Power grid equipment efficient utilization method based on dynamic demand prediction

Abstract

The invention discloses a power grid equipment efficient utilization method based on dynamic demand prediction, and relates to the technical field of power regulation and control, and the key points of the technical scheme are that the method comprises a monitoring collection module which is used for obtaining the historical power consumption condition and the real-time power consumption condition of each charging station in a region in each time period, and can also collect the weather forecast condition and the power consumption condition of holidays and festivals; the information processing module is used for analyzing the collected data, judging the predicted power consumption condition of the charging station in each time period according to the influence of each parameter through an established prediction model, and synchronizing new data in real time, and the power regulation and control module is used for regulating and controlling the power supply condition of each charging station according to the information processing result. The method has the effect of realizing dynamic demand prediction and high-efficiency management of the power of the charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power regulation and control, and more specifically, to a method for efficiently utilizing power grid equipment based on dynamic demand prediction. Background Art

[0002] With the continuous development of my country's economy and the increasing awareness of environmental protection, the promotion and application of new energy vehicles (such as electric vehicles, hybrid vehicles, etc.) have become an important part of the national energy strategy and environmental protection policy. The emergence of a large number of new energy vehicles has directly promoted the rapid construction and popularization of charging pile infrastructure. However, with the sharp increase in the number of charging piles, the demand for electricity has also risen sharply, which has brought new challenges to the existing power grid system.

[0003] Traditional power grid planning and management models often distribute electricity based on static or historical data, but this method is inadequate for power supply management of new energy vehicle charging piles. Due to the high dynamics and uncertainty of new energy vehicle charging demand, not only is power demand difficult to predict, but it also poses a great challenge to the power regulation of charging stations in various regions. This mismatch between supply and demand often leads to waste or shortage of power resources, which in turn affects the efficiency of charging piles and the charging experience of users.

[0004] In recent years, the concept of microgrids has provided new ideas for urban electricity planning. Microgrids can achieve fine management of power demand and supply in local areas, and are expected to solve the shortcomings of traditional power grids in dealing with dynamic demand. However, in the power supply management of new energy vehicle charging piles, how to effectively combine microgrid technology with dynamic demand forecasting is still an urgent problem to be solved.

[0005] At present, it is often difficult to make fine adjustments to the power supply planning of charging piles according to the actual usage in each area and at each time period. This is mainly due to the lack of an effective dynamic demand forecasting mechanism and a matching strategy for efficient utilization of power grid equipment. This power supply uncoordination problem may not only lead to waste of power resources, but also cause insufficient power supply during certain peak hours, thus affecting the normal operation and service quality of charging piles.

[0006] Therefore, developing a method for efficient utilization of power grid equipment based on dynamic demand forecasting is of great significance for improving the power supply efficiency and service quality of new energy vehicle charging piles. This method needs to be able to collect and analyze the usage data of charging piles in real time, accurately predict future power demand, and dynamically adjust the power supply plan of power grid equipment according to the prediction results to achieve the optimal configuration and utilization of power resources. Through such a method, not only can the redundancy or shortage of electricity be avoided, but also the efficient and stable operation of charging piles can be ensured, thereby promoting the healthy and sustainable development of my country's new energy vehicle industry.

[0007] Therefore, in order to solve the above technical problems, the present invention proposes a method for efficient utilization of power grid equipment based on dynamic demand forecasting. Summary of the invention

[0008] In view of the deficiencies in the prior art, an object of the present invention is to provide a method for efficiently utilizing power grid equipment based on dynamic demand forecasting.

[0009] To achieve the above object, the present invention provides the following technical solution: a method for efficient utilization of power grid equipment based on dynamic demand forecasting, comprising:

[0010] The monitoring and collection module is used to obtain the historical power consumption of each charging station in the region at each time period, as well as the real-time power consumption. It can also collect weather forecasts and holiday power consumption.

[0011] The information processing module is used to analyze the collected data, determine the expected power consumption of the charging station in each time period according to the influence of various parameters through the established prediction model, and synchronize the new data in real time;

[0012] The power control module is used to control the power supply of each charging station according to the results of information processing.

[0013] Furthermore, the monitoring and collection module also includes a charging information collection unit arranged in the app or mini program. The charging information collection unit can facilitate users to make reservations in advance, input the expected charging time and the expected amount of energy to be replenished, facilitate power regulation, and prevent the driver from running out of energy.

