CN113408820B - Adjustable potential mining method, system and equipment for heat accumulating type electric boiler user - Google Patents
Adjustable potential mining method, system and equipment for heat accumulating type electric boiler user Download PDFInfo
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Abstract
The invention discloses a method, a system and equipment for mining adjustable potential of a heat accumulating type electric boiler user, wherein S1, historical data of the user are obtained, the historical data are preprocessed, and the preprocessed historical data are converted into daily load curve sequence vectors; s2, clustering the sequence vectors of the daily load curves based on a k-means algorithm, extracting typical daily loads, and drawing typical daily load curves; s3, solving the residual capacity, the heat supply demand constraint, the heat accumulating type electric boiler power constraint, the maximum heat accumulation constraint and the like in the previous day based on the predicted daily load curve and the typical daily load curve; and S4, solving a load optimization curve with the minimum electricity price by combining attribute constraints of the heat accumulating type electric boiler through a bubbling sequencing method. According to the invention, the constraint conditions can be reversely solved only through the typical daily load data of the heat accumulating type electric boiler.
Description
Technical Field
The invention relates to the technical field of new energy industry, in particular to a method and a system for mining adjustable potential of a heat accumulating type electric boiler user.
Background
In China, new energy industry is developed vigorously in recent years, the electric energy level of new energy is the first in the world, however, due to unbalanced supply and demand of new energy markets, the problem of wind and light abandonment in the three north areas is serious, a large amount of resources are wasted, and the new energy industry is not in accordance with the original intention of wind power green energy. In the face of the current situation of insufficient new energy consumption, how to utilize a data mining technology and efficiently utilize fine-grained data obtained by smart grid infrastructure to intelligently manage a smart grid is to solve the problem that the new energy consumption in the three north areas of China is insufficient and becomes a key concern.
The wind power consumption in winter is mainly caused by three reasons: 1. the heating in winter is just needed, the thermal power needs to be operated in a minimum mode, the peak regulation potential of the wind power is reduced, and the wind power consumption space is lowered; 2. the power market is not perfect enough, and the power demand is not enough; 3. in part of areas, the winter is in a strong wind period, and the wind power is sufficient. These restrict the development of wind power. The heat accumulating type electric boiler is additionally arranged in the power grid area with insufficient wind power consumption, wind power can be consumed on the spot by utilizing the characteristic that the heat accumulating type electric boiler is flexible in power utilization, and wind power utilization can be effectively developed. However, many users of the heat accumulating type electric boiler do not use electricity in the valley period, so that the heat accumulating type electric boiler does not play a good role in stabilizing the power grid.
Therefore, aiming at the problem that the excavation of the adjustable value of the heat accumulating type electric boiler user is not sufficient, how to provide the excavation method and the excavation system of the adjustable potential of the heat accumulating type electric boiler user is a problem to be solved urgently by technical personnel in the field.
Disclosure of Invention
In view of the above, the invention provides a method, a system and equipment for mining the user-adjustable potential of a heat accumulating type electric boiler, and the constraint conditions of the heat accumulating type electric boiler can be reversely solved only through typical daily load data of the heat accumulating type electric boiler.
