Method for constructing carbon assessment classification indicator system of zero-carbon power supply station taking carbon flow analysis into consideration

Abstract

The present invention relates to the technical field of carbon emission monitoring. Disclosed is a method for constructing a carbon assessment classification indicator system of a zero-carbon power supply station taking carbon flow analysis into consideration. The method comprises: acquiring an output result of a power network steady-state carbon flow model of a zero-carbon power supply station; on the basis of the output result and a goal of the zero-carbon power supply station, obtaining carbon assessment classification indicators of zero-carbon power supply; assigning weights to the carbon assessment classification indicators by means of a frequency statistics method; and integrating weighted carbon assessment classification indicators, so as to obtain a carbon assessment classification indicator system of the zero-carbon power supply station. In the present invention, multidimensional comprehensive evaluation is performed taking into account carbon flow analysis, thereby assessing the zero-carbon level of a power supply station more comprehensively and more accurately. Moreover, by means of carbon flow analysis, carbon flow and transformation processes within the power supply station can be precisely tracked, so as to assess the carbon emissions of the power supply station more accurately, and thus an assessment result is more comparable and operable.

Description

A method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission monitoring, in particular to a method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis. BACKGROUND

[0002] Carbon emissions are the main factors causing global climate change and energy crisis. At the same time, as the main participant in carbon emissions, the power industry urgently needs to reform "low carbonization". A zero-carbon power supply station is a micro power system that integrates new energy power generation, energy storage, charging piles, and power utilization facilities, and realizes all clean energy supply and zero-carbon operation within the power supply station. Such a power supply station is an important part of promoting the construction of a new power system and provides an effective implementation path for achieving the "double carbon" goal. For a zero-carbon power supply station, a carbon evaluation classification index system helps to measure its carbon emission level and evaluate the effectiveness of low-carbon measures, thereby promoting its development in a lower-carbon and more environmentally friendly direction.

[0003] The current carbon evaluation classification index system mainly analyzes carbon evaluation through relevant indicators set and combined with indicator weights. Although this method can have reference value, it does not analyze carbon flow, which can easily cause the evaluation results of the evaluation system to be inaccurate.

[0004] Therefore, a method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis is needed.

[0005] SUMMARY

[0006] In view of the problem in the prior art that the carbon evaluation classification index system does not analyze carbon flow, resulting in inaccurate evaluation results, the present application provides a method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis, which can more comprehensively and accurately evaluate the carbon emission situation of a zero-carbon power supply station. The specific technical solutions are as follows:

[0007] A method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis, comprising:

[0008] obtaining an output result of a power network steady-state carbon flow model of the zero-carbon power supply station;

[0009] obtaining each carbon evaluation classification index of the zero-carbon power supply station according to the output result and a target of the zero-carbon power supply station;

[0010] weighting each carbon evaluation classification index by a frequency statistical method;

[0011] integrating the weighted carbon evaluation classification indexes to obtain a carbon evaluation classification index system of the zero-carbon power supply station.

[0012] Preferably, the output results of the power network steady-state carbon flow model of the zero-carbon power supply station include:

[0013] The calculated carbon flow rate data of each node, branch and unit of the power network steady-state carbon flow model are obtained.

[0014] The carbon flow rate data of each node, branch and unit are analyzed to obtain the results of the main source of carbon emissions, distribution characteristics and trends over time.

[0015] Preferably, each carbon evaluation classification index of the zero-carbon power supply includes clean energy generation index, terminal load power consumption index, system evaluation index, zero-carbon control platform index, carbon sink index, low-carbon atmosphere creation index, safety management index, and additional classification index.

[0016] Preferably, the weighting of each carbon evaluation classification index by the frequency statistical method includes:

[0017] The key secondary indicators of each carbon evaluation classification index are obtained.

[0018] The key secondary indicators of each carbon evaluation classification index are weighted by the frequency statistical method to obtain the weighted carbon evaluation classification index.

[0019] Preferably, the key evaluation indicators of each carbon evaluation classification index include:

[0020] The key secondary indicators of each carbon evaluation classification index are selected by the expert trust propagation-decision laboratory analysis method, and the selection results are:

[0021] The key secondary indicators of the clean energy generation index include photovoltaic power generation proportion, wind power generation proportion and energy storage proportion.

[0022] The key secondary indicators of the terminal load power consumption index include new energy vehicle proportion, two-way charging pile facility proportion and building direct current power consumption proportion.

[0023] The key secondary indicators of the system evaluation index include green building material application proportion and building energy saving rate.

[0024] The key secondary indicators of the zero-carbon control platform index include carbon flow analysis and calculation, and zero-carbon power station green electricity trading balance calculation.

[0025] The key secondary indicators of the carbon sink index include greening coverage and offsetting carbon emissions by purchasing certified voluntary emission reduction.

[0026] The key secondary indicators of the low-carbon atmosphere creation index include electrical equipment material recycling and wastewater green irrigation.

[0027] The key secondary indicators of the safety management class indicator include regular equipment maintenance;

[0028] The key secondary indicators of the additional classification indicators include green building certification and carbon neutral certification.

[0029] Preferably, the screening of the key secondary indicators from each carbon assessment classification indicator by the expert trust propagation-decision laboratory analysis method comprises:

[0030] Obtain the scores of all the secondary indicators of each carbon assessment classification indicator by multiple experts under preset influence degree scoring rules, and obtain multiple direct influence matrices corresponding to the number of experts according to the scoring results;

[0031] Obtain the weight results of the multiple experts;

[0032] Obtain a comprehensive influence matrix according to the multiple direct influence matrices and the weight results;

[0033] Screen the key secondary indicators from all the secondary indicators of each carbon assessment classification indicator through the calculation results of the comprehensive influence matrix.

