CN110994703A - A FM capacity requirement allocation method considering multiple FM resources - Google Patents

A FM capacity requirement allocation method considering multiple FM resources Download PDF

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CN110994703A
CN110994703A CN201911209072.6A CN201911209072A CN110994703A CN 110994703 A CN110994703 A CN 110994703A CN 201911209072 A CN201911209072 A CN 201911209072A CN 110994703 A CN110994703 A CN 110994703A
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周前
胡泽春
刘礼恺
程亮
朱寰
吴盛军
汪成根
赵静波
陈哲
岑炳成
黄成�
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Tsinghua University
State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

本发明提出一种考虑多种调频资源的调频容量需求分配方法，属于电力系统自动发电控制领域。该方法首先以模型驱动的方式计算在不同储能调频资源占比下的储能调频资源对传统调频资源的替代比；然后，对于未来任一AGC考核时段，以数据驱动的方式根据历史数据计算该时段储能调频资源折算为传统调频资源后的调频容量需求，并根据替代比获取使得替代后总调频资源容量达到该调频容量需求的所有调频资源分配组合方案；最后，从所有组合方案中选取使得总调频资源成本最低的方案作为该未来该时段的调频容量需求分配方案。本发明可考虑多种调频资源调节特性差异，充分发挥储能调频资源优良的调节性能。The invention proposes a frequency regulation capacity demand allocation method considering multiple frequency regulation resources, and belongs to the field of automatic power generation control of electric power systems. This method firstly calculates the substitution ratio of energy storage frequency regulation resources to traditional frequency regulation resources under different energy storage frequency regulation resource proportions in a model-driven way; The energy storage frequency regulation resources in this period are converted into the frequency regulation capacity requirements after traditional frequency regulation resources, and all the frequency regulation resource allocation combinations that make the total frequency regulation resource capacity after replacement meet the frequency regulation capacity requirements are obtained according to the substitution ratio; finally, select from all the combination schemes The scheme with the lowest total frequency modulation resource cost is used as the frequency modulation capacity demand allocation scheme in the future period. The present invention can take into account the differences in the regulation characteristics of various frequency modulation resources, and give full play to the excellent regulation performance of the energy storage frequency modulation resources.

Description

一种考虑多种调频资源的调频容量需求分配方法A FM capacity requirement allocation method considering multiple FM resources

技术领域technical field

本发明属于电力系统自动发电控制(AGC)领域，具体涉及一种考虑多种调频资源的调频容量需求分配方法。The invention belongs to the field of automatic generation control (AGC) of electric power systems, and in particular relates to a frequency regulation capacity demand allocation method considering multiple frequency regulation resources.

背景技术Background technique

可再生能源出力具有较强的波动性，随着可再生能源的大量接入，电力系统有功不平衡的幅值和频次都呈增长趋势。储能相对于传统可再生能源，具有更快的调节速度，因此在调频方面具有更高的效能，即单位容量的储能调频资源能够在不损害调频表现的前提下替代多个单位容量的火电、水电等调频资源(下文统称为传统调频资源)。The output of renewable energy has strong volatility. With the large-scale access of renewable energy, the magnitude and frequency of active power imbalance in the power system are increasing. Compared with traditional renewable energy, energy storage has a faster regulation speed, so it has higher efficiency in frequency regulation, that is, energy storage and frequency regulation resources per unit capacity can replace multiple thermal power units of unit capacity without compromising the performance of frequency regulation. , hydropower and other frequency modulation resources (hereinafter collectively referred to as traditional frequency modulation resources).

目前，大多数电力市场仍然以传统调频资源为主，已有方法根据AGC控制区的历史数据建立调频表现、传统调频资源容量和净负荷波动的相关关系，再根据未来净负荷标准差的预测结果计算传统调频资源的容量需求。近年来越来越多的储能装置参与调频服务，储能调频资源与传统调频资源调节能力有较大差异，单位容量的储能调频资源能够等效替代多个单位容量的传统调频资源，因而在计算调频容量需求时需区分储能调频资源与传统调频资源，而已有方法在计算调频容量需求时不能区分储能调频资源与传统调频资源。At present, most power markets are still dominated by traditional frequency regulation resources. There are existing methods to establish the correlation between frequency regulation performance, traditional frequency regulation resource capacity and net load fluctuations based on the historical data of the AGC control area, and then based on the forecast results of the standard deviation of the future net load. Calculate the capacity requirements of traditional FM resources. In recent years, more and more energy storage devices have participated in frequency regulation services. The regulation capacity of energy storage frequency regulation resources is quite different from that of traditional frequency regulation resources. Energy storage frequency regulation resources of unit capacity can equivalently replace traditional frequency regulation resources of multiple unit capacities. When calculating frequency regulation capacity requirements, it is necessary to distinguish energy storage frequency regulation resources from traditional frequency regulation resources. Existing methods cannot distinguish energy storage frequency regulation resources from traditional frequency regulation resources when calculating frequency regulation capacity requirements.

发明内容SUMMARY OF THE INVENTION

本发明的目的是为克服已有技术的不足之处，提出一种考虑多种调频资源的调频容量需求分配方法。本发明可考虑多种调频资源调节特性差异，充分发挥储能调频资源优良的调节性能。The purpose of the present invention is to provide a method for allocating frequency modulation capacity requirements considering various frequency modulation resources in order to overcome the shortcomings of the prior art. The present invention can take into account the differences in the regulation characteristics of various frequency modulation resources, and give full play to the excellent regulation performance of the energy storage frequency modulation resources.

本发明提出一种考虑多种调频资源的调频容量需求分配方法，其特征在于，包括以下步骤：The present invention proposes a method for allocating frequency modulation capacity requirements considering multiple frequency modulation resources, which is characterized by comprising the following steps:

1)计算在不同储能调频资源占比下的储能调频资源对传统调频资源替代比；具体步骤如下：1) Calculate the substitution ratio of energy storage frequency regulation resources to traditional frequency regulation resources under different energy storage frequency regulation resource ratios; the specific steps are as follows:

1-1)将前一年中每月15号之前的一个工作日和15号之后的一个非工作日作为典型日，共得到24个典型日构成典型日集合；按照AGC考核单位时间长度Γ，将所有典型日划分为对应的AGC典型考核时段；1-1) Take a working day before the 15th and a non-working day after the 15th as a typical day in the previous year, and obtain a total of 24 typical days to form a typical day set; according to the AGC assessment unit time length Γ, Divide all typical days into corresponding AGC typical assessment periods;

对每一个AGC典型考核时段，选取该时段内每一个离散时间点的负荷功率、风电发电功率和光伏发电功率数据，计算该时段内每一个离散时间点的净负荷：For each AGC typical assessment period, select the load power, wind power generation power and photovoltaic power generation data at each discrete time point in the period, and calculate the net load at each discrete time point in the period:

Figure BDA0002297642220000021
Figure BDA0002297642220000021

其中，q表示AGC典型考核时段编号，

Figure BDA0002297642220000022
分别为第q个AGC典型考核时段中第k个离散时间点净负荷和负荷功率，
Figure BDA0002297642220000023
分别为该时段第k个离散时间点风电发电功率和光伏发电功率；离散时间点数据的采样周期与AGC的指令周期τ相同，每个AGC考核时段包含K＝Γ/τ个AGC指令周期，K为每个时段内的离散时间点的采样总数；Among them, q represents the number of the typical AGC assessment period,
Figure BDA0002297642220000022
are the net load and load power at the kth discrete time point in the qth AGC typical assessment period, respectively,
Figure BDA0002297642220000023
are the wind power generation power and photovoltaic power generation power at the kth discrete time point in this period, respectively; the sampling period of the discrete time point data is the same as the AGC command cycle τ, and each AGC assessment period includes K=Γ/τ AGC command cycles, K is the total number of samples at discrete time points in each period;

计算每个离散时间点的净负荷波动

Figure BDA0002297642220000024
表达式如下：Calculate net load fluctuations at each discrete point in time
Figure BDA0002297642220000024
The expression is as follows:

Figure BDA0002297642220000025
Figure BDA0002297642220000025

其中，

Figure BDA0002297642220000026
为第q个AGC典型考核时段的净负荷均值，计算表达式为：in,
Figure BDA0002297642220000026
is the average net load of the qth AGC typical assessment period, and the calculation expression is:

Figure BDA0002297642220000027
Figure BDA0002297642220000027

每个AGC典型考核时段内所有离散时间点的净负荷波动

Figure BDA0002297642220000028
构成的序列组成该时段的净负荷波动
Figure BDA0002297642220000029
Net load fluctuations at all discrete time points within a typical assessment period of each AGC
Figure BDA0002297642220000028
The constituted series makes up the net load fluctuations for the period
Figure BDA0002297642220000029

1-2)建立优化模型求解AGC典型考核时段净负荷波动为

Figure BDA00022976422200000210
储能调频资源容量占系统总调频容量比例为η时，该AGC控制区的调频容量需求
Figure BDA00022976422200000211
所述优化模型以
Figure BDA00022976422200000212
和η为输入变量，以最小化该AGC控制区的调频容量需求
Figure BDA00022976422200000213
为优化目标，表达式如下：1-2) Establish an optimization model to solve the net load fluctuation of the typical AGC assessment period as
Figure BDA00022976422200000210
When the ratio of energy storage frequency regulation resource capacity to the total frequency regulation capacity of the system is η, the frequency regulation capacity demand of the AGC control area
Figure BDA00022976422200000211
The optimized model is
Figure BDA00022976422200000212
and η are input variables to minimize the FM capacity requirement for this AGC control area
Figure BDA00022976422200000213
For the optimization goal, the expression is as follows:

Figure BDA00022976422200000214
Figure BDA00022976422200000214

s.t.s.t.

