Power control method, device, equipment and medium for charging station to participate in substation

Abstract

The present application discloses a method, device, equipment and medium for a charging station to participate in power control of a substation. The method includes: controlling the operation of a charging station inverter based on a virtual synchronous machine control model, wherein the moment of inertia and damping coefficient are adaptively adjusted according to the electromagnetic power change rate; controlling the active and reactive power of the charging station in real time in a rolling optimization manner based on a two-layer control model. In the two-layer control model, the real-time scheduling layer is used to optimize the charging and discharging power of the charging station in future periods with the goal of reducing the peak-valley difference of the equivalent load, and the power distribution layer is used to determine the charging and discharging power of the current period with the goal of minimizing the system network loss and taking the charging and discharging power of the current period in the optimization result of the real-time scheduling layer as a constraint. The above technical solution adaptively adjusts the moment of inertia and damping coefficient according to the electromagnetic power change rate based on the virtual synchronous machine control model, and performs rolling optimization based on the two-layer control model, improving the flexibility of active and reactive power control of the charging station.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of coding technologies, and in particular, to a method, device, equipment, and medium for a charging station to participate in the power control of a substation. Background Art

[0002] In recent years, the charging electricity demand of electric vehicles has been growing at a high speed. As an important intermediate link for power transmission and distribution in the distribution network, the substation plays important roles such as power transmission, power flow optimization, voltage regulation, and power quality management. At present, the operation mode of "substation + charging station" with multi-station integration has been promoted and pilot applied. However, the controllable load of the power electronic converter in the electric vehicle charging station has a very fast response speed, and it does not have the inherent rotational inertia and damping components of a synchronous motor, so the control flexibility is poor. The access of small-inertia, fast, and high-frequency power electronic systems to large-inertia, low-speed, and power-frequency power systems has caused many adaptability problems such as not participating in power grid regulation, not supporting power grid fault recovery, difficult management and control, and weak robustness in the dynamic and steady-state processes. When too many power electronic loads are connected to the power system, it will surely threaten the safe and stable operation of the power system.

[0003] It should be noted that the penetration rate of flexible loads with power electronic converters such as electric vehicles and variable-frequency air conditioners is increasing continuously, and the load regulation potential is huge. Coordinating the load side and the power supply side to regulate the power supply-demand balance of the power grid has become an effective measure to solve this problem. In addition, with the improvement of the power equipment manufacturing level and the growth of power users' demand for different forms of electricity consumption, it is difficult to support the safe and stable operation of the adjacent substation if the charging station load cannot be flexibly controlled. Summary of the Invention

[0004] The present application provides a method, device, equipment, and medium for a charging station to participate in the power control of a substation to improve the flexibility of charging station power control.

[0005] In a first aspect, the embodiments of the present application provide a method for a charging station to participate in the power control of a substation, including:

[0006] Controlling the operation of the inverter of the charging station based on a virtual synchronous machine control model, wherein the moment of inertia and damping coefficient in the virtual synchronous machine control model are adaptively adjusted according to the change rate of electromagnetic power;

[0007] Based on a two-layer control model, the active and reactive power of the charging station is controlled in real time in a rolling optimization manner, wherein the two-layer control model includes a real-time scheduling layer and a power distribution layer. The real-time scheduling layer is used to optimize the charging and discharging power of the charging station in the future period with the goal of reducing the peak-valley difference of the equivalent load, and the power distribution layer is used to determine the charging and discharging power of the current period with the goal of minimizing the system network loss and with the charging and discharging power of the current period in the optimization result of the real-time scheduling layer as a constraint.

[0008] In a second aspect, an embodiment of the present application further provides a power control device for a charging station to participate in a substation, including:

[0009] A first control module for controlling the operation of the inverter of the charging station based on a virtual synchronous machine control model, wherein the moment of inertia and damping coefficient in the virtual synchronous machine control model are adaptively adjusted according to the change rate of the electromagnetic power;

[0010] A second control module for controlling the active and reactive power of the charging station in real time in a rolling optimization manner based on a two-layer control model, wherein the two-layer control model includes a real-time scheduling layer and a power distribution layer. The real-time scheduling layer is used to optimize the charging and discharging power of the charging station in a future period with the goal of reducing the peak-valley difference of the equivalent load, and the power distribution layer is used to determine the charging and discharging power of the current period with the goal of minimizing the system network loss and taking the charging and discharging power of the current period in the optimization result of the real-time scheduling layer as a constraint.

[0011] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0012] One or more processors;

[0013] A storage device for storing one or more programs;

[0014] When the one or more programs are executed by the one or more processors, the one or more processors implement the power control method for the charging station to participate in the substation as described in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the power control method for the charging station to participate in the substation as described in the first aspect.

[0016] An embodiment of the present application provides a method, device, equipment and medium for a charging station to participate in the power control of a substation, including: controlling the operation of the inverter of the charging station based on a virtual synchronous machine control model, wherein the moment of inertia and damping coefficient in the virtual synchronous machine control model are adaptively adjusted according to the change rate of electromagnetic power; controlling the active and reactive power of the charging station in real time in a rolling optimization manner based on a double-layer control model, wherein the double-layer control model includes a real-time scheduling layer and a power distribution layer, the real-time scheduling layer is used to optimize the charging and discharging power of the charging station in the future period with the goal of reducing the peak-valley difference of the equivalent load, and the power distribution layer is used to determine the charging and discharging power of the current period with the goal of minimizing the system network loss and with the charging and discharging power of the current period in the optimization result of the real-time scheduling layer as a constraint. The above technical solution adaptively adjusts the moment of inertia and damping coefficient according to the change rate of electromagnetic power based on the virtual synchronous machine control model, and performs rolling optimization based on the double-layer control model, improving the flexibility of the active and reactive power control of the charging station. Description of the Drawings

[0017] Combined with the drawings and referring to the following specific embodiments, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more obvious. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the original components and elements are not necessarily drawn to scale.

