CN103023066A - Optimal configuration method suitable for energy storage power of electrical power system with wind electricity - Google Patents

Abstract

The invention discloses an optimal configuration method suitable for the energy storage power of an electrical power system with wind electricity. The method comprises the following steps of: S1, obtaining the sample data of the wind power and the load of the electrical power system with wind electricity; S2, obtaining a positive rotation spare capacity and a negative rotation spare capacity according to the sample data and an energy storage power configuration model, wherein the energy storage power configuration model takes that the energy storage power used by the electrical power system in a dispatching cycle is the minimum as an object function, takes that the sum of the rated total force output upper limit of thermal power generating units in the electrical power system and the energy storage power upper limit is greater than the actually generated net load value as a positive rotation spare chance constraint, and takes that the sum of the rated total force output lower limit of the thermal power generating units in the electrical power system and the energy storage power lower limit is less than the actually generated net load value as a negative rotation spare chance constraint; and S3, obtaining the optimal configuration for the energy storage power needed by the electrical power system with wind electricity for coping a net load prediction error according to the positive rotation spare capacity and the negative rotation spare capacity. Via the method disclosed by the invention, the minimum configuration for the energy storage power can be obtained, safe operation can be ensured, and cost can be saved.

Description

An optimal allocation method for energy storage power in power systems containing wind power

technical field

The invention belongs to the technical field of wind power generation, and more specifically relates to an optimal configuration method for energy storage power of a power system containing wind power.

Background technique

进行频谱分析，将风电频谱中不同周期的风电功率归一化处理后并逐次累加，得到风电场所发出的不同频段的风电能力之和E_i（0<E_i<1）与周期时间t的对应关系曲线，在该曲线上设定风电能量的阀值a（0<a<1），由所设定的阀值对应的t_a确定出相应的储能装置容量为

The anti-peaking characteristics, unpredictability and strong random fluctuation of wind power have brought great troubles to the power balance of the power system. It is difficult to meet the requirements only by conventional units, and it is also difficult to formulate daily dispatch methods. The system needs to have a strong Positive/negative power quick adjustment capability. Energy storage device is one of the effective ways to solve the power balance of power system including wind power. In the Chinese invention patent specification CN102593853A, an energy storage system capacity optimization configuration method for improving the wind power acceptance capacity is disclosed. This method reasonably optimizes the energy storage system by coordinating the impact of energy storage costs and the benefits of receiving more wind power on the energy storage system configuration capacity. System capacity to maximize total revenue. In the Chinese invention patent specification CN102005771A, a method for selecting energy storage capacity for wind, light, and storage microgrid systems is disclosed. The method predicts the power difference between the generated power and the predicted demand in the grid-connected mode and island mode through simulation, and calculates The capacity requirements of energy storage equipment in the two modes are calculated, and then the energy storage capacity of the microgrid system is calculated. In the Chinese invention patent specification CN102255328A, a method for determining the capacity of a wind farm connected to an energy storage device based on spectrum analysis is disclosed.

Carry out spectrum analysis, normalize the wind power of different periods in the wind power spectrum and accumulate it successively, and obtain the correspondence between the sum E _i (0<E _i <1) of the wind power capacity of different frequency bands emitted by the wind farm and the cycle time t The relationship curve, on which the threshold value a of wind power energy is set (0<a<1), and the corresponding energy storage device capacity is determined from the t _a corresponding to the set threshold value as

In the above three methods, the selection of energy storage device capacity is calculated based on the determined fluctuation characteristics of historical wind power, which fails to consider the stable statistical distribution characteristics of wind power and load forecast errors, and the configured energy storage capacity is difficult to reflect Scenarios other than historical data. And it is based on solving the power/capacity configuration problem of a single energy storage device.

Contents of the invention

Aiming at the defects of the prior art, the purpose of the present invention is to provide an optimal allocation method of energy storage power suitable for wind power and load forecast deviations (called net load forecast deviations) for power systems containing wind power, aiming to solve Including the problem of the minimum energy storage power and spinning reserve required for large-scale wind power systems to deal with wind power and load forecast errors, it aims to solve the problem that the selection of energy storage device capacity in the prior art is based on historical wind power data, which fails to consider Due to the stable statistical distribution characteristics of wind power and load forecast errors, it is difficult for the configured energy storage capacity to reflect scenarios other than historical data.