In order to achieve the purpose, the invention adopts the following technical scheme:
a heat accumulating type electric boiler user adjustable potential mining method comprises the following steps:
s1, acquiring historical data of a user, preprocessing the historical data, and converting the preprocessed historical data into daily load curve sequence vectors;
s2, clustering the sequence vectors of the daily load curves based on a k-means algorithm, extracting typical daily loads, and drawing typical daily load curves;
s3, solving the residual heat storage capacity, the heat supply demand constraint, the maximum power constraint of the heat storage type electric boiler, the maximum heat storage constraint and the like in the previous day based on the predicted daily load curve provided by the existing load prediction system of the power grid and the typical daily load curve extracted in the step two, and if the data collected by the power grid has relevant constraints, using the data collected by the power grid;
wherein, the heat supply demand constraint and the maximum heat storage capacity constraint are respectively:
Ph(j)*h≤(Eah+Ed(j)-Eh(j))
0≤Pd*h≤Emax
in the formula, Ph(j) The virtual heating demand of the user at moment j, h is the number of hours, EahThe heat accumulating type electric boiler remains for the previous dayThe remainder, Ed(j) Integrating the actual power consumption for time j, Eh(j) Accumulating the virtual heat supply amount for j moment; pdTo the actual power demand of the user, EmaxTaking the maximum value of the residual capacity on the day as the maximum heat storage capacity of the heat storage type electric boiler;
and S4, solving a load optimization curve with the minimum electricity price by combining attribute constraints of the heat accumulating type electric boiler through a bubbling sequencing method.
Preferably, the virtual heating demand P of the user at time jh(j) The specific calculation method comprises the following steps:
wherein, Pa(j) And T is the total number of the points in the time period.
Preferably, the remaining amount of the regenerative electric boiler of the previous day EahThe specific calculation method comprises the following steps:
Ps(0)＝0
Ps(j)＝Ps(j-1)+Pd(j)-Ph(j)
Eah＝-min(Ps)/z
in the formula, wherein Ps(j) The residual capacity of a user at the moment j, z is the frequency of collected power per hour, and the residual capacity of the heat accumulating type electric boiler on the previous day is set to be 0, Pd(j) Is the actual power demand of the user at time j, Ph(j) The virtual heating demand of the user at time j.
Where, if data is collected once in 15 minutes, 60/15 is 4, and 4 times an hour, z is 4.
Preferably, the actual electricity consumption E is accumulated at time jd(j) J, the virtual heat supply E is accumulated at timeh(j) And maximum heat storage capacity E of heat storage type electric boilermaxThe calculation methods are respectively as follows:
preferably, the specific contents of S4 include:
1) through making heat accumulation formula electric boiler charges of electricity minimum for the user saves the charges of electricity, and the electric wire netting is despatched the peak and is filled valley, and steady load is undulant, sets up the objective function:
in the formula, P is load power, C is electric charge, T is electricity utilization time, and T is an optimization time interval;
2) comparing adjacent power points in the daily load power curve: under the condition of meeting the constraint, after the two sequences are exchanged, if the former has lower cost than the latter, the sequences of two adjacent power points are exchanged;
3) sequentially carrying out the work of the step 2 on each pair of adjacent power points in the daily load power curve from beginning to end;
4) performing the switching step in the step 3 on all the power points except the last power point;
5) and (4) fixing the last power point each time step 4 is executed, and circularly executing the steps to fix the remaining non-fixed power points until no power point needs to be compared.
A user-adjustable potential mining system for regenerative electric boilers, comprising: the system comprises a data preprocessing module, a typical daily load drawing module, a heat accumulating type electric boiler constraint solving module and an adjustable potential mining module;
the data preprocessing module is used for acquiring historical data of a user, preprocessing the historical data and converting the preprocessed historical data into daily load curve sequence vectors;
the typical daily load drawing module is used for clustering the daily load curve sequence vectors based on a k-means algorithm, extracting typical daily loads and drawing typical daily load curves;
the constraint solving module of the heat accumulating type electric boiler is used for solving the residual heat accumulation capacity, the heat supply requirement constraint, the maximum power constraint of the heat accumulating type electric boiler, the maximum heat accumulation constraint and the like in the previous day based on a predicted daily load curve provided by the existing load prediction system of the power grid and a typical daily load curve extracted based on a k-means algorithm, and if relevant constraints exist in the data collected by the power grid, the data collected by the power grid is used.