[0018] Figure 1 It is a flowchart of a method for a charging station to participate in the power control of a substation provided by an embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of the VSG topology structure of an electric vehicle charging pile provided by an embodiment;

[0020] Figure 3 It is a schematic diagram of rolling optimization based on a double-layer control model provided by an embodiment;

[0021] Figure 4 It is a schematic diagram of the relationship between the moment of inertia, damping coefficient and the change rate of electromagnetic power provided by an embodiment;

[0022] Figure 5 It is a schematic diagram of the full-range operation of charging and discharging power in four quadrants provided by an embodiment;

[0023] Figure 6 It is a schematic diagram of the phasor diagram of a bidirectional AC / DC converter provided by an embodiment;

[0024] Figure 7 It is a schematic diagram of the structure of a device for a charging station to participate in the power control of a substation provided by an embodiment of the present application;

[0025] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0026] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the sake of convenience of description, only parts related to the present application rather than all structures are shown in the accompanying drawings.

[0027] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the steps as sequential processes, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0028] It should be noted that the concepts such as "first" and "second" mentioned in the embodiments of the present application are only used to distinguish different devices, modules, units, or other objects, rather than to limit the order of the functions performed by these devices, modules, units, or other objects or their interdependent relationships.

[0029] In addition, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0030] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of national laws and regulations.

[0031] Figure 1 A flowchart of a method for a charging station to participate in the power control of a substation provided by an embodiment of the present application. This embodiment is applicable to the case of real-time encoding of video data. Specifically, the method for the charging station to participate in the power control of the substation can be executed by a power control device for the charging station to participate in the substation. The power control device for the charging station to participate in the substation can be implemented in software and / or hardware and integrated in an electronic device. The electronic device includes, but is not limited to, devices with image processing functions such as a computer, a smart phone, or a server.

[0032] As Figure 1 shown, the method specifically includes the following steps:

[0033] S110. Control the operation of the inverter of the charging station based on a virtual synchronous machine control model, where the moment of inertia and damping coefficient in the virtual synchronous machine control model are adaptively adjusted according to the change rate of the electromagnetic power.

[0034] In this embodiment, the charging pile of the charging station adopts a circuit containing an inverter. On the basis of controlling the inverter using a Virtual Synchronous Generator (VSG), in order to improve the dynamic stability characteristics and active power support ability of the VSG system, considering that low-frequency oscillations and steady-state deviations will occur when the power suddenly changes, the moment of inertia and damping coefficient are adaptively designed for this.

[0035] Specifically, regardless of the operating state of the VSG, when the operating condition changes, the electromagnetic power will also increase or decrease correspondingly. The moment of inertia and damping coefficient can operate with dynamic parameters. Exemplarily, when the rate of change of electromagnetic power rises or falls rapidly, the moment of inertia can gradually decrease according to the rate of change of electromagnetic power and the acceleration coefficient, and the damping coefficient can gradually increase, thereby suppressing the generation of low-frequency oscillations of the VSG active power; when the rate of change of electromagnetic power decreases or tends to be stable, the moment of inertia can gradually increase according to the rate of change of electromagnetic power and the acceleration coefficient, and the damping coefficient can gradually decrease, even tending to 0, thereby reducing the possibility of the VSG generating active low-frequency oscillations, while maintaining a small active steady-state deviation.

[0036] On this basis, the organic combination of the dynamic characteristics and static characteristics of the VSG can be realized, the problems of system low-frequency oscillations and steady-state power deviation in the "substation + charging station" multi-operation modes can be solved, and the stable and reliable operation of electric vehicles in different operation modes can be achieved.

[0037] Figure 2 It is a schematic diagram of the VSG topology structure of an electric vehicle charging pile provided by an embodiment. As Figure 2 shown, V dc is the DC-side voltage, C dc is the DC-side capacitor, E a , E b , E c are the voltages at the output end of the inverter respectively, L f , R f are the filter inductor and parasitic resistance at the output end of the inverter respectively, i f is the current flowing through the filter inductor of the inverter, C f is the filter capacitor, v oa , v ob , v oc are the terminal voltages on each filter capacitor respectively, L g , R g are the inductor and resistance on the grid side, v ga , v gb , v gc are the grid voltages.

[0038] S120. Based on a double - layer control model, the active and reactive power of the charging station is controlled in real - time in a rolling optimization manner. Among them, the double - layer control model includes a real - time scheduling layer and a power distribution layer. The real - time scheduling layer is used to optimize the charging and discharging power of the charging station in future periods with the goal of reducing the peak - valley difference of the equivalent load. The power distribution layer is used to determine the charging and discharging power of the current period with the goal of minimizing the system network loss, taking the charging and discharging power of the current period in the optimization result of the real - time scheduling layer as a constraint.

