Power grid capacity scheduling method, medium and control device

Abstract

The invention relates to the technical field of power distribution, particularly provides a power grid capacity scheduling method, a medium and a control device, and aims to solve the problems of low utilization rate, high power utilization control difficulty and high investment of an energy storage device of an existing charging station. The method is suitable for a plurality of power utilization units connected to the same power distribution network, each power utilization unit comprises at least one power device, and all the power utilization units are in communication connection with the same superior controller; the power grid capacity scheduling method comprises the steps that each power utilization unit predicts the time-sharing capacity demand of the power utilization unit in the next control period; all the power utilization units upload respective time-sharing capacity requirements to a superior controller; the superior controller calculates the dynamic capacity of each power utilization unit in the next control period based on all the time-sharing capacity requirements; and based on the calculation result, distributing capacity to each power utilization unit in the next control period. According to the invention, the capacity bearing capability and the service capability of the power utilization unit can be improved, and the investment and control complexity are reduced.

Description

Technical Field

[0001] This invention relates to the field of power distribution technology, specifically providing a power grid capacity scheduling method, medium, and control device. Background Technology

[0002] Currently, my country's power system is in a transitional period from traditional energy (thermal power, hydropower, etc.) power generation as the main source to new energy (wind power, solar power, etc.) power generation as the main source. The new energy power system has two distinct characteristics: First, from the power supply side, new energy power generation exhibits a significant imbalance in both time and spatial scales compared to traditional energy power generation. Second, from the electricity consumption side, due to the rapid growth in the number of new energy vehicles, especially electric vehicles, in recent years, the impact of charging load on the distribution network has begun to emerge in cities with high electric vehicle penetration rates, and the existing distribution network planning is no longer sufficient to cope with the increasing charging demand.

[0003] One important way to solve these problems is to greatly enhance the regulation and balancing capabilities of the power system on both the generation and load sides, especially the regulation on the load side. For example, research and practice on virtual power plants have been a hot topic in recent years.

[0004] As facilities such as charging and battery swapping stations and energy storage devices, which are highly related to the means of replenishing energy for new energy vehicles, can achieve peak shaving and valley filling of the power distribution network to a certain extent, the following problems still exist: First, most charging and battery swapping stations on the market are equipped with separate transformers, and the large fluctuations in charging load lead to low equipment utilization; Second, virtual power plants are mainly concentrated in the top-down control mode. Although time-sharing control can regulate the electricity consumption on the load side to a certain extent, the control accuracy is low and the control is difficult due to the frequent changes in the electricity demand of charging and battery swapping stations; Third, the configuration of energy storage devices is more of an individual station behavior, which easily leads to redundant investment.

[0005] Accordingly, a new technical solution is needed in this field to solve the above problems. Summary of the Invention

[0006] This application aims to solve at least one of the above-mentioned technical problems, namely, to address the issues of low utilization rate of existing charging and battery swapping stations, difficulty in power control, and high investment in energy storage devices.

[0007] In a first aspect, this application provides a power grid capacity scheduling method applicable to multiple power-consuming units with spatiotemporal complementary characteristics connected under the same distribution network. Each power-consuming unit includes at least one power device, and all power-consuming units are communicatively connected to the same upper-level controller. The power device is at least one of a charging pile, a battery swapping station, and an energy storage device. The power grid capacity scheduling method includes: each power-consuming unit predicting its time-of-use capacity demand in the next control cycle; all power-consuming units uploading their respective time-of-use capacity demands to the upper-level controller; the upper-level controller calculating the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands; and allocating capacity to each power-consuming unit in the next control cycle based on the calculation results.

[0008] By adopting the above technical solution, this application can coordinate multiple distributed power consumption units within the same distribution network, fully utilizing the spatiotemporal complementarity of different power consumption units. This enhances the capacity carrying capacity and service capabilities of power consumption units within the same distribution network, while significantly reducing investment in distribution network capacity expansion or energy storage device installation, and lowering the control complexity of the upper-level virtual power plant. This addresses the energy replenishment needs under conditions of high electric vehicle penetration, providing stable ancillary services to the power system. Specifically, due to differences in scenarios and user habits, electric vehicle energy replenishment facilities, such as battery swapping stations, public charging piles, and home charging piles, naturally exhibit spatiotemporal differences. For example, home charging piles typically concentrate on charging during off-peak hours at night, while public charging piles usually have two distinct peak periods: midday and early morning. Battery swapping stations, due to different peak-shaving charging strategies and user types, exhibit different types of load characteristics. Overall, battery swapping stations, public charging piles, and home charging piles possess complementary spatiotemporal characteristics. Compared to traditional individual power consumption units connected to the grid, this application effectively utilizes this complementary characteristic. By predicting and reporting the real-time capabilities of power consumption units on the same distribution lines and equipment, the time-sharing determinism of power and electricity consumption after demand aggregation of multiple power consumption units with spatiotemporal complementary characteristics is greatly improved. Based on the demand aggregation, capacity is allocated to each power consumption unit, enabling time-sharing capacity scheduling of loads of multiple intelligently interconnected power consumption units. This enhances the carrying capacity and service capacity of electric vehicles within the same distribution network, reduces the complexity of upper-level dispatch control, and only requires a small amount of energy storage devices to provide flexibility for short-term capacity deviations, avoiding redundant investment in energy storage and delaying the transformation of distribution lines.

[0009] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the step of "each power consumption unit predicting its time-of-use capacity demand in the next control cycle" further includes: each power consumption unit predicting the maximum and minimum operating power available in the next control cycle based on its current operating conditions and corresponding power consumption constraints; wherein, the power consumption constraints include the physical limit maximum power and the minimum power required by the user experience of the power consumption unit.

[0010] By using the current operating conditions to predict the maximum and minimum operating power available for the next control cycle, load power prediction on a short timescale can be achieved, improving prediction accuracy and capacity scheduling accuracy.

[0011] In the preferred technical solution of the above-mentioned power grid capacity scheduling method, the step of "the upper-level controller calculating the dynamic capacity of each power consumption unit in the next control cycle based on all time-sharing capacity demands" further includes: calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity data of each power consumption unit before the next control cycle, the time-sharing capacity demand of each power consumption unit in the next control cycle, and the maximum capacity of the distribution network.

[0012] By calculating the dynamic capacity of each power consumption unit in the next control cycle based on historical capacity data, time-of-use capacity demand, and the maximum capacity of the distribution network, not only can smooth power regulation be achieved, but also, compared with the current application of virtual power plants, since the capacity constraints of multiple power consumption units and the distribution network are considered, the capacity over-sizing of power consumption units in high-penetration scenarios can be achieved through dynamic capacity constraints to realize spatiotemporal complementarity of different types of energy replenishment stations, avoiding significant modifications to distribution lines.

[0013] In the preferred embodiment of the above-mentioned power grid capacity dispatching method, the historical capacity data includes historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power. The historical time-of-use capacity utilization rate is the ratio of the historical time-of-use average power to the historical dynamic capacity. The step of "calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity data of each power user, the time-of-use capacity demand of each power user in the next control cycle, and the maximum capacity of the distribution network" further includes: determining the historical capacity margin of each power user based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power user; and calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity margin, the time-of-use capacity demand, and the maximum capacity of each power user.

[0014] In the preferred embodiment of the above-mentioned power grid capacity dispatching method, the step of "determining the historical capacity margin of each power consumption unit based on its historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power" further includes: determining the historical capacity margin of each power consumption unit in the following manner: ; where m i,k-1 This represents the historical capacity margin of the i-th power consumption unit in the (k-1)th control cycle. D i,k-1 Let P be the historical time-of-use capacity utilization rate of the i-th power consumption unit in the (k-1)-th control cycle. avg_i,k-1 P represents the historical time-sharing average power of the i-th power consumption unit during the (k-1)-th control cycle; cap_i,k-1 P represents the historical dynamic capacity of the i-th power consumption unit in the (k-1)-th control cycle; min_i,k-1 and P max_i,k-1 Let be the minimum and maximum operating power available for the i-th power consumption unit in the (k-1)-th control cycle, respectively; c is a constant, and 0 < c < 1.

