A method, system and device for evaluating carbon index of source-grid-load power grid

Abstract

The present invention provides a source-grid-load power grid carbon index assessment method, system and equipment, including: obtaining distributed energy operation data, flow data, regional area and regional carbon emission livelihood data; calculating the source-grid-load carbon peak index based on the regional area, flow data and the distributed energy operation data; calculating the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data; and assessing the source-grid-load power grid carbon index based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index. The present invention can analyze and assess the carbon emission and emission reduction of electricity, track the carbon footprint of the power grid according to the carbon emission intensity of regional generators, display the carbon flow density of substations and lines and the carbon potential of any node, support the low-carbon power dispatch of power companies and the low-carbon operation of power grids, assist in the formulation of economic, livelihood, low-carbon and other policies, as well as low-carbon power regulation, and create the value of electricity-carbon + value-added services.

Description

Technical Field

[0001] The present invention relates to the field of electric carbon technology, and in particular to a source-grid-load electric network carbon index assessment method, system and device. Background Art

[0002] The new power system is a revolutionary change in energy and electricity. The key to building a new power system lies in the effective development and utilization of new energy sources such as photovoltaics and wind power. The high proportion of new energy access will have a profound impact on the reliable supply of electricity and the safe and stable operation of the system. The power system source, network, load, and storage will also be deeply adjusted. The carbon index of a region is the key indicator of this adjustment. The minimum carbon emissions of a region is used as a constraint condition, together with the network power balance constraint, voltage constraint, and power source active and reactive constraints to guide low-carbon power regulation.

[0003] At present, the power industry is the single largest industry in terms of coal consumption and carbon emissions. Unlike other energy systems, the power system has a strict real-time balance characteristic of "power generation-power consumption". The power grid connects power production and consumption, is an important energy network platform, and is the core hub leading the reduction of power carbon emissions. It must not only ensure the large-scale development and efficient use of new energy, but also meet the electricity demand for economic and social development. The above characteristics determine that the "carbon reduction" of the power system is not just a task on the source side, but requires the coordination and cooperation of the entire chain of "source-grid-load".

[0004] Existing technologies generally use macro-statistical methods and full life cycle carbon footprint to calculate the carbon emission index of electricity on the source, grid and load sides. These two methods are introduced below.

[0005] (1) Macro-statistical method

[0006] Accurate carbon measurement and carbon tracking are prerequisites for the low-carbon transformation of the power energy system. The original electricity carbon emissions accounting method was mainly based on macro-statistical methods, that is, carbon emissions were only calculated based on the annual, provincial / regional power generation fuel consumption. The core idea is to construct activity data and emission factors for emission sources, and use the product of activity data and emission factors as the carbon emissions estimate for the emission project. The emission factor is a unit activity carbon emission coefficient, such as the carbon dioxide emissions corresponding to unit fossil fuel consumption. For direct carbon emissions from the power system, the carbon emissions can be calculated based on the fuel consumption and the corresponding emission factors. The calculation formula for direct carbon emissions from the power system based on the fuel emission factor is as follows:

[0007]

[0008] Where E represents the direct carbon emissions from fuel consumption in the power system; is the activity data of the jth fossil fuel, that is, the heat generated by the fuel during combustion; N represents the set of all types of fossil fuels, represents the energy carbon emission factor, and j represents the energy type. This method is simple and intuitive, highly operational, and has a wide range of applications. It is widely used in macro-electricity carbon emission accounting.

[0009] (2) Carbon footprint throughout the entire life cycle

[0010] Carbon footprint technology based on life cycle assessment refers to the collection of emissions and absorption of major greenhouse gases by products from raw materials, use, disposal and other procedures during the production process. Therefore, carbon emission monitoring for power systems needs to take into account carbon emissions on the electricity consumption side.

[0011] The traditional full life cycle method and macro-statistical carbon emission accounting methods have defects, such as long statistical time, failure to consider the grid structure characteristics and power flow distribution of the power system, and failure to clarify the temporal and spatial transfer mechanism of carbon emissions in various links of the power system, leading to unreasonable allocation of carbon emission responsibilities between the power generation side and the power consumption side. It is unable to reflect the temporal and spatial differences of users' electricity carbon emission factors, fails to consider the "network" characteristics of the new power system, and is difficult to characterize the indirect carbon emissions generated by the evolution of the power grid and related transmission losses. It is impossible to accurately calculate the carbon emissions of all links of the power system, and its calculation results can only represent one of the important indicators of the carbon emissions of electricity.

[0012] With the development of carbon emission flow theory, the research on carbon emissions in power systems has been expanded in terms of time scale. Relying on the data support of power flow and power generation links, carbon emission flow analysis can cover from minutes to years. Power system carbon emission flow is a basic concept. In order to form a theoretical framework for power system carbon emission flow analysis, it is necessary to establish a number of corresponding indicators. At present, this method is mostly used in the exploration and practice of indirect carbon emission measurement and analysis in the engineering community. According to the calculation results of the above indicators and the application of the actual measurement method, a power supply company developed a carbon meter device and a carbon metering platform based on the carbon emission flow theory. Figure 1 As shown in the figure, the platform displays the panoramic carbon footprint information of a region, including: the city's carbon emission intensity curve, carbon emissions and substation carbon potential ranking, carbon emission proportion of each region, carbon emission intensity and carbon flow rate, etc. Due to the complexity of power industrial control equipment on the distribution side, terminal collection and other equipment have tended to be multifunctional and integrated, and the carbon meter device with independent functions is difficult to install on the user side, which consumes a lot of costs. Therefore, the carbon meter device is difficult to apply to rural counties and other areas.

[0013] With the access of distributed clean power sources such as photovoltaics and wind power and the popularization of green electricity trading, the carbon emissions of regional power grids have dropped significantly. Therefore, the existing source-grid-load power carbon system not only needs to calculate the carbon emissions and carbon footprint impact in the power grid, but also needs to consider the carbon reduction and emission reduction effects brought by clean energy, such as the calculation of green electricity emission reduction and electricity carbon neutrality rate. Such indicators are not included in the existing carbon assessment system. At the same time, due to the access of a large number of small self-generated power equipment, it is not sufficient to track the carbon footprint of large power plants and power grids in the transmission and distribution process only at the macro level. The carbon indicators on the source-grid-load side should have a more detailed hierarchical display, and the regional carbon emissions situation also needs to be further refined according to the administrative level. Summary of the invention

[0014] In order to solve the problem that the traditional full life cycle method and macro-statistical carbon emission accounting method cannot clarify the temporal and spatial transfer mechanism of carbon emissions in various links of the power system, resulting in unreasonable allocation of carbon emission responsibilities between the power generation side and the power consumption side, and cannot reflect the temporal and spatial differences of users' electricity carbon emission factors, and cannot accurately calculate the carbon emissions of all links of the power system, the present invention proposes a source-grid-load power grid carbon index evaluation method, including:

[0015] Obtain distributed energy operation data, flow data, regional area and regional carbon emission livelihood data;

[0016] Calculate the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data;

[0017] Calculate the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data;

[0018] The source-grid-load power grid carbon index is evaluated based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index.

[0019] Optionally, the calculating of the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data includes:

[0020] Based on the area of ​​the region and the distributed energy operation data, the electricity carbon emission algorithm is used to calculate the electricity carbon emission monitoring indicators;

[0021] Based on the power flow data and the pre-built distributed energy model, power flow analysis is performed to obtain the electricity carbon footprint accounting index;

[0022] Among them, the source-grid carbon peak indicators include: electricity carbon emission monitoring indicators and electricity carbon footprint accounting indicators;

[0023] The distributed energy model is constructed by distributed energy parameters using simulation software.

[0024] Optionally, the calculation of the electricity carbon emission monitoring index based on the area of ​​the region and the distributed energy operation data by using an electricity carbon emission algorithm includes:

[0025] Based on the source-side data, grid-side data and load-side data in the distributed energy operation data, direct carbon emissions, grid-side carbon emissions and load-side carbon emissions are calculated respectively;

[0026] The electric carbon emission density is obtained based on the direct carbon emissions, grid-side carbon emissions and load-side carbon emissions combined with the area of ​​the region;

[0027] Among them, the electricity carbon emissions monitoring indicators include: direct carbon emissions, grid-side carbon emissions, load-side carbon emissions and regional electricity carbon emissions density.