[0039] In this embodiment, fully considering the spatio - temporal distribution characteristics of electric vehicle charging and discharging, a double - layer control model for electric vehicles to participate in power grid scheduling is established, including a real - time scheduling layer and a power distribution layer. Through the real - time scheduling layer, the charging and discharging power of the charging station in future periods is preliminarily optimized to obtain the optimization results for each future period. Through the power distribution layer, the charging and discharging power of the current period is finally determined according to the optimization result of the current period. In addition, considering that there are deviations between the predicted values and actual values of renewable energy output and load, and when performing grouped scheduling of electric vehicles, in order to fully consider the application information of each electric vehicle and the possible temporary changes in the application information due to the subjective charging and discharging behavior of the vehicle owners, the active and reactive power of the charging station can be controlled in real - time in a rolling optimization manner.

[0040] Exemplarily, the process of rolling optimization based on the double - layer control model includes:

[0041] 1) Demand information release: The dispatching center formulates a reasonable time - of - use electricity price according to the next - day load forecast and the application output plan of the renewable energy power station and releases it to the users of electric vehicles.

[0042] 2) Demand information acquisition: Users apply to any charging station for information such as the charging and discharging time period and the charging and discharging amount according to the next - day electric vehicle usage. These information can be called application information, spatio - temporal information or reservation information.

[0043] 3) Demand information processing: Each charging period is divided according to the peak - valley electricity price interval. Electric vehicles with the same electricity price interval for the applied charging and discharging time period are grouped together, and the electronic device uploads the sorted information to the dispatching center.

[0044] 4) Real - time scheduling of charging power: At the start of the current period, the daily equivalent load curve is corrected by combining ultra - short - term prediction data. Based on the corrected equivalent load curve, with the goal of reducing the peak - valley difference of the equivalent load, the real - time scheduling layer optimizes the charging and discharging power of electric vehicles in future periods.

[0045] 5) Regulation of power flow power distribution (power flow calculation): Combining the owner reservation information received by each charging station currently, with the optimization results of the real-time scheduling layer as the constraint, reasonably allocate the charging and discharging power of electric vehicles at each charging station. At the same time, considering the capacity limit of the bidirectional converter, optimize the reactive power magnitude and direction of each charging station with the minimum system network loss as the objective function;

[0046] 6) Rolling optimization: Add a feedback correction link to cope with the uncertainty of photovoltaic power output and electric vehicle charging and discharging, so that the electric vehicle charging facilities have the ability to adaptively respond to the voltage / frequency disturbances of the power grid.

[0047] Figure 3 FIG. is a schematic diagram of rolling optimization based on a two-layer control model provided for an embodiment. As Figure 3 shown, the rolling optimization process includes:

[0048] Initialize the daily equivalent load curve and the owner reservation information; Starting from the start time of the current time period, combining the renewable energy output and load demand data of the previous time periods, ultra-short-term prediction and day-ahead prediction data of the current day, and the reservation information of all owners, the charging and discharging power of electric vehicles in each future time period of the current day can be optimized through the real-time scheduling layer, and the optimal charging and discharging power of the entire system in the current time period can be obtained therefrom; In the power distribution layer, the charging and discharging power of electric vehicles at each charging station can be reasonably allocated by combining the owner reservation information of each charging station currently, with the optimization results of the real-time scheduling layer as the constraint. At the same time, considering the capacity limit of the bidirectional converter, optimize the reactive power magnitude and direction of each charging station;

[0049] For the next time period, update the previous time period, ultra-short-term prediction and day-ahead prediction data, and update the owner reservation information of each charging station;

[0050] Repeat the above process, and finally realize the optimal scheduling of all time periods of the current day in a rolling manner.

[0051] It should be noted that the number of electric vehicles in the charging station is huge, and it is too complicated to formulate a scheduling plan for each electric vehicle separately. In this embodiment, a reasonable time-of-use electricity price system can be adopted to attract electric vehicle users to charge and discharge at the charging facilities during peak and valley electricity price periods. At the same time, adopt a day-ahead application mechanism and rely on the support of smart grid-related technologies to realize the information interaction between electric vehicle users, charging piles and the dispatching center. Through the coordination and complementarity between multi-layer control models, the effective allocation of power among the distribution network, electric vehicle charging equipment and electric vehicles is realized.

[0052] A power control method for a charging station to participate in a substation provided by an embodiment of the present application, on the one hand, realizes the organic combination of the dynamic characteristics and static characteristics of the VSG by dynamically and adaptively designing the rotational inertia and damping coefficient; on the other hand, utilizes a double-layer control model for electric vehicles to participate in grid dispatching to adaptively optimize the active and reactive power of the charging station, enabling the electric vehicle charging facilities to have the ability to adaptively respond to substation voltage / frequency disturbances. On this basis, give full play to the flexible control role of the active and reactive power of the bi-directional charging pile (V2G), perform double-layer rolling optimization dispatching on the active and reactive charging and discharging power of the charging station, realize the adaptive response of the electric vehicle charging facilities in the charging station to the substation frequency and voltage fluctuations, and realize the efficient and stable operation of the "substation + charging station" multi-station integration.

[0053] In one embodiment, the virtual synchronous generator control model satisfies:

[0054]

[0055] Wherein, P n is the rated active power, k thred is the rate threshold, k1 is the acceleration coefficient, k2 is the deceleration coefficient, J n is the rated rotational inertia, J1, J2, and J3 are all intermediate variables, J is the rotational inertia, D p is the damping coefficient, D pn is the rated damping coefficient, k D is the damping coefficient rate, ΔP e is the change in electromagnetic power within Δt time.

