CN112994129A - Distributed intelligent charging network control method and distributed intelligent power grid controller - Google Patents
Distributed intelligent charging network control method and distributed intelligent power grid controller Download PDFInfo
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Abstract
The invention discloses a distributed intelligent charging network control method, an intelligent power grid controller, a distributed intelligent charging network and a power grid energy optimization control method.
Description
Technical Field
The present invention relates to a control method and a controller, and more particularly, to a distributed intelligent charging network control method and a distributed intelligent grid controller.
Background
The automobile is a transportation means widely used by users at present, but the emission of carbon in the environment is greatly increased due to the large number of automobiles. Therefore, in order to reduce the amount of carbon discharged in the living environment, electric vehicles and electric locomotives have been actively driven in recent years to replace the conventional fuel-powered vehicles in many countries.
As mentioned above, when the number of electric vehicles is increased, the charging points should be increased correspondingly under ideal conditions, so that the energy demand of the conventional power network is increased. Therefore, except that the number of the charging points is not increased to a sufficient number, the vehicle to be charged may be congested at the charging points, and the energy demand of the real-time power grid is excessive, so that the capacity of the existing line is not sufficient, and therefore, the most effective electric quantity management and allocation for charging the electric vehicle is required.
Furthermore, if the charging points are located around the building and the commercial power provided by the building is used as the charging source, and since a plurality of buildings have assigned contract capacities and cannot exceed the instantaneous rated power consumption, if too many electric vehicles are concentrated at the same charging point for charging during the charging period, the charging is not possible for the user end to be charged, and it is inconvenient to separately search for other charging points.
Besides, the commercial power supplies the charging electric energy, the solar energy is also converted into the electric energy as the charging electric energy. However, under the condition of the tight mains supply, although solar energy can be used as another charging electric energy, not every building, charging station and charging point are provided with the function of solar charging. Besides the charging by the commercial power, the building, the charging station and the charging point with the solar charging function should have sufficient electric energy to charge the electric vehicle. However, in other buildings, charging stations and charging points where solar charging is not provided, the problem of uneven power distribution is caused in comparison to the situation where the mains supply is tight.
Accordingly, how to provide a management and distribution that can perform charging for a plurality of regions, a plurality of buildings, a plurality of charging stations, and a plurality of charging points has become a subject of urgent research.
Disclosure of Invention
In view of the above problems, the present invention provides a distributed intelligent charging network control method, comprising the following steps: the method comprises the steps of obtaining statistical data, obtaining statistical quantity information, statistical power supply information, statistical load information and statistical energy storage information of a charging network in the statistical data, and generating an initial prediction charging and discharging information model according to the statistical information, wherein the charging network is formed by a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions. The method comprises the steps of obtaining instant information, obtaining instant quantity information, instant power supply information, instant load information and instant energy storage information of the charging network, and generating an optimized charging and discharging control schedule of the charging network according to the instant information and the initial prediction charging and discharging information model. And transmitting the optimized charging and discharging control schedule to a plurality of clients. And transmitting the optimized charging and discharging control schedule to a management terminal, so that the management terminal controls the switches of the electric power among a plurality of areas, a plurality of buildings, a plurality of charging stations and a plurality of charging points according to the optimized charging and discharging control schedule.
The invention discloses a distributed intelligent power grid controller which comprises a storage module, a communication connection port, a data operation module and a power control switch. The storage module stores statistical quantity information, statistical power supply information, statistical load information and statistical energy storage information of a charging network in statistical data, wherein the charging network is formed by a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions. The communication connection port transmits and receives instant quantity information, instant power supply information, instant load information and instant energy storage information of the charging network in a wired or wireless instant mode, and the instant quantity information, the instant power supply information, the instant load information and the instant energy storage information are stored in the storage module. The data operation module is electrically connected with the storage module, and generates an initial prediction charging and discharging information model according to the statistical information, and then generates an optimized charging and discharging control schedule of the charging network according to the instant information and the initial prediction charging and discharging information model. The power control switch controls the power on and off of the charging network. The optimized charging and discharging control schedule is transmitted to a plurality of user terminals through the communication connection ports, and the power control switch controls the power of the charging network to be switched on and off according to the optimized charging and discharging control schedule.
