Power system multi-scale electricity-carbon energy efficiency optimization calculation method and device based on photovoltaic power generation cluster mode

Abstract

The present invention relates to the technical field of power information. Disclosed are a power system multi-scale electricity-carbon energy efficiency optimization calculation method and device based on a photovoltaic power generation cluster mode, mainly aiming to improve the multi-scale electricity-carbon energy efficiency optimization calculation accuracy of a power system. The method comprises: first designing an improved power system carbon flow calculation model taking into account a photovoltaic cluster ancillary service, equivalently converting a system power lossy transmission electric network into a lossless electric network, and calculating a carbon flow of a power system taking into account a photovoltaic cluster reactive-power ancillary service; then, designing a power system low-carbon operation decision-making system based on electricity-carbon energy efficiency coupling, and calculating the electric quantity and carbon flow relevance of the power system taking into account the effect of the photovoltaic cluster ancillary service, wherein an output result is changed in a range of 0-1, and the greater a value is, the stronger the relevance of the electricity-carbon energy efficiency is; designing a DEA three-stage low-carbon operation decision-making method based on input-output dynamic feedback, identifying the low-carbon operation efficiency of the system, and eliminating the effect of external environment variables on the system operation efficiency; finally, designing a photovoltaic cluster division method taking into account the carbon flow and the energy efficiency of the power system, and analyzing the effect of the photovoltaic cluster ancillary service on the carbon flow and the energy efficiency of the power system; and calculating the contribution degree of a photovoltaic cluster to the carbon reduction and efficiency improvement of the system to achieve power system multi-scale carbon flow energy efficiency optimization. The present invention is suitable for optimization calculation of the power system multi-scale electricity-carbon energy efficiency.

Description

A multi-scale electricity-carbon energy efficiency optimization calculation method and device for power systems based on photovoltaic power generation cluster mode Technical Field

[0001] The present invention relates to the field of electric power information technology, and in particular to a method and device for optimizing and calculating the multi-scale electricity-carbon energy efficiency of an electric power system based on a photovoltaic power generation cluster mode. Background Art

[0002] Building a new power system is an effective way to achieve energy transition and achieve the "dual carbon" goals. Photovoltaic power generation has become the mainstay of renewable energy generation and a key tool for reducing carbon emissions in the power system. Photovoltaic clusters can address the challenges of individual PV power plants, including limited flexibility, risk resilience, and support capacity.

[0003] Based on the control capacity, carbon flow, energy efficiency and other indicators of photovoltaic power stations, a photovoltaic cluster division method that couples carbon flow and energy efficiency is constructed. Combined with the carbon flow and energy efficiency path coefficients, the system operation carbon flow and energy efficiency are analyzed, and the photovoltaic cluster with the greatest impact on the system carbon flow and energy efficiency is determined. The power system operation control of layered clusters is studied, and a control strategy for the coordination of multiple photovoltaic clusters is formulated. Various types of photovoltaic resources are mobilized to participate in the system's deep carbon reduction and efficiency improvement, providing key technical support and institutional mechanism guarantees for the large-scale development and utilization of renewable energy, and helping to achieve the "dual carbon" goals.

[0004] Summary of the Invention

[0005] The present invention provides a method and device for optimizing the calculation of multi-scale electricity-carbon energy efficiency of a power system based on a photovoltaic power generation cluster mode, which mainly aims to improve the efficiency of the calculation of multi-scale electricity-carbon energy efficiency of the power system.

[0006] According to a first aspect of the present invention, a multi-scale electricity-carbon energy efficiency optimization calculation method for a power system based on a photovoltaic power generation cluster mode includes:

[0007] Obtaining carbon flows in the power system taking into account ancillary services provided by photovoltaic clusters.

[0008] The system power lossy transmission network is converted into a lossless network, and the carbon flow of the power system taking into account the reactive auxiliary services of the photovoltaic cluster is calculated and obtained.

[0009] Design a low-carbon operation decision-making system for the power system based on electricity-carbon energy efficiency coupling, and calculate and obtain the correlation between the electrical quantity and carbon flow of the power system for photovoltaic cluster auxiliary services.

[0010] Design a photovoltaic cluster partitioning method that considers power system carbon flow and energy efficiency, analyze the impact of photovoltaic cluster ancillary services on power system carbon flow and energy efficiency, calculate and obtain the contribution of photovoltaic clusters to system carbon reduction and efficiency improvement, and achieve multi-scale carbon flow and energy efficiency optimization of the power system.

[0011] According to a second aspect of the present invention, there is provided a multi-scale electricity-carbon energy efficiency optimization calculation device for a power system based on a photovoltaic power generation cluster mode, comprising:

[0012] The device comprises: a connection module, an acquisition module, and a display module.

[0013] The connection module is used for information connection, interaction and calculation with the power system.

