CN110877546B - Weather prediction-based photovoltaic charging station charging control method and device - Google Patents
Weather prediction-based photovoltaic charging station charging control method and device Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/50—Charging stations characterised by energy-storage or power-generation means
- B60L53/51—Photovoltaic means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/50—Charging stations characterised by energy-storage or power-generation means
- B60L53/53—Batteries
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/64—Optimising energy costs, e.g. responding to electricity rates
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
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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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
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Abstract
The application discloses a method and a device for controlling charging of a photovoltaic charging station based on weather prediction, wherein the method comprises the following steps: acquiring a weather prediction data set; adjusting the energy storage maximum discharge power of an energy storage battery in the photovoltaic charging station on the same day according to the weather prediction data set to determine the initial energy storage maximum discharge power; dividing the initial energy storage maximum discharge power according to a preset step length to obtain a plurality of discharge powers; according to historical data of the power distribution network, predicting a load curve of the power distribution network on the current day, and in each preset time period on the current day, acquiring each power grid electricity purchasing expense when electricity is purchased to the power distribution network under each discharging power according to the load curve, vehicle charging demand data in the station and photovoltaic power generation data of a photovoltaic charging station, and then taking the discharging power corresponding to the lowest power grid electricity purchasing expense as the maximum discharging power of energy storage on the current day; and orderly charging the vehicles in the station based on a time-interval charging strategy according to the maximum energy storage discharge power of the day and the current time interval.
Description
Technical Field
The application relates to the technical field of power control, in particular to a charging control method and device for a photovoltaic charging station based on weather prediction.
Background
The photovoltaic charging station is a charging station which converts light energy into electric energy to be stored and is used for charging a new energy automobile. The method can realize the on-site consumption and utilization of renewable energy sources, effectively improve the utilization rate of the renewable energy sources and reduce the carbon emission, and can reduce the burden of the development of electric vehicles on the construction of power grids and the adjustment of energy structures.
The existing charging control method for the photovoltaic charging station divides a day into time intervals according to photovoltaic conditions and load distribution characteristics, classifies vehicles according to vehicle charging demand information, determines a charging strategy of the photovoltaic charging station according to the time intervals and the vehicle types, and accordingly performs charging control on the photovoltaic charging station according to the charging strategy. However, when the prior art is adopted to perform charging control on the photovoltaic charging station, it is found that, because the prediction of the load curve of the power distribution network based on the power grid load prediction is not considered, the discharging power cannot be adjusted according to the power grid condition, so that the situation that the power grid needs to provide too much electric quantity and the power grid power consumption is too high may occur, the electric power resource is wasted, and the change of the weather can cause an influence on the energy storage of the photovoltaic charging station, so that the current charging strategy cannot be well executed due to the energy storage, and the charging effect of the vehicle is influenced.
Disclosure of Invention
The technical problem to be solved in the embodiments of the present application is to provide a method and an apparatus for controlling charging of a photovoltaic charging station based on weather prediction, so as to save power resources and reduce the influence of weather fluctuation on charging of the photovoltaic charging station.
In order to solve the above problem, an embodiment of the present application provides a method for controlling charging of a photovoltaic charging station based on weather prediction, which is suitable for being executed in a computing device, and at least includes the following steps:
acquiring a weather prediction data set; wherein the weather projection data set includes at least a current day's weather projection data and a next day's weather projection data;
adjusting the maximum energy storage discharge power of the photovoltaic charging station on the same day according to the weather prediction data set to determine the initial maximum energy storage discharge power;
dividing the initial energy storage maximum discharge power according to a preset step length to obtain a plurality of discharge powers;
predicting a load curve of the power distribution network on the current day according to historical data of the power distribution network, and after acquiring each power grid electricity purchasing cost when electricity is purchased to the power distribution network under each discharging power according to the load curve, vehicle charging demand data in the station and photovoltaic power generation data of the photovoltaic charging station in each preset time period on the current day, taking the discharging power corresponding to the lowest power grid electricity purchasing cost as the maximum discharging power of the energy storage on the current day; wherein, the power grid electricity purchasing cost comprises the current day electricity purchasing cost and the next day electricity purchasing cost;
orderly charging the vehicles in the station based on a time-sharing charging strategy according to the maximum energy storage discharge power of the current day and the current time period; the current time interval is one of a plurality of time intervals obtained by dividing time intervals for one day according to photovoltaic conditions and load distribution characteristics.
