A carbon emission-based new energy power system power flow optimization method

Abstract

The application relates to the technical field of new energy power systems, in particular to a new energy power system power flow optimization method based on carbon emission. The method comprises the following steps: setting a plurality of primary device points and a plurality of secondary device points according to the device parameters of a region to be dispatched; generating a primary dispatch strategy in a current dispatch period according to a preset dispatch model; generating an expected carbon emission curve of each primary device point according to the primary dispatch strategy, and judging whether to generate a correction instruction according to all the carbon emission curves; generating a secondary dispatch strategy in the current dispatch period according to the correction instruction and the dispatch model; establishing a dispatch model based on a cost optimization principle, performing optimization processing on daily operation and dispatch of the power system, generating the primary dispatch strategy according to the optimization result, further calculating carbon emission power flow in the power system and carbon emission of each device node according to the primary dispatch strategy, and adjusting the device points with excessive carbon emission, so that two-stage regulation of the new energy power system power flow is realized.

Description

Technical Field

[0001] This application relates to the field of new energy power system technology, and in particular to a power flow optimization method for new energy power systems based on carbon emissions. Background Technology

[0002] For a long time, thermal power capacity has accounted for an excessively high proportion of my country's power generation structure, making the power system the largest emitter of carbon in the country. Therefore, optimizing the dispatch of renewable energy power systems to reduce carbon emissions and achieve low-carbon operation of the power system is of significant theoretical and practical importance.

[0003] Current optimization methods for power system flow do not take carbon emission flow into account. With the increasing proportion of new energy sources in new power systems, existing optimization methods for power system flow cannot effectively reduce the overall carbon emissions of the power system, thus affecting the overall benefits of the power system. Summary of the Invention

[0004] The purpose of this application is to provide a power flow optimization method for a new energy power system based on carbon emissions in order to solve the above-mentioned technical problems, thereby reducing the carbon emissions of the power system and increasing the operating benefits of the power system.

[0005] In some embodiments of this application, a scheduling model is established based on the principle of cost optimization to optimize the daily operation scheduling of the power system. A first-level scheduling strategy is generated based on the optimization results. The carbon emission flow and carbon emissions of each equipment node within the power system are further calculated based on the first-level scheduling strategy. Equipment nodes with excessive carbon emissions are adjusted and fed back to the scheduling model, thereby achieving two-level regulation of the power flow of the new energy power system, reducing the overall carbon emissions and improving the operating benefits of the power system.

[0006] In some embodiments of this application, a power flow optimization method for a new energy power system based on carbon emissions is provided, including:

[0007] Multiple primary equipment points and multiple secondary equipment points are set according to the equipment parameters of the area to be scheduled;

[0008] Generate a first-level scheduling strategy for the current scheduling period based on a preset scheduling model;

[0009] The expected carbon emission curves for each primary equipment point are generated based on the primary scheduling strategy, and a correction instruction is generated based on all carbon emission curves.

[0010] The secondary scheduling strategy for the current scheduling period is generated based on the correction instructions and the scheduling model; wherein, multiple primary device points and multiple secondary device points are set, including:

[0011] Establish a sequence A1 of primary equipment points, A1 = (a 11 a 12 …a 1i …a 1n1 ), where a 1i Let be the i-th primary equipment point; n1 is the number of primary equipment points;

[0012] Establish a sequence of secondary equipment points A2, A2 = (a 21 a 22 …a 2i …a 2n2 ), where a 2i Let be the i-th secondary equipment point; n2 is the number of secondary equipment points.

[0013] In some embodiments of this application, the expected carbon emission curves for each primary equipment point are generated, including:

[0014] Based on the sequence of primary equipment points A1, a1 is set sequentially. i For target level 1 equipment points;

[0015] Set up a simulation sub-model for the target primary equipment point;

[0016] Generate the runtime data packet for the target primary device point according to the primary scheduling strategy;

[0017] Generate the expected carbon emission curves for the target primary equipment points based on the simulation sub-model and the runtime data package;

[0018] The expected carbon emission curves for each primary equipment point are generated sequentially.

