TWM622791U - Distributed smart grid controller - Google Patents

Abstract

The present invention discloses a distributed smart grid controller, which acquires the charging station system, production capacity information, Taipower information, various loads and energy storage system information of the power network, and performs statistical calculation and analysis on the ground according to the historical information. Go to the cloud to obtain the best charging path in the network with artificial intelligence calculation, and generate the best energy scheduling control strategy in the power network before, and further combine the real-time information of the day and the actual operation of the user, and optimize the energy adjustment based on this information. , so that the overall power network can achieve a balance between supply and demand, avoid contract capacity exceeding the agreed load, and provide detailed charging solutions for the user and the optimal scheduling strategy for the management.

Description

Decentralized Smart Grid Controller

The present invention relates to a control method and a controller, in particular to a distributed smart charging network control method and a distributed smart grid controller.

Automobiles and locomotives are currently widely used means of transportation, but due to the large number of automobiles and locomotives, carbon emissions in the environment have also increased significantly. Therefore, in order to reduce carbon emissions in the living environment, in recent years, a number of countries have actively promoted electric vehicles and electric scooters to replace traditional gasoline-powered cars and locomotives.

As mentioned above, when the number of electric locomotives used increases, under ideal conditions, the number of charging points set up should also increase accordingly, so the traditional power network will also increase the energy demand. Therefore, in addition to the fact that when the number of charging points has not been increased to a sufficient number, the cars and locomotives that need to be charged may be congested at the charging points. Due to the excessive energy demand of the grid, the capacity of the existing lines may be insufficient. Therefore, it is necessary to The charging of electric vehicle locomotives carries out the most effective power management and deployment.

Further, if the charging point is set around the building and uses the city power provided by the building as the charging source, and since multiple buildings have signed electricity contract capacity, the instantaneous rated electricity consumption cannot be exceeded. During the charging period, too many electric vehicle locomotives are charged at the same charging point, which not only causes the electricity load of the building, but also causes the inconvenience of finding other charging points for the users who want to be charged.

In addition, in addition to supplying electric energy for charging with commercial power, there are also electric energy converted from solar energy as electric energy for charging. In the case of tight mains power supply, although solar energy can be used as another kind of electric energy for charging, not every building, charging station and charging point are equipped with solar charging function. In addition to charging with commercial power, buildings, charging stations and charging points with solar charging functions should have sufficient electrical energy to charge electric vehicles and locomotives. However, in the case of other buildings, charging stations and charging points that are not equipped with solar charging and the mains power supply is tight, in comparison, the problem of uneven power distribution will be caused.

Accordingly, how to provide a management and distribution of charging for a plurality of regions, a plurality of buildings, a plurality of charging stations and a plurality of charging points has become an urgent research topic.

In view of the above problem, the present invention provides a distributed smart charging network control method, which includes the following steps: a step of acquiring statistical data, acquiring a statistical quantity information, a statistical power supply information, a statistical load information of the charging network in the statistical data, and a statistical energy storage information, and an initial predicted charging and discharging information model is generated 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 step of retrieving real-time information: retrieving real-time quantity information, real-time power supply information, real-time load information and real-time energy storage information of the charging network, and generating the charging according to the real-time information and the initial predicted charging and discharging information model One of the networks optimizes the charging and discharging control schedule. Send the optimized charge-discharge control schedule to multiple clients. The optimized charge-discharge control schedule is sent to a management terminal, so that the management terminal controls the switching of electric power among the plurality of areas, the plurality of buildings, the plurality of charging stations, and the plurality of charging points according to the optimized charge-discharge control schedule.