Further, the current time period comprises a photovoltaic power generation time period;
the orderly charging control of the vehicles in the station based on the time-sharing charging strategy according to the maximum energy storage discharging power and the current time period on the same day comprises the following steps:
when the fact that the maximum discharge power of the energy storage in the current day is larger than the maximum charging power requirement of the vehicles in the station in the photovoltaic power generation time period is detected, the vehicles in the station are charged according to the maximum charging power requirement in the photovoltaic power generation time period, and after the maximum charging power requirement of the vehicles in the station is met, the residual power of the maximum discharge power of the energy storage in the current day is used for charging an energy storage battery.
Further, the method also comprises the following steps:
when the fact that the maximum discharge power of the energy storage in the current day is larger than the minimum charging power requirement of the vehicles in the station and smaller than the maximum charging power requirement of the vehicles in the station is detected, in the photovoltaic power generation time period, the vehicles in the station are charged according to the minimum charging power requirement, and after the minimum charging power requirement of the vehicles in the station is met, the residual power of the maximum discharge power of the energy storage in the current day is sequentially charged according to the arrival sequence of the vehicles in the station.
Further, the method also comprises the following steps:
and when the fact that the maximum discharge power of the energy storage in the day is smaller than the minimum charging power requirement of the vehicles in the station in the photovoltaic power generation period is detected, charging the vehicles in the station according to a total power discharge rate formed by the maximum discharge power of the energy storage in the day and the storage power of the energy storage battery.
Further, the current time interval also comprises an energy storage and power generation time interval;
the orderly charging control of the vehicles in the station based on the time-sharing charging strategy according to the maximum energy storage discharging power and the current time period on the same day comprises the following steps:
and according to the storage power of the energy storage battery, supplying power to the vehicles in the station in the energy storage and power generation time period.
Further, the current time period also comprises a night load curve valley time period;
the orderly charging control of the vehicles in the station based on the time-sharing charging strategy according to the maximum energy storage discharging power and the current time period on the same day comprises the following steps:
according to the charging demand of the vehicles in the station, the charge state of the energy storage battery and the load curve of the power distribution network in the night load curve valley period, corresponding power purchasing power is obtained from the power distribution network at different moments of the night load curve valley period to charge the vehicles in the station, so that the load curve in the night load curve valley period tends to be gentle.
Further, still provide a photovoltaic charging station charge control device based on weather prediction, include:
the data acquisition module is used for acquiring a weather prediction data set; wherein the weather projection data set includes at least a current day's weather projection data and a next day's weather projection data;
the initial power determination module is used for adjusting the maximum energy storage discharge power of the photovoltaic charging station on the same day according to the weather prediction data set so as to determine the maximum initial energy storage discharge power;
the power dividing module is used for dividing the initial energy storage maximum discharge power according to a preset step length to obtain a plurality of discharge powers;
the maximum power determining module is used for predicting a load curve of the power distribution network on the current day according to historical data of the power distribution network, acquiring each power grid electricity purchasing cost when electricity is purchased to the power distribution network under each discharging power according to the load curve, vehicle charging demand data in the station and photovoltaic power generation data of the photovoltaic charging station in each preset time period on the current day, and taking the discharging power corresponding to the lowest power grid electricity purchasing cost as the maximum energy storage discharging power on the current day; wherein, the power grid electricity purchasing cost comprises the current day electricity purchasing cost and the next day electricity purchasing cost;
the charging control module is used for orderly charging the vehicles in the station based on a time-sharing charging strategy according to the maximum energy storage discharging power of the day and the current time period; the current time interval is one of a plurality of time intervals obtained after time interval division is carried out on one day according to photovoltaic conditions and load distribution characteristics, and the charging mode of the vehicles in the station comprises photovoltaic charging, power distribution network charging and energy storage battery charging.