[0028] Optionally, the electricity carbon emission density includes: regional electricity carbon emission density, power generation carbon emission density, and electricity consumption carbon emission density; the regional electricity carbon emission density is calculated according to the following formula:

[0029]

[0030] in, represents the spatial carbon emission intensity, represents the carbon emissions of region a, S a represents the area of ​​region a;

[0031] The carbon emission density of power generation is calculated as follows:

[0032]

[0033] In the formula, is the carbon emission density of power generation, and They represent the direct power supply area and indirect power supply area of ​​a power plant / generator set respectively. is the total direct carbon emissions in the region;

[0034] The carbon emission density of electricity consumption is calculated as follows:

[0035]

[0036] In the formula, is the carbon emission density of electricity consumption on the user side, is the load-side carbon emissions in the region, represents the area of ​​region a, Represents the number of grid load nodes, is the load power consumption of the user, The node carbon potential for each user node.

[0037] Optionally, the power flow analysis is performed based on the power flow data in combination with a pre-built distributed energy model to obtain an electricity carbon footprint accounting indicator, including:

[0038] Based on the carbon flow of the branch in the flow data, calculate the carbon flow of the branch passing through the flow in unit time;

[0039] Calculate branch carbon flow density based on the carbon flow that passes through the branch with the tidal current in unit time and the branch active power flow in the tidal current data;

[0040] The node carbon potential is calculated based on the active power flow of the branch flowing into the node and the carbon flow density flowing into the node in the power flow data;

[0041] Calculating the carbon potential fluctuation of a node based on the carbon potentials of two adjacent nodes, and calculating the average carbon potential fluctuation rate of a power grid based on the carbon potential fluctuation of the node;

[0042] Among them, the electricity carbon footprint accounting indicators include: carbon flow density, node carbon potential, node carbon potential fluctuation and grid average carbon potential fluctuation rate.

[0043] Optionally, the node carbon potential fluctuation is calculated as follows:

[0044]

[0045] In the formula, represents the carbon potential fluctuation of node i, Denoted as the node carbon potential of node i, Denote it as the node carbon potential of the node after node i.

[0046] Optionally, the average carbon potential fluctuation rate of the power grid is calculated as follows:

[0047]

[0048] In the formula, is the average carbon potential fluctuation rate of the power grid, i is the node number, and n is the number of nodes.

[0049] Optionally, the node carbon potential is calculated as follows:

[0050]

[0051] In the formula, Indicates all branches; represents the active power flow from branch i into the node; represents the carbon flow density flowing into node i; represents the branch carbon flow rate; represents the carbon flow density flowing into node n; i represents the node number.

[0052] Optionally, the node carbon potential fluctuation is calculated as follows:

[0053]

[0054] In the formula, Denoted as the node carbon potential of node i, It is recorded as the node carbon potential of the node after node i, and the direction is the carbon flow direction. Represents the carbon potential fluctuation at node i.

[0055] Optionally, the calculating of the source-grid-load carbon neutrality index based on the distributed energy operation data and the regional carbon emission livelihood data includes:

[0056] Calculate carbon and livelihood analysis indicators based on regional carbon emissions and livelihood data;

[0057] Calculate the electricity carbon emission reduction analysis index based on distributed energy operation data and electricity carbon emission reduction index algorithm;

[0058] Among them, the source, grid and load carbon neutrality indicators include: carbon and people's livelihood analysis indicators and electricity carbon emission reduction analysis indicators.

[0059] Optionally, the calculation of carbon and livelihood analysis indicators based on regional carbon emission livelihood data includes:

[0060] Calculate the carbon emission elasticity coefficient of the power economy based on indirect carbon emissions from electricity consumption and GDP growth rate;

[0061] Calculate the social carbon emission elasticity coefficient of electricity based on indirect carbon emissions from electricity consumption and per capita growth rate;

[0062] Among them, the carbon and people's livelihood analysis indicators include: electricity economic carbon emission elasticity coefficient and electricity social carbon emission elasticity coefficient.

[0063] Optionally, the electricity economic carbon emission elasticity coefficient is calculated as follows:

[0064]

[0065] In the formula, represents the carbon emission elasticity coefficient of electricity economy, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the GDP at time t+1 and time t respectively.

[0066] Optionally, the electricity social carbon emission elasticity coefficient is calculated as follows:

[0067]

[0068] In the formula, represents the social carbon emission elasticity coefficient of electricity, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the regional population at time t+1 and time t respectively.

[0069] Optionally, the calculation of the electricity carbon emission reduction analysis index based on the distributed energy operation data combined with the electricity carbon emission reduction index algorithm includes:

[0070] Calculate the carbon emission reduction of power generation based on the energy supply structure ratio, carbon emission factor of fossil energy and the total amount of clean energy power generation in the region;

[0071] Calculate electricity emission reduction based on user electricity consumption, clean energy electricity consumption, fossil energy required to complete activities, and average electricity carbon emission factor;

[0072] Calculate emission reductions from green electricity trading based on the green electricity volume traded by users in the region / area and the average electricity carbon emission factor;

[0073] The electricity carbon emission reduction rate is calculated based on the sum of electricity carbon emission reduction and the carbon emissions generated by external grid power supply and power generation.

[0074] Optionally, the carbon emission reduction of power generation is calculated as follows:

[0075]

[0076]

[0077] In the formula, is the carbon emission reduction of clean energy power generation, c is the type of clean energy, is the total amount of clean energy power generation within a certain period of time, The average carbon emission factor of electricity from fossil fuels that are replaced to supply electricity to the region, is the energy supply structure ratio, is the carbon emission factor of fossil energy j.

[0078] Optionally, the electricity consumption reduction is calculated as follows:

[0079]

[0080]

[0081] In the formula, is the total amount of emission reduction on the user side, Represents the amount of direct electricity reduction by users over a period of time, Represents the amount of clean energy consumption and emission reduction by users over a period of time. It represents the emission reduction when users use electricity instead of fossil energy for a certain activity. , Represent the user's electricity consumption at time t and t+1 respectively, , Represent the clean energy electricity consumption at time t and t+1 respectively, represents the amount of fossil energy required to complete activity x, represents the carbon emission factor of fossil energy j, represents the amount of electricity required to complete activity x, r e represents the average electricity carbon emission factor, x represents the activity number, and M represents the number of activities.

[0082] Optionally, the emission reduction amount of green electricity trading is calculated as follows:

[0083]

[0084] In the formula, Green electricity trading emission reduction, W k is the green electricity traded by region / user k, Buy green electricity for k, Sell ​​green electricity for k, r e is the average electricity carbon emission factor.

[0085] Optionally, the electricity carbon emission reduction rate is calculated as follows:

[0086]

[0087] In the formula, is the carbon emission reduction rate of electricity, c is the clean energy power generation node, j represents the power generation energy node that generates carbon emissions, k represents the number of green electricity transactions, ui represents the user load node, Di represents the number of grid lines, Indicates other carbon reduction methods that have not been classified. To reduce electricity consumption, is the total amount of clean energy power generation within a certain period of time, is the total power generation of energy source j, is the load power consumption of the user, is the node carbon potential of each user node, is the network loss of line Di, is the carbon flow density of line Di, r e is the average electricity carbon emission factor, W k For green electricity trading, The average carbon emission factor of electricity from fossil fuels that are replaced to supply electricity to the region, is the carbon emission factor of fossil energy j, To buy green electricity, To sell green electricity.

[0088] In another aspect, the present invention further provides a source-grid-load power grid carbon index assessment system, comprising:

[0089] Parameter acquisition module, used to obtain distributed energy operation data, flow data, regional area and regional carbon emission livelihood data;

[0090] A carbon peak index calculation module, used to calculate the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data;

[0091] A carbon neutrality index calculation module, used to calculate the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data;

[0092] A carbon index evaluation module is used to evaluate the source-grid-load power grid carbon index based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index.

[0093] Optionally, the carbon peak index calculation module includes:

[0094] The monitoring index calculation submodule is used to calculate the electricity carbon emission monitoring index based on the area of ​​the region and the distributed energy operation data using the electricity carbon emission algorithm;

[0095] The accounting index calculation submodule is used to perform power flow analysis based on power flow data combined with a pre-built distributed energy model to obtain the electricity carbon footprint accounting index;

[0096] Among them, the source-grid carbon peak indicators include: electricity carbon emission monitoring indicators and electricity carbon footprint accounting indicators;

[0097] The distributed energy model is constructed by distributed energy parameters using simulation software.

[0098] Optionally, the monitoring indicator calculation submodule is specifically used for:

[0099] Based on the source-side data, grid-side data and load-side data in the distributed energy operation data, direct carbon emissions, grid-side carbon emissions and load-side carbon emissions are calculated respectively;

[0100] Based on the direct carbon emissions, grid-side carbon emissions and load-side carbon emissions combined with the area of ​​the region, the regional electricity carbon emission density is obtained;

[0101] Among them, the electricity carbon emissions monitoring indicators include: direct carbon emissions, grid-side carbon emissions, load-side carbon emissions and regional electricity carbon emissions density.

