Scheduling optimization method for source network load storage system

Abstract

The embodiment of the invention provides a scheduling optimization method for a source network load storage system, and relates to the technical field of power grid management. The method comprises the following steps: inputting obtained first environment prediction data in a target period and a first operation parameter of a source network load storage system into a source network load storage twin model of the source network load storage system to obtain a power dispatching total scheme in the target period; in the target period, collecting second environment prediction data in the current time period and a second operation parameter of the source network load storage system every preset duration; inputting the second environment prediction data and the second operation parameter into the source network load storage twin model to obtain the current prediction electricity consumption and the current prediction electricity generation amount in the current time period; and determining a power dispatching sub-scheme in the current time period by using the power dispatching total scheme, the current predicted power consumption, the current predicted power generation amount and the second operation parameter. According to the invention, normal operation of the source network load storage system can be ensured.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of power grid management, and more particularly, embodiments of the present application relate to a scheduling optimization method of a source-grid-load-storage system. BACKGROUND

[0002] In recent years, with the rapid development of clean energy, the existing source-grid-load-storage system usually increases the way of using clean energy to generate electricity. Therefore, in the process of planning the power scheduling mode, the source-grid-load-storage system needs to consider the coexistence of thermal power generation and clean energy generation.

[0003] At present, the power scheduling of the source-grid-load-storage system in the next 24 hours is usually planned in a cycle of 24 hours. However, it is found in practice that the current power scheduling planning mode cannot respond to sudden conditions (such as sudden weather changes, etc.) after 24 hours, and cannot adjust the power scheduling in a timely manner, thereby affecting the normal operation of the source-grid-load-storage system. SUMMARY

[0004] In this context, embodiments of the present application aim to provide a scheduling optimization method of a source-grid-load-storage system, thereby ensuring the normal operation of the source-grid-load-storage system.

[0005] In a first aspect of the embodiments of the present application, a scheduling optimization method of a source-grid-load-storage system is provided, comprising:

[0006] digitally twin modeling the source-grid-load-storage system to obtain a source-grid-load-storage twin model of the source-grid-load-storage system; wherein the source-grid-load-storage twin model comprises a power generation system model, a transmission model, a power storage model, and a power consumption model;

[0007] inputting the obtained first environment prediction data in a target period and the first operation parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain a total power scheduling scheme in the target period;

[0008] In the target period, second environment prediction data and second operation parameters of the source-grid-load-storage system in a current time period are collected every preset time interval;

[0009] inputting the second environment prediction data and the second operation parameters into the source-grid-load-storage twin model to obtain a current predicted power consumption and a current predicted power generation in the current time period;

[0010] using the total power scheduling scheme, the current predicted power consumption, the current predicted power generation, and the second operation parameters to determine a power scheduling sub-scheme in the current time period;

[0011] apply the power dispatch sub-scheme to the source grid load storage system in the current time period.

[0012] In one embodiment of the present embodiment, the first environment prediction data in the target period and the first operating parameter of the source grid load storage system are input into the source grid load storage twin model to obtain the power dispatch total scheme in the target period, specifically comprising:

[0013] obtain first environment prediction data in a target period; wherein the first environment prediction data at least includes wind power prediction data, illumination prediction data, water flow prediction data and weather prediction data of the environment in which the source grid load storage system is located in the target period;

[0014] obtain the first operating parameter of the source grid load storage system in the target period; wherein the first operating parameter at least includes power generation equipment parameter, power transmission line parameter, fault data and power consumption equipment parameter of the source grid load storage system;

[0015] input the first environment prediction data and the first operating parameter into the source grid load storage twin model to obtain the total power consumption, thermal power generation information, clean energy power generation information, maximum storage capacity, current storage capacity, charging and discharging loss rate, power transmission loss rate and redundant power in the target period;

[0016] use the total power consumption, the thermal power generation information, the clean energy power generation information, the maximum storage capacity, the current storage capacity, the charging and discharging loss rate, the power transmission loss rate and the redundant power to determine the power dispatch total scheme in the target period.

[0017] In one embodiment of the present embodiment, the use of the total power consumption, the thermal power generation information, the clean energy power generation information, the maximum storage capacity, the current storage capacity, the charging and discharging loss rate, the power transmission loss rate and the redundant power to determine the power dispatch total scheme in the target period, specifically comprising:

[0018] use the total power consumption, the current storage capacity, the charging and discharging loss rate, the power transmission loss rate and the redundant power to calculate the total power demand;

[0019] use the total power demand and the maximum clean energy power generation in the clean energy power generation information to determine the clean energy power generation demand and the thermal power generation demand;

[0020] use the clean energy power generation equipment information in the clean energy power generation information and the clean energy power generation demand to determine the clean energy dispatch total scheme;

[0021] determine a total scheme of thermal power dispatching by using the thermal power generation information in the thermal power generation information and the total demand of thermal power generation;

[0022] determine the total scheme of power dispatching in the target period by using the total scheme of clean energy dispatching, the total scheme of thermal power dispatching and the redundant power quantity.

[0023] In one embodiment of the present embodiment, the second operating parameter at least includes a current power transmission loss rate, a current charging and discharging loss rate of the source network load storage system, and a current storage power and a maximum storage power of the storage power device; and the determining of the sub-scheme of power dispatching in the current time period by using the total scheme of power dispatching, the current predicted power consumption, the current predicted power generation and the second operating parameter specifically includes:

[0024] obtain an actual clean energy power generation and a clean energy power generation in the total scheme of clean energy dispatching;

[0025] if the actual clean energy power generation does not reach the clean energy power generation, determine a current clean energy predicted power generation from the current predicted power generation;

[0026] if the current clean energy predicted power generation is greater than or equal to the current predicted power consumption, determine the current clean energy predicted power generation equal to the current predicted power consumption as a current clean energy power consumption, and determine a difference between the current clean energy predicted power generation and the current predicted power consumption as an initial clean energy storage power; and calculate an updated current storage power by using the current power transmission loss rate, the current charging and discharging loss rate, the current storage power and the initial clean energy storage power; and determine the current clean energy power consumption and the initial clean energy storage power as the sub-scheme of power dispatching in the current time period.