Further, the current time period comprises a photovoltaic power generation time period;
the charging control module is specifically configured to:
when the fact that the maximum discharge power of the energy storage in the current day is larger than the maximum charging power requirement of the vehicles in the station in the photovoltaic power generation time period is detected, the vehicles in the station are charged according to the maximum charging power requirement in the photovoltaic power generation time period, and after the maximum charging power requirement of the vehicles in the station is met, the residual power of the maximum discharge power of the energy storage in the current day is used for charging an energy storage battery.
Further, the current time interval also comprises an energy storage and power generation time interval;
the charging control module is specifically configured to:
and according to the storage power of the energy storage battery, supplying power to the vehicles in the station in the energy storage and power generation time period.
Further, the current time period also comprises a night load curve valley time period;
the charging control module is specifically configured to:
according to the charging demand of the vehicles in the station, the charge state of the energy storage battery and the load curve of the power distribution network in the night load curve valley period, corresponding power purchasing power is obtained from the power distribution network at different moments of the night load curve valley period to charge the vehicles in the station, so that the load curve in the night load curve valley period tends to be gentle.
The embodiment of the application has the following beneficial effects:
compared with the prior art, the energy storage maximum discharge power of the energy storage battery in the photovoltaic charging station on the same day is adjusted through the weather prediction data set, the charging influence of weather fluctuation on the photovoltaic is reduced, after the energy storage maximum discharge power on the same day is determined based on the energy storage maximum discharge power through the prediction load curve of the power distribution network on the same day, vehicle charging demand data in the station and photovoltaic power generation data, the vehicles in the station are charged in order based on a time-sharing charging strategy, so that the photovoltaic charging station can adjust the discharge power according to the power grid condition, and the photovoltaic power generation and the energy storage battery are fully utilized to reduce the power consumption of the power grid.
In addition, according to the curve change of the load curve in the low valley period of the load curve at night, the corresponding electricity purchasing power is obtained from the power distribution network in the time period when the load curve is in the low valley period to charge the vehicles in the station, so that the load curve tends to be smooth, the purposes of reducing the electricity purchasing cost of the charging station and reducing the peak valley difference of the power distribution network are achieved, and the impact on the power distribution network caused by disordered charging of a large number of electric vehicles in the low valley period of the electricity price is further avoided
Drawings
Fig. 1 is a schematic flowchart of a method for controlling charging of a photovoltaic charging station based on weather prediction according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a photovoltaic charging station charging control apparatus based on weather prediction according to a second embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a schematic flowchart of a method for controlling charging of a photovoltaic charging station based on weather prediction according to an embodiment of the present application is shown. The method comprises the following steps:
in step S11, a weather prediction data set is obtained.
Wherein the weather projection data set includes at least the weather projection data for the current day and the weather projection data for the next day.
In this embodiment, the weather forecast data includes weather forecast data on sunny days, cloudy days, rainy days, and the like.
And step S12, adjusting the maximum energy storage discharge power of the energy storage battery in the photovoltaic charging station on the same day according to the weather prediction data set to determine the initial maximum energy storage discharge power.
In this embodiment, for example, on a sunny day and a cloudy day, the maximum discharge depth of the stored energy on the day is increased to supplement the charging requirement on the day, so as to reduce the electricity purchasing cost of the power grid on the day, and the energy storage discharge reduction part on the day uses the time-of-use electricity price to purchase electricity to the power grid in the time period with the lowest electricity price, so as to achieve the purpose of transferring the electricity purchasing from the peak time period of the day to the valley time period of the day, thereby reducing the electricity purchasing cost to the power grid, and achieving the effect of peak clipping and valley filling, so as to realize the sequential charging strategy on successive days. Similarly, under the conditions of cloudy days on the current day and sunny days on the next day, the maximum discharge depth of the stored energy on the current day is reduced, so that a large amount of stored energy is discharged on the current day to charge the electric automobile, enough battery capacity is reserved for photovoltaic charging on the next day, and the electricity purchase cost of the power grid on the current day is reduced.