[0015] In the preferred embodiment of the above-mentioned power grid capacity scheduling method, the step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity margin of each power consumption unit, the time-sharing capacity demand, and the maximum capacity" further includes: letting the objective function be... And preset conditions , , ; Solve the P-set that minimizes the value of f(k) through programming. cap_i,k and P cap_i,k As the dynamic capacity of each of the aforementioned power-consuming units in the next control cycle; wherein, P min_i,k and P max_i,k These represent the minimum and maximum operating power available to the i-th power consumption unit in the k-th control cycle, respectively; P totalmax_k This represents the maximum capacity of the distribution network during the k-th control cycle.

[0016] In the preferred embodiment of the above-mentioned power grid capacity scheduling method, the power grid capacity scheduling method further includes: each power consumption unit predicting its time-of-use capacity demand in multiple future control cycles; all power consumption units uploading their respective time-of-use capacity demands to the upper-level controller; and the upper-level controller calculating the dynamic capacity of each power consumption unit in multiple future control cycles based on all time-of-use capacity demands.

[0017] By predicting time-of-use capacity demand for multiple future control cycles and uploading this data to the upper-level controller, which then calculates the dynamic capacity of each power-consuming unit for these cycles, the power-consuming units gain long-term load forecasting capabilities. This allows for advance prediction of capacity demand over a period of time (e.g., one hour, one day, one month), facilitating proactive grid planning and resource allocation. Furthermore, the addition of short-term load forecasting capabilities enables short-term capacity demand correction, improving the accuracy of time-of-use capacity demand calculations and preventing localized line overloads during sudden load surges.

[0018] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the step of "each power consumption unit predicting its time-of-use capacity demand in multiple future control cycles" further includes: each power consumption unit predicting the time-of-use capacity demand curve for multiple future control cycles based on at least one of charging and swapping order prediction data and historical load data; wherein, the time-of-use capacity demand curve includes at least one of electricity demand curve, power demand curve, maximum operating electricity curve and minimum operating electricity curve.

[0019] In the preferred technical solution of the above-mentioned power grid capacity scheduling method, the step of "the upper-level controller calculating the dynamic capacity of each power consumption unit in multiple future control cycles based on all time-sharing capacity demands" further includes: for any control cycle in the multiple future control cycles, calculating the dynamic capacity of each power consumption unit in the control cycle based on the capacity demand data of each power consumption unit before the control cycle, the time-sharing capacity demand of each power consumption unit in the control cycle, and the maximum capacity of the distribution network.

[0020] In the preferred embodiment of the above-mentioned power grid capacity scheduling method, the power grid capacity scheduling method further includes: the upper-level controller sending the dynamic capacity and capacity margin of each power-consuming unit in the next control cycle to the cloud; the cloud displaying the dynamic capacity and capacity margin of each power-consuming unit to the user.

[0021] By sending the dynamic capacity and capacity margin of the next control cycle to the cloud and displaying it to users, users can be guided to naturally divert power, thereby reducing the difficulty of virtual power plant control and improving the user experience.

[0022] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the upper-level controller is set up locally, in the cloud, or in a virtual power plant.

[0023] In a second aspect, this application provides a power grid capacity scheduling method applicable to multiple power-consuming units with spatiotemporal complementary characteristics connected under the same distribution network. Each power-consuming unit includes at least one power device, and all power-consuming units are communicatively connected to the same upper-level controller. The power device is at least one of a charging pile, a battery swapping station, and an energy storage device. The power grid capacity scheduling method includes: each power-consuming unit predicting its time-of-use capacity demand in the next control cycle; all power-consuming units uploading their respective time-of-use capacity demands to the upper-level controller; so that the upper-level controller calculates the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands, and allocates capacity to each power-consuming unit in the next control cycle based on the calculation results.

[0024] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the step of "each power consumption unit predicting its time-of-use capacity demand in the next control cycle" further includes: each power consumption unit predicting the maximum and minimum operating power available in the next control cycle based on its current operating conditions and corresponding power consumption constraints; wherein, the power consumption constraints include the physical limit maximum power and the minimum power required by the user experience of the power consumption unit.

[0025] In a preferred embodiment of the above-mentioned power grid capacity scheduling method, the power grid capacity scheduling method further includes: each power-consuming unit predicting its time-of-use capacity demand in multiple future control cycles; all power-consuming units uploading their respective time-of-use capacity demands to the upper-level controller; so that the upper-level controller can calculate the dynamic capacity of each power-consuming unit in multiple future control cycles based on all time-of-use capacity demands.

[0026] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the step of "each power consumption unit predicting its time-of-use capacity demand in multiple future control cycles" further includes: each power consumption unit predicting the time-of-use capacity demand curve for multiple future control cycles based on at least one of charging and swapping order prediction data and historical load data; wherein, the time-of-use capacity demand curve includes at least one of electricity demand curve, power demand curve, maximum operating electricity curve and minimum operating electricity curve.

[0027] In a third aspect, this application provides a computer-readable storage medium storing a plurality of program codes adapted to be loaded and run by a processor to perform the power grid capacity scheduling method described in any one of the technical solutions of the second aspect above.

[0028] In a fourth aspect, this application provides a control device, comprising: a processor; and a memory adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to execute the power grid capacity scheduling method described in any one of the technical solutions of the second aspect above.

[0029] In a fifth aspect, this application provides a power grid capacity scheduling method applicable to multiple power-consuming units with spatiotemporal complementary characteristics connected under the same distribution network. Each power-consuming unit includes at least one power device, and all power-consuming units are communicatively connected to the same upper-level controller. The power device is at least one of a charging pile, a battery swapping station, and an energy storage device. The power grid capacity scheduling method includes: the upper-level controller receiving time-of-use capacity demands and calculating the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands; and allocating capacity to each power-consuming unit in the next control cycle based on the calculation results. The time-of-use capacity demands are predicted and uploaded by each power-consuming unit for the next control cycle.

[0030] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on all time-of-use capacity demands" further includes: calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity data of each power consumption unit before the next control cycle, the time-of-use capacity demand of each power consumption unit in the next control cycle, and the maximum capacity of the distribution network.

[0031] In the preferred embodiment of the above-mentioned power grid capacity dispatching method, the historical capacity data includes historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power. The historical time-of-use capacity utilization rate is the ratio of the historical time-of-use average power to the historical dynamic capacity. The step of "calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity data of each power user, the time-of-use capacity demand of each power user in the next control cycle, and the maximum capacity of the distribution network" further includes: determining the historical capacity margin of each power user based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power user; and calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity margin, the time-of-use capacity demand, and the maximum capacity of each power user.

[0032] In the preferred embodiment of the above-mentioned power grid capacity dispatching method, the step of "determining the historical capacity margin of each power consumption unit based on its historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power" further includes: determining the historical capacity margin of each power consumption unit in the following manner: ; where m i,k-1 This represents the historical capacity margin of the i-th power consumption unit in the (k-1)th control cycle. D i,k-1 Let P be the historical time-of-use capacity utilization rate of the i-th power consumption unit in the (k-1)-th control cycle. avg_i,k-1 P represents the historical time-sharing average power of the i-th power consumption unit during the (k-1)-th control cycle; cap_i,k-1 P represents the historical dynamic capacity of the i-th power consumption unit in the (k-1)-th control cycle; min_i,k-1 and P max_i,k-1 Let be the minimum and maximum operating power available for the i-th power consumption unit in the (k-1)-th control cycle, respectively; c is a constant, and 0 < c < 1.

