CN117575175B - Carbon emission evaluation method, device, electronic equipment and storage medium - Google Patents
Carbon emission evaluation method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The present disclosure provides a carbon emission assessment method, a device, an electronic apparatus, and a storage medium. The specific implementation scheme is as follows: and constructing a simulation power generation model of the first regional power grid aiming at the first parameter value of each carbon emission index of the first regional power grid. And the control model generates power in each power generation scene respectively to obtain the reference carbon emission factor in each power generation scene. And then, disturbing the simulation conditions of the model to change the parameter value of any index, and controlling the disturbed model to generate power in each power generation scene respectively so as to obtain the carbon emission factor difference value of each carbon emission index in each power generation scene. Finally, using the difference, a weight value for each carbon emission index can be obtained. And processing the first parameter values and the weights of the carbon emission indexes of the first regional power grid, so that the comprehensive carbon emission fraction of the first regional power grid can be accurately obtained. By adopting the technical scheme disclosed by the invention, the carbon reduction capacity of the regional power grid can be accurately estimated.
Description
Technical Field
The present disclosure relates to the field of power technology, and in particular to the field of carbon emission assessment. The disclosure relates specifically to a carbon emission assessment method, a device, an electronic apparatus, and a storage medium.
Background
In current power systems, with the massive grid connection of renewable energy sources and the increasing complexity of the grid structure, green low-carbon conversion of the grid faces unprecedented challenges. With the wide application of renewable energy sources such as wind energy, solar energy and the like, how to accurately evaluate the power generation contribution of the energy sources in the whole power grid in real time becomes a great difficulty. This involves not only the acquisition and processing of real-time data, but also the influence of weather changes, seasonal factors, etc. on the amount of power generation. The grid requires sufficient flexible resources such as energy storage devices, demand response mechanisms, etc. in order to accommodate the instability and unpredictability of renewable energy sources. How to effectively configure these resources to improve the stability and efficiency of the power grid is a current urgent problem to be solved. With the diversification of the user structure and the electricity utilization mode of the power grid, real-time monitoring and adjusting the supply and demand matching of the power grid becomes a challenge. This not only relates to the power supply capability of the grid, but also to the demand management and response capability on the user side. The elastic load configuration rate and the green electricity consumption ratio in the power grid are evaluated, and the method is very important for guiding the low-carbon transformation of the power grid. This requires accurate collection and analysis of large amounts of distributed data while also taking into account uncertainty in user behavior. Different carbon reduction ability evaluation indexes need to be calculated based on different data sets, and the acquisition frequency and the update frequency of the data sets are often inconsistent. Such inconsistencies result in difficulty in synchronously obtaining up-to-date results when comprehensively evaluating the carbon reduction capability of the power grid, affecting the accuracy and timeliness of the evaluation. In actual operation, too long calculation time of the evaluation index may result in failure to evaluate the key index in time, and influence the timeliness of the power grid operation decision; too short calculation time may not guarantee the correctness of the evaluation result due to insufficient data or hurry in the processing process. Therefore, in the process of green low-carbon transformation of the power grid, a main technical challenge is how to accurately and real-timely evaluate and monitor the key indexes so as to ensure the efficient and stable operation of the power grid and realize the low-carbon emission target. This requires a new evaluation method to overcome the deficiencies of the prior art to accommodate rapid development and changes in the power grid.
Disclosure of Invention
The present disclosure provides a carbon emission evaluation method, apparatus, electronic device, and storage medium, capable of solving the above-described problems.
According to an aspect of the present disclosure, there is provided a carbon emission assessment method including:
constructing a simulation power generation model of a first regional power grid aiming at first parameter values of the carbon emission indexes of the first regional power grid;
controlling the simulation power generation model to generate power in each power generation scene respectively to obtain the reference carbon emission and the reference load power consumption of the first regional power grid in each power generation scene;
determining a reference carbon emission factor of the first regional power grid in each power generation scene based on the reference carbon emission of the first regional power grid in each power generation scene and the reference load electricity consumption;
the simulation conditions of the simulation power generation model are disturbed aiming at any carbon emission index, so that a first parameter value of the carbon emission index is disturbed, the disturbed simulation power generation model is controlled to generate power in each power generation scene respectively, carbon emission and load power consumption of the first regional power grid in each power generation scene after the carbon emission index is disturbed are obtained, and carbon emission factors of the first regional power grid in each power generation scene after the carbon emission index is disturbed are determined based on the carbon emission and load power consumption of the first regional power grid in each power generation scene after the carbon emission index is disturbed;
Calculating a carbon emission factor difference value of a carbon emission factor of the first regional power grid in each power generation scene and after the carbon emission index is disturbed and a reference carbon emission factor in a corresponding power generation scene aiming at any carbon emission index, so as to obtain a carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
determining a weight value of each carbon emission index of the first regional power grid based on a carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
and determining the comprehensive carbon emission fraction of the first regional power grid based on the first parameter value of each carbon emission index and the weight value of each carbon emission index of the first regional power grid.
