An energy storage optimization method and device based on wind power consumption

Abstract

The present invention provides an energy storage optimization method and device based on wind power consumption. The method includes: acquiring the operation data of the power system to be optimized, including thermal power unit data, load data, wind power output data, and energy storage data; constructing a fuzzy set of wind power output with the goal of minimizing the wind power curtailment amount, and performing distributionally robust parameter planning, thereby constructing an operation model of the power system under wind power uncertainty, and simultaneously constructing corresponding constraint conditions; solving the model under the constraint conditions to generate an analytical relationship between the wind power curtailment amount and the energy storage capacity of the power system to be optimized; and optimizing the configuration of the energy storage device. Compared with the prior art, based on the operation data of the power system, the configuration is optimized, taking into account the wind power output data and the uncertainty of wind power output. The analytical relationship between the wind power curtailment amount and the energy storage capacity has better robustness than the prior art, improving the accuracy of the optimization result.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and particularly to an energy storage optimization method and device based on wind power consumption. Background Art

[0002] Offshore wind power has characteristics such as cleanness and high efficiency. The output of wind power is difficult to predict itself and has a large difference from the periodic law of the load. Therefore, its volatility and uncertainty also bring certain difficulties to the consumption of offshore wind power. For the problem of wind curtailment and power rationing, one of the more effective ways is to configure a certain cluster energy storage for wind power. Considering the energy storage cost, in order to reasonably optimize the energy storage capacity configuration of offshore wind power, a balance needs to be found between the wind power consumption capacity and the energy storage economic cost. Therefore, it is necessary to accurately evaluate the impact of energy storage on the consumption of offshore wind power.

[0003] The optimization of existing technologies for energy storage is based on the evaluation results of offshore wind power systems. Usually, a limited number of scenarios are compared, such as comparing the wind power consumption with and without energy storage, or comparing the wind power consumption under several specified energy storage capacities. This method can only roughly describe the impact of the energy storage capacity on the wind power consumption capacity, and cannot accurately determine the analytical relationship between the energy storage capacity and the wind power consumption. In addition, existing technologies usually perform operation simulations based on the given wind power output data, lacking consideration of the uncertainty of wind power output. Therefore, the evaluation results lack robustness, which in turn affects the accuracy of the optimization results and is not suitable for the actual application of offshore wind power systems. Summary of the Invention

[0004] The present invention provides an energy storage optimization method and device based on wind power consumption, which solves the technical problem of the lack of consideration of the uncertainty of wind power output in the existing technology and improves the accuracy of the optimization results.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides an energy storage optimization method based on wind power consumption, including:

[0006] Obtaining the operation data of the power system to be optimized; wherein, the operation data includes thermal power unit data, load data, wind power output data, and energy storage data; the power system to be optimized includes a wind power generation module and an energy storage device;

[0007] Taking minimizing the wind curtailment amount as the goal, constructing a fuzzy set of wind power output according to the wind power output data, and performing distributionally robust parameter programming on the operation data according to the fuzzy set, thereby constructing a power system operation model under wind power uncertainty; and constructing constraint conditions of the power system operation model based on the operation data;

[0008] Solve the operation model of the power system under the above constraints to generate the analytical relationship between the wind curtailment amount and the energy storage capacity of the power system to be optimized; and optimize the configuration of the energy storage device according to the analytical relationship.

[0009] As a preferred solution, the construction of the fuzzy set of wind power output is specifically:

[0010]

[0011]

[0012]

[0013] where S is the fuzzy set, N is the number of groups of wind power output data, d(P, P 0 ) is the distance between the actual wind power output distribution P and the reference distribution P 0 , ρ i is the probability of the i-th group of wind power output data, ε is the radius of the fuzzy set, and β is the confidence probability.

[0014] As a preferred solution, the operation model of the power system is specifically:

[0015]

[0016] s.t. Ax i + By i ≤ b + Cw i + Fθ, i = 1, 2,..., N;

[0017] Hp ≤ h: λ;

[0018] θ ∈ Θ;

[0019] where v(θ) is the distributionally robust parameter programming expression, c is a constant, w i is the maximum wind power output curve of the i-th group of wind power output data, λ is the dual variable of the fuzzy set constraint, and A, B, C, b, c, F, H, and h are all constants;

[0020] Moreover, θ is a parameter, y is an integer variable, x is a continuous variable, and Θ is the domain of the parameter θ. Specifically:

[0021]

[0022]

[0023]

[0024] where P s is the maximum charge and discharge power of the energy storage, is the lower energy limit of the energy storage device, is the lower energy limit of the energy storage device, curt(t) is the wind curtailment at time t, and z s (t) is the charge-discharge state of the energy storage device, and z s (t) = 0 indicates that the energy storage device discharges at time t, and z s (t) = 1 indicates that the energy storage device charges at time t, and p g (t) is the active power output of the thermal power unit at time t, and p sc (t) is the energy storage charging power at time t, and p sd (t) is the energy storage discharging power at time t.