[0014] Furthermore, the power control module includes a microgrid power supply unit arranged around each charging station to supply power to the charging station, and an energy storage unit matched with the microgrid power supply unit. The power control module can control the power flow between each microgrid power supply unit and the energy storage unit.

[0015] Further, S1: the monitoring and acquisition module obtains the power production (Gt) of the microgrid equipped by each charging station, the power generation within the time t, and sends the obtained data to the information processing module;

[0016] S2: The information processing module determines the power demand forecast (Dt) of each charging station in each time period according to the prediction model, the expected power consumption at time t, and calculates the power supply and demand gap (ΔPt), ΔPt = Dt-Gt. If ΔPt < 0, it means that the power of the charging pile can meet the usage in the predicted time period;

[0017] S3: In step S2, if ΔPt>0, it means that the charging pile cannot meet the power requirements in the predicted time period and needs to be recharged;

[0018] S4: In step S3, when the charging pile has a power supply gap in the predicted time period, the information processing module analyzes the energy storage loss (ΔEa) caused by storing electricity in the energy storage unit, and calculates the transmission loss (ΔEb) caused by transmitting electricity from other microgrid power supply units with a power supply and demand gap (ΔPt) less than 0, and compares multiple transmission losses (ΔEb) with energy storage losses (ΔEa), screens out transmission losses (ΔEb) that are larger than energy storage losses (ΔEa), and sorts the remaining transmission losses (ΔEb) from small to large losses, and prioritizes supplying power to the charging station with a power supply and demand gap from the microgrid power supply unit with a small sequence number;

[0019] S5: In step S4, if there is no transmission loss (ΔEb) that is smaller than the energy storage loss (ΔEa), or the transmission loss that is smaller than the energy storage loss (ΔEa) is not enough to fill the power supply and demand gap (ΔPt) of the charging station, the energy storage unit will be started to store the power required by the charging station.

[0020] Furthermore, the calculation formula for the energy storage loss (ΔEa) is: ΔEa=I^2×R×Δt, where I is the current (ampere, A), indicating the current in the storage device, R is the internal resistance of the storage device (ohm, Ω), indicating the degree of resistance of the storage device to the current, and Δt is the storage time (seconds, s), indicating the storage time of electricity in the storage device.

[0021] Furthermore, the calculation formula for the transmission loss (ΔEb) is: ΔEb=I^2×R×t, where I is the current (ampere, A), R is the resistance of the wire (ohm, Ω), and the resistance is usually proportional to the length of the wire (i.e., the transmission distance), which can be expressed as R=ρ×l / A, where ρ is the resistivity of the wire, l is the length of the wire (i.e., the transmission distance), A is the cross-sectional area of ​​the wire, and t is the transmission time (seconds, s).

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. In the present invention, various information factors can be collected by the monitoring and collection module. The information processing module can predict the power demand of each charging station based on these information factors, and control the power transmission between the microgrid power supply unit and the energy storage unit according to the analyzed situation, so as to realize the dynamic demand prediction and efficient management of the power of the charging station, thereby solving the problem of unreasonable power allocation of the charging station in the prior art, resulting in power redundancy or shortage in some charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0026] like Figure 1 As shown, the present invention provides a method for efficient utilization of power grid equipment based on dynamic demand forecasting, including a monitoring and acquisition module for obtaining historical power consumption of each charging station in a region at each time period, as well as real-time power consumption, and can also collect weather forecasts and holiday power consumption;

[0027] The information processing module is used to analyze the collected data, determine the expected power consumption of the charging station in each time period according to the influence of various parameters through the established prediction model, and synchronize the new data in real time;

[0028] The power control module is used to control the power supply of each charging station according to the results of information processing;

[0029] The monitoring and collection module also includes a charging information collection unit set in the app or mini-program. The charging information collection unit can facilitate users to make reservations in advance, input the expected charging time and the expected amount of energy to be replenished, facilitate power regulation, and also prevent drivers from running out of energy.

[0030] The power control module includes a microgrid power supply unit arranged around each charging station to supply energy to the charging station, and an energy storage unit matched with the microgrid power supply unit. The power control module can control the power flow between each microgrid power supply unit and the energy storage unit.