According to another aspect of the present disclosure, there is provided a carbon emission assessment device including:
the simulation power generation model construction module is used for constructing a simulation power generation model of the first regional power grid aiming at first parameter values of the carbon emission indexes of the first regional power grid;
the simulation power generation module is used for controlling the simulation power generation model to generate power respectively in each power generation scene to obtain the reference carbon emission and the reference load power consumption of the first regional power grid in each power generation scene;
The first carbon emission calculation module is used for determining a reference carbon emission factor of the first regional power grid in each power generation scene based on the reference carbon emission amount of the first regional power grid in each power generation scene and the reference load electricity consumption;
the second carbon emission calculation module is used for disturbing the simulation conditions of the simulation power generation model aiming at any carbon emission index to enable a first parameter value of the carbon emission index to be disturbed, controlling the disturbed simulation power generation model to generate power respectively in each power generation scene to obtain carbon emission and load power consumption of the first regional power grid in each power generation scene after the carbon emission index is disturbed, and determining carbon emission factors of the first regional power grid in each power generation scene after the carbon emission index is disturbed based on the carbon emission and load power consumption of the first regional power grid in each power generation scene;
the third carbon emission calculation module is used for calculating a carbon emission factor difference value between the carbon emission factor of the first regional power grid in each power generation scene and the carbon emission factor of the carbon emission index subjected to disturbance and the reference carbon emission factor in the corresponding power generation scene according to any carbon emission index so as to obtain a carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
The weight calculation module is used for determining weight values of the carbon emission indexes of the first regional power grid based on carbon emission factor difference values of the carbon emission indexes of the first regional power grid in each power generation scene;
and a fourth carbon emission calculation module, configured to determine a comprehensive carbon emission score of the first regional power grid based on the first parameter value of each carbon emission index of the first regional power grid and the weight value of each carbon emission index.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor, and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the carbon emission estimation methods of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any one of the carbon emission estimation methods of the embodiments of the present disclosure.
According to the technology of the present disclosure, a simulated power generation model of a first regional power grid is constructed with a first parameter value for each of the carbon emission indicators of the first regional power grid. And controlling the simulation power generation model to generate power in each power generation scene respectively to obtain the reference carbon emission and the reference load power consumption in each power generation scene, and further obtaining the reference carbon emission factor in each power generation scene. And then, disturbing the simulation conditions of the simulation power generation model to change the first parameter value of any carbon emission index, controlling the disturbed simulation power generation model to generate power in each power generation scene respectively, further obtaining carbon emission factors in each power generation scene after disturbance of each carbon emission index, and differentiating the carbon emission factors with the reference carbon emission factors of the corresponding scenes to obtain the carbon emission factor differences of each carbon emission index in each power generation scene. Finally, using the difference, a weight value for each carbon emission index can be obtained. The weight value of each carbon emission index is obtained by disturbing the simulation model, so that the first parameter value of each carbon emission index of the first regional power grid is processed, and the comprehensive carbon emission fraction of the first regional power grid can be accurately obtained.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow chart of a carbon emission assessment method of an embodiment of the present disclosure;
FIG. 2 is a flow chart of a carbon emission assessment method of another embodiment of the present disclosure;
FIG. 3 is a flow chart of a carbon emission assessment method of another embodiment of the present disclosure;
FIG. 4 is a block diagram of a carbon emission estimation device according to an embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device of a carbon emission estimation method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of a carbon emission assessment method according to an embodiment of the present disclosure.
As shown in fig. 1, the carbon emission assessment method may include:
s110, constructing a simulation power generation model of the first regional power grid aiming at first parameter values of all carbon emission indexes of the first regional power grid;
s120, controlling the simulation power generation model to generate power in each power generation scene respectively, and obtaining the reference carbon emission and the reference load power consumption of the first regional power grid in each power generation scene;
s130, determining a reference carbon emission factor of the first regional power grid in each power generation scene based on the reference carbon emission and the reference load electricity consumption of the first regional power grid in each power generation scene;
s140, for any carbon emission index, disturbing the simulation condition of the simulation power generation model to enable a first parameter value of the carbon emission index to be disturbed, controlling the disturbed simulation power generation model to generate power in each power generation scene respectively to obtain carbon emission and load power consumption of the first regional power grid in each power generation scene after the carbon emission index is disturbed, and determining carbon emission factors of the first regional power grid in each power generation scene after the carbon emission index is disturbed based on the carbon emission and load power consumption of the first regional power grid in each power generation scene;
S150, calculating a carbon emission factor difference value of a carbon emission factor of the first regional power grid in each power generation scene and after the carbon emission index is disturbed and a reference carbon emission factor in a corresponding power generation scene aiming at any carbon emission index, so as to obtain a carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
s160, determining weight values of all carbon emission indexes of the first regional power grid based on carbon emission factor difference values of all carbon emission indexes of the first regional power grid in all power generation scenes;
s170, determining the comprehensive carbon emission fraction of the first regional power grid based on the first parameter value of each carbon emission index of the first regional power grid and the weight value of each carbon emission index.
