CN114819591A - A power demand response potential assessment method, system and related equipment - Google Patents

A power demand response potential assessment method, system and related equipment Download PDF

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CN114819591A
CN114819591A CN202210415872.9A CN202210415872A CN114819591A CN 114819591 A CN114819591 A CN 114819591A CN 202210415872 A CN202210415872 A CN 202210415872A CN 114819591 A CN114819591 A CN 114819591A
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李磊
麻吕斌
郁春雷
潘一洲
闻安
林振智
沈亚萍
王思睿
吴迪
王韵楚
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Zhejiang University ZJU
State Grid Zhejiang Electric Power Co Ltd
Zhejiang Huayun Information Technology Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
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Abstract

本发明公开了一种电力需求响应潜力评估方法、系统及相关设备，方法包括：S1、构建基于STL算法的专变用户负荷分解模型，用于将专变用户的负荷进行分解获得负荷周期分量；S2、构建基于S‑G滤波算法的负荷曲线平台功率确定模型，确定可代表负荷曲线平台的功率，经过S‑G滤波算法处理后的负荷周期性分量中每一个局部极小值点可代表所在负荷曲线平台的功率；S3，根据指定的需求响应的起始时间，利用专变用户负荷分解模型、负荷曲线平台功率确定模型确定负荷周期性分量在响应起始时间的实时负荷功率，对所有小于实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率。可以较为准确地获得专变用户的需求响应潜力，提高了管理效率。

Figure 202210415872

The invention discloses a power demand response potential evaluation method, system and related equipment. The method includes: S1. Constructing a dedicated variable user load decomposition model based on an STL algorithm, which is used for decomposing the dedicated variable user load to obtain a load period component; S2. Build a load curve platform power determination model based on the S-G filtering algorithm to determine the power that can represent the load curve platform. Each local minimum point in the periodic component of the load processed by the S-G filtering algorithm can represent the The power of the load curve platform; S3, according to the specified start time of the demand response, the real-time load power of the load periodic component at the start time of the response is determined by using the special variable user load decomposition model and the load curve platform power determination model. The load curve platform power difference of real-time load power is calculated, and the maximum value is the demand response potential power. The demand response potential of dedicated users can be obtained more accurately, and the management efficiency is improved.

Figure 202210415872

Description

一种电力需求响应潜力评估方法、系统及相关设备A method, system and related equipment for evaluating power demand response potential

技术领域technical field

本发明涉及电力智能管理技术领域，特别是涉及一种电力需求响应潜力评估方法、系统及相关设备。The invention relates to the technical field of power intelligent management, in particular to a power demand response potential assessment method, system and related equipment.

背景技术Background technique

随着近年来电网负荷不断上升，负荷的峰谷差不断增加，调峰调频成为建设可靠性高的电网的难点。面对短暂的高峰负荷需求时，以往都是采用增加建设发电机组和配套的输配电网络来应对，这些设备利用率较低，经济效益也不高。With the continuous increase of the power grid load in recent years, the peak-to-valley difference of the load continues to increase, and the peak and frequency regulation has become a difficulty in building a highly reliable power grid. In the face of short-term peak load demand, in the past, it was solved by increasing the construction of generator sets and supporting transmission and distribution networks. The utilization rate of these equipment is low and the economic benefits are not high.

需求侧管理作为智能用电的一种重要方式。通过制定有效合理的规则，在不影响用户的基础用电需求的条件下，引导用户群体按有利于电网运行的方向用电，提高用电效率并增强电网可靠性。Demand side management is an important way of smart electricity consumption. By formulating effective and reasonable rules, without affecting the basic electricity demand of users, guide the user group to use electricity in the direction that is conducive to the operation of the power grid, improve the efficiency of power consumption and enhance the reliability of the power grid.

需求响应是一种市场行为，电网侧提供各种价格政策和激励政策，当用户选择进行响应时，可以通过改变自身用电方式获得收益；对于电网来说可以改善电网系统负荷压力。需求响应以智能电网为实施状态，帮助用户主动参与到电网调节中，提高用电经济性，节约资源降低能耗，加快电力市场机制发展，提升电网可靠性与稳定性。Demand response is a market behavior. The grid side provides various price policies and incentive policies. When users choose to respond, they can obtain benefits by changing their own electricity consumption methods; for the grid, it can improve the load pressure of the grid system. Demand response takes the smart grid as the implementation state, helps users to actively participate in grid regulation, improves electricity economy, saves resources and reduces energy consumption, accelerates the development of electricity market mechanism, and improves the reliability and stability of the grid.

用户可以自己决定是否参与需求响应，这决定了需求侧响应的效果依赖于用户用电行为与响应习惯。对需求侧全息数据的分析以及用户历史用电行为的深度挖掘，挑选出优质的潜在用户有利于提高需求响应的实施效率。Users can decide whether to participate in demand response, which determines that the effect of demand response depends on the user's electricity consumption behavior and response habits. The analysis of demand-side holographic data and the in-depth mining of users' historical electricity consumption behaviors to select high-quality potential users is conducive to improving the implementation efficiency of demand response.

发明内容SUMMARY OF THE INVENTION

本发明的目的是提供了一种电力需求响应潜力评估方法、系统及相关设备，可以较为准确地获得专变用户的需求响应潜力，为电力公司施行需求侧管理提供科学指导，用于筛选优质的参与需求响应的用户以及定向激励参与需求响应态度消极的用户。The purpose of the present invention is to provide a power demand response potential evaluation method, system and related equipment, which can more accurately obtain the demand response potential of dedicated users, provide scientific guidance for power companies to implement demand side management, and be used for screening high-quality Users who participate in demand response and users who have a negative attitude towards participating in demand response are targeted and motivated.

为解决上述技术问题，本发明实施例提供了一种电力需求响应潜力评估方法，包括：In order to solve the above technical problems, an embodiment of the present invention provides a power demand response potential assessment method, including:

S1、构建基于STL算法的专变用户负荷分解模型，用于将专变用户的负荷进行分解获得负荷周期分量，所述专变用户负荷分解模型中的输入量为所述专变用户在响应日前的指定时长的负荷观测量；S1. Build a dedicated user load decomposition model based on the STL algorithm, which is used to decompose the dedicated user load to obtain a load cycle component, and the input in the dedicated user load decomposition model is the dedicated user before the response date. load observations for a specified duration;

S2、构建基于S-G滤波算法的负荷曲线平台功率确定模型，确定可代表负荷曲线平台的功率，经过S-G滤波算法处理后的所述负荷周期性分量中每一个局部极小值点可代表所在负荷曲线平台的功率；S2. Build a load curve platform power determination model based on the S-G filtering algorithm, determine the power that can represent the load curve platform, and each local minimum point in the load periodic component processed by the S-G filtering algorithm can represent the load curve where it is located. the power of the platform;

S3，根据指定的需求响应的起始时间，利用所述专变用户负荷分解模型、所述负荷曲线平台功率确定模型确定所述负荷周期性分量在响应起始时间的实时负荷功率，对所有小于所述实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率。S3: Determine the real-time load power of the periodic component of the load at the start time of the response by using the dedicated variable user load decomposition model and the load curve platform power determination model according to the specified start time of the demand response. The difference value of the load curve platform power of the real-time load power, the maximum value of which is the demand response potential power.