Figure BDA0002297642220000031
Figure BDA0002297642220000031

ACE[k-1]＝BΔf[k-1]ACE[k-1]=BΔf[k-1]

Figure BDA0002297642220000032
Figure BDA0002297642220000032

Figure BDA0002297642220000033
Figure BDA0002297642220000033

Figure BDA0002297642220000034
Figure BDA0002297642220000034

Figure BDA0002297642220000035
Figure BDA0002297642220000035

Figure BDA0002297642220000036
Figure BDA0002297642220000036

Figure BDA0002297642220000037
Figure BDA0002297642220000037

Figure BDA0002297642220000038
Figure BDA0002297642220000038

πg[k]＝min{max{π[k],ΔPg m[k]},ΔPg M[k]}π g [k]=min{max{π[k],ΔP g m [k]},ΔP g M [k]}

πs[k]＝π[k]-πg[k]π s [k]=π[k]-π g [k]

ΔPg[k]＝πg[k]ΔP g [k] = π g [k]

Ps[k]＝min{max{πs[k],Ps m[k]},Ps M[k]}P s [k]=min{max{π s [k],P s m [k]},P s M [k]}

e[k]＝e[k-1]-Ps[k]δe[k]=e[k-1]-P s [k]δ

Figure BDA0002297642220000039
Figure BDA0002297642220000039

Figure BDA00022976422200000310
Figure BDA00022976422200000310

Figure BDA00022976422200000311
Figure BDA00022976422200000311

Figure BDA00022976422200000312
Figure BDA00022976422200000312

式中，Δf是系统频率偏差，

Figure BDA00022976422200000313
是ACE考核指标中A2指标所规定的限值，B表示系统频率响应常数，IACE表示ACE的PI滤波积分项，
Figure BDA00022976422200000314
为传统调频资源的爬坡速率上限，λ为调频资源到达最大调频容量允许的最长时间，
Figure BDA00022976422200000315
为储能调频资源容量，
Figure BDA00022976422200000316
为第k个离散时间点传统调频资源向上最大可调功率，
Figure BDA0002297642220000041
为第k个离散时间点传统调频资源向下最大可调功率，
Figure BDA0002297642220000042
为第k个离散时间点储能调频资源向上最大可调功率，
Figure BDA0002297642220000043
为第k个离散时间点储能调频资源向下最大可调功率，eM和em分别为储能资源电量的最大值与最小值，e[k]为第k个离散时间点储能资源电量，π[k]为第k个离散时间点系统调节功率需求，πg[k]和πs[k]分别为第k个离散时间点分配给传统机组与储能资源的调节功率，ΔPg[k]和Ps[k]分别为传统调频资源与储能资源实际承担的调节功率，rg[k]和rnl[k]分别为第k个离散时间点传统调频资源和净负荷的变化率，a[k]为第k个离散时间点计算频率偏差所需的常数，D为系统负荷阻尼常数，H为系统惯性常数；where Δf is the system frequency deviation,
Figure BDA00022976422200000313
is the limit specified by the A 2 index in the ACE assessment index, B represents the system frequency response constant, IACE represents the PI filter integral term of ACE,
Figure BDA00022976422200000314
is the upper limit of the ramp rate of traditional FM resources, λ is the maximum time allowed for FM resources to reach the maximum FM capacity,
Figure BDA00022976422200000315
Frequency regulation resource capacity for energy storage,
Figure BDA00022976422200000316
is the maximum upward adjustable power of traditional FM resources at the kth discrete time point,
Figure BDA0002297642220000041
is the maximum downward adjustable power of traditional FM resources at the kth discrete time point,
Figure BDA0002297642220000042
is the maximum upward adjustable power of the energy storage frequency modulation resource at the kth discrete time point,
Figure BDA0002297642220000043
is the maximum downward adjustable power of the energy storage frequency regulation resource at the kth discrete time point, e M and em are the maximum and minimum values of the energy storage resource, respectively, and e[ k ] is the energy storage resource at the kth discrete time point Electricity, π[k] is the regulated power demand of the system at the kth discrete time point, πg [k] and π s [ k] are the regulated power allocated to traditional units and energy storage resources at the kth discrete time point, respectively, ΔP g [k] and P s [k] are the regulation power actually undertaken by traditional frequency regulation resources and energy storage resources, respectively, r g [k] and r nl [k] are the traditional frequency regulation resources and the net load at the kth discrete time point, respectively The rate of change of , a[k] is the constant required to calculate the frequency deviation at the kth discrete time point, D is the system load damping constant, and H is the system inertia constant;

1-3)选取任一AGC典型考核时段q，令储能调频资源占比η的取值以1％作为步长从0％取到100％，根据该时段对应的净负荷波动

Figure BDA0002297642220000044
利用步骤1-2)建立的模型，计算储能调频资源占比为η时系统在该时段调频容量需求
Figure BDA0002297642220000045
1-3) Select any AGC typical assessment period q, let the value of energy storage frequency regulation resource ratio η take 1% as a step from 0% to 100%, according to the net load fluctuation corresponding to this period
Figure BDA0002297642220000044
Using the model established in step 1-2), calculate the frequency regulation capacity demand of the system in this period when the energy storage frequency regulation resource ratio is η
Figure BDA0002297642220000045

1-4)重复步骤1-3)，对步骤1-1)的每一个AGC典型考核时段，计算储能调频资源占比为η时系统在每个时段调频容量需求得到对应的

Figure BDA0002297642220000046
1-4) Repeat step 1-3), for each AGC typical assessment period in step 1-1), calculate the frequency modulation resource ratio of energy storage to be η when the frequency modulation capacity requirement of the system in each period is obtained correspondingly.
Figure BDA0002297642220000046

根据下式计算在不同η下储能调频资源相对于传统调频资源的替代比：The substitution ratio of energy storage frequency regulation resources to traditional frequency regulation resources under different η is calculated according to the following formula:

Figure BDA0002297642220000047
Figure BDA0002297642220000047

其中，Q为AGC典型考核时段的总数；Among them, Q is the total number of typical AGC assessment periods;

2)选取未来任一AGC考核时段记为F时段，根据历史数据计算该时段储能调频资源折算为传统调频资源后的调频容量需求；具体步骤如下：2) Select any AGC assessment period in the future and record it as period F, and calculate the frequency regulation capacity demand after the energy storage frequency regulation resources in this period are converted into traditional frequency regulation resources according to historical data; the specific steps are as follows:

2-1)收集自动发电控制AGC控制区内过去N年的历史数据；所述历史数据包括：每分钟负荷功率、每分钟风电发电功率、每分钟光伏发电功率，每个AGC考核时段的A2指标、每个AGC考核时段的传统调频资源容量和储能调频资源容量；2-1) Collect historical data of the past N years in the AGC control area of automatic power generation control; the historical data include: load power per minute, wind power generation power per minute, photovoltaic power generation power per minute, A2 of each AGC assessment period Indicators, traditional frequency regulation resource capacity and energy storage frequency regulation resource capacity for each AGC assessment period;

2-2)根据步骤2-1)中每个AGC考核时段的传统调频资源容量

Figure BDA0002297642220000048
和储能调频资源容量Ps h，得到历史数据中每个AGC考核时段对应的储能调频资源占比ηh值；其中，h为历史数据的时段编号；2-2) According to the traditional FM resource capacity of each AGC assessment period in step 2-1)
Figure BDA0002297642220000048
and energy storage frequency regulation resource capacity P s h , to obtain the value η h of energy storage frequency regulation resources corresponding to each AGC assessment period in the historical data; where h is the period number of the historical data;

Figure BDA0002297642220000051
Figure BDA0002297642220000051

根据步骤1)的结果，获取在该ηh值下对应的储能调频资源相对于传统调频资源的替代比，将历史数据中每个AGC考核时段的储能调频资源折算为传统调频资源，得到折算后历史数据中每个AGC考核时段的总调频容量

Figure BDA0002297642220000052
每个AGC考核时段的折算根据下式进行：According to the result of step 1), obtain the substitution ratio of the energy storage frequency regulation resources corresponding to the traditional frequency regulation resources under the η h value, and convert the energy storage frequency regulation resources in each AGC assessment period in the historical data into the traditional frequency regulation resources to obtain The total FM capacity of each AGC assessment period in the converted historical data
Figure BDA0002297642220000052
The conversion of each AGC assessment period is carried out according to the following formula:

Figure BDA0002297642220000053
Figure BDA0002297642220000053

2-3)计算历史数据中每个AGC考核时段内的负荷标准差δh,l：2-3) Calculate the load standard deviation δ h,l in each AGC assessment period in the historical data:

Figure BDA0002297642220000054
Figure BDA0002297642220000054

式中，Z表示以1分钟为采样周期时每个AGC考核时段内离散时间点个数，z为第z个离散时间采样点，

Figure BDA0002297642220000055
为时段h中第z分钟的负荷功率；
Figure BDA0002297642220000056
为时段h内的负荷均值，计算方式如下：In the formula, Z represents the number of discrete time points in each AGC assessment period with 1 minute as the sampling period, z is the zth discrete time sampling point,
Figure BDA0002297642220000055
is the load power of the zth minute in the period h;
Figure BDA0002297642220000056
is the average load in the period h, and is calculated as follows:

Figure BDA0002297642220000057
Figure BDA0002297642220000057

计算历史数据中每个AGC考核时段内光伏发电功率的标准差：Calculate the standard deviation of photovoltaic power generation in each AGC assessment period in the historical data:

Figure BDA0002297642220000058
Figure BDA0002297642220000058

其中，

Figure BDA0002297642220000059
为时段h中第z分钟的光伏发电功率，
Figure BDA00022976422200000510
为时段h内光伏发电功率的均值，计算方式为：in,
Figure BDA0002297642220000059
is the photovoltaic power generation at the zth minute in the period h,
Figure BDA00022976422200000510
is the average value of photovoltaic power generation in the period h, and the calculation method is:

Figure BDA00022976422200000511
Figure BDA00022976422200000511

计算历史数据中每个AGC考核时段内风电发电功率的均值：Calculate the average value of wind power generation power in each AGC assessment period in the historical data:

Figure BDA00022976422200000512
Figure BDA00022976422200000512

其中，

Figure BDA00022976422200000513
为时段h中第z分钟的风电发电功率；in,
Figure BDA00022976422200000513
is the wind power generation power at the zth minute in the period h;

2-4)将历史数据中每个AGC考核时段的A2,

Figure BDA0002297642220000061
δh,l,δh,pv和
Figure BDA0002297642220000062
组成该时段对应的样本，对历史数据中的所有样本根据下式进行筛选：2-4) A 2 of each AGC assessment period in the historical data,
Figure BDA0002297642220000061
δ h,l , δ h,pv and
Figure BDA0002297642220000062
The samples corresponding to this period are formed, and all samples in the historical data are filtered according to the following formula:

Figure BDA0002297642220000063
Figure BDA0002297642220000063

具体筛选方法为：对历史数据中的每个样本i，统计历史数据中除i外换算后调频容量

Figure BDA0002297642220000064
大于
Figure BDA0002297642220000065
且调频表现绝对值
Figure BDA0002297642220000066
大于
Figure BDA0002297642220000067
的样本所占比例；若该比例高于设定的样本判定阈值l，则判定样本i是一个正常样本并予以保留，否则予以删除；对所有样本处理完毕后，得到所有正常样本组成的集合；The specific screening method is: for each sample i in the historical data, the frequency modulation capacity after conversion except for i in the statistical historical data
Figure BDA0002297642220000064
more than the
Figure BDA0002297642220000065
And the absolute value of FM performance
Figure BDA0002297642220000066
more than the
Figure BDA0002297642220000067
If the proportion is higher than the set sample judgment threshold l, then the sample i is judged to be a normal sample and kept, otherwise it will be deleted; after all samples are processed, a set of all normal samples is obtained;