[0019] In some embodiments of this application, determining whether to generate a correction instruction based on the complete carbon emission curve includes:

[0020] Set multiple time intervals within the current scheduling cycle;

[0021] Establish a time interval sequence T, T = (t1, t2, ..., tt3). i ...t m ), where ti is the i-th time interval; m is the sequence of time intervals;

[0022] Generate adjustment evaluation values ​​for each time interval;

[0023] Establish a series of moderated evaluation values ​​C, where C = (c1, c2, ..., c3). i ...c m ), where c i Let be the adjustment evaluation value for the i-th time interval;

[0024] Determine whether to generate a correction instruction based on the adjustment evaluation value sequence C.

[0025] In some embodiments of the present application, generating adjustment evaluation values for each time interval includes:

[0026] Sequentially setting the i-th time interval as the target time interval according to the time interval sequence T; generating the adjustment evaluation value c of the target time interval;

[0027]

[0028] Where, e1 is a preset first weight coefficient, e2 is a preset second weight coefficient, Q1 is a preset first fixed coefficient, Q2 is a preset second fixed coefficient, β i is the influence factor of the i-th first-level equipment point, k i is the expected carbon emission of the i-th first-level equipment point within the target time interval, k′ is the carbon emission threshold within the target time interval, Y(i) is a selection coefficient. If (k i -k′)>0, Y(i)=1; if (k i -k′)<0, Y(i)=0.

[0029] In some embodiments of the present application, judging whether to generate a correction instruction according to the adjustment evaluation value sequence C includes:

[0030] Generating a correction evaluation value d;

[0031]

[0032] Where, e3 is a preset third weight coefficient, e4 is a preset fourth weight coefficient, Q3 is a preset third fixed coefficient, Q4 is a preset fourth fixed coefficient, and c′ is the average value of all data in the adjustment evaluation value sequence C;

[0033] Presetting a correction evaluation value threshold D1;

[0034] If d>D1, generating a secondary correction instruction and correcting the scheduling model according to the secondary correction instruction;

[0035] If d<D1, setting an adjustment evaluation value threshold C1. If c i >C1, setting a primary correction instruction for the i-th time interval.

[0036] In some embodiments of the present application, generating a secondary scheduling strategy within the current scheduling period includes:

[0037] Generating primary sub-strategies for each time interval according to the primary scheduling strategy;

[0038] Sequentially setting the i-th time interval as the target time interval according to the time interval sequence T; judging whether there is a primary correction instruction within the target time interval;

[0039] If it does not exist, set the first-level sub-strategy of the target time interval as the second-level sub-strategy;

[0040] If it exists, set the correction strategy for the target time interval, correct the first-level sub-strategy within the target time interval according to the correction strategy, and generate the second-level sub-strategy within the target time interval according to the correction result.

[0041] Generate secondary sub-policies for each time interval sequentially;

[0042] Generate the secondary scheduling policy for the current scheduling period based on all secondary sub-policies.

[0043] In some embodiments of this application, the correction strategy for setting the target time interval includes:

[0044] Generate carbon emission adjustment amounts for the target time period based on all expected carbon emission curves;

[0045] A primary allocation strategy is generated based on carbon emission adjustment amounts;

[0046] The load variation within the target time interval is generated based on the primary allocation strategy.

[0047] A two-level allocation strategy is generated based on load fluctuations;

[0048] A correction strategy for the target time interval is generated based on the primary and secondary allocation strategies.

[0049] In some embodiments of this application, the generation of a first-level allocation strategy includes:

[0050] Sequentially set the i-th primary equipment point as the target primary equipment point;

[0051] Generate the emission assessment value d for the target primary equipment point;

[0052]

[0053] Where e5 is the preset fifth weighting coefficient, e6 is the preset sixth weighting coefficient, Q5 is the preset fifth fixed coefficient, Q6 is the preset sixth fixed coefficient, Δk is the expected carbon emissions of the target primary equipment point within the target time interval, θ1 is the number of equipment evaluation indicators, and η i Let g be the influencing factor of the i-th equipment evaluation index. i The reference value for the evaluation index of the i-th equipment at the target level within the target time interval;

[0054] The emission evaluation values ​​for each primary equipment point within the target time interval are generated sequentially.