[0034] Preferably, the screening of the key secondary indicators from all the secondary indicators of each carbon assessment classification indicator through the calculation results of the comprehensive influence matrix comprises:

[0035] By comparing the calculation results of the comprehensive influence matrix with preset influence factor thresholds, when the calculation result of a secondary indicator is greater than the influence factor threshold, the secondary indicator is screened as a key secondary indicator, otherwise, the secondary indicator is not a key secondary indicator.

[0036] Compared with the prior art, the beneficial effects of the present application are:

[0037] The construction method of the zero-carbon power supply station carbon assessment classification indicator system considering carbon flow analysis of the present application obtains the output results of the power network steady-state carbon flow model of the zero-carbon power supply station; obtains each carbon assessment classification indicator of the zero-carbon power supply station according to the output results and the target of the zero-carbon power supply station; weights each carbon assessment classification indicator by frequency statistics; integrates each carbon assessment classification indicator after weighting to obtain the zero-carbon power supply station carbon assessment classification indicator system. The present application combines carbon flow analysis to perform multi-dimensional comprehensive evaluation, more comprehensively and accurately assesses the zero-carbon level of the power supply station. At the same time, through carbon flow analysis, the flow and conversion process of carbon in the power supply station can be accurately tracked, so that the carbon emission situation of the power supply station can be more accurately evaluated, and the evaluation results are more comparable and operable. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference signs. In the drawings, the elements or parts are not necessarily drawn according to the actual proportions.

[0039] Fig. 1 is a flow chart of a method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis according to an embodiment of the present application.

[0040] Fig. 2 is a flow chart of another embodiment of a method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis according to an embodiment of the present application.

[0041] Fig. 3 is a schematic diagram of an energy station steady-state carbon flow model according to an embodiment of the present application.

[0042] Fig. 4 is a schematic diagram of the relationship between the carbon flow density of a power distribution network and the corresponding node carbon potential according to an embodiment of the present application.

[0043] Fig. 5 is a schematic diagram of the relationship between the carbon flow density of a gas distribution network and the corresponding node carbon potential according to an embodiment of the present application.

[0044] Fig. 6 is a schematic diagram of the relationship between the carbon flow density of a heat supply network and the corresponding node carbon potential according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of the present application.

[0046] It should be understood that, when used in the specification and the appended claims, the terms "comprise" and "include" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0047] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0048] It should be further understood that the term "and / or" used in the description and claims of the application means one or more of the associated listed items as well as all possible combinations of the items and includes these combinations.

[0049] The following embodiments refer to Figures 1 to 6.

[0050] The embodiment of the present application provides a construction method of a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis, comprising:

[0051] Step S1, obtaining an output result of a power network steady-state carbon flow model of a zero-carbon power supply station;

[0052] The power network steady-state carbon flow model in the embodiment is a model for analyzing and calculating the carbon emission flow in the building power system. Based on the power flow distribution of the power system, the carbon emission of each node and branch in the power system is quantified by introducing the concept of carbon flow, so as to realize the fine management of carbon emission of the power system.

[0053] Specifically, the output result of the power network steady-state carbon flow model of the zero-carbon power supply station comprises:

[0054] The carbon flow rate data of each node, branch and unit calculated by the power network steady-state carbon flow model are obtained.

[0055] The carbon flow rate data of each node, branch and unit are analyzed to obtain the results of the main source of carbon emission, distribution characteristics and change trend over time.

[0056] Step S2, obtaining each carbon evaluation classification index of zero-carbon power supply according to the output result and the target of the zero-carbon power supply station.

[0057] The output result of the power network steady-state carbon flow model includes the carbon flow rate data of each node, branch and unit, and the corresponding carbon emission data, and the target of the zero-carbon power supply station is to realize zero-carbon emission in the power supply process. The output data in the power network steady-state carbon flow model are combined with the target of the zero-carbon power supply station to obtain each carbon evaluation classification index of zero-carbon power supply.

[0058] Specifically, each carbon evaluation classification index of zero-carbon power supply includes clean energy generation index, terminal load power consumption index, system evaluation index, zero-carbon control platform index, carbon increment index, low-carbon atmosphere creation index, safety management index and additional classification index.

[0059] The reason for setting up the evaluation index of clean energy power generation: Clean energy power generation does not produce or produces very little greenhouse gas emissions, which plays a key role in promoting the transformation of the power industry to low-carbon and carbon-free. By setting up the clean energy power generation index, the target and effect of clean energy utilization in power supply can be clearly defined, which helps to guide the power industry to develop in a more environmentally friendly direction.

[0060] The reason for setting up the terminal load power consumption index: By setting up the terminal load power consumption index, the power supply can more accurately grasp the change rule of power load, so as to optimize the dispatching and operation of power grid. This helps to improve the efficiency of power grid operation, reduce energy loss and reduce operating costs.

[0061] The reason for setting up the building system evaluation index: Ensure that the building part is coordinated with the overall zero-carbon target and promote the low-carbon transformation of energy structure.

[0062] The reason for setting up the zero-carbon management platform evaluation index: It helps to establish a carbon emission management system and regulate the carbon emission behavior of power supply. Through real-time monitoring, data analysis and early warning functions, it can timely find out the abnormal situation of carbon emission and take effective measures to correct it, so as to ensure that the carbon emission of power supply meets the relevant standards and requirements.