[0027] In one embodiment of the present embodiment, if the current clean energy predicted power generation is less than the current predicted power consumption, the method further includes:

[0028] determine the current clean energy predicted power generation as a current clean energy power consumption;

[0029] obtain an actual thermal power generation and a thermal power generation in the total scheme of thermal power dispatching;

[0030] if the actual thermal power generation amount does not reach the thermal power generation amount, determining a difference between the current predicted power consumption amount and the current clean energy power consumption amount as a current thermal power consumption amount, and determining a difference between the redundancy power amount and the current storage power amount as a storage power difference value, and using the current power transmission loss rate, the current charging and discharging loss rate, the current storage power amount, and the storage power difference value to calculate a current thermal power storage amount, and determining the current clean energy power consumption amount, the current thermal power consumption amount, and the current thermal power storage amount as the power dispatch sub-scheme in the current time period.

[0031] In one embodiment of the present embodiment, if the actual thermal power generation amount reaches the thermal power generation amount, the method further comprises:

[0032] if the current storage power amount is greater than or equal to the current predicted power consumption amount, determining the same storage power amount as the current predicted power consumption amount from the current storage device as a current storage power consumption amount, and determining the current storage power consumption amount as the power dispatch sub-scheme in the current time period;

[0033] if the current storage power amount is less than the current predicted power consumption amount, determining the current storage power amount as a current storage power consumption amount, and determining a difference between the current predicted power consumption amount and the current storage power amount as a current thermal power consumption amount, and determining the current storage power consumption amount and the current thermal power consumption amount as the power dispatch sub-scheme in the current time period.

[0034] In a second aspect of the present embodiment, a dispatch optimization device of a source-grid-load-storage system is provided, comprising:

[0035] a modeling unit configured to digitally twin model the source-grid-load-storage system to obtain a source-grid-load-storage twin model of the source-grid-load-storage system; wherein the source-grid-load-storage twin model comprises a power generation system model, a transmission model, a storage model, and a power consumption model;

[0036] a first input unit configured to input the obtained first environment prediction data in a target period and first operation parameters of the source-grid-load-storage system to the source-grid-load-storage twin model to obtain a total power dispatch scheme in the target period;

[0037] a collection unit configured to collect second environment prediction data and second operation parameters of the source-grid-load-storage system in a current time period every preset time length in the target period;

[0038] a second input unit configured to input the second environment prediction data and the second operation parameters to the source-grid-load-storage twin model to obtain a current predicted power consumption amount and a current predicted power generation amount in the current time period;

[0039] determining unit configured to determine, using the power dispatch overall scheme, the current predicted power consumption, the current predicted power generation, and the second operation parameter, a power dispatch sub-scheme in the current time period;

[0040] applying unit configured to apply the power dispatch sub-scheme to the source grid load storage system in the current time period.

[0041] In a third aspect of the embodiments of the present application, a computing device is provided, comprising at least one processor, a memory, and an input output unit; wherein the memory is configured to store a computer program, and the processor is configured to invoke the computer program stored in the memory to execute the method of any one of the first aspect.

[0042] In a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, comprising instructions which, when executed on a computer, cause the computer to perform the method of any one of the first aspect.

[0043] In a fifth aspect of the embodiments of the present application, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method of any one of the first aspect.

[0044] According to the power dispatch optimization method, device, equipment, medium and product of the source grid load storage system of the embodiments of the present application, the source grid load storage system can be digitally twin modeled to obtain a source grid load storage twin model, and the power dispatch in a target period can be planned using the source grid load storage twin model to obtain a total power dispatch overall scheme; and in the target period, second environment prediction data and second operation parameters of the source grid load storage system in a current time period can be collected every preset time length, so that the source grid load storage twin model can process the second environment prediction data and the second operation parameters to obtain current predicted power consumption and current predicted power generation in the current time period, and the power dispatch sub-scheme in the current time period can be determined by analyzing the power dispatch overall scheme, the current predicted power consumption, the current predicted power generation, and the second operation parameters, so that the power dispatch can be adjusted in a timely manner as the environment changes, thereby ensuring the normal operation of the source grid load storage system. BRIEF DESCRIPTION OF DRAWINGS

[0045] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which a number of embodiments of the application are illustrated by way of example, in which:

[0046] FIG. 1 A flowchart of a power dispatch optimization method of a source grid load storage system provided by an embodiment of the present application is shown.

[0047] FIG. 2 A structural schematic diagram of a scheduling optimization device of a source network and cargo storage system according to an embodiment of the present application is shown.

[0048] FIG. 3 A structural schematic diagram of a medium according to an embodiment of the present application is shown.

[0049] FIG. 4 A structural schematic diagram of a computing device according to an embodiment of the present application is shown.

[0050] In the drawings, the same or similar notations represent the same or similar parts. DETAILED DESCRIPTION

[0051] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present application, and in no way limit the scope of the present application. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0052] Those skilled in the art know that the embodiments of the present application can be implemented as a system, a device, a device, a method or a computer program product. Therefore, the present disclosure can be embodied as a complete hardware, a complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0053] According to the embodiments of the present application, a scheduling optimization method, device, equipment, medium and product of a source network and cargo storage system are provided.

[0054] It should be noted that the number of any elements in the drawings is used for example and not for limitation, and any naming is only used for distinction and does not have any limiting meaning.

[0055] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present application, and in no way limit the scope of the present application. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0056] Exemplary method

[0057] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present application, and in no way limit the scope of the present application. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art. FIG. 1 , FIG. 1 A flowchart of a scheduling optimization method of a source network and cargo storage system according to an embodiment of the present application is shown. It should be noted that the embodiments of the present application can be applied to any applicable scenario.

[0058] FIG. 1 The flow of the scheduling optimization method of the source network and cargo storage system according to an embodiment of the present application is shown.

[0059] Step S101: Perform digital twin modeling on the source-grid-load-storage system to obtain the source-grid-load-storage twin model of the system.

[0060] In this embodiment of the invention, the source-grid-load-storage system integrates four key links: energy supply (source), grid transmission (grid), user load (load), and energy storage (storage). Through intelligent management and collaborative optimization, it improves energy utilization efficiency and ensures the reliability and stability of power supply.

[0061] Source: refers to the energy supply side, including traditional energy (such as coal, oil, and natural gas) and clean energy (such as solar energy, wind energy, and hydropower).

[0062] The power grid is responsible for the transmission and distribution of energy, delivering energy from the supply side to the user side.

[0063] Load: refers to user load, that is, the energy consumption end, including various fields such as industry, commerce, and residence.