[0033] In the preferred embodiment of the above-mentioned power grid capacity scheduling method, the step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity margin of each power consumption unit, the time-sharing capacity demand, and the maximum capacity" further includes: letting the objective function be... And preset conditions , , ; Solve by planning to find a new set of P that minimizes the value of f(k). cap_i,k and P cap_i,k As the dynamic capacity of each of the aforementioned power-consuming units in the next control cycle; wherein, P min_i,k and P max_i,k These represent the minimum and maximum operating power available to the i-th power consumption unit in the k-th control cycle, respectively; P totalmax_k This represents the maximum capacity of the distribution network during the k-th control cycle.

[0034] In the preferred embodiment of the above-mentioned power grid capacity scheduling method, the power grid capacity scheduling method further includes: the upper-level controller receiving time-sharing capacity demand, and calculating the dynamic capacity of each power-consuming unit in the future multiple control cycles based on all time-sharing capacity demands; wherein, the time-sharing capacity demand is the time-sharing capacity demand predicted and uploaded by each power-consuming unit in the future multiple control cycles.

[0035] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the step of "calculating the dynamic capacity of each of the power consumption units in the future multiple control cycles based on all time-sharing capacity demands" further includes: for any control cycle in the future multiple control cycles, calculating the dynamic capacity of each of the power consumption units in the control cycle based on the capacity demand data of each power consumption unit before the control cycle, the time-sharing capacity demand of each power consumption unit in the control cycle, and the maximum capacity of the distribution network.

[0036] In the preferred embodiment of the above-mentioned power grid capacity scheduling method, the power grid capacity scheduling method further includes: the upper-level controller sending the dynamic capacity and capacity margin of each power-consuming unit in the next control cycle to the cloud; so that the cloud can display the dynamic capacity and capacity margin of each power-consuming unit to the user.

[0037] In the preferred technical solution of the above-mentioned power grid capacity dispatching method, the upper-level controller is set up locally, in the cloud, or in a virtual power plant.

[0038] In a sixth aspect, this application provides a computer-readable storage medium storing a plurality of program codes adapted for loading and execution by a processor of the power grid capacity scheduling method described in any one of the technical solutions of the fifth aspect above.

[0039] In a seventh aspect, this application provides a control device, comprising: a processor; and a memory adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to perform the power grid capacity scheduling method described in any one of the technical solutions of the fifth aspect above.

[0040] Scheme 1. A power grid capacity scheduling method, characterized in that it is applicable to multiple power-consuming units with spatiotemporal complementary characteristics connected under the same distribution network, each power-consuming unit including at least one power device, all power-consuming units being communicatively connected to the same upper-level controller, the power device being at least one of charging piles, battery swapping stations, and energy storage devices; the power grid capacity scheduling method includes: each power-consuming unit predicting its time-of-use capacity demand in the next control cycle; all power-consuming units uploading their respective time-of-use capacity demands to the upper-level controller; the upper-level controller calculating the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands; and allocating capacity to each power-consuming unit in the next control cycle based on the calculation results.

[0041] Scheme 2. According to the power grid capacity scheduling method described in Scheme 1, the step of "each power consumption unit predicting its time-of-use capacity demand in the next control cycle" further includes: each power consumption unit predicting the maximum and minimum operating power available in the next control cycle based on its current operating conditions and corresponding power consumption constraints; wherein, the power consumption constraints include the physical limit maximum power and the minimum power required by the user experience of the power consumption unit.

[0042] Scheme 3. According to the power grid capacity scheduling method described in Scheme 2, the step of "the upper-level controller calculating the dynamic capacity of each power consumption unit in the next control cycle based on all time-sharing capacity demands" further includes: calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity data of each power consumption unit before the next control cycle, the time-sharing capacity demand of each power consumption unit in the next control cycle, and the maximum capacity of the distribution network.

[0043] Scheme 4. The power grid capacity scheduling method according to Scheme 3, characterized in that the historical capacity data includes historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power, wherein the historical time-of-use capacity utilization rate is the ratio of the historical time-of-use average power to the historical dynamic capacity, and the step of "calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity data of each power user, the time-of-use capacity demand of each power user in the next control cycle, and the maximum capacity of the distribution network" further includes: determining the historical capacity margin of each power user based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power user; and calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity margin, the time-of-use capacity demand, and the maximum capacity of each power user.

[0044] Scheme 5. The power grid capacity dispatching method according to Scheme 4, characterized in that the step of "determining the historical capacity margin of each power consumption unit based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power consumption unit" further includes: determining the historical capacity margin of each power consumption unit in the following manner: ; where m i,k-1 This represents the historical capacity margin of the i-th power consumption unit in the (k-1)th control cycle. D i,k-1 Let P be the historical time-of-use capacity utilization rate of the i-th power consumption unit in the (k-1)-th control cycle. avg_i,k-1 P represents the historical time-sharing average power of the i-th power consumption unit during the (k-1)-th control cycle; cap_i,k-1P represents the historical dynamic capacity of the i-th power consumption unit in the (k-1)-th control cycle; min_i,k-1 and P max_i,k-1 Let be the minimum and maximum operating power available for the i-th power consumption unit in the (k-1)-th control cycle, respectively; c is a constant, and 0 < c < 1.

[0045] Scheme 6. The power grid capacity scheduling method according to Scheme 5, characterized in that the step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity margin of each power consumption unit, the time-of-use capacity demand, and the maximum capacity" further includes: letting the objective function be... And preset conditions , , ; Solve by planning to find a new set of P that minimizes the value of f(k). cap_i,k and P cap_i,k As the dynamic capacity of each of the aforementioned power-consuming units in the next control cycle; wherein, P min_i,k and P max_i,k These represent the minimum and maximum operating power available to the i-th power consumption unit in the k-th control cycle, respectively; P totalmax_k This represents the maximum capacity of the distribution network during the k-th control cycle.

[0046] Scheme 7. The power grid capacity scheduling method according to Scheme 1, characterized in that the power grid capacity scheduling method further includes: each power consumption unit predicting its time-of-use capacity demand in multiple future control cycles; all power consumption units uploading their respective time-of-use capacity demands to the upper-level controller; the upper-level controller calculating the dynamic capacity of each power consumption unit in multiple future control cycles based on all time-of-use capacity demands.

[0047] Scheme 8. The power grid capacity dispatching method according to Scheme 7 is characterized in that the step of "each power consumption unit predicting its time-of-use capacity demand in multiple future control cycles" further includes: each power consumption unit predicting the time-of-use capacity demand curve for multiple future control cycles based on at least one of charging and swapping order prediction data and historical load data; wherein the time-of-use capacity demand curve includes at least one of electricity demand curve, power demand curve, maximum operating electricity curve and minimum operating electricity curve.

[0048] Scheme 9. The power grid capacity scheduling method according to Scheme 8 is characterized in that the step of "the upper-level controller calculating the dynamic capacity of each power consumption unit in multiple future control cycles based on all time-sharing capacity demands" further includes: for any control cycle in the multiple future control cycles, calculating the dynamic capacity of each power consumption unit in the control cycle based on the capacity demand data of each power consumption unit before the control cycle, the time-sharing capacity demand of each power consumption unit in the control cycle, and the maximum capacity of the distribution network.

[0049] Scheme 10. The power grid capacity scheduling method according to Scheme 4, characterized in that the power grid capacity scheduling method further includes: the upper-level controller sending the dynamic capacity and capacity margin of each power-consuming unit in the next control cycle to the cloud; the cloud displaying the dynamic capacity and capacity margin of each power-consuming unit to the user.

