New energy power system power flow optimization method based on carbon emission

Abstract

The invention 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. Comprising the following steps: setting a plurality of first-level equipment points and a plurality of second-level equipment points according to equipment parameters of a to-be-scheduled area; generating a first-level scheduling strategy in the current scheduling period according to a preset scheduling model; an expected carbon emission curve of each primary equipment point is generated according to the primary scheduling strategy, and whether a correction instruction is generated or not is judged according to all the carbon emission curves; generating a secondary scheduling strategy in the current scheduling period according to the correction instruction and the scheduling model; the method comprises the following steps: establishing a scheduling model based on a cost optimization principle, carrying out optimization processing on daily operation scheduling of a power system, generating a primary scheduling strategy according to an optimization result, further calculating a carbon emission power flow in the power system and carbon emission of each equipment node according to the primary scheduling strategy, and adjusting the equipment node with excessive carbon emission, so as to complete the scheduling of the power system. Therefore, two-stage regulation of the power flow of the new energy power system is realized.

Description

Technical Field

[0001] The present application relates to the technical field of new energy power systems, and in particular to a method for optimizing power flow in new energy power systems based on carbon emissions. Background Art

[0002] For a long time, the proportion of thermal power installed capacity in my country's power structure has been too high, causing the power system to become the industry with the largest carbon emissions in my country. Therefore, optimizing the dispatch of new energy power system trends to reduce the carbon emission level of the power system and achieve low-carbon operation of the power system has important theoretical and practical significance.

[0003] At present, the optimization methods for power system flows do not take carbon emission flows into consideration. Faced with the increasing proportion of new energy in new power systems, the existing optimization methods for power system flows cannot effectively reduce the overall carbon emissions of the power system, thus affecting the comprehensive benefits of the power system. Summary of the invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides a new energy power system flow optimization method based on carbon emissions, aiming to reduce the carbon emissions of the power system and improve the operating income of the power system.

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

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

[0007] Set multiple first-level equipment points and multiple second-level equipment points according to the equipment parameters of the area to be scheduled;

[0008] Generate a first-level scheduling strategy within the current scheduling cycle according to the preset scheduling model;

[0009] Generate the expected carbon emission curve for each first-level equipment point according to the first-level scheduling strategy, and determine whether to generate a correction instruction based on all carbon emission curves;

[0010] Generate a secondary scheduling strategy within the current scheduling cycle according to the correction instruction and the scheduling model; wherein multiple primary equipment points and multiple secondary equipment points are set, including:

[0011] Establish a first-level equipment point array A1, A1 = (a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th first-level equipment point; n1 is the number of first-level equipment points;

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

[0013] In some embodiments of the present application, generating an expected carbon emission curve for each primary equipment point includes:

[0014] Set a1 in sequence according to the first-level equipment point column A1 i It is the target first-level equipment point;

[0015] Set the simulation sub-model of the target first-level equipment point;

[0016] Generate an operation data packet for the target first-level equipment point according to the first-level scheduling strategy;

[0017] Generate the expected carbon emission curve of the target primary equipment point based on the simulation sub-model and the operation data package;

[0018] Generate the expected carbon emission curve for each primary equipment point in turn.

[0019] In some embodiments of the present application, determining whether to generate a correction instruction based on all carbon emission curves includes:

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

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

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

[0023] Establish the adjustment evaluation value sequence C, C = (c1, c2...c i ...c m ), where c i is the adjustment evaluation value of the i-th time interval;

[0024] It is determined based on the adjustment evaluation value sequence C whether a correction instruction should be generated.

[0025] In some embodiments of the present application, generating the 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 for 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, determining 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 the 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; determining whether there is a primary correction instruction within the target time interval;

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

[0040] If it exists, set the correction strategy for the target time interval, and 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-strategies in each time interval in sequence;

[0042] Generate the secondary scheduling strategy for the current scheduling cycle based on all secondary sub-strategies.

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

[0044] Generate carbon emission adjustments for the target time interval based on all expected carbon emission curves;

[0045] Generate a primary allocation strategy based on the carbon emission adjustment amount;

[0046] Generate the load variation within the target time interval according to the first-level allocation strategy;

[0047] Generate a secondary allocation strategy based on load variation;

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

[0049] In some embodiments of the present application, generating a primary allocation strategy includes:

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

[0051] Generate the emission evaluation value d of the target first-level equipment point;

[0052]

[0053] Among them, 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 first-level equipment point within the target time interval, θ1 is the number of equipment evaluation indicators, η i is the influencing factor of the i-th equipment evaluation index, g i is the reference value of the i-th equipment evaluation index of the target first-level equipment point within the target time interval;

[0054] Generate emission evaluation values ​​of each primary equipment point in the target time interval in sequence;

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

[0056] Generate a first allocation order according to the emission evaluation value sequence D;

[0057] Generate a first-level allocation strategy based on the preset first-level optimization model and the first-level order.