[0056] In the traditional VSG control strategy, the rotational inertia J and the damping coefficient D p are both constant values, and low-frequency oscillations and steady-state deviations will occur in the V2G when the power suddenly changes. In this embodiment, the rotational inertia J and the damping coefficient D p can be adaptively adjusted with the change of electromagnetic power. Referring to the formula of the above virtual synchronous generator control model, because of the existence of the integral part in the rotational inertia, an additional amount appears in the steady-state deviation of the active power. However, in the actual control process, there will be a limiter for the rotational inertia finally, and the limiting effect will make both J1 and J2 equal to 0, that is, the steady-state deviation caused by the dynamic adaptive rate will finally disappear automatically.

[0057] Figure 4 It is a schematic diagram of the relationship between the rotational inertia, damping coefficient and the change rate of electromagnetic power provided by an embodiment. Taking the case where the electromagnetic power P e rises as an example, with the rate threshold k thred as the dividing line, according to the electromagnetic power P eThe magnitude of the absolute value of the rate of change is divided into an acceleration region and a deceleration region, that is, the VSG is in either the acceleration region or the deceleration region at a certain moment.

[0058] When the VSG is in the acceleration region, J1 plays a major role. At this time, the corresponding J3 accumulates and increases, and the increasing rate mainly depends on k1. As J3 increases, the moment of inertia J of the VSG continuously decreases. In the acceleration region, due to the large absolute value of the electromagnetic power change rate, the D at this time p is also in an increasing state. Then, in the entire acceleration region, the damping ratio of the VSG is in an increasing state, which helps to avoid the occurrence of active power low-frequency oscillations.

[0059] When the VSG is in the deceleration region, the VSG is often in or has already been in a new stable state. When the VSG is about to reach the new stable state, J2 plays a major role. At this time, the corresponding J3 accumulates and decreases, and the decreasing rate mainly depends on k2. As J3 decreases, the moment of inertia J of the VSG continuously increases. Due to the small absolute value of the electromagnetic power change rate, the D at this time p is also in a decreasing state, and the decrease of D p helps to reduce the steady-state deviation of the active power. After the VSG reaches the new stable state, at this time, the absolute value of the electromagnetic power change rate reaches the minimum, and J3 will further decrease until J is equal to J n , so that the additional steady-state quantity will disappear. At this time, under the action of k D , D p is also close to 0. At this time, the steady-state deviation of the active power reaches the minimum value, and the VSG maintains dynamic balance at the new steady-state operating point.

[0060] Based on the above virtual synchronous control strategy, J and D p will operate with dynamic parameters. When the power change rate rises or falls rapidly, J will gradually decrease with the power change rate and the selection of the acceleration coefficient, and D p will gradually increase, thereby suppressing the generation of active power low-frequency oscillations of the VSG. When the power change rate decreases or tends to be stable, J will gradually increase with the power change rate and the selection of the deceleration coefficient, and D p will gradually decrease and even tend to 0, thereby reducing the possibility of the VSG generating active power low-frequency oscillations, while maintaining a small active power steady-state deviation, solving the system low-frequency oscillation and steady-state power deviation problems in the "substation + charging station" multi-operation modes, and realizing the stable and reliable operation of electric vehicles in different operation modes.

[0061] In one embodiment, the charging pile adopts a two-stage topology; the two-stage topology includes: the front stage adopts a voltage-source three-phase bridge circuit including an AC / DC inverter, which is used to control the active and reactive power transmissions respectively, and provide reactive power compensation and power factor correction for charging and discharging. The voltage-source three-phase bridge circuit contains an inverter, and this inverter is controlled based on a virtual synchronous machine control model; the rear stage adopts a dual-active full-bridge DC / DC converter, which is used to achieve electrical isolation between the electric vehicle and the power grid and bidirectional power flow.

[0062] In this embodiment, the charging piles in the charging station have the function of bidirectional power flow control, so bidirectional charging piles (V2G) are adopted. By constructing an active and reactive power transmission model for the electric vehicle charging station, full-range operation of the charging and discharging power of the charging pile in all four quadrants is realized.

[0063] Figure 5 It is a schematic diagram showing the full-range operation of the charging and discharging power in all four quadrants provided for one embodiment. Specifically, the charging pile adopts a two-stage topology. The front stage adopts a voltage-source three-phase bridge circuit and a control strategy for separately controlling the active and reactive currents. The converter can transmit active and reactive powers simultaneously, perform reactive power compensation and power factor correction while charging and discharging the battery, and realize the full-range operation of the charging and discharging power of the charging pile in all four quadrants. The rear stage realizes electrical isolation between the grid side and the electric vehicle battery side and bidirectional power flow by adopting a dual-active full-bridge DC / DC converter.

[0064] Exemplarily, the front-stage AC / DC stage is a voltage-source three-phase full-bridge PWM converter (Voltage Source PWM Converter, VSC), and the rear-stage DC / DC stage is a dual-active bridge converter (Dual Active Bridge, DAB). The V2G converter realizes electrical isolation between the AC side and the battery side, avoids the influence of direct electrical contact on the battery or the power grid, and reduces the requirements for device and circuit protection. At the same time, this circuit has a high power level, a large power density, a high transfer efficiency, small stress on the switches, and reasonable control can achieve soft switching, reduce losses, and is suitable for high-power application scenarios such as off-vehicle charging piles.