In summary, the distributed intelligent power grid controller and the distributed intelligent charging network control method of the present invention generate an optimized charging and discharging control schedule by obtaining various information, classifying, calculating and judging by an algorithm, and transmitting the optimized charging and discharging control schedule to the user terminal and the management terminal. For the user terminal, the charging point to be charged can be selected according to various schedules provided by the optimized charging and discharging control schedule. For the management terminal, the electric power among a plurality of buildings, a plurality of charging stations and a plurality of charging points in a plurality of areas in the charging network can be effectively balanced and transmitted, and the management of distributed intelligent charging is further achieved.
The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.
Drawings
FIG. 1 is a flow chart of a distributed intelligent charging network control method of the present invention; and
FIG. 2 is a diagram of a distributed intelligent grid controller according to the present invention; and
fig. 3 is another schematic diagram of the distributed intelligent grid controller according to the present invention.
Detailed Description
The invention will be described in detail with reference to the following drawings, which are provided for illustration purposes and the like:
please refer to fig. 1, which is a flowchart illustrating a distributed intelligent charging network control method according to the present invention. First, it should be noted that in the embodiment of the present invention, the charging network refers to a charging network formed by a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions, and the buildings, the charging stations and the charging points include locations that can be charged by commercial power, solar photovoltaic and fuel cell. In the step S11 of obtaining statistical data, the distributed intelligent charging network control method includes obtaining statistical quantity information, statistical power supply information, statistical load information, and statistical energy storage information of the charging network in the statistical data, and generating an initial prediction charging and discharging information model according to the statistical information. In the step S13 of obtaining the instant information, the instant quantity information, the instant power supply information, the instant load information and the instant energy storage information of the charging network are obtained, and an optimized charging and discharging control schedule of the charging network is generated according to the instant information and the initial predicted charging and discharging information model. In step S15, the optimized charging/discharging control schedule is transmitted to the plurality of clients. In step S17, the optimized charging/discharging control schedule is transmitted to a management end, so that the management end controls the power switches among a plurality of areas, a plurality of buildings, a plurality of charging stations, and a plurality of charging points in the charging network according to the optimized charging/discharging control schedule.
In addition, the step of obtaining statistical data S11 further includes obtaining statistical electricity price information and statistical weather information, and the step of obtaining instant message S13 further includes obtaining instant electricity price information and instant weather information. The statistical load information and the instantaneous load information include electric contract capacity load information and electric power consumption information of a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions. The statistical energy storage information and the real-time energy storage information include reserve electric energy information and contract electric capacity information of a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions. The statistical power rate information and the instant power rate information include commercial power time power rate information provided by a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions. The statistical weather information and the instant weather information include weather information, temperature information, wind power information, humidity information, weather cloud map information, and ultraviolet information of areas where a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of areas are located. The statistical power supply information and the instant power supply information comprise the generated energy and power consumption information of the solar photovoltaic inverter, commercial power information and fuel cell information. The statistical quantity information includes charging pile quantity information of a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions. The real-time quantity information comprises charging pile quantity information of a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions, vehicle quantity information waiting for charging and charging time information of charging vehicles, wherein electric energy of the charging pile is generated by converting light energy through a solar photovoltaic inverter. The above-mentioned statistical quantity information, statistical power supply information, statistical load information, statistical energy storage information, statistical electricity price information, statistical weather information, instant quantity information, instant power supply information, instant load information, instant energy storage information, instant electricity price information and instant weather information are stored in a storage module, a database or a cloud network.