As a preferred example of this embodiment, the initial energy storage maximum discharge power may be adjusted in such a way that there are four preset energy storage maximum discharge powers on the same day, and the weather prediction data sets corresponding to the four energy storage maximum discharge powers on the same day are: and calling the corresponding preset current-day energy storage maximum discharge power as the initial energy storage maximum discharge power according to the obtained weather prediction data set. The four preset current-day energy storage maximum discharge powers can be preset according to historical charging information, and a specific preset method can adopt a common preset model and is not limited herein.
And step S13, dividing the initial energy storage maximum discharge power according to a preset step length to obtain a plurality of discharge powers.
In this embodiment, taking an energy storage battery with a rated capacity of 1000MWh as an example, the preset step size is set to 50MW, and the divided discharge powers are 1000MWh, 950MWh, 900MWh, 850MWh, and 800MWh … 300MWh, respectively. The preset step length can be adjusted according to the rated capacity of the energy storage battery, or adjusted a priori according to the power supply condition.
And step S14, predicting a load curve of the power distribution network on the current day according to the historical data of the power distribution network, acquiring the power purchasing cost of each power grid under each discharging power according to the load curve, the charging demand data of the vehicles in the station and the photovoltaic power generation data of the photovoltaic charging station, and taking the discharging power corresponding to the lowest power purchasing cost of the power grid as the maximum discharging power of the energy storage on the current day.
Wherein, the electricity purchasing cost of the power grid comprises the current day electricity purchasing cost and the next day electricity purchasing cost.
In this embodiment, the historical data of the power distribution network includes historical load curves of the power distribution network, historical in-station vehicle information for the charging stations, and historical weather forecast data sets. And obtaining all historical load curves corresponding to the searched vehicle information and historical weather prediction data sets in the historical stations by searching all historical vehicle information and all historical weather prediction data sets in the historical data, which are the same as the vehicle information and weather prediction data sets in the station on the current day, so as to predict the load curve of the power distribution network on the current day. If the vehicle information in the station on the current day is 100, and the weather prediction data set is the next sunny day of the current day, searching all dates of which the vehicle information in the historical station is 100 and the weather prediction data set is the next sunny day of the current day, and predicting the load curve of the power distribution network on the current day according to the historical load curves on the dates. The prediction method may be averaging or other existing calculation methods for predicting data, and is not limited herein.
And step S15, orderly charging the vehicles in the station based on the time-interval charging strategy according to the maximum energy storage discharging power of the day and the current time interval.
The current time interval is one of a plurality of time intervals obtained by dividing one day in time intervals according to photovoltaic conditions and load distribution characteristics.
The embodiment adjusts the energy storage maximum discharge power of the energy storage battery in the photovoltaic charging station on the same day through the weather forecast data set, reduces the charging influence of weather fluctuation on the photovoltaic, and then determines the energy storage maximum discharge power on the same day based on the energy storage maximum discharge power through the forecast load curve of the power distribution network on the same day, the charging demand data of the vehicles in the station and the photovoltaic power generation data, and orderly charges the vehicles in the station based on the time-sharing charging strategy, so that the photovoltaic charging station can adjust the discharge power according to the power grid condition, and the photovoltaic power generation and the energy storage battery are fully utilized to reduce the power consumption of the power grid.
In the present embodiment, the time period of one day may be divided into a photovoltaic power generation time period, an energy storage power generation time period, a load curve valley time period at night, and a load curve valley rising time period according to photovoltaic conditions and load distribution characteristics.
In this embodiment, the charging control during the photovoltaic power generation period is specifically:
and when detecting that the maximum discharge power of the energy storage in the day is greater than the maximum charge power requirement of the vehicles in the station in the photovoltaic power generation period, charging the vehicles in the station according to the maximum charge power requirement in the photovoltaic power generation period, and charging the residual power of the maximum discharge power of the energy storage in the day for the energy storage battery after the maximum charge power requirement of the vehicles in the station is met.
In this embodiment, if the energy storage maximum discharge power can meet the maximum charge power requirement of the vehicles in the station on the same day, the vehicle meeting the latest charge time is charged preferentially.
