CN111181151A - Smart power grid control method for estimating and controlling power load - Google Patents
Smart power grid control method for estimating and controlling power load Download PDFInfo
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- CN111181151A CN111181151A CN201910994569.7A CN201910994569A CN111181151A CN 111181151 A CN111181151 A CN 111181151A CN 201910994569 A CN201910994569 A CN 201910994569A CN 111181151 A CN111181151 A CN 111181151A
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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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
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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
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
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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
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a smart power grid control method for estimating and controlling power load, which is characterized by comprising the following steps of: s1: estimating the electric load of a region according to historical data in an expected time period and dividing the electric load into a high load, a normal load and a low load; s2: the free-load electrical loads in the region, which can be temporarily deactivated, are deactivated when a high load is estimated. The invention provides an intelligent power grid control method for reducing and controlling pre-estimated power load of a load at the peak of a power grid.
Description
Technical Field
The invention relates to the field of power utilization control, in particular to a smart power grid control method for predicting and controlling power utilization load.
Background
At present, no better method is available for load regulation of a power grid, and as the power consumption is gradually increased and the peak-to-valley ratio of the load of the power grid is gradually increased, the control of the power load of the power grid is more difficult.
The invention provides an energy management method and system based on a building photovoltaic microgrid, which is invented and created by Chinese patent publication No. CN104269849A, published on 2015, 01/07, and the application comprises the steps of firstly obtaining operation historical data of a building photovoltaic power generation system and synchronous meteorological condition information and establishing a building photovoltaic power generation power prediction model; acquiring historical data of electrical loads in a building and synchronous environmental monitoring information, and establishing a building load demand prediction model according to the historical data and the synchronous environmental monitoring information; according to the demand application of the electric vehicle governed by the building, an operation scheduling plan of the electric vehicle is made in advance, and the charge state of the electric vehicle when the electric vehicle leaves the building and the charge state when the electric vehicle returns to the building are estimated; and establishing an objective function of the whole building photovoltaic microgrid by combining the three functions, and planning the charge and discharge power of each electric vehicle in each time period when the electric vehicle stops at the building in advance through an optimization algorithm. The scheme limits the charging and discharging power of the electric vehicle, which can cause that some electric vehicles are damaged when the charging and discharging power is changed, and the charging time of the electric vehicle can not be determined, which is very inconvenient for users.
Disclosure of Invention
The invention provides an intelligent power grid control method for reducing and controlling estimated power load of a power grid peak load, aiming at overcoming the problem that the power grid peak load is difficult to reduce in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
the technical scheme adopted by the invention for solving the technical problems is as follows: a smart power grid control method for estimating and controlling power loads is characterized by comprising the following steps:
s1: estimating the electric load of a region according to historical data in an expected time period and dividing the electric load into a high load, a normal load and a low load;
s2: the operating free electrical load in the region, which can be temporarily stopped, is stopped when a high load is estimated. Because the power load of the power grid changes in real time, the power load data of the power grid cannot be directly used as the starting and stopping conditions of the free power load, and because the power load of the power grid may fluctuate above and below the power load threshold value for stopping the free power load within a period of time, the free power load can be started and stopped for a moment, the damage of the free power load can be caused, the service life of the free power load is reduced, the power load of the power grid at the expected time needs to be estimated, the free power load stops working when the power grid is in a high-load state, and the effect of 'peak clipping' is achieved.
Preferably, the step S1 includes the steps of:
s11: acquiring historical power load data and date from a power grid platform, acquiring weather data in an expected time period from a related weather website, acquiring date data in the expected time period from a calendar and judging whether special dates exist in the expected time period;
s12: analyzing historical power load data and historical weather data, and then obtaining the weight values of influences of weather and special dates on the power load;
s13: obtaining the electricity load data in the expected time period according to the historical electricity load data, the weather data in the expected time period and the special date in the expected time period;
s14: setting a high power load threshold value and a low power load threshold value, dividing the power load into high loads if the power load is greater than the high power load threshold value, dividing the power load into general loads if the power load is less than the high power load threshold value and greater than the low power load threshold value, and dividing the power load into low loads if the power load is less than the low power load threshold value. Because the power load of the power grid changes in real time, the power load data of the power grid cannot be directly used as the starting and stopping conditions of the free power load, the power load of the power grid may fluctuate above and below a certain power load threshold value within a period of time, at this time, an execution unit using the power load threshold value as the starting or stopping condition is started and stopped for a while, damage to the execution unit may be caused, and the service life of the execution unit is reduced.