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

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

[0060] Generate the operation evaluation value f of the target secondary equipment point;

[0061]

[0062] Among them, θ2 is the number of operation evaluation indicators, r i is the influencing factor of the i-th operation evaluation index, h i is the reference value of the i-th operation evaluation index of the target secondary equipment point within the target time interval;

[0063] Generate the operation evaluation value of each secondary equipment point in sequence;

[0064] Establish the running evaluation value series F, F = (f1, f2...f i ...f n2 ), where f i is the operation evaluation value of the i-th secondary equipment point within the target time interval;

[0065] Generate a second allocation order according to the running evaluation value sequence F;

[0066] A secondary allocation strategy is generated according to the preset secondary optimization model and the second allocation order.

[0067] Compared with the prior art, the method for optimizing the power flow of a new energy power system based on carbon emissions in the embodiment of the present application has the following beneficial effects:

[0068] A dispatching model is established based on the principle of cost optimization, and the daily operation dispatch of the power system is optimized. A first-level dispatching strategy is generated based on the optimization results. The carbon emission trend within the power system and the carbon emissions of each equipment node are further calculated based on the first-level dispatching strategy. The equipment points with excessive carbon emissions are adjusted and fed back to the dispatching model, thereby realizing two-level regulation of the trend of the new energy power system, reducing the overall carbon emissions and improving the operating income of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of a method for optimizing power flow of a new energy power system based on carbon emissions in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0070] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0071] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

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

[0073] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

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

[0075] S101: setting a plurality of first-level equipment points and a plurality of second-level equipment points according to equipment parameters of the area to be scheduled;

[0076] S102: Generate a first-level scheduling strategy within the current scheduling period according to a preset scheduling model;

[0077] S103: generating an expected carbon emission curve for each first-level equipment point according to the first-level scheduling strategy, and determining whether to generate a correction instruction according to all carbon emission curves;

[0078] S104: Generate a secondary scheduling strategy within the current scheduling cycle according to the modified instruction and the scheduling model;

[0079] Among them, multiple first-level equipment points and multiple second-level equipment points are set, including:

[0080] Create a first-level equipment point array A1, A1 = (a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th first-level equipment point; n1 is the number of first-level equipment points;

[0081] Create a secondary equipment point array A2, A2 = (a 21 , a 22 …a 2i …a 2n2 ), where a 2i is 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 first-level equipment point, and each new energy unit in the area to be dispatched is set as a second-level equipment point.

[0083] Specifically, a dispatching model is established based on the goal of minimizing the cost of power system operation, that is, optimizing the operating economy, so as to achieve daily operation dispatch of the power system in the area to be dispatched.

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

[0085] Specifically, the expected carbon emission curve of each primary equipment point is generated, including:

[0086] Set a1 in sequence according to the first-level equipment point column A1 i It is the target first-level equipment point;

[0087] Set the simulation sub-model of the target first-level equipment point;

[0088] Generate an operation data packet for the target first-level equipment point according to the first-level scheduling strategy;

[0089] Generate the expected carbon emission curve of the target primary equipment point based on the simulation sub-model and the operation data package;

[0090] Generate the expected carbon emission curve for each primary equipment point in turn.

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

[0092] It can be understood that in the above embodiment, a scheduling model is established based on the principle of cost optimization, the daily operation scheduling of the power system is optimized, and a first-level scheduling strategy is generated based on the optimization results. The carbon emission trend within the power system and the carbon emissions of each equipment node are further calculated based on the first-level scheduling strategy, and the equipment points with excessive carbon emissions are adjusted to reduce the overall carbon emissions and improve the operating income of the power system.

[0093] In some embodiments of the present application, determining whether to generate a correction instruction based on all carbon emission curves includes:

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

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

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

[0097] Establish the adjustment evaluation value sequence C, C = (c1, c2...c i ...c m ), where c i is the adjustment evaluation value of the i-th time interval;

[0098] It is determined based on the adjustment evaluation value sequence C whether a correction instruction should be generated.

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

[0100] Specifically, the adjustment evaluation value of each time interval is generated, including:

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

[0102]

[0103] Among them, e1 is the preset first weight coefficient, e2 is the preset second weight coefficient, Q1 is the preset first fixed coefficient, Q2 is the preset second fixed coefficient, β i is the impact factor of the i-th primary equipment point, k 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 perform iterative optimization on 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 can be understood that in the above embodiment, by continuously optimizing the scheduling model, the alternating optimization of the two-level model is achieved, thereby improving the optimal control of the power flow of the new energy power system, improving the operating income of the system, and reducing the overall carbon emissions.

