected optimization reference power;
The solving unit is used for solving the optimal battery capacity configuration scheme by adopting an improved particle swarm algorithm combining reverse learning early-stage optimization with variant cross later-stage optimization and a nested mathematical programming optimizer.
The invention also includes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the method as described above when executing the computer program.
The invention also includes a storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
The beneficial effects of the invention are as follows:
1. According to the invention, energy storage is controlled to realize stable fluctuation demand, and the residual power of the energy storage is used for participating in frequency modulation standby of the wind power plant, so that the battery utilization rate and energy storage benefit can be effectively improved, the grid-connected fluctuation coefficient is further reduced, and the wind power standby and energy storage control are combined to greatly reduce the wind power load reduction cost and the wind discarding quantity, so that the frequency modulation capability of the wind power plant is enhanced;
2. the invention embeds the dual battery control strategy into the dual working condition for operation. The strategy can effectively reduce the total capacity of the energy storage configuration and prolong the service life of the battery;
3. The invention provides a method for optimizing grid-connected reference power by considering that a wind power plant participates in a frequency modulation working condition in combination with a variation modal decomposition method. The wind power grid-connected optimization reference power obtained by updating and calculating is more reasonable and is closer to the actual running condition;
4. The invention adopts an improved particle swarm algorithm nested mathematical programming optimizer combining reverse learning early-stage optimization with variant cross later-stage optimization for optimization calculation. The algorithm has the advantages of less evaluation times for obtaining the feasible solution and higher convergence rate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a flow chart of the method of example 1;
FIG. 2 is a schematic view of the structure of the device in example 1;
FIG. 3 is a schematic diagram of a combined wind and energy system according to example 2;
FIG. 4 is a flowchart of the algorithm in example 2;
FIG. 5 is a graph showing the comparison of the convergence curves in example 2;
FIG. 6 is a graph showing the electric field power stabilizing effect of example 2;
FIG. 7 is a graph showing the frequency modulation effect of the wind power plant in example 2;
fig. 8 is a schematic diagram of the state of charge of the packet battery in example 2;
fig. 9 is a schematic diagram of charge and discharge power of the assembled battery in example 2;
Fig. 10 is a schematic structural diagram of a computer device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
As shown in fig. 1: a wind farm battery capacity optimizing configuration method comprises the following steps:
combining energy storage stabilizing power fluctuation and primary frequency modulation working condition requirements, and constructing a planning layer model considering energy storage cost according to a net present value method;
An operation layer model considering the energy storage cooperative frequency modulation cost of the fan is constructed by taking optimal allocation of frequency modulation power of the fan and the energy storage as a target;
the method comprises the steps of combining a planning layer model and an operation layer model, constructing a capacity optimization configuration model, and embedding a battery grouping control strategy and a variation modal decomposition method to update grid-connected optimization reference power;
and adopting an improved particle swarm algorithm combining reverse learning early-stage optimization with variant cross later-stage optimization, and solving a battery capacity optimal configuration scheme by using a nested mathematical programming optimizer.
In this embodiment, the planning layer model includes: constructing an objective function of energy storage participation double scenes:
；
Wherein f ou is the annual average effect of energy storage, S flu is the effect of energy storage to stabilize wind power fluctuation, B fre is the effect of energy storage to assist primary frequency modulation, and C life is the annual cost of energy storage and the like;
Wherein: ;
；
Wherein S 1 is punishment and cost reduction; s 2 is increasing the power utility of surfing the Internet; p W (t) is the original output of the fan at the moment t; p G (t) is the actual grid-connected power of the wind farm at the moment t; c qf is unit wind abandon punishment cost; c qd units of electricity deficiency punishment cost; Δt is the sampling period; t annual run time; c w is the unit online electricity price of the wind power plant, and when the annual increased electricity generation amount of the wind power plant is positive after energy storage is configured, the utility is obtained; otherwise, the cost is born as negative;
；
；
Wherein, B 1 is the utility of the FM service; b 2 energy storage and electricity purchase cost; b 3 energy storage frequency modulation charge-discharge loss cost; k tp is the FM service charge; p bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; a buy、asell is the unit price of energy storage electricity purchase and electricity selling respectively; η c and η d are respectively the charge and discharge efficiencies of the stored energy;
；
；
Wherein, C inve is the energy storage construction investment cost; c main is the energy storage operation maintenance cost; l Y is a present value coefficient, represents a change coefficient of energy storage maintenance cost along with service life, and C reco is recovery cost; e total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; c rec is the rejection cost ratio; b is the discount rate; y is the service life of the energy storage battery;
The service life of the battery is the ratio of the total discharge capacity of the battery under the service life period at the rated discharge depth to the annual discharge capacity converted into the rated discharge depth:
；
wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e (i) is the actual discharge capacity of the battery in the ith switching stage; n r is the number of battery cycles at rated depth of discharge; i is the discharge phase count; n (i) is the number of battery cycles at the actual depth of discharge for the ith discharge stage.
The planning layer model further includes:
Wind power grid-connected power fluctuation constraint conditions:
；
Wherein, P G is the actual output of the wind turbine; p WN is the installed capacity of the wind farm; a b is a grid-connected fluctuation rate limit value, which indicates that the power change rate of the output of the wind power plant in a fixed time interval should not exceed a certain proportion of the installed capacity of the wind power plant;
Energy storage constraint conditions:
；
；
；
Wherein S min is the lower limit of the remaining battery power; s (t) is the residual capacity of the battery at the moment t, and E r is the rated capacity of the battery; p min is the stabilizing power required to make the wind power fluctuation meet the defined standard; p r is the maximum rated charge-discharge power of the energy storage battery pack; and ψ max、Ψmin is the maximum and minimum values of the battery SOC.