Preferably, the special dates in step S11 include weekends, legal holidays, overnight, 24 days in 12 months, and 25 days in 12 months. The power consumption load in different areas on the dates can change remarkably, the power consumption load in a certain area is different on different dates, the power consumption load in residential areas on weekends and holidays can rise, the power consumption of working day industrial parks and office buildings can rise, and therefore the dates can influence the power consumption load.
Preferably, the process of step S12 is: analyzing historical electricity load data and historical weather data, enabling the historical weather data to correspond to historical electricity loads, enabling historical date data to correspond to historical electricity loads, enabling each weather to correspond to a variable, enabling each special date to correspond to a variable, enabling each variable to correspond to a weight value queue, traversing the historical electricity load data, and using a weight value calculation method, wherein the weight value calculation method comprises the following steps: finding two historical electric load data with different variables from the historical electric load data, recording the variable as a selected variable, dividing the historical electric load data with the selected variable by the historical electric load data without the selected variable to obtain a weight value of the selected variable, adding the weight value of the selected variable into a weight value queue corresponding to the selected queue, and calculating the finally obtained weight value queue to obtain the weight value of each variable, wherein the calculating process is as follows: and taking the average value of the data in the weight value queue as the weight value of the variable corresponding to the weight value. Influence factors are controlled according to a control variable method, so that only one variable influencing two historical data is provided, and the variable is changed when the two historical data are changed, so that the influence weight value of the variable on the electric load can be found out, an average value is taken as the weight value of the variable after all the conditions are calculated, and the accuracy is improved.
Preferably, the process of step S13 is: and according to the weather and special date data of the expected time period, multiplying the weight values of the weather and the special date of the expected time period on the original data to obtain the power load data in the expected time period. The influence of historical weather and special dates in the historical electric load data is eliminated, and then the electric load data in the expected time period can be obtained by multiplying the influences by the weight values of the weather and the special dates in the expected time period.
Preferably, in step S2, the free electric loads are electric vehicles and energy storage stations using a timed full charge mode. The energy storage station can play a role in 'peak clipping and valley filling' of power load of a power grid, and the electric vehicle using a timing full-filling mode can play a role in 'peak clipping'.
Preferably, the timed full mode is as follows: the method comprises the steps of reading a timing full-charging time point input by an electric vehicle user to a controller arranged on the electric vehicle, calculating full-charging time required by full-charging of a battery by the controller according to the residual electric quantity of the electric vehicle and the battery capacity of the electric vehicle, calculating a charging time difference from a charging starting time point to the timing full-charging time, starting a full-charging state as soon as possible if the charging time difference is smaller than the full-charging time, otherwise, subtracting the full-charging time from the charging time difference to obtain free time, then starting charging, and controlling the electric vehicle to stop charging by the controller when the power load in an area is in a high load, wherein the charging stopping time is. When the mode is selected to be full-charged regularly, the time which is not needed to be charged can be left in the middle, the time is set when the load of the power grid is at a high load, the peak clipping effect can be achieved, and the means of full-charging regularly is adopted, so that a user can obtain an electric vehicle which is just fully charged in a fixed time, and for some used electric vehicles, after a period of time, the battery capacity is not full, so that the user can obtain a full-charged electric vehicle after a period of time, and the electric energy and the time are wasted.
