A multi-level power carbon emission management path library construction method and system

Abstract

The application discloses a kind of multi-level power carbon emission management path library construction method and system, method includes: determining the power generation increase or decrease of first level in transformation target year compared with starting year, determine transformation direction;Calculate the initial value of non-fossil energy power generation of first level starting year, set target value of target year;According to the initial value and target value of first level, the set of first level path is generated to constitute first level path library;For remaining levels, each level is the child level of its last level, perform path decomposition from parent level to its child level, generate the path of each level and check;When all remaining levels complete path generation and verification, integrate each level path library constituted by the set of each level path, combined with first level path library, form multi-level path library;The application can support multi-level object, multi-stage energy power low-carbon transformation path planning, with good adaptability and policy response capability.

Description

Technical Field

[0001] This invention belongs to the field of energy and power low-carbon transformation planning technology, and in particular relates to a method and system for constructing a multi-level power carbon emission management path library. Background Technology

[0002] With the rapid development of new power systems, the proportion of renewable energy generation such as wind and solar power continues to increase, but the inherent volatility and uneven distribution of regional resources are becoming increasingly prominent issues. Against this backdrop, how to construct a multi-level, interconnected low-carbon development path system under carbon emission management targets, and promote overall coordination and responsibility allocation at the national, regional, provincial, and municipal levels, has become a key issue in achieving green and low-carbon development.

[0003] However, current research on carbon emission management pathway planning still has many limitations. First, the pathway generation methods mostly adopt linear growth or static allocation models, which are difficult to reflect the dynamic evolution characteristics of the energy system. Second, the target decomposition mechanism does not fully consider the differences in regional resource endowment, technological and economic levels, resulting in insufficient pathway adaptability. Third, there is a lack of hierarchical coordination mechanisms; most existing models are unidirectional hierarchical decompositions and lack a global backtracking mechanism when sub-level pathways are infeasible, leading to a disconnect between planning results and actual implementation. Fourth, the dynamic updating capability is weak, making it difficult to effectively cope with the uncertainties brought about by technological progress, cost changes, and policy orientation changes.

[0004] Therefore, there is an urgent need to propose a carbon emission management path generation method that can support multi-level collaboration, differentiated adjustment, dynamic path evolution, and upper and lower level feedback linkage, so as to realize the step-by-step decomposition of carbon emission management objectives and the dynamic evolution of paths in multi-level energy systems. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a method and system for constructing a multi-level electricity carbon emission management path library that can support decomposition from the top level down.

[0006] Technical solution: The present invention provides a method for constructing a multi-level electricity carbon emission management path library, comprising:

[0007] Based on the administrative divisions, several levels are divided from high to low. The increase or decrease in power generation of the first level in the target year of transformation compared to the starting year is determined to determine the direction of transformation. The initial value of the proportion of non-fossil energy power generation in the total power generation in the starting year of the first level is calculated, and the target value of the proportion of non-fossil energy power generation in the total power generation in the target year of the first level is set.

[0008] Based on the initial and target values ​​of the first level, several time-series trajectories of the proportion of non-fossil energy power generation are generated. Through clustering and dimensionality reduction, several paths are retained as a set of first-level paths. The set of first-level paths constitutes the first-level path library.

[0009] For the remaining levels, each level is a sub-level of the level above it. The resource endowment score, time-varying dynamic synergy coefficient and resource endowment adjustment coefficient of each level are determined. Path decomposition from the parent level to its sub-level is performed to generate the path of each level and verify it.

[0010] Once all remaining levels have completed path generation and verification, the path libraries for each level, which consist of the sets of paths from each level, are integrated and combined with the first-level path library to form a multi-level path library.

[0011] Furthermore, for the remaining levels, each level is a sub-level of its parent level. A time-varying dynamic coordination coefficient and a resource endowment adjustment coefficient are set. Path decomposition from the parent level to its sub-levels is performed to generate paths for each level and to verify them, including:

[0012] The quantitative evaluation method was used to determine the first Tier-specific resource endowment score , ; The number of levels; the quantitative evaluation method includes any one or more combinations of the following:

[0013] The multi-criteria decision analysis method includes evaluation indicators such as wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity.

[0014] Statistical regression analysis based on historical data;

[0015] Artificial intelligence prediction methods based on data mining;

[0016] According to the Tier-based resource endowment scoring Set the adjustment rate of its non-fossil energy power generation change in the target year relative to the change in its parent level's non-fossil energy power generation, and set the change trend to be faster than, slower than, or in sync with the parent level according to demand, and initialize the first level. Time-varying dynamic synergy coefficients at different levels and resource endowment adjustment coefficient ,in, To minimize adjustment intensity, Maximum adjustment intensity; For the year variable, , The starting year, For the target year; time-varying dynamic synergy coefficient The rules for determining the value are as follows:

[0017] When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is greater than that of the parent tier. ;

[0018] When the When the target value for the proportion of non-fossil energy power generation in the total power generation at each tier is equal to that of the parent tier. ;

[0019] When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is less than that of the parent tier. ;

[0020] The first Hierarchical Path Decompose down to the first Hierarchy, generating the first Hierarchical path: ; The number of paths reserved for the corresponding level;

[0021] Execute the Perform a hierarchical path feasibility check. If the check passes, proceed to... The hierarchical path decomposition process is followed; otherwise, a local adjustment priority strategy is implemented until the path passes the verification.