In this embodiment, the operation layer model includes:
consider the objective function of the annual primary frequency modulation composite cost:
；
；
Wherein f in is the annual primary frequency modulation comprehensive cost; w 1 is wind power load reduction standby cost, and W 2 is wind power frequency modulation cost; b 2 is the energy storage frequency modulation electricity purchasing cost; b 3 is the energy storage frequency modulation charge-discharge loss cost; b 4 is the punishment cost of insufficient frequency modulation, and c w is the unit online price of the wind power plant; p gref (t) is the theoretical grid-connected initial reference power of the wind power plant at the t moment; p wdown (t) is the downward frequency modulation power of the wind turbine generator; a pe is punishment unit price of insufficient frequency modulation of the system; p p2 (t) is energy storage primary frequency modulation power, P de (t) is optimal load shedding active output of the wind farm, and the expression is as follows;
；
Wherein P act (t) is the actual total frequency modulation power provided by the wind power storage system; p ins (t) is wind farm frequency modulation required power, and the expression is shown as follows;
；
；
D opt (t) is the optimal load shedding rate of the wind power plant; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; f (t) is the actual sampling frequency of the system at the moment t; f N is the system nominal frequency; r t is a difference adjustment coefficient.
The runtime layer model further includes:
Energy storage frequency modulation power constraint condition:
；
；
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable of upward and downward frequency modulation, when 1 is used for indicating participation in system frequency modulation, when 0 is used for indicating non-participation in system frequency modulation, P up_max、Pdown_max is a declaration upper limit which energy storage upward and downward frequency modulation power cannot exceed, and the expression is as follows:
；
Wherein P imax、Pomax is the set maximum total charge and discharge power of the stored energy; delta in、δout is the distribution coefficient of the energy storage frequency modulation charge and discharge power; p s (t) is a wave suppression power action domain, and the calculation formula is as follows:
；
Wind farm load shedding power constraint conditions:
；
Wherein d max is the maximum load shedding rate; p de_max (t) is the maximum load shedding power of the wind farm;
Fan frequency modulation power constraint condition:
；
Wherein P gref (t) is the theoretical output of the wind farm; p de (t) is the optimal derate power; p de_max (t) is the maximum off-load power;
Frequency modulation capability constraint:
the probability that the wind storage system meets the frequency modulation requirement is required to be larger than a certain confidence coefficient beta 1, and for the ith frequency modulation, a variable m (i) of 0-1 is set:
；
if the wind storage system meets the frequency modulation requirement, m (i) is 1, otherwise 0 is 0, the probability is replaced by the frequency, and the frequency modulation capacity constraint is as follows:
。
in this embodiment, updating the grid-connected optimization reference power includes the following steps:
After the original wind power is decomposed by adopting a variational mode decomposition method, high-frequency, medium-frequency and low-frequency components are distinguished based on the central frequency, a fluctuation index is regulated according to a wind power plant access power system, the medium-frequency and low-frequency components are taken as theoretical grid-connected initial reference power P gref (t), and the variational model is as follows:
；
wherein { u k } is the IMF component; { ω k } is the center frequency; delta is a pulse function; f is the original input signal;
The theoretical grid-connected initial reference power is transmitted into an embedded battery grouping control strategy and a variation modal decomposition method, the up-down frequency modulation quantity and the dynamic load reduction quantity which are required to be executed by a wind power plant are obtained through optimization calculation, the grid-connected initial reference power is updated to obtain grid-connected optimized reference power P gopt (t), and then the optimal charge and discharge power P p1 (t) required by energy storage stabilizing fluctuation is obtained; when the stabilized power is positive, the battery is required to discharge to supplement the deficiency; when the stabilized power is negative, the battery is charged to absorb the balance; the calculation formula of the grid-connected optimization reference power P gopt (t) and the energy storage optimal wave suppression power P p1 (t) is as follows:
；
；
Wherein, P de (t) wind farm optimally subtracts the active output; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; p W (t) is the original output of the fan;
The inner layer model also calculates and obtains primary frequency modulation power P p2 (t) required to be provided by energy storage; when the system frequency is too high, the energy storage is downwards modulated to absorb power; when the system frequency is too low, the energy storage is upwards modulated, and then power is emitted, so that the energy storage frequency modulation power expression is:
；
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable for energy storage up and down frequency modulation;
the total charge-discharge power P sum (t) of the energy storage tracking comprises energy storage wave suppression power P p1 (t) and energy storage frequency modulation power P p2 (t), and the whole charge-discharge power expression is as follows:
；
；
Wherein P ch(t)、Pdis (t) is the total charge power and discharge power of the stored energy at time t;
Embedding a grouping charge-discharge battery control strategy: the BESS1 absorbs power from a power grid at the initial moment and is used as a rechargeable battery pack; the BESS2 outputs power to the power grid, and is a discharge battery pack; the expression is as follows:
；
If the discharge amount of the BESS2 is large, and reaches the SOC lower limit first, the BESS2 immediately exchanges charge and discharge roles with the BESS1, namely, the BESS1 serves as a discharge battery pack, and the BESS2 serves as a charge battery pack, and the expression is as follows:
；
Wherein P b1(t)、Pb2 (t) is the output of BESS1 and BESS2 at time t.
