Charging pile cluster regulation and control method and system for clean energy consumption and system frequency modulation

Abstract

The invention discloses a charging pile cluster regulation and control method and system for clean energy consumption and system frequency modulation. The method comprises the following steps: acquiring related data of a power system; establishing a power system optimization scheduling model considering charging pile cluster regulation, and calculating the power system optimization scheduling model to obtain charging pile cluster power A; calculating a system frequency modulation capacity demand and a charging pile cluster frequency modulation capability to obtain a charging pile cluster power B participating in system frequency modulation; calculating the power C of the charging pile cluster when the user receives the ordered power regulation, generating the power D of each charging pile when the user receives the ordered power regulation in the cluster according to the power C, issuing the power D to each corresponding charging pile controller, converting each charging pile controller into a control signal according to the power D, and sending the control signal to the corresponding charging pile controller; and charging and discharging power control is carried out on the charging pile of which the user receives ordered power regulation through the control signal. According to the invention, an effective regulation and control strategy is provided for improving clean energy consumption and frequency modulation capability of a novel power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging pile cluster control, and specifically relates to a charging pile cluster control method and system for clean energy consumption and system frequency modulation. Background Art

[0002] Currently, energy and environmental issues are becoming increasingly prominent. Building a new power system that is clean, low-carbon, safe, abundant, economically efficient, coordinated in supply and demand, and flexible and intelligent is a key platform for achieving the "dual carbon" goals. On the one hand, due to the randomness, intermittency, and volatility of new energy sources, the large-scale grid integration of new energy sources has made it increasingly difficult to balance power in the power system, and the situation of clean energy consumption has become increasingly serious. Improving the level of clean energy consumption has become an increasingly important research topic. On the other hand, the large-scale grid integration of new energy sources has squeezed the installed capacity of traditional thermal and hydropower units. Since new energy units themselves do not have frequency regulation capabilities, the volatility and intermittency of their output increase the frequency regulation burden of the power grid. Improving the frequency regulation capabilities of the new power system is a pressing issue.

[0003] As my country's automotive industry progresses toward electrification, the number of new energy vehicles (NEVs) on the road is rapidly increasing. By 2024, the number of NEVs on the road in China will exceed 31.4 million. By the end of March 2025, the number of EV charging stations in China will reach 13.749 million, a year-on-year increase of 47.6%. The energy storage properties of EV batteries enable charging stations to function as distributed energy storage devices, enabling bidirectional power transmission between the grid and charging stations. Exploiting the existing capacity of charging stations and EVs to contribute to grid peak and frequency regulation, fully utilizing their resource value to enhance the power system's clean energy absorption and frequency regulation capabilities, is a highly valuable research topic.

[0004] Currently, there are limited research results on leveraging the energy storage characteristics of electric vehicles to participate in power system peak and frequency regulation. Reference [A Frequency Response Method for Charging Pile Aggregate Load Participation. Patent Application No. 202311397564.9] proposes rapid frequency regulation of charging pile aggregate loads based on variable parameter control and rapid frequency regulation of charging pile aggregate loads based on variable target control. Reference [A Charging Pile Frequency Regulation Control Method, Device, and Medium. Patent Application No. 202210242565.5] proposes a charging pile frequency regulation control method. This method includes obtaining the measured frequency of the power grid connected to the charging pile, calculating a frequency change value based on the measured frequency and a standard frequency, determining an output power change value based on the frequency change value, generating a control signal based on the output power change value, and regulating the output power of the charging pile. On the one hand, the above research results do not consider whether users are willing to participate in system frequency regulation, which may infringe on the personal rights of electric vehicle users; on the other hand, the above research results all require charging piles to have real-time frequency monitoring functions, and to participate in frequency regulation by autonomously adjusting power through real-time monitoring of grid frequency. Due to the lack of control requirement feedback from the grid dispatching agency, the autonomous and spontaneous frequency regulation response of massive electric vehicles may cause chaos in the frequency regulation of the power system, which is not conducive to the safe and stable operation of the power system; at the same time, the above research results do not consider the need for charging piles to participate in clean energy consumption. Summary of the Invention

[0005] In view of the deficiencies of the existing technology, the present invention provides a charging pile cluster control method and system for clean energy consumption and system frequency modulation.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a charging pile cluster control method for clean energy consumption and system frequency modulation, comprising the following steps:

[0008] Step S1: Acquire power system related data, including charging pile data, wind power data, photovoltaic data, and load data;

[0009] Step S2: Establishing a power system optimization scheduling model that takes into account the regulation of the charging pile cluster, with the objective function being to maximize the amount of clean energy consumed. The power system-related data is used as input data for the power system optimization scheduling model, and the power system optimization scheduling model is calculated using an optimization solution algorithm to obtain the charging pile cluster power A.

[0010] Step S3: Calculate the system frequency regulation capacity requirement and the charging pile cluster frequency regulation capability to obtain the charging pile cluster power B participating in the system frequency regulation;

[0011] Step S4: The charging pile cluster power A obtained in step S2 and the charging pile cluster power B obtained in step S3 are combined to obtain the charging pile cluster power C for which users accept orderly power regulation. Based on the charging pile cluster power C, the power D of each charging pile for which users accept orderly power regulation in the cluster is generated, and the power D of each charging pile is sent to the corresponding charging pile controllers. Each charging pile controller converts the power D of each charging pile into a control signal, and then controls the charging and discharging power of the charging piles for which users accept orderly power regulation through the control signal.

[0012] Furthermore, in step S1, the charging pile data includes the rated charging and discharging power of the charging pile, whether the user is willing to accept orderly power regulation, and the charging and discharging power forecast data for the next T hours. The wind power data is the wind power forecast data for the next T hours. The photovoltaic power generation data is the photovoltaic power forecast data for the next T hours. The load data is the load power forecast data for the next T hours. The power system-related data also includes thermal power data, hydropower data, energy storage data, grid structure data, transmission section transmission capacity, and backup capacity data.

[0013] Furthermore, in step S2, a power system optimization scheduling model considering the charging pile cluster regulation is established, and the objective function is to maximize the clean energy consumption, which is expressed as:

[0014]

[0015] Where, For clean energy consumption, N w 、N s 、N h are the number of wind farms, photovoltaic power stations, and hydropower stations, respectively. are the power consumption of the i-th wind farm, photovoltaic power station and hydropower station at time t, N area is the number of grid partitions, T is the time period considered for optimal dispatch of the power system;

[0016] Constraints include system power balance constraints, reserve capacity constraints, charging pile power constraints, tie line and transmission section constraints, new energy power constraints, energy storage operation constraints, thermal power unit constraints, and cascade hydropower unit constraints.

