CN109378816B - Method and system for searching running opportunity of electricity conversion gas in energy system - Google Patents
Method and system for searching running opportunity of electricity conversion gas in energy system Download PDFInfo
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- CN109378816B CN109378816B CN201811093110.1A CN201811093110A CN109378816B CN 109378816 B CN109378816 B CN 109378816B CN 201811093110 A CN201811093110 A CN 201811093110A CN 109378816 B CN109378816 B CN 109378816B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The application provides a method and a system for searching for an opportunity of running electricity in an energy system, comprising the following steps: obtaining the operation opportunity of the electric conversion device in the area or between seasons according to the obtained area temperature data and energy consumption data; when the electric power conversion device is in the area or Ji Jiejian, the electric power conversion device is operated in the day according to the acquired distribution data of the electricity price and the load in the day. The method and the system utilize the big data method to excavate the regional, seasonal and daily peak Gu Xing differences of the energy load and the energy price, seek the opportunity of arbitrage and the opportunity of stabilizing the peak-valley difference of the electric power system to stabilize the operation of the electric power system, and realize the economic, efficient and valuable operation of the electric power device.
Description
Technical Field
The application belongs to the technical field of energy data analysis, and particularly relates to a method and a system for searching an opportunity of running electricity-to-gas in an energy system.
Background
The new energy makes outstanding contributions in the aspects of coping with environmental pollution, energy crisis and the like, but the intermittent property and the fluctuation of the new energy can not track the load well, and the regional, seasonal and daily peak-valley energy difference is easily caused, so that a large amount of new energy generated energy is discarded, and the energy waste is generated. Therefore, the electric energy which is not easy to store on a large scale is converted into natural gas which is easy to store or hydrogen which is directly used for chemical industry by utilizing an electric conversion technology, so that the use efficiency of new energy is improved; the operation of the electric conversion device solves the problems of wind and light abandoning of electric power energy, has the advantage of reducing natural gas energy, and is beneficial to national energy safety. However, at present, the conversion efficiency of the electric conversion device is lower, the cost is higher, and the opportunity of seeking a larger difference in arbitrage is needed to make the operation of the electric conversion device have economic benefit.
Disclosure of Invention
To overcome the above-mentioned deficiencies of the prior art, the present application provides a method and system for searching for opportunities for operation of an electrical power conversion gas in an energy system. The method and the system utilize the big data method to mine regional, seasonal and daily peak Gu Xing differences of energy load and energy price, and seek the opportunity of arbitrage of the electric power conversion device and the opportunity of stabilizing the peak-valley differences to stabilize the stable operation of the electric power system.
The solution adopted for achieving the purpose is as follows:
in a method of searching for opportunities for operation of an electrical power conversion gas in an energy system, the improvement comprising:
obtaining the operation opportunity of the electric conversion device in the area or between seasons according to the obtained area temperature data and energy consumption data;
when the electric power conversion device is in the area or Ji Jiejian, the electric power conversion device is operated in the day according to the acquired distribution data of the electricity price and the load in the day.
The first preferred technical scheme provided by the application is improved in that the method for acquiring the operation opportunity of the electric conversion device between areas according to the acquired regional temperature data and energy consumption data comprises the following steps:
dividing the temperature comfort categories of different areas according to the acquired regional temperature data and a preset temperature range, and respectively calculating the distribution difference of the temperature comfort categories between every two areas;
according to the energy data and the temperature data of different areas for years, calculating the correlation coefficient between the energy data and the temperature of each area respectively;
when the absolute values of the correlation coefficients of the two areas are both larger than a preset correlation threshold value and the distribution difference of the temperature comfort categories between the two areas is larger than a preset distribution threshold value, the operation opportunity of the electric switching device between the two areas exists;
the temperature comfort category comprises a first category, a second category and a third category, wherein the temperature interval of the first category is lower than the temperature interval of the second category, and the temperature interval of the third category is higher than the temperature interval of the third category.
The second preferred technical scheme provided by the application is improved in that the calculation of the distribution difference of the temperature comfort categories between the two areas comprises the following steps:
calculating a distribution of jensen-shannon divergences of temperature comfort categories between the two regions;
the distribution difference of the temperature comfort class between two regions is taken as the jensen-shannon divergence of the distribution of the temperature comfort class between the two regions.