[0102] Optionally, the electricity carbon emission density includes: regional electricity carbon emission density, power generation carbon emission density, and electricity consumption carbon emission density; the regional electricity carbon emission density is calculated according to the following formula:

[0103]

[0104] In the formula, represents the regional electricity carbon emission density, represents the carbon emissions of region a, S a represents the area of ​​region a;

[0105] The carbon emission density of power generation is calculated as follows:

[0106]

[0107] In the formula, is the carbon emission density of power generation, and They represent the direct power supply area and indirect power supply area of ​​a power plant / generator set respectively. is the total direct carbon emissions in the region;

[0108] The carbon emission density of electricity consumption is calculated as follows:

[0109]

[0110] In the formula, is the carbon emission density of electricity consumption on the user side, is the load-side carbon emissions in the region, represents the area of ​​region a, Represents the number of grid load nodes, is the load power consumption of the user, The node carbon potential for each user node.

[0111] Optionally, the accounting indicator calculation submodule is specifically used for:

[0112] Based on the carbon flow of the branch in the flow data, calculate the carbon flow of the branch passing through the flow in unit time;

[0113] Calculate branch carbon flow density based on the carbon flow that passes through the branch with the tidal current in unit time and the branch active power flow in the tidal current data;

[0114] The node carbon potential is calculated based on the active power flow of the branch flowing into the node and the carbon flow density flowing into the node in the power flow data;

[0115] Calculating the carbon potential fluctuation of a node based on the carbon potentials of two adjacent nodes, and calculating the average carbon potential fluctuation rate of a power grid based on the carbon potential fluctuation of the node;

[0116] Among them, the electricity carbon footprint accounting indicators include: carbon flow density, node carbon potential, node carbon potential fluctuation and grid average carbon potential fluctuation rate.

[0117] Optionally, the node carbon potential is calculated as follows:

[0118]

[0119] In the formula, Indicates all branches; represents the active power flow from branch i into the node; represents the carbon flow density flowing into node i; represents the branch carbon flow rate; represents the carbon flow density flowing into node n; i represents the node number.

[0120] Optionally, the node carbon potential fluctuation is calculated as follows:

[0121]

[0122] In the formula, Denoted as the node carbon potential of node i, It is recorded as the node carbon potential of the node after node i, and the direction is the carbon flow direction. Represents the carbon potential fluctuation at node i.

[0123] Optionally, the average carbon potential fluctuation rate of the power grid is calculated as follows:

[0124]

[0125] In the formula, is the average carbon potential fluctuation rate of the power grid, i is the node number, and n is the number of nodes.

[0126] Optionally, the carbon neutrality index calculation module includes:

[0127] The first calculation submodule is used to calculate carbon and livelihood analysis indicators based on regional carbon emission and livelihood data;

[0128] The second calculation submodule is used to calculate the electric carbon emission reduction analysis index based on the distributed energy operation data combined with the electric carbon emission reduction index algorithm;

[0129] Among them, the source, grid and load carbon neutrality indicators include: carbon and people's livelihood analysis indicators and electricity carbon emission reduction analysis indicators.

[0130] Optionally, the first computing submodule is specifically used for:

[0131] Calculate the carbon emission elasticity coefficient of the power economy based on indirect carbon emissions from electricity consumption and GDP growth rate;

[0132] Calculate the social carbon emission elasticity coefficient of electricity based on indirect carbon emissions from electricity consumption and per capita growth rate;

[0133] Among them, the carbon and people's livelihood analysis indicators include: electricity economic carbon emission elasticity coefficient and electricity social carbon emission elasticity coefficient.

[0134] Optionally, the electricity economic carbon emission elasticity coefficient is calculated as follows:

[0135]

[0136] In the formula, represents the carbon emission elasticity coefficient of electricity economy, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the GDP at time t+1 and time t respectively.

[0137] Optionally, the electricity social carbon emission elasticity coefficient is calculated as follows:

[0138]

[0139] In the formula, represents the social carbon emission elasticity coefficient of electricity, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the regional population at time t+1 and time t respectively.

[0140] Optionally, the second computing submodule is specifically configured to:

[0141] Calculate the carbon emission reduction of power generation based on the energy supply structure ratio, carbon emission factor of fossil energy and the total amount of clean energy power generation in the region;

[0142] Calculate electricity emission reduction based on user electricity consumption, clean energy electricity consumption, fossil energy required to complete activities, and average electricity carbon emission factor;

[0143] Calculate emission reductions from green electricity trading based on the green electricity volume traded by users in the region / area and the average electricity carbon emission factor;

[0144] The electricity carbon emission reduction rate is calculated based on the sum of electricity carbon emission reduction and the carbon emissions generated by external grid power supply and power generation.

[0145] The carbon emission reduction of power generation is calculated as follows:

[0146]

[0147]

[0148] In the formula, is the carbon emission reduction of clean energy power generation, c is the type of clean energy, is the total amount of clean energy power generation within a certain period of time, The average carbon emission factor of electricity from fossil fuels that are replaced to supply electricity to the region, is the energy supply structure ratio, is the carbon emission factor of fossil energy j.

[0149] Optionally, the electricity consumption reduction is calculated as follows:

[0150]

[0151]

[0152] In the formula, is the total amount of emission reduction on the user side, Represents the amount of direct electricity reduction by users over a period of time, Represents the amount of clean energy consumption and emission reduction by users over a period of time. It represents the emission reduction when users use electricity instead of fossil energy for a certain activity. , Represent the user's electricity consumption at time t and t+1 respectively, , Represent the clean energy electricity consumption at time t and t+1 respectively, represents the amount of fossil energy required to complete activity x, represents the carbon emission factor of fossil energy j, represents the amount of electricity required to complete activity x, r e represents the average electricity carbon emission factor, x represents the activity number, and M represents the number of activities.

[0153] Optionally, the emission reduction amount of green electricity trading is calculated as follows:

[0154]

[0155] In the formula, Green electricity trading emission reduction, W k is the green electricity traded by region / user k, Buy green electricity for k, Sell ​​green electricity for k, r e is the average electricity carbon emission factor.

[0156] Optionally, the electricity carbon emission reduction rate is calculated as follows:

[0157]

[0158] In the formula, is the carbon emission reduction rate of electricity, c is the clean energy power generation node, j represents the power generation energy node that generates carbon emissions, k represents the number of green electricity transactions, ui represents the user load node, Di represents the number of grid lines, Indicates other carbon reduction methods that have not been classified. To reduce electricity consumption, is the total amount of clean energy power generation within a certain period of time, is the total power generation of energy source j, is the load power consumption of the user, is the node carbon potential of each user node, is the network loss of line Di, is the carbon flow density of line Di, r e is the average electricity carbon emission factor, W k Trading electricity for green electricity.

[0159] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the processor and the memory are connected via a bus;

[0160] The memory is used to store one or more programs;

[0161] When the one or more programs are executed by the at least one processor, a source-grid-load power grid carbon index assessment method as described above is implemented.

[0162] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, a source-grid-load power grid carbon index assessment method as described above is implemented.

[0163] Compared with the prior art, the present invention has the following beneficial effects:

[0164] The present invention provides a source-grid-load power grid carbon index assessment method, including: obtaining distributed energy operation data, flow data, area of ​​the region and regional carbon emission livelihood data; calculating the source-grid-load carbon peak index based on the area of ​​the region, flow data and the distributed energy operation data; calculating the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data; and assessing the source-grid-load power grid carbon index based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index. The present invention can analyze and assess the carbon emission and emission reduction of electricity, track the carbon footprint of the power grid according to the carbon emission intensity of regional generators (including distributed energy such as photovoltaic / wind power / biomass power generation / small hydropower), display the carbon flow density of substations and lines and the carbon potential of any node, support the low-carbon power dispatching of power companies and the low-carbon operation of power grids, assist in the formulation of economic, livelihood, low-carbon and other policies, and create the value of electricity-carbon + value-added services. BRIEF DESCRIPTION OF THE DRAWINGS

[0165] Figure 1 This is a schematic diagram of the structure of a regional carbon metering platform of the present invention;

[0166] Figure 2 This is a schematic diagram of the source-grid-load power grid carbon index evaluation index system of the present invention;

[0167] Figure 3 A flow chart of a source-grid-load power grid carbon index assessment method of the present invention;

[0168] Figure 4 This is a flow chart of the source-grid-load power grid carbon index assessment of the present invention;

