Electric power flexible capacity demand measuring and calculating method and device based on body digital twinning and scene perception

Abstract

The invention relates to the technical field of electric power capacity measurement and calculation, and discloses an electric power flexible capacity demand measurement and calculation method and device based on body digital twinning and scene perception. According to the invention, for a power insurance supply scene and a new energy consumption scene, a power insurance supply double-layer measurement and calculation model and a new energy consumption double-layer measurement and calculation model are respectively established. Based on the model, the reliability of the flexible resources in the power system is dynamically evaluated by adopting the digital twinning technology, the load and power generation state changes are sensed in real time, an optimized resource scheduling strategy is provided, and the flexibility and reliability of the power system in coping with a complex operation environment are effectively improved. And multi-scene adaptability evaluation is carried out by adopting a scene perception technology, and a scheduling strategy of flexible resources is dynamically adjusted, so that the new energy consumption level is remarkably improved. And meanwhile, through down-regulation capability optimization and real-time abandoned electricity quantity control, the abandoned electricity quantity of new energy power generation is effectively reduced, and efficient utilization of renewable energy sources is facilitated.

Description

Technical Field

[0001] The present application relates to the technical field of power capacity calculation, and specifically to a method and device for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception. Background Art

[0002] With the accelerated transformation of the global energy structure and the continued increase in the proportion of renewable energy power generation, the operation and development model of the power system are undergoing profound changes. The randomness, intermittency, and volatility of renewable energy sources such as wind power and photovoltaics pose significant challenges to the safe and stable operation of the power system. At the same time, to ensure the reliability of power supply, more flexible and efficient capacity planning methods are needed to cope with the complex and changing power supply and demand situation. This places new demands on the scheduling and optimization of flexible resources in the power system and poses significant challenges to traditional capacity planning methods.

[0003] Current methods for estimating power capacity demand primarily focus on the planning and evaluation of power generation resources, but most rely on static reliability assessment models. These models typically assume that capacity resource failures are independent and identically distributed, ignoring dynamically changing environmental factors and the dynamic scheduling capabilities of flexibility resources. This makes it difficult to effectively address the complex demands of multiple scenarios under conditions of high renewable energy penetration. Furthermore, existing methods typically employ a single objective function optimization approach when dealing with multi-level models. This lacks systematic consideration of the demands of flexibility resources across different scenarios, leading to significant deviations in the estimation of flexibility resource capacity and weakening the accuracy and adaptability of capacity planning. Summary of the Invention

[0004] This application provides a method for calculating the flexible capacity demand of electricity based on embodied digital twins and scenario-based perception to solve the problem in the existing technology that the static reliability assessment model ignores the dynamically changing environmental factors and the dynamic scheduling capabilities of flexibility resources, and the existing method lacks systematic consideration of the flexibility resource requirements for different scenarios when processing multi-level models.

[0005] Correspondingly, the present application also provides a device for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception, an electronic device, and a computer-readable storage medium to ensure the implementation and application of the above method.

[0006] In order to solve the above technical problems, this application discloses a method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception, which includes:

[0007] For power supply guarantee scenarios, a two-layer power supply guarantee calculation model is established; the power supply guarantee two-layer calculation model includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer reliability assessment model;

[0008] A two-layer calculation model for new energy consumption is established for new energy consumption scenarios. The two-layer calculation model for new energy consumption includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer new energy consumption model.

[0009] Solve the two-layer calculation model for power supply security or the two-layer calculation model for new energy consumption to obtain capacity clearing results;

[0010] Among them, the two-layer measurement model for power supply uses embodied digital twin technology for global and local real-time status perception, dynamic reliability assessment, and adaptive scheduling of flexibility resources; the two-layer measurement model for new energy consumption uses scenario-based perception technology for multi-scenario adaptability assessment, dynamic downward adjustment and scheduling of flexibility resources, real-time power curtailment control, and flexible adjustment of new energy consumption level constraints.

[0011] Preferably, the objective function of the upper-layer flexibility resource capacity demand calculation model of the two-layer calculation model for ensuring power supply is used to minimize the annual capacity demand for inter-provincial flexibility resources;

[0012] The constraints of the upper-layer flexibility resource capacity demand calculation model of the two-layer calculation model for power supply security include capacity balance constraints, capacity upper and lower limit constraints, and reliability constraints.

[0013] Preferably, the objective function of the lower layer reliability assessment model of the two-layer power supply guarantee calculation model is used to minimize the system load reduction;

[0014] The constraints of the lower-level reliability assessment model of the two-level calculation model for power supply security include power balance constraints, load loss constraints, existing power output constraints, flexible resource equivalent output constraints, and new energy equivalent capacity constraints.

[0015] Preferably, the objective function of the upper-layer flexibility resource capacity demand calculation model of the new energy consumption double-layer calculation model is used to optimize the effect of the flexibility resource downward adjustment capacity on the new energy consumption;

[0016] The constraints of the upper-level flexibility resource capacity demand calculation model of the two-level calculation model for new energy consumption include capacity balance constraints, new energy consumption level constraints, and capacity upper and lower limit constraints.

[0017] Preferably, the objective function of the lower layer new energy consumption model of the new energy consumption double-layer estimation model is used to minimize the amount of new energy curtailment;

[0018] The constraints of the lower-layer new energy consumption model of the two-layer calculation model for new energy consumption include power balance constraints, new energy consumption level constraints, and flexible resource equivalent output constraints.

[0019] Preferably, solving a two-layer calculation model for power supply security or a two-layer calculation model for new energy consumption to obtain capacity clearing results includes:

[0020] Monte Carlo simulation is used to sample the physical power system state and obtain the capacity series used in the reliability assessment process;

[0021] Perform multivariate convex fitting on the sample capacity in the capacity sequence, establish the relationship between power generation resource capacity and system reliability, and determine the fitting parameters;

[0022] The fitting parameters are substituted into the two-layer calculation model for power supply security or the two-layer calculation model for new energy consumption, and the piecewise linearization method is used to solve the annual capacity demand of the optimal flexibility resource to obtain the capacity clearing result.