The third preferred technical scheme provided by the application is improved in that the correlation coefficient between the energy consumption data and the temperature is shown as the following formula:
wherein r is TL Representing the correlation coefficient between the energy consumption data and the temperature, N 1 The number of days, T, representing the correlation coefficient between the analysis temperature and the energy consumption load i For the ith day temperature, L i Is the energy load for the ith day.
The fourth preferred technical scheme provided by the application is improved in that the method for obtaining the operation opportunity of the electric conversion device in the season according to the obtained energy data comprises the following steps:
calculating the average energy load ratio of every two different months in the same area respectively;
when the difference value between the ratio of two different months and 1 is larger than a preset ratio threshold value, an operation opportunity of the electric switching device between the two months exists.
The fifth preferred technical scheme provided by the application is improved in that the ratio of average energy loads of two different months in the same area is calculated, and the ratio is shown as the following formula:
wherein r is a.b Represents the ratio of average energy load between month a and month b, N a Represents the number of days of month a, N b Represents the number of days of the month b, L ai Represents the energy load on the ith day of month a, L bi The energy load on day i of month b is shown.
The sixth preferred technical scheme provided by the application is improved in that the method for acquiring the operation opportunity of the electric conversion device in the day according to the acquired distribution data of the electricity price and the load in the day comprises the following steps:
respectively counting the first and second days of each period in a preset statistical day according to the electricity price and load distribution data in the day;
judging whether the distribution similarity of the first number of days and the second number of days in each period meets a preset distribution standard, and when the distribution similarity of the first number of days and the second number of days in each period meets the preset distribution standard, running opportunities of the electric conversion device in the day exist between load peaks and valleys in the days of statistics days;
the first number of days of the time period is the number of days of the maximum current load, the second number of days of the time period is the number of days of the maximum current price.
The seventh preferred technical solution provided by the present application is improved by the method for determining whether the distribution similarity of the first number of days and the second number of days in each period meets a preset distribution standard, including:
calculating a jensen-shannon divergence of the first and second day distributions for each time period;
if the value of the jensen-shannon divergence is smaller than a preset divergence threshold value, the distribution similarity of the first number of days and the second number of days accords with a preset distribution standard; otherwise, the distribution standard does not accord with the preset distribution standard.
In a system for searching for opportunities for operation of an electrical power conversion gas in an energy system, the improvement comprising: an area/inter-season operation opportunity module and an intra-day operation opportunity module;
the regional/seasonal operation opportunity module is used for obtaining the operation opportunity of the electric conversion device in the region or the season according to the acquired regional temperature data and energy consumption data;
the daily operation opportunity module is used for acquiring the operation opportunity of the electric power conversion device in the day according to the acquired daily electricity price and load distribution data when the electric power conversion device is in the area or Ji Jiejian.
The eighth preferred technical solution provided by the present application is improved in that the area/inter-season operation opportunity module includes: a temperature comfort difference unit, an energy consumption temperature correlation coefficient unit and a region operation opportunity unit;
the temperature comfort difference unit is used for dividing the temperature comfort categories of different areas according to the acquired regional temperature data and the preset temperature range and respectively calculating the distribution difference of the temperature comfort categories between every two areas;
the energy utilization temperature correlation coefficient unit is used for respectively calculating correlation coefficients between energy utilization data and temperature of each area according to energy utilization data and temperature data of different areas for many years;
the region operation opportunity unit is used for judging that the operation opportunity of the electric conversion device between the two regions exists when the absolute values of the correlation coefficients of the two regions are larger than a preset correlation threshold value and the distribution difference of the temperature comfort categories between the two regions is larger than a preset distribution threshold value;
the temperature comfort category comprises a first category, a second category and a third category, wherein the temperature interval of the first category is lower than the temperature interval of the second category, and the temperature interval of the third category is higher than the temperature interval of the third category.
