A cloud-computing-based dynamic adjustment carbon emission management method and terminal

Abstract

The application discloses a kind of based on cloud computing's dynamic adjustment carbon emission management method and terminal, establish the initial grid-connected output model of all power stations in region and the initial power consumption model of load side, carry out power flow calculation;Monitoring the initial output value of each power station, whether the power grid system operation in region meets the output constraint condition by initial power flow calculation judging that active and reactive power in power grid are balanced, if not satisfied, then according to constraint condition calculation region inside power supply side needs to increase total output;Under the premise of meeting output optimization constraint and carbon emission constraint, the output amplitude of each power station is calculated as the optimization strategy of regional power supply side.In the constraint condition of each power station in region, further enter the limitation of carbon emission quota in this region and introduce carbon emission factor, not only pay attention to the stability of system, but also pay attention to the low carbon level of each power station output, give consideration to meet the demand of load side and the limitation of local carbon emission quota.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission management, and in particular to a cloud computing-based dynamic carbon emission management method and terminal. Background Art

[0002] The current market demand is to accelerate the optimization of energy structure, strictly control fossil energy consumption, and actively promote the development of clean energy such as wind power and photovoltaics.

[0003] Of course, the current market context is that the energy structure is mostly coal-based, with a large number of thermal power plants, while oil and natural gas are relatively expensive and scarce. Considering the current market consensus on carbon neutrality management, the current energy structure faces a relatively urgent need for transformation. At the same time, as new energy technologies fully mature and are deployed, how to timely manage the traditional energy side, especially how to optimize the management of grid-connected energy companies so that corresponding structural energy conservation and emission reduction can be quickly achieved based on existing technologies and production scale, is a problem that must be faced in the current market trend of carbon neutrality.

[0004] In existing technologies, regional power grid flow calculations and carbon emission levels are usually considered relatively independently. This is due to the relative maturity of traditional flow calculation methods and the fact that carbon neutrality strategies are new requirements. Therefore, existing technologies currently lack mature output regulation strategies to meet the stability requirements of traditional flow calculations while ensuring that the carbon emissions after power station output adjustment meet regional quota requirements. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a cloud computing-based dynamic carbon emission management method and terminal to meet the stability requirements of traditional tidal current calculations while ensuring that the carbon emissions after power station output adjustment meet the regional quota requirements.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for dynamically adjusting carbon emission management based on cloud computing, comprising the steps of:

[0008] S1. Establish the initial grid-connected output model of all power stations in the region and the initial power consumption model on the load side to perform power flow calculations;

[0009] S2. Monitor the initial output value of each power station and determine whether the power grid system in the region satisfies the output constraint conditions while maintaining a balance between active and reactive power within the grid through initial power flow calculation. If not, calculate the total output that needs to be increased on the power supply side in the region based on the constraint conditions and execute step S3.

[0010] S3. Under the premise of satisfying the output optimization constraints and carbon emission constraints, the increased output amplitude of each power station is calculated as the regional power supply side optimization strategy.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A terminal for dynamically adjusting carbon emission management method based on cloud computing includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described above is implemented.

[0013] The beneficial effects of the present invention are: a cloud computing-based dynamic adjustment carbon emission management method and terminal, which further enters the carbon emission quota restrictions in the region and introduces carbon emission factors in the constraints of each power station in the region. Therefore, when adjusting the output level of each power station, it not only pays attention to the stability of the system, but also pays attention to the low-carbon level of the output of each power station, fully considering the respective advantages of traditional energy power stations and new energy power stations, and taking into account the satisfaction of load-side demand and the restrictions of local carbon emission quotas. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of a method for dynamically adjusting carbon emission management based on cloud computing according to an embodiment of the present invention is shown;

[0015] Figure 2 This is a schematic structural diagram of a cloud computing-based dynamic carbon emission management terminal according to an embodiment of the present invention;

[0016] Description of labels:

[0017] 1. A cloud computing-based dynamically adjusted carbon emission management terminal; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0018] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0019] Please refer to Figure 1 , a method for dynamically adjusting carbon emission management based on cloud computing, comprising the steps of:

[0020] S1. Establish the initial grid-connected output model of all power stations in the region and the initial power consumption model on the load side to perform power flow calculations;

[0021] S2. Monitor the initial output value of each power station and determine whether the power grid system in the region satisfies the output constraint conditions while maintaining a balance between active and reactive power within the grid through initial power flow calculation. If not, calculate the total output that needs to be increased on the power supply side in the region based on the constraint conditions and execute step S3.