[0025] As a preferred solution, solving the power system operation model to generate the analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity specifically includes:

[0026] Performing dual processing on the distributionally robust parameter programming expression;

[0027] Uniformly sampling several points in the domain of the parameter to obtain integer solutions at each point;

[0028] According to the integer solutions at each point, obtaining the parameter linear programming expressions at each integer solution and comparing them, and generating the analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity according to the smallest parameter programming expression.

[0029] As a preferred solution, the constraint conditions of the power system operation model include power balance constraint, branch capacity constraint, thermal power unit output and ramp constraint, wind power output constraint, energy storage power constraint, energy storage electricity constraint, and energy storage electricity initial and final balance constraint.

[0030] Correspondingly, an energy storage optimization device based on wind power consumption provided by an embodiment of the present invention includes a data acquisition module, a model establishment module, and an optimization module; wherein,

[0031] The data acquisition module is used to acquire the operation data of the power system to be optimized; wherein, the operation data includes thermal power unit data, load data, wind power output data, and energy storage data; the power system to be optimized includes a wind power generation module and an energy storage device;

[0032] The model establishment module is used to minimize the wind curtailment as the goal, construct a fuzzy set of wind power output according to the wind power output data, perform distributionally robust parameter programming on the operation data according to the fuzzy set, and then construct a power system operation model under wind power uncertainty; and construct the constraint conditions of the power system operation model based on the operation data;

[0033] The optimization module is used to solve the power system operation model under the constraint conditions to generate an analytical relationship between the wind curtailment amount and the energy storage capacity of the power system to be optimized; and optimize the configuration of the energy storage device according to the analytical relationship.

[0034] As a preferred solution, the model establishment module constructs a fuzzy set of wind power output, specifically:

[0035]

[0036]

[0037]

[0038] where S is the fuzzy set, N is the number of groups of wind power output data, d(P, P 0 ) is the distance between the actual wind power output distribution P and the reference distribution P 0 , ρ i is the probability of the i-th group of wind power output data, ε is the radius of the fuzzy set, and β is the confidence probability.

[0039] As a preferred solution, the power system operation model is specifically:

[0040]

[0041] s.t. Ax i + By i ≤ b + Cw i + Fθ, i = 1, 2,..., N;

[0042] Hp ≤ h: λ;

[0043] θ ∈ Θ;

[0044] where v(θ) is the distributionally robust parameter programming expression, c is a constant, w i is the maximum wind power output curve of the i-th group of wind power output data, λ is the dual variable of the fuzzy set constraint, and A, B, C, b, c, F, H, and h are all constants;

[0045] Moreover, θ is a parameter, y is an integer variable, x is a continuous variable, and Θ is the domain of the parameter θ. Specifically:

[0046]

[0047]

[0048]

[0049] where P s is the maximum charge and discharge power of the energy storage. is the lower energy limit of the energy storage device, is the lower energy limit of the energy storage device, curt(t) is the wind curtailment at time t, z s (t) is the charge and discharge state of the energy storage device, z s (t) = 0 indicates that the energy storage device discharges at time t, z s (t) = 1 indicates that the energy storage device charges at time t, p g (t) is the active power output of the thermal power unit at time t, p sc (t) is the energy storage charging power at time t, p sd (t) is the energy storage discharging power at time t.

[0050] As a preferred solution, the optimization module solves the power system operation model to generate an analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity, specifically:

[0051] The optimization module performs dual processing on the distributionally robust parameter programming expression;

[0052] Uniformly sample a number of points in the domain of the parameter to obtain integer solutions at each point;

[0053] According to the integer solutions at each point, obtain the parameter linear programming expressions at each integer solution and compare them, and generate an analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity according to the smallest parameter programming expression.