[0063] The reason for setting up the carbon sequestration evaluation index: The core goal of zero-carbon power supply is to achieve zero or negative carbon emissions within the power supply. Carbon sequestration, as an effective measure to reduce carbon dioxide emissions, can increase the absorption and fixation of carbon dioxide through afforestation, forest management and vegetation restoration, thereby significantly reducing the concentration of greenhouse gases in the atmosphere.

[0064] The reason for setting up the low-carbon atmosphere creation evaluation index: The low-carbon atmosphere creation evaluation index can quantitatively evaluate the efforts and achievements of power supply in promoting the concept of green and low carbon. By setting specific evaluation indexes such as office power consumption reduction rate and green plant coverage rate, it can guide power supply to actively take measures to create a strong low-carbon cultural atmosphere, thereby improving the image and reputation of power supply in the society.

[0065] The reason for setting up the safety management index: Safety management is the foundation of the power industry, which is directly related to the stable operation of power supply. By setting up the safety management index, it can ensure that the power supply pursues zero carbon emission while also has high safety and reliability.

[0066] The reason for setting up the extra points index: The extra points index, as a positive incentive mechanism, can encourage power supply to pursue higher energy efficiency, technological innovation and environmental performance on the basis of achieving zero carbon emission target.

[0067] Step S3, weight each carbon evaluation classification index by frequency statistics method;

[0068] Specifically, the weighting of each carbon assessment classification index by frequency statistics includes:

[0069] Obtaining key secondary indicators of each carbon assessment classification index;

[0070] Weighting the key secondary indicators of each carbon assessment classification index by frequency statistics to obtain each carbon assessment classification index after weighting.

[0071] For each index, group all expert scoring values according to a certain group interval, and calculate the frequency of scoring in each group. The group median (or average) of the group with the maximum frequency is the maximum weight of the index. This method can reduce the influence of extreme scoring values and make the results more stable.

[0072] Frequency statistics mainly relies on the experience and subjective judgment of experts in the relevant field, and is a subjective weighting method. It believes that the essence of weight is the quantification of the relative importance of the evaluation index to the evaluation target.

[0073] Step S4, integrating each carbon assessment classification index after weighting to obtain a carbon assessment classification index system of zero-carbon power supply.

[0074] According to the carbon assessment classification index system of zero-carbon power supply obtained by fusion, the carbon emission level and green development level of zero-carbon power supply are comprehensively and accurately evaluated.

[0075] In this embodiment, the carbon flow rate data of each node, branch and unit calculated by the power network steady-state carbon flow model. In this embodiment, the relevant carbon flow rate is selected for reference explanation:

[0076] The construction of the power network steady-state carbon flow model obtains the topological structure information of the power network, including the connection relationship of nodes, branches, transformers and other elements; collects the carbon emission intensity, efficiency and other parameters of each unit, which may come from the unit manufacturer, historical statistical data or real-time monitoring system; obtains the load data of the power network, including the load size, load curve and other data of each node. In the steady state of the power grid, the power flow calculation is carried out to obtain the active power, reactive power and other power flow data of each branch. Based on the power flow distribution of the power system and the carbon emission characteristics of the unit, a carbon flow calculation model is established. Carbon flow depends on power flow, and its distribution is affected by factors such as power grid topology, operating state and unit carbon emission intensity. The carbon emission intensity of the unit is combined with the power flow data to calculate the carbon emission rate of each branch. The power flow tracking method is used to calculate the contribution of each unit to the system loss, and the carbon emission of the loss is allocated to each unit accordingly. At the same time, the carbon emission amount that the system load should bear is determined.

[0077] (1) Building-scale power network steady-state carbon flow model: When considering the line loss of power network, the line carbon flow rate represents the carbon flow passing through the branch per unit time with the active power flow. If the carbon emission generated by line loss is considered, the carbon flow rate is represented as follows:

[0078] wherein, and

[0079] According to the aforementioned PSP basic condition, the carbon potential of a node is determined by the carbon emission flow generated by the generator set connected to the node and the carbon emission flow from other nodes into the node, and is represented as follows:

[0080] wherein, NP and KP are the number of other nodes and the number of generator sets connected to node n in the power network, respectively; and is the active power of the kth generator set; is the carbon flow density of the jth branch connected to node n in the power network; is the carbon potential of the kth generator set connected to node n in the power network.

[0081] (2) Building-scale natural gas network steady-state carbon flow model: Natural gas is a primary energy source, so the carbon emission in the natural gas network is regarded as the potential carbon emission of natural gas. When considering the access of compressors to the natural gas pipeline network, the energy consumption of the compressors needs to be considered to cause additional carbon emissions, and the natural gas network line carbon flow rate is represented as follows:

[0082] wherein, and and gas is the natural gas flow and the natural gas flow consumed by the compressor of branch j in the natural gas network; q

[0083] Similar to the power network, the carbon potential of a node in the natural gas network is determined by the carbon emission flow generated by the upper natural gas gate station connected to the node and the carbon emission flow from other nodes into the node, and is represented as follows:

[0084] wherein, NG and KG are the number of other nodes and the number of upper natural gas gate stations connected to node n in the natural gas network, respectively; and and gas is the natural gas flow and the natural gas flow consumed by the compressor of branch j connected to node n in the natural gas network, and the input natural gas flow of the kth natural gas gate station. carbon flow density of the jth branch connected to node n of the natural gas network; carbon potential of the kth natural gas gate station connected to node n of the natural gas network.