[0023] Preferably, before solving the two-layer calculation model for power supply security or the two-layer calculation model for new energy consumption and obtaining the capacity clearing result, the method further includes:

[0024] It is assumed that resource failures in the two-layer calculation model for power supply security and the two-layer calculation model for new energy consumption are independently distributed, and transmission failures and the correlation between transmission failures are ignored.

[0025] This application also discloses a device for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception, the device comprising:

[0026] A two-layer calculation model construction module is used to establish a two-layer calculation model for power supply security scenarios. The two-layer calculation model for power supply security includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer reliability assessment model.

[0027] The two-layer calculation model construction module is also used to establish a two-layer calculation model for new energy consumption scenarios; the new energy consumption two-layer calculation model includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer new energy consumption model;

[0028] The calculation module is used to solve the two-layer calculation model for power supply guarantee or the two-layer calculation model for new energy consumption to obtain capacity clearing results;

[0029] Among them, the two-layer measurement model for power supply uses embodied digital twin technology for global and local real-time status perception, dynamic reliability assessment, and adaptive scheduling of flexibility resources; the two-layer measurement model for new energy consumption uses scenario-based perception technology for multi-scenario adaptability assessment, dynamic downward adjustment and scheduling of flexibility resources, real-time power curtailment control, and flexible adjustment of new energy consumption level constraints.

[0030] The present application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, one or more methods described in the present application are implemented.

[0031] The present application also discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, one or more methods described in the present application are implemented.

[0032] In this application, for power supply guarantee scenarios and new energy consumption scenarios, a two-tiered calculation model for power supply guarantee that considers reliability assessment and a two-tiered calculation model for new energy consumption that considers the level of new energy consumption are proposed, respectively. The two-tiered calculation model for power supply guarantee uses embodied digital twin technology, which can dynamically assess the reliability of flexible resources in the power system, perceive changes in load and power generation status in real time, and provide optimized resource scheduling strategies. This effectively improves the flexibility and reliability of the power system in dealing with complex operating environments, especially in power supply guarantee scenarios, which can significantly reduce load reduction and ensure a safe and stable power supply. The two-tiered calculation model for new energy consumption uses scenario-based perception technology to conduct multi-scenario adaptability assessments, dynamically adjust the scheduling strategy of flexible resources, and significantly improve the level of new energy consumption. At the same time, through downward capacity optimization and real-time curtailment control, the curtailment of new energy power generation is effectively reduced, facilitating the efficient utilization of renewable energy and providing technical support for the transformation of the energy structure.

[0033] In this application, a two-layer measurement model and Monte Carlo simulation method are used to accurately calculate the annual capacity demand of flexible resources and improve the accuracy of capacity planning. In addition, the introduction of piecewise linearization and multivariate convex fitting methods greatly improves the computational efficiency and adaptability of the model. This application demonstrates strong practicality in medium- and long-term power planning and provides reliable data and technical support for the construction of intelligent power systems.

[0034] Additional aspects and advantages of the present application will be given in the following description, which will become apparent from the following description, or will be understood through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0036] Figure 1 A flowchart of a method for estimating flexible power capacity demand based on embodied digital twins and scenario-based perception provided in an embodiment of the present application;

[0037] Figure 2A flowchart of the overall solution of the method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception provided in an embodiment of the present application;

[0038] Figure 3 A block diagram of the design of an algorithm for optimizing and clearing annual capacity requirements for flexible resources taking into account reliability assessment, provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of a device for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following describes embodiments of the present application in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0042] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0043] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless specifically defined as herein, will not be interpreted in an idealized or overly formal sense.

[0044] The solution provided in the embodiments of the present application can be executed by any electronic device, such as a terminal device or a server, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and this application does not limit this. With respect to the technical problems existing in the prior art, the method and device for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception provided in this application are intended to solve at least one of the technical problems of the prior art.

[0045] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0046] The embodiment of the present application provides a method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception, such as Figure 1 and Figure 2 As shown, the method may include the following steps:

[0047] Step 101: For the power supply guarantee scenario, a two-layer power supply guarantee calculation model is established; the two-layer power supply guarantee calculation model includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer reliability assessment model.

[0048] Among them, the two-layer measurement model for power supply security uses embodied digital twin technology to perform global and local real-time status perception, dynamic reliability assessment, and adaptive scheduling of flexibility resources to achieve real-time optimization and scheduling.

[0049] Step 102: For the new energy consumption scenario, a two-layer calculation model for new energy consumption is established; the two-layer calculation model for new energy consumption includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer new energy consumption model.

[0050] Among them, the two-layer measurement model for new energy consumption uses scenario-based perception technology to conduct multi-scenario adaptability assessment, dynamic reduction and scheduling of flexibility resources, real-time power abandonment control, and flexible adjustment of new energy consumption level constraints.

[0051] Step 103: Solve the two-layer calculation model for power supply or the two-layer calculation model for new energy consumption to obtain the capacity clearing result.

[0052] The method in the embodiment of the present application should be used in the long-term capacity planning of the power system. For the power supply guarantee scenario and the new energy consumption scenario, a two-layer calculation model for power supply guarantee considering reliability assessment and a two-layer calculation model for new energy consumption considering the new energy consumption level are proposed respectively. Among them, the two-layer calculation model for power supply guarantee adopts embodied digital twin technology, which can dynamically evaluate the reliability of flexible resources in the power system, perceive changes in load and power generation status in real time, and provide optimized resource scheduling strategies. It effectively improves the flexibility and reliability of the power system in dealing with complex operating environments, especially in the power supply guarantee scenario, it can significantly reduce the load reduction and ensure the safety and stability of power supply. The two-layer calculation model for new energy consumption adopts scenario-based perception technology to conduct multi-scenario adaptability assessment, dynamically adjust the scheduling strategy of flexible resources, and significantly improve the new energy consumption level. At the same time, through downward capacity optimization and real-time power abandonment control, the power abandonment of new energy power generation is effectively reduced, which helps the efficient utilization of renewable energy and provides technical support for the transformation of energy structure.