[0014] This acquisition module is used to obtain grid topology data, load data, photovoltaic power station data, obtain the carbon flow of the power system taking into account photovoltaic cluster auxiliary services, lossless power network conversion results, obtain low-carbon operation decision results of the power system based on electricity-carbon energy efficiency coupling, obtain the correlation results of the power system electrical quantity and carbon flow of photovoltaic cluster auxiliary services, obtain the photovoltaic cluster division results taking into account the carbon flow and energy efficiency of the power system, obtain the contribution of the photovoltaic cluster to the system carbon reduction and efficiency improvement, and the multi-scale carbon flow energy efficiency optimization results of the power system.

[0015] The display module is used to send the multi-scale electricity-carbon energy efficiency optimization calculation results of the power system in the photovoltaic power generation cluster mode to a display terminal for display.

[0016] According to a third aspect of the present invention, a multi-scale electricity-carbon energy efficiency optimization calculation device for a power system based on a photovoltaic power generation cluster mode implements the following steps:

[0017] Obtaining carbon flows in the power system considering ancillary services provided by photovoltaic clusters.

[0018] Based on the multi-scale operation scenarios of the power system considering photovoltaic cluster ancillary services, three operation scenarios of photovoltaic inverters and SVG devices are designed according to the light intensity. The dynamic carbon flow changes of the power system before and after the 24-hour photovoltaic cluster division are calculated and obtained, and the supporting role of photovoltaic cluster ancillary services for the low-carbon operation of the system is analyzed.

[0019] The system power is equivalently converted from a lossy transmission network to a lossless network, and the network loss generated by the operation of the power grid is distributed to the load of each node. Combined with the unit power carbon emission intensity of different generator sets, the power vector matrix of each unit is constructed to measure the carbon emission flow corresponding to the system dynamic network loss, realize accurate calculation of carbon emissions and obtain carbon indicators of each link of the power system, effectively track the carbon emission footprint of the power system, and realize the equivalent conversion of lossy and lossless networks.

[0020] Obtain the carbon flow of the power system taking into account the reactive auxiliary service of the photovoltaic cluster, consider all branch flow distribution matrices, unit injection matrices, node flux matrices, carbon emission factors, node real-time load values, and carbon emission intensity matrices of the generator units in the equivalent lossless network of the photovoltaic power generation cluster auxiliary service, calculate and obtain the carbon potential matrix of each node, and combine the equivalent node load in the lossless network to calculate and obtain the load carbon flow rate and dynamic carbon emissions.

[0021] Obtain a low-carbon operation decision-making method for power systems based on electricity-carbon energy efficiency coupling.

[0022] Calculate and obtain the correlation between electrical quantities and carbon flows in the power system based on photovoltaic cluster ancillary services. The output ranges from 0 to 1, with larger values ​​indicating a stronger correlation between electricity and carbon efficiency. Based on this electricity and carbon efficiency index, a low-carbon operation efficiency indicator system for the power system is constructed, providing a basis for identifying low-carbon operation efficiency and making decisions about low-carbon operations.

[0023] A three-stage DEA low-carbon operation decision-making method based on input-output dynamic feedback is designed, including identification of system low-carbon operation efficiency, elimination of the impact of external environmental variables on system operation efficiency, and system low-carbon operation analysis based on DEA dynamic cycle feedback.

[0024] Obtain photovoltaic clusters that take into account carbon flows and energy efficiency of the power system.

[0025] Cluster division indicators include four aspects: photovoltaic power generation system control capacity, net load carbon flow rate, node carbon potential, and node energy efficiency. The photovoltaic power generation system control capacity includes the upper and lower limits of the photovoltaic power generation system's reactive power regulation capability, the photovoltaic inverter's grid-connected capacity, and the upper limit of the photovoltaic active power regulation capability. The node net load is expressed as the difference between the node load correction value in the lossless equivalent network and the output of the connected photovoltaic system. The net load carbon flow rate is the product of the net load value and the carbon potential of the node. Photovoltaic systems with similar node carbon potentials are divided into a cluster. By balancing the node carbon potential, the photovoltaic cluster can achieve a balanced distribution of carbon flows within the power system, effectively avoiding an imbalance in carbon emissions. Photovoltaic power generation systems with similar node energy efficiency are divided into a photovoltaic cluster. A full understanding of the energy efficiency of each node in the cluster is achieved to assist in power system energy planning and improve the overall energy utilization efficiency of the photovoltaic cluster.

[0026] Node similarity matrix,PV access nodes within the same cluster should have similar operating characteristics.,The similarity of the operating characteristics between PV nodes is reflected,by calculating and obtaining the similarity of each node indicator.

[0027] The cluster division method uses the Fast Unfolding clustering algorithm to divide the clusters. The modularity function is used as the basis for cluster division. The larger the modularity function value Q is, the more reasonable the cluster division result is.