The ninth preferred technical solution provided by the present application is improved in that the area/inter-season operation opportunity module includes: an energy load ratio unit and a season operation opportunity unit;
the energy load ratio unit is used for calculating the average energy load ratio of each two different months in the same area respectively;
and the seasonal operation opportunity unit is used for judging that the operation opportunity of the electric conversion device between the two months exists when the difference value between the ratio of the two different months and 1 is larger than a preset ratio threshold value.
The tenth preferred technical solution provided by the present application is improved in that the daily operation opportunity module includes: the daily number counting unit and the daily operation opportunity unit;
the daily number statistics unit is used for respectively counting the first daily number and the second daily number of each period in the preset statistical days according to the distribution data of electricity prices and loads in the days;
the daily operation opportunity unit is used for judging whether the distribution similarity of the first number of days and the second number of days in each period accords with a preset distribution standard, and when the distribution similarity of the first number of days and the second number of days in each period accords with the preset distribution standard, the daily operation opportunity of the electric conversion device exists between daily load peaks and valleys in the statistical days;
the first number of days of the time period is the number of days of the maximum current load, the second number of days of the time period is the number of days of the maximum current price.
Compared with the closest prior art, the application has the following beneficial effects:
based on the great increase of the existing renewable energy installation capacity and the fluctuation and randomness defects thereof, the electric conversion technology is utilized to convert electric energy which is not easy to store in a great quantity into methane or hydrogen for utilization, but on the other hand, the problem is that the electric conversion technology has higher cost and lower operation efficiency in the current stage, a good operation opportunity background is required to be sought, the regional temperature data and the energy consumption data are utilized to obtain the operation opportunity of the electric conversion device in regions or seasons, and when the electric conversion device is in the regions or Ji Jiejian, the operation opportunity of the electric conversion device in the day is obtained according to the obtained daily electricity price and load distribution data, so that the economic and efficient valuable operation of the electric conversion device is realized.
Drawings
FIG. 1 is a schematic flow chart of a method for searching for an opportunity of electric power conversion gas to run in an energy system;
FIG. 2 is a schematic diagram of the basic structure of a system for searching the chance of electric power conversion gas running in an energy system;
FIG. 3 is a schematic diagram of a detailed structure of a system for searching for opportunities for electric power conversion to run in an energy system according to the present application.
Detailed Description
The following describes the embodiments of the present application in further detail with reference to the drawings.
Example 1:
the flow chart of the method for searching the running opportunity of the electricity conversion gas in the energy system is shown in fig. 1, and the method comprises the following steps:
step 1: obtaining the operation opportunity of the electric conversion device in the area or between seasons according to the obtained area temperature data and energy consumption data;
step 2: when the electric power conversion device is in the area or Ji Jiejian, the electric power conversion device is operated in the day according to the acquired distribution data of the electricity price and the load in the day.
The specific flow of the method for searching the chance of running electricity in the energy system is as follows. The order of steps 101 and 102 is only an example, and steps 101 or 102 may be performed simultaneously, steps 102 may be performed before steps 101 are performed, or only one step of steps 101 or 102 may be performed.
Step 101: the research method for searching the operation opportunity of the electricity conversion gas in the energy system by utilizing big data is used for searching the operation opportunity of the electricity conversion gas device under the condition that energy utilization differences exist in different areas, and specifically comprises the following steps:
step 101-1: according to the fact that energy load is generally strong in correlation with air temperature, different areas are classified into 3 types according to temperature comfort DATC for comfort comparison, and the running opportunity of electricity conversion is searched: a first type of cold non-comfortable region (less than 8 ℃), a second type of temperature comfortable region (8 ℃ -26 ℃), and a third type of summer non-comfortable region (greater than 26 ℃), namely:
step 101-2: statistics is carried out on energy data of different areas for years, and a correlation coefficient r between the energy data and temperature is analyzed TL The value range is [ -1,1]，|r TL The magnitude of i indicates the degree of temperature and load correlation, and the closer to 1, the stronger the correlation, and the closer to 0, the weaker the correlation coefficient. R is |r TL The 'E' (0.8-1.0) is extremely strongly correlated, r TL Strong correlation of I E (0.6-0.8), I r TL The |E (0.4-0.6) is moderately relevant, r TL The ∈ (0.2-0.4) is weakly correlated, r TL The ∈ (0.0-0.2) is very weakly correlated or uncorrelated. r is (r) TL The calculation formula is as follows:
wherein N is 1 The number of days, T, representing the correlation coefficient between the analysis temperature and the energy consumption load i For the ith day temperature, L i Is the energy load for the ith day.
