Power dispatching method for demand response capability of optical storage and charging virtual power plant

Abstract

The invention discloses a power dispatching method for the demand response capability of an optical storage and charging virtual power plant, and relates to the technical field of power dispatching, and the method comprises the steps: determining the aggregated charging power corresponding to any unique account number at any moment based on charging pile resources, and determining a first demand request of any unique account number in real time based on the aggregated charging power; determining all first demand requests in each optical storage and charging station area, and determining a first power dispatching scheme of any optical storage and charging station area by taking demand response income and grid-connected photovoltaic income maximization as a target and taking a first preset condition as a constraint; and further determining a second demand request corresponding to any first power dispatching scheme, and determining a second power dispatching scheme of the to-be-dispatched optical storage and charging virtual power plant by taking demand response income maximization and response deviation minimization as targets and taking a second preset condition as a constraint. The economic benefit and the energy value of the light storage and charging facility are fully excavated, and meanwhile safe and stable operation of the system is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant. Background Technology

[0002] Virtual power plants are a crucial component of modern power system innovation and energy transition, providing technological support for the development of clean and renewable energy. Among them, the photovoltaic-storage-charging virtual power plant is a power dispatch and management system based on new energy and energy storage technologies. It integrates photovoltaic power generation, energy storage systems, charging piles, and other distributed power sources, using information technology and intelligent methods for unified dispatch, optimization, and management. The integrated photovoltaic-storage-charging virtual power plant system enables efficient utilization of distributed energy resources, peak-valley load regulation, and the integration of renewable energy, improving the flexibility and stability of the power system. However, the addition of distributed resources and equipment makes the power system's hierarchical structure more complex, and the massive amounts of control data and diverse control methods place higher demands on the operational efficiency of existing photovoltaic-storage-charging virtual power plant platforms.

[0003] In practice, the photovoltaic-storage-charging virtual power plant integrates a large number of regional distributed photovoltaic, charging pile loads, energy storage systems and other distributed resources. The distributed characteristics of its internal equipment, the massive amount of control data and the flexible and varied control methods make the traditional centralized control method, which integrates global operation data through a dispatch center for unified judgment and scheduling, very limited in terms of system scalability and compatibility. All new information from all units needs to be processed and communicated bidirectionally through the dispatch center. Summary of the Invention

[0004] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a power dispatching method for demand response capabilities of photovoltaic-storage-charging virtual power plants, as detailed below:

[0005] 1) In a first aspect, the present invention provides a power dispatching method for the demand response capability of a photovoltaic-storage-charging virtual power plant, the specific technical solution of which is as follows:

[0006] Collect the charging pile resources corresponding to each unique account in the virtual power plant of photovoltaic storage and charging to be scheduled, determine the aggregated charging power corresponding to any unique account at any time based on the charging pile resources, and determine the first demand request of any unique account in real time based on the aggregated charging power.

[0007] Taking the photovoltaic-storage-charging station area as a unit, determine all the first demand requests in each photovoltaic-storage-charging station area, and determine the first power dispatch scheme for any photovoltaic-storage-charging station area with the goal of maximizing demand response revenue and grid-connected photovoltaic revenue and the first preset condition as a constraint.

[0008] Based on the first power dispatch scheme, the second demand request corresponding to any first power dispatch scheme is determined. With the goal of maximizing demand response revenue and minimizing response deviation, and with the second preset condition as constraint, the second power dispatch scheme of the photovoltaic-storage-charging virtual power plant to be dispatched is determined.

[0009] The beneficial effects of the power dispatching method for demand response capability of photovoltaic-storage-charging virtual power plants provided by this invention are as follows:

[0010] By accurately collecting charging pile resources corresponding to each household number, the aggregated charging power and primary demand requests are determined in real time, providing an accurate data foundation for subsequent power dispatch and ensuring the scientific and timely nature of dispatch decisions. Integrating the primary demand requests on a regional basis, and aiming to maximize demand response revenue and grid-connected photovoltaic revenue, combined with constraint optimization under primary preset conditions, effectively improves the utilization efficiency of power resources within the region, fully explores the economic benefits and energy value of photovoltaic, energy storage, and charging facilities, while ensuring the safe and stable operation of the system. Further, based on the primary power dispatch scheme, the secondary demand requests are determined, aiming to maximize demand response revenue and minimize response deviation, combined with constraint optimization under secondary preset conditions. This enables higher-level overall coordination of the power resources of the entire virtual power plant to be dispatched, achieving global optimized dispatch, further improving energy utilization efficiency, reducing operating costs, enhancing the virtual power plant's support capacity for the grid, and improving its competitiveness and economics in the electricity market. This provides strong support for the stable operation of the grid and the efficient use of energy, resulting in significant economic and social benefits.

[0011] Based on the above solution, the present invention can be further improved as follows.

[0012] Furthermore, the method for determining the aggregated charging power is as follows:

[0013] In the correspondence between predicted charging power and target aggregate charging power, the aggregate charging power corresponding to the target predicted charging power in the charging pile resources is determined.

[0014] Furthermore, the first preset condition includes:

[0015] The constraints include peak-shaving demand response capacity, load balance constraints for photovoltaic-storage-charging stations, transformer capacity limits, energy storage system operation constraints, distributed photovoltaic operation constraints, and charging pile electricity consumption compliance constraints.

[0016] Furthermore, the second preset condition includes:

[0017] Demand response capacity constraints for peak-shaving virtual power plants, load balance constraints for virtual power plants, and power consumption constraints for distributed charging piles.

[0018] Furthermore, it also includes:

[0019] According to the second power dispatch scheme, at least one trading period corresponding to any photovoltaic-storage-charging station area is predicted, as well as the charging volume corresponding to any trading period.

[0020] 2) Secondly, the present invention also provides a power dispatching system for the demand response capability of photovoltaic-storage-charging virtual power plants, the specific technical solution of which is as follows:

[0021] The data acquisition module is used to: acquire the charging pile resources corresponding to each unique account number in the virtual power plant to be scheduled; determine the aggregated charging power corresponding to any unique account number at any time based on the charging pile resources; and determine the first demand request of any unique account number in real time based on the aggregated charging power.

[0022] The first scheduling module is used to: determine all first demand requests in each photovoltaic-storage-charging station area, taking the photovoltaic-storage-charging station area as a unit, and determine the first power scheduling scheme for any photovoltaic-storage-charging station area with the goal of maximizing demand response revenue and grid-connected photovoltaic revenue and with the first preset conditions as constraints.

[0023] The second scheduling module is used to: determine the second demand request corresponding to any first power scheduling scheme based on the first power scheduling scheme, and determine the second power scheduling scheme of the photovoltaic-storage-charging virtual power plant to be scheduled with the goal of maximizing demand response benefits and minimizing response deviation, and with the second preset conditions as constraints.

[0024] Based on the above solution, the present invention can be further improved as follows.

[0025] Furthermore, the method for determining the aggregated charging power is as follows:

[0026] In the correspondence between predicted charging power and target aggregate charging power, the aggregate charging power corresponding to the target predicted charging power in the charging pile resources is determined.

[0027] Furthermore, the first preset condition includes:

[0028] The constraints include peak-shaving demand response capacity, load balance constraints for photovoltaic-storage-charging stations, transformer capacity limits, energy storage system operation constraints, distributed photovoltaic operation constraints, and charging pile electricity consumption compliance constraints.

[0029] Furthermore, the second preset condition includes:

[0030] Demand response capacity constraints for peak-shaving virtual power plants, load balance constraints for virtual power plants, and power consumption constraints for distributed charging piles.

[0031] Furthermore, it also includes:

[0032] The prediction module is used to: predict at least one trading period corresponding to any photovoltaic-storage-charging station area, and the charging amount corresponding to any trading period, according to the second power dispatch scheme.

[0033] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the electronic device to perform any of the methods described above.

[0034] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to perform any of the above methods.

[0035] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0036] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0037] Figure 1 This is a flowchart illustrating a power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant according to an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the model architecture of a power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant according to an embodiment of the present invention.

[0039] Figure 3 This is a schematic diagram of a fixed energy state for a power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant according to an embodiment of the present invention.