[0028] Analyze the impact of photovoltaic cluster ancillary services on the carbon flow and energy efficiency of the power system. Calculate and obtain the contribution of photovoltaic clusters to system carbon reduction and efficiency improvement, and achieve multi-scale carbon flow and energy efficiency optimization of the power system.

[0029] Obtain the contribution of the photovoltaic cluster to system carbon reduction and efficiency improvement.

[0030] The structural equation model (SEM) was used to analyze the impact of photovoltaic cluster ancillary services on system carbon flow, and the indirect impact of photovoltaic ancillary services on system carbon flow and energy efficiency through other variables was obtained.

[0031] By mining and analyzing the dynamic changes in the carbon flow path coefficient, the total energy efficiency path coefficient, and the system load curve based on the system's 24-hour operating data, we can clarify the supporting mechanism of photovoltaic cluster auxiliary services on the system's carbon flow and energy efficiency, as well as the contribution of each photovoltaic cluster to the system's carbon reduction and efficiency improvement. At the same time, we can clearly determine the adjustment amount of photovoltaic auxiliary services to improve the system's low-carbon and high-efficiency operation capabilities.

[0032] The present invention provides a multi-scale electricity-to-carbon energy efficiency optimization calculation method and device for a power system based on a photovoltaic power generation cluster model. Compared with current power system energy efficiency calculation and evaluation methods, this method can accurately measure the carbon metering of the power system by obtaining the dynamic carbon flow of the power system that takes into account photovoltaic cluster ancillary services. It also obtains the three-stage electricity-to-carbon energy efficiency of DEA based on input-output dynamic feedback, eliminating the impact of environmental or random errors on the accuracy of the system's low-carbon operation decisions. It also obtains the contribution of the photovoltaic power generation cluster coupled with carbon flow and energy efficiency to the system's carbon reduction and efficiency improvement, clarifying the impact mechanism of photovoltaic cluster ancillary services on the carbon flow and energy efficiency of the power system. This invention reduces the difficulty of calculating electricity-to-carbon energy efficiency, thereby improving the efficiency of electricity-to-carbon energy efficiency calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are provided to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0034] FIG1 shows a flow chart of a multi-scale electricity-carbon energy efficiency optimization calculation method for a power system based on a photovoltaic power generation cluster mode provided by an embodiment of the present invention;

[0035] FIG2 shows a schematic structural diagram of a multi-scale electricity-carbon energy efficiency optimization calculation device for a power system based on a photovoltaic power generation cluster mode provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless there is a conflict, the features in the accompanying drawings and embodiments of this application can be combined with each other.

[0037] Currently, low-carbon power system operations focus on the "source side" of the power system, using optimized planning to reduce direct carbon emissions. This approach fails to consider the extent to which photovoltaic cluster ancillary services can support carbon reduction in the power system. This results in low efficiency in calculating the power-to-carbon efficiency. Furthermore, errors in calculations increase with higher photovoltaic penetration rates, leading to lower accuracy in these calculations.

[0038] To solve the above problems, an embodiment of the present invention provides a multi-scale electricity-carbon energy efficiency optimization calculation method for a power system based on a photovoltaic power generation cluster mode, as shown in FIG1 . The method includes:

[0039] 101. Obtaining the carbon flow of the power system of photovoltaic cluster ancillary services includes the following steps.

[0040] Step 1: Obtain a multi-scale operation scenario of the power system taking into account photovoltaic cluster auxiliary services.

[0041] Specifically, three operating scenarios of the photovoltaic inverter and SVG device in the system are considered. Scenario 1: From 18:00 to 7:00, there is no light intensity, the active output of the photovoltaic system is 0, and the remaining reactive capacity of the inverter reaches the maximum value. During this period, the inverter alone provides reactive support for the system; Scenario 2: From 9:00 to 16:00, the sunlight is strong, and the photovoltaic system operates at full power. During this period, the SVG device alone provides reactive support for the system; Scenario 3: From 7:00 to 9:00 and 16:00 to 18:00, the light intensity is weak. Assuming that photovoltaic power generation operates at 50% of the rated power, the inverter and SVG device cooperate to provide reactive support to the grid during this period. The total reactive support value of the photovoltaic power generation system is Q, and its active output value is the ratio p of the total reactive support value to the phase angle tangent value of the photovoltaic power generation system access node. pv =Q / tanθ, and respectively calculate and obtain the dynamic carbon flow changes of the power system before and after the 24-hour PV cluster division based on the above three scenarios, and obtain the supporting effect of the PV cluster auxiliary service on the system carbon flow and low-carbon operation.

[0042] Step 2: Equivalent conversion of the system power lossy transmission network to a lossless network.