It will be appreciated that for any regional power grid, the carbon emission index may include: clean generating capacity contribution degree, flexible resource allocation rate, clean power supply and demand matching degree, elastic load allocation rate and green electricity consumption ratio.
In one example, the clean power generation contribution may characterize the carbon emission abatement capability of the power source side of the regional power grid. It can be calculated using the following formula:
；
wherein,representing the degree of contribution of the cleaning power generation amount, TFor the total number of sampling periods>Indicating that the regional power grid is at the firsttClean energy generation of a sampling period, +.>Indicating that the regional power grid is at the firsttTotal power generation of each sampling period.
In the first place for regional power gridtThe clean energy generation of each sampling period can be calculated by the following formula:
wherein,is indicated at->Clean energy generation power at various moments in time period +.>Is the time interval length. In the ideal case, the time interval +.>It should approach infinity, thereby measuring the contribution of the regional clean energy generated power to the regional total generated power over a period of time. Considering the universality of the index and the actual data acquisition difficulty, calculating the proper time interval length according to the actual acquired data frequency>. It should be noted that the length of the time interval chosen by the index calculation should be ensured during the lateral comparison between the regions>The same, thereby ensuring comparability of the evaluation results. The above-mentioned information about the length of the time interval->The discussion of (2) is also applicable to the calculation of low-carbon level evaluation indexes of the power grid side and the load side, and is not repeated.
It will be appreciated that the flexible resource allocation rate and clean power supply and demand matching may reflect grid-side carbon reduction capabilities of the regional grid.
In one example, the flexible resource allocation rate may be calculated using the following formula:
；
wherein,representing the flexible resource allocation rate, +.>Representing the adjustable capacity of the grid-side flexible resource of the regional grid at the t-th sampling instant,/->、/>And->And respectively representing the electric power for the rigid load, the wind power generation power and the photovoltaic power generation power of the regional power grid at the t sampling moment.
It will be appreciated that grid side flexible resources may include photovoltaic energy storage, pumped storage, etc.
In one example, the clean power supply-demand match may be calculated using the following formula:
；
；
wherein,indicating the matching degree of the supply and the demand of the clean power, < >>Representing the load power consumption of the regional power grid in the t sampling period,/for the t sampling period>For punishment and punishment functions, the recipe is->Representing the ratio of the clean energy generating capacity to the load power consumption of the regional power grid in the t sampling period,/or%>And (3) representing N times of the ratio of the clean energy generating capacity to the load power consumption of the regional power grid in the t sampling period, wherein N is an integer greater than 1.
For the above punishment and punishment functions, the design achieves the following effects: when the ratio of the amount of clean energy generation to the amount of load electricity consumption is less than 1, that is, when the supply of clean electricity (amount of clean energy generation) is insufficient to meet the regional load demand (amount of load electricity consumption), multiple clean electricity is encouraged, or the regional load demand is reduced, at which time the closer the ratio is to 1, the higher the score is obtained accordingly. When the ratio is equal to 1, i.e. the clean power supply is exactly matched with the regional load demand, the power system achieves an optimal low-carbon operation effect, and the score obtained correspondingly is highest, namely 1. When the ratio is further increased, i.e. the clean power supply is greater than the regional load demand, it is necessary to To send clean power to the outside of the area, the power generation and cleaning of other areas are facilitated in a certain range. However, when the ratio reaches a certain multiple (useRepresenting this value), this output has a negative effect on the safe and stable operation of the overall grid. Therefore, when the ratio is greater than 1 and less than +.>When the score is correspondingly reduced; at a ratio equal toWhen the time is 0, the fraction obtained by continuously increasing the proportion is negative; to prevent the penalty from being too large, when the ratio is further increased, it reaches +.>Thereafter, the score obtained accordingly was fixed at-1.
It will be appreciated that the elastic load configuration rate and green electricity consumption duty cycle may reflect the load side carbon reduction capability of the regional power grid.