Wherein, the heat supply demand constraint and the maximum heat storage capacity constraint are respectively:
Ph(j)*h≤(Eah+Ed(j)-Eh(j))
0≤Pd*h≤Emax
in the formula, Ph(j) The virtual heating demand of the user at moment j, h is the number of hours, EahFor the surplus of the heat accumulating type electric boiler of the previous day, Ed(j) Integrating the actual power consumption for time j, Eh(j) Accumulating the virtual heat supply amount for j moment; pdTo the actual power demand of the user, EmaxTaking the maximum value of the residual capacity on the day as the maximum heat storage capacity of the heat storage type electric boiler;
the adjustable potential mining module is used for solving a load optimization curve with the minimum electricity price by combining attribute constraints of the heat accumulating type electric boiler through a bubbling sequencing method.
A user-adjustable potential excavating device for a heat accumulating type electric boiler comprises a processor and a memory connected with the processor;
the memory is used for storing a computer program;
the processor is used for calling and executing the computer program in the memory so as to execute the user-adjustable potential mining method of the regenerative electric boiler.
Compared with the prior art, the invention discloses and provides the method, the system and the equipment for excavating the adjustable potential of the heat accumulating type electric boiler user, and the method, the system and the equipment have the following beneficial effects:
1) compared with the violent enumeration method, the method has the calculation complexity of O (kn! ) The content is reduced to O (klogn), and the working efficiency is greatly improved;
2) complicated parameters of the heat accumulating type electric boiler and artificial wish investigation results do not need to be collected, and based on historical electric power data driving, the heat accumulating type electric boiler residual capacity, the maximum heat accumulating capacity of the heat accumulating type electric boiler and the like of a user are simulated in a reverse mode, so that the constraint of the heat accumulating type electric boiler is met by the scheme design;
3) based on the segmented electricity price policy, the potential that the load of the user can be adjusted is excavated, the electricity price of the user is reduced, the integral peak clipping and valley filling of the power grid are facilitated, the power grid is more stable, and natural resources are more efficiently utilized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method for mining adjustable potential of a heat accumulating type electric boiler user provided by the invention;
FIG. 2 is a flow chart of constraint solving of a heat accumulating type electric boiler in the heat accumulating type electric boiler user adjustable potential mining method provided by the invention;
FIG. 3 is a graph showing an overall heat supply load per unit value curve in the method for mining the user-adjustable potential of the heat accumulating type electric boiler provided by the invention;
fig. 4 is a flowchart of S4 in the method for mining the user-adjustable potential of the heat accumulating type electric boiler provided by the 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.
The embodiment of the invention discloses a method for mining adjustable potential of a heat accumulating type electric boiler user, which comprises the following steps as shown in figure 1:
s1, acquiring historical data of a user, preprocessing the historical data, and converting the preprocessed historical data into daily load curve sequence vectors;
s2, clustering the sequence vectors of the daily load curves based on a k-means algorithm, extracting typical daily loads, and drawing typical daily load curves;
s3, as shown in the figure 2, solving the residual heat storage capacity, the heat supply requirement constraint, the maximum power constraint and the maximum heat storage constraint of the heat storage type electric boiler and the like in the previous day based on a predicted daily load curve provided by the existing load prediction system of the power grid and the typical daily load curve extracted in the step two, and if relevant constraints exist in the data collected by the power grid, using the data collected by the power grid;
wherein, the heat supply demand constraint and the maximum heat storage capacity constraint are respectively:
Ph(j)*h≤(Eah+Ed(j)-Eh(j))
0≤Pd*h≤Emax
in the formula, Ph(j) The virtual heating demand of the user at moment j, h is the number of hours, EahFor the surplus of the heat accumulating type electric boiler of the previous day, Ed(j) Integrating the actual power consumption for time j, Eh(j) Accumulating the virtual heat supply amount for j moment; pdTo the actual power demand of the user, EmaxTaking the maximum value of the residual capacity on the day as the maximum heat storage capacity of the heat storage type electric boiler;
and S4, solving a load optimization curve with the minimum electricity price by combining attribute constraints of the heat accumulating type electric boiler through a bubbling sequencing method.