The present invention provides a method for optimal configuration of energy storage power suitable for wind power systems, comprising the following steps:

S1: Obtain sample data of wind power and load including wind power system;

S2: Obtain the positive and negative spinning reserve capacity according to the sample data and the energy storage power configuration model; the energy storage power configuration model takes the minimum energy storage power used by the power system in the dispatch period as the objective function, and takes the thermal power unit in the power system The sum of the rated total output upper limit and the energy storage power upper limit of the power system is greater than the actual net load value. The value is a negative spinning reserve opportunity constraint;

S3: According to the positive and negative spinning reserve capacity, the optimal energy storage power configuration required by the wind power system to cope with the net load forecast error is obtained.

表示第t时段为保证电力系统功率平衡所需配置的储能功率，T表示优化周期。Furthermore, the objective function is

Indicates the energy storage power that needs to be configured to ensure the power balance of the power system in the t-th period, T represents the optimization period.

所述负旋转备用机会约束为

N_G表示火电机组的台数， $P_t^{L,A}$ 表示第t时段电力系统负荷实际值，

表示第t时段电力系统风功率实际值，

表示第i台火电机组的额定出力上限，

表示第i台火电机组的额定出力下限，a^rs表示置信度水平，Pr表示概率。Furthermore, the positive spinning reserve opportunity constraint is

The negative spinning reserve opportunity constraint is

N _G represents the number of thermal power units, $P_t^{L,A}$ Indicates the actual value of the power system load in the tth period,

Indicates the actual value of the wind power of the power system in the t-th period,

Indicates the upper limit of the rated output of the i-th thermal power unit,

Represents the lower limit of the rated output of the i-th thermal power unit, a ^rs represents the confidence level, and Pr represents the probability.

Further, the step S2 is specifically:

S21: Obtain the net load forecast error according to the historical load data, historical wind power data, net load forecast deviation and load forecast deviation in the corresponding time period;

S22: Obtain the cumulative distribution function and its inverse function of the net load forecast error by using a non-parametric estimation method;

S23: Transform the positive and negative spinning reserve opportunity constraints into positive and negative spinning reserve deterministic constraints according to the inverse function of the cumulative distribution function;

S24: Obtain the positive and negative spinning reserve capacity according to the positive and negative spinning reserve deterministic constraints.

为t时段的风功率预测偏差，为t时段的负荷预测偏差，

为第t时段的风功率的预测值，为第t时段的负荷预测值。Furthermore, the net load prediction error is

is the forecast deviation of wind power in period t, is the load forecast deviation in period t,

is the predicted value of wind power in the tth period, is the load forecast value for the tth period.

为第t时段净负荷预测误差累积分布函数的逆函数。Furthermore, the positive and negative spinning reserve deterministic constraints are respectively $C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\&Greater\; Equal;(P_t^{L,f}-P_t^{W,f})(1+f_t^{-1}\left(a^{\mathrm{rs}}\right))$ and $\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\&le;(P_t^{L,f}-P_t^{W,f})(1-f_t^{-1}\left(a^{\mathrm{rs}}\right)),$

is the inverse function of the cumulative distribution function of the net load forecast error in the t-th period.

和

Furthermore, the positive and negative spinning reserves are respectively

and

Further, the step S3 specifically includes:

S31: Obtain the positive energy storage power required by the power system according to the sum of the maximum output of all thermal power units in the power system and the positive rotation reserve capacity;

S32: Obtain the negative energy storage power required by the power system according to the sum of the minimum output of all thermal power units in the power system and the negative spinning reserve capacity;

S33: Use the maximum value of the absolute value of the positive energy storage power and the negative energy storage power as the optimal energy storage power configuration required by the wind power-containing power system to cope with the net load prediction error.

Furthermore, the optimal energy storage power configuration required by the wind power system to cope with the net load forecast error in the tth period is: $C_t^{\mathrm{ES}}=\max \{(P_t^{L,f}-P_t^{W,f})(1+f^{-1}\left(a^{\mathrm{rs}}\right))-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G,\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-(P_t^{L,f}-P_t^{W,f})(1-f^{-1}\left(a^{\mathrm{rs}}\right))\}.$

On the basis of historical wind power and load data, the present invention excavates the random statistical law of wind power and load, obtains the statistical distribution law of stable wind power and load forecast error, and establishes an energy storage capacity configuration model and proposes a solution method based on this, so that all The determined energy storage capacity can not only reflect the historical working conditions, but also adapt to the possible future working conditions, so as to better solve the power balance problem of the wind power grid-connected power system.