[0055] Establish an emission evaluation value sequence D, where D = (d1, d2, ..., dn). i ...dn1 ), where d i This represents the emission evaluation value of the i-th primary equipment point within the target time interval;

[0056] The first allocation order is generated based on the emission assessment value sequence D;

[0057] A first-level allocation strategy is generated based on the preset first-level optimization model and the first-level score order.

[0058] In some embodiments of this application, the generation of a secondary allocation strategy includes:

[0059] Sequentially set the i-th secondary device point as the target secondary device point;

[0060] Generate the operational evaluation value f for the target secondary equipment point;

[0061]

[0062] Where θ2 represents the number of performance evaluation indicators, r i h is the influencing factor of the i-th operational evaluation index. i This is the reference value for the i-th operational evaluation index of the target secondary equipment point within the target time interval;

[0063] The operational evaluation values ​​for each secondary equipment point are generated sequentially.

[0064] Establish a sequence of operational evaluation values ​​F, F = (f1, f2, ..., f3). i ...f n2 ), where f i This represents the operational evaluation value of the i-th secondary equipment point within the target time interval;

[0065] A second allocation order is generated based on the sequence of operational evaluation values ​​F;

[0066] A secondary allocation strategy is generated based on a preset secondary optimization model and a secondary allocation order.

[0067] Compared with existing technologies, the power flow optimization method for new energy power systems based on carbon emissions proposed in this application has the following advantages:

[0068] A scheduling model is established based on the principle of cost optimization to optimize the daily operation scheduling of the power system. A primary scheduling strategy is generated based on the optimization results. The carbon emission flow and carbon emissions of each equipment node within the power system are further calculated based on the primary scheduling strategy. Equipment nodes with excessive carbon emissions are adjusted and fed back to the scheduling model, thereby achieving two-level regulation of the power flow of the new energy power system, reducing the overall carbon emissions and improving the operating benefits of the power system. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating a preferred embodiment of a new energy power system power flow optimization method based on carbon emissions. Detailed Implementation

[0070] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0071] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0072] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0073] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0074] like Figure 1 As shown in the preferred embodiment of this application, a power flow optimization method for a new energy power system based on carbon emissions includes:

[0075] S101: Set multiple primary equipment points and multiple secondary equipment points according to the equipment parameters of the area to be scheduled;

[0076] S102: Generate the first-level scheduling strategy for the current scheduling period based on the preset scheduling model;

[0077] S103: Generate the expected carbon emission curves for each primary equipment point according to the primary scheduling strategy, and determine whether to generate correction instructions based on all carbon emission curves;

[0078] S104: Generate the secondary scheduling strategy for the current scheduling period based on the correction instructions and the scheduling model;

[0079] This includes setting up multiple primary equipment points and multiple secondary equipment points, including:

[0080] Establish a sequence A1 of primary equipment points, A1 = (a 11 a 12 …a 1i …a 1n1 ), where a 1i Let be the i-th primary equipment point; n1 is the number of primary equipment points;

[0081] Establish a sequence of secondary equipment points A2, A2 = (a 21 a 22 …a 2i …a 2n2 ), where a 2i Let be the i-th secondary equipment point; n2 is the number of secondary equipment points.

[0082] Specifically, each thermal power unit in the area to be dispatched is set as a primary equipment point, and each new energy unit in the area to be dispatched is set as a secondary equipment point.

[0083] Specifically, a scheduling model is established with the goal of minimizing the operating cost of the power system, i.e., optimizing the operating economy, so as to realize the daily operation scheduling of the power system in the area to be scheduled.

[0084] Specifically, the preferred duration of the scheduling cycle is one day.

[0085] Specifically, the expected carbon emission curves for each primary equipment point are generated, including:

[0086] Based on the sequence of primary equipment points A1, a1 is set sequentially. i For target level 1 equipment points;

[0087] Set up a simulation sub-model for the target primary equipment point;

[0088] Generate the runtime data packet for the target primary device point according to the primary scheduling strategy;

[0089] Generate the expected carbon emission curves for the target primary equipment points based on the simulation sub-model and the runtime data package;

[0090] The expected carbon emission curves for each primary equipment point are generated sequentially.