[0022] Furthermore, the characteristic quantities of the path also include any one or more of the following combinations:

[0023] The proportion of wind and solar power generation in the structure;

[0024] Configure the ratio of coal-fired power plants for carbon capture, utilization, and storage;

[0025] The proportion of electricity generated from fossil fuels.

[0026] Furthermore, the multi-level path library supports arbitrary nested modeling and also includes a feedback correction mechanism between upper and lower levels. Specifically, when a lower-level path fails the verification, it is allowed to feed back and correct the time-varying dynamic collaboration coefficient and resource endowment adjustment coefficient of the parent level until the lower-level path passes the verification.

[0027] Furthermore, the feasibility verification is performed using the following constraints:

[0028] Out-of-bounds constraints: ;

[0029] Resource endowment constraints: The installed capacity of wind and solar power shall not exceed the upper limit of exploitable resources;

[0030] Electricity supply and demand balance constraints:

[0031] Regions where local power generation cannot meet local demand: Local power generation + Imported electricity Load demand;

[0032] Regions where local power generation can meet local demand: Local power generation - Power transmitted to other regions Load demand;

[0033] Carbon emission constraints: Cumulative carbon emissions must not exceed carbon allowances.

[0034] Furthermore, the local adjustment priority strategy includes:

[0035] When a path at a certain level fails the verification, the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of this level are adjusted first to achieve path adaptation.

[0036] If the local adjustment still fails the verification, then adjust the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of the parent level.

[0037] Furthermore, it also includes a dynamic feedback and update mechanism, specifically including:

[0038] Regularly assess and update resource endowment scores at each level;

[0039] Recalibrate the time-varying dynamic synergy coefficient and resource endowment adjustment coefficient;

[0040] Dynamically adjust the trend judgment of the growth rate of power generation at each level relative to the level above;

[0041] Based on the latest data and parameters, all steps are repeated to generate an updated multi-level path library, enabling dynamic evolution of path planning.

[0042] Based on the same inventive concept, this invention also provides a multi-level power carbon emission management path library construction system, comprising:

[0043] The initialization module is used to divide the administrative divisions into several levels from high to low, determine the increase or decrease in power generation of the first level in the transformation target year compared with the starting year, and determine the transformation direction; calculate the initial value of the proportion of non-fossil energy power generation in the total power generation in the starting year of the first level, and set the target value of the proportion of non-fossil energy power generation in the total power generation in the target year of the first level.

[0044] The top-level path generation module is used to generate several time-series trajectories of the proportion of non-fossil energy power generation based on the initial and target values ​​of the first level. Through clustering and dimensionality reduction, several paths are retained as a set of first-level paths, and the set of first-level paths constitutes the first-level path library.

[0045] The multi-level path decomposition module is used to determine the resource endowment score, time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of each level for the remaining levels, each level being a sub-level of the level above it. It performs path decomposition from the parent level to its sub-level, generates paths for each level and performs verification.

[0046] The path library integration and output module is used to integrate the path libraries of each level, which consist of the set of paths of each level, after all the remaining levels have completed path generation and verification. Combined with the first-level path library, a multi-level path library is formed.

[0047] Furthermore, for the remaining levels, each level is a sub-level of its parent level. A time-varying dynamic coordination coefficient and a resource endowment adjustment coefficient are set. Path decomposition from the parent level to its sub-levels is performed to generate paths for each level and to verify them, including:

[0048] The quantitative evaluation method was used to determine the first Tier-specific resource endowment score , ; The number of levels; the quantitative evaluation method includes any one or more combinations of the following:

[0049] The multi-criteria decision analysis method includes evaluation indicators such as wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity.

[0050] Statistical regression analysis based on historical data;

[0051] Artificial intelligence prediction methods based on data mining;

[0052] According to the Tier-based resource endowment scoring Set the adjustment rate of its non-fossil energy power generation change in the target year relative to the change in its parent level's non-fossil energy power generation, and set the change trend to be faster than, slower than, or in sync with the parent level according to demand, and initialize the first level. Time-varying dynamic synergy coefficients at different levels and resource endowment adjustment coefficient ,in, To minimize adjustment intensity, Maximum adjustment intensity; For the year variable, , The starting year, For the target year; time-varying dynamic synergy coefficient The rules for determining the value are as follows:

[0053] When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is greater than that of the parent tier. ;

[0054] When the When the target value for the proportion of non-fossil energy power generation in the total power generation at each tier is equal to that of the parent tier. ;

[0055] When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is less than that of the parent tier. ;

[0056] The first Hierarchical Path Decompose down to the first Hierarchy, generating the first Hierarchical path: ; The number of paths reserved for the corresponding level;

[0057] Execute the Perform a hierarchical path feasibility check. If the check passes, proceed to... The hierarchical path decomposition process is followed; otherwise, a local adjustment priority strategy is implemented until the path passes the verification.

[0058] Furthermore, the characteristic quantities of the path also include any one or more of the following combinations:

[0059] The proportion of wind and solar power generation in the structure;

[0060] Configure the ratio of coal-fired power plants for carbon capture, utilization, and storage;

[0061] The proportion of electricity generated from fossil fuels.

[0062] Furthermore, the multi-level path library supports arbitrary nested modeling and also includes a feedback correction mechanism between upper and lower levels. Specifically, when a lower-level path fails the verification, it is allowed to feed back and correct the time-varying dynamic collaboration coefficient and resource endowment adjustment coefficient of the parent level until the lower-level path passes the verification.