[0065] The reactive power response ability of the charging pile comes from the bidirectional AC / DC converter. Under steady-state operating conditions, taking phase A as an example, there is the following expression: U sa = jωL s I sa + RI sa + U aN ; where Usa is the fundamental wave phasor of the phase voltage of phase A of the power grid, Isa is the fundamental wave phasor of the phase current of phase A, and UaN is the phase voltage of phase A at the AC end of the three-phase bridge. Figure 6Schematic diagram of a phasor diagram of a bidirectional AC / DC converter provided for an embodiment. As Figure 6 shown, the grid voltage remains unchanged. By controlling the amplitude and phase of U sa , the amplitude and phase of I sa can be changed, thereby realizing the bidirectional flow of active and reactive power.

[0066] During operation, the active and reactive power transmitted by the front-stage AC / DC bidirectional converter is limited by the maximum apparent capacity: P + Q ≤ S max ; where P and Q are the active and reactive power passing through the converter respectively; Smax is the maximum apparent capacity of the converter.

[0067] The bidirectional operation of active and reactive power mainly includes the following three modes:

[0068] 1) G2V (Grid-to-Vehicle) mode: The active power flows forward from the AC side to the battery side;

[0069] 2) V2G (Vehicle-to-Grid) mode: The active power flows backward from the battery side to the AC side;

[0070] 3) Reactive power mode: The working condition of the converter is similar to that of a Static Synchronous Compensator (STATCOM), realizing the bidirectional regulation of capacitive and inductive reactive power factors and reactive power transmission control.

[0071] The front-stage VSC is mainly used to control the transmission of bidirectional active and reactive power on the grid side, realizing four-quadrant operation on the AC side. The rear-stage DAB is mainly used for electrical isolation, adjusting the active power transmission on the battery side, and controlling the charging and discharging process of the battery. The specific working modes of the converter include:

[0072] (1) In the G2V (Grid-to-Vehicle) mode, the active power flows forward from the AC side to the battery side. The AC-DC converter operates in the rectification state, presenting the boost circuit boost characteristic, controlling the input current on the AC side and stabilizing the DC side voltage; the DC-DC converter transports the power from the primary side to the secondary side, controlling the constant current-constant voltage (CC-CV) charging of the battery and stabilizing the output voltage or current on the battery side.

[0073] (2) In the V2G (Vehicle-to-Grid) mode, the active power flows backward from the battery side to the AC side. The AC-DC converter operates in the inversion state, presenting the buck circuit buck characteristic, controlling the output current on the AC side. The DC-DC converter transports the power from the secondary side to the primary side, discharges the battery, and stabilizes the DC side voltage.

[0074] (3) In the reactive power mode, the operation of the converter is similar to that of a static var generator (STATCOM), achieving power factor regulation and reactive power transmission control. The front stage mainly controls the absorption or injection of reactive power, while the rear stage is still used for battery charging and discharging.

[0075] The two-stage topology enables independent control of the front and rear stages. Coupled with the control strategy of the AC / DC stage that can separately control the active and reactive currents, the converter can transmit both active and reactive powers simultaneously, perform reactive power compensation and power factor correction while charging and discharging the battery, and achieve full-range operation in all four quadrants of the power circle on the AC side.

[0076] In one embodiment, the first objective function corresponding to the real-time scheduling layer is:

[0077] where T is the total number of future time periods; P L (t) is the average power consumed by the base load in the t-th time period, P DG is the output of the distributed power source in the t-th time period; P EV (t) is the charging and discharging power of the electric vehicle in the t-th time period, with charging being positive and discharging being negative, is the average value of the equivalent load, Alternatively, the first objective function corresponding to the real-time scheduling layer is:

[0078] where x is the charging and discharging power of the electric vehicle in each time period to be optimized, G is equal to 2 times the identity matrix, and r is a constant matrix, and r is determined according to P L (t), P DG (t) and is determined.

[0079] Specifically, at the start of the current time period, the daily equivalent load curve is corrected by combining the ultra-short-term prediction data. Based on the corrected equivalent load curve, with the goal of reducing the peak-to-valley difference of the equivalent load, the charging and discharging power of the electric vehicle in each future time period is optimized. One day can be divided into 24 time periods in hourly units.

[0080] In one embodiment, the constraint conditions corresponding to the first objective function include:

[0081] System power balance constraint, denoted as s.t.(1): P DG (t) + P grid (t) = P L (t) + P loss (t) + P EV (t), where P grid is the transmission power of the tie line, and P loss is the active power network loss of the system;

[0082] Battery capacity constraint for the current time period, denoted as s.t.(2): Among them, P EV (t c ) is the scheduling power of electric vehicles in the current time period, Nc is the number of electric vehicles whose application time periods include the current time period in the current reservation information, and C EV (n) is the applied charge and discharge amount of the nth electric vehicle;

[0083] Battery capacity constraint within each electricity price interval, denoted as s.t.(3): Among them, P EV (t) is the scheduling power of electric vehicles in the corresponding time period, and T inl is the set of all time periods included in a certain electricity price interval; N inl is the number of electric vehicles whose application time periods are within this electricity price interval;

[0084] Charging and discharging power constraint of electric vehicles, denoted as s.t.(4): Among them, is the maximum discharge power of the electric vehicle, is the maximum charging power of the electric vehicle;

[0085] Distributed power output constraint, denoted as s.t.(5): Among them, are the lower limit value and upper limit value of the active power output of the distributed power source respectively;

[0086] Power transmission capacity limit of the distribution transformer: Among them, are the upper limit value and lower limit value of the power transmission capacity of the distribution transformer respectively.