[0017] The calculation formula for the system power balance constraint considering the charging and discharging power of the charging pile cluster is as follows:

[0018]

[0019] Where k is the grid partition number, M area (k) represents the set of resources in the grid partition; {i g ,i w ,is ,i h} represents the number of thermal power plants, wind farms, photovoltaic power plants, and hydropower plants in the grid zone; They represent the power of thermal power plant, hydropower station, wind farm and photovoltaic power station at time t respectively; are the input and output powers of the tie line between the partitioned grid k and the external grid, respectively; represents the total load power of the partitioned power grid k; represents the grid loss, Respectively represent the charging and discharging power of energy storage; They represent the charging and discharging power of the charging pile cluster at time t in the k-partition power grid;

[0020] The calculation formula for the reserve capacity constraint considering the charging and discharging power of the charging pile cluster is as follows:

[0021]

[0022] In the formula, RT represents spare, K w.RT , K s.RT , K ld.RT Respectively represent the reserve rates of wind power, photovoltaic power, and load;

[0023] The power constraints of the charging pile cluster are as follows:

[0024]

[0025] Where, They represent the charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; They represent the adjustable charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; N represents the rated charging power of charging pile i with only charging function in the partitioned power grid k; k.in N represents the number of charging piles with only charging function in the partitioned power grid k; k.out represents the number of charging piles with charging and discharging functions in the partitioned power grid k; They represent the charging and discharging states of the charging pile j with charging and discharging functions in the partitioned power grid k. When in the charging state otherwise When in discharge state otherwise They represent the rated charging power and rated discharging power of the charging pile j with charging and discharging functions in the partitioned power grid k respectively; W k.i 、W k.jThey represent the willingness of users of charging pile i and charging pile j to accept orderly power regulation. When the value is 1, it means that the user accepts the orderly regulation of the charging and discharging power of the charging pile; when the value is 0, it means that the user does not accept the orderly regulation of the charging and discharging power of the charging pile.

[0026] In the above formula The calculation formula is:

[0027]

[0028] The calculation formula for tie line and transmission section constraints is as follows:

[0029]

[0030] Where, They represent the transmission capacity of the jth transmission section at time t, which is the transmission section between different sections of the target power grid and the interconnection transmission section between the target power grid and the external power grid;

[0031] The power constraints of new energy sources are as follows:

[0032]

[0033] Where, They represent the theoretical power of wind farm and photovoltaic power station at time t respectively;

[0034] The energy storage operation constraints are:

[0035]

[0036] Where, Indicates the energy storage charging and discharging power, Indicates the maximum value of energy storage charging and discharging power. Indicates the energy storage charging and discharging status. Indicates the state of charge of the energy storage. Indicates the minimum and maximum energy storage capacity; η ES.in ,η ES.out Indicates the energy storage charging and discharging efficiency.

[0037] Thermal power unit constraints include upper and lower power limits, ramp power constraints, minimum start and stop time constraints, and the number of times the unit receives start and stop status commands within a cycle. The upper and lower power limits of thermal power units are:

[0038]

[0039] Where, Indicates the maximum and minimum power of thermal power units. Indicates start / stop status;

[0040] The ramp power constraint of thermal power units is:

[0041]

[0042] Where, Indicates the maximum power of thermal power units climbing up and down;

[0043] The minimum start and stop time constraints of thermal power units are:

[0044]

[0045] Where, t tempt Represents any time, T on.min 、T off.min Indicates the minimum start and stop time;

[0046] The number of times a thermal power unit receives start and stop status instructions within a cycle is constrained as follows:

[0047]

[0048] Where, T period represents the total scheduling period; Indicates the maximum number of times start and stop state commands are accepted;

[0049] The constraints of cascade hydropower units include the water balance constraints of each cascade hydropower station, the output power constraints of the hydropower station, the water storage capacity constraints of the reservoir, and the power generation flow constraints of the hydropower station. Among them, the water balance constraints of each cascade hydropower station are:

[0050] For the first-stage hydropower station:

[0051] V i.t+1 =V i.t +q 1i.t -Q 1i.t -S 1i.t

[0052] Where V i.t 、V i.t+1 represents the storage capacity of hydropower station i at time t and t+1, respectively, 1i.t represents the natural water inflow of hydropower station i during period t, Q 1i.t represents the power generation flow of hydropower station i during period t, S 1i.t represents the amount of water abandoned by hydropower station i during period t;

[0053] For other levels of hydropower stations:

[0054] V i.t+1 =V i.t +q i.t -Q i.t -S i.t +Q i-1.t-τ +S i-1.t-τ

[0055] Where q i.t , Q i.t 、S 1i.t They represent the natural water inflow, power generation flow, and abandoned water volume of hydropower station i during period t, τ represents the arrival time of water flow from the i-1th level hydropower station to the i-th level hydropower station, Q i-1.t-τ 、S i-1.t-τ They represent the power generation flow and abandoned water volume of the i-1th-level hydropower station after τ time;

[0056] The output power constraint of the hydropower station is:

[0057]

[0058] Where, represents the power of the i-th hydropower station at time t, H i.t represents the water head of the i-th hydropower station at time t, η i represents the power generation efficiency of the i-th level hydropower station, They represent the minimum and maximum active power of the i-th hydropower station at time t respectively;

[0059] The reservoir storage capacity constraint is:

[0060]

[0061] Where, They represent the maximum and minimum water storage capacity of the reservoir of the i-th hydropower station at time t respectively;

[0062] The power generation flow constraint of the hydropower station is:

[0063]

[0064] Where, They respectively represent the maximum and minimum daily flow values ​​available for the hydropower units.

[0065] Furthermore, in step S2, the wind power forecast data for the next T hours, photovoltaic power forecast data, load power forecast data, and other power system related data obtained in step S1 are used as input data, and an optimization solution algorithm is used to calculate the power system optimization scheduling model considering the charging pile cluster regulation established in step S2 to obtain the charging pile cluster power A for the next T hours.

[0066] Furthermore, in step S3, the system frequency modulation capacity requirement calculation formula is as follows:

[0067] P f =α×P max.T

[0068] Where, Pf is the system frequency regulation capacity requirement for the next T hours, α is the frequency regulation coefficient, P max.T The maximum load power forecast for the next T hours.

[0069] The calculation formula for the frequency regulation capability of the charging pile cluster is as follows:

[0070]

[0071] Where, They respectively represent the maximum upper regulation power and the maximum lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k.

[0072] According to the operating rules of the power frequency regulation auxiliary service market, combined with the system frequency regulation capacity requirements and the frequency regulation capabilities of the charging pile cluster, the power B of the charging pile cluster participating in the system frequency regulation in the next T hours can be obtained, which is expressed as:

[0073]

[0074] Where, β, P f.β They are the maximum proportion and maximum frequency regulation capacity allowed by the charging pile cluster stipulated in the power frequency regulation auxiliary service market operation rules. They respectively represent the upper regulation power and lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k.

