Intelligent power grid dynamic planning method and system based on multi-dimensional space-time coupling

Abstract

The invention discloses an intelligent power grid dynamic planning method and system based on multi-dimensional space-time coupling, and belongs to the technical field of power grid planning, and the method comprises the following steps: carrying out original data collection, carrying out multi-dimensional modeling, and carrying out dynamic planning; according to the method, short-term scheduling and long-term planning can be dynamically coordinated through multi-dimensional modeling, cross-regional resource complementation can also be achieved, insufficient or waste of local resources is reduced, and the dynamic characteristics of the power grid are accurately captured through high-resolution spatio-temporal data. According to the method, the uncertainty of wind and light output prediction errors, load fluctuation and the like can be effectively handled, the model can be dynamically planned, flexible resources can be reserved, extreme weather or equipment faults can be handled, time and space separation is broken through, resource utilization efficiency maximization is achieved, and overall economical efficiency, reliability and low-carbon performance are cooperatively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid planning, and in particular relates to a smart grid dynamic planning method and system based on multi-dimensional spatiotemporal coupling. Background Art

[0002] Grid planning is a mathematical method that aims to cope with the complex changes in the power system in multiple dimensions such as time, space, and resources, and to achieve efficient operation of the power grid through phased optimization decisions.

[0003] Chinese patent CN117332965A discloses a method and device for dynamic planning of a distribution network, belonging to the field of power grid planning technology. The method includes: dividing the planning period into multiple planning cycles with preset years; obtaining the topological structure of the distribution network and the historical load data of the distribution network in the previous historical planning cycle of the planning period, and determining the optimal planning scheme of the distribution network in the first planning cycle of the planning period based on the topological structure and historical load data; controlling the distribution network to operate in the first planning cycle corresponding to the optimal planning scheme; for each planning cycle other than the first planning cycle of the planning period, obtaining the operating status of the optimal planning scheme corresponding to the previous planning cycle at the beginning of the planning cycle, and determining and executing the optimal planning scheme for the current planning cycle based on the operating status of the previous planning cycle. This method can plan the distribution network under the condition of dynamic changes in the load of users in the distribution network. Although current planning methods can realize the planning of power grids and the distribution of electricity, they do not perform multi-dimensional modeling, resulting in the inability to dynamically coordinate short-term scheduling and long-term planning, the inability to complement resources across regions, the inability to reduce local resource shortages or waste, and the inability to deal with some emergencies in a timely manner. The actual application effect is poor, and there is an urgent need for smart grid dynamic planning methods and systems based on multi-dimensional spatiotemporal coupling. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems that although the current planning methods can realize the planning of power grids and the distribution of electricity, they do not perform multi-dimensional modeling, resulting in the inability to dynamically coordinate short-term scheduling and long-term planning, and the inability to complement cross-regional resources, and the inability to reduce local resource shortages or waste. Some emergencies cannot be handled in a timely manner, and the actual application effect is poor. A smart grid dynamic planning method and system based on multi-dimensional spatiotemporal coupling are proposed.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling includes the following steps: S1, collect original data; S2, conduct multi-dimensional modeling; S3, perform dynamic planning; S4, performing optimization and solution; S5, conduct spatiotemporal coordination and resource allocation; S6. Perform verification and iteration.

[0006] As a further description of the above technical solution: In S1, raw data collection is performed, and the collected data include historical power grid operation data, meteorological data and geographical data, and the spatiotemporal resolution of these data is set. The historical power grid operation data specifically includes historical power grid load curves and data, historical power grid power supply data and historical electricity prices; the meteorological data specifically includes wind speed, light, temperature and humidity data; the geographical data specifically includes power grid topology, energy storage / power generation equipment distribution location information and load center location information. The spatiotemporal resolution setting includes time dimension and space dimension, wherein the time dimension is the dynamic demand by time, day and season, and the space dimension is the regional power grid division.