[0050] Scheme 11. The power grid capacity dispatching method according to Scheme 1, characterized in that the upper-level controller is set up locally, in the cloud, or in a virtual power plant.

[0051] Scheme 12. A power grid capacity scheduling method, characterized in that it is applicable to multiple power-consuming units with spatiotemporal complementary characteristics connected under the same distribution network, each power-consuming unit including at least one power device, all power-consuming units being communicatively connected to the same upper-level controller, the power device being at least one of charging piles, battery swapping stations, and energy storage devices; the power grid capacity scheduling method includes: each power-consuming unit predicting its time-of-use capacity demand in the next control cycle; all power-consuming units uploading their respective time-of-use capacity demands to the upper-level controller; so that the upper-level controller calculates the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands, and allocates capacity to each power-consuming unit in the next control cycle based on the calculation results.

[0052] Scheme 13. The power grid capacity scheduling method according to Scheme 12 is characterized in that the step of "each power consumption unit predicting its time-of-use capacity demand in the next control cycle" further includes: each power consumption unit predicting the maximum and minimum operating power available in the next control cycle based on its current operating conditions and corresponding power consumption constraints; wherein the power consumption constraints include the physical limit maximum power of the power consumption unit and the minimum power required by the user experience.

[0053] Scheme 14. The power grid capacity scheduling method according to Scheme 12, characterized in that the power grid capacity scheduling method further includes: each power consumption unit predicting its time-sharing capacity demand in multiple future control cycles; all power consumption units uploading their respective time-sharing capacity demands to the upper-level controller; so that the upper-level controller calculates the dynamic capacity of each power consumption unit in multiple future control cycles based on all time-sharing capacity demands.

[0054] Scheme 15. The power grid capacity dispatching method according to Scheme 14 is characterized in that the step of "each power consumption unit predicting its time-of-use capacity demand in multiple future control cycles" further includes: each power consumption unit predicting the time-of-use capacity demand curve for multiple future control cycles based on at least one of charging and swapping order prediction data and historical load data; wherein the time-of-use capacity demand curve includes at least one of electricity demand curve, power demand curve, maximum operating electricity curve and minimum operating electricity curve.

[0055] Scheme 16. A computer-readable storage medium storing a plurality of program codes, characterized in that the program codes are adapted to be loaded and run by a processor to perform the power grid capacity scheduling method described in any one of Schemes 12 to 15.

[0056] Scheme 17. A control device, characterized in that it comprises: a processor; a memory adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to execute the power grid capacity scheduling method as described in any one of Schemes 12 to 15.

[0057] Scheme 18. A power grid capacity scheduling method, characterized in that it is applicable to multiple power-consuming units with spatiotemporal complementary characteristics connected under the same distribution network, each power-consuming unit including at least one power device, all power-consuming units being communicatively connected to the same upper-level controller, the power device being at least one of charging piles, battery swapping stations, and energy storage devices; the power grid capacity scheduling method includes: the upper-level controller receiving time-of-use capacity demands, and calculating the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands; based on the calculation results, allocating capacity to each power-consuming unit in the next control cycle; wherein, the time-of-use capacity demand is the time-of-use capacity demand predicted and uploaded by each power-consuming unit in the next control cycle.

[0058] Scheme 19. The power grid capacity scheduling method according to Scheme 18 is characterized in that the step of "calculating the dynamic capacity of each of the power consumption units in the next control cycle based on all time-sharing capacity demands" further includes: calculating the dynamic capacity of each of the power consumption units in the next control cycle based on the historical capacity data of each of the power consumption units before the next control cycle, the time-sharing capacity demand of each of the power consumption units in the next control cycle, and the maximum capacity of the distribution network.

[0059] Scheme 20. The power grid capacity scheduling method according to Scheme 19, characterized in that the historical capacity data includes historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power, wherein the historical time-of-use capacity utilization rate is the ratio of the historical time-of-use average power to the historical dynamic capacity, and the step of "calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity data of each power user, the time-of-use capacity demand of each power user in the next control cycle, and the maximum capacity of the distribution network" further includes: determining the historical capacity margin of each power user based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power user; and calculating the dynamic capacity of each power user in the next control cycle based on the historical capacity margin, the time-of-use capacity demand, and the maximum capacity of each power user.

[0060] Scheme 21. The power grid capacity dispatching method according to Scheme 20, characterized in that the step of "determining the historical capacity margin of each power consumption unit based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power consumption unit" further includes: determining the historical capacity margin of each power consumption unit in the following manner: ; where m i,k-1 This represents the historical capacity margin of the i-th power consumption unit in the (k-1)th control cycle. D i,k-1 Let P be the historical time-of-use capacity utilization rate of the i-th power consumption unit in the (k-1)-th control cycle. avg_i,k-1 P represents the historical time-sharing average power of the i-th power consumption unit during the (k-1)-th control cycle; cap_i,k-1 P represents the historical dynamic capacity of the i-th power consumption unit in the (k-1)-th control cycle; min_i,k-1 and P max_i,k-1 Let be the minimum and maximum operating power available for the i-th power consumption unit in the (k-1)-th control cycle, respectively; c is a constant, and 0 < c < 1.

[0061] Scheme 22. The power grid capacity scheduling method according to Scheme 21, characterized in that the step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity margin of each power consumption unit, the time-of-use capacity demand, and the maximum capacity" further includes: letting the objective function be... And preset conditions , , ; Solve by planning to find a new set of P that minimizes the value of f(k). cap_i,k and P cap_i,k As the dynamic capacity of each of the aforementioned power-consuming units in the next control cycle; wherein, P min_i,k and P max_i,k These represent the minimum and maximum operating power available to the i-th power consumption unit in the k-th control cycle, respectively; P totalmax_k This represents the maximum capacity of the distribution network during the k-th control cycle.

[0062] Scheme 23. The power grid capacity scheduling method according to Scheme 18, characterized in that the power grid capacity scheduling method further includes: the upper-level controller receiving time-sharing capacity demand, and calculating the dynamic capacity of each power-consuming unit in the future multiple control cycles based on all time-sharing capacity demands; wherein the time-sharing capacity demand is the time-sharing capacity demand predicted and uploaded by each power-consuming unit in the future multiple control cycles.

[0063] Scheme 24. The power grid capacity scheduling method according to Scheme 23 is characterized in that the step of "calculating the dynamic capacity of each of the power consumption units in the future multiple control cycles based on all time-sharing capacity demands" further includes: for any control cycle in the future multiple control cycles, calculating the dynamic capacity of each of the power consumption units in the control cycle based on the capacity demand data of each power consumption unit before the control cycle, the time-sharing capacity demand of each power consumption unit in the control cycle, and the maximum capacity of the distribution network.

[0064] Scheme 25. The power grid capacity scheduling method according to Scheme 20 is characterized in that the power grid capacity scheduling method further includes: the upper-level controller sending the dynamic capacity and capacity margin of each power-consuming unit in the next control cycle to the cloud; so that the cloud can display the dynamic capacity and capacity margin of each power-consuming unit to the user.

[0065] Scheme 26. The power grid capacity dispatching method according to Scheme 18, characterized in that the upper-level controller is set up locally, in the cloud, or in a virtual power plant.

[0066] Scheme 27. A computer-readable storage medium storing a plurality of program codes, characterized in that the program codes are adapted to be loaded and run by a processor to perform the power grid capacity scheduling method described in any one of Schemes 18 to 26.

[0067] Scheme 28. A control device, characterized in that it comprises: a processor; a memory adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to execute the power grid capacity scheduling method as described in any one of Schemes 18 to 26. Attached Figure Description

[0068] The preferred embodiments of this application will now be described with reference to the accompanying drawings.

[0069] Figure 1 This is a system diagram of the application scenario of the power grid capacity scheduling method of this application.