[0022] S3. Under the premise of satisfying the output optimization constraints and carbon emission constraints, the increased output amplitude of each power station is calculated as the regional power supply side optimization strategy.

[0023] From the above description, it can be seen that the beneficial effects of the present invention are: a cloud computing-based dynamic adjustment carbon emission management method and terminal, which further enters the carbon emission quota restrictions in the region and introduces carbon emission factors in the constraints of each power station in the region. Therefore, when adjusting the output level of each power station, it not only pays attention to the stability of the system, but also pays attention to the low-carbon level of the output of each power station, fully considering the respective advantages of traditional energy power stations and new energy power stations, and taking into account the satisfaction of load-side demand and local carbon emission quota restrictions.

[0024] Furthermore, the output optimization constraint is:

[0025]

[0026] Where, ΔMP ia Refers to the actual increase in output amplitude of the i-th power station in the new energy power station, ΔNP ja It refers to the actual increase in output amplitude of the jth power station in the traditional energy power station. is the fuel energy conversion efficiency value of the jth power station in the traditional energy power station, E j is the carbon emission factor per unit fuel used by the jth power station in the traditional energy power station, ΔC N The total carbon emissions of all traditional energy power plants increased when the power supply side of region D increases its output by ΔP, where ΔP is the total output that needs to be increased on the power supply side in the region;

[0027] The carbon emission constraints are:

[0028] ΔC N ≤C dif ;

[0029] Where C dif It refers to the difference between the current carbon emissions in area D and the carbon emission quota.

[0030] From the above description, it can be seen that the load side demand and the local carbon emission quota can be met according to the constraints.

[0031] Furthermore, in step S3, the calculation of the increased output amplitude of each power station must also meet the stability constraint condition:

[0032]

[0033] Where, δ kris the real part of the eigenvalue of each power station after the regional power supply side optimization strategy is executed, and ε kr is the real part of the eigenvalue of each power station before the regional power supply side optimization strategy is implemented;

[0034] The eigenvalues ​​are obtained by decomposing the Jacobian matrix of the power flow calculation.

[0035] As can be seen from the above description, using the eigenvalues ​​in traditional power flow calculations can further improve system stability, prevent the pursuit of excessively low carbon emissions that leads to a high proportion of output from renewable energy power stations on the grid, and avoid possible system oscillation problems.

[0036] Furthermore, the calculation of the increased output amplitude of each power station is performed based on a minimization index, which is one of the total time of output adjustment, the carbon emission economic index and the carbon emission content corresponding to each kilowatt-hour of electricity, or the weighted sum of more than one of the total time of output adjustment, the carbon emission economic index and the carbon emission content corresponding to each kilowatt-hour of electricity.

[0037] From the above description, it can be seen that according to the minimization index, the optimal output adjustment plan can be effectively designed according to the needs.

[0038] Furthermore, the total time T of the output adjustment ad Calculate according to the following formula:

[0039] T ad =∑T Mi +∑T Ni ;

[0040] Where, T Mi T is the time required for the i-th power station in the new energy power station to complete the increase in output amplitude, Ni It is the time required for the jth power station in the traditional energy power station to actually increase the output amplitude.

[0041] From the above description, it can be seen that the time for output adjustment is minimized.

[0042] Furthermore, the carbon emission economic index is calculated according to the following formula:

[0043]

[0044] Where Z j It refers to the fuel purchase price of the jth power station in the traditional energy power station.

[0045] From the above description, it can be seen that economic optimization of carbon emissions is achieved.

[0046] Furthermore, the carbon emission content R corresponding to each kilowatt-hour of electricity is calculated according to the following formula:

[0047]

[0048] From the above description, it can be seen that the carbon emission content per kilowatt-hour of electricity is minimized.