[0054] As a preferred solution, the constraint conditions of the power system operation model include power balance constraint, branch capacity constraint, thermal power unit output and ramp constraint, wind power output constraint, energy storage power constraint, energy storage electricity constraint, and energy storage electricity start and end balance constraint.

[0055] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0056] An embodiment of the present invention provides an energy storage optimization method and device based on wind power consumption. The method includes: obtaining operation data of a power system to be optimized; wherein the operation data includes thermal power unit data, load data, wind power output data, and energy storage data; the power system to be optimized includes a wind power generation module and an energy storage device; with the goal of minimizing the wind power curtailment amount, according to the wind power output data, constructing a fuzzy set of wind power output, and performing distributionally robust parameter programming on the operation data according to the fuzzy set, thereby constructing a power system operation model under wind power uncertainty; and constructing constraint conditions of the power system operation model based on the operation data; solving the power system operation model under the constraint conditions to generate an analytical relationship between the wind power curtailment amount and the energy storage capacity of the power system to be optimized; and optimizing the configuration of the energy storage device according to the analytical relationship. Compared with the prior art, based on the operation data of the power system, the configuration is optimized, taking into account the wind power output data and the uncertainty of wind power output. The analytical relationship between the wind power curtailment amount and the energy storage capacity has better robustness than the prior art, improving the accuracy of the optimization result. Description of the Drawings

[0057] Figure 1 : A flowchart of an embodiment of the energy storage optimization method provided by the present invention based on wind power consumption.

[0058] Figure 2 : A schematic diagram of the solution result of an embodiment of the solution method for the mixed-integer parameter programming problem proposed by the present invention.

[0059] Figure 3 : A schematic diagram of the structure of an embodiment of the energy storage optimization device provided by the present invention based on wind power consumption. Detailed Embodiments

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Embodiment 1:

[0062] Please refer to Figure 1 , Figure 1 An energy storage optimization method provided by an embodiment of the present invention based on wind power consumption, (for remote offshore wind farms), includes steps S1 to S3, wherein,

[0063] Step S1: Obtain the operation data of the power system to be optimized. Among them, the operation data includes thermal power unit data, wind power output data, and energy storage data. The power system to be optimized includes thermal power units (traditional units), a wind power generation module, and an energy storage device.

[0064] Step S2: With the goal of minimizing the wind curtailment volume, construct a fuzzy set of wind power output according to the wind power output data, and perform distributionally robust parameter programming on the operation data according to the fuzzy set, thereby constructing an operation model of the power system under wind power uncertainty; and construct the constraint conditions of the power system operation model based on the operation data.

[0065] Step S3: Solve the power system operation model under the constraint conditions to generate an analytical relationship between the wind curtailment volume and the energy storage capacity of the power system to be optimized; and optimize the configuration of the energy storage device according to the analytical relationship.

[0066] For step S1, in the present invention, the above thermal power unit data includes the active power output p g (t) of the thermal power unit at time t, the power transfer distribution factor π gl of the thermal power unit with respect to branch l, the lower limit of the thermal power unit output the upper limit of the thermal power unit output the maximum downward ramp power R D of the thermal power unit, and the maximum upward ramp power R U of the thermal power unit.

[0067] The above wind power output data includes the power transfer distribution factor π wl of wind farm w with respect to branch l, the actual wind power output p w (t) at time t, and the maximum wind power output at time t

[0068] The above energy storage data includes the power transfer distribution factor π sl of energy storage s with respect to branch l, the energy storage charging power p sc (t) at time t, the energy storage discharging power p sd (t) at time t, the maximum charge-discharge power P s of the energy storage, the lower limit of the energy storage device the upper limit of the energy storage device the charging efficiency η c of the energy storage, the discharging efficiency η d of the energy storage, and the initial power e0 of the energy storage device and the charge-discharge state z s (t) of the energy storage device, where z s (t) = 0 indicates that the energy storage device discharges at time t, and z s(t) = 1 indicates that the energy storage device is being charged at time t.

[0069] The above load data includes the upper power capacity limit F of branch l l , the load magnitude p at time t d (t), and the power transfer distribution factor π of the load d with respect to branch l dl .

[0070] The above operating data (thermal power unit data, load data, wind power output data, and energy storage data) are all known data, which are the historical operating data of the power system to be optimized.