Description of drawings

Figure 1(a) is a flow chart of the implementation of the optimal configuration method suitable for the energy storage power of the power system including wind power provided by the embodiment of the present invention;

Fig. 1 (b) is the sub-flow chart of step S2;

Fig. 1 (c) is the sub-flow chart of step S3;

Fig. 2 is a schematic diagram of the wind power forecast curve, load forecast curve, net load forecast curve, thermal power unit maximum output curve and minimum output curve of the power system provided by the embodiment of the present invention on January 1, 2012;

Fig. 3 is a schematic diagram of a cumulative probability distribution function and an empirical distribution function of net load forecast errors in a certain period of time of the power system provided by an embodiment of the present invention;

Fig. 4 is a schematic diagram of an actual net load curve, a predicted net load curve, and two set output scenarios of a power system on a certain day provided by an embodiment of the present invention;

Fig. 5 is a schematic diagram of positive energy storage power, negative energy storage power, actual output of energy storage corresponding to scenario 1, and actual output of energy storage corresponding to scenario 2 provided by an embodiment of the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

The present invention is based on solving the problem of the minimum energy storage power and spinning reserve required for large-scale wind power systems to cope with wind power and load forecast errors, and overcomes the existing energy storage capacity allocation technology that only relies on historical wind power already determined However, it fails to consider and utilize the stable random statistical laws of wind power and load forecast errors, and it is difficult to meet the energy storage demand scenarios other than historical data. It provides a storage system that can simultaneously consider the statistical characteristics of wind power and load forecast deviations. Energy power optimization configuration method; on the basis of historical wind power and load data, the present invention excavates the random statistical law of wind power and load forecast errors, based on this, establishes an energy storage power configuration model and proposes a solution method, so that the determined energy storage The capacity can not only reflect the historical working conditions, but also adapt to the possible future working conditions, so as to better solve the power balance problem of the wind power grid-connected power system.

In the present invention, as shown in Fig. 1(a), Fig. 1(b) and Fig. 1(c), a method suitable for configuring the minimum energy storage power of a wind power system specifically includes:

为第t时段为保证系统功率平衡所需配置的储能装置功率，

f和

的单位为MW，T为优化周期；(1) With the goal of using as little energy storage power as possible in the entire dispatch period, the minimum energy storage power optimization objective function is established. The method is as follows: In the formula,

is the power of the energy storage device configured to ensure the system power balance in the t-th period,

f and

The unit of is MW, and T is the optimization period;

(2) Establish the positive and negative spinning reserve opportunity constraints of the system, the method is as follows:

Positive rotation alternate constraint: $\mathrm{PR}\{P_t^{L,A}-P_t^{W,A}\&le;C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\}\&Greater\; Equal;a^{\mathrm{rs}}......\left(2\right)$

为第t时段系统负荷实际值，

为第t时段系统风功率实际值，

为第i台火电机组的额定出力上限，

为第i台火电机组的额定出力下限，a^rs为置信度水平，Pr表示概率；。其中式(2)的物理含义：系统中火电机组的额定总出力上限与储能功率上限之和应在一定置信度水平（通常不小于95%）下大于实际发生的净负荷值，称式(2)为正旋转备用约束；式(3)的物理含义：系统中火电机组的额定出力总下限与储能功率下限之和应在一定置信度水平（通常不小于95%）下小于实际发生的净负荷值，称式(3)为负旋转备用约束；并假设

即本发明中考虑负荷值大于风功率的装机容量的系统。Positive rotation alternate constraints: $\mathrm{PR}\{P_t^{L,A}-P_t^{W,A}\&Greater\; Equal;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\}\&Greater\; Equal;a^{\mathrm{rs}}......\left(3\right)$ In the formula, N _G is the number of thermal power units,

is the actual value of the system load in the t-th period,

is the actual value of the wind power of the system in the t-th period,

is the rated output upper limit of the i-th thermal power unit,

is the lower limit of the rated output of the i-th thermal power unit, a ^rs is the confidence level, and Pr is the probability; The physical meaning of formula (2): the sum of the rated total output upper limit of thermal power units and the upper limit of energy storage power in the system should be greater than the actual net load value under a certain confidence level (usually not less than 95%), which is called formula ( 2) is the positive spinning reserve constraint; the physical meaning of formula (3): the sum of the total lower limit of the rated output of thermal power units in the system and the lower limit of energy storage power should be less than the actual Net load value, said formula (3) is the negative spinning reserve constraint; and assume

That is, in the present invention, the system whose load value is greater than the installed capacity of wind power is considered.