As a preferred embodiment of the above embodiment, the particle swarm algorithm is improved, comprising the steps of:
the particle update process in the particle swarm algorithm is as follows:
；
Wherein w is an inertial weight; c 1、c2 is a learning factor; r 1、r2 is a random number in interval [0,1 ]; Position and velocity for the ith particle, the d-th dimension, the kth iteration; the/> is an individual extremum and a global extremum;
After the individual updating and the group optimal updating are completed, reverse learning is performed in the early stage, and meanwhile, the solutions in the current direction and the opposite direction are considered to approach the global optimal more quickly, wherein the formula is as follows:
；
if the particles are out of range, reinitializing:
；
is a new solution of the current population optimal individuals obtained by a reverse learning strategy; the/> and/> values are the maximum and minimum boundary values; k is a random number in interval [0,1 ];
the later period adopts the variation of differential evolution and a crossing strategy to carry out improved optimization, and the variation process is as follows:
；
the crossover operation is as follows:
；
Wherein z i,d is a variation vector; f is a scaling factor; m 1、m2 is a random number in the interval [1, N ], N is the total size of the population; u i,d is the d-th dimension of heuristic vector u i; c is the crossover probability; n i is a random integer in the interval [1, D ], D is the total dimension of the particle swarm.
The beneficial effect of the scheme in this embodiment is:
1. According to the invention, the energy storage is controlled to realize the stable fluctuation demand, and the residual power of the energy storage is used for the frequency modulation standby of the wind power plant, so that the battery utilization rate and the energy storage benefit can be effectively improved, the grid-connected fluctuation coefficient is further reduced, the wind power standby and the energy storage control are combined, the wind power load reduction cost and the wind discarding quantity can be greatly reduced, and the frequency modulation capability of the wind power plant is enhanced.
2. The invention embeds the dual battery control strategy into the dual working condition for operation. The strategy can effectively reduce the total capacity of the energy storage configuration and prolong the service life of the battery.
3. The invention provides a method for optimizing grid-connected reference power by considering that a wind power plant participates in a frequency modulation working condition in combination with a variation modal decomposition method. The wind power grid-connected optimization reference power obtained by updating and calculating is more reasonable and is closer to the actual running condition.
4. The invention adopts an improved particle swarm algorithm nested mathematical programming optimizer combining reverse learning early-stage optimization with variant cross later-stage optimization for optimization calculation. The algorithm has the advantages of less evaluation times for obtaining the feasible solution and higher convergence rate.
As shown in fig. 2, the embodiment further includes a device for optimizing and configuring the battery capacity of the wind farm, where the method includes:
The planning model building unit is used for combining the energy storage stabilizing power fluctuation and the primary frequency modulation working condition requirement, and building a planning layer model considering the energy storage cost according to a net present value method;
The operation model building unit is used for building an operation layer model considering the energy storage cooperative frequency modulation cost of the fan by taking optimal allocation of frequency modulation power of the fan and the energy storage as a target;
the double-layer model unit is used for jointly planning a layer model and an operation layer model, constructing a capacity optimization configuration model, and embedding a battery grouping control strategy and a variation modal decomposition method to update grid-connected optimization reference power;
the solving unit is used for solving the optimal configuration scheme of the battery capacity by adopting an improved particle swarm algorithm combining reverse learning early-stage optimization with variant cross later-stage optimization and a nested mathematical programming optimizer.
Example 2:
As shown in fig. 3, the method for optimizing and configuring the capacity of the wind power plant battery according to the dual working conditions comprises the following steps:
Step 1: combining energy storage stabilizing power fluctuation and primary frequency modulation working condition requirements, and constructing a planning layer model considering energy storage cost-benefit according to a net present value method;
1.1: and constructing a cost-benefit objective function of energy storage participation double scenes.
Wherein f ou is the annual average net benefit of energy storage, S flu is the net benefit of energy storage stabilizing wind power fluctuation, B fre is the net benefit of energy storage auxiliary primary frequency modulation, and C life is the annual investment cost of energy storage and the like.
The net benefit expression for stabilizing wind power fluctuation is as follows:
Wherein S 1 is punishment and cost reduction; s 2 is increasing the power efficiency of surfing the Internet; p W (t) is the original output of the fan at the moment t; p G (t) is the actual grid-connected power of the wind farm at the moment t; c qf is unit wind abandon punishment cost; c qd units of electricity deficiency punishment cost; Δt is the sampling period; t annual run time; and c w is the unit internet electricity price of the wind power plant. When the annual increased power generation amount of the wind power station is positive after energy storage is configured, obtaining benefits; otherwise, the cost is born as negative.
The primary frequency modulation net gain expression is as follows:
Wherein, B 1 is the income of the FM service; b 2 energy storage and electricity purchase cost; b 3 energy storage frequency modulation charge-discharge loss cost; k tp is the FM service charge; p bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; a buy、asell is the unit price of energy storage electricity purchase and electricity selling respectively; η c and η d are the charge and discharge efficiencies of the stored energy, respectively.
The annual investment cost expression of energy storage and the like is as follows:
Wherein, C inve is the investment cost of energy storage construction; c main is the energy storage operation maintenance cost; l Y is the present value coefficient and represents the change coefficient of the energy storage maintenance cost along with the service life. C reco is the recovery cost; e total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; c rec is the rejection cost ratio; b is the discount rate; y is the life of the energy storage battery.
The service life of a battery is the ratio of the total discharge capacity of the battery at a rated discharge depth under the life cycle to the annual discharge capacity converted into the rated discharge depth.
Wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e (i) is the actual discharge capacity of the battery in the ith switching stage; n r is the number of battery cycles at rated depth of discharge; i is the discharge phase count; n (i) is the number of battery cycles at the actual depth of discharge for the ith discharge stage.