[0071] For step S2, the constraint conditions for constructing the power system operation model based on the above operating data are specifically as follows:

[0072] Power balance constraint:

[0073]

[0074] Branch capacity constraint:

[0075]

[0076] Thermal power unit output and ramp rate constraint:

[0077]

[0078]

[0079] Wind power output constraint:

[0080]

[0081]

[0082] Energy storage power constraint:

[0083]

[0084]

[0085] Energy storage capacity constraint:

[0086]

[0087] Energy storage capacity initial and final balance constraint:

[0088]

[0089] Among them, p g (t), p sc (t), p sd (t), pw (t) and curt(t) are variables, △t is the time interval, and the rest are constants. It should be noted that the integer variable z is introduced s (t) can prevent the energy storage device from charging and discharging at the same time. If this restriction is removed, the simultaneous charging and discharging of the energy storage will lead to the unnecessary consumption of wind power, thus underestimating the amount of wind curtailment. In addition, the energy storage device has the same amount of electricity at the beginning and end, ensuring the rationality of the energy storage device's cyclic operation. And the various variables and parameters can be recorded as:

[0090] parameter

[0091] Integer variables

[0092] Continuous variables

[0093] This can be organized into the following form:

[0094]

[0095] stAx+By≤b+Fθ;

[0096] θ∈Θ; where Θ={θ|Hθ≤h} is the domain of the parameter θ (A, B, b, F, H and h are all constants). The set of parameters Θ can be divided into a series of polyhedrons, and in each polyhedron, the optimal value v is an affine function of the parameter θ. Therefore, for the set Θ as a whole, v is a piecewise affine function of the parameter θ. This is equivalent to converting the mixed integer linear programming problem into a mixed integer parameter programming problem, and the solution to the mixed integer parameter programming problem includes the following steps (the results refer to Figure 2 ):

[0097] (1) Sampling to obtain integer solutions: uniformly sample K points θ1, θ2, ..., θ in the set Θ K , so that each θ can be obtained k The integer solution y of the optimization problem under (k=1, 2, ..., K) k ; Remove duplicate integer solutions and obtain the set of integer solutions Y = {y1, y2, ..., y M}.

[0098] (2) Obtain the parameter linear programming expression under each integer solution: For a given y m ∈Y, v(θ) can be simplified as:

[0099]

[0100] stAx≤(b-By m )+Fθ:γ;

[0101] θ ∈ Θ; where v m The integer programming optimal value under the given integer solution y m is: γ is the dual variable.

[0102] Therefore, the corresponding dual problem is:

[0103]

[0104] s.t. A T γ = c;

[0105] θ ∈ Θ; where c is a constant.

[0106] It can be understood that the optimal solution is obtained at the extreme points of the set Γ = {γ | A T γ = c}. Therefore, the extreme points of this set can be enumerated, and the objective values corresponding to the extreme points can be compared to obtain the analytical expression of the parametric programming. Among them, γ i is the optimal solution, and its corresponding parameter space is:

[0107]

[0108] It can be briefly recorded as:

[0109]

[0110] Then its corresponding optimal value function is

[0111] (3) Compare the parametric programming expressions under different integer solutions to obtain the parametric programming expression v m under each integer solution y m (θ). After that, compare each piecewise affine expression v m (θ) segment by segment, and take the smallest part of each v m (θ). That is, the final mixed-integer parametric programming expression, which is also the solution result, is:

[0112] v(θ) = min{v1(θ), v2(θ),..., v m (θ)}.

[0113] Furthermore, to consider the impact of uncertainty, extract the maximum wind power output at time t and denote it as:

[0114]

[0115] Thus, it can be sorted out as:

[0116]

[0117] s.t. Ax + By ≤ b + Cw + Fθ;

[0118] θ belongs to Θ;

[0119] For N groups of historical wind power output data w1, w2,..., w N , each group of data contains T time periods (if there are 24 hours in a day, then N = 24), and each group of wind power output corresponds to a scenario, so its reference distribution P 0 Preferably:

[0120] where I is the indicator function. When the wind power output is w i , otherwise it is 0. It can be understood that the probability of each scenario appearing in the reference distribution is

[0121] The distance between two distributions is measured by the infinity norm,

[0122]

[0123] where ρ i is the probability of the occurrence of scenario i (i.e., the probability of the i-th group of wind power output data). And

[0124] Furthermore, based on the wind power output data, a fuzzy set of wind power output is constructed, and the fuzzy set is specifically:

[0125]

[0126]

[0127]

[0128] where S is the fuzzy set, N is the number of groups of wind power output data, d(P, P 0 ) is the distance between the true wind power output distribution P and the reference distribution P 0 , ρ i is the probability of the i-th group of wind power output data, ε is the radius of the fuzzy set, β is the confidence probability, and in this embodiment, the confidence probability is preferably 0.95.