(3) Transform the chance constraints (2) and (3) into deterministic constraints, as follows:

为第t时段系统负荷预测值，

为随机变量，表示第t时段系统负荷预测偏差，

为第t时段系统风功率预测值，

为随机变量，表示第t时段系统风功率预测偏差。(3.1) The actual values of wind power and load are represented by their predicted values and predicted deviations respectively, which are recorded as: $P_t^{L,A}=P_t^{L,f}+\mathrm{\&Delta;}{P}_t^L......\left(4\right),$ $P_t^{W,A}=P_t^{W,f}+\mathrm{\&Delta;}{P}_t^W......\left(5\right),$ In the formula,

is the predicted value of the system load in the t-th period,

is a random variable, representing the system load forecast deviation in the t-th period,

is the predicted value of wind power of the system in the t-th period,

is a random variable, representing the forecast deviation of the system wind power in the t-th period.

(3.2) Substitute formula (4) and formula (5) into formula (2) and (3) to get formula (6) and formula (7): $\mathrm{PR}\{\mathrm{\&Delta;}{P}_t^L-\mathrm{\&Delta;}{P}_t^W\&le;C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,f}+P_t^{W,f}\}\&Greater\; Equal;a^{\mathrm{rs}}......\left(6\right),$ $\mathrm{PR}\{\mathrm{\&Delta;}{P}_t^L-\mathrm{\&Delta;}{P}_t^W\&Greater\; Equal;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}-P_t^{L,f}+P_t^{W,f}\}\&Greater\; Equal;a^{\mathrm{rs}}......\left(7\right).$

可将含双随机变量的式子（6）和式子（7）转换为含单随机变量的式子（8）和式子（9）： $\mathrm{Pr}({\left(\mathrm{\&Delta;}{P}_t^{L-W}\right)}^{*}\&le;(C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,F}+P_t^{W,F})/(P_t^{L,F}-P_t^{W,F}))\&GreaterEqual;a^{\mathrm{rs}}......\left(8\right),$ $\mathrm{Pr}({\left(\mathrm{\&Delta;}{P}_t^{L-W}\right)}^{*}\&le;(C_t^{\mathrm{ES}}-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G+P_t^{L,F}-P_t^{W,F})/(P_t^{L,F}-P_t^{W,F}))\&GreaterEqual;a^{\mathrm{rs}}......\left(9\right)$ (3.3) Let net load forecast error

Formulas (6) and (7) with double random variables can be transformed into formulas (8) and (9) with single random variables: $\mathrm{PR}({\left(\mathrm{\&Delta;}{P}_t^{L-W}\right)}^{*}\&le;(C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,f}+P_t^{W,f})/(P_t^{L,f}-P_t^{W,f}))\&Greater\; Equal;a^{\mathrm{rs}}......\left(8\right),$ $\mathrm{PR}({\left(\mathrm{\&Delta;}{P}_t^{L-W}\right)}^{*}\&le;(C_t^{\mathrm{ES}}-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G+P_t^{L,f}-P_t^{W,f})/(P_t^{L,f}-P_t^{W,f}))\&Greater\; Equal;a^{\mathrm{rs}}......\left(9\right)$

和负荷预测偏差从而计算出净负荷预测误差

(3.4) According to the historical data (actual value and predicted value) of wind power and load in recent years, calculate the wind power forecast deviation for the corresponding period t

Deviation from Load Forecast to calculate the net load forecast error

数值，采用非参数估计的方法，估计出净负荷预测误差

的累积分布函数值F_t=Pr(X≤a^rs)，并计算其逆函数值

(3.5) According to the calculated net load forecast error in recent years

Numerical values, using a non-parametric estimation method to estimate the net load forecast error

Cumulative distribution function value F _t =Pr(X≤a ^rs ), and calculate its inverse function value

(3.6) Defined by the cumulative probability distribution function F _t (x)=Pr(X≤x), the formula (8) can be transformed into: $f_t((C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G-P_t^{L,f}+P_t^{W,f})/(P_t^{L,f}-P_t^{W,f}))\&Greater\; Equal;a^{\mathrm{rs}},$ which is thus equivalent to:

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; ES</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; rs</span>}}<span\; class="notranslate">\; )</span>$

(3.7) Defined by the cumulative probability distribution function F _t (x)=Pr(X≤x), the formula (9) can be transformed into: $f_t((C_t^{\mathrm{ES}}-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G+P_t^{L,f}-P_t^{W,f})/(P_t^{L,f}-P_t^{W,f}))\&Greater\; Equal;a^{\mathrm{rs}},$ which is thus equivalent to:

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; ES</span>}}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\underset{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; rs</span>}}<span\; class="notranslate">\; )</span>$

可将式子（8）和式子（9）变换为式子（10）和式子（11）： $C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_{i,t}^G\&GreaterEqual;(P_t^{L,F}-P_t^{W,F})(1+F_t^{-1}\left(a^{\mathrm{rs}}\right))......\left(10\right)$ (3.7) According to the calculated inverse function value

Formula (8) and formula (9) can be transformed into formula (10) and formula (11): $C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_{i,t}^G\&Greater\; Equal;(P_t^{L,f}-P_t^{W,f})(1+f_t^{-1}\left(a^{\mathrm{rs}}\right))......\left(10\right)$

$\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_{i,t}^G-C_t^{\mathrm{ES}}\&le;(P_t^{L,f}-P_t^{W,f})(1-f_t^{-1}\left(a^{\mathrm{rs}}\right))......\left(11\right);$ In this way, probabilistic constraints can be transformed into deterministic constraints.

(4) Calculate the positive/negative spinning reserve coefficient required for the system during period t and the positive/negative spinning reserve capacity required by the system. The calculation method is:

Positive spinning reserve factor:

Negative spinning reserve factor:

Spinning spare capacity: $(P_t^{L,f}-P_t^{W,f})(1+f_t^{-1}\left(a^{\mathrm{rs}}\right));$

Negative spinning reserve capacity: $(P_t^{L,f}-P_t^{W,f})(1-f_t^{-1}\left(a^{\mathrm{rs}}\right));$

(5) Calculate the minimum energy storage power configuration required by the power system including wind power. The calculation method is as follows: (5.1) Formula (12) is obtained from formula (10) and formula (11):

$\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; ES</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; rs</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; G</span>}}\\ {\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; ES</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\underset{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; rs</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>\end{array}\right.<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

(5.2) According to the formula (12), the minimum energy storage power required by the power system with wind power in the t-th period can be obtained by calculating according to the formula (13):

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; ES</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; max</span>}<span\; class="notranslate">\; \{</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; rs</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; ,</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\underset{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}^{\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; f</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; rs</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

Based on the statistical characteristics of the two random variables of wind power and load prediction error, the present invention determines the rotating reserve required by the system at a predetermined confidence level, and obtains a wind power system suitable for wind power-load (net load) The minimum energy storage power configuration required for prediction errors, the calculation results of this method are derived from historical data samples, and are not limited to historical data samples, which can better reflect scenarios different from historical data samples. Capacity allocation provides a theoretical basis and reference to ensure the safe and stable operation of the power system, and at the same time saves the cost investment caused by excessive allocation of energy storage capacity.

In order to further illustrate the method for optimally configuring the energy storage power of an electric power system including wind power provided by the embodiment of the present invention, it is now described in detail in conjunction with specific examples as follows:

The predicted and actual values of wind farms and loads contained in a certain system for one year are used as the sample data for statistical net load forecasting errors, and the data samples are taken as sampling intervals of 15 minutes. The method in this paper is illustrated by calculating the positive/negative spinning reserve power and energy storage power that the system needs to configure on a certain day. The load data, wind power data and thermal power unit output data of the planned day are shown in Figure 2.

即认为在置信度为95%的水平下，净负荷预测误差的区间为±10.6%，实心方框所示的点即为 Implementation step 1: Obtain the cumulative probability distribution function of the net load forecast error, as shown in Figure 3, the cumulative probability distribution function of the net load forecast error in the first period. Assuming a ^rs =0.95, the calculation can be

That is to say, at the confidence level of 95%, the range of net load forecast error is ±10.6%, and the point shown by the solid box is

