CN113095669A - Comprehensive energy scheduling method and system based on energy consumption coupling coordination - Google Patents
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Abstract
The invention discloses a comprehensive energy scheduling method and a comprehensive energy scheduling system based on energy consumption coupling coordination, wherein the comprehensive energy scheduling method comprises the following steps: acquiring multi-element energy load data; acquiring secondary energy consumption behavior characteristics according to the multivariate energy consumption load data, acquiring weights according to the contrast strength and the conflict of the secondary energy consumption behavior characteristics, and giving weights to the secondary energy consumption behavior characteristics to obtain primary energy consumption behavior characteristics; and obtaining the energy utilization coupling coordination variation trend according to the coupling coordination of the primary energy utilization behavior characteristics, and scheduling the supply of the comprehensive energy according to the energy utilization coupling coordination variation trend. The method is characterized in that a comprehensive energy dynamic scheduling method aiming at the coupling coordination of the medium-term energy utilization and the long-term energy utilization of the comprehensive energy user is firstly established, and the medium-term energy utilization characteristic quantitative index system of the comprehensive energy user is utilized to carry out qualitative and quantitative analysis on the coupling characteristic of the energy utilization from multiple aspects, so that favorable conditions are provided for the construction, planning and scheduling of the comprehensive energy system.
Description
Technical Field
The invention relates to the technical field of comprehensive energy scheduling, in particular to a comprehensive energy scheduling method and system based on energy consumption coupling coordination.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The comprehensive energy system takes an electric power system as a core, integrates various energy supply forms such as electricity, gas, cold and heat and the like, and improves the energy supply efficiency through coordination and optimization in a multi-energy supply link. In the comprehensive energy system, different energy forms have the characteristic of high coupling, and if the coupling mechanism between the energy is analyzed in detail and corresponding scheduling is carried out according to the analysis, the utilization efficiency of the comprehensive energy can be obviously improved, and the user service is improved.
The inventor finds that with the continuous promotion of the construction of the comprehensive energy system, the energy consumption form of a user is diversified and developed, the coupling of electric energy and other energy forms becomes more intimate, and the traditional comprehensive energy system is not only lack of a quantitative analysis method for the coupling characteristics among the energy forms; and the traditional comprehensive energy analysis generally stays in stages of qualitative analysis or short-term qualitative and quantitative analysis and the like, and has defects on medium-long term user energy utilization characteristic mechanism research.
Disclosure of Invention
In order to solve the problems, the invention provides a comprehensive energy scheduling method and a comprehensive energy scheduling system based on energy consumption coupling coordination, aiming at the comprehensive energy dynamic scheduling method of the energy consumption coupling coordination in the middle and long term of a comprehensive energy user, firstly, a quantitative index system of the characteristics of the energy consumption in the middle and long term of the comprehensive energy user is established, and the index system is utilized to carry out qualitative and quantitative analysis on the energy consumption coupling characteristics from multiple aspects, thereby providing favorable conditions for the construction, planning and scheduling of the comprehensive energy system.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a comprehensive energy scheduling method based on energy consumption coupling coordination, including:
acquiring multi-element energy load data;
acquiring secondary energy consumption behavior characteristics according to the multivariate energy consumption load data, acquiring weights according to the contrast strength and the conflict of the secondary energy consumption behavior characteristics, and giving weights to the secondary energy consumption behavior characteristics to obtain primary energy consumption behavior characteristics;
and obtaining the energy utilization coupling coordination variation trend according to the coupling coordination of the primary energy utilization behavior characteristics, and scheduling the supply of the comprehensive energy according to the energy utilization coupling coordination variation trend.