[0064] Storage: refers to energy storage systems, such as battery storage and pumped hydro storage, which are used to balance energy supply and demand and improve the flexibility and reliability of the power system.

[0065] Therefore, the source-grid-load-storage system can include a power generation system, a transmission system, a storage system, and electrical equipment. The power generation system model, transmission model, storage model, and electrical equipment model included in the source-grid-load-storage twin model can also correspond one-to-one with the power generation system, transmission system, storage system, and electrical equipment included in the source-grid-load-storage system.

[0066] In this embodiment of the invention, the modeling method for the source-grid-load-storage twin model can be system modeling, simulation analysis, and iterative optimization, specifically as follows:

[0067] System modeling: CAD, CAE and other tools can be used to create a three-dimensional model of the physical entity of the source-grid-load-storage system.

[0068] Simulation analysis: The performance of the source-grid-load-storage twin model can be analyzed using simulation software, such as power generation, transmission rate, and energy storage loss rate, to verify the rationality of the design.

[0069] Iterative optimization: The source-grid-load-storage twin model can be modified and optimized based on simulation results until the design requirements are met.

[0070] Step S102: Input the first environmental prediction data within the target period and the first operating parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain the overall power dispatch scheme within the target period.

[0071] As an optional implementation, step S102, which involves inputting the first environmental prediction data for the target period and the first operating parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain the overall power dispatch scheme for the target period, may include:

[0072] Acquire first environmental prediction data within the target period; wherein, the first environmental prediction data includes at least wind prediction data, sunshine prediction data, water flow prediction data, and weather prediction data of the environment in which the source-grid-load-storage system is located within the target period;

[0073] Obtain the first operating parameters of the power generation, grid, load and storage system within the target period; wherein, the first operating parameters include at least the power generation equipment parameters, transmission line parameters, fault data and power consumption equipment parameters of the power generation, grid, load and storage system;

[0074] The first environmental prediction data and the first operating parameters are input into the source-grid-load-storage twin model to obtain the total electricity consumption, thermal power generation information, clean energy generation information, maximum storage capacity, current storage capacity, charge-discharge loss rate, transmission loss rate, and redundant power within the target period.

[0075] Using the total electricity consumption, thermal power generation information, clean energy generation information, maximum energy storage capacity, current energy storage capacity, charging and discharging loss rate, transmission loss rate, and redundant energy, determine the overall power dispatch plan for the target period.

[0076] This implementation method integrates the first environmental forecast data (covering multiple dimensions such as wind, sunlight, water flow, and weather) within the target period with the first operating parameters of the power generation, grid, load, and storage system (including parameters of power generation equipment, transmission lines, faults, and power consumption equipment). This data is then input into a power generation, grid, load, and storage twin model for precise simulation and analysis. This allows for a comprehensive assessment of the system's operating status and energy flow characteristics under different environmental conditions, thereby accurately calculating key indicators such as total electricity consumption, various power generation information, energy storage status, and loss rates. Based on this, a scientific overall power dispatch plan is formulated. This approach effectively improves the foresight and accuracy of power dispatch, enhances the system's ability to absorb clean energy, optimizes the allocation efficiency of energy storage resources, reduces transmission losses and redundant power, and significantly improves the overall operating efficiency and reliability of the power generation, grid, load, and storage system.

[0077] In this embodiment of the invention, the target period can be the next day (i.e., 24 hours from 0:00 to 24:00) or the next half day (i.e., the next 12 hours). This embodiment of the invention does not limit the time period.

[0078] In this embodiment of the invention, wind force prediction data is based on meteorological principles, historical meteorological data, real-time observation data, and advanced prediction models to predict the magnitude, direction, and trend of future wind force. Sunlight prediction data is based on meteorological principles, historical meteorological data, real-time observation data, and advanced prediction models to predict the intensity, duration, and trend of future sunlight. Water flow prediction data is based on hydrological principles, historical hydrological data, real-time observation data, and advanced prediction models to predict parameters such as the magnitude, velocity, direction, and water level of future water flows. Weather prediction data is based on meteorological principles, historical meteorological data, real-time observation data, and advanced prediction models to predict future weather conditions (such as temperature, humidity, wind speed, wind direction, precipitation, and air pressure). The above prediction data can be predicted using a pre-built neural network model. By training the neural network model with a large amount of historical data, the model learns the changing patterns of wind force, sunlight, water flow, and weather, and then predicts the wind force, sunlight, water flow, and weather conditions in the environment of the source-grid-load-storage system.

[0079] In this embodiment of the invention, the parameters of the power generation equipment may include parameters of thermal power generation equipment and parameters of clean energy power generation equipment. These parameters may include rated power, power generation efficiency, voltage, and current. Clean energy power generation equipment may include wind turbines, solar photovoltaic panels, and hydroelectric generators. The parameters of a wind turbine may further include cut-in wind speed, cut-out wind speed, rated wind speed, rotor diameter, number of blades, materials, and design. The parameters of a solar photovoltaic panel may further include maximum power point, open-circuit voltage, short-circuit current, conversion efficiency, and temperature coefficient. The parameters of a hydroelectric generator may further include head, flow rate, turbine type and efficiency, and runner diameter.

[0080] In this embodiment of the invention, the transmission line parameters may include resistance, reactance, susceptance, conductance, capacitance, line length, conductor type, laying method, insulation level, and thermal stability current, etc.

[0081] In this embodiment of the invention, the fault data of the source-grid-load-storage system may include equipment fault data, operational anomaly data, communication fault data, and environmental fault data, etc.

[0082] Equipment fault data can include generator faults, transformer faults, transmission line faults, energy storage system faults, and load equipment faults.

[0083] Abnormal operating data can include voltage abnormalities, current abnormalities, frequency abnormalities, and power abnormalities.

[0084] Communication failure data can include data transmission interruptions, data errors, or data loss.

[0085] Environmental failure data can include natural disasters and human-caused damage.

[0086] In this embodiment of the invention, the electrical equipment parameters may include civil electrical equipment and industrial electrical equipment. The electrical equipment parameters may include rated voltage, rated current, rated power, efficiency, efficiency factor, starting current, temperature rise, load characteristics, insulation class, protection class, frequency, number of phases, etc. This embodiment of the invention does not limit these parameters.

