A spatiotemporal coupling power system carbon flow distribution discrete analysis calculation method

Abstract

The application discloses a kind of spatiotemporal coupling's power system carbon flow distribution discrete analysis calculation method, belong to power system coupling analysis technical field.The present application is based on the time-space coupling characteristics of power supply and load in multiple continuous time section, and the rapid solution of branch / node carbon flow rate is carried out to carbon emission for efficient tracing, can guarantee the carbon balance of system as a whole and each link of electricity use, simultaneously, load node can realize emission reduction by optimizing electricity time sequence under the condition of not reducing total electricity consumption, promote new energy consumption.This application is a kind of new basic theory and calculation method for the statistics, tracing and analysis of carbon emission of power system.

Description

Technical Field

[0001] This invention belongs to the field of power system coupling analysis technology, and is particularly related to a spatiotemporally coupled discrete analysis and calculation method for carbon flow distribution in power systems. Background Technology

[0002] my country has the world's largest power grid. To achieve the goal of carbon neutrality, clarifying the responsibilities for carbon emissions, activating the emission reduction potential of each link, and promoting the consumption of new energy are key issues that the power system urgently needs to overcome from the perspective of "carbon". Due to the randomness and intermittency of new energy, different electricity consumption behaviors on the user side will generate different amounts of electricity carbon emissions. The existing control methods mainly have the following shortcomings: (1) The statistical methods for load-side carbon emissions that are currently put into practical application, including the carbon emission measurement standards and statistical principles in the fields of residential, commercial buildings, and transportation, are essentially macro-statistical methods based on carbon emission factors. They are insufficient in terms of refinement and analysis of the electricity-carbon generation mechanism; (2) The carbon flow analysis theory proposed in recent years couples "current flow" and "carbon flow" for calculation. Its main feature is that it adopts the principle of proportional sharing in the source tracing process, and the results focus on the evaluation of the "carbon emission responsibility" of the load. Considering that the key to the decarbonization of the power system lies in breaking through the bottleneck of high-proportion new energy consumption, the user's electricity consumption habits and time sequence patterns are one of the key factors for new energy consumption, but the carbon flow analysis theory does not take this into account. Therefore, while the quantitative results of carbon flow analysis theory have a certain degree of rationality and accuracy, they are not clearly guiding users' energy consumption behavior. Furthermore, because the analysis only uses a single time segment, the carbon emission statistics at different time segments are almost identical, which is detrimental to users' ability to obtain and efficiently utilize carbon emission data. Therefore, in order to incorporate users' electricity consumption behavior into the carbon emission allocation system, a completely new theory and calculation method for carbon flow analysis of the power system is needed. Summary of the Invention

[0003] To address the problems existing in the aforementioned background technology, this invention proposes a spatiotemporally coupled discrete analysis and calculation method for carbon flow distribution in power systems. This invention establishes a completely new fundamental theory and calculation method for the statistics, source tracing, and analysis of carbon emissions in power systems.

[0004] Therefore, the present invention adopts the following technical solution: a spatiotemporally coupled discrete analysis and calculation method for carbon flow distribution in a power system, comprising the following steps:

[0005] Step 1: Characterize the node coupling relationship. Obtain the absorption relationship between power source and load using a breadth-first search algorithm. The absorption relationship is a dynamic coupling relationship related to the power flow direction, represented by 0 and 1.

[0006]

[0007] In the formula, Power supply G q and load L p , the coupling relationship between the load L p and the power supply G q , the power consumption of the load L R , in the time period T, for any given power flow distribution, the accommodation relationship of all power sources and loads can be represented by a matrix Γ, as shown in equation (2):

[0008]

[0009] Step 2: Evaluate the accommodation contribution of the load to the new energy, calculate the accommodation contribution of each load node to the new energy based on the dynamic coupling relationship of the power source and the load, assuming that the observation time series of the new energy unit and the load node in the time period T are ω L and ω R , as shown in equation (3):

[0010]