[0070] Figure 2 This is the main flowchart of the power grid capacity scheduling method of this application. Detailed Implementation

[0071] Preferred embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application. It should be noted that although the detailed steps of the method of this application are described in detail below, those skilled in the art can combine, split, and rearrange the following steps without departing from the basic principles of this application. Such modifications do not change the basic concept of this application and therefore also fall within the scope of protection of this application.

[0072] First refer to Figure 1 This paper briefly introduces the applicable scenarios of the power grid capacity scheduling method of this application.

[0073] like Figure 1 As shown, in one possible application scenario, multiple power consumption units with spatiotemporal complementary characteristics are connected under the same power distribution network. These power consumption units are all connected to a local controller, which in turn is connected to a cloud server, and the cloud server is connected to a virtual power plant. Figure 1In the diagram, solid arrows represent top-down instruction distribution, while dashed arrows represent various status information feedbacks from the power-consuming unit and the aggregated overall status information feedback. Each power-consuming unit includes at least one electrical device. In this application, the electrical device can be a charging pile, a battery swapping station, an energy storage device, or other controllable electrical equipment such as air conditioners or factory production equipment. A power-consuming unit may include only one electrical device; for example, it may include a battery swapping station, an energy storage device (energy storage power station or photovoltaic energy storage system), or a home charging pile. Alternatively, a power-consuming unit may include multiple electrical devices, such as a public charging pile cluster, multiple battery swapping stations, or multiple air conditioners.

[0074] For multiple power consumption units with spatiotemporal complementary characteristics connected under the same distribution network, in one specific scenario, these could be power consumption units from different operators located at the same substation and connected to the same transformer. For example, under the same transformer, charging piles, battery swapping stations, energy storage devices, and other controllable power equipment from different operators could be connected. In another specific scenario, these could be power consumption units located at the same substation and connected to the same distribution line but under different transformers. For example, a main transformer could have multiple sub-transformers connected to it, each sub-transformer connecting to a power consumption unit from one operator. Yet another specific scenario could be multiple power consumption units located at different substations connected to the same distribution line. For example, a distribution bus could have two main transformers connected to it, each main transformer corresponding to one substation. A single substation could be any of the two scenarios described above. In summary, as long as multiple power consumption units with spatiotemporal complementary characteristics are connected under the same distribution network, regardless of whether these units are located at the same substation, the method described in this application can be implemented. In one practical application scenario, power consumption units with spatiotemporal complementary characteristics from different substations can be selectively connected to the same distribution network. For example, public charging stations in commercial areas, home charging stations in some residential communities, and battery swapping stations in service areas can be connected to the same power distribution network. This can be achieved by connecting the busbars of public charging stations, home charging stations, and battery swapping stations via a switch, ensuring that when the switch is closed, they are all located on the same power grid. Home charging stations typically operate during off-peak hours at night, while public charging stations usually have two distinct peak electricity consumption periods: midday and early morning. Battery swapping stations exhibit different electricity consumption habits due to off-peak charging strategies and varying user types (paying users and users with battery swapping privileges). Therefore, these types of electricity-consuming units have complementary characteristics in terms of their electricity consumption habits in terms of time and space.

[0075] All power-consuming units in this application possess self-prediction and communication capabilities. Power-consuming units can predict capacity demand in a future control cycle (such as the next 5 minutes, 15 minutes, 30 minutes, etc.) or multiple control cycles (such as the next 1 hour, 12 hours, 24 hours, etc., with 5 minutes as a control cycle), and report the demand to the local controller. The local controller further uploads the received demand to the cloud server. The cloud server interacts with the virtual power plant and sends the demands uploaded by different local controllers to the virtual power plant. The upper-level controller set in the virtual power plant performs dynamic capacity allocation based on the demand and issues allocation instructions in the opposite direction. Finally, the local controller distributes the allocated capacity to each power-consuming unit, and the power-consuming unit operates according to the allocated capacity.

[0076] Of course, the above-described method of setting up a higher-level controller for capacity allocation via a virtual power plant is merely exemplary. Those skilled in the art will understand that the specific location of the higher-level controller is not unique. As long as the dynamic capacity of each power consumption unit can be calculated based on the aggregated capacity demand, the higher-level controller can be located anywhere. For example, the higher-level controller can also be located in the cloud or a local controller. Furthermore, the communication links between the virtual power plant, cloud server, and local controller described above are also merely exemplary. In other embodiments, the connection method can be changed according to the different access requirements of the power consumption units. For example, the virtual power plant can directly connect to the power consumption units, or one of the local controller and cloud server can be omitted. Additionally, although the type of distribution network is not described above, those skilled in the art will understand that both AC and DC distribution networks are applicable to this application.

[0077] The following is combined with Figure 2 This paper provides a brief introduction to the power grid capacity dispatching method proposed in this application. Figure 2 As shown, in order to solve the problems of low utilization rate of existing charging and battery swapping stations, difficulty in power consumption control, and high investment in energy storage devices, the power grid capacity dispatching method includes the following steps S101~S107.

[0078] S101, each power consumption unit predicts its time-of-use capacity demand for the next control cycle. For example, each power consumption unit can predict its time-of-use capacity demand for the next control cycle (the next 5 minutes) based on currently executing orders. For instance, if a charging pile cluster has 4 vehicles charging, and the sum of their maximum power demands is 400kW, and if these 4 vehicles continue charging in the next control cycle and their combined power demands do not exceed the maximum physical capacity limit of the charging pile cluster (such as transformer capacity limits), then the maximum time-of-use capacity demand in the next 5-minute control cycle is 400kW. However, due to factors such as decreased vehicle power demand and other vehicles joining the charging process, the actual power of the charging piles will be reduced. The minimum power demand per pile can be reduced to 0, but to ensure a good user experience, each charging pile typically has a lower power limit. If this power limit is 20kW, then the minimum time-of-use capacity demand for the charging pile cluster in the next 5-minute control cycle is 80kW. Therefore, the minimum time-of-use capacity demand for this charging pile cluster in the next 5-minute control cycle is 80kW, and the maximum is 400kW. In another alternative implementation, the time-sharing capacity demand for the next control cycle can be jointly determined by combining the current demand power and the charging / swapping order forecast. For example, if there are 3 batteries charging in a battery swapping station, the sum of the maximum demand power is 300kW. The cloud server sends a new battery swapping order to the battery swapping station at a certain time in the future, which requires an additional battery power. If the maximum additional power is 100kW, the maximum capacity demand for the next 5-minute control cycle can be predicted to be 400kW. The minimum demand is similar to the above and will not be repeated.

[0079] S103, all power consumption units upload their respective time-of-use capacity requirements to the upper-level controller. For example, after predicting the time-of-use capacity requirements for the next control cycle, all power consumption units upload their requirements to the local controller, which then uploads these requirements to the cloud server. The cloud server further uploads these time-of-use capacity requirements to the upper-level controller of the virtual power plant.

[0080] S105, the upper-level controller calculates the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands. For example, the upper-level controller aggregates the received time-of-use capacity demands and allocates grid power based on these demands. After calculation, it obtains the dynamic capacity of each power-consuming unit in the next control cycle, so that the power load is more balanced in time and space. For example, power-consuming units with greater demand are allocated more dynamic capacity, while power-consuming units with less demand are allocated less dynamic capacity.

[0081] S107, Based on the calculation results, allocate capacity to each power consumption unit in the next control cycle. After calculating the dynamic capacity of each power consumption unit, allocate capacity to each power consumption unit according to the calculation results at the beginning of the next control cycle.

