Power system non-iterative low-carbon scheduling method and system based on carbon emission flow calculation and power flow decomposition

Abstract

The invention relates to the field of low-carbon electric power technology and electric power system optimization scheduling, in particular to an electric power system non-iterative low-carbon scheduling method and system based on carbon emission flow calculation and power flow decomposition, and the method comprises the steps: obtaining various types of input data in an electric power system, constructing an electric power carbon emission flow model based on power flow direction decomposition, a node carbon emission factor real-time calculation method and a carbon flow balance model suitable for non-iterative optimization solution are obtained; and finally, establishing a low-carbon dispatching optimization model of the power system, carrying out calculation and solution, and finally obtaining output results such as a unit output plan, a system power flow result and a node carbon emission factor. By establishing an electric power carbon emission flow model, synchronous calculation of a power flow equation and a carbon flow equation is achieved, the problem that traditional carbon flow calculation depends on a power flow calculation result is solved, load node carbon emission constraint is embedded in an optimized dispatching model, and a new technical path is provided for an electric power system to achieve refined low-carbon dispatching.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of low-carbon power, and more particularly, relates to a non-iterative low-carbon scheduling method and system for a power system based on carbon emission flow calculation and power flow decomposition. BACKGROUND

[0002] Traditional low-carbon economic scheduling of a power system mainly starts from the power supply side, and realizes carbon emission control at the system level by incorporating carbon emissions at the power generation side into an optimization scheduling model in the form of an objective function or a constraint condition. However, with the continuous improvement of the international carbon regulatory system, low-carbon scheduling modes for controlling carbon emissions at the user side or guiding user-side interactive carbon reduction have gradually attracted attention from the academic and industrial circles. In existing research, a dynamic carbon emission factor is a key signal for measuring carbon emissions at the user side, and the signal is mainly obtained according to a carbon emission flow calculation model. Therefore, in order to realize more reasonable low-carbon optimization scheduling of a power system, it is necessary to introduce the carbon emission flow model into the optimization model.

[0003] However, there are significant challenges in integrating the carbon emission flow model into the optimization scheduling framework, one of which is that the calculation of carbon emission flow must be based on determined power flow and power, and is realized through recursive calculation of the node carbon emission factor, which has strict topological dependence. This serial solving mechanism of “first power flow calculation and then carbon flow calculation” makes it impossible to directly embed the carbon emission flow model into the power system optimization scheduling model. The fundamental reason is that the power flow distribution of the system has not yet converged during the scheduling optimization process, and therefore the carbon emission flow of the power system cannot be directly solved.

[0004] Due to this technical bottleneck, existing research on user-side low-carbon optimization generally adopts an iterative solving strategy, the specific steps of which are as follows: first, perform traditional economic scheduling to obtain the power flow distribution and the node carbon emission factor, then perform carbon flow calculation, and finally feed back the carbon emission index to the scheduling model in the form of a penalty function or a constraint condition, and approximate the optimal solution through multiple iterations. It should be recognized that this method has three inherent defects: (1) convergence is not guaranteed, and may fall into a local optimum; (2) the calculation complexity is high, and the solving efficiency is low; (3) the optimization effect is subject to the number of iterations and the convergence threshold. The above limitations result in the fact that the iterative solving method is currently mainly limited to academic research, and is difficult to meet the strict requirements of calculation efficiency and reliability in actual engineering applications. SUMMARY

[0005] The purpose of the present application is to provide a non-iterative low-carbon scheduling method and system for a power system based on carbon emission flow calculation and power flow decomposition, to solve the problems in the prior art.

[0006] The present application is implemented by the following technical solutions: In a first aspect, the present application provides a non-iterative low-carbon scheduling method for a power system based on carbon emission flow calculation and power flow decomposition, comprising: obtaining basic parameters of the power system, constructing a power carbon emission flow model based on power flow direction decomposition, the power carbon emission flow model comprising a node carbon emission factor model and a carbon flow balance model, solving the node carbon emission factor based on the node carbon emission factor model, and solving the power flow and the carbon flow based on the carbon flow balance model; establishing a low-carbon scheduling optimization model for the power system with the minimum total operating cost of system units as the target, establishing an economic dispatch constraint set for the power system based on the basic parameters, and establishing a low-carbon dispatch constraint set for the power system based on the power carbon emission flow model; solving the low-carbon scheduling optimization model for the power system based on the economic dispatch constraint set for the power system and the low-carbon dispatch constraint set for the power system, and outputting the solution.