And when detecting that the maximum discharge power of the energy storage in the day is greater than the minimum charge power requirement of the vehicles in the station and less than the maximum charge power requirement of the vehicles in the station in the photovoltaic power generation period, charging the vehicles in the station according to the minimum charge power requirement in the photovoltaic power generation period, and after the minimum charge power requirement of the vehicles in the station is met, sequentially charging the vehicles in the station according to the arrival sequence of the vehicles in the station with the residual power of the maximum discharge power of the energy storage in the day.
In this embodiment, after the minimum charging power requirement is completed, the remaining power is sequentially charged to other vehicles at the station according to the arrival sequence until the photovoltaic power is used up.
And when detecting that the maximum discharge power of the energy storage in the day is smaller than the minimum charge power requirement of the vehicles in the station in the photovoltaic power generation period, charging the vehicles in the station according to the total power discharge rate formed by the maximum discharge power of the energy storage in the day and the storage power of the energy storage battery.
In this embodiment, if the maximum discharge power of the energy storage cannot meet the minimum charging power of the vehicles in the station on the same day, the energy storage battery and the power grid are required to be supplemented, and the minimum charging power is considered as a basic principle to be met in the peak load period, so that electricity is purchased to the power distribution network as little as possible. The specific ordered charging strategy is divided into the following two cases: 1. and if the photovoltaic power generation and energy storage battery can meet the minimum charging power, the photovoltaic power generation and the energy storage supply power together. And 2, if the photovoltaic power generation and the energy storage battery are supplied together and cannot meet the minimum charging power, supplementing the power by a power grid.
In this embodiment, the charging control during the energy storage and power generation period is specifically:
and according to the storage power of the energy storage battery, supplying power to the vehicles in the station in the energy storage and power generation time period.
In the embodiment, the energy storage and power generation period is a period without photovoltaic power generation, and the power distribution network is in a load peak in any part of the period. In order to avoid the burden of increasing the load of the power grid as much as possible, the energy storage battery is preferentially utilized for charging in order to meet the minimum charging power requirement during the period, and the energy storage battery is supplemented by the power distribution grid when the energy storage battery cannot meet the requirement.
In this embodiment, the charging control during the low valley period of the load curve at night is specifically as follows:
according to the charging requirements of the vehicles in the station, the charge states of the energy storage batteries and the load curves of the power distribution network in the load curve valley period at night, the corresponding electricity purchasing powers are obtained from the power distribution network at different moments of the load curve valley period at night to charge the vehicles in the station, so that the load curves in the load curve valley period at night tend to be gentle.
In order to avoid the above situation, in this embodiment, when a time node enters the time period, charging demand information of all vehicles on the time node is integrated, a total charging demand of the vehicles to be charged is calculated, and meanwhile, after determining power supply power required to be provided by the power distribution network in the time period according to a total charging demand gap, according to curve change of the load curve in the time period, corresponding power purchasing power is obtained from the power distribution network in the time period when the load curve is in the valley to charge the vehicles in the station, and at this time, the load curve in the valley is pulled high, so that the load curve tends to be smooth, and the purposes of reducing power purchasing cost of the charging station and reducing peak-valley difference of the power distribution network are achieved, and then avoid a large amount of electric automobile to charge in disorder at the low ebb period of price of electricity and lead to the impact that produces the electric wire netting. The time period when the load curve is in the valley may be a time period when the load curve is in the valley at night, and the actual load of the power grid is smaller than the average load of the power grid in the valley time of the load curve at night.
In this embodiment, the charging control manner during the load curve valley rising period is similar to the control manner during the energy storage and power generation period, and is not repeated herein.
Further, refer to fig. 2, which is a schematic structural diagram of a photovoltaic charging station charging control apparatus based on weather prediction according to a second embodiment of the present application. The method comprises the following steps:
a data acquisition module 101, configured to acquire a weather prediction data set; wherein the weather projection data set includes at least a current day's weather projection data and a next day's weather projection data.
The initial power determination module 102 is configured to adjust the maximum energy storage discharge power of the photovoltaic charging station on the same day according to the weather prediction data set, so as to determine the initial maximum energy storage discharge power.