[0082] By adopting the above technical solution, this application can coordinate multiple distributed power consumption units in the same distribution network, make full use of the spatiotemporal complementary characteristics of different power consumption units, improve the capacity carrying capacity and service capacity of power consumption units in the same distribution network, significantly reduce the investment in distribution capacity expansion or energy storage device installation, and reduce the control complexity of the upper-level virtual power plant, thereby meeting the energy replenishment demand under the condition of high electric vehicle penetration and providing stable auxiliary services for the power system. Specifically, compared with traditional individual power consumption units connected to the power grid, this application effectively utilizes the spatiotemporal complementary characteristics of power consumption units such as battery swapping stations, public charging piles, and home charging piles. By predicting and reporting the real-time capabilities of power consumption units on the same distribution lines and equipment, the time-sharing determinism of power and electricity consumption after demand aggregation of multiple power consumption units is greatly improved. Based on the demand aggregation, capacity is allocated to each power consumption unit, enabling time-sharing capacity scheduling of loads of multiple intelligent interconnected power consumption units. This enhances the carrying capacity and service capacity of electric vehicles within the same distribution network, reduces the complexity of upper-level dispatch control, and only requires a small amount of energy storage devices to provide flexibility for short-term capacity deviations, avoiding redundant investment in energy storage and delaying the transformation of distribution lines.

[0083] The power grid capacity scheduling method of this application will be further described below.

[0084] In one specific implementation, the step of "predicting the time-of-use capacity demand of each power consumption unit in the next control cycle" further includes: each power consumption unit predicts the maximum and minimum operating power available in the next control cycle based on its current operating conditions and corresponding power consumption constraints. For example, for charging piles, the current operating conditions can be determined by reading parameters such as requested power, current operating power, and current, including whether a vehicle is charging, the amount of requested power, and the current charging power and current. For battery swapping stations, the current operating conditions can be determined by reading the number of batteries being charged, the remaining battery charge (SOC), battery capacity, requested current, and battery temperature. Power consumption constraints include the physical maximum power limit of the power consumption unit and the minimum power required by the user experience. Typically, for power consumption units such as charging piles and battery swapping stations, there is a power limit due to the physical limitations of transformers and other circuit components; that is, the operating power must not exceed the physical maximum power limit. To ensure user experience, although the power of devices like charging piles can be reduced to 0, a certain basic power is usually reserved, which is the minimum power required by the user experience. When predicting the maximum and minimum operating power available for the next control cycle, it is necessary to consider the current operating conditions and power consumption constraints. For example, if a charging pile has a physical maximum power limit of 200kW and a user's minimum power demand is 20kW, and a vehicle is currently charging at that charging pile with a requested power of 150kW, then the maximum operating power of the charging pile is determined to be 150kW and the minimum operating power to be 20kW. If the vehicle's requested power becomes 220kW, and the charging pile's physical maximum power limit is 200kW, then the maximum operating power of the charging pile is again determined to be 200kW and the minimum operating power to be 20kW. In other words, the maximum and minimum operating power reflect the maximum and minimum power that can be allocated to the power consumption unit in the next control cycle. Of course, the principle is similar for charging pile clusters, battery swapping stations, energy storage devices, and other controllable power equipment. However, for charging pile clusters, it is necessary to collect and integrate the demand of all charging piles within the cluster. For bidirectional battery swapping stations and energy storage devices, there may be situations where power is supplied to the grid, in which case the power can be defined as a negative value.

[0085] By using the current operating conditions to predict the maximum and minimum operating power available for the next control cycle, load power prediction on a short timescale can be achieved, improving prediction accuracy and capacity scheduling accuracy.

[0086] In one specific implementation, the step of "the upper-level controller calculating the dynamic capacity of each power consumption unit in the next control cycle based on all time-of-use capacity demands" further includes: calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity data of each power consumption unit before the next control cycle, the time-of-use capacity demand of each power consumption unit in the next control cycle, and the maximum capacity of the distribution network. More specifically, firstly, based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power consumption unit, the historical capacity margin of each power consumption unit is determined; then, based on the historical capacity margin, time-of-use capacity demand, and maximum capacity of each power consumption unit, the dynamic capacity of each power consumption unit in the next control cycle is calculated. The historical capacity data includes the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power, where the historical time-of-use capacity utilization rate is the ratio of historical time-of-use average power to historical dynamic capacity. The historical capacity data can be uniformly stored in the upper-level controller, or it can be stored in a cloud server or a local controller for the upper-level controller to access.

[0087] In this application, time-sharing capacity utilization rate is defined. , where D i,k P represents the time-of-use capacity utilization rate of the i-th power consumption unit in the k-th control cycle; avg_i,k P represents the time-sharing average power of the i-th power consumption unit in the k-th control cycle; cap_i,k Let be the dynamic capacity of the i-th power consumption unit in the k-th control cycle. In other words, this capacity utilization rate can reflect the proportion of the actual operating power of the i-th power consumption unit in the dynamic capacity allocated by the power grid in the k-th control cycle.

[0088] Define capacity margin , where m i,k P represents the capacity margin of the i-th power consumption unit in the k-th control cycle. min_i,k and P max_i,k Let be the minimum and maximum operating power available to the i-th power consumption unit in the k-th control cycle, respectively; c is a constant, and 0 < c < 1. This capacity margin reflects the dynamic capacity surplus of the power consumption unit in the k-th control cycle.

[0089] For example, taking c=0.05 as an example, in the k-th control cycle, the time-sharing average power P of the i-th power consumption unit is... avg_i,k If it is greater than the minimum operating power P of that cycle min_i,k And capacity utilization rate D i,k Minimum operating power P less than the kth control cycle min_i,k With dynamic capacity P cap_i,kAdding 0.05 to the ratio indicates that the power unit is using less capacity at this time, resulting in wasted capacity. In this case, the capacity margin is determined to be 1. Conversely, if the time-sharing average power P of the power unit in the k-th control cycle... avg_i,k If it is less than the maximum operating power P of that cycle max_i,k And capacity utilization rate D i,k If the value is greater than 0.95, it indicates that the power consumption unit is using too much capacity and the capacity margin is insufficient; in this case, the capacity margin is determined to be -1. If the time-of-use average power of the power consumption unit is not within either of the above two ranges, then the capacity margin of the power consumption unit is considered sufficient, and is determined to be 0. Specifically, if a power consumption unit has no load, then P is considered to be... min_i,k= P max_i,k =0,m i,k =0. Of course, c=0.05 is merely an example, and those skilled in the art can adjust it based on specific application scenarios.

[0090] Under the above definition, the step of "determining the historical capacity margin of each power consumption unit based on its historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power" further includes: first determining the historical capacity margin of each power consumption unit using the following formula: ; where m i,k-1 This represents the historical capacity margin of the i-th power consumption unit in the (k-1)th control cycle. D i,k-1 Let P be the historical time-of-use capacity utilization rate of the i-th power consumption unit in the (k-1)-th control cycle. avg_i,k-1 P represents the historical time-sharing average power of the i-th power consumption unit during the (k-1)-th control cycle; cap_i,k-1 P represents the historical dynamic capacity of the i-th power consumption unit in the (k-1)-th control cycle; min_i,k-1 and P max_i,k-1 Let be the minimum and maximum operating power available for the i-th power consumption unit in the (k-1)-th control cycle, respectively; c is a constant, and 0 < c < 1.

[0091] In the above formula, the (k-1)th control cycle is the cycle preceding the kth control cycle. In other words, if it is necessary to calculate the dynamic capacity of each power consumption unit in the next control cycle, the data from the current cycle is used. Specifically, in the current cycle, the historical time-of-use capacity utilization rate D... i,k-1 The actual time-sharing average power P generated in the current period avg_i,k-1 The dynamic capacity P allocated by the power grid to the power consumption unit cap_i,k-1 The ratio, based on this ratio and the time-sharing average power P avg_i,k-1 This allows us to determine the historical capacity margin m for the current period. i,k-1 .

