Power grid capacity configuration method and system and electronic equipment

Abstract

The invention provides a power grid capacity configuration method and system and electronic equipment, and relates to the technical field of power supply setup, and the method comprises the steps: constructing a multi-element complementary energy system of a photovoltaic unit, an energy storage unit and a gas unit, and carrying out the optimal configuration based on the full-time-sequence operation simulation and the optimal economical efficiency. Multi-day continuous supply guarantee can be realized; and the utilization rate of clean energy is improved.

Description

Technical Field

[0001] This invention relates to the field of power supply setup technology, and in particular to a power grid capacity configuration method, system, and electronic equipment. Background Technology

[0002] In end-point power supply scenarios, especially in remote mountainous areas, rural power grids, and long single-circuit power supply areas, there are common problems of low power supply reliability and significant power quality fluctuations. Due to geographical constraints, the lines in these areas are complex and have a large maintenance radius. Once the main grid fails or is hit by a natural disaster, the repair cycle is long and the restoration of power is difficult.

[0003] There are two main approaches to improving power supply reliability and emergency power supply: 1. Large-scale hardware upgrades and extensions on the distribution network side: This involves large-scale infrastructure investment, such as building new high-voltage (e.g., 35kV) transmission and transformation projects. By extending transmission lines and adding substations, the length of power supply lines can be shortened, the grid structure can be strengthened, and the power supply margin can be increased. However, due to the complex geographical environment in mountainous areas, this approach results in extremely high unit investment costs and long construction periods. The asset utilization rate of newly added equipment (e.g., the load rate of substation main transformers) is often very low.

[0004] 2. Reliance on short-term emergency backup power: For critical loads, uninterruptible power supplies (UPS) or small diesel generator sets are typically configured as emergency backups. The core objective of this type of solution is to ensure a smooth transition of the system in the initial stage of a main grid failure, or to maintain power supply for only a short period (e.g., the common emergency design standard in the industry is usually around 2 hours) while waiting for the main grid failure to be resolved or for professional rescue forces to arrive. These devices often operate in isolation, making it difficult to achieve efficient and coordinated dynamic scheduling with clean energy sources such as distributed photovoltaic power.

[0005] In existing technologies, microgrid network architecture is mainly based on AC grid connection, and operation control follows the principle of "prioritizing grid connection and consumption, and providing emergency backup for islanding". In grid-connected mode, distributed renewable energy is fully consumed first, and the remaining power is fed into the main grid. When the main grid fails, islanding is triggered by relay protection devices, and energy storage and backup power (such as diesel generators) ensure power supply to important loads. However, there is a lack of specific specifications for key issues such as multi-day continuous power supply requirements and source-load timing matching optimization in end-point power supply scenarios.

[0006] In typical end-user scenarios such as remote mountainous areas, existing configurations often employ a dual structure of photovoltaic (PV) + energy storage, or simply add diesel generators as backups. For example, in areas with relatively small load fluctuations, PV capacity is typically configured at 1.5 times the maximum load, and energy storage capacity at 25% of the PV capacity to meet basic peak shaving and valley filling needs. In areas prone to natural disasters, additional diesel generators with peak power equivalent to the maximum load are configured to ensure power supply startup capability in emergency situations. The core consideration of such solutions focuses on controlling equipment investment costs, without fully considering the load characteristics, resource endowment, and power supply duration requirements of the end-user scenario for refined design.

[0007] Specifically, the existing capacity configuration and power supply upgrade solutions have the following four major objective drawbacks: The continuous power supply capacity is severely insufficient and cannot cope with extreme disasters: Traditional emergency power supply solutions typically only provide power for about 2 hours. When faced with main grid failures lasting for several days (such as 3 days for townships and 7 days for counties), ice storms, or earthquakes, this extremely short power supply time is completely unable to meet the long-term, continuous power supply needs of basic infrastructure such as hospitals, communications, and water supply, as well as the living loads of residents in the region.

[0008] Poor economic efficiency and low return on investment: Traditional solutions involving the construction or expansion of transmission and transformation lines result in huge total investments due to the large scale of the project and the difficulty of the route. However, since the end load is usually not large, the equipment utilization efficiency (load rate) resulting from such huge investments is extremely low, which is not economically viable and leads to a waste of resources.

[0009] Supply and demand imbalance and low utilization rate of clean energy: The existing configuration of distributed power sources such as photovoltaics lacks systematic source-load coordination optimization, resulting in obvious backfeeding (power backfeeding) phenomenon in the power grid during peak photovoltaic output periods, which may cause equipment overload and voltage over-limit; while at night or during photovoltaic off-peak periods, there is excessive reliance on the main grid, making the system's self-balancing ability extremely poor, unable to meet the high requirements of new microgrids for clean energy power self-sufficiency rate (such as more than 50%).

[0010] Lack of ability to cope with extreme weather: Relying solely on a combination of photovoltaics and energy storage, the stored energy will be quickly depleted under extreme weather conditions such as continuous rain and insufficient sunlight, posing a risk of system failure. Existing technologies lack backup power configuration methods that offer rapid response, relatively flexible refueling, and the ability to coordinate with new energy sources, resulting in insufficient resilience of the system to harsh operating conditions. Summary of the Invention

[0011] In view of this, the purpose of the present invention is to provide a grid capacity configuration method, system and electronic equipment. The method constructs a multi-complementary energy system of photovoltaic + energy storage + gas turbine units and optimizes the configuration based on full-time operation simulation and the goal of optimal economic efficiency, which can achieve continuous supply for multiple days and improve the utilization rate of clean energy.

[0012] In a first aspect, embodiments of the present invention provide a power grid capacity configuration method, the method comprising: Steps for determining the duration of power supply guarantee: Determine the load power parameters based on the power grid demand parameters and climate parameters corresponding to the target power supply area, and use the load power parameters to determine the duration of power supply guarantee corresponding to the target power supply area; Power grid time series analysis steps: Calculate the power difference between photovoltaic power generation and load demand power within the target time period based on the photovoltaic power supply curve and load characteristic curve corresponding to the target power supply area, and use the power difference and its corresponding power supply period to determine the time series analysis results corresponding to the target power supply area; Constraint integration steps: Using the power load parameters and grid operation parameters of the target power supply area, as well as the power generation capacity and output of clean energy in the target power supply area, determine the set of constraints corresponding to the target power supply area; Capacity optimization iteration steps: Construct a capacity configuration model corresponding to the target power supply area based on the time series analysis results and the set of constraints, and use the capacity configuration model to obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration. Configuration scheme output steps: Based on the capacity configuration strategy, obtain the equipment capacity parameters corresponding to photovoltaic equipment, energy storage equipment and gas turbine equipment in the target power supply area, and use the coordinated control parameters corresponding to the equipment capacity parameters to determine the grid capacity configuration strategy corresponding to the target power supply area.

[0013] Optional steps for determining the power supply duration include: Obtain the geographical area data corresponding to the target power supply area, and use the power supply demand duration corresponding to the geographical area data to determine the power grid power supply demand parameters. Obtain the climate parameters and grid condition parameters corresponding to the target power supply area. Use the climate parameters to obtain the sunshine duration and number of rainy days corresponding to the target power supply area, and use the grid condition parameters to obtain the line load rate and transformer capacity corresponding to the target power supply area. The output power parameters of distributed power sources, energy storage devices and gas turbine units in the target power supply area are determined based on sunshine duration, number of rainy days, line load rate and transformer capacity. Determine the load power parameters corresponding to the target power supply area based on the power grid demand parameters and output power parameters. The duration of power supply protection for the target power supply area is determined by using the duration corresponding to the load power parameters.

[0014] Optional, power grid time series analysis steps include: Obtain meteorological data corresponding to the target power supply area within the first target time period, generate photovoltaic power supply curves corresponding to the meteorological data using Markov chain prediction models, and obtain the photovoltaic power generation power corresponding to the target power supply area based on the photovoltaic power supply curves. Obtain load data corresponding to the target power supply area within the second target time period, use K-means clustering algorithm to obtain load characteristic curves corresponding to the load data, and determine the load demand power corresponding to the target power supply area based on the load characteristic curves; Calculate the power difference between photovoltaic power generation and load demand at the same time based on the power supply period corresponding to the target power supply area, and determine the supply-demand gap period and reverse transmission period included in the power supply period based on the power difference; The timing analysis results corresponding to the target power supply area are determined based on the power difference, the supply-demand gap period, and the reverse transmission period.

[0015] Optional constraint integration steps include: Based on the power generation capacity and output of clean energy in the target power supply area, determine the installed capacity of renewable energy power generation and the output of clean energy power generation in the target power supply area. Based on the power load parameters of the target power supply area, determine the maximum load and total power consumption of the power grid corresponding to the target power supply area. Based on the power grid operation parameters of the target power supply area, determine the maximum load limit, distributed power generation power, equivalent power load, voltage and frequency values, energy storage device power value, and gas turbine start-up response time of the target power supply area. The first constraint condition corresponding to the target power supply area is determined by using the first ratio of the installed capacity of renewable energy power generation to the maximum load of the power grid. The second constraint condition corresponding to the target power supply area is determined by using the second ratio of clean energy power generation to the total power consumption of the power grid. The reverse load rate of the transformer in the target power supply area is calculated using the maximum load limit of the power grid, the power of distributed power sources, and the equivalent power of the electrical load. Based on the reverse load rate, the third constraint condition corresponding to the target power supply area is determined. The fourth constraint condition corresponding to the target power supply area is determined by comparing the voltage frequency value, the energy storage device power value, and the gas turbine start-up response time with the preset threshold. The set of constraints corresponding to the target power supply area is determined based on the first constraint, the second constraint, the third constraint, and the fourth constraint.