[0075] Furthermore, in step S4, based on the charging pile cluster power A obtained in step S2, combined with the willingness of users of each charging pile to accept orderly power regulation and their charging and discharging power, the charging pile cluster power E at which users accept orderly power regulation is obtained, and the calculation formula is:

[0076]

[0077] Where, are respectively the charging power and the discharging power at time t in the charging pile cluster power A obtained in step S2; They are respectively the charging power and discharging power at time t in the charging pile cluster power E when the user accepts orderly power regulation.

[0078] The power E of the charging pile cluster that accepts orderly power regulation by the user and the power B of the charging pile cluster that participates in the system frequency modulation obtained in step S3 are combined to obtain the power C of the charging pile cluster that accepts orderly power regulation by the user. According to the principle of proportionality of the rated power of the charging piles, the power C of the charging pile cluster is converted into the power D of each charging pile in the cluster that accepts orderly power regulation by the user, and the power D is sent to each charging pile controller that accepts orderly power regulation by the user. Each charging pile controller converts the power D into a control signal, and then controls the charging and discharging power of the charging piles that accept orderly power regulation by the user through the control signal.

[0079] In a second aspect, the present invention provides a charging pile cluster control system for clean energy consumption and system frequency modulation, comprising:

[0080] A data acquisition module is used to acquire power system related data, including charging pile data, wind power data, photovoltaic data, and load data;

[0081] The power system optimization and dispatching module is used to establish a power system optimization and dispatching model that takes into account the control of the charging pile cluster, and calculate the model through the optimization solution algorithm to obtain the charging pile cluster power A;

[0082] The charging pile cluster frequency regulation module is used to calculate the system frequency regulation capacity demand and the charging pile cluster frequency regulation capability, and obtain the charging pile cluster power B participating in the system frequency regulation;

[0083] The charging pile cluster power control module is used to obtain the charging pile cluster power C for users to accept orderly power regulation based on the charging pile cluster power A obtained by the power system optimization and scheduling module and the charging pile cluster power B obtained by the charging pile cluster frequency modulation module. Based on the charging pile cluster power C, the power D of each charging pile in the cluster to which users accept orderly power regulation is generated, and the power D is sent to the corresponding charging pile controllers. Each charging pile controller converts the power D into a control signal, and then controls the charging and discharging power of the charging piles to which users accept orderly power regulation through the control signal.

[0084] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the above-mentioned charging pile cluster control method for clean energy consumption and system frequency modulation.

[0085] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the above-mentioned charging pile cluster control method for clean energy consumption and system frequency modulation.

[0086] This invention establishes a power system optimization scheduling model that considers the regulation of charging pile clusters. This model calculates the controlled power of charging pile clusters under the goal of maximizing clean energy consumption. Based on the willingness of charging pile users to accept orderly power regulation, a calculation model is proposed for the charging pile cluster's ability to participate in system frequency regulation. Combined with the operating rules of the power frequency regulation auxiliary service market, the actual charging pile cluster power participating in system regulation is calculated. Furthermore, a charging pile cluster regulation strategy is developed that comprehensively considers the willingness of charging pile users to accept orderly power regulation and the need for the charging pile cluster to participate in both clean energy consumption and system frequency regulation. This invention considers both the actual usage of charging pile users and the realities of the power frequency regulation auxiliary service market, thereby increasing clean energy consumption and improving system frequency regulation capabilities in new power systems. Furthermore, this invention overcomes the risk of chaotic system frequency regulation control caused by local autonomous participation of charging piles in system frequency regulation. This invention provides an effective regulation strategy for improving the clean energy consumption and frequency regulation capabilities of new power systems, effectively tapping into the grid regulation value of existing charging piles and providing new ideas for the safe and stable operation of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0088] Figure 1 A flowchart of a charging pile cluster control method for clean energy consumption and system frequency regulation provided by an exemplary embodiment of the present invention;

[0089] Figure 2 A schematic diagram of the structure of a charging pile cluster control system for clean energy consumption and system frequency regulation provided by an exemplary embodiment of the present invention;

[0090] Figure 3 A structural block diagram of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0091] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0092] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0093] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0094] It should also be understood that, in the embodiments of the present invention, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0095] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0096] In addition, the term "and / or" in the present invention is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0097] In addition, the character “ / ” in the present invention generally indicates that the preceding and following related objects are in an “or” relationship.

[0098] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0099] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0100] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0101] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0102] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0103] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate in conjunction with numerous other general-purpose or specialized computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above.

[0104] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system-executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0105] Exemplary Methods

[0106] Figure 1 This is a flow chart of a method for controlling a charging pile cluster for clean energy consumption and system frequency modulation provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, a charging pile cluster control method for clean energy consumption and system frequency regulation includes the following steps:

[0107] Step S1: Acquire power system related data, which includes charging pile data, wind power data, photovoltaic data, and load data.

[0108] Specifically, the charging pile data includes the rated charging and discharging power of the charging pile, whether the user is willing to accept orderly power regulation, and the charging and discharging power forecast data for the next T hours. The wind power data is the wind power forecast data for the next T hours; the photovoltaic power generation data is the photovoltaic power forecast data for the next T hours; the load data is the load power forecast data for the next T hours; the thermal power data includes the maximum and minimum power of the thermal power unit, the maximum power of up and down ramps, the minimum start and stop time, the maximum number of start and stop status instructions accepted, and the current start and stop status; the hydropower data includes the reservoir capacity of the hydropower station, the natural water inflow, the power generation flow, the abandoned water volume, the head, the power generation efficiency of the hydropower station, the minimum and maximum active power of the hydropower station, the maximum and minimum water storage capacity of the reservoir, and the maximum and minimum daily flow values ​​available for the hydropower unit; the energy storage data includes the maximum energy storage charging and discharging power, the energy storage charging and discharging status, the energy storage charge state, the minimum and maximum energy storage power, and the energy storage charging and discharging efficiency; the transmission capacity of the transmission section, the reserve rate of wind power, photovoltaic power, and load, etc.

[0109] Step S2: Establish a power system optimization scheduling model that takes into account the regulation of charging pile clusters. The objective function is to maximize the amount of clean energy consumed. The model is calculated through an optimization solution algorithm to obtain the charging pile cluster power A.

[0110] Specifically, a power system optimization scheduling model considering the charging pile cluster regulation is established, and the objective function is to maximize the clean energy consumption, which is expressed as:

[0111]

[0112] Where, For clean energy consumption, N w 、N s 、N h are the number of wind farms, photovoltaic power stations, and hydropower stations, respectively. are the power consumption of the i-th wind farm, photovoltaic power station and hydropower station at time t, N area is the number of grid partitions, T is the time period considered for optimal dispatch of the power system;

[0113] Constraints include system power balance constraints, reserve capacity constraints, charging pile power constraints, tie line and transmission section constraints, new energy power constraints, energy storage operation constraints, thermal power unit constraints, and cascade hydropower unit constraints.

