CN114819591A - Power demand response potential evaluation method, system and related equipment - Google Patents

Power demand response potential evaluation 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
Zhejiang Huayun Information Technology Co Ltd
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Abstract

The invention discloses a power demand response potential evaluation method, a system and related equipment, wherein the method comprises the following steps: s1, constructing a load decomposition model of the special transformer user based on the STL algorithm, and decomposing the load of the special transformer user to obtain a load period component; 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 periodic component of the load at the response starting time by using the load decomposition model of the special transformer user and the load curve platform power determination model according to the specified starting time of the demand response, and solving the difference of all load curve platform powers smaller than the real-time load power, wherein the maximum value is the demand response potential power. The demand response potential of the special transformer user can be accurately obtained, and the management efficiency is improved.

Description

Power demand response potential evaluation method, system and related equipment
Technical Field
The invention relates to the technical field of intelligent power management, in particular to a method, a system and related equipment for evaluating power demand response potential.
Background
With the increasing of the load of the power grid in recent years, the peak-to-valley difference of the load is increasing, and the peak-load modulation and frequency modulation become the difficulty for building the power grid with high reliability. In the face of transient peak load demands, the conventional method is to increase and build a generator set and a matched transmission and distribution network for coping, the utilization rate of the equipment is low, and the economic benefit is not high.
Demand side management is an important way to intelligently consume power. By formulating effective and reasonable rules, under the condition of not influencing the basic power demand of users, the power utilization of user groups is guided according to the direction favorable for the running of the power grid, the power utilization efficiency is improved, and the reliability of the power grid is enhanced.
The demand response is a market behavior, the power grid side provides various price policies and incentive policies, and when a user selects to respond, the user can obtain income by changing the self power utilization mode; it is possible to improve the grid system load pressure for the grid. The demand response takes the intelligent power grid as an implementation state, helps users actively participate in power grid regulation, improves power utilization economy, saves resources, reduces energy consumption, accelerates development of a power market mechanism, and improves reliability and stability of the power grid.
The user can decide whether to participate in demand response or not, and the effect of the demand side response depends on the electricity utilization behavior and the response habit of the user. And (3) analyzing the holographic data of the demand side and deeply mining the historical power utilization behavior of the user, and selecting high-quality potential users, which is favorable for improving the implementation efficiency of demand response.
Disclosure of Invention
The invention aims to provide a power demand response potential evaluation method, a power demand response potential evaluation system and related equipment, which can accurately obtain the demand response potential of a special transformer user, provide scientific guidance for power company to implement demand side management, and are used for screening high-quality users participating in demand response and directionally exciting users participating in demand response attitude negative.
In order to solve the above technical problem, an embodiment of the present invention provides a power demand response potential evaluation method, including:
s1, constructing a load decomposition model of the special transformer user based on an STL algorithm, and decomposing the load of the special transformer user to obtain a load cycle component, wherein the input quantity in the load decomposition model of the special transformer user 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.
Wherein the S1 includes:
the load of the special transformer user is taken as an input load sequence to remove a load trend component, and a load subsequence is subjected to low-flux filtering to obtain a load period component and a load residual component;
wherein, the load trend component represents the load which is continuously operated and not cut off in a plurality of preset sampling days in the production process of the specific transformer user, and is used for reflecting the change of the production scale in the day of the sampling day; 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.
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.
Wherein the determining the inner loop by the load component comprises:
s11, removing the trend amount of the last iteration from the load sequence sampled by multiple days
Figure BDA0003605930920000021
Figure BDA0003605930920000031
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 smoothed result is noted as
Figure BDA0003605930920000032
S12, smoothing the result
Figure BDA0003605930920000033
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 BDA0003605930920000034
S13, obtaining the periodic component of the multi-day load sequence,
Figure BDA0003605930920000035
s14, a de-cycling,
Figure BDA0003605930920000036
s15, for
Figure BDA0003605930920000037
Smoothing by using LOESS algorithm to obtain the load trend component
Figure BDA0003605930920000038
Judgment of
Figure BDA0003605930920000039
Convergence, if convergence, outputting the result, otherwise returning to step S11,
Figure BDA00036059309200000310
wherein,
Figure BDA00036059309200000311
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 BDA00036059309200000312
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 loses smoothing parameters in S12, S13, S14, respectively.