[0085] (3) Steady-state carbon flow model of building-level heating network: The heating network is composed of heat sources, heat loads and supply and return water pipes. Heat is generated by heat sources, transported to loads through water circulation in the supply pipe, and finally returned to the heat source through the return pipe. Therefore, the heating network is different from the power network and the natural gas network in the physical layer. When analyzing the steady-state carbon flow model, the carbon emission flow rate of the supply and return pipes and the node carbon potential need to be considered respectively, and the pipe carbon flow rate loss corresponding to the heat power loss of the supply and return pipes also needs to be taken into account.

[0086] The heat power loss of the supply and return pipes of the heating network is represented as follows:

[0087] In the formula, and are the heat power losses of the jth supply and return pipe of the heating network; m j is the medium flow rate of the jth pipe; and are the temperatures at the first and last ends of the jth supply pipe, respectively; and are the temperatures at the first and last ends of the jth return pipe, respectively.

[0088] When the carbon emissions generated by pipe loss are taken into account, the carbon flow rate of the supply and return pipes of the heating network is represented as follows:

[0089] In the formula, and are the carbon emission flow rates of the jth supply and return pipe of the heating network; and are the node carbon potentials of the jth pipe; and are the heat power and heat power loss of the jth supply and return pipe, respectively.

[0090] Similarly, the carbon potential of the node of the heating network contains two parts of supply and return water, which is determined by the carbon emission flow generated by the heat source connected to the node and the carbon emission flow flowing into the node from other nodes, and is represented as follows:

[0091] In the formula, NH and KH are the number of other nodes and the number of heat sources connected to node n of the heating network, respectively; and The jth branch thermal power and thermal power loss of the water supply network connection node n, and the kth heat source output thermal power, respectively; The carbon flow density of the jth branch of the water supply network connection node n; The carbon potential of the kth heat source of the water supply network connection node n; And The jth branch thermal power and thermal power loss of the return water network connection node n, and the kth heat source output thermal power, respectively; The carbon flow density of the jth branch of the return water network connection node n; The carbon potential of the kth heat source of the return water network connection node n.

[0092] In actual engineering applications, regional heating systems are usually operated by energy stations and supply heat energy, so the heat supply network source node is the heat output node of the energy station, and the carbon potential and input carbon flow rate of this node are determined by the energy station steady-state carbon flow model, which is described in detail in the next section.

[0093] (4) Energy hub-based power supply station carbon flow distribution: In the energy station lossless carbon flow model, carbon emissions are transferred from one energy system to another with energy conversion, only considering the conversion and distribution of multi-energy carbon flow in devices, and not considering the loss of carbon flow in the energy station, i.e. in the building-level integrated energy system carbon emission responsibility allocation process, the energy station is not considered to bear the carbon emission responsibility.

[0094] Based on the energy station lossless carbon flow model, energy conversion devices are divided into two types: single-input-single-output conversion devices such as gas boilers, electric boilers, and refrigeration units, and single-input-multiple-output conversion devices such as combined heat and power units and combined cooling, heating and power units. In this lossless model, the total amount of carbon emission flow at the output end should be equal to the total amount of carbon emission flow at the input end, and the steady-state carbon flow models of the two types of devices are established as follows: in ·P in = e out ·P out

[0095] In the formula, e in and e out are the carbon potentials of the input and output ports of the single-input-single-output device, respectively; P in and P out are the active powers of the input and output, respectively.

[0096] In the formula, is the carbon potential of the input port of the single-input-multiple-output device; and are the carbon potentials of the multiple output ports; is the active power of the input port; and Pn, Pn, Pn are the active power of the multi-output port active power.

[0097] To describe the distribution of carbon emission flow at the output of single-input multi-output devices, the nodal carbon potential of different energy output ports is usually defined to be proportional to the efficiency of the corresponding energy.

[0098] where η I and η II are the energy conversion efficiencies of different energy conversion devices.

[0099] Classical calculation method of carbon flow model of building-level integrated energy system

[0100] (1) Carbon flow proportional sharing principle of building-level energy system: In order to explore the relationship between branch carbon flow rate R and corresponding nodal carbon potential e, the proportional sharing principle (PSP) is introduced as the basic condition: node n injects power from several incoming branches in a given proportion, then it will allocate power to each outgoing branch in the original proportion. PSP is usually applied to ideal lossless networks. In fact, for any given node n, regardless of line loss, the PSP it should follow is only related to the outgoing power of the incoming branch of the node and the incoming power of the outgoing branch.

[0101] PSP is also applicable to the carbon flow calculation model of building-level integrated energy system, and its physical meaning is: the carbon flow density ρ of all outgoing branch currents is independent of the branch, i.e. the carbon flow density ρ of the outgoing branch is equal to the carbon potential e of the node. The relationship between carbon flow density ρ and nodal carbon potential e is shown in Figure 2.

[0102] where ρ and ρ are the carbon flow densities of branch j in the power network, natural gas network and heat network, respectively. and R are the carbon flow rates of branch j in the power network, natural gas network and heat network, respectively. and P are the active powers of branch j in the power network, natural gas network and heat network, respectively. and e

[0103] ​(2) Building-level carbon flow matrix calculation method: the basic idea of the carbon flow matrix algorithm is to obtain the matrix representation of the system node carbon potential through the matrix representation of the power system power flow data, and then obtain the carbon emission flow rate and other "carbon element" data of each branch and key section of the system. The difficulty of the carbon flow matrix algorithm lies in constructing various matrices suitable for computer operation, and its advantage lies in the simple calculation method, which can obtain the global carbon emission flow distribution information of the system at one time, and its disadvantage is that when the system size increases, the complexity of matrix operation increases greatly, and the solving speed decreases.