[0007] Preferably, the basic parameters include network topology, load prediction data, new energy output prediction data, generator unit parameters, line parameters, generator-node affiliation matrix, load-node affiliation matrix, line-node affiliation matrix, and PTDF matrix.

[0008] Preferably, the node carbon emission factor model comprises:

[0009] wherein, is the carbon emission factor of node , is the adjacent node set of node injecting power to node , is the set of generator units on node , is the power flowing from node to node , is the injection power of generator units on node , is the carbon emission factor of node , is the fuel emission factor vector of generator units.

[0010] Preferably, the carbon flow balance model comprises:

[0011] wherein, is the node set adjacent to node , is the carbon emission factor of node , a flow component from node k to node n, a flow component from node n to node k, a flow component from node n to node k, an upper load an outflow power, a set of upper loads of node n.

[0012] Preferably, the establishing of the low-carbon dispatching optimization model of the power system comprises:

[0013] wherein, a dispatching period, a first-order item offering cost vector of the generator unit, a column vector of the generator unit output at time t, a constant item offering cost vector of the generator unit, a column vector of the start-stop state of the generator unit at time t, a spare offering cost vector of the generator unit, a column vector of the spare of the generator unit at time t, a spare offering cost vector of the generator unit, a column vector of the spare of the generator unit at time t.

[0014] Preferably, the economic dispatching constraint set of the power system comprises a generator unit output upper and lower limit constraint, a generator unit ramping constraint, a generator unit minimum start-stop time constraint, a line transmission power constraint, a PTDF constraint, a system power balance constraint, and a system spare constraint, and the low-carbon dispatching constraint set of the power system comprises a total carbon emission amount constraint of the power system, a load node carbon emission factor constraint, and a load node carbon emission amount constraint.

[0015] Preferably, the total carbon emission amount constraint of the power system comprises:

[0016] wherein, a maximum carbon emission amount of the system in the dispatching period T.

[0017] Preferably, the load node carbon emission factor constraint comprises:

[0018] wherein, a load and node affiliation matrix, a column vector of the node carbon emission factor of all nodes at time period t, a column vector of the node carbon emission factor of all load nodes at time period t, a column vector of the upper limit of the node carbon emission factor of the load node at time period t.

[0019] Preferably, the load node carbon emission constraint comprises:

[0020] In the formula, is a column vector that sets an upper limit for the node carbon emission of the load node in the time period t, is load prediction data of the power system in the t time period, is a matrix operation of element-wise multiplication.

[0021] In a second aspect, the present application also provides a non-iterative low-carbon scheduling system for a power system based on carbon emission flow calculation and power flow decomposition, comprising: A model construction module is configured to acquire basic parameters of the power system, construct a power carbon emission flow model based on power flow direction decomposition based on the basic parameters, the power carbon emission flow model comprising a node carbon emission factor model and a carbon flow balance model, solve the node carbon emission factor based on the node carbon emission factor model, and solve the power flow and the carbon flow based on the carbon flow balance model; A target function module is configured to take the minimum total operation cost of the system as the target, establish a low-carbon scheduling optimization model for the power system, establish a set of economic scheduling constraints for the power system based on the basic parameters, and establish a set of low-carbon scheduling constraints for the power system based on the power carbon emission flow model; A solving module is configured to solve the low-carbon scheduling optimization model for the power system based on the set of economic scheduling constraints for the power system and the set of low-carbon scheduling constraints for the power system, and output the solving result.