[0114] The calculation formula for the system power balance constraint considering the charging and discharging power of the charging pile cluster is as follows:

[0115]

[0116] Where k is the grid partition number, M area(k) represents the set of resources in the grid partition; {i g ,i w ,i s ,i h} represents the number of thermal power plants, wind farms, photovoltaic power plants, and hydropower plants in the grid zone; They represent the power of thermal power plant, hydropower station, wind farm and photovoltaic power station at time t respectively; are the input and output powers of the tie line between the partitioned grid k and the external grid, respectively; represents the total load power of the partitioned power grid k; represents the grid loss, Respectively represent the charging and discharging power of energy storage; represent the charging and discharging power of the charging pile cluster at time t in the k-zone power grid. In this exemplary embodiment, the charging pile clusters are divided according to the power grid zones, and all the charging piles in a power grid zone constitute a charging pile cluster;

[0117] The calculation formula for the reserve capacity constraint considering the charging and discharging power of the charging pile cluster is as follows:

[0118]

[0119] In the formula, RT represents spare, K w.RT , K s.RT , K ld.RT Respectively represent the reserve rates of wind power, photovoltaic power, and load;

[0120] The power constraints of the charging pile cluster are as follows:

[0121]

[0122] Where, They represent the charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; They represent the adjustable charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; N represents the rated charging power of charging pile i with only charging function in the partitioned power grid k; k.in N represents the number of charging piles with only charging function in the partitioned power grid k; k.out represents the number of charging piles with charging and discharging functions in the partitioned power grid k; They represent the charging and discharging states of the charging pile j with charging and discharging functions in the partitioned power grid k. When in the charging state otherwise When in discharge state otherwise They represent the rated charging power and rated discharging power of the charging pile j with charging and discharging functions in the partitioned power grid k respectively; W k.i 、W k.j They represent the willingness of users of charging pile i and charging pile j to accept orderly power regulation. When the value is 1, it means that the user accepts the orderly regulation of the charging and discharging power of the charging pile; when the value is 0, it means that the user does not accept the orderly regulation of the charging and discharging power of the charging pile.

[0123] In the above formula The calculation formula is:

[0124]

[0125] The calculation formula for tie line and transmission section constraints is as follows:

[0126]

[0127] Where, They represent the transmission capacity of the jth transmission section at time t, which is the transmission section between different sections of the target power grid and the interconnection transmission section between the target power grid and the external power grid;

[0128] The power constraints of new energy sources are as follows:

[0129]

[0130] Where, They represent the theoretical power of wind farm and photovoltaic power station at time t respectively;

[0131] The energy storage operation constraints are:

[0132]

[0133] Where, Indicates the energy storage charging and discharging power, Indicates the maximum value of energy storage charging and discharging power. Indicates the energy storage charging and discharging status. Indicates the state of charge of the energy storage. Indicates the minimum and maximum energy storage capacity; η ES.in ,η ES.out Indicates the energy storage charging and discharging efficiency.

[0134] Thermal power unit constraints include upper and lower power limits, ramp power constraints, minimum start and stop time constraints, and the number of times the unit receives start and stop status commands within a cycle. The upper and lower power limits of thermal power units are:

[0135]

[0136] Where, Indicates the maximum and minimum power of thermal power units. Indicates start / stop status;

[0137] The ramp power constraint of thermal power units is:

[0138]

[0139] Where, Indicates the maximum power of thermal power units climbing up and down;

[0140] The minimum start and stop time constraints of thermal power units are:

[0141]

[0142] Where, t tempt Represents any time, T on.min 、T off.min Indicates the minimum start and stop time;

[0143] The number of times a thermal power unit receives start and stop status instructions within a cycle is constrained as follows:

[0144]

[0145] Where, T period represents the total scheduling period; Indicates the maximum number of times start and stop state commands are accepted;

[0146] The constraints of cascade hydropower units include the water balance constraints of each cascade hydropower station, the output power constraints of the hydropower station, the water storage capacity constraints of the reservoir, and the power generation flow constraints of the hydropower station. Among them, the water balance constraints of each cascade hydropower station are:

[0147] For the first-stage hydropower station:

[0148] V i.t+1 =V i.t +q 1i.t -Q 1i.t -S 1i.t

[0149] Where V i.t 、V i.t+1 represents the storage capacity of hydropower station i at time t and t+1, respectively, 1i.t represents the natural water inflow of hydropower station i during period t, Q 1i.t represents the power generation flow of hydropower station i during period t, S 1i.t represents the amount of water abandoned by hydropower station i during period t;

[0150] For other levels of hydropower stations:

[0151] V i.t+1 =V i.t +q i.t-Q i.t -S i.t +Q i-1.t-τ +S i-1.t-τ

[0152] Where q i.t , Q i.t 、S 1i.t They represent the natural water inflow, power generation flow, and abandoned water volume of hydropower station i during period t, τ represents the arrival time of water flow from the i-1th level hydropower station to the i-th level hydropower station, Q i-1.t-τ 、S i-1.t-τ They represent the power generation flow and abandoned water volume of the i-1th-level hydropower station after τ time;

[0153] The output power constraint of the hydropower station is:

[0154]

[0155] Where, represents the power of the i-th hydropower station at time t, H i.t represents the water head of the i-th hydropower station at time t, η i represents the power generation efficiency of the i-th level hydropower station, They represent the minimum and maximum active power of the i-th hydropower station at time t respectively;

[0156] The reservoir storage capacity constraint is:

[0157]

[0158] Where, They represent the maximum and minimum water storage capacity of the reservoir of the i-th hydropower station at time t respectively;

[0159] The power generation flow constraint of the hydropower station is:

[0160]

[0161] Where, They respectively represent the maximum and minimum daily flow values ​​available for the hydropower units.

[0162] Specifically, the wind power forecast data for the next T hours, photovoltaic power forecast data, load power forecast data, and other power system-related data obtained in step S1 are used as input data, and the optimization solution algorithm is used to calculate the power system optimization scheduling model considering the charging pile cluster regulation established in step S2 to obtain the charging pile cluster power A for the next T hours.

[0163] Step S3: Calculate the system frequency regulation capacity requirement and the charging pile cluster frequency regulation capability to obtain the charging pile cluster power B participating in the system frequency regulation.

