CN114498767A - Wind power plant energy storage capacity optimization method and device and electronic equipment - Google Patents

Wind power plant energy storage capacity optimization method and device and electronic equipment Download PDF

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Publication number
CN114498767A
CN114498767A CN202210097188.0A CN202210097188A CN114498767A CN 114498767 A CN114498767 A CN 114498767A CN 202210097188 A CN202210097188 A CN 202210097188A CN 114498767 A CN114498767 A CN 114498767A
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energy storage
constraint
wind power
storage equipment
parameter
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陆秋瑜
杨银国
李力
于珍
刘洋
杨璧瑜
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Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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Abstract

The invention discloses a method and a device for optimizing energy storage capacity of a wind power plant and electronic equipment, wherein the method comprises the following steps: acquiring an energy storage planning operation model of a wind power plant; the target function of the energy storage planning operation model is to minimize energy storage investment cost, and the constraint conditions comprise energy storage equipment charge state constraint, energy storage equipment charging and discharging linear constraint in hours, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group; calculating a parameter feasible region of the energy storage equipment by using a projection method based on an energy storage planning operation model; and calculating the optimal configuration parameters of the energy storage equipment according to the parameter feasible region of the energy storage equipment. According to the method, the parameter feasible region of the energy storage equipment can be obtained by solving the energy storage planning operation model of the wind power plant, and the optimal configuration parameters can be further calculated according to the parameter feasible region, so that the wind power plant energy storage system can reasonably absorb the wind power output based on the configuration parameters, and the economy of energy storage investment and the safety of wind power curtailment constraint are balanced.

Description

Wind power plant energy storage capacity optimization method and device and electronic equipment
Technical Field
The invention relates to the technical field of new energy, in particular to a method and a device for optimizing energy storage capacity of a wind power plant and electronic equipment.
Background
In order to relieve the global energy crisis and greatly popularize green energy, the domestic offshore wind power industry is rapidly developing at present. The offshore wind power output is influenced by meteorological factors and often has strong fluctuation and randomness. The consideration of energy storage has the effects of stabilizing wind power fluctuation, improving power supply reliability and the like, so that the wind power storage combined operation becomes an important mode of wind power development.
However, the construction and maintenance costs of the energy storage system are often high, and if the energy storage capacity is configured too much, the scheduling and energy storage costs will be increased, and the randomness of the wind power output also easily causes the problem of wind abandonment, and increases the wind power absorption pressure.
Therefore, how to configure the optimal wind power matching energy storage capacity is a problem that needs to be solved urgently by technical personnel in the field.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method and a device for optimizing the energy storage capacity of a wind power plant and electronic equipment, which can calculate and obtain the optimal configuration parameters of the energy storage equipment of the wind power plant based on linear programming.
In a first aspect, the invention provides a method for optimizing energy storage capacity of a wind power plant, which comprises the following steps:
acquiring an energy storage planning operation model of a wind power plant; wherein the content of the first and second substances,
the objective function of the energy storage planning operation model is the minimized energy storage investment cost, and the constraint conditions comprise energy storage equipment charge state constraint, energy storage equipment charging and discharging linear constraint in hours, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group;
calculating a parameter feasible region of the energy storage equipment by using a projection method based on the energy storage planning operation model;
and calculating the optimal configuration parameters of the energy storage equipment according to the parameter feasible region of the energy storage equipment.
Optionally, the linear constraint of charging and discharging in the energy storage device in hours specifically includes:
the sum of the charging power and the discharging power of the energy storage device in the hour does not exceed the power limit value of the energy storage device.
Optionally, the wind power curtailment linear constraint group is determined by the following method:
acquiring an initial wind power curtailment rate constraint, wherein the initial wind power curtailment rate constraint is determined based on output information and curtailment information of a wind power plant;
and converting the initial wind power curtailment constraint into a wind power curtailment linear constraint group according to the strong dual property of the linear programming.
Optionally, the calculating the parameter feasible region of the energy storage device by using the projection method specifically includes:
determining the full quantity parameters of the constraint conditions and the parameters of the energy storage equipment based on the energy storage planning operation model;
obtaining a projection result of the full parameter on the energy storage equipment parameter by using a projection method;
and determining the parameter feasible region of the energy storage equipment according to the projection result.