[0063] Furthermore, the feasibility verification is performed using the following constraints:

[0064] Out-of-bounds constraints: ;

[0065] Resource endowment constraints: The installed capacity of wind and solar power shall not exceed the upper limit of exploitable resources;

[0066] Electricity supply and demand balance constraints:

[0067] Regions where local power generation cannot meet local demand: Local power generation + Imported electricity Load demand;

[0068] Regions where local power generation can meet local demand: Local power generation - Power transmitted to other regions Load demand;

[0069] Carbon emission constraints: Cumulative carbon emissions must not exceed carbon allowances.

[0070] Furthermore, the local adjustment priority strategy includes:

[0071] When a path at a certain level fails the verification, the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of this level are adjusted first to achieve path adaptation.

[0072] If the local adjustment still fails the verification, then adjust the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of the parent level.

[0073] Furthermore, it also includes a dynamic feedback and update mechanism, specifically including:

[0074] Regularly assess and update resource endowment scores at each level;

[0075] Recalibrate the time-varying dynamic synergy coefficient and resource endowment adjustment coefficient;

[0076] Dynamically adjust the trend judgment of the growth rate of power generation at each level relative to the level above;

[0077] Based on the latest data and parameters, all steps are repeated to generate an updated multi-level path library, enabling dynamic evolution of path planning.

[0078] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories, and one or more programs, the programs being stored in the memory and configured to be executed by the processor, wherein when the programs are loaded onto the processor, they implement the steps of the multi-level power carbon emission management path library construction method according to any of the preceding claims.

[0079] Based on the same inventive concept, the present invention also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the multi-level power carbon emission management path library construction method according to any one of the preceding claims.

[0080] Beneficial effects: Compared with existing technologies, this invention can support layer-by-layer nested path generation from the national to the local level, realizing cross-level target decomposition and path adaptation; by introducing time-varying dynamic coordination coefficients and resource endowment adjustment coefficients, it enhances the flexibility and differentiated processing capabilities of path allocation; simultaneously, it constructs a feedback correction mechanism between upper and lower levels, which can feed back and correct upper-level parameters when a sub-level path is infeasible, enhancing path feasibility and system robustness; in addition, it designs a dynamic path update mechanism to periodically evaluate and adjust key parameters to adapt to the uncertainties in the energy transition process; the method of this invention can support multi-level, multi-stage energy and power low-carbon transition path planning, has good policy responsiveness and broad engineering application potential, and has important practical significance and promotional value for fields such as new power system planning and carbon emission management. Attached Figure Description

[0081] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0082] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0083] Example 1, such as Figure 1 As shown, the method for constructing a multi-level electricity carbon emission management path library in this embodiment includes:

[0084] Step 1: Divide the administrative divisions into several levels from high to low, determine the increase or decrease in power generation of the first level in the target year of transformation compared to the starting year, and determine the direction of transformation; calculate the initial value of the proportion of non-fossil energy power generation in the total power generation in the starting year of the first level, and set the target value of the proportion of non-fossil energy power generation in the total power generation in the target year of the first level.

[0085] Step 2: Based on the initial and target values ​​of the first level, generate several time-series trajectories of the proportion of non-fossil energy power generation. Through clustering and dimensionality reduction, retain several paths as a set of first-level paths. The set of first-level paths constitutes the first-level path library.

[0086] Step 3: For the remaining levels, each level is a sub-level of the level above it. Determine the resource endowment score, time-varying dynamic synergy coefficient and resource endowment adjustment coefficient for each level. Perform path decomposition from the parent level to its sub-levels to generate the path for each level and verify it.

[0087] Step 4: After all remaining levels have completed path generation and verification, integrate the path libraries of each level, which consist of the sets of paths of each level, and combine them with the first-level path library to form a multi-level path library.

[0088] Specifically, in step 1, the top layer (level 1) is determined. (e.g., countries or regions) in the year of transformation goals Compared to the starting year The magnitude of power generation increases or decreases will determine the overall direction of the transformation; based on this, the initial value of the top-level non-fossil energy power generation ratio will be calculated. Set the target value corresponding to its target year. Define the year variable ;

[0089] In step 2, based on the given initial value of the proportion of non-fossil energy power generation... and target value Multiple time-series trajectories of the proportion of non-fossil energy power generation are generated according to different curve shapes. Through clustering and dimensionality reduction, the remaining data are preserved. The typical paths are the set of paths at the first level (top level), denoted as . The set of top-level paths constitutes the top-level path library.

[0090] In step 3, regarding the hierarchy Perform the following operations sequentially to achieve the result from the first... Hierarchical to the first Hierarchical path decomposition, where the first The level is the Hierarchical direct subsystems:

[0091] Step 3.1: Determine the first step using quantitative assessment methods. Tier-specific resource endowment score Including but not limited to any one or more combinations of the following:

[0092] (a) Multi-criteria decision analysis method, with evaluation indicators including but not limited to wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity;

[0093] (b) Classical statistical regression analysis based on historical data;

[0094] (c) Artificial intelligence prediction methods based on data mining;

[0095] (d) Other models and algorithms applicable to the quantitative assessment of resource endowments;

[0096] Step 3.2: Based on the first Based on the tiered energy resource endowment and development positioning, the change in its power generation in the target year relative to the first tier is set. The adjustment speed at each level should clearly indicate whether the trend of change is faster than, slower than, or synchronized with the first level. Hierarchy, based on which the first level is initialized. Time-varying dynamic synergy coefficients at different levels and resource endowment adjustment coefficient ;