[0011] E L (t1) and E ω (t1) represent the power generation of the new energy unit and the power consumption of the load in the first Δt step, respectively, N R is the number of sampling points in the time period T; assuming that the total cumulative power of ω L and ω R in the time period T is E L and E L-R , respectively, the simultaneous rate ξ L of the load L and the new energy R is as shown in equation (4):

[0012]

[0013] In the formula, r(t) and l(t) are the normalized value curves of the new energy and load power time series, respectively, d is the Euclidean distance of the first-order differential of the two curves, and λ is the similarity coefficient; the value range of ξ is [0, 1]; when the similarity of the load curve and the new energy curve decreases, the value of d increases, and the value of ξ decreases, which can be infinitely close to 0 when the load power is 0, and ξ = 0; on the contrary, when the load curve and the new energy output curve are close, d decreases, and ξ increases, until they are completely consistent, d = 0, and ξ = 1;

[0014] Define the green electricity index Ω L of the load node, which represents the contribution of the load node L to the accommodation of the new energy in the network under the coupling relationship, as shown in equation (5):

[0015]

[0016] In the formula, N is the number of new energy units coupled with the load node L. Total power of the generator set coupled with the load node L;

[0017] Step 3: Establish the node carbon conductance matrix, take the inverse of Ω L , define it as the node carbon conductance Φ L , as shown in formula (6):

[0018] Φ L = 1 / Ω L (6)

[0019] The carbon conductance properties of all nodes in the entire network can be characterized as the node carbon conductance matrix Φ, as shown in formula (7):

[0020]

[0021] Step 4: Set up a discrete allocation model objective, the direct carbon emissions Π G produced by the unit G in period T can be represented as formula (8):

[0022]

[0023] In the formula, E G is the power generation of the unit G, ρ G is the carbon density of energy; C(t i ) is the average coal consumption coefficient corresponding to the sampling point t i , unit: kgCO2 / kW.h, π G is the carbon emission factor corresponding to the fuel, E G (t i ) is the cumulative power generation of the sampling interval;

[0024] For a network composed of M units and N load nodes, the allocation objective is shown in formula (9):

[0025]

[0026] Step 5: Calculate the constraint conditions to be met, the direct carbon emissions generated by the system in any period T are equal to the equivalent indirect carbon emissions allocated by the load, as shown in formula (10):

[0027]

[0028] In the formula, N T is the total number of periods;

[0029] In any period T, the direct carbon emissions generated by any thermal power unit in the system are equal to the equivalent carbon emissions allocated by all load nodes for the unit, as shown in formula (11):

[0030]

[0031] At any time period T, the carbon emissions flowing into each load node balances with the carbon emissions flowing out, as shown in equation (12):

[0032]

[0033] Step 6: Solution method of discrete allocation, assuming that the electricity of all power sources and loads at T time period is shown in equation (13):

[0034]

[0035] and are the total electricity of the pth load node and qth power source at time period T, respectively, if the allocation coefficient is used to represent the proportion of the electricity of the allocated power source G p in the load node L q to the total electricity of L p , then the coefficient matrix of the whole network allocation can be represented as equation (14):

[0036]

[0037] By combining equations (1)-(7), the following balance equation is obtained, as shown in equation (15):

[0038]

[0039] The general solution of matrix R is derived as shown in equation (16):

[0040]

[0041] At this time, the carbon flow frame of the generator set G q transferred to the load node L p is shown in equation (17):

[0042]

[0043] The node carbon flow throughput at T time period can be obtained from the coupling relationship, and the node carbon flow rate is calculated from the throughput, as shown in equation (18):

[0044] The statistics, tracing and analysis of carbon emissions of the power system are completed.