2-5)建立基于极限学习机ELM的负荷标准差的区间预测模型：该模型的输入

Figure BDA0002297642220000068
为预测时段i之前M日中每日与i相同AGC考核时段及该时段前后各2个相邻AGC考核时段内的负荷标准差组成的向量；输入层至隐藏层的权重矩阵kl和偏置向量bl为随机生成的取值在0～1之间的数，隐藏层中每个单元均含有一个激活函数σ，隐藏层至输出层的权重
Figure BDA0002297642220000069
经过优化生成，模型的输出是时段i该模型输入至输出所对应的映射
Figure BDA00022976422200000610
为：2-5) Establish an interval prediction model of load standard deviation based on extreme learning machine ELM: the input of the model
Figure BDA0002297642220000068
is the vector composed of the load standard deviations in the same AGC assessment period as i and the two adjacent AGC assessment periods before and after the forecast period i in M days before the forecast period i; the weight matrix k l and the bias from the input layer to the hidden layer The vector b l is a randomly generated number between 0 and 1. Each unit in the hidden layer contains an activation function σ, and the weight from the hidden layer to the output layer
Figure BDA0002297642220000069
After optimization and generation, the output of the model is the mapping corresponding to the input to the output of the model in period i
Figure BDA00022976422200000610
for:

Figure BDA00022976422200000611
Figure BDA00022976422200000611

从步骤2-4)得到的正常样本组成的集合中随机抽取75％的样本组成该预测模型的训练集Il，形成极限学习机隐藏层至输出层的权重优化模型如下：From the set of normal samples obtained in step 2-4), 75% of the samples are randomly selected to form the training set I l of the prediction model, and the weight optimization model from the hidden layer to the output layer of the extreme learning machine is formed as follows:

Figure BDA00022976422200000612
Figure BDA00022976422200000612

Figure BDA00022976422200000613
Figure BDA00022976422200000613

Figure BDA00022976422200000614
Figure BDA00022976422200000614

Figure BDA00022976422200000615
Figure BDA00022976422200000615

Figure BDA00022976422200000616
Figure BDA00022976422200000616

其中，α代表分位数，

Figure BDA00022976422200000617
为训练集中第i个AGC考核时段内的负荷标准差，
Figure BDA00022976422200000618
和
Figure BDA00022976422200000619
是辅助变量；where α represents the quantile,
Figure BDA00022976422200000617
is the standard deviation of the load in the ith AGC assessment period in the training set,
Figure BDA00022976422200000618
and
Figure BDA00022976422200000619
is an auxiliary variable;

求解该权重优化模型对权重

Figure BDA00022976422200000620
进行优化，得到训练完毕的负荷标准差的区间预测模型；Solve the weights to optimize the model for the weights
Figure BDA00022976422200000620
Perform optimization to obtain the interval prediction model of the trained load standard deviation;

2-6)建立基于极限学习机的光伏发电功率标准差的区间预测模型：该模型的输入

Figure BDA00022976422200000621
为预测时段i之前M日中每日与i相同时段及该时段前后各2个相邻AGC考核时段内的光伏发电功率标准差组成的向量；输入层至隐藏层的权重矩阵kpv和偏置向量bpv为随机生成的取值在0～1之间的数，隐藏层中每个单元均含有一个激活函数σ，隐藏层至输出层的权重
Figure BDA0002297642220000071
经过优化生成，模型的输出是时段i光伏发电功率标准差的分位数
Figure BDA0002297642220000072
和
Figure BDA0002297642220000073
的预测值；；该模型输入至输出所对应的映射
Figure BDA0002297642220000074
为：2-6) Establish an interval prediction model of standard deviation of photovoltaic power generation power based on extreme learning machine: the input of the model
Figure BDA00022976422200000621
is the vector composed of the standard deviation of photovoltaic power generation power in the same time period as i and the two adjacent AGC assessment periods before and after the prediction period i in the M days before the forecast period i; the weight matrix k pv and the bias from the input layer to the hidden layer The vector b pv is a randomly generated number between 0 and 1. Each unit in the hidden layer contains an activation function σ, and the weight from the hidden layer to the output layer
Figure BDA0002297642220000071
After optimization and generation, the output of the model is the quantile of the standard deviation of photovoltaic power generation in period i.
Figure BDA0002297642220000072
and
Figure BDA0002297642220000073
The predicted value of ;; the mapping corresponding to the input to the output of the model
Figure BDA0002297642220000074
for:

Figure BDA0002297642220000075
Figure BDA0002297642220000075

从步骤2-4)得到的正常样本组成的集合中随机抽取75％的样本组成该预测模型的训练集Ipv，形成极限学习机隐藏层至输出层的权重优化模型如下：75% of the samples are randomly selected from the set of normal samples obtained in step 2-4) to form the training set I pv of the prediction model, and the weight optimization model from the hidden layer to the output layer of the extreme learning machine is formed as follows:

Figure BDA0002297642220000076
Figure BDA0002297642220000076

Figure BDA0002297642220000077
Figure BDA0002297642220000077

Figure BDA0002297642220000078
Figure BDA0002297642220000078

Figure BDA0002297642220000079
Figure BDA0002297642220000079

Figure BDA00022976422200000710
Figure BDA00022976422200000710

其中，α代表分位数，

Figure BDA00022976422200000711
为训练集中第i个AGC考核时段内的光伏发电功率标准差，
Figure BDA00022976422200000712
和
Figure BDA00022976422200000713
是辅助变量；where α represents the quantile,
Figure BDA00022976422200000711
is the standard deviation of photovoltaic power generation during the i-th AGC assessment period in the training set,
Figure BDA00022976422200000712
and
Figure BDA00022976422200000713
is an auxiliary variable;

求解该优化模型对权重

Figure BDA00022976422200000714
进行优化，得到训练完毕的光伏发电功率标准差的的区间预测模型；Solve the optimization model for the weights
Figure BDA00022976422200000714
Carry out optimization to obtain the interval prediction model of the standard deviation of photovoltaic power generation power after training;

2-7)建立基于极限学习机的风电发电量的区间预测模型：该模型的输入

Figure BDA00022976422200000715
为预测时段i之前M日中每日与i相同AGC考核时段及该相同时段前后各2个相邻的AGC考核时段的风电发电量，输入层至隐藏层的权重矩阵kw和偏置向量bw为随机生成的取值在0～1之间的数，隐藏层中每个单元均含有一个激活函数σ，隐藏层至输出层的权重
Figure BDA00022976422200000716
经过优化生成，模型的输出是时段i风电发电功率的分位数
Figure BDA00022976422200000717
和
Figure BDA00022976422200000718
的预测值；该模型输入至输出所对应的映射
Figure BDA00022976422200000719
为：2-7) Establish an interval prediction model of wind power generation based on extreme learning machine: the input of the model
Figure BDA00022976422200000715
In the M days before the forecast period i, the daily wind power generation in the same AGC assessment period as i and the two adjacent AGC assessment periods before and after the same period, the weight matrix k w and the bias vector b from the input layer to the hidden layer w is a randomly generated number between 0 and 1, each unit in the hidden layer contains an activation function σ, the weight from the hidden layer to the output layer
Figure BDA00022976422200000716
After optimization and generation, the output of the model is the quantile of wind power generation in period i
Figure BDA00022976422200000717
and
Figure BDA00022976422200000718
the predicted value of ; the mapping from the input to the output of the model
Figure BDA00022976422200000719
for:

Figure BDA00022976422200000720
Figure BDA00022976422200000720

从步骤2-4)得到的正常样本组成的集合中随机抽取75％的样本作为该预测模型的训练集Iw，来形成极限学习机隐藏层至输出层的权重优化模型如下：75% of the samples are randomly selected from the set of normal samples obtained in step 2-4) as the training set Iw of the prediction model to form the weight optimization model from the hidden layer to the output layer of the extreme learning machine as follows:

Figure BDA0002297642220000081
Figure BDA0002297642220000081

Figure BDA0002297642220000082
Figure BDA0002297642220000082

Figure BDA0002297642220000083
Figure BDA0002297642220000083

Figure BDA0002297642220000084
Figure BDA0002297642220000084

Figure BDA0002297642220000085
Figure BDA0002297642220000085

其中，α代表分位数，

Figure BDA0002297642220000086
为训练集中第i个AGC考核时段内的风电发电功率均值，
Figure BDA0002297642220000087
和
Figure BDA0002297642220000088
是辅助变量；where α represents the quantile,
Figure BDA0002297642220000086
is the mean value of wind power generation during the i-th AGC assessment period in the training set,
Figure BDA0002297642220000087
and
Figure BDA0002297642220000088
is an auxiliary variable;

求解该优化模型对权重

Figure BDA0002297642220000089
进行优化，得到训练完毕的风电发电量的区间预测模型；Solve the optimization model for the weights
Figure BDA0002297642220000089
Carry out optimization to obtain the interval prediction model of wind power generation after training;

2-8)选取未来任一AGC考核时段记为时段F，将F所在日之前M日每日与F相同的AGC考核时段及该相同时段前后各2个AGC考核时段的负荷标准差输入经过步骤2-5)训练完毕的模型

Figure BDA00022976422200000810
得到F时段负荷标准差的区间的预测值
Figure BDA00022976422200000811
2-8) Select any AGC assessment period in the future to be recorded as period F, and input the AGC assessment period that is the same as F every day M days before the day where F is located, and the load standard deviation of the two AGC assessment periods before and after the same period. 2-5) The trained model
Figure BDA00022976422200000810
Obtain the predicted value of the interval of the load standard deviation in the F period
Figure BDA00022976422200000811

将F所在日之前M日每日与F相同的AGC考核时段及该相同时段前后各2个AGC考核时段的光伏发电功率标准差输入经过步骤2-6)训练完毕的模型

Figure BDA00022976422200000812
得到F时段光伏发电功率标准差的区间的预测值
Figure BDA00022976422200000813
Input the standard deviation of photovoltaic power generation power in the same AGC assessment period as F and the standard deviation of the photovoltaic power generation in the two AGC assessment periods before and after the same period on the M days before the date of F into the model that has been trained in step 2-6).
Figure BDA00022976422200000812
Obtain the predicted value of the interval of the standard deviation of photovoltaic power generation in the F period
Figure BDA00022976422200000813

将F所在日之前M日每日与F相同的AGC考核时段及该相同时段前后各2个AGC考核时段的风电发电功率输入经过步骤2-7)训练完毕的极限学习机模型

Figure BDA00022976422200000814
得到F时段风电发电功率标准差的区间的预测值
Figure BDA00022976422200000815
Input the wind power generation power of the same AGC assessment period as F and the wind power generation power of the two AGC assessment periods before and after the same period on the M days before the date of F, and enter the extreme learning machine model that has been trained in step 2-7).
Figure BDA00022976422200000814
Obtain the predicted value of the interval of the standard deviation of wind power generation in period F
Figure BDA00022976422200000815

2-9)设定F时段折算成传统调频资源后初始调频容量

Figure BDA00022976422200000816
2-9) Set the initial FM capacity after converting the F period into traditional FM resources
Figure BDA00022976422200000816

2-10)对步骤2-4)得到所有正常样本，根据下式计算折算后调频容量

Figure BDA00022976422200000817
属于区间
Figure BDA00022976422200000818
的样本中调频达标与不达标的概率比
Figure BDA00022976422200000819
其中ΔPr′为调频容量区间的范围量；2-10) For all normal samples obtained in step 2-4), calculate the converted frequency modulation capacity according to the following formula
Figure BDA00022976422200000817
belong to the interval
Figure BDA00022976422200000818
The probability ratio of FM meeting the standard and not meeting the standard in the sample of
Figure BDA00022976422200000819
where ΔP r ' is the range of the frequency modulation capacity interval;