[0043] In order to clarify the dynamic carbon emissions corresponding to the energy loss in the power system during energy consumption and power generation and transmission, the network losses generated by the operation of the power grid are allocated to the load of each node. Combined with the unit power carbon emission intensity of different generating units, the power vector matrix of each unit is constructed. The carbon emissions corresponding to the dynamic network loss of the system can be calculated, and the carbon emissions can be accurately calculated, and the carbon indicators of each link of the power system can be obtained, and the carbon emission footprint of the power system can be effectively tracked.

[0044] Specifically, the equivalent conversion method between lossy network and lossless network is as follows.

[0045] Equivalent power flow calculation for each node in the lossless network.

[0046] The system power flow of the lossy network is obtained through power flow calculation. At the same time, it is assumed that the N×N lossless network is equivalent to the actual lossy network, and the equivalent power flow of each node in the lossless network is obtained; according to the classical power flow tracking calculation method and its network topology characteristics, the node power flow matrix is ​​equal to the product of the unit injection power matrix and the downstream tracking matrix, as shown in Equations (1)-(2). G1 +P X1 =A u P g (1)

[0047] Where, P g P represents the active power flow vector of each node in the system after it is equivalent to a lossless network, kW (the units of active power flow and load are all kW); G1 is the generator output vector; P X1 =[p 1,pv ,…,p i,pv ] T A is the photovoltaic cluster output vector considering ancillary services; u is the n×n order downstream tracking matrix, P j-i is the active power flow from node j to node i in branch ji in the actual lossy network, P j is the active power flow through node j in the actual lossy network, is the set of nodes where active power flows into node i.

[0048] Calculation of equivalent power flow for each branch of lossless network.

[0049] Since the unit injection power is not affected by the network conversion, the unit injection matrix of the lossy network is equal to that of the lossless network. Therefore, the equivalent power flow of each node in the lossless network can be calculated and obtained. Taking branch ij as an example, since the line loss is relatively small, the power flow ratio of branch ij to node i in the lossless network is infinitely close to the power flow ratio of branch ij to node i in the lossy network. Therefore, the power flow of branch ij in the equivalent lossless network is the product of the ratio of the power flow of branch ij to node i in the lossy network and the power flow of node i in the lossless network, as shown in Equation (3).

[0050] is the active power flow from node i to node j in branch ij in the equivalent lossless network, P i is the active power flow through node i in the actual lossy network, is the active power flow through node i in the equivalent lossless network.

[0051] Calculation of equivalent load of nodes in lossless networks.

[0052] Taking node j as an example, according to the proportional principle, the ratio of the load of node j to the power flow of node i in the equivalent lossless network is equal to the ratio of the load of node j to the power flow of node i in the actual lossy network. Therefore, the equivalent load of node j in the lossless network is the product of the load of node j in the lossy network divided by the power flow of node i and the power flow of node i in the lossless network, as shown in Equation (4).

[0053] is the load correction value of node i at time t, P Li is the comprehensive load of node i in the actual lossy network.

[0054] Step 3: Obtain the carbon flow of the power system taking into account the reactive auxiliary services of the photovoltaic cluster.

[0055] Specifically, the power flow distribution matrix of all branches in the equivalent lossless network considering the auxiliary service of photovoltaic power generation cluster is P B , unit injection matrix P Z , node flux matrix P N , carbon emission intensity matrix E of different generator sets G , according to E N =(P N -P B T ) -1 P Z T E G Calculate and obtain the carbon potential matrix E of each node N , where the carbon potential of the i-th node is represented by e Ni ; Load carbon flow rate R Li R is the carbon emissions per unit time transferred from the power generation side to each node load, Li The node load value is multiplied by the carbon emission intensity of the node. According to the existing carbon flow theory, the carbon emission intensity of the node load electricity consumption is equal to the carbon potential of the node. Combined with the equivalent node load in the lossless network, Calculate the load carbon flow rate The system carbon emission factor η is the ratio of the carbon potential of all nodes in the system to the sum of the loads of all nodes. The carbon emission factor is multiplied by the sum of the real-time load values ​​of all nodes in the power system to obtain the dynamic carbon emissions of the system at a certain moment; according to the above method, the load carbon flow rate takes into account the impact of reactive auxiliary services and includes the carbon transfer caused by network losses, making the system carbon emission calculation more accurate.

[0056] 102. Obtaining a low-carbon operation decision-making system for the power system coupled with electricity and carbon energy efficiency includes the following steps.

[0057] Step 1: Obtain the correlation between the electrical quantity and carbon flow of the power system considering the auxiliary services of the photovoltaic cluster.