In a second aspect, the present invention provides an integrated energy scheduling system based on energy consumption coupling coordination, including:
the data acquisition module is configured to acquire the multi-element energy load data;
the correlation analysis module is configured to acquire secondary energy utilization behavior characteristics according to the multivariate energy utilization load data, obtain weights according to the contrast strength and the conflict of the secondary energy utilization behavior characteristics, and obtain primary energy utilization behavior characteristics after the weights are given to the secondary energy utilization behavior characteristics;
and the coupling coordination module is configured to obtain an energy utilization coupling coordination change trend according to the coupling coordination of the primary energy utilization behavior characteristics, and to schedule the supply of the comprehensive energy according to the energy utilization coupling coordination change trend.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a comprehensive energy scheduling method for dynamically analyzing the medium-term and long-term energy utilization coupling coordination characteristics of a comprehensive energy user. Firstly, aiming at energy consumption load data curves of cold, heat, electricity and the like of a comprehensive energy user, establishing a medium-long term energy consumption behavior characteristic quantization index system, wherein the system consists of three primary indexes of electricity consumption behavior characteristics, electricity consumption behavior characteristics and electricity consumption behavior characteristics, each primary index consists of a plurality of secondary indexes, and each secondary index is subjected to weight calculation by a CRITIC method and added with weights to obtain a corresponding primary index; then, according to an energy consumption behavior characteristic index system, performing correlation analysis on each primary index and a secondary index of the comprehensive energy user, calculating coupling co-scheduling among the primary indexes of the three types of energy behavior characteristics, and performing qualitative and quantitative analysis and prediction on energy consumption coupling coordination of the energy user from multiple aspects by taking the coupling co-scheduling as a basis; and favorable conditions are provided for the construction, planning and scheduling of the comprehensive energy system, user service and the like.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of an integrated energy scheduling method based on energy consumption coupling coordination according to embodiment 1 of the present invention;
fig. 2 is a result of correlation analysis between the behavior feature indexes of each secondary energy consumption provided in embodiment 1 of the present invention;
fig. 3 is a line diagram of monthly coupling co-scheduling according to embodiment 1 of the present invention;
fig. 4 is a diagram of the fitting and prediction effects of the ARIMA model on the monthly coupling co-scheduling curve provided in embodiment 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the embodiment provides an integrated energy scheduling method based on energy consumption coupling coordination, including:
s1: acquiring multi-element energy load data;
s2: acquiring secondary energy consumption behavior characteristics according to the multivariate energy consumption load data, acquiring weights according to the contrast strength and the conflict of the secondary energy consumption behavior characteristics, and giving weights to the secondary energy consumption behavior characteristics to obtain primary energy consumption behavior characteristics;
s3: and obtaining the energy utilization coupling coordination variation trend according to the coupling coordination of the primary energy utilization behavior characteristics, and scheduling the supply of the comprehensive energy according to the energy utilization coupling coordination variation trend.
In step S1, in this embodiment, the multivariate energy consumption load data of the cold, heat and electricity of the comprehensive energy user to be analyzed within a certain time period is obtained, and the multivariate energy consumption load data is subjected to data cleaning;
preferably, the data cleansing includes abnormal data repair and missing data repair, wherein: and if the data missing is detected, repairing the missing value of the current time by using the data of the same time period of the previous day, and if the abnormal data is detected, repairing the missing value of the current time by using the data of the same time period of the previous day.
Preferably, the multivariate energy load data is acquired in units of months, and the multivariate energy load data in one year is acquired in the embodiment.
In the step S2, the primary energy consumption behavior features include medium-and long-term electricity consumption behavior features, medium-and long-term heat consumption behavior features, and medium-and long-term cold consumption behavior features, and each primary behavior feature index includes multiple secondary energy consumption behavior features, and a medium-and long-term energy consumption behavior feature index system of the comprehensive energy user is constructed by the primary energy consumption behavior features and the secondary energy consumption behavior features.