|r TL The correlation of temperature and energy load is represented by r TL High, indicating that the peak or valley energy usage is due to temperature, it is feasible to exploit the temperature differences in different regions with the opportunity to electrically switch between different regions. Analyzing the distribution diagrams of the temperature comfort DATC between every two different areas, wherein the larger the difference of the distribution diagrams of the DATC is, |r TL The higher the correlation is, the greater the running opportunity of the electric conversion gas in the different analyzed areas is, and the more possible energy transfer and storage in the different areas can be realized by using the temperature difference of the areas. In determining DATC profile differences, a determination can be made using jensen-shannon divergence JS divergence: when the JS divergence of the temperature comfort class distribution of the two areas is larger than a preset distribution threshold, the DATC distribution diagram difference is considered to be large enough; when the correlation is judged, a correlation threshold value can be set, and when |r TL When the I is larger than a preset correlation threshold, judging the I r TL The correlation is high enough. The concrete values of the variance threshold and the correlation threshold can be obtained by calculating economic value by referring to parameters such as electricity price, electricity-to-gas cost, efficiency, operation life and the like. When the absolute value of the correlation coefficient of the two areas is larger than a preset correlation threshold value and the distribution difference of the temperature comfort categories between the two areas is larger than a preset distribution threshold value, the electric switching device has an operation opportunity between the two areas.
Step 102: the research method for searching the operation opportunity of the electricity conversion gas in the energy system by utilizing big data is used for searching the operation opportunity of the electricity conversion gas device under the condition that the seasonal difference of energy consumption exists in the same area, and specifically comprises the following steps:
step 102-1: calculate the sameAverage energy load of different months in the region is in a ratio r of a month to b month a.b The following formula is shown:
step 102-2: calculating matrix R a.b 。R a.b Is a 12×12 matrix, the elements of the matrix are r obtained in step 102-1 a.b 。R a.b The form is as follows:
r a.b a closer to 1 means that the difference in energy usage between a and b months is smaller. When r is a.b The larger the indicated month energy consumption difference is, the larger the seasonal difference between the month a and the month b is in the region, the larger the running opportunity of the electric conversion gas in the month a and the month b is, and the seasonal difference is more likely to be used for stabilizing seasonal energy fluctuation, so that the seasonal long-time storage of energy is realized. When seasonal difference judgment is carried out, a ratio threshold value can be set, and when r is a.b And when the difference value between the first power supply and the second power supply is larger than the ratio threshold value, judging that the difference of the month power consumption is large enough, and operating the electric conversion device between the season nodes in the same area, namely between the month a and the month b. The specific value of the ratio threshold can be calculated by referring to the parameters such as electricity price, electricity-to-gas cost, efficiency, operation life and the like.
Step 103: when the electricity-to-gas conversion device has operation opportunities in the area or Ji Jiejian, the big data technology is utilized to count the daily hour data of the power load and the price for many years, the relation between the daily hour electricity price and the daily hour load is excavated, the daily hour electricity price and the daily hour load characteristics are counted, the operation opportunities of electricity-to-gas conversion in different periods of the same season in the area are searched, namely, the operation in the period with lower electricity price in the same day realizes the energy storage arbitrage or is used as the load on the demand side to perform peak elimination, valley filling and load fluctuation stabilization on the power grid, and the safe and stable operation of the power system is assisted. The method specifically comprises the following steps:
step 103-1:for N pi For counting the maximum value of the energy consumption price per hour in the day, namely counting the second day, the running opportunity of electricity conversion is mined, wherein the running opportunity is represented by the following formula:
wherein N is ！i For statistical days N ！ The ith period (i e [1, 24)]) For the statistics of the highest electricity price time period, X ！j.i Is binary number, when the highest electricity price period in the pj day is P ji Time X ！j.i 1, otherwise 0 is taken.