[0087] The first objective function is a quadratic function, and the constraint conditions are all linear. This model can be transformed into a convex quadratic programming problem for solution:

[0088]

[0089] In the formula: x is the charging and discharging power of electric vehicles in each time period to be optimized, G is equal to twice the identity matrix, r is a constant matrix, and can be obtained through P L , P DG , P.

[0090] In an embodiment, the second objective function corresponding to the power distribution layer is: Among them, r k is the resistance of branch k; I k (t) is the current flowing through branch k at time t; Δt is the time period length; N b is the number of system branches.

[0091] Specifically, according to the optimization results of the real-time scheduling layer, the optimal charging and discharging power of all-system electric vehicles in this period can be obtained. Taking this as a constraint, the active and reactive power input and output of each charging station in this period are optimized in the power distribution layer, with the minimum system network loss as the objective function.

[0092] In one embodiment, the constraint conditions of the second objective function include:

[0093] The planned power constraint of the real-time scheduling layer, denoted as s.t. ①:

[0094] Among them, P stg (m, t) is the active power of charging station m in the t-th time period, and P EV (t) is the charging and discharging power of electric vehicles in the t-th time period. N stg is the number of charging devices;

[0095] The battery capacity constraint, denoted as s.t. ②: Among them, P stg (m, t c ) is the scheduling power of electric vehicles at charging station m in the current time period t c . N m,c is the number of electric vehicles whose application time periods in the reservation information of the current charging station m include the current time period; C EV (n) is the applied charging and discharging amount of the n-th electric vehicle;

[0096] The charging and discharging power constraint, denoted as s.t. ③: Among them, is the maximum discharge power of electric vehicles at time t in charging station m, is the maximum charging power of electric vehicles at time t in charging station m;

[0097] The current-carrying capacity limit constraint of the bidirectional converter, denoted as s.t. ④: Among them, P stg , Q stg are the active power and reactive power of the charging station respectively, and S stg,max is the maximum apparent capacity of the converter;

[0098] The system security constraint, denoted as s.t. ⑤: Among them, V j min , V j max are the upper limit value and lower limit value of the voltage amplitude of node j respectively, is the maximum current that line k allows to transmit.

[0099] Through the second-order cone relaxation technology, the power distribution model can be expressed as:

[0100] The model can be solved by second-order cone programming.

[0101] The two-layer control model for the electric vehicle to participate in power grid dispatching in this embodiment takes into account the reactive power response ability of the electric vehicle charging pile, gives full play to the functions of V2G active and reactive power, and optimizes the active and reactive power charging and discharging of multiple charging stations. First, with the lowest load peak-valley difference rate as the goal, the quadratic programming method is used to obtain the optimal charging and discharging power of the electric vehicle at each time period; on this basis, with the lowest system network loss as the goal, the second-order cone programming is used to optimize the active and reactive power of each charging station at this time period, and the rolling optimization method is adopted to add a feedback correction link to cope with the uncertainty of photovoltaic power output and electric vehicle charging and discharging, so that the electric vehicle charging facility has the ability to adaptively respond to the voltage / frequency disturbance of the power grid.

[0102] Figure 7 It is a schematic structural diagram of a power control device for a charging station to participate in a substation provided by an embodiment of the present application. The power control device for a charging station to participate in a substation provided by this embodiment includes:

[0103] The first control module 210 is used to control the operation of the inverter of the charging station based on the virtual synchronous machine control model, wherein the moment of inertia and damping coefficient in the virtual synchronous machine control model are adaptively adjusted according to the change rate of the electromagnetic power;

[0104] The second control module 220 is used to control the active and reactive power of the charging station in real time in a rolling optimization manner based on the two-layer control model, wherein the two-layer control model includes a real-time scheduling layer and a power distribution layer. The real-time scheduling layer is used to optimize the charging and discharging power of the charging station in the future time period with the goal of reducing the equivalent load peak-valley difference, and the power distribution layer is used to determine the charging and discharging power at the current time period with the goal of the lowest system network loss and the charging and discharging power at the current time period in the optimization result of the real-time scheduling layer as the constraint.

[0105] The device uses a neural network model to pre-determine the coding parameters corresponding to different complexity levels, and selects appropriate target coding parameters according to the complexity level during the coding process to improve the quality and efficiency of the power control of the charging station participating in the substation.

[0106] On the basis of the above embodiment, the virtual synchronous machine control model satisfies:

[0107]

[0108] Among them, P n is the rated active power, k thred is the rate threshold, k1 is the acceleration coefficient, k2 is the deceleration coefficient, J nis the rated moment of inertia, J1, J2, and J3 are all intermediate variables, J is the moment of inertia, D p is the damping coefficient, D pn is the rated damping coefficient, k D is the damping coefficient rate, ΔP e is the change in electromagnetic power within the time period of Δt.

[0109] Based on the above embodiments, the charging pile of the charging station adopts a two-stage topology structure; the two-stage topology structure includes:

[0110] The latter stage adopts a dual-active full-bridge DC / DC converter, which is used to achieve electrical isolation between the electric vehicle and the power grid and bidirectional power flow;

[0111] The former stage adopts a voltage-source three-phase bridge circuit including an AC / DC inverter, which is used to control the active and reactive power transmission respectively, and provide reactive power compensation and power factor correction for charging and discharging.

[0112] Based on the above embodiments, the first objective function corresponding to the real-time scheduling layer is: where T is the total number of future time periods; P L (t) is the average power consumed by the base load in the t-th time period, P DG is the distributed power generation output in the t-th time period; P EV (t) is the charging and discharging power of the electric vehicle in the t-th time period, is the average value of the equivalent load,

[0113] Or, the first objective function is: where x is the charging and discharging power of the electric vehicle in each time period to be optimized, G is equal to 2 times the identity matrix, r is a constant matrix, and r is determined according to P L (t), P DG (t) and determined.