[0049] Furthermore, the output constraint condition is specifically:

[0050]

[0051] Where, ΔMP ir It refers to the maximum increase in the output active power of the i-th new energy power station during the grid regulation cycle, ∑ΔMP ir It refers to the extent to which the total output active power of all new energy power stations can be increased during the grid regulation cycle; ΔNP jr It refers to the increase in the output active power of the jth traditional energy power station during the grid control cycle, ∑ΔNP jr It refers to the maximum increase in the total active power output of all traditional energy power stations in region D during time period T; ΔN refers to the increased network line loss in the region after all traditional energy power stations and new energy power stations increase by the specified amount during the grid regulation cycle; and ΔL D It refers to the maximum increase in the load side during the regional power grid regulation cycle relative to the rated power consumption, δ is the active redundancy coefficient, U k is the actual voltage value of node k in the region, U k0 The voltage rating or initial steady-state voltage of node k in the region, β refers to the maximum deviation of the voltage of each node, and G refers to the total number of nodes in the region; P l Refers to the actual active power value of device 1 in the area, P l0 Refers to the rated active power value of device 1 in the area.

[0052] From the above description, it can be seen that the required increased output is calculated and the load side demand is met.

[0053] Furthermore, the method further comprises the steps of:

[0054] S4. Obtain the grid-connected output model and load-side power consumption model of all power stations in the region after optimization according to the regional power supply side optimization strategy;

[0055] S5. Performing power flow calculation based on the optimized grid-connected output model of all power stations in the region and the load-side power consumption model;

[0056] After the power control cycle T ends, step S6 is executed to determine whether the actual carbon emissions on the power supply side are less than the carbon emission quota. If so, the regional power supply side optimization strategy is saved and used as the regional power supply side optimization strategy for the next cycle. If not, the carbon emission constraint condition is modified to:

[0057] ΔC N ≤Cdif -ΔC;

[0058] Wherein, ΔC is the quota gap value;

[0059] Save the modified carbon emission constraints and regional power supply optimization strategy.

[0060] From the above description, it can be seen that self-adjustment of the optimization strategy is achieved.

[0061] Please refer to Figure 2 A cloud computing-based dynamically adjusted carbon emission management terminal includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the method described above is implemented.

[0062] The present invention is used to dynamically adjust the overall carbon emissions of a region, ensuring timely and effective monitoring and energy consumption control of high-energy-consuming enterprises in the region, and achieving regional carbon neutrality and management.

[0063] Please refer to Figure 1 , embodiment 1 of the present invention is:

[0064] A method for dynamically adjusting carbon emission management based on cloud computing includes the following steps:

[0065] S1. Establish the initial grid-connected output model of all power stations in the region and the initial power consumption model on the load side, perform power flow calculations, and set the grid control cycle.

[0066] Specifically, the initial grid-connected output model P0(t) is:

[0067] P0(t)=∑P ir (t)+∑P jf (t);

[0068] Among them, P ir (t) is the output of the i-th renewable energy power station in region D at time t, and P if (t) is the output of the jth traditional energy power station in the region at time t.

[0069] The initial power consumption model on the load side can be substituted by various load power consumption models in the existing technology. Since it is an existing technology, it will not be described in detail here.

[0070] Set the grid control period T.

[0071] At the same time, in order to meet the current carbon neutrality emission requirements, it is preferred that when setting the initial grid-connected output model, consideration should be given to appropriately increasing the proportion of grid-connected power generation of new energy power stations, so that the carbon emissions of traditional energy power stations are reduced accordingly.

[0072] S2. Monitor the initial output value of each power station and determine whether the operation of the power grid system in the area meets the output constraint conditions while maintaining the balance of active and reactive power in the power grid through initial power flow calculation. If not, calculate the total output that needs to be increased on the power supply side in the area based on the constraint conditions and execute step S3.

[0073] Specifically, under the condition that both active and reactive power are balanced in the power grid of region D, the following output constraints are added:

[0074]

[0075] Among them, ΔMP ir It refers to the maximum increase in the output active power of the i-th new energy power station within the time period T, where 1≤i≤M, ∑ΔMP ir It refers to the increase in the total output active power of all new energy power stations in area D during time period T; ΔΔNP jr It refers to the increase in the output active power of the jth traditional energy power station in the time period T, where 1≤j≤N, ∑ΔNP jr It refers to the maximum increase in the total active power output of all traditional energy power stations in area D during time period T; ΔN refers to the increased network line loss value in area D after all traditional energy power stations and new energy power stations in time period T increase by the specified amount; and ΔL D It refers to the maximum increase in load side due to seasonal factors, power consumption cycle or other factors in area D and time period T relative to the rated power consumption. δ is the active redundancy coefficient, that is, the output amplitude of the power supply side needs to be moderately higher than the power demand of the power consumption side to prevent oscillation caused by grid voltage drop when the power demand of the power consumption side cannot be met. k is the actual voltage value of node k in region D, U k0 The voltage rating or initial steady-state voltage of node k in region D, β refers to the maximum deviation of the voltage of each node, where 1≤k≤G, G refers to the total number of nodes in the region; P l Refers to the actual active power value of device 1 in the area, P l0 It refers to the rated active power value of device 1 in the area, where 1≤l≤L, and L refers to the total number of devices in the area.