[0087] In this embodiment of the invention, the total electricity consumption within the target period can be the total electricity consumption of the electrical equipment within the target period. The thermal power generation information can include the total thermal power generation within the target period and the thermal power generation per hour, etc. The clean energy power generation information can include the total clean energy power generation within the target period and the clean energy power generation per hour, etc. The maximum energy storage capacity can be the maximum total energy storage capacity of each energy storage device. The current energy storage capacity can be the energy currently stored by the energy storage device. The redundant energy capacity can be the extra energy reserved, which can be used in case of emergency power demand.

[0088] Optionally, the method for determining the overall power dispatch plan within the target period using the total electricity consumption, the thermal power generation information, the clean energy power generation information, the maximum energy storage capacity, the current energy storage capacity, the charge / discharge loss rate, the transmission loss rate, and the redundant energy may include:

[0089] The total electricity demand is calculated using the total electricity consumption, the current energy storage capacity, the charging and discharging loss rate, the transmission loss rate, and the redundant energy.

[0090] Using the total electricity demand and the maximum clean energy power generation in the clean energy power generation information, determine the clean energy power generation demand and the thermal power generation demand;

[0091] Using the clean energy power generation equipment information and the clean energy power generation demand from the clean energy power generation information, a general clean energy dispatch plan is determined.

[0092] Using the thermal power generation equipment information and the total thermal power generation demand in the thermal power generation information, a total thermal power dispatch plan is determined.

[0093] The overall clean energy dispatch plan, the overall thermal power dispatch plan, and the redundant power are collectively determined as the overall power dispatch plan for the target period.

[0094] This implementation method first involves accurately calculating the total electricity demand based on total electricity consumption, current energy storage capacity, charging and discharging loss rate, transmission loss rate, and redundant power. Then, it rationally allocates the power generation demand of clean energy and thermal power based on the maximum power generation capacity of clean energy sources, ensuring a dynamic balance between energy supply and demand. Subsequently, based on the power generation equipment information of clean energy and thermal power plants and their respective power generation demands, targeted overall dispatch plans are formulated, achieving optimized allocation and efficient utilization of various energy resources. This process not only fully considers the priority consumption of clean energy and the flexible adjustment of the energy storage system, but also enhances the system's ability to cope with emergencies through the reasonable reservation of redundant power.

[0095] In this embodiment of the invention, the formula for calculating the total electricity demand can be:

[0096]

[0097] In this embodiment of the invention, the overall clean energy dispatch plan may include the clean energy power generation demand and the expected power generation of each clean energy power generation unit. The overall thermal power dispatch plan may include the total thermal power generation demand and the expected power generation of each thermal power generation unit.

[0098] Step S103: Within the target period, collect the second environmental prediction data and the second operating parameters of the source-grid-load-storage system at preset intervals during the current time period.

[0099] In this embodiment of the invention, the second operating parameter includes at least the current transmission loss rate and current charge / discharge loss rate of the source-grid-load-storage system, as well as the current and maximum storage capacity of the energy storage device. The second operating parameter has the same meaning as the first operating parameter, and the second environmental prediction data also has the same meaning as the first environmental prediction data, which will not be repeated here. The preset duration can be 15 minutes, 20 minutes, 30 minutes, etc., and this embodiment of the invention does not limit this.

[0100] Step S104: Input the second environmental prediction data and the second operating parameters into the source-grid-load-storage twin model to obtain the current predicted electricity consumption and the current predicted power generation in the current time period.

[0101] Step S105: Using the overall power dispatch plan, the current predicted power consumption, the current predicted power generation, and the second operating parameters, determine the power dispatch sub-plan for the current time period.

[0102] As an optional implementation, step S105, using the overall power dispatch plan, the current predicted electricity consumption, the current predicted power generation, and the second operating parameters, to determine the power dispatch sub-plan for the current time period may include:

[0103] Obtain the actual power generation of clean energy and the power generation that should be generated by clean energy in the overall clean energy dispatch plan;

[0104] If the actual power generation of the clean energy source does not reach the expected power generation of the clean energy source, then the current predicted power generation of the clean energy source is determined from the current predicted power generation.

[0105] If the current predicted clean energy power generation is greater than or equal to the current predicted power consumption, then the portion of the current predicted clean energy power generation that is equal to the current predicted power consumption is determined as the current clean energy power consumption; the difference between the current predicted clean energy power generation and the current predicted power consumption is determined as the initial clean energy storage capacity; and the updated current storage capacity is calculated using the current transmission loss rate, the current charge / discharge loss rate, the current storage capacity, and the initial clean energy storage capacity; and the current clean energy power consumption and the initial clean energy storage capacity are determined as the power dispatch sub-scheme for the current time period.

[0106] This implementation method effectively addresses the volatility and uncertainty of clean energy generation by dynamically adjusting power dispatch strategies through real-time monitoring of actual clean energy generation and the required generation in the overall dispatch plan. When actual clean energy generation is insufficient, it can be quickly supplemented based on current forecasts to ensure the stability of power supply. If current forecasts of clean energy generation are sufficient, priority is given to meeting electricity demand, and excess electricity is rationally stored, improving the utilization rate of clean energy and enhancing the regulation capacity of the energy storage system. By accurately calculating and updating the current storage capacity and formulating power dispatch sub-plans for the current time period, the optimal allocation and efficient utilization of power resources are achieved, reducing dependence on traditional energy sources such as thermal power and minimizing energy waste and carbon emissions.

[0107] In this embodiment of the invention, the initial clean energy storage capacity = the current predicted clean energy power generation - the current predicted power consumption.

[0108] In this embodiment of the invention, the updated formula for calculating the current storage capacity is as follows:

[0109] Updated current storage capacity

[0110] = Current energy storage capacity + Initial clean energy energy storage capacity * (1 - Current transmission loss rate)

[0111] -Current charge / discharge loss rate)

[0112] If the current predicted clean energy power generation is less than the current predicted power consumption, the current predicted clean energy power generation shall be determined as the current clean energy power consumption;

[0113] Obtain the actual power generation of thermal power plants and the required power generation of thermal power plants in the overall thermal power dispatch plan;

[0114] If the actual power generation of the thermal power plant does not reach the expected power generation, the difference between the current predicted power consumption and the current clean energy power consumption is determined as the current thermal power consumption; the difference between the redundant power and the current energy storage is determined as the energy storage difference; and the current thermal power energy storage is calculated using the current transmission loss rate, the current charge and discharge loss rate, the current energy storage, and the energy storage difference; and the current clean energy power consumption, the current thermal power power consumption, and the current thermal power energy storage are determined as the power dispatch sub-scheme for the current time period.