其中，所述S1包括：Wherein, the S1 includes:

对所述专变用户的负荷作为输入负荷序列去除负荷趋势分量，对负荷子序列进行低通量过滤后得到负荷周期分量和负荷残余分量；Taking the load of the specialized user as the input load sequence to remove the load trend component, and performing low-flux filtering on the load subsequence to obtain the load period component and the load residual component;

其中，所述负荷趋势分量代表所述专变用户的生产过程中多个预定的采样日的日内持续运行不切除的负荷，用于体现所述采样日的日间生产规模的变化；所述负荷周期分量代表从所述采样日中提取出的规律性用电负荷，用于反映日内生产或者营业的计划，体现日内用电负荷变化的规律；所述负荷残余分量代表计划生产之外的突发性负荷波动。Wherein, the load trend component represents the unremoved load that continues to run during a plurality of predetermined sampling days in the production process of the special change user, and is used to reflect the change of the production scale during the day on the sampling day; the load The periodic component represents the regular electricity load extracted from the sampling day, which is used to reflect the production or business plan in the day, and reflects the law of changes in the electricity load during the day; the load residual component represents the sudden change outside the planned production. Sexual load fluctuations.

其中，所述S1包括:Wherein, described S1 includes:

通过负荷分量确定内循环；Determine the inner circulation by the load component;

计算鲁棒性权重项，以控制负荷分解的过程中数据产生异常值，并将权重值代入所述内循环中进行运算，实现鲁棒性权重平衡外循环；Calculate the robust weight item to control the abnormal value generated by the data in the process of load decomposition, and substitute the weight value into the inner loop for operation, so as to realize the robust weight balance outer loop;

在循环结束后，对所述负荷周期分量基于局部二次拟合进行后平滑。After the end of the cycle, the duty cycle component is post-smoothed based on a local quadratic fit.

其中，所述通过负荷分量确定内循环包括：Wherein, the determining the inner loop by the load component includes:

S11，对多日采样的负荷序列去除上次迭代的趋势量

Figure BDA0003605930920000021
Figure BDA0003605930920000031
S11, remove the trend quantity of the last iteration from the load sequence sampled for multiple days
Figure BDA0003605930920000021
Figure BDA0003605930920000031

S11，对每个负荷子序列进行LOESS回归处理，前后各延长一个循环周期，平滑参数为n(s)，平滑结果记为

Figure BDA0003605930920000032
S11, LOESS regression processing is performed on each load subsequence, and one cycle period is extended before and after each, the smoothing parameter is n (s) , and the smoothing result is recorded as
Figure BDA0003605930920000032

S12，对所述平滑结果

Figure BDA0003605930920000033
依次做长度为n(p)、n(p)、3的滑动平均，再进行参数为n(l)的LOESS回归，得到长度为N的序列
Figure BDA0003605930920000034
S12, for the smoothed result
Figure BDA0003605930920000033
Do the moving average of length n (p) , n (p) , 3 in turn, and then perform LOESS regression with parameter n (l) to get a sequence of length N
Figure BDA0003605930920000034

S13，获得多日负荷序列的周期分量，

Figure BDA0003605930920000035
S13, obtain the periodic component of the multi-day load sequence,
Figure BDA0003605930920000035

S14，去周期，

Figure BDA0003605930920000036
S14, go to cycle,
Figure BDA0003605930920000036

S15，对

Figure BDA0003605930920000037
使用LOESS算法进行平滑，得到所述负荷趋势分量
Figure BDA0003605930920000038
判断
Figure BDA0003605930920000039
收敛性，若收敛则输出结果，否则返回步骤S11，
Figure BDA00036059309200000310
S15, yes
Figure BDA0003605930920000037
Use the LOESS algorithm for smoothing to obtain the load trend component
Figure BDA0003605930920000038
judge
Figure BDA0003605930920000039
Convergence, if converged, output the result, otherwise return to step S11,
Figure BDA00036059309200000310

其中，

Figure BDA00036059309200000311
为负荷分量确定内循环中第k-1次循环结束时的负荷趋势分量和负荷周期分量，初始时刻
Figure BDA00036059309200000312
n(i)为内循环层数，n(o)为外循环层数，n(p)为周期样本数，n(s)、n(l)、n(t)分别为S12，S13，S14中的LOESS平滑参数。in,
Figure BDA00036059309200000311
Determine the load trend component and the load cycle component at the end of the k-1th cycle in the inner loop for the load component, the initial time
Figure BDA00036059309200000312
n (i) is the number of inner circulation layers, n (o) is the number of outer circulation layers, n (p) is the number of periodic samples, and n (s) , n (l) , and n (t) are S12, S13, and S14, respectively. The LOESS smoothing parameter in .

其中，所述计算鲁棒性权重项包括：Wherein, the calculation robustness weights include:

采用以下公式计算所述鲁棒性权重项，The robustness weight term is calculated using the following formula,

δv＝6*fmedian(|Rv|)；δ v =6*f median (|R v |);

Figure BDA0003605930920000041
Figure BDA0003605930920000041

Figure BDA0003605930920000042
Figure BDA0003605930920000042

其中，v为负荷序列中负荷点的位置，δv为鲁棒性权重。Among them, v is the position of the load point in the load sequence, and δ v is the robustness weight.

其中，在所述S3之后还包括：Wherein, after the S3, it also includes:

判断所述需求响应潜力功率是否大于当前的最大负荷供应能力；Determine whether the demand response potential power is greater than the current maximum load supply capacity;

若是，增加所述最大负荷供应能力并输出警报信息。If so, increase the maximum load supply capacity and output an alarm message.

除此之外，本申请的实施例还提供了一种电力需求响应潜力评估系统，包括：In addition, the embodiments of the present application also provide a power demand response potential assessment system, including:

专变用户负荷分解模型构建模块、用于构建基于STL算法的专变用户负荷分解模型，将专变用户的负荷进行分解获得负荷周期分量，所述专变用户负荷分解模型中的输入量为所述专变用户在响应日前的指定时长的负荷观测量；The dedicated user load decomposition model building module is used to construct a dedicated user load decomposition model based on the STL algorithm, and the load of the dedicated user is decomposed to obtain the load cycle component. The input in the dedicated user load decomposition model is: The load observation amount for the specified period of time before the response date of the above-mentioned special change user;

平台功率确定模型构建模块、用于构建基于S-G滤波算法的负荷曲线平台功率确定模型，确定可代表负荷曲线平台的功率，经过S-G滤波算法处理后的所述负荷周期性分量中每一个局部极小值点可代表所在负荷曲线平台的功率；The platform power determination model building module is used to construct a load curve platform power determination model based on the S-G filtering algorithm, and determine the power that can represent the load curve platform. The value point can represent the power of the load curve platform where it is located;

需求响应潜力功率计算模块，用于根据指定的需求响应的起始时间，利用所述专变用户负荷分解模型、所述负荷曲线平台功率确定模型确定所述负荷周期性分量在响应起始时间的实时负荷功率，对所有小于所述实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率。The demand-response potential power calculation module is used to determine, according to the start time of the specified demand response, the specific variable user load decomposition model and the load curve platform power determination model to determine the magnitude of the periodic component of the load at the start time of the response. For real-time load power, the difference is calculated for all load curve platform powers smaller than the real-time load power, and the maximum value is the demand response potential power.