[0169] Figure 5 This is a flow chart of the electric carbon emission monitoring and analysis method of the present invention;

[0170] Figure 6 is a flow chart of the electric carbon footprint method of the present invention;

[0171] Figure 7 This is a flow chart of the electricity carbon neutrality module of the present invention;

[0172] Figure 8 This is a flow chart of the carbon and livelihood analysis method of the present invention;

[0173] Fig. 9 It is a 33-node structure diagram in a specific embodiment of the present invention;

[0174] Fig.10 This is a schematic diagram of the architecture of the source-grid-load-grid carbon index assessment system of the present invention;

[0175] Fig.11 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0176] At present, with the access of massive distributed energy, there is a gap in the relevant technologies and services in the distributed energy power carbon index. Therefore, the main problems solved by this application are: based on the basic carbon index related indicators, considering the impact of distributed clean energy on the carbon reduction and emission reduction of the power grid, establish a comprehensive source-grid-charge-carbon collaborative analysis system architecture such as Figure 2 As shown, analyze and evaluate the carbon emissions and emission reduction of electricity. According to the carbon emission intensity of local generators (including distributed power sources such as photovoltaic / wind power / biomass power generation / small hydropower), track the carbon footprint of the power grid, display the carbon flow density of substations and lines and the carbon potential of any node, support the low-carbon power dispatch of power companies and the low-carbon operation of the power grid, assist in the formulation of economic, livelihood, low-carbon and other policies, and create the value of electricity carbon + value-added services.

[0177] This application designs a source-grid-load power grid carbon index assessment method. The indicators are mainly divided into two levels. The source-grid-load carbon assessment targets the early carbon peak indicators of the new power system in the initial green transformation, including the analysis and assessment of power carbon emissions and power carbon footprint; the source-grid-load carbon emission reduction analysis mainly targets the later carbon neutrality indicators of the new power system, including the calculation of power carbon emission reduction effects, such as Figure 2 shown.

[0178] Relying on algorithms such as electricity carbon emissions and electricity-carbon flow, the carbon index of the source, network, and load sides of the active power grid is calculated and analyzed in a targeted manner. The proposed carbon index assessment system consists of electricity carbon emissions monitoring, electricity carbon footprint accounting, electricity carbon emission reduction analysis, and carbon and people's livelihood analysis.

[0179] In order to better understand the present application, the contents of the present application are further described below in conjunction with the accompanying drawings and embodiments of the specification.

[0180] Embodiment 1:

[0181] A source-grid-load grid carbon index assessment method, such as Figure 3 As shown, including:

[0182] Step S1: Obtaining distributed energy operation data, flow data, area of ​​the region, and regional carbon emission livelihood data;

[0183] Step S2: Calculate the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data;

[0184] Step S3: Calculate the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data;

[0185] Step S4: Evaluate the source-grid-load power grid carbon index based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index.

[0186] The process steps of the source-grid-load power grid carbon index evaluation index calculation method proposed in this application are as follows: Figure 4 As shown:

[0187] 1. Import data samples and algorithm models;

[0188] 2. Carry out carbon emission assessment and calculation on the source-grid-load side based on data such as electricity and flow, including carbon emission monitoring and carbon footprint;

[0189] 3. Based on the above carbon emission results, clean energy data and social data, conduct source-grid-load carbon emission reduction analysis and calculation.

[0190] The following is a detailed description of each step:

[0191] Step S1: Obtaining distributed energy operation data, flow data, regional area and regional carbon emission livelihood data, including:

[0192] Obtain the type of energy, the total power generation of each energy source, the carbon emission factor of each energy source, the total regional electricity consumption, the number of grid load nodes, the user's load power consumption, the node carbon potential of each user node, the grid loss power, the regional area, the regional carbon emissions, the direct power supply area and indirect power supply area of ​​a power plant / generator set, the coverage area of ​​a node, the branch active power flow, the user's clean energy power consumption, the user's energy consumption reduction due to energy conservation and emission reduction, the green power volume traded by the region / user, the local population, etc.

[0193] Step S2: Calculate the source-grid-load carbon peak index based on the area, flow data and the distributed energy operation data, including:

[0194] Based on the area of ​​the region and the distributed energy operation data, the electricity carbon emission algorithm is used to calculate the electricity carbon emission monitoring indicators;

[0195] Calculate electricity carbon footprint accounting indicators based on power flow data using carbon flow algorithm and power flow algorithm;

[0196] Among them, the source-grid carbon peak indicators include: electricity carbon emission monitoring indicators and electricity carbon footprint accounting indicators.

[0197] Furthermore, the electric carbon emission monitoring index is calculated based on the area of ​​the region and the distributed energy operation data using the electric carbon emission algorithm, including:

[0198] Based on the source-side data, grid-side data and load-side data in the distributed energy operation data, direct carbon emissions, grid-side carbon emissions and load-side carbon emissions are calculated respectively;

[0199] Based on the direct carbon emissions, grid-side carbon emissions and load-side carbon emissions combined with the area of ​​the region, the regional electricity carbon emission density is obtained;

[0200] Among them, the electricity carbon emissions monitoring indicators include: direct carbon emissions, grid-side carbon emissions, load-side carbon emissions and regional electricity carbon emissions density.

[0201] Furthermore, the regional electricity carbon emission density Calculate as follows:

[0202]

[0203] In the formula, represents the regional electricity carbon emission density, represents the carbon emissions of region a, S a Represents the area of ​​region a.

[0204] Furthermore, the calculation of the electricity carbon footprint accounting index based on the power flow data using the carbon flow algorithm and the power flow algorithm includes:

[0205] Based on the carbon flow of the branch, the carbon flow of the branch passing through the branch with the tidal current in unit time is calculated;

[0206] Calculate branch carbon flow density based on the carbon flow that passes through the branch with the tidal current in unit time and the branch active power flow in the tidal current data;

[0207] The node carbon potential is calculated based on the active power flow of the branch flowing into the node and the carbon flow density flowing into the node in the power flow data;

[0208] Calculating the carbon potential fluctuation of a node based on the carbon potentials of two adjacent nodes, and calculating the average carbon potential fluctuation rate of a power grid based on the carbon potential fluctuation of the node;

[0209] Among them, the electricity carbon footprint accounting indicators include: carbon flow density, node carbon potential, node carbon potential fluctuation and grid average carbon potential fluctuation rate.

[0210] Furthermore, the node carbon potential is calculated as follows:

[0211]

[0212] In the formula, Indicates all branches; represents the active power flow from branch i into the node; represents the carbon flow density flowing into node i; represents the branch carbon flow rate; represents the carbon flow density flowing into node n; i represents the node number.

[0213] Furthermore, the node carbon potential fluctuation is calculated as follows:

[0214]

[0215] In the formula, Denoted as the node carbon potential of node i, It is recorded as the node carbon potential of the node after node i, and the direction is the carbon flow direction. Represents the carbon potential fluctuation at node i.

[0216] Step S2 specifically includes: (1) Electricity carbon emission monitoring

[0217] Electricity carbon emission monitoring is mainly reflected in the carbon emissions and carbon emission intensity (carbon emission factor) of the power system source-grid-load side, and realizes the monitoring and analysis of electricity carbon emissions in the selected area. The calculation results obtained can provide data support for the local power grid carbon emission assessment work, such as Figure 5 shown.

[0218] 1) Electricity carbon emissions

[0219] Electricity carbon emissions refers to an indicator that measures the amount of greenhouse gas emissions caused to the environment during the electricity production process. Carbon emissions are basically generated in the power generation, transmission and distribution, and power consumption processes. In this application, it is classified according to source-grid-load side carbon emissions.

[0220] ① Carbon emissions on the power generation side also refer to direct carbon emissions, and the calculation formula is as follows:

[0221]

[0222] in, is the total direct carbon emissions in the region, j is the type of energy, is the total power generation of energy j, r j is the carbon emission factor of energy j. The carbon emission factor, also known as the carbon emission coefficient, refers to the amount of carbon emissions generated by each unit of energy during the combustion or use of each energy. In theory, the carbon emission factor on the power generation side is about 0.875 kgCO2 / kWh, because most thermal power plants use coal combustion to produce carbon emissions. Biomass power generation uses biogas and other biomass carbon emission factors, which is about 0.018 kgCO2 / kWh. Clean energy such as photovoltaic wind power is uniformly considered to have zero carbon emissions, but in fact, the calculation of carbon emissions on the power generation side needs to take into account the proportion of energy. The current global average carbon emission factor for the power grid is about 0.57 kgCO2 / kWh.