[0115] This implementation method allows for the rapid activation of a thermal power supplementation mechanism when the predicted clean energy generation is insufficient to meet current predicted electricity demand. By accurately calculating the gap between actual and expected thermal power generation, it dynamically adjusts thermal power consumption, ensuring the continuity and stability of power supply. Furthermore, this scheme innovatively incorporates the difference between redundant power and current energy storage as a storage difference factor. Combined with transmission loss rate and charge / discharge loss rate, it accurately calculates the current thermal power storage capacity, achieving optimized allocation and efficient utilization of energy storage resources. This mechanism not only effectively alleviates the intermittency of clean energy generation but also enhances the overall regulation and resilience of the power system through the synergistic effect of thermal power and energy storage systems. The resulting power dispatch sub-scheme ensures the reliability of power supply while promoting the priority consumption of clean energy and flexible adjustment of thermal power.

[0116] In this embodiment of the invention, the formula for calculating the current thermal power storage capacity is as follows:

[0117] Current thermal power storage capacity

[0118] = Current stored capacity + Storage difference * (1 - Current transmission loss rate)

[0119] -Current charge / discharge loss rate)

[0120] If the actual power generation of the thermal power plant reaches the power generation that the thermal power plant should generate, and if the current power storage capacity is greater than or equal to the current predicted power consumption, then the power storage capacity that is the same as the current predicted power consumption is determined from the current power storage device as the current power storage capacity, and the current power storage capacity is determined as the power dispatch sub-scheme for the current time period.

[0121] If the current energy storage capacity is less than the current predicted energy consumption, then the current energy storage capacity is taken as the current energy storage consumption; the difference between the current predicted energy consumption and the current energy storage capacity is determined as the current thermal power consumption; and the current energy storage consumption and the current thermal power consumption are determined as the power dispatch sub-scheme for the current time period.

[0122] This implementation method provides a flexible and efficient power dispatch strategy, regardless of whether the actual power generation from thermal power plants meets demand and energy storage is sufficient or insufficient. When energy storage is sufficient to cover the current predicted electricity demand, the corresponding electricity is directly allocated from the energy storage devices, avoiding unnecessary thermal power generation, reducing energy waste and carbon emissions, and prioritizing the use of clean energy. When energy storage is insufficient, the gap between energy storage and electricity demand is accurately calculated, and thermal power is rationally allocated to supplement it, ensuring the continuity and stability of power supply. This intelligent dispatch strategy not only optimizes the allocation efficiency of thermal power and energy storage resources but also significantly improves the flexibility and response speed of the power system, effectively addressing the volatility and uncertainty of electricity demand.

[0123] Step S106: Apply the power dispatch sub-scheme to the source-grid-load-storage system within the current time period.

[0124] This invention enables power dispatch to be adjusted promptly in response to environmental changes, thereby ensuring the normal operation of the power generation, grid, load, and storage system. Furthermore, this invention improves the overall operational efficiency and reliability of the power generation, grid, load, and storage system. It also enhances the system's ability to cope with emergencies. Moreover, this invention achieves optimized allocation and efficient utilization of power resources, reducing dependence on traditional energy sources such as thermal power, and minimizing energy waste and carbon emissions. Furthermore, this invention ensures the reliability of power supply while promoting the priority consumption of clean energy and flexible adjustment of thermal power. Finally, this invention improves the flexibility and response speed of the power system, effectively addressing the volatility and uncertainty of electricity demand.

[0125] Exemplary apparatus

[0126] After introducing the method of exemplary embodiments of the present invention, the following references are made. FIG. 2 An exemplary embodiment of the present invention provides a scheduling optimization device for a source-grid-load-storage system, the device comprising:

[0127] Modeling unit 201 is used to perform digital twin modeling of the power generation, grid, load and storage system to obtain the power generation, grid, load and storage twin model of the power generation, grid, load and storage system; wherein, the power generation, grid, load and storage twin model includes a power generation system model, a transmission model, a power storage model and a power consumption model;

[0128] The first input unit 202 is used to input the first environmental prediction data within the target period and the first operating parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain the overall power dispatch scheme within the target period.

[0129] The acquisition unit 203 is used to acquire second environmental prediction data and second operating parameters of the source-grid-load-storage system at preset intervals within the target period.

[0130] The second input unit 204 is used to input the second environmental prediction data and the second operating parameters into the source-grid-load-storage twin model to obtain the current predicted electricity consumption and the current predicted power generation in the current time period.

[0131] The determining unit 205 is used to determine the power dispatch sub-scheme for the current time period using the overall power dispatch scheme, the current predicted power consumption, the current predicted power generation, and the second operating parameters;

[0132] Application unit 206 is used to apply the power dispatch sub-scheme to the source-grid-load-storage system during the current time period.

[0133] As an optional implementation, the first input unit 202 inputs the first environmental prediction data within the target period and the first operating parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain the overall power dispatch scheme within the target period. Specifically, this can be achieved by:

[0134] Acquire first environmental prediction data within the target period; wherein, the first environmental prediction data includes at least wind prediction data, sunshine prediction data, water flow prediction data, and weather prediction data of the environment in which the source-grid-load-storage system is located within the target period;

[0135] Obtain the first operating parameters of the power generation, grid, load and storage system within the target period; wherein, the first operating parameters include at least the power generation equipment parameters, transmission line parameters, fault data and power consumption equipment parameters of the power generation, grid, load and storage system;

[0136] The first environmental prediction data and the first operating parameters are input into the source-grid-load-storage twin model to obtain the total electricity consumption, thermal power generation information, clean energy generation information, maximum storage capacity, current storage capacity, charge-discharge loss rate, transmission loss rate, and redundant power within the target period.

[0137] Using the total electricity consumption, thermal power generation information, clean energy generation information, maximum energy storage capacity, current energy storage capacity, charging and discharging loss rate, transmission loss rate, and redundant energy, determine the overall power dispatch plan for the target period.