Preferably, the primary energy consumption behavior characteristic and the secondary energy consumption behavior characteristic are calculated in a month unit;
specifically, S2-1-1: the monthly electricity consumption behavior characteristics comprise monthly average load, monthly load rate, average peak-to-hour electricity consumption rate, average valley electricity coefficient, average peak-to-valley difference, maximum peak-to-valley difference, monthly load standard difference, average detail fluctuation, average load fluctuation rate, average peak regulation capacity, maximum peak regulation capacity, average demand response potential entropy and maximum demand response potential entropy, the calculation of the monthly energy consumption index depends on the daily energy index, wherein the calculation formula of each index is as follows:
the daily average load is the average value of the daily load curve of the user, the energy consumption level of the user is comprehensively reflected, and the calculation formula is as follows:
in the formula: l1 represents the daily average load, n is the number of samples per day, and L (t) is the load value at time t.
The daily load rate is the ratio of the average load of the user per day to the maximum load of the user per day, and represents the fluctuation of the user energy behavior, and the calculation formula is as follows:
L2＝Lav/Lmax (2)
in the formula: l2 denotes the daily load rate, LavAnd LmaxThe daily average load and the daily maximum load are indicated, respectively.
The peak power consumption rate refers to the ratio of the power consumed by the user in the peak power consumption period to the total power consumed in one day, and the calculation formula is as follows:
L3＝Lp/LZ (3)
in the formula: l3 denotes the peak power consumption, LpAnd LZRespectively representing the peak-time electricity consumption of the user and the total electricity consumption of the day.
The valley power coefficient refers to the ratio of the power consumed by the user in the power consumption valley period to the total power consumed in the day, and the calculation formula is as follows:
L4＝Lv/LZ (4)
in the formula: l4 denotes the valley power coefficient, LvAnd LZRespectively representing the user valley time electricity consumption and the total electricity consumption of the day.
The daily peak-valley difference refers to the difference between the maximum load and the minimum load of the user in each day, and the calculation formula is as follows:
S1＝Lmax-Lmin (5)
in the formula: s1 denotes the difference between the daily peak and trough, LmaxAnd LminMaximum load and minimum load values in a day, respectively.
The detail volatility refers to the volatility of the energy used by the user at adjacent time, and the mutation degree of the energy used by the user is measured, and the calculation formula is as follows:
in the formula: s2 represents detail fluctuation, n represents the number of load sampling points of the user every day, and L (t) and L (t-1) are the load values at the time t and the previous time respectively.
The daily load fluctuation rate refers to the total fluctuation of the load in one day, and the larger the daily load fluctuation rate is, the stronger the daily load fluctuation is, and the calculation formula is as follows:
in the formula: s3 represents daily load fluctuation rate, S represents standard deviation of daily load, and x represents average daily load.
The peak shaving capability refers to the magnitude of load that the user may reduce, and this embodiment considers that the larger the difference between the load at peak and the load at valley every day is, the larger the variable amount of user energy utilization is, that is, the larger the peak shaving capability is, and the calculation formula is as follows:
in the formula: d1Indicating the peak shaving ability, T, of the userpFor the moment when the daily energy power of the user is the highest, Lp(t) represents the load value at time t, and min (L) is the minimum value of the daily load of the user.
Demand response potential entropy the potential of demand response of a user is described by using information entropy, and the calculation formula is as follows:
in the formula: d2 represents demand response potential entropy, n represents load points per day, xiRepresenting the load of the user at the time i after normalization.
The average monthly load is the average value of the total load of the user in each month and each day, and the calculation formula is as follows:
in the formula: ML1 is average load, n is days per month, LiThe daily average load on day i.
The monthly load rate is the ratio of the average load of the current month day to the maximum load of the current month day, and the calculation formula is as follows:
in the formula: ML2 is the monthly load rate, LavAnd LmaxThe average load in the month and the maximum load in the month and the day, respectively.
The average peak-time power consumption rate is the average value of the peak-time power consumption rates of the current month and the day, and the calculation formula is as follows:
in the formula: ML3 is average peak hour power consumption rate, n is the number of days of the month, L3iThe peak-time power consumption rate on day i.
The average valley electric coefficient is the average value of the valley electric coefficients in one month, and the calculation formula is as follows:
in the formula: ML4 is the average valley power coefficient, n is the number of days of the month, L4iThe day-to-day electrical factor on day i.