1.2: And constructing a wind power grid-connected power fluctuation constraint condition.
Wherein, P G is the actual output of the wind turbine generator; p WN is the installed capacity of the wind farm; a b is a grid-connected fluctuation rate limit value, which indicates that the power change rate of the wind farm output in a fixed time interval should not exceed a certain proportion of the installed capacity.
1.3: And constructing an energy storage constraint condition.
Wherein S min is the lower limit of the remaining battery power; s (t) is the residual capacity of the battery at the moment t, and E r is the rated capacity of the battery; p min is the required stabilizing power for enabling the wind power fluctuation to meet the limit standard in GB/T19963.1-2021; p r is the maximum rated charge-discharge power of the energy storage battery pack; and ψ max、Ψmin is the maximum and minimum values of the battery SOC.
Step 2: an operation layer model considering the energy storage cooperative frequency modulation cost of the fan is constructed for determining the optimal frequency modulation power distribution of the fan and the energy storage;
2.1: an objective function is constructed that considers the annual primary frequency modulation composite cost.
/>
Wherein f in is the annual primary frequency modulation comprehensive cost; w 1 is wind power load reduction standby cost, and W 2 is wind power frequency modulation cost; b 2 is the energy storage frequency modulation electricity purchasing cost; b 3 is the energy storage frequency modulation charge-discharge loss cost; b 4 is the cost of punishment of insufficient frequency modulation. c w is the unit on-line electricity price of the wind power plant; p gref (t) is the theoretical grid-connected initial reference power of the wind power plant at the t moment; p wdown (t) is the downward frequency modulation power of the wind turbine generator; a pe is punishment unit price of insufficient frequency modulation of the system; p p2 (t) is the energy storage primary frequency modulation power. P de (t) is the optimal load-shedding active output of the wind farm, and the expression is shown as follows;
p act (t) is the actual total frequency modulation power provided by the wind power storage system; p ins (t) is wind farm frequency modulation required power, and the expression is shown as follows;
D opt (t) is the optimal load shedding rate of the wind power plant; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; f (t) is the actual sampling frequency of the system at the moment t; f N is the system nominal frequency; r t is a difference adjustment coefficient.
2.2: And constructing an energy storage frequency modulation power constraint condition.
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable of up and down frequency modulation, when 1 is used for participating in system frequency modulation, and when 0 is used for not participating in system frequency modulation. P up_max、Pdown_max is a declaration upper limit which can not be exceeded by the upward and downward frequency modulation power of the stored energy, and the expression is as follows:
Wherein P imax、Pomax is the set maximum total charge and discharge power of the stored energy; delta in、δout is the distribution coefficient of the energy storage frequency modulation charge and discharge power; p s (t) is a wave suppression power reporting scope, and the calculation formula is as follows:
2.3: and constructing a wind power plant load shedding power constraint condition.
Wherein d max is the maximum load shedding rate; p de_max (t) is the maximum load shedding power of the wind farm.
2.4: And constructing a fan frequency modulation power constraint condition.
Wherein P gref (t) is the theoretical output of the wind farm; p de (t) is the optimal derate power; p de_max (t) is the maximum derated power.
2.5: And constructing a frequency modulation capacity constraint condition. The energy storage can greatly increase the cost for fully tracking the frequency modulation requirement, and the gain is limited, so according to the opportunity constraint planning theory, due to the occurrence of random variables, the decision making is allowed to be not met to a certain extent when adverse conditions occur. The probability that the wind storage system meets the frequency modulation requirement is required to be larger than a certain confidence coefficient beta 1, and for the ith frequency modulation, a variable m (i) of 0-1 is set:
If the wind storage system meets the frequency modulation requirement, m (i) is 1, otherwise 0. The probability is replaced by the frequency, and the frequency modulation capacity constraint is as follows:
step 3: embedding a battery grouping control strategy and a variation modal decomposition method in the model to update grid-connected optimization reference power;
3.1: after the original wind power is decomposed by adopting a variation modal decomposition method, high-frequency medium-frequency and low-frequency components are distinguished based on the central frequency, and then the medium-frequency and low-frequency components are taken as theoretical grid-connected initial reference power P gref (t) according to a fluctuation index specified by a wind power plant access power system technology. The variational model is as follows:
Wherein { u k } is the IMF component; { ω k } is the center frequency; delta is a pulse function; f is the original input signal.
3.2: And (3) introducing the theoretical grid-connected initial reference power into an inner layer model for optimization calculation to obtain upward and downward frequency modulation quantity and dynamic load reduction quantity which are required to be executed by the wind farm, updating the grid-connected initial reference power to obtain grid-connected optimized reference power P gopt (t), and further obtaining optimal charge and discharge power P p1 (t) required by energy storage stabilizing fluctuation. When the stabilized power is positive, the battery is required to discharge to supplement the deficiency; when the stabilized power is negative, the battery is charged to absorb the balance. The calculation formula of the grid-connected optimization reference power P gopt (t) and the energy storage optimal wave suppression power P p1 (t) is as follows:
Wherein, P de (t) wind farm optimally subtracts the active output; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; p W (t) is the original output of the fan.
3.3: The inner layer model also calculates and obtains primary frequency modulation power P p2 (t) required to be provided by energy storage. When the system frequency is too high, the energy storage is downwards modulated to absorb power; when the system frequency is too low, the energy storage is upwards modulated, and then power is emitted, so that the energy storage frequency modulation power expression is:
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable for energy storage up and down frequency modulation.