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 BDA0003605930920000041
Figure BDA0003605930920000042
where v is the position of the load point in the load sequence, δ v Are robustness weights.
Wherein after the S3, the method further comprises:
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.
In addition, an embodiment of the present application further provides a power demand response potential evaluation system, including:
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;
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.
The construction module of the load decomposition model of the special transformer user comprises an inner loop determination unit, a robustness weight term calculation unit and a smoothing processing 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 performing low-flux filtering on 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 in a plurality of preset sampling days in the production process of the special transformer user and is not cut off, and is used for reflecting the change of the production scale in 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.
Besides, an embodiment of the present application provides a power demand response potential evaluation apparatus, including:
a memory and a processor; wherein the memory is configured to store a computer program and the processor is configured to implement the steps of the power demand response potential assessment method as described above when executing the computer program.
In addition, embodiments of the present application also provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the power demand response potential evaluation apparatus method as described above.
Compared with the prior art, the power demand response potential evaluation method, the power demand response potential evaluation system and the related equipment provided by the embodiment of the invention have the following advantages:
the power demand response potential evaluation method, the system and the related equipment decompose the load of the special transformer user by adopting the STL algorithm to obtain the load periodic component, then the load periodic component is processed by the S-G filtering algorithm, each local minimum value point can represent the power of the load curve platform, finally, the real-time load power of the load periodic component at the response starting time is determined according to the specified starting time of the demand response, the difference value is calculated for all load curve platform powers smaller than the real-time load power, the maximum value is demand response potential power, the demand response potential of the special transformer user can be accurately obtained by the method, scientific guidance is provided for the power company to implement demand side management, users who are screened for good quality participation in demand responses and users who are oriented to encourage negative participation in demand response attitude.
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In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions in the prior art are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart illustrating steps of an embodiment of a power demand response potential assessment method according to the present invention;
FIG. 2 is a flow chart illustrating the steps of inner loop determination in an embodiment of the power demand response potential assessment method provided by the present invention,
FIG. 3 is a schematic structural diagram of a power demand response potential evaluation system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of load split results and components of an STL algorithm based system for assessing the potential of a power demand response provided by the present invention;
fig. 5 is a comparison diagram before and after the load periodicity curve filtering based on the S-G algorithm of the power demand response potential evaluation system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 5, fig. 1 is a schematic flow chart illustrating steps of an embodiment of a power demand response potential evaluation method according to the present invention; fig. 2 is a schematic flow chart illustrating steps of inner loop determination in an embodiment of a power demand response potential evaluation method provided by the present invention, and fig. 3 is a schematic structural diagram illustrating a power demand response potential evaluation system provided by an embodiment of the present invention; FIG. 4 is a schematic diagram of load split results and components of an STL algorithm based system for assessing the potential of a power demand response provided by the present invention; fig. 5 is a comparison diagram before and after the load periodicity curve filtering based on the S-G algorithm of the power demand response potential evaluation system according to an embodiment of the present invention.
In one embodiment, the power demand response potential assessment method includes:
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.
The method comprises the steps of decomposing the load of a special variable user by adopting an STL algorithm to obtain a load periodic component, processing the load periodic component by an S-G filtering algorithm, wherein each local minimum value point can represent the power of a load curve platform where the load periodic component is located, finally determining the real-time load power of the load periodic component at the response starting time according to the specified starting time of demand response, and calculating 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.
The present application does not limit the decomposition obtaining manner of the periodic component of the load, and in an embodiment, the S1 includes:
the load of the special transformer user is taken as an input load sequence to remove a load trend component, and a load subsequence is subjected to low-flux filtering to obtain a load period component and a load residual component;
wherein, the load trend component represents the load which is continuously operated and not cut off in a plurality of preset sampling days in the production process of the specific transformer user, and is used for reflecting the change of the production scale in the day of the sampling day; 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 filter used for providing the filtering is not limited, and a worker can select a proper filter according to needs.