[0007] As a further description of the above technical solution: In S2, multi-dimensional modeling is performed, and the specific steps are: establishing a time-space coupling model and generating a mathematical expression. The time-space coupling model includes time coupling and space coupling. Time coupling is the temporal correlation between power demand and renewable energy fluctuations. Spatial coupling is the power transmission constraint between grid nodes. The mathematical expression is the objective function of minimizing total cost. The constraint adjustment is power balance, equipment capacity, renewable energy absorption rate, and carbon emission cap.

[0008] As a further description of the above technical solution: In S3, dynamic planning is performed, and cycle planning and division are performed first. The planning unit is year, month or week. Optimization iteration is performed for each stage, and the large power grid is divided into multiple sub-areas. Distributed optimization is adopted, and the optimization algorithm adopts the ADMM algorithm.

[0009] As a further description of the above technical solution: In S4, an optimization solution is performed, and the optimization solution algorithm includes a mixed integer linear programming algorithm, a dynamic programming algorithm, and a reinforcement learning algorithm. In S5, spatiotemporal coordination and resource allocation are performed, specifically: flexible resource scheduling and cross-regional collaboration are performed, spatiotemporal charging and discharging strategies for energy storage systems (batteries, pumped storage) are formulated, demand-side response is performed, power mutual assistance between multiple regions, and spare capacity sharing is performed.

[0010] As a further description of the above technical solution: In S6, verification and iteration are performed, and the specific steps are as follows: first, simulation testing is performed to verify the robustness of the model based on typical scenarios, such as extreme weather and load surges; then, sensitivity analysis is performed to analyze the impact of changes in key parameters on the planning results. Key data include electricity prices and energy storage costs, and the planning strategy is dynamically adjusted according to actual operating data.

[0011] The present invention also discloses a smart grid dynamic planning system based on multi-dimensional space-time coupling, including an original data acquisition module, a multi-dimensional modeling module, a dynamic planning module, an optimization solution module, a space-time coordination and resource allocation module, and a verification and iteration module. The original data acquisition module is used for collecting and accumulating original related data, the multi-dimensional modeling module is used for establishing a space-time coupling model and establishing the temporal correlation between power demand and renewable energy fluctuations, the dynamic planning module is used for dynamic division of time and region, the optimization solution module is used for linear programming calculation of divided states, the space-time coordination and resource allocation module is used for flexible resource scheduling and allocation, and the verification and iteration module is used for simulation verification of resource allocation.

[0012] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: In the present invention, by conducting multi-dimensional modeling, it is possible to dynamically coordinate short-term scheduling and long-term planning, and to achieve cross-regional resource complementarity, reduce local resource shortages or waste, and accurately capture the dynamic characteristics of the power grid through high-resolution spatiotemporal data. This method effectively responds to uncertainties such as wind and solar output forecast errors and load fluctuations through random programming or robust optimization. It can dynamically plan the model to reserve flexibility resources to deal with extreme weather or equipment failures. This method breaks the separation of time and space, maximizes resource utilization efficiency, and synergistically improves overall economy, reliability, and low carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Schematic diagram of the module structure of the smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention. Example