[0045] This invention achieves the following beneficial effects: 1. By leveraging the spatiotemporal coupling characteristics of power sources and loads across multiple continuous time segments, this invention enables efficient source tracing of carbon emissions and rapid calculation of branch / node carbon flow rates. This ensures carbon balance across the entire system and all aspects of electricity consumption, while allowing load nodes to achieve emission reductions through optimized electricity timing without reducing total electricity consumption, thus promoting the integration of renewable energy. 2. By establishing a discrete analysis theory for carbon flow distribution in power systems, this invention extends the analysis and calculation of carbon flow distribution from a single time segment to multiple continuous time segments. Furthermore, it eliminates the need for carbon emission factors, allowing for decoupled calculation of power flow and carbon flow. This invention also establishes a carbon emission responsibility sharing mechanism based on renewable energy consumption contributions. From the perspective of emission reduction contributions, it incorporates the spatiotemporal coupling characteristics of power sources and loads into the carbon emission sharing system, enabling users to achieve emission reductions by outputting regulation capacity to the grid rather than reducing load. By constructing a discrete carbon emission sharing model and its calculation method, it achieves rapid solution and efficient source tracing of carbon emissions without relying on active power flow, improving the real-time performance and usability of the method. Attached Figure Description

[0046] Fig. 1 This is a schematic diagram of the carbon flow discrete analysis of the present invention.

[0047] Fig. 2 This is a schematic diagram of the discrete analysis method for carbon flow distribution in power systems according to the present invention.

[0048] Fig. 3 The flowchart of the carbon flow discrete allocation algorithm of the present invention is as follows. Detailed Implementation

[0049] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The described embodiments are only for illustration and explanation of the present invention and do not constitute the only limitation of the present invention.

[0050] like Figs. 1-3 As shown, the contribution of load to renewable energy consumption should take into account network constraints, that is, the allocation of carbon emissions must be based on the consumption relationship. The specific steps are as follows:

[0051] Step 1: Characterize node coupling relationships. The absorption relationship between power sources and loads can be obtained using a breadth-first search algorithm. The absorption relationship is a dynamic coupling relationship related to the power flow direction, represented by 0 and 1:

[0052]

[0053] In the formula, Indicates power supply G q With load L p The coupling relationship between them For load L p Absorption power supply G qpower. In period T, for any given power flow distribution, the accommodation relationship of all power sources and loads can be represented by matrix Γ as shown in equation (2).

[0054]

[0055] Step 2: Evaluate the accommodation contribution of loads to new energy. Based on the dynamic coupling relationship of power sources and loads, the accommodation contribution of each load node to new energy is calculated, assuming that the observed time series of new energy units and load nodes in period T are ω R and ω L as shown in equation (3):

[0056]

[0057] In the formula, E R (t1) and E L (t1) represent the power generation of new energy units and the power consumption of loads in the first Δt step, N ω is the number of sampling points in period T. Assuming that the total cumulative power of ω R and ω L in period T is E R and E L , the simultaneous rate ξ L-R of load L and new energy R is as shown in equation (4):

[0058]

[0059] In the formula, r(t) and l(t) are the normalized value curves of new energy and load power time series respectively, d is the Euclidean distance of the first-order derivatives of the two curves, and λ is the similarity coefficient. The value of ξ ranges from 0 to 1. When the similarity between the load curve and the new energy curve decreases, the value of d increases, and the value of ξ decreases, which can be infinitely close to 0 when the load power is 0; on the contrary, when the load curve is close to the new energy output curve, the value of d decreases, and the value of ξ increases, until they are completely consistent, d = 0, and ξ = 1.

[0060] Define the green electricity index Ω L of the load node, which represents the contribution of the load node L to the accommodation of new energy in the network under the coupling relationship as shown in equation (5):

[0061]

[0062] In the formula, N is the number of new energy units coupled with the load node L. is the total power of the generator units coupled with the load node L.

[0063] Step 3: Establish the node carbon guide matrix. Take Ω LThe inverse of the carbon conductance of a node is defined as the node carbon resistance Φ L As shown in equation (6):

[0064] Φ L = 1 / Ω L (6)

[0065] The carbon conductance properties of all nodes in the entire network can be characterized as a node carbon conductance matrix Φ, as shown in equation (7):

[0066]

[0067] An efficient solution algorithm is the key to the implementation of discrete analysis theory.