[0097] Step 3.3: Place the first Hierarchical Path Decompose down to the first Hierarchy, generating the first Hierarchical path: ;in, Used to define the degree of responsibility for sub-level objectives; Based on the energy resource endowment and development positioning of this level, a differentiated adjustment is made reflecting its rate of change relative to the previous level—that is, whether it is "faster, slower, or synchronous"; this step realizes the transition from the parent level ( ) target to sub-level ( The transfer and differentiated decomposition of responsibilities for objectives reflect... Nested dependency relationships;

[0098] Step 3.4: Execute the... The hierarchical path feasibility is checked to determine whether the constraints of that level are met; if the check passes, the process proceeds to the next level (the next level). The path decomposition process is hierarchical; otherwise, the "local adjustment priority" strategy is executed until the path is feasible.

[0099] In step 4, when After the lowest level (such as city or county) completes path generation and verification, the results of the path database, which consists of the sets of paths from each level, are integrated to form a complete path database. Multi-level path library ;

[0100] This also includes step 5: establishing a regular evaluation mechanism (e.g., every 5 years) to perform the following operations based on factors such as technological advancements and policy changes:

[0101] (a) Reassess resource endowment scores at each level ;

[0102] (b) Calibration of time-varying dynamic coordination coefficients and resource endowment adjustment coefficient ;

[0103] (c) Update the judgment of the growth trend of power generation at each level (faster / slower / synchronous with the upper level);

[0104] (d) Based on the latest data and parameters, repeat steps 1 to 4 to generate an updated multi-level path library, realizing the dynamic evolution and iterative update of path planning.

[0105] A further feature of the above technical solution is that: the path feature quantity This includes, but is not limited to, the proportion of non-fossil energy power generation, but also includes, but is not limited to, any one or more of the following combinations:

[0106] (a) The proportion of wind and solar power generation in the structure;

[0107] (b) Configure the proportion of coal-fired power plants for carbon capture, utilization and storage (CCUS);

[0108] (c) Percentage of electricity generated from fossil fuels;

[0109] (d) Other dimensionless key indicators that can reflect the progress of the low-carbon transformation of the energy system;

[0110] A further feature of the above technical solution is that the time-varying dynamic cooperative coefficient Used to characterize the degree of responsibility assumed by a sub-level relative to the goal of its parent level, its value is determined according to the following rules:

[0111] (a) When a child level needs to exceed the target of its parent level, This reflects either prioritizing development or sharing responsibility;

[0112] (b) When the child-level goal completely follows the parent-level goal, ;

[0113] (c) When a child level can be lower than the parent level's target within a certain range, It is suitable for regions with limited resources or lagging development stages;

[0114] (d) It can be set as a piecewise function or a dynamic parameter that evolves over time to reflect the phased policy orientation;

[0115] A further feature of the above technical solution is that the resource endowment adjustment coefficient The range of values ​​is This is used to adjust the intensity of the impact of resource endowment on target setting, and its setting principles are as follows:

[0116] (a) Minimize adjustment intensity to ensure that resource endowment differences have an effective impact and avoid correction failure;

[0117] (b) : Maximum adjustment intensity to prevent the path from deviating too far from the parent level baseline and to ensure the consistency of the overall goal;

[0118] (c) The interval can achieve a balance between “parent-level goal consistency” and “child-level resource differences”, and can be flexibly adjusted according to the actual application scenario;

[0119] A further feature of the above technical solution is that the multi-level path collaboration framework supports arbitrary-level nested modeling and can reflect the interaction relationships between different levels, including:

[0120] (a) Dynamic decomposition of top-level carbon emission reduction targets and electricity scale growth direction to sub-levels;

[0121] (b) Inter-regional power exchange and resource allocation coordination;

[0122] (c) A hierarchical feedback and correction mechanism allows lower-level paths to feed back and correct parent-level parameters when they cannot be implemented due to technical, resource, or economic constraints. or This enables the collaborative generation logic of a carbon emission management path that is "multi-level coordination, collaborative adaptation, and precise implementation."

[0123] A further feature of the above technical solution is that, during the path feasibility verification phase, the following constraints are used to verify the path's rationality:

[0124] (a) Over-limit constraint: ;

[0125] (b) Resource endowment constraints: The installed capacity of wind and solar power shall not exceed the upper limit of the exploitable resources. ;

[0126] (c) Electricity supply and demand balance constraints:

[0127] Resource-scarce regions (regions where local power generation cannot meet local demand): Local power generation + External power Load demand ;

[0128] Resource-rich regions (regions where local power generation can meet local demand): Local power generation -External power supply Load demand ;

[0129] (d) Carbon emission constraints: Cumulative carbon emissions shall not exceed carbon allowances. ;

[0130] A further feature of the above technical solution is that the "local adjustment priority" strategy includes:

[0131] (a) When a path at a certain level is not feasible, adjust the parameters of that level first. and Implement path adaptation (partial adjustments);

[0132] (b) If local adjustments are ineffective, an upward feedback mechanism is triggered to adjust the parent level parameters to improve overall coordination (global backtracking).

[0133] A further feature of the above technical solution is that it also includes a dynamic feedback and update mechanism, specifically including:

[0134] (a) Regularly assess and update resource endowment scores at each level. ;

[0135] (b) Recalibrate and parameter;

[0136] (c) Dynamically adjust the trend judgment of the growth rate of power generation at each level relative to the upper level (faster / slower / synchronous).