3.4: The total charge-discharge power P sum (t) of the energy storage tracking comprises energy storage wave suppression power P p1 (t) and energy storage frequency modulation power P p2 (t), and the whole charge-discharge power expression is as follows:
wherein P ch(t)、Pdis (t) is the total charge power and discharge power of the stored energy at time t.
3.5: And embedding a grouping charge-discharge battery control strategy. The BESS1 absorbs power from a power grid at the initial moment and is used as a rechargeable battery pack; the BESS2 outputs power to the power grid, which is a discharge battery pack. The expression is as follows:
If the discharge amount of the BESS2 is large, and reaches the SOC lower limit first, the BESS2 immediately exchanges charge and discharge roles with the BESS1, namely, the BESS1 serves as a discharge battery pack, and the BESS2 serves as a charge battery pack, and the expression is as follows:
Wherein P b1(t)、Pb2 (t) is the output of BESS1 and BESS2 at time t.
Step 4: as shown in fig. 4, an improved particle swarm algorithm nest Gurobi combining reverse learning early-stage optimization with variant crossover late-stage optimization is adopted to solve the energy storage optimal configuration scheme with the annual net benefit maximization.
4.1: The particle updating process in the improved particle swarm algorithm is as follows:
Wherein w is an inertial weight; c 1、c2 is a learning factor; r 1、r2 is a random number in interval [0,1 ]; Position and velocity for the ith particle, the d-th dimension, the kth iteration; and/> is an individual extremum and a global extremum.
4.2: After the individual updating and the group optimal updating are completed, reverse learning is performed in the early stage, and meanwhile, the solutions in the current direction and the opposite direction are considered to approach the global optimal more quickly, wherein the formula is as follows:
if the particles are out of range, reinitializing:
is a new solution of the current population optimal individuals obtained by a reverse learning strategy; the/> and/> values are the maximum and minimum boundary values; k is a random number in interval [0,1 ]. /(I)
4.3: The later period adopts the variation of differential evolution and a crossing strategy to carry out improved optimization, and the variation process is as follows:
the crossover operation is as follows:
Wherein z i,d is a variation vector; f is a scaling factor; m 1、m2 is a random number in the interval [1, N ], N is the total size of the population; u i,d is the d-th dimension of heuristic vector u i; c is the crossover probability; n i is a random integer in the interval [1, D ], D is the total dimension of the particle swarm.
In this example, a 450MW wind farm in Jiangsu is selected as an example. The improved particle swarm optimization is adopted to nest Gurobi an optimizer for solving, and compared with the convergence curve contrast diagram of the traditional particle swarm algorithm, the convergence curve contrast diagram is shown in fig. 5. The best configuration scheme is obtained as follows: two battery packs with a rated capacity of 40.6MWh and a DC/AC converter with a rated power of 32.9MW were assembled. At the moment, the service life of the battery is 16 years, the average annual income is 5075.4 ten thousand yuan, the net income of energy storage and wave suppression is 3395.2 ten thousand yuan, the net income of energy storage and frequency modulation is 5512.6 ten thousand yuan, and the annual investment cost of energy storage and the like is 3832.3 ten thousand yuan.
The power fluctuation before and after the stabilization of the output of the wind power plant on a certain day and the system frequency adjusting effect before and after the primary frequency modulation of the wind storage system are respectively shown in fig. 6 and 7. The obtained energy storage configuration scheme can not only effectively smooth wind power output fluctuation, but also effectively adjust the system frequency exceeding the frequency modulation dead zone.
Table 1 is a comparison table of configuration results and related parameters of energy storage in a dual-working-condition application scenario and a single-working-condition application scenario.
Table 1 comparison of different operating mode configuration schemes
It can be seen from the table that when the stored energy is applied in a dual condition scenario, the total net gain during life is much greater than for a single condition. And energy storage under the condition is fully utilized, the grid-connected fluctuation coefficient, the wind farm load shedding capacity and the wind farm frequency modulation comprehensive cost are smaller than those of a single application scene, and the satisfaction degree and the completion degree of participation in stabilization and frequency modulation are higher.
Comparing the grouped battery with the traditional overall battery configuration scheme, optimally configuring two battery packs with the capacity of 40.6MWh by the grouped battery, namely 81.2MWh in total; while the cell stack needs to be configured with 108.6MWh. The grouping control strategy can be seen to reduce the total capacity of the energy storage to be configured, thereby reducing the investment cost of the energy storage. The charge states and charge/discharge powers of the grouped batteries configured in this example are shown in fig. 8 and 9. The battery switching frequency is seen to decrease, and thus the life of the grouped battery is three years longer than the overall battery pack.
Fig. 10 is a schematic structural diagram of a computer device according to an embodiment of the present application. The computer device 400 provided in the embodiment of the present application includes: a processor 410 and a memory 420, the memory 420 storing a computer program executable by the processor 410, which when executed by the processor 410 performs the method as described above.
The embodiment of the present application also provides a storage medium 430, on which storage medium 430 a computer program is stored which, when executed by the processor 410, performs a method as above.