The invention discloses a distributed smart grid controller, which comprises a storage module, a communication connection port, a data operation module and a power control switch. The storage module stores a statistical quantity information, a statistical power supply information, a statistical load information and a statistical energy storage information of the charging network in the statistical data, wherein the charging network is formed by a plurality of charging points of a plurality of charging stations in a plurality of buildings in a plurality of regions . The communication port transmits and receives real-time quantity information, real-time power supply information, real-time load information, and real-time energy storage information of the charging network in real time by wired or wireless, and store them in the storage module. The data computing module is electrically connected to the storage module, and after generating an initial predicted charge and discharge information model according to the statistical information, an optimized charge and discharge control of the charging network is generated according to the real-time information and the initial predicted charge and discharge information model schedule. The power control switch controls the power on and off of the charging network. The optimized charge and discharge control schedule is transmitted to a plurality of users through the communication port, and the power control switch controls the power on and off of the charging network according to the optimized charge and discharge control schedule.

As mentioned above, the distributed smart grid controller and the distributed smart charging network control method of the present invention generate an optimized charging and discharging control schedule by capturing various information, classifying, calculating and judging by an algorithm, and transmitting to the client and management. 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 side, it can effectively balance and transmit the power between multiple buildings, multiple charging stations and multiple charging points in the charging network, and further achieve the management of decentralized smart charging.

Please refer to FIG. 1 , which is a flow chart of the novel decentralized smart charging network control method. First of all, it should be noted that in the embodiment of the present invention, the so-called charging network refers to a charging network formed by a plurality of charging points of a plurality of buildings, a plurality of buildings, and a plurality of charging stations in a plurality of regions. , solar photovoltaic and fuel cell charging locations. In the step S11 of retrieving statistical data, the distributed smart charging network control method includes retrieving 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 the data according to the statistical information. an initial predictive charge-discharge information model. In the step S13 of acquiring real-time information, real-time quantity information, real-time power supply information, real-time load information and real-time energy storage information of the charging network are acquired, and a maximum value of the charging network is generated according to the real-time information and the initial predicted charging and discharging information model. Optimized charging and discharging control schedule. In step S15, the optimized charge-discharge control schedule is transmitted to a plurality of clients. In step S17, the optimized charge and discharge control schedule is transmitted to a management terminal, so that the management terminal controls the charging network according to the optimized charge and discharge control schedule among the plurality of areas, the plurality of buildings, the plurality of charging stations and the plurality of charging points. power switch.

In addition, the above step S11 of retrieving statistical data further includes retrieving statistical electricity price information and statistical weather information, and the step S13 of retrieving real-time information further includes retrieving real-time electricity price information and real-time weather information. The statistical load information and the real-time load information include the electricity contract capacity load information and the electricity consumption information of the plurality of charging points of the plurality of buildings and the plurality of charging stations in the plurality of regions. The statistical energy storage information and the real-time energy storage information include the reserved electric energy information and the contracted electric capacity information of the plural charging points of plural charging stations in plural buildings in plural regions. The statistical electricity price information and the real-time electricity price information include the time electricity price information of the commercial power provided by the plurality of charging points of the plurality of charging stations in the plurality of buildings in the plurality of regions. Statistical weather information and real-time weather information include weather information, temperature information, wind information, humidity information, weather cloud map information and ultraviolet information of the area where the plurality of charging stations of the plurality of buildings in the plurality of regions are located. Statistical power supply information and real-time power supply information include solar photovoltaic inverter power generation and power consumption information, utility power information and fuel cell information. The statistical quantity information includes information on the quantity of charging piles of the plurality of charging points of the plurality of buildings, the plurality of buildings, and the plurality of charging points. The real-time quantity information includes the information on the number of charging piles, the information on the number of vehicles waiting to be charged, and the information on the charging time of the charging vehicles, in which the electric energy of the charging piles is generated by the photovoltaic inverter. Converted light energy is generated. The above statistical quantity information, statistical power supply information, statistical load information, statistical energy storage information, statistical electricity price information, statistical weather information, real-time quantity information, real-time power supply information, real-time load information, real-time energy storage information, real-time electricity price information and real-time weather Information is stored in storage modules, databases or cloud networks.