In one example, the elastic load configuration rate may be calculated using the following formula:
；
wherein,representing the elastic load configuration rate, +.>An elastic load-adjustable capacity of the load side of the regional power grid at the t-th sampling time,/->The load side representing the regional power grid represents the total adjustable capacity of the regional power grid.
In one example, the green electricity consumption ratio may be calculated using the following formula:
；
wherein,representing the green consumption duty cycle +.>Represents the annual green electricity consumption of the regional power grid,/- >Representing the total annual power consumption of the regional power grid.
It will be appreciated that the simulation conditions of the simulated power generation model are different for different power generation scenarios.
It is understood that, for the s-th power generation scenario, the reference carbon emission factor of the first regional power grid is a ratio of the reference carbon emission amount of the first regional power grid in the s-th power generation scenario to the reference load power consumption amount.
It can be understood that, for the s-th power generation scene, after the disturbance is performed by the i-th carbon emission index, the carbon emission factor of the first regional power grid is the ratio of the carbon emission amount of the first regional power grid in the s-th power generation scene and after the disturbance of the i-th carbon emission index to the load electricity consumption.
For example, the reference carbon emission factor or the perturbed carbon emission factor may be calculated using the following formula, specifically as follows:
；
wherein,is carbon emission factor, < >>Indicating that regional power grid is at the firstCarbon emission in t sampling periods, < >>Representing the load electricity consumption of the regional power grid in the t sampling time period.
As shown in fig. 2, in practical application, for the carbon emission factor after disturbance, the calculation process is as follows: setting S typical scenes as boundary conditions of a power system dispatching simulation model according to typical day data of the power system, and obtaining electricity consumption carbon emission factors of the power system in the S-th scene through simulation operation As the s-th scene->A carbon emission level reference value of (2), whereinThe method comprises the steps of carrying out a first treatment on the surface of the Then, in the s-th scene, let +.>The individual indexes are slightly disturbed so as to change the boundary conditions of the simulation model, and simulation is carried out again to obtain the +.>Electric carbon emission factor after individual index disturbance>。
For a simulated power generation model, its modeling should meet the following constraints:
1. the objective function of the model is to minimize the scheduling costs, including the cost of generating power for the genset and the cost of invoking the adjustable load.
2. In the running process, the real-time balance between the power supply and the power demand needs to be ensured. Wherein the power supply comprises the generation power of the generator setNovel energy storage deviceElectric power->. The electricity demand includes rigid load->Adjustable load->Charging power of novel energy storage>。
3. For each generator set, it is necessary to ensure that the generated power does not exceed the capacity limit and the hill climbing limit. Wherein each line powerAnd solving by using a direct current power flow model.
4. The line flows may not exceed the line steady state capacity limit.
5. Novel physical constraints of novel grid-connected main bodies such as novel energy storage. For example, the regulated power of the adjustable load may not exceed its regulation power for a period of time The novel energy storage regulating power cannot exceed the maximum regulating power of the novel energy storage regulating power, and the novel energy storage charge state is required to be kept within a safe range.
6. The novel energy storage can not be in a charging and discharging state at the same time, so that 0-1 integer variable is definedRepresenting the charge and discharge state of the stored energy and limiting it to only one state at a time.
It can be appreciated that for any one of the power generation scenarios, for the firstThe carbon emission factor after each index disturbance is differenced with the reference carbon emission factor to obtain +.>The carbon emission factor difference of each index in each power generation scene. Then, for->The difference value of the carbon emission factors of the indexes in each power generation scene is calculated to obtain the +.>Weight value of each index. For example, entropy weighting method can be used for the +.>And calculating the carbon emission factor difference value of each index in each power generation scene. As another example, the mean or variance can be obtained by summing to obtain +.>Weight value of each index.
It may be appreciated that after the weight values of the carbon emission indicators are obtained, the first parameter values of the carbon emission indicators of the first regional power grid and the weight values of the carbon emission indicators are weighted and summed to obtain the integrated carbon emission score of the first regional power grid.
According to the above embodiment, the simulated power generation model of the first regional power grid is constructed with the first parameter values for the respective carbon emission indexes of the first regional power grid. And controlling the simulation power generation model to generate power in each power generation scene respectively to obtain the reference carbon emission and the reference load power consumption in each power generation scene, and further obtaining the reference carbon emission factor in each power generation scene. And then, disturbing the simulation conditions of the simulation power generation model to change the first parameter value of any carbon emission index, controlling the disturbed simulation power generation model to generate power in each power generation scene respectively, further obtaining carbon emission factors in each power generation scene after disturbance of each carbon emission index, and differentiating the carbon emission factors with the reference carbon emission factors of the corresponding scenes to obtain the carbon emission factor differences of each carbon emission index in each power generation scene. Finally, using the difference, a weight value for each carbon emission index can be obtained. The weight value of each carbon emission index is obtained by disturbing the simulation model, so that the first parameter value of each carbon emission index of the first regional power grid is processed, and the comprehensive carbon emission fraction of the first regional power grid can be accurately obtained.