In the step S11 of obtaining statistical data, the statistical quantity information, the statistical power supply information, the statistical load information, the statistical energy storage information, the statistical electricity price information, and the statistical weather information of the charging network in the statistical data are obtained as a data prediction model for predicting the charging and discharging states of the future charging network, that is, generally speaking, under normal use conditions, the use states of a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions do not change suddenly and drastically, therefore, the charging network can use data in the past period of time as an initial model for predicting charging and discharging. The period of time in which the statistical data is acquired includes acquiring statistical quantity information, statistical power supply information, statistical load information, statistical energy storage information, statistical electricity price information, and statistical weather of the charging network for the past day, two days, three days, one week, one month, one season, one half year, or one year, and is not limited in the present invention. Alternatively, the statistical data may be obtained according to the perpetual calendar information, the lunar calendar information, the calendar information, and the constellation and movement information. Furthermore, for example, according to the calendar, on the regular day of work, the number of the charging points available to the user side may be small, that is, the load is low, so that the power can be stored in the charging network on the regular day of work. On the contrary, if a large amount of traffic tides may occur in each area during holidays, national holidays, continuous holidays or special festivals, and thus the user end has a large demand for charging at this time, during the festivals, the electric energy stored in the ordinary working days can be provided to the charging network, or the electric power of the charging point with a low load capacity and a large power supply capacity can be transmitted to the charging point with a large load capacity and a small power supply capacity, so that the balance and distribution of the electric power in the charging network are achieved, and the situation that the single charging point is overloaded or the charging point has sufficient electric power but is not used by the user is further avoided. Similarly, the statistical quantity information, the statistical power supply information, the statistical load information, the statistical energy storage information, the statistical electricity price information, the statistical weather information, the real-time quantity information, the real-time power supply information, the real-time load information, the real-time energy storage information, the real-time electricity price information, and the real-time weather information may also affect the power distribution and use of a plurality of areas, a plurality of buildings, a plurality of charging stations, and a plurality of charging points in the charging network, and the optimized charging and discharging control schedule may perform the management, correction, allocation, and balance of charging and discharging for the charging network according to the information.
The optimized charging and discharging control schedule comprises a route planning schedule of a plurality of charging points of a plurality of charging stations of a plurality of buildings in the area where the user terminal is located (when the user starts the positioning function of the intelligent device), a time planning schedule and a calculation schedule of the residual number of the plurality of charging points of the plurality of charging stations of the plurality of buildings which can be charged. The optimized charging and discharging control schedule also comprises activity information schedules around a plurality of charging points of a plurality of charging stations of a plurality of buildings. The activity information schedule comprises a charging discount offer schedule and an art activity information schedule. The optimized charging and discharging control schedule can display various planned information schedules in an energy management map together so that a user can quickly browse and master all information. The user end can receive the optimized charging and discharging control schedule by the intelligent device, the computer device or the cloud network, so that the user end can immediately know the position in the charging network according to the current position of the user end, can reach the position of the surrounding charging point according to the optimal path planning schedule, or can reach the position of the surrounding charging point at the fastest speed according to the shortest time planning schedule, or can display the residual quantity of the charging points available in the surrounding according to the position in the charging network of the user end, or can also display the residual quantity in the energy management map for the user to refer if the preferential activity schedule of charging exists. The optimized charging and discharging control schedule also comprises personal charging records of the user terminal, namely, the optimized charging and discharging control schedule can be quickly generated from the past charging records of the user terminal. In addition, the optimized charging and discharging control schedule also comprises an electric energy storage schedule which is carried out aiming at the charging network at the off-peak time and an electric energy generation schedule which is carried out by the solar electric inverter to generate electric energy at the peak time. The reserved electric energy can be stored in a fuel cell or other devices capable of storing electric energy, and is not limited in the present invention. Similarly, the management end receives the optimized charging and discharging control schedule through the intelligent device, the computer device or the cloud network, and the difference is that the optimized charging and discharging control schedule transmits different optimized charging and discharging control schedule contents according to the identity of the management end or the user end. For example, the optimal charging and discharging control schedule that the management terminal needs to receive includes an electric energy storage schedule and an electric energy generation schedule so as to manage charging and discharging for the charging network, and the user terminal does not need to receive such an optimal charging and discharging control schedule.
