A power regulation control method and device based on dynamic electric carbon factor, terminal equipment and storage medium

Abstract

The application discloses a kind of based on dynamic electric carbon factor, power regulation control method, device, terminal equipment and storage medium, obtain the operation data of power system;Based on the operation data, the electric carbon factor model of each node of power system is constructed;Under the constraint condition of default, the electric carbon factor model is constructed based on each described electric carbon factor model;The electric carbon emission model is solved by vector weighted average algorithm, obtains the carbon emission degree capacity instruction of power system, and then based on the carbon emission degree capacity instruction, the power system is regulated and controlled.The application is based on the operation data of power system and constructs electric carbon factor model, and electric carbon factor model is used as the input of electric carbon emission model, and the electric carbon emission model is solved by vector weighted average algorithm, and then based on the result of solving, the regulation and control of power grid are optimized, the accuracy of power regulation is greatly improved, and then the carbon emission of power system can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power regulation, and particularly relates to a power regulation control method and device based on a dynamic electric carbon factor, a terminal device and a storage medium. BACKGROUND

[0002] In recent years, methods and strategies for reducing carbon emissions of power users include the following methods: time-of-use pricing, customizing electricity prices at different times according to current demand and supply, encouraging power consumers to shift consumption to non-peak periods with lower carbon intensity; critical peak pricing, higher rates during periods of high demand or high carbon intensity, encouraging reduced use; coordinating multiple users through demand response aggregators to aggregate demand response capabilities, optimizing economic and carbon efficiency; providing real-time data on energy consumption and carbon intensity through smart meters and advanced metering infrastructure; on-site power generation, installing solar panels or small wind turbines at homes or businesses; smart grid, implementing smart grid technology to enable real-time monitoring and management of energy use. However, existing technologies do not take into account the impact of grid-related equipment (generators, etc.) on power regulation, resulting in excessive carbon emissions in the power system.

[0003] Therefore, there is an urgent need for a power regulation control strategy to solve the problem of excessive carbon emissions in the power system. SUMMARY

[0004] The embodiments of the present application provide a power regulation control method and device based on a dynamic electric carbon factor, a terminal device and a storage medium to solve the problem of excessive carbon emissions in the power system.

[0005] To solve the above problems, an embodiment of the present application provides a power regulation control method based on a dynamic electric carbon factor, comprising:

[0006] obtaining operation data of a power system;

[0007] based on the operation data, constructing an electric carbon factor model for each node of the power system;

[0008] under a predetermined constraint condition, constructing a power carbon emission model based on each electric carbon factor model;

[0009] dynamically solving the power carbon emission model by a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the power system, and then regulating the power system based on the carbon emission degree capacity instruction.

[0010] As an improvement of the above scheme, the operation data includes: total power generation, total number of generators, injection power of the generator, total amount of power of the power generation end connected to the node, number of power generation ends of the power system, active power of the load connected to the node, total number of loads connected to the node, node power flow injection connected to the current node, and total number of nodes connected to the current node; the electric carbon factor model of each node of the power system is constructed based on the operation data, including:

[0011] According to the total power generation, the total number of generators, and the injection power of the generator, the random load calculation formula is substituted to obtain the random load data of each node of the power system; wherein the random load calculation formula satisfies the following conditions:

[0012]

[0013] In the formula, D i is the random load data, G max is the total power generation, g max is the total number of generators, is the injection power of the jth generator to the ith node, b is a proportional parameter, a i is a random parameter, a j is the proportion of the jth load to the total load, and d is the number of nodes.

[0014] Each random load data is taken as input, and the total amount of power of the power generation end connected to the node, the number of power generation ends of the power system, the active power of the load connected to the node, the total number of loads connected to the node, the node power flow injection connected to the current node, and the total number of nodes connected to the current node are combined to construct the electric carbon factor model of each node of the power system; wherein the electric carbon factor model satisfies the following conditions:

[0015]

[0016] In the formula, g i and respectively represent the total amount of power produced by the kth power generation end connected to the ith node in the power system, the total number of power generation ports included in the power system, and the corresponding electric carbon factor; and d i respectively represent the active power of the load connected to the ith node and the total number of loads connected to the ith node; and n i respectively represent the power flow injection from the jth node to the ith node and the total number of nodes connected to the ith node; is the diagonal component in the matrix, representing the total power flowing out of the ith node; P i jis the non-diagonal component, and is the power injected from the jth node to the ith node.