[0223] ② Load-side carbon emissions refer to the indirect carbon emissions generated by the electricity consumption process in the region. The calculation formula is as follows:

[0224]

[0225] in, is the load-side carbon emissions in the region, is the total electricity consumption in the area, r e is the average electricity carbon emission factor. More specifically, if the power flow distribution of the regional power grid is known, in order to calculate more accurate load carbon emissions, the following method can be used:

[0226]

[0227] Among them, ui represents the number of grid load nodes, is the load power consumption of the user, The node carbon potential for each user node.

[0228] ③ Grid-side carbon emissions refer to the carbon emissions indirectly generated by the power grid in the region during the power transmission and distribution process. In this process, the carbon flow is transferred to the user side along with the power grid flow. Grid loss is one of the important reasons for its carbon emissions. Therefore, when calculating grid-side carbon emissions, it is briefly recorded as:

[0229]

[0230] in, is the carbon emission of network loss, is the grid loss power, r e is the average electricity carbon emission factor.

[0231] Similar to the load side, if the grid structure and power output mode are known, the carbon emissions caused by grid losses can be calculated more accurately:

[0232]

[0233] in, represents the carbon emission of network loss, Di represents the number of lines, is the network loss of line Di, is the carbon flow density of line Di.

[0234] 2) Regional electricity carbon emission density

[0235] Combined with the above electricity carbon emission accounting indicators, this application proposes an indicator to measure the regional electricity carbon level: regional electricity carbon emission density. Electricity carbon emission density mainly measures the spatial carbon emission intensity of a region, and the unit is tCO2 / m 2 Its indicators It can be expressed as:

[0236]

[0237] In the formula, represents the regional electricity carbon emission density, represents the carbon emissions of region a, S a Represents the area of ​​region a.

[0238] According to the different meanings of carbon emissions from the source and load grid, it can be further divided into: carbon emission density of power generation and carbon emission density of electricity consumption.

[0239] Among them, the carbon emission density of power generation is a measure of the carbon emission density of the area covered by the power generation of a power plant / generator unit. :

[0240]

[0241] in, is the total direct carbon emissions in the region, S a is the area of ​​region a, S d and Respectively represent the direct power supply area and indirect power supply area of ​​a power plant / generator unit (i.e. the excess power is transmitted to other areas)

[0242] Electricity carbon emission density is a measure of the carbon emission density of electricity consumption on the user side of a region. :

[0243]

[0244] In the formula, is the carbon emission density of electricity consumption on the user side, represents the area of ​​region a, Represents the number of grid load nodes, is the load power consumption of the user, The node carbon potential for each user node.

[0245] Going deeper, the carbon emission density per node can be calculated, i.e. :

[0246]

[0247] ui Represents a power supply node, which can be a power supply unit, substation, enterprise or user. Represents the coverage area of ​​a node.

[0248] This indicator further classifies and plans the carbon emissions of sources and loads in space, and fully explores the carbon emission level of a region, power plant or node. For example, multiple power plants produce the same carbon emissions in the same period of time, but their power supply areas are different, which will lead to differences in the evaluation of their own carbon emission levels. Nodes with wider power supply areas have lower carbon emission levels. Therefore, when evaluating the carbon emission level of a region / power plant / node, one cannot only consider the total carbon emissions, but also further evaluate its carbon emission density. This density is different from the carbon emission factor and node carbon potential. It further explores carbon emission information from a spatial perspective. The calculation results can provide a basis for regional carbon emission planning.

[0249] (2) Electricity carbon footprint accounting

[0250] Electricity carbon footprint accounting mainly refers to the process in which the carbon emission flow generated by energy consumption reaches the user side along with the transmission of the power grid flow. The carbon flow forms a carbon footprint as the power flow passes through nodes and branches, which is an important indicator for measuring the carbon index of the power grid, such as Figure 6 shown.

[0251] 1) Carbon flow density and nodal carbon potential

[0252] Carbon flow density refers to the amount of carbon emissions per unit of electricity flow, in kgCO2 / kWh. The calculation method is as follows:

[0253]

[0254]

[0255] Among them, F represents the branch carbon flow rate kgCO2 / h, that is, the carbon flow rate of the branch passing through the tidal current in unit time, R is the carbon emission, t represents time, refers to the carbon and sulfur density of the branch, and P represents the active power flow of the branch (kW).

[0256] Node carbon potential refers to the carbon emissions corresponding to unit electricity consumption at a circuit node. Nodes generally include power nodes, load nodes, substations and other equipment.

[0257]

[0258] In the formula, Indicates all branches; represents the active power flow from branch i into the node (kW); represents the carbon flow density flowing into node i (kgCO2 / kWh); Indicates branch carbon flow rate (kgCO2 / h); represents the carbon flow density flowing into node n; i represents the node number.

[0259] 2) Grid carbon potential fluctuation coefficient

[0260] Through the above indicators, this application proposes an indicator to measure the carbon footprint of a regional power grid: the carbon potential fluctuation coefficient of the power grid. The carbon flow flows with the tide, and the dynamic changes of carbon potential at its nodes - whether the carbon potential increases or decreases, this process is called "node carbon potential fluctuation", which accurately describes the increase or decrease in the content or intensity of the carbon flow when it is transferred between different nodes, reflects the dynamic balance and imbalance of each link in the electric carbon system, and represents the spatial carbon footprint distribution and development level of a regional power grid. The calculation formula can be expressed as:

[0261]

[0262]

[0263] in, Denoted as the node carbon potential of node i, It is recorded as the node carbon potential of the node after node i, and the direction is the carbon flow direction. represents the carbon potential fluctuation of node i, which is described as the process of the carbon potential change of the node. is the average carbon potential fluctuation rate of the power grid, representing the fluctuation direction of the carbon flow in a power grid, i is the node number, and n is the total number of nodes.

[0264] (3) Statistical analysis of carbon emission reduction in electricity

[0265] The statistical analysis method of power carbon emission reduction is mainly aimed at the analysis of source-grid-load carbon emission reduction in the selected region at multiple temporal and spatial scales. The obtained carbon neutrality analysis results can provide support for carbon trading and green electricity trading, such as Figure 7 shown.

[0266] Distributed clean energy can promote the low-carbon transformation of the power industry and improve its functional structure. The carbon emission reduction analysis of the power system is summarized into the following three indicators from the aspects of power generation, power consumption and green power trading:

[0267] 1) Carbon emissions reduction from power generation

[0268] The carbon emission reduction of power generation refers to the carbon emission of fossil energy offset by the use of clean energy such as photovoltaic, wind power, and hydropower on the power generation side, which can be expressed as:

[0269]

[0270]

[0271] In the formula, is the carbon emission reduction of clean energy power generation, c is the type of clean energy, is the total amount of clean energy power generation within a certain period of time, The average carbon emission factor of electricity from fossil fuels that are replaced to supply electricity to the region, is the energy supply structure ratio, is the carbon emission factor of fossil energy j.

[0272] 2) Electricity Emission Reduction

[0273] Electricity carbon emission reduction refers to the carbon emission of fossil energy offset by energy saving in electricity consumption by users, which can be expressed as:

[0274]

[0275] in,

[0276] in, is the total amount of emission reduction on the user side, Represents the amount of direct electricity reduction by users over a period of time, Represents the amount of clean energy consumption and emission reduction by users over a period of time. It represents the emission reduction when users use electricity instead of fossil energy for a certain activity. , Represent the user's electricity consumption at time t and t+1 respectively, , Represent the clean energy electricity consumption at time t and t+1 respectively, represents the amount of fossil energy required to complete activity x, represents the carbon emission factor of fossil energy j, represents the amount of electricity required to complete activity x, r e represents the average electricity carbon emission factor, x represents the activity number, and M represents the number of activities.

[0277] 3) Emission reduction from green electricity trading

[0278] Green electricity trading emission reduction refers to the carbon emissions of fossil energy offset by green electricity trading in the region / user, which can be expressed as:

[0279]

[0280] in, Green electricity trading emission reduction, W k is the green electricity traded by region / user k, Buy green electricity for k, Sell ​​green electricity for k, r e is the average electricity carbon emission factor.