The average peak-valley difference is the average value of the peak-valley differences in the current month, and the calculation formula is as follows:
in the formula: MS2 is mean peak-to-valley difference, n is the number of days of the month, S1iThe peak-to-valley difference on day i.
The maximum peak-valley difference is the maximum value of the peak-valley difference of the current month day, and the calculation formula is as follows:
MS3＝max(S1i) (15)
in the formula: MS3 maximum Peak to Val Difference, S1iThe peak-to-valley difference on day i.
The standard deviation of the monthly load is calculated as follows:
in the formula: MS4 is standard deviation of monthly load, n is the number of days of the month, LiThe load value on day i is shown.
The average detail volatility is the average value of detail volatility in the current month and day, and the calculation formula is as follows:
in the formula: MS5 is mean detail volatility, n is the number of days of the month, S2iThe daily detail fluctuation on day i.
The average load fluctuation rate is the average value of the load fluctuation rate in the current month day, and the calculation formula is as follows:
in the formula: MS6 is mean load fluctuation rate, n is the number of days of the month, S3iThe daily load fluctuation rate on day i.
The average peak regulation capacity is the average value of the peak regulation capacity in the current month, and the calculation formula is as follows:
in the formula: MD1 is the average peak shaving ability, n is the number of days of the month, D1iDaily peak shaver capacity on day i.
The maximum peak regulation capacity is the maximum value of the peak regulation capacity in the current month and day, and the calculation formula is as follows:
MD2＝max(D1i) (20)
in the formula: MD2 maximum Peak Regulation capability, S1iDaily peak shaver capacity on day i.
The average demand response potential entropy is an average value of demand response potential entropies in the current month, and the calculation formula is as follows:
in the formula: MD3 is the mean demand response potential entropy, D2iResponse potential entropy was required for the day demand on day i.
The maximum demand response potential entropy is the maximum value of the demand response potential entropy in the current month, and the calculation formula is as follows:
MD4＝max(D2i) (22)
in the formula: MD4 is the maximum demand response potential entropy, D2iResponse potential entropy was required for the day demand on day i.
S2-1-2: the monthly hot behavior characteristics and the monthly cold behavior characteristics comprise monthly average load, monthly load rate, average peak-valley difference, maximum peak-valley difference and monthly load standard deviation, and the calculation formulas are the same as the formulas (10), (11), (14), (15) and (16) in the electricity behavior characteristics.
Calculating a secondary energy consumption behavior characteristic index by adopting the methods of the steps S2-1-1 and S2-1-2 according to the acquired comprehensive energy user multivariate energy consumption data; and calculating the weight value of each secondary energy consumption behavior characteristic by adopting a CRITIC weight calculation method, and adding the weights to obtain a primary energy consumption behavior characteristic index comprising three aspects of medium-long term electricity consumption behavior characteristics, medium-long term heat consumption behavior characteristics and medium-long term cold consumption behavior characteristics.
Specifically, S2-2: the CRITIC method is a weight calculation method, the weight of each characteristic index is calculated according to the contrast strength and the conflict strength among the characteristic indexes of the secondary energy consumption behaviors, and the calculation steps are as follows:
1) normalization:
assuming that m months and n indexes are provided, different normalization methods are adopted for the forward indexes and the reverse indexes, wherein:
the method for normalizing the forward indexes comprises the following steps:
the reverse index normalization method comprises the following steps:
in the formula: i is 1,2, …, m, j is 1,2, …, n, aijActual value of j index representing i monthijAnd (4) an index value of the j-th item in the ith month after normalization.
2) And (3) correlation coefficient matrix calculation:
the correlation coefficient is a measure value for researching linear correlation degree between indexes, and in the CRITIC method, the correlation between the indexes is described according to the correlation coefficient between the indexes, and the specific formula is as follows:
in the formula: i 1,2, …, n, j 1,2, …, n, rijAnd the correlation coefficient between the i index and the j index is shown.