The power dividing module 103 is configured to divide the initial energy storage maximum discharge power according to a preset step length to obtain a plurality of discharge powers.
And the maximum power determining module 104 is configured to predict a load curve of the power distribution network on the current day according to historical data of the power distribution network, and after acquiring each power grid electricity purchasing cost when electricity is purchased to the power distribution network under each discharging power according to the load curve, vehicle charging demand data in the station and photovoltaic power generation data of the photovoltaic charging station on the current day, use the discharging power corresponding to the lowest power grid electricity purchasing cost as the maximum energy storage discharging power on the current day.
Wherein, the electricity purchasing cost of the power grid comprises the current day electricity purchasing cost and the next day electricity purchasing cost.
And the charging control module 105 is used for orderly charging the vehicles in the station based on the time-sharing charging strategy according to the maximum energy storage discharging power of the day and the current time period.
The current time interval is one of a plurality of time intervals obtained after time interval division is carried out on one day according to photovoltaic conditions and load distribution characteristics, and the charging mode of the vehicles in the station comprises photovoltaic charging, power distribution network charging and energy storage battery charging.
The embodiment adjusts the energy storage maximum discharge power of the energy storage battery in the photovoltaic charging station on the same day through the weather forecast data set, reduces the charging influence of weather fluctuation on the photovoltaic, and then determines the energy storage maximum discharge power on the same day based on the energy storage maximum discharge power through the forecast load curve of the power distribution network on the same day, the charging demand data of the vehicles in the station and the photovoltaic power generation data, and orderly charges the vehicles in the station based on the time-sharing charging strategy, so that the photovoltaic charging station can adjust the discharge power according to the power grid condition, and the photovoltaic power generation and the energy storage battery are fully utilized to reduce the power consumption of the power grid.
In the present embodiment, the time period of one day may be divided into a photovoltaic power generation time period, an energy storage power generation time period, a load curve valley time period at night, and a load curve valley rising phase according to the photovoltaic conditions and the load distribution characteristics.
In this embodiment, when in the photovoltaic power generation period, the charging control module 105 is specifically configured to:
and when detecting that the maximum discharge power of the energy storage in the day is greater than the maximum charge power requirement of the vehicles in the station in the photovoltaic power generation period, charging the vehicles in the station according to the maximum charge power requirement in the photovoltaic power generation period, and charging the residual power of the maximum discharge power of the energy storage in the day for the energy storage battery after the maximum charge power requirement of the vehicles in the station is met.
And when detecting that the maximum discharge power of the energy storage in the day is greater than the minimum charge power requirement of the vehicles in the station and less than the maximum charge power requirement of the vehicles in the station in the photovoltaic power generation period, charging the vehicles in the station according to the minimum charge power requirement in the photovoltaic power generation period, and after the minimum charge power requirement of the vehicles in the station is met, sequentially charging the vehicles in the station according to the arrival sequence of the vehicles in the station with the residual power of the maximum discharge power of the energy storage in the day.
And when detecting that the maximum discharge power of the energy storage in the day is smaller than the minimum charge power requirement of the vehicles in the station in the photovoltaic power generation period, charging the vehicles in the station according to the total power discharge rate formed by the maximum discharge power of the energy storage in the day and the storage power of the energy storage battery.
In the embodiment, when in the energy storage and power generation period, the charging control module 105 is specifically configured to supply power to the in-station vehicle during the energy storage and power generation period according to the storage power of the energy storage battery.
In this embodiment, when the load curve valley period is in the night time, the charging control module 105 is specifically configured to, according to the charging demand of the vehicles in the station, the state of charge of the energy storage battery, and the load curve of the power distribution network during the night time load curve valley period, obtain corresponding power purchasing power from the power distribution network at different times of the night time load curve valley period to charge the vehicles in the station, so that the load curve during the night time load curve valley period tends to be gentle.
In this embodiment, when the load curve is in the valley rising period, the charging control module 105 is configured to perform the same operation as in the energy storage and power generation period, which is not described herein again.
Still another embodiment of the present application further provides a photovoltaic charging station charging control terminal device based on weather prediction, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the photovoltaic charging station charging control method based on weather prediction as described in the above embodiment is implemented.