In order to further implement the technical scheme, the virtual of the user at the moment jHeat supply requirement Ph(j) The specific calculation method comprises the following steps:
wherein, Pa(j) And T is the total number of the points in the time period.
It should be noted that:
the overall curve of the per unit value of the heating load is shown in fig. 3, the overall trend is that the heating is high at night, the heating is low in the daytime, and is inversely proportional to the temperature, T is the total number of points calculated in the time period, if the daily load curve of 96 users, T is 96, and in this embodiment, T is 96.
In order to further implement the technical scheme, the previous day heat accumulating type electric boiler residual quantity EahThe specific calculation method comprises the following steps:
Ps(0)＝0
Ps(j)＝Ps(j-1)+Pd(j)-Ph(j)
Eah＝-min(Ps)/z
in the formula, wherein Ps(j) The residual capacity of the user at the moment j, z is the collected power frequency per hour, if data are collected once in 15 minutes, 60/15 is 4, and 4 times are collected once in one hour, z is 4, the residual capacity of the previous day heat accumulating type electric boiler is 0, and P isd(j) Is the actual power demand of the user at time j, Ph(j) The virtual heating demand of the user at the moment j.
It should be noted that:
accumulating the daily residual capacity to obtain the residual capacity of each point in the time interval; eahThe previous day is left, because if P iss(j) The minimum value is negative, namely the initial value is less than 0, which indicates that the boiler has residual capacity in the day before, so the negative value of the minimum residual capacity at each point in the time interval is taken as the residual capacity in the day before, if the minimum residual capacity is a daily load value of 96 points, namely data is collected once in 15 minutes and is collected 4 times in one hour, and therefore, the electric quantity value is the power value divided by 4.
To further realizeApplying the technical scheme, j is the accumulated actual electricity consumption Ed(j) J, the virtual heat supply E is accumulated at timeh(j) And maximum heat storage capacity E of heat storage type electric boilermaxThe calculation methods are respectively as follows:
it should be noted that:
Emaxthe maximum value of the residual capacity on the day is taken as the maximum heat storage capacity, and the actual power utilization requirement is met, so that the power utilization habit and the power utilization requirement of a user are met.
In order to further implement the above technical solution, as shown in fig. 4, the specific content of S4 includes:
1) through making heat accumulation formula electric boiler charges of electricity minimum for the user saves the charges of electricity, and the electric wire netting is despatched and is filled valley, and steady load is undulant, sets up the objective function:
in the formula, P is load power, C is electric charge, T is electricity utilization time, and T is an optimization time interval;
2) comparing adjacent power points in the daily load power curve: under the condition of meeting the constraint, after the two sequences are exchanged, if the former has lower cost than the latter, the sequences of two adjacent power points are exchanged;
3) sequentially carrying out the work of the step 2 on each pair of adjacent power points in the daily load power curve from beginning to end;
4) performing the switching step in the step 3 on all the power points except the last power point;
5) and (4) fixing the last power point each time step 4 is executed, and circularly executing the steps to fix the remaining non-fixed power points until no power point needs to be compared.