Figure BDA00022976422200000820
Figure BDA00022976422200000820

2-11)判断

Figure BDA0002297642220000091
是否大于置信比γLimit或者
Figure BDA0002297642220000092
是否等于步骤2-4)得到的正常样本组成的集合中所有样本对应的折算后调频容量的最大值
Figure BDA0002297642220000093
如果两个条件中至少有一个满足，则
Figure BDA0002297642220000094
为F时段储能调频资源折算为传统调频资源后的调频容量需求，然后进入步骤3)；否则，令
Figure BDA0002297642220000095
增加20MW，然后重新返回步骤2-10)；2-11) Judgment
Figure BDA0002297642220000091
is greater than the confidence ratio γ Limit or
Figure BDA0002297642220000092
Is it equal to the maximum value of the converted FM capacity corresponding to all samples in the set of normal samples obtained in step 2-4)
Figure BDA0002297642220000093
If at least one of the two conditions is satisfied, then
Figure BDA0002297642220000094
Calculate the frequency regulation capacity demand after converting the energy storage frequency regulation resources into traditional frequency regulation resources in the F period, and then enter step 3); otherwise, let
Figure BDA0002297642220000095
Add 20MW, then go back to steps 2-10);

γLimit根据调频表现达标的置信度α计算，表达式如下：γ Limit is calculated according to the confidence α that the frequency modulation performance meets the standard, and the expression is as follows:

Figure BDA0002297642220000096
Figure BDA0002297642220000096

3)根据步骤1)得到的储能调频资源与传统调频资源的替代比，计算F时段将储能调频资源等效替代为传统调频资源后，使得替代后总调频资源容量达到步骤2-9)计算所得

Figure BDA0002297642220000097
的调频资源分配组合方案，即找出所有满足下式的解
Figure BDA0002297642220000098
其中
Figure BDA0002297642220000099
为F时段储能调频资源容量，
Figure BDA00022976422200000910
为F时段传统调频资源容量：3) According to the substitution ratio of energy storage frequency regulation resources and traditional frequency regulation resources obtained in step 1), calculate the equivalent replacement of energy storage frequency regulation resources with traditional frequency regulation resources in F period, so that the total frequency regulation resource capacity after replacement reaches step 2-9) Calculated
Figure BDA0002297642220000097
FM resource allocation combination scheme, that is, find all solutions that satisfy the following equation
Figure BDA0002297642220000098
in
Figure BDA0002297642220000099
is the energy storage frequency regulation resource capacity in the F period,
Figure BDA00022976422200000910
For the traditional FM resource capacity in the F period:

Figure BDA00022976422200000911
Figure BDA00022976422200000911

4)根据储能调频资源的成本Cs和传统调频资源的成本Cg，从步骤3)得到的所有组合方案中选取使得总成本

Figure BDA00022976422200000912
最低的最优组合方案，该最优组合方案即为步骤2)中选取的F时段的调频容量需求分配方案。4) According to the cost C s of energy storage frequency regulation resources and the cost C g of traditional frequency regulation resources, select from all the combination schemes obtained in step 3) such that the total cost
Figure BDA00022976422200000912
The lowest optimal combination scheme, the optimal combination scheme is the frequency modulation capacity demand allocation scheme in the F period selected in step 2).

本发明的特点及有益效果在于：The characteristics and beneficial effects of the present invention are:

该方法使用数据驱动和模型驱动融合的方式在传统调频资源和储能等新型调频资源之间分配调频容量，充分发挥储能调频资源优良的调节性能，并提高调频资源分配方案的合理性，提升电力系统自动发电控制的调节效果。This method uses data-driven and model-driven fusion methods to allocate frequency regulation capacity between traditional frequency regulation resources and new frequency regulation resources such as energy storage, give full play to the excellent regulation performance of energy storage frequency regulation resources, and improve the rationality of the frequency regulation resource allocation scheme. Adjustment effect of automatic generation control of power system.

具体实施方式Detailed ways

本发明提出一种考虑多种调频资源的调频容量需求分配方法，下面结合具体实施例对本发明进一步详细说明如下。The present invention proposes a method for allocating frequency modulation capacity requirements considering multiple frequency modulation resources. The present invention is further described in detail below with reference to specific embodiments.

本发明提出一种考虑多种调频资源的调频容量需求分配方法，包括以下步骤：The present invention proposes a method for allocating frequency modulation capacity requirements considering multiple frequency modulation resources, comprising the following steps:

1)以模型驱动的方式计算在不同储能调频资源占比下的储能调频资源与传统调频资源替代比。具体步骤如下：1) Calculate the replacement ratio of energy storage frequency regulation resources and traditional frequency regulation resources under different energy storage frequency regulation resource proportions in a model-driven manner. Specific steps are as follows:

1-1)将前一年中每月15号之前的一个工作日和之后的一个非工作日作为典型日，组成典型日集合(本实施例典型日集合中共包含2*12＝24个典型日)，并按AGC考核单位时间长度Γ(根据AGC考核准则可查，本实施例取为15min)将所有典型日划分为24*24*4＝2304个AGC典型考核时段。1-1) Take a working day before the 15th of each month in the previous year and a non-working day after that as a typical day to form a typical day set (the typical day set in this embodiment contains a total of 2*12=24 typical days ), and divides all typical days into 24*24*4=2304 typical AGC assessment periods according to the AGC assessment unit time length Γ (which can be checked according to the AGC assessment criteria, and is taken as 15min in this embodiment).

对于每一个AGC典型考核时段(以q表示其编号，q＝1,2…2304)，选取AGC控制区所记录的该时段内每一个离散时间点的负荷功率、风电发电功率和光伏发电功率数据，计算该时段内每一个离散时间点的净负荷：For each typical assessment period of AGC (representing its number with q, q=1, 2...2304), select the load power, wind power generation power and photovoltaic power generation power data at each discrete time point in the period recorded by the AGC control area , calculate the net load at each discrete time point in the period:

Figure BDA0002297642220000101
Figure BDA0002297642220000101

其中，

Figure BDA0002297642220000102
分别为第q个AGC典型考核时段中第k个离散时间点净负荷和负荷功率，
Figure BDA0002297642220000103
分别为该时段第k个离散时间点风电发电功率和光伏发电功率。上述离散时间点数据的采样周期与AGC的指令周期τ相同，每个AGC考核时段包含K＝Γ/τ个AGC指令周期，τ可根据AGC控制区确定，K为每个时段内的离散时间点的采样总数。计算每个离散时间点的净负荷波动
Figure BDA0002297642220000104
表达式如下：in,
Figure BDA0002297642220000102
are the net load and load power at the kth discrete time point in the qth AGC typical assessment period, respectively,
Figure BDA0002297642220000103
are the wind power generation power and photovoltaic power generation power at the kth discrete time point in this period, respectively. The sampling period of the above discrete time point data is the same as the instruction period τ of the AGC. Each AGC assessment period includes K=Γ/τ AGC instruction periods. τ can be determined according to the AGC control area, and K is the discrete time point in each period. the total number of samples. Calculate net load fluctuations at each discrete point in time
Figure BDA0002297642220000104
The expression is as follows:

Figure BDA0002297642220000105
Figure BDA0002297642220000105

其中，

Figure BDA0002297642220000106
为第q个AGC典型考核时段的净负荷均值，计算表达式为：in,
Figure BDA0002297642220000106
is the average net load of the qth AGC typical assessment period, and the calculation expression is:

Figure BDA0002297642220000107
Figure BDA0002297642220000107

每个AGC典型考核时段内所有离散时间点的净负荷波动

Figure BDA0002297642220000108
构成的序列组成该时段的净负荷波动
Figure BDA0002297642220000109
Net load fluctuations at all discrete time points within a typical assessment period of each AGC
Figure BDA0002297642220000108
The constituted series makes up the net load fluctuations for the period
Figure BDA0002297642220000109

1-2)建立优化模型求解AGC典型考核时段净负荷波动为

Figure BDA00022976422200001010
(由
Figure BDA00022976422200001011
构成的序列)、储能调频资源容量占系统总调频容量比例为η(以下简称为储能调频资源占比)时，该AGC控制区的调频容量需求
Figure BDA00022976422200001012
所述优化模型以
Figure BDA00022976422200001013
和η为输入变量，以最小化该AGC控制区的调频容量需求
Figure BDA0002297642220000111
为优化目标，其他符号代表系统常量或中间状态量，可由输入量和系统常量计算得到。表达式如下：1-2) Establish an optimization model to solve the net load fluctuation of the typical AGC assessment period as
Figure BDA00022976422200001010
(Depend on
Figure BDA00022976422200001011
When the ratio of the energy storage frequency regulation resource capacity to the total system frequency regulation capacity is η (hereinafter referred to as the energy storage frequency regulation resource ratio), the frequency regulation capacity demand of the AGC control area
Figure BDA00022976422200001012
The optimized model is
Figure BDA00022976422200001013
and η are input variables to minimize the FM capacity requirement for this AGC control area
Figure BDA0002297642220000111
For optimization goals, other symbols represent system constants or intermediate state quantities, which can be calculated from input quantities and system constants. The expression is as follows:

Figure BDA0002297642220000112
Figure BDA0002297642220000112

s.t.s.t.

Figure BDA0002297642220000113
Figure BDA0002297642220000113

ACE[k-1]＝BΔf[k-1]ACE[k-1]=BΔf[k-1]

Figure BDA0002297642220000114
Figure BDA0002297642220000114

Figure BDA0002297642220000115
Figure BDA0002297642220000115

Figure BDA0002297642220000116
Figure BDA0002297642220000116

Figure BDA0002297642220000117
Figure BDA0002297642220000117

Figure BDA0002297642220000118
Figure BDA0002297642220000118

Figure BDA0002297642220000119
Figure BDA0002297642220000119

Figure BDA00022976422200001110
Figure BDA00022976422200001110

πg[k]＝min{max{π[k],ΔPg m[k]},ΔPg M[k]}π g [k]=min{max{π[k],ΔP g m [k]},ΔP g M [k]}

πs[k]＝π[k]-πg[k]π s [k]=π[k]-π g [k]

ΔPg[k]＝πg[k]ΔP g [k] = π g [k]