[0040] Figure 4 This is a schematic diagram of a flexible energy state of a power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant, according to an embodiment of the present invention.

[0041] Figure 5 This is a structural framework diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0043] like Figures 1 to 4 As shown in the figure, a power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant according to an embodiment of the present invention includes the following steps:

[0044] S1: Collect the charging pile resources corresponding to each unique account number in the virtual power plant to be scheduled (photovoltaic, energy storage and charging), determine the aggregated charging power corresponding to any unique account number at any time based on the charging pile resources, and determine the first demand request of any unique account number in real time based on the aggregated charging power.

[0045] S2, taking the photovoltaic-storage-charging station area as a unit, determine all the first demand requests in each photovoltaic-storage-charging station area, and determine the first power dispatch scheme for any photovoltaic-storage-charging station area with the goal of maximizing demand response revenue and grid-connected photovoltaic revenue and the first preset condition as a constraint.

[0046] S3. Based on the first power dispatch scheme, determine the second demand request corresponding to any first power dispatch scheme, and determine the second power dispatch scheme for the photovoltaic-storage-charging virtual power plant to be dispatched with the goal of maximizing demand response revenue and minimizing response deviation, and with the second preset condition as constraint.

[0047] The beneficial effects of the power dispatching method for demand response capability of photovoltaic-storage-charging virtual power plants provided by this invention are as follows:

[0048] By accurately collecting charging pile resources corresponding to each household number, the aggregated charging power and primary demand requests are determined in real time, providing an accurate data foundation for subsequent power dispatch and ensuring the scientific and timely nature of dispatch decisions. Integrating the primary demand requests on a regional basis, and aiming to maximize demand response revenue and grid-connected photovoltaic revenue, combined with constraint optimization under primary preset conditions, effectively improves the utilization efficiency of power resources within the region, fully explores the economic benefits and energy value of photovoltaic, energy storage, and charging facilities, while ensuring the safe and stable operation of the system. Further, based on the primary power dispatch scheme, the secondary demand requests are determined, aiming to maximize demand response revenue and minimize response deviation, combined with constraint optimization under secondary preset conditions. This enables higher-level overall coordination of the power resources of the entire virtual power plant to be dispatched, achieving global optimized dispatch, further improving energy utilization efficiency, reducing operating costs, enhancing the virtual power plant's support capacity for the grid, and improving its competitiveness and economics in the electricity market. This provides strong support for the stable operation of the grid and the efficient use of energy, resulting in significant economic and social benefits.

[0049] In S1, the photovoltaic-storage-charging virtual power plant is an energy management system that aggregates dispersed photovoltaic power generation, energy storage systems, and charging facilities (such as charging piles) into a unified "virtual power generation unit" through IoT, big data, and AI technologies. It is not a physical power plant in the traditional sense, but a virtual and flexible energy dispatching platform that enables intelligent energy dispatching and market participation.

[0050] In addition, for ease of understanding, the relationship between the virtual power plant (PV-energy storage-charging system), the PV-energy storage-charging station (the actual land area corresponding to any PV-energy storage-charging station is referred to as the PV-energy storage-charging station area), and the charging piles is summarized as follows:

[0051] Photovoltaic-storage-charging stations are the physical foundation and an important component of photovoltaic-storage-charging virtual power plants. These stations organically combine photovoltaic power generation systems, energy storage systems, and charging piles to form a small, self-sufficient energy system. Within the architecture of a photovoltaic-storage-charging virtual power plant, multiple stations are connected through an intelligent management system to achieve optimized energy allocation and coordinated operation.

[0052] Charging piles are the terminal equipment of a photovoltaic-storage-charging virtual power plant, used to provide charging services for electric vehicles. In this virtual power plant, charging piles are not only energy consumers but also, through V2G (vehicle-to-grid) technology, utilize the electric vehicle's battery as an energy storage resource, participating in peak shaving and valley filling of the power grid. The layout and operation strategy of charging piles directly affect the energy dispatch efficiency and economic benefits of the photovoltaic-storage-charging virtual power plant.