The embodiment of the invention also discloses a heat accumulating type electric boiler user adjustable potential excavating system, which comprises: the system comprises a data preprocessing module, a typical daily load drawing module, a heat accumulating type electric boiler constraint solving module and an adjustable potential mining module;
the data preprocessing module is used for acquiring historical data of a user, preprocessing the historical data and converting the preprocessed historical data into daily load curve sequence vectors;
the typical daily load drawing module is used for clustering the daily load curve sequence vectors based on a k-means algorithm, extracting typical daily loads and drawing typical daily load curves;
the heat accumulating type electric boiler constraint solving module is used for solving the residual heat accumulation capacity, the heat supply requirement constraint, the heat accumulating type electric boiler maximum power constraint, the maximum heat accumulation constraint and the like in the previous day based on a predicted daily load curve provided by the existing load prediction system of the power grid and a typical daily load curve extracted based on a k-means algorithm, and if relevant constraints exist in the data collected by the power grid, the data collected by the power grid is used;
wherein, the heat supply demand constraint and the maximum heat storage capacity constraint are respectively:
Ph(j)*h≤(Eah+Ed(j)-Eh(j))
0≤Pd*h≤Emax
in the formula, Ph(j) The virtual heating demand of the user at moment j, h is the number of hours, EahFor the surplus of the heat accumulating type electric boiler of the previous day, Ed(j) Integrating the actual power consumption for time j, Eh(j) Accumulating the virtual heat supply amount for j moment; pdTo the actual power demand of the user, EmaxTaking the maximum value of the residual capacity on the day as the maximum heat storage capacity of the heat storage type electric boiler;
the adjustable potential mining module is used for solving a load optimization curve with the minimum electricity price by combining attribute constraints of the heat accumulating type electric boiler through a bubbling sequencing method.
The embodiment of the invention also discloses a heat accumulating type electric boiler user adjustable potential excavating device which comprises a processor and a memory connected with the processor;
the memory is used for storing a computer program;
the processor is used for calling and executing a computer program in the memory so as to execute the user-adjustable potential mining method for the heat accumulating type electric boiler.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A heat accumulating type electric boiler user adjustable potential mining method is characterized by comprising the following steps:
s1, acquiring historical data of a user, preprocessing the historical data, and converting the preprocessed historical data into daily load curve sequence vectors;
s2, clustering the sequence vectors of the daily load curves based on a k-means algorithm, extracting typical daily loads, and drawing typical daily load curves;
s3, solving the residual heat storage capacity, the heat supply requirement constraint, the maximum power constraint and the maximum heat storage constraint of the heat storage type electric boiler on the previous day based on a predicted daily load curve provided by the existing load prediction system of the power grid and the typical daily load curve extracted in the step two, and using the data collected by the power grid if relevant constraints exist in the data collected by the power grid;
wherein, the heat supply demand constraint and the maximum heat storage capacity constraint are respectively:
Ph(j)*h≤(Eah+Ed(j)-Eh(j))
0≤Pd*h≤Emax
in the formula, Ph(j) The virtual heating demand of the user at moment j, h is the number of hours, EahThe surplus of the heat accumulating type electric boiler on the previous day, Ed(j) Integrating the actual power consumption for time j, Eh(j) Accumulating the virtual heat supply amount for j moment; pdTo the actual power demand of the user, EmaxTaking the maximum value of the residual capacity on the day as the maximum heat storage capacity of the heat storage type electric boiler;
s4, solving a load optimization curve with the minimum electricity price by combining attribute constraints of the heat accumulating type electric boiler through a bubbling sequencing method;
the specific content of S4 includes:
1) through making heat accumulation formula electric boiler charges of electricity minimum for the user saves the charges of electricity, and the electric wire netting is despatched the peak and is filled valley, and steady load is undulant, sets up the objective function:
in the formula, P is load power, C is electric charge, T is electricity utilization time, and T is an optimization time interval;
2) comparing adjacent power points in the daily load power curve: under the condition of meeting the constraint, after the two sequences are exchanged, if the former has lower cost than the latter, the sequences of two adjacent power points are exchanged;
3) sequentially carrying out the work of the step 2 on each pair of adjacent power points in the daily load power curve from beginning to end;
4) performing the switching step in the step 3 on all the power points except the last power point;
5) and (4) fixing the last power point each time step 4 is executed, and circularly executing the steps to fix the remaining non-fixed power points until no power point needs to be compared.
2. A method as claimed in claim 1, wherein the virtual heat demand P of the user at time j is the virtual heat demand of the userh(j) The specific calculation method comprises the following steps:
wherein, Pa(j) And T is the total number of the points calculated in the time period.