[0017] As an improvement of the above scheme, the constraint condition satisfies the following condition:

[0018]

[0019] wherein f(X i ) represents the actual target calculation value of the ith group of influence factors; F(X i ) represents the practicality calculation value of the ith group; is the minimum power constraint of the system, representing the minimum power output allowed; ΔP C (k) is the power change at the kth data update, representing the dynamic power load change of the system at the current time; is the maximum power constraint of the system, representing the maximum power output allowed; X i (j) is the contribution value of the jth influence factor in the ith group to the power, representing the power distribution of each influence factor in the system.

[0020] As an improvement of the above scheme, the power carbon emission model satisfies the following condition:

[0021]

[0022] wherein, represents the carbon emission capacity instruction obtained by the power data carbon emission degree at the kth data update; is the carbon emission capacity instruction obtained by the power data carbon emission degree at the k+1th data update; p i is an n-dimensional stable vector, representing the proportion of the ith influence factor in the entire power carbon emission; represents the dynamic electric carbon factor of the ith node at the kth update; and are the minimum and maximum allowed power of the i+1th node, respectively, limiting the power output range of the system; and are the minimum and maximum power output constraints of the jth node, respectively; and are the minimum and maximum power output constraints of the i+2-nth node, respectively, used to calculate the contribution of different nodes in power distribution.

[0023] As an improvement of the above scheme, the power carbon emission model is solved by the vector weighted average algorithm to obtain the carbon emission capacity instruction of the power system, comprising:

[0024] The power carbon emission model operation is cyclically executed, and the carbon emission degree capacity instruction when the cycle number is the cycle threshold is output;

[0025] The power carbon emission model operation is specifically:

[0026] A new vector is obtained, wherein the new vector satisfies the following conditions:

[0027]

[0028] wherein, is the current position of the solution, and is the position virtual random number, ρ is the position judgment random number, and r1 and r2 are weighted mean values;

[0029] Based on the preset exploration function, the weighted average factor and the scaling factor are calculated, wherein the exploration function satisfies the following conditions:

[0030] δ=2r1×r-r

[0031] σ=2r2×r-r

[0032]

[0033] wherein, r1 and r2 represent random numbers between 0 and 1; l is the latest iteration in the optimization process, r, r1 and r2 represent weighted mean values, k max is the total number of iterations; δ is the weighted average factor, and σ is the scaling factor;

[0034] The active power of the load connected with the node in the electric carbon factor model and the node power flow injection connected with the current node are taken as the initial vector, and the new vector and the random number are combined to obtain a combined vector;

[0035] Based on the weighted average factor, the scaling factor and the combined vector, the calculation of the power carbon emission model is performed to obtain the carbon emission degree capacity instruction;

[0036] It is judged whether the current carbon emission degree capacity instruction meets the preset instruction condition;

[0037] If not, a new new vector is reacquired as the input of the next power carbon emission model operation, and the power carbon emission model operation is re-executed;

[0038] If yes, the current new vector is updated, the updated new vector is verified based on the preset verification condition, the verified new vector is taken as the input of the next power carbon emission model operation, and the power carbon emission model operation is re-executed.

[0039] As an improvement of the above scheme, the updating of the current new vector comprises:

[0040] updating the current new vector according to a preset vector updating formula to obtain an updated new vector, wherein the vector updating formula is specifically:

[0041]

[0042] wherein d1, d2, dn, rand and φ are random numbers between 0 and 1; d1 is used to determine which updating strategy is selected; X best is the position of the optimal individual in the current population, which usually corresponds to the optimal solution of the current carbon emission optimization target; dn is used to adjust the step length and affect the updating range of the vector; is the adjustment matrix of the ith individual in the lth iteration, which is used to control the migration degree of the individual to the optimal solution and is used to further refine the selection of the updating strategy; X w is the center position of the calculation, which is usually a weighted average of multiple individual positions, and is used to balance exploration and development; v1 and v2 are auxiliary variables that control the selection results in some conditional branches; rand is a random number; X a , X b , X c are the positions of three different individuals, which are used to calculate the population center or for random search to increase the diversity of search.