Ps[k]＝min{max{πs[k],Ps m[k]},Ps M[k]}P s [k]=min{max{π s [k],P s m [k]},P s M [k]}

e[k]＝e[k-1]-Ps[k]δe[k]=e[k-1]-P s [k]δ

Figure BDA00022976422200001111
Figure BDA00022976422200001111

Figure BDA00022976422200001112
Figure BDA00022976422200001112

Figure BDA00022976422200001113
Figure BDA00022976422200001113

Figure BDA00022976422200001114
Figure BDA00022976422200001114

式中，Δf是系统频率偏差，

Figure BDA0002297642220000121
是ACE考核指标中A2指标所规定的限值，B表示系统频率响应常数，IACE表示ACE的PI滤波积分项，
Figure BDA0002297642220000122
为传统调频资源的爬坡速率上限，λ为调频资源到达最大调频容量允许的最长时间，
Figure BDA0002297642220000123
为储能调频资源容量，
Figure BDA0002297642220000124
为第k个离散时间点传统调频资源向上最大可调功率(正值)，
Figure BDA0002297642220000125
为第k个离散时间点传统调频资源向下最大可调功率(负值)，
Figure BDA0002297642220000126
为第k个离散时间点储能调频资源向上最大可调功率(正值)，
Figure BDA0002297642220000127
为第k个离散时间点储能调频资源向下最大可调功率(负值)，eM和em分别为储能资源电量的最大值与最小值，e[k]为第k个离散时间点储能资源电量，πg[k]和πs[k]分别为第k个离散时间点分配给传统机组与储能资源的调节功率，ΔPg[k]和Ps[k]分别为传统调频资源与储能资源实际承担的调节功率，rg[k]和rnl[k]分别为第k个离散时间点传统调频资源和净负荷的变化率，a[k]为第k个离散时间点计算频率偏差所需的常数，D为系统负荷阻尼常数，H为系统惯性常数。where Δf is the system frequency deviation,
Figure BDA0002297642220000121
is the limit specified by the A 2 index in the ACE assessment index, B represents the system frequency response constant, IACE represents the PI filter integral term of ACE,
Figure BDA0002297642220000122
is the upper limit of the ramp rate of traditional FM resources, λ is the maximum time allowed for FM resources to reach the maximum FM capacity,
Figure BDA0002297642220000123
Frequency regulation resource capacity for energy storage,
Figure BDA0002297642220000124
is the maximum upward adjustable power (positive value) of traditional FM resources at the kth discrete time point,
Figure BDA0002297642220000125
is the maximum downward adjustable power (negative value) of traditional FM resources at the kth discrete time point,
Figure BDA0002297642220000126
is the maximum upward adjustable power (positive value) of the energy storage frequency modulation resource at the kth discrete time point,
Figure BDA0002297642220000127
is the maximum downward adjustable power (negative value) of the energy storage frequency modulation resource at the kth discrete time point, e M and em are the maximum and minimum values of the energy storage resources, respectively, and e[ k ] is the kth discrete time energy of point energy storage resources, π g [k] and π s [k] are the regulated power allocated to traditional units and energy storage resources at the kth discrete time point, respectively, ΔP g [k] and P s [k] are respectively The regulation power actually undertaken by traditional frequency regulation resources and energy storage resources, r g [k] and rnl [k] are the rate of change of traditional frequency regulation resources and net load at the kth discrete time point, respectively, and a[k] is the kth The constant required to calculate the frequency deviation at discrete time points, D is the system load damping constant, and H is the system inertia constant.

1-3)选取任一AGC典型考核时段q，令储能调频资源占比η的取值以1％作为步长从0％取到100％，根据该时段对应的净负荷波动

Figure BDA0002297642220000128
利用步骤1-2)建立的模型，计算储能调频资源占比为η时系统在该时段调频容量需求
Figure BDA0002297642220000129
1-3) Select any AGC typical assessment period q, let the value of energy storage frequency regulation resource ratio η take 1% as a step from 0% to 100%, according to the net load fluctuation corresponding to this period
Figure BDA0002297642220000128
Using the model established in step 1-2), calculate the frequency regulation capacity demand of the system in this period when the energy storage frequency regulation resource ratio is η
Figure BDA0002297642220000129

1-4)重复步骤1-3)，对步骤1-1)的每一个AGC典型考核时段，计算储能调频资源占比为η时系统在每个时段调频容量需求得到对应的

Figure BDA00022976422200001210
1-4) Repeat step 1-3), for each AGC typical assessment period in step 1-1), calculate the frequency modulation resource ratio of energy storage to be η when the frequency modulation capacity requirement of the system in each period is obtained correspondingly.
Figure BDA00022976422200001210

根据下式计算在不同η下储能调频资源相对于传统调频资源的替代比：The substitution ratio of energy storage frequency regulation resources to traditional frequency regulation resources under different η is calculated according to the following formula:

Figure BDA00022976422200001211
Figure BDA00022976422200001211

其中，Q为AGC典型考核时段的总数。Among them, Q is the total number of typical AGC assessment periods.

2)选取未来任一AGC考核时段记为F时段，以数据驱动的方式根据历史数据计算该时段储能调频资源折算为传统调频资源后的调频容量需求；具体步骤如下：2) Select any future AGC assessment period and record it as the F period, and calculate the frequency regulation capacity demand after the energy storage frequency regulation resources are converted into traditional frequency regulation resources in a data-driven manner based on historical data; the specific steps are as follows:

2-1)收集自动发电控制AGC控制区内过去N年(N的取值范围为2～3)的历史数据，所述历史数据包括：每分钟负荷功率、每分钟风电发电功率、每分钟光伏发电功率，每个AGC考核时段的A2指标、每个AGC考核时段的传统调频资源容量和储能调频资源容量；2-1) Collect historical data of the past N years (the value range of N is 2 to 3) in the AGC control area of automatic power generation control, the historical data includes: load power per minute, wind power generation power per minute, photovoltaic power per minute Power generation, A2 index of each AGC assessment period, traditional frequency regulation resource capacity and energy storage frequency regulation resource capacity of each AGC assessment period ;

2-2)根据步骤2-1)中每个AGC考核时段(以h进行编号，h为历史数据的时段编号；)的传统调频资源容量

Figure BDA0002297642220000131
和储能调频资源容量
Figure BDA0002297642220000132
得到历史数据中每个AGC考核时段对应的储能调频资源占比ηh值：2-2) According to the traditional frequency modulation resource capacity of each AGC assessment period (numbered with h, h is the period number of the historical data;) in step 2-1)
Figure BDA0002297642220000131
and energy storage frequency regulation resource capacity
Figure BDA0002297642220000132
Obtain the η h value of energy storage frequency regulation resources corresponding to each AGC assessment period in the historical data:

Figure BDA0002297642220000133
Figure BDA0002297642220000133

根据步骤1)的结果，获取在该ηh值下对应的储能调频资源相对于传统调频资源的替代比，将历史数据中每个AGC考核时段的储能调频资源折算为传统调频资源，得到折算后历史数据中每个AGC考核时段的总调频容量

Figure BDA0002297642220000134
每个AGC考核时段的折算根据下式进行：According to the result of step 1), obtain the substitution ratio of the energy storage frequency regulation resources corresponding to the traditional frequency regulation resources under the η h value, and convert the energy storage frequency regulation resources in each AGC assessment period in the historical data into the traditional frequency regulation resources to obtain The total FM capacity of each AGC assessment period in the converted historical data
Figure BDA0002297642220000134
The conversion of each AGC assessment period is carried out according to the following formula:

Figure BDA0002297642220000135
Figure BDA0002297642220000135

2-3)计算历史数据中每个AGC考核时段内的负荷标准差δh,l：2-3) Calculate the load standard deviation δ h,l in each AGC assessment period in the historical data:

Figure BDA0002297642220000136
Figure BDA0002297642220000136

式中，Z表示以1分钟为采样周期时每个AGC考核时段内离散时间点个数，z为第z个离散时间采样点，

Figure BDA0002297642220000137
为时段h中第z分钟的负荷功率；
Figure BDA0002297642220000138
为时段h内的负荷均值，计算方式如下：In the formula, Z represents the number of discrete time points in each AGC assessment period with 1 minute as the sampling period, z is the zth discrete time sampling point,
Figure BDA0002297642220000137
is the load power of the zth minute in the period h;
Figure BDA0002297642220000138
is the average load in the period h, and is calculated as follows:

Figure BDA0002297642220000139
Figure BDA0002297642220000139

计算历史数据中每个AGC考核时段内光伏发电功率的标准差：Calculate the standard deviation of photovoltaic power generation in each AGC assessment period in the historical data:

Figure BDA00022976422200001310
Figure BDA00022976422200001310

其中，

Figure BDA00022976422200001311
为时段h中第z分钟的光伏发电功率，
Figure BDA00022976422200001312
为历史数据中AGC考核时段内光伏发电功率的均值，计算方式为：in,
Figure BDA00022976422200001311
is the photovoltaic power generation at the zth minute in the period h,
Figure BDA00022976422200001312
is the average value of photovoltaic power generation during the AGC assessment period in the historical data, and the calculation method is as follows:

Figure BDA0002297642220000141
Figure BDA0002297642220000141

计算历史数据中每个AGC考核时段内风电发电功率的均值：Calculate the average value of wind power generation power in each AGC assessment period in the historical data:

Figure BDA0002297642220000142
Figure BDA0002297642220000142

其中，

Figure BDA0002297642220000143
为时段h中第z分钟的风电发电功率；in,
Figure BDA0002297642220000143
is the wind power generation power at the zth minute in the period h;

2-4)将历史数据中每个AGC考核时段的A2,

Figure BDA0002297642220000144
δh,l,δh,pv和
Figure BDA0002297642220000145
组成该时段对应的样本，对历史数据中的所有样本根据下式进行筛选：2-4) A 2 of each AGC assessment period in the historical data,
Figure BDA0002297642220000144
δ h,l , δ h,pv and
Figure BDA0002297642220000145
The samples corresponding to this period are formed, and all samples in the historical data are filtered according to the following formula:

Figure BDA0002297642220000146
Figure BDA0002297642220000146

具体实施方法为：对历史数据中的每个样本i，统计历史数据中除i外换算后调频容量

Figure BDA0002297642220000147
大于
Figure BDA0002297642220000148
且调频表现绝对值
Figure BDA0002297642220000149
大于
Figure BDA00022976422200001410
的样本所占比例；若该比例高于设定的样本判定阈值l(l取为0.01％～0.05％)，则判定样本i是一个正常样本并予以保留，否则予以删除；对所有样本处理完毕后，得到所有正常样本组成的集合。The specific implementation method is: for each sample i in the historical data, the frequency modulation capacity after conversion except for i in the statistical historical data
Figure BDA0002297642220000147
more than the
Figure BDA0002297642220000148
And the absolute value of FM performance
Figure BDA0002297642220000149
more than the
Figure BDA00022976422200001410
If the proportion is higher than the set sample judgment threshold l (l is taken as 0.01% to 0.05%), the sample i is judged to be a normal sample and kept, otherwise it will be deleted; all samples are processed. After that, a set of all normal samples is obtained.