[0129] Thus, it can be further obtained that: Pr||P - P 0 || ∞ ≤ ε ≥ β; where Pr refers to probability, that is, the probability that the infinity norm distance between the true distribution P and the reference distribution P 0 is not higher than ε is not less than β.

[0130] Since the fuzzy set S is a polyhedron in the probability space, thus:

[0131] S = {ρ | Hρ ≤ h}; where ρ = [ρ1, ρ2,..., ρ N T is a column vector composed of the probabilities of each scenario.

[0132] According to the fuzzy set, perform distributionally robust parameter programming on the operating data, and then construct an operating model of the power system under wind power uncertainty:

[0133]

[0134] s.t. Ax i + By i ≤ b + Cw i + Fθ, i = 1, 2,..., N;

[0135] Hp ≤ h: λ;

[0136] θ ∈ Θ;

[0137] where v(θ) is the distributionally robust parameter programming expression, c is a constant, w i is the maximum wind power output curve of the wind power output data of the i-th group, λ is the dual variable of the fuzzy set constraint, and A, B, C, b, c, F, H, and h are all constants.

[0138] In step S3, to solve this model, the solution method for the mixed-integer parameter programming problem described above can be referred to. Therefore, solving the power system operating model under the constraint conditions to generate the analytical relationship between the wind power curtailment amount and the energy storage capacity of the power system to be optimized includes:

[0139] Perform dual processing on the distributionally robust parameter programming expression. Since the constraints corresponding to the outer layer max problem and the inner layer min problem are decoupled, the bilevel programming problem can be converted into a single-level programming problem using the duality principle. After performing dual processing on the distributionally robust parameter programming expression, the robust optimization problem is sorted out into a standard mixed-integer parameter linear programming problem:

[0140]

[0141] s.t. Ax i + By i ≤ b + Cw i + Fθ, i = 1, 2,..., N;

[0142]

[0143] λ ≥ 0;

[0144] θ ∈ Θ;

[0145] ​Furthermore, a number of points are evenly sampled in the domain of the parameters to obtain integer solutions at each point;

[0146] According to the integer solutions at each point, the parametric linear programming expressions at each integer solution are obtained and compared, and the analytical relationship (the analytical expression of the curtailment wind volume with respect to the energy storage capacity in the worst-case scenario) between the curtailment wind volume of the power system to be optimized and the energy storage capacity is generated according to the smallest parametric programming expression. This analytical relationship reflects the influence of the energy storage capacity on the wind power consumption capacity. Therefore, the configuration of the energy storage device can be optimized according to the analytical relationship.

[0147] Correspondingly, referring to Figure 3 , an energy storage optimization device based on wind power consumption provided by an embodiment of the present invention further includes a data acquisition module 101, a model establishment module 102, and an optimization module 103; wherein,

[0148] The data acquisition module 101 is used to acquire the operation data of the power system to be optimized; wherein, the operation data includes thermal power unit data, load data, wind power output data, and energy storage data; the power system to be optimized includes a wind power generation module and an energy storage device;

[0149] The model establishment module 102 is used to construct a fuzzy set of wind power output with the goal of minimizing the curtailment wind volume, and perform distributionally robust parametric programming on the operation data according to the fuzzy set, and then construct a power system operation model under wind power uncertainty; and construct the constraint conditions of the power system operation model based on the operation data;

[0150] The optimization module 103 is used to solve the power system operation model under the constraint conditions to generate the analytical relationship between the curtailment wind volume of the power system to be optimized and the energy storage capacity; and optimize the configuration of the energy storage device according to the analytical relationship.

[0151] As a preferred solution, the model establishment module 102 constructs a fuzzy set of wind power output, specifically:

[0152]

[0153]

[0154]

[0155] wherein, S is the fuzzy set, N is the number of groups of wind power output data, d(P, P 0 ) is the distance between the true wind power output distribution P and the reference distribution P 0 , and ρ iis the probability of the i-th group of wind power output data, ε is the radius of the fuzzy set, and β is the confidence probability.