Step 103-2: for N Li For counting the maximum value of energy load in one day per hour in the counting days, namely counting the first day, the operation opportunity of electricity conversion is mined, and the operation opportunity is represented by the following formula:
wherein N is Li For statistical days N L The ith period (i e [1, 24)]) To count the highest energy load time period, X Lj.i Is binary number, when the highest electricity price period in Lj days is K ji Time X Lj.i 1, otherwise 0 is taken.
The greater the energy load difference per time period, the greater the chance of operation of the electric converter, i.e. in combination with N ！i Distribution of (2) with N Li The higher the similarity, the higher the peak electricity price is due to peak load, then N Li The larger the more likely the electric power is used to run in the load off-peak period or off-peak powerThe price period realizes the reduction of peak load in the day, stabilizes the fluctuation of the daily energy consumption load in the short time and realizes the daily short time translation of energy. In particular, judging N pi And N Li In the case of similarity of distribution, N can be calculated pi And N Li The distribution of jensen-shannon divergence, JS divergence. The value range of JS divergence is [0,1 ]]The higher the two profile similarities, the closer the JS divergence value is to 0, otherwise the closer to 1. When the value of JS divergence is smaller than a preset divergence threshold value, N is considered to be broken pi And N Li The distribution similarity is high enough, and the operation opportunity of the electric switching device in the day exists between load peaks and valleys in the same region and in the same season and statistics days. The specific value of the divergence threshold value can be obtained by calculating the economic value by referring to the parameters such as electricity price, electricity-to-gas cost, efficiency, operation life and the like.
Example 2:
based on the same conception, the application also provides a system for searching the opportunity of the electric transfer gas to run in the energy system, and as the principle of solving the technical problems by the equipment is similar to that of the method for searching the opportunity of the electric transfer gas to run in the energy system, the repetition is not repeated.
The basic structure of the system is shown in fig. 2, and comprises: an area/inter-season operation opportunity module and an intra-day operation opportunity module;
the regional/seasonal operation opportunity module is used for obtaining the operation opportunity of the electric conversion device in the region or the season according to the acquired regional temperature data and energy consumption data;
and the daily operation opportunity module is used for acquiring the operation opportunity of the electric power conversion device in the day according to the acquired daily electricity price and load distribution data when the electric power conversion device is in the area or Ji Jiejian.
A specific configuration of a system for seeking opportunities for electrical transfer to operate in an energy system is shown in fig. 3. Wherein the region/inter-season operation opportunity module comprises: a temperature comfort difference unit, an energy consumption temperature correlation coefficient unit and a region operation opportunity unit;
the temperature comfort difference unit is used for dividing the temperature comfort categories of different areas according to the acquired regional temperature data and the preset temperature range and respectively calculating the distribution difference of the temperature comfort categories between every two areas;
the energy-consumption temperature correlation coefficient unit is used for respectively calculating correlation coefficients between energy consumption data and temperature of each region according to energy consumption data and temperature data of different regions for years;
the regional operation opportunity unit is used for judging that the operation opportunity of the electric conversion device between the two regions exists when the absolute value of the correlation coefficient of the two regions is larger than a preset correlation threshold value and the distribution difference of the temperature comfort categories between the two regions is larger than a preset distribution threshold value;
the temperature comfort type comprises a first type, a second type and a third type, wherein the temperature interval of the first type is lower than that of the second type, and the temperature interval of the third type is higher than that of the third type.
When the temperature comfort difference unit calculates the distribution difference of the temperature comfort categories between the two areas, calculating the jensen-shannon divergence of the temperature comfort category distribution between the two areas; the jensen-shannon divergence, which is a distribution of temperature comfort categories between two regions, is the difference in the distribution of temperature comfort categories between the two regions.
Wherein, the correlation coefficient between the energy data and the temperature is calculated by the energy temperature correlation coefficient unit, and the following formula is shown:
wherein r is TL Representing the correlation coefficient between the energy consumption data and the temperature, N 1 The number of days, T, representing the correlation coefficient between the analysis temperature and the energy consumption load i For the ith day temperature, L i Is the energy load for the ith day.