[0016] Optional capacity optimization iterative steps include: The objective function of the capacity configuration model under the power supply duration is determined based on the continuous power supply duration corresponding to the target power supply area, equipment investment cost, and clean energy utilization rate. Using the photovoltaic installed capacity, energy storage capacity, and gas turbine capacity corresponding to the target power supply area as optimization variables, the time series analysis results as input data, and the set of constraint conditions as constraint conditions, a capacity configuration model corresponding to the target power supply area is constructed. The initial capacity combination corresponding to photovoltaic installed capacity, energy storage capacity, and gas turbine capacity is generated based on the particle swarm optimization algorithm. After time-series simulation and iteration of the initial capacity combination using the objective function control capacity configuration model, the capacity configuration strategy corresponding to the target power supply area under the power supply duration is obtained.

[0017] Optionally, the step of determining the objective function of the capacity configuration model under the power supply duration based on the continuous power supply guarantee period corresponding to the target power supply area, equipment investment cost, and clean energy utilization rate includes: Obtain the continuous power supply duration, equipment investment cost, and clean energy utilization rate for the target power supply area; Based on the comparison between continuous power supply duration and power supply duration, the reliability objective function corresponding to the target power supply area is determined. Based on the comparison between equipment investment costs and preset cost thresholds, the objective function for investment costs corresponding to the target power supply area is determined; Based on the comparison results between the clean energy utilization rate and the preset utilization rate threshold, the utilization rate objective function corresponding to the target power supply area is determined; The weight values ​​of the reliability objective function, investment cost objective function, and utilization rate objective function are determined using the entropy weight method. The objective function for the capacity allocation model is constructed by using the reliability objective function, investment cost objective function, and utilization rate objective function, along with their corresponding weight values.

[0018] Optional, the configuration scheme output steps include: Based on the capacity configuration strategy, obtain the corresponding photovoltaic capacity, energy storage capacity and gas turbine capacity for photovoltaic equipment, energy storage equipment and gas turbine equipment respectively; Determine the equipment capacity parameters corresponding to the target power supply area based on photovoltaic capacity, energy storage capacity, and gas turbine capacity; The scheduling instructions corresponding to photovoltaic equipment, energy storage equipment and gas turbine equipment in the target power supply area are determined by using equipment capacity parameters, and the coordinated control parameters corresponding to photovoltaic equipment, energy storage equipment and gas turbine equipment are generated according to the scheduling instructions; The grid capacity configuration strategy for the target power supply area is determined by coordinating control parameters.

[0019] Optionally, the step of determining the grid capacity configuration strategy corresponding to the target power supply area through coordinated control parameters includes: Based on the collaborative control parameters, the grid access strategy and equipment operation strategy corresponding to the target power supply area are determined. The grid access strategy is used to control the photovoltaic equipment, energy storage equipment and gas turbine equipment to connect to the power grid corresponding to the target power supply area. The equipment operation strategy is used to control the photovoltaic equipment, energy storage equipment and gas turbine equipment to supply power. The grid capacity configuration strategy for the target power supply area is determined by utilizing grid access strategy and equipment operation strategy.

[0020] In a second aspect, the present invention provides a power grid capacity configuration system, the system comprising: Power supply duration determination module: used to determine load power parameters based on the power grid demand parameters and climate parameters corresponding to the target power supply area, and use the load power parameters to determine the power supply duration corresponding to the target power supply area; The power grid time series analysis module is used to calculate the power difference between photovoltaic power generation and load demand power within the target time period based on the photovoltaic power supply curve and load characteristic curve corresponding to the target power supply area, and to determine the time series analysis results corresponding to the target power supply area using the power difference and its corresponding power supply period. Constraint integration module: used to determine the set of constraints corresponding to the target power supply area by utilizing the power load parameters and grid operation parameters of the target power supply area, as well as the power generation capacity and output of clean energy in the target power supply area; Capacity optimization iteration module: used to construct a capacity configuration model corresponding to the target power supply area based on time series analysis results and constraint set, and to obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration using the capacity configuration model; Configuration scheme output module: used to obtain the equipment capacity parameters corresponding to the target power supply area according to the capacity configuration strategy, and use the cooperative control parameters corresponding to the equipment capacity parameters to determine the power grid capacity configuration strategy corresponding to the target power supply area.

[0021] Thirdly, embodiments of the present invention also provide an electronic device, which includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, and the processor executing the computer-executable instructions to implement the steps of the power grid capacity configuration method provided in the first aspect.

[0022] Fourthly, embodiments of the present invention also provide a storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the steps of the power grid capacity configuration method provided in the first aspect.

[0023] This invention provides a grid capacity configuration method, system, and electronic device. In configuring grid capacity for end-point power supply scenarios, the method first determines load power parameters based on grid power demand parameters and climate parameters corresponding to the target power supply area, and then uses these load power parameters to determine the power supply duration corresponding to the target power supply area. Next, it calculates the power difference between photovoltaic power generation and load demand power within the target time period based on the photovoltaic power supply curve and load characteristic curve corresponding to the target power supply area, and uses the power difference and its corresponding power supply period to determine the time series analysis results corresponding to the target power supply area. Subsequently, it uses the electricity load parameters and grid operation parameters of the target power supply area, as well as the power generation and output of clean energy in the target power supply area, to determine the set of constraints corresponding to the target power supply area. Then, it constructs a capacity configuration model corresponding to the target power supply area using the time series analysis results and the set of constraints, and uses the capacity configuration model to obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration. Finally, it obtains the equipment capacity parameters corresponding to photovoltaic equipment, energy storage equipment, and gas turbine equipment in the target power supply area based on the capacity configuration strategy, and uses the coordinated control parameters corresponding to the equipment capacity parameters to determine the grid capacity configuration strategy corresponding to the target power supply area. This method constructs a multi-complementary energy system consisting of photovoltaics, energy storage, and gas turbines, and optimizes its configuration based on full-time operation simulation and the goal of optimal economic efficiency. This enables continuous supply for multiple days and improves the utilization rate of clean energy.

[0024] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 A flowchart of a power grid capacity configuration method provided in an embodiment of the present invention; Figure 2 This is a flowchart of step S101, which involves determining the power supply duration, in a power grid capacity configuration method according to an embodiment of the present invention. Figure 3 This is a flowchart of the power grid timing analysis step S102 in a power grid capacity configuration method provided by an embodiment of the present invention; Figure 4 This is a flowchart of the constraint integration step S103 in a power grid capacity configuration method provided by an embodiment of the present invention; Figure 5 This is a flowchart of the capacity optimization iteration step S104 in a power grid capacity configuration method provided by an embodiment of the present invention; Figure 6 A flowchart of step S501 in a power grid capacity configuration method provided in an embodiment of the present invention; Figure 7 This is a flowchart of step S105, which is a configuration scheme output step in a power grid capacity configuration method provided in an embodiment of the present invention. Figure 8 A flowchart of step S704 of a power grid capacity configuration method provided in an embodiment of the present invention; Figure 9 A flowchart of another power grid capacity configuration method provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of a power grid capacity configuration system provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0028] icon: 1010 - Power Supply Duration Determination Module; 1020 - Power Grid Timing Analysis Module; 1030 - Constraint Integration Module; 1040 - Capacity Optimization Iteration Module; 1050 - Configuration Scheme Output Module; 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] To facilitate understanding of this embodiment, a power grid capacity configuration method disclosed in this embodiment will first be introduced, such as... Figure 1 As shown, the method includes: Step S101 for determining the duration of power supply guarantee: Determine the load power parameters based on the power grid demand parameters and climate parameters corresponding to the target power supply area, and use the load power parameters to determine the duration of power supply guarantee corresponding to the target power supply area.

[0031] This step forms the basis of capacity configuration, with the core objective of clarifying the duration of the required power supply guarantee. First, two key parameters for the target power supply area need to be collected: First, power grid guarantee requirements parameters, including the importance level and minimum operating power of essential loads such as hospitals, communication base stations, and water pumping stations within the area, as well as the scale of core residential loads. This also requires reference to local power grid planning standards for guaranteeing power supply during extreme faults (e.g., 3 days for townships and 7 days for counties). Second, climate parameters, covering the duration and frequency of historical extreme weather events (e.g., ice storms, continuous rain). Based on these parameters, load power parameters (including average load, peak load, and load fluctuation characteristics) are derived through load statistics and simulation calculations. Finally, combining this with the core principle of ensuring continuous load operation, the duration of the power supply guarantee for the target area under extreme scenarios is determined.