2-5)建立基于极限学习机(ELM)的负荷标准差的区间预测模型：该模型的输入

Figure BDA00022976422200001411
为预测时段i之前M(M取值为5～7)日中每日与i相同的AGC考核时段及该时段前后各2个相邻AGC考核时段内的负荷标准差，输入层至隐藏层的权重矩阵kl和偏置向量bl为随机生成的取值在0～1之间的数，隐藏层中每个单元均含有一个激活函数σ(采用signoid函数)，隐藏层至输出层的权重
Figure BDA00022976422200001412
经过优化生成，模型的输出是时段i负荷标准差的分位数
Figure BDA00022976422200001413
和
Figure BDA00022976422200001414
的预测值(分别对应上下分位
Figure BDA00022976422200001415
和α)；该模型输入至输出所对应的映射
Figure BDA00022976422200001416
为：2-5) Establish an interval prediction model of load standard deviation based on extreme learning machine (ELM): the input of the model
Figure BDA00022976422200001411
is the standard deviation of the load in the AGC assessment period that is the same as that of i every day before the forecast period i (M is 5 to 7) and the two adjacent AGC assessment periods before and after this period, the input layer to the hidden layer. The weight matrix k l and the bias vector b l are randomly generated numbers between 0 and 1. Each unit in the hidden layer contains an activation function σ (using the signoid function), and the weight from the hidden layer to the output layer
Figure BDA00022976422200001412
After optimization, the output of the model is the quantile of the standard deviation of the load in period i
Figure BDA00022976422200001413
and
Figure BDA00022976422200001414
The predicted value of (corresponding to the upper and lower quantiles, respectively
Figure BDA00022976422200001415
and α ); the mapping corresponding to the input to the output of the model
Figure BDA00022976422200001416
for:

Figure BDA00022976422200001417
Figure BDA00022976422200001417

从步骤2-4)得到的正常样本组成的集合中随机抽取75％的样本组成该预测模型的训练集Il，形成极限学习机隐藏层至输出层的权重优化模型如下：From the set of normal samples obtained in step 2-4), 75% of the samples are randomly selected to form the training set I l of the prediction model, and the weight optimization model from the hidden layer to the output layer of the extreme learning machine is formed as follows:

Figure BDA00022976422200001418
Figure BDA00022976422200001418

Figure BDA00022976422200001419
Figure BDA00022976422200001419

Figure BDA00022976422200001420
Figure BDA00022976422200001420

Figure BDA00022976422200001421
Figure BDA00022976422200001421

Figure BDA00022976422200001422
Figure BDA00022976422200001422

其中，α代表分位数，

Figure BDA0002297642220000151
为训练集中第i个AGC考核时段内的负荷标准差，
Figure BDA0002297642220000152
和
Figure BDA0002297642220000153
是辅助变量；where α represents the quantile,
Figure BDA0002297642220000151
is the standard deviation of the load in the ith AGC assessment period in the training set,
Figure BDA0002297642220000152
and
Figure BDA0002297642220000153
is an auxiliary variable;

求解该优化模型对权重

Figure BDA0002297642220000154
进行优化，得到训练完毕的负荷标准差的区间预测模型。Solve the optimization model for the weights
Figure BDA0002297642220000154
Perform optimization to obtain the interval prediction model of the trained load standard deviation.

2-6)建立基于极限学习机的光伏发电功率标准差的区间预测模型：该模型的输入

Figure BDA0002297642220000155
为预测时段i之前M日中与i相同时段及该时段前后各2个相邻AGC考核时段内的光伏发电功率标准差组成的向量；输入层至隐藏层的权重矩阵kpv和偏置向量bpv为随机生成的取值在0～1之间的数，隐藏层中每个单元均含有一个激活函数σ(采用signoid函数)，隐藏层至输出层的权重
Figure BDA0002297642220000156
经过优化生成，模型的输出是时段i光伏发电功率标准差的分位数
Figure BDA0002297642220000157
和
Figure BDA0002297642220000158
的预测值(分别对应上下分位
Figure BDA0002297642220000159
和α)。该模型输入至输出所对应的映射
Figure BDA00022976422200001510
为：2-6) Establish an interval prediction model of standard deviation of photovoltaic power generation power based on extreme learning machine: the input of the model
Figure BDA0002297642220000155
is the vector composed of the standard deviation of photovoltaic power generation in M days before the prediction period i and in the same period as i and the two adjacent AGC assessment periods before and after the period; the weight matrix k pv and the bias vector b from the input layer to the hidden layer pv is a randomly generated number between 0 and 1. Each unit in the hidden layer contains an activation function σ (using the signoid function), and the weight from the hidden layer to the output layer
Figure BDA0002297642220000156
After optimization and generation, the output of the model is the quantile of the standard deviation of photovoltaic power generation in period i.
Figure BDA0002297642220000157
and
Figure BDA0002297642220000158
The predicted value of (corresponding to the upper and lower quantiles, respectively
Figure BDA0002297642220000159
and α ). The mapping from the input to the output of the model
Figure BDA00022976422200001510
for:

Figure BDA00022976422200001511
Figure BDA00022976422200001511

从步骤2-4)得到的正常样本组成的集合中随机抽取75％的样本组成该预测模型的训练集Ipv，形成极限学习机隐藏层至输出层的权重优化模型如下：75% of the samples are randomly selected from the set of normal samples obtained in step 2-4) to form the training set I pv of the prediction model, and the weight optimization model from the hidden layer to the output layer of the extreme learning machine is formed as follows:

Figure BDA00022976422200001512
Figure BDA00022976422200001512

Figure BDA00022976422200001513
Figure BDA00022976422200001513

Figure BDA00022976422200001514
Figure BDA00022976422200001514

Figure BDA00022976422200001515
Figure BDA00022976422200001515

Figure BDA00022976422200001516
Figure BDA00022976422200001516

其中，α代表分位数，

Figure BDA00022976422200001517
为训练集中第i个AGC考核时段内的光伏发电功率标准差，
Figure BDA00022976422200001518
和
Figure BDA00022976422200001519
是辅助变量；where α represents the quantile,
Figure BDA00022976422200001517
is the standard deviation of photovoltaic power generation during the i-th AGC assessment period in the training set,
Figure BDA00022976422200001518
and
Figure BDA00022976422200001519
is an auxiliary variable;

求解该优化模型对权重

Figure BDA00022976422200001520
进行优化，得到训练完毕的光伏发电功率标准差的的区间预测模型。Solve the optimization model for the weights
Figure BDA00022976422200001520
Carry out optimization to obtain the interval prediction model of the standard deviation of photovoltaic power generation power after training.

2-7)建立基于极限学习机的风电发电量的区间预测模型：该模型的输入

Figure BDA00022976422200001521
为预测时段i之前M日中每日与i相同AGC考核时段及该相同时段前后各2个相邻的AGC考核时段的风电发电量，输入层至隐藏层的权重矩阵kw和偏置向量bw为随机生成的取值在0～1之间的数，隐藏层中每个单元均含有一个激活函数σ(采用signoid函数)，隐藏层至输出层的权重
Figure BDA0002297642220000161
经过优化生成，模型的输出是时段i风电发电功率的分位数
Figure BDA0002297642220000162
和
Figure BDA0002297642220000163
的预测值(分别对应上下分位
Figure BDA0002297642220000164
和α)；该模型输入至输出所对应的映射
Figure BDA0002297642220000165
为：2-7) Establish an interval prediction model of wind power generation based on extreme learning machine: the input of the model
Figure BDA00022976422200001521
In the M days before the forecast period i, the daily wind power generation in the same AGC assessment period as i and the two adjacent AGC assessment periods before and after the same period, the weight matrix k w and the bias vector b from the input layer to the hidden layer w is a randomly generated number between 0 and 1, each unit in the hidden layer contains an activation function σ (using the signoid function), and the weight from the hidden layer to the output layer
Figure BDA0002297642220000161
After optimization and generation, the output of the model is the quantile of wind power generation in period i
Figure BDA0002297642220000162
and
Figure BDA0002297642220000163
The predicted value of (corresponding to the upper and lower quantiles, respectively
Figure BDA0002297642220000164
and α ); the mapping corresponding to the input to the output of the model
Figure BDA0002297642220000165
for:

Figure BDA0002297642220000166
Figure BDA0002297642220000166

从步骤2-4)得到的正常样本组成的集合中随机抽取75％的样本作为该预测模型的训练集Iw，形成极限学习机隐藏层至输出层的权重优化模型如下：75% of the samples are randomly selected from the set of normal samples obtained in step 2-4) as the training set Iw of the prediction model, and the weight optimization model from the hidden layer to the output layer of the extreme learning machine is formed as follows:

Figure BDA0002297642220000167
Figure BDA0002297642220000167

Figure BDA0002297642220000168
Figure BDA0002297642220000168

Figure BDA0002297642220000169
Figure BDA0002297642220000169

Figure BDA00022976422200001610
Figure BDA00022976422200001610

Figure BDA00022976422200001611
Figure BDA00022976422200001611

其中，α代表分位数，

Figure BDA00022976422200001612
为训练集中第i个AGC考核时段内的风电发电功率均值，
Figure BDA00022976422200001613
和
Figure BDA00022976422200001614
是辅助变量；where α represents the quantile,
Figure BDA00022976422200001612
is the mean value of wind power generation during the i-th AGC assessment period in the training set,
Figure BDA00022976422200001613
and
Figure BDA00022976422200001614
is an auxiliary variable;

求解该优化模型对权重

Figure BDA00022976422200001615
进行优化，得到训练完毕的风电发电量的区间预测模型；Solve the optimization model for the weights
Figure BDA00022976422200001615
Carry out optimization to obtain the interval prediction model of wind power generation after training;

2-8)选取未来任一AGC考核时段记为时段F，将F所在日之前M日每日与F相同的AGC考核时段及该相同时段前后各2个AGC考核时段的负荷标准差输入经过步骤2-5)训练完毕的模型

Figure BDA00022976422200001616
得到F时段负荷标准差的区间的预测值
Figure BDA00022976422200001617
2-8) Select any AGC assessment period in the future to be recorded as period F, and input the AGC assessment period that is the same as F every day M days before the day where F is located, and the load standard deviation of the two AGC assessment periods before and after the same period. 2-5) The trained model
Figure BDA00022976422200001616
Obtain the predicted value of the interval of the load standard deviation in the F period
Figure BDA00022976422200001617

将F所在日之前M日每日与F相同的AGC考核时段及该相同时段前后各2个AGC考核时段的光伏发电功率标准差输入经过步骤2-6)训练完毕的模型

Figure BDA00022976422200001618
得到F时段光伏发电功率标准差的区间的预测值
Figure BDA00022976422200001619
Input the standard deviation of photovoltaic power generation power in the same AGC assessment period as F and the standard deviation of the photovoltaic power generation in the two AGC assessment periods before and after the same period on the M days before the date of F into the model that has been trained in step 2-6).
Figure BDA00022976422200001618
Obtain the predicted value of the interval of the standard deviation of photovoltaic power generation in the F period
Figure BDA00022976422200001619

将F所在日之前M日每日与F相同的AGC考核时段及该相同时段前后各2个AGC考核时段的风电发电功率输入经过步骤2-7)训练完毕的极限学习机模型

Figure BDA00022976422200001620
得到F时段风电发电功率标准差的区间的预测值
Figure BDA00022976422200001621
Input the wind power generation power of the same AGC assessment period as F and the wind power generation power of the two AGC assessment periods before and after the same period on the M days before the date of F, and enter the extreme learning machine model that has been trained in step 2-7).
Figure BDA00022976422200001620
Obtain the predicted value of the interval of the standard deviation of wind power generation in period F
Figure BDA00022976422200001621

2-9)设定F时段折算成传统调频资源后初始调频容量

Figure BDA00022976422200001622
(单位：MW)；2-9) Set the initial FM capacity after converting the F period into traditional FM resources
Figure BDA00022976422200001622
(unit: MW);