[0022] The technical scheme of the present application has at least the following advantages and beneficial effects: The method provided by the present application first acquires various input data such as network topology, source and load prediction data, generator set parameters, line parameters, etc. in the power system; secondly, a power carbon emission flow model based on power flow direction decomposition is constructed, and then a real-time calculation method for the node carbon emission factor and a carbon flow balance model suitable for non-iterative optimization solving are obtained; finally, a low-carbon scheduling optimization model for the power system is established, and the carbon emission of the load node is embedded in the model in the form of a constraint for calculation and solving, and finally the output results such as the unit output plan, the system power flow result, and the node carbon emission factor are obtained. By using the above method, the power carbon emission flow model is established, the synchronous calculation of the power flow equation and the carbon flow equation is realized, the problem that the traditional carbon flow calculation depends on the power flow calculation result is solved, and the problem of low efficiency of iterative calculation is solved; by establishing a node-level carbon emission constraint mechanism, the carbon emission constraint of the load node is embedded in the optimization scheduling model, which provides a new technical path for the fine low-carbon scheduling of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0024] Fig. 1 The flowchart of the present application is shown in the figure. Fig. 2 The modified PJM5 node system diagram of the present application is shown in the figure. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all of the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0026] The independently described modules or sub-modules can be physically separated or not physically separated, can be software implemented or hardware implemented, and part of the modules or sub-modules can be implemented by software, and the functions of the part of the modules or sub-modules can be called by the processor, and the other part of the modules or sub-modules can be implemented by hardware, such as hardware circuit.

[0027] Please refer to Figs. 1-2 The present application provides a non-iterative low-carbon scheduling method for power system based on carbon emission flow calculation and power flow decomposition, which comprises: S101: obtaining the basic parameters of the power system, constructing the power carbon emission flow model based on the power flow direction decomposition, the power carbon emission flow model comprising a node carbon emission factor model and a carbon flow balance model, solving the node carbon emission factor based on the node carbon emission factor model, and solving the power flow and carbon flow based on the carbon flow balance model; S102: taking the minimum total operation cost of the system as the target, establishing a low-carbon scheduling optimization model for the power system, establishing a set of economic dispatching constraints for the power system based on the basic parameters, and establishing a set of low-carbon dispatching constraints for the power system based on the power carbon emission flow model; S103: solving the low-carbon scheduling optimization model for the power system based on the set of economic dispatching constraints for the power system and the set of low-carbon dispatching constraints for the power system, and outputting the solution.

[0028] Among them, the solution to the objective function can adopt the Lagrange multiplier method to integrate the constraint into the objective function, and convert it into an unconstrained problem. KKT condition: generalized Lagrange multiplier method, process inequality constraint. Projection gradient method, project back to the feasible region after gradient descent. Interior point method, convert the constraint optimization into a sequence of unconstrained problems by the barrier function, suitable for convex optimization.

[0029] The solution result includes unit output plan, system power flow result, node carbon emission factor and the like.

[0030] By adopting the method provided in the application, firstly, various input data such as network topology, source load prediction data, generator set parameters, line parameters and the like in the power system are acquired; secondly, a power carbon emission flow model based on power flow direction decomposition is constructed, and then a node carbon emission factor real-time calculation method and a carbon flow balance model suitable for non-iterative optimization solution are obtained; finally, a power system low-carbon dispatching optimization model is established, and the load node carbon emission amount is embedded into the model in the form of constraint, calculation and solution are carried out, and finally unit output plan, system power flow result, node carbon emission factor and the like output results are obtained. By adopting the above method, synchronous calculation of power flow equation and carbon flow equation is realized, the problem that traditional carbon flow calculation depends on power flow calculation result is solved, and the problem of low efficiency of iterative calculation is solved; by establishing a node-level carbon emission constraint mechanism, the load node carbon emission amount constraint is embedded in the optimization dispatching model, and a new technical path is provided for realizing fine low-carbon dispatching of the power system.

[0031] In an example embodiment of the application, the basic parameters include network topology, load prediction data, new energy output prediction data, generator set parameters, line parameters, generator and node affiliation matrix, load-node affiliation matrix, line-node affiliation matrix and PTDF matrix.