[0053] In an optional embodiment, the objective function of the upper-layer flexibility resource capacity demand calculation model of the power supply guarantee two-layer calculation model is used to minimize the annual capacity demand of inter-provincial flexibility resources;

[0054] The constraints of the upper-layer flexibility resource capacity demand calculation model of the two-layer calculation model for power supply security include capacity balance constraints, capacity upper and lower limit constraints, and reliability constraints.

[0055] In this embodiment, the upper-layer flexibility resource capacity demand calculation model of the two-layer power supply guarantee model minimizes the annual inter-provincial flexibility resource capacity demand. The capacity units considered are conventional power sources such as thermal power and hydropower. To simplify the model, the capacity value of the output of new energy units is not considered. Instead, the output of new energy units is randomly sampled from historical data, and a random production simulation is performed on the power generation system.

[0056] The objective function and constraints of the upper-layer flexibility resource capacity demand calculation model of the two-layer calculation model for power supply security are as follows:

[0057] 1.1 Objective Function

[0058] In order to find the lower limit of the annual capacity demand of flexibility resources, the objective function of the upper-layer flexibility resource capacity demand calculation model of the two-layer power supply guarantee model is to minimize the annual capacity demand of flexibility resources of the system, which is expressed as:

[0059]

[0060] In the formula, the variable is the equivalent capacity of flexibility resource k in province j; jis the set of flexibility resources in province j.

[0061] 1.2 Reliability Constraints

[0062] Reliability constraints indicate that the power capacity reliability provided by various flexible resources (sources, grids, loads, and storage) and other conventional resources in the system must meet the overall system reliability requirements. System reliability is measured by LOLP.

[0063] LOLP(ΔD s,t )≤R0 (2)

[0064] Where R0 represents the system's reliability requirement; LOLP(ΔD s,t ) is the system reliability obtained by the lower-level reliability assessment model, or the relationship between the load loss probability and the power source capacity.

[0065] 1.3 Capacity Balance Constraints

[0066] The capacity balance constraint means that the power capacity that can be provided by the system should be greater than the predicted peak load demand:

[0067]

[0068] In the formula, the variable is the capacity of conventional generator set i in province j; Θ j is the set of conventional generators in province j; represents the predicted peak load of province j.

[0069] When the sum of the power supply capacities on the left side of formula (3) is equal to the capacity demand of the system on the right side, since this model is coupled with the reliability assessment model, it will inevitably fail to meet the reliability constraint requirements expressed by formula (2). Therefore, in the annual capacity demand calculation model for flexible resources considering reliability assessment, the sum of the power supply capacities on the left side of the capacity balance constraint should be greater than the system capacity demand on the right side.

[0070] 1.4 Capacity upper and lower limit constraints

[0071] The upper and lower capacity constraints represent the feasible range of the capacity supply of the existing power supply. Specifically, the upper and lower capacity constraints of the existing power supply are:

[0072]

[0073] Where, represents the minimum capacity that conventional generator set i can provide in province j, represents the maximum capacity that conventional generator set i can provide in province j, which is related to the minimum output and installed capacity of conventional generator sets.

[0074] Similarly, the upper and lower bounds of the equivalent capacity of flexible resources are:

[0075]

[0076] Where, represents the minimum equivalent capacity that flexibility resource k in province j can provide, represents the maximum equivalent capacity that flexibility resource k can provide in province j.

[0077] In an optional embodiment, the objective function of the lower layer reliability assessment model of the power supply guarantee two-layer measurement model is used to minimize the system load reduction;

[0078] The constraints of the lower-level reliability assessment model of the two-level calculation model for power supply security include power balance constraints, load loss constraints, existing power output constraints, flexible resource equivalent output constraints, and new energy equivalent capacity constraints.

[0079] Among them, the objective function and constraints of the lower layer reliability assessment model of the two-layer calculation model for power supply security are as follows:

[0080] 2.1 Objective Function

[0081] The objective function of the lower layer reliability assessment model of the two-layer calculation model for power supply security can be expressed as:

[0082]

[0083] Where ΔD s,t is the load loss at time t in scenario s, represents the total load loss of the system in scenario s. Therefore, the objective function To minimize the load loss of the system in each scenario.

[0084] 2.2 Power balance constraints

[0085] The power balance constraint means that the power available in the system should be greater than the load demand. The resources that can provide power here include existing power sources, new energy, and flexible resources:

[0086]

[0087] Where, represents the output of stock power source i in province j at time t in scenario s; In scenario s, the consumption output of new energy in province j at time r is represents the equivalent output of flexibility resource k in province j at time t in scenario s; Γ j is the set of new energy sources in province j; D j,tis the load forecast value of province j at time t; ΔD j,s,t is the load loss of province j at time t in scenario s.

[0088] 2.3 Load loss constraints

[0089] The load loss constraint states that the system load loss cannot be negative:

[0090] 0≤ΔD j,s,t ≤D j,t ,j=1,…,N Z ,t=1,…,N T (8)

[0091] Where ΔD j,t It represents the load loss of province j at time t.

[0092] 2.4 Output Constraints on Existing Power Sources

[0093] The output constraint of existing power sources can be expressed as:

[0094]

[0095] Where, represents the minimum output of the stock power source i in province j during operation; It represents the maximum output of the stock power source i in province j during operation.

[0096] 2.5 Flexible Resource Equivalent Output Constraints

[0097] For load-side resources, the equivalent output in this constraint represents the amount of load that can be responded to:

[0098]

[0099] Where, represents the minimum equivalent output of flexibility resource k in province j during operation; represents the maximum equivalent output of flexibility resource k in province j during operation.