[0091] Specifically, a corresponding simulation sub-model is established based on the historical operating parameters of the target primary equipment point, and the operating parameters of the target primary equipment point are generated according to the primary scheduling strategy. Based on the operating parameters and the simulation sub-model, the expected carbon emission curve of the target primary equipment point in the current adjustment cycle is generated, thereby determining the carbon emission cost of the target primary equipment point.

[0092] It is understood that in the above embodiments, a scheduling model is established based on the principle of cost optimization to optimize the daily operation scheduling of the power system. A first-level scheduling strategy is generated based on the optimization results. The carbon emission flow and carbon emission of each equipment node within the power system are further calculated based on the first-level scheduling strategy. Equipment nodes with excessive carbon emissions are adjusted to reduce the overall carbon emission and improve the operating benefits of the power system.

[0093] In some embodiments of this application, determining whether to generate a correction instruction based on the complete carbon emission curve includes:

[0094] Set multiple time intervals within the current scheduling cycle;

[0095] Establish a time interval sequence T, T = (t1, t2, ..., tt3). i ...t m ), where ti is the i-th time interval; m is the sequence of time intervals;

[0096] Generate adjustment evaluation values ​​for each time interval;

[0097] Establish a series of moderated evaluation values ​​C, where C = (c1, c2, ..., c3). i ...c m ), where c i Let be the adjustment evaluation value for the i-th time interval;

[0098] Determine whether to generate a correction instruction based on the adjustment evaluation value sequence C.

[0099] Specifically, the duration of each time interval is the same.

[0100] Specifically, the adjusted evaluation values ​​for each time interval are generated, including:

[0101] Based on the time interval sequence T, the i-th time interval is sequentially set as the target time interval; the adjustment evaluation value c of the target time interval is generated;

[0102]

[0103] Where e1 is a preset first weighting coefficient, e2 is a preset second weighting coefficient, Q1 is a preset first fixed coefficient, Q2 is a preset second fixed coefficient, and β i Let k be the influence factor of the i-th primary equipment point. iis the expected carbon emission of the i-th primary equipment point within the target time interval, k′ is the carbon emission threshold within the target time interval; Y(i) is the selection coefficient; if (k i - k′)>0, Y(i) = 1; if (k i - k′)<0, Y(i) = 0.

[0104] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient and the second fixed coefficient, so that each parameter is within the same value range.

[0105] Specifically, the larger the adjustment evaluation value, the greater the carbon emission cost in the current time interval, the more serious the pollution to the environment, and the more necessary it is to correct the scheduling strategy.

[0106] Specifically, its carbon emission threshold and the influence factors of each primary equipment point can be set according to historical parameters.

[0107] Specifically, it is judged whether to generate a correction instruction according to the adjustment evaluation value sequence C, including:

[0108] Generate a correction evaluation value d;

[0109]

[0110] Among them, e3 is the preset third weight coefficient, e4 is the preset fourth weight coefficient, Q3 is the preset third fixed coefficient, Q4 is the preset fourth fixed coefficient, and c′ is the average value of all data in the adjustment evaluation value sequence C;

[0111] Preset a correction evaluation value threshold D1;

[0112] If d > D1, generate a secondary correction instruction and correct the scheduling model according to the secondary correction instruction;

[0113] If d < D1, set an adjustment evaluation value threshold C1. If c i > C1, set the primary correction instruction for the i-th time interval.

[0114] Specifically, all parameters in the model are normalized by presetting the third fixed coefficient and the fourth fixed coefficient, so that each parameter is within the same value range.

[0115] Specifically, the primary correction instruction refers to correcting the scheduling strategy within the current time interval. The secondary correction instruction means that there is a large error in the scheduling processing result of the current scheduling model. It is necessary to iteratively optimize the scheduling model, regenerate the primary scheduling strategy according to the optimized scheduling model, and then judge the newly generated primary scheduling strategy according to the above method.

[0116] It is understandable that in the above embodiments, by continuously optimizing the scheduling model, the two-level model is alternately optimized, thereby improving the optimized control of the power flow of the new energy power system, increasing the system's operating benefits, and reducing the overall carbon emissions.