其中，所述专变用户负荷分解模型构建模块包括内循环确定单元、鲁棒性权重项计算单元、平滑处理单元；其中,Wherein, the specific variable user load decomposition model building module includes an inner loop determination unit, a robust weight item calculation unit, and a smoothing processing unit; wherein,

所述内循环确定单元，用于通过负荷分量确定内循环，对所述专变用户的负荷作为输入负荷序列去除负荷趋势分量，对负荷子序列进行低通量过滤后得到负荷周期分量和负荷残余分量，所述负荷趋势分量代表所述专变用户的生产过程中多个预定的采样日的日内持续运行不切除的负荷，用于体现所述采样日的日间生产规模的变化；所述负荷周期分量代表从所述采样日中提取出的规律性用电负荷，用于反映日内生产或者营业的计划，体现日内用电负荷变化的规律；所述负荷残余分量代表计划生产之外的突发性负荷波动；The inner cycle determination unit is configured to determine the inner cycle through the load component, take the load of the special user as an input load sequence to remove the load trend component, and perform low-flux filtering on the load subsequence to obtain the load cycle component and the load residual. component, the load trend component represents the unremoved load that continues to run during a plurality of predetermined sampling days in the production process of the special change user, and is used to reflect the change of the production scale during the day on the sampling day; the load The periodic component represents the regular electricity load extracted from the sampling day, which is used to reflect the production or business plan in the day, and reflects the law of changes in the electricity load during the day; the load residual component represents the sudden change outside the planned production. Sexual load fluctuations;

所述鲁棒性权重项计算模块，用于计算鲁棒性权重项，以控制负荷分解的过程中数据产生异常值，并将权重值代入所述内循环中进行运算，实现鲁棒性权重平衡外循环；The robustness weight item calculation module is used to calculate the robustness weight item, so as to control the abnormal value generated by the data in the process of load decomposition, and substitute the weight value into the inner loop for calculation, so as to realize the robust weight balance outside loop;

所述平滑处理单元，用于在在循环结束后，对所述负荷周期分量基于局部二次拟合进行后平滑。The smoothing processing unit is configured to perform post-smoothing on the load period component based on local quadratic fitting after the cycle ends.

除此之外，本申请的实施例一种电力需求响应潜力评估设备，包括：In addition, an embodiment of the present application is a power demand response potential assessment device, including:

存储器和处理器；其中，所述存储器用于存储计算机程序，所述处理器用于执行所述计算机程序时实现如上所述电力需求响应潜力评估方法的步骤。A memory and a processor; wherein the memory is used to store a computer program, and the processor is used to implement the steps of the above-mentioned method for evaluating the power demand response potential when the computer program is executed.

除此之外，本申请的实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上所述电力需求响应潜力评估设备方法的步骤。In addition, embodiments of the present application also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, realizes the power demand response potential as described above Steps for evaluating device methods.

本发明实施例所提供的电力需求响应潜力评估方法、系统及相关设备，与现有技术相比，具有以下优点：Compared with the prior art, the power demand response potential assessment method, system and related equipment provided by the embodiments of the present invention have the following advantages:

所述电力需求响应潜力评估方法、系统及相关设备，通过采用STL算法将专变用户的负荷进行分解获得负荷周期分量，然后对负荷周期性分量经过S-G滤波算法处理，其中的每一个局部极小值点可代表所在负荷曲线平台的功率，最后根据指定的需求响应的起始时间，确定所述负荷周期性分量在响应起始时间的实时负荷功率，对所有小于所述实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率，通过该方法可以较为准确地获得专变用户的需求响应潜力，为电力公司施行需求侧管理提供科学指导，用于筛选优质的参与需求响应的用户以及定向激励参与需求响应态度消极的用户。In the power demand response potential evaluation method, system and related equipment, the load period component is obtained by decomposing the load of the variable user by using the STL algorithm, and then the load period component is processed by the S-G filtering algorithm, each of which is locally extremely small. The value point can represent the power of the load curve platform where it is located. Finally, according to the start time of the specified demand response, the real-time load power of the periodic component of the load at the start time of the response is determined. For all load curves less than the real-time load power The difference between the platform power is calculated, and the maximum value is the demand response potential power. Through this method, the demand response potential of the dedicated users can be obtained more accurately, which can provide scientific guidance for the power company to implement demand side management and be used to screen high-quality participants. Demand response users and users who have a negative attitude towards demand response with targeted incentives.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to illustrate the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are For some embodiments of the present invention, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative effort.

图1为本发明提供的电力需求响应潜力评估方法的一个实施例的具体实施方式的步骤流程示意图；FIG. 1 is a schematic flow chart of steps of an embodiment of a specific implementation of a power demand response potential assessment method provided by the present invention;

图2为本发明提供的电力需求响应潜力评估方法的一实施例中的内循环确定的步骤流程示意图，FIG. 2 is a schematic flowchart of the steps of determining the inner loop in an embodiment of the power demand response potential assessment method provided by the present invention,

图3为本申请发明实施例提供的电力需求响应潜力评估系统的结构示意图；3 is a schematic structural diagram of a power demand response potential assessment system provided by an embodiment of the present invention;

图4为本发明的提供的电力需求响应潜力评估系统的一个实施例中基于STL算法的负荷分解结果及各部分分量示意图；FIG. 4 is a schematic diagram of the load decomposition result based on the STL algorithm and the components of each part in an embodiment of the power demand response potential assessment system provided by the present invention;

图5为本发明提供的电力需求响应潜力评估系统的一个实施例的基于S-G算法的负荷周期性曲线滤波前后对比图。FIG. 5 is a comparison diagram before and after filtering of the load periodic curve based on the S-G algorithm according to an embodiment of the power demand response potential evaluation system provided by the present invention.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

请参考图1～5，图1为本发明提供的电力需求响应潜力评估方法的一个实施例的具体实施方式的步骤流程示意图；图2为本发明提供的电力需求响应潜力评估方法的一实施例中的内循环确定的步骤流程示意图，图3为本申请发明实施例提供的电力需求响应潜力评估系统的结构示意图；图4为本发明的提供的电力需求响应潜力评估系统的一个实施例中基于STL算法的负荷分解结果及各部分分量示意图；图5为本发明提供的电力需求响应潜力评估系统的一个实施例的基于S-G算法的负荷周期性曲线滤波前后对比图。Please refer to FIGS. 1 to 5. FIG. 1 is a schematic flow chart of steps of a specific implementation of an embodiment of a power demand response potential assessment method provided by the present invention; FIG. 2 is an embodiment of a power demand response potential assessment method provided by the present invention. Figure 3 is a schematic structural diagram of a power demand response potential assessment system provided by an embodiment of the present invention; Figure 4 is an embodiment of the power demand response potential assessment system provided by the present invention. The load decomposition result of the STL algorithm and the schematic diagram of each component; FIG. 5 is a comparison diagram before and after filtering of the load periodic curve based on the S-G algorithm of an embodiment of the power demand response potential assessment system provided by the present invention.