[0100] 2.6 New Energy Equivalent Capacity Constraints:

[0101] Among them, when the goal is to "meet the system load supply guarantee", new energy does not need to be fully absorbed. For this reason, the new energy equivalent capacity constraint is established as:

[0102]

[0103] Where, It represents the guaranteed output of renewable energy r in province j at time t, that is, the available capacity at 95% confidence level.

[0104] Obviously, in the long-term capacity planning problem of power systems, the larger the capacity parameter of the power source, the higher the unit construction cost, but at the same time the system can achieve higher reliability. In the proposed two-layer measurement model for power supply security considering reliability assessment, the reliability constraint expressed in formula (2) can well balance the economy of flexible resource purchase and the capacity adequacy of the system.

[0105] In an optional embodiment, the two-tiered power supply measurement model uses embodied digital twin technology to perform global and local real-time status perception, dynamic reliability assessment, and adaptive scheduling of flexibility resources. The specific implementation method is as follows:

[0106] In power supply guarantee scenarios, embodied digital twin technology is introduced to dynamically evaluate and optimize the reliability of flexible resources in real time. The digital twin system can deeply integrate the operating status of the physical power system with the virtual model, and achieve dynamic perception and rapid feedback by real-time monitoring of the health status of each generator set, energy storage equipment, and load response. Specifically, it includes:

[0107] 1) Global and local real-time status perception

[0108] Embodied digital twin technology combines the physical equipment of the power system (such as generators, energy storage devices, and power grids) with a two-layer measurement model for power supply assurance. By sensing the real-time operating status of the equipment, load changes, weather changes, and other factors, it provides a dynamic, constantly updated virtual replica. This virtual replica is synchronized with the actual system and can reflect the health status, performance degradation, and possible failure risks of the equipment in real time. By combining it with the reliability constraints in formula (2), the system reliability assessment results can be dynamically adjusted to reflect the abundance of flexible resources and power supply capabilities.

[0109] 2) Dynamic reliability assessment

[0110] Traditional reliability assessments often rely on static predictive data, while embodied digital twins enable dynamic monitoring based on real-time changes in the power system. In power supply assurance scenarios, digital twins can simulate not only the operating status of power generation equipment and energy storage systems, but also changes in grid topology and even the impact of weather conditions on equipment output. Based on this real-time data, the system can assess equipment health and adjust capacity planning in real time to ensure a balance between system reliability and economic efficiency.

[0111] 3) Adaptive Scheduling of Flexible Resources

[0112] Flexible resources (such as adjustable energy storage systems and demand response mechanisms) can be adjusted according to fluctuations in power demand. With the assistance of embodied digital twins, the system can perceive factors such as load fluctuations and changes in power generation resources in real time and dynamically adjust the scheduling strategy of flexible resources. It can provide more accurate input data for the load loss calculation in formula (2). Real-time feedback on factors such as flexible resource scheduling, load demand fluctuations, and equipment reliability enables the system to reduce scheduling costs and improve the economic efficiency of the system while ensuring power supply.

[0113] In an optional embodiment, the objective function of the upper-layer flexibility resource capacity demand estimation model of the new energy consumption two-layer estimation model is used to optimize the effect of the flexibility resource downward adjustment capability on the new energy consumption;

[0114] The constraints of the upper-level flexibility resource capacity demand calculation model of the two-level calculation model for new energy consumption include capacity balance constraints, new energy consumption level constraints, and capacity upper and lower limit constraints.

[0115] The objective function and constraints of the upper-layer flexibility resource capacity demand calculation model of the two-layer calculation model for new energy consumption are as follows:

[0116] 3.1 Objective Function

[0117] The objective function of the upper-layer flexibility resource capacity demand calculation model of the new energy consumption two-layer calculation model can be expressed as:

[0118]

[0119] In the formula, the variable is the equivalent capacity of flexibility resource k in province j; j is the set of flexibility resources in province j.

[0120] 3.2 Constraints on the level of new energy consumption

[0121] The constraint on the new energy consumption level can be expressed as:

[0122]

[0123] In the formula, the variable is the capacity of new energy generator r in province j; Indicates the system's requirements for the level of new energy consumption; It is the evaluation function of new energy consumption level.

[0124] 3.3 Capacity balance constraints:

[0125]

[0126] In the formula, the variable is the capacity of conventional generator set i in province j; Θ j is the set of conventional generators in province j; Γ j is the set of new energy generating units in province j; represents the predicted peak load of province j.

[0127] 3.4 Capacity upper and lower limit constraints:

[0128] The upper and lower capacity constraints represent the feasible range of the capacity supply of the existing power supply. Specifically, the upper and lower capacity constraints of the existing power supply are:

[0129]

[0130] Where, represents the minimum capacity that conventional generator set i can provide in province j, represents the maximum capacity that conventional generator set i can provide in province j, which is related to the minimum output and installed capacity of conventional generator sets.

[0131] Similarly, the upper and lower bounds of the equivalent capacity of flexible resources are:

[0132]

[0133] Where, represents the minimum equivalent capacity that flexibility resource k in province j can provide, represents the maximum equivalent capacity that flexibility resource k can provide in province j.

[0134] In an optional embodiment, the objective function of the lower layer new energy consumption model of the new energy consumption double-layer estimation model is used to minimize the amount of new energy curtailment;

[0135] The constraints of the lower-layer new energy consumption model of the two-layer calculation model for new energy consumption include power balance constraints, new energy consumption level constraints, and flexible resource equivalent output constraints.

[0136] Among them, the objective function and constraints of the lower layer of the new energy consumption model of the two-layer calculation model for new energy consumption are as follows:

[0137] 4.1 Objective Function

[0138] The objective function of the lower layer new energy consumption model of the two-layer estimation model for new energy consumption can be expressed as:

[0139]

[0140] Where, is the amount of renewable energy curtailment at time t in scenario s, that is, the system has the minimum renewable energy curtailment in each scenario.