Therefore, the invention has the following beneficial effects: (1) because the power load of the power grid changes in real time, the power load data of the power grid cannot be directly used as the starting and stopping conditions of the free power load, the power load of the power grid may fluctuate above and below a certain power load threshold value within a period of time, at this time, an execution unit using the power load threshold value as the starting or stopping condition is started and stopped for a while, damage to the execution unit may be caused, the service life of the execution unit is reduced, and therefore, the starting or stopping conditions of the execution unit can be determined by estimating the power load of the power grid at the expected time;
(2) calculating the influence of weather and special dates on the load of a power grid, wherein the influence of weather factors on electricity utilization is particularly large, people like air conditioners in hot days and cold days, particularly, refrigerators are used more in hot days, so that the electricity utilization load is increased, the electricity utilization load of a certain area is different on different dates, the electricity utilization load of residential areas on weekends and holidays can be increased, the electricity utilization amount of an industrial park and an office building on a working day can be increased, and the electricity utilization load can be influenced by the dates;
(3) controlling influence factors according to a control variable method, so that only one variable influencing two historical data is provided, and the variable which causes the change of the two historical data is the variable, so that the influence weight value of the variable on the electric load can be found out, and an average value is taken as the weight value of the variable after all the conditions are calculated, so that the accuracy is improved;
(4) when the mode is selected to be full-charged regularly, the time which is not needed to be charged can be left in the middle, the time is set when the load of the power grid is at a high load, the peak clipping effect can be achieved, and the means of full-charging regularly is adopted, so that a user can obtain an electric vehicle which is just fully charged in a fixed time, and for some used electric vehicles, after a period of time, the battery capacity is not full, so that the user can obtain a full-charged electric vehicle after a period of time, and the electric energy and the time are wasted.
Detailed Description
The invention is further described with reference to specific embodiments.
Example (b): a smart power grid control method for estimating and controlling power loads is characterized by comprising the following steps:
s1: estimating the electric load of a region according to historical data in an expected time period and dividing the electric load into a high load, a normal load and a low load;
s11: acquiring historical power load data and dates from a power grid platform, acquiring weather data in an expected time period from a related weather website, acquiring date data in the expected time period from a calendar and judging whether special dates exist in the expected time period, wherein the special dates include weekends, statutory holidays, overnight festivals, 12 months, 24 days and 12 months, 25 days;
s12: analyzing historical electricity load data and historical weather data, and then obtaining the influence weighted value of weather and special date on the electricity load, wherein the specific process is as follows: analyzing historical electricity load data and historical weather data, enabling the historical weather data to correspond to historical electricity loads, enabling historical date data to correspond to historical electricity loads, enabling each weather to correspond to a variable, enabling each special date to correspond to a variable, enabling each variable to correspond to a weight value queue, traversing the historical electricity load data, and using a weight value calculation method, wherein the weight value calculation method comprises the following steps: finding two historical electric load data with different variables from the historical electric load data, recording the variable as a selected variable, dividing the historical electric load data with the selected variable by the historical electric load data without the selected variable to obtain a weight value of the selected variable, adding the weight value of the selected variable into a weight value queue corresponding to the selected queue, and calculating the finally obtained weight value queue to obtain the weight value of each variable, wherein the calculating process is as follows: taking the average value of the data in the weight value queue as the weight value of the variable corresponding to the weight value;
s13: obtaining the electricity load data in the expected time period according to the historical electricity load data, the weather data in the expected time period and the special date in the expected time period, wherein the specific process is as follows: the method comprises the steps that data, from historical electric load data in an expected time period, are subjected to influence elimination of weather and special dates according to weight values of the weather and the special dates are used as original data, and the weight values of the weather and the special dates in the expected time period are multiplied by the original data according to the weather and the special date data in the expected time period, so that electric load data in the expected time period are obtained;
s14: setting a high power load threshold value and a low power load threshold value, dividing the power load into high loads if the power load is greater than the high power load threshold value, dividing the power load into general loads if the power load is less than the high power load threshold value and greater than the low power load threshold value, and dividing the power load into low loads if the power load is less than the low power load threshold value.
S2: stopping the free power loads in the area, which can be temporarily stopped, when the high load is estimated, wherein the free power loads are the electric vehicle and the energy storage station which use a timing full-charging mode, and the timing full-charging mode comprises the following steps: the method comprises the steps of reading a timing full-charging time point input by an electric vehicle user to a controller arranged on the electric vehicle, calculating full-charging time required by full-charging of a battery by the controller according to the residual electric quantity of the electric vehicle and the battery capacity of the electric vehicle, calculating a charging time difference from a charging starting time point to the timing full-charging time, starting a full-charging state as soon as possible if the charging time difference is smaller than the full-charging time, otherwise, subtracting the full-charging time from the charging time difference to obtain free time, then starting charging, and controlling the electric vehicle to stop charging by the controller when the power load in an area is in a high load, wherein the charging stopping time is.