[0137] (d) Based on the latest data and parameters, an updated multi-level path library is generated to realize the dynamic evolution of path planning.

[0138] Example 2 discloses a method for constructing a multi-level electricity carbon emission management path library:

[0139] Step 1: Determine the national level (Tier 1) In the year of transformation goals Compared to the starting year The magnitude of power generation increases or decreases will determine the overall direction of the transformation; based on this, the initial value of the top-level non-fossil energy power generation ratio will be calculated. Set the target value corresponding to its target year. Define the year variable ; , , , , ;

[0140] Step 2: Based on the given initial value of the proportion of non-fossil energy power generation. and target value Multiple time-series trajectories of the proportion of non-fossil energy power generation are generated according to different curve shapes. Through clustering and dimensionality reduction, three typical paths are retained as a set of paths at the national level, denoted as... The set of national-level paths constitutes the national-level path library, as shown in Table 1.

[0141] Table 1. National-level Path Set Table

[0142]

[0143] Step 3.1: For the second level (region, i.e., the subsystem of the country), perform the following operations sequentially to achieve path decomposition from the country level to the region level:

[0144] Step 3.1.1: Determine the resource endowment score at the regional level using quantitative assessment methods. Including but not limited to any one or more combinations of the following:

[0145] (a) Multi-criteria decision analysis method, with evaluation indicators including but not limited to wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity;

[0146] (b) Classical statistical regression analysis based on historical data;

[0147] (c) Artificial intelligence prediction methods based on data mining;

[0148] (d) Other models and algorithms applicable to the quantitative assessment of resource endowments; ;

[0149] Step 3.1.2: Based on the region's energy resource endowment and development positioning, set the adjustment speed of its annual power generation change relative to the national average, clarifying whether the change trend is faster, slower, or synchronized with the national average, and initialize the time-varying dynamic coordination coefficient at the regional level accordingly. and resource endowment adjustment coefficient ; , ;

[0150] Step 3.1.3: Path to the national level Decompose down to the region layer to generate region layer paths: ;in, Used to define the degree of responsibility for regional level objectives; Based on the energy resource endowment and development positioning of this level, a differentiated adjustment is made reflecting its rate of change relative to the national level—that is, whether it is "faster, slower, or synchronous"; this step realizes the transition from the parent level ( ) target to sub-level ( The transfer and differentiated decomposition of responsibilities for objectives reflect... Path generation logic;

[0151] Step 3.1.4: Perform a regional layer path feasibility check to determine whether the constraints at this level are met.

[0152] (a) Over-limit constraint: ;

[0153] (b) Resource endowment constraints: By 2060, approximately 320GW of new wind and solar power capacity will be needed, which is less than the region's developable potential of 380GW.

[0154] (c) Electricity supply and demand balance constraints: The region's electricity self-sufficiency rate and external transmission capacity meet the planning requirements;

[0155] (d) Carbon emission constraints: Cumulative carbon emissions have not exceeded the regional carbon quota;

[0156] The set of regional layer paths is obtained through verification. And then enter the path decomposition process at the third level (provincial level);

[0157] Step 3.2: For the third level (province, i.e., the subsystem of a region), perform the following operations sequentially to achieve path decomposition from the regional level to the provincial level:

[0158] Step 3.2.1: Determine the resource endowment score at the provincial level using quantitative assessment methods. Including but not limited to any one or more combinations of the following:

[0159] (a) Multi-criteria decision analysis method, with evaluation indicators including but not limited to wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity;

[0160] (b) Classical statistical regression analysis based on historical data;

[0161] (c) Artificial intelligence prediction methods based on data mining;

[0162] (d) Other models and algorithms applicable to the quantitative assessment of resource endowments; ;

[0163] Step 3.2.2: Based on the province's energy resource endowment and development positioning, set the adjustment speed of its annual power generation change relative to the region, clarifying whether the change trend is faster, slower, or synchronized with the region, and initialize the time-varying dynamic coordination coefficient at the provincial level accordingly. and resource endowment adjustment coefficient ; , ;

[0164] Step 3.2.3: Convert the region layer path Decompose down to the provincial level to generate provincial level paths: ;in, Used to define the degree of responsibility for provincial-level targets; Based on the energy resource endowment and development positioning of this level, a differentiated adjustment is made reflecting its rate of change relative to the regional level—that is, whether it is "faster, slower, or synchronous"; this step realizes the transition from the parent level ( ) target to sub-level ( The transfer and differentiated decomposition of responsibilities for objectives reflect... The path generation logic, together with the preceding hierarchy, constitutes Nested dependency chains;

[0165] Step 3.2.4: Perform a provincial-level path feasibility check to determine whether the constraints at this level are met; the check revealed that... The required new wind and solar power capacity exceeds the province's exploitable potential, violating resource constraints;

[0166] Implement a "local adjustment priority" strategy:

[0167] Prioritize returning to step 3.2.2 for adjustment. , ;

[0168] Recalculate The rate dropped to 92.5%, and the required new installation capacity is within the scope of development.