In the above step S11 of retrieving the statistical data, the statistical data of the 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 in the statistical data are extracted and used for predicting the future charging network The data prediction model based on the charging and discharging state of the system, that is, in general, under normal use conditions, the use state of the plurality of charging points of the plurality of buildings, the plurality of buildings, and the plurality of charging points will not change suddenly and drastically. Emergence, therefore, that the charging network can use data from the past period as an initial model for building predictions of charging and discharging. Retrieving statistical data The past period of time includes capturing the statistical quantity information, statistical power supply information, statistical load information, statistical storage information of the charging network in the past one day, two days, three days, one week, one month, one quarter, six months or one year Energy information, statistical electricity price information and statistical weather are not limited in the present invention. Alternatively, statistics can be extracted based on perpetual calendar information, farmer calendar information, calendar information, and horoscope information. Further, for example, according to the calendar, the number of charging points used by the user terminal may be less on normal work days, that is, the load is low. Therefore, on normal work days, energy can be stored in the charging network. Actions. On the other hand, if there is a large number of traffic surges in various regions during regular holidays, national holidays, consecutive holidays or special holidays, the user terminal has a large demand for charging at this time. The daily stored electric energy is supplied to the charging network, or the power of the charging point with lower load and larger power supply capacity is transmitted to the charging point with larger load and smaller power supply capacity, so as to achieve the balance of power in the charging network. and distribution, to further avoid the overload of a single charging point, or the situation that the charging point has sufficient power but no user is using it. Similarly, the above statistical quantity information, statistical power supply information, statistical load information, statistical energy storage information, statistical electricity price information, statistical weather information, real-time quantity information, real-time power supply information, real-time load information, real-time energy storage information, real-time electricity price information And real-time weather information will also affect the power distribution and use of multiple areas, multiple buildings, multiple charging stations, and multiple charging points in the charging network, and the optimal charging and discharging control schedule can be based on this information. Discharge management, correction, deployment and balancing.

The optimal charging and discharging control schedule includes the path planning schedule, time planning schedule, and multiple buildings of multiple charging points in the area where the client is located (when the user turns on the positioning function of the smart device) A calculation schedule for the remaining number of charging points that can be charged by the plurality of charging stations. The optimal charge-discharge control schedule also includes a schedule of activity information around the plurality of charging points of the plurality of charging stations in the plurality of buildings. The event information schedule includes the charging discount discount schedule and the art and cultural activity information schedule. The optimized charging and discharging control schedule can display the information schedule of various plans together in an energy management map, so that the user can quickly browse and grasp all the information. The user terminal can receive the optimal charging and discharging control schedule through the smart device, computer device or cloud network, so that the user terminal can immediately know the current position in the charging network. If the schedule is planned according to the best path, The location of the surrounding charging points can be reached, or, if the schedule is planned according to the shortest time, the location of the surrounding charging points can be reached as soon as possible, or, according to the current location of the user terminal in the charging network, the available charging points in the surrounding area are displayed. The remaining number of points, or, if there is a schedule of preferential activities for charging, can also be displayed in the energy management map for users' reference. The optimized charge and discharge control schedule also includes the personal charge records of the client, that is, the optimized charge and discharge control schedule can be quickly generated from the previous charge records of the client. In addition, the optimal charge-discharge control schedule also includes a power reserve schedule for the charging network during off-peak hours and a power generation schedule for generating electrical energy with the solar photovoltaic inverter during peak hours. The stored electrical energy can be stored in a fuel cell or other devices capable of storing electrical energy, which is not limited in the present invention. Similarly, the management terminal also receives the optimal charging and discharging control schedule through the smart device, computer device or cloud network, the difference is that the optimal charging and discharging control schedule is transmitted according to the identity of the management terminal or the user terminal. The optimized charge and discharge control schedule content. For example, the optimal charge and discharge control schedule that the management terminal needs to receive includes the power reserve schedule and the power generation schedule, so as to facilitate the charging and discharging management of the charging network, while the user terminal does not need to receive such optimal charge and discharge schedules. Charge and discharge control schedule.