[0032] Power grid time series analysis step S102: Calculate the power difference between photovoltaic power generation and load demand power within the target time period based on the photovoltaic power supply curve and load characteristic curve corresponding to the target power supply area, and use the power difference and its corresponding power supply period to determine the time series analysis results corresponding to the target power supply area.

[0033] This step focuses on the source-load timing matching problem, providing data support for subsequent configuration. The core is to clarify when and how much power is lacking. First, a precise curve model of the target area needs to be constructed: the photovoltaic power supply curve needs to be combined with historical solar irradiance data, seasonal variation patterns, and photovoltaic module conversion efficiency to simulate hourly photovoltaic power generation within the target time period (usually covering typical days, typical seasons, or extreme weather cycles); the load characteristic curve needs to distinguish the electricity consumption patterns of different load types (such as peak residential loads during mealtimes and stable 24-hour loads for hospitals) to generate hourly load demand power. By comparing the two curves hourly, the power difference between photovoltaic power generation and load demand power for each time period is calculated. When the difference is positive, it means that photovoltaic output is excessive, and energy storage or clean energy consumption needs to be considered; when the difference is negative, it indicates insufficient photovoltaic output, requiring energy storage discharge or supplementary energy from gas turbine units. Based on these power differences and the distribution characteristics of the corresponding time periods, the grid timing analysis results are formed, clearly showing the specific time periods and scale of the source-load imbalance.

[0034] Constraint integration step S103: Using the power load parameters and grid operation parameters of the target power supply area, as well as the power generation capacity and output of clean energy in the target power supply area, determine the set of constraints corresponding to the target power supply area.

[0035] This step establishes the boundary framework for capacity configuration, ensuring the solution is implemented safely, compliantly, and efficiently. It requires integrating four key constraints to form a set of constraints: first, electricity load constraints, including minimum power supply and voltage frequency tolerance ranges to prevent power outages or substandard power quality; second, grid operation constraints, covering the upper limit of regional distribution network access capacity, line current carrying capacity limits, and voltage stability thresholds to prevent equipment overload caused by backfeeding; third, clean energy constraints, specifying the maximum output and power generation fluctuation range of distributed power sources such as photovoltaics, as well as clean energy self-sufficiency targets (e.g., above 50%); and fourth, equipment operation constraints, including the charging and discharging efficiency and cycle life limits of energy storage devices, and the start-up response time, minimum output, and fuel replenishment capacity of gas turbine units. These constraints collectively constitute the hard boundary of capacity configuration, ensuring the practical feasibility of the solutions generated during subsequent optimization.

[0036] Capacity optimization iteration step S104: Construct a capacity configuration model corresponding to the target power supply area through time series analysis results and constraint condition set, and use the capacity configuration model to obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration.

[0037] This step is the core of the method, aiming to achieve the optimal configuration goal, namely, determining the appropriate capacity through model calculation. First, based on the power grid time-series analysis results (source-load gap data), a capacity configuration model is constructed in conjunction with a set of constraints. The core objective of the model is dual-dimensional optimization: first, a full-time-series power supply guarantee goal, ensuring that the power supply needs of all important loads are met through the coordinated operation of photovoltaic, energy storage, and gas turbine units during the power supply guarantee duration; second, an economic optimization goal, comprehensively considering equipment investment costs, operation and maintenance costs, fuel procurement costs, and the benefits brought by photovoltaic integration. During the model solution process, photovoltaic capacity, energy storage capacity, and gas turbine power are used as core variables. Iterative optimization algorithms (such as genetic algorithms and particle swarm optimization) are used to continuously adjust the variable parameters, ultimately obtaining a capacity configuration strategy that achieves the required power supply reliability and minimizes overall cost while satisfying all constraints.

[0038] Configuration scheme output step S105: Obtain the equipment capacity parameters corresponding to photovoltaic equipment, energy storage equipment and gas turbine equipment in the target power supply area according to the capacity configuration strategy, and determine the grid capacity configuration strategy corresponding to the target power supply area using the coordinated control parameters corresponding to the equipment capacity parameters.

[0039] This step is the final implementation stage of the configuration method, transforming the optimization results into a concrete, executable solution. First, based on the capacity configuration strategy derived from the capacity optimization iteration steps, the core parameters of the photovoltaic (PV) equipment, energy storage equipment, and gas turbine units are clarified, including the installed capacity of the PV modules, the rated capacity and charging / discharging power of the energy storage system, and the rated power of the gas turbine units. Building on this, the coordinated control parameters for the three types of equipment are further determined, clarifying the operating rules under different scenarios: for example, in grid-connected mode, "PV is prioritized for consumption, excess energy is charged to storage, and surplus electricity can be connected to the grid"; in the event of a main grid failure, "energy storage discharges first to ensure power supply, and gas turbine units start to supplement energy when the energy storage capacity falls below a threshold"; in extreme cloudy / rainy weather, "gas turbine units are the main power source, and energy storage smooths load fluctuations," etc. Finally, the equipment capacity parameters and coordinated control parameters are integrated to form a complete grid capacity configuration scheme, providing clear guidance for project implementation and operation scheduling.

[0040] Optionally, the power supply duration determination step S101, such as... Figure 2 As shown, it includes: Step S201: Obtain the area data corresponding to the target power supply area, and determine the power grid power supply demand parameters using the power supply demand duration corresponding to the area data.

[0041] The core of this step is to establish the correlation between regional characteristics and power supply needs, providing demand guidance for subsequent analysis. First, accurately obtain the geographical area data of the target power supply region. The geographical area directly affects the difficulty and duration of power supply needs; for example, in remote mountainous areas, a larger area often means more dispersed power lines, longer travel times for maintenance personnel to reach the fault point, and a correspondingly longer repair cycle after a main grid fault. Based on this, and combining the correspondence between geographical area and power supply demand duration in regional power grid planning standards, the area data is transformed into quantified power grid power supply demand parameters. These parameters clearly define the basic minimum duration for regional power supply needs, serving as the demand benchmark for all subsequent analyses.

[0042] Step S202: Obtain the climate parameters and grid condition parameters corresponding to the target power supply area. Use the climate parameters to obtain the sunshine duration and number of rainy days corresponding to the target power supply area, and use the grid condition parameters to obtain the line load rate and transformer capacity corresponding to the target power supply area.

[0043] This step focuses on data input, comprehensively collecting core environmental and grid parameters affecting power supply capacity and transforming them into quantifiable analytical indicators. On one hand, it acquires climate parameters for the target area, including historical and predicted solar intensity distribution and seasonal rainfall characteristics. Through data statistics, it extracts core indicators: sunshine duration (directly determining photovoltaic output potential) and number of rainy days (affecting photovoltaic efficiency and the probability of extreme weather). On the other hand, it collects regional grid condition parameters, covering basic information such as line type, grid structure, and transformer specifications, thereby calculating line load factor (reflecting line power supply bottlenecks) and transformer capacity (defining the regional power supply ceiling). These indicators collectively constitute the foundational data pool for subsequent equipment output calculations.

[0044] Step S203: Determine the output power parameters of distributed power sources, energy storage devices and gas turbine units in the target power supply area based on sunshine duration, number of rainy days, line load rate and transformer capacity.

[0045] This step transforms basic data into equipment capabilities, clarifying the output potential of each device in the diversified and complementary system. Combining the four key indicators extracted in step S202—sunshine duration, number of rainy days (affecting photovoltaic output), line load factor (limiting total output power), and transformer capacity (constraining power supply scale)—calculations are performed using equipment operation characteristic models: For photovoltaics, its maximum output power and fluctuation range are determined based on sunshine duration and rainfall impact; for energy storage, its reasonable charging and discharging power is determined by combining photovoltaic output fluctuations and line load limitations; for gas turbine units, their maximum output power is determined based on transformer capacity and line carrying capacity. Ultimately, the output power parameters for each of the three types of equipment are formed, clearly defining the regional energy supply capacity boundaries.

[0046] Step S204: Determine the load power parameters corresponding to the target power supply area based on the power grid demand parameters and output power parameters.

[0047] This step is the demand-supply balancing stage, precisely identifying the core load parameters that need to be guaranteed. Guided by the power grid guarantee demand parameters (bottom line for guarantee duration, critical load level) determined in step S201, and combined with the equipment output power parameters (upper limit of supply capacity) obtained in step S203, load power parameters are determined through load matching analysis. These parameters not only include basic data such as the region's average load and peak load, but also clearly define the core load power that must be guaranteed within the equipment's supply capacity, ensuring that the subsequent determination of the guarantee duration is both demand-driven and feasible.

[0048] Step S205: Determine the power supply duration corresponding to the target power supply area using the duration corresponding to the load power parameters.

[0049] This step is the final output stage for determining the power supply guarantee duration, transforming load parameters into a clear power supply guarantee time standard. The core logic is that the continuous operation requirement of the load determines the power supply guarantee duration. By analyzing the load power parameters determined in step S204, the focus is on extracting the continuous operation time requirements of core loads (such as hospitals requiring 24-hour continuous power supply and residential core loads requiring all-day guarantee). Combined with the results of the previous equipment supply capacity assessment, the final power supply guarantee duration for the target power supply area is determined. This duration not only meets the continuous operation requirements of core loads but also ensures that in extreme scenarios (such as main grid failures or natural disasters), the multi-functional system of photovoltaic + energy storage + gas turbine units can support the grid until it is restored or rescue arrives.