[0141] 4.2 Power balance constraints

[0142] Similar to the upper-level flexibility resource capacity demand calculation model, in the lower-level new energy consumption model, the downward adjustment capacity of flexibility resources is included in the load side:

[0143]

[0144] Where, represents the output of stock power source i in province j at time t in scenario s; In scenario s, the consumption output of new energy in province j at time r is represents the equivalent output of flexibility resource k in province j at time t in scenario s; Γ j is the set of new energy sources in province j; D j,t is the load forecast value of province j at time t.

[0145] 4.3 Constraints on the level of new energy consumption

[0146]

[0147] Where, represents the predicted output of renewable energy r in province j at time t.

[0148] 4.4 Output Constraints on Existing Power Sources

[0149] The output constraint of existing power sources can be expressed as:

[0150]

[0151] Where, represents the minimum output of the stock power source i in province j during operation; It represents the maximum output of the stock power source i in province j during operation.

[0152] 4.5 Flexible Resource Equivalent Output Constraints

[0153] For load-side resources, the equivalent output in this constraint represents the amount of load that can be responded to:

[0154]

[0155] Where, represents the minimum equivalent output of flexibility resource k in province j during operation; represents the maximum equivalent output of flexibility resource k in province j during operation.

[0156] In an optional embodiment, the two-tiered calculation model for renewable energy consumption uses scenario-based sensing technology to perform multi-scenario adaptability assessment, dynamic downsizing and scheduling of flexibility resources, real-time curtailment control, and flexible adjustment of renewable energy consumption level constraints. The specific implementation method is as follows:

[0157] In the new energy consumption scenario, scenario-based perception technology optimizes the downward adjustment capability of flexible resources through fine-grained modeling and dynamic evaluation of the volatility of new energy. Based on scenario-based perception, the model can capture the changing characteristics of new energy such as wind energy and solar energy in real time, thereby providing more accurate guidance for the new energy consumption constraints in formulas (13) and (19). Specifically, it includes:

[0158] 1) Multi-scenario adaptability evaluation

[0159] Renewable energy output is highly uncertain, and scenario-based awareness can provide power systems with forecasts for various weather and load scenarios. For example, for a specific time period or season, the system can generate multiple "scenarios," each simulating renewable energy output under different weather conditions and evaluating the renewable energy consumption in each scenario. Through in-depth analysis of these scenarios, the system can identify the optimal consumption strategy for each scenario, ensuring maximum energy utilization.

[0160] 2) Dynamic downsizing and scheduling of flexible resources

[0161] During the renewable energy consumption process, due to the volatility of energy sources such as wind and solar power, the system needs to flexibly adjust its regulatory resources to maintain a stable power supply. Scenario-based sensing technology can detect and predict renewable energy fluctuations in real time, determining the scheduling needs of flexible resources (such as energy storage, electric vehicle charging, and load response) in different time periods. When renewable energy output is excessive, the system can quickly mobilize flexible resources to reduce it; when renewable energy generation is insufficient, the system can quickly mobilize resources such as energy storage to supplement it, thereby ensuring the improvement of renewable energy consumption levels.

[0162] 3) Real-time power curtailment control

[0163] A key application of scenario-based sensing technology in optimizing renewable energy consumption is real-time curtailment control. Based on multi-scenario sensing, the system can assess the discrepancy between actual renewable energy generation and grid load in real time and dynamically adjust load-side resources (such as demand response and energy storage) to absorb excess renewable energy. When system load is low, scenario-based sensing technology can quickly detect this information and activate energy storage facilities or load response mechanisms to absorb excess power, thereby reducing curtailment.

[0164] 4) Flexible adjustment of constraints on new energy consumption levels

[0165] In renewable energy consumption scenarios, the system needs to flexibly adjust based on real-time scenario-based data to maximize renewable energy consumption while avoiding grid overload. Based on real-time monitoring based on scenario-based perception, renewable energy consumption level constraints can be flexibly adjusted to ensure that renewable energy can be fully absorbed under varying load and weather conditions without causing grid stability issues.

[0166] In an optional embodiment, if Figure 2 As shown, before solving the two-layer calculation model for power supply security or the two-layer calculation model for new energy consumption and obtaining the capacity clearing result, the method further includes:

[0167] It is assumed that resource failures in the two-layer calculation model for power supply security and the two-layer calculation model for new energy consumption are independently distributed, and transmission failures and the correlation between transmission failures are ignored.

[0168] Since the two-layer calculation model for power supply and the two-layer calculation model for new energy consumption in the embodiments of the present application are designed for medium- and long-term power planning, the model does not consider the correlation between transmission failures and failures, that is, it ignores the topological structure and operation mode of the power grid, and assumes that capacity resource failures are independent and identically distributed.

[0169] In an optional embodiment, if Figure 2 As shown in , solve the two-layer calculation model for power supply or the two-layer calculation model for new energy consumption to obtain capacity clearing results, including:

[0170] Monte Carlo simulation is used to sample the physical power system state and obtain the capacity series used in the reliability assessment process;

[0171] Perform multivariate convex fitting on the sample capacity in the capacity sequence, establish the relationship between power generation resource capacity and system reliability, and determine the fitting parameters;

[0172] The fitting parameters are substituted into the two-layer calculation model for power supply security or the two-layer calculation model for new energy consumption, and the piecewise linearization method is used to solve the annual capacity demand of the optimal flexibility resource to obtain the capacity clearing result.

[0173] Monte Carlo simulation is an effective method for solving the sample mean function in power generation expansion planning problems. It is superior to Latin Hypercube sampling, which may encounter dimensionality explosion problems due to stratified sampling, especially when applied to large complex systems. Therefore, the embodiment of this application adopts Monte Carlo Simulation (MCS). g

[0174] MCS is a widely used power system reliability assessment technology that can simulate the operating state of the power system to avoid listing all possible contingencies. When there are sufficient samples, the reliability index can be statistically estimated by analyzing the sample state. In summary, this method is highly effective in the reliability assessment of large power systems. Although it sacrifices precision and the accuracy of the results depends on the number of samples, it simplifies a complex process. Monte Carlo simulation does not require explicitly listing all contingencies, but instead samples the system state according to the probability distribution function. With a sufficient number of samples, the reliability index of the system is estimated by analyzing and counting the system state of each sample. Specifically, reliability assessment can be divided into three steps: (1) system state sampling; (2) system state assessment; (3) reliability index calculation.