和

t=1,2,…,T，如表一所示：Implementation Step 2: Determine the positive and negative spinning reserves for each time period required by the system to be configured as

and

t=1,2,…,T, as shown in Table 1:

period of time 1 2 3 4 5 6 7 8 9 10 11 12 spare 326 323 319 320 314 317 317 315 309 311 309 308 period of time 13 14 15 16 17 18 19 20 twenty one twenty two twenty three twenty four spare 305 306 308 311 306 309 309 310 307 312 312 316 period of time 25 26 27 28 29 30 31 32 33 34 35 36 spare 316 324 334 338 338 338 329 330 331 339 342 346 period of time 37 38 39 40 41 42 43 44 45 46 47 48 spare 347 350 351 357 354 359 355 355 355 355 345 342 period of time 49 50 51 52 53 54 55 56 57 58 59 60 spare 339 339 338 337 340 338 341 339 339 341 341 342 period of time 61 62 63 64 65 66 67 68 69 70 71 72 spare 346 350 356 362 364 374 383 375 380 385 377 379 period of time 73 74 75 76 77 78 79 80 81 82 83 84 spare 371 369 370 365 369 363 364 362 352 354 349 346 period of time 85 86 87 88 89 90 91 92 93 94 95 96 spare 342 371 374 375 368 370 361 356 343 340 333 328

Table 1 System spinning reserve (±MW)

Implementation step 3: Calculate and obtain the energy storage power required for each time period, as shown in Table 2;

period of time 1 2 3 4 5 6 7 8 9 10 11 12 energy storage 0 0 0 0 9 0 0 5 53 36 51 61 period of time 13 14 15 16 17 18 19 20 twenty one twenty two twenty three twenty four energy storage 86 80 66 38 80 57 50 45 70 26 29 0 period of time 25 26 27 28 29 30 31 32 33 34 35 36 energy storage 0 0 0 0 0 0 0 0 0 0 0 0 period of time 37 38 39 40 41 42 43 44 45 46 47 48 energy storage 0 0 0 0 0 0 0 0 0 0 0 0 period of time 49 50 51 52 53 54 55 56 57 58 59 60 energy storage 0 0 0 0 0 0 0 0 0 0 0 0 period of time 61 62 63 64 65 66 67 68 69 70 71 72 energy storage 0 0 0 0 0 102 198 115 165 217 138 151 period of time 73 74 75 76 77 78 79 80 81 82 83 84 energy storage 71 47 56 5 46 0 0 0 0 0 0 0 period of time 85 86 87 88 89 90 91 92 93 94 95 96 energy storage 0 75 101 112 42 65 0 0 0 0 0 0

Table 2 Energy storage power to be configured in the system (±MW)

Implementation Step 4: System Operation Test

In order to verify the effectiveness of the energy storage power set in the present invention, the following two extreme scenarios (scenario 1, scenario 2) and the actual value of the net load (scenario 3) are calculated in three scenarios, and the configured storage power is checked. Whether it can cope with the forecast deviation of net load.

Scenario 1: Assume that the load forecast is at the maximum positive deviation (103%), and the wind power forecast is at the maximum negative deviation (80%);

Scenario 2: Assume that the load forecast is at the maximum negative deviation (97%), and the wind power forecast is at the maximum negative deviation (120%);

Scenario 3: Actual occurring wind power and loads.

The corresponding scene settings are shown in Figure 4; from Figure 4, it can be seen that only conventional generator sets (the uppermost line represents the upper limit value of the generator set, and the lower line represents the lower limit value of the generator set) can no longer balance scene 1 And the power of the system in Scenario 2, the actual net load curve also exceeds the upper or lower limit of the thermal power unit, and energy storage is needed to balance the system power at this time.

Calculate the energy storage output required to meet the power balance of the set scene, as shown in Figure 5. It can be seen that in Scenario 1, the system only needs positive spinning reserve, which is close to and smaller than the configured energy storage capacity; in Scenario 2, the system only needs negative spinning reserve, and the absolute value is close to and smaller than the configured energy storage capacity; the actual The net load basically operates within the adjustable range of the thermal power unit, and only a small amount of energy storage is needed to balance it at peak times. It can be seen that the energy storage power configured by the method of the present invention satisfies both the actual operation of the system and the requirements of preset extreme scenarios.

It is easy for those skilled in the art to understand that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, All should be included within the protection scope of the present invention.