[0058] Specifically, the node carbon potential, load carbon flow rate, system dynamic carbon emission factor, and system carbon emissions are used as carbon flow indicators, while the system active power consumption, reactive power consumption, and line loss rate are used as core indicators of energy efficiency. The grey correlation analysis method is used to verify the coupling relationship between electricity-carbon quantity and energy efficiency. The active power consumption, reactive power consumption, and line loss rate are used as characteristic sequences (independent variables), and the dynamic carbon flow rate and system carbon emissions are used as parent sequences (dependent variables). Modeling and simulation are carried out, and the variables are adjusted cyclically. The output of the calculation is the correlation value, which varies in the range of 0-1. The larger the value, the stronger the correlation between electricity-carbon energy efficiency. Based on the electricity-carbon energy efficiency indicators, a low-carbon operation efficiency indicator system for the power system is constructed to provide a basis for identifying the system's low-carbon operation efficiency and making low-carbon operation decisions.

[0059] Step 2: Obtain a three-stage DEA low-carbon operation decision-making method based on input-output dynamic feedback.

[0060] Phase 1: Identify system low-carbon operational efficiencies.

[0061] The system's corresponding operating status every hour is used as the decision-making unit (DMU) of the DEA model. At the same time, the real-time carbon flow rate of the node or the system carbon emissions are used as the input indicators of the DEA model, and the functional consumption, non-functional consumption, and line loss rate are used as the output indicators of the DEA model. By inputting the input and output indicator data into the DEA model, the efficiency value, input relaxation value S-, and output relaxation value S+ of each decision-making unit can be calculated. If the input and output relaxation values ​​are both 0, it means that the decision-making unit has achieved optimal efficiency. If any relaxation value is greater than 0, it means that the decision-making unit is weakly effective, but there is still room for efficiency improvement.

[0062] Phase 2: Eliminate the impact of external environmental variables on system operational efficiency.

[0063] Operational efficiency is not only affected by the decision-making unit, but also by external environmental factors such as light intensity, ambient temperature, and random errors in the system input. Based on the first stage, environmental factors and random errors are used as independent variables, and the slack value of input indicators is used as the dependent variable. A regression equation based on the stochastic frontier method (SFA) is constructed to eliminate the impact of environmental factors and random errors on system efficiency, so that each decision-making unit is on a unified benchmark and the system operational efficiency is effectively determined.

[0064] The third stage: obtaining the system low-carbon operation efficiency of DEA dynamic cycle feedback.

[0065] If the system operating efficiency is not DEA-effective, it means that the system operating efficiency is unqualified. The third-stage DEA projection value analysis method can be used to determine the redundancy of input factors to determine the proportion of system input indicators that need to be adjusted. The system dynamic operating efficiency analysis can be repeated until the system operating efficiency value reaches the optimal value. At the same time, based on the law of changes in system operating efficiency and combined with the system electricity-carbon indicator data, the key links affecting the system's low-carbon operating efficiency are analyzed to lay the foundation for adjusting the system operation or photovoltaic cluster auxiliary service mode and achieving efficient decision-making.

[0066] 103. Obtain a photovoltaic cluster that takes into account the carbon flow and energy efficiency of the power system, analyze the impact of the photovoltaic cluster's ancillary services on the carbon flow and energy efficiency of the power system, calculate and obtain the photovoltaic cluster's contribution to system carbon reduction and efficiency improvement, and achieve multi-scale carbon flow and energy efficiency optimization of the power system. This process includes the following steps.

[0067] Step 1: Obtain cluster division indicators.

[0068] Obtain the control capacity of the photovoltaic power generation system.

[0069] Specifically, the regulation capacity reflects the regulation capability of the photovoltaic power generation system. Dividing photovoltaics with similar regulation capabilities into a cluster is conducive to ensuring node voltage safety. The regulation capacity is mainly divided into active regulation capacity and reactive regulation capacity. Each photovoltaic system in the cluster should have sufficient reactive regulation capacity to ensure reactive support in the event of an emergency in the power grid. When the grid voltage exceeds the upper limit, the photovoltaic reactive output can be reduced to adjust the grid voltage level; when the reactive regulation capacity exceeds the limit, the photovoltaic active output can be controlled to further ensure the safe operation of the power grid. Photovoltaic reactive and active regulation capacity Q j , P j As shown in formulas (5) and (6). 0≤P j ≤P j,max (6)

[0070] Where Q j,max , Q j,min are the upper and lower limits of the reactive power regulation capability of the photovoltaic power generation system, kVar; S j,max is the grid-connected capacity of the photovoltaic inverter, kVA; P j,max The upper limit is the photovoltaic active power regulation capability, and the lower limit is 0.

[0071] Get the net load carbon flow rate.

[0072] Specifically, in order to quantify the contribution of each photovoltaic power station to the carbon reduction of the power system, the photovoltaic power station is treated as a negative load. The net load of the node is expressed as the difference between the node load correction value in the lossless equivalent network and the output of the connected photovoltaic system. The net load carbon flow rate is the product of the net load value and the carbon potential of the node, as shown in Equation (7). The contribution of different photovoltaic power stations to the reduction of carbon emissions in the power system is obtained.