In one embodiment, the method may further include:
fitting a curve of the second parameter value of each carbon emission index changing along with the power grid region based on the second parameter value of each carbon emission index of each regional power grid in the regional power grids to obtain a parameter value distribution curve of each carbon emission index;
and determining a first parameter value of each carbon emission index of the first regional power grids in the regional power grids based on a parameter value distribution curve of each carbon emission index changing along with the power grid region.
It is understood that the second parameter value is an actual parameter value, and the first parameter value is a corrected parameter value obtained by adjusting the actual parameter value.
It can be understood that, by using the position information of the first regional power grid, the parameter value corresponding to the position information is searched in the parameter value distribution curve of each carbon emission index changing along with the power grid region, so as to obtain the first parameter value of each carbon emission index of the first regional power grid.
In an example, a maximum likelihood estimation method may be used to generate a spatial distribution curve (parameter value distribution curve) in which each carbon emission index varies with the position change of the regional power grid.
The spatial distribution curve may employ a sample distribution functionTo characterize. Wherein (1)>Representing the carbon emission index and its corresponding parameter values, respectively. After the estimated parameter values, the carbon emission index can be used>The position in the resulting distribution gives the relative fraction of the region index +.>. From the probability properties and definitions, the relative score +.>The value range is 0, 100]。
；
Wherein the absolute index of each region is givenIs>. Assuming that the index meets a standard normal distribution, +.>For the value to be estimated, a likelihood function is constructed as follows:
；
thus, the parameters to be estimated and the samples are obtained by solvingIs a relationship of (3).
Finally, according to the estimated regional indexThe relative scores of the region indicators are obtained at the positions in the distribution as follows:
。
according to the embodiment, the actual parameter values of the carbon emission indexes of the regional power grids can be fitted to obtain the parameter value distribution curve. And then, searching a parameter value corresponding to the position information in a parameter value distribution curve of each carbon emission index changing along with the power grid region by utilizing the position information of the first regional power grid, and obtaining a corrected parameter value of each carbon emission index of the first regional power grid.
In one embodiment, the determining the weight value of each carbon emission index of the first regional power grid based on the carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene includes:
normalizing the carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene to obtain a normalized carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
for any carbon emission index, carrying out information entropy calculation on normalized carbon emission factor difference values of the carbon emission index of the first regional power grid in each power generation scene to obtain information entropy of the carbon emission index of the first regional power grid;
and determining the weight value of each carbon emission index of the first regional power grid based on the information entropy of each carbon emission index of the first regional power grid.
In an example, a sensitivity matrix may be constructed for carbon emission factor differences of each of the carbon emission indicators of the first regional power grid in each power generation scenario. It can be expressed as:。
in one example, each element in the sensitivity matrix is normalized or normalized to obtain a normalized sensitivity matrix . One element in the sensitivity matrix is:
。
wherein,representing one element in a normalized sensitivity matrix.
In one example, for a normalized sensitivity matrixNormalizing to obtain a normalized sensitivity matrix. The normalization process is as follows:
。
and calculating the normalized sensitivity matrix to obtain the information entropy of each index. The specific calculation process is as follows:
；
the weight of each index can be determined according to the information entropy of each index. The specific calculation process is as follows:
。
according to the above embodiment, the weight value of each carbon emission index can be accurately calculated.
Fig. 3 is a flowchart of a carbon emission assessment method according to another embodiment of the present disclosure.
As shown in fig. 3, actual parameter values of a plurality of carbon emission indicators of the respective regions, such as a source side indicator (power source side indicator), a grid side indicator (grid side indicator), and a load side indicator (load side indicator), may be acquired for each of the regional power grids 1 to N. And then, fitting the second parameter values of the indexes according to the distribution condition of the regional power grid to obtain parameter value distribution curves of the indexes. For example, a parameter value distribution curve of each index from index 1 to index I. Further, second parameter values of the respective indexes of the first regional power grid (the region to be evaluated) are obtained by using the parameter value distribution curves of the respective indexes.
Then, as shown in fig. 2, a simulation power generation model (simulation scheduling power system) is constructed by using the second parameter values of each index of the first regional power grid, and simulation is performed for each different power generation scene, for example, from scene 1 to scene S, respectively, to obtain the electricity consumption carbon emission factorAnd electrical carbon emission factor->A sensitivity matrix is constructed based on the difference between the two. And calculating the weight value of each index by using the sensitivity matrix.