[0092] Of course, using data from the current period to calculate the dynamic capacity for the next period is merely an example. Those skilled in the art can also use multiple historical data for calculation, such as using two, three, or more historical capacity data from before the next control period to estimate the dynamic capacity for the next control period.

[0093] After calculating the historical capacity margin, the step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity margin, time-of-use capacity demand, and maximum capacity of each unit" further includes: setting the objective function as... And preset conditions , , ; Solve by planning to find a new set of P that minimizes the value of f(k). cap_i,k and P cap_i,k This represents the dynamic capacity of each power-consuming unit in the next control cycle; where P min_i,k and P max_i,k These represent the minimum and maximum operating power available to the i-th power consumption unit in the k-th control cycle, respectively; P totalmax_k This represents the maximum capacity of the distribution network during the k-th control cycle.

[0094] Specifically, after the historical capacity margin is calculated, the number of power consumption units with insufficient or wasted capacity margin can be determined based on the number of 1s and -1s in the historical capacity margin of each power consumption unit. In other words, these power consumption units are not operating under appropriate dynamic capacity, while power consumption units with a historical capacity margin of 0 have been allocated appropriate dynamic capacity. At this time, the historical dynamic capacity P of each power consumption unit can be... cap_i,k-1 The purpose of adjustment is to allocate appropriate dynamic capacity to as many electrical units as possible after adjustment. In other words, by adjusting the historical dynamic capacity P of each electrical unit... cap_i,k-1 , making m i,k-1 The number of values ​​equal to ±1 is minimized. This application employs a programming approach to solve the objective function. The adjusted dynamic capacity is solved using this method, supplemented by constraints. , , The set of solutions that minimizes the value of f(k) through programming is denoted as a new set P. cap_i,k The solution set is taken as the adjusted new dynamic capacity, which is the dynamic capacity P of the k-th control cycle. cap_i,k .

[0095] It should be noted that for cases where energy storage devices are connected to the distribution network, P totalmax_kThe maximum capacity of the distribution network in the k-th control cycle is defined as the sum of the physical maximum capacity of the distribution network and the discharge power of the energy storage device over the k control cycles. It should also be noted that the above method of determining dynamic capacity by solving the objective function through planning is merely exemplary. Those skilled in the art can adjust this method, as long as it can effectively calculate m... i,k-1 The minimum number of dynamic capacities with values ​​of ±1 is required.

[0096] By calculating the dynamic capacity of each power consumption unit in the next control cycle based on historical capacity data, time-of-use capacity demand, and the maximum capacity of the distribution network, not only can smooth power regulation be achieved, but also, compared with the current application of virtual power plants, since the capacity constraints of multiple power consumption units and the distribution network are considered, the capacity over-sizing of power consumption units in high-penetration scenarios can be achieved through dynamic capacity constraints to realize spatiotemporal complementarity of different types of energy replenishment stations, avoiding significant modifications to distribution lines.

[0097] In one specific implementation, the power grid capacity dispatching method further includes: each power-consuming unit predicting its time-of-use capacity demand over multiple control cycles; all power-consuming units uploading their respective time-of-use capacity demands to the upper-level controller; and the upper-level controller calculating the dynamic capacity of each power-consuming unit over multiple control cycles based on all time-of-use capacity demands. Specifically, for some power-consuming units, especially large-scale charging stations, battery swapping stations, or office buildings, their daily demand exhibits certain regularities. Therefore, the time-of-use capacity demand over multiple control cycles can be predicted by the power-consuming units and uploaded to the upper-level controller. This allows the virtual power plant to understand and plan the power demand of these units and calculate the dynamic capacity of each power-consuming unit over multiple control cycles based on these demands, facilitating power planning and resource allocation. For example, the time-of-use capacity demand for the next 1 hour, 12 hours, 24 hours, etc., can be predicted in a control cycle of 5 minutes.

[0098] Furthermore, the step of "predicting the time-of-use capacity demand of each power consumption unit in multiple future control cycles" further includes: each power consumption unit predicts the time-of-use capacity demand curve for multiple future control cycles based on at least one of the following: charging / swapping order prediction data and historical load data. For example, for charging piles, energy storage devices, and other controllable power equipment, their historical load data can be read through daily data records. By integrating several historical load data points, future electricity demand can be predicted. For instance, by statistically analyzing data from the past week, month, or even year, and calculating the average demand and weighted average for each control cycle, the capacity demand for the next day can be predicted. Alternatively, by further classifying the data and statistically analyzing weekdays, weekends, and holidays separately, the capacity demand for the next day can be predicted more accurately. For battery swapping stations, most swapping orders are pre-booked orders. If the battery swapping station is equipped with an order prediction module, the order and capacity demand for a future period can be predicted based on this module. If no order forecasting module is configured, capacity demand can be predicted using historical load data, such as through Long Short-Term Memory (LSTM) network algorithms or Seasonally Differential Autoregressive Moving Average (SARIMA) models. Alternatively, both order forecasting models and historical load data can be combined for prediction.

[0099] The forecast results of capacity demand are presented in the form of curves. The time-of-use capacity demand curve includes at least one of the following: electricity demand curve, power demand curve, maximum operating electricity demand curve, and minimum operating electricity demand curve. The electricity demand curve and power demand curve represent the power and electricity requested by the electricity users, while the maximum operating electricity demand curve and minimum operating electricity demand curve represent the maximum and minimum electricity that the electricity users can provide.

[0100] By predicting time-of-use capacity demand for multiple future control cycles and uploading this information to the upper-level controller, which then calculates the dynamic capacity of each power-consuming unit for these cycles, the power-consuming units gain long-term load forecasting capabilities. This allows for advance prediction of capacity demand over a period of time (e.g., one hour, one day, one month), facilitating proactive grid planning and resource allocation. Furthermore, by integrating short-term load forecasting capabilities, the power-consuming units gain not only preliminary forecasting capabilities but also short-term capacity demand correction capabilities, improving the accuracy of time-of-use capacity demand calculations and preventing localized line overloads during sudden load surges. Of course, the above implementation is not mandatory; those skilled in the art can choose whether or not to include this capacity demand forecasting feature. Including the above control steps further reduces the control complexity of the virtual power grid.

[0101] In one specific implementation, the step of "the upper-level controller calculating the dynamic capacity of each power consumption unit in multiple future control cycles based on all time-of-use capacity demands" further includes: for any control cycle among the multiple future control cycles, calculating the dynamic capacity of each power consumption unit in that control cycle based on the capacity demand data of each power consumption unit before that control cycle, the time-of-use capacity demand of each power consumption unit in that control cycle, and the maximum capacity of the distribution network. In this implementation, a method similar to the short-term load forecasting capability described above can be used to determine the dynamic capacity of multiple future control cycles. Specifically, for any control cycle among the multiple future control cycles, based on the capacity demand data, dynamic capacity, and time-of-use capacity utilization rate (which can be determined based on the capacity demand curve) of each power consumption unit in the previous control cycle, the capacity margin of each power consumption unit in the previous control cycle is determined. Then, based on the capacity margin of each capacity unit in the previous control cycle, the time-of-use capacity demand in the currently calculated control cycle, and the maximum capacity, the dynamic capacity of each power consumption unit in the currently calculated control cycle is calculated. The calculation process can refer to the above implementation method. The specific difference is that the historical capacity data is replaced with the predicted data of the previous control cycle of the control cycle to be calculated or the data calculated based on the predicted data of the previous control cycle. This will not be elaborated here.