[0068] Step 4: Establish the objective of the discrete allocation model. In period T, the direct carbon emissions Π G can be represented as shown in equation (8):

[0069]

[0070] In the equation, E G is the power generation of unit G, ρ G is the carbon density of energy. C(t i ) is the average coal consumption coefficient corresponding to sampling point t i , with units of kgCO2 / kW.h, π G is the carbon emission factor corresponding to the fuel, and E G (t i ) is the cumulative power generation of the sampling interval.

[0071] For a network consisting of M units and N load nodes, the allocation objective is shown in equation (9):

[0072]

[0073] Step 5: Calculate the constraints that need to be met. In any period T, the direct carbon emissions generated by the system are equal to the equivalent indirect carbon emissions allocated to the load, as shown in equation (10):

[0074]

[0075] In the equation, N T is the total number of periods.

[0076] In any period T, the direct carbon emissions generated by any thermal power unit in the system are equal to the equivalent carbon emissions allocated to all load nodes for that unit, as shown in equation (11):

[0077]

[0078] For any time period T, the carbon emissions flowing into and out of each load node are balanced, as shown in equation (12):

[0079]

[0080] Step 6: Solution method for discrete allocation. Assume that the power consumption of all power sources and loads in time period T is as shown in equation (13):

[0081]

[0082] and Let T represent the total electrical power of the p-th load node and the q-th power source during time period T, respectively. If an allocation factor is used... Indicates load node L p Centrally distributed power supply G q The amount of electricity accounts for L p The proportion of total electricity consumption can be represented by the coefficient matrix of the entire network as shown in equation (14):

[0083]

[0084] Combining equations (1) to (7), it is not difficult to derive the following equilibrium equation, as shown in equation (15):

[0085]

[0086] Furthermore, the general solution of matrix R is derived as shown in equation (16):

[0087]

[0088] At this time, generator set G q Transmitted to load node L p The carbon flow frame is shown in equation (17):

[0089]

[0090] The node carbon flux throughput during time period T can be obtained from the coupling relationship, and the node carbon flux rate can be calculated from the throughput as shown in equation (18):

[0091]

[0092] The analytical method of this invention mainly solves three main problems as follows:

[0093] 1) Multi-period: Quantify the contribution of load to emission reduction based on time-series characteristics.

[0094] One of the key factors affecting new energy consumption is the user's electricity habit and timing law. Therefore, the carbon emission responsibility allocation can be established on the basis of a low-carbon-friendly electricity habit or timing law, and the analysis of a single time section is extended to the analysis of multiple continuous time periods.

[0095] 2) Process-oriented: Quantify the emission reduction contribution of the load operation process.

[0096] The purpose of carbon emission responsibility allocation is to promote user emission reduction, and the key to emission reduction is to improve the contribution of new energy consumption by load. Therefore, the carbon emission responsibility allocation mechanism should be established from the perspective of "emission reduction contribution". Under this allocation mechanism, the coupling between "power flow" and "carbon flow" is no longer established through the carbon emission factor, but the coupling between new energy consumption contribution (decarbonization target) and load timing flexibility (decarbonization method) is established based on timing characteristics.

[0097] 3) Real-time evaluation: Promote real-time consumption of high-proportion intermittent new energy through grid-load collaborative control.

[0098] New energy represented by wind power and photovoltaic power has significant randomness and intermittency. In the emission reduction process, high-proportion new energy access will bring great challenges to power grid dispatching. Under the condition of insufficient flexibility, the power grid is difficult to realize large-scale intermittent new energy access and consumption. Therefore, long-time-scale evaluation cannot effectively solve the new energy consumption problem, and the emission reduction process needs to consider the output characteristics of new energy, real-time evaluation, and adopt pre-forecasting, fine guidance, and precise load control to solve the real-time consumption problem of high-proportion new energy. In recent years, demand side represented by electric vehicles and temperature control load has developed on a large scale, and the flexibility and interaction ability have been greatly improved. The load state can be accurately adjusted based on Internet of Things technology, and online tracking of new energy output is realized, thereby effectively breaking through the bottleneck problem of intermittent new energy real-time consumption.