在一种具体实施方式中，所述电力需求响应潜力评估方法，包括：In a specific embodiment, the power demand response potential assessment method includes:

S1、构建基于STL算法的专变用户负荷分解模型，用于将专变用户的负荷进行分解获得负荷周期分量，所述专变用户负荷分解模型中的输入量为所述专变用户在响应日前的指定时长的负荷观测量；S1. Build a dedicated user load decomposition model based on the STL algorithm, which is used to decompose the dedicated user load to obtain a load cycle component, and the input in the dedicated user load decomposition model is the dedicated user before the response date. load observations for a specified duration;

S2、构建基于S-G滤波算法的负荷曲线平台功率确定模型，确定可代表负荷曲线平台的功率，经过S-G滤波算法处理后的所述负荷周期性分量中每一个局部极小值点可代表所在负荷曲线平台的功率；S2. Build a load curve platform power determination model based on the S-G filtering algorithm, determine the power that can represent the load curve platform, and each local minimum point in the load periodic component processed by the S-G filtering algorithm can represent the load curve where it is located. the power of the platform;

S3，根据指定的需求响应的起始时间，利用所述专变用户负荷分解模型、所述负荷曲线平台功率确定模型确定所述负荷周期性分量在响应起始时间的实时负荷功率，对所有小于所述实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率。S3: Determine the real-time load power of the periodic component of the load at the start time of the response by using the dedicated variable user load decomposition model and the load curve platform power determination model according to the specified start time of the demand response. The difference value of the load curve platform power of the real-time load power, the maximum value of which is the demand response potential power.

通过采用STL算法将专变用户的负荷进行分解获得负荷周期分量，然后对负荷周期性分量经过S-G滤波算法处理，其中的每一个局部极小值点可代表所在负荷曲线平台的功率，最后根据指定的需求响应的起始时间，确定所述负荷周期性分量在响应起始时间的实时负荷功率，对所有小于所述实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率，通过该方法可以较为准确地获得专变用户的需求响应潜力，为电力公司施行需求侧管理提供科学指导，用于筛选优质的参与需求响应的用户以及定向激励参与需求响应态度消极的用户。By using the STL algorithm to decompose the load of the dedicated user to obtain the load period component, and then process the load period component through the S-G filtering algorithm, each local minimum point in it can represent the power of the load curve platform, and finally according to the specified determine the real-time load power of the periodic component of the load at the start time of the demand response, calculate the difference between all load curve platform powers less than the real-time load power, and the maximum value is the demand response Potential power, through this method, the demand response potential of dedicated users can be obtained more accurately, which provides scientific guidance for power companies to implement demand side management, and is used to screen high-quality users participating in demand response and directional incentives to participate in demand response. Users with negative attitudes .

本申请对于负荷周期性分量的分解获得方式不做限定，在一个实施例中，所述S1包括：The present application does not limit the decomposition and acquisition method of the load periodic component. In one embodiment, the S1 includes:

对所述专变用户的负荷作为输入负荷序列去除负荷趋势分量，对负荷子序列进行低通量过滤后得到负荷周期分量和负荷残余分量；Taking the load of the specialized user as the input load sequence to remove the load trend component, and performing low-flux filtering on the load subsequence to obtain the load period component and the load residual component;

其中，所述负荷趋势分量代表所述专变用户的生产过程中多个预定的采样日的日内持续运行不切除的负荷，用于体现所述采样日的日间生产规模的变化；所述负荷周期分量代表从所述采样日中提取出的规律性用电负荷，用于反映日内生产或者营业的计划，体现日内用电负荷变化的规律；所述负荷残余分量代表计划生产之外的突发性负荷波动。Wherein, the load trend component represents the unremoved load that continues to run during a plurality of predetermined sampling days in the production process of the special change user, and is used to reflect the change of the production scale during the day on the sampling day; the load The periodic component represents the regular electricity load extracted from the sampling day, which is used to reflect the production or business plan in the day, and reflects the law of changes in the electricity load during the day; the load residual component represents the sudden change outside the planned production. Sexual load fluctuations.

本申请对于其提供滤波采用的滤波器不做限定，工作人员可以根据需要选择合适的滤波器。The present application does not limit the filter used for filtering, and the staff can select an appropriate filter as required.

由于在本申请中主要是采用STL算法将专变用户的负荷进行分解获得负荷周期分量，对于该过程中的具体计算过程不做限定，在一个实施例中，所述S1包括:Because the STL algorithm is mainly used in this application to decompose the load of the dedicated user to obtain the load period component, the specific calculation process in this process is not limited, in one embodiment, the S1 includes:

通过负荷分量确定内循环；Determine the inner circulation by the load component;

计算鲁棒性权重项，以控制负荷分解的过程中数据产生异常值，并将权重值代入所述内循环中进行运算，实现鲁棒性权重平衡外循环；Calculate the robust weight item to control the abnormal value generated by the data in the process of load decomposition, and substitute the weight value into the inner loop for operation, so as to realize the robust weight balance outer loop;

在循环结束后，对所述负荷周期分量基于局部二次拟合进行后平滑。After the end of the cycle, the duty cycle component is post-smoothed based on a local quadratic fit.

由于主要是通过内循环获得符合周期分量，对于其具体的计算过程不做限定，在一个具体的实施例中，所述通过负荷分量确定内循环包括：Since the conforming periodic component is mainly obtained through the inner loop, the specific calculation process thereof is not limited. In a specific embodiment, the determination of the inner loop through the load component includes:

S11，对多日采样的负荷序列去除上次迭代的趋势量

Figure BDA0003605930920000081
Figure BDA0003605930920000082
S11, remove the trend quantity of the last iteration from the load sequence sampled for multiple days
Figure BDA0003605930920000081
Figure BDA0003605930920000082

S12，对每个负荷子序列进行LOESS回归处理，前后各延长一个循环周期，平滑参数为n(s)，平滑结果记为

Figure BDA0003605930920000083
S12, perform LOESS regression processing on each load subsequence, extend one cycle period before and after each, the smoothing parameter is n (s) , and the smoothing result is recorded as
Figure BDA0003605930920000083

S13，对所述平滑结果

Figure BDA0003605930920000091
依次做长度为n(p)、n(p)、3的滑动平均，再进行参数为n(l)的LOESS回归，得到长度为N的序列
Figure BDA0003605930920000092
S13, for the smoothed result
Figure BDA0003605930920000091
Do the moving average of length n (p) , n (p) , 3 in turn, and then perform LOESS regression with parameter n (l) to get a sequence of length N
Figure BDA0003605930920000092

S14，获得多日负荷序列的周期分量，

Figure BDA0003605930920000093
S14, obtain the periodic component of the multi-day load sequence,
Figure BDA0003605930920000093

S15，去周期，

Figure BDA0003605930920000094
S15, go to cycle,
Figure BDA0003605930920000094

S16，对

Figure BDA0003605930920000095
使用LOESS算法进行平滑，得到所述负荷趋势分量
Figure BDA0003605930920000096
判断
Figure BDA0003605930920000097
收敛性，若收敛则输出结果，否则返回步骤S11，
Figure BDA0003605930920000098
S16, yes
Figure BDA0003605930920000095
Use the LOESS algorithm for smoothing to obtain the load trend component
Figure BDA0003605930920000096
judge
Figure BDA0003605930920000097
Convergence, if converged, output the result, otherwise return to step S11,
Figure BDA0003605930920000098

其中，

Figure BDA0003605930920000099
为负荷分量确定内循环中第k-1次循环结束时的负荷趋势分量和负荷周期分量，初始时刻
Figure BDA00036059309200000910
n(i)为内循环层数，n(o)为外循环层数，n(p)为周期样本数，n(s)、n(l)、n(t)分别为S12，S13，S14中的LOESS平滑参数。in,
Figure BDA0003605930920000099
Determine the load trend component and the load cycle component at the end of the k-1th cycle in the inner loop for the load component, the initial time
Figure BDA00036059309200000910
n (i) is the number of inner circulation layers, n (o) is the number of outer circulation layers, n (p) is the number of periodic samples, and n (s) , n (l) , and n (t) are S12, S13, and S14, respectively. The LOESS smoothing parameter in .