[0053] The photovoltaic-storage-charging virtual power plant integrates resources such as photovoltaic, energy storage, and charging stations, achieving efficient energy utilization and flexible dispatch. The photovoltaic-storage-charging station, as the physical node of the virtual power plant, provides the infrastructure for photovoltaic power generation, energy storage, and charging; the charging pile, as the terminal equipment, connects electric vehicles and the power grid, enabling bidirectional energy flow. This model not only improves energy utilization efficiency but also supports the stable operation of the power grid.

[0054] It should be noted that each photovoltaic-storage-charging station corresponds to multiple unique account numbers, and each unique account number corresponds to at least one charging pile.

[0055] In another embodiment of this solution, modeling is performed using the household number as the dimension, aggregating all charging pile resources under the same household number (i.e., under the same household number), and the charging power curve is the predicted power curve minus the adjustable capacity:

[0056]

[0057] in, The aggregated charging power of all charging piles under user number i during time period t; The predicted charging power is the aggregated charging power of all charging piles under user number i in time period t. The response ratio that all charging piles can provide under user number i during time period t.

[0058] The process of determining the first demand request for any unique account number in real time based on aggregated charging power is as follows:

[0059] Demand (E) = Aggregate charging power (P) × Charging time (t).

[0060] In S2, the process of determining all first demand requests within each optical storage and charging station area is as follows:

[0061] Identify all unique account numbers corresponding to the photovoltaic storage and charging station area, and then sum up the demand quantities in the first demand request corresponding to all unique account numbers.

[0062] In another embodiment of this scheme, the process of determining the first power dispatch scheme for any photovoltaic-storage-charging station area is as follows:

[0063] Objective function:

[0064] Maximizing demand response revenue and grid-connected photovoltaic revenue, i.e.

[0065]

[0066] in, The response capacity (first demand request) of photovoltaic-storage charging station i in time period t, in kW; The demand response / peak shaving subsidy price for time period t is expressed in yuan / kWh. The grid-connected power of distributed photovoltaic (PV) unit p during time period t is expressed in kW. The transformer conversion efficiency coefficient; The grid-connected energy price of photovoltaic p in time period t is expressed in yuan / kWh. The power purchased by photovoltaic-storage charging station i during time period t is expressed in kW. P represents the energy price for time period t, expressed in yuan / kWh, where T is the time period and P is the number of photovoltaic, energy storage, and charging stations.

[0067] Constraints:

[0068] (1) Demand response capacity (peak shaving type)

[0069]

[0070] in, The response capacity of photovoltaic-storage charging station i during time period t, in kW; The baseline power of the photovoltaic-storage charging station is expressed in kW. The power purchased by photovoltaic-storage charging station i during time period t is expressed in kW.

[0071] (2) System load balance constraints

[0072]

[0073] in, Let e ​​be the discharge power of the energy storage system e during time period t, in kW; The charging power of energy storage system e during time period t is expressed in kW. The self-generated and self-consumed power of distributed photovoltaic power generation p during time period t is expressed in kW. denoted as the grid-connected conversion efficiency coefficient of distributed photovoltaic (PV) p; The power consumption of the charging piles included in household number i during time period t is expressed in kW. The rigid load of the photovoltaic-storage charging station is expressed in kW.

[0074] (3) Transformer capacity limitation

[0075]

[0076] in, η is the transformer capacity. i λ is the power factor of the transformer. i This refers to the transformer efficiency.

[0077] (4) Energy storage system operation constraints

[0078] ①Energy storage power unit electrical quantity relationship

[0079]

[0080] Among them, E e,t and E e,t-1 These represent the electrical energy of energy storage system e at time periods t and t-1, respectively, in kWh. and Here, represents the charging efficiency and discharging efficiency of energy storage system e, respectively, in percentage (%).

[0081] ②SOC constraints of energy storage system batteries

[0082]

[0083] in, and These represent the lower and upper limits of the State of Charge (SOC) for energy storage system e, respectively, in percentages; E e,t Let e ​​be the electrical energy of energy storage system e during time period t, in kWh; This represents the upper limit of electrical energy of the energy storage system e, expressed in kWh.