[0031] In summary, since the charging of new energy vehicles has highly dynamic and uncertain factors, for example, during working hours on weekdays, the power usage of charging stations near industrial areas and office buildings is high, while during rest time, the power usage of charging stations near communities and residential buildings is high, resulting in inaccurate power supply in each charging station, causing power redundancy or insufficient problems in some charging stations. The present invention can read the historical power consumption of each charging station in the area through the monitoring and collection module, including the energy consumption in each time period, and can also collect weather and environmental factors. The information processing module analyzes the collected information, and can also integrate the real-time collected power consumption into the historical power consumption data, and judge the power consumption in each time period of the area according to the power prediction model, and then dispatch the power by controlling the microgrid power supply unit around each charging station that supplies power to the charging station, and the energy storage unit matched with the microgrid power supply unit;

[0032] The following power control methods are also included:

[0033] S1: The monitoring and acquisition module obtains the power production (Gt) of the microgrid equipped by each charging station, the power generation within time t, and sends the obtained data to the information processing module;

[0034] S2: The information processing module determines the power demand forecast (Dt) of each charging station in each time period according to the prediction model, the expected power consumption at time t, and calculates the power supply and demand gap (ΔPt), ΔPt = Dt-Gt. If ΔPt < 0, it means that the power of the charging pile can meet the usage in the predicted time period;

[0035] S3: In step S2, if ΔPt>0, it means that the charging pile cannot meet the power requirements in the predicted time period and needs to be recharged;

[0036] S4: In step S3, when the charging pile has a power supply gap in the predicted time period, the information processing module analyzes the energy storage loss (ΔEa) caused by storing electricity in the energy storage unit, and calculates the transmission loss (ΔEb) caused by transmitting electricity from other microgrid power supply units with a power supply and demand gap (ΔPt) less than 0, and compares multiple transmission losses (ΔEb) with energy storage losses (ΔEa), screens out transmission losses (ΔEb) that are larger than energy storage losses (ΔEa), and sorts the remaining transmission losses (ΔEb) from small to large losses, and prioritizes supplying power to the charging station with a power supply and demand gap from the microgrid power supply unit with a small sequence number;

[0037] S5: In step S4, if there is no transmission loss (ΔEb) smaller than the energy storage loss (ΔEa), or the transmission loss smaller than the energy storage loss (ΔEa) is not enough to fill the power supply and demand gap (ΔPt) of the charging station, the energy storage unit will be started to store the power required by the charging station;

[0038] 5. The calculation formula for energy storage loss (ΔEa) is: ΔEa = I^2 × R × Δt, where I is the current (ampere, A), which indicates the current in the storage device.

[0039] R is the internal resistance of the storage device (ohms, Ω), which indicates the degree of resistance of the storage device to the current flow, Δt is the storage time (seconds, s), which indicates the storage time of electricity in the storage device;

[0040] 6. The calculation formula of transmission loss (ΔEb) is: ΔEb = I^2 × R × t, where I is the current (ampere, A), R is the resistance of the wire (ohm, Ω), and the resistance is usually proportional to the length of the wire (i.e., the transmission distance), which can be expressed as R = ρ × l / A, where ρ is the resistivity of the wire, l is the length of the wire (i.e., the transmission distance), A is the cross-sectional area of ​​the wire, and t is the transmission time (seconds, s);

[0041] The information processing module will determine the relationship between the maximum transmission capacity (Ebm) and maximum energy storage capacity (Eam) of the microgrid power supply units around each charging station and the energy storage units matched with the microgrid power supply units and the power supply and demand gap (ΔPt). If the transmission loss (ΔEb) is the least, power transmission will be used to fill the supply and demand gap (ΔPt) first, and multiple transmission stations can be used for supply;

[0042] Among them, the system will screen out the supply stations with a power supply-demand gap less than 0, and then sort them according to the loss, sort them according to Arabic numerals, and delete the supply stations with losses greater than the energy storage station, and give priority to supplying power to the supply station with the smallest loss, and judge whether the power supply-demand gap (ΔPt) can be met according to its transmission volume (ΔG1), Gt1 minus Dt, and if it cannot be met, the next transmission volume (ΔG2) is driven to join, and so on, thereby completing the power regulation between new energy charging stations, facilitating the dynamic management of charging stations, and ensuring the efficient and stable operation of charging piles;

[0043] For example, when a microgrid power supply unit of a charging station has a power supply and demand gap (ΔPt>0), the microgrid power supply units of the five charging stations around it have no power supply and demand gap (ΔPt<0). Compared with the energy storage loss (ΔEa), the transmission loss (ΔEb) of the microgrid power supply units of four charging stations is less than the energy storage loss (ΔEa) of the energy storage unit of the charging station. The microgrid power supply units of these four charging stations are sorted by Arabic numerals. First, the microgrid power supply unit with the lowest energy consumption is used to supply energy to the charging station with a power supply and demand gap (ΔPt>0). If there is still a power supply and demand gap, the microgrid power supply unit of the charging station with the next sequence number is started to supply energy to the charging station with the power supply and demand gap (ΔPt>0) again, and so on. Finally, the energy storage unit can be used to store energy until the expected power demand for that time period is reached.