Since the load of the specific variable user is mainly decomposed by using the STL algorithm to obtain the load period component in the present application, a specific calculation process in the process is not limited, and in an embodiment, 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.
Since the coincidence period component is mainly obtained through the inner loop, the specific calculation process is not limited, and in a specific embodiment, the determining the inner loop through the load component includes:
s11, removing the trend quantity of the last iteration from the load sequence sampled by multiple days
Figure BDA0003605930920000081
Figure BDA0003605930920000082
S12, 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 BDA0003605930920000083
S13, smoothing the result
Figure BDA0003605930920000091
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 BDA0003605930920000092
S14, obtaining the periodic component of the multi-day load sequence,
Figure BDA0003605930920000093
s15, a de-cycling,
Figure BDA0003605930920000094
s16, for
Figure BDA0003605930920000095
Smoothing by using LOESS algorithm to obtain the load trend component
Figure BDA0003605930920000096
Judgment of
Figure BDA0003605930920000097
Convergence, if convergence, outputting the result, otherwise returning to step S11,
Figure BDA0003605930920000098
wherein,
Figure BDA0003605930920000099
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 BDA00036059309200000910
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 loses smoothing parameters in S12, S13, S14, respectively.
The method includes, but is not limited to the above calculation method, and the staff may also choose to adopt other types of calculation methods.
In this application, the calculation and operation process of the robustness weight term are not limited, and in one embodiment, the calculation of the robustness weight term includes:
the robustness weight term is calculated using the following formula,
δ v ＝6*f median (|R v| )；
Figure BDA0003605930920000101
Figure BDA0003605930920000102
where v is the position of the load point in the load sequence, δ v Are robustness weights.
The main objective of the present application is to calculate and obtain the demand response potential of the user, implement demand-side management, and improve management efficiency, and inevitably, the situation of insufficient power supply will occur, so that it is necessary to implement appropriate increase in charge of power supply, so as to improve the power utilization reliability of the power consumer and meet the demand of power consumption, thereby ensuring its normal production life, and therefore, in an embodiment, after S3, the present application further includes:
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.
Of course, it should be noted that, due to changes in management, production efficiency, production volume, and the like, different enterprises or units may not only increase loads but also decrease power loads, and in order to achieve efficient power management, dynamic management of loads of different users may be achieved, thereby achieving more efficient power management.
In the application, a load curve platform is defined in a summary mode to be a power range of a state that equipment combinations put into use in production of a special transformer user are not changed, when new equipment is put into use or the equipment in use is cut off, a load curve can suddenly rise or suddenly fall, and a new load curve platform is achieved. In the load periodic component, a certain load fluctuation still exists on one load curve platform, and the load platform with too short duration is less than the time required by the demand response, so that the load curve platform is not suitable to be used as a basis for evaluating the demand response potential of the special transformer user.
Thus, the power that can represent the load curve plateau is determined using the S-G filter algorithm. The principle of the S-G filtering algorithm, i.e. the load curve plateau representative power, is determined as follows.
Each local minimum value point in the load periodic component processed by the S-G filtering algorithm can represent the power P of the load curve platform where the local minimum value point is located Loc_min ＝{P 1,L_min ,P 2,L_min ,……,P i,L_min ,……,P k,L_min }。
The real-time load power of the load periodic component of the special transformer user at the response starting time is determined according to the starting time of the demand response required by the power company, and the difference value is solved for all load curve platform powers of the user which are smaller than the real-time load power of the user, wherein the maximum value is the demand response potential power of the user.