The algorithm includes an artificial intelligence algorithm, but is not limited in the present invention. Another preferred embodiment of the algorithm comprises the following steps: the method comprises the steps of updating statistical quantity information, statistical power supply information, statistical load information and statistical energy storage information of the charging network in the statistical data by means of immediately acquiring the real-time quantity information, the real-time power supply information, the real-time load information and the real-time energy storage information of the charging network, so as to update an initial prediction charging and discharging information model, and transmitting the initial prediction charging and discharging information model to a cloud network. The real-time load size and the remaining usable quantity of a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of areas in the updated charging network are judged whether to reach a preset critical value or not by the data operation module of the cloud network. If the current value does not reach the preset critical value, the data operation module generates an optimized charging and discharging control schedule and transmits the optimized charging and discharging control schedule to the user terminal and the management terminal. If the current charging time reaches the preset critical value, an optimal charging and discharging control schedule is generated according to the distance between the area where the user side is located and a plurality of buildings, a plurality of charging stations and a plurality of charging points, the time length and the remaining chargeable quantity, and the optimal charging and discharging control schedule is transmitted to the user side and the management side. And when the instant load size of the charging point reaches a preset critical value, the management terminal allocates the electric power of other charging points to the charging point of which the instant load size reaches the preset critical value according to the optimized charging and discharging control schedule so as to maintain the electric power balance and distribution of the whole charging network, and a new optimized charging and discharging control schedule is generated again after the electric power balance and distribution. Therefore, it should be noted that the above-mentioned algorithm is a loop process executed repeatedly, and may be executed at a predetermined time interval, or may be uploaded to the cloud network in real time when any information change occurs, so as to generate a new optimized charging and discharging control schedule in real time, which is not limited in the present invention.
Please refer to fig. 2, which is a diagram illustrating a distributed intelligent network controller according to the present invention. The distributed intelligent grid controller 1 includes a storage module 11, a communication connection port 12, a data operation module 13 and a power control switch 14. The storage module 11 stores statistical quantity information, statistical power supply information, statistical load information, and statistical energy storage information of a charging network in statistical data, wherein the charging network is formed by a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions. The communication connection port 12 transmits and receives the instant quantity information, the instant power supply information, the instant load information and the instant energy storage information of the charging network in a wired or wireless manner in real time, and stores the information in the storage module 11. The data operation module 13 is electrically connected to the storage module 11, and generates an initial predicted charging and discharging information model according to the statistical information, and then generates an optimized charging and discharging control schedule of the charging network according to the instant information and the initial predicted charging and discharging information model. The power control switch 14 controls the power on and off of the charging network. The optimized charging and discharging control schedule is transmitted to a plurality of user terminals through the communication connection port 12, and the power control switch 14 controls the power on and off of the charging network according to the optimized charging and discharging control schedule.
It should be noted that although the drawings show the region including the building including the charging station, the charging station including the charging point, the charging station and the charging point may be disposed outside the building, and may be independent of the solar-powered inverter to generate electric energy or supply electric energy by the fuel cell, rather than being supplied by the commercial power of the building. The stored electrical energy may be stored in a fuel cell or other device that may store electrical energy. However, whether powered through a building or not, charging stations and points that generate power from an ethernet solar power inverter or provide power from a fuel cell can provide additional power in other areas or when the building is under power.
Fig. 3 is another schematic diagram of the distributed intelligent grid controller according to the present invention. The distributed intelligent grid controller 1 further includes a voltage and current calculation module 15, which calculates and measures the instant power supply information, the instant load information and the instant energy storage information in the charging network received by the communication connection port 12, further calculates and measures the power supply amount, the load amount and the reserve energy amount of a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of areas in the charging network, and transmits the power supply amount, the load amount and the reserve energy amount to the data calculation module 13 for determination, and then transmits a control signal to the power control switch 14, so as to transmit the power of the building, the charging station or the charging point with abundant reserve energy to the building, the charging station or the charging point with large load and tight power supply, so as to maintain the power balance and distribution of the whole charging network.
The distributed intelligent grid controller 1 further includes the software using the distributed intelligent charging network control method in addition to the hardware devices, and accordingly, the efficiency achieved by the distributed intelligent grid controller 1 through the distributed intelligent charging network control method is as described above, and is not described herein again.
In addition, the storage module 11 and the data operation module 13 include plug-and-play modules, which can be plugged into the distributed intelligent power grid controller 1 at any time for use, or can be unplugged from the distributed intelligent power grid controller 1 at any time when not in use, which is not limited by the invention. Furthermore, the statistical information and the instant message are stored in the storage module 11, the database or the cloud network, and the present invention is not limited thereto. The communication connection port receives and transmits the instant data in a wired or wireless manner. The statistical data stored in the storage module 11 includes statistical quantity information, statistical power supply information, statistical load information, statistical energy storage information, statistical electricity price information, and statistical weather information of the charging network of the past day, two days, three days, one week, one month, one season, one half year, or one year.