Optionally, the calculating the optimal configuration parameter of the energy storage device according to the parameter feasible region of the energy storage device specifically includes:
establishing a linear programming function according to the parameter feasible region and the energy storage investment cost of the energy storage equipment;
and obtaining the optimal configuration parameters of the energy storage equipment by solving the linear programming function.
Optionally, the energy storage investment cost includes an energy storage power cost and an energy storage capacity cost;
the parameters of the energy storage device include energy storage power and energy storage capacity.
In a second aspect, the present invention further provides a wind farm energy storage capacity optimization system, including:
the model obtaining unit is used for obtaining an energy storage planning operation model of the wind power plant; wherein the content of the first and second substances,
the objective function of the energy storage planning operation model is the minimized energy storage investment cost, and the constraint conditions comprise energy storage equipment charge state constraint, energy storage equipment charging and discharging linear constraint in hours, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group;
the first calculation unit is used for calculating a parameter feasible region of the energy storage equipment by using a projection method based on the energy storage planning operation model;
and the second calculation unit is used for calculating the optimal configuration parameters of the energy storage equipment according to the parameter feasible region of the energy storage equipment.
Optionally, the first computing unit is specifically configured to:
determining a full parameter of a constraint condition and an energy storage device parameter based on the energy storage planning operation model;
obtaining a projection result of the full parameter on the energy storage equipment parameter by using a projection method;
and determining the parameter feasible region of the energy storage equipment according to the projection result.
Optionally, the second computing unit is specifically configured to:
establishing a linear programming function according to the parameter feasible region and the energy storage investment cost of the energy storage equipment;
and obtaining the optimal configuration parameters of the energy storage equipment by solving the linear programming function.
In a third aspect, the invention also provides an electronic device comprising one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a wind farm energy storage capacity optimization method as described in the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
according to the wind power plant energy storage capacity optimization method, the parameter feasible region of the energy storage equipment can be obtained by solving the wind power plant energy storage planning operation model based on the linear constraint condition, the optimal configuration parameters of the energy storage equipment can be obtained by further calculating according to the parameter feasible region of the energy storage equipment, so that the wind power plant energy storage system can reasonably absorb the wind power output based on the configuration parameters, and the economy of energy storage investment and the safety of wind power abandoned wind constraint are effectively balanced.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for optimizing energy storage capacity of a wind farm provided by an embodiment of the invention;
fig. 2 is a schematic structural diagram of an energy storage system associated with an offshore wind farm according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a wind farm energy storage capacity optimization system provided by an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a first aspect, as shown in fig. 1, an embodiment of the present invention provides a method for optimizing energy storage capacity of a wind farm, including the following steps.
S1: and acquiring an energy storage planning operation model of the wind power plant.
Specifically, the objective function of the energy storage planning operation model is to minimize energy storage investment cost, and the constraint conditions include energy storage device state-of-charge constraint, energy storage device charging and discharging linear constraint within an hour, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group.
The linear constraint of charging and discharging of the energy storage equipment in an hour is that the sum of the charging power and the discharging power of the energy storage equipment in the hour does not exceed the power limit value of the energy storage equipment.
In this embodiment, the wind power curtailment linear constraint group may be obtained by:
acquiring an initial wind power curtailment rate constraint, wherein the initial wind power curtailment rate constraint is determined based on output information and curtailment information of a wind power plant; and converting the initial wind power curtailment constraint into a wind power curtailment linear constraint group according to the strong dual property of the linear programming.
It should be noted that the energy storage investment cost includes an energy storage power cost and an energy storage capacity cost.
S2: and calculating the parameter feasible region of the energy storage equipment by using a projection method based on the energy storage planning operation model.
Specifically, the total parameters of the constraint conditions and the parameters of the energy storage equipment can be determined based on the energy storage planning operation model, then the projection result of the total parameters on the parameters of the energy storage equipment is obtained by using a projection method, and the parameter feasible region of the energy storage equipment is determined according to the projection result.
Wherein the parameters of the energy storage device comprise energy storage power and energy storage capacity.
S3: and calculating the optimal configuration parameters of the energy storage equipment according to the parameter feasible region of the energy storage equipment.
In this embodiment, a linear programming function may be established according to the parameter feasible region and the energy storage investment cost of the energy storage device, and the optimal configuration parameters of the energy storage device may be obtained by solving the linear programming function.