[0104] The matrix calculation formula of the power system node carbon potential is as follows:

[0105] In the formula, is the power system node carbon potential matrix; is the node active power flow matrix; is the branch active power flow matrix; is the generator active power matrix; is the source end carbon emission intensity vector of the power system.

[0106] The power system unit-node carbon flow correlation matrix can distinguish the way of the power flow injected by the unit into the system from the node where the unit is located to the target node, and the matrix calculation formula is as follows:

[0107] In the formula, is the power system unit-node carbon flow correlation matrix; is the unit-load carbon flow correlation matrix; ξ m is an m-dimensional row vector, and all elements in the formula are 1.

[0108] Specifically, the key evaluation indexes of each carbon evaluation classification index include:

[0109] The key secondary indexes of each carbon evaluation classification index are screened out by the expert trust propagation-decision laboratory analysis method, and the screening includes the following steps:

[0110] Step S31, obtain multiple experts to score all secondary indexes of each carbon evaluation classification index under the preset influence degree scoring rule (as shown in Table 1), and obtain multiple direct influence matrices corresponding to the number of experts according to the scoring results;

[0111] The preset influence degree scoring rule is shown in Table 1, which is a unified scoring rule for quantifying the expert's judgment on the influence degree of each secondary indicator. This rule is based on a three-point scoring system. Invite multiple experts with relevant professional knowledge and experience to score all secondary indicators under each carbon assessment classification indicator according to the preset scoring rule. The score should reflect the expert's subjective judgment of the importance of each secondary indicator in the classification indicator. The scoring results of each expert will be used to construct a direct influence matrix. Each row and column of the matrix corresponds to a secondary indicator, and the elements in the matrix represent the influence degree of one secondary indicator on another. Since each expert will provide such a matrix, multiple direct influence matrices will be obtained, each corresponding to an expert's evaluation result.

[0112] Table 1 Influence degree scoring table

[0113] Step S32, obtaining the weight results of multiple experts;

[0114] The weight of an expert reflects their authority and credibility in the evaluation process. This can be determined in various ways, such as based on the expert's qualifications, experience, published works in the relevant field, etc. The weight result can be a value between 0 and 1, and the sum of the weights of all experts should be 1.

[0115] Step S33, obtaining a comprehensive influence matrix according to the multiple direct influence matrices and the weight results;

[0116] All weighted direct influence matrices are combined, usually through simple arithmetic mean or weighted mean, to obtain a comprehensive influence matrix that integrates the opinions of multiple experts.

[0117] Step S34, screening key secondary indicators from all secondary indicators of each carbon assessment classification indicator through the calculation results of the comprehensive influence matrix.

[0118] The elements in the comprehensive influence matrix reflect the relative importance or influence degree between the secondary indicators. By analyzing the element values in the matrix, it can be identified which secondary indicators have a significant influence on other indicators or the entire classification indicator. According to the analysis results, those secondary indicators with the highest influence degree and key importance can be screened from all secondary indicators of each carbon assessment classification indicator. For example, by comparing the calculation results of the comprehensive influence matrix with the preset influence factor threshold, when the calculation result of a secondary indicator is greater than the influence factor threshold, it is screened as a key secondary indicator, otherwise it is not a key secondary indicator.

[0119] The following results can be obtained through the above calculation and analysis:

[0120] The key sub-indicators of the clean energy power generation category include the proportion of photovoltaic power generation, the proportion of wind power generation, and the proportion of energy storage; clean energy power generation does not produce or produces very little greenhouse gas emissions, and plays a key role in promoting the low-carbon and carbon-free transformation of the power industry. The establishment of clean energy power generation indicators can clearly define the goals and effectiveness of clean energy utilization in power supply, and help guide the power industry towards a more environmentally friendly direction. The specific key sub-indicators are as follows:

[0121] (1) Proportion of photovoltaic power generation: the proportion of photovoltaic power generation in the evaluation period is the ratio of the amount of photovoltaic power generation built by the power supply to the total amount of energy consumption in the evaluation period. The larger this indicator, the greater the proportion of clean energy in the power supply, and the more conducive it is to achieving low carbon in the power supply.

[0122] E 光伏 The amount of photovoltaic power generation in the evaluation period is in tons of standard coal equivalent (tce); N 综合消耗 The total amount of energy consumption in the evaluation period is in tons of standard coal equivalent (tce).

[0123] (2) Proportion of wind power generation: the proportion of wind power generation in the evaluation period is the ratio of the amount of wind power generation in the power supply to the total amount of energy consumption in the evaluation period. The larger this indicator, the greater the proportion of clean energy in the power supply, and the more conducive it is to achieving low carbon in the power supply.

[0124] E 风力 The amount of wind power generation is in tons of standard coal equivalent (tce); N 合 The total amount of energy consumption is in tons of standard coal equivalent (tce).

[0125] (3) Proportion of energy storage: when the system power supply is greater than the demand, energy storage is equivalent to load and can store the unabsorbed power; when the supply is less than the demand, energy storage can also be equivalent to power supply to the load, so energy storage is crucial in achieving clean energy supply and zero carbon in power supply. At the same time, energy storage also consumes energy while storing, so this indicator refers to the ratio of the difference between the amount of power supplied by energy storage and the energy consumption of energy storage in the evaluation period to the total amount of energy consumption in the power supply in the evaluation period.

[0126] E 储能供电 The amount of power supplied by energy storage is in tons of standard coal equivalent (tce); E 储能损耗 The energy consumption of energy storage is in tons of standard coal equivalent (tce); N 综合消耗 The total amount of energy consumption is in tons of standard coal equivalent (tce).