The algorithm includes the method of artificial intelligence calculation, but is not limited in the present invention. Another preferred embodiment of the algorithm includes the following steps: replacing the statistical quantity information of the charging network and the statistical power supply in the statistical data by updating the real-time quantity information, real-time power supply information, real-time load information and real-time energy storage information of the charging network in real time information, statistical load information, and statistical energy storage information to update the initial predicted charging and discharging information model, and send it to a cloud network. The data computing module of the cloud network determines whether the real-time load size of the plurality of charging points of the plurality of charging stations in the plurality of buildings in the plurality of areas in the updated charging network and whether the remaining available quantity reaches a predetermined threshold value. If the preset threshold value is not reached, the optimized charging and discharging control schedule is generated by the data computing module and sent to the user terminal and the management terminal. If the preset critical value is reached, the optimal charging and discharging control schedule is generated according to the distance, time, and remaining number of charging points from the area where the client is located to multiple buildings, charging stations, and charging points, and sent to the client. and management side. Furthermore, when the real-time load size of the charging point reaches the preset threshold value, the management terminal allocates the power of other charging points to the charging point where the real-time load size reaches the preset threshold value according to the optimized charging and discharging control schedule, so as to maintain the The power balance and distribution of the entire charging network, and after the power balance and distribution, a new optimal charge and discharge control schedule is regenerated. Therefore, it should be noted that the steps of the above algorithm are a loop process that is repeatedly executed, and it can be executed at a predetermined time interval, or when any information changes are generated, it can be uploaded to the cloud network in real time for real-time generation. The new optimized charge-discharge control schedule is not limited in the present invention.

Please refer to FIG. 2 , which is a schematic diagram of the novel distributed smart grid controller. The distributed smart grid controller 1 includes a storage module 11 , a communication 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 the charging network in the statistical data, wherein the charging network is formed by a plurality of charging points of a plurality of charging stations in a plurality of buildings in a plurality of regions. The communication port 12 transmits and receives the real-time quantity information, real-time power supply information, real-time load information and real-time energy storage information of the charging network by wired or wireless, and is stored in the storage module 11 . The data computing module 13 is electrically connected to the storage module 11, and after generating an initial predicted charging and discharging information model according to the statistical information, the optimized charging and discharging of the charging network is generated according to the real-time information and the initial predicted charging and discharging information model Control scheduling. The power control switch 14 controls the power on and off of the charging network. The optimized charge-discharge control schedule is transmitted to a plurality of clients through the communication port 12, and the power control switch 14 controls the power on and off of the charging network according to the optimized charge-discharge control schedule.

In addition, it should be noted that although the area includes the building, the building includes the charging station, and the charging station includes the charging point, the charging station and the charging point can also be set outside the building in addition to supplying power through the building. , and independently use solar photovoltaic inverters to generate electricity or fuel cells to provide electricity, not necessarily the mains power supply of the building. The stored electrical energy can be stored in a fuel cell or other device that can store electrical energy. But whether or not it is powered by the building, charging stations and charging points powered by solar photovoltaic inverters or fuel cells can provide additional power in other areas or when the building is under power.

FIG. 3 is another schematic diagram of the novel distributed smart grid controller. The distributed smart grid controller 1 further includes a voltage and current calculation module 15, which calculates and measures the real-time power supply information, real-time load information and real-time energy storage information in the charging network received by the communication port 12, and further calculates and measures the charging network. The power supply, load, and reserve energy of the plurality of charging points of the plurality of buildings in the plurality of buildings in the middle and plural areas are sent to the data computing module 13 for judgment, and then the control signal is sent to the power control switch 14 to store the energy. The power of the more abundant building, charging station or charging point is transmitted to the building, charging station or charging point with heavy load and tight power supply, so as to maintain the power balance and distribution of the whole charging network.