The specific process of applying the wind farm energy storage capacity optimization method to offshore wind farm matching energy storage capacity configuration will be described below by an embodiment, and the structure of the offshore wind farm matching energy storage system is shown in fig. 2.
In this embodiment, a pre-constructed energy storage planning operation model of the offshore wind farm is obtained first.
Specifically, the constraint conditions of the obtained energy storage planning operation model comprise energy storage equipment state-of-charge constraint, energy storage equipment charging and discharging linear constraint in hours, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group.
For the energy storage device state of charge constraint, it can be specifically expressed as:
Figure BDA0003490912910000061
αlem≤ent≤αhem，
Figure BDA0003490912910000062
in the formula, entRepresenting the state of charge of the energy storage device at t hours on the nth day, etac/ηdRepresenting respectively the charging and discharging efficiencies, Δ, of the energy storage devicetThe duration of the time period t is indicated,
Figure BDA0003490912910000063
and
Figure BDA0003490912910000064
respectively representing the charging power and the discharging power of the energy storage device at t hours on the nth day, emRepresenting the energy storage capacity, alphalE (0,0.5) and alphahAnd E (0.9,1) is respectively the upper and lower limit constant constraints of the operation of the energy storage equipment.
The linear constraint on the charging and discharging in the hour of the energy storage device can be specifically expressed as follows:
Figure BDA0003490912910000065
in the formula (I), the compound is shown in the specification,
Figure BDA0003490912910000066
and
Figure BDA0003490912910000067
respectively representing the charging power and the discharging power of the energy storage equipment at t hours on the nth day, pmRepresenting the stored energy power.
It should be noted that, different from the energy storage charge-discharge constraint of the conventional model, the linear constraint of charge-discharge in the energy storage device in the hour provided by the embodiment can avoid the occurrence of integer variables, and effectively simplify the calculation. Meanwhile, the linear hourly scheduling of the energy storage equipment can be represented according to the linear charging and discharging constraint of the energy storage equipment in the hour, namely the energy storage equipment can be divided into different time periods for charging and discharging in the hour.
For the transmission capacity constraint, it can be expressed specifically as:
Figure BDA0003490912910000068
in the formula (I), the compound is shown in the specification,
Figure BDA0003490912910000069
represents the discharge power of the energy storage device at t hours on the nth day,
Figure BDA00034909129100000610
representing the output power of the offshore wind farm at t hours on the nth day, FmThe upper limit of the capacity of the long-distance transmission line.
For the wind farm output constraint, it can be specifically expressed as:
Figure BDA0003490912910000071
in the formula (I), the compound is shown in the specification,
Figure BDA0003490912910000072
representing the output power of the offshore wind farm at t hours on the nth day,
Figure BDA0003490912910000073
representing the charging power of the energy storage device at t hours on the nth day,
Figure BDA0003490912910000074
representing the power curtailment of the offshore wind farm at t hours on the nth day,
Figure BDA0003490912910000075
and (4) representing the offshore wind farm output at t hours on the nth day.
For the wind power curtailment linear constraint group, the embodiment can be obtained by performing linear conversion on the initial wind power curtailment constraint.
Specifically, the initial wind curtailment power constraint is expressed as:
Figure BDA0003490912910000076
where ρ isnRepresenting the empirical probability of a typical scenario under random force of an offshore wind farm,
Figure BDA0003490912910000077
the output of the offshore wind farm at t hours on the nth day is shown,
Figure BDA0003490912910000078
and expressing the electricity abandoning amount of the offshore wind farm at t hours on the nth day, wherein sigma represents the electricity abandoning rate.
In the present embodiment, for a given power curtailment rate, the following should be satisfied:
Figure BDA0003490912910000079
s.t.ρ≤1·Γ+ρ0:μ+
ρ≥ρ0-1·Γ:μ-
1Tρ＝1:λ
ρ≥0
where ρ isn∈Π。μ+,μ-And λ are dual variables of corresponding constraint conditions respectively, then corresponding pairsThe even problem is represented as:
Figure BDA00034909129100000710
Figure BDA00034909129100000711
μ+≥0,μ-≤0
it will be appreciated that a strong dual in linear programming is always true, so the optima of the two optimization problems described above must be equal.