[0138] This implementation method integrates the first environmental forecast data (covering multiple dimensions such as wind, sunlight, water flow, and weather) within the target period with the first operating parameters of the power generation, grid, load, and storage system (including parameters of power generation equipment, transmission lines, faults, and power consumption equipment). This data is then input into a power generation, grid, load, and storage twin model for precise simulation and analysis. This allows for a comprehensive assessment of the system's operating status and energy flow characteristics under different environmental conditions, thereby accurately calculating key indicators such as total electricity consumption, various power generation information, energy storage status, and loss rates. Based on this, a scientific overall power dispatch plan is formulated. This approach effectively improves the foresight and accuracy of power dispatch, enhances the system's ability to absorb clean energy, optimizes the allocation efficiency of energy storage resources, reduces transmission losses and redundant power, and significantly improves the overall operating efficiency and reliability of the power generation, grid, load, and storage system.

[0139] As an optional implementation, the first input unit 202 may determine the overall power dispatch plan for the target period using the total electricity consumption, the thermal power generation information, the clean energy power generation information, the maximum energy storage capacity, the current energy storage capacity, the charge / discharge loss rate, the transmission loss rate, and the redundant energy.

[0140] The total electricity demand is calculated using the total electricity consumption, the current energy storage capacity, the charging and discharging loss rate, the transmission loss rate, and the redundant energy.

[0141] Using the total electricity demand and the maximum clean energy power generation in the clean energy power generation information, determine the clean energy power generation demand and the thermal power generation demand;

[0142] Using the clean energy power generation equipment information and the clean energy power generation demand from the clean energy power generation information, a general clean energy dispatch plan is determined.

[0143] Using the thermal power generation equipment information and the total thermal power generation demand in the thermal power generation information, a total thermal power dispatch plan is determined.

[0144] The overall clean energy dispatch plan, the overall thermal power dispatch plan, and the redundant power are collectively determined as the overall power dispatch plan for the target period.

[0145] This implementation method first involves accurately calculating the total electricity demand based on total electricity consumption, current energy storage capacity, charging and discharging loss rate, transmission loss rate, and redundant power. Then, it rationally allocates the power generation demand of clean energy and thermal power based on the maximum power generation capacity of clean energy sources, ensuring a dynamic balance between energy supply and demand. Subsequently, based on the power generation equipment information of clean energy and thermal power plants and their respective power generation demands, targeted overall dispatch plans are formulated, achieving optimized allocation and efficient utilization of various energy resources. This process not only fully considers the priority consumption of clean energy and the flexible adjustment of the energy storage system, but also enhances the system's ability to cope with emergencies through the reasonable reservation of redundant power.

[0146] As an optional implementation, the second operating parameters include at least the current transmission loss rate, current charge / discharge loss rate of the power generation, grid, load, and storage system, and the current and maximum storage capacity of the energy storage devices. Specifically, the determining unit 205 uses the overall power dispatch plan, the current predicted power consumption, the current predicted power generation, and the second operating parameters to determine the power dispatch sub-plan for the current time period.

[0147] Obtain the actual power generation of clean energy and the power generation that should be generated by clean energy in the overall clean energy dispatch plan;

[0148] If the actual power generation of the clean energy source does not reach the expected power generation of the clean energy source, then the current predicted power generation of the clean energy source is determined from the current predicted power generation.

[0149] If the current predicted clean energy power generation is greater than or equal to the current predicted power consumption, then the portion of the current predicted clean energy power generation that is equal to the current predicted power consumption is determined as the current clean energy power consumption; the difference between the current predicted clean energy power generation and the current predicted power consumption is determined as the initial clean energy storage capacity; and the updated current storage capacity is calculated using the current transmission loss rate, the current charge / discharge loss rate, the current storage capacity, and the initial clean energy storage capacity; and the current clean energy power consumption and the initial clean energy storage capacity are determined as the power dispatch sub-scheme for the current time period.

[0150] This implementation method effectively addresses the volatility and uncertainty of clean energy generation by dynamically adjusting power dispatch strategies through real-time monitoring of actual clean energy generation and the required generation in the overall dispatch plan. When actual clean energy generation is insufficient, it can be quickly supplemented based on current forecasts to ensure the stability of power supply. If current forecasts of clean energy generation are sufficient, priority is given to meeting electricity demand, and excess electricity is rationally stored, improving the utilization rate of clean energy and enhancing the regulation capacity of the energy storage system. By accurately calculating and updating the current storage capacity and formulating power dispatch sub-plans for the current time period, the optimal allocation and efficient utilization of power resources are achieved, reducing dependence on traditional energy sources such as thermal power and minimizing energy waste and carbon emissions.

[0151] As an optional implementation, the determining unit 205 is further configured to:

[0152] If the current predicted clean energy power generation is less than the current predicted power consumption, the current predicted clean energy power generation shall be determined as the current clean energy power consumption;

[0153] Obtain the actual power generation of thermal power plants and the required power generation of thermal power plants in the overall thermal power dispatch plan;

[0154] If the actual power generation of the thermal power plant does not reach the expected power generation, the difference between the current predicted power consumption and the current clean energy power consumption is determined as the current thermal power consumption; the difference between the redundant power and the current energy storage is determined as the energy storage difference; and the current thermal power energy storage is calculated using the current transmission loss rate, the current charge and discharge loss rate, the current energy storage, and the energy storage difference; and the current clean energy power consumption, the current thermal power power consumption, and the current thermal power energy storage are determined as the power dispatch sub-scheme for the current time period.

[0155] This implementation method allows for the rapid activation of a thermal power supplementation mechanism when the predicted clean energy generation is insufficient to meet current predicted electricity demand. By accurately calculating the gap between actual and expected thermal power generation, it dynamically adjusts thermal power consumption, ensuring the continuity and stability of power supply. Furthermore, this scheme innovatively incorporates the difference between redundant power and current energy storage as a storage difference factor. Combined with transmission loss rate and charge / discharge loss rate, it accurately calculates the current thermal power storage capacity, achieving optimized allocation and efficient utilization of energy storage resources. This mechanism not only effectively alleviates the intermittency of clean energy generation but also enhances the overall regulation and resilience of the power system through the synergistic effect of thermal power and energy storage systems. The resulting power dispatch sub-scheme ensures the reliability of power supply while promoting the priority consumption of clean energy and flexible adjustment of thermal power.