[0169] Other provincial-level constraints (electricity supply and demand balance, carbon emissions, etc.) are all met;

[0170] The verification passed, and the provincial-level path set was obtained. ;

[0171] Step 4: When (lowest level) After the path generation and verification are completed at the provincial level, the results of the path databases at each level are integrated to form a complete path database. Multi-level path library ;

[0172] Step 5: Establish a regular evaluation mechanism every 5 years, and perform the following operations based on factors such as technological advancements and policy changes:

[0173] (a) Reassess resource endowment scores at each level ;

[0174] (b) Calibration of time-varying dynamic coordination coefficients and resource endowment adjustment coefficient ;

[0175] (c) Update the judgment of the growth trend of power generation at each level (faster / slower / synchronous with the upper level);

[0176] (d) Based on the latest data and parameters, repeat steps 1 to 4 to generate an updated multi-level path library, realizing the dynamic evolution and iterative update of path planning. The dynamically updated parameters for 2030 are shown in Table 2; path regeneration... The original path has been further optimized.

[0177] Table 2 Parameter Dynamic Update Table

[0178]

[0179] Example 3, based on the same inventive concept, also provides a multi-level power carbon emission management path library construction system, including:

[0180] The initialization module is used to divide the administrative divisions into several levels from high to low, determine the increase or decrease in power generation of the first level in the transformation target year compared with the starting year, and determine the transformation direction; calculate the initial value of the proportion of non-fossil energy power generation in the total power generation in the starting year of the first level, and set the target value of the proportion of non-fossil energy power generation in the total power generation in the target year of the first level.

[0181] The top-level path generation module is used to generate several time-series trajectories of the proportion of non-fossil energy power generation based on the initial and target values ​​of the first level. Through clustering and dimensionality reduction, several paths are retained as a set of first-level paths, and the set of first-level paths constitutes the first-level path library.

[0182] The multi-level path decomposition module is used to determine the resource endowment score, time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of each level for the remaining levels, each level being a sub-level of the level above it. It performs path decomposition from the parent level to its sub-level, generates paths for each level and performs verification.

[0183] The path library integration and output module is used to integrate the path libraries of each level, which consist of the set of paths of each level, after all the remaining levels have completed path generation and verification. Combined with the first-level path library, a multi-level path library is formed.

[0184] Furthermore, for the remaining levels, each level is a sub-level of its parent level. A time-varying dynamic coordination coefficient and a resource endowment adjustment coefficient are set. Path decomposition from the parent level to its sub-levels is performed to generate paths for each level and to verify them, including:

[0185] Step 3.1: Determine the first step using quantitative assessment methods. Tier-specific resource endowment score , ; The number of levels; the quantitative evaluation method includes any one or more combinations of the following:

[0186] The multi-criteria decision analysis method includes evaluation indicators such as wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity.

[0187] Statistical regression analysis based on historical data;

[0188] Artificial intelligence prediction methods based on data mining.

[0189] Step 3.2: Based on the first Tier-based resource endowment scoring Set the adjustment rate of its non-fossil energy power generation change in the target year relative to the change in its parent level's non-fossil energy power generation, and set the change trend to be faster than, slower than, or in sync with the parent level according to demand, and initialize the first level. Time-varying dynamic synergy coefficients at different levels and resource endowment adjustment coefficient ,in, To minimize adjustment intensity, Maximum adjustment intensity; For the year variable, , The starting year, For the target year; time-varying dynamic synergy coefficient The rules for determining the value are as follows:

[0190] When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is greater than that of the parent tier. ;

[0191] When the When the target value for the proportion of non-fossil energy power generation in the total power generation at each tier is equal to that of the parent tier. ;

[0192] When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is less than that of the parent tier. ;

[0193] Step 3.3: Place the first Hierarchical Path Decompose down to the first Hierarchy, generating the first Hierarchical path: ; The number of paths reserved for the corresponding level;

[0194] Step 3.4: Execute the... Perform a hierarchical path feasibility check. If the check passes, proceed to... The hierarchical path decomposition process is followed; otherwise, a local adjustment priority strategy is implemented until the path passes the verification.

[0195] Furthermore, the characteristic quantities of the path also include any one or more of the following combinations:

[0196] The proportion of wind and solar power generation in the structure;

[0197] Configure the ratio of coal-fired power plants for carbon capture, utilization, and storage;

[0198] The proportion of electricity generated from fossil fuels.

[0199] Furthermore, the multi-level path library supports arbitrary nested modeling and also includes a feedback correction mechanism between upper and lower levels. Specifically, when a lower-level path fails the verification, it is allowed to feed back and correct the time-varying dynamic collaboration coefficient and resource endowment adjustment coefficient of the parent level until the lower-level path passes the verification.

[0200] Furthermore, the feasibility verification described in step 3.4 is performed using the following constraints:

[0201] Out-of-bounds constraints: ;

[0202] Resource endowment constraints: The installed capacity of wind and solar power shall not exceed the upper limit of exploitable resources;

[0203] Electricity supply and demand balance constraints:

[0204] Regions where local power generation cannot meet local demand: Local power generation + Imported electricity Load demand;

[0205] Regions where local power generation can meet local demand: Local power generation - Power transmitted to other regions Load demand;

[0206] Carbon emission constraints: Cumulative carbon emissions must not exceed carbon allowances.

[0207] Furthermore, the local adjustment priority strategy includes:

[0208] When a path at a certain level fails the verification, the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of this level are adjusted first to achieve path adaptation.

[0209] If the local adjustment still fails the verification, then adjust the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of the parent level.

[0210] Furthermore, it also includes a dynamic feedback and update mechanism, specifically including:

[0211] Regularly assess and update resource endowment scores at each level;

[0212] Recalibrate the time-varying dynamic synergy coefficient and resource endowment adjustment coefficient;

[0213] Dynamically adjust the trend judgment of the growth rate of power generation at each level relative to the level above;

[0214] Based on the latest data and parameters, all steps are repeated to generate an updated multi-level path library, enabling dynamic evolution of path planning.