[0050] Optionally, the power grid time series analysis step S102, such as Figure 3 As shown, it includes: Step S301: Obtain meteorological data corresponding to the target power supply area within the first target time period, generate photovoltaic power supply curves corresponding to the meteorological data using a Markov chain prediction model, and obtain the photovoltaic power generation power corresponding to the target power supply area based on the photovoltaic power supply curves.

[0051] This step focuses on accurate prediction of photovoltaic (PV) output, with the core being the transformation of meteorological data into quantifiable power generation data. First, a target time period is defined (covering cycles with significant fluctuations in PV output, such as typical years, seasons, or extreme weather cycles), and refined meteorological data for the target area within this period is collected, including hourly solar irradiance, cloud cover, temperature, humidity, and other key parameters. These data directly determine the photoelectric conversion efficiency of PV modules. Subsequently, a Markov chain prediction model is introduced. This model excels at capturing the random fluctuations and state transition patterns of meteorological data. By learning from historical meteorological data, the model mines the transition probabilities of different weather states (such as sunny, cloudy, overcast), thereby generating a PV power supply curve that matches the meteorological change trend within the first target time period. Finally, hourly PV power generation data is extracted from this curve, forming the time-series foundation data for PV output.

[0052] Step S302: Obtain the load data corresponding to the target power supply area within the second target time period, use the K-means clustering algorithm to obtain the load characteristic curve corresponding to the load data, and determine the load demand power corresponding to the target power supply area based on the load characteristic curve.

[0053] The core of this step is to extract load operation patterns and address the issues of fragmented and unclear load data. First, a second target time period is determined, aligned with the first target time period, ensuring consistency in the analysis cycle of photovoltaic output and load demand. Then, hourly load data for the target area within this time period is collected, covering electricity consumption information for various loads, including residential, public service, and industrial loads. Considering the strong periodicity and volatility of load data, a K-means clustering algorithm is introduced for load data classification. Based on characteristics such as load size and variation amplitude, the algorithm clusters time periods with similar electricity consumption patterns into several categories (e.g., weekday peak periods, weekday valley periods, holiday load periods, etc.), and fits a load characteristic curve reflecting the regional electricity consumption patterns based on the clustering results. Hourly load demand power is extracted from this curve to clarify the load scale and changing trends in different time periods.

[0054] Step S303: Calculate the power difference between photovoltaic power generation and load demand power at the same time based on the power supply period corresponding to the target power supply area, and determine the supply-demand gap period and reverse transmission period included in the power supply period based on the power difference.

[0055] This step is the core comparison step in time-series analysis. By matching source and load power hourly, the specific characteristics of the supply-demand imbalance are clarified. Using the complete power supply period of the target power supply area as the time axis, the hourly photovoltaic power generation obtained in step S301 is matched one-to-one with the hourly load demand power obtained in step S302 to calculate the power difference at each moment (the calculation formula is: power difference = photovoltaic power generation - load demand power). Based on the positive or negative attribute of the power difference, the power supply period is precisely divided: when the power difference is <0, it indicates that the photovoltaic output cannot meet the current load demand, and this period is a supply-demand gap period, which needs to rely on energy storage discharge or gas turbine units to supplement energy; when the power difference is ≥0, it means that the photovoltaic output is sufficient or even excessive, and this period is a reverse power supply period, which needs to consider energy storage charging or reasonable consumption of surplus power. Through this step, the specific period of source-load imbalance and the scale of gap / surplus are clearly located.

[0056] Step S304: Determine the time sequence analysis results corresponding to the target power supply area based on the power difference, the supply-demand gap period, and the reverse transmission period.

[0057] This step is the output stage of the time-series analysis, transforming scattered data into systematic analytical conclusions. Based on the calculation results of step S303, three types of key information are integrated to form the final time-series analysis results: first, hourly power difference data, which intuitively reflects the matching degree of source and load at each moment; second, detailed information on the supply-demand gap period, including the gap start and end times, gap duration, maximum gap power, and average gap power, providing a basis for energy storage discharge and gas turbine startup strategies; and third, core parameters of the reverse transmission period, covering the reverse transmission start and end times, reverse transmission duration, maximum reverse transmission power, and average reverse transmission power, guiding energy storage charging planning and surplus power consumption schemes. The final time-series analysis results comprehensively present the source-load time-series characteristics of the target area within the target time period, providing accurate data input for the subsequent construction of capacity optimization models.

[0058] Optionally, constraint integration step S103, such as Figure 4 As shown, it includes: Step S401: Determine the installed capacity of renewable energy generation and the amount of clean energy generated in the target power supply area based on the power generation capacity and amount of clean energy generated in the target power supply area.

[0059] This step focuses on clean energy supply capacity, providing foundational data for subsequent green power generation constraints. The core is to quantify this from two dimensions: installed capacity and total power generation, based on actual operational data of clean energy sources such as photovoltaics within the target region. This involves determining the installed capacity of renewable energy generation (reflecting the upper limit of clean energy supply potential) by statistically analyzing the maximum power output of clean energy and combining this with parameters such as equipment operating efficiency; and simultaneously clarifying the amount of clean energy generated (reflecting the actual contribution capacity of clean energy) by accumulating power generation data over the target period. These two parameters together constitute the core basis for clean energy-related constraints.

[0060] Step S402: Determine the maximum load and total power consumption of the power grid corresponding to the target power supply area based on the power load parameters of the target power supply area, and determine the maximum load limit, distributed power source power, equivalent power load, voltage frequency value, energy storage device power value, and gas turbine start-up response time of the target power supply area based on the power grid operation parameters of the target power supply area.

[0061] This step involves dual data mining of load demand and grid capacity to construct a pool of fundamental parameters for constraints. On one hand, based on the electricity load parameters of the target area (such as hourly load data and load type proportions), statistical analysis is used to derive the maximum grid load (reflecting peak load pressure) and total grid electricity consumption (reflecting long-term load demand). On the other hand, combined with grid operating parameters (such as line specifications, equipment rated parameters, and dispatching procedures), a series of key operational boundary values ​​are extracted, including: the maximum grid load limit (the upper limit of the load that the grid can safely carry), distributed power generation (the access and output limits of distributed power sources), equivalent load power (the actual load demand after considering factors such as network losses), voltage and frequency values ​​(core indicators of power quality, which must meet national standards), energy storage device capacity (energy storage charging and discharging capacity and SOC limits), and gas turbine start-up response time (energy replenishment speed requirements in emergency scenarios). These parameters cover the core operating characteristics of load, grid, and equipment.

[0062] Step S403: Determine the first constraint condition corresponding to the target power supply area using the first ratio of the installed capacity of renewable energy power generation to the maximum load of the power grid.

[0063] The core of this step is to define the matching boundary between renewable energy and grid load, avoiding an imbalance between renewable energy installed capacity and grid capacity. This is achieved by calculating the first ratio of renewable energy generation capacity to the grid's maximum load. This ratio reflects the renewable energy capacity's ability to cover peak loads; a ratio that is too high may lead to excessive impact on the grid from fluctuations in renewable energy output, while a ratio that is too low will fail to fully realize the environmental benefits of renewable energy. By combining the regional grid's regulation capacity and industry standards, a reasonable range for this ratio is set, forming the first constraint condition to ensure that the scale of renewable energy installed capacity is compatible with the grid's load-carrying capacity.

[0064] Step S404: Determine the second constraint condition corresponding to the target power supply area using the second ratio of clean energy power generation to the total power consumption of the power grid.

[0065] This step focuses on the target of clean energy consumption, which is a core constraint for promoting the green transformation of the energy structure. A second ratio is calculated between clean energy generation and total grid electricity consumption; this ratio directly reflects the region's self-sufficiency rate in clean energy. By setting a minimum threshold for this ratio, a second constraint is formed, forcibly ensuring the efficient use of clean energy and avoiding waste of renewable energy output.

[0066] Step S405: Calculate the reverse load rate of the transformer in the target power supply area using the maximum load limit of the power grid, the power of the distributed power source, and the equivalent power of the electrical load, and determine the third constraint condition corresponding to the target power supply area based on the reverse load rate.

[0067] This step addresses transformer safety constraints to resolve the common backfeeding overload problem in the end-point power grid. Based on the maximum grid load limit, distributed generation power, and equivalent load power extracted in step S402, the reverse load rate of the transformer (reflecting the transformer's load pressure under backfeeding scenarios) is calculated using a power flow calculation model. To prevent insulation aging and equipment damage due to excessive reverse load, a safety threshold for the reverse load rate is set, forming a third constraint to ensure the safe operation of core power distribution equipment.

[0068] Step S406: Determine the fourth constraint condition corresponding to the target power supply area by comparing the voltage frequency value, the energy storage device power value, and the gas turbine start-up response time with the preset threshold.