[0044] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A method for efficient utilization of power grid equipment based on dynamic demand forecasting, characterized in that: include: The monitoring and collection module is used to obtain the historical power consumption of each charging station in the region at each time period, as well as the real-time power consumption. It can also collect weather forecasts and holiday power consumption. The information processing module is used to analyze the collected data, determine the expected power consumption of the charging station in each time period according to the influence of various parameters through the established prediction model, and synchronize the new data in real time; The power control module is used to control the power supply of each charging station according to the results of information processing.

2. The method for efficient utilization of power grid equipment based on dynamic demand forecasting according to claim 1, characterized in that: The monitoring and collection module also includes a charging information collection unit set in the app or mini-program, which can facilitate users to make reservations in advance and input the expected charging time and expected energy replenishment.

3. The method for efficient utilization of power grid equipment based on dynamic demand forecasting according to claim 2 is characterized in that: The power control module includes a microgrid power supply unit arranged around each charging station to supply energy to the charging station, and an energy storage unit matched with the microgrid power supply unit. The power control module can control the power flow between each microgrid power supply unit and the energy storage unit.

4. The method for efficient utilization of power grid equipment based on dynamic demand forecasting according to any one of claims 1 to 3, characterized in that: The following steps are also included: S1: The monitoring and acquisition module obtains the power production (Gt) of the microgrid equipped at each charging station, and sends the obtained data to the information processing module; S2: The information processing module determines the power demand forecast (Dt) of each charging station in each time period according to the prediction model, and calculates the power supply and demand gap (ΔPt), ΔPt = Dt-Gt. If ΔPt < 0, it means that the power of the charging pile can meet the usage in the predicted time period; S3: In step S2, if ΔPt>0, it means that the charging pile cannot meet the power requirements in the predicted time period and needs to be recharged; S4: In step S3, when the charging pile has a power supply gap in the predicted time period, the information processing module analyzes the energy storage loss (ΔEa) caused by storing electricity in the energy storage unit, and calculates the transmission loss (ΔEb) caused by transmitting electricity from other microgrid power supply units with a power supply and demand gap (ΔPt) less than 0, and compares multiple transmission losses (ΔEb) with energy storage losses (ΔEa), screens out transmission losses (ΔEb) that are larger than energy storage losses (ΔEa), and sorts the remaining transmission losses (ΔEb) from small to large losses, and prioritizes supplying power to the charging station with a power supply and demand gap from the microgrid power supply unit with a small sequence number; S5: In step S4, if there is no transmission loss (ΔEb) that is smaller than the energy storage loss (ΔEa), or the transmission loss that is smaller than the energy storage loss (ΔEa) is not enough to fill the power supply and demand gap (ΔPt) of the charging station, the energy storage unit will be started to store the power required by the charging station.

5. The method for efficient utilization of power grid equipment based on dynamic demand forecasting according to claim 4 is characterized in that: The calculation formula of the energy storage loss (ΔEa) is: ΔEa=I^2×R×Δt, where I is the current (ampere, A), which indicates the current in the storage device, R is the internal resistance of the storage device (ohm, Ω), which indicates the degree of resistance of the storage device to the current, and Δt is the storage time (seconds, s), which indicates the storage time of electricity in the storage device.

6. The method for efficient utilization of power grid equipment based on dynamic demand forecasting according to claim 4 is characterized in that: The calculation formula for the transmission loss (ΔEb) is: ΔEb=I^2×R×t, where I is the current (ampere, A), R is the resistance of the wire (ohm, Ω), and the resistance is usually proportional to the length of the wire (i.e., the transmission distance), which can be expressed as R=ρ×l / A, where ρ is the resistivity of the wire, l is the length of the wire (i.e., the transmission distance), A is the cross-sectional area of ​​the wire, and t is the transmission time (seconds, s).

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