[0281] 4) Electricity carbon emission reduction rate

[0282] Carbon neutrality generally refers to offsetting the carbon dioxide or greenhouse gas emissions generated by itself through energy conservation and emission reduction, achieving positive and negative offsets, and achieving relative "zero emissions". In the power industry, it can be understood that power carbon neutrality refers to reducing carbon dioxide emissions in the production and use of electricity through various means until a relatively net-zero level is reached. In theory, the carbon neutrality amount is the sum of the carbon reduction in electricity and the carbon sequestration reduction, but it is difficult to obtain accurate carbon sequestration data. Therefore, this application proposes a method for calculating carbon neutrality indicators for the power industry, power carbon emission reduction and power carbon emission reduction rate:

[0283]

[0284]

[0285]

[0286] in, is the carbon emission reduction of electricity, R represents the carbon emissions generated by the external grid power supply and power generation, is the electricity carbon emission reduction rate, c is the clean energy power generation node, j represents the power generation energy node that generates carbon emissions, k represents the green electricity transaction quantity serial number, ui represents the user load node, and Di represents the number of grid lines. In other carbon reduction methods that have not been classified, such as carbon sinks and carbon capture, the higher the carbon reduction degree, the greater the ratio; if , which shows that the power grid is effective in energy conservation and carbon emission reduction, with carbon emission reduction offsetting carbon emissions and approaching relative "zero carbon". Emission reduction for green electricity trading, Carbon emissions reduction for clean energy power generation, To reduce electricity consumption, is the total amount of clean energy power generation within a certain period of time, is the total power generation of energy source j, is the load power consumption of the user, is the node carbon potential of each user node, is the network loss of line Di, is the carbon flow density of line Di, r e is the average electricity carbon emission factor, W k For green electricity trading, It is the carbon emission of grid-side network loss.

[0287] The higher the carbon emission reduction, the greater the ratio; if , which shows that the power grid is effective in energy conservation and carbon emission reduction, with carbon emission reduction offsetting carbon emissions and approaching relative "zero carbon".

[0288] Step S3: Calculating the source-grid-load carbon neutrality index based on the distributed energy operation data and the regional carbon emission livelihood data, including:

[0289] Calculate carbon and livelihood analysis indicators based on regional carbon emissions and livelihood data;

[0290] Calculate the electricity carbon emission reduction analysis index based on distributed energy operation data and electricity carbon emission reduction index algorithm;

[0291] Among them, the source, grid and load carbon neutrality indicators include: carbon and people's livelihood analysis indicators and electricity carbon emission reduction analysis indicators.

[0292] Furthermore, the carbon and livelihood analysis indicators calculated based on regional carbon emissions and livelihood data include:

[0293] Calculate the carbon emission elasticity coefficient of the power economy based on indirect carbon emissions from electricity consumption and GDP growth rate;

[0294] Calculate the social carbon emission elasticity coefficient of electricity based on indirect carbon emissions from electricity consumption and per capita growth rate;

[0295] Among them, the carbon and people's livelihood analysis indicators include: electricity economic carbon emission elasticity coefficient and electricity social carbon emission elasticity coefficient.

[0296] Furthermore, the electricity economic carbon emission elasticity coefficient is calculated as follows:

[0297]

[0298] In the formula, represents the carbon emission elasticity coefficient of electricity economy, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the GDP at time t+1 and time t respectively.

[0299] Furthermore, the electricity social carbon emission elasticity coefficient is calculated as follows:

[0300]

[0301] In the formula, represents the social carbon emission elasticity coefficient of electricity, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the regional population at time t+1 and time t respectively.

[0302] Step S3 specifically includes:

[0303] (4) Analysis of carbon and people’s livelihood

[0304] Carbon and livelihood analysis mainly involves social indicators related to economic society and carbon, such as economic carbon emission intensity, economic carbon emission reduction measurement, population carbon emission intensity, etc. Figure 8 shown.

[0305] Electricity carbon productivity refers to electricity carbon emissions per unit of GDP, which is used to measure the economic carbon emission intensity of the region at the electricity level.

[0306]

[0307] in, refers to the carbon productivity of electricity, represents indirect carbon emissions from electricity consumption, and GDP is gross domestic product.

[0308] In order to make the relationship between economic development and electricity carbon emissions clearer, the electricity economic carbon emissions elasticity coefficient is proposed, which refers to the ratio of the growth rate of electricity carbon emissions to the growth rate of GDP:

[0309]

[0310] t represents time; if , indicating that the degree of economic growth is smaller than the degree of carbon emission growth in electricity, and low-carbon electricity and economic development are not balanced; if , indicating that the economic growth rate is greater than the growth rate of electricity carbon emissions. With the development of economic technology, the implementation of energy-saving policies for electricity consumption has achieved good results. There are two possible results. One is that the carbon emission growth rate is less than 0 and the economic growth rate is greater than 0, which means that with economic growth, electricity carbon emissions decrease instead, and the carbon emission reduction policy is very effective. On the contrary, it means that with economic decline, electricity carbon emissions increase, indicating that the development of the two is extremely unbalanced, and adjustments need to be made to the electricity carbon reduction plan at the economic capacity level.

[0311] Carbon emissions per capita from electricity consumption p The formula is:

[0312]

[0313] Among them, x p Represents the local population, Represents indirect carbon emissions from local electricity consumption.

[0314] In order to express the relationship between population growth rate and electricity carbon emission growth rate, the electricity social carbon emission elasticity coefficient is proposed, and the formula is:

[0315]

[0316] represents the social carbon emission elasticity coefficient of electricity, t represents time; if , indicating that the population growth rate is smaller than the electricity carbon emission growth rate, and low carbon and population development are relatively unbalanced; if , indicating that the population growth rate is greater than the carbon emission growth rate of electricity. As the population index increases, the effect of implementing the energy-saving policy is good. There are two possible results. One is that the carbon emission growth rate is less than 0 and the population growth rate is greater than 0, which means that with the rapid population growth, electricity carbon emissions decrease and the implementation of carbon emission reduction policies is very effective. On the contrary, it means that as the population growth rate decreases, the carbon emission growth rate increases instead, which shows that the development of the two is extremely unbalanced and it is necessary to adjust the electricity carbon reduction plan at the level of people's livelihood publicity.

[0317] Example 2

[0318] The application is further introduced below in conjunction with a source-grid-load power grid carbon index calculation example.

[0319] Considering the reality of distributed energy access, the above carbon emission assessment indicators are calculated using 33 nodes as an example:

[0320] like Fig. 9 As shown in the figure, node 1 of the distribution network is a thermal power plant with a power of 3.1MW, and its carbon emission factor is 0.875 tCO2 / MWh. Nodes 3, 19, 20, and 23 are connected to photovoltaics with a capacity of 200kW respectively. The network topology is shown in the figure. According to the carbon emission and carbon footprint flow calculation, based on 1 hour as the standard, the total carbon emission on the power generation side of the power grid is 2.71t, the line loss carbon emission on the grid side is 0.131 tCO2, and the carbon emission on the load side is 2.268 tCO2. The node carbon potential is shown in Table 1:

[0321] Table 1 Calculation results of 33-node carbon potential

[0322]

[0323] According to the carbon emission reduction accounting method, it can be calculated that, taking 1 hour as the standard, the carbon emission reduction on the power generation side in this example is about 0.44 tCO2, and the carbon emission reduction on the power consumption side is 0.663 tCO2. This example does not involve green electricity trading and external power transmission, and its electricity carbon emission reduction rate is about 21.6%.

[0324] Example 3

[0325] The present application based on the same inventive concept also provides a source-grid-load power grid carbon index assessment system, comprising:

[0326] Parameter acquisition module, used to obtain distributed energy operation data, flow data, regional area and regional carbon emission livelihood data;

[0327] A carbon peak index calculation module, used to calculate the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data;

[0328] A carbon neutrality index calculation module, used to calculate the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data;

[0329] A carbon index evaluation module is used to evaluate the source-grid-load power grid carbon index based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index.

[0330] Optionally, the carbon peak index calculation module includes:

[0331] The monitoring index calculation submodule is used to calculate the electricity carbon emission monitoring index based on the area of ​​the region and the distributed energy operation data using the electricity carbon emission algorithm;

[0332] The accounting index calculation submodule is used to calculate the electricity carbon footprint accounting index based on the power flow data by using the carbon flow algorithm and the power flow algorithm;

[0333] Among them, the source-grid carbon peak indicators include: electricity carbon emission monitoring indicators and electricity carbon footprint accounting indicators.

[0334] Optionally, the monitoring indicator calculation submodule is specifically used for:

[0335] Based on the source-side data, grid-side data and load-side data in the distributed energy operation data, direct carbon emissions, grid-side carbon emissions and load-side carbon emissions are calculated respectively;

[0336] Based on the direct carbon emissions, grid-side carbon emissions and load-side carbon emissions combined with the area of ​​the region, the regional electricity carbon emission density is obtained;

[0337] Among them, the electricity carbon emissions monitoring indicators include: direct carbon emissions, grid-side carbon emissions, load-side carbon emissions and regional electricity carbon emissions density.