In addition to the above-mentioned hardware device, the distributed smart grid controller 1 further includes the above-mentioned software using the distributed smart charging network control method. Accordingly, the distributed smart grid controller 1 is achieved by the distributed smart charging network control method. The effect is as described above, and will not be repeated here.

In addition, the storage module 11 and the data computing module 13 include plug-and-play modules, which can be inserted into the distributed smart grid controller 1 at any time for use, or, when not in use, can also be used by the distributed smart grid controller 1 at any time. Unplugging is not limited in the present invention. Furthermore, the above statistical information and real-time information are all stored in the storage module 11, the database or the cloud network, which is not limited in the present invention. The communication port is wired or wireless to receive and transmit real-time data. The statistical data stored by the storage module 11 includes statistical quantity information, statistical power supply information, statistical load information, and statistical energy storage information of the charging network in the past one day, two days, three days, one week, one month, one quarter, six months or one year. , statistical electricity price information and statistical weather information.

The distributed smart grid controller 1 further includes a display device 16 , which displays the charging points in the current charging network that need to perform charging and discharging operations, so as to facilitate the management terminal or artificial intelligence to perform charging and discharging management control. The display device 16 can display the information on the screen or on the APP of the smart device.

The distributed smart grid controller further includes a power supply module 17 that is electrically connected to the power control switch 14 to provide charging network power through the power control switch 14 . According to the above-mentioned data calculation module 13, it transmits the control signal to the power control switch 14, and the power supply module 17 transmits the power to the building, charging station or charging point with heavy load and tight power supply, so as to maintain the power balance of the entire charging network. and distribution.

In addition, the plurality of buildings, the plurality of charging stations, and the plurality of charging points in the charging network include at least one monitoring device (not shown). If an abnormal state occurs, an abnormal signal can be sent to the distributed smart grid controller 1 through the monitoring device. And management terminal, and repair and eliminate according to abnormal conditions. Alternatively, the monitoring device can also be set on the distributed smart grid controller 1 to monitor whether an abnormal condition occurs in a plurality of buildings, a plurality of charging stations and a plurality of charging points in a plurality of regions within a preset range, and repair and repair according to the abnormal condition. exclude.

To sum up, the present invention discloses a distributed smart grid controller, a distributed smart charging network control method, and a distributed smart charging network and grid energy optimization control method. Taipower information, various loads and energy storage system information, and perform statistical calculations and analysis on the ground based on historical information, and upload it to the cloud to obtain the best charging path in the network with artificial intelligence calculations, and produce the best charging path in the current power network. The energy scheduling control strategy further combines the real-time information of the day and the actual operation of the user, and optimizes the energy adjustment based on this information, so that the overall power network can achieve a balance between supply and demand, avoid contract capacity exceeding the contract, and provide the user with detailed charging solutions and The optimal scheduling strategy on the management side. Furthermore, the distributed smart grid controller and the distributed smart charging network control method generate an optimized charging and discharging control schedule by capturing various information, classifying, calculating and judging by an algorithm, and sending it to the client and managing it. end. 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 end, it can effectively balance and transmit the power between the plurality of charging points of the plurality of buildings and the plurality of buildings in the charging network, and further achieve the management of decentralized smart charging.

1: Decentralized smart grid controller 11: Storage Module 12: Communication port 13: Data operation module 14: Power control switch 15: Voltage and current calculation module 16: Display device 17: Power supply module

FIG. 1 is a flow chart of the novel decentralized smart charging network control method; and FIG. 2 is a schematic diagram of the novel decentralized smart grid controller; and FIG. 3 is another schematic diagram of the novel distributed smart grid controller.