[0076] It is worth noting that the active power can be increased by an amount ∑ΔNP jr It refers to the maximum output active power amplitude that can be increased by each power station by increasing the power generation materials, regulating the energy conversion efficiency of equipment or adding more grid-connected units, based on the active output nominal value of each power station in area D that meets the requirements of power flow calculation.

[0077] S3. Under the premise of satisfying the output optimization constraints and carbon emission constraints, the increased output amplitude of each power station is calculated as the regional power supply side optimization strategy.

[0078] In the initial stage of cycle T, the load side demand is usually relatively stable and the power supply side output fluctuation is small, so the flow calculation converges quickly. However, with the fluctuation of load side power consumption, such as a substantial increase in load side power consumption, it is necessary to increase the grid-connected power of regional D power station. At this time, not only the power system flow may need to be recalculated, but also the economic factors of carbon emissions after readjusting the power supply side output need to be considered to meet the regional carbon neutrality emission requirements.

[0079] Assume that the total output required to be increased on the power supply side in the region is ΔP. ​​In this case, the following strategy is used to optimize the output of traditional energy power stations and new energy power stations in region D. The output optimization constraints are as follows:

[0080]

[0081] Among them, ΔMP ia It refers to the actual increase in output amplitude of the i-th power station in the new energy power station, where 1≤i≤M, ΔNP ja It refers to the actual increase in output amplitude of the jth power station in the traditional energy power station, where 1≤j≤N, is the fuel energy conversion efficiency value of the jth power station in the traditional energy power station, E j is the carbon emission factor per unit fuel used by the jth power station in the traditional energy power station, ΔC N The total carbon emissions of all traditional energy power stations increased when the power supply side of region D increases its output ΔP.

[0082] Also includes carbon emission constraints:

[0083] ΔC N ≤C sif ;

[0084] Among them, C sif It refers to the difference between the current carbon emissions in area D and the carbon emission quota.

[0085] Under the above optimization constraints, the following optimization results are obtained:

[0086] Min(T ad =∑T Mi +∑T Ni );

[0087] Among them, T ad It refers to the total time it takes for all new energy power stations and traditional power stations in area D to complete output adjustment. MiRefers to the actual increase in output amplitude ΔMP completed by the i-th power station in the new energy power station ia The time required, T Ni The actual increased output amplitude ΔNP of the jth power station in the traditional energy power station ja The time required.

[0088] The above optimization results are set up to take into account the regional power grid flow regulation. Generally, it is hoped that the convergence speed of the flow calculation will be fast and the adjustment cycle will be short. The output adjustment time of traditional energy power stations is relatively fast, such as directly activating idle units or increasing the output value of the current unit. However, the output adjustment of new energy power stations has a certain time curve, which is longer than the output adjustment time of traditional energy power stations. Therefore, it is necessary to constrain the output adjustment time of all power stations in area D to quickly meet the time requirements of the power grid flow calculation.

[0089] In an optional embodiment, carbon emission economy can be used as an indicator for power supply side optimization from the perspective of carbon emission economy:

[0090]

[0091] Among them, Q is the carbon emission economic index, Z j It refers to the fuel purchase price of the jth power station in the traditional energy power station;

[0092] In another optional embodiment, from the perspective of low carbonization of electricity, the carbon emission content per kilowatt-hour of electricity can be used as an indicator for optimizing the power supply side:

[0093]

[0094] Among them, R is the carbon emission index corresponding to each additional kilowatt-hour of electricity in area D.

[0095] Of course, in areas with more complex situations, the three indicators of Tad, Q and R mentioned above can be used as optimization factors, and the corresponding weight coefficients can be added to comprehensively set the regional carbon emission optimization strategy:

[0096] Min(Z=γ1×T ad +γ2×Q+γ3×R);

[0097] Among them, γ1, γ2, and γ3 are T ad The optimization weight values ​​of the three indicators , Q and R, can be set by the operator according to the focus of the optimization. For example, if you want to focus on the optimization of carbon emission economic indicators, you can achieve this by increasing the weight value of γ2.