[0015] See also Figure 1 The present invention provides a technical solution: a smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling, comprising the following steps: S1. Collect raw data, including historical grid operation data, meteorological data, and geographic data. The temporal and spatial resolutions of these data are set. The historical grid operation data specifically includes historical grid load curves and data, historical grid power supply data, and historical electricity prices. The meteorological data specifically includes wind speed, light, temperature, and humidity data. The geographic data specifically includes grid topology, energy storage / generation equipment distribution location information, and load center location information. The temporal and spatial resolution settings include time and space dimensions. The time dimension represents dynamic demand by hour, day, and season, and the spatial dimension represents regional grid division. S2. Conduct multi-dimensional modeling. The specific steps are as follows: establish a spatiotemporal coupling model and generate a mathematical expression. The spatiotemporal coupling model includes time coupling and spatial coupling. Time coupling refers to the temporal correlation between power demand and renewable energy fluctuations. Spatial coupling refers to the power transmission constraints between grid nodes. The mathematical expression is the objective function of minimizing total cost. The constraints are power balance, equipment capacity, renewable energy absorption rate, and carbon emission cap. S3. Dynamic planning is performed. First, periodic planning and division are performed. The planning unit is year, month or week. Optimization iteration is performed for each stage. The large power grid is divided into multiple sub-areas. Distributed optimization is adopted. The optimization algorithm is ADMM algorithm. S4. Optimization solution is performed. The optimization solution algorithm includes mixed integer linear programming algorithm, dynamic programming algorithm, and reinforcement learning algorithm; S5. Conduct spatiotemporal coordination and resource allocation, specifically: flexible resource scheduling and cross-regional collaboration, develop spatiotemporal charging and discharging strategies for energy storage systems (batteries, pumped hydro), implement demand-side response, inter-regional power coordination, and share reserve capacity. S6. Verify and iterate. The specific steps are as follows: first conduct simulation tests to verify the robustness of the model based on typical scenarios, such as extreme weather and load surges. Then conduct sensitivity analysis to analyze the impact of changes in key parameters on the planning results. Key data include electricity prices and energy storage costs. Dynamically adjust the planning strategy based on actual operating data.

[0016] In this embodiment, by conducting multi-dimensional modeling, it is possible to dynamically coordinate short-term scheduling and long-term planning, and to achieve cross-regional resource complementarity, reduce local resource shortages or waste, and accurately capture the dynamic characteristics of the power grid through high-resolution spatiotemporal data. This method effectively responds to uncertainties such as wind and solar output forecast errors and load fluctuations through random programming or robust optimization. It can dynamically plan the model to reserve flexibility resources to deal with extreme weather or equipment failures. This method breaks the separation of time and space, maximizes resource utilization efficiency, and synergistically improves overall economy, reliability, and low carbon. Example

[0017] This application also proposes a smart grid dynamic planning system based on multi-dimensional spatiotemporal coupling, including an original data acquisition module, a multi-dimensional modeling module, a dynamic planning module, an optimization solution module, a spatiotemporal coordination and resource allocation module, and a verification and iteration module. The original data acquisition module is used to collect and accumulate original related data, the multi-dimensional modeling module is used to establish a spatiotemporal coupling model, and to establish the temporal correlation between power demand and renewable energy fluctuations. The dynamic planning module is used for dynamic division of time and region, the optimization solution module is used for linear programming calculation of divided states, the spatiotemporal coordination and resource allocation module is used for flexible resource scheduling and allocation, and the verification and iteration module is used for simulation verification of resource allocation. The present application also proposes an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of the embodiment of the present application. Example

[0018] In addition, to achieve the above-mentioned purpose, the embodiments of the present application further provide a computer-readable storage medium storing a computer program, which implements the method of the embodiments of the present application when executed by a processor; The following is a detailed introduction to the various components of electronic equipment: The term "processor" refers to the control center of an electronic device and can be a single processor or a collective term for multiple processing elements. For example, the processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0019] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0020] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0021] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processor via an interface circuit of the electronic device, and this is not specifically limited in the embodiments of the present invention.

[0022] A transceiver is used to communicate with network devices or terminal devices.

[0023] Optionally, the transceiver may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0024] Optionally, the transceiver may be integrated with the processor, or may exist independently and be coupled to the processor via an interface circuit of the router, which is not specifically limited in the embodiment of the present invention.

[0025] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method described in the above method embodiment, and will not be repeated here.

[0026] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), but may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0027] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0028] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0029] It should be understood that the term "and / or" in this article merely describes the association relationship between associated objects based on the smart grid dynamic planning method for multi-dimensional spatiotemporal coupling, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship based on the smart grid dynamic planning method for multi-dimensional spatiotemporal coupling, but it may also indicate an "and / or" relationship based on the smart grid dynamic planning method for multi-dimensional spatiotemporal coupling. Please refer to the previous and next context for specific understanding.