[0043] As an improvement of the above scheme, the verification condition satisfies the following condition:

[0044]

[0045] wherein, and are the minimum allowable power and the maximum allowable power, which limit the power output range of the system; j is the jth individual, representing the ith node; n is the total number of individuals, that is, the total number of nodes; is the position of the ith individual after the (l+1)th iteration; is the position of the ith individual after the lth iteration; is a vector position calculation value of the ith group after the lth iteration; is a practicality calculation value of the ith group after the lth iteration.

[0046] Correspondingly, an embodiment of the present application also provides an electric power regulation and control device based on a dynamic electric carbon factor, comprising a data acquisition module, a first model construction module, a second model construction module and a result generation module.

[0047] The data acquisition module is used to acquire the operation data of the electric power system.

[0048] The first model construction module is configured to construct an electricity-carbon factor model of each node of the power system based on the operation data.

[0049] The second model construction module is configured to construct a power carbon emission model based on each electricity-carbon factor model under a preset constraint condition.

[0050] The result generation module is configured to dynamically solve the power carbon emission model by a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the power system, and further regulate and control the power system based on the carbon emission degree capacity instruction.

[0051] Correspondingly, an embodiment of the present application further provides a computer terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the power regulation and control method based on dynamic electricity-carbon factors when executing the computer program.

[0052] Correspondingly, an embodiment of the present application further provides a computer readable storage medium, which comprises a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the power regulation and control method based on dynamic electricity-carbon factors when the computer program runs.

[0053] As can be seen from the above, the present application has the following beneficial effects:

[0054] The present application provides a power regulation and control method based on dynamic electricity-carbon factors, obtains operation data of a power system, constructs an electricity-carbon factor model of each node of the power system based on the operation data, constructs a power carbon emission model based on each electricity-carbon factor model under a preset constraint condition, solves the power carbon emission model by a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the power system, and further regulates and controls the power system based on the carbon emission degree capacity instruction. The present application constructs an electricity-carbon factor model based on operation data of a power system, takes the electricity-carbon factor model as an input of a power carbon emission model, can track carbon emission related to power generation, solves the power carbon emission model based on a vector weighted average algorithm, further optimizes regulation and control of a power grid based on a result of the solving, considers the influence of the power system on power regulation and control, greatly improves the accuracy of power regulation and control, and further can reduce the carbon emission amount of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a flowchart of the power regulation and control method based on dynamic electricity-carbon factors provided by an embodiment of the present application;

[0056] Figure 2 is a structural schematic diagram of a power regulation and control device based on a dynamic electric carbon factor provided by an embodiment of the present application.

[0057] Figure 3 is a structural schematic diagram of a terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0059] Embodiment one

[0060] Reference is made to Figure 1 , Figure 1 is a flowchart of a power regulation and control method based on a dynamic electric carbon factor provided by an embodiment of the present application, as shown in the figure, the embodiment includes steps 101 to 104, and each step is specifically as follows: Figure 1

[0061] Step 101: Obtain operation data of a power system.

[0062] In the embodiment,

[0063] Step 102: Based on the operation data, build an electric carbon factor model of each node of the power system.

[0064] In the embodiment, the operation data includes total power generation, total number of generators, injection power of the generators, total amount of electric energy of a power generation end connected to the node, number of power generation ends of the power system, active power of a load connected to the node, total number of loads connected to the node, node power flow injection connected to the current node, and total number of nodes connected to the current node; the step of building the electric carbon factor model of each node of the power system based on the operation data includes:

[0065] According to the total power generation, total number of generators, and injection power of the generators, substitute into a random load calculation formula to obtain random load data of each node of the power system; wherein the random load calculation formula satisfies the following conditions:

[0066]

[0067] In the formula, D i is random load data, G max is total power generation, g max is total number of generators, ​Pijis the injection power of the jth generator to the ith node, b is a proportional parameter, a i is a random parameter, a j is the proportion of the jth load to the total load, d is the number of nodes;

[0068] Each of the random load data is taken as input, combined with the total amount of power of the power generation end connected to the node, the number of power generation ends of the power system, the active power of the load connected to the node, the total number of loads connected to the node, the node power flow injection connected to the current node, and the total number of nodes connected to the current node, to construct an electric carbon factor model of each node of the power system; wherein the electric carbon factor model satisfies the following conditions:

[0069]

[0070] In the formula, g i and represent the total amount of power produced by the kth power generation end connected to the ith node in the power system, the total number of power generation ports included in the power system, and the corresponding electric carbon factor, respectively; and d i represent the active power of the load connected to the ith node and the total number of loads connected to the ith node, respectively; and n i represent the power flow injection of the jth node to the ith node and the total number of nodes connected to the ith node, respectively; is a diagonal component in the matrix, representing the total power flowing out of the ith node; P i j is a non-diagonal component, which is the power flow injected by the jth node to the ith node.

[0071] In a specific embodiment, the carbon emission factor can be calculated based on the active power flow between network nodes, and the current size and corresponding carbon emission intensity of the power input from the power generation end directly determine the carbon emission factor of the node.

[0072] It should be noted that the dynamic electric carbon factor is based on the calculation of carbon emission flow, which combines the data concentration of the data source area and the carbon emission factor of the regional power supply to accurately quantify and analyze the real-time carbon emission of the power system network, and gives users targeted carbon emission reduction electricity technology guidance.

[0073] It can be understood that based on the above electric carbon factor calculation model, the carbon emission of each node is analyzed. Because the power flow of each node changes is difficult to control, the load of each node is selected as an input variable, and the calculation results of the electric carbon factor of each node As output, a d-dimensional load input and n-dimensional electrical carbon factor output model is converted.

[0074] According to the maximum power generation of the system, the total load maximum range is set, in order to ensure that the input load data is random data, n+1 random numbers between 0 and 1 are set, and n random parameters are the proportion of load to total load a i One random parameter is the proportion parameter b (0<b<1) of the power generation transferred to the load area.

[0075] Step 103: Under the preset constraint condition, an electrical carbon emission model is constructed based on each of the electrical carbon factor models.

[0076] In this embodiment, the constraint condition satisfies the following condition:

[0077]

[0078] In the formula, f(X i ) represents the actual target calculation value of the i-th group of influence factors; F(X i ) represents the practicality calculation value of the i-th group; is the minimum power constraint of the system, representing the minimum power output allowed; ΔP C (k) is the power change at the k-th data update, representing the dynamic power load change of the system at the current time; is the maximum power constraint of the system, representing the maximum power output allowed; X i (j) represents the contribution value of the j-th influence factor in the i-th group to power, representing the power distribution of each influence factor in the system.

[0079] In this embodiment, the electrical carbon emission model satisfies the following condition:

[0080]

[0081] In the formula, represents the carbon emission capacity instruction obtained by the power data carbon emission degree at the k-th data update; is the carbon emission capacity instruction obtained by the power data carbon emission degree at the k+1-th data update; p i is an n-dimensional stable vector, representing the proportion of the i-th influence factor in the entire electrical carbon emission; represents the dynamic electrical carbon factor of the i-th node at the k-th update; and are the minimum and maximum allowed power of the i+1-th node, respectively, limiting the power output range of the system; and respectively, are the minimum and maximum power output constraints of the jth node; and respectively, are the minimum and maximum power output constraints of the i+2-nth node, which are used to calculate the contribution of different nodes in power allocation.

[0082] Step 104: dynamically solving the power carbon emission model by the vector weighted average algorithm to obtain the carbon emission degree capacity instruction of the power system, and then regulating and controlling the power system based on the carbon emission degree capacity instruction.

[0083] In a specific embodiment, the vector weighted average algorithm is a method of obtaining a weighted average of a set of vectors by searching different operators in the population in the search space to update the position of each generation vector in the iteration process. There are four stages respectively. Before the algorithm starts, the data is preprocessed, the total number of nodes in the power network is initialized as the population size, which is set as N, the maximum number of optimization iterations is set as kmax, and the initial solution set is X0∈R N×2(n-1) , and the evaluation of the minimum value of the total carbon emission of the power network is Two most important factors affecting search ability are initialized respectively: weighted average factor δ and scaling factor σ, which dynamically change according to the generation condition. The initial solution is set as X0∈R N×2(n-1) , and the initial fitness rate includes random initialization of the regulated source power, which is set as

[0084] It should be noted that the vector weighted average algorithm includes four stages:

[0085] The first stage: update rule stage, using the mean-based rule to update the vector position. First, extract from the weighted mean of a set of random vectors. At the same time, convergence acceleration is added as the rule of the update operator, which improves the global search ability. Two main initialization random parameters are weighted average factor δ and scaling factor σ, and an exploration function for increasing the amount of space search.