[0175] It should be noted that the two-layer model proposed in the embodiment of the present application (the two-layer measurement model for power supply or the two-layer measurement model for new energy consumption) can be directly combined into a single-layer model. However, if there are n elements in the system, the number of scenarios in the lower model will be 2n, and it will be difficult to list all sub-problems. Mathematical models that contain such optimization problems are usually called semi-infinite programming models. To this end, the embodiment of the present application decomposes the entire problem into two sub-problems, embeds the sub-problems of the lower model into the reliability constraints of the upper model to obtain the optimal solution, and uses the Monte Carlo sampling method to fit the relationship between the power generation resource capacity and the system reliability to reduce the number of sub-problems.

[0176] When solving a two-tiered power supply measurement model that considers reliability assessment, the greatest challenge lies in the difficulty of determining an analytical expression that accurately represents the relationship between reliability and capacity. To this end, the present embodiment proposes a solution algorithm based on multivariate convex fitting, which mainly includes: (1) Monte Carlo simulation (MCS); (2) multivariate convex fitting technology.

[0177] The designed solution algorithm flow chart is as follows Figure 3 As shown in Figure 2, the algorithm uses a two-layer Monte Carlo sampling scheme. The first layer of sampling is used to obtain the capacity sequence used in the evaluation process, which can greatly reduce the sample space. The second layer of sampling is the state duration sampling performed when executing MCS.

[0178] Step 1: Read in the original data and calculate the system parameters, including capacity bids and prices, technical parameters of capacity power sources, power load curves, and other parameters; calculate the reliability parameters of each component in the system, including system capacity requirements, MTTR, MTTF, forced outage rate, etc.

[0179] Step 2: Set the initialization parameters of the reliability assessment program, including the total number of sampling times N of the programg , the number of calculations per day M, etc.; according to the declared parameters of each power source, set the capacity parameter range of the reliability program evaluation and set the sampling number g=0.

[0180] Step 3: Use the Monte Carlo method to extract the capacity parameter sequence used for evaluation and perform Monte Carlo simulation; at the same time, set the number of simulations m = 0.

[0181] Step 4: Determine whether m is less than M. If yes, go to step 5; if not, go to step 6.

[0182] Step 5: Perform continuous state sampling according to the following formulas (22)-(23), and perform reliability evaluation according to formulas (24)-(27), where m=m+1.

[0183] Step 6: g=g+1.

[0184] Step 7: Determine whether g is greater than the total number of sampling times N g If yes, go to step 8; if no, return to step 3.

[0185] Step 8: Output the system capacity parameter sequence and reliability index (LOLP) sequence, and perform multivariate convex fitting calculation according to formula (35).

[0186] Step 9: Substitute the fitting parameters into the system reliability constraints expressed by formula (37) for piecewise linearization.

[0187] Step 10: Output the capacity clearing result and the program ends.

[0188] In step 5, the specific method of system status sampling is as follows:

[0189] First, assume that each thermal power unit, energy storage device, new energy unit, and demand-side response resource in each time period has two states: fault and normal. c,t Expressed as the state of the cth element, it can be given by:

[0190]

[0191] Considering that each component in the system is independent and identically distributed, the failure rate of each component is p r , the state scenario generated by Monte Carlo simulation method is {s 1,t ,…,s k,t ,…,s M,t}, the sampling state of the system in the mth scene and time t is the joint distribution function of each component, which can be expressed as Then the probability of the occurrence of the mth scenario in the system can be approximately expressed as follows:

[0192]

[0193] Where n(s m,t ) is the frequency of the mth scene in the M states sampled by the system.

[0194] In step 5, the specific method for performing system status assessment is as follows:

[0195] For the sampling state of the system in each period under the mth scenario, the total load loss can be expressed by the following model:

[0196]

[0197] 0≤ΔD j,m,t ≤D j,t ,j=1,…,N Z ,t=1,…,N T (26)

[0198]

[0199] Among them, formula (24) is the objective function, and formulas (24)-(27) are constraints.

[0200] At the same time, in order to judge the convergence and calculation accuracy of MCS, the variance coefficient is used to measure the convergence of the results after each iteration. The calculation method is as follows:

[0201]

[0202] Where, represents the expected value estimator of the function; V(·) represents the variance of the function; represents the variance estimator of the function.

[0203] In step 5, the specific method for calculating the reliability index is as follows:

[0204] The reliability index used in the model is LOLP. The reliability of the system in each period under scenario m can be measured by the following formula:

[0205]

[0206] Where, It represents the total load loss of the system in state m; It represents the total load loss of the entire region at time t under system state m.

[0207] In step 8, multivariate fitting technology is used to perform multivariate convex fitting calculations, as follows:

[0208] The proposed two-layer model is connected by a nonlinear reliability constraint that is difficult to express analytically. Through multiple sampling experiments, it is found that the unavailability and increment of the power system decrease with the increase of capacity and eventually approach zero. This phenomenon provides an opportunity to realize multivariate convex fitting approximation. Specifically, using the kernel function e -x The exponential function of the fitting function is written in the form of "product sum". Specifically, the embodiment of the present application adopts the following formula to approximately characterize the relationship between system reliability and power supply main capacity parameters:

[0209]

[0210] Where R0 represents the reliability parameter of the system; A n represents the nth fitting parameter of the multivariate convex fitting function; c n Represents the nth power supply reliability input variable, that is, the capacity of each thermal power unit τ n Is a positive number.