[0127] In addition to the above-mentioned three key secondary indicators, the secondary indicators of clean energy power generation evaluation indicators include photovoltaic power generation daily utilization hours (different typical days), annual utilization hours, wind turbine power generation daily utilization hours (different typical days), annual utilization hours, roof photovoltaic power generation efficiency in different time periods, photovoltaic carport power generation efficiency in different time periods, regional wind speed, wind direction and wind power density in different time periods, wind turbine power generation efficiency in different time periods, photovoltaic power generation consumption rate or power abandonment rate in different typical days, wind turbine power generation consumption rate or wind abandonment rate in different typical days, and energy storage device charging and discharging efficiency, etc.

[0128] The key secondary indicators of the terminal load power consumption class include the proportion of new energy vehicles, the proportion of two-way charging piles, and the proportion of building direct current power consumption. By establishing terminal load power consumption indicators, power supply stations can more accurately grasp the variation law of power load, thereby optimizing power grid dispatching and operation. This helps to improve the efficiency of power grid operation, reduce energy loss, and reduce operating costs. In addition, terminal load power consumption indicators help to promote the development of distributed power and clean energy. Smart grid can adjust the power generation plan of distributed power or clean energy according to the terminal load power consumption, and realize compatible grid connection with large power grid. This not only expands the resource selection range, but also improves the flexibility and reliability of power grid operation. The specific key secondary indicators are as follows:

[0129] (1) Proportion of new energy vehicles: The proportion of new energy vehicles refers to the ratio of new energy vehicles in the power supply station to the total number of vehicles in the power supply station.

[0130] Wherein: E 新能源汽车 represents the number of new energy vehicles in the power supply station, and the unit is vehicle; N 所内汽车 represents the total number of vehicles in the power supply station, and the unit is vehicle.

[0131] (2) Proportion of two-way charging piles: The proportion of two-way charging piles refers to the ratio of the number of charging piles in the power supply station to the number of new energy vehicles in the power supply station.

[0132] Wherein: E 双向充电桩 is the number of charging piles, and the unit is vehicle; N 新能源车 is the total number of new energy vehicles, and the unit is vehicle.

[0133] (3) Proportion of building direct current power consumption: This indicator refers to the ratio of the total direct current power consumption in the buildings in the power supply station to the total energy consumption of the buildings in the evaluation period.

[0134] Wherein: E 直流用电 is the total power consumption of the building, and the unit is ton of standard coal equivalent (tce); N 建筑总能耗 is the total energy consumption of the building, and the unit is ton of standard coal equivalent (tce).

[0135] In addition to the above key secondary indicators, the terminal load electricity evaluation index secondary indicators include building alternating current electricity proportion, accommodation electricity proportion, terminal electricity activity CO2 emission intensity, peak load management, smart meter usage rate, power demand side management effect, electricity information collection system coverage, etc.

[0136] The key secondary indicators of the building system evaluation index include green building material application proportion, building energy saving rate. The establishment of building system indicators can ensure that the building part is coordinated with the overall zero-carbon target and promote the low-carbon transformation of energy structure. Building system indicators can comprehensively evaluate the energy consumption of buildings, including energy supply cleanization, energy use efficiency, etc., providing data support for reducing energy consumption. Building system indicators require the selection of low-carbon, renewable and recycled materials to reduce energy consumption and carbon emissions during material production and transportation and promote resource recycling. The specific key secondary indicators are as follows:

[0137] (1) Green building material application proportion: The green building material application proportion is calculated by the ratio of the use amount of green building materials to the total building material use amount.

[0138] Wherein: E 绿色建材 is the use amount of green building materials, unit is ton standard coal equivalent (tce); N 所内总建材 is the total building material use amount in the power supply station, unit is ton standard coal equivalent (tce).

[0139] (2) Building energy saving rate: The building energy saving rate is usually expressed as the energy saving efficiency achieved after energy saving measures, expressed in percentage.

[0140] Wherein: E 基准能耗 represents the reference energy consumption of the power supply station, which refers to the energy consumption of the building without any energy saving measures, which can be estimated based on building design, equipment configuration and expected use. E 实际能耗 represents the actual energy consumption of the power supply station, which refers to the actual energy consumption of the building after energy saving measures, which usually needs to be determined by actual operation data.

[0141] In addition to the above 2 key secondary indicators, the secondary indicators of the building system evaluation index include indoor air quality, landscape maintenance, energy consumption monitoring.

[0142] The key secondary indicators of the zero-carbon management platform include carbon flow analysis and calculation, and zero-carbon power supply green electricity trading balance calculation. The evaluation indicators of the zero-carbon management platform help establish a carbon emission management system and regulate the carbon emission behavior of power supply stations. Through real-time monitoring, data analysis, and early warning and reminder functions, abnormal carbon emission situations can be discovered in a timely manner, and effective measures can be taken to correct them, ensuring that the carbon emission of the power supply station meets the relevant standards and requirements. By encouraging and supporting the development and utilization of new energy, the low-carbon transformation of the energy structure of the power supply station is promoted, the dependence on traditional energy is reduced, and carbon emissions are reduced. The specific key secondary indicators are as follows:

[0143] (1) Carbon flow analysis and calculation: Carbon flow analysis and calculation indicators include the carbon potential of the upper grid M 41 , the power supply of the upper grid M 42 , and the carbon flow rate of the power supply node of the upper grid M 43 , network loss carbon flow rate monitoring M 44 .