[0098] However, it is worth noting that although renewable energy power generation meets the needs of a low-carbon economy, the increase in output of renewable energy power stations may further bring about grid oscillation problems, thereby reducing the stability of the grid flow. At this time, the corresponding energy storage equipment can be started to reduce the grid oscillation problem. At the same time, the eigenvalue method for small-disturbance system stability analysis of the power system can be used to analyze the eigenvalue σ from the Jacobian matrix of the flow calculation. k , where 1≤k≤N+M, the eigenvalue or the real part of the eigenvalue is selected as the stability evaluation indicator. When the eigenvalue is to the right of the Y-axis of the complex plane and the eigenvalue is far from the imaginary axis of the complex plane, it means that the system after output regulation is relatively stable under small disturbances. Based on the fact that the larger the real part of the eigenvalue on the complex plane, the stronger the requirement for system stability, the regional manager can also set the eigenvalue threshold ε0. Using the eigenvalue threshold as a constraint, the characteristics of each power station in region D are traversed and constrained, further limiting the above-mentioned regional power supply side optimization strategy to ensure that after the regional power supply side optimization strategy is executed, the system can still maintain a low degree of oscillation and meet the system stability requirements. Taking the real part of the eigenvalue as an example:

[0099]

[0100] Among them, δ kr is the real part of the eigenvalue of each power station after the regional power supply side optimization strategy is executed, and ε kr is the real part of the eigenvalue corresponding to each power station before the regional power supply side optimization strategy is implemented.

[0101] Afterwards, the grid personnel can adjust the output level of each power station in the region according to the obtained regional power supply optimization strategy and provide feedback on the adjusted results.

[0102] S4. Obtain the grid-connected output model and load-side power consumption model of all power stations in the region after optimization according to the regional power supply side optimization strategy;

[0103] S5. Performing power flow calculation based on the optimized grid-connected output model of all power stations in the region and the load-side power consumption model;

[0104] The power system flow calculation is performed based on the output levels of each power station in the adjusted area to ensure balanced load at each node in the network.

[0105] After the power control cycle T ends, step S6 is executed to determine whether the actual carbon emissions on the power supply side are less than the carbon emission quota. If so, the regional power supply side optimization strategy is saved and used as the regional power supply side optimization strategy for the next cycle. If not, the carbon emission constraint condition is modified to:

[0106] ΔC N ≤C dif -ΔC;

[0107] Among them, ΔC is the quota gap value, and the modified carbon emission constraint conditions and regional power supply side optimization strategy are saved.

[0108] After the end of period T, the difference between the actual carbon emissions on the power supply side in region D and the carbon emission quota is calculated. If the actual carbon emissions are less than the quota, it proves that the above-mentioned power supply side optimization strategy is effective. The power supply side optimization strategy executed within this period T is recorded in the server and can be continued in the next period. If the actual carbon emissions are less than the quota, the difference between the two is used as the carbon emission quota gap value, which proves that the above-mentioned power supply side optimization strategy is not good, especially the carbon emission constraints need to be further increased. Therefore, it is necessary to first purchase the quota gap value from the market in region D, and then further modify the new carbon emission constraints in step S3 executed within this period T to:

[0109] ΔC N ≤C dif -ΔC;

[0110] Wherein, ΔC is the quota gap value, and the modified constraint conditions and optimization strategy are recorded in the server for execution in the next cycle.

[0111] Please refer to Figure 2 , the second embodiment of the present invention is:

[0112] A cloud computing-based dynamically adjusted carbon emission management terminal 1 includes a memory 3, a processor 2, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps of the above-mentioned embodiment 1 are implemented.

[0113] To sum up, the present invention provides a cloud computing-based dynamic adjustment carbon emission management method and terminal, which further enters the carbon emission quota restriction in the region and introduces the carbon emission factor in the constraint conditions of each power station in the region. Therefore, when adjusting the output level of each power station, it not only pays attention to the stability of the system, but also pays attention to the low-carbon level of the output of each power station, fully considering the respective advantages of traditional energy power stations and new energy power stations, and taking into account the satisfaction of load-side demand and the restriction of local carbon emission quota.