[0117] In a preferred embodiment of this application, generating a secondary scheduling strategy for the current scheduling period includes:

[0118] First-level sub-policies are generated for each time interval based on the first-level scheduling policy;

[0119] Based on the time interval sequence T, the i-th time interval is sequentially set as the target time interval; determine whether there is a first-level correction instruction within the target time interval;

[0120] If it does not exist, set the first-level sub-strategy of the target time interval as the second-level sub-strategy;

[0121] If it exists, set the correction strategy for the target time interval, correct the first-level sub-strategy within the target time interval according to the correction strategy, and generate the second-level sub-strategy within the target time interval according to the correction result.

[0122] Generate secondary sub-policies for each time interval sequentially;

[0123] Generate the secondary scheduling policy for the current scheduling period based on all secondary sub-policies.

[0124] Specifically, the correction strategy for setting the target time interval includes:

[0125] Generate carbon emission adjustment amounts for the target time period based on all expected carbon emission curves;

[0126] A primary allocation strategy is generated based on carbon emission adjustment amounts;

[0127] The load variation within the target time interval is generated based on the primary allocation strategy.

[0128] A two-level allocation strategy is generated based on load fluctuations;

[0129] A correction strategy for the target time interval is generated based on the primary and secondary allocation strategies.

[0130] Specifically, the primary allocation strategy refers to setting the carbon emission reduction for each primary equipment point, while the secondary allocation strategy refers to setting the unit load increase for each secondary equipment point.

[0131] Specifically, the generation of a primary allocation strategy includes:

[0132] Sequentially set the i-th primary equipment point as the target primary equipment point;

[0133] Generate the emission assessment value d for the target primary equipment point;

[0134]

[0135] Where e5 is the preset fifth weight coefficient, e6 is the preset sixth weight coefficient, Q5 is the preset fifth fixed coefficient, Q6 is the preset sixth fixed coefficient, Δk is the expected carbon emissions of the target primary equipment point in the target time interval, θ1 is the number of equipment evaluation indicators, ηi is the influence factor of the i-th equipment evaluation indicator, and gi is the reference value of the i-th equipment evaluation indicator of the target primary equipment point in the target time interval.

[0136] The emission evaluation values ​​for each primary equipment point within the target time interval are generated sequentially.

[0137] Establish an emission evaluation value sequence D, where D = (d1, d2, ..., dn). i ...d n1 ), where d i This represents the emission evaluation value of the i-th primary equipment point within the target time interval;

[0138] The first allocation order is generated based on the emission assessment value sequence D;

[0139] A first-level allocation strategy is generated based on the preset first-level optimization model and the first-level score order.

[0140] Specifically, the equipment evaluation indicators include, but are not limited to, the carbon capture capacity of the thermal power unit corresponding to the primary equipment point, the liquid storage capacity in the storage tank, the historical operating time of the thermal power unit, the fuel type of the thermal power unit, and other parameters. The higher the emission evaluation value, the stronger the current thermal power unit's ability to reduce carbon emissions.

[0141] Specifically, the first allocation order is set according to the order of their operation evaluation values ​​from largest to smallest, and the optimal carbon emission reduction of the first primary equipment point is set according to the first allocation order based on the first-level optimization model. The optimal carbon emission reduction of each primary equipment point is set in turn until the carbon emission adjustment amount is reached, thereby generating the corresponding primary allocation strategy.

[0142] Specifically, by determining the carbon emission costs of all primary equipment points, demand response can be implemented for primary equipment points with adjustment capabilities, and the adjusted load after demand response is fed back to the scheduling model. Through two-level regulation, the overall carbon emission is reduced.

[0143] Specifically, by pre-setting the fifth and sixth fixed coefficients, all parameters in the model are normalized, so that all parameters are within the same range of values.

[0144] Specifically, the generation of secondary allocation strategies includes:

[0145] Sequentially set the i-th secondary device point as the target secondary device point;

[0146] Generate the operational evaluation value f for the target secondary equipment point;

[0147]

[0148] Where θ2 represents the number of performance evaluation indicators, r i h is the influencing factor of the i-th operational evaluation index. i This is the reference value for the i-th operational evaluation index of the target secondary equipment point within the target time interval;

[0149] The operational evaluation values ​​for each secondary equipment point are generated sequentially.