[0114] Based on the above embodiments, the constraint conditions corresponding to the first objective function include:

[0115] System power balance constraint: P DG (t)+P grid (t) = P L (t)+P loss (t)+P EV (t), where P grid is the transmission power of the tie line, P loss is the active power network loss of the system;

[0116] Battery capacity constraint in the current time period: where PEV (t c ) is the scheduling power of electric vehicles in the current time period, Nc is the number of electric vehicles whose application time periods in the current reservation information include the current time period, and C EV (n) is the applied charge and discharge amount of the nth electric vehicle;

[0117] Battery capacity constraint within each electricity price interval: Among them, P EV (t) is the scheduling power of electric vehicles in the corresponding time period, and T inl is the set of all time periods included in a certain electricity price interval; N inl is the number of electric vehicles whose application time periods are within this electricity price interval;

[0118] Charge and discharge power constraint of electric vehicles: Among them, is the maximum discharge power of the electric vehicle, and is the maximum charge power of the electric vehicle;

[0119] Output constraint of distributed power source: Among them, are respectively the lower limit value and the upper limit value of the active output of the distributed power source;

[0120] Transmission power capacity limit of distribution transformer: Among them, are respectively the upper limit value and the lower limit value of the transmission power capacity of the distribution transformer.

[0121] On the basis of the above embodiments, the second objective function corresponding to the power distribution layer is: Among them, r k is the resistance of branch k; I k (t) is the current flowing through branch k at time t; Δt is the time period length; N b is the number of system branches.

[0122] On the basis of the above embodiments, the constraint conditions of the second objective function include:

[0123] Planned power constraint: Among them, P stg (m, t) is the active power of charging station m at the tth time period, and P EV (t) is the charge and discharge power of electric vehicles at the tth time period, and N stg is the number of charging devices;

[0124] Battery capacity constraint: Among them, P stg (m, t c ) is the scheduling power of electric vehicles at charging station m in the current time period t c ; Nm,c The number of electric vehicles whose application time periods in the reservation information of the current charging station m include the current time period; C EV (n) is the applied charge and discharge power of the nth electric vehicle;

[0125] Charge and discharge power constraint: Among them, is the maximum charge and discharge power of the electric vehicle at time t in charging station m, is the maximum charge and discharge power of the electric vehicle at time t in charging station m;

[0126] Current-carrying capacity limit constraint of the bidirectional converter: Among them, P stg and Q stg are the active power and reactive power of the charging station respectively, and S stg,max is the maximum apparent capacity of the converter;

[0127] System security constraint: Among them, V j min and V j max are the upper limit value and lower limit value of the voltage amplitude of node j respectively, is the maximum current that line k allows to transmit.

[0128] The power control device for a charging station participating in a substation provided by an embodiment of the present application can be used to execute the power control method for a charging station participating in a substation provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0129] Figure 8 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, user equipment, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described herein and / or claimed.

[0130] As Figure 8As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0131] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, wireless networks.

[0132] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above.

[0133] In some embodiments, the method of the above embodiments can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method of any of the above embodiments by any other appropriate means (e.g., by means of firmware).

[0134] This embodiment also provides a power system, including a charging station, a substation, and an electronic device as described in any of the above embodiments.

[0135] An embodiment of the present application further provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the power control method of a charging station participating in a substation as described in any of the above embodiments.

[0136] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] The computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to the processors of general-purpose computers, special-purpose computers, or other programmable data processing devices, such that when the computer programs are executed by the processors, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0139] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 10 having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device 10. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0140] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0141] The computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0142] The embodiment of the present application also provides a computer program product, including a computer program and / or instructions, which when executed by a processor implement the power control method of the charging station participating in the substation as described in any of the above embodiments.

[0143] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present application can be achieved, and no limitation is made herein.

[0144] The above specific embodiments do not constitute a limitation to the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this application shall be included within the scope of protection of this application.

Claims

1. A method for a charging station to participate in power control of a substation, characterized in that, Including: Controlling the operation of the inverter of the charging station based on a virtual synchronous machine control model, wherein the moment of inertia and damping coefficient in the virtual synchronous machine control model are adaptively adjusted according to the change rate of electromagnetic power; Based on a two-layer control model, the active and reactive power of the charging station is controlled in real time in a rolling optimization manner, wherein the two-layer control model includes a real-time scheduling layer and a power distribution layer. The real-time scheduling layer is used to optimize the charging and discharging power of the charging station in future time periods with the goal of reducing the peak-valley difference of the equivalent load. The power distribution layer is used to determine the charging and discharging power in the current time period with the goal of minimizing the system network loss and with the charging and discharging power in the current time period in the optimization result of the real-time scheduling layer as a constraint; The virtual synchronous machine control model satisfies: Among them, P n is the rated active power, k thred is the speed threshold, k1 is the acceleration coefficient, k2 is the deceleration coefficient, J n is the rated moment of inertia, J1, J2, and J3 are all intermediate variables, J is the moment of inertia, D p is the damping coefficient, D pn is the rated damping coefficient, k D is the damping coefficient rate, ΔP e is the change in electromagnetic power within the time of Δt; The first objective function corresponding to the real-time scheduling layer is as follows: where T is the total number of future time periods; P L (t) is the average power consumed by the base load in the t-th time period, P DG is the output of the distributed power source in the t-th time period; P EV (t) is the charging and discharging power of the electric vehicle in the t-th time period, is the average value of the equivalent load, Alternatively, the first objective function is: where x is the charging and discharging power of electric vehicles in each period to be optimized, G is equal to twice the identity matrix, r is a constant matrix, and r is determined according to P L (t), P DG (t) and determined; The second objective function corresponding to the power distribution layer is as follows: where r k is the resistance of branch k; I k (t) is the current flowing through branch k at time t; Δt is the time interval length; N b is the number of system branches.