2-10)对步骤2-4)得到所有正常样本，根据下式计算折算后调频容量

Figure BDA00022976422200001623
属于区间
Figure BDA0002297642220000171
的样本中调频达标与不达标的概率比
Figure BDA0002297642220000172
其中
Figure BDA0002297642220000173
为调频容量区间的范围量，在本实施例中设定为20MW；2-10) For all normal samples obtained in step 2-4), calculate the converted frequency modulation capacity according to the following formula
Figure BDA00022976422200001623
belong to the interval
Figure BDA0002297642220000171
The probability ratio of FM meeting the standard and not meeting the standard in the sample of
Figure BDA0002297642220000172
in
Figure BDA0002297642220000173
is the range of the frequency modulation capacity interval, which is set to 20MW in this embodiment;

Figure BDA0002297642220000174
Figure BDA0002297642220000174

2-11)判断

Figure BDA0002297642220000175
是否大于置信比γLimit或者
Figure BDA0002297642220000176
是否等于步骤2-4)得到的正常样本组成的集合中所有样本对应的折算后调频容量中的最大值
Figure BDA0002297642220000177
如果两个条件中至少有一个满足，则
Figure BDA0002297642220000178
为F时段储能调频资源折算为传统调频资源后的调频容量需求，然后进入步骤3)；否则，令
Figure BDA0002297642220000179
增加20MW，然后重新返回步骤2-10)；2-11) Judgment
Figure BDA0002297642220000175
is greater than the confidence ratio γ Limit or
Figure BDA0002297642220000176
Is it equal to the maximum value in the converted frequency modulation capacity corresponding to all samples in the set of normal samples obtained in step 2-4)
Figure BDA0002297642220000177
If at least one of the two conditions is satisfied, then
Figure BDA0002297642220000178
Calculate the frequency regulation capacity demand after converting the energy storage frequency regulation resources into traditional frequency regulation resources in the F period, and then enter step 3); otherwise, let
Figure BDA0002297642220000179
Add 20MW, then go back to steps 2-10);

γLimit根据调频表现达标的置信度α计算，表达式如下：γ Limit is calculated according to the confidence α that the frequency modulation performance meets the standard, and the expression is as follows:

Figure BDA00022976422200001710
Figure BDA00022976422200001710

3)根据步骤1)得到的储能调频资源与传统调频资源的替代比，计算F时段将储能调频资源等效替代为传统调频资源后，使得替代后总调频资源容量达到步骤2-9)计算所得

Figure BDA00022976422200001711
的调频资源分配组合方案，即找出所有满足下式的解
Figure BDA00022976422200001712
其中
Figure BDA00022976422200001713
为F时段储能调频资源容量，
Figure BDA00022976422200001714
为F时段传统调频资源容量：3) According to the substitution ratio of energy storage frequency regulation resources and traditional frequency regulation resources obtained in step 1), calculate the equivalent replacement of energy storage frequency regulation resources with traditional frequency regulation resources in F period, so that the total frequency regulation resource capacity after replacement reaches step 2-9) Calculated
Figure BDA00022976422200001711
FM resource allocation combination scheme, that is, find all solutions that satisfy the following equation
Figure BDA00022976422200001712
in
Figure BDA00022976422200001713
is the energy storage frequency regulation resource capacity in the F period,
Figure BDA00022976422200001714
For the traditional FM resource capacity in the F period:

Figure BDA00022976422200001715
Figure BDA00022976422200001715

4)根据储能调频资源的成本Cs和传统调频资源的成本Cg，从步骤3)得到的所有组合方案中选取使得总成本

Figure BDA00022976422200001716
最低的最优组合方案，该最优组合方案即为步骤2)中选取的F时段的调频容量需求分配方案。4) According to the cost C s of energy storage frequency regulation resources and the cost C g of traditional frequency regulation resources, select from all the combination schemes obtained in step 3) such that the total cost
Figure BDA00022976422200001716
The lowest optimal combination scheme, the optimal combination scheme is the frequency modulation capacity demand allocation scheme in the F period selected in step 2).

Claims (1)