[0069] This step integrates three major safety objectives—power quality, energy storage reliability, and emergency response speed—to form a comprehensive constraint. The three key parameters extracted in step S402 are compared with preset thresholds: voltage and frequency values ​​must be within the safe range specified by national standards (e.g., voltage deviation ±7%, frequency deviation ±0.5Hz); the energy storage device's charge level must avoid overcharging and over-discharging (e.g., maintaining SOC between 20% and 80%); and the gas turbine unit's start-up response time must meet emergency needs (e.g., no more than 10 seconds). These comparison standards are integrated to form a fourth constraint, ensuring qualified power grid quality, stable energy storage device lifespan, and timely emergency replenishment response.

[0070] Step S407: Determine the set of constraints corresponding to the target power supply area based on the first constraint, the second constraint, the third constraint, and the fourth constraint.

[0071] This step is the final stage of constraint integration, realizing the transformation from "dispersed constraints to a system set." It systematically integrates the four types of constraints formed in the previous steps: the first constraint ensures matching of new energy sources with loads; the second constraint ensures the utilization of clean energy; the third constraint ensures equipment safety; and the fourth constraint ensures operational and emergency safety. These four types of constraints complement each other and provide comprehensive coverage, collectively constituting the constraint set for the target power supply area. This sets clear boundaries for subsequent capacity optimization iterations, ensuring that the optimized capacity configuration scheme meets both policy and environmental requirements while also possessing safety and feasibility.

[0072] Optionally, capacity optimization iteration step S104, such as Figure 5 As shown, it includes: Step S501: Determine the objective function of the capacity configuration model under the power supply duration based on the continuous power supply duration corresponding to the target power supply area, equipment investment cost, and clean energy utilization rate.

[0073] The core of this step is to establish a guiding principle for capacity optimization, transforming the core needs of end-point power supply scenarios into quantifiable mathematical objectives. The objective function is constructed around three core dimensions: first, continuous power supply reliability, using the duration of power supply continuity in the target area as a benchmark, requiring the model's output capacity combination to ensure no power outages within that duration, quantified through indicators such as power supply compliance rate; second, economic efficiency, encompassing the entire lifecycle investment of photovoltaic, energy storage, and gas turbine units, including equipment purchase costs, installation costs, and operation and maintenance costs, aiming to minimize total costs; and third, clean energy utilization efficiency, with the utilization rate of clean energy such as photovoltaics as a core indicator, aligning with the high self-sufficiency requirements of new microgrids for clean energy. By balancing the priorities of the three dimensions through weighted coefficients (e.g., increasing the weight of power supply reliability in areas prone to extreme disasters), a multi-objective function that considers reliability, economic efficiency, and environmental friendliness is ultimately formed, providing evaluation criteria for subsequent optimization.

[0074] Step S502: Using the photovoltaic installed capacity, energy storage capacity, and gas turbine capacity corresponding to the target power supply area as optimization variables, the time series analysis results as input data, and the set of constraints as constraints, construct the capacity configuration model corresponding to the target power supply area.

[0075] This step is crucial for transforming optimization requirements into a mathematical model, clarifying the three core elements of the model: variables, inputs, and constraints. First, the optimization variables are determined: photovoltaic installed capacity, rated capacity of the energy storage system, and rated power of the gas turbine unit. These three parameters directly determine energy supply capacity and are the core objects of model optimization. Second, the input data is defined, with grid time-series analysis results as the core, including hourly source-load power differences, supply-demand gap distribution during periods, and characteristics of reverse power transmission periods, providing full-time operational scenario support for the model. Finally, constraints are set, directly introducing the constraint set formed in the constraint integration step, covering constraints on matching new energy installed capacity with load, clean energy utilization rate, equipment safe operation, and power quality, ensuring that the capacity combination output by the model is within safe and compliant boundaries. By integrating variables, inputs, and constraints, a capacity configuration model capable of simulating actual operational scenarios is constructed.

[0076] Step S503: Generate initial capacity combinations corresponding to photovoltaic installed capacity, energy storage capacity, and gas turbine unit capacity based on particle swarm optimization algorithm. After performing time-series simulation iteration on the initial capacity combinations using the objective function control capacity configuration model, obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration.

[0077] This step is the execution phase of the model solution, achieving efficient optimization of capacity combinations through relevant intelligent algorithms. The particle swarm optimization algorithm is chosen as the optimization tool because its advantage lies in its fast convergence speed when handling complex optimization problems with multiple variables and constraints, and its ability to effectively avoid local optima. First, the algorithm randomly generates a batch of initial capacity combinations (i.e., "particles"), each combination corresponding to a set of capacity parameters for photovoltaic, energy storage, and gas turbine units. Then, these initial combinations are substituted into the capacity configuration model, and a full-time simulation is performed based on the objective function. This simulates the power supply reliability (whether continuous power supply is met), economic efficiency (total cost), and clean energy utilization rate (photovoltaic absorption) under different capacity combinations within the power supply guarantee duration. Based on the simulation results, the algorithm iterates through particle position updates, retaining the best-performing capacity combinations and eliminating those that do not meet the requirements, continuously approaching the optimal value of the objective function. After multiple iterations, when the performance indicators of the capacity combinations (power supply guarantee compliance rate, cost, and utilization rate) tend to stabilize, the final capacity configuration strategy is output. This strategy is the optimal solution that meets multiple objective requirements within the power supply guarantee duration.

[0078] Optionally, step S501, which determines the objective function of the capacity configuration model under the power supply duration based on the continuous power supply duration corresponding to the target power supply area, equipment investment cost, and clean energy utilization rate, is as follows: Figure 6 As shown, it includes: Step S601: Obtain the continuous power supply duration, equipment investment cost, and clean energy utilization rate corresponding to the target power supply area.

[0079] This step is the data input stage, comprehensively collecting three types of core data required to construct the objective function, providing support for subsequent analysis. First, continuous power supply duration data, i.e., the power supply duration to be guaranteed under extreme scenarios determined through step S101 (e.g., 3 days for townships, 7 days for counties), is the core basis for reliability targets. Second, equipment investment cost data, covering the entire lifecycle economic parameters of photovoltaic modules, energy storage systems, and gas turbine units, including purchase costs, installation costs, operation and maintenance costs, and fuel costs, is the quantitative basis for economic targets. Third, clean energy utilization rate data, including regional photovoltaic resource endowment and historical absorption rates, is the core reference for green development targets. All three types of data must be accurate and matched to the actual scenarios of the target power supply area.

[0080] Step S602: Based on the comparison results of continuous power supply duration and power supply duration, determine the reliability objective function corresponding to the target power supply area.

[0081] This step transforms the rigid requirement of continuous power supply into a mathematically constrained objective. The core logic is to compare the actual power supply duration supported by the capacity combination output by the model with the preset continuous power supply duration: when the actual power supply duration is greater than or equal to the preset duration, reliability is achieved; otherwise, it is not. By introducing quantitative indicators such as the power supply compliance rate and the proportion of power outage time, a reliability objective function is constructed, requiring the function value to be maximized. This means minimizing the risk of power outages and ensuring that the continuous power supply needs of core loads are met under extreme scenarios—the primary objective for end-point power supply scenarios.

[0082] Step S603: Based on the comparison results of equipment investment cost and preset cost threshold, determine the investment cost objective function corresponding to the target power supply area.

[0083] This step, guided by cost optimization, constructs an economic objective function. By comparing the total lifecycle investment cost corresponding to different capacity combinations with a preset cost threshold (determined in conjunction with the regional power grid investment budget), the cost control effect is quantified. The core of the objective function is to minimize the total investment cost, specifically encompassing the cumulative calculation of various expenditures such as equipment procurement, construction and installation, daily operation and maintenance, and fuel consumption. Simultaneously, the function must consider the time value of money, discounting costs at different stages to ensure the accuracy of cost calculations and avoid investment waste caused by blindly pursuing power supply protection.

[0084] Step S604: Based on the comparison results of clean energy utilization rate and preset utilization rate threshold, determine the utilization rate objective function corresponding to the target power supply area.

[0085] This step transforms the need to improve clean energy utilization into an optimizable objective. By comparing the actual clean energy utilization rate under the capacity combination output by the model with a preset utilization rate threshold (such as above 70% required by new microgrids), a utilization rate objective function is constructed. The function focuses on maximizing clean energy utilization, specifically quantified by the ratio of actual photovoltaic power generation to the theoretical maximum photovoltaic power generation, while also taking into account the backfeeding problem caused by excessive photovoltaic output, ensuring that clean energy is used efficiently without affecting grid security.

[0086] Step S605: Use the entropy weight method to determine the weight values ​​of the reliability objective function, investment cost objective function, and utilization rate objective function.

[0087] This step addresses the issue of balancing the priorities of multiple objectives by using the entropy weighting method to achieve objective weight allocation and avoid biases from subjective judgment. The core logic of the entropy weighting method is to determine the weight based on the information entropy of each objective function; the smaller the information entropy, the greater the numerical difference of the objective, the more effective information it contains, and the more significant its impact on the optimization result, thus resulting in a higher weight. For example, in remote mountainous areas prone to extreme disasters, the numerical differences in the reliability objective function may be more pronounced, leading to a correspondingly higher weight; while in economically underdeveloped regions, the cost objective function may have a higher weight. The entropy weighting method calculates the weight values ​​for the three objective functions—reliability, cost, and utilization rate—providing a basis for constructing the subsequent overall objective function.