Correspondingly, the initial wind curtailment constraint may be equivalent to searching for a feasible solution to the following linear problem:
1T(μ+-μ-)Γ+(μ++μ-)Tρ0+λ≤0
Figure BDA0003490912910000081
μ+≥0,μ-≤0
the embodiment can set the feasible solution of the above linear problem as a linear constraint group of wind power curtailment, where ρ ∈ Π can be represented by a finite number of dual variables.
According to the method, the initial wind power curtailment constraint can be converted into a group of linear constraints by applying the strong duality of linear programming, so that a wind power curtailment linear constraint group is obtained, the effect of simplifying constraint conditions is achieved, and the calculation efficiency is improved.
In this embodiment, the objective function of the energy storage planning operation model of the offshore wind farm is set to minimize the energy storage investment cost. Wherein the energy storage investment cost comprises an energy storage capacity cost and an energy storage power cost.
Further, after the energy storage planning operation model of the offshore wind farm is obtained, the parameter feasible region of the energy storage equipment matched with the offshore wind farm can be calculated based on a projection method.
In particular, the settable parameter x represents a set of offshore wind farm scheduling variables, including in particular
Figure BDA0003490912910000082
ent、
Figure BDA0003490912910000083
Figure BDA0003490912910000084
And a dual variable mu+、μ-λ; meanwhile, the setting parameter θ represents a parameter of the energy storage device.
The parameter vector θ includes the energy storage power and capacity parameters, and may be expressed as θ ═ pm,em]T。
Further, the constraint conditions of the offshore wind farm are converted into a feasibility problem of solving the parameter theta.
Figure BDA0003490912910000091
Since all constraints in the above feasibility problem are linear constraints, the above equation can be expressed in matrix form:
Λ(θ)＝{x∣Ax+Bθ≤b}
where the equality constraint may be represented by two inequality constraints of opposite sign.
Further, the cost per unit capacity of the energy storage device is set to ceThe unit power cost is cpInvestment budget of energy storage equipment is ximThen the available vector c ═ cp,ce]TAnd (4) performing representation.
In this embodiment, the feasible fields of the parameter θ can be defined as:
Figure BDA0003490912910000092
meanwhile, a polyhedron is defined with respect to the parameter θ and the parameter x:
Figure BDA0003490912910000093
wherein, theta is
Figure BDA0003490912910000094
Projection onto the parameter theta.
According to the definition of projection, Θ is a polyhedron and can be expressed as:
Figure BDA0003490912910000095
D＝{γ∣ATγ＝0,-1≤γ≤0}
wherein vert (D) represents all poles of D.
After the setting and the definition of the parameters are completed, the parameter feasible region of the energy storage device matched with the offshore wind farm is further calculated through the following steps.
S21: setting initial values of parameter feasible domains of the energy storage equipment: thetatemp＝{θ∣θ≥0,cTθ≤ξm}。
S22: updating ΘtempAll pole sets of
Figure BDA0003490912910000101
And record the new poles that have not yet participated in the calculation.
S23: according to the new poles which do not participate in the calculation, the following linear programming problem is calculated:
Figure BDA0003490912910000102
s.t.ATγ＝0,-1≤γ≤0
in the calculation process, the optimal solution and the optimal value of the k-th problem are respectively recorded as
Figure BDA0003490912910000103
And
Figure BDA0003490912910000104
it can be understood that, among the K problems, the largest one
Figure BDA0003490912910000105
Then, for the global optimal solution, the present embodiment calculates the global optimal solution v according to the following formula*：
Figure BDA0003490912910000106
Figure BDA0003490912910000107
Figure BDA0003490912910000108
S24: for global optimum solution v*And (6) judging. In particular, if v*When the value is equal to 0, the calculation is terminated; if v is*>0, then Θ in the settempAdding tangent plane (gamma)*)TBθ≥(γ*)Tb, and returns to S22 to continue the calculation.
Through the calculation of the steps, the final parameter feasible domain theta of the energy storage device can be obtained.
In this embodiment, after the parameter feasible region Θ of the energy storage device is obtained, the energy storage investment parameter c ═ c may be determined according top,ce]TAnd further calculating the optimal configuration parameters of the energy storage equipment of the offshore wind farm.