The storage medium 430 may be implemented by any type or combination of volatile or nonvolatile memory devices, such as static random access memory (Static Random Access Memory, SRAM), electrically erasable programmable read-only memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-only memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The meaning of "a plurality of" is two or more, unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily for the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (9)
1. The wind farm battery capacity optimizing configuration method is characterized by comprising the following steps:
combining energy storage stabilizing power fluctuation and primary frequency modulation working condition requirements, and constructing a planning layer model considering energy storage cost according to a net present value method;
An operation layer model considering the energy storage cooperative frequency modulation cost of the fan is constructed by taking optimal allocation of frequency modulation power of the fan and the energy storage as a target;
The planning layer model and the operation layer model are combined to construct a capacity optimization configuration model, and a battery grouping control strategy and a variation modal decomposition method are embedded to update grid-connected optimization reference power;
adopting an improved particle swarm algorithm combining reverse learning early-stage optimization with variant cross later-stage optimization, and solving a battery capacity optimal configuration scheme by a nested mathematical programming optimizer;
the improved particle swarm algorithm comprises the following steps:
the particle update process in the particle swarm algorithm is as follows:
；
Wherein w is an inertial weight; c 1、c2 is a learning factor; r 1、r2 is a random number in interval [0,1 ]; Position and velocity for the ith particle, the d-th dimension, the kth iteration; the/> is an individual extremum and a global extremum;
After the individual updating and the group optimal updating are completed, reverse learning is performed in the early stage, and meanwhile, the solutions in the current direction and the opposite direction are considered to approach the global optimal more quickly, wherein the formula is as follows:
；
if the particles are out of range, reinitializing:
；
is a new solution of the current population optimal individuals obtained by a reverse learning strategy; the/> and/> values are the maximum and minimum boundary values; k is a random number in interval [0,1 ];
the later period adopts the variation of differential evolution and a crossing strategy to carry out improved optimization, and the variation process is as follows:
；
the crossover operation is as follows:
；
Wherein z i,d is a variation vector; f is a scaling factor; m 1、m2 is a random number in the interval [1, N ], N is the total size of the population; u i,d is the d-th dimension of heuristic vector u i; c is the crossover probability; n i is a random integer in the interval [1, D ], D is the total dimension of the particle swarm.
2. The method for optimizing configuration of wind farm battery capacity according to claim 1, wherein the planning layer model comprises: constructing an objective function of energy storage participation double scenes:
；
Wherein f ou is the annual average effect of energy storage, S flu is the effect of energy storage to stabilize wind power fluctuation, B fre is the effect of energy storage to assist primary frequency modulation, and C life is the annual cost of energy storage and the like;
wherein: ;
；
Wherein S 1 is punishment and cost reduction; s 2 is increasing the power utility of surfing the Internet; p W (t) is the original output of the fan at the moment t; p G (t) is the actual grid-connected power of the wind farm at the moment t; c qf is unit wind abandon punishment cost; c qd units of electricity deficiency punishment cost; Δt is the sampling period; t annual run time; c w is the unit online electricity price of the wind power plant, and when the annual increased electricity generation amount of the wind power plant is positive after energy storage is configured, the utility is obtained; otherwise, the cost is born as negative;
；
；
Wherein, B 1 is the utility of the FM service; b 2 energy storage and electricity purchase cost; b 3 energy storage frequency modulation charge-discharge loss cost; k tp is the FM service charge; p bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; a buy、asell is the unit price of energy storage electricity purchase and electricity selling respectively; η c and η d are respectively the charge and discharge efficiencies of the stored energy;
；
；
Wherein, C inve is the energy storage construction investment cost; c main is the energy storage operation maintenance cost; l Y is a present value coefficient, represents a change coefficient of energy storage maintenance cost along with service life, and C reco is recovery cost; e total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; c rec is the rejection cost ratio; b is the discount rate; y is the service life of the energy storage battery;
The service life of the battery is the ratio of the total discharge capacity of the battery under the service life period at the rated discharge depth to the annual discharge capacity converted into the rated discharge depth:
；
wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e (i) is the actual discharge capacity of the battery in the ith switching stage; n r is the number of battery cycles at rated depth of discharge; i is the discharge phase count; n (i) is the number of battery cycles at the actual depth of discharge for the ith discharge stage.
3. The method for optimizing configuration of a battery capacity of a wind farm according to claim 2, wherein the planning layer model further comprises:
Wind power grid-connected power fluctuation constraint conditions:
；
Wherein, P G is the actual output of the wind turbine; p WN is the installed capacity of the wind farm; a b is a grid-connected fluctuation rate limit value, which indicates that the power change rate of the output of the wind power plant in a fixed time interval should not exceed a certain proportion of the installed capacity of the wind power plant;
Energy storage constraint conditions:
；
；
；
Wherein S min is the lower limit of the remaining battery power; s (t) is the residual capacity of the battery at the moment t, and E r is the rated capacity of the battery; p min is the stabilizing power required to make the wind power fluctuation meet the defined standard; p r is the maximum rated charge-discharge power of the energy storage battery pack; and ψ max、Ψmin is the maximum and minimum values of the battery SOC.
4. The method for optimizing configuration of wind farm battery capacity according to claim 1, wherein the operation layer model comprises:
consider the objective function of the annual primary frequency modulation composite cost:
；
；
Wherein f in is the annual primary frequency modulation comprehensive cost; w 1 is wind power load reduction standby cost, and W 2 is wind power frequency modulation cost; b 2 is the energy storage frequency modulation electricity purchasing cost; b 3 is the energy storage frequency modulation charge-discharge loss cost; b 4 is the punishment cost of insufficient frequency modulation, and c w is the unit online price of the wind power plant; p gref (t) is the theoretical grid-connected initial reference power of the wind power plant at the t moment; p wdown (t) is the downward frequency modulation power of the wind turbine generator; a pe is punishment unit price of insufficient frequency modulation of the system; p p2 (t) is energy storage primary frequency modulation power, P de (t) is optimal load shedding active output of the wind farm, and the expression is as follows;
；
Wherein P act (t) is the actual total frequency modulation power provided by the wind power storage system; p ins (t) is wind farm frequency modulation required power, and the expression is shown as follows;
；
；
D opt (t) is the optimal load shedding rate of the wind power plant; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; f (t) is the actual sampling frequency of the system at the moment t; f N is the system nominal frequency; r t is a difference adjustment coefficient.