[0211] make Then f n The second-order derivative of is shown below. When all fitting parameters are positive, the convexity of the fitting function can be guaranteed, that is:

[0212]

[0213] In step 9, piecewise linearization is performed using piecewise linearization technology, as follows:

[0214] Due to physical limitations, the capacity parameter c n Should be within the range Inside. Then N seg -1 The endpoints of a segment can be represented as And x n ,y n We can use the continuous variable w h Indicates that there is

[0215]

[0216] Therefore, the original optimization model can be transformed into a simple linear programming model, that is,

[0217]

[0218] l min ≤x≤l max (40)

[0219] B T y≤R0 (41)

[0220] Where, l min,l max is a vector consisting of limit constraints; B T The coefficient matrix representing the reliability constraints.

[0221] More generally, the model containing (38)-(41) can be reformulated as:

[0222]

[0223] Where S is the objective function of the original problem; P is the row vector containing the objective lens coefficients; X is the column vector consisting of non-negative decision variables; A is the coefficient matrix of the linear programming, and b is the column vector consisting of non-negative constants.

[0224] In the embodiment of the present application, a double-layer Monte Carlo sampling method is adopted to improve the calculation accuracy by sampling the state duration; based on the sampling results, the system reliability index is statistically evaluated to verify the convergence and calculation accuracy; the system state is evaluated in multiple scenarios, and the model parameters are dynamically adjusted in combination with the sample sequence to improve the solution efficiency.

[0225] In the embodiments of this application, the application of Monte Carlo simulation and piecewise linearization techniques in optimization algorithms provides theoretical support for multi-scenario capacity demand estimation for complex power systems. Monte Carlo simulation enables large-scale sampling of system states, capturing the distribution characteristics of capacity sequences during reliability assessment. Piecewise linearization effectively addresses nonlinearities in solving complex objective functions, improving computational efficiency and accuracy.

[0226] Based on the same principle as the method provided in the embodiment of the present application, the embodiment of the present application also provides a power flexible capacity demand calculation device based on embodied digital twins and scenario-based perception, such as Figure 4 As shown, the device includes:

[0227] A two-layer calculation model building module 401 is used to establish a two-layer calculation model for power supply guarantee scenarios; the two-layer calculation model for power supply guarantee includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer reliability assessment model;

[0228] The two-layer calculation model construction module 401 is further used to establish a two-layer calculation model for new energy consumption according to the new energy consumption scenario; the two-layer calculation model for new energy consumption includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer new energy consumption model;

[0229] A solution calculation module 402 is used to solve a two-layer calculation model for power supply or a two-layer calculation model for new energy consumption to obtain a capacity clearing result;

[0230] Among them, the two-layer measurement model for power supply uses embodied digital twin technology for global and local real-time status perception, dynamic reliability assessment, and adaptive scheduling of flexibility resources; the two-layer measurement model for new energy consumption uses scenario-based perception technology for multi-scenario adaptability assessment, dynamic downward adjustment and scheduling of flexibility resources, real-time power curtailment control, and flexible adjustment of new energy consumption level constraints.

[0231] In the embodiments of the present application, for the scenarios of power supply guarantee and new energy consumption, a two-layer calculation model for power supply guarantee considering reliability assessment and a two-layer calculation model for new energy consumption considering the level of new energy consumption are proposed respectively. Among them, the two-layer calculation model for power supply guarantee adopts embodied digital twin technology, which can dynamically evaluate the reliability of flexible resources in the power system, perceive changes in load and power generation status in real time, and provide optimized resource scheduling strategies. It effectively improves the flexibility and reliability of the power system in dealing with complex operating environments, especially in the scenario of power supply guarantee, it can significantly reduce the amount of load reduction and ensure the safety and stability of power supply. The two-layer calculation model for new energy consumption adopts scenario-based perception technology to conduct multi-scenario adaptability assessment, dynamically adjust the scheduling strategy of flexible resources, and significantly improve the level of new energy consumption. At the same time, through downward capacity optimization and real-time power abandonment control, the power abandonment of new energy power generation is effectively reduced, which helps the efficient utilization of renewable energy and provides technical support for the transformation of energy structure.

[0232] The power flexible capacity demand calculation device based on embodied digital twin and scenario perception provided in the embodiment of the present application can achieve Figures 1 to 3 To avoid repetition, the various processes implemented in the method embodiment will not be described again here.

[0233] The device for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception in the embodiment of the present application can execute the method for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception provided in the embodiment of the present application. The implementation principles are similar. The actions performed by each module and unit in the device for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception in each embodiment of the present application correspond to the steps in the method for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception in each embodiment of the present application. For the detailed functional description of each module of the device for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception, please refer to the description of the corresponding method for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception shown in the previous text, which will not be repeated here.

[0234] Based on the same principles as the methods shown in the embodiments of the present application, the embodiments of the present application also provide an electronic device, which may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception shown in any optional embodiment of the present application by calling the computer program. Compared with the existing technology, the method for calculating flexible capacity demand for electricity based on embodied digital twins and scenario-based perception provided by the present application proposes a two-layer calculation model for power supply guarantee that considers reliability assessment and a two-layer calculation model for new energy consumption that considers the level of new energy consumption for power supply guarantee scenarios and new energy consumption scenarios, respectively. Among them, the two-layer calculation model for power supply guarantee adopts embodied digital twin technology, which can dynamically evaluate the reliability of flexible resources in the power system, perceive changes in load and power generation status in real time, and provide optimized resource scheduling strategies. It effectively improves the flexibility and reliability of the power system in dealing with complex operating environments, especially in the power supply guarantee scenario, it can significantly reduce load reduction and ensure safe and stable power supply. The two-tiered model for new energy consumption uses scenario-based sensing technology to conduct multi-scenario adaptability assessments and dynamically adjust the scheduling strategy for flexible resources, significantly improving the level of new energy consumption. Furthermore, through downward capacity optimization and real-time curtailment control, it effectively reduces curtailment of new energy generation, promotes the efficient utilization of renewable energy, and provides technical support for the transformation of the energy structure.