3) Calculating weights
Calculating the contrast strength and the conflict of each secondary energy consumption behavior characteristic index by using the correlation coefficient matrix obtained by calculation, wherein the contrast strength and the conflict are shown as the following formula:
in the formula: j is 1,2, …, n, σjIs mean square error, CI, of the j-th indexjContrast intensity, CT, representing the j-th indexjAnd the quantization index represents the conflict between the j-th index and other indexes.
And based on the contrast strength and the conflict of the secondary energy consumption behavior characteristic indexes, the information content contained in the secondary energy consumption behavior characteristic indexes is obtained by the following formula:
in the formula: j is 1,2, …, n, GjThe larger the value is, the larger the information content of the j-th index is represented, and the larger the empowerment is.
Objective weight W of j-th secondary energy consumption behavior characteristic indexjComprises the following steps:
and weighting the secondary energy consumption behavior characteristics according to the weight to obtain a primary energy consumption behavior characteristic index.
In step S3, before calculating the coupling co-scheduling of the primary energy consumption behavior characteristics, correlation analysis is performed on all the energy consumption behavior characteristic indicators, and the correlation degree between the secondary energy consumption behavior characteristic indicators and the primary energy consumption behavior characteristic indicators are analyzed.
Preferably, by adopting a Person correlation coefficient, the correlation and the correlation degree between the primary energy consumption behavior characteristic indexes and between the secondary energy consumption behavior characteristic indexes of each month in a year of the user are analyzed, and the calculation formula is as follows:
wherein x and y represent two indexes for calculating the correlation coefficient respectively, i represents the ith sample, and N represents the number of samples.
And secondly, calculating the coupling co-scheduling of the first-level energy consumption behavior characteristic indexes, and analyzing the static characteristics and the dynamic characteristics of the coupling co-scheduling of each month.
Preferably, since the coupling degree represents the degree of mutual influence among different energy consumption behavior characteristic indexes, but cannot reflect the level of each index, the embodiment introduces a coupling co-scheduling index, and the coupling co-scheduling index reflects the magnitude of benign coupling degree after coupling of each energy consumption behavior index, and reflects both the coupling degree among the indexes and the value of each index.
In this embodiment, three energy sources, i.e., electricity, cold and heat, are taken as an example, and the calculation formula of the coupling degree is as follows:
wherein, U1、U2、U3Respectively is a first-level evaluation index of each energy every day; and C is the coupling degree of the three indexes in the day.
The calculation formula of the coordination degree by taking three energy sources of electricity, cold and heat as an example is as follows:
T＝β1U1+β2U2+β3U3 (31)
wherein, beta1、β2、β3The weight coefficients of each energy source are respectively required to be specified in the calculation process.
The coupling co-scheduling calculation formula is as follows:
in the step S3, fitting the monthly energy consumption behavior coupling co-scheduling based on an arima model to obtain a variation trend of the coupling co-scheduling; the arima model refers to a difference integration moving average autoregressive model, which comprises an autoregressive order p, a difference order d and a moving average order q, and is expressed as:
wherein L is a hysteresis operator;
in the embodiment, the fitting and trend analysis of monthly coupling co-scheduling are carried out by adopting a spssau data analysis tool, the prediction can be completed only by setting parameters, and the principle is not expanded in detail because arima is a common time series prediction analysis method.
Preferably, the static characteristic analysis is to analyze the volatility and the stationarity of the annual monthly coupling coordinated scheduling from the minimum value, the maximum value, the average value, the standard deviation and the median of the annual monthly coupling coordinated characteristic; the dynamic characteristic analysis means that the volatility and the stationarity of coupling co-scheduling of each month are analyzed by taking the month as a unit.