[0144] For AC and DC loads in the power supply station, the input active power and the output node carbon potential, and the output carbon flow rate have the following relationship: R = EP

[0145] Where: P represents the input active power of AC and DC loads in the power supply station, unit kW, E represents the output node carbon potential of the load, unit kgCO2 / kWh, and R represents the output carbon flow rate of the load, unit kgCO2 / h.

[0146] The carbon potential of the upper grid node, the power supply of the upper grid, and the carbon flow rate of the power supply node of the upper grid all satisfy the above formula, i.e. the carbon flow rate of the power supply node of the upper grid M 43 is equal to the product of the carbon potential of the upper grid node M 41 and the power supply of the upper grid M 42 .

[0147] The calculation of the loss carbon flow rate M 44 needs to consider the problem of two-way allocation coefficient, i.e. part of the loss is borne by the network (power supply line) and part is borne by the load.

[0148] (2) Zero-carbon power supply station green electricity trading balance calculation: The power supply station has a period of one year, and if it achieves zero carbon, it needs to satisfy the following relationship: the carbon emission of the power supply of the upper grid minus the green electricity sold to the upper grid M45- the green electricity purchased M46≤ 0. The green electricity sold to the upper grid is the excess electricity after the wind and photovoltaic power generation meets the microgrid power supply demand, which is equivalent to the green electricity sold to the upper grid, and can be used to offset the green electricity purchased.

[0149] In addition to the above key secondary indicators, the secondary indicators of the zero-carbon management platform evaluation index include carbon emission factors of coal-fired power plants and nuclear power plants, energy management platform construction for power supply, real-time monitoring and data collection capabilities, data security and privacy protection, etc.

[0150] The key secondary indicators of the carbon sequestration index include greening coverage rate, offsetting carbon emissions through purchasing certified voluntary emission reductions, etc. As an effective measure to reduce carbon dioxide emissions, carbon sequestration can increase the absorption and fixation of carbon dioxide through greening methods such as afforestation, forest management, and vegetation restoration, thereby significantly reducing the concentration of greenhouse gases in the atmosphere. Therefore, including carbon sequestration in the zero-carbon power supply index evaluation system helps to more accurately measure and evaluate the progress and effectiveness of power supply in achieving carbon neutrality. The specific key secondary indicators are as follows:

[0151] (1) Greening coverage rate: The greening coverage rate refers to the ratio of the total area of various green spaces within the power supply to the total area of land within the power supply planning range.

[0152] Where: E 绿地 refers to the total area of various green spaces within the power supply, in square meters; N 所用地 refers to the total area of land within the power supply, in square meters.

[0153] (2) Offset carbon emissions through purchasing certified voluntary emission reductions, etc.: Certified voluntary emission reductions (CCER) are the quantification and certification of greenhouse gas emission reduction effects of specific projects within China, and the registration of greenhouse gas emission reductions in the national voluntary greenhouse gas emission trading registration system.

[0154] As a key emission unit, power supply will generate a large amount of carbon dioxide emissions during its operation. By purchasing CCER, power supply can offset part of its carbon emissions, thereby directly reducing its carbon emissions.

[0155] The key secondary indicators of the low-carbon atmosphere creation index include electrical equipment material recycling and wastewater irrigation for greening. The low-carbon atmosphere creation evaluation index can quantify and evaluate the efforts and achievements of power supply in promoting green and low-carbon concepts. By setting specific evaluation indicators such as office power consumption reduction rate and green plant coverage rate, power supply can be guided to actively take measures to create a strong low-carbon cultural atmosphere, thereby improving the image and reputation of power supply in society. The specific key secondary indicators are as follows.

[0156] 1. Electrical equipment material recycling: Electrical equipment material recycling refers to the ratio of the amount of electrical equipment material recycled within the evaluation period to the total amount of recycled materials.

[0157] Where, E回收利用 Refers to the number of electrical equipment class materials collected and utilized, in units of pieces; N 总回收 Refers to the number of all recycled materials of the power supply station, in units of pieces.

[0158] 2. Green irrigation with wastewater: The green irrigation with wastewater index refers to the ratio of the amount of wastewater used for green irrigation to the total amount of wastewater in the evaluation period.

[0159] E 废水绿化灌溉 is the amount of wastewater used for green irrigation of the power supply station, in cubic meters; N 总废水 is the total amount of wastewater generated by the power supply station, in cubic meters.

[0160] In addition to the above key secondary indicators, the secondary indicators of the low-carbon atmosphere creation evaluation index include office digital management, low-carbon energy-saving propaganda, and domestic sewage treatment.

[0161] The key secondary indicators of the safety management index include equipment regular maintenance; safety management is the foundation of the power industry, directly related to the stable operation of the power supply station. The establishment of safety management indicators can ensure that the power supply station pursues zero carbon emissions while also having high safety and reliability. Through safety management indicators, unsafe factors and safety hazards in the operation process of the power supply station can be discovered in a timely manner, so that appropriate preventive measures and emergency plans can be taken to ensure safety in production. Safety management indicators mainly include emergency disposal, hazardous waste disposal, punishment for illegal behavior, network security monitoring, equipment regular maintenance, and no illegal operation rate.

[0162] The key secondary indicators of the additional classification index include green building certification and carbon neutral certification. The additional points index, as a positive incentive mechanism, can encourage the power supply station to pursue higher energy efficiency, technological innovation, and environmental protection on the basis of achieving the zero carbon emission target. By setting up the bonus item, the power supply station will have more motivation to implement energy saving and emission reduction, improve energy utilization efficiency, promote green technological innovation, etc., so as to further promote the realization of the zero carbon target. The additional points index includes green building (two-star and above rating) certification, carbon neutral certification, and energy saving investment recovery period. Table 2 shows the key secondary indicators of each carbon assessment classification index that is not screened out.