[0164] Specifically, the total frequency regulation capacity demand in the frequency regulation market is generally 1%-5% of the maximum daily dispatch load forecast. The specific value is determined by the power dispatching agency and announced to the operating entity. The system frequency regulation capacity demand calculation formula is as follows:

[0165] P f =α×P max.T

[0166] Where, P f is the system frequency regulation capacity demand in the next T hours, α is the frequency regulation coefficient, which is determined by the power dispatching agency, and P max.T The maximum load power forecast for the next T hours.

[0167] The calculation formula for the frequency regulation capability of the charging pile cluster is as follows:

[0168]

[0169] Where, They respectively represent the maximum upper regulation power and the maximum lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k.

[0170] To ensure the safe and stable operation of the power system, in principle, the total frequency regulation capacity awarded by new business entities such as new energy storage, virtual power plants, and load aggregators consisting of electric vehicle charging piles must not exceed a certain proportion of the total frequency regulation capacity demand. This proportion will be adjusted based on the operation of the frequency regulation ancillary service market. According to the operating rules of the power frequency regulation ancillary service market, combined with the system frequency regulation capacity demand and the frequency regulation capacity of the charging pile cluster, the power B of the charging pile cluster participating in the system frequency regulation in the next T hours can be obtained, expressed as:

[0171]

[0172] Where, β, P f.β They are the maximum proportion and maximum frequency regulation capacity allowed by the charging pile cluster stipulated in the power frequency regulation auxiliary service market operation rules. They respectively represent the upper regulation power and lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k.

[0173] Step S4: The charging pile cluster power A obtained in step S2 and the charging pile cluster power B obtained in step S3 are combined to obtain the charging pile cluster power C for which users accept orderly power regulation. Based on the charging pile cluster power C, the power D of each charging pile in the cluster for which users accept orderly power regulation is generated, and the power D is sent to each corresponding charging pile controller. Each charging pile controller converts the power D into a control signal, and then controls the charging and discharging power of the charging piles for which users accept orderly power regulation through the control signal.

[0174] Specifically, based on the charging pile cluster power A obtained in step S2, combined with the willingness of users of each charging pile to accept orderly power regulation and their charging and discharging power, the charging pile cluster power E when users accept orderly power regulation is obtained, and the calculation formula is:

[0175]

[0176] Where, are respectively the charging power and the discharging power at time t in the charging pile cluster power A obtained in step S2; They are respectively the charging power and discharging power at time t in the charging pile cluster power E when the user accepts orderly power regulation.

[0177] The power E of the charging pile cluster that accepts orderly power regulation by the user and the power B of the charging pile cluster that participates in the system frequency modulation obtained in step S3 are combined to obtain the power C of the charging pile cluster that accepts orderly power regulation by the user; according to the principle of proportionality of the rated power of the charging piles, the power C of the charging pile cluster is converted into the power D of each charging pile in the cluster that accepts orderly power regulation by the user; the power D is sent to each charging pile controller that accepts orderly power regulation by the user, and each charging pile controller converts the power D into a control signal, and then controls the charging and discharging power of the charging piles that accept orderly power regulation by the user through the control signal.

[0178] Exemplary Systems

[0179] Figure 2 This is a schematic diagram of a charging pile cluster control system for clean energy consumption and system frequency regulation provided by an exemplary embodiment of the present invention. Figure 2 As shown, the apparatus 200 includes:

[0180] The data acquisition module 201 is used to acquire power system related data, including charging pile data, wind power data, photovoltaic data, and load data;

[0181] The power system optimization and scheduling module 202 is used to establish a power system optimization and scheduling model that takes into account the control of the charging pile cluster. The power system related data obtained by the data acquisition module 201 is used as input data. The power system optimization and scheduling model is calculated through the optimization solution algorithm to obtain the charging pile cluster power A;

[0182] The charging pile cluster frequency modulation module 203 is used to calculate the system frequency modulation capacity requirement and the charging pile cluster frequency modulation capability, and obtain the charging pile cluster power B participating in the system frequency modulation;

[0183] The charging pile cluster power control module 204 is used to obtain the charging pile cluster power C for which users accept orderly power regulation based on the charging pile cluster power A obtained by the power system optimization and scheduling module 202 and the charging pile cluster power B obtained by the charging pile cluster frequency modulation module 203, generate the power D of each charging pile for which users in the cluster accept orderly power regulation based on the charging pile cluster power C, and send the power D of each charging pile to the corresponding charging pile controllers. Each charging pile controller converts the power D of each charging pile into a control signal, and then controls the charging and discharging power of the charging piles for which users accept orderly power regulation through the control signal.

[0184] Optionally, the data acquisition module 201 acquires charging pile data including the rated charging and discharging power of the charging pile, whether the user is willing to accept orderly power regulation, and charging and discharging power forecast data for the next T hours; acquires wind power data including wind power forecast data for the next T hours; acquires photovoltaic power generation data including photovoltaic power forecast data for the next T hours; acquires load data including load power forecast data for the next T hours; and acquires power system related data including thermal power data, hydropower data, energy storage data, grid structure data, transmission capacity of transmission sections, and backup capacity data, etc.

[0185] Optionally, the power system optimization and scheduling module 202 establishes a power system optimization and scheduling model that takes into account the control of the charging pile cluster. The objective function is to maximize the amount of clean energy consumed, which is expressed as:

[0186]

[0187] Where, For clean energy consumption, N w 、N s 、N h are the number of wind farms, photovoltaic power stations, and hydropower stations, respectively. are the power consumption of the i-th wind farm, photovoltaic power station and hydropower station at time t, N area is the number of grid partitions, T is the time period considered for optimal dispatch of the power system;

[0188] Constraints include system power balance constraints, reserve capacity constraints, charging pile power constraints, tie line and transmission section constraints, new energy power constraints, energy storage operation constraints, thermal power unit constraints, and cascade hydropower unit constraints.

[0189] The calculation formula for the system power balance constraint considering the charging and discharging power of the charging pile cluster is as follows:

[0190]

[0191] Where k is the grid partition number, M area(k) represents the set of resources in the grid partition; {i g ,i w ,i s ,i h} represents the number of thermal power plants, wind farms, photovoltaic power plants, and hydropower plants in the grid zone; They represent the power of thermal power plant, hydropower station, wind farm and photovoltaic power station at time t respectively; are the input and output powers of the tie line between the partitioned grid k and the external grid, respectively; represents the total load power of the partitioned power grid k; represents the grid loss, Respectively represent the charging and discharging power of energy storage; They represent the charging and discharging power of the charging pile cluster at time t in the k-partition power grid;

[0192] The calculation formula for the reserve capacity constraint considering the charging and discharging power of the charging pile cluster is as follows:

[0193]

[0194] In the formula, RT represents spare, K w.RT , K s.RT , K ld.RT Respectively represent the reserve rates of wind power, photovoltaic power, and load;

[0195] The power constraints of the charging pile cluster are as follows:

[0196]

[0197] Where, They represent the charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; They represent the adjustable charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; N represents the rated charging power of charging pile i with only charging function in the partitioned power grid k; k.in N represents the number of charging piles with only charging function in the partitioned power grid k; k.out represents the number of charging piles with charging and discharging functions in the partitioned power grid k; They represent the charging and discharging states of the charging pile j with charging and discharging functions in the partitioned power grid k. When in the charging state otherwise When in discharge state otherwise They represent the rated charging power and rated discharging power of the charging pile j with charging and discharging functions in the partitioned power grid k respectively; W k.i 、W k.jThey represent the willingness of users of charging pile i and charging pile j to accept orderly power regulation. When the value is 1, it means that the user accepts the orderly regulation of the charging and discharging power of the charging pile; when the value is 0, it means that the user does not accept the orderly regulation of the charging and discharging power of the charging pile.