[0086] The second stage: vector merging stage, vector merging is to combine the previous vector and newly created vector and the random number ρ used in the merging process.

[0087] The third stage: regional exploration update, in order to prevent the selected influencing factors from falling into local optimal solution, the combined vector will be updated according to the given operation.

[0088] The fourth stage: vector refresh, according to the above process, after considering the optimal rule, the worst rule and the weighted average rule, a new vector will be created to update the vector.

[0089] In the embodiment, the power carbon emission model is solved by the vector weighted average algorithm to obtain the carbon emission degree capacity instruction of the power system, comprising:

[0090] The power carbon emission model operation is cyclically executed, and the carbon emission degree capacity instruction when the cycle threshold is reached is output;

[0091] The power carbon emission model operation is specifically:

[0092] A new vector is obtained; wherein the new vector satisfies the following conditions:

[0093]

[0094] wherein, is the current position of the solution, and is the position virtual random number, ρ is the position judgment random number, and r1 and r2 are weighted mean values;

[0095] Based on the preset exploration function, the weighted average factor and the scaling factor are calculated; wherein the exploration function satisfies the following conditions:

[0096] δ = 2r1x r - r

[0097] σ = 2r2x r - r

[0098]

[0099] wherein, r1 and r2 represent random numbers between 0 and 1; l is the latest iteration in the optimization process, r, r1 and r2 represent weighted mean values, k max is the total number of iterations; δ is the weighted average factor, and σ is the scaling factor;

[0100] The active power of the load connected with the node in the electric carbon factor model and the node power flow injection connected with the current node are taken as the initial vector, and the new vector and the random number are combined to obtain a merged vector;

[0101] Based on the weighted average factor, the scaling factor and the merged vector, the calculation of the power carbon emission model is performed to obtain the carbon emission degree capacity instruction;

[0102] It is judged whether the current carbon emission degree capacity instruction meets the preset instruction condition;

[0103] If not, a new new vector is reacquired as the input of the next power carbon emission model operation, and the power carbon emission model operation is re-executed;

[0104] If yes, the current new vector is updated, the updated new vector is verified based on a preset verification condition, the new vector that passes the verification is taken as an input of a next power carbon emission model operation, and the power carbon emission model operation is re-executed.

[0105] In the embodiment, the updating of the current new vector comprises:

[0106] The current new vector is updated according to a preset vector updating formula to obtain an updated new vector; wherein the vector updating formula is specifically:

[0107]

[0108] In the formula, d1, d2, dn, rand, and φ are random numbers between 0 and 1; d1 is used to determine which updating strategy is selected; X best is a position of an optimal individual in the current population, which usually corresponds to an optimal solution of the current carbon emission optimization target; dn is used to adjust a step length and affect the updating amplitude of the vector; is an adjustment matrix of the ith individual in the lth iteration, which is used to control the migration degree of the individual to the optimal solution and is used to further refine the selection of the updating strategy; X w is a center position of calculation, which is usually a weighted average of positions of multiple individuals, and is used to balance exploration and development; v1 and v2 are auxiliary variables, which control the selection results in some conditional branches; rand is a random number; X a , X b , X c are positions of three different individuals, which are used to calculate a population center or are used for random search to increase the diversity of search.

[0109] In the embodiment, the verification condition satisfies the following condition:

[0110]

[0111] In the formula, P i min and P i max are minimum and maximum allowed powers, which limit the power output range of the system; j is the jth individual, representing the ith node; n is the total number of individuals, i.e., the total number of nodes; is a position of the ith individual after the l+1th iteration; is a position of the ith individual after the lth iteration; is a vector position calculation value of the ith group after the lth iteration; is a practicability calculation value of the ith group after the lth iteration.