[0030] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0031] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0032] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

Claims

1. A smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling, characterized by: The steps include: S1, collect original data; S2, conduct multi-dimensional modeling; S3, perform dynamic planning; S4, performing optimization and solution; S5, conduct spatiotemporal coordination and resource allocation; S6. Perform verification and iteration.

2. The smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling according to claim 1 is characterized in that: In S1, raw data collection is performed, and the collected data include historical power grid operation data, meteorological data and geographical data, and the spatiotemporal resolution of these data is set. The historical power grid operation data specifically includes historical power grid load curves and data, historical power grid power supply data and historical electricity prices; the meteorological data specifically includes wind speed, light, temperature and humidity data; the geographical data specifically includes power grid topology, energy storage / power generation equipment distribution location information and load center location information. The spatiotemporal resolution setting includes time dimension and space dimension, wherein the time dimension is the dynamic demand by time, day and season, and the space dimension is the regional power grid division.

3. The smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling according to claim 1 is characterized in that: In S2, multi-dimensional modeling is performed, and the specific steps are: establishing a time-space coupling model and generating a mathematical expression. The time-space coupling model includes time coupling and space coupling. Time coupling is the temporal correlation between power demand and renewable energy fluctuations. Spatial coupling is the power transmission constraint between grid nodes. The mathematical expression is the objective function of minimizing total cost. The constraint adjustment is power balance, equipment capacity, renewable energy absorption rate, and carbon emission cap.

4. The smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling according to claim 1 is characterized in that: In S3, dynamic planning is performed, and cycle planning and division are performed first. The planning unit is year, month or week. Optimization iteration is performed for each stage, and the large power grid is divided into multiple sub-areas. Distributed optimization is adopted, and the optimization algorithm adopts the ADMM algorithm.

5. The smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling according to claim 1 is characterized in that: In S4, an optimization solution is performed, and the optimization solution algorithm includes a mixed integer linear programming algorithm, a dynamic programming algorithm, and a reinforcement learning algorithm. In S5, spatiotemporal coordination and resource allocation are performed, specifically: flexible resource scheduling and cross-regional collaboration are performed, spatiotemporal charging and discharging strategies for energy storage systems (batteries, pumped storage) are formulated, demand-side response is performed, power mutual assistance between multiple regions, and spare capacity sharing is performed.

6. The smart grid dynamic planning method based on multi-dimensional spatiotemporal coupling according to claim 1, characterized in that: In S6, verification and iteration are performed, and the specific steps are as follows: first, simulation testing is performed to verify the robustness of the model based on typical scenarios, such as extreme weather and load surges; then, sensitivity analysis is performed to analyze the impact of changes in key parameters on the planning results. Key data include electricity prices and energy storage costs, and the planning strategy is dynamically adjusted according to actual operating data.

7. A smart grid dynamic planning system based on multi-dimensional spatiotemporal coupling, characterized by: It includes an original data acquisition module, a multi-dimensional modeling module, a dynamic programming module, an optimization solution module, a spatiotemporal coordination and resource allocation module, and a verification and iteration module. The original data acquisition module is used to collect and accumulate original relevant data. The multi-dimensional modeling module is used to establish a spatiotemporal coupling model and establish the temporal correlation between power demand and renewable energy fluctuations. The dynamic programming module is used for dynamic division of time and region. The optimization solution module is used for linear programming calculation of divided states. The spatiotemporal coordination and resource allocation module is used for flexible resource scheduling and allocation. The verification and iteration module is used for simulation verification of resource allocation.

8. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the smart grid dynamic planning system based on multi-dimensional spatiotemporal coupling as claimed in claim 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the smart grid dynamic planning system based on multi-dimensional spatiotemporal coupling as claimed in claim 7.

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