[0198] In the above formula The calculation formula is:

[0199]

[0200] The calculation formula for tie line and transmission section constraints is as follows:

[0201]

[0202] Where, They represent the transmission capacity of the jth transmission section at time t, which is the transmission section between different sections of the target power grid and the interconnection transmission section between the target power grid and the external power grid;

[0203] The power constraints of new energy sources are as follows:

[0204]

[0205] Where, They represent the theoretical power of wind farm and photovoltaic power station at time t respectively;

[0206] The energy storage operation constraints are:

[0207]

[0208] Where, Indicates the energy storage charging and discharging power, Indicates the maximum value of energy storage charging and discharging power. Indicates the energy storage charging and discharging status. Indicates the state of charge of the energy storage. Indicates the minimum and maximum energy storage capacity; η ES.in ,η ES.out Indicates the energy storage charging and discharging efficiency.

[0209] Thermal power unit constraints include upper and lower power limits, ramp power constraints, minimum start and stop time constraints, and the number of times the unit receives start and stop status commands within a cycle. The upper and lower power limits of thermal power units are:

[0210]

[0211] Where, Indicates the maximum and minimum power of thermal power units. Indicates start / stop status;

[0212] The ramp power constraint of thermal power units is:

[0213]

[0214] Where, Indicates the maximum power of thermal power units climbing up and down;

[0215] The minimum start and stop time constraints of thermal power units are:

[0216]

[0217] Where, t tempt Represents any time, T on.min 、T off.min Indicates the minimum start and stop time;

[0218] The number of times a thermal power unit receives start and stop status instructions within a cycle is constrained as follows:

[0219]

[0220] Where, T period represents the total scheduling period; Indicates the maximum number of times start and stop state commands are accepted;

[0221] The constraints of cascade hydropower units include the water balance constraints of each cascade hydropower station, the output power constraints of the hydropower station, the water storage capacity constraints of the reservoir, and the power generation flow constraints of the hydropower station. Among them, the water balance constraints of each cascade hydropower station are:

[0222] For the first-stage hydropower station:

[0223] V i.t+1 =V i.t +q 1i.t -Q 1i.t -S 1i.t

[0224] Where V i.t 、V i.t+1 represents the storage capacity of hydropower station i at time t and t+1, respectively, 1i.t represents the natural water inflow of hydropower station i during period t, Q 1i.t represents the power generation flow of hydropower station i during period t, S 1i.t represents the amount of water abandoned by hydropower station i during period t;

[0225] For other levels of hydropower stations:

[0226] V i.t+1 =V i.t +q i.t -Q i.t -S i.t +Q i-1.t-τ +S i-1.t-τ

[0227] Where q i.t , Q i.t 、S 1i.t They represent the natural water inflow, power generation flow, and abandoned water volume of hydropower station i during period t, τ represents the arrival time of water flow from the i-1th level hydropower station to the i-th level hydropower station, Q i-1.t-τ 、S i-1.t-τ They represent the power generation flow and abandoned water volume of the i-1th-level hydropower station after τ time;

[0228] The output power constraint of the hydropower station is:

[0229]

[0230] Where, represents the power of the i-th hydropower station at time t, H i.t represents the water head of the i-th hydropower station at time t, η i represents the power generation efficiency of the i-th level hydropower station, They represent the minimum and maximum active power of the i-th hydropower station at time t respectively;

[0231] The reservoir storage capacity constraint is:

[0232]

[0233] Where, They represent the maximum and minimum water storage capacity of the reservoir of the i-th hydropower station at time t respectively;

[0234] The power generation flow constraint of the hydropower station is:

[0235]

[0236] Where, They respectively represent the maximum and minimum daily flow values ​​available for the hydropower units.

[0237] Optionally, the wind power forecast data, photovoltaic power forecast data, load power forecast data, and other power system-related data for the next T hours obtained by the data acquisition module 201 are used as input data, and an optimization solution algorithm is used to calculate the power system optimization scheduling model considering the charging pile cluster regulation established by the power system optimization scheduling module 202 to obtain the charging pile cluster power A for the next T hours.

[0238] Optionally, the charging pile cluster frequency modulation module 203 calculates the system frequency modulation capacity requirement using the following formula:

[0239] P f =α×P max.T

[0240] Where, P f is the system frequency regulation capacity requirement for the next T hours, α is the frequency regulation coefficient, P max.T The maximum load power forecast for the next T hours.

[0241] The calculation formula for the frequency regulation capability of the charging pile cluster is as follows:

[0242]

[0243] Where, They respectively represent the maximum upper regulation power and the maximum lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k.

[0244] According to the operating rules of the power frequency regulation auxiliary service market, combined with the system frequency regulation capacity requirements and the frequency regulation capabilities of the charging pile cluster, the power B of the charging pile cluster participating in the system frequency regulation in the next T hours can be obtained, which is expressed as:

[0245]

[0246] Where, β, P f.β They are the maximum proportion and maximum frequency regulation capacity allowed by the charging pile cluster stipulated in the power frequency regulation auxiliary service market operation rules. They respectively represent the upper regulation power and lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k.

[0247] Optionally, the charging pile cluster power control module 204 obtains the charging pile cluster power E when users accept orderly power regulation based on the charging pile cluster power A obtained by the power system optimization and scheduling module 202 and the willingness of users of each charging pile to accept orderly power regulation and their charging and discharging power. The calculation formula is:

[0248]

[0249] Where, are respectively the charging power and the discharging power at time t of the charging pile cluster power A obtained by the power system optimization scheduling module 202; They are respectively the charging power and discharging power at time t in the charging pile cluster power E when the user accepts orderly power regulation.