The foregoing is a preferred embodiment of the present application, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations are also regarded as the protection scope of the present application.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (10)
1. A photovoltaic charging station charging control method based on weather prediction is characterized by at least comprising the following steps:
acquiring a weather prediction data set; wherein the weather projection data set includes at least a current day's weather projection data and a next day's weather projection data;
adjusting the maximum energy storage discharge power of the photovoltaic charging station on the same day according to the weather prediction data set to determine the initial maximum energy storage discharge power;
dividing the initial energy storage maximum discharge power according to a preset step length to obtain a plurality of discharge powers;
predicting a load curve of the power distribution network on the current day according to historical data of the power distribution network, and after acquiring each power grid electricity purchasing cost when electricity is purchased to the power distribution network under each discharging power according to the load curve, vehicle charging demand data in the station and photovoltaic power generation data of the photovoltaic charging station in each preset time period on the current day, taking the discharging power corresponding to the lowest power grid electricity purchasing cost as the maximum discharging power of the energy storage on the current day; wherein, the power grid electricity purchasing cost comprises the current day electricity purchasing cost and the next day electricity purchasing cost;
orderly charging the vehicles in the station based on a time-sharing charging strategy according to the maximum energy storage discharge power of the current day and the current time period; the current time interval is one of a plurality of time intervals obtained by dividing time intervals for one day according to photovoltaic conditions and load distribution characteristics.
2. The weather prediction-based photovoltaic charging station charge control method of claim 1, wherein the current time period comprises a photovoltaic power generation time period;
the orderly charging control of the vehicles in the station based on the time-sharing charging strategy according to the maximum energy storage discharging power and the current time period on the same day comprises the following steps:
when the fact that the maximum discharge power of the energy storage in the current day is larger than the maximum charging power requirement of the vehicles in the station in the photovoltaic power generation time period is detected, the vehicles in the station are charged according to the maximum charging power requirement in the photovoltaic power generation time period, and after the maximum charging power requirement of the vehicles in the station is met, the residual power of the maximum discharge power of the energy storage in the current day is used for charging an energy storage battery.
3. The weather prediction-based photovoltaic charging station charge control method of claim 2, further comprising:
when the fact that the maximum discharge power of the energy storage in the current day is larger than the minimum charging power requirement of the vehicles in the station and smaller than the maximum charging power requirement of the vehicles in the station is detected, in the photovoltaic power generation time period, the vehicles in the station are charged according to the minimum charging power requirement, and after the minimum charging power requirement of the vehicles in the station is met, the residual power of the maximum discharge power of the energy storage in the current day is sequentially charged according to the arrival sequence of the vehicles in the station.
4. The weather prediction-based photovoltaic charging station charge control method of claim 2, further comprising:
and when the fact that the maximum discharge power of the energy storage in the day is smaller than the minimum charging power requirement of the vehicles in the station in the photovoltaic power generation period is detected, charging the vehicles in the station according to a total power discharge rate formed by the maximum discharge power of the energy storage in the day and the storage power of the energy storage battery.
5. The weather prediction-based photovoltaic charging station charge control method of claim 1, wherein the current time period further comprises an energy storage and power generation time period;
the orderly charging control of the vehicles in the station based on the time-sharing charging strategy according to the maximum energy storage discharging power and the current time period on the same day comprises the following steps:
and according to the storage power of the energy storage battery, supplying power to the vehicles in the station in the energy storage and power generation time period.
6. The weather prediction-based photovoltaic charging station charge control method of claim 1, wherein the current time period further comprises a nighttime load curve trough time period;
the orderly charging control of the vehicles in the station based on the time-sharing charging strategy according to the maximum energy storage discharging power and the current time period on the same day comprises the following steps:
according to the charging demand of the vehicles in the station, the charge state of an energy storage battery and the load curve of the power distribution network in the night load curve valley period, corresponding power purchasing power is obtained from the power distribution network at different moments of the night load curve valley period to charge the vehicles in the station, so that the load curve in the night load curve valley period tends to be gentle.