[0084] ③ Charge and discharge power constraints of energy storage power units

[0085]

[0086] in, The maximum charging power of energy storage system e is expressed in kW. The maximum discharge power of energy storage system e is expressed in kW.

[0087] ④ Energy control at the end of the energy storage power unit cycle

[0088]

[0089] in, E represents the permissible fluctuation range of the state of charge (SOC) at the end of the day for the energy storage system, expressed as a percentage. e,t＝T The energy state of energy storage system e at the end of the day (T) is expressed in kWh.

[0090] ⑤ Limitation on the number of cycles for energy storage power units

[0091] The number of cycles is approximately the sum of the charging capacity during the time period divided by the stored energy capacity.

[0092]

[0093] in, This represents the upper limit of the daily equivalent cycle count for energy storage system k, expressed in cycles.

[0094] (5) Operational constraints of distributed photovoltaic systems:

[0095]

[0096] in, The grid-connected power of distributed photovoltaic (PV) unit p during time period t is expressed in kW. This represents the predicted power of distributed photovoltaic (PV) unit p during time period t, expressed in kW.

[0097] (6) Power load constraints for charging piles:

[0098]

[0099] in, The predicted power of the charging piles included in household number i during time period t, in kW. Let be the adjustable coefficient of photovoltaic-storage charging station i during time period t.

[0100] Based on the above, it can be determined that when the goal is to maximize the revenue from demand response and grid-connected photovoltaic power, the current demand response volume can be determined, and a first power dispatch plan can be generated based on the current demand response volume. At the same time, a second demand request can be determined based on the current demand response volume and the corresponding power generation of the photovoltaic, energy storage and charging station area.

[0101] In another embodiment of this scheme, the process of determining the second power dispatch scheme for the virtual power plant to be dispatched (photovoltaic, energy storage, and charging) is as follows:

[0102] Objective function:

[0103] Maximizing benefits from demand response:

[0104]

[0105] in, The virtual power plant's response reporting capacity (second demand request) for time period t is expressed in kW. The demand response / peak-shaving subsidy price for time period t; The grid-connected power of distributed photovoltaic power generation p in time period t is obtained by optimization by the regional coordination layer, and the unit is kW; The transformer conversion efficiency coefficient is expressed as a percentage (%). The grid-connected energy price of photovoltaic p in time period t is expressed in yuan / kWh. This represents the power purchased by the virtual power plant during time period t, in kW. The energy price for time period t; The response power of photovoltaic-storage charging station i during time period t is obtained by optimization by the regional coordination layer, and the unit is kW; The price is a subsidy for the demand response of agent user l during time period t.

[0106] Constraints:

[0107] (1) Virtual power plant demand response capacity (peak shaving type)

[0108]

[0109] in, The baseline power of the distributed charging pile user number c is in kW; The power consumption of distributed charging pile user number c during time period t is expressed in kW. The power of the reported response of agent user l during time period t is expressed in kW.

[0110] (2) System load balance constraints

[0111]

[0112] in, The power purchased by photovoltaic-storage charging station i during time period t is obtained by optimization by the regional coordination layer, and the unit is kW.

[0113] (3) Power constraints of distributed charging piles

[0114]

[0115] in, The predicted power is for user number c of the distributed charging station, in kW. The charging power curve is the predicted power curve minus the adjustable capacity.

[0116] Objective function:

[0117] Minimize response bias:

[0118]

[0119] Among them, M slack For the i-command deviation penalty item of the photovoltaic-storage charging station; The response command deviation of the photovoltaic-storage charging station during time period t is expressed in kW.

[0120] Constraints:

[0121] (1) Demand response capacity of photovoltaic-storage charging stations:

[0122]

[0123] in, The decomposed response capacity of the photovoltaic-storage charging station during time period t is expressed in kW. The response command deviation of the photovoltaic-storage charging station during time period t is expressed in kW. The response power of the photovoltaic-storage charging station i is decomposed by the virtual power plant's overall management layer, and the unit is kW. The baseline power of the photovoltaic-storage charging station is expressed in kW. Let s represent the power purchased by photovoltaic-storage charging station i during time period t under random scenario s, in kW.