[0073] Where: P i,net Net load of grid node i, P i,pv Active power output of the PV system at grid node i.

[0074] Get the nodal carbon potential.

[0075] Specifically, PV systems with similar node carbon potentials are grouped together. By balancing the node carbon potentials, PV clusters can achieve a balanced distribution of carbon flows within the power system, effectively avoiding imbalances in carbon emissions. Maintaining the proper node carbon potential helps optimize the overall operation of the power system, ensuring that PV clusters not only provide clean energy but also develop in coordination with other energy sources, ultimately achieving the goal of a low-carbon power system.

[0076] Get node energy efficiency.

[0077] Specifically, node energy efficiency can directly reflect the energy utilization efficiency of the photovoltaic cluster. Photovoltaic power generation systems with similar node energy efficiency can be divided into a photovoltaic cluster. The energy efficiency of each node in the cluster can be fully understood to assist in power system energy planning and improve the overall energy utilization efficiency of the photovoltaic cluster.

[0078] Step 2: Get the node similarity matrix.

[0079] Specifically, the similarity of the operating characteristics of the photovoltaic nodes is reflected by calculating and obtaining the similarity of each node indicator. Different cluster division indicators have different units. All indicators need to be normalized before cluster division to ensure that their range is between 0 and 1, as shown in formula (8).

[0080] Where: x i,m ′ is the mth index value of the i-th node after normalization, x i,m is the mth index value of the i-th node, max(x i,m ) is the maximum value of the mth index of node i, min(x i,m ) is the minimum value among the m-th index of node i.

[0081] The calculation formula of the similarity matrix of the photovoltaic power generation system is shown in formula (9).

[0082] Where, αm is the weight of the mth indicator, x i,m ′、x j,m ′ are the normalized values ​​of the mth index of the i-th and j-th nodes, respectively. Equation (9) quantifies the similarity between nodes in the photovoltaic power generation system. The larger the vector inner product, the more similar the nodes are.

[0083] Step 3: Obtain the optimal cluster division.

[0084] Specifically, the Fast Unfolding clustering algorithm is used to divide clusters. It uses the modularity function as the basis for cluster division. The larger the modularity function value Q, the more reasonable the cluster division result. The modularity function is shown in Equation (10).

[0085] Where Aij = 1 when there is a connecting branch between nodes i and j, and Aij = 0 when there is no connecting branch between nodes i and j; ki is the number of branches connected to node i; and m is the total number of branches in the network. If nodes i and j are in the same cluster, function = 1, otherwise = 0.

[0086] Based on the equivalent lossless network parameters, photovoltaic power generation system and load data, the photovoltaic cluster division index value is calculated and obtained, and the index value is normalized according to formula (8), and the similarity value is calculated and obtained using formula (9); each photovoltaic power node in the network is regarded as a cluster, and the modularity function value at this time is calculated and obtained using formula (10); the cluster where the node i in the network is located is randomly selected and merged with other clusters to form a new cluster, and the modularity function increment value ΔQ′ of the network is calculated and obtained respectively. If ΔQ′ max > 0, the cluster where node i is located is connected to ΔQ′ max The corresponding clusters are merged into new clusters, otherwise no new cluster structure is formed; the formed clusters are used as new nodes, and the similarity between nodes is the sum of the similarities between the two clusters. Repeat the previous step until the modularity function value Q′ of the entire network no longer increases. This is the optimal cluster division result.

[0087] Step 4: Analyze the impact of photovoltaic cluster auxiliary services on system carbon flow.

[0088] Specifically, six indicators, H1: auxiliary services, H2: node carbon potential, H3: node equivalent load, H4: node load carbon flow rate, H5: system dynamic carbon emission factor, and H6: system carbon emissions, are set as latent variables. First, the structural equation model (SEM) is used to test the indirect influence of variable H1 on the dependent variable H4 through the mediating variables H2 and H3, as well as the direct influence on H5 and H6. Secondly, confirmatory factor analysis is used to test the influence between the latent variables, and the path coefficients H1-2 between the latent variables can be calculated. , H1-3, H1-4, H2-4, H3-4, H1-5, H1-6; the direct impact of photovoltaic ancillary services on system carbon flow can be represented by the path coefficient between latent variables. On the other hand, the impact of photovoltaic ancillary services on system carbon flow through other variables can be obtained by multiplying the path coefficients between different latent variables (for example, the indirect impact of H1 on H5 through H2 is H1-2*H2-5); the total impact effect is the sum of direct and indirect effects, that is, the carbon flow path coefficient HT, which represents the total impact of photovoltaic ancillary services on system carbon flow.

[0089] Step 5: Analyze the impact of photovoltaic cluster auxiliary services on system energy efficiency.