[0088] Step S606: Construct the objective function corresponding to the capacity configuration model through the reliability objective function, investment cost objective function, and utilization rate objective function and their corresponding weight values.

[0089] This step is the final stage in constructing the objective function, integrating the sub-objectives with the overall objective. The three sub-objective functions determined in steps S602-604 are multiplied by their corresponding weights obtained in step S605, and then a weighted sum is performed to form the overall objective function of the capacity allocation model. The core characteristic of the overall objective function is multi-objective balance optimization, ensuring continuous power supply compliance, minimizing investment costs, and maximizing clean energy utilization, ultimately providing a clear optimization direction for the subsequent construction of the capacity allocation model.

[0090] Optionally, the configuration scheme output step S105, such as Figure 7 As shown, it includes: Step S701: Obtain the photovoltaic capacity, energy storage capacity, and gas turbine capacity corresponding to the photovoltaic equipment, energy storage equipment, and gas turbine equipment respectively according to the capacity configuration strategy.

[0091] The core of this step is to extract key capacity indicators for three types of core equipment from the optimization results, providing a data foundation for subsequent scheme refinement. The capacity configuration strategy is the optimal result formed after iterative optimization using the particle swarm optimization algorithm, which explicitly includes the suitable capacity for photovoltaic, energy storage, and gas turbine units. In this step, the core capacity parameters of the three types of equipment need to be accurately extracted from the strategy: for photovoltaic equipment, the focus is on photovoltaic installed capacity, i.e., the total installed power of photovoltaic modules that meets the needs of clean energy utilization and load matching; for energy storage equipment, the core is energy storage capacity, covering the rated energy storage capacity and maximum charging and discharging power of the energy storage system; for gas turbine units, the focus is on the gas turbine unit capacity, i.e., the rated output power of the unit required in emergency energy replenishment scenarios. The extraction process must ensure that the data is completely consistent with the output results of the optimization model, laying the foundation for the accuracy of the scheme.

[0092] Step S702: Determine the equipment capacity parameters corresponding to the target power supply area based on the photovoltaic capacity, energy storage capacity, and gas turbine unit capacity.

[0093] This step represents a systematic upgrade of individual capacity data, transforming the extracted basic capacity into a complete system of equipment parameters. It's not simply a list of capacity values ​​for photovoltaic, energy storage, and gas turbine units, but rather a combination of equipment selection and engineering practice to supplement and form equipment capacity parameters: for example, photovoltaic capacity needs to be associated with module model, installation tilt angle, and number of arrays; energy storage capacity needs to specify battery type, charge / discharge efficiency, cycle life, and SOC (State of Charge) operating range; and gas turbine unit capacity needs to be matched with fuel type, thermal efficiency, and rated speed. Through integration, scattered capacity data is transformed into a standardized parameter system that can directly guide equipment procurement and installation, ensuring a precise match between equipment selection and capacity requirements.

[0094] Step S703: Determine the dispatch instructions corresponding to photovoltaic equipment, energy storage equipment and gas turbine equipment in the target power supply area using equipment capacity parameters, and generate the corresponding collaborative control parameters for photovoltaic equipment, energy storage equipment and gas turbine equipment based on the dispatch instructions.

[0095] This step is the core guarantee for the feasibility of the solution, transforming equipment capacity into coordinated operation instructions by formulating scheduling rules. Based on equipment capacity parameters and combined with the operational needs of end-point power supply scenarios (such as grid-connected / islanded mode switching and extreme weather response), scheduling instructions for three types of equipment are formulated: In grid-connected mode, a power allocation rule is clearly defined, prioritizing photovoltaic power consumption, charging excess energy to storage, and rationally connecting surplus power to the grid; in the event of a main grid failure, a switching logic is set where energy storage discharges first to replenish energy, and the gas turbine automatically starts when the SOC drops to a threshold (e.g., 20%); during continuous rainy weather, an operation mode is established where the gas turbine is the main power source, with photovoltaic and energy storage assisting in load mitigation. These scheduling instructions are quantified into coordinated control parameters, including the start-up threshold, power adjustment range, and response time of each device, ensuring clear division of labor and smooth coordination among the three types of equipment during operation.

[0096] Step S704: Determine the grid capacity configuration strategy corresponding to the target power supply area through collaborative control parameters.

[0097] This step is the final stage of the solution output, integrating equipment parameters and control rules into a comprehensive execution plan. Centered on collaborative control parameters and incorporating the equipment capacity parameter system, it forms a complete grid capacity configuration strategy covering equipment configuration, operation scheduling, and emergency response: the equipment configuration section clarifies engineering requirements such as capacity, model, and installation location for photovoltaic, energy storage, and gas turbine units; the operation scheduling section specifies operational procedures for equipment start-up and shutdown, power regulation, etc., under different scenarios; and the emergency response section details the handling procedures for emergencies such as main grid failures and extreme weather. The final configuration strategy is highly practical and can directly serve as the core basis for engineering construction, equipment commissioning, and daily operation scheduling, realizing the transformation from theoretical optimization to practical application.

[0098] Optionally, step S704, which determines the grid capacity configuration strategy corresponding to the target power supply area through coordinated control parameters, such as... Figure 8 As shown, it includes: Step S801: Determine the grid access strategy and equipment operation strategy corresponding to the target power supply area based on the collaborative control parameters; wherein, the grid access strategy is used to control the photovoltaic equipment, energy storage equipment and gas turbine equipment to connect to the power grid corresponding to the target power supply area; the equipment operation strategy is used to control the photovoltaic equipment, energy storage equipment and gas turbine equipment to supply power.

[0099] The core of this step is to break down the abstract collaborative control parameters into specific rules that guide how devices connect and operate, thus transforming parameters into strategies. The collaborative control parameters include both the technical requirements for device access and the scheduling logic for operation, which are then differentiated into two key strategies: One type is the grid connection strategy, which focuses on how to safely and compliantly connect equipment to the power grid. Based on the access capacity limits and interface standards in the coordinated control parameters, the connection schemes for photovoltaic, energy storage, and gas turbine units are clearly defined: including the selection of grid connection points for photovoltaic modules (such as proximity to load centers to reduce line losses), the grid connection topology of energy storage systems (such as centralized or distributed access), and the emergency access interface specifications for gas turbine units; at the same time, the safety protection measures after access are clearly defined, such as configuring anti-islanding protection devices and setting the voltage and frequency tolerance range of the access point, to ensure that the access of the three types of equipment does not affect the stable operation of the original power grid and complies with the access specifications of the regional distribution network.

[0100] Another category is equipment operation strategies, focusing on how equipment can coordinate power supply. Based on power regulation rules and start / stop thresholds in the coordinated control parameters, operational specifications are formulated for all scenarios: In grid-connected mode, the priority consumption path of photovoltaic power output, the charging and discharging timing of energy storage (e.g., peak photovoltaic charging, peak load discharging), and the standby mode of gas turbine units are clearly defined; when switching to islanded mode due to a main grid failure, the initial discharge power of energy storage, the start-up delay of gas turbine units, and the power ramp-up rate are specified; in extreme weather (continuous rain), the basic output of gas turbine units and the auxiliary adjustment range of photovoltaic and energy storage are clearly defined. Through these rules, it is ensured that the three types of equipment can coordinate efficiently in different scenarios to meet load demands.

[0101] Step S802: Determine the grid capacity configuration strategy corresponding to the target power supply area using the grid access strategy and equipment operation strategy.

[0102] This step is the system integration phase of the strategy, merging the grid access and equipment operation strategies to form a grid capacity configuration strategy covering the entire lifecycle of the equipment. The grid access strategy is the fundamental guarantee, solving the physical access and security issues of the equipment; the equipment operation strategy is the core driving force, solving the problems of coordinated operation and power supply efficiency. The two complement each other and are indispensable. The integrated configuration strategy needs to clearly define the connection relationships: for example, the photovoltaic grid connection point determined in the grid access strategy must correspond to the photovoltaic power transmission path in the equipment operation strategy; the access interface specifications of the gas turbine unit must match the start-up power requirements in its operation strategy. The final complete strategy includes not only the engineering construction standards for equipment access, but also the scheduling procedures for daily operation, and clear emergency response plans, providing one-stop practical guidance for grid capacity configuration in the target power supply area.

[0103] In the specific implementation process, you can refer to Figure 9 Another grid capacity configuration method is shown, which includes the following steps: S1: Defining supply demand and classifying scenarios (corresponding to step S101 for determining power supply duration).

[0104] Supply guarantee levels are categorized as follows: township level corresponds to a 72-hour continuous power supply guarantee requirement, and county level corresponds to a 168-hour continuous power supply guarantee requirement. The minimum power supply guarantee duration constraints are clearly defined for each level. In the event of a main grid power outage, the microgrid must maintain a continuous and stable power supply to critical loads in the region through islanded operation. This method explicitly requires a continuous guarantee capability of several days, far exceeding the traditional 2-hour emergency standard. The following formula represents the core constraint on the power supply guarantee duration: ; In the formula, P d P s P gas P represents the output (kW) of distributed power sources, energy storage, and small gas turbine units at time t, respectively. load Let T represent the load at time t, in kW. end =72 hours (township level) or 168 hours (county level).