The embodiment adopts comprehensive energy data of a Campus Metabolism project platform of the State university of Arizona, USA, and uses cold, heat and electricity load data of 2018 years in a building in a Tempe school district Phys Sci A _ B _ C (Phy) provided by the project platform, wherein the sampling frequency is 1 point per hour; the specific framework of the obtained energy use behavior index system is shown in table 1;
TABLE 1 comprehensive energy user daily energy consumption behavior characteristic index system
As shown in table 2, the secondary indexes of the building under the primary indexes of 12 months in 2018;
TABLE 2 evaluation index of monthly energy consumption characteristics of phy building
Based on the calculated monthly secondary energy consumption behavior characteristic index score, the weight coefficient of each secondary evaluation index is calculated through a CRITIC method, the primary index of the monthly energy consumption behavior characteristic of the comprehensive energy user is calculated with the weight, and the weight distribution of each secondary index and the average value of the primary index score from 1 month to 12 months are shown in Table 3.
TABLE 2 short-term energy consumption characteristic comprehensive evaluation index weight distribution
As can be seen from table 3, the monthly average peak shaving capacity MD1 has a higher weight in the aspect of the power consumption index, the weight coefficients of the other indexes are not very different, the average load has a higher weight value in the aspects of the heat consumption index and the cold consumption behavior characteristic index, and the weight values of the other indexes are similar.
The Pearson correlation coefficient is used for simultaneously carrying out correlation analysis on all secondary indexes of electricity consumption, cold consumption and heat consumption, the result is shown in figure 2, it can be seen that obvious correlation exists among most secondary indexes of energy consumption behaviors, no matter positive correlation or negative correlation exists, the fact that obvious coupling relation exists among all energy utilization behaviors of the comprehensive user can be shown, the analysis result proves the necessity of carrying out deeper coupling analysis, and qualitative and quantitative analysis among all the secondary indexes is given.
Calculating the coupling coordination of the first-level energy consumption behavior characteristic indexes, and analyzing the monthly coupling coordination static and dynamic characteristics; the dynamic coupling co-scheduling calculation results of each month are shown in table 4, and it can be seen that the comprehensive energy coupling degrees of each month are very close to 1, which indicates that the energy coupling degrees are very high.
The monthly coupling co-scheduling line graph is shown in fig. 3, it can be seen that the coupling co-scheduling is also high and stable, and this result shows that the user has a high coupling relationship among the annual energy sources, and the scores of the coupled energy sources are always at a high level, that is, the annual user electricity consumption behavior characteristics have certain stability while maintaining a high coupling degree. Compared with correlation analysis, the result quantitatively analyzes the change trend of the user energy consumption behavior characteristics when the coupling degree of each primary index at different time is coupled with each energy source more accurately.
TABLE 4 dynamic coupling co-scheduling calculation results for each month
Table 5 shows static statistics results of the coupling co-scheduling in one year, and it can be seen that although the minimum value and the maximum value have a large difference, the standard deviation is small, and the median and the average value are also relatively close, which indicates that the coupling coordination of the user has certain stationarity, i.e., when various energy sources are coupled, the comprehensive energy consumption behavior characteristics have certain stationarity.
TABLE 5 dynamic coupling co-scheduling static statistics for each month
Fitting monthly energy consumption behavior coupling co-scheduling indexes based on an arima model, and predicting the coupling co-scheduling change trend of the future periods; the model used in this embodiment is ARIMA (0, 1, 0), the model parameters are shown in table 6 below, and the model fitting and predicting effects are shown in fig. 4, which shows that the ARIMA model used in this embodiment can well fit the annual coupling co-scheduling curve and can reasonably predict the variation trend of the following two periods of coupling co-scheduling, and the curve prediction of the following period number approaches to a value due to certain randomness.
TABLE 6 ARIMA (0, 1, 0) model parameter Table
Example 2
The embodiment provides a comprehensive energy scheduling system based on energy consumption coupling coordination, including:
the data acquisition module is configured to acquire the multi-element energy load data;
the correlation analysis module is configured to acquire secondary energy utilization behavior characteristics according to the multivariate energy utilization load data, obtain weights according to the contrast strength and the conflict of the secondary energy utilization behavior characteristics, and obtain primary energy utilization behavior characteristics after the weights are given to the secondary energy utilization behavior characteristics;
and the coupling coordination module is configured to obtain an energy utilization coupling coordination change trend according to the coupling coordination of the primary energy utilization behavior characteristics, and to schedule the supply of the comprehensive energy according to the energy utilization coupling coordination change trend.