[0099] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for discrete analysis and calculation of carbon flow distribution of a spatiotemporally coupled power system, characterized in that The power system carbon flow distribution discrete analysis calculation method comprises the following steps: Step 1: Characterizing node coupling relationship, obtaining the accommodation relationship between power supply and load by breadth-first algorithm, the accommodation relationship is a dynamic coupling relationship related to the power flow direction, and is represented by 0 and 1: （1） In the formula, represents the coupling relationship between the power supply and the load , is the power of the load that can be accommodated by the power supply , and for any given power flow distribution in the time period T, the accommodation relationship of all power supplies and loads is represented by a matrix , as shown in formula (2): （2） Step 2: Evaluate the contribution of load to the accommodation of new energy, calculate the contribution of each load node to the accommodation of new energy based on the dynamic coupling relationship of power supply and load, and the observation time sequence of new energy units and load nodes within the time period T is and As shown in equation (3): （3） In the formula and respectively represent the first The power generation of the step new energy unit and the power consumption of the load, is the sampling point number in the time period T; the total cumulative power of the load L and the new energy R in the time period T is and respectively. and The simultaneous rate of the load L and the new energy R is is as shown in formula (4): （4） In the formula, and are the normalized value curves of new energy and load power time sequence respectively, d is the Euclidean distance of the first-order differential of the two curves, is the similarity coefficient; The value range is [0, 1]; when the similarity of the load curve and the new energy curve decreases, d increases, decreases, and the minimum is infinitely close to 0 when the load power is 0, =0; on the contrary, when the load curve and the new energy output curve are close, d decreases, increases until d=0 when they are completely consistent, =1; Defining a load node green electricity index , which represents the contribution of the load node L to the consumption of new energy in the network under the coupling relationship, as shown in equation (5): （5） In the formula, is the number of new energy units coupled with the load node L; is the total power of the generator units coupled with the load node L; Step 3: Establish the node carbon conductance matrix, take the reciprocal, define the node carbon conductance as shown in equation (6): （6） The carbon conductive properties of all nodes in the entire network are characterized as a node carbon conductance matrix As shown in equation (7): （7） Step 4: Set up discrete allocation model objective, direct carbon emissions from units G in period T is represented as formula (8): （8） wherein, is the power generation of the unit G, is the carbon density of the fuel; is the sampling point is the corresponding average coal consumption factor, in kgCO2 / kW.h, is the carbon emission factor of the fuel, is the cumulative power generation of the sampling interval; For a network composed of M units and N load nodes, the allocation target is shown as formula (9): （9） Step 5: Calculating the constraint condition to be met, the direct carbon emission generated by the system in any time period T is equal to the equivalent indirect carbon emission allocated by the load, as shown in formula (10): （10） In the formula, is the total number of time periods; In any time period T, the direct carbon emission generated by any thermal power unit in the system is equal to the equivalent carbon emission allocated by all load nodes for the unit, as shown in formula (11): （11） In any time period T, the carbon emission flowing into each load node is balanced with the carbon emission flowing out, as shown in formula (12): （12） Step 6: Solution method of discrete allocation, the electric quantity of all power supply and load in T time period is shown as formula (13): （13） and The total electrical energy of the p-th load node and the q-th power source during time period T is represented by the allocation factor. Indicates load node Centralized power supply The amount of electricity accounted for The proportion of total electricity consumption is then represented by the coefficient matrix for the entire network as shown in equation (14): （14） Comprehensive formula (1)-(7), the following balance equation is obtained, as shown in formula (15): （15） The general solution of matrix R is derived as shown in formula (16): （16） At this time, the power supply The carbon flow frame delivered to the load node is shown as equation (17): （17） The node carbon flow throughput in T time period is obtained from the coupling relationship, and the node carbon flow rate is calculated from the throughput, as shown in formula (18): (18) complete the statistics, tracing and analysis of carbon emissions of the power system.

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