[0150] Establish a sequence of operational evaluation values ​​F, F = (f1, f2, ..., f3). i ...f n2 ), where f i This represents the operational evaluation value of the i-th secondary equipment point within the target time interval;

[0151] A second allocation order is generated based on the sequence of operational evaluation values ​​F;

[0152] A secondary allocation strategy is generated based on a preset secondary optimization model and a secondary allocation order.

[0153] Specifically, the operational evaluation indicators include, but are not limited to, the difference between the expected output and the maximum output of the unit at the secondary equipment point, the historical operating time of the unit, and the failure probability of the unit. The higher the operational evaluation value, the stronger the current load regulation capability of the secondary equipment point.

[0154] Specifically, the secondary allocation order is set according to the order of the operation evaluation values ​​from largest to smallest. The secondary optimization model sets the optimal load increase of the first secondary equipment point according to the secondary allocation order, and sets the optimal load increase of each secondary equipment point in turn until the load change is allocated, and then generates the secondary allocation strategy.

[0155] Based on the first concept of this application, a scheduling model is established based on the principle of cost optimization to optimize the daily operation scheduling of the power system. A first-level scheduling strategy is generated based on the optimization results. The carbon emission flow and carbon emissions of each equipment node within the power system are further calculated based on the first-level scheduling strategy. Equipment nodes with excessive carbon emissions are adjusted and fed back to the scheduling model, thereby achieving two-level regulation of the power flow of the new energy power system, reducing the overall carbon emissions and improving the operating benefits of the power system.

[0156] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A power flow optimization method for a new energy power system based on carbon emissions, characterized in that, include: Multiple primary equipment points and multiple secondary equipment points are set according to the equipment parameters of the area to be scheduled; Generate a first-level scheduling strategy for the current scheduling period based on a preset scheduling model; The expected carbon emission curves for each primary equipment point are generated based on the primary scheduling strategy, and a correction instruction is generated based on all carbon emission curves. Generate a secondary scheduling strategy for the current scheduling cycle based on the correction instructions and the scheduling model; This includes setting up multiple primary equipment points and multiple secondary equipment points, including: Establish a sequence A1 of primary equipment points, A1 = (a 11 a 12 …a 1i …a 1n1 ), where a 1i Let be the i-th primary equipment point; n1 is the number of primary equipment points; Establish a sequence of secondary equipment points A2, A2 = (a 21 a 22 …a 2i …a 2n2 ), where a 2i Let n be the i-th secondary equipment point; n2 is the number of secondary equipment points; Generate the expected carbon emission curves for each primary equipment point, including: Based on the sequence of primary equipment points A1, set a sequentially. 1i For target level 1 equipment points; Define the simulation sub-model for the target primary equipment point; Generate the runtime data packet for the target primary device point according to the primary scheduling strategy; Generate the expected carbon emission curves for the target primary equipment points based on the simulation sub-model and the runtime data package; The expected carbon emission curves for each primary equipment point are generated sequentially. Determine whether to generate a correction directive based on the complete carbon emission curve, including: Set multiple time intervals within the current scheduling cycle; Establish a time interval sequence T, T = (t1, t2, ..., tt3). i ...t m ), where ti is the i-th time interval; m is the sequence of time intervals; Generate adjustment evaluation values ​​for each time interval; Establish a series of moderated evaluation values ​​C, where C = (c1, c2, ..., c3). i ...c m ), where c i Let be the adjustment evaluation value for the i-th time interval; Determine whether to generate a correction instruction based on the adjustment evaluation value sequence C; Generate adjusted evaluation values ​​for each time interval, including: Based on the time interval sequence T, the i-th time interval is sequentially set as the target time interval; Generate the adjusted evaluation value c for the target time interval; Where e1 is a preset first weighting coefficient, e2 is a preset second weighting coefficient, Q1 is a preset first fixed coefficient, Q2 is a preset second fixed coefficient, and β i Let k be the influence factor of the i-th primary equipment point. i Let k be the expected carbon emissions of the i-th primary equipment point within the target time interval; k′ be the carbon emission threshold within the target time interval; and Y(i) be the selection coefficient. If (k′) i -k′)>0, Y(i)=1; if (k i -k′)<0, Y(i)=0; Based on the adjustment evaluation value sequence C, determine whether to generate a correction instruction, including: Generate a corrected evaluation value d; Where e3 is the preset third weight coefficient, e4 is the preset fourth weight coefficient, Q3 is the preset third fixed coefficient, Q4 is the preset fourth fixed coefficient, and c′ is the average value of all data in the adjusted evaluation value series C. Preset correction evaluation value threshold D1; If d > D1, generate a second-level correction instruction and correct the scheduling model according to the second-level correction instruction; If d < D1, set the adjustment evaluation value threshold C1. If c i > C1, set the first-level correction instruction for the i-th time interval.