[0124] (2) System load balance constraints:

[0125]

[0126] in, Let s be the discharge power of energy storage system e in time period t under random scenario s, in kW; The charging power of energy storage system e in time period t under random scenario s is expressed in kW. The power generation of distributed photovoltaic p in time period t under random scenario s is expressed in kW. Let s be the power consumption of charging pile c in time period t under random scenario s, in kW; The power purchased by photovoltaic-storage charging station i during time period t is expressed in kW.

[0127] (3) Transformer capacity limitation:

[0128]

[0129] in, This refers to the upper limit of the transmission power of the transformer in the photovoltaic-storage charging station, in kW. η is the transformer capacity. i λ is the power factor of the transformer. i This refers to the transformer efficiency.

[0130] In another embodiment of this scheme, after the response capability is optimized, the power consumption curves of each charging pile can be obtained, and the summation can be compared with the baseline load of the household number to obtain the response capacity.

[0131] After algorithm analysis, by updating the charging pile response behavior simulation algorithm to simulate the vehicle access situation of each charging gun, the transaction time and charging volume of each charging gun in the station can be predicted.

[0132] Furthermore, the method for determining the aggregated charging power is as follows:

[0133] In the correspondence between predicted charging power and target aggregate charging power, the aggregate charging power corresponding to the target predicted charging power in the charging pile resources is determined.

[0134] Furthermore, the first preset condition includes:

[0135] The constraints include peak-shaving demand response capacity, load balance constraints for photovoltaic-storage-charging stations, transformer capacity limits, energy storage system operation constraints, distributed photovoltaic operation constraints, and charging pile electricity consumption compliance constraints.

[0136] Furthermore, the second preset condition includes:

[0137] Demand response capacity constraints for peak-shaving virtual power plants, load balance constraints for virtual power plants, and power consumption constraints for distributed charging piles.

[0138] Furthermore, it also includes:

[0139] According to the second power dispatch scheme, at least one trading period corresponding to any photovoltaic-storage-charging station area is predicted, as well as the charging volume corresponding to any trading period.

[0140] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0141] This invention also provides a power dispatching system for demand response capabilities of photovoltaic-storage-charging virtual power plants, the specific technical solution of which is as follows:

[0142] The data acquisition module is used to: acquire the charging pile resources corresponding to each unique account number in the virtual power plant to be scheduled; determine the aggregated charging power corresponding to any unique account number at any time based on the charging pile resources; and determine the first demand request of any unique account number in real time based on the aggregated charging power.

[0143] The first scheduling module is used to: determine all first demand requests in each photovoltaic-storage-charging station area, taking the photovoltaic-storage-charging station area as a unit, and determine the first power scheduling scheme for any photovoltaic-storage-charging station area with the goal of maximizing demand response revenue and grid-connected photovoltaic revenue and with the first preset conditions as constraints.

[0144] The second scheduling module is used to: determine the second demand request corresponding to any first power scheduling scheme based on the first power scheduling scheme, and determine the second power scheduling scheme of the photovoltaic-storage-charging virtual power plant to be scheduled with the goal of maximizing demand response benefits and minimizing response deviation, and with the second preset conditions as constraints.

[0145] It should be noted that the beneficial effects of the power dispatching system for demand response capability of photovoltaic-storage-charging virtual power plants provided in the above embodiments are the same as those of the power dispatching method for demand response capability of photovoltaic-storage-charging virtual power plants described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0146] like Figure 5 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330, which is loaded and executed by the processor 320 to enable the electronic device 300 to implement any of the above-mentioned methods. Specifically:

[0147] The electronic device 300 can vary considerably due to differences in configuration or performance. It may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The memories 310 store at least one computer program 330, which is loaded and executed by the processors 320 to enable the electronic device 300 to implement the power dispatching method for demand response capabilities of a photovoltaic-storage-charging virtual power plant provided in the above embodiments. Of course, the electronic device 300 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input / output. It may also include other components for implementing device functions, which will not be elaborated upon here.

[0148] An embodiment of the present invention provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-described methods.