And finally, carrying out weighted summation on the second parameter values of each index of the first regional power grid (the region to be evaluated) and the corresponding weight values thereof to obtain the comprehensive carbon emission fraction of the first regional power grid. The score may characterize the low carbon (carbon reduction) capability of the regional power grid.
Fig. 4 is a block diagram of a carbon emission estimation device according to an embodiment of the present disclosure.
As shown in fig. 4, the carbon emission estimation device may include:
a simulation power generation model construction module 410, configured to construct a simulation power generation model of a first regional power grid for a first parameter value of each carbon emission index of the first regional power grid;
the simulation power generation module 420 is configured to control the simulation power generation model to generate power in each power generation scene, so as to obtain a reference carbon emission and a reference load power consumption of the first regional power grid in each power generation scene;
A first carbon emission calculation module 430, configured to determine a reference carbon emission factor of the first regional power grid in each power generation scenario based on a reference carbon emission amount of the first regional power grid in each power generation scenario and the reference load electricity consumption amount;
the second carbon emission calculation module 440 is configured to, for any one of the carbon emission indexes, perform disturbance on a simulation condition of the simulation power generation model, so that a first parameter value of the carbon emission index is disturbed, and control the disturbed simulation power generation model to generate power in each power generation scene, so as to obtain a carbon emission amount and a load power consumption amount of the first regional power grid in each power generation scene after the disturbance of the carbon emission index, and determine a carbon emission factor of the first regional power grid in each power generation scene after the disturbance of the carbon emission index based on the carbon emission amount and the load power consumption amount of the first regional power grid in each power generation scene after the disturbance of the carbon emission index;
a third carbon emission calculation module 450, configured to calculate, for any one of the carbon emission indexes, a carbon emission factor difference value between a carbon emission factor of the first regional power grid in each power generation scenario and a reference carbon emission factor of the first regional power grid in the corresponding power generation scenario after the disturbance of the carbon emission index occurs, so as to obtain a carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scenario;
A weight calculation module 460, configured to determine a weight value of each carbon emission indicator of the first regional power grid based on a carbon emission factor difference value of each carbon emission indicator of the first regional power grid in each power generation scenario;
a fourth carbon emission calculation module 470 configured to determine a comprehensive carbon emission score of the first regional power grid based on the first parameter value of each of the carbon emission indicators of the first regional power grid and the weight value of each of the carbon emission indicators.
In one embodiment, the apparatus may further include:
the curve fitting module is used for fitting a curve of the second parameter value of each carbon emission index changing along with the power grid area based on the second parameter values of each carbon emission index of each regional power grid in the regional power grids to obtain a parameter value distribution curve of each carbon emission index;
and the parameter value determining module is used for determining a first parameter value of each carbon emission index of the first regional power grids in the regional power grids based on a parameter value distribution curve of each carbon emission index changing along with the power grid region.
In one embodiment, the weight calculation module 460 includes:
The normalization unit is used for normalizing the carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene to obtain a normalized carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
the information entropy calculation unit is used for carrying out information entropy calculation on the normalized carbon emission factor difference value of the carbon emission index of the first regional power grid in each power generation scene aiming at any carbon emission index to obtain the information entropy of the carbon emission index of the first regional power grid;
and the weight calculation unit is used for determining the weight value of each carbon emission index of the first regional power grid based on the information entropy of each carbon emission index of the first regional power grid.
In one embodiment, the carbon emission index of the regional power grid includes a clean power generation contribution, a flexible resource allocation rate, a clean power supply and demand matching degree, an elastic load allocation rate, and a green electricity consumption ratio of the regional power grid.
In one embodiment, the clean power generation amount contribution degree is:
；
wherein,representing the degree of contribution of the cleaning power generation amount, TFor the total number of sampling periods>Indicating that the regional power grid is at the firsttClean energy generation of a sampling period, +.>Indicating that the regional power grid is at the firsttTotal power generation of each sampling period.
In one embodiment, the flexible resource allocation rate is:
；
wherein,representing the flexible resource allocation rate, +.>Representing the adjustable capacity of the grid-side flexible resource of the regional grid at the t-th sampling instant,/->、/>And->And respectively representing the electric power for the rigid load, the wind power generation power and the photovoltaic power generation power of the regional power grid at the t sampling moment.
In one embodiment, the clean power supply and demand matching degree is:
；
；
wherein,indicating the matching degree of the supply and the demand of the clean power, < >>Representing the load power consumption of the regional power grid in the t sampling period,/for the t sampling period>For punishment and punishment functions, the recipe is->Representing the ratio of the clean energy generating capacity to the load power consumption of the regional power grid in the t sampling period,/or%>And (3) representing N times of the ratio of the clean energy generating capacity to the load power consumption of the regional power grid in the t sampling period, wherein N is an integer greater than 1.