[0105] Scene feature extraction: Combine regional climate (such as sunshine duration and number of rainy days) and grid conditions (such as line load rate and transformer capacity) to determine extreme scene parameters. For example, in rainy mountainous areas, the worst-case scenario of no effective sunshine for 7 consecutive days needs to be considered, and in remote mountainous areas, the grid constraint that the transformer reverse load rate does not exceed 80% needs to be considered.

[0106] S2: Full-time analysis of source-load characteristics (corresponding to power grid time-series analysis step S102).

[0107] To address the issue of coarse source-load matching in existing technologies, this solution constructs a three-dimensional analysis model of "resource-load-time series" to achieve refined data support: Renewable energy characteristics analysis: Based on the region's meteorological data (sunshine and wind speed) over the past 5 years, a Markov chain prediction model was used to generate a photovoltaic power output curve for 8760 hours throughout the year, clarifying the power output characteristics of different seasons (e.g., the peak power output is at noon in summer, and the peak power output in winter is significantly lower than in summer); combined with the conditions for distributed power generation access, the maximum grid-connected photovoltaic capacity was determined (not exceeding 80% of the transformer capacity).

[0108] Load characteristic analysis: Load data for the past three years was obtained through the electricity information collection system. Typical daily load curves were extracted using the K-means clustering algorithm to identify typical load characteristics such as "double peaks" and load fluctuation patterns. Combined with load priority, load demand curves under different power supply scenarios were generated (only loads with priority 1 and 2 are retained in islanded mode).

[0109] Source-load timing matching analysis: Calculate the net power of source and load (difference between load and photovoltaic output) at each time of the year, identify the periods of supply-demand gap and reverse transmission, and clarify the key periods of photovoltaic output surplus and load demand gap, so as to provide precise targeting for capacity allocation.

[0110] S3: Multi-dimensional constraint integration (corresponding to constraint integration step S103).

[0111] Based on the premise that the core function of a microgrid is "end-point supply assurance," and thereby setting quantifiable targets and strict constraints to guide subsequent capacity configuration optimization: Given that existing technological constraints are incomplete, this solution integrates technical, policy, and security constraints to form a unified set of constraints. Renewable energy capacity constraints: In the formula, P re P represents the installed capacity of renewable energy power generation. all This represents the maximum load within the microgrid, expressed in kW.

[0112] Clean energy self-sufficiency constraints: In the formula, E ce E represents clean energy power generation. all This represents the total electricity consumption of the microgrid, expressed in kWh.

[0113] Safe operation constraints: Maximum power load constraint is satisfied. In the formula, P mg P represents the maximum load within the microgrid. max This indicates the maximum power load limit of the microgrid. The system capacity (maximum power load) of the microgrid should, in principle, not exceed 20 megawatts.

[0114] In the process of distributed photovoltaic (PV) grid integration, the carrying capacity constraints of the distribution network must be considered. To avoid problems such as reverse power flow and overload caused by large-scale centralized PV grid integration, the reverse load factor is typically required to not exceed 0.8, meaning the PV installed capacity should be less than 80% of the maximum load to ensure the safety and stability of the system operation. Specifically, the reverse load factor should satisfy the following relationship: In the formula, P represents the reverse load rate. D Power output for distributed generation; P L Equivalent electrical load; S e This is the maximum load.

[0115] Operational quality constraints: voltage and frequency constraints (voltage deviation ±7% and frequency deviation ±0.5Hz in islanded mode), equipment operation constraints (energy storage SOC range 20%-80%, gas turbine start-up response time ≤10 seconds), and grid safety constraints (transformer reverse load rate ≤80%).

[0116] S4: Timing simulation and capacity optimization (corresponding to capacity optimization iteration step S104).

[0117] This is the core innovation of this solution. Unlike the empirical formulas of existing technologies, this solution adopts a combination of "particle swarm optimization algorithm + 8760 hours of time-series simulation throughout the year" to achieve precise capacity allocation. Optimization objective setting: Construct a multi-objective optimization function with the objective of "maximizing power supply reliability (continuous power supply duration ≥ T)". end With the goals of "lowest investment cost and highest clean energy utilization rate (≥70%)", the entropy weight method is used to determine the weight of each target (power supply reliability weight 0.5, cost weight 0.3, utilization rate weight 0.2).

[0118] Input variables and constraints: Photovoltaic installed capacity (P... re ), energy storage capacity (P) s (including power and electricity), gas turbine unit capacity (P) gas To optimize the model, we input source load time series data and multi-dimensional constraints.

[0119] Time-series simulation and iterative optimization: Initial capacity combinations are generated based on particle swarm optimization algorithm. The operating status under different working conditions is simulated through 8760 hours of time-series simulation throughout the year. For example, the power supply capability of energy storage and gas turbine is verified in the scenario of three consecutive days without sunlight. The objective function value of each combination is calculated, and the optimal solution is selected through iterative optimization. Finally, the capacity configuration scheme of "photovoltaic + energy storage + gas turbine" is output.

[0120] S5: Timing simulation and capacity optimization (corresponding to configuration scheme output step S105).

[0121] Equipment capacity configuration output: Taking a township-level microgrid as an example, the optimized configuration scheme can meet the 72-hour continuous power supply requirements, and the self-sufficiency rate of clean energy and the local consumption rate of photovoltaic power are significantly improved, effectively solving the problems of high back-feeding rate and insufficient power supply capacity of the existing scheme.

[0122] Collaborative Control System Design (Generation of Collaborative Control Strategies): The system comprises three parts: a data acquisition unit, an energy management unit, and an execution control unit. The data acquisition unit collects real-time data on photovoltaic output, energy storage SOC, and load status. Based on real-time data and optimization strategies, the energy management unit outputs scheduling commands (such as controlling energy storage charging when photovoltaic output is excessive, and controlling energy storage discharging and gas turbine startup during peak load periods). The execution control unit uses a PLC to achieve equipment linkage control, ensuring that "backflow does not exceed limits" when connected to the grid and "power supply is not interrupted" when isolated.

[0123] The grid structure adopts an "AC-based, source-load coordinated" grid structure, connecting multiple units such as distributed photovoltaics, centralized + distributed energy storage, small gas turbine units, and electric vehicle charging piles, forming a flexible structure that can be grid-connected or isolated.

[0124] The operation strategy is divided into two modes: grid-connected and islanded. When grid-connected, the principle of "photovoltaic priority consumption, energy storage peak shaving and valley filling, and surplus power not being fed into the grid" is followed, and photovoltaic backfeed is avoided through energy storage regulation. When islanded, the mechanism of "energy storage priority power supply and gas turbine standby" is activated to ensure power supply according to load priority. The gas turbine automatically starts when the energy storage SOC is lower than 20% to ensure continuous power supply.

[0125] As can be seen from the above grid capacity configuration method, this method can achieve continuous supply for multiple days and improve the utilization rate of clean energy by constructing a multi-complementary energy system of photovoltaic + energy storage + gas turbine units and optimizing the configuration based on full-time operation simulation and the goal of optimal economic efficiency.

[0126] Corresponding to the above-described embodiments of the power grid capacity configuration method, this embodiment of the invention also provides a power grid capacity configuration system, such as... Figure 10 As shown, the system includes: Power supply duration determination module 1010: used to determine load power parameters based on the power grid power supply demand parameters and climate parameters corresponding to the target power supply area, and to determine the power supply duration corresponding to the target power supply area using the load power parameters; The power grid time series analysis module 1020 is used to calculate the power difference between photovoltaic power generation and load demand power within the target time period based on the photovoltaic power supply curve and load characteristic curve corresponding to the target power supply area, and to determine the time series analysis results corresponding to the target power supply area using the power difference and its corresponding power supply period. Constraint integration module 1030: Used to determine the set of constraints corresponding to the target power supply area by utilizing the power load parameters and grid operation parameters of the target power supply area, as well as the power generation capacity and power generation of clean energy in the target power supply area; Capacity optimization iteration module 1040: It is used to construct a capacity configuration model corresponding to the target power supply area through time series analysis results and constraint condition set, and to obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration using the capacity configuration model; Configuration scheme output module 1050: used to obtain the equipment capacity parameters corresponding to the target power supply area according to the capacity configuration strategy, and use the cooperative control parameters corresponding to the equipment capacity parameters to determine the power grid capacity configuration strategy corresponding to the target power supply area.

[0127] As can be seen from the above power grid capacity configuration system, by constructing a multi-complementary energy system of photovoltaic + energy storage + gas turbine units, and optimizing the configuration based on full-time operation simulation and the goal of optimal economic efficiency, the system can achieve continuous supply for multiple days and improve the utilization rate of clean energy.

[0128] The power grid capacity configuration system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned power grid capacity configuration method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned power grid capacity configuration method embodiment.

[0129] This embodiment also provides an electronic device, the structural schematic diagram of which is shown below. Figure 11 As shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the above-described power grid capacity configuration method.