[0338] Optionally, the regional electricity carbon emission density Calculate as follows:

[0339]

[0340] in, represents the regional electricity carbon emission density, represents the carbon emissions of region a, S a Represents the area of ​​region a.

[0341] Optionally, the accounting indicator calculation submodule is specifically used for:

[0342] Based on the carbon flow of the branch, the carbon flow of the branch passing through the branch with the tidal current in unit time is calculated;

[0343] Calculate branch carbon flow density based on the carbon flow passing through the branch with the tidal current in unit time and the branch active power flow in the tidal current data;

[0344] The node carbon potential is calculated based on the active power flow of the branch flowing into the node and the carbon flow density flowing into the node in the power flow data;

[0345] Calculating the carbon potential fluctuation of a node based on the carbon potentials of two adjacent nodes, and calculating the average carbon potential fluctuation rate of a power grid based on the carbon potential fluctuation of the node;

[0346] Among them, the electricity carbon footprint accounting indicators include: carbon flow density, node carbon potential, node carbon potential fluctuation and grid average carbon potential fluctuation rate.

[0347] Optionally, the node carbon potential is calculated as follows:

[0348]

[0349] In the formula, Indicates all branches; represents the active power flow from branch i into the node; represents the carbon flow density flowing into node i; represents the branch carbon flow rate; represents the carbon flow density flowing into node n; i represents the node number.

[0350] Optionally, the node carbon potential fluctuation is calculated as follows:

[0351]

[0352] In the formula, Denoted as the node carbon potential of node i, It is recorded as the node carbon potential of the node after node i, and the direction is the carbon flow direction. Represents the carbon potential fluctuation at node i.

[0353] Optionally, the carbon neutrality index calculation module includes:

[0354] The first calculation submodule is used to calculate carbon and livelihood analysis indicators based on regional carbon emission and livelihood data;

[0355] The second calculation submodule is used to calculate the electric carbon emission reduction analysis index based on the distributed energy operation data combined with the electric carbon emission reduction index algorithm;

[0356] Among them, the source, grid and load carbon neutrality indicators include: carbon and people's livelihood analysis indicators and electricity carbon emission reduction analysis indicators.

[0357] Optionally, the first computing submodule is specifically used for:

[0358] Calculate the carbon emission elasticity coefficient of the power economy based on indirect carbon emissions from electricity consumption and GDP growth rate;

[0359] Calculate the social carbon emission elasticity coefficient of electricity based on indirect carbon emissions from electricity consumption and per capita growth rate;

[0360] Among them, the carbon and people's livelihood analysis indicators include: electricity economic carbon emission elasticity coefficient and electricity social carbon emission elasticity coefficient.

[0361] Optionally, the electricity economic carbon emission elasticity coefficient is calculated as follows:

[0362]

[0363] In the formula, represents the carbon emission elasticity coefficient of electricity economy, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the GDP at time t+1 and time t respectively.

[0364] Optionally, the electricity social carbon emission elasticity coefficient is calculated as follows:

[0365]

[0366] In the formula, represents the social carbon emission elasticity coefficient of electricity, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the regional population at time t+1 and time t respectively.

[0367] Optionally, the second computing submodule is specifically configured to:

[0368] Calculate the carbon emission reduction of power generation based on the energy supply structure ratio, carbon emission factor of fossil energy and the total amount of clean energy power generation in the region;

[0369] Calculate electricity emission reduction based on user electricity consumption, clean energy electricity consumption, fossil energy required to complete activities, and average electricity carbon emission factor;

[0370] Calculate emission reductions from green electricity trading based on the green electricity volume traded by users in the region / area and the average electricity carbon emission factor;

[0371] The electricity carbon emission reduction rate is calculated based on the sum of electricity carbon emission reduction and the carbon emissions generated by external grid power supply and power generation.

[0372] The carbon emission reduction of power generation is calculated as follows:

[0373]

[0374] In the formula, is the carbon emission reduction of clean energy power generation, c is the type of clean energy, is the total amount of clean energy power generation within a certain period of time, The average carbon emission factor of electricity from fossil fuels that were replaced to supply electricity to the region.

[0375] Optionally, the electricity consumption reduction is calculated as follows:

[0376]

[0377] In the formula, is the total amount of emission reduction on the user side, Represents the amount of direct electricity reduction by users over a period of time, Represents the amount of clean energy consumption and emission reduction by users over a period of time. It represents the emission reduction when a user uses electricity instead of fossil energy when doing a certain activity.

[0378] Optionally, the emission reduction amount of green electricity trading is calculated as follows:

[0379]

[0380] In the formula, Green electricity trading emission reduction, W k is the green electricity traded by region / user k, Buy green electricity for k, Sell ​​green electricity for k, r e is the average electricity carbon emission factor.

[0381] Example 4

[0382] This application proposes a source-grid-load grid carbon index assessment system, such as Fig.10 As shown, its main evaluation object is the carbon emissions and carbon footprint of the active power grid in a certain area. Its method relies on algorithms such as electricity carbon emissions, electricity carbon flow, and electricity carbon emission reduction to specifically analyze the carbon index of the source, network, and load sides of the active power grid at multiple spatiotemporal scales. The proposed method for calculating the grid carbon index evaluation index is mainly implemented by functional modules, including integrated applications. The integrated application includes electricity carbon emissions analysis, electricity carbon footprint analysis, electricity carbon emission reduction analysis, carbon and people's livelihood analysis, etc. The integrated application realizes the function of a source-grid-load power grid carbon index evaluation system of the present application. Auxiliary functions include basic applications, application support layer, and data management layer. Among them, the basic applications include model management, data management, and topology management. The application support layer includes: model services, data services, and algorithm services. The data management layer includes: data calculation and data analysis. The architectural relationship is as follows Fig.10 shown.

[0383] A source-grid-load power grid carbon index assessment method and system considering distributed energy realizes multi-dimensional power carbon index analysis and display, and supports carbon index analysis on the source, grid, and load sides for new power systems. Through electricity carbon emission monitoring, it is helpful to grasp the electricity carbon emission situation and trend of regional sources and loads, support carbon emission dual control and electricity carbon trading; track the carbon footprint of the power grid, support low-carbon power dispatch and low-carbon operation; statistical analysis of electricity carbon emission reduction can provide reference for local governments to formulate electricity carbon credit, electricity carbon tax, and carbon trading policies; people's livelihood and electricity carbon big data assist in the formulation of economic and people's livelihood policies; with the minimum regional carbon emission as a constraint, provide technical support for low-carbon power regulation.

[0384] Example 5

[0385] like Fig.11 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0386] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a source-grid-load power grid carbon index assessment method in the above embodiment.

[0387] Example 6

[0388] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a source-grid-load grid carbon index assessment method in the above embodiment.

[0389] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0390] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0391] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0392] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0393] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A source-grid-load power grid carbon index assessment method, characterized in that: include: Obtain distributed energy operation data, flow data, regional area and regional carbon emission livelihood data; Calculate the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data; Calculate the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data; Evaluate the source-grid-load power grid carbon index based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index; The calculation of the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data includes: Based on the area of ​​the region and the distributed energy operation data, the electricity carbon emission algorithm is used to calculate the electricity carbon emission monitoring indicators; Based on the power flow data and the pre-built distributed energy model, power flow analysis is performed to obtain the electricity carbon footprint accounting index; Among them, the source-grid carbon peak indicators include: electricity carbon emission monitoring indicators and electricity carbon footprint accounting indicators; The distributed energy model is constructed using simulation software based on distributed energy parameters; The calculation of the source-grid-load carbon neutrality index based on the distributed energy operation data and the regional carbon emission livelihood data includes: Calculate carbon and livelihood analysis indicators based on regional carbon emissions and livelihood data; Calculate the electricity carbon emission reduction analysis index based on distributed energy operation data and electricity carbon emission reduction index algorithm; Among them, the source, grid and load carbon neutrality indicators include: carbon and people's livelihood analysis indicators and electricity carbon emission reduction analysis indicators.

2. The method according to claim 1, characterized in that The electricity carbon emission monitoring indicators are calculated based on the area of ​​the region and the distributed energy operation data using the electricity carbon emission algorithm, including: Based on the source-side data, grid-side data and load-side data in the distributed energy operation data, direct carbon emissions, grid-side carbon emissions and load-side carbon emissions are calculated respectively; The electric carbon emission density is obtained based on the direct carbon emissions, grid-side carbon emissions and load-side carbon emissions combined with the area of ​​the region; Among them, the electricity carbon emissions monitoring indicators include: direct carbon emissions, grid-side carbon emissions, load-side carbon emissions and regional electricity carbon emissions density.