For descriptions of specific functions and examples of each module and sub-module of the apparatus in the embodiments of the present disclosure, reference may be made to the related descriptions of corresponding steps in the foregoing method embodiments, which are not repeated herein.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 5 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 601 performs the respective methods and processes described above, for example, a carbon emission evaluation method. For example, in some embodiments, a carbon emission assessment method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of one carbon emission assessment method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform a carbon emission assessment method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. that are within the principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (5)
1. A carbon emission evaluation method, characterized by comprising:
fitting a curve of the second parameter value of each carbon emission index changing along with the power grid region based on the second parameter value of each carbon emission index of each regional power grid in the regional power grids to obtain a parameter value distribution curve of each carbon emission index;
determining a first parameter value of each carbon emission index of the first regional power grid in the regional power grids based on a parameter value distribution curve of each carbon emission index changing along with the power grid region, wherein the second parameter value is an actual parameter value, and the first parameter value is a correction parameter value obtained after the actual parameter value is adjusted;
Constructing a simulation power generation model of a first regional power grid aiming at first parameter values of the carbon emission indexes of the first regional power grid;
controlling the simulation power generation model to generate power in each power generation scene respectively to obtain the reference carbon emission and the reference load power consumption of the first regional power grid in each power generation scene;
determining a reference carbon emission factor of the first regional power grid in each power generation scene based on the reference carbon emission of the first regional power grid in each power generation scene and the reference load electricity consumption;
the simulation conditions of the simulation power generation model are disturbed aiming at any carbon emission index, so that a first parameter value of the carbon emission index is disturbed, the disturbed simulation power generation model is controlled to generate power in each power generation scene respectively, carbon emission and load power consumption of the first regional power grid in each power generation scene after the carbon emission index is disturbed are obtained, and carbon emission factors of the first regional power grid in each power generation scene after the carbon emission index is disturbed are determined based on the carbon emission and load power consumption of the first regional power grid in each power generation scene after the carbon emission index is disturbed;
Calculating a difference value between a carbon emission factor of the first regional power grid in each power generation scene and after the carbon emission index is disturbed and a reference carbon emission factor in a corresponding power generation scene aiming at any carbon emission index to obtain a carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
determining a weight value of each carbon emission index of the first regional power grid based on a carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
determining a comprehensive carbon emission fraction of the first regional power grid based on a first parameter value of each carbon emission index of the first regional power grid and a weight value of each carbon emission index;
wherein, the carbon emission index of regional power grid includes: clean generating capacity contribution degree, flexible resource allocation rate, clean power supply and demand matching degree, elastic load allocation rate and green electricity consumption ratio;
wherein the clean power generation contribution degree is:
；
wherein,representing the degree of contribution of the cleaning power generation amount,Tfor the total number of sampling periods>Indicating that the regional power grid is at the first tClean energy generation of a sampling period, +.>Indicating that the regional power grid is at the firsttTotal power generation of the sampling period;
wherein the flexible resource allocation rate is:
；
wherein,representing the flexible resource allocation rate, +.>Grid-side flexible resources representing the regional grid at the firsttAdjustable capacity at each sampling instant +.>、/>And->Respectively represent the regional power grid in the first placetElectric power, wind power generation power and photovoltaic power generation power for the rigid load at each sampling moment;
wherein, clean electric power supply and demand matching degree is:
；
；
wherein,indicating the matching degree of the supply and the demand of the clean power, < >>Indicating that the regional power grid is at the firsttLoad power consumption for each sampling period, +.>For punishment and punishment functions, the recipe is->Indicating that the regional power grid is at the firsttThe ratio of the clean energy power generation to the load power consumption in the sampling period +.>Indicating that the regional power grid is at the firsttN times the ratio of the clean energy power generation amount to the load power consumption amount in each sampling period, wherein N is an integer greater than 1;
wherein, the elastic load configuration rate is:
；
wherein,representing the elastic load configuration rate, +.>Indicating that the load side of the regional power grid is on the first sidetElastic load adjustable capacity at each sampling instant, < > >Indicating that the load side of the regional power grid is on the first sidetTotal adjustable capacity for each sampling instant;
wherein, the green electricity consumption ratio is:
；
wherein,representing the green electricity consumption duty cycle, +.>Represents the annual green electricity consumption of the regional power grid,/->Representing the total annual power consumption of the regional power grid.