[0090] Specifically, six indicators, including H1: auxiliary services, H4: node load carbon flow rate, H6: system carbon emissions, H7: active power consumption, H8: passive power consumption, and H9: line loss rate, are selected as latent variables. The calculation process and method are the same as those in step 3. The total energy efficiency path coefficient HN represents the impact of photovoltaic auxiliary services on system energy efficiency.

[0091] Step 6: Obtain the multi-scale carbon flow energy efficiency optimization decision of the power system.

[0092] Specifically, by mining and analyzing the dynamic relationship and patterns among HT, HN and the system load curve based on the system's 24-hour operating data, we can clarify the supporting mechanism of photovoltaic cluster auxiliary services on the system's carbon flow and energy efficiency, as well as the contribution of each photovoltaic cluster to the system's carbon reduction and efficiency improvement. At the same time, we can clearly determine the adjustment amount of photovoltaic auxiliary services to improve the system's low-carbon and efficient operation capabilities, laying the foundation for achieving efficient system decision-making.

[0093] Furthermore, as a specific implementation of the method described in Figure 1, an embodiment of the present application provides a multi-scale electricity-carbon energy efficiency optimization calculation device for a power system based on a photovoltaic power generation cluster mode. As shown in Figure 2, the device includes: a connection module 21, an acquisition module 22, and a display module 23.

[0094] The connection module 21 is used for information connection, interaction and calculation with the power system.

[0095] The acquisition module 22 is used to obtain grid topology data, load data, photovoltaic power station data, obtain the carbon flow of the power system taking into account photovoltaic cluster auxiliary services, lossless power network conversion results, obtain the low-carbon operation decision results of the power system based on electricity-carbon energy efficiency coupling, obtain the correlation results of the power system electrical quantity and carbon flow of photovoltaic cluster auxiliary services, obtain the photovoltaic cluster division results taking into account the carbon flow and energy efficiency of the power system, obtain the contribution of the photovoltaic cluster to the system carbon reduction and efficiency improvement, and the multi-scale carbon flow energy efficiency optimization results of the power system.

[0096] The display module 23 is used to send the multi-scale electricity-carbon energy efficiency optimization calculation results of the power system in the photovoltaic power generation cluster mode to the display terminal for display.

[0097] Those skilled in the art may understand the accompanying drawings as schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application.

[0098] Those skilled in the art can understand the modules in the implementation scenario device as being distributed in the device according to the implementation scenario description, and one or more devices different from this implementation scenario can also be changed accordingly. The modules of the above implementation scenario can be combined into one module or further divided into multiple sub-modules.

[0099] The above application serial numbers are for description only and do not represent the advantages or disadvantages of the implementation scenarios.

[0100] The above disclosure is only a specific implementation scenario of the present application. However, the present application is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A multi-scale electricity-carbon energy efficiency optimization calculation method for a power system based on a photovoltaic power generation cluster mode, the characteristics of which include: Design an improved model for calculating carbon flow in the power system taking into account the auxiliary services of photovoltaic clusters. Convert the power lossy transmission network of the system into a lossless network, calculate and obtain the carbon flow of the power system taking into account the reactive auxiliary services of photovoltaic clusters; Design a low-carbon operation decision-making system for the power system based on electricity-carbon energy efficiency coupling, calculate and obtain the correlation between the electrical quantity and carbon flow of the power system for photovoltaic cluster auxiliary services; Design a photovoltaic cluster division method that takes into account the carbon flow and energy efficiency of the power system, and analyze the impact of photovoltaic cluster auxiliary services on the carbon flow and energy efficiency of the power system. Calculate and obtain the contribution of photovoltaic clusters to system carbon reduction and efficiency improvement, and achieve multi-scale carbon flow energy efficiency optimization of the power system.

2. According to the method of claim 1, the improved model for calculating carbon flow in the power system of photovoltaic cluster auxiliary services comprises: Design multi-scale operation scenarios of the power system considering photovoltaic cluster auxiliary services, design three operation scenarios of photovoltaic inverters and SVG devices according to light intensity, calculate and obtain the dynamic carbon flow changes of the power system before and after the 24-hour photovoltaic cluster division, and analyze the supporting role of photovoltaic cluster auxiliary services for the low-carbon operation of the system; The equivalent conversion of the system power lossy transmission network to the lossless network, the network loss generated by the operation of the power grid is allocated to each node load, and the power vector matrix of each unit is constructed in combination with the unit power carbon emission intensity of different generator sets. The carbon emissions generated by the dynamic network loss under different paths of the system can be calculated, and the carbon flow of the system can be accurately calculated and the carbon indicators of each link of the power system can be obtained. The carbon emission footprint of the power system can be tracked to achieve the equivalent conversion of lossy network and lossless network; Carbon flow calculation of power system taking into account the reactive auxiliary service of photovoltaic cluster, including all branch flow distribution matrix, unit injection matrix, node flux matrix, carbon emission factor, node real-time load value, and carbon emission of generator set in the equivalent lossless network taking into account the auxiliary service of photovoltaic power generation cluster The intensity matrix is ​​used to calculate and obtain the carbon potential matrix of each node, and combined with the equivalent node load in the lossless network, the load carbon flow rate and dynamic carbon emissions are calculated and obtained.