Wherein the region/inter-season operation opportunity module comprises: an energy load ratio unit and a season operation opportunity unit;
the energy load ratio unit is used for calculating the average energy load ratio of each two different months in the same area respectively;
and the season operation opportunity unit is used for judging that the operation opportunity of the electric conversion device between the season nodes exists between the two months when the difference value between the ratio of the two different months and 1 is larger than a preset ratio threshold value.
The energy load ratio unit is used for calculating the average energy load ratio of two different months in the same area, and the average energy load ratio is shown in the following formula:
wherein r is a.b Represents the ratio of average energy load between month a and month b, N a Represents the number of days of month a, N b Represents the number of days of the month b, L ai Represents the energy load on the ith day of month a, L bi The energy load on day i of month b is shown.
Wherein, the intra-day operation opportunity module includes: the daily number counting unit and the daily operation opportunity unit;
the daily number statistics unit is used for respectively counting the first daily number and the second daily number of each period in the preset statistical days according to the daily electricity price and the load distribution data;
the intra-day operation opportunity unit is used for judging whether the distribution similarity of the first number of days and the second number of days in each period accords with a preset distribution standard, and when the distribution similarity of the first number of days and the second number of days in each period accords with the preset distribution standard, an operation opportunity of the electric conversion device in the day exists between load peaks and valleys in the days of statistics days;
the first number of days of the time period is the number of days of the maximum current load, the second number of days of the time period is the number of days of the maximum current price, and the electricity price of the time period is the number of days of the maximum current price.
Wherein, the intra-day operation opportunity unit includes: a divergence calculating subunit and a standard judging subunit;
a divergence calculating subunit for calculating a jensen-shannon divergence of the first and second day distributions for each period;
the standard judging subunit is used for enabling the distribution similarity of the first number of days and the second number of days to accord with a preset distribution standard if the value of the Jansen-shannon divergence is smaller than a preset divergence threshold value; otherwise, the distribution standard does not accord with the preset distribution standard.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the scope of protection thereof, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the application after reading the present application, and these changes, modifications or equivalents are within the scope of protection of the claims appended hereto.
Claims (10)
1. A method of searching for opportunities for electrical switching to operate in an energy system, comprising:
obtaining the operation opportunity of the electric conversion device in the area or between seasons according to the obtained area temperature data and energy consumption data;
when the electric power conversion device has operation opportunities in the area or the season, acquiring the operation opportunities of the electric power conversion device in the day according to the acquired distribution data of the electricity price and the load in the day;
the method for acquiring the operation opportunity of the electric conversion device in the day according to the acquired distribution data of the electricity price and the load in the day comprises the following steps:
respectively counting the first and second days of each period in a preset statistical day according to the electricity price and load distribution data in the day;
judging whether the distribution similarity of the first number of days and the second number of days in each period meets a preset distribution standard, and when the distribution similarity of the first number of days and the second number of days in each period meets the preset distribution standard, running opportunities of the electric conversion device in the day exist between load peaks and valleys in the days of statistics days;
the first number of days of the time period is the number of days of the maximum current load, the second number of days of the time period is the number of days of the maximum current price.
2. The method of claim 1, wherein the acquiring the operating opportunity of the electric converting device between areas based on the acquired regional temperature data and energy consumption data comprises:
dividing the temperature comfort categories of different areas according to the acquired regional temperature data and a preset temperature range, and respectively calculating the distribution difference of the temperature comfort categories between every two areas;
according to the energy data and the temperature data of different areas for years, calculating the correlation coefficient between the energy data and the temperature of each area respectively;
when the absolute values of the correlation coefficients of the two areas are both larger than a preset correlation threshold value and the distribution difference of the temperature comfort categories between the two areas is larger than a preset distribution threshold value, the operation opportunity of the electric switching device between the two areas exists;
the temperature comfort category comprises a first category, a second category and a third category, wherein the temperature interval of the first category is lower than the temperature interval of the second category, and the temperature interval of the third category is higher than the temperature interval of the second category.
3. The method of claim 2, wherein the calculating of the temperature comfort class distribution difference between the two regions comprises:
calculating jensen-shannon divergence of the temperature comfort class distribution between the two regions;
the jensen-shannon divergence, which is a distribution of temperature comfort categories between two regions, is the difference in the distribution of temperature comfort categories between the two regions.