[0156] As an optional implementation, the determining unit 205 is further configured to:

[0157] If the actual power generation of the thermal power plant reaches the power generation that the thermal power plant should generate, and if the current power storage capacity is greater than or equal to the current predicted power consumption, then the power storage capacity that is the same as the current predicted power consumption is determined from the current power storage device as the current power storage capacity, and the current power storage capacity is determined as the power dispatch sub-scheme for the current time period.

[0158] If the current energy storage capacity is less than the current predicted energy consumption, then the current energy storage capacity is taken as the current energy storage consumption; the difference between the current predicted energy consumption and the current energy storage capacity is determined as the current thermal power consumption; and the current energy storage consumption and the current thermal power consumption are determined as the power dispatch sub-scheme for the current time period.

[0159] This implementation method provides a flexible and efficient power dispatch strategy, regardless of whether the actual power generation from thermal power plants meets demand and energy storage is sufficient or insufficient. When energy storage is sufficient to cover the current predicted electricity demand, the corresponding electricity is directly allocated from the energy storage devices, avoiding unnecessary thermal power generation, reducing energy waste and carbon emissions, and prioritizing the use of clean energy. When energy storage is insufficient, the gap between energy storage and electricity demand is accurately calculated, and thermal power is rationally allocated to supplement it, ensuring the continuity and stability of power supply. This intelligent dispatch strategy not only optimizes the allocation efficiency of thermal power and energy storage resources but also significantly improves the flexibility and response speed of the power system, effectively addressing the volatility and uncertainty of electricity demand.

[0160] This invention enables power dispatch to be adjusted promptly in response to environmental changes, thereby ensuring the normal operation of the power generation, grid, load, and storage system. Furthermore, this invention improves the overall operational efficiency and reliability of the power generation, grid, load, and storage system. It also enhances the system's ability to cope with emergencies. Moreover, this invention achieves optimized allocation and efficient utilization of power resources, reducing dependence on traditional energy sources such as thermal power, and minimizing energy waste and carbon emissions. Furthermore, this invention ensures the reliability of power supply while promoting the priority consumption of clean energy and flexible adjustment of thermal power. Finally, this invention improves the flexibility and response speed of the power system, effectively addressing the volatility and uncertainty of electricity demand.

[0161] Exemplary medium

[0162] After introducing the methods and apparatus of exemplary embodiments of the present invention, the following references are made. FIG. 3 A computer-readable storage medium according to exemplary embodiments of the present invention will be described, please refer to... FIG. 3The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above-described method implementation, such as performing digital twin modeling of the source-grid-load-storage system to obtain a source-grid-load-storage twin model of the source-grid-load-storage system; wherein the source-grid-load-storage twin model includes a power generation system model, a transmission model, a power storage model, and a power consumption model; inputting the first environmental prediction data within the target period and the first operating parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain the overall power dispatch scheme within the target period; Within the target period, second environmental prediction data and second operating parameters of the power generation, grid, load, and storage system are collected at preset intervals for the current time period. The second environmental prediction data and the second operating parameters are input into the power generation, grid, load, and storage twin model to obtain the current predicted electricity consumption and current predicted power generation for the current time period. Using the overall power dispatch plan, the current predicted electricity consumption, the current predicted power generation, and the second operating parameters, a power dispatch sub-plan for the current time period is determined. The power dispatch sub-plan is applied to the power generation, grid, load, and storage system for the current time period. The specific implementation methods of each step will not be repeated here.

[0163] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0164] Exemplary computing device

[0165] After introducing the methods, apparatus, and media of exemplary embodiments of the present invention, the following references are made. FIG. 4 A computing device for scheduling optimization of a source-grid-load-storage system according to an exemplary embodiment of the present invention.

[0166] FIG. 4 A block diagram is shown of an exemplary computing device 40 suitable for implementing embodiments of the present invention. The computing device 40 may be a computer system or a server. FIG. 4 The computing device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0167] like FIG. 4As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0168] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, and removable and non-removable media.

[0169] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media (…). FIG. 4 Not shown in the image (usually referred to as a "hard drive"). Although not shown in FIG. 4 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0170] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 4024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0171] The computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 405. Furthermore, the computing device 40 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 406. FIG. 4 As shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401) via bus 403. It should be understood that, although...​ As not shown, it can be used in conjunction with computing device 40 with other hardware and / or software modules.

[0172] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, it performs digital twin modeling of the power generation, grid, load, and storage system to obtain a power generation, grid, load, and storage twin model, which includes a power generation system model, a transmission model, a storage model, and a consumption model. It inputs the first environmental prediction data for the target period and the first operating parameters of the power generation, grid, load, and storage system into the power generation, grid, load, and storage twin model to obtain a general power dispatch plan for the target period. Within the target period, it collects second environmental prediction data and second operating parameters of the power generation, grid, load, and storage system at preset intervals. It inputs the second environmental prediction data and the second operating parameters into the power generation, grid, load, and storage twin model to obtain the current predicted electricity consumption and current predicted power generation for the current time period. Using the general power dispatch plan, the current predicted electricity consumption, the current predicted power generation, and the second operating parameters, it determines a power dispatch sub-plan for the current time period. Finally, it applies the power dispatch sub-plan to the power generation, grid, load, and storage system for the current time period. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the scheduling optimization device for the source-grid-load-storage system are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0173] In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0174] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0175] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus 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.

[0176] 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.

[0177] 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.

[0178] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] 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.

[0180] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0181] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

Claims

1. A scheduling optimization method for a source-grid-load-storage system, characterized in that, The method includes: A digital twin model of the power generation, grid, load, and energy storage system is performed to obtain the power generation, grid, load, and energy storage twin model of the system; wherein, the power generation, grid, load, and energy storage twin model includes a power generation system model, a transmission model, an energy storage model, and an energy consumption model; The first environmental prediction data within the target period and the first operating parameters of the source-grid-load-storage system are input into the source-grid-load-storage twin model to obtain the overall power dispatch scheme within the target period. Within the target period, second environmental prediction data and second operating parameters of the source-grid-load-storage system are collected at preset intervals for the current time period; The second environmental prediction data and the second operating parameters are input into the source-grid-load-storage twin model to obtain the current predicted electricity consumption and the current predicted power generation in the current time period. Using the overall power dispatch plan, the current predicted electricity consumption, the current predicted power generation, and the second operating parameters, determine the power dispatch sub-plan for the current time period; The power dispatch sub-scheme is applied to the source-grid-load-storage system during the current time period.