本申请中包括但是不局限于上述的计算方法，工作人员还可以选择采用其它类型的计算方式。This application includes but is not limited to the above-mentioned calculation methods, and the staff may also choose to adopt other types of calculation methods.

本申请中鲁棒性权重项的计算以及运算过程不做限定，在一个实施例中，所述计算鲁棒性权重项包括：The calculation and operation process of the robustness weight item in this application are not limited. In one embodiment, the calculation robustness weight item includes:

采用以下公式计算所述鲁棒性权重项，The robustness weight term is calculated using the following formula,

δv＝6*fmedian(|Rv|)；δ v =6*f median (|R v| );

Figure BDA0003605930920000101
Figure BDA0003605930920000101

Figure BDA0003605930920000102
Figure BDA0003605930920000102

其中，v为负荷序列中负荷点的位置，δv为鲁棒性权重。Among them, v is the position of the load point in the load sequence, and δ v is the robustness weight.

本申请的主要目的在于计算获得用户的需求响应潜力，实现需求侧管理，提高管理效率，而不可避免的会出现供电力应不足的情况，因此需要对电力供应的负责实现适当的增加，从而提高用电户的用电可靠性以及满足用电量的需求，从而保证其正常的生产生活，因此，在一个实施例中，在所述S3之后还包括：The main purpose of this application is to calculate and obtain the demand response potential of users, realize demand side management, and improve management efficiency. However, there will inevitably be insufficient power supply. Therefore, it is necessary to appropriately increase the responsibility for power supply, so as to improve the management efficiency. The electricity consumption reliability of the electricity user and the demand for electricity consumption are met, so as to ensure their normal production and life. Therefore, in an embodiment, after the S3, the following further includes:

判断所述需求响应潜力功率是否大于当前的最大负荷供应能力；Determine whether the demand response potential power is greater than the current maximum load supply capacity;

若是，增加所述最大负荷供应能力并输出警报信息。If so, increase the maximum load supply capacity and output an alarm message.

当然，需要指出的是，不同的企业或者单位由于管理、生产效率以及生产量等的变化，不仅会出现负荷增加的情况，也可能会出现用电负荷下降的情况，为了实现高效的用电管理，可以实现对不同用户的负荷动态管理，实现更高效率的用电管理。Of course, it should be pointed out that due to changes in management, production efficiency and production volume of different enterprises or units, not only will the load increase, but the electricity load may also decrease. In order to achieve efficient electricity management , which can realize the dynamic management of the load of different users and achieve more efficient power management.

在本申请汇总定义负荷曲线平台为专变用户生产中投入使用的设备组合不变的状态的功率范围，当投入新设备或者切除正在使用的设备时，负荷曲线会出现骤升或骤降，并达到新的负荷曲线平台。在负荷周期性分量中，一个负荷曲线平台上仍存在一定的负荷波动，且持续时间过短的负荷平台小于需求响应所需时间，不宜作为评估专变用户需求响应潜力的依据。In this application, the load curve platform is defined as a power range in which the combination of equipment put into use in the production of dedicated users remains unchanged. When new equipment is put in or the equipment in use is removed, the load curve will suddenly rise or fall, and A new load curve plateau is reached. In the periodic component of load, there is still a certain load fluctuation on a load curve platform, and the load platform with too short duration is less than the time required for demand response, so it should not be used as a basis for evaluating the demand response potential of dedicated users.

因此，利用S-G滤波算法确定可代表负荷曲线平台的功率。S-G滤波算法的原理即负荷曲线平台代表功率的确定如下。Therefore, the S-G filtering algorithm is used to determine the power that can represent the plateau of the load curve. The principle of the S-G filtering algorithm, that is, the determination of the representative power of the load curve platform is as follows.

经过S-G滤波算法处理后的负荷周期性分量中每一个局部极小值点可代表其所在负荷曲线平台的功率PLoc_min＝{P1,L_min,P2,L_min,……,Pi,L_min,……,Pk,L_min}。Each local minimum point in the load periodic component processed by the SG filter algorithm can represent the power P Loc_min of the load curve platform where it is located . ..., P k, L_min }.

根据电力公司要求的需求响应的起始时间，确定专变用户的负荷周期性分量在响应起始时间的实时负荷功率，对于该用户所有小于其自身实时负荷功率的负荷曲线平台功率，求差值，最大值即为该用户的需求响应潜力功率。According to the starting time of demand response required by the power company, determine the real-time load power of the periodic component of the load of the special variable user at the starting time of the response, and calculate the difference for all the load curve platform powers of the user that are less than its own real-time load power , the maximum value is the demand response potential power of the user.

Figure BDA0003605930920000111
Figure BDA0003605930920000111

除此之外，本申请的实施例还提供了一种电力需求响应潜力评估系统，包括：In addition, the embodiments of the present application also provide a power demand response potential assessment system, including:

专变用户负荷分解模型构建模块10、用于构建基于STL算法的专变用户负荷分解模型，将专变用户的负荷进行分解获得负荷周期分量，所述专变用户负荷分解模型中的输入量为所述专变用户在响应日前的指定时长的负荷观测量；The dedicated user load decomposition model building module 10 is used to construct a dedicated user load decomposition model based on the STL algorithm, decompose the dedicated user load to obtain a load cycle component, and the input in the dedicated user load decomposition model is: The load observation amount for the specified period of time before the response date of the special change user;

平台功率确定模型构建模块20、用于构建基于S-G滤波算法的负荷曲线平台功率确定模型，确定可代表负荷曲线平台的功率，经过S-G滤波算法处理后的所述负荷周期性分量中每一个局部极小值点可代表所在负荷曲线平台的功率；The platform power determination model building module 20 is used to construct a load curve platform power determination model based on the S-G filtering algorithm, to determine the power that can represent the load curve platform, and each local pole of the load periodic component processed by the S-G filtering algorithm. The small value point can represent the power of the load curve platform where it is located;

需求响应潜力功率计算模块30，用于根据指定的需求响应的起始时间，利用所述专变用户负荷分解模型、所述负荷曲线平台功率确定模型确定所述负荷周期性分量在响应起始时间的实时负荷功率，对所有小于所述实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率。The demand response potential power calculation module 30 is configured to determine, according to the specified start time of demand response, the specific variable user load decomposition model and the load curve platform power determination model to determine the load periodic component at the response start time The real-time load power is calculated as the difference between all load curve platform powers smaller than the real-time load power, and the maximum value is the demand response potential power.

由于所述电力需求响应潜力评估系统为上述的电力需求响应潜力评估方法对应的系统，具有相同的有益效果，本申请对此不做赘述。Since the power demand response potential assessment system is a system corresponding to the power demand response potential assessment method described above, it has the same beneficial effects, and will not be described in detail in this application.