[0163] Table 2 Key secondary indicators of carbon assessment classification index

[0164] The construction method of the zero-carbon power supply station carbon evaluation classification index system considering carbon flow analysis comprises the following steps: obtaining the output result of the power network steady-state carbon flow model of the zero-carbon power supply station; obtaining each carbon evaluation classification index of the zero-carbon power supply station according to the output result and the target of the zero-carbon power supply station; weighting each carbon evaluation classification index by using the frequency statistical method; and integrating each weighted carbon evaluation classification index to obtain the zero-carbon power supply station carbon evaluation classification index system. The zero-carbon power supply station is comprehensively evaluated in multiple dimensions in combination with carbon flow analysis, so that the zero-carbon level of the power supply station is more comprehensively and accurately evaluated. Meanwhile, through carbon flow analysis, the flow and conversion process of carbon in the power supply station can be accurately tracked, so that the carbon emission of the power supply station is more accurately evaluated, and the evaluation result is more comparable and operable.

[0165] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.

Claims

1. A method for constructing a carbon evaluation classification index system of a zero-carbon power supply station considering carbon flow analysis, characterized in that, The application relates to a carbon evaluation method for a zero-carbon power supply station. The method comprises the following steps: obtaining output results of a power network steady-state carbon flow model of the zero-carbon power supply station; obtaining various carbon evaluation classification indexes of the zero-carbon power supply according to the output results and targets of the zero-carbon power supply station; weighting the various carbon evaluation classification indexes by using a frequency statistical method; 2.The method of claim 1, wherein, integrating the weighted various carbon evaluation classification indexes to obtain a carbon evaluation classification index system of the zero-carbon power supply station. The method for obtaining the output results of the power network steady-state carbon flow model of the zero-carbon power supply station comprises the following steps: obtaining carbon flow rate data of each node, branch and unit calculated by the power network steady-state carbon flow model; 3. The method for constructing a carbon assessment classification index system of zero-carbon power supply considering carbon flow analysis according to claim 2, characterized in that, analyzing the carbon flow rate data of each node, branch and unit to obtain results of main sources of carbon emission, distribution characteristics and variation trends over time.

4. The method for constructing a carbon assessment classification index system of zero-carbon power supply considering carbon flow analysis according to claim 3, characterized in that, The various carbon evaluation classification indexes of the zero-carbon power supply station comprise clean energy generation indexes, terminal load power consumption indexes, system evaluation indexes, zero-carbon control platform indexes, carbon increment indexes, low-carbon atmosphere creation indexes, safety management indexes and additional classification indexes. The method for weighting the various carbon evaluation classification indexes by using the frequency statistical method comprises the following steps: obtaining key secondary indexes of each carbon evaluation classification index; 5. The method for constructing a carbon assessment classification index system of zero-carbon power supply considering carbon flow analysis according to claim 4, characterized in that, weighting the key secondary indexes of each carbon evaluation classification index by using the frequency statistical method to obtain the weighted various carbon evaluation classification indexes. The method for obtaining the key evaluation indexes of each carbon evaluation classification index comprises the following steps: filtering out the key secondary indexes from each carbon evaluation classification index by using an expert trust propagation-decision laboratory analysis method, and the filtering results are as follows: The key secondary indexes of the clean energy generation indexes comprise a photovoltaic power generation proportion, a wind power generation proportion and an energy storage proportion; the key secondary indexes of the terminal load power consumption indexes comprise a new energy vehicle proportion, a two-way charging pile facility proportion and a building direct current power consumption proportion; The key secondary indexes of the system evaluation indexes comprise a green building material application proportion and a building energy consumption saving rate; The key secondary indexes of the zero-carbon control platform indexes comprise carbon flow analysis calculation and zero-carbon power supply station green power transaction balance calculation; The key secondary indexes of the carbon increment indexes comprise a greening coverage rate and a method for offsetting carbon emission by purchasing a certified voluntary emission reduction; The key secondary indexes of the low-carbon atmosphere creation indexes comprise electrical equipment material recycling and wastewater green irrigation; The key secondary indexes of the safety management indexes comprise equipment regular maintenance; 6. The method for constructing a carbon assessment classification index system of zero-carbon power supply considering carbon flow analysis according to claim 5, characterized in that, The key secondary indexes of the additional classification indexes comprise green building certification and carbon neutralization certification. The method for filtering out the key secondary indexes from each carbon evaluation classification index by using the expert trust propagation-decision laboratory analysis method comprises the following steps: obtaining scores of all secondary indexes of each carbon evaluation classification index given by multiple experts under preset influence degree scoring rules, and obtaining multiple direct influence matrices corresponding to the number of experts according to the scoring results; obtaining weight results of the multiple experts; obtaining a comprehensive influence matrix according to the multiple direct influence matrices and the weight results; filtering out the key secondary indexes from all secondary indexes of each carbon evaluation classification index by using a calculation result of the comprehensive influence matrix.

7. The method for constructing a carbon assessment classification index system of zero-carbon power supply considering carbon flow analysis according to claim 6, characterized in that, The screening of the key secondary indicators from all the secondary indicators of the carbon assessment classification index through the calculation result of the comprehensive influence matrix comprises: By comparing the calculation result of the comprehensive influence matrix with a preset influence factor threshold, when the calculation result of the secondary indicator is greater than the influence factor threshold, the secondary indicator is screened as a key secondary indicator, otherwise, the secondary indicator is not a key secondary indicator.

Images