It should be noted that the modules correspond to the steps described in embodiment 1, and the modules are the same as the corresponding steps in the implementation examples and application scenarios, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A comprehensive energy scheduling method based on energy consumption coupling coordination is characterized by comprising the following steps:
acquiring multi-element energy load data;
acquiring secondary energy consumption behavior characteristics according to the multivariate energy consumption load data, acquiring weights according to the contrast strength and the conflict of the secondary energy consumption behavior characteristics, and giving weights to the secondary energy consumption behavior characteristics to obtain primary energy consumption behavior characteristics;
and obtaining the energy utilization coupling coordination variation trend according to the coupling coordination of the primary energy utilization behavior characteristics, and scheduling the supply of the comprehensive energy according to the energy utilization coupling coordination variation trend.
2. The comprehensive energy scheduling method based on energy consumption coupling coordination according to claim 1, wherein the secondary energy consumption behavior characteristics comprise one or more combinations of monthly average load, monthly load rate, average peak-to-time power consumption rate, average valley power coefficient, average peak-to-valley difference, maximum peak-to-valley difference, monthly load standard deviation, average detail volatility, average load fluctuation rate, average peak-to-peak capacity, maximum peak-to-peak capacity, average demand response potential entropy and maximum demand response potential entropy;
alternatively, the first and second electrodes may be,
the primary energy consumption behavior characteristics comprise electricity consumption behavior characteristics, hot consumption behavior characteristics and cold consumption behavior characteristics.
3. The comprehensive energy scheduling method based on energy consumption coupling coordination as claimed in claim 1, wherein a correlation coefficient matrix of the secondary energy consumption behavior characteristics is calculated by a CRITIC method, the contrast strength and the conflict are obtained according to the correlation coefficient matrix, the information content included in the secondary energy consumption behavior characteristics is obtained according to the contrast strength and the conflict, and the weight is obtained according to the information content.
4. The comprehensive energy scheduling method based on energy consumption coupling coordination as claimed in claim 1, wherein before the coupling coordination of the primary energy consumption behavior characteristics is calculated, a Person correlation coefficient is used to perform correlation analysis between the primary energy consumption behavior characteristics and between the secondary energy consumption behavior characteristics.
5. The method of claim 1, wherein the coupling coordination is a method of energy-consumption coupling coordination
C is the coupling degree of the first-level energy consumption behavior characteristics, and T is the coordination degree of the first-level energy consumption behavior characteristics.
6. The integrated energy dispatching method based on energy utilization coupling coordination as claimed in claim 5,
the degree of coupling of the first-order energy consumption behavior characteristics is
U1、U2、U3Respectively is a first-level energy consumption behavior characteristic index of each energy source every day;
alternatively, the first and second electrodes may be,
the coordination degree of the first-level energy utilization behavior characteristic is T ═ beta1U1+β2U2+β3U3，β1、β2、β3Respectively, the weighting factor for each energy source.
7. The comprehensive energy scheduling method based on energy consumption coupling coordination as claimed in claim 1, wherein the coupling coordination of the primary energy consumption behavior characteristics is fitted based on an arima model to obtain the energy consumption coupling coordination variation trend.
8. An integrated energy scheduling system based on energy consumption coupling coordination, comprising:
the data acquisition module is configured to acquire the multi-element energy load data;
the correlation analysis module is configured to acquire secondary energy utilization behavior characteristics according to the multivariate energy utilization load data, obtain weights according to the contrast strength and the conflict of the secondary energy utilization behavior characteristics, and obtain primary energy utilization behavior characteristics after the weights are given to the secondary energy utilization behavior characteristics;
and the coupling coordination module is configured to obtain an energy utilization coupling coordination change trend according to the coupling coordination of the primary energy utilization behavior characteristics, and to schedule the supply of the comprehensive energy according to the energy utilization coupling coordination change trend.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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