[0250] The charging pile cluster power E of the charging piles that accept orderly power regulation by the user and the charging pile cluster power B participating in system frequency modulation obtained by the charging pile cluster frequency modulation module 203 are combined to obtain the charging pile cluster power C of the charging piles that accept orderly power regulation by the user. According to the principle of proportionality of the rated power of the charging piles, the charging pile cluster power C is converted into the power D of each charging pile in the cluster that accepts orderly power regulation by the user, and the power D of each charging pile is sent to each charging pile controller that accepts orderly power regulation by the user. Each charging pile controller converts the power D of each charging pile into a control signal, and then controls the charging and discharging power of the charging piles that accept orderly power regulation by the user through the control signal.

[0251] Exemplary electronic devices

[0252] Figure 3 FIG1 is a block diagram of an electronic device provided by an exemplary embodiment of the present invention. Figure 3 As shown, the electronic device 300 includes one or more processors 301 and a memory 302 .

[0253] The processor 301 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0254] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM), cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may further include: an input device 303 and an output device 304, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0255] In addition, the input device 303 may also include a keyboard, a mouse, etc.

[0256] The output device 304 can output various information to the outside, and can include a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0257] Of course, to simplify, Figure 3Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0258] Exemplary computer program products and computer-readable storage media

[0259] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to perform the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0260] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0261] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0262] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0263] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in the present invention are merely illustrative and non-limiting, and should not be construed as necessarily possessed by each embodiment of the present invention. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, and are not intended to be limiting. These details do not necessarily limit the present invention to being implemented using these specific details.

[0264] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the device embodiments, since they are essentially similar to the method embodiments, their descriptions are relatively simple. For relevant parts, reference can be made to the descriptions of the method embodiments.

[0265] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0266] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above, unless otherwise specified. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers recording media that store programs for executing the method according to the present invention.

[0267] It should also be noted that, in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in this field to make or use the present invention. Various modifications to these aspects will be very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but according to the widest scope consistent with the principles disclosed here and novel features.

[0268] The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. While various exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize that certain variations, modifications, alterations, additions, and sub-combinations thereof are intended to fall within the scope of the claims of the present invention.

Claims

1. A charging pile cluster control method for clean energy consumption and system frequency modulation, characterized in that: include: Step S1: Acquire power system related data, including charging pile data, wind power data, photovoltaic data, and load data; Step S2: Establishing a power system optimization scheduling model that takes into account the regulation of the charging pile cluster, with the objective function being to maximize the amount of clean energy consumed. The power system-related data is used as input data for the power system optimization scheduling model, and the power system optimization scheduling model is calculated using an optimization solution algorithm to obtain the charging pile cluster power A. Step S3: Calculate the system frequency regulation capacity requirement and the charging pile cluster frequency regulation capability to obtain the charging pile cluster power B participating in the system frequency regulation; Step S4: The charging pile cluster power A obtained in step S2 and the charging pile cluster power B obtained in step S3 are combined to obtain the charging pile cluster power C for which users accept orderly power regulation. Based on the charging pile cluster power C, the power D of each charging pile for which users accept orderly power regulation in the cluster is generated, and the power D of each charging pile is sent to the corresponding charging pile controllers. Each charging pile controller converts the power D of each charging pile into a control signal, and then controls the charging and discharging power of the charging piles for which users accept orderly power regulation through the control signal.

2. The method according to claim 1, characterized in that In step S1, the charging pile data includes the rated charging and discharging power of the charging pile, whether the user is willing to accept orderly power regulation, and the charging and discharging power forecast data for the next T hours. The wind power data is the wind power forecast data for the next T hours. The photovoltaic power generation data is the photovoltaic power forecast data for the next T hours. The load data is the load power forecast data for the next T hours. The power system related data also includes thermal power data, hydropower data, energy storage data, grid structure data, transmission section transmission capacity and standby capacity data.

3. The method according to claim 1, characterized in that In step S2, a power system optimization scheduling model considering the charging pile cluster control is established, and the objective function is to maximize the clean energy consumption, which is expressed as: Where, For clean energy consumption, N w 、N s 、N h are the number of wind farms, photovoltaic power stations, and hydropower stations, respectively. are the power consumption of the i-th wind farm, photovoltaic power station and hydropower station at time t, N area is the number of grid partitions, T is the time period considered for optimal dispatch of the power system; Constraints include system power balance constraints, reserve capacity constraints, charging pile power constraints, tie line and transmission section constraints, new energy power constraints, energy storage operation constraints, thermal power unit constraints, and cascade hydropower unit constraints. The calculation formula for the system power balance constraint considering the charging and discharging power of the charging pile cluster is as follows: Where k is the grid partition number, M area (k) represents the set of resources in the grid partition; {i g ,i w ,i s ,i h } represents the number of thermal power plants, wind farms, photovoltaic power plants, and hydropower plants in the grid zone; They represent the power of thermal power plant, hydropower station, wind farm and photovoltaic power station at time t respectively; are the input and output powers of the tie line between the partitioned grid k and the external grid, respectively; represents the total load power of the partitioned power grid k; Represents the grid loss, P t ES.in 、P t ES.out Respectively represent the charging and discharging power of energy storage; They represent the charging and discharging power of the charging pile cluster at time t in the k-partition power grid; The calculation formula for the reserve capacity constraint considering the charging and discharging power of the charging pile cluster is as follows: In the formula, RT represents spare, K w.RT , K s.RT , K ld.RT Respectively represent the reserve rates of wind power, photovoltaic power, and load; The power constraints of the charging pile cluster are as follows: Where, They represent the charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; They represent the adjustable charging power and discharging power of the charging pile cluster at time t in the partitioned power grid k; N represents the rated charging power of charging pile i with only charging function in the partitioned power grid k; k.in N represents the number of charging piles with only charging function in the partitioned power grid k; k.out represents the number of charging piles with charging and discharging functions in the partitioned power grid k; They represent the charging and discharging states of the charging pile j with charging and discharging functions in the partitioned power grid k. When in the charging state otherwise When in discharge state otherwise They represent the rated charging power and rated discharging power of the charging pile j with charging and discharging functions in the partitioned power grid k respectively; W k.i 、W k.j Respectively represent the willingness of users of charging pile i and charging pile j to accept orderly power regulation. When the value is 1, it means that the user accepts the orderly regulation of charging and discharging power of the charging pile; when the value is 0, it means that the user does not accept the orderly regulation of charging and discharging power of the charging pile; In the above formula The calculation formula is: The calculation formula for tie line and transmission section constraints is as follows: Where, They represent the transmission capacity of the jth transmission section at time t, which is the transmission section between different sections of the target power grid and the interconnection transmission section between the target power grid and the external power grid; The power constraints of new energy sources are as follows: Where, They represent the theoretical power of wind farm and photovoltaic power station at time t respectively; The energy storage operation constraints are: Where, P t ES.in 、P t ES.out Indicates the energy storage charging and discharging power, P t ES.in.max 、P t ES.out.max Indicates the maximum value of energy storage charging and discharging power. Indicates the energy storage charging and discharging status. Indicates the state of charge of the energy storage. Indicates the minimum and maximum energy storage capacity; η ES.in ,η ES.out Indicates the energy storage charging and discharging efficiency. Thermal power unit constraints include upper and lower power limits, ramp power constraints, minimum start and stop time constraints, and the number of times the unit receives start and stop status commands within a cycle. The upper and lower power limits of thermal power units are: Where, P i g.max 、P i g.min Indicates the maximum and minimum power of thermal power units. Indicates start / stop status; The ramp power constraint of thermal power units is: Where, P i g.clup.max 、P i g.cldw.max Indicates the maximum power of thermal power units climbing up and down; The minimum start and stop time constraints of thermal power units are: Where, t tempt Represents any time, T on.min 、T off.min Indicates the minimum start and stop time; The number of times a thermal power unit receives start and stop status instructions within a cycle is constrained as follows: Where, T period represents the total scheduling period; Indicates the maximum number of times start and stop state commands are accepted; The constraints of cascade hydropower units include the water balance constraints of each cascade hydropower station, the output power constraints of the hydropower station, the water storage capacity constraints of the reservoir, and the power generation flow constraints of the hydropower station. Among them, the water balance constraints of each cascade hydropower station are: For the first-stage hydropower station: V i.t+1 =V i.t +q 1i.t -Q 1i.t -S 1i.t Where V i.t 、V i.t+1 represents the storage capacity of hydropower station i at time t and t+1, respectively, 1i.t represents the natural water inflow of hydropower station i during period t, Q 1i.t represents the power generation flow of hydropower station i during period t, S 1i.t represents the amount of water abandoned by hydropower station i during period t; For other levels of hydropower stations: V i.t+1 =V i.t +q i.t -Q i.t -S i.t +Q i-1.t-τ +S i-1.t-τ Where q i.t , Q i.t 、S 1i.t They represent the natural water inflow, power generation flow, and abandoned water volume of hydropower station i during period t, τ represents the arrival time of water flow from the i-1th level hydropower station to the i-th level hydropower station, Q i-1.t-τ 、S i-1.t-τ They represent the power generation flow and abandoned water volume of the i-1th-level hydropower station after τ time; The output power constraint of the hydropower station is: Where, represents the power of the i-th hydropower station at time t, H i.t represents the water head of the i-th hydropower station at time t, η i represents the power generation efficiency of the i-th level hydropower station, They represent the minimum and maximum active power of the i-th hydropower station at time t respectively; The reservoir storage capacity constraint is: Where, They represent the maximum and minimum water storage capacity of the reservoir of the i-th hydropower station at time t respectively; The power generation flow constraint of the hydropower station is: Where, They respectively represent the maximum and minimum daily flow values ​​available for the hydropower units.