The invention is further illustrated by the following specific examples: the controller is a common electric vehicle controller in the market.
S1: estimating the electric load of a region with more industrial plants according to historical data in an expected time period, and dividing the electric load into a high load, a normal load and a low load, wherein the expected time period is 30 minutes from 11 am of 3 and 12 months of 2022 to 11 am of 3 and 12 months of 2022;
s11: acquiring historical electricity load data of 72.3 kilowatts, 84.6 kilowatts, 91.8 kilowatts and 79.8 kilowatts from 11 am to 11 hours 30 minutes of 26 days at 2 months in 2022, 5 days at 3 months in 2022, 10 days at 3 months in 2022 and 11 days at 3 months in 2022 from a power grid platform, acquiring weather data of medium snow in an expected time period from a related weather website, acquiring historical weather data of 26 days at 2 months in 2022, 5 days at 3 months in 2022, 10 days at 3 months in 2022 and 11 days at 3 months in 2022 as sunny, medium snow and fair calendar from the related weather website, acquiring date data in the expected time period from the related weather website and judging that no special date exists in the expected time period, and acquiring that 26 days at 2 months in 2022 and 5 days at 3 months in 2022 as saturday and 5 days at 3 months in 2022 from the calendar as special dates which belong to weekdays, including weekends, holidays, festivals, 12 months, 24 days at 12 months and 25 days at 12 months;
s12: analyzing historical electricity load data and historical weather data, and then obtaining the influence weighted value of weather and special date on the electricity load, wherein the specific process is as follows: analyzing historical power consumption load data and historical weather data, respectively corresponding 26 days at 2 months and 26 days at 2022 years, 5 days at 3 months and 5 days at 2022 years, 10 days at 3 months and 11 days at 2022 years to be clear, medium snow and clear, respectively corresponding 30 minutes from 11 hours to 11 hours of the load data at 2 months and 26 days at 2022 months and 5 days at 3 months and 10 days at 2022 years and 3 months and 11 days at 2022 years to be 72.3 kilowatts, 84.6 kilowatts, 91.8 kilowatts and 79.8 kilowatts of the historical power consumption load data, respectively corresponding a variable x at medium snow and normal weather at sunny days as no variables, corresponding a variable y at saturday as no variable at date other than special date as no variable, and corresponding variables x and y to a weight value queue, and traversing the historical power consumption load data by using a weight value calculation method, wherein the process is as follows: 84.6 by one more variable x than 72.3, marking the variable x as a selected variable, dividing 84.6 by 72.3 to obtain a number 1.170124481, which is equal to about 1.17, adding 1.17 to the weight value queue corresponding to the selected variable x, marking the variable y as a selected variable by 72.3 by 79.8, dividing 72.3 by 79.8 to obtain a number 0.906015038, adding 0.906015038 to the weight value queue corresponding to the selected variable x, adding 84.6 by 91.8 by one more variable y, marking the variable y as a selected variable, dividing 84.6 by 91.8 to obtain a number 0.921568627, adding 0.921568627 to the weight value queue corresponding to the selected variable x, adding 91.8658 by one more variable x than 79.8, marking the variable x as a selected variable, dividing 91.8 by 79.8 to obtain a number 5, adding 1.15037594 to the weight value queue corresponding to the selected variable x, averaging 1.170124481 and 1.15037594 in the weight value queue corresponding to the variable x to 1.16025021, averaging the variable x to 1.24, averaging the weight value of 0.906015038 and 38723, the variable y is weighted to 0.914.