Note that, in order to simplify the calculation process, in this embodiment, the parameter feasible field of the energy storage device is denoted as Θ ═ θ | H θ ≦ g }, that is: Θ ═ θ | Hppm+Heem≤g}。
Specifically, by solving for the following linesThe optimal configuration parameters of the energy storage equipment of the offshore wind farm can be obtained by the sexual planning problem
Figure BDA0003490912910000109
min cθ
s.t.Hθ≤g
In this embodiment, the linear programming problem can be further expanded:
min cppm+ceem
s.t.Hppm+Heem≤g
it should be noted that, if other parameters of the offshore wind farm are adjusted, for example, the investment cost parameter c ═ cp,ce]TWind power curtailment parameter sigma or long-distance transmission line capacity parameter FmAnd the different optimal energy storage configuration parameters can be obtained through corresponding calculation.
In the actual planning operation, the selected value of the parameter can be adjusted according to the actual demand, so that a more reasonable parameter configuration scheme is planned correspondingly, and more accurate guidance is provided for demand evaluation of the energy storage equipment matched with the offshore wind farm.
In the method for optimizing the energy storage capacity of the wind power plant provided by the embodiment of the invention, the linear constraint of energy storage scheduling in hours is considered in the acquired energy storage planning operation model, so that the application of integer variables in a conventional energy storage model can be effectively avoided; and aiming at the wind power curtailment constraint of the offshore wind power plant, the strong dual property of linear programming is applied, and the wind power curtailment constraint is converted into a group of linear constraints, so that the calculation process of the model is effectively simplified.
Meanwhile, the method also adopts a projection algorithm based on linear programming, and generates tangent planes by iteratively solving a plurality of groups of linear programming, so as to obtain more accurate parameter feasible regions of the energy storage equipment; and further calculating based on the parameters of the energy storage equipment in a territory to obtain the optimal configuration parameters of the energy storage equipment, so that the obtained optimal configuration parameters can meet the wind power curtailment rate constraint, the energy storage investment budget constraint and the operation constraint of the offshore wind farm, and the feasibility of the operation of the offshore wind farm and the safety of the energy storage construction investment are effectively considered.
In a second aspect, as shown in fig. 3, another embodiment of the present invention further provides a wind farm energy storage capacity optimization system, which includes a model obtaining unit 101, a first calculating unit 102, and a second calculating unit 103.
The model obtaining unit 101 is configured to obtain an energy storage planning operation model of the wind farm.
The objective function of the energy storage planning operation model is to minimize energy storage investment cost, and the constraint conditions comprise energy storage equipment charge state constraint, energy storage equipment charging and discharging linear constraint in hours, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group.
The first calculating unit 102 is configured to calculate a parameter feasible region of the energy storage device by using a projection method based on the energy storage planning operation model.
The second calculating unit 103 is configured to calculate an optimal configuration parameter of the energy storage device according to the parameter feasible region of the energy storage device.
In this embodiment, the first calculating unit 102 is specifically configured to: determining a full parameter of a constraint condition and an energy storage device parameter based on an energy storage planning operation model; obtaining a projection result of the full parameter on the energy storage equipment parameter by using a projection method; and determining the parameter feasible region of the energy storage equipment according to the projection result.
In this embodiment, the second calculating unit 103 is specifically configured to: establishing a linear programming function according to the parameter feasible region and the energy storage investment cost of the energy storage equipment; and obtaining the optimal configuration parameters of the energy storage equipment by solving the linear programming function.
The content of information interaction, execution process and the like among the units in the system is based on the same concept as the embodiment of the method for optimizing the energy storage capacity of the wind power plant, and specific content can be referred to the description in the embodiment of the method of the invention, and is not described herein again.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device may include: one or more processors, and a memory. A memory is coupled to the processor for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method for optimizing energy storage capacity of a wind farm according to any one of the embodiments, and achieve the technical effects consistent with the method.
The processor is used for controlling the overall operation of the electronic equipment so as to complete all or part of the steps of the wind power plant energy storage capacity optimization method. The memory is used to store various types of data to support operation at the electronic device, and the data may include, for example, instructions for any application or method operating on the electronic device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic or optical disk.
In an exemplary embodiment, the electronic Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, for performing the wind farm energy storage capacity optimization method described above, and achieving technical effects consistent with the above-described method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the wind farm energy storage capacity optimization method described above is also provided. For example, the computer readable storage medium may be the above-mentioned memory including program instructions executable by a processor of an electronic device to perform the above-mentioned wind farm energy storage capacity optimization method, and to achieve technical effects consistent with the above-mentioned method.