[0114] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for dynamically adjusting carbon emission management based on cloud computing, characterized in that: Including steps: S1. Establish the initial grid-connected output model of all power stations in the region and the initial power consumption model on the load side to perform power flow calculations; S2. Monitor the initial output value of each power station and determine whether the power grid system in the region satisfies the output constraint conditions while maintaining a balance between active and reactive power within the grid through initial power flow calculation. If not, calculate the total output that needs to be increased on the power supply side in the region based on the constraint conditions and execute step S3. The output constraint conditions are specifically: ； Where, It refers to the maximum increase in the output active power of the i-th new energy power station during the grid regulation cycle. It refers to the extent to which the total output active power of all renewable energy power stations can be increased during the grid regulation cycle; It refers to the increase in the output active power of the jth traditional energy power station during the power grid regulation cycle. It refers to the maximum increase in the total active power output of all traditional energy power stations in area D during time period T; It refers to the increased network line loss in the region after all traditional energy power stations and new energy power stations are increased by the above amount during the power grid regulation cycle; It refers to the maximum increase in load side during the regional power grid regulation cycle relative to the rated power consumption. is the active redundancy coefficient, is the actual voltage value of node k in the region, The voltage rating or initial steady-state voltage value of node k in the region, It refers to the maximum deviation of the voltage of each node, and G refers to the total number of nodes in the region; Refers to the equipment in the area l Actual active power value, Refers to the equipment in the area l Rated active power value; S3. Calculate the increased output amplitude of each power station as the regional power supply side optimization strategy under the premise of meeting the output optimization constraints and carbon emission constraints; The output optimization constraint is: ； Where, It refers to the actual increased output amplitude of the i-th power station in the new energy power station. It refers to the actual increase in output amplitude of the jth power station in the traditional energy power station. is the fuel energy conversion efficiency value of the jth power station in the traditional energy power station, is the carbon emission factor per unit fuel used by the jth power station in the traditional energy power station, Increase the output of the power supply side of area D Under this scenario, the total carbon emissions of all traditional energy power plants will increase by The total output that needs to be increased on the power supply side within the region; The carbon emission constraints are: ； Where, It refers to the difference between the current carbon emissions and the carbon emission quota in area D; The calculation of the increased output amplitude of each power station is performed based on a minimization index, wherein the index is one of the total output adjustment time, the carbon emission economic index, and the carbon emission content per kilowatt-hour, or a weighted sum of more of the total output adjustment time, the carbon emission economic index, and the carbon emission content per kilowatt-hour; In step S3, the calculation of the increased output amplitude of each power station must also meet the stability constraint condition: ； Where, is the real part of the eigenvalue of each power station after the regional power supply side optimization strategy is executed, and is the real part of the eigenvalue of each power station before the regional power supply side optimization strategy is implemented; The eigenvalues ​​are obtained by decomposing the Jacobian matrix of the power flow calculation.

2. The method for dynamically adjusting carbon emission management based on cloud computing according to claim 1 is characterized in that: The total time of the output adjustment Calculated according to the following formula: ； Where, The time required for the i-th power station in the new energy power station to complete the increase in output amplitude, It is the time required for the jth power station in the traditional energy power station to actually increase the output amplitude.

3. The method for dynamically adjusting carbon emission management based on cloud computing according to claim 1 is characterized in that: The carbon emission economic index is calculated according to the following formula: Q= ； Where Z j It refers to the fuel purchase price of the jth power station in the traditional energy power station.

4. The method for dynamically adjusting carbon emission management based on cloud computing according to claim 1 is characterized in that: The carbon emission content R per kilowatt-hour of electricity is calculated according to the following formula: R= 。 5. The method for dynamically adjusting carbon emission management based on cloud computing according to claim 1 is characterized in that: Also includes the steps: S4. Obtain the grid-connected output model and load-side power consumption model of all power stations in the region after optimization according to the regional power supply side optimization strategy; S5. Performing power flow calculation based on the optimized grid-connected output model of all power stations in the region and the load-side power consumption model; After the power control cycle T ends, step S6 is executed to determine whether the actual carbon emissions on the power supply side are less than the carbon emission quota. If so, the regional power supply side optimization strategy is saved and used as the regional power supply side optimization strategy for the next cycle. If not, the carbon emission constraint condition is modified to: ； Where, is the quota gap value; Save the modified carbon emission constraints and regional power supply optimization strategy.

6. A cloud computing-based dynamic carbon emission management terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

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