2. The method of claim 1, wherein the determining a weight value for each of the carbon emission indicators of the first regional power grid based on the carbon emission factor difference for each of the carbon emission indicators of the first regional power grid in each power generation scenario comprises:
normalizing the carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene to obtain a normalized carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
for any carbon emission index, carrying out information entropy calculation on normalized carbon emission factor difference values of the carbon emission index of the first regional power grid in each power generation scene to obtain information entropy of the carbon emission index of the first regional power grid;
and determining the weight value of each carbon emission index of the first regional power grid based on the information entropy of each carbon emission index of the first regional power grid.
3. A carbon emission evaluation device, characterized by comprising:
the curve fitting module is used for fitting a curve of the second parameter value of each carbon emission index changing along with the power grid area based on the second parameter values of each carbon emission index of each regional power grid in the regional power grids to obtain a parameter value distribution curve of each carbon emission index;
the parameter value determining module is used for determining a first parameter value of each carbon emission index of the first regional power grid in the regional power grids based on a parameter value distribution curve of each carbon emission index changing along with the power grid region, wherein the second parameter value is an actual parameter value, and the first parameter value is a corrected parameter value obtained after the actual parameter value is adjusted;
the simulation power generation model construction module is used for constructing a simulation power generation model of the first regional power grid aiming at first parameter values of the carbon emission indexes of the first regional power grid;
the simulation power generation module is used for controlling the simulation power generation model to generate power respectively in each power generation scene to obtain the reference carbon emission and the reference load power consumption of the first regional power grid in each power generation scene;
The first carbon emission calculation module is used for determining a reference carbon emission factor of the first regional power grid in each power generation scene based on the reference carbon emission amount of the first regional power grid in each power generation scene and the reference load electricity consumption;
the second carbon emission calculation module is used for disturbing the simulation conditions of the simulation power generation model aiming at any carbon emission index to enable a first parameter value of the carbon emission index to be disturbed, controlling the disturbed simulation power generation model to generate power respectively in each power generation scene to obtain carbon emission and load power consumption of the first regional power grid in each power generation scene after the carbon emission index is disturbed, and determining carbon emission factors of the first regional power grid in each power generation scene after the carbon emission index is disturbed based on the carbon emission and load power consumption of the first regional power grid in each power generation scene;
the third carbon emission calculation module is used for calculating the difference value between the carbon emission factor of the first regional power grid in each power generation scene and the carbon emission factor of the first regional power grid after the carbon emission index is disturbed and the reference carbon emission factor of the corresponding power generation scene aiming at any carbon emission index so as to obtain the carbon emission factor difference value of each carbon emission index of the first regional power grid in each power generation scene;
The weight calculation module is used for determining weight values of the carbon emission indexes of the first regional power grid based on carbon emission factor difference values of the carbon emission indexes of the first regional power grid in each power generation scene;
a fourth carbon emission calculation module, configured to determine a comprehensive carbon emission score of the first regional power grid based on a first parameter value of each carbon emission index of the first regional power grid and a weight value of each carbon emission index;
wherein, the carbon emission index of regional power grid includes: clean generating capacity contribution degree, flexible resource allocation rate, clean power supply and demand matching degree, elastic load allocation rate and green electricity consumption ratio;
wherein the clean power generation contribution degree is:
；
wherein,representing the degree of contribution of the cleaning power generation amount,Tfor the total number of sampling periods>Indicating that the regional power grid is at the firsttClean energy generation of a sampling period, +.>Indicating that the regional power grid is at the firsttTotal power generation of the sampling period;
wherein the flexible resource allocation rate is:
；
wherein,representing the flexible resource allocation rate, +.>Grid-side flexible resources representing the regional grid at the first tAdjustable capacity at each sampling instant +.>、/>And->Respectively represent the regional power grid in the first placetElectric power, wind power generation power and photovoltaic power generation power for the rigid load at each sampling moment;
wherein, clean electric power supply and demand matching degree is:
；
；
wherein,indicating the matching degree of the supply and the demand of the clean power, < >>Indicating that the regional power grid is at the firsttLoad power consumption for each sampling period, +.>For punishment and punishment functions, the recipe is->Indicating that the regional power grid is at the firsttThe ratio of the clean energy power generation to the load power consumption in the sampling period +.>Indicating that the regional power grid is at the firsttN times the ratio of the clean energy power generation amount to the load power consumption amount in each sampling period, wherein N is an integer greater than 1;
wherein, the elastic load configuration rate is:
；
wherein,representing the elastic load configuration rate, +.>Indicating that the load side of the regional power grid is on the first sidetElastic load adjustable capacity at each sampling instant, < >>Indicating that the load side of the regional power grid is on the first sidetTotal adjustable capacity for each sampling instant;
wherein, the green electricity consumption ratio is:
；
wherein,representing the green electricity consumption duty cycle, +.>Represents the annual green electricity consumption of the regional power grid,/->Representing the total annual power consumption of the regional power grid.
4. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-2.
5. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-2.
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