7. A photovoltaic charging station charge control device based on weather prediction is characterized by comprising:
the data acquisition module is used for acquiring a weather prediction data set; wherein the weather projection data set includes at least a current day's weather projection data and a next day's weather projection data;
the initial power determination module is used for adjusting the maximum energy storage discharge power of the photovoltaic charging station on the same day according to the weather prediction data set so as to determine the maximum initial energy storage discharge power;
the power dividing module is used for dividing the initial energy storage maximum discharge power according to a preset step length to obtain a plurality of discharge powers;
the maximum power determining module is used for predicting a load curve of the power distribution network on the current day according to historical data of the power distribution network, acquiring each power grid electricity purchasing cost when electricity is purchased to the power distribution network under each discharging power according to the load curve, vehicle charging demand data in the station and photovoltaic power generation data of the photovoltaic charging station in each preset time period on the current day, and taking the discharging power corresponding to the lowest power grid electricity purchasing cost as the maximum energy storage discharging power on the current day; wherein, the power grid electricity purchasing cost comprises the current day electricity purchasing cost and the next day electricity purchasing cost;
the charging control module is used for orderly charging the vehicles in the station based on a time-sharing charging strategy according to the maximum energy storage discharging power of the day and the current time period; the current time interval is one of a plurality of time intervals obtained after time interval division is carried out on one day according to photovoltaic conditions and load distribution characteristics, and the charging mode of the vehicles in the station comprises photovoltaic charging, power distribution network charging and energy storage battery charging.
8. The weather prediction-based photovoltaic charging station charge control apparatus of claim 7, wherein the current time period comprises a photovoltaic generation time period;
the charging control module is specifically configured to:
when the fact that the maximum discharge power of the energy storage in the current day is larger than the maximum charging power requirement of the vehicles in the station in the photovoltaic power generation time period is detected, the vehicles in the station are charged according to the maximum charging power requirement in the photovoltaic power generation time period, and after the maximum charging power requirement of the vehicles in the station is met, the residual power of the maximum discharge power of the energy storage in the current day is used for charging an energy storage battery.
9. The weather prediction-based photovoltaic charging station charge control apparatus of claim 7, wherein the current time period further comprises an energy storage and power generation time period;
the charging control module is specifically configured to:
and according to the storage power of the energy storage battery, supplying power to the vehicles in the station in the energy storage and power generation time period.
10. The weather prediction-based photovoltaic charging station charge control apparatus of claim 7, wherein the current time period further comprises a nighttime load curve trough time period;
the charging control module is specifically configured to:
according to the charging demand of the vehicles in the station, the charge state of the energy storage battery and the load curve of the power distribution network in the night load curve valley period, corresponding power purchasing power is obtained from the power distribution network at different moments of the night load curve valley period to charge the vehicles in the station, so that the load curve in the night load curve valley period tends to be gentle.
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| CN113067370B (en) * | 2021-03-15 | 2023-07-18 | 远景智能国际私人投资有限公司 | Charging control method and device of V2G charging station, server and storage medium |
| CN113060036B (en) * | 2021-03-15 | 2023-07-21 | 远景智能国际私人投资有限公司 | Charging control method and device of optical storage charging station, server and storage medium |
| CN113733963B (en) * | 2021-08-31 | 2023-05-02 | 国网北京市电力公司 | Day-ahead scheduling method, system and device for optical storage and charging integrated station and storage medium |
| CN113964872A (en) * | 2021-11-25 | 2022-01-21 | 内蒙古民族大学 | Photovoltaic power generation energy storage control method and system |
| CN115258861B (en) * | 2022-07-28 | 2023-11-07 | 广州广日电梯工业有限公司 | Elevator energy management method, system, device and storage medium |
| CN115378007B (en) * | 2022-10-25 | 2022-12-23 | 国网安徽省电力有限公司合肥供电公司 | Control method of intelligent photovoltaic energy storage integrated charging station |
| CN116780583B (en) * | 2023-06-06 | 2024-04-16 | 广东中特建设集团有限公司 | Intelligent energy storage method, system, equipment and medium for photovoltaic power generation |
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