[0117] In a preferred embodiment of the present application, generating a secondary scheduling strategy within the current scheduling cycle includes:

[0118] Generate first-level sub-strategies for each time interval based on the first-level scheduling strategy;

[0119] According to the time interval sequence T, the i-th time interval is sequentially set as the target time interval; and it is determined whether there is a first-level correction instruction in the target time interval;

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

[0121] If it exists, set the correction strategy for the target time interval, and 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-strategies in each time interval in sequence;

[0123] Generate the secondary scheduling strategy for the current scheduling cycle based on all secondary sub-strategies.

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

[0125] Generate carbon emission adjustments for the target time interval based on all expected carbon emission curves;

[0126] Generate a primary allocation strategy based on the carbon emission adjustment amount;

[0127] Generate the load variation within the target time interval according to the first-level allocation strategy;

[0128] Generate a secondary allocation strategy based on load variation;

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

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

[0131] Specifically, a first-level allocation strategy is generated, including:

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

[0133] Generate the emission evaluation value d of the target first-level equipment point;

[0134]

[0135] Among them, 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 first-level equipment point in the target time interval, θ1 is the number of equipment evaluation indicators, ηi is the influencing factor of the i-th equipment evaluation indicator, and gi is the reference value of the i-th equipment evaluation indicator of the target first-level equipment point in the target time interval;

[0136] Generate emission evaluation values ​​of each primary equipment point in the target time interval in sequence;

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

[0138] Generate a first allocation order according to the emission evaluation value sequence D;

[0139] Generate a first-level allocation strategy based on the preset first-level optimization model and the first-level 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 first-level equipment point, the liquid storage volume in the storage tank, the historical operating hours of the thermal power unit, the fuel type of the thermal power unit and other parameters. The larger the emission evaluation value, the stronger the ability of the current thermal power unit to reduce carbon emissions.

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

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

[0143] Specifically, all parameters in the model are normalized by presetting the fifth fixed coefficient and the sixth fixed coefficient, so that each parameter is within the same value range.

[0144] Specifically, a secondary allocation strategy is generated, including:

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

[0146] Generate the operation evaluation value f of the target secondary equipment point;

[0147]

[0148] Among them, θ2 is the number of operation evaluation indicators, r i is the influencing factor of the i-th operation evaluation index, h i is the reference value of the i-th operation evaluation index of the target secondary equipment point within the target time interval;

[0149] Generate the operation evaluation value of each secondary equipment point in sequence;

[0150] Establish the running evaluation value series F, F = (f1, f2...f i ...f n2 ), where f i is the operation evaluation value of the i-th secondary equipment point within the target time interval;

[0151] Generate a second allocation order according to the running evaluation value sequence F;

[0152] A secondary allocation strategy is generated according to the preset secondary optimization model and the second allocation order.

[0153] Specifically, the operation evaluation indicators include but are not limited to the difference between the expected output value and the maximum output value of the unit at the secondary equipment point, the historical operating time of the unit, the failure probability of the unit and other parameters. The larger the operation evaluation value, the stronger the current secondary equipment point's ability to perform load regulation.

[0154] Specifically, the secondary allocation order is set according to the order of the operation evaluation values ​​from large to small. 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 the secondary allocation strategy is generated.

[0155] According to the first concept of the present application, a dispatching model is established based on the principle of cost optimization, the daily operation dispatch of the power system is optimized, and a first-level dispatching strategy is generated according to the optimization results. The carbon emission trend within the power system and the carbon emissions of each equipment node are further calculated according to the first-level dispatching strategy, and the equipment points with excessive carbon emissions are adjusted and fed back to the dispatching model, thereby realizing two-level regulation of the trend of the new energy power system, reducing the overall carbon emissions, and improving the operating income of the power system.

[0156] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A new energy power system flow optimization method based on carbon emissions, characterized in that: include: Set multiple first-level equipment points and multiple second-level equipment points according to the equipment parameters of the area to be scheduled; Generate a first-level scheduling strategy within the current scheduling cycle according to the preset scheduling model; Generate the expected carbon emission curve for each first-level equipment point according to the first-level scheduling strategy, and determine whether to generate a correction instruction based on all carbon emission curves; Generate a secondary scheduling strategy within the current scheduling cycle according to the revised instructions and the scheduling model; Among them, multiple first-level equipment points and multiple second-level equipment points are set, including: Create a first-level equipment point array A1, A1 = (a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th first-level equipment point; n1 is the number of first-level equipment points; Create a secondary equipment point column A2, A2 = (a 21 , a 22 …a 2i …a 2n2 ), where a 2i is the i-th secondary equipment point; n2 is the number of secondary equipment points.

2. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 1, characterized in that: Generate expected carbon emission curves for each primary equipment point, including: According to the first-level equipment point column A1, set a 1i It is the target first-level equipment point; Set the simulation sub-model of the target first-level equipment point; Generate an operation data packet for the target first-level equipment point according to the first-level scheduling strategy; Generate the expected carbon emission curve of the target primary equipment point based on the simulation sub-model and the operation data package; Generate the expected carbon emission curve for each primary equipment point in turn.

3. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 2, characterized in that: Determine whether to generate correction instructions based on the entire carbon emission curve, including: Set multiple time intervals within the current scheduling cycle; Establish a time interval sequence T, T = (t1, t2...t i ...t m ), where ti is the i-th time interval; m is the time interval sequence; Generate adjustment evaluation values ​​for each time interval; Establish the adjustment evaluation value sequence C, C = (c1, c2...c i ...c m ), where c i is the adjustment evaluation value of the i-th time interval; It is determined based on the adjustment evaluation value sequence C whether a correction instruction should be generated.

4. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 3, characterized in that: Generate adjustment evaluation values ​​for each time interval, including: According to the time interval sequence T, the i-th time interval is sequentially set as the target time interval; Generate an adjustment evaluation value c for the target time interval; Wherein, e1 is the preset first weight coefficient, e2 is the preset second weight coefficient, Q1 is the preset first fixed coefficient, Q2 is the preset second fixed coefficient, β i is the impact factor of the i-th primary equipment point, k i is the expected carbon emission of the i-th primary equipment point in the target time interval; k′ is the carbon emission threshold in 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.

5. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 3, characterized in that: Determining whether to generate a correction instruction according to the adjustment evaluation value sequence C includes: Generate a modified evaluation value d; Wherein, 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 modified 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.

6. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 5, characterized in that: Generate the secondary scheduling strategy within the current scheduling cycle, including: Generate first-level sub-strategies for each time interval based on the first-level scheduling strategy; According to 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; If it does not exist, the first-level sub-strategy of the target time interval is set as the second-level sub-strategy; If it exists, set the correction strategy for the target time interval, and 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-strategies in each time interval in sequence; Generate the secondary scheduling strategy for the current scheduling cycle based on all secondary sub-strategies.

7. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 6, characterized in that: Correction strategies for setting target time intervals include: Generate carbon emission adjustments for the target time interval based on all expected carbon emission curves; Generate a primary allocation strategy based on the carbon emission adjustment amount; Generate the load variation within the target time interval according to the first-level allocation strategy; Generate a secondary allocation strategy based on load variation; A revised strategy for the target time interval is generated based on the primary allocation strategy and the secondary allocation strategy.

8. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 7, characterized in that: Generate a first-level allocation strategy, including: Sequentially set the i-th first-level equipment point as the target first-level equipment point; Generate the emission evaluation value d of the target first-level equipment point; Among them, 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 first-level equipment point within the target time interval, θ1 is the number of equipment evaluation indicators, η i is the influencing factor of the i-th equipment evaluation index, g i is the reference value of the i-th equipment evaluation index of the target first-level equipment point within the target time interval; Generate emission evaluation values ​​of each primary equipment point in the target time interval in sequence; Establish the emission evaluation value series D, D = (d1, d2…d i ...d n1 ), where d i is the emission evaluation value of the i-th primary equipment point within the target time interval; Generate a first allocation order according to the emission evaluation value sequence D; Generate a first-level allocation strategy based on the preset first-level optimization model and the first-level order.

9. The method for optimizing the power flow of a new energy power system based on carbon emissions according to claim 8, characterized in that: Generate a secondary allocation strategy, including: Sequentially set the i-th secondary equipment point as the target secondary equipment point; Generate the operation evaluation value f of the target secondary equipment point; Among them, θ2 is the number of operation evaluation indicators, r i is the influencing factor of the i-th operation evaluation index, h i is the reference value of the i-th operation evaluation index of the target secondary equipment point within the target time interval; Generate the operation evaluation value of each secondary equipment point in sequence; Establish the running evaluation value series F, F = (f1, f2...f i ...f n2 ), where f i is the operation evaluation value of the i-th secondary equipment point within the target time interval; Generate a second allocation order according to the running evaluation value sequence F; A secondary allocation strategy is generated according to the preset secondary optimization model and the second allocation order.

Images