S13: obtaining the electricity load data in the expected time period according to the historical electricity load data, the weather data in the expected time period and the special date in the expected time period, wherein the specific process is as follows: dividing 72.3 by original data with a y variable weight value of 0.914 to be 79.1 kilowatts, dividing 84.6 by original data with an x variable weight value of 1.16 and a y variable weight value of 0.914 to be 79.79 kilowatts, dividing 91.8 by original data with an x variable weight value of 1.16 to be 79.14 kilowatts, and dividing 91.8 by original data with an x variable weight value of 1.16 to be 79.8 kilowatts, wherein the average value of the original data is 79.46 kilowatts, so that the original data in an expected time period is determined to be 79.46, and the weight value of 79.46 multiplied by the variable x is 1.16 to be 92.17 kilowatts, thereby obtaining power load data in the expected time period to be 92.17 kilowatts;
s14: the method comprises the steps of setting a high power load threshold value of 90 kilowatts and a low power load threshold value of 75 kilowatts, dividing the power load into high loads if the power load is larger than the high power load threshold value, dividing the power load into ordinary loads if the power load is smaller than the high power load threshold value and larger than the low power load threshold value, dividing the power load into low loads if the power load is smaller than the low power load threshold value, and dividing 92.17 into high loads if the power load is larger than the high power load threshold value, so that the power load is the high loads within 30 minutes of 11 am of 3 and 12 am of 2022 to 11 am of 3 and 12 am of 2022.
Because the power load of the power grid changes in real time, the power load data of the power grid cannot be directly used as the starting and stopping conditions of the free power load, and because the power load of the power grid may fluctuate above and below the power load threshold value for stopping the free power load within a period of time, the free power load can be started and stopped for a moment, the damage of the free power load can be caused, the service life of the free power load is reduced, the power load of the power grid at the expected time needs to be estimated, the free power load stops working when the power grid is in a high-load state, and the effect of 'peak clipping' is achieved.
Because the power load of the power grid changes in real time, the power load data of the power grid cannot be directly used as the starting and stopping conditions of the free power load, the power load of the power grid may fluctuate above and below a certain power load threshold value within a period of time, at this time, an execution unit using the power load threshold value as the starting or stopping condition is started and stopped for a while, damage to the execution unit may be caused, and the service life of the execution unit is reduced. The power consumption load in different areas on the dates can change remarkably, the power consumption load in a certain area is different on different dates, the power consumption load in residential areas on weekends and holidays can rise, the power consumption of working day industrial parks and office buildings can rise, and therefore the dates can influence the power consumption load.
Influence factors are controlled according to a control variable method, so that only one variable influencing two historical data is provided, and the variable is changed when the two historical data are changed, so that the influence weight value of the variable on the electric load can be found out, an average value is taken as the weight value of the variable after all the conditions are calculated, and the accuracy is improved. The influence of historical weather and special dates in the historical electric load data is eliminated, and then the electric load data in the expected time period can be obtained by multiplying the influences by the weight values of the weather and the special dates in the expected time period.
S2: stopping the operation of the free power load which can be temporarily stopped in the area when the high load is estimated, wherein the free power load is the electric vehicle and the energy storage station which use the timing full-charge mode, the timing full-charge mode is described as an example of an electric vehicle which is charged in the area, and the operation process of the timing full-charge mode of the electric vehicle which uses the timing full-charge mode is as follows: the timing full-charging time point which reads the input of a user is 2022 year 3 month 12 day 20 hour, the controller calculates the full-charging time required by the full-charging of the battery according to the residual capacity of the electric vehicle and the battery capacity of the electric vehicle, the residual capacity of the electric vehicle is 21 percent, the battery capacity is 32AH, the full-charging time required by the full-charging of the battery is calculated according to the residual capacity of the electric vehicle and the battery capacity of the electric vehicle, the disclosed conventional technology can also adopt the following technical means, according to different using conditions of the battery, the average time of 0.08 hour is consumed when charging for 1 percent in the last time, so the battery is recharged for 79 percent to the full-charging time required for 6.32 hours, the charging time difference from the charging starting time point to the timing full-charging time is calculated for 12 hours, the current time is 2022 year 3 month 12 day 8 hour, because the charging time difference is greater than the full-charging time, the charging time difference is reduced to the, then, charging is started, because the electric load is high load from 11 am of 3/12 th 2022 to 11 am of 3/12 th 2022, the controller controls the electric vehicle to stop charging, because the free time 5.68 hours is more than 0.5 hours, the electric vehicle stops charging from 30 am of 3/12 th 2022 to 11 am of 3/12 th 2022. The energy storage station can play a role in 'peak clipping and valley filling' of power load of a power grid, and the electric vehicle using a timing full-filling mode can play a role in 'peak clipping'. When the mode is selected to be full-charged regularly, the time which is not needed to be charged can be left in the middle, the time is set when the load of the power grid is at a high load, the peak clipping effect can be achieved, and the means of full-charging regularly is adopted, so that a user can obtain an electric vehicle which is just fully charged in a fixed time, and for some used electric vehicles, after a period of time, the battery capacity is not full, so that the user can obtain a full-charged electric vehicle after a period of time, and the electric energy and the time are wasted.