Figure BDA0003605930920000111
In addition, an embodiment of the present application further provides a power demand response potential evaluation system, including:
the dedicated transformer user load decomposition model building module 10 is used for building a dedicated transformer user load decomposition model based on an STL algorithm, and decomposing the load of a dedicated transformer user to obtain a load period component, wherein the input quantity in the dedicated transformer user load decomposition model is a load observation quantity of the dedicated transformer user in a specified time before a response day;
the platform power determination model construction module 20 is configured to construct a load curve platform power determination model based on an S-G filtering algorithm, determine power capable of representing a load curve platform, and determine that 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 30 is configured to determine, according to the specified start time of the demand response, the real-time load power of the load periodic component at the response start time by using the dedicated-transformer user load decomposition model and the load curve platform power determination model, and calculate a difference value for all load curve platform powers smaller than the real-time load power, where a maximum value is the demand response potential power.
Since the power demand response potential evaluation system is a system corresponding to the power demand response potential evaluation method, the same beneficial effects are achieved, and details are not repeated in the present application.
In the application, the process of load decomposition by using the STL algorithm is not limited, and in one embodiment, the dedicated transformer user load decomposition model construction module includes 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 performing low-flux filtering on 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 in a plurality of preset sampling days in the production process of the special transformer user and is not cut off, and is used for reflecting the change of the production scale in 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;
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.
In the application, the main links for load decomposition by using the STL algorithm comprise three parts of load component determination inner circulation, robustness weight balance outer circulation and load period component smoothing.
(1) Load component determination inner loop
In the step of determining the internal circulation link of the load component, the load trend component T is removed from the input load sequence v Low-pass filtering the load subsequence to obtain a load period component S v And a load residual component R v . Is provided with
Figure BDA0003605930920000121
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 BDA0003605930920000122
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 process of the LOESS smoothing parameters in the second step, the third step and the fourth step is shown in the attached figure 1.
Removing last iteration trend quantity of load sequence sampled from multiple days
Figure BDA0003605930920000123
Figure BDA0003605930920000124
Secondly, performing LOESS regression processing on each load subsequence, and respectively prolonging a cycle period before and after the LOESS regression processing, wherein the smoothing parameter is n (s) The smoothing result is recorded as
Figure BDA0003605930920000125
Thirdly, carrying out LOESS regression processing on the subsequence, namely, smoothing results in the second step
Figure BDA0003605930920000131
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 BDA0003605930920000132
Obtaining the periodic component of the multi-day load sequence:
Figure BDA0003605930920000133
and fifthly, removing the cycle:
Figure BDA0003605930920000134
sixthly, to
Figure BDA0003605930920000135
Smoothing by using LOESS algorithm to obtain load trend component
Figure BDA0003605930920000136
Judgment of
Figure BDA0003605930920000137
Convergence, if convergence, outputting the result, otherwise returning to the step (r):
Figure BDA0003605930920000138
(2) robust weight balancing outer loop
The outer loop is used for calculating a robustness weight item to control abnormal values generated by data in the process of load decomposition, and substituting the weight value into the inner loop for operation, and the following steps are provided:
δ v ＝6*f median (|R v |)
Figure BDA0003605930920000139
Figure BDA00036059309200001310
where v is the position of the load point in the load sequence, δ v Are robustness weights.
(3) Load cycle component post smoothing
After the loop is over, the load in the periodic component may have glitches because the smoothing in the inner loop is only performed in every window. After the load sequences are integrated according to the load sample time, the smoothness of the whole load sample sequence cannot be guaranteed. Post-smoothing of the load cycle component is based on local quadratic fit and does not require robustness iteration in loess.
Three components obtained after STL decomposition, wherein the load trend component T v The continuous operation of the load without cutting off in the days of a plurality of sampling days in the production process of the special transformer user can represent the change of the production scale in the daytime of the sampling days and the load period component S v The system represents regular electric load extracted from a plurality of sampling days, can reflect daily production or business plans, and reflects the change rule of the daily electric load. Residual component of loadR v Representing sudden load fluctuations outside of the planned production.
And defining a load curve platform as a power range of a state that the equipment combination put into use in the production of the special transformer user is not changed, wherein when new equipment is put into or used equipment is cut off, the load curve can suddenly rise or fall, and the new load curve platform is reached. In the load periodic component, a certain load fluctuation still exists on one load curve platform, and the load platform with too short duration is less than the time required by the demand response, so that the load curve platform is not suitable to be used as a basis for evaluating the demand response potential of the special transformer user. Thus, the power that can represent the load curve plateau is determined using the S-G filter algorithm.