2. The method according to claim 1, characterized in that, The charging piles of the charging station adopt a two-stage topology structure; the two-stage topology structure includes: The front stage adopts a voltage-source three-phase bridge circuit including an AC / DC inverter, which is used to control the active and reactive power transmission respectively and provide reactive power compensation and power factor correction for charging and discharging; The rear stage adopts a dual-active full-bridge DC / DC converter, which is used to realize electrical isolation between the electric vehicle and the power grid and bidirectional power flow.

3. The method according to claim 1, wherein The constraint conditions corresponding to the first objective function include: System power balance constraint: P DG (t) + P grid (t) = P L (t) + P loss (t) + P EV (t), where P grid is the transmission power of the tie line, and P loss is the active power network loss of the system; Battery capacity constraint for the current time period: Among them, P EV (t c ) is the scheduling power of electric vehicles in the current time period, Nc is the number of electric vehicles whose application time periods in the current reservation information include the current time period, and C EV (n) is the applied charge and discharge amount of the nth electric vehicle; Battery capacity constraint within each electricity price interval: Among them, P EV (t) is the scheduling power of electric vehicles during the corresponding period, and T inl is the set of all periods included in a certain electricity price interval; N inl is the number of electric vehicles during the application period within this electricity price interval; Charging and discharging power constraints of electric vehicles: Among them, is the maximum discharging power of the electric vehicle, is the maximum charging power of the electric vehicle; Distributed power output constraint: Among them, are respectively the lower limit value and the upper limit value of the active power output of the distributed power source; Power transmission capacity limit of distribution transformer: Wherein, are respectively the upper limit value and the lower limit value of the power transmission capacity of the distribution transformer.

4. The method according to claim 1, wherein The constraint conditions of the second objective function include: Planned power constraint: Where, P stg (m, t) is the active power of charging station m in the t-th time period, P EV (t) is the charging and discharging power of electric vehicles in the t-th time period, N stg is the number of charging devices; Battery capacity constraint: Among them, P stg (m, t c ) is the scheduling power of electric vehicles at charging station m during the current period t c ; N m,c is the number of electric vehicles whose application periods in the reservation information of the current charging station m include the current period; C EV (n) is the applied charge and discharge amount of the nth electric vehicle; Charge and discharge power constraint: Wherein, is the maximum discharge power of the electric vehicle at time t in charging station m, is the maximum charging power of the electric vehicle at time t in charging station m; Current-carrying capacity limit constraint of bidirectional converter: where P stg , Q stg are the active power and reactive power of the charging station respectively, and S stg,max is the maximum apparent capacity of the converter; System security constraints: Among them, V j min and V j max are the upper and lower limit values of the voltage amplitude of node j respectively, is the maximum current that line k allows to transmit.

5. A power control device for a charging station to participate in the power control of a substation, characterized in that, Including: A first control module for controlling the operation of the inverter of the charging station based on a virtual synchronous machine control model, wherein the moment of inertia and damping coefficient in the virtual synchronous machine control model are adaptively adjusted according to the change rate of electromagnetic power; A second control module for controlling the active and reactive power of the charging station in real time in a rolling optimization manner based on a two-layer control model, wherein the two-layer control model includes a real-time scheduling layer and a power distribution layer. The real-time scheduling layer is used to optimize the charging and discharging power of the charging station in future time periods with the goal of reducing the peak-valley difference of the equivalent load. The power distribution layer is used to determine the charging and discharging power in the current time period with the goal of minimizing the system network loss and with the charging and discharging power in the current time period in the optimization result of the real-time scheduling layer as a constraint; The virtual synchronous machine control model satisfies: Among them, P n is the rated active power, k thred is the speed threshold, k1 is the acceleration coefficient, k2 is the deceleration coefficient, J n is the rated moment of inertia, J1, J2, and J3 are all intermediate variables, J is the moment of inertia, D p is the damping coefficient, D pn is the rated damping coefficient, k D is the damping coefficient rate, ΔP e is the change in electromagnetic power within the time of Δt; The first objective function corresponding to the real-time scheduling layer is as follows: where T is the total number of future time periods; P L (t) is the average power consumed by the base load in the t-th time period, and P DG is the output of the distributed power source in the t-th time period; P EV (t) is the charging and discharging power of the electric vehicle in the t-th time period, is the average value of the equivalent load, Alternatively, the first objective function is: where x is the charging and discharging power of electric vehicles in each period to be optimized, G is equal to twice the identity matrix, r is a constant matrix, and r is determined according to P L (t), P DG (t) and determined; The second objective function corresponding to the power distribution layer is as follows: where r k is the resistance of branch k; I k (t) is the current flowing through branch k at time t; Δt is the length of the time period; N b is the number of system branches.

6. A scheduling device, characterized in that, Including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for a charging station to participate in power control of a substation as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for a charging station to participate in power control of a substation as described in any one of claims 1-4.

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