[0032] Specifically, the network topology is: The node set in the power system is ; the dispatching period set is ; the line set is ; the generator set is , wherein the new energy unit set is , the fossil energy unit set is , and there are ; the load set is .

[0033] The load prediction data is: The power system load prediction data at the t period is , . is a column vector containing D elements, and each element is the predicted value of the load d at the t period.

[0034] New energy output prediction data: The new energy output prediction data at time period t is , . is a column vector containing R elements, each element is the predicted output per unit of new energy unit r at time period t.

[0035] Generator set parameters: Generator set output upper and lower limit vectors , , , .

[0036] and is a column vector containing G elements, each element and represent the output upper limit and lower limit of the gth unit, respectively.

[0037] Generator set first-order term, constant term bid cost vectors , , , . , are column vectors containing G elements, each element , is the first-order term, constant term bid cost of unit g.

[0038] Generator set upper and lower reserve bid cost vectors , , , . , are column vectors containing G elements, each element , is the upper and lower reserve bid cost of unit g.

[0039] Generator set fuel emission factor vector , . is a column vector containing G elements, each element is the fuel emission factor of unit g.

[0040] Generator set maximum upper and lower ramp rate vectors , , , . and is a column vector containing G elements, each element and denote the maximum up and down ramp rates of the gth unit, respectively.

[0041] maximum power output vector of the initial and final start-up period of the generator units , , , . and is a column vector containing G elements, each element and denote the maximum power output of the gth unit at the initial and final start-up period, respectively.

[0042] minimum start-up and shut-down time vector of the generator units , , , . and is a column vector containing G elements, each element and denote the minimum start-up and shut-down time of the gth unit, respectively.

[0043] line parameters: line transmission power upper limit vector , . is a column vector containing L elements, each element denotes the transmission power upper limit of the lth line. generator and node affiliation matrix:

[0044] generator and node affiliation matrix , , . is an N×G matrix, each element is 1 if unit g belongs to node i, and 0 if unit g does not belong to node i.

[0045] load and node affiliation matrix: load and node affiliation matrix , . is an N×D matrix, each element is 1 if load d belongs to node i, and 0 if load d does not belong to node i.

[0046] line and node affiliation matrix: line and node affiliation matrix , . is an N x L matrix, each element is 1 if node i is the start node of line l, is -1 if node i is the end node of line l, is 0 if node i has no connection with line l.

[0047] PTDF matrix: Power Transfer Distribution Factor matrix , . is an N x L matrix, each element represents the Power Transfer Distribution Factor of node i to line l.

[0048] In one example embodiment of the present application, the carbon emission flow is a responsibility allocation framework that conceptualizes carbon emissions as virtual tags attached to power flows. This approach enables explicit tracking of emission transmission and accumulation within the power grid. Traditional regional carbon accounting methods uniformly allocate carbon responsibility to all users within a geographical boundary, regardless of their actual energy consumption characteristics. For example, users relying on clean energy (e.g., solar, wind) are assigned the same carbon responsibility as users using fossil fuels (e.g., coal, natural gas), which does not effectively incentivize the adoption of low-carbon energy. In contrast, the CEF, by calculating based on the actual physical distribution of power flows, decouples carbon responsibility from subjective regional divisions.

[0049] Establish a node power balance model and a carbon emission flow balance model.

[0050] Since carbon emission flows strictly follow the active power distribution law, to improve computational efficiency, power loss caused by network loss is ignored, and a direct current flow is used to establish a node power balance model as follows:

[0051] In the formula, , respectively represent the adjacent node set that injects power into node n and the adjacent node set that flows out power from node n through a single line; , respectively represent the set of generator units and the set of loads on node n; , respectively represent the power flowing from node i to node n and the power flowing from node n to node j; , respectively represent the injection power of generator unit g and the outflow power of load d on node n.

[0052] In the framework of virtual material flow, the inflow and outflow of carbon emission of a node must also follow the law of conservation of mass. The carbon emission flow balance model is shown as follows

[0053] wherein, , respectively represent the carbon flow from node i to node n and from node n to node j; , respectively represent the injected carbon flow of generator g and the outflow carbon flow of load d of node n.

[0054] According to the principle of fair mixing, all carbon emissions of a node have the same carbon intensity, i.e. equal to the carbon emission factor of the node. Therefore, the node carbon emission flow balance model can be rewritten as:

[0055] According to the combination of the node power balance model and the node carbon emission flow balance model, the node carbon emission factor model is obtained:

[0056] wherein, is a fuel emission factor vector of generator, , respectively represent the node carbon emission factor of node and node .

[0057] In an example embodiment of the present application, the line carbon flow is obtained by multiplying the line power flow by the node carbon emission factor of the source node of the line flow, so that the calculation of carbon flow depends on the determination of the direction of line flow. However, the specific direction of line flow cannot be determined before the calculation of power flow, so that the node carbon flow balance equation shown in the node carbon flow balance model cannot be directly embedded into the optimization scheduling model, which is the reason why the traditional research adopts the serial solution mode of “first power flow calculation, then carbon flow calculation”.

[0058] To solve this problem, the present application proposes a power flow direction decomposition method, which eliminates the dependence of carbon flow calculation on the direction of line flow, so that the synchronous non-iterative solution of power flow calculation and carbon flow calculation can be realized. The specific implementation method is as follows: for a line , the start node and the end node of the line are , , the power flow direction from to is defined as the positive direction, and the actual power flow of the line Perform forward and reverse disassembly, that is, define and are the forward and reverse power flow components of line l, respectively, satisfying the following constraints:

[0059]

[0060] Establish a carbon flow balance model suitable for non-iterative solution; After the tidal direction decomposition, the carbon flow balance model can be rewritten from the node carbon emission flow balance model into the following two forms:

[0061] Where, For nodes The set of adjacent nodes, For nodes The carbon emission factor, is the power flow component from node k to node n, is the power flow component from node n to node k, For nodes Load outflow power.

[0062] Comparing the node carbon emission flow balance model with the carbon flow balance model reveals that the node carbon emission flow balance model requires screening the lines and corresponding adjacent nodes with actual flow input and flow out of node n in order to write the carbon flow balance equation, making it impossible to solve it programmatically. The carbon flow balance model, on the other hand, only requires the adjacent lines and nodes of node n to write the carbon flow balance equation, allowing it to be directly embedded in the power system optimization dispatch model for solution.

[0063] In an exemplary embodiment of the present invention, establishing a low-carbon dispatch optimization model for a power system includes:

[0064] Where, For the scheduling period, is the primary bidding cost vector of the generator set, is the generator output column vector at time t, is the constant term bid cost vector for the generator set, is the start and stop state column vector of the generator set at time t, is the reserve bid cost vector for the generator set, is the spare column vector of the generator set at time t, is the reserve bid cost vector of the generator set, is the reserve column vector of the generator set at time t.

[0065] Secondly, the constraint set of the economic dispatch of the power system includes the upper and lower limits of the output of the generator set, the ramping constraint of the generator set, the minimum start-stop time constraint of the generator set, the transmission power constraint of the line, the PTDF constraint, the system power balance constraint and the system reserve constraint, and the constraint set of the low-carbon dispatch of the power system includes the total carbon emission constraint of the power system, the carbon emission factor constraint of the load node and the carbon emission constraint of the load node.

[0066] Specifically, the upper and lower limits of the output of the generator set are as follows:

[0067] In particular, for the new energy unit, there are:

[0068] In the formula, “ ” represents the matrix operation of element-by-element multiplication, is the lower limit of the output of the new energy unit, is the upper limit of the output of the new energy unit, is the start-stop state column vector of the new energy unit at time t.

[0069] The ramping constraint of the generator set is as follows: The generator set, especially the coal-fired unit, cannot have a dramatic change in power output in a time period due to the physical limitations such as the heating of the boiler and the inertia of the steam turbine, so the output change must be subject to the ramping rate constraint.

[0070]

[0071] The minimum start-stop time constraint of the generator set is as follows: Due to the technical requirements of the boiler, steam turbine and other equipment, once the unit is started, it must be continuously operated for a period of time and cannot be immediately shut down, and similarly, after the unit is shut down, it must be kept for a certain period of time before it can be restarted, so the power generation dispatch of the generator set must satisfy the minimum start-up time and shutdown time constraints.

[0072]

[0073] In the formula, represents the start-up or shutdown time column vector of the generator set at time t, , element represents the start-up or shutdown time of the unit g at time t, which is positive for start-up and negative for shutdown.

[0074] The transmission power constraint of the line is as follows:

[0075] In the formula, denotes the transmission power column vector of the line i at time period t, , element denotes the transmission power of the line i at time period t.

[0076] PTDF constraint: Since the carbon emission flow is only transmitted with the active power flow, the PTDF-based DC power flow calculation method can simplify the network parameters of the power grid, thereby realizing fast solution of the power flow and the carbon flow.

[0077]

[0078] In the formula: denotes the injection power column vector of the system at time period t.

[0079] System power balance constraint:

[0080] System reserve constraint:

[0081] Secondly, the carbon flow balance equation constraint is also included, and the carbon flow balance model is included in the low-carbon scheduling constraint set of the power system in the form of an equality constraint, wherein, is the reserve demand capacity of the system at time period t.

[0082] The present application proposes three low-carbon scheduling constraints for the carbon reduction demand of the power system, which are respectively the total carbon emission amount constraint of the power system, the carbon emission factor constraint of the load node and the carbon emission amount constraint of the load node. In actual application, appropriate constraints and constraint strength can be selected according to specific conditions.

[0083] The total carbon emission amount constraint of the power system comprises:

[0084] In the formula, is the maximum carbon emission amount preset for the system in the scheduling period T.

[0085] The carbon emission factor constraint of the load node comprises:

[0086] In the formula, is the load and node affiliation matrix, is the node carbon emission factor column vector of all nodes at time period t, is the node carbon emission factor column vector of all load nodes at time period t, is the node carbon emission factor upper limit column vector of the load node at time period t.

[0087] Load node carbon emission constraint: in actual industrial production, regulatory policies usually have requirements for the total carbon emissions of production enterprises. The corresponding node-level total carbon emission constraint is:

[0088] In the formula, is a column vector that sets an upper limit for the node carbon emission of the load node at period t, is the load prediction data of the power system at period t, is a matrix operation of element-by-element multiplication.

[0089] A power system non-iterative low-carbon scheduling system based on carbon emission flow calculation and power flow decomposition, comprising: A model construction module configured to obtain basic parameters of a power system, construct a power carbon emission flow model based on power flow direction decomposition based on the basic parameters, the power carbon emission flow model comprising a node carbon emission factor model and a carbon flow balance model, solve the node carbon emission factor based on the node carbon emission factor model, and solve the power flow and the carbon flow based on the carbon flow balance model; A target function module configured to take the minimum total operating cost of the system as the target, establish a power system low-carbon scheduling optimization model, establish a power system economic dispatch constraint set based on the basic parameters, and establish a power system low-carbon dispatch constraint set based on the power carbon emission flow model; A solving module configured to solve the power system low-carbon scheduling optimization model based on the power system economic dispatch constraint set and the power system low-carbon dispatch constraint set, and output the solving result.

[0090] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0091] The embodiment of the present application takes a modified version of the PJM-5 node system as the implementation object, and a schematic diagram of the modified version of the PJM-5 node system is as follows: Fig. 2The system includes five units, of which G1 is a wind turbine unit, G2 is a photovoltaic turbine unit, G3 is a gas turbine unit, and G4 and G5 are coal-fired turbine units. The parameters of the turbine units are shown in Tables 1 and 2. The system includes loads D1-D3, of which D1 is an industrial high-energy consumption load. The system source load prediction data are shown in Table 3. Three operating scenarios are set in the embodiment of the present application, of which scenario A is a benchmark scenario, i.e., traditional economic dispatch without considering carbon emission constraints; scenario B is low-carbon economic dispatch considering the total system carbon emission constraints, and the carbon emission reduction intensity is set to 1%-6% of the total system carbon emission; and scenario C is low-carbon economic dispatch considering the node carbon emission constraints, and the constraint node is D3, and the carbon emission reduction intensity is also set to 1%-6% of the total system carbon emission. The optimization model is written in Python language and is solved by using a mature commercial solver.

[0092] Table 1 Reference table of power generation cost and direct carbon emission factor of turbine units

[0093] Table 2 Table of technical parameters of turbine units

[0094] The optimization scheduling results under each scenario are shown in Table 4, and the carbon emissions of loads 1-3 under each scenario are shown in Table 5. It can be seen that, by using the non-iterative solving method proposed in the present application, the simultaneous calculation of power flow and carbon flow can be realized by setting different levels of carbon emission constraints, so as to realize the precise carbon control target.

[0095] From the perspective of the whole system, it can be seen from the comparison between scenario A and scenario C that, with the increase of the carbon emission reduction ratio, the optimization scheduling result of scenario A remains unchanged at the carbon emission reduction ratio of 1%-2%, and then presents the trend of increasing total cost and reducing total carbon emission, the maximum total carbon emission reduction is 426.47 tons, and the maximum carbon emission reduction cost is 48.83 dollars / ton. From the perspective of load 3, with the increase of the carbon emission reduction ratio, the carbon emission reduction of load 3 strictly meets the requirement of the node carbon emission constraint, and presents the approximate linear downward trend.

[0096] Table 3 Table of system source load prediction data

[0097] Comparing scenario B with scenario C, from the perspective of the whole system, as the carbon emission reduction ratio increases, the total cost of scenario B gradually increases, the total carbon emission gradually decreases, the total carbon emission reduction strictly meets the system carbon emission constraint requirement, and presents a linear downward trend, the maximum total carbon emission reduction is 750.44 tons, and the maximum carbon emission reduction cost is 42.77 dollars / ton. From the perspective of load 3, as the carbon emission reduction ratio increases, the carbon emission reduction of load 3 as a whole presents an increasing trend, but the proportion of the carbon emission reduction of load 3 in the total carbon emission reduction is between 23.59% and 42.25%.

[0098] Table 4 Reference table of optimization scheduling results of each scenario

[0099] Table 5 Reference table of carbon emissions of loads 1-3 under each scenario

[0100] By comparing the scheduling results of scenario A and scenario B, it can be found that implementing the whole system carbon emission constraint cannot realize node-level precise carbon reduction, nor can it realize synchronous emission reduction of all nodes. In fact, as the carbon emission reduction intensity increases, in order to realize the reduction of the whole system carbon emission, the proportion of low-carbon or zero-carbon units in the total power generation will increase, which causes the node close to the new energy unit (such as the node where load 2 is located in this embodiment) to possibly consume more low-carbon power, thereby reducing its node carbon emission factor, while the node carbon emission factor of other nodes (such as the node where load 1 is located in this embodiment) may increase due to the change of power flow. Therefore, when implementing the whole system carbon emission constraint, the carbon emission quota of all enterprises in the dispatching area must be considered comprehensively to avoid unreasonable carbon emission responsibility transfer. When implementing the single-node carbon emission constraint, it can be observed that the carbon emission of load 3 gradually decreases, the carbon emissions of load 1 and load 3 remain basically unchanged after the carbon emission reduction ratio reaches 3%, and the total carbon emission of the system first remains unchanged and then decreases. This is because the system adjusts the output power of the generator unit, and reorients the new energy carbon flow originally preferentially flowing to load 2 to load 3, which is completely opposite to the case of requiring all generator units to preferentially supply power to adjacent nodes under the whole system constraint.

[0101] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. The computer software product stored in a storage medium includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0102] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A non-iterative low-carbon dispatching method for power systems based on carbon emission flow calculation and power flow decomposition, characterized in that, The method comprises the following steps: acquiring basic parameters of a power system, constructing a power carbon emission flow model based on power flow direction decomposition, the power carbon emission flow model comprising a node carbon emission factor model and a carbon flow balance model, solving node carbon emission factors based on the node carbon emission factor model, and solving power flow and carbon flow based on the carbon flow balance model; establishing a power system low-carbon dispatch optimization model with the minimum total operating cost of system units as the target, establishing a power system economic dispatch constraint set based on the basic parameters, and establishing a power system low-carbon dispatch constraint set based on the power carbon emission flow model; solving the power system low-carbon dispatch optimization model based on the power system economic dispatch constraint set and the power system low-carbon dispatch constraint set, and outputting a solution result.

2. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 1, characterized in that, The basic parameters comprise network topology, load prediction data, new energy output prediction data, generator unit parameters, line parameters, a generator-node affiliation matrix, a load-node affiliation matrix, a line-node affiliation matrix, and a PTDF matrix.

3. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 2, characterized in that, The node carbon emission factor model comprises: wherein, is the carbon emission factor of node , is the set of adjacent nodes injecting power to node via a single line, is the set of generator units on node , is the power flowing to node , is the power flowing to node , is the injection power of generator units on node , is the carbon emission factor of node , is the vector of generator unit fuel emission factors.

4. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 3, characterized in that, The carbon flow balance model comprises: wherein, is a set of nodes adjacent to node is a carbon emission factor for node is a flow component from node k to node n, is a flow component from node n to node k, is an outflow power of node is a set of loads on node n.​​​​ 5. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 4, characterized in that, The establishment of the power system low-carbon dispatch optimization model comprises: wherein, is the dispatch period, is the generator unit constant term offer cost vector, is the generator unit output column vector at t, is the generator unit constant term offer cost vector, is the generator unit start-up and shut-down state column vector at t, is the generator unit on-standby offer cost vector, is the generator unit on-standby column vector at t, is the generator unit off-standby offer cost vector, is the generator unit off-standby column vector at t.

6. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 5, characterized in that, The power system economic dispatch constraint set comprises generator unit output upper and lower limit constraints, generator unit ramping constraints, generator unit minimum start-stop time constraints, line transmission power constraints, PTDF constraints, system power balance constraints, and system reserve constraints, and the power system low-carbon dispatch constraint set comprises a total power system carbon emission amount constraint, a load node carbon emission factor constraint, and a load node carbon emission amount constraint.

7. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 6, characterized in that, The total power system carbon emission amount constraint comprises: In the formula, is the maximum carbon emission preset by the system within the dispatch period T.

8. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 7, characterized in that, The load node carbon emission factor constraint comprises: wherein, is a matrix of load and node dependencies, is a column vector of node carbon emission factors for all nodes at period t, is a column vector of node carbon emission factors for all load nodes at period t, is a column vector of prescribed upper limits of node carbon emission factors for load nodes at period t.

9. The non-iterative low-carbon dispatching method for power system based on carbon emission flow calculation and power flow decomposition according to claim 7, characterized in that, The load node carbon emission amount constraint comprises: wherein is a vector of upper bounds on node carbon emissions for load nodes at time period t, is the power system load forecast data for time period t, is an element-wise multiplication of matrices.

10. A non-iterative low-carbon dispatching system for power systems based on carbon emission flow calculation and power flow decomposition, characterized in that, The method comprises the following steps: a model construction module configured to acquire basic parameters of a power system, construct a power carbon emission flow model based on power flow direction decomposition based on the basic parameters, the power carbon emission flow model comprising a node carbon emission factor model and a carbon flow balance model, solve node carbon emission factors based on the node carbon emission factor model, and solve power flow and carbon flow based on the carbon flow balance model; a target function module configured to establish a power system low-carbon dispatch optimization model with the minimum total operating cost of system units as the target, establish a power system economic dispatch constraint set based on the basic parameters, and establish a power system low-carbon dispatch constraint set based on the power carbon emission flow model; a solution module configured to solve the power system low-carbon dispatch optimization model based on the power system economic dispatch constraint set and the power system low-carbon dispatch constraint set, and output a solution result.

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