[0112] It can be understood that after each iteration is completed, the qualified vector will be updated for vector optimization update in subsequent iterations. If the current iteration number exceeds the maximum iteration number kmax, the optimization process will end. According to the recommended optimization result, the factor that has the most obvious influence on carbon emissions in the power data will be obtained, which is used to provide technical guidance for demand regulation of power users.

[0113] Referring to Figure 2 , Figure 2 is a structural schematic diagram of an electric power regulation and control device based on a dynamic electric carbon factor provided by an embodiment of the present application, comprising a data acquisition module 201, a first model construction module 202, a second model construction module 203 and a result generation module 204.

[0114] The data acquisition module is configured to acquire operation data of a power system.

[0115] The first model construction module is configured to construct an electric carbon factor model of each node of the power system based on the operation data.

[0116] The second model construction module is configured to construct a power carbon emission model based on each electric carbon factor model under a preset constraint condition.

[0117] The result generation module is configured to dynamically solve the power carbon emission model by a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the power system, and then regulate and control the power system based on the carbon emission degree capacity instruction.

[0118] It can be understood that the above system item embodiments correspond to the method item embodiments of the present application, and can realize the electric power regulation and control method based on the dynamic electric carbon factor provided by any one of the above method item embodiments of the present application.

[0119] The embodiment acquires operation data of a power system, constructs an electric carbon factor model of each node of the power system based on the operation data, constructs a power carbon emission model based on each electric carbon factor model under a preset constraint condition, solves the power carbon emission model by a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the power system, and then regulates and controls the power system based on the carbon emission degree capacity instruction. The present application constructs an electric carbon factor model based on operation data of a power system, takes the electric carbon factor model as an input of a power carbon emission model, can track carbon emission related to power generation, solves the power carbon emission model based on a vector weighted average algorithm, and then optimizes regulation and control of a power grid based on a result of the solving, considers the influence of the power system on electric power regulation and control, greatly improves the accuracy of electric power regulation and control, and then can reduce the carbon emission of the power system.

[0120] Embodiment Two

[0121] Referring to Figure 3 , Figure 3 is a schematic diagram of a terminal device structure according to an embodiment of the present application.

[0122] The terminal device of this embodiment includes a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. The processor 301 implements the steps of the power regulation and control method based on dynamic electricity-carbon factors in the embodiments when executing the computer program, such as all the steps of the power regulation and control method based on dynamic electricity-carbon factors shown in Figure 1 . Alternatively, the processor 301 implements the functions of the modules in the system embodiments when executing the computer program, such as all the modules of the power regulation and control apparatus based on dynamic electricity-carbon factors shown in Figure 2 .

[0123] In addition, the embodiment of the present application further provides a computer readable storage medium, which includes a stored computer program, wherein when the computer program is executed, the device where the computer readable storage medium is located performs the power regulation and control method based on dynamic electricity-carbon factors according to any one of the above embodiments.

[0124] Those skilled in the art can understand that the schematic diagram is only an example of the terminal device and does not limit the terminal device, which can include more or fewer components than the diagram, or combine certain components, or different components, for example, the terminal device can also include an input and output device, a network access device, a bus, etc.

[0125] The processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like, and the processor 301 is the control center of the terminal device, which connects all parts of the terminal device through various interfaces and lines.

[0126] The memory 302 can be used to store the computer programs and / or modules, and the processor 301 realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store operating systems, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0127] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of the above-mentioned various method embodiments can be realized. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or system capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0128] It should be noted that the system embodiments described above are only illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the system embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0129] The above is the preferred embodiment of the present application. It should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements are also considered within the scope of protection of the present application.