The distributed intelligent grid controller 1 further comprises a display device 16 for displaying the charging points required to be charged and discharged in the current charging network, so as to facilitate management control of charging and discharging by a management terminal or artificial intelligence. The display device 16 can display information on a screen or on an APP of the smart device.
The distributed intelligent grid controller further includes a power supply module 17 electrically connected to the power control switch 14 for providing the charging grid power through the power control switch 14. After the above-mentioned data operation module 13 determines, it transmits a control signal to the power control switch 14, and the power supply module 17 transmits the power to a building, a charging station or a charging point with a large load and a tight power supply, so as to maintain the power balance and distribution of the whole charging network.
In addition, the charging network includes at least one monitoring device (not shown) in a plurality of areas, a plurality of buildings, a plurality of charging stations, and a plurality of charging points, and if an abnormal state occurs, the monitoring device can send an abnormal signal to the distributed intelligent power network controller 1 and the management terminal, and the maintenance and the removal can be performed according to the abnormal state. Alternatively, the monitoring device may be disposed on the distributed smart grid controller 1, and monitor whether abnormal conditions occur in a plurality of buildings, a plurality of charging stations, and a plurality of charging points in a plurality of areas within a preset range, and repair and remove the abnormal conditions according to the abnormal conditions.
In summary, the present invention discloses a distributed intelligent power grid controller, a distributed intelligent charging network control method, a distributed intelligent charging network and a power grid energy optimization control method, wherein a power network charging station system, capacity information, station electricity information, various types of load and energy storage system information are obtained, statistical operation and analysis are performed at the ground end according to historical information, the information is thrown to the cloud end to obtain an optimal charging path in the network through artificial intelligence calculation, an optimal energy scheduling control strategy in the power network at the day time is generated, and further, in combination with the instant information at the day time and the actual operation condition of a user end, optimal energy modulation is performed according to the information, so that the overall power network is balanced in supply and demand, and a detailed charging scheme at the user end and an optimal scheduling strategy at a management end are provided. Furthermore, the distributed intelligent power grid controller and the distributed intelligent charging network control method generate an optimized charging and discharging control schedule by acquiring various information, classifying, calculating and judging through an algorithm, and transmitting the optimized charging and discharging control schedule to the user side and the management side. For the user terminal, the charging point to be charged can be selected according to various schedules provided by the optimized charging and discharging control schedule. For the management terminal, the electric power among the charging points of the charging stations of the buildings in the areas in the charging network can be effectively balanced and transmitted, and the management of distributed intelligent charging is further achieved.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it should be understood that various changes and modifications can be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (22)
1. A distributed intelligent charging network control method is characterized by comprising the following steps:
acquiring statistical data, namely acquiring statistical quantity information, statistical power supply information, statistical load information and statistical energy storage information of a charging network in the statistical data, and generating an initial prediction charging and discharging information model according to the statistical information; wherein the charging network is formed by a plurality of charging points of a plurality of charging stations of a plurality of buildings of a plurality of regions;
a step of obtaining instant information, namely obtaining instant quantity information, instant power supply information, instant load information and instant energy storage information of the charging network, and generating an optimized charging and discharging control schedule of the charging network according to the instant information and the initial prediction charging and discharging information model;
transmitting the optimized charging and discharging control schedule to a plurality of clients; and
transmitting the optimized charging and discharging control schedule to a management terminal, so that the management terminal controls the switching of the electric power among the buildings, the charging stations and the charging points in the areas according to the optimized charging and discharging control schedule.
2. The method as claimed in claim 1, wherein the statistical load information and the real-time load information comprise electric contract capacity load information and power consumption information of the plurality of buildings, the plurality of charging stations and the plurality of charging points in the plurality of regions;
the statistical energy storage information and the instant energy storage information comprise reserve electric energy information and contract electric capacity information of the buildings, the charging stations and the charging points.
3. The method of claim 1, wherein the step of obtaining statistical data further comprises obtaining statistical electricity rate information and statistical weather information, and the step of obtaining instant information further comprises obtaining instant electricity rate information and instant weather information.