1: Decentralized smart grid controller

11: Storage Module

12: Communication port

13: Data operation module

14: Power control switch

Claims (10)

A distributed smart grid controller includes: a storage module, which stores a statistical quantity information, a statistical power supply information, a statistical load information and a statistical energy storage information of a charging network in the statistical data, wherein the charging network is composed of a plurality of Formed by a plurality of charging points of a plurality of charging stations in a plurality of buildings in a region; a communication port for real-time transmission and reception of a real-time quantity information, a real-time power supply information, a real-time load information and a real-time storage information of the charging network by wired or wireless energy information is stored in the storage module; a data operation module is electrically connected to the storage module, and after generating an initial predicted charge and discharge information model according to the statistical information, according to the real-time information and the initial The predictive charging and discharging information model generates an optimized charging and discharging control schedule of the charging network; a power control switch controls the power on and off of the charging network; and a power supply module is electrically connected to the power control switch, and The power of the charging network is provided by the power control switch; wherein the optimized charge and discharge control schedule is transmitted to a plurality of users through the communication port, and the power control switch is controlled according to the optimized charge and discharge control schedule The power of the charging network is switched on and off. The distributed smart grid controller of claim 1, wherein the statistical load information and the real-time load information include power contract capacity load information of the plurality of areas, the plurality of buildings, the plurality of charging stations, and the plurality of charging points, and Electricity consumption information; wherein the statistical energy storage information and the real-time energy storage information include the stored energy information and contracted electricity capacity information of the plurality of buildings, the plurality of charging stations, and the plurality of charging points. The distributed smart grid controller of claim 1, wherein the storage module further stores a statistical electricity price information, a statistical weather information, a real-time electricity price information and a real-time weather information, and the storage module stores the statistics The data also includes the past one day, two days, three days, one week, one week 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 meteorological information of the charging network for a month, a quarter, half a year or a year. The distributed smart grid controller according to claim 3, wherein the statistical electricity price information and the real-time electricity price information include time electricity price information provided by the plurality of buildings, the plurality of charging stations and the plurality of charging points; wherein the statistical weather information And the real-time weather information includes weather information, temperature information, wind information, humidity information, weather cloud map information, and ultraviolet information of the area where the plurality of buildings, the plurality of charging stations, and the plurality of charging points are located. The distributed smart grid controller according to claim 1, wherein the statistical power supply information and the real-time power supply information include power generation and power consumption information of a solar photovoltaic inverter, mains power information and fuel cell information. The distributed smart grid controller according to claim 5, further comprising a voltage and current calculation module for calculating and measuring the real-time power supply information, the real-time load information and the real-time load information in the charging network received by the communication port Energy storage information. The distributed smart grid controller according to claim 5, wherein the optimized charging and discharging control schedule includes a path planning schedule between the area where the client is located and the plurality of buildings, the plurality of charging stations and the plurality of charging points schedule, a time planning schedule, a calculation schedule of the remaining number of charging points that can be charged at the plurality of charging stations of the plurality of buildings, and an activity information around the plurality of charging points of the plurality of charging stations of the plurality of buildings A schedule, wherein the activity information schedule includes a charging discount offer schedule and an art and cultural activity information schedule. The distributed smart grid controller of claim 7, wherein the optimized charge-discharge control schedule further includes a power reserve schedule for the charging network at an off-peak time and a power reserve schedule for the charging network at a peak time The photovoltaic inverter generates a power generation schedule of power. The distributed smart grid controller of claim 7, wherein the statistical quantity information includes the plurality of areas, the plurality of buildings, the plurality of charging stations, and the charging pile quantity information of the plurality of charging points, and the real-time quantity information includes the Information on the number of charging piles, information on the number of vehicles waiting to be charged, and information on the charging time of a charging vehicle in the plurality of areas, the plurality of charging stations in the plurality of buildings; wherein the electric energy of the charging piles is generated by the solar Photovoltaic inverters convert light energy to produce. The distributed smart grid controller of claim 7, wherein the optimized charge-discharge control schedule is displayed in an energy management map.

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