[0156] As a preferred solution, the power system operation model is specifically:

[0157]

[0158] s.t. Ax i + By i ≤ b + Cw i + Fθ, i = 1, 2,..., N;

[0159] Hp ≤ h: λ;

[0160] θ ∈ Θ;

[0161] Among them, v(θ) is the distributionally robust parameter programming expression, c is a constant, w i is the maximum wind power output curve of the i-th group of wind power output data, λ is the dual variable of the fuzzy set constraint, and A, B, C, b, c, F, H, and h are all constants;

[0162] And, θ is a parameter, y is an integer variable, x is a continuous variable, and Θ is the domain of the parameter θ. Specifically:

[0163]

[0164]

[0165]

[0166] Among them, P s is the maximum charge and discharge power of the energy storage, is the lower energy limit of the energy storage device, is the lower energy limit of the energy storage device, curt(t) is the wind curtailment at time t, z s (t) is the charge and discharge state of the energy storage device, z s (t) = 0 indicates that the energy storage device discharges at time t, z s (t) = 1 indicates that the energy storage device charges at time t, p g (t) is the active power output of the thermal power unit at time t, p sc (t) is the energy storage charging power at time t, p sd (t) is the energy storage discharging power at time t.

[0167] As a preferred solution, the optimization module 103 solves the power system operation model to generate the analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity, specifically:

[0168] The optimization module 103 performs dual processing on the distributionally robust parameter programming expression;

[0169] Uniformly sample a number of points in the domain of the parameter, and obtain the integer solutions at each point;

[0170] According to the integer solutions at each point, obtain the parameter linear programming expressions at each integer solution and compare them, and generate the analytical relationship between the curtailed wind volume and the energy storage capacity of the power system to be optimized according to the smallest parameter programming expression.

[0171] As a preferred solution, the constraint conditions of the power system operation model include power balance constraint, branch capacity constraint, thermal power unit output and ramp constraint, wind power output constraint, energy storage power constraint, energy storage power capacity constraint, and energy storage power capacity initial and final balance constraint.

[0172] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0173] The embodiments of the present invention provide a method and device for energy storage optimization based on wind power consumption. The method includes: obtaining the operation data of the power system to be optimized; wherein the operation data includes thermal power unit data, load data, wind power output data, and energy storage data; the power system to be optimized includes a wind power generation module and an energy storage device; with the goal of minimizing the curtailed wind volume, according to the wind power output data, construct a fuzzy set of wind power output, and perform distributionally robust parameter programming on the operation data according to the fuzzy set, and then construct a power system operation model under wind power uncertainty; and construct the constraint conditions of the power system operation model based on the operation data; solve the power system operation model under the constraint conditions to generate the analytical relationship between the curtailed wind volume and the energy storage capacity of the power system to be optimized; and optimize the configuration of the energy storage device according to the analytical relationship. Compared with the prior art, based on the operation data of the power system, the configuration is optimized, including considering the wind power output data and the uncertainty of wind power output. The analytical relationship between the curtailed wind volume and the energy storage capacity has better robustness than the prior art, and the accuracy of the optimization result is improved.

[0174] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An energy storage optimization method based on wind power consumption, characterized in that, Including: Obtain the operation data of the power system to be optimized; wherein, the operation data includes thermal power unit data, load data, wind power output data, and energy storage data; the power system to be optimized includes a wind power generation module and an energy storage device; Taking minimizing the wind curtailment as the goal, according to the wind power output data, construct a fuzzy set of wind power output, and perform distributionally robust parameter programming on the operation data according to the fuzzy set, and then construct a power system operation model under wind power uncertainty; and construct the constraint conditions of the power system operation model based on the operation data; Solve the power system operation model under the constraint conditions to generate an analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity; and optimize the configuration of the energy storage device according to the analytical relationship; The power system operation model is specifically: ; s.t. ; ; θ ∈ Θ; Among them, v(θ) is the distributionally robust parameter programming expression, and w i is the maximum wind power output curve of the wind power output data of the i-th group. λ is the dual variable of the fuzzy set constraint. A, B, C, b, F, H, and h are all constants, and N is the number of groups of wind power output data; And, θ is a parameter, y is an integer variable, x is a continuous variable, and Θ is the domain of definition of the parameter θ. Specifically: ; ; ; Among them, P s is the maximum charge and discharge power of energy storage, is the lower energy limit of the energy storage device, is the upper energy limit of the energy storage device, curt(t) is the wind curtailment at time t, z s (t) is the charge and discharge state of the energy storage device, z s (t) = 0 indicates that the energy storage device discharges at time t, z s (t) = 1 indicates that the energy storage device charges at time t, p g (t) is the active power output of the thermal power unit at time t, p sc (t) is the energy storage charging power at time t, p sd (t) is the energy storage discharge power at time t; The solving of the power system operation model to generate an analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity is specifically: Perform dual processing on the distributionally robust parameter programming expression; Uniformly sample a number of points in the domain of definition of the parameter to obtain integer solutions at each point; According to the integer solutions at each point, obtain the parameter linear programming expressions at each integer solution and compare them, and generate an analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity according to the smallest parameter linear programming expression.