本申请中对于采用STL算法进行负荷分解的过程不做限定，在一个实施例中，所述专变用户负荷分解模型构建模块包括内循环确定单元、鲁棒性权重项计算单元、平滑处理单元；其中,The process of using the STL algorithm for load decomposition is not limited in this application. In one embodiment, the dedicated user load decomposition model building module includes an inner loop determination unit, a robust weight item calculation unit, and a smoothing processing unit; in,

所述内循环确定单元，用于通过负荷分量确定内循环，对所述专变用户的负荷作为输入负荷序列去除负荷趋势分量，对负荷子序列进行低通量过滤后得到负荷周期分量和负荷残余分量，所述负荷趋势分量代表所述专变用户的生产过程中多个预定的采样日的日内持续运行不切除的负荷，用于体现所述采样日的日间生产规模的变化；所述负荷周期分量代表从所述采样日中提取出的规律性用电负荷，用于反映日内生产或者营业的计划，体现日内用电负荷变化的规律；所述负荷残余分量代表计划生产之外的突发性负荷波动；The inner cycle determination unit is configured to determine the inner cycle through the load component, take the load of the special user as an input load sequence to remove the load trend component, and perform low-flux filtering on the load subsequence to obtain the load cycle component and the load residual. component, the load trend component represents the unremoved load that continues to run during a plurality of predetermined sampling days in the production process of the special change user, and is used to reflect the change of the production scale during the day on the sampling day; the load The periodic component represents the regular electricity load extracted from the sampling day, which is used to reflect the production or business plan in the day, and reflects the law of changes in the electricity load during the day; the load residual component represents the sudden change outside the planned production. Sexual load fluctuations;

所述鲁棒性权重项计算模块，用于计算鲁棒性权重项，以控制负荷分解的过程中数据产生异常值，并将权重值代入所述内循环中进行运算，实现鲁棒性权重平衡外循环；The robustness weight item calculation module is used to calculate the robustness weight item, so as to control the abnormal value generated by the data in the process of load decomposition, and substitute the weight value into the inner loop for calculation, so as to realize the robust weight balance outside loop;

所述平滑处理单元，用于在在循环结束后，对所述负荷周期分量基于局部二次拟合进行后平滑。The smoothing processing unit is configured to perform post-smoothing on the load period component based on local quadratic fitting after the cycle ends.

本申请中，利用STL算法进行负荷分解的主要环节包含负荷分量确定内循环、鲁棒性权重平衡外循环和负荷周期分量后平滑三个部分。In this application, the main link of load decomposition using STL algorithm includes three parts: load component determination inner loop, robust weight balance outer loop and load cycle component smoothing.

(1)负荷分量确定内循环(1) The load component determines the inner loop

在负荷分量确定内循环环节，对输入负荷序列去除负荷趋势分量Tv，对负荷子序列进行低通量过滤以得到负荷周期分量Sv和负荷残余分量Rv。设

Figure BDA0003605930920000121
为负荷分量确定内循环中第k-1次循环结束时的负荷趋势分量和负荷周期分量，初始时刻
Figure BDA0003605930920000122
n(i)为内循环层数，n(o)为外循环层数，n(p)为周期样本数，n(s)、n(l)、n(t)分别为②，③，④中的LOESS平滑参数，具体过程如附图1所示。In the inner loop link of load component determination, the load trend component T v is removed from the input load sequence, and the load subsequence is subjected to low-flux filtering to obtain the load period component S v and the load residual component R v . Assume
Figure BDA0003605930920000121
Determine the load trend component and the load cycle component at the end of the k-1th cycle in the inner loop for the load component, the initial time
Figure BDA0003605930920000122
n (i) is the number of inner circulation layers, n (o) is the number of outer circulation layers, n (p) is the number of periodic samples, n (s) , n (l) , n (t) are ②, ③, ④ respectively The LOESS smoothing parameter in , the specific process is shown in Figure 1.

①对多日采样的负荷序列去除上次迭代的趋势量

Figure BDA0003605930920000123
①Remove the trend quantity of the last iteration from the load sequence sampled for multiple days
Figure BDA0003605930920000123

Figure BDA0003605930920000124
Figure BDA0003605930920000124

②对每个负荷子序列进行LOESS回归处理，前后各延长一个循环周期，平滑参数为n(s)，平滑结果记为

Figure BDA0003605930920000125
② Perform LOESS regression processing on each load sub-sequence, extend one cycle before and after each, the smoothing parameter is n (s) , and the smoothing result is recorded as
Figure BDA0003605930920000125

③对子序列进行LOESS回归处理，即对②中的平滑结果

Figure BDA0003605930920000131
依次做长度为n(p)、n(p)、3的滑动平均，再进行参数为n(l)的LOESS回归，得到长度为N的序列
Figure BDA0003605930920000132
③ Perform LOESS regression processing on the subsequence, that is, to smooth the results in ②
Figure BDA0003605930920000131
Do the moving average of length n (p) , n (p) , 3 in turn, and then perform LOESS regression with parameter n (l) to get a sequence of length N
Figure BDA0003605930920000132

④获得多日负荷序列的周期分量：④ Obtain the periodic component of the multi-day load sequence:

Figure BDA0003605930920000133
Figure BDA0003605930920000133

⑤去周期：⑤ Go cycle:

Figure BDA0003605930920000134
Figure BDA0003605930920000134

⑥对

Figure BDA0003605930920000135
使用LOESS算法进行平滑，得到负荷趋势分量
Figure BDA0003605930920000136
判断
Figure BDA0003605930920000137
收敛性，若收敛则输出结果，否则返回步骤①：⑥Yes
Figure BDA0003605930920000135
Use the LOESS algorithm for smoothing to get the load trend component
Figure BDA0003605930920000136
judge
Figure BDA0003605930920000137
Convergence, if converged, output the result, otherwise return to step ①:

Figure BDA0003605930920000138
Figure BDA0003605930920000138

(2)鲁棒性权重平衡外循环(2) Robust weight balance outer loop

外循环的作用是计算鲁棒性权重项，以控制负荷分解的过程中数据产生异常值，并将权重值代入内循环中进行运算,设：The function of the outer loop is to calculate the robustness weight item to control the abnormal value generated by the data in the process of load decomposition, and substitute the weight value into the inner loop for operation. Let:

δv＝6*fmedian(|Rv|)δ v =6*f median (|R v |)

Figure BDA0003605930920000139
Figure BDA0003605930920000139

Figure BDA00036059309200001310
Figure BDA00036059309200001310

其中，v为负荷序列中负荷点的位置，δv为鲁棒性权重。Among them, v is the position of the load point in the load sequence, and δ v is the robustness weight.

(3)负荷周期分量后平滑(3) Smoothing after the load cycle component

循环结束后，由于内循环中的平滑只在每一个窗口中进行，周期分量中的负荷会存在毛刺。在按照负荷采样时间将负荷序列整合在一起之后，整个负荷采样序列的平滑性不能保证。负荷周期分量的后平滑基于局部二次拟合，且不需要在loess中进行稳健性迭代。After the loop is over, since the smoothing in the inner loop is only done in each window, there will be glitches in the load in the periodic component. After the load sequence is integrated according to the load sampling time, the smoothness of the whole load sampling sequence cannot be guaranteed. The post-smoothing of the load cycle components is based on a local quadratic fit and does not require robust iterations in loess.

经过STL分解后获得的三个分量，其中负荷趋势分量Tv代表专变用户的生产过程中数个采样日的日内持续运行不切除的负荷，可以体现采样日的日间生产规模的变化，负荷周期分量Sv代表从数个采样日中提取出的规律性用电负荷，可以反映日内生产或者营业的计划，体现日内用电负荷变化的规律。负荷残余分量Rv代表计划生产之外的突发性负荷波动。Three components are obtained after STL decomposition, among which the load trend component T v represents the unremoved load that continues to run during several sampling days in the production process of the variable user, which can reflect the change of the production scale during the day on the sampling day. The periodic component S v represents the regular electricity load extracted from several sampling days, which can reflect the production or business plan in the day, and reflect the change law of the electricity load in the day. The load residual component R v represents sudden load fluctuations outside the planned production.