[0215] Example 4, based on the same inventive concept, also provides a computing device, including: one or more processors, one or more memories, and one or more programs, the programs being stored in the memory and configured to be executed by the processor, the programs being loaded onto the processor to implement the steps of the multi-level power carbon emission management path library construction method according to any of the above.

[0216] Example 5, based on the same inventive concept, also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the multi-level power carbon emission management path library construction method according to any one of the above.

Claims

1. A method for constructing a multi-level electricity carbon emission management path library, characterized in that, include: Based on the administrative divisions, several levels are divided from high to low. The increase or decrease in power generation in the first level in the year of transformation target compared to the starting year is determined, and the direction of transformation is determined. Calculate the initial value of the proportion of non-fossil energy power generation in the total power generation in the first year of the first tier, and set the target value of the proportion of non-fossil energy power generation in the total power generation in the target year of the first tier; Based on the initial and target values ​​of the first level, several time-series trajectories of the proportion of non-fossil energy power generation are generated. Through clustering and dimensionality reduction, several paths are retained as a set of first-level paths. The set of first-level paths constitutes the first-level path library. For the remaining levels, each level is a sub-level of the level above it. The resource endowment score, time-varying dynamic synergy coefficient and resource endowment adjustment coefficient of each level are determined. Path decomposition from the parent level to its sub-level is performed to generate the path of each level and verify it. Once all remaining levels have completed path generation and verification, the path libraries for each level, which consist of the sets of paths from each level, are integrated and combined with the first-level path library to form a multi-level path library.

2. The method for constructing a multi-level power carbon emission management path library according to claim 1, characterized in that, For the remaining levels, each level is a sub-level of its parent level. A time-varying dynamic coordination coefficient and a resource endowment adjustment coefficient are set. Path decomposition from the parent level to its sub-levels is performed to generate paths for each level and to validate them, including: The quantitative evaluation method was used to determine the first Tier-specific resource endowment score , ; The number of levels; the quantitative evaluation method includes any one or more combinations of the following: The multi-criteria decision analysis method includes evaluation indicators such as wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity. Statistical regression analysis based on historical data; Artificial intelligence prediction methods based on data mining; According to the Tier-based resource endowment scoring Set the adjustment rate of its non-fossil energy power generation change in the target year relative to the change in its parent level's non-fossil energy power generation, and set the change trend to be faster than, slower than, or in sync with the parent level according to demand, and initialize the first level. Time-varying dynamic synergy coefficients at different levels and resource endowment adjustment coefficient ,in, To minimize adjustment intensity, Maximum adjustment intensity; For the year variable, , The starting year, For the target year; time-varying dynamic synergy coefficient The rules for determining the value are as follows: When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is greater than that of the parent tier. ; When the When the target value for the proportion of non-fossil energy power generation in the total power generation at each tier is equal to that of the parent tier. ; When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is less than that of the parent tier. ; The first Hierarchical Path Decompose down to the first Hierarchy, generating the first Hierarchical path: ; The number of paths reserved for the corresponding level; Execute the Perform a hierarchical path feasibility check. If the check passes, proceed to... The hierarchical path decomposition process is followed; otherwise, a local adjustment priority strategy is implemented until the path passes the verification.

3. The method for constructing a multi-level electricity carbon emission management path library according to claim 1, characterized in that, The path's features also include any one or more of the following combinations: The proportion of wind and solar power generation in the structure; Configure the ratio of coal-fired power plants for carbon capture, utilization, and storage; The proportion of electricity generated from fossil fuels.

4. The method for constructing a multi-level electricity carbon emission management path library according to claim 1, characterized in that, The multi-level path library supports arbitrary nested modeling and also includes a feedback correction mechanism between upper and lower levels. Specifically, when a lower-level path fails the verification, it is allowed to feed back and correct the time-varying dynamic collaboration coefficient and resource endowment adjustment coefficient of the parent level until the lower-level path passes the verification.

5. The method for constructing a multi-level power carbon emission management path library according to claim 2, characterized in that, The feasibility verification is performed using the following constraints: Out-of-bounds constraints: ; Resource endowment constraints: The installed capacity of wind and solar power shall not exceed the upper limit of exploitable resources; Electricity supply and demand balance constraints: Regions where local power generation cannot meet local demand: Local power generation + Imported electricity Load demand; Regions where local power generation can meet local demand: Local power generation - Power transmitted to other regions Load demand; Carbon emission constraints: Cumulative carbon emissions must not exceed carbon allowances.

6. The method for constructing a multi-level power carbon emission management path library according to claim 2, characterized in that, The local adjustment priority strategy includes: When a path at a certain level fails the verification, the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of this level are adjusted first to achieve path adaptation. If the local adjustment still fails the verification, then adjust the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of the parent level.

7. The method for constructing a multi-level power carbon emission management path library according to claim 1, characterized in that, It also includes dynamic feedback and update mechanisms, specifically including: Regularly assess and update resource endowment scores at each level; Recalibrate the time-varying dynamic synergy coefficient and resource endowment adjustment coefficient; Dynamically adjust the trend judgment of the growth rate of power generation at each level relative to the level above; Based on the latest data and parameters, all steps are repeated to generate an updated multi-level path library, enabling dynamic evolution of path planning.