4. The method as claimed in claim 3, wherein the statistical electricity rate information and the real-time electricity rate information comprise utility time electricity rate information provided by the buildings, the charging stations and the charging points;
the statistical weather information and the instant weather information comprise weather information, temperature information, wind power information, humidity information, weather cloud picture information and ultraviolet information of the areas where the buildings, the charging stations and the charging points are located.
5. The method as claimed in claim 1, wherein the client and the manager receive the optimized charging/discharging control schedule via an intelligent device, a computer device or a cloud network.
6. The distributed intelligent charging network control method according to claim 3, wherein the statistical quantity information, the statistical power supply information, the statistical load information, the statistical energy storage information, the statistical electricity rate information, and the statistical weather information of the charging network of the past day, two days, three days, one week, one month, one season, one half year, or one year are acquired in the acquiring statistical data step.
7. The method of claim 1, wherein the statistical power supply information and the real-time power supply information comprise power generation and power consumption information of a solar photovoltaic inverter, utility power information, and fuel cell information.
8. The method as claimed in claim 7, wherein the optimized charging/discharging control schedule includes a route planning schedule for the user terminal to be located at the area away from the plurality of buildings, the plurality of charging stations and the plurality of charging points, a time planning schedule, a calculation schedule for a remaining number of the plurality of charging points of the plurality of charging stations of the plurality of buildings that can be charged, and an activity information schedule around the plurality of charging points of the plurality of charging stations of the plurality of buildings, wherein the activity information schedule includes a charging discount offer schedule and an art activity information schedule.
9. The method of claim 8, wherein the optimal charging/discharging control schedule further comprises an electrical energy reserve schedule for the charging network at an off-peak time and an electrical energy generation schedule for the solar-photovoltaic inverter to generate electrical energy at a peak time.
10. The method of claim 9, wherein the statistical quantity information comprises the charging pile quantity information of the plurality of regions, the plurality of buildings, the plurality of charging stations, and the plurality of charging points, and the real-time quantity information comprises charging pile quantity information of the plurality of charging points of the plurality of charging stations of the plurality of buildings, a quantity of vehicles waiting to be charged, and charging time information of a vehicle performing charging;
wherein the electric energy of the charging pile is generated by converting light energy through the solar photovoltaic inverter.
11. The distributed intelligent charging network control method of claim 1, wherein the algorithm comprises:
updating the initial prediction charging and discharging information model by means of immediately acquiring the instant quantity information, the instant power supply information, the instant load information and the instant energy storage information of the charging network to replace the statistical quantity information, the statistical power supply information, the statistical load information and the statistical energy storage information of the charging network in the statistical data, and transmitting the initial prediction charging and discharging information model to a cloud network;
judging whether the real-time load size and the residual available quantity of the plurality of areas, the plurality of buildings, the plurality of charging stations and the plurality of charging points in the updated charging network reach a preset critical value by using a data operation module of the cloud network;
if the current charge-discharge control schedule does not reach the preset critical value, generating the optimized charge-discharge control schedule by the data operation module and transmitting the optimized charge-discharge control schedule to the user terminal and the management terminal; and
if the preset critical value is reached, generating the optimized charging and discharging control schedule according to the distance, the time length and the remaining chargeable quantity of the area where the user side is located from the buildings, the charging stations and the charging points, and transmitting the optimized charging and discharging control schedule to the user side and the management side.
12. A distributed smart grid controller, comprising:
the storage module is used for storing statistical quantity information, statistical power supply information, statistical load information and statistical energy storage information of a charging network in statistical data, wherein the charging network is formed by a plurality of charging points of a plurality of charging stations of a plurality of buildings in a plurality of regions;
a communication connection port for transmitting and receiving an instant quantity message, an instant power supply message, an instant load message and an instant energy storage message of the charging network in a wired or wireless instant manner, and storing the instant quantity message, the instant power supply message, the instant load message and the instant energy storage message in the storage module;
a data operation module, which is electrically connected with the storage module, generates an initial prediction charging and discharging information model according to the statistical information, and then generates an optimized charging and discharging control schedule of the charging network according to the instant information and the initial prediction charging and discharging information model; and
the power control switch controls the power on and off of the charging network;
the optimal charging and discharging control schedule is transmitted to a plurality of clients through the communication connection port, and the power control switch controls the power of the charging network to be switched on and off according to the optimal charging and discharging control schedule.