3. The method according to claim 1, characterized in that The low-carbon operation decision of the power system based on electricity-carbon energy efficiency coupling includes: Calculate and obtain the correlation between the electrical quantity and carbon flow of the power system based on photovoltaic cluster auxiliary services. The output results vary in the range of 0-1, and the larger the value, the stronger the correlation between electricity and carbon energy efficiency. Construct a low-carbon operation efficiency index system for the power system based on the electricity-carbon energy efficiency index to provide a basis for the identification of low-carbon operation efficiency of the system and low-carbon operation decision-making; A three-stage DEA low-carbon operation decision-making method based on input-output dynamic feedback is designed, including identification of system low-carbon operation efficiency, elimination of the impact of external environmental variables on system operation efficiency, and system low-carbon operation analysis based on DEA dynamic cycle feedback.

4. The method according to claim 1, characterized in that: The photovoltaic cluster division method considering carbon flow and energy efficiency of the power system includes: Cluster division indicators include four aspects: photovoltaic power generation system control capacity, net load carbon flow rate, node carbon potential, and node energy efficiency. The photovoltaic power generation system control capacity includes the upper and lower limits of the photovoltaic power generation system's reactive power regulation capability, the photovoltaic inverter grid-connected capacity, and the upper limit of the photovoltaic active power regulation capability. The node net load is expressed as the difference between the node load correction value and the output of the connected photovoltaic system in the lossless equivalent network, and the net load carbon flow rate is the product of the net load value and the node carbon potential. Photovoltaic systems with similar node carbon potential are divided into a cluster. Through the balance of node carbon potential, the photovoltaic cluster can achieve a balanced distribution of carbon flow within the power system, thereby effectively avoiding an unbalanced situation of carbon emissions. Photovoltaic power generation systems with similar node energy efficiency are divided into a photovoltaic cluster, and the energy efficiency of each node in the cluster is fully understood to assist in the energy planning of the power system and improve the overall energy utilization efficiency of the photovoltaic cluster; Node similarity matrix: the photovoltaic access nodes within the same cluster should have similar The similarity of the operating characteristics of the photovoltaic access nodes is reflected by calculating and obtaining the similarity of each node indicator; The cluster division method uses the Fast Unfolding clustering algorithm to divide the clusters, and the modularity function is used as the basis for cluster division. The larger the modularity function value Q is, the more reasonable the cluster division result is.

5. The method according to claim 1, characterized in that Obtain the impact of photovoltaic cluster auxiliary services on the carbon flow and energy efficiency of the power system, and calculate and obtain the contribution of photovoltaic clusters to system carbon reduction and efficiency improvement. Including: The structural equation model (SEM) is used to analyze the impact of photovoltaic cluster auxiliary services on system carbon flow, and the indirect impact of photovoltaic auxiliary services on system carbon flow and energy efficiency through other variables is obtained. Based on the system's 24-hour operation data, the dynamic changes in the carbon flow path coefficient, the total energy efficiency path coefficient and the system load curve are analyzed. This can clarify the supporting mechanism of photovoltaic cluster auxiliary services on the system's carbon flow and energy efficiency, as well as the contribution of each photovoltaic cluster to the system's carbon reduction and efficiency improvement. At the same time, the adjustment amount of photovoltaic auxiliary services to improve the system's low-carbon and high-efficiency operation capabilities can be clarified.

6. A multi-scale electricity-carbon energy efficiency optimization calculation device for a power system based on a photovoltaic power generation cluster mode, characterized in that: include: Connection module, used for information connection, interaction and calculation with the power system; An acquisition module is used to acquire grid topology data, load data, and photovoltaic power station data, to acquire the carbon flow of the power system taking into account photovoltaic cluster auxiliary services, lossless power network conversion results, to acquire the low-carbon operation decision results of the power system based on electricity-carbon energy efficiency coupling, to acquire the correlation results between the electrical quantity and carbon flow of the power system for photovoltaic cluster auxiliary services, to acquire the photovoltaic cluster division results taking into account the carbon flow and energy efficiency of the power system, to acquire the contribution of the photovoltaic cluster to the system carbon reduction and efficiency increase, and the multi-scale carbon flow energy efficiency optimization results of the power system; The display module is used to send the multi-scale electricity-carbon energy efficiency optimization calculation results of the power system in the photovoltaic power generation cluster mode to the display terminal for display.

Images