2. The scheduling optimization method for a source-grid-load-storage system according to claim 1, characterized in that, The step of inputting the first environmental prediction data within the target period and the first operating parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain the overall power dispatch scheme within the target period specifically includes: Acquire first environmental prediction data within the target period; wherein, the first environmental prediction data includes at least wind prediction data, sunshine prediction data, water flow prediction data, and weather prediction data of the environment in which the source-grid-load-storage system is located within the target period; Obtain the first operating parameters of the power generation, grid, load and storage system within the target period; wherein, the first operating parameters include at least the power generation equipment parameters, transmission line parameters, fault data and power consumption equipment parameters of the power generation, grid, load and storage system; The first environmental prediction data and the first operating parameters are input into the source-grid-load-storage twin model to obtain the total electricity consumption, thermal power generation information, clean energy generation information, maximum storage capacity, current storage capacity, charge-discharge loss rate, transmission loss rate, and redundant power within the target period. Using the total electricity consumption, thermal power generation information, clean energy generation information, maximum energy storage capacity, current energy storage capacity, charging and discharging loss rate, transmission loss rate, and redundant energy, determine the overall power dispatch plan for the target period.

3. The scheduling optimization method for a source-grid-load-storage system according to claim 2, characterized in that, The process of determining the overall power dispatch plan for the target period using the total electricity consumption, thermal power generation information, clean energy generation information, maximum energy storage capacity, current energy storage capacity, charge / discharge loss rate, transmission loss rate, and redundant energy specifically includes: The total electricity demand is calculated using the total electricity consumption, the current energy storage capacity, the charging and discharging loss rate, the transmission loss rate, and the redundant energy. Using the total electricity demand and the maximum clean energy power generation in the clean energy power generation information, determine the clean energy power generation demand and the thermal power generation demand; Using the clean energy power generation equipment information and the clean energy power generation demand from the clean energy power generation information, a general clean energy dispatch plan is determined. Using the thermal power generation equipment information and the total thermal power generation demand in the thermal power generation information, a total thermal power dispatch plan is determined. The overall clean energy dispatch plan, the overall thermal power dispatch plan, and the redundant power are collectively determined as the overall power dispatch plan for the target period.

4. The scheduling optimization method for a source-grid-load-storage system according to claim 3, characterized in that, The second operating parameters include at least the current transmission loss rate, current charge / discharge loss rate of the power generation, grid, load, and storage system, and the current and maximum storage capacity of the energy storage devices; the step of using the overall power dispatch plan, the current predicted power consumption, the current predicted power generation, and the second operating parameters to determine the power dispatch sub-plan for the current time period specifically includes: Obtain the actual power generation of clean energy and the power generation that should be generated by clean energy in the overall clean energy dispatch plan; If the actual power generation of the clean energy source does not reach the expected power generation of the clean energy source, then the current predicted power generation of the clean energy source is determined from the current predicted power generation. If the current predicted clean energy power generation is greater than or equal to the current predicted power consumption, then the portion of the current predicted clean energy power generation that is equal to the current predicted power consumption is determined as the current clean energy power consumption; the difference between the current predicted clean energy power generation and the current predicted power consumption is determined as the initial clean energy storage capacity; and the updated current storage capacity is calculated using the current transmission loss rate, the current charge / discharge loss rate, the current storage capacity, and the initial clean energy storage capacity; and the current clean energy power consumption and the initial clean energy storage capacity are determined as the power dispatch sub-scheme for the current time period.

5. The scheduling optimization method for a source-grid-load-storage system according to claim 4, characterized in that, If the current predicted clean energy power generation is less than the current predicted electricity consumption, the method further includes: The current predicted clean energy power generation is determined as the current clean energy electricity consumption. Obtain the actual power generation of thermal power plants and the required power generation of thermal power plants in the overall thermal power dispatch plan; If the actual power generation of the thermal power plant does not reach the expected power generation, the difference between the current predicted power consumption and the current clean energy power consumption is determined as the current thermal power consumption; the difference between the redundant power and the current energy storage is determined as the energy storage difference; and the current thermal power energy storage is calculated using the current transmission loss rate, the current charge and discharge loss rate, the current energy storage, and the energy storage difference; and the current clean energy power consumption, the current thermal power power consumption, and the current thermal power energy storage are determined as the power dispatch sub-scheme for the current time period.

6. The scheduling optimization method for a source-grid-load-storage system according to claim 5, characterized in that, If the actual power generation of the thermal power plant reaches the required power generation of the thermal power plant, the method further includes: If the current energy storage capacity is greater than or equal to the current predicted energy consumption, then the energy storage capacity that is the same as the current predicted energy consumption is determined from the current energy storage device as the current energy storage energy consumption, and the current energy storage energy consumption is determined as the power dispatch sub-scheme for the current time period; If the current energy storage capacity is less than the current predicted energy consumption, then the current energy storage capacity is taken as the current energy storage consumption; the difference between the current predicted energy consumption and the current energy storage capacity is determined as the current thermal power consumption; and the current energy storage consumption and the current thermal power consumption are determined as the power dispatch sub-scheme for the current time period.

7. A scheduling optimization device for a source-grid-load-storage system, characterized in that, The device includes: The modeling unit is used to perform digital twin modeling of the power generation, grid, load and storage system to obtain the power generation, grid, load and storage twin model of the power generation, grid, load and storage system; wherein, the power generation, grid, load and storage twin model includes a power generation system model, a transmission model, a power storage model and a power consumption model; The first input unit is used to input the first environmental prediction data within the target period and the first operating parameters of the source-grid-load-storage system into the source-grid-load-storage twin model to obtain the overall power dispatch scheme within the target period. The acquisition unit is used to acquire second environmental prediction data and second operating parameters of the source-grid-load-storage system at preset intervals within the target period. The second input unit is used to input the second environmental prediction data and the second operating parameters into the source-grid-load-storage twin model to obtain the current predicted electricity consumption and the current predicted power generation in the current time period. The determining unit is used to determine the power dispatch sub-scheme for the current time period using the overall power dispatch plan, the current predicted power consumption, the current predicted power generation, and the second operating parameters; An application unit is used to apply the power dispatch sub-scheme to the source-grid-load-storage system during the current time period.

8. A computing device, characterized in that, The computing device includes: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to invoke the computer programs stored in the memory to execute the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium comprising instructions, characterized in that, When it is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-6.

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