Claims (7)
1. A smart power grid control method for estimating and controlling power loads is characterized by comprising the following steps:
s1: estimating the electric load of a region according to historical data in an expected time period and dividing the electric load into a high load, a normal load and a low load;
s2: the free-load electrical loads in the region, which can be temporarily deactivated, are deactivated when a high load is estimated.
2. The method for controlling a smart grid according to claim 1, wherein the step S1 comprises the steps of:
s11: acquiring historical power load data and date from a power grid platform, acquiring weather data in an expected time period from a related weather website, acquiring date data in the expected time period from a calendar and judging whether special dates exist in the expected time period;
s12: analyzing historical power load data and historical weather data, and then obtaining the weight values of influences of weather and special dates on the power load;
s13: obtaining the electricity load data in the expected time period according to the historical electricity load data, the weather data in the expected time period and the special date in the expected time period;
s14: setting a high power load threshold value and a low power load threshold value, dividing the power load into high loads if the power load is greater than the high power load threshold value, dividing the power load into general loads if the power load is less than the high power load threshold value and greater than the low power load threshold value, and dividing the power load into low loads if the power load is less than the low power load threshold value.
3. The method as claimed in claim 2, wherein the special dates in step S11 include weekends, legal holidays, overnight, 24 days in 12 months, and 25 days in 12 months.
4. The method for controlling a smart grid according to claim 2, wherein the step S12 comprises the steps of: analyzing historical electricity load data and historical weather data, enabling the historical weather data to correspond to historical electricity loads, enabling historical date data to correspond to historical electricity loads, enabling each weather to correspond to a variable, enabling each special date to correspond to a variable, enabling each variable to correspond to a weight value queue, traversing the historical electricity load data, and using a weight value calculation method, wherein the weight value calculation method comprises the following steps: finding two historical electric load data with different variables from the historical electric load data, recording the variable as a selected variable, dividing the historical electric load data with the selected variable by the historical electric load data without the selected variable to obtain a weight value of the selected variable, adding the weight value of the selected variable into a weight value queue corresponding to the selected queue, and calculating the finally obtained weight value queue to obtain the weight value of each variable, wherein the calculating process is as follows: and taking the average value of the data in the weight value queue as the weight value of the variable corresponding to the weight value.
5. The method for controlling a smart grid according to claim 2, wherein the step S13 comprises the steps of: and according to the weather and special date data of the expected time period, multiplying the weight values of the weather and the special date of the expected time period on the original data to obtain the power load data in the expected time period.
6. The method as claimed in claim 1, wherein the free power loads are electric vehicles and energy storage stations using a timed full charging mode in step S2.
7. The method as claimed in claim 6, wherein the timing full mode is: the method comprises the steps of reading a timing full-charging time point input by an electric vehicle user to a controller arranged on the electric vehicle, calculating full-charging time required by full-charging of a battery by the controller according to the residual electric quantity of the electric vehicle and the battery capacity of the electric vehicle, calculating a charging time difference from a charging starting time point to the timing full-charging time, starting a full-charging state as soon as possible if the charging time difference is smaller than the full-charging time, otherwise, subtracting the full-charging time from the charging time difference to obtain free time, then starting charging, and controlling the electric vehicle to stop charging by the controller when the power load in an area is in a high load, wherein the charging stopping time is.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910994569.7A CN111181151A (en) | 2019-10-18 | 2019-10-18 | Smart power grid control method for estimating and controlling power load |
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