It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. The terms "first", "second", and the like in the present invention are used for distinguishing different objects, not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A wind power plant energy storage capacity optimization method is characterized by comprising the following steps:
acquiring an energy storage planning operation model of a wind power plant; wherein the content of the first and second substances,
the objective function of the energy storage planning operation model is the minimized energy storage investment cost, and the constraint conditions comprise energy storage equipment charge state constraint, energy storage equipment charging and discharging linear constraint in hours, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group;
calculating a parameter feasible region of the energy storage equipment by using a projection method based on the energy storage planning operation model;
and calculating the optimal configuration parameters of the energy storage equipment according to the parameter feasible region of the energy storage equipment.
2. The wind farm energy storage capacity optimization method according to claim 1, wherein the linear constraints of charging and discharging of the energy storage device in hours are specifically:
the sum of the charging power and the discharging power of the energy storage device in the hour does not exceed the power limit value of the energy storage device.
3. The wind farm energy storage capacity optimization method according to claim 1, wherein the wind curtailment linear constraint group is determined by:
acquiring an initial wind power curtailment rate constraint, wherein the initial wind power curtailment rate constraint is determined based on the output information and the curtailment information of the wind power plant;
and converting the initial wind power curtailment constraint into a wind power curtailment linear constraint group according to the strong dual property of the linear programming.
4. The method for optimizing the energy storage capacity of the wind farm according to claim 1, wherein the calculation of the parameter feasible region of the energy storage device by using a projection method specifically comprises:
determining the full quantity parameters of the constraint conditions and the parameters of the energy storage equipment based on the energy storage planning operation model;
obtaining a projection result of the full parameter on the energy storage equipment parameter by using a projection method;
and determining the parameter feasible region of the energy storage equipment according to the projection result.
5. The method for optimizing the energy storage capacity of the wind farm according to claim 1, wherein the optimal configuration parameters of the energy storage device are calculated according to the parameter feasible region of the energy storage device, and specifically:
establishing a linear programming function according to the parameter feasible region of the energy storage equipment and the energy storage investment cost;
and obtaining the optimal configuration parameters of the energy storage equipment by solving the linear programming function.
6. Wind farm energy storage capacity optimization method according to any of the claims 1 to 5,
the energy storage investment cost comprises an energy storage power cost and an energy storage capacity cost;
the parameters of the energy storage device include energy storage power and energy storage capacity.
7. A wind farm energy storage capacity optimization system, comprising:
the model obtaining unit is used for obtaining an energy storage planning operation model of the wind power plant; wherein the content of the first and second substances,
the objective function of the energy storage planning operation model is the minimized energy storage investment cost, and the constraint conditions comprise energy storage equipment charge state constraint, energy storage equipment charging and discharging linear constraint in hours, power transmission capacity constraint, wind power plant output constraint and wind power curtailment linear constraint group;
the first calculation unit is used for calculating a parameter feasible region of the energy storage equipment by using a projection method based on the energy storage planning operation model;
and the second calculation unit is used for calculating the optimal configuration parameters of the energy storage equipment according to the parameter feasible region of the energy storage equipment.
8. The wind farm energy storage capacity optimization system according to claim 7, wherein the first calculation unit is specifically configured to:
determining the full quantity parameters of the constraint conditions and the parameters of the energy storage equipment based on the energy storage planning operation model;
obtaining a projection result of the full parameter on the energy storage equipment parameter by using a projection method;
and determining the parameter feasible region of the energy storage equipment according to the projection result.
9. The wind farm energy storage capacity optimization system according to claim 7, wherein the second calculation unit is specifically configured to:
establishing a linear programming function according to the parameter feasible region of the energy storage equipment and the energy storage investment cost;
and obtaining the optimal configuration parameters of the energy storage equipment by solving the linear programming function.
10. An electronic device, comprising,
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a wind farm energy storage capacity optimization method as claimed in any one of claims 1 to 6.
CN202210097188.0A 2022-01-26 2022-01-26 Wind power plant energy storage capacity optimization method and device and electronic equipment Pending CN114498767A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114971088A (en) * 2022-07-26 2022-08-30 中国华能集团清洁能源技术研究院有限公司 Optimal configuration method and device for wind power plant energy storage and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114971088A (en) * 2022-07-26 2022-08-30 中国华能集团清洁能源技术研究院有限公司 Optimal configuration method and device for wind power plant energy storage and storage medium

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