定义负荷曲线平台为专变用户生产中投入使用的设备组合不变的状态的功率范围，当投入新设备或者切除正在使用的设备时，负荷曲线会出现骤升或骤降，并达到新的负荷曲线平台。在负荷周期性分量中，一个负荷曲线平台上仍存在一定的负荷波动，且持续时间过短的负荷平台小于需求响应所需时间，不宜作为评估专变用户需求响应潜力的依据。因此，利用S-G滤波算法确定可代表负荷曲线平台的功率。The load curve platform is defined as the power range in which the combination of equipment put into use in the user's production is unchanged. When new equipment is put in or the equipment in use is removed, the load curve will suddenly rise or fall, and the new load will be reached. Curved platform. In the periodic component of load, there is still a certain load fluctuation on a load curve platform, and the load platform with too short duration is less than the time required for demand response, so it should not be used as a basis for evaluating the demand response potential of dedicated users. Therefore, the S-G filtering algorithm is used to determine the power that can represent the plateau of the load curve.

经过S-G滤波算法处理后的负荷周期性分量中每一个局部极小值点可代表其所在负荷曲线平台的功率PLoc_min＝{P1,L_min,P2,L_min,……,Pi,L_min,……,Pk,L_min}。Each local minimum point in the load periodic component processed by the SG filter algorithm can represent the power P Loc_min of the load curve platform where it is located . ..., P k, L_min }.

根据电力公司要求的需求响应的起始时间，确定专变用户的负荷周期性分量在响应起始时间的实时负荷功率，对于该用户所有小于其自身实时负荷功率的负荷曲线平台功率，求差值，最大值即为该用户的需求响应潜力功率。According to the starting time of demand response required by the power company, determine the real-time load power of the periodic component of the load of the special variable user at the starting time of the response, and calculate the difference for all the load curve platform powers of the user that are less than its own real-time load power , the maximum value is the demand response potential power of the user.

Figure BDA0003605930920000141
Figure BDA0003605930920000141

除此之外，本申请的实施例一种电力需求响应潜力评估设备，包括：In addition, an embodiment of the present application is a power demand response potential assessment device, including:

存储器和处理器；其中，所述存储器用于存储计算机程序，所述处理器用于执行所述计算机程序时实现如上所述电力需求响应潜力评估方法的步骤。A memory and a processor; wherein the memory is used to store a computer program, and the processor is used to implement the steps of the above-mentioned method for evaluating the power demand response potential when the computer program is executed.

由于所述电力需求响应潜力评估设备的处理器用于执行所述计算机程序时实现如上所述电力需求响应潜力评估方法的步骤，具有相同的有益效果，本申请对此不作限定。Since the processor of the power demand response potential assessment device is configured to execute the computer program to implement the steps of the power demand response potential assessment method described above, it has the same beneficial effect, which is not limited in this application.

本申请对于电力需求响应潜力评估设备的类型不做赘述。This application will not go into details on the types of power demand response potential assessment devices.

除此之外，本申请的实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上所述电力需求响应潜力评估设备方法的步骤。In addition, embodiments of the present application also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, realizes the power demand response potential as described above Steps for evaluating device methods.

同理，所述由于所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上所述电力需求响应潜力评估设备方法的步骤，具有相同的有益效果，本申请对此不作赘述。In the same way, because the computer readable storage medium stores a computer program, when the computer program is executed by the processor, the steps of implementing the power demand response potential assessment device method as described above have the same beneficial effects. This will not be repeated.

本申请对于计算机可读存储介质的类型不做限定，可以是CDROM，可以是EEPROM，还可以是U盘，移动硬盘等存储介质，本申请对此不作限定。This application does not limit the type of the computer-readable storage medium, which may be a CDROM, an EEPROM, or a storage medium such as a U disk, a mobile hard disk, etc., which is not limited in this application.

在一个实施例中，用户所在行业为铸造业，在响应日前15天的负荷分解结果如图4所示。分析其负荷分解结果，由负荷周期性分量Sv可知，该用户的生产模式为白日生产，夜晚休息，中午出现较轻微的避峰现象；由负荷趋势分量Tv可知，该用户在这15天有变化地安排生产规模，在一定的波动的基础上整体呈现下降趋势；有负荷残差分量Rv可知，在生产计划之外，还有一定的突发性负荷波动。In one embodiment, the industry of the user is the foundry industry, and the load decomposition result 15 days before the response date is shown in FIG. 4 . Analyzing the load decomposition results, it can be seen from the load periodic component S v that the user's production mode is daytime production, night rest, and a slight peak avoidance phenomenon at noon; from the load trend component T v , it can be seen that the user is in these 15 The production scale is arranged with changes in the day, showing a downward trend as a whole on the basis of certain fluctuations; it can be seen from the load residual component R v that there are certain sudden load fluctuations in addition to the production plan.

提取一日内负荷周期性分量并进行S-G滤波，该用户的负荷周期分量及经过S-G滤波结果如图5所示。The periodic component of load within a day is extracted and S-G filtering is performed. The periodic component of the user's load and the result of S-G filtering are shown in Figure 5.

对比滤波前后，可发现微小负荷波动已被平滑，目的是不影响针对需求响应能力的负荷平台确定。在上午8:00附近出现的较大的负荷波动也被平滑了，这是合理的。虽然该波动产生的负荷差很大，但持续时间很短，通常并不能满足需求响应的持续半个小时及以上的削减负荷的要求。Comparing before and after filtering, it can be found that the small load fluctuation has been smoothed, and the purpose is not to affect the determination of the load platform for the demand response capability. Larger load fluctuations around 8:00 am are also smoothed out, which is reasonable. Although the load difference generated by this fluctuation is very large, the duration is very short, and it usually cannot meet the requirement of load reduction for half an hour or more of demand response.

最终，利用基于S-G滤波算法的客观需求响应能力确定模型得到的负荷平台有3个，对应的功率值分别为P1,L_min＝559.93kW，P2,L_min＝1816.73kW，P3,L_min＝0，响应起始时刻对应的响应前负荷PDR_p＝2187.09kW客观响应能力为2187.09kW，参与当次需求响应的响应功率为1772.4kW，响应水平为81.04％。可以说明该需求响应客观能力确定模型的合理性，以及确定结果的准确性。Finally, there are three load platforms obtained by using the objective demand response capability determination model based on the SG filter algorithm, and the corresponding power values are P 1, L_min = 559.93kW, P 2, L_min = 1816.73kW, P 3, L_min = 0 , the corresponding pre-response load P DR_p = 2187.09kW at the start of the response is 2187.09kW, the objective response capacity is 2187.09kW, the response power participating in the current demand response is 1772.4kW, and the response level is 81.04%. It can illustrate the rationality of the model for determining the objective capability of demand response and the accuracy of the determination results.