4. The method of claim 2, wherein the correlation coefficient between the computational energy data and the temperature is represented by the formula:
wherein r is TL Representing the correlation coefficient between the energy consumption data and the temperature, N 1 The number of days, T, representing the correlation coefficient between the analysis temperature and the energy consumption load i For the ith day temperature, L i Is the energy load for the ith day.
5. The method of claim 1, wherein the obtaining the opportunity for the electric switching apparatus to operate between season nodes based on the obtained energy usage data comprises:
calculating the average energy load ratio of every two different months in the same area respectively;
when the difference value between the ratio of two different months and 1 is larger than a preset ratio threshold value, an operation opportunity of the electric switching device between the two months exists.
6. The method of claim 5, wherein the ratio of average energy loads for two different months in the same area is calculated as follows:
wherein r is a.b Represents the ratio of average energy load between month a and month b, N a Represents the number of days of month a, N b Represents the number of days of the month b, L ai Represents the energy load on the ith day of month a, L bi The energy load on day i of month b is shown.
7. The method of claim 1, wherein determining whether the distribution similarity of the first number of days and the second number of days for each period meets a preset distribution criterion comprises:
calculating a jensen-shannon divergence of the first and second day distributions for each time period;
if the value of the jensen-shannon divergence is smaller than a preset divergence threshold value, the distribution similarity of the first number of days and the second number of days accords with a preset distribution standard; otherwise, the distribution standard does not accord with the preset distribution standard.
8. A system for searching for opportunities for electrical switching to operate in an energy system, comprising: an area/inter-season operation opportunity module and an intra-day operation opportunity module;
the regional/seasonal operation opportunity module is used for obtaining the operation opportunity of the electric conversion device in the region or the season according to the acquired regional temperature data and energy consumption data;
the daily operation opportunity module is used for acquiring the operation opportunity of the electric power conversion device in the day according to the acquired daily electricity price and load distribution data when the electric power conversion device has the operation opportunity in the area or the season;
the daily operation opportunity module comprises: the daily number counting unit and the daily operation opportunity unit;
the daily number statistics unit is used for respectively counting the first daily number and the second daily number of each period in the preset statistical days according to the distribution data of electricity prices and loads in the days;
the daily operation opportunity unit is used for judging whether the distribution similarity of the first number of days and the second number of days in each period accords with a preset distribution standard, and when the distribution similarity of the first number of days and the second number of days in each period accords with the preset distribution standard, the daily operation opportunity of the electric conversion device exists between daily load peaks and valleys in the statistical days;
the first number of days of the time period is the number of days of the maximum current load, the second number of days of the time period is the number of days of the maximum current price.
9. The system of claim 8, wherein the region/inter-season run opportunity module comprises: a temperature comfort difference unit, an energy consumption temperature correlation coefficient unit and a region operation opportunity unit;
the temperature comfort difference unit is used for dividing the temperature comfort categories of different areas according to the acquired regional temperature data and the preset temperature range and respectively calculating the distribution difference of the temperature comfort categories between every two areas;
the energy utilization temperature correlation coefficient unit is used for respectively calculating correlation coefficients between energy utilization data and temperature of each area according to energy utilization data and temperature data of different areas for many years;
the region operation opportunity unit is used for judging that the operation opportunity of the electric conversion device between the two regions exists when the absolute values of the correlation coefficients of the two regions are larger than a preset correlation threshold value and the distribution difference of the temperature comfort categories between the two regions is larger than a preset distribution threshold value;
the temperature comfort category comprises a first category, a second category and a third category, wherein the temperature interval of the first category is lower than the temperature interval of the second category, and the temperature interval of the third category is higher than the temperature interval of the second category.
10. The system of claim 8, wherein the region/inter-season run opportunity module comprises: an energy load ratio unit and a season operation opportunity unit;
the energy load ratio unit is used for calculating the average energy load ratio of each two different months in the same area respectively;
and the seasonal operation opportunity unit is used for judging that the operation opportunity of the electric conversion device between the two months exists when the difference value between the ratio of the two different months and 1 is larger than a preset ratio threshold value.
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