4. The method according to claim 2, characterized in that In step S2, the wind power forecast data for the next T hours, photovoltaic power forecast data, load power forecast data, and other power system related data obtained in step S1 are used as input data, and an optimization solution algorithm is used to calculate the power system optimization scheduling model considering the charging pile cluster regulation established in step S2 to obtain the charging pile cluster power A for the next T hours.

5. The method according to claim 1, characterized in that In step S3, the system frequency modulation capacity requirement calculation formula is as follows: P f =α×P max.T Where, P f is the system frequency regulation capacity requirement for the next T hours, α is the frequency regulation coefficient, P max.T The maximum load power prediction for the next T hours; The calculation formula for the frequency regulation capability of the charging pile cluster is as follows: Where, They represent the maximum upper regulation power and the maximum lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k; According to the operating rules of the power frequency regulation auxiliary service market, combined with the system frequency regulation capacity requirements and the frequency regulation capabilities of the charging pile cluster, the power B of the charging pile cluster participating in the system frequency regulation in the next T hours is obtained, which is expressed as: Where, β, P f.β They are the maximum proportion and maximum frequency regulation capacity allowed by the charging pile cluster stipulated in the power frequency regulation auxiliary service market operation rules. They respectively represent the upper regulation power and lower regulation power of the charging pile cluster participating in frequency regulation at time t in the partitioned power grid k.

6. The method according to claim 1, characterized in that In step S4, the charging pile cluster power A obtained in step S2 is combined with the willingness of users of each charging pile to accept orderly power regulation and their charging and discharging power to obtain the charging pile cluster power E when users accept orderly power regulation. The calculation formula is: Where, are respectively the charging power and the discharging power at time t in the charging pile cluster power A obtained in step S2; are the charging power and discharging power at time t in the charging pile cluster power E when the user accepts orderly power regulation; The power E of the charging pile cluster that accepts orderly power regulation by the user and the power B of the charging pile cluster that participates in the system frequency modulation obtained in step S3 are combined to obtain the power C of the charging pile cluster that accepts orderly power regulation by the user. According to the principle of proportionality of the rated power of the charging piles, the power C of the charging pile cluster is converted into the power D of each charging pile in the cluster that accepts orderly power regulation by the user, and the power D is sent to each charging pile controller that accepts orderly power regulation by the user. Each charging pile controller converts the power D into a control signal, and then controls the charging and discharging power of the charging piles that accept orderly power regulation by the user through the control signal.

7. A charging pile cluster control system for clean energy consumption and system frequency modulation, characterized by: include: A data acquisition module is used to acquire power system related data, including charging pile data, wind power data, photovoltaic data, and load data; The power system optimization and dispatching module is used to establish a power system optimization and dispatching model that takes into account the control of the charging pile cluster, and calculate the model through the optimization solution algorithm to obtain the charging pile cluster power A; The charging pile cluster frequency regulation module is used to calculate the system frequency regulation capacity demand and the charging pile cluster frequency regulation capability, and obtain the charging pile cluster power B participating in the system frequency regulation; The charging pile cluster power control module is used to obtain the charging pile cluster power C for users to accept orderly power regulation based on the charging pile cluster power A obtained by the power system optimization and scheduling module and the charging pile cluster power B obtained by the charging pile cluster frequency modulation module. According to the charging pile cluster power C, the power D of each charging pile for users in the cluster to accept orderly power regulation is generated, and each charging pile power D is sent to the corresponding charging pile controllers. Each charging pile controller converts the power D of each charging pile into a control signal, and then controls the charging and discharging power of the charging piles for users to accept orderly power regulation through the control signal.

8. An electronic device, characterized in that: It includes a processor and a memory, and the processor is used to execute a computer program stored in the memory to implement the charging pile cluster control method for clean energy consumption and system frequency modulation as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the charging pile cluster control method for clean energy consumption and system frequency modulation as described in any one of claims 1 to 6.

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