Claims

1. A power regulation control method based on dynamic electrical carbon factor, characterized in that, The method comprises the following steps: acquiring operation data of a power system; constructing an electric carbon factor model of each node of the power system based on the operation data; constructing a power carbon emission model based on each electric carbon factor model under a preset constraint condition; solving the power carbon emission model by a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the power system, and then regulating and controlling the power system based on the carbon emission degree capacity instruction; wherein the operation data comprises total power generation, total number of generators, injection power of the generators, total power of power generation ends connected to the nodes, number of power generation ends of the power system, active power of loads connected to the nodes, total number of loads connected to the nodes, node power flow injection connected to the current nodes, and total number of nodes connected to the current nodes; the electric carbon factor model of each node of the power system is constructed based on the operation data, which comprises the following steps: obtaining random load data of each node of the power system by substituting the total power generation, total number of generators, and injection power of the generators into a random load calculation formula; wherein the random load calculation formula satisfies the following conditions: where D i is random load data, G max is total power generation, g max is the total number of generators, is the injection power of the jth generator to the ith node, b is a proportional parameter, a i is a random parameter, a j is the proportion of the jth load to the total load, d is the number of nodes, and n is the number of system nodes; constructing the electric carbon factor model of each node of the power system by taking each random load data as input, combining the total power of power generation ends connected to the nodes, number of power generation ends of the power system, active power of loads connected to the nodes, total number of loads connected to the nodes, node power flow injection connected to the current nodes, and total number of nodes connected to the current nodes; wherein the electric carbon factor model satisfies the following conditions: wherein, g i and respectively represent the total amount of power produced by the kth power generation terminal connected to the ith node, the total number of power generation terminals included in the power system, and the corresponding carbon factor of electricity; and d i respectively represent the active power of the load connected to the ith node and the total number of loads connected to the ith node; and n i respectively represent the power flow injection from the jth node to the ith node and the total number of nodes connected to the ith node; is the diagonal component in the matrix, representing the total power flowing out of the ith node; is the non-diagonal component, representing the power flow injected from the jth node to the ith node.

2. The dynamic electrical carbon factor based power regulation control method of claim 1, wherein, the constraint condition satisfies the following conditions: where f(X i ) represents the actual target calculated value of the i-th group of influencing factors; F(X i ) represents the practicality calculated value of the i-th group; Pmin represents the minimum power constraint of the system, indicating the minimum power output allowed; ΔP C (k) represents the power change at the k-th data update, indicating the dynamic power load change of the system at the current time; Pmax represents the maximum power constraint of the system, indicating the maximum power output allowed; X i (j) represents the contribution value of the j-th influencing factor in the i-th group to the power, representing the power distribution of each influencing factor in the system.

3. The dynamic electrical carbon factor based power regulation control method of claim 2, wherein, the power carbon emission model satisfies the following conditions: In the formula, represents the carbon emission capacity instruction obtained by the power data carbon emission degree at the kth data update; is the carbon emission capacity instruction obtained by the power data carbon emission degree at the k+1th data update; i is an n-dimensional stable vector representing the proportion of the ith influencing factor in the entire power carbon emission; represents the dynamic electric carbon factor of the ith node at the kth update; and are the minimum and maximum allowed power of the i+1th node, respectively, limiting the power output range of the system; and are the minimum and maximum power output constraints of the jth node, respectively; and are the minimum and maximum power output constraints of the i+2-nth node, respectively, used to calculate the contribution of different nodes in power distribution.

4. The dynamic electrical carbon factor based power regulation control method of claim 1, wherein, the power carbon emission model is solved by the vector weighted average algorithm to obtain the carbon emission degree capacity instruction of the power system, which comprises the following steps: performing the power carbon emission model operation in a loop, and outputting the carbon emission degree capacity instruction when the number of loop times reaches a loop threshold value; wherein the power carbon emission model operation specifically comprises the following steps: obtaining a newly created vector; wherein the newly created vector satisfies the following conditions: wherein is the current position of the solution, and is the position virtual random number, p is the position judgment random number, and r1 and r2 are weighted mean values. calculating a weighted average factor and a scaling factor based on a preset exploration function; wherein the exploration function satisfies the following conditions: δ=2r1×r-r σ=2r2×r-r where r1and r2represent random numbers between 0 and 1; l is the latest iteration in the optimization process, r, r1and r2represent weighted mean values, k max is the total number of iterations; δ is the weighted average factor, and σ is the scaling factor; combining the active power of loads connected to the nodes in the electric carbon factor model and the node power flow injection connected to the current nodes with the newly created vector and a random number to obtain a combined vector; calculating the power carbon emission model based on the weighted average factor, the scaling factor, and the combined vector to obtain the carbon emission degree capacity instruction; judging whether the current carbon emission degree capacity instruction meets a preset instruction condition; if not, a new newly created vector is obtained as input of the next power carbon emission model operation, and the power carbon emission model operation is performed again; if yes, the current newly created vector is updated, the updated newly created vector is verified based on a preset verification condition, and the verified newly created vector is taken as input of the next power carbon emission model operation, and the power carbon emission model operation is performed again.