3. The method according to claim 2, characterized in that The electricity carbon emission density includes: regional electricity carbon emission density, power generation carbon emission density, and electricity consumption carbon emission density; The regional electricity carbon emission density is calculated as follows: In the formula, represents the spatial carbon emission intensity, represents the carbon emissions of region a, S a represents the area of ​​region a; The carbon emission density of power generation is calculated as follows: In the formula, is the carbon emission density of power generation, and Respectively represent the direct power supply area and indirect power supply area of ​​a power plant / generator set. is the total direct carbon emissions in the region; The carbon emission density of electricity consumption is calculated as follows: In the formula, is the carbon emission density of electricity consumption on the user side, is the load-side carbon emissions in the region, Represents the number of grid load nodes, is the load power consumption of the user, The node carbon potential for each user node.

4. The method according to claim 1, characterized in that The power flow analysis is performed based on the power flow data combined with the pre-built distributed energy model to obtain the electricity carbon footprint accounting indicators, including: Based on the carbon flow of the branch in the flow data, calculate the carbon flow of the branch passing through the flow in unit time; Calculate branch carbon flow density based on the carbon flow passing through the branch with the tidal current in unit time and the branch active power flow in the tidal current data; The node carbon potential is calculated based on the active power flow of the branch flowing into the node and the carbon flow density flowing into the node in the power flow data; Calculating the carbon potential fluctuation of a node based on the carbon potentials of two adjacent nodes, and calculating the average carbon potential fluctuation rate of a power grid based on the carbon potential fluctuation of the nodes; Among them, the electricity carbon footprint accounting indicators include: carbon flow density, node carbon potential, node carbon potential fluctuation and grid average carbon potential fluctuation rate.

5. The method according to claim 4, characterized in that The node carbon potential fluctuation is calculated as follows: In the formula, represents the carbon potential fluctuation of node i, Denoted as the node carbon potential of node i, Denote it as the node carbon potential of the node after node i.

6. The method according to claim 5, characterized in that The average carbon potential fluctuation rate of the power grid is calculated as follows: In the formula, is the average carbon potential fluctuation rate of the power grid, i is the node number, and n is the number of nodes.

7. The method according to claim 1, characterized in that The carbon and livelihood analysis indicators calculated based on regional carbon emissions and livelihood data include: Calculate the carbon emission elasticity coefficient of the power economy based on indirect carbon emissions from electricity consumption and GDP growth rate; Calculate the social carbon emission elasticity coefficient of electricity based on indirect carbon emissions from electricity consumption and per capita growth rate; Among them, the carbon and people's livelihood analysis indicators include: electricity economic carbon emission elasticity coefficient and electricity social carbon emission elasticity coefficient.

8. The method according to claim 7, characterized in that The electricity economic carbon emission elasticity coefficient is calculated as follows: In the formula, represents the carbon emission elasticity coefficient of electricity economy, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the GDP at time t+1 and time t respectively.

9. The method according to claim 7, characterized in that The electricity social carbon emission elasticity coefficient is calculated as follows: In the formula, represents the social carbon emission elasticity coefficient of electricity, and They represent the indirect carbon emissions from regional electricity consumption at time t+1 and time t, and They represent the regional population at time t+1 and time t respectively.

10. The method according to claim 1, characterized in that The calculation of the electricity carbon emission reduction analysis index based on the distributed energy operation data combined with the electricity carbon emission reduction index algorithm includes: Calculate the carbon emission reduction of power generation based on the energy supply structure ratio, carbon emission factor of fossil energy and the total amount of clean energy power generation in the region; Calculate electricity emission reduction based on user electricity consumption, clean energy electricity consumption, fossil energy required to complete activities, and average electricity carbon emission factor; Calculate emission reductions from green electricity trading based on the green electricity volume traded by users in the region / area and the average electricity carbon emission factor; The electricity carbon emission reduction rate is calculated based on the sum of electricity carbon emission reduction and the carbon emissions generated by external grid power supply and power generation.

11. The method according to claim 10, characterized in that The carbon emission reduction of power generation is calculated as follows: In the formula, is the carbon emission reduction of clean energy power generation, c is the type of clean energy, is the total amount of clean energy power generation within a certain period of time, The average carbon emission factor of electricity from fossil energy sources that were replaced to supply electricity to the region is the energy supply structure ratio, is the carbon emission factor of fossil energy j.

12. The method according to claim 10, characterized in that The electricity reduction is calculated as follows: In the formula, is the total amount of emission reduction on the user side, Represents the amount of direct electricity reduction by users over a period of time, Represents the amount of clean energy consumption and emission reduction by users over a period of time. It represents the emission reduction when users use electricity instead of fossil energy for a certain activity. , Represent the user's electricity consumption at time t and t+1 respectively, , Represent the clean energy electricity consumption at time t and t+1 respectively, represents the amount of fossil energy required to complete activity x, represents the carbon emission factor of fossil energy j, represents the amount of electricity required to complete activity x, represents the average electricity carbon emission factor, x represents the activity number, and M represents the number of activities.

13. The method according to claim 10, characterized in that The emission reduction amount of green electricity trading is calculated as follows: In the formula, Emission reduction for green electricity trading, is the green electricity traded by region / user k, Buy green electricity for k, Sell ​​green electricity for k, is the average electricity carbon emission factor.

14. The method according to claim 10, characterized in that The electricity carbon emission reduction rate is calculated as follows: In the formula, is the electricity carbon emission reduction rate, c is the clean energy power generation node, j represents the power generation energy node that generates carbon emissions, k represents the number of green electricity transactions, ui represents the user load node, Di represents the number of grid lines, Indicates other carbon reduction methods that have not been classified. To reduce electricity consumption, is the total amount of clean energy power generation within a certain period of time, is the total power generation of energy source j, is the load power consumption of the user, is the node carbon potential of each user node, is the network loss of line Di, is the carbon flow density of line Di, is the average electricity carbon emission factor, For green electricity trading, The average carbon emission factor of electricity from fossil fuels that are replaced to supply electricity to the region, is the carbon emission factor of fossil energy j, To buy green electricity, To sell green electricity.

15. A source-grid-load power grid carbon index assessment system, characterized in that: include: Parameter acquisition module, used to obtain distributed energy operation data, flow data, regional area and regional carbon emission livelihood data; A carbon peak index calculation module, used to calculate the source-grid-load carbon peak index based on the area of ​​the region, the flow data and the distributed energy operation data; A carbon neutrality index calculation module, used to calculate the source-grid-load carbon neutrality index based on the distributed energy operation data and regional carbon emission livelihood data; A carbon index evaluation module, used to evaluate the source-grid-load power grid carbon index based on the source-grid-load carbon peak index and the source-grid-load carbon neutrality index; The carbon peak index calculation module includes: The monitoring index calculation submodule is used to calculate the electricity carbon emission monitoring index based on the area of ​​the region and the distributed energy operation data using the electricity carbon emission algorithm; The accounting index calculation submodule is used to perform power flow analysis based on power flow data combined with a pre-built distributed energy model to obtain the electricity carbon footprint accounting index; Among them, the source-grid carbon peak indicators include: electricity carbon emission monitoring indicators and electricity carbon footprint accounting indicators; The distributed energy model is constructed using simulation software based on distributed energy parameters; Carbon neutrality index calculation module, including: The first calculation submodule is used to calculate carbon and livelihood analysis indicators based on regional carbon emission and livelihood data; The second calculation submodule is used to calculate the electric carbon emission reduction analysis index based on the distributed energy operation data combined with the electric carbon emission reduction index algorithm; Among them, the source, grid and load carbon neutrality indicators include: carbon and people's livelihood analysis indicators and electricity carbon emission reduction analysis indicators.

16. The system of claim 15, wherein: The monitoring indicator calculation submodule is specifically used for: Based on the source-side data, grid-side data and load-side data in the distributed energy operation data, direct carbon emissions, grid-side carbon emissions and load-side carbon emissions are calculated respectively; Based on the direct carbon emissions, grid-side carbon emissions and load-side carbon emissions combined with the area of ​​the region, the regional electricity carbon emission density is obtained; Among them, the electricity carbon emissions monitoring indicators include: direct carbon emissions, grid-side carbon emissions, load-side carbon emissions and regional electricity carbon emissions density.

17. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a source-grid-load power grid carbon index assessment method as described in any one of claims 1 to 14 is implemented.

18. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a source-grid-load power grid carbon index assessment method as described in any one of claims 1 to 14 is implemented.

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