3. A method according to claim 1, wherein the user-adjustable potential of the regenerative electric boiler is derived from the remaining quantity E of the regenerative electric boiler on the previous dayahThe specific calculation method comprises the following steps:
Ps(0)＝0
Ps(j)＝Ps(j-1)+Pd(j)-Ph(j)
Eah＝-min(Ps)/z
in the formula, wherein Ps(j) The residual capacity of a user at the moment j, z is the frequency of collected power per hour, and the residual capacity of the heat accumulating type electric boiler on the previous day is set to be 0, Pd(j) Is the actual power demand of the user at time j, Ph(j) The virtual heating demand of the user at the moment j.
4. The method as claimed in claim 1, wherein the accumulated actual power consumption E at time j is used as the total potential of the regenerative electric boilerd(j) J, the virtual heat supply E is accumulated at timeh(j) And the maximum heat storage quantity E of the heat storage type electric boilermaxThe calculation methods are respectively as follows:
wherein z is the collected power frequency per hour.
5. A user-adjustable potential excavation system for a heat accumulating type electric boiler is characterized by comprising: the system comprises a data preprocessing module, a typical daily load drawing module, a heat accumulating type electric boiler constraint solving module and an adjustable potential mining module;
the data preprocessing module is used for acquiring historical data of a user, preprocessing the historical data and converting the preprocessed historical data into daily load curve sequence vectors;
the typical daily load drawing module is used for clustering the daily load curve sequence vectors based on a k-means algorithm, extracting typical daily loads and drawing a typical daily load curve;
the heat accumulating type electric boiler constraint solving module is used for solving the residual heat accumulation capacity, the heat supply requirement constraint, the heat accumulating type electric boiler maximum power constraint and the maximum heat accumulation constraint in the previous day based on a predicted daily load curve provided by the existing load prediction system of the power grid and a typical daily load curve extracted based on a k-means algorithm, and if relevant constraints exist in the data collected by the power grid, the data collected by the power grid is used;
wherein, the heat supply demand constraint and the maximum heat storage capacity constraint are respectively:
Ph(j)*h≤(Eah+Ed(j)-Eh(j))
0≤Pd*h≤Emax
in the formula, Ph(j) The virtual heating demand of the user at moment j, h is the number of hours, EahFor the surplus of the heat accumulating type electric boiler of the previous day, Ed(j) Integrating the actual power consumption for time j, Eh(j) Accumulating the virtual heat supply amount for j moment; pdTo the actual power demand of the user, EmaxTaking the maximum value of the residual capacity on the day as the maximum heat storage capacity of the heat storage type electric boiler;
the adjustable potential mining module is used for solving a load optimization curve with the minimum electricity price by combining attribute constraints of the heat accumulating type electric boiler through a bubbling sequencing method; the method specifically comprises the following steps:
1) through making heat accumulation formula electric boiler charges of electricity minimum for the user saves the charges of electricity, and the electric wire netting is despatched and is filled valley, and steady load is undulant, sets up the objective function:
in the formula, P is load power, C is electric charge, T is electricity utilization time, and T is an optimization time interval;
2) comparing adjacent power points in the daily load power curve: under the condition of meeting the constraint, after the two sequences are exchanged, if the former has lower cost than the latter, the sequences of two adjacent power points are exchanged;
3) sequentially carrying out the work of the step 2 on each pair of adjacent power points in the daily load power curve from beginning to end;
4) performing the switching step in the step 3 on all the power points except the last power point;
5) and (4) fixing the last power point each time step 4 is executed, and circularly executing the steps to fix the remaining non-fixed power points until no power point needs to be compared.
6. A user-adjustable potential excavating device for a heat accumulating type electric boiler is characterized by comprising a processor and a memory connected with the processor;
the memory is used for storing a computer program;
the processor is used for calling and executing the computer program in the memory to execute the user adjustable potential mining method of the regenerative electric boiler according to any one of claims 1 to 4.
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