2. The energy storage optimization method based on wind power consumption as described in claim 1, characterized in that, The construction of the fuzzy set of wind power output is specifically: ； ； ； where S is the fuzzy set, d(P, P 0 ) is the distance between the actual wind power output distribution P and the reference distribution P 0 , ρ i is the probability of the i-th group of wind power output data, ε is the radius of the fuzzy set, and β is the confidence probability.

3. The energy storage optimization method based on wind power consumption as claimed in claim 1 or 2, wherein The constraint conditions of the power system operation model include power balance constraint, branch capacity constraint, thermal power unit output and ramp constraint, wind power output constraint, energy storage power constraint, energy storage capacity constraint, and energy storage capacity initial and final balance constraint.

4. An energy storage optimization device based on wind power consumption, characterized in that, Including a data acquisition module, a model establishment module, and an optimization module; wherein, The data acquisition module is used to obtain the operation data of the power system to be optimized; wherein, the operation data includes thermal power unit data, load data, wind power output data, and energy storage data; the power system to be optimized includes a wind power generation module and an energy storage device; The model establishment module is used to take minimizing the wind curtailment as the goal, according to the wind power output data, construct a fuzzy set of wind power output, and perform distributionally robust parameter programming on the operation data according to the fuzzy set, and then construct a power system operation model under wind power uncertainty; and construct the constraint conditions of the power system operation model based on the operation data; The optimization module is used to solve the power system operation model under the constraint conditions to generate an analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity; and optimize the configuration of the energy storage device according to the analytical relationship; The power system operation model is specifically: ; s.t. ; ; θ ∈ Θ; where \(v(\theta)\) is the distributionally robust parameter programming expression, and \(w\) i is the maximum wind power output curve of the \(i\)-th group of wind power output data, \(\lambda\) is the dual variable of the fuzzy set constraint, and \(A\), \(B\), \(C\), \(b\), \(F\), \(H\), and \(h\) are all constants; And, θ is a parameter, y is an integer variable, x is a continuous variable, and Θ is the domain of definition of the parameter θ. Specifically: ; ; ; Among them, P s is the maximum charge-discharge power of energy storage, is the lower energy limit of the energy storage device, is the lower energy limit of the energy storage device, curt(t) is the wind curtailment at time t, z s (t) is the charge-discharge state of the energy storage device, z s (t) = 0 indicates that the energy storage device discharges at time t, z s (t) = 1 indicates that the energy storage device charges at time t, p g (t) is the active power output of the thermal power unit at time t, p sc (t) is the energy storage charging power at time t, p sd (t) is the energy storage discharge power at time t, and N is the number of groups of wind power output data; The optimization module solves the power system operation model to generate an analytical relationship between the wind curtailment of the power system to be optimized and the energy storage capacity, specifically: The optimization module performs dual processing on the distributionally robust parameter programming expression; Uniformly sample a number of points in the domain of the parameter, and obtain the integer solutions at each point; According to the integer solutions at each point, obtain the parameter linear programming expressions at each integer solution and compare them, and generate the analytical relationship between the curtailed wind power and the energy storage capacity of the power system to be optimized according to the smallest parameter programming expression.

5. The energy storage optimization device based on wind power consumption as claimed in claim 4, wherein The model establishment module constructs a fuzzy set of wind power output, specifically: ； ； ； Among them, S is the fuzzy set, and d(P, P 0 ) is the distance between the actual wind power output distribution P and the reference distribution P 0 , ρ i is the probability of the i-th group of wind power output data, ε is the radius of the fuzzy set, and β is the confidence probability.

6. An energy storage optimization device based on wind power consumption as claimed in claim 4 or 5, characterized in that The constraint conditions of the power system operation model include power balance constraint, branch capacity constraint, thermal power unit output and ramp constraint, wind power output constraint, energy storage power constraint, energy storage capacity constraint, and energy storage capacity initial and end balance constraint.

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