Each local minimum value point in the load periodic component processed by the S-G filtering algorithm can represent the power P of the load curve platform where the local minimum value point is located Loc_min ＝{P 1,L_min ,P 2,L_min ,……,P i,L_min ,……,P k,L_min }。
The real-time load power of the load periodic component of the special transformer user at the response starting time is determined according to the starting time of the demand response required by the power company, and the difference value is solved for all load curve platform powers of the user which are smaller than the real-time load power of the user, wherein the maximum value is the demand response potential power of the user.
Figure BDA0003605930920000141
Besides, an embodiment of the present application provides a power demand response potential evaluation apparatus, including:
a memory and a processor; wherein the memory is configured to store a computer program and the processor is configured to implement the steps of the power demand response potential assessment method as described above when executing the computer program.
The processor of the power demand response potential evaluation device is used for implementing the steps of the power demand response potential evaluation method described above when executing the computer program, and the method has the same advantages, which are not limited in the present application.
The type of the power demand response potential evaluation device is not described in detail in the present application.
In addition, embodiments of the present application also provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the power demand response potential evaluation apparatus method as described above.
Similarly, since the computer readable storage medium stores the computer program, and the computer program is executed by the processor to implement the steps of the power demand response potential assessment apparatus method described above, the same beneficial effects are achieved, and details of the method are not repeated herein.
The type of the computer-readable storage medium is not limited in the present application, and may be a CDROM, an EEPROM, a usb disk, a removable hard disk, or other storage media.
In one embodiment, the customer industry is foundry, and the results of the load split 15 days before the response day are shown in FIG. 4. Analyzing the load decomposition result, and obtaining a load periodic component S v Therefore, the production mode of the user is day production, rest at night and slight peak avoidance at noon; by the load trend component T v It can be known that the user variably schedules the production scale in the 15 days, and the whole body shows a descending trend on the basis of certain fluctuation; loaded residual component R v It is known that there are certain sudden load fluctuations outside the production plan.
The periodic load component of the user in one day is extracted and S-G filtered, and the periodic load component of the user and the S-G filtered result are shown in FIG. 5.
Comparing before and after filtering, it can be found that minor load fluctuations have been smoothed in order not to affect the load platform determination for demand response capability. It is reasonable that large load fluctuations occurring around 8:00 a.m. are also smoothed. Although the load difference due to the fluctuation is large, the duration is short, and the load reduction requirement that the demand response lasts for half an hour or more is not always satisfied.
Finally, 3 load platforms are obtained by utilizing an objective demand response capacity determination model based on an S-G filtering algorithm, and the corresponding power values are P 1,L_min ＝559.93kW，P 2,L_min ＝1816.73kW，P 3,L_min 0, response front load P corresponding to response start time DR_p 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%. The reasonability of the demand response objective ability determination model can be illustrated, and the accuracy of the determination result can be determined.
In summary, the power demand response potential evaluation method, system and related apparatus provided by the embodiments of the invention, decomposing the load of the special transformer user by adopting an STL algorithm to obtain a load periodic component, then processing the load periodic component by an S-G filtering algorithm, each local minimum value point can represent the power of the load curve platform, finally, the real-time load power of the load periodic component at the response starting time is determined according to the specified starting time of the demand response, the difference value is calculated for all load curve platform powers smaller than the real-time load power, the maximum value is demand response potential power, the demand response potential of the special transformer user can be accurately obtained by the method, scientific guidance is provided for the power company to implement demand side management, for screening users who participate in demand response with good quality and for targeting users who participate in demand response with negative attitude.
The power demand response potential evaluation method, system and related devices provided by the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the 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.
CN202210415872.9A 2022-04-20 2022-04-20 Power demand response potential evaluation method, system and related equipment Pending CN114819591A (en)

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