13. The distributed intelligent grid controller according to claim 12, wherein the statistical load information and the instantaneous load information comprise power contract capacity load information and power usage information for the plurality of regions, the plurality of buildings, the plurality of charging stations, and the plurality of charging points;
the statistical energy storage information and the instant energy storage information comprise reserve electric energy information and contract electric capacity information of the buildings, the charging stations and the charging points.
14. The distributed intelligent grid controller method as claimed in claim 12, wherein the storage module further stores a statistical electricity rate information, a statistical weather information, a real-time electricity rate information and a real-time weather information, and the statistical data stored by the storage module further comprises the statistical quantity information, the statistical power supply information, the statistical load information, the statistical energy storage information, the statistical electricity rate information and the statistical weather information of the charging network for the past day, two days, three days, one week, one month, one season, one half year or one year.
15. The distributed intelligent grid controller as claimed in claim 14, wherein the statistical electricity rate information and the real-time electricity rate information comprise utility time electricity rate information provided by the plurality of buildings, the plurality of charging stations, and the plurality of charging points;
the statistical weather information and the instant weather information comprise weather information, temperature information, wind power information, humidity information, weather cloud picture information and ultraviolet information of the areas where the buildings, the charging stations and the charging points are located.
16. The distributed intelligent grid controller according to claim 12, wherein the statistical power supply information and the real-time power supply information comprise power generation and power consumption information of a solar-photovoltaic inverter, a utility power information, and a fuel cell information.
17. The distributed intelligent grid controller according to claim 16, further comprising a voltage-to-current calculation module that calculates and measures the real-time power supply information, the real-time load information, and the real-time energy storage information received at the communication connection port in the charging network.
18. The distributed intelligent grid controller as claimed in claim 16, wherein the optimized charging and discharging control schedule comprises a route planning schedule for the area where the user terminal is located away from the plurality of buildings, the plurality of charging stations and the plurality of charging points, a time planning schedule, a calculation schedule for a remaining number of the plurality of charging points of the plurality of charging stations of the plurality of buildings that are chargeable, and an activity information schedule around the plurality of charging points of the plurality of charging stations of the plurality of buildings, wherein the activity information schedule comprises a charging discount offer schedule and an art activity information schedule.
19. The distributed intelligent power grid controller of claim 18, wherein the optimized charging and discharging control schedule further comprises an electrical energy reserve schedule for the charging network at an off-peak time and an electrical energy generation schedule for the solar-photovoltaic inverter to generate electrical energy at a peak time.
20. The distributed intelligent grid controller as claimed in claim 18, wherein the statistical quantity information includes the charging pile quantity information of the plurality of regions, the plurality of buildings, the plurality of charging stations and the plurality of charging points, and the real-time quantity information includes a charging pile quantity information of the plurality of charging points of the plurality of charging stations of the plurality of buildings, a quantity of vehicles waiting to be charged and a charging time information of a vehicle performing charging;
wherein the electric energy of the charging pile is generated by converting light energy through the solar photovoltaic inverter.
21. The distributed intelligent grid controller as recited in claim 18, wherein the optimized charge-discharge control schedule is displayed in an energy management map.
22. The distributed intelligent power grid controller according to claim 12, further comprising a power supply module electrically connected to the power control switch and configured to provide power to the charging network via the power control switch.
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| Application Number | Priority Date | Filing Date | Title |
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| TW108145580A TW202123156A (en) | 2019-12-12 | 2019-12-12 | Distributed smart charging network control method and distributed smart grid controller capable of avoiding contract capacity exceeding the agreed load |
| TW108145580 | 2019-12-12 |
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| CN112994129A true CN112994129A (en) | 2021-06-18 |
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| TW202427915A (en) * | 2022-12-22 | 2024-07-01 | 國家中山科學研究院 | Household energy management and dispatching system including a power generation unit, and converter units connected to the power generation unit and a mains power grid unit, an energy storage unit, and an energy management control unit |
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