2. The power flow optimization method for new energy power systems based on carbon emissions as described in claim 1, characterized in that, Generate the secondary scheduling policy for the current scheduling period, including: First-level sub-policies are generated for each time interval based on the first-level scheduling policy; Based on the time interval sequence T, the i-th time interval is sequentially set as the target time interval; Determine whether a Level 1 correction instruction exists within the target time interval; If it does not exist, set the first-level sub-strategy of the target time interval as the second-level sub-strategy; If it exists, set the correction strategy for the target time interval, correct the first-level sub-strategy within the target time interval according to the correction strategy, and generate the second-level sub-strategy within the target time interval according to the correction result. Generate secondary sub-policies for each time interval sequentially; Generate the secondary scheduling policy for the current scheduling period based on all secondary sub-policies.

3. The power flow optimization method for new energy power systems based on carbon emissions as described in claim 2, characterized in that, The correction strategy for setting the target time interval includes: Generate carbon emission adjustment amounts for the target time period based on all expected carbon emission curves; A primary allocation strategy is generated based on carbon emission adjustment amounts; The load variation within the target time interval is generated based on the primary allocation strategy. A two-level allocation strategy is generated based on load fluctuations; A correction strategy for the target time interval is generated based on the primary and secondary allocation strategies.

4. The power flow optimization method for new energy power systems based on carbon emissions as described in claim 3, characterized in that, Generate a primary allocation strategy, including: Sequentially set the i-th primary equipment point as the target primary equipment point; Generate the emission assessment value d for the target primary equipment point; Where e5 is the preset fifth weighting coefficient, e6 is the preset sixth weighting coefficient, Q5 is the preset fifth fixed coefficient, Q6 is the preset sixth fixed coefficient, Δk is the expected carbon emissions of the target primary equipment point within the target time interval, θ1 is the number of equipment evaluation indicators, and η i Let g be the influencing factor of the i-th equipment evaluation index. i The reference value for the evaluation index of the i-th equipment at the target level within the target time interval; The emission evaluation values ​​for each primary equipment point within the target time interval are generated sequentially. Establish an emission assessment value sequence D, where D = (d1, d2, ..., dn) i ...dn1), where d i This represents the emission evaluation value of the i-th primary equipment point within the target time interval; The first allocation order is generated based on the emission assessment value sequence D; A first-level allocation strategy is generated based on the preset first-level optimization model and the first-level score order.

5. The power flow optimization method for new energy power systems based on carbon emissions as described in claim 4, characterized in that, Generate a secondary allocation strategy, including: Sequentially set the i-th secondary device point as the target secondary device point; Generate the operational evaluation value f for the target secondary equipment point; Where θ2 represents the number of performance evaluation indicators, r i h is the influencing factor of the i-th operational evaluation index. i This is the reference value for the i-th operational evaluation index of the target secondary equipment point within the target time interval; The operational evaluation values ​​for each secondary equipment point are generated sequentially. Establish a sequence of operational evaluation values ​​F, F = (f1, f2, ..., f3). i ...f n2 ), where f i This represents the operational evaluation value of the i-th secondary equipment point within the target time interval; A second allocation order is generated based on the sequence of operational evaluation values ​​F; A secondary allocation strategy is generated based on a preset secondary optimization model and a secondary allocation order.

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