综上所述，本发明实施例提供的所述电力需求响应潜力评估方法、系统及相关设备，通过采用STL算法将专变用户的负荷进行分解获得负荷周期分量，然后对负荷周期性分量经过S-G滤波算法处理，其中的每一个局部极小值点可代表所在负荷曲线平台的功率，最后根据指定的需求响应的起始时间，确定所述负荷周期性分量在响应起始时间的实时负荷功率，对所有小于所述实时负荷功率的负荷曲线平台功率求差值，其中的最大值即为需求响应潜力功率，通过该方法可以较为准确地获得专变用户的需求响应潜力，为电力公司施行需求侧管理提供科学指导，用于筛选优质的参与需求响应的用户以及定向激励参与需求响应态度消极的用户。To sum up, the power demand response potential assessment method, system, and related equipment provided by the embodiments of the present invention use the STL algorithm to decompose the load of the dedicated user to obtain the load period component, and then the load period component is processed by S-G. Filter algorithm processing, where each local minimum point can represent the power of the load curve platform where it is located, and finally determine the real-time load power of the load periodic component at the response start time according to the specified demand response start time, Calculate the difference between all load curve platform powers less than the real-time load power, and the maximum value is the demand response potential power. Through this method, the demand response potential of the dedicated user can be obtained more accurately, and the demand side is implemented for the power company. Management provides scientific guidance for screening high-quality users who participate in demand response and targeting and motivating users with negative attitudes to participate in demand response.

以上对本发明所提供的所述电力需求响应潜力评估方法、系统及相关设备进行了详细介绍。本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想。应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以对本发明进行若干改进和修饰，这些改进和修饰也落入本发明权利要求的保护范围内。The power demand response potential assessment method, system and related equipment provided by the present invention have been described in detail above. The principles and implementations of the present invention are described herein by using specific examples, and the descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can also be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims (10)

1. A power demand response potential assessment method, comprising:
s1, constructing a special transformer user load decomposition model based on an STL algorithm, and decomposing the load of a special transformer user to obtain a load period component, wherein the input quantity in the special transformer user load decomposition model is the load observed quantity of the special transformer user in the appointed time before the response day;
s2, constructing a load curve platform power determination model based on an S-G filtering algorithm, determining power capable of representing a load curve platform, wherein each local minimum value point in the load periodic component processed by the S-G filtering algorithm can represent the power of the load curve platform;
and S3, determining the real-time load power of the load periodic component at the response starting time by using the special variable user load decomposition model and the load curve platform power determination model according to the specified starting time of the demand response, and solving the difference value of all load curve platform powers smaller than the real-time load power, wherein the maximum value is the demand response potential power.
2. The power demand response potential evaluation method according to claim 1, wherein the S1 includes:
the load of the special transformer user is used as an input load sequence to remove load trend components, and the load subsequence is subjected to low-pass filtering to obtain a load period component and a load residual component;
the load trend component represents the load which is continuously operated and not cut off in a plurality of scheduled sampling days in the production process of the specific transformer user, and is used for reflecting the change of the day production scale of the sampling days; the load cycle component represents the regular power load extracted from the sampling day, is used for reflecting the daily production or business plan and reflecting the change rule of the daily power load; the load residual component represents sudden load fluctuations outside of the planned production.
3. The power demand response potential evaluation method according to claim 2, wherein the S1 includes:
determining an inner loop from the load component;
calculating a robustness weight item to control abnormal values generated by data in the process of load decomposition, substituting the weight value into the inner loop for operation, and realizing robustness weight balance outer loop;
after the cycle is over, post-smoothing the load cycle component based on a local quadratic fit.
4. The power demand response potential assessment method according to claim 3, wherein the determining an inner loop by load component comprises:
s11, removing the trend quantity of the last iteration from the load sequence sampled by multiple days
Figure FDA0003605930910000021
Figure FDA0003605930910000022
S11, carrying out LOESS regression treatment on each load subsequence, respectively extending a cycle period before and after each load subsequence, wherein the smoothing parameter is n (s) The smoothing result is recorded as
Figure FDA0003605930910000023
S12, smoothing the result
Figure FDA0003605930910000024
Make the length n in sequence (p) 、n (p) 3, then the parameter is n (l) LOESS regression of (D) to obtain a length N sequence
Figure FDA0003605930910000025
S13, obtaining the periodic component of the multi-day load sequence,
Figure FDA0003605930910000026
s14, a de-cycling,
Figure FDA0003605930910000027
s15, for
Figure FDA0003605930910000028
Smoothing by using LOESS algorithm to obtain the load trend component
Figure FDA0003605930910000029
Judgment of
Figure FDA00036059309100000210
Convergence, if convergence, outputting the result, otherwise returning to step S11,
Figure FDA00036059309100000211
wherein,
Figure FDA00036059309100000212
determining the load trend component and the load period component at the end of the (k-1) th cycle in the inner cycle, the initial moment
Figure FDA00036059309100000213
n (i) Number of internal circulation layers, n (o) Number of outer circulation layers, n (p) Is the number of periodic samples, n (s) 、n (l) 、n (t) The LOESS smoothing parameters in S12, S13, and S14, respectively.
5. The power demand response potential assessment method according to claim 4, wherein the calculating the robustness weight term comprises:
the robustness weight term is calculated using the following formula,
δ v ＝6*f median (|R v |)；
Figure FDA0003605930910000031
Figure FDA0003605930910000032
where v is the position of the load point in the load sequence, δ v Are robustness weights.
6. The power demand response potential assessment method according to claim 5, further comprising, after the step S3:
determining whether the demand response potential power is greater than a current maximum load supply capacity;
and if so, increasing the maximum load supply capacity and outputting alarm information.
7. A power demand response potential assessment system, comprising:
the load decomposition model construction module of the special transformer user is used for constructing a special transformer user load decomposition model based on an STL algorithm, and decomposing the load of the special transformer user to obtain a load period component, wherein the input quantity in the special transformer user load decomposition model is a load observed quantity of the special transformer user in a specified time before a response day;
the platform power determination model construction module is used for constructing a load curve platform power determination model based on an S-G filtering algorithm, determining power capable of representing a load curve platform, and each local minimum value point in the load periodic component processed by the S-G filtering algorithm can represent the power of the load curve platform where the local minimum value point is located;
and the demand response potential power calculation module is used for determining the real-time load power of the load periodic component at the response starting time by utilizing the special variable user load decomposition model and the load curve platform power determination model according to the specified starting time of the demand response, and solving difference values of all load curve platform powers smaller than the real-time load power, wherein the maximum value is the demand response potential power.
8. The power demand response potential evaluation system of claim 7, wherein the specific transformer user load decomposition model building module comprises an inner loop determination unit, a robustness weight term calculation unit, and a smoothing unit; wherein,
the internal circulation determining unit is used for determining internal circulation through a load component, removing a load trend component of the load of the special transformer user as an input load sequence, and filtering a low flux of a load subsequence to obtain a load period component and a load residual component, wherein the load trend component represents a load which is continuously operated and not cut in a plurality of preset sampling days in the production process of the special transformer user and is used for reflecting the change of the production scale in the day of the sampling days; the load cycle component represents the regular power load extracted from the sampling day, is used for reflecting the daily production or business plan and reflecting the change rule of the daily power load; the load residual component represents sudden load fluctuations outside of the planned production;
the robust weight item calculation module is used for calculating a robust weight item to control data to generate an abnormal value in the process of load decomposition, and substituting the weight value into the inner loop for operation to realize a robust weight balance outer loop;
and the smoothing processing unit is used for carrying out post-smoothing on the load period component based on local quadratic fitting after the circulation is finished.
9. An electric power demand response potential evaluation apparatus characterized by comprising:
a memory and a processor; wherein the memory is adapted to store a computer program which when executed by the processor implements the steps of the power demand response potential assessment method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the steps of the power demand response potential assessment apparatus method according to any one of claims 1 to 6.
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