[0102] In one specific implementation, the power grid capacity scheduling method further includes: the upper-level controller sending the dynamic capacity and capacity margin of each power-consuming unit in the next control cycle to the cloud; and the cloud displaying the dynamic capacity and capacity margin of each power-consuming unit to the user. For example, after calculating the dynamic capacity for the next control cycle, the capacity margin for the next control cycle can be calculated based on the actual time-of-use average power, and this dynamic capacity and capacity margin can be sent to the cloud server. The cloud server then provides basic information about the power-consuming units (such as power equipment occupancy and queuing status) and displays the status of each power-consuming unit to the user on the cloud platform, allowing the user to choose as needed. For example, the capacity margin of each power-consuming unit can be displayed to the user on a map on the cloud platform in the form of a heat map.

[0103] By sending the dynamic capacity and capacity margin of the next control cycle to the cloud and displaying them to users, users can be guided to naturally divert power, thus reducing the difficulty of controlling the virtual power plant and improving the user experience. Of course, the above control method is merely exemplary, and those skilled in the art can choose whether to display the information of the power consumption unit to users on the cloud platform based on the specific application scenario.

[0104] Corresponding to the aforementioned power consumption unit, this application provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that executes the power grid capacity scheduling method of the above-described method embodiment. This program can be loaded and run by a processor to implement the above-described power grid capacity scheduling method. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0105] Corresponding to the aforementioned power consumption unit, this application provides a control device. In one embodiment of the control device according to this application, the control device includes a processor and a memory. The memory can be configured to store a program for executing the power grid capacity scheduling method of the above-described method embodiments, and the processor can be configured to execute the program in the memory. This program includes, but is not limited to, a program for executing the power grid capacity scheduling method of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. This control device can be a device comprising various electronic devices.

[0106] Corresponding to the aforementioned upper-level controller, this application provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that executes the power grid capacity scheduling method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described power grid capacity scheduling method. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0107] Corresponding to the aforementioned upper-level controller, this application provides a control device. In one embodiment of the control device according to this application, the control device includes a processor and a memory. The memory can be configured to store a program for executing the power grid capacity scheduling method of the above-described method embodiments, and the processor can be configured to execute the program in the memory. This program includes, but is not limited to, a program for executing the power grid capacity scheduling method of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. This control device can be a device comprising various electronic devices.

[0108] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the server or client according to the embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a PC program and PC program products) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a PC-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0109] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such order. They can be executed simultaneously (in parallel) or in reverse order. These simple changes are all within the protection scope of this application.

[0110] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, any of the claimed embodiments in the claims of this application can be used in any combination.

[0111] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A power grid capacity dispatching method, characterized in that, This invention is applicable to multiple power consumption units with spatiotemporal complementary characteristics connected under the same power distribution network. Each power consumption unit includes at least one power device. All power consumption units are communicatively connected to the same upper-level controller. The power device is at least one of a charging pile, a battery swapping station, and an energy storage device. The power grid capacity scheduling method includes: Each of the aforementioned power consumption units predicts its time-of-use capacity demand in the next control cycle; All the power-consuming units upload their respective time-of-use capacity requirements to the upper-level controller; The upper-level controller calculates the dynamic capacity of each power-consuming unit in the next control cycle based on all time-sharing capacity demands. Based on the calculation results, capacity is allocated to each of the power-consuming units in the next control cycle.

2. The power grid capacity dispatching method according to claim 1, characterized in that, The step of "predicting the time-of-use capacity demand of each of the power consumption units in the next control cycle" further includes: Each power consumption unit predicts the maximum and minimum operating power available in the next control cycle based on its current operating conditions and corresponding power consumption constraints. The power consumption constraints include the physical maximum power limit of the power consumption unit and the minimum power requirement for user experience.

3. The power grid capacity dispatching method according to claim 2, characterized in that, The step of "the upper-level controller calculating the dynamic capacity of each power-consuming unit in the next control cycle based on all time-of-use capacity demands" further includes: Based on the historical capacity data of each power consumption unit before the next control cycle, the time-of-use capacity demand of each power consumption unit in the next control cycle, and the maximum capacity of the distribution network, the dynamic capacity of each power consumption unit in the next control cycle is calculated.

4. The power grid capacity dispatching method according to claim 3, characterized in that, The historical capacity data includes historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power. The historical time-of-use capacity utilization rate is the ratio of the historical time-of-use average power to the historical dynamic capacity. The step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity data of each power consumption unit, the time-of-use capacity demand of each power consumption unit in the next control cycle, and the maximum capacity of the distribution network" further includes: Based on the historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power of each power consumption unit, the historical capacity margin of each power consumption unit is determined. Based on the historical capacity margin of each power consumption unit, the time-of-use capacity demand, and the maximum capacity, the dynamic capacity of each power consumption unit in the next control cycle is calculated.

5. The power grid capacity dispatching method according to claim 4, characterized in that, The step of "determining the historical capacity margin of each power consumption unit based on its historical time-of-use capacity utilization rate, historical dynamic capacity, and historical time-of-use average power" further includes: The historical capacity margin of each power consumption unit is determined in the following manner: Where, m i,k-1 This represents the historical capacity margin of the i-th power consumption unit in the (k-1)th control cycle. D i,k-1 Let P be the historical time-of-use capacity utilization rate of the i-th power consumption unit in the (k-1)-th control cycle. avg_i,k-1 P represents the historical time-sharing average power of the i-th power consumption unit during the (k-1)-th control cycle; cap_i,k-1 P represents the historical dynamic capacity of the i-th power consumption unit in the (k-1)-th control cycle; min_i,k-1 and P max_i,k-1 Let be the minimum and maximum operating power available for the i-th power consumption unit in the (k-1)-th control cycle, respectively; c is a constant, and 0 < c < 1.

6. The power grid capacity dispatching method according to claim 5, characterized in that, The step of "calculating the dynamic capacity of each power consumption unit in the next control cycle based on the historical capacity margin of each power consumption unit, the time-of-use capacity demand, and the maximum capacity" further includes: Let the objective function be And preset conditions , , ; By solving a new set of P that minimizes the value of f(k), we can find the solution. cap_i,k and P cap_i,k As the dynamic capacity of each of the aforementioned power-consuming units in the next control cycle; Among them, P min_i,k and P max_i,k These represent the minimum and maximum operating power available to the i-th power consumption unit in the k-th control cycle, respectively; P totalmax_k This represents the maximum capacity of the distribution network during the k-th control cycle.

7. The power grid capacity dispatching method according to claim 1, characterized in that, The power grid capacity scheduling method also includes: Each of the aforementioned power consumption units predicts its time-of-use capacity demand over multiple future control cycles; All the power-consuming units upload their respective time-of-use capacity requirements to the upper-level controller; The upper-level controller calculates the dynamic capacity of each power-consuming unit in the future multiple control cycles based on all time-sharing capacity requirements.

8. The power grid capacity dispatching method according to claim 7, characterized in that, The step of "predicting the time-of-use capacity demand of each of the aforementioned power consumption units over multiple future control cycles" further includes: Each of the aforementioned power consumption units predicts the time-of-use capacity demand curve for multiple future control cycles based on at least one of the charging and swapping order forecast data and historical load data. The time-of-use capacity demand curve includes at least one of the following: electricity demand curve, power demand curve, maximum operating electricity demand curve, and minimum operating electricity demand curve.

9. The power grid capacity dispatching method according to claim 8, characterized in that, The step of "the upper-level controller calculating the dynamic capacity of each power-consuming unit in multiple control cycles based on all time-of-use capacity demands" further includes: For any control cycle among multiple future control cycles, the dynamic capacity of each power consumption unit in the control cycle is calculated based on the capacity demand data of each power consumption unit before the control cycle, the time-of-use capacity demand of each power consumption unit in the control cycle, and the maximum capacity of the distribution network.

10. The power grid capacity dispatching method according to claim 4, characterized in that, The power grid capacity scheduling method also includes: The upper-level controller sends the dynamic capacity and capacity margin of each power-consuming unit to the cloud in the next control cycle; The cloud displays the dynamic capacity and capacity margin of each power consumption unit to the user.

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