5. The method for optimizing configuration of wind farm battery capacity according to claim 4, wherein the operation layer model further comprises:
Energy storage frequency modulation power constraint condition:
；
；
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable of upward and downward frequency modulation, when 1 is used for indicating participation in system frequency modulation, when 0 is used for indicating non-participation in system frequency modulation, P up_max、Pdown_max is a declaration upper limit which energy storage upward and downward frequency modulation power cannot exceed, and the expression is as follows:
；
Wherein P imax、Pomax is the set maximum total charge and discharge power of the stored energy; delta in、δout is the distribution coefficient of the energy storage frequency modulation charge and discharge power; p s (t) is a wave suppression power action domain, and the calculation formula is as follows:
；
Wind farm load shedding power constraint conditions:
；
Wherein d max is the maximum load shedding rate; p de_max (t) is the maximum load shedding power of the wind farm;
Fan frequency modulation power constraint condition:
；
Wherein P gref (t) is the theoretical output of the wind farm; p de (t) is the optimal derate power; p de_max (t) is the maximum off-load power;
Frequency modulation capability constraint:
The probability that the wind power storage system meets the frequency modulation requirement is required to be larger than the set confidence coefficient beta 1, and for the ith frequency modulation, a variable m (t) of 0-1 is set:
；
If the wind storage system meets the frequency modulation requirement, m (t) is 1, otherwise 0 is 0, the probability is replaced by the frequency, and the frequency modulation capacity constraint is as follows:
。
6. The method for optimizing configuration of battery capacity of a wind farm according to claim 1, wherein the updating of grid-connected optimized reference power comprises the steps of:
After the original wind power is decomposed by adopting a variational mode decomposition method, high-frequency, medium-frequency and low-frequency components are distinguished based on the central frequency, a fluctuation index is regulated according to a wind power plant access power system, the medium-frequency and low-frequency components are taken as theoretical grid-connected initial reference power P gref (t), and the variational model is as follows:
；
wherein { u k } is the IMF component; { ω k } is the center frequency; delta is a pulse function; f is the original input signal;
The theoretical grid-connected initial reference power is transmitted into an embedded battery grouping control strategy and a variation modal decomposition method, the up-down frequency modulation quantity and the dynamic load reduction quantity which are required to be executed by a wind power plant are obtained through optimization calculation, the grid-connected initial reference power is updated to obtain grid-connected optimized reference power P gopt (t), and then the optimal charge and discharge power P p1 (t) required by energy storage stabilizing fluctuation is obtained; when the stabilized power is positive, the battery is required to discharge to supplement the deficiency; when the stabilized power is negative, the battery is charged to absorb the balance; the calculation formula of the grid-connected optimization reference power P gopt (t) and the energy storage optimal wave suppression power P p1 (t) is as follows:
；
；
Wherein, P de (t) wind farm optimally subtracts the active output; p wup(t)、Pwdown (t) is the upward and downward frequency modulation power of the fan; p W (t) is the original output of the fan;
The inner layer model also calculates and obtains primary frequency modulation power P p2 (t) required to be provided by energy storage; when the system frequency is too high, the energy storage is downwards modulated to absorb power; when the system frequency is too low, the energy storage is upwards modulated, and then power is emitted, so that the energy storage frequency modulation power expression is:
；
Wherein P bup(t)、Pbdown (t) is energy storage upward and downward frequency modulation power; u up(t)、udown (t) is a state control variable for energy storage up and down frequency modulation;
the total charge-discharge power P sum (t) of the energy storage tracking comprises energy storage wave suppression power P p1 (t) and energy storage frequency modulation power P p2 (t), and the whole charge-discharge power expression is as follows:
；
；
Wherein P ch(t)、Pdis (t) is the total charge power and discharge power of the stored energy at time t;
Embedding a grouping charge-discharge battery control strategy: the BESS1 absorbs power from a power grid at the initial moment and is used as a rechargeable battery pack; the BESS2 outputs power to the power grid, and is a discharge battery pack; the expression is as follows:
；
If the discharge amount of the BESS2 is large, and reaches the SOC lower limit first, the BESS2 immediately exchanges charge and discharge roles with the BESS1, namely, the BESS1 serves as a discharge battery pack, and the BESS2 serves as a charge battery pack, and the expression is as follows:
；
Wherein P b1(t)、Pb2 (t) is the output of BESS1 and BESS2 at time t.
7. A wind farm battery capacity optimizing configuration apparatus, characterized by using the method according to any one of claims 1 to 6, comprising:
The planning model building unit is used for combining the working condition requirements of energy storage stabilizing power fluctuation and primary frequency modulation and building a planning layer model considering energy storage cost according to a net present value method;
The operation model building unit is used for building an operation layer model considering the energy storage cooperative frequency modulation cost of the fan by taking optimal allocation of frequency modulation power of the fan and energy storage as a target;
The double-layer model unit is used for combining the planning layer model and the operation layer model, constructing a capacity optimization configuration model, and embedding a battery grouping control strategy and a variation modal decomposition method to update grid-connected optimization reference power;
The solving unit is used for solving the optimal battery capacity configuration scheme by adopting an improved particle swarm algorithm combining reverse learning early-stage optimization with variant cross later-stage optimization and a nested mathematical programming optimizer.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-6 when the computer program is executed.
9. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410168456.2A CN117713177B (en) | 2024-02-06 | 2024-02-06 | Method, device, equipment and medium for optimizing and configuring battery capacity of wind farm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410168456.2A CN117713177B (en) | 2024-02-06 | 2024-02-06 | Method, device, equipment and medium for optimizing and configuring battery capacity of wind farm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117713177A CN117713177A (en) | 2024-03-15 |
| CN117713177B true CN117713177B (en) | 2024-04-16 |
Family
ID=90162904
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410168456.2A Active CN117713177B (en) | 2024-02-06 | 2024-02-06 | Method, device, equipment and medium for optimizing and configuring battery capacity of wind farm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117713177B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113888204A (en) * | 2021-09-06 | 2022-01-04 | 中国能源建设集团天津电力设计院有限公司 | Multi-subject game virtual power plant capacity optimization configuration method |
| CN116345502A (en) * | 2023-03-14 | 2023-06-27 | 华北电力大学 | Method, system, equipment and medium for capacity optimization configuration of hybrid energy storage double-layer model |
| CN117375035A (en) * | 2023-11-01 | 2024-01-09 | 华北电力大学 | Wind-solar-electricity grid-connected double-layer energy storage capacity intelligent optimal configuration method based on improved Archimedes algorithm |
| CN117394451A (en) * | 2023-10-12 | 2024-01-12 | 国网福建省电力有限公司 | Active power distribution network source network storage multi-element coordination planning method considering flexibility |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11163271B2 (en) * | 2018-08-28 | 2021-11-02 | Johnson Controls Technology Company | Cloud based building energy optimization system with a dynamically trained load prediction model |
| US11159022B2 (en) * | 2018-08-28 | 2021-10-26 | Johnson Controls Tyco IP Holdings LLP | Building energy optimization system with a dynamically trained load prediction model |
-
2024
- 2024-02-06 CN CN202410168456.2A patent/CN117713177B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113888204A (en) * | 2021-09-06 | 2022-01-04 | 中国能源建设集团天津电力设计院有限公司 | Multi-subject game virtual power plant capacity optimization configuration method |
| CN116345502A (en) * | 2023-03-14 | 2023-06-27 | 华北电力大学 | Method, system, equipment and medium for capacity optimization configuration of hybrid energy storage double-layer model |
| CN117394451A (en) * | 2023-10-12 | 2024-01-12 | 国网福建省电力有限公司 | Active power distribution network source network storage multi-element coordination planning method considering flexibility |
| CN117375035A (en) * | 2023-11-01 | 2024-01-09 | 华北电力大学 | Wind-solar-electricity grid-connected double-layer energy storage capacity intelligent optimal configuration method based on improved Archimedes algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117713177A (en) | 2024-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021114849A1 (en) | Island power grid energy storage system hierarchical control method for ameliorating new energy power generation fluctuation | |
| WO2017161787A1 (en) | Dynamic stabilizing method for photovoltaic power fluctuation based on future information | |
| CN108110795B (en) | Wind power plant group outgoing power transmission capacity and energy storage configuration joint optimization method | |
| CN103986190A (en) | Wind and solar storage combining power generation system smooth control method based on power generation power curves | |
| WO2015070480A1 (en) | Method for managing energy of battery energy storage power station based on wind power prediction | |
| Faisal et al. | Backtracking search algorithm based fuzzy charging-discharging controller for battery storage system in microgrid applications | |
| CN105140942B (en) | Consider the hybrid energy-storing optimization power distribution method of state-of-charge deviation | |
| CN107124002A (en) | Independent micro-grid energy optimal control method based on bi-fuzzy control | |
| Meng et al. | Energy storage auxiliary frequency modulation control strategy considering ACE and SOC of energy storage | |
| CN104377724A (en) | Coordinated optimization control method for improving economical efficiency of wind power/photovoltaic hybrid energy storage system | |
| CN114784907B (en) | Wind power plant energy storage capacity configuration method considering battery running state | |
| CN113644693B (en) | Distributed operation control method for renewable energy/hydrogen-containing alternating current-direct current hybrid system | |
| CN106505604A (en) | The photovoltaic energy storage cooperation unit optimization collocation method of access area power distribution network | |
| CN116388252B (en) | Wind farm energy storage capacity optimal configuration method, system, computer equipment and medium | |
| CN114629139A (en) | Wind-storage combined system frequency modulation control method considering energy storage capacity optimization | |
| CN107273968A (en) | A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization | |
| CN107565576A (en) | A kind of active distribution network reactive Voltage Optimum method that more active management means are mutually coordinated | |
| CN105896575A (en) | Hundred megawatt energy storage power control method and system based on self-adaptive dynamic programming | |
| Han et al. | Optimization control and economic evaluation of energy storage combined thermal power participating in frequency regulation based on multivariable fuzzy double-layer optimization | |
| CN117713177B (en) | Method, device, equipment and medium for optimizing and configuring battery capacity of wind farm | |
| Li et al. | Research on the control strategy of energy storage participation in power system frequency regulation | |
| CN110854910B (en) | Thermal power generating unit climbing pressure relieving method under high-proportion photovoltaic grid connection | |
| CN107147115A (en) | A kind of user side energy management method based on multiple time scale model | |
| CN116805192A (en) | Comprehensive energy system double-layer planning optimization method considering optimal energy rejection rate and application thereof | |
| CN115549130A (en) | Method, device and equipment for configuring hybrid energy storage capacity of primary frequency modulation of photovoltaic power station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |