CN116976598A - Demand response low-carbon scheduling method and system based on carbon responsibility allocation - Google Patents
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Abstract
The application discloses a demand response low-carbon scheduling method and system based on carbon responsibility allocation. According to the application, by inputting the related operation data of the power generation side and the load side and combining with the load regulation and control target, the demand side response optimization scheduling strategy considering the carbon cost can be obtained, and the related departments are supported to formulate a scientific and reasonable demand response mode, so that the method has important significance for the intelligent and low-carbonization development of the power industry.
Description
Technical Field
The application belongs to the field of low-carbon economic dispatching of electric power systems, relates to a carbon emission flow theory of an electric power system, and particularly relates to a demand response low-carbon dispatching method and system based on carbon responsibility allocation.
Background
The high-proportion renewable energy source is connected into the power grid, so that the power supply and demand balance situation is more severe, reasonable demand side management is carried out, and the method is an important means for realizing the power grid supply and demand balance and reducing the fluctuation and peak-valley difference of the power demand. Since the user demand response will change carbon emissions, the carbon emission indicators are not considered in the traditional response strategy, making it difficult to evaluate the impact on the user's carbon quota and the carbon trade market. The current demand response strategy developed by the power grid enterprises aiming at the power users is mainly based on demand response of electricity prices, and in the background of a carbon market, carbon emission cost change caused by changing electricity consumption behaviors of the users can be utilized as an excitation signal. Therefore, the demand response improvement strategy under the carbon market environment is explored, the change of the carbon market can be better dealt with, the government, enterprises and individual consumers are promoted to actively participate in the carbon emission reduction movement, the carbon emission is reduced, and the development of clean energy is promoted.
Disclosure of Invention
The application aims to overcome the defects of the prior art, and provides a demand response low-carbon scheduling method and system based on carbon responsibility allocation, which solve the problem that the power demand response is difficult to consider carbon emission.
The application solves the technical problems by adopting the following technical scheme:
a demand response low-carbon scheduling method and system based on carbon responsibility allocation comprises the following steps:
step one, acquiring and constructing a novel power system carbon emission database facing to demand response;
step two, combining a carbon emission flow theory and a carbon emission responsibility allocation technology of an electric power system, identifying high-carbon nodes, and obtaining a carbon emission responsibility allocation result of users in an area where the demand response is carried out;
thirdly, constructing a low-carbon demand response optimization scheduling model by taking minimized carbon emission cost and demand response cost as targets and taking safe and stable operation conditions and the like of the power system as constraints;
step four, on the basis of a standard particle swarm optimization algorithm, the inertia weight and the acceleration coefficient of the particle swarm algorithm are improved, and an improved particle swarm algorithm suitable for solving a demand response optimization scheduling model is provided;
and fifthly, solving through a model, finally outputting response strategies of different demand response user nodes, and analyzing and obtaining benefits of the scheduling strategy in terms of load adjustment and carbon emission.
In addition, the construction of the novel power system carbon emission database facing the demand response in the first step is to combine the relevant data characteristics of the demand response of the user side, construct the novel power system carbon emission database with the main purpose of promoting the demand response of the user side and the supplementary purpose of allocating the carbon emission responsibility under the coordination of the enterprise greenhouse gas emission accounting and reporting guidelines and multiple policies, and mainly comprises the following steps:
step 1, constructing a database for user side demand response. The method comprises the steps of electric power market transaction data, user side load characteristic indexes and renewable energy source output data;
and 2, constructing a database based on enterprise greenhouse gas emission accounting and report guidance rechecking. The method comprises the following steps of generating carbon emission generated by burning fossil fuel at the power generation side and economic, production and meteorological indexes at the user side;
and 3, constructing a carbon emission database based on carbon emission responsibility allocation under the cooperation of multiple policies. The method comprises renewable energy quota and price, green certificate, price of green electricity transaction and thermal power generating unit cost data.
In addition, the carbon emission responsibility allocation technology in the second step adopts a load side carbon emission responsibility allocation method based on a node carbon trace strength-fairness theory, and mainly comprises the following steps:
step 1, quantitatively evaluating the trend distribution condition of a system by adopting a trend and carbon flow tracking method according to a carbon emission flow theory;
step 2, constructing a counter-current distribution matrix considering network loss and a column vector of carbon flow injection quantity of power generation nodes by applying a node carbon intensity evaluation method, and calculating by combining a tide result to obtain node carbon trace intensity (Footprint Carbon Intensity, FCI) of a system network and a carbon emission responsibility allocation result (constant value) under the FCI method;
and step 3, introducing a fairness interval concept applicable to the power system by referring to a related theory of distribution fairness and node related data, and constructing a carbon emission responsibility allocation fairness interval (interval value) aiming at a load side.
The FCI value calculation method of each node on the load side comprises the following steps:
step 1, under the condition of considering extra carbon emission of network loss, selecting the active power of the head end of each branch to calculate a countercurrent distribution matrix, and calculating the carbon flow relation of the system as follows:
in the method, in the process of the application,a total carbon flow vector for the node flow; c (C) fG And injecting column vectors for carbon flows of each power generation node, wherein the corresponding element of the non-power generation node is 0. In addition, for the novel power system,the proportion of renewable energy power generation is increased year by year, so that the carbon emission intensity of certain power generation nodes is 0, and therefore, the carbon flow injection amount of the power generation nodes is also 0./>To consider the reverse flow distribution matrix of net losses, its matrix elements can be expressed as:
wherein P is mn The active power of the head end of the branch mn flows into the node n; p (P) n Flow-through power (equal to the sum of injection or outflow power) for node n; Γ_ (m) is the incoming line set of node m.
Step 2, based on the calculated system carbon flow relation, constructing the following formula to represent the FCI vector of the system:
wherein P is n The power vector is flowed for the node.
Step 3, calculating to obtain the carbon emission responsibility allocation quantity of each load node at the load side by using the following formula:
x i ＝P Di F f(i)
wherein P is Di The amount of active power consumed for load member i; f (F) f(i) The FCI value of the node where the load member i is located.
And step 4, combining the principles of historical responsibility and individual equality, and constructing a responsibility allocation fairness interval according to the result and fairness theory after obtaining a quantitative responsibility allocation value based on the FCI allocation method, thereby providing a decision space with higher variability for a decision maker.
Moreover, the specific steps of high carbon node identification are as follows:
step 1, based on a carbon emission flow theory, carrying out carbon flow analysis and calculation on the whole power system to obtain data such as carbon flow, carbon flow density, node carbon potential and the like of each node;
step 2, calculating the carbon emission of the load side according to the output result of the key parameters;
step 3, calculating the carbon emission responsibility allocation quantity of the load side of the system according to the load flow and the carbon flow calculation result;
step 4, collecting actual measurement carbon emission summary results of the system, comparing and analyzing actual carbon and virtual carbon, and setting carbon emission and carbon emission responsibility allocation critical values (average values) according to carbon emission responsibility allocation results;
and 5, performing high-carbon node identification. To reasonably set the carbon emission threshold, the average value of the sum of the total emissions in both the "actual carbon" and "virtual carbon" scenarios is set as its threshold. In the two situations that the carbon emission amount and the carbon emission responsibility allocation amount both exceed the average value and that the carbon emission amount exceeds the average value but the carbon emission responsibility allocation amount does not exceed the average value, the node is judged to be a high carbon node, and in the other situations, the node is judged to be a non-high carbon node.
Moreover, the construction method of the low-carbon demand response optimization scheduling model comprises the following steps:
step 1, constructing an objective function by taking the minimum sum of the load side carbon emission cost and the demand response cost as the target,wherein N is T And N N Respectively the total time period and the total power grid node number, C CE,i,t The step-type carbon emission cost of the node i at the moment t is c DR Cost per unit for demand response->And->Respectively adjusting power up and down for the load of the node i at the moment t;
step 2, defining a calculation method of the carbon emission cost at the load side, taking the ladder-type carbon transaction cost as a principle, wherein the calculation method comprises the following steps:
wherein E is i,t The carbon emission amount of the node i at the time t is c CE,1 、c CE,2 、c CE,3 、c CE,4 For the unit cost of stepped carbon emission, E Bnd,1 、E Bnd,2 、E Bnd,3 Boundary quantity of a carbon emission price interval;
step 3, a low-carbon demand response optimization scheduling model mainly considers conditions such as load adjustment capability and carbon emission of a user, and sets model constraint as follows:
wherein D is DR,i,t Andthe load requirements after and before the node i responds at the moment t are respectively; lambda (lambda) i For the adjustable proportion of the node i load, the 1 st row and the 2 nd row respectively represent the upper and lower adjustable proportion limiting constraint of the node load, and the 3 rd row responds to the equality relation of the front and rear node loads.
Moreover, on the basis of a standard particle swarm optimization algorithm, the inertia weight and the acceleration coefficient of the particle swarm algorithm are improved, and a comprehensively improved particle swarm algorithm is provided to find the optimal demand side load index, and the method specifically comprises the following steps:
step 1, constructing and forming a low-carbon demand response optimization scheduling model, and determining the scale and credibility space of a population space;
step 2, completing the steps of determining fitness functions, setting parameters, initializing and the like on the basis of a traditional particle swarm algorithm;
and 3, when the inertia weight is adjusted, the standard particle swarm optimization algorithm usually depends on human experience to take values, so that dynamic adjustment is difficult, and the relation between local search and global search cannot be well balanced. Therefore, the inertia weight parameter which is dynamically and linearly decreased is set in the step, and compared with a fixed value, the method can achieve better optimization effect;
step 4, when setting dynamic acceleration coefficients, an improved thought of self-adjustment of a particle swarm optimization algorithm is provided, so that particles can more accurately approach an optimal solution in an iterative process, and the dynamic selection acceleration coefficients are considered, so that the particles are in the whole search space at the initial stage of particle search, the social information capacity is enhanced, the searching efficiency of the particles is improved as much as possible at the later stage of search, and the local searching capacity is enhanced;
step 5, providing a comprehensively improved particle swarm optimization algorithm by improving the inertia weight and the acceleration coefficient in a standard particle swarm algorithm, and preventing particles from flying out of a search range by controlling the flying speed of the particles and limiting the maximum flying distance of the particles;
and 6, taking the solution of the 0-1 variable as a sequence to be separated, outputting an optimal node parameter combination for regulating and controlling the resource at the demand side, and repeatedly solving to obtain an optimal solution of the scheduling strategy.
And the obtained optimal scheduling strategy comprises the response quantity and the response mode of each power system node user participating in the demand response, and before and after the user participates in the demand response, the system can respectively store the data such as load power, node carbon potential, node carbon emission and the like before and after each node response for demand response settlement and benefit analysis.
Moreover, the benefit analysis method of the low-carbon demand response strategy comprises the following steps:
step 1, recording the load of a node user, the carbon potential of the node and the carbon emission of the node before demand response scheduling;
step 2, calculating the carbon potential and the load after response of each user node after the demand response, and comparing the carbon potential and the load after response with corresponding data before response to obtain the influence of a low-carbon demand response strategy on system trend and carbon flow;
and step 3, calculating the carbon emission quantity of each user node after demand response, and comparing the carbon emission conditions of each node and the whole system respectively compared with the data before response and the traditional demand response strategy without considering carbon emission responsibility, thereby obtaining the benefits of the low-carbon demand response strategy in terms of load adjustment and carbon emission reduction.
The application has the advantages and positive effects that:
1. according to the application, factors of carbon emission are introduced into a demand response strategy, so that the maximum exploitation of the carbon reduction potential of the novel power system is realized. Aiming at the defect that the traditional carbon emission responsibility allocation method cannot be combined with the demand response value of the user side, a carbon emission responsibility allocation mechanism which takes fairness principles into consideration is provided for coupling the demand response low-carbon effect, and accurate identification of high-carbon emission nodes of the system is completed.
2. According to the application, an improved particle swarm algorithm is adopted to solve the demand response low-carbon scheduling model, and the method and the system can reduce the total carbon emission of the power system under the condition of meeting safe and stable operation, so that a more scientific and reasonable demand response strategy is formulated in the future by the support related departments, and the method and the system have important significance for the low-carbon development of the power industry.
Drawings
FIG. 1 is an overall flow chart of the present application;
FIG. 2 is a flow chart of the carbon flow calculation of the present application;
FIG. 3 is a carbon emission liability allocation flow chart of the FCI-fairness method of the present application;
fig. 4 is a flowchart of an implementation of the improved particle swarm algorithm of the present application.
Detailed Description
Embodiments of the application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the application and are not to be construed as limiting the application.
In the description of the application, it should be understood that the terms "center," "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate describing the application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the application. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The application discloses a demand response low-carbon scheduling method and system based on carbon responsibility allocation, which are creatively characterized in that: as shown in fig. 1, the method comprises the following steps:
firstly, constructing a novel power system carbon emission database facing to demand response, and firstly constructing a database facing to demand response of a user side, wherein the database comprises electric power market transaction data, user side load characteristic indexes and renewable energy output data; secondly, constructing a database based on enterprise greenhouse gas emission accounting and report guideline rechecking, wherein the database comprises carbon emission generated by burning fossil fuel at the power generation side and economic, production and meteorological indexes at the user side; and finally, constructing a carbon emission database based on carbon emission responsibility allocation under the cooperation of multiple policies. The method comprises renewable energy quota and price, green certificate, price of green electricity transaction and thermal power generating unit cost data.
Quantitatively evaluating the trend and the carbon flow distribution condition of the system by adopting a trend and carbon flow tracking method according to a carbon emission flow theory, wherein a carbon flow calculation flow chart is shown in figure 2;
thirdly, constructing a counter-current distribution matrix considering network loss and a column vector of carbon flow injection quantity of power generation nodes by applying a node carbon intensity evaluation method, and calculating by combining a tide result to obtain node carbon trace intensity (Footprint Carbon Intensity, FCI) of a system network and a carbon emission responsibility allocation result (constant value) under the FCI method;
and step four, introducing a fairness interval concept applicable to the power system by referring to a related theory of distribution fairness and node related data, and constructing a carbon emission responsibility allocation fairness interval (interval value) aiming at a load side, wherein a carbon emission responsibility allocation flow chart is shown in fig. 3.
Calculating the FCI value of each node at the load side, wherein the method comprises the following specific steps:
step 1, under the condition of considering extra carbon emission of network loss, selecting the active power of the head end of each branch to calculate a countercurrent distribution matrix, and calculating the carbon flow relation of the system as follows:
in the method, in the process of the application,a total carbon flow vector for the node flow; c (C) fG And injecting column vectors for carbon flows of each power generation node, wherein the corresponding element of the non-power generation node is 0. In addition, for the novel power system, the renewable energy power generation proportion is increased year by year, so that the carbon emission intensity of certain power generation nodes is 0, and therefore the carbon flow injection amount is also 0./>To consider the reverse flow distribution matrix of net losses, its matrix elements can be expressed as:
wherein P is mn The active power of the head end of the branch mn flows into the node n; p (P) n Flow-through power (equal to the sum of injection or outflow power) for node n; Γ_ (m) is the incoming line set of node m.
Step 2, based on the calculated system carbon flow relation, constructing the following formula to represent the FCI vector of the system:
wherein P is n The power vector is flowed for the node.
Step 3, calculating to obtain the carbon emission responsibility allocation quantity of each load node at the load side by using the following formula:
x i ＝P Di F f(i)
wherein P is Di The amount of active power consumed for load member i; f (F) f(i) The FCI value of the node where the load member i is located.
And step 4, combining the principles of historical responsibility and individual equality, and constructing a responsibility allocation fairness interval according to the result and fairness theory after obtaining a quantitative responsibility allocation value based on the FCI allocation method, thereby providing a decision space with higher variability for a decision maker.
Step six, identifying high-carbon nodes, which comprises the following specific steps:
step 1, based on a carbon emission flow theory, carrying out carbon flow analysis and calculation on the whole power system to obtain data such as carbon flow, carbon flow density, node carbon potential and the like of each node;
step 2, calculating the carbon emission of the load side according to the output result of the key parameters;
step 3, calculating the carbon emission responsibility allocation quantity of the load side of the system according to the load flow and the carbon flow calculation result;
step 4, collecting actual measurement carbon emission summary results of the system, comparing and analyzing actual carbon and virtual carbon, and setting carbon emission and carbon emission responsibility allocation critical values (average values) according to carbon emission responsibility allocation results;
and 5, performing high-carbon node identification. To reasonably set the carbon emission threshold, the average value of the sum of the total emissions in both the "actual carbon" and "virtual carbon" scenarios is set as its threshold. In the two situations that the carbon emission amount and the carbon emission responsibility allocation amount both exceed the average value and that the carbon emission amount exceeds the average value but the carbon emission responsibility allocation amount does not exceed the average value, the node is judged to be a high carbon node, and in the other situations, the node is judged to be a non-high carbon node.
Step seven, constructing a low-carbon demand response optimization scheduling model, which comprises the following specific steps:
step 1, constructing an objective function by taking the minimum sum of the load side carbon emission cost and the demand response cost as the target,wherein N is T And N N Respectively the total time period and the total power grid node number, C CE,i,t The step-type carbon emission cost of the node i at the moment t is c DR Cost per unit for demand response->And->Respectively adjusting power up and down for the load of the node i at the moment t;
step 2, defining a calculation method of the carbon emission cost at the load side, taking the ladder-type carbon transaction cost as a principle, wherein the calculation method comprises the following steps:
wherein E is i,t The carbon emission amount of the node i at the time t is c CE,1 、c CE,2 、c CE,3 、c CE,4 For the unit cost of stepped carbon emission, E Bnd,1 、E Bnd,2 、E Bnd,3 Boundary quantity of a carbon emission price interval;
step 3, a low-carbon demand response optimization scheduling model mainly considers conditions such as load adjustment capability and carbon emission of a user, and sets model constraint as follows:
wherein D is DR,i,t Andthe load requirements after and before the node i responds at the moment t are respectively; lambda (lambda) i For the adjustable proportion of the node i load, the 1 st row and the 2 nd row respectively represent the upper and lower adjustable proportion limiting constraint of the node load, and the 3 rd row responds to the equality relation of the front and rear node loads.
Step eight, solving the model in the step seven by adopting an improved particle swarm algorithm, wherein the algorithm flow chart is shown in fig. 4, and the steps are as follows:
step 1, constructing and forming a low-carbon demand response optimization scheduling model, and determining the scale and credibility space of a population space;
step 2, completing the steps of determining fitness functions, setting parameters, initializing and the like on the basis of a traditional particle swarm algorithm;
and 3, when the inertia weight is adjusted, the standard particle swarm optimization algorithm usually depends on human experience to take values, so that dynamic adjustment is difficult, and the relation between local search and global search cannot be well balanced. Therefore, the inertia weight parameter which is dynamically and linearly decreased is set in the step, and compared with a fixed value, the method can achieve better optimization effect;
step 4, when setting dynamic acceleration coefficients, an improved thought of self-adjustment of a particle swarm optimization algorithm is provided, so that particles can more accurately approach an optimal solution in an iterative process, and the dynamic selection acceleration coefficients are considered, so that the particles are in the whole search space at the initial stage of particle search, the social information capacity is enhanced, the searching efficiency of the particles is improved as much as possible at the later stage of search, and the local searching capacity is enhanced;
step 5, providing a comprehensively improved particle swarm optimization algorithm by improving the inertia weight and the acceleration coefficient in a standard particle swarm algorithm, and preventing particles from flying out of a search range by controlling the flying speed of the particles and limiting the maximum flying distance of the particles;
and 6, taking the solution of the 0-1 variable as a sequence to be separated, outputting an optimal node parameter combination for regulating and controlling the resource at the demand side, and repeatedly solving to obtain an optimal solution of the scheduling strategy.
Step nine, benefit analysis of a low-carbon demand response strategy, which comprises the following specific steps:
step 1, recording the load of a node user, the carbon potential of the node and the carbon emission of the node before demand response scheduling;
step 2, calculating the carbon potential and the load after response of each user node after the demand response, and comparing the carbon potential and the load after response with corresponding data before response to obtain the influence of a low-carbon demand response strategy on system trend and carbon flow;
and step 3, calculating the carbon emission quantity of each user node after demand response, and comparing the carbon emission conditions of each node and the whole system respectively compared with the data before response and the traditional demand response strategy without considering carbon emission responsibility, thereby obtaining the benefits of the low-carbon demand response strategy in terms of load adjustment and carbon emission reduction.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be emphasized that the embodiments of the present application are illustrative and not restrictive, and that this application includes not limited to the embodiments shown in the detailed description, but may be practiced by those skilled in the art to which the application pertains.
Claims (10)
1. A demand response low-carbon scheduling method and system based on carbon responsibility allocation is characterized in that: the method comprises the following steps:
step one, acquiring and constructing a novel power system carbon emission database facing to demand response;
step two, combining a carbon emission flow theory and a carbon emission responsibility allocation technology of an electric power system, identifying high-carbon nodes, and obtaining a carbon emission responsibility allocation result of users in an area where the demand response is carried out;
thirdly, constructing a low-carbon demand response optimization scheduling model by taking minimized carbon emission cost and demand response cost as targets and taking safe and stable operation conditions and the like of the power system as constraints;
step four, on the basis of a standard particle swarm optimization algorithm, the inertia weight and the acceleration coefficient of the particle swarm algorithm are improved, and an improved particle swarm algorithm suitable for solving a demand response optimization scheduling model is provided;
and fifthly, solving through a model, finally outputting response strategies of different demand response user nodes, and analyzing and obtaining benefits of the scheduling strategy in terms of load adjustment and carbon emission.
2. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 1, wherein: the first step is a construction of a novel power system carbon emission database facing to demand response, which is constructed by combining relevant data characteristics of demand response of a user side, and aims to promote the demand response of the user side, and the novel power system carbon emission database is constructed by taking enterprise greenhouse gas emission accounting, report guideline review and carbon emission responsibility allocation under multiple policy collaboration as supplement aims, and mainly comprises the following steps:
step 1, constructing a database for user side demand response; the method comprises the steps of electric power market transaction data, user side load characteristic indexes and renewable energy source output data;
step 2, constructing a database based on enterprise greenhouse gas emission accounting and report guideline rechecking; the method comprises the following steps of generating carbon emission generated by burning fossil fuel at the power generation side and economic, production and meteorological indexes at the user side;
step 3, constructing a carbon emission database for apportionment of carbon emission responsibilities under the cooperation of multiple policies; the method comprises renewable energy quota and price, green certificate, price of green electricity transaction and thermal power generating unit cost data.
3. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 1, wherein: the carbon emission responsibility allocation technology in the second step adopts a load side carbon emission responsibility allocation method based on a node carbon trace strength-fairness theory:
step 1, quantitatively evaluating the trend distribution condition of a system by adopting a trend and carbon flow tracking method according to a carbon emission flow theory;
step 2, constructing a counter-current distribution matrix considering network loss and a column vector of carbon flow injection quantity of power generation nodes by applying a node carbon intensity evaluation method, and calculating by combining a tide result to obtain node carbon trace intensity of a system network and a carbon emission responsibility allocation result under an FCI (fuzzy c-means) method, namely a fixed value;
and step 3, introducing a fairness interval concept applicable to the power system by referring to a related theory of distribution fairness and node related data, and constructing a carbon emission responsibility allocation fairness interval for a load side, namely an interval value.
4. A demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 3, wherein: the FCI value calculation method of each node at the load side comprises the following steps:
step 1, under the condition of considering extra carbon emission of network loss, selecting the active power of the head end of each branch to calculate a countercurrent distribution matrix, and calculating the carbon flow relation of the system as follows:
in the method, in the process of the application,a total carbon flow vector for the node flow; c (C) fG Injecting column vectors for carbon flows of all power generation nodes, wherein the corresponding element of the non-power generation node is 0; in addition, for the novel power system, the renewable energy power generation proportion is improved year by year, so that the carbon emission intensity of certain power generation nodes is 0, and therefore, the carbon flow injection amount is also 0; />To consider the reverse flow distribution matrix of net losses, its matrix elements can be expressed as:
wherein P is mn The active power of the head end of the branch mn flows into the node n; p (P) n The power flowing through the node n is equal to the sum of the power injected or flowing out; Γ_ (m) is the incoming line set of node m;
step 2, based on the calculated system carbon flow relation, constructing the following formula to represent the FCI vector of the system:
wherein P is n Flowing power vectors for the nodes;
step 3, calculating to obtain the carbon emission responsibility allocation quantity of each load node at the load side by using the following formula:
x i ＝P Di F f(i)
wherein P is Di The amount of active power consumed for load member i; f (F) f(i) The FCI value of the node where the load member i is located;
and step 4, combining the principles of historical responsibility and individual equality, and constructing a responsibility allocation fairness interval according to the result and fairness theory after obtaining a quantitative responsibility allocation value based on the FCI allocation method, thereby providing a decision space with higher variability for a decision maker.
5. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 1, wherein: the specific steps of high-carbon node identification are as follows:
step 1, based on a carbon emission flow theory, carrying out carbon flow analysis and calculation on the whole power system to obtain data such as carbon flow, carbon flow density, node carbon potential and the like of each node;
step 2, calculating the carbon emission of the load side according to the output result of the key parameters;
step 3, calculating the carbon emission responsibility allocation quantity of the load side of the system according to the load flow and the carbon flow calculation result;
step 4, collecting actual measurement carbon emission summary results of the system, comparing and analyzing actual carbon and virtual carbon, and setting carbon emission and carbon emission responsibility allocation critical values, namely average values, according to carbon emission responsibility allocation results;
step 5, performing high-carbon node identification work; setting the average value of the sum of total emissions under two situations of actual carbon and virtual carbon as a critical value of the carbon emission threshold value for reasonably setting the carbon emission threshold value; in the two situations that the carbon emission amount and the carbon emission responsibility allocation amount both exceed the average value and that the carbon emission amount exceeds the average value but the carbon emission responsibility allocation amount does not exceed the average value, the node is judged to be a high carbon node, and in the other situations, the node is judged to be a non-high carbon node.
6. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 1, wherein: the construction method of the low-carbon demand response optimization scheduling model objective function comprises the following steps:
step 1, constructing an objective function by taking the minimum sum of the load side carbon emission cost and the demand response cost as the target,wherein N is T And N N Respectively the total time period and the total power grid node number, C CE,i,t The step-type carbon emission cost of the node i at the moment t is c DR Cost per unit for demand response->And->Respectively adjusting power up and down for the load of the node i at the moment t;
step 2, defining a calculation method of the carbon emission cost at the load side, taking the ladder-type carbon transaction cost as a principle, wherein the calculation method comprises the following steps:
wherein E is i,t The carbon emission amount of the node i at the time t is c CE,1 、c CE,2 、c CE,3 、c CE,4 For the unit cost of stepped carbon emission, E Bnd,1 、E Bnd,2 、E Bnd,3 Is the boundary quantity of the carbon emission price interval.
7. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 1, wherein: the low-carbon demand response optimization scheduling model mainly considers conditions such as load adjustment capability and carbon emission of users, and sets model constraint as follows:
wherein D is DR,i,t Andthe load requirements after and before the node i responds at the moment t are respectively; lambda (lambda) i For the adjustable proportion of the node i load, the 1 st row and the 2 nd row respectively represent the upper and lower adjustable proportion limiting constraint of the node load, and the 3 rd row responds to the equality relation of the front and rear node loads.
8. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 1, wherein: on the basis of a standard particle swarm optimization algorithm, the inertia weight and the acceleration coefficient of the particle swarm algorithm are improved to obtain a comprehensively improved particle swarm algorithm so as to find an optimal demand side load index, and the method specifically comprises the following steps:
step 1, constructing and forming a low-carbon demand response optimization scheduling model, and determining the scale and credibility space of a population space;
step 2, completing the steps of determining fitness functions, setting parameters, initializing and the like on the basis of a traditional particle swarm algorithm;
step 3, when the inertia weight is adjusted, the standard particle swarm optimization algorithm usually depends on human experience to take values, so that dynamic adjustment is difficult, and the relation between local search and global search cannot be well balanced; therefore, the inertia weight parameter which is dynamically and linearly decreased is set in the step, and compared with a fixed value, the method can achieve better optimization effect;
step 4, when setting dynamic acceleration coefficients, an improved thought of self-adjustment of a particle swarm optimization algorithm is provided, so that particles can more accurately approach an optimal solution in an iterative process, and the dynamic selection acceleration coefficients are considered, so that the particles are in the whole search space at the initial stage of particle search, the social information capacity is enhanced, the searching efficiency of the particles is improved as much as possible at the later stage of search, and the local searching capacity is enhanced;
step 5, providing a comprehensively improved particle swarm optimization algorithm by improving the inertia weight and the acceleration coefficient in a standard particle swarm algorithm, and preventing particles from flying out of a search range by controlling the flying speed of the particles and limiting the maximum flying distance of the particles;
and 6, taking the solution of the 0-1 variable as a sequence to be separated, outputting an optimal node parameter combination for regulating and controlling the resource at the demand side, and repeatedly solving to obtain an optimal solution of the scheduling strategy.
9. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as set forth in claim 8, wherein: the obtained optimal scheduling strategy comprises the response quantity and the response mode of each power system node user participating in the demand response, and before and after the user participates in the demand response, the system can respectively store the data such as load power, node carbon potential, node carbon emission and the like before and after each node response for demand response settlement and benefit analysis.
10. The demand response low-carbon scheduling method and system based on carbon responsibility allocation as claimed in claim 9, wherein the method is characterized in that: the benefit analysis method of the low-carbon demand response strategy comprises the following steps:
step 1, recording the load of a node user, the carbon potential of the node and the carbon emission of the node before demand response scheduling;
step 2, calculating the carbon potential and the load after response of each user node after the demand response, and comparing the carbon potential and the load after response with corresponding data before response to obtain the influence of a low-carbon demand response strategy on system trend and carbon flow;
and step 3, calculating the carbon emission quantity of each user node after demand response, and comparing the carbon emission conditions of each node and the whole system respectively compared with the data before response and the traditional demand response strategy without considering carbon emission responsibility, thereby obtaining the benefits of the low-carbon demand response strategy in terms of load adjustment and carbon emission reduction.
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