8. A multi-level power carbon emission management path library construction system, characterized in that, include: The initialization module is used to divide the administrative divisions into several levels from high to low, determine the increase or decrease in power generation of the first level in the transformation target year compared with the starting year, and determine the transformation direction; calculate the initial value of the proportion of non-fossil energy power generation in the total power generation in the starting year of the first level, and set the target value of the proportion of non-fossil energy power generation in the total power generation in the target year of the first level. The top-level path generation module is used to generate several time-series trajectories of the proportion of non-fossil energy power generation based on the initial and target values ​​of the first level. Through clustering and dimensionality reduction, several paths are retained as a set of first-level paths, and the set of first-level paths constitutes the first-level path library. The multi-level path decomposition module is used to determine the resource endowment score, time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of each level for the remaining levels, each level being a sub-level of the level above it. It performs path decomposition from the parent level to its sub-level, generates paths for each level and performs verification. The path library integration and output module is used to integrate the path libraries of each level, which consist of the set of paths of each level, after all the remaining levels have completed path generation and verification. Combined with the first-level path library, a multi-level path library is formed.

9. The multi-level power carbon emission management path library construction system according to claim 8, characterized in that, For the remaining levels, each level is a sub-level of its parent level. A time-varying dynamic coordination coefficient and a resource endowment adjustment coefficient are set. Path decomposition from the parent level to its sub-levels is performed to generate paths for each level and to validate them, including: The quantitative evaluation method was used to determine the first Tier-specific resource endowment score , ; The number of levels; the quantitative evaluation method includes any one or more combinations of the following: The multi-criteria decision analysis method includes evaluation indicators such as wind and solar exploitable resources, inter-regional power transmission capacity, and energy storage regulation capacity. Statistical regression analysis based on historical data; Artificial intelligence prediction methods based on data mining; According to the Tier-based resource endowment scoring Set the adjustment rate of its non-fossil energy power generation change in the target year relative to the change in its parent level's non-fossil energy power generation, and set the change trend to be faster than, slower than, or in sync with the parent level according to demand, and initialize the first level. Time-varying dynamic synergy coefficients at different levels and resource endowment adjustment coefficient ,in, To minimize adjustment intensity, Maximum adjustment intensity; For the year variable, , The starting year, For the target year; time-varying dynamic synergy coefficient The rules for determining the value are as follows: When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is greater than that of the parent tier. ; When the When the target value for the proportion of non-fossil energy power generation in the total power generation at each tier is equal to that of the parent tier. ; When the When the target value for the proportion of non-fossil energy power generation in the total power generation of a tier is less than that of the parent tier. ; The first Hierarchical Path Decompose down to the first Hierarchy, generating the first Hierarchical path: ; The number of paths reserved for the corresponding level; Execute the Perform a hierarchical path feasibility check. If the check passes, proceed to... The hierarchical path decomposition process is followed; otherwise, a local adjustment priority strategy is implemented until the path passes the verification.

10. The multi-level power carbon emission management path library construction system according to claim 8, characterized in that, The path's features also include any one or more of the following combinations: The proportion of wind and solar power generation in the structure; Configure the ratio of coal-fired power plants for carbon capture, utilization, and storage; The proportion of electricity generated from fossil fuels.

11. The multi-level power carbon emission management path library construction system according to claim 8, characterized in that, The multi-level path library supports arbitrary nested modeling and also includes a feedback correction mechanism between upper and lower levels. Specifically, when a lower-level path fails the verification, it is allowed to feed back and correct the time-varying dynamic collaboration coefficient and resource endowment adjustment coefficient of the parent level until the lower-level path passes the verification.

12. The multi-level power carbon emission management path library construction system according to claim 9, characterized in that, The feasibility verification is performed using the following constraints: Out-of-bounds constraints: ; Resource endowment constraints: The installed capacity of wind and solar power shall not exceed the upper limit of exploitable resources; Electricity supply and demand balance constraints: Regions where local power generation cannot meet local demand: Local power generation + Imported electricity Load demand; Regions where local power generation can meet local demand: Local power generation - Power transmitted to other regions Load demand; Carbon emission constraints: Cumulative carbon emissions must not exceed carbon allowances.

13. The multi-level power carbon emission management path library construction system according to claim 9, characterized in that, The local adjustment priority strategy includes: When a path at a certain level fails the verification, the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of this level are adjusted first to achieve path adaptation. If the local adjustment still fails the verification, then adjust the time-varying dynamic coordination coefficient and resource endowment adjustment coefficient of the parent level.

14. The multi-level power carbon emission management path library construction system according to claim 8, characterized in that, It also includes dynamic feedback and update mechanisms, specifically including: Regularly assess and update resource endowment scores at each level; Recalibrate the time-varying dynamic synergy coefficient and resource endowment adjustment coefficient; Dynamically adjust the trend judgment of the growth rate of power generation at each level relative to the level above; Based on the latest data and parameters, all steps are repeated to generate an updated multi-level path library, enabling dynamic evolution of path planning.

15. A computing device, characterized in that, include: One or more processors, one or more memories, and one or more programs, said programs being stored in the memory and configured to be executed by the processor, said programs being loaded onto the processor to implement the steps of the method for constructing a multi-level electricity carbon emission management path library according to any one of claims 1 to 7.

16. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, cause the processor to perform the steps of the method for constructing a multi-level power carbon emission management path library according to any one of claims 1 to 7.

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