5. The dynamic electrical carbon factor based power regulation control method of claim 4, wherein, the updated newly created vector comprises the following steps: Updating the current new vector according to a preset vector updating formula to obtain an updated new vector; wherein the vector updating formula is specifically: In the formula, d1, d2, dn, rand, and φ are random numbers between 0 and 1; d1 is used to determine which update strategy is selected; X best is the position of the optimal individual in the current population, and the optimal solution corresponding to the current carbon emission optimization target; dn is used to adjust the step size and affect the update amplitude of the vector; is the adjustment matrix of the ith individual in the lth iteration, which is used to control the migration degree of the individual to the optimal solution, and is used to further refine the selection of the update strategy; X w is the center position of the calculation, which is the weighted average of the positions of multiple individuals, and is used to balance exploration and development; v1 and v2 are auxiliary variables that control the selection results in some conditional branches; rand is a random number; X a , X b , X c are the positions of three different individuals, which are used to calculate the population center or for random search, increasing the diversity of the search.

6. The dynamic electrical carbon factor based power regulation control method of claim 5, wherein, The verification condition satisfies the following conditions: where P i min and P i max Pmin and Pmax are the minimum and maximum allowed power, which limit the power output range of the system; j is the jth individual, i represents the ith node; n is the total number of individuals, i.e. the total number of nodes; is the position of the ith individual after the (l+1)th iteration; is the position of the ith individual after the lth iteration; is the vector position calculation value of the ith group after the lth iteration; is the utility calculation value of the ith group after the lth iteration.

7. A power regulation control device based on dynamic electrical carbon factor, characterized by, The method comprises the following steps: The method comprises a data acquisition module, a first model construction module, a second model construction module and a result generation module; The data acquisition module is configured to acquire operation data of the power system; The first model construction module is configured to construct an electric carbon factor model of each node of the power system based on the operation data; The second model construction module is configured to construct a power carbon emission model based on each electric carbon factor model under a preset constraint condition; The result generation module is configured to dynamically solve the power carbon emission model by a vector weighted average algorithm to obtain a carbon emission degree capacity instruction of the power system, and further regulate and control the power system based on the carbon emission degree capacity instruction; The operation data comprises total power generation, total number of generators, injected power of the generators, total amount of electric energy of a power generation end connected to the node, number of power generation ends of the power system, active power of a load connected to the node, total number of loads connected to the node, node power flow injection connected to the current node, and total number of nodes connected to the current node; the first model construction module is configured to construct an electric carbon factor model of each node of the power system based on the operation data, comprising: According to the total power generation, total number of generators and injected power of the generators, the random load calculation formula is substituted to obtain random load data of each node of the power system; wherein the random load calculation formula satisfies the following conditions: where D i is random load data, G max is total power generation, g max is the total number of generators, is the injection power of the jth generator to the ith node, b is a proportional parameter, a i is a random parameter, a j is the proportion of the jth load to the total load, d is the number of nodes, and n is the number of system nodes; Each random load data is taken as input, and the electric carbon factor model of each node of the power system is constructed in combination with the total amount of electric energy of the power generation end connected to the node, the number of power generation ends of the power system, the active power of the load connected to the node, the total number of loads connected to the node, the node power flow injection connected to the current node, and the total number of nodes connected to the current node; wherein the electric carbon factor model satisfies the following conditions: wherein, g i and respectively represent the total amount of power produced by the kth power generation terminal connected to the ith node, the total number of power generation terminals included in the power system, and the corresponding carbon factor of electricity; and d i respectively represent the active power of the load connected to the ith node and the total number of loads connected to the ith node; and n i respectively represent the power flow injection from the jth node to the ith node and the total number of nodes connected to the ith node; is the diagonal component in the matrix, representing the total power flowing out of the ith node; is the non-diagonal component, representing the power flow injected from the jth node to the ith node.

8. A computer terminal device, characterized by The computer readable storage medium comprises a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the power regulation and control method based on the dynamic electric carbon factor.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the power regulation and control method based on the dynamic electric carbon factor.

Images