[0130] Figure 11 The electronic device shown also includes a bus 103 and a communication interface 104, with the processor 101, communication interface 104 and memory 102 connected via the bus 103.

[0131] The memory 102 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0132] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0133] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0137] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for configuring power grid capacity, characterized in that, The method includes: Steps for determining power supply duration: Determine load power parameters based on the power grid demand parameters and climate parameters corresponding to the target power supply area, and use the load power parameters to determine the power supply duration corresponding to the target power supply area; Power grid time series analysis steps: Calculate the power difference between photovoltaic power generation and load demand power within the target time period based on the photovoltaic power supply curve and load characteristic curve corresponding to the target power supply area, and use the power difference and its corresponding power supply period to determine the time series analysis results corresponding to the target power supply area; Constraint integration step: Using the power load parameters and grid operation parameters of the target power supply area, as well as the power generation capacity and output of clean energy in the target power supply area, determine the set of constraints corresponding to the target power supply area; Capacity optimization iteration steps: Construct a capacity configuration model corresponding to the target power supply area using the time series analysis results and the set of constraints, and use the capacity configuration model to obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration. Configuration scheme output steps: According to the capacity configuration strategy, obtain the equipment capacity parameters corresponding to photovoltaic equipment, energy storage equipment and gas turbine equipment in the target power supply area, and use the collaborative control parameters corresponding to the equipment capacity parameters to determine the grid capacity configuration strategy corresponding to the target power supply area.

2. The power grid capacity configuration method according to claim 1, characterized in that, The steps for determining the power supply duration include: Obtain the geographical area data corresponding to the target power supply area, and use the power supply demand duration corresponding to the geographical area data to determine the power grid power supply demand parameters. Obtain the climate parameters and grid condition parameters corresponding to the target power supply area; use the climate parameters to obtain the sunshine duration and number of rainy days corresponding to the target power supply area; and use the grid condition parameters to obtain the line load rate and transformer capacity corresponding to the target power supply area. The output power parameters of the distributed power sources, energy storage devices and gas turbine units in the target power supply area are determined based on the sunshine duration, the number of rainy days, the line load rate and the transformer capacity. The load power parameters corresponding to the target power supply area are determined based on the power grid demand parameters and the output power parameters. The duration of power supply protection for the target power supply area is determined by using the duration corresponding to the load power parameters.

3. The power grid capacity configuration method according to claim 1, characterized in that, The power grid time series analysis steps include: Obtain meteorological data corresponding to the target power supply area within the first target time period, generate a photovoltaic power supply curve corresponding to the meteorological data using a Markov chain prediction model, and obtain the photovoltaic power generation power corresponding to the target power supply area based on the photovoltaic power supply curve. Obtain load data corresponding to the target power supply area within the second target time period, use K-means clustering algorithm to obtain the load characteristic curve corresponding to the load data, and determine the load demand power corresponding to the target power supply area based on the load characteristic curve; Calculate the power difference between the photovoltaic power generation and the load demand power at the same moment based on the power supply period corresponding to the target power supply area, and determine the supply-demand gap period and the reverse transmission period included in the power supply period based on the power difference; The time-series analysis results corresponding to the target power supply area are determined based on the power difference, the supply-demand gap period, and the reverse transmission period.

4. The power grid capacity configuration method according to claim 1, characterized in that, The constraint integration step includes: Based on the power generation capacity and output of clean energy in the target power supply area, determine the installed capacity of renewable energy power generation and the output of clean energy power generation corresponding to the target power supply area; Based on the power load parameters of the target power supply area, determine the maximum power grid load and total power consumption of the target power supply area, and based on the power grid operation parameters of the target power supply area, determine the maximum power grid load limit, distributed power generation power, equivalent power load, voltage frequency value, energy storage device power value, and gas turbine start-up response time of the target power supply area. The first constraint condition corresponding to the target power supply area is determined by using the first ratio of the installed capacity of renewable energy power generation to the maximum load of the power grid. The second constraint condition corresponding to the target power supply area is determined by using the second ratio of the clean energy power generation to the total power consumption of the power grid. The reverse load rate of the transformer in the target power supply area is calculated using the maximum load limit of the power grid, the power of the distributed power source, and the equivalent power of the electrical load. Based on the reverse load rate, the third constraint condition corresponding to the target power supply area is determined. The fourth constraint condition corresponding to the target power supply area is determined by comparing the voltage frequency value, the energy storage device power value, and the gas turbine start-up response time with a preset threshold. The set of constraints corresponding to the target power supply area is determined based on the first constraint, the second constraint, the third constraint, and the fourth constraint.

5. The power grid capacity configuration method according to claim 1, characterized in that, The capacity optimization iteration steps include: The objective function of the capacity configuration model under the power supply duration is determined based on the continuous power supply duration corresponding to the target power supply area, equipment investment cost, and clean energy utilization rate. Using the photovoltaic installed capacity, energy storage capacity, and gas turbine capacity corresponding to the target power supply area as optimization variables, the time series analysis results as input data, and the set of constraints as constraints, a capacity configuration model corresponding to the target power supply area is constructed. The initial capacity combination corresponding to the photovoltaic installed capacity, the energy storage capacity, and the gas turbine unit capacity is generated based on the particle swarm optimization algorithm. After the initial capacity combination is subjected to time-series simulation iteration using the objective function to control the capacity configuration model, the capacity configuration strategy corresponding to the target power supply area under the power supply duration is obtained.

6. The power grid capacity configuration method according to claim 5, characterized in that, The steps for determining the objective function of the capacity configuration model under the power supply duration based on the continuous power supply duration corresponding to the target power supply area, equipment investment cost, and clean energy utilization rate include: Obtain the continuous power supply duration, equipment investment cost, and clean energy utilization rate corresponding to the target power supply area; Based on the comparison between the continuous power supply duration and the power supply duration, the reliability objective function corresponding to the target power supply area is determined. Based on the comparison between the equipment investment cost and the preset cost threshold, the investment cost objective function corresponding to the target power supply area is determined; Based on the comparison between the clean energy utilization rate and the preset utilization rate threshold, the utilization rate objective function corresponding to the target power supply area is determined; The weight values ​​of the reliability objective function, the investment cost objective function, and the utilization rate objective function are determined using the entropy weight method. The objective function corresponding to the capacity configuration model is constructed by the reliability objective function, the investment cost objective function, and the utilization rate objective function, along with their corresponding weight values.

7. The power grid capacity configuration method according to claim 1, characterized in that, The configuration scheme output steps include: According to the capacity configuration strategy, the photovoltaic capacity, energy storage capacity and gas turbine capacity corresponding to the photovoltaic equipment, the energy storage equipment and the gas turbine equipment are obtained respectively. The equipment capacity parameters corresponding to the target power supply area are determined based on the photovoltaic capacity, the energy storage capacity, and the gas turbine unit capacity. The scheduling instructions corresponding to the photovoltaic equipment, energy storage equipment, and gas turbine equipment in the target power supply area are determined using the equipment capacity parameters, and the coordinated control parameters corresponding to the photovoltaic equipment, energy storage equipment, and gas turbine equipment are generated according to the scheduling instructions; The grid capacity configuration strategy corresponding to the target power supply area is determined by the cooperative control parameters.

8. The power grid capacity configuration method according to claim 7, characterized in that, The step of determining the grid capacity configuration strategy corresponding to the target power supply area through the cooperative control parameters includes: Based on the collaborative control parameters, the grid access strategy and equipment operation strategy corresponding to the target power supply area are determined; wherein, the grid access strategy is used to control the photovoltaic equipment, the energy storage equipment and the gas turbine equipment to connect to the power grid corresponding to the target power supply area; the equipment operation strategy is used to control the photovoltaic equipment, the energy storage equipment and the gas turbine equipment to supply power; The grid capacity configuration strategy corresponding to the target power supply area is determined using the grid access strategy and the equipment operation strategy.

9. A power grid capacity configuration system, characterized in that, The system includes: Power supply duration determination module: used to determine load power parameters based on the power grid demand parameters and climate parameters corresponding to the target power supply area, and use the load power parameters to determine the power supply duration corresponding to the target power supply area; Power grid time series analysis module: used to calculate the power difference between photovoltaic power generation and load demand power within the target time period based on the photovoltaic power supply curve and load characteristic curve corresponding to the target power supply area, and to determine the time series analysis results corresponding to the target power supply area using the power difference and its corresponding power supply period; Constraint integration module: used to determine the set of constraints corresponding to the target power supply area by utilizing the power load parameters and grid operation parameters of the target power supply area, as well as the power generation capacity and power generation of clean energy in the target power supply area; Capacity optimization iteration module: used to construct a capacity configuration model corresponding to the target power supply area based on the time series analysis results and the set of constraints, and to obtain the capacity configuration strategy corresponding to the target power supply area under the power supply duration using the capacity configuration model; Configuration scheme output module: used to obtain the equipment capacity parameters corresponding to the target power supply area according to the capacity configuration strategy, and use the cooperative control parameters corresponding to the equipment capacity parameters to determine the power grid capacity configuration strategy corresponding to the target power supply area.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the power grid capacity configuration method according to any one of claims 1 to 8.

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