[0235] In an optional embodiment, an electronic device is also provided, such as Figure 5 As shown, Figure 5 The electronic device 500 shown may be a server, including a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502. Optionally, the electronic device 500 may further include a transceiver 504. It should be noted that in actual applications, the number of transceivers 504 is not limited to one, and the structure of the electronic device 500 does not constitute a limitation on the embodiments of the present application.

[0236] The processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor 501 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0237] The bus 502 may include a path for transmitting information between the above components. The bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 502 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0238] The memory 503 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0239] The memory 503 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the application code stored in the memory 503 to implement the content shown in the above method embodiment.

[0240] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0241] The server provided in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to these. The terminal and the server can be directly or indirectly connected through wired or wireless communication, and this application does not limit this.

[0242] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0243] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0244] It should be noted that the computer-readable storage medium mentioned above in this application may also be a computer-readable signal medium or a combination of a computer-readable storage medium and a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0245] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0246] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0247] According to one aspect of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method and apparatus for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception, as provided in the various optional implementations described above.

[0248] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0249] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0250] The modules described in the embodiments of the present application may be implemented in software or hardware. The name of a module does not, in some cases, limit the module itself. For example, a two-layer calculation model construction module may also be described as a "two-layer calculation model construction module for establishing a two-layer calculation model for power supply security scenarios."

[0251] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for estimating flexible power capacity demand based on embodied digital twins and scenario-based perception, characterized in that: The method comprises: For power supply guarantee scenarios, a two-layer power supply guarantee calculation model is established; the two-layer power supply guarantee calculation model includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer reliability assessment model; A two-layer calculation model for new energy consumption is established for new energy consumption scenarios; the two-layer calculation model for new energy consumption includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer new energy consumption model; Solving the two-layer calculation model for power supply security or the two-layer calculation model for new energy consumption to obtain a capacity clearing result; Among them, the two-layer measurement model for power supply security adopts embodied digital twin technology to perform global and local real-time status perception, dynamic reliability assessment and adaptive scheduling of flexibility resources; the two-layer measurement model for new energy consumption adopts scenario-based perception technology to perform multi-scenario adaptability assessment, dynamic downward adjustment and scheduling of flexibility resources, real-time power abandonment control and flexible adjustment of new energy consumption level constraints.

2. The method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception according to claim 1 is characterized in that: The objective function of the upper-layer flexibility resource capacity demand calculation model of the two-layer power supply guarantee calculation model is used to minimize the annual capacity demand of inter-provincial flexibility resources; The constraints of the upper-layer flexibility resource capacity demand calculation model of the two-layer calculation model for power supply security include capacity balance constraints, capacity upper and lower limit constraints, and reliability constraints.

3. The method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception according to claim 2 is characterized in that: The objective function of the lower layer reliability assessment model of the two-layer power supply guarantee calculation model is used to minimize the system load reduction; The constraints of the lower reliability assessment model of the two-layer power supply guarantee calculation model include power balance constraints, load loss constraints, existing power output constraints, flexible resource equivalent output constraints, and new energy equivalent capacity constraints.

4. The method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception according to claim 1 is characterized in that: The objective function of the upper-layer flexibility resource capacity demand calculation model of the new energy consumption double-layer calculation model is used to optimize the effect of the flexibility resource downward adjustment capacity on the new energy consumption; The constraints of the upper-level flexibility resource capacity demand calculation model of the new energy consumption two-level calculation model include capacity balance constraints, new energy consumption level constraints, and capacity upper and lower limit constraints.

5. The method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception according to claim 4 is characterized in that: The objective function of the lower layer new energy consumption model of the new energy consumption double-layer estimation model is used to minimize the amount of new energy curtailment; The constraints of the lower-layer new energy consumption model of the two-layer calculation model for new energy consumption include power balance constraints, new energy consumption level constraints, and flexible resource equivalent output constraints.

6. The method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception according to claim 1 is characterized in that: Solving the two-layer calculation model for power supply assurance or the two-layer calculation model for new energy consumption to obtain a capacity clearing result includes: Monte Carlo simulation is used to sample the physical power system state and obtain the capacity series used in the reliability assessment process; Performing multivariate convex fitting on the sample capacity in the capacity sequence, establishing a relationship between the power generation resource capacity and the system reliability, and determining fitting parameters; Substitute the fitting parameters into the two-layer calculation model for power supply or the two-layer calculation model for new energy consumption, use the piecewise linearization method to solve the optimal annual capacity demand of flexibility resources, and obtain the capacity clearing result.

7. The method for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception according to claim 1 is characterized in that: Before solving the two-layer calculation model for power supply assurance or the two-layer calculation model for new energy consumption and obtaining a capacity clearing result, the method further includes: It is assumed that resource failures in the power supply dual-layer calculation model and the new energy consumption dual-layer calculation model are independently distributed, and transmission failures and the correlation between transmission failures are ignored.

8. A device for calculating flexible power capacity demand based on embodied digital twins and scenario-based perception, characterized in that: The device comprises: A two-layer calculation model construction module is used to establish a two-layer calculation model for power supply security for power supply security scenarios; the two-layer calculation model for power supply security includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer reliability assessment model; The two-layer calculation model construction module is also used to establish a two-layer calculation model for new energy consumption according to the new energy consumption scenario; the two-layer calculation model for new energy consumption includes an upper-layer flexibility resource capacity demand calculation model and a lower-layer new energy consumption model; A solution calculation module, used to solve the power supply guarantee two-layer calculation model or the new energy consumption two-layer calculation model to obtain a capacity clearing result; Among them, the two-layer measurement model for power supply security adopts embodied digital twin technology to perform global and local real-time status perception, dynamic reliability assessment and adaptive scheduling of flexibility resources; the two-layer measurement model for new energy consumption adopts scenario-based perception technology to perform multi-scenario adaptability assessment, dynamic downward adjustment and scheduling of flexibility resources, real-time power abandonment control and flexible adjustment of new energy consumption level constraints.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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