Claims (9)

1. an Optimal Configuration Method that is suitable for containing wind-powered electricity generation electric power system energy storage power is characterized in that, comprises the steps:

S1: obtain the wind power that contains the wind-powered electricity generation electric power system and the sample data of load;

S2: obtain positive and negative spinning reserve capacity according to described sample data and energy storage power configuration model; The energy storage power minimum that described energy storage power configuration model uses with electric power system in dispatching cycle is as target function, the net load value that the specified gross capability upper limit of fired power generating unit and energy storage power upper limit sum occur greater than reality in the electric power system is as positive rotation chance constraint for subsequent use, and the total lower limit of rated output of fired power generating unit and energy storage power lower limit sum turn chance constraint for subsequent use less than the net load value of reality generation as negative rotation in the electric power system;

S3: obtain to contain the required optimum energy storage power configuration of wind-powered electricity generation electric power system reply net load predicated error according to positive and negative spinning reserve capacity.

2. Optimal Configuration Method as claimed in claim 1 is characterized in that, described target function is

Represent that the t period is the energy storage power that guarantees the required configuration of Power Systems balance,

T represents optimization cycle.

3. Optimal Configuration Method as claimed in claim 2 is characterized in that, described positive rotation guest machine can be constrained to $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&le;C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\}\&GreaterEqual;a^{\mathrm{rs}},$ Described negative rotation turns guest machine and can be constrained to $\mathrm{Pr}\{P_t^{L,A}-P_t^{W,A}\&GreaterEqual;\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\}\&GreaterEqual;a^{\mathrm{rs}};$ N _GThe number of units of expression fired power generating unit,

Represent t period power system load actual value,

Represent t period electric power system wind power actual value,

The rated output upper limit that represents i platform fired power generating unit, The rated output lower limit that represents i platform fired power generating unit, a ^RsThe expression level of confidence, Pr represents probability.

4. such as claim 1 or 3 described Optimal Configuration Methods, it is characterized in that, described step S2 is specially:

S21: according to the load data of history, historical wind power data and the wind power prediction deviation of corresponding period and load prediction deviation acquisition net load predicated error;

S22: cumulative distribution function and its inverse function of adopting the method acquisition net load predicated error of non-parametric estmation;

S23: convert positive and negative spinning reserve chance constraint to positive and negative spinning reserve certainty constraint according to inverse function;

S24: obtain positive and negative spinning reserve capacity according to positive and negative spinning reserve certainty constraint.

5. Optimal Configuration Method as claimed in claim 4 is characterized in that, described net load predicated error is

Be the wind power prediction deviation of t period,

Be the load prediction deviation of t period,

Represent t period Load Prediction In Power Systems value,

Represent t period electric power system wind power prediction value.

6. Optimal Configuration Method as claimed in claim 4 is characterized in that, described positive and negative spinning reserve certainty constraint is respectively $C_t^{\mathrm{ES}}+\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G\&GreaterEqual;(P_t^{L,F}-P_t^{W,F})(1+F_t^{-1}\left(a^{\mathrm{rs}}\right))$ With $\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-C_t^{\mathrm{ES}}\&le;(P_t^{L,F}-P_t^{W,F})(1-F_t^{-1}\left(a^{\mathrm{rs}}\right));$

It is the inverse function of t period net load predicated error cumulative distribution function.

7. Optimal Configuration Method as claimed in claim 4 is characterized in that, described positive and negative spinning reserve capacity is respectively With

8. Optimal Configuration Method as claimed in claim 4 is characterized in that, described step S3 specifically comprises:

S31: maximum output sum and positive rotation reserve capacity according to all fired power generating unit in the electric power system obtain the required positive energy storage power of electric power system;

S32: minimum load sum and negative rotation according to all fired power generating unit in the electric power system turn the required negative energy storage power of reserve capacity acquisition electric power system;

S33: the maximum of described positive energy storage power and described negative energy storage power absolute value is tackled the required optimum energy storage power configuration of net load predicated error as containing the wind-powered electricity generation electric power system.

9. Optimal Configuration Method as claimed in claim 8 is characterized in that, describedly contains the wind-powered electricity generation electric power system t period and tackles the required optimum energy storage power configuration of net load error and be: $C_t^{\mathrm{ES}}=\max \{(P_t^{L,F}-P_t^{W,F})(1+F^{-1}\left(a^{\mathrm{rs}}\right))-\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{P}}_i^G,\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{\underset{\&OverBar;}{P}}_i^G-(P_t^{L,F}-P_t^{W,F})(1-F^{-1}\left(a^{\mathrm{rs}}\right))\}.$

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