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

Abstract

The present application relates to low-carbon power technology and power system optimization scheduling field, more particularly, to a kind of power system non-iterative low-carbon scheduling method and system based on carbon emission flow calculation and power flow decomposition, including obtaining various types of input data in power system, construct the power carbon emission flow model based on power flow direction decomposition, then obtain node carbon emission factor real-time calculation method and carbon flow balance model suitable for non-iterative optimization solution;Finally, establish power system low-carbon scheduling optimization model, carry out calculation and solution, finally obtain unit output plan, system power flow result, node carbon emission factor and other output results.By establishing power carbon emission flow model, the 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, the load node carbon emission amount constraint is embedded in optimization scheduling model, which provides a new technical path for realizing fine low-carbon scheduling of power system.

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) the convergence is not guaranteed, and it 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 it 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:

[0007] In a first aspect, the present invention provides a non-iterative low-carbon dispatching method for power systems based on carbon emission flow calculation and power flow decomposition, comprising:

[0008] Obtain basic parameters of the power system and construct a power carbon emission flow model based on power flow direction decomposition. The power carbon emission flow model includes a node carbon emission factor model and a carbon flow balance model. The node carbon emission factor is solved based on the node carbon emission factor model, and the power flow and carbon flow are solved based on the carbon flow balance model.

[0009] With the goal of minimizing the total operating cost of the system units, a low-carbon dispatch optimization model for the power system is established. Based on the aforementioned basic parameters, an economic dispatch constraint set for the power system is established, and based on the aforementioned power carbon emission flow model, a low-carbon dispatch constraint set for the power system is established.

[0010] The low-carbon dispatch optimization model of the power system is solved based on the economic dispatch constraint set and the low-carbon dispatch constraint set of the power system, and the solution results are output.

[0011] Preferably, the basic parameters include network topology, load forecast data, new energy output forecast data, generator parameters, line parameters, generator-node dependency matrix, load-node dependency matrix, line-node dependency matrix, and PTDF matrix.

[0012] Preferably, the node carbon emission factor model includes:

[0013]

[0014] In the formula, For nodes carbon emission factors, To reach the node via a single line The set of neighboring nodes to which the injected power is applied. For nodes The assembly of the generator sets, For nodes Flow to Node power, For nodes Up generator set Injection power, For nodes carbon emission factors, This is the generator set fuel emission factor vector.

[0015] Preferably, the carbon flow balance model includes:

[0016]

[0017] In the formula, For nodes a set of adjacent nodes, a carbon emission factor of a node a carbon emission factor of a node a power flow component from node k to node n, a power flow component from node n to node k, a set of nodes outgoing power of a node outgoing power of a node a set of nodes

[0018] Preferably, the establishing of the low-carbon dispatching optimization model of the power system comprises:

[0019]

[0020] wherein, a dispatching time period, a first term offering cost vector of a generator unit, a column vector of generator unit output at time t, a constant term offering cost vector of a generator unit, a column vector of generator unit start-stop state at time t, a spare offering cost vector of a generator unit, a column vector of spare of a generator unit at time t, a spare offering cost vector of a generator unit, a column vector of spare of a generator unit at time t.

[0021] Preferably, the set of economic dispatching constraints of the power system 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 spare constraints, and the set of low-carbon dispatching constraints of the power system comprises total carbon emission amount constraints of the power system, carbon emission factor constraints of a load node, and carbon emission amount constraints of a load node.

[0022] Preferably, the total carbon emission amount constraints of the power system comprise:

[0023]

[0024] wherein, a preset maximum carbon emission amount of the system within a dispatching time period T.

[0025] Preferably, the carbon emission factor constraints of a load node comprise:

[0026]

[0027] wherein, a load and node affiliation matrix, a column vector of node carbon emission factors for all nodes at time period t, a column vector of node carbon emission factors for all load nodes at time period t, a column vector of upper limit of node carbon emission factors for load nodes at time period t.

[0028] Preferably, the load node carbon emission amount constraint comprises:

[0029]

[0030] wherein, a column vector of upper limit of node carbon emission factors for load nodes at time period t, a load prediction data of the power system at time period t, a matrix operation of element-wise multiplication.

[0031] In a second aspect, the present application further provides a non-iterative low-carbon scheduling system for a power system based on carbon emission flow calculation and power flow decomposition, comprising:

[0032] a model construction module 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;

[0033] a target function module configured to establish a low-carbon scheduling optimization model for the power system with the minimum total operation 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;

[0034] a solving module configured to solve the low-carbon scheduling optimization model for the power system based on the power system economic dispatch constraint set and the power system low-carbon dispatch constraint set, and output a solving result.

[0035] The technical scheme of the present application has at least the following advantages and beneficial effects:

[0036] The method provided by the application firstly acquires various input data such as network topology, source and load prediction data, generator set parameters and line parameters in the power system; 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 a constraint, calculation and solution are performed, and finally output results such as unit output plan, system power flow result and node carbon emission factor are obtained. By using the above method, through the establishment of the power carbon emission flow model, 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 the 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 the power system to realize fine low-carbon dispatching. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the 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 embodiments of the 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 on the basis of these drawings.

[0038] Fig. 1 The flowchart of the application;

[0039] Fig. 2 The modified version of the schematic diagram of the PJM5 node system of the application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the application more clear, the following will combine the drawings in the embodiments of the application to clearly and completely describe the technical solutions in the embodiments of the application. Obviously, the described embodiments are some of the embodiments of the application, not all the embodiments. The components