[0149] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0150] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the methods described above.

[0151] It should be noted that the terms "first" and "second" in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0152] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product in one or more computer-readable media containing computer-readable program code.

[0153] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0154] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A power dispatching method for demand response capability of photovoltaic-storage-charging virtual power plants, characterized in that, include: Collect the charging pile resources corresponding to each unique account in the virtual power plant of photovoltaic storage and charging to be scheduled, determine the aggregated charging power corresponding to any unique account at any time based on the charging pile resources, and determine the first demand request of any unique account in real time based on the aggregated charging power. Taking the photovoltaic-storage-charging station area as a unit, determine all the first demand requests in each photovoltaic-storage-charging station area, and determine the first power dispatch scheme for any photovoltaic-storage-charging station area with the goal of maximizing demand response revenue and grid-connected photovoltaic revenue and the first preset condition as a constraint. Based on the first power dispatch scheme, a second demand request corresponding to any first power dispatch scheme is determined, and with the goal of maximizing demand response benefits and minimizing response deviation, and with the second preset condition as a constraint, a second power dispatch scheme for the virtual power plant to be dispatched (photovoltaic, energy storage, and charging) is determined.

2. The power dispatching method for demand response capability of photovoltaic-storage-charging virtual power plants according to claim 1, characterized in that, The aggregated charging power is determined as follows: In the correspondence between predicted charging power and target aggregate charging power, the aggregate charging power corresponding to the target predicted charging power in the charging pile resources is determined.

3. The power dispatching method for demand response capability of photovoltaic-storage-charging virtual power plants according to claim 1, characterized in that, The first preset conditions include: The constraints include peak-shaving demand response capacity, load balance constraints for photovoltaic-storage-charging stations, transformer capacity limits, energy storage system operation constraints, distributed photovoltaic operation constraints, and charging pile electricity consumption compliance constraints.

4. A power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant according to claim 1, characterized in that, The second preset condition includes: Demand response capacity constraints for peak-shaving virtual power plants, load balance constraints for virtual power plants, and power consumption constraints for distributed charging piles.

5. A power dispatching method for demand response capability of a photovoltaic-storage-charging virtual power plant according to claim 1, characterized in that, Also includes: According to the second power dispatch scheme, at least one trading period corresponding to any photovoltaic-storage-charging station area is predicted, as well as the charging volume corresponding to any trading period.

6. A power dispatching system for demand response capability of photovoltaic-storage-charging virtual power plants, characterized in that, include: The data acquisition module is used to: acquire the charging pile resources corresponding to each unique account in the virtual power plant to be scheduled; determine the aggregated charging power corresponding to any unique account at any time based on the charging pile resources; and determine the first demand request of any unique account in real time based on the aggregated charging power. The first scheduling module is used to: determine all first demand requests in each photovoltaic-storage-charging station area, taking the photovoltaic-storage-charging station area as a unit, and determine the first power scheduling scheme for any photovoltaic-storage-charging station area with the goal of maximizing demand response revenue and grid-connected photovoltaic revenue and with the first preset conditions as constraints. The second scheduling module is used to: determine the second demand request corresponding to any first power scheduling scheme based on the first power scheduling scheme, and determine the second power scheduling scheme of the virtual power plant to be scheduled, with the goal of maximizing demand response benefits and minimizing response deviation, and with the second preset condition as constraint.

7. A power dispatching system for demand response capability of a photovoltaic-storage-charging virtual power plant according to claim 6, characterized in that, The aggregated charging power is determined as follows: In the correspondence between predicted charging power and target aggregate charging power, the aggregate charging power corresponding to the target predicted charging power in the charging pile resources is determined.

8. A power dispatching system for demand response capability of a photovoltaic-storage-charging virtual power plant according to claim 6, characterized in that, The first preset conditions include: The constraints include peak-shaving demand response capacity, load balance constraints for photovoltaic-storage-charging stations, transformer capacity limits, energy storage system operation constraints, distributed photovoltaic operation constraints, and charging pile electricity consumption compliance constraints.

9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to perform the method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to perform the method as described in any one of claims 1 to 5.

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