1. A method for demand allocation of frequency modulation capacity considering a plurality of frequency modulation resources is characterized by comprising the following steps:
1) calculating the substitution ratio of the energy storage frequency modulation resources to the traditional frequency modulation resources under different energy storage frequency modulation resource occupation ratios; the method comprises the following specific steps:
1-1) taking a working day before 15 and a non-working day after 15 every month in the previous year as typical days, and obtaining 24 typical days to form a typical day set; dividing all typical days into corresponding AGC typical examination time periods according to the AGC examination unit time length gamma;
for each AGC typical evaluation period, selecting the load power, wind power generation power and photovoltaic power generation power data of each discrete time point in the period, and calculating the net load of each discrete time point in the period:
Figure FDA0002297642210000011
wherein q represents the typical evaluation period number of the AGC,
Figure FDA0002297642210000012
respectively the net load and the load power at the kth discrete time point in the qth AGC typical examination period,
Figure FDA0002297642210000013
respectively in the time intervalk discrete time points are wind power generation power and photovoltaic power generation power; the sampling period of the discrete time point data is the same as the instruction period tau of the AGC, each AGC assessment period comprises K-gamma/tau AGC instruction periods, and K is the total number of samples of the discrete time point in each period;
calculating the net load fluctuation of each discrete time point
Figure FDA00022976422100000113
The expression is as follows:
Figure FDA0002297642210000014
wherein ,
Figure FDA0002297642210000016
for the net load mean value of the qth AGC typical examination period, the calculation expression is as follows:
Figure FDA0002297642210000015
net load fluctuation of all discrete time points in each AGC typical examination period
Figure FDA0002297642210000017
The constructed sequence constitutes the net load fluctuation of the time period
Figure FDA0002297642210000018
1-2) establishing an optimization model to solve the problem of AGC typical examination period net load fluctuation
Figure FDA0002297642210000019
When the ratio of the energy storage frequency modulation resource capacity to the total frequency modulation capacity of the system is η, the frequency modulation capacity requirement of the AGC control area
Figure FDA00022976422100000111
The optimization model is based on
Figure FDA00022976422100000110
And η as input variables to minimize the FM capacity requirement of the AGC control region
Figure FDA00022976422100000112
For optimization purposes, the expression is as follows:
Figure FDA0002297642210000021
s.t.
Figure FDA0002297642210000022
ACE[k-1]＝BΔf[k-1]
Figure FDA0002297642210000023
Figure FDA0002297642210000024
Figure FDA0002297642210000025
Figure FDA0002297642210000026
Figure FDA0002297642210000027
Figure FDA0002297642210000028
Figure FDA0002297642210000029
Figure FDA00022976422100000214
πs[k]＝π[k]-πg[k]
ΔPg[k]＝πg[k]
Ps[k]＝min{max{πs[k],Ps m[k]},Ps M[k]}
e[k]＝e[k-1]-Ps[k]δ
Figure FDA00022976422100000210
Figure FDA00022976422100000211
Figure FDA00022976422100000212
Figure FDA00022976422100000213
where, Δ f is the system frequency deviation,
Figure FDA00022976422100000215
is A in ACE examination index2The limit value specified by the index, B represents a system frequency response constant, IACE represents a PI filtering integral term of ACE,
Figure FDA00022976422100000216
the upper limit of the climbing rate of the traditional frequency modulation resource is shown, lambda is the maximum time allowed by the frequency modulation resource to reach the maximum frequency modulation capacity,
Figure FDA0002297642210000034
in order to store the capacity of the frequency modulation resource,
Figure FDA0002297642210000032
the maximum adjustable power of the conventional fm resource for the kth discrete time point,
Figure FDA0002297642210000033
for the kth discrete time point, the maximum adjustable power, P, downwards of the conventional FM resources M[k]Storing the upward maximum adjustable power, P, of the FM resource for the kth discrete time points m[k]Storing the maximum downward adjustable power of the frequency modulation resource for the kth discrete time point, eM and emRespectively the maximum value and the minimum value of the electric quantity of the energy storage resource, e [ k ]]Storing energy resource electricity quantity for kth discrete time point, pi [ k [ [ k ]]Adjusting the power requirement, pi, for the kth discrete-time systemg[k] and πs[k]The regulation power, delta P, respectively allocated to the conventional units and energy storage resources at the kth discrete time pointg[k] and Ps[k]The regulated power r actually born by the traditional frequency modulation resource and the energy storage resource respectivelyg[k] and rnl[k]Respectively the change rate of the traditional frequency modulation resource and the net load at the kth discrete time point, a [ k ]]Calculating a constant required by frequency deviation for the kth discrete time point, wherein D is a system load damping constant, and H is a system inertia constant;
1-3) selecting any AGC typical assessment time period q, taking the value of the energy storage frequency modulation resource ratio η from 0% to 100% by taking 1% as a step length, and fluctuating according to the net load corresponding to the time period
Figure FDA0002297642210000037
Calculating the frequency modulation capacity requirement of the system in the period when the energy storage frequency modulation resource occupation ratio is η by using the model established in the step 1-2)
Figure FDA0002297642210000038
1-4) repeating the steps 1-3), and calculating the energy storage frequency modulation resource ratio of η for each AGC typical assessment period in the step 1-1) by the systemEach time interval frequency modulation capacity requirement is obtained correspondingly
Figure FDA0002297642210000039
The substitution ratio of the energy storage frequency modulation resource relative to the traditional frequency modulation resource under different η is calculated according to the following formula:
Figure FDA0002297642210000031
wherein Q is the total number of typical evaluation periods of AGC;
2) selecting any AGC (automatic gain control) assessment time period in the future as an F time period, and calculating the frequency modulation capacity requirement after the energy storage frequency modulation resource in the time period is converted into the traditional frequency modulation resource according to historical data; the method comprises the following specific steps:
2-1) collecting historical data of the past N years in an automatic generation control AGC control area; the historical data includes: load power per minute, wind power generation power per minute, photovoltaic power generation power per minute, and A of each AGC (automatic gain control) examination period2Indexes, the capacity of traditional frequency modulation resources and the capacity of energy storage frequency modulation resources in each AGC assessment period;
2-2) according to the traditional frequency modulation resource capacity of each AGC assessment period in the step 2-1)
Figure FDA00022976422100000310
And the capacity P of energy storage frequency modulation resources hObtaining the energy storage frequency modulation resource proportion η corresponding to each AGC examination period in the historical datahA value; h is a time interval number of the historical data;
Figure FDA0002297642210000041
according to the result of step 1), ηhThe substitution ratio of the corresponding energy storage frequency modulation resources under the value relative to the traditional frequency modulation resources is converted into the traditional frequency modulation resources from the energy storage frequency modulation resources of each AGC (automatic gain control) assessment period in the historical data, and the converted energy storage frequency modulation resources of each AGC assessment period in the historical data are obtainedTotal modulation capacity P ofｒ n′(ii) a The conversion for each AGC qualification period is performed according to the following equation:
Figure FDA0002297642210000042
2-3) calculating the load standard deviation delta in each AGC examination period in historical datah,l：
Figure FDA0002297642210000043
In the formula, Z represents the number of discrete time points in each AGC examination period by taking 1 minute as a sampling period, Z is the Z th discrete time sampling point, and P isl h[z]Load power for the z-th minute in time period h; pl h，avgThe load mean value in the time period h is calculated as follows:
Figure FDA0002297642210000044
calculating the standard deviation of the photovoltaic power generation power in each AGC examination period in historical data:
Figure FDA0002297642210000045
wherein ,
Figure FDA00022976422100000411
for the photovoltaic power generation power of the z minute in the period h,
Figure FDA00022976422100000412
the calculation method is the average value of the photovoltaic power generation power in the time period h:
Figure FDA0002297642210000046
calculating the average value of the wind power generation power in each AGC assessment period in historical data:
Figure FDA0002297642210000047
wherein ,
Figure FDA00022976422100000413
the power generated by the wind power generation in the z minute in the time period h;
2-4) comparing A of each AGC examination period in historical data2,Pｒ n′；δh,l,δh,pvAnd
Figure FDA0002297642210000059
forming samples corresponding to the time period, and screening all samples in the historical data according to the following formula:
Figure FDA0002297642210000051
the specific screening method comprises the following steps: for each sample i in the historical data, counting the frequency modulation capacity after the conversion except i in the historical data
Figure FDA00022976422100000510
Is greater than
Figure FDA00022976422100000511
And the frequency modulation shows an absolute value
Figure FDA00022976422100000512
Is greater than
Figure FDA00022976422100000513
The proportion of the sample (c); if the ratio is higher than the set sample determination threshold
Figure FDA00022976422100000515
Then the sample i is judged to be a normal sample andreserving, otherwise, deleting; after all samples are processed, a set consisting of all normal samples is obtained;
2-5) establishing an interval prediction model based on the load standard deviation of the extreme learning machine ELM: input of the model
Figure FDA00022976422100000514
The vector is formed by the load standard deviations of AGC assessment time periods which are the same as i every day in M days before the prediction time period i and 2 adjacent AGC assessment time periods before and after the time period; weight matrix k from input layer to hidden layerlAnd an offset vector blFor randomly generated numbers with the value between 0 and 1, each unit in the hidden layer contains an activation function sigma, and the weight from the hidden layer to the output layer
Figure FDA00022976422100000516
Optimized generation is carried out, the output of the model is the mapping corresponding to the input to the output of the model in the time period i
Figure FDA00022976422100000517
Comprises the following steps:
Figure FDA0002297642210000052
randomly extracting 75% of samples from the set consisting of the normal samples obtained in the step 2-4) to form a training set I of the prediction modellForming a weight optimization model from a hidden layer to an output layer of the extreme learning machine as follows:
Figure FDA0002297642210000053
Figure FDA0002297642210000054
Figure FDA0002297642210000055
Figure FDA0002297642210000056
Figure FDA0002297642210000057
wherein, α represents the quantile,
Figure FDA00022976422100000518
for the standard deviation of the load in the ith AGC assessment period in the training set,
Figure FDA00022976422100000519
and
Figure FDA00022976422100000520
is an auxiliary variable;
solving the weight optimization model pair weight
Figure FDA00022976422100000521
Optimizing to obtain an interval prediction model of the trained load standard deviation;
2-6) establishing an interval prediction model of the standard deviation of the photovoltaic power generation power based on the extreme learning machine: input of the model
Figure FDA00022976422100000522
The vector is formed by the standard deviations of the photovoltaic power generation power in the same time interval as i every day and in each 2 adjacent AGC assessment time intervals before and after the time interval in M days before the prediction time interval i; weight matrix k from input layer to hidden layerpvAnd an offset vector bpvFor randomly generated numbers with the value between 0 and 1, each unit in the hidden layer contains an activation function sigma, and the weight from the hidden layer to the output layer
Figure FDA0002297642210000068
Through optimized generation, moduleThe output of the model is quantile of standard deviation of photovoltaic power generation power in the time period i
Figure FDA00022976422100000610
And
Figure FDA0002297642210000069
the predicted value of (2); (ii) a Mapping of the model input to output
Figure FDA00022976422100000611
Comprises the following steps:
Figure FDA0002297642210000061
randomly extracting 75% of samples from the set consisting of the normal samples obtained in the step 2-4) to form a training set I of the prediction modelpvForming a weight optimization model from a hidden layer to an output layer of the extreme learning machine as follows:
Figure FDA0002297642210000062
Figure FDA0002297642210000063
Figure FDA0002297642210000064
Figure FDA0002297642210000065
Figure FDA0002297642210000066
wherein α represents quantile, δi pvξ standard deviation of photovoltaic power generation power in the ith AGC examination period in the training seti pv，α，θi pv，αAnd
Figure FDA00022976422100000614
is an auxiliary variable;
solving the optimized model pair weights
Figure FDA00022976422100000615
Optimizing to obtain an interval prediction model of the standard deviation of the trained photovoltaic power generation power;
2-7) establishing an interval prediction model of wind power generation based on an extreme learning machine: input of the model
Figure FDA00022976422100000616
Inputting a weight matrix k from the layer to the hidden layer for predicting the wind power generation amount of the AGC examination time period which is the same as i every day in M days before the time period i and 2 adjacent AGC examination time periods before and after the same time periodwAnd an offset vector bwFor randomly generated numbers with the value between 0 and 1, each unit in the hidden layer contains an activation function sigma, and the weight from the hidden layer to the output layer
Figure FDA00022976422100000617
Through optimized generation, the output of the model is the quantile of the wind power generation power in the time period i
Figure FDA00022976422100000619
And
Figure FDA00022976422100000618
the predicted value of (2); mapping of the model input to output
Figure FDA00022976422100000620
Comprises the following steps:
Figure FDA0002297642210000067
randomly extracting 75% of samples from the set consisting of the normal samples obtained in the step 2-4) as a training set I of the prediction modelwThe weight optimization model from the hidden layer to the output layer of the extreme learning machine is formed as follows:
Figure FDA0002297642210000071
Figure FDA0002297642210000072
Figure FDA0002297642210000073
Figure FDA0002297642210000074
Figure FDA0002297642210000075
wherein α represents quantile, Pi w，avgFor the mean value of the wind power generation power in the ith AGC examination period in the training set,
Figure FDA00022976422100000710
and
Figure FDA00022976422100000711
is an auxiliary variable;
solving the optimized model pair weights
Figure FDA00022976422100000712
Optimizing to obtain a trained interval prediction model of the wind power generation capacity;
2-8) selecting any AGC (automatic gain control) assessment period in the future as a period F, and performing AGC assessment at the same AGC assessment period as F every day on M days before the day of F and 2 AGC assessment periods before and after the same periodInputting the standard deviation of the load of the section into the model trained in the step 2-5)
Figure FDA00022976422100000713
Obtaining the predicted value of the interval of the F time interval load standard deviation
Figure FDA0002297642210000076
Inputting the photovoltaic power generation power standard deviation of the AGC assessment time period which is the same as that of the F every day in M days before the day of the F and 2 AGC assessment time periods before and after the same time period into the model trained in the step 2-6)
Figure FDA00022976422100000714
Obtaining a predicted value of a region of the standard deviation of the photovoltaic power generation power in the F time period
Figure FDA0002297642210000077
Inputting the wind power generation power of the AGC assessment time period which is the same as that of the F every day for M days before the day of the F and 2 AGC assessment time periods before and after the same time period into the extreme learning machine model trained in the step 2-7)
Figure FDA00022976422100000715
Obtaining the predicted value of the interval of the standard deviation of the wind power generation power in the F time interval
Figure FDA0002297642210000078
2-9) setting F time interval to convert into traditional frequency modulation resource and then obtaining initial frequency modulation capacity Pr F′＝20MW；
2-10) obtaining all normal samples in the step 2-4), and calculating the converted frequency modulation capacity P according to the following formular h′Belong to the interval
Figure FDA0002297642210000079
The probability ratio gamma (P) of the frequency modulation reaching standard and failing to reach standard in the sampler F′), wherein ΔPr' is the range quantity of the frequency modulation capacity interval;
Figure FDA0002297642210000081
2-11) determination of gamma (P)r F′) Whether or not it is greater than the confidence ratio gammaLimitOr Pr F′Whether the maximum value of the converted frequency modulation capacity corresponding to all samples in the set consisting of the normal samples obtained in the step 2-4) is equal to or not
Figure FDA0002297642210000087
If at least one of the two conditions is satisfied, Pr F′Converting the energy storage frequency modulation resource in the F time period into the frequency modulation capacity requirement of the traditional frequency modulation resource, and then entering the step 3); otherwise, let Pr F′Increasing by 20MW, and then returning to the step 2-10);
γLimitand calculating according to the confidence α that the frequency modulation performance reaches the standard, wherein the expression is as follows:
Figure FDA0002297642210000082
3) according to the substitution ratio of the energy storage frequency modulation resource obtained in the step 1) and the traditional frequency modulation resource, after the energy storage frequency modulation resource is equivalently substituted into the traditional frequency modulation resource in the F time period, the total capacity of the substituted frequency modulation resource reaches the P obtained by calculation in the step 2-9)r F′The frequency modulation resource allocation combination scheme of (1) is to find all solutions satisfying the following formula
Figure FDA0002297642210000084
wherein Ps FThe capacity of the frequency modulation resource is stored for the F time period,
Figure FDA0002297642210000085
for the conventional fm resource capacity in the F period:
Figure FDA0002297642210000083
4) cost C according to energy storage frequency modulation resourcesAnd cost C of conventional frequency modulation resourcesgSelecting the combination schemes obtained from step 3) to obtain the total cost
Figure FDA0002297642210000086
The lowest optimal combination scheme, which is the frequency modulation capacity demand allocation scheme of the F time period selected in the step 2).
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112350344A (en) * 2020-05-25 2021-02-09 清华大学 Energy storage system-thermal power generating unit combined frequency modulation control method considering frequency modulation performance examination
CN112350344B (en) * 2020-05-25 2022-03-25 清华大学 Energy storage system-thermal power generating unit combined frequency modulation control method
CN112838621A (en) * 2021-01-22 2021-05-25 上海交通大学 The realization method of frequency regulation capacity of power system considering the growth of new energy
CN112821424A (en) * 2021-01-29 2021-05-18 国网辽宁省电力有限公司大连供电公司 Power system frequency response analysis method based on data-model fusion drive
CN112821424B (en) * 2021-01-29 2023-07-14 国网辽宁省电力有限公司大连供电公司 A frequency response analysis method of power system driven by data-model fusion
CN113011030A (en) * 2021-03-23 2021-06-22 南方电网科学研究院有限责任公司 CPS 1-based frequency modulation capacity allocation method and device and storage medium
CN115189371A (en) * 2022-08-03 2022-10-14 东南大学溧阳研究院 Power curve dynamic matching-based auxiliary frequency modulation capacity calculation method for power system
CN115189371B (en) * 2022-08-03 2023-04-07 东南大学溧阳研究院 Power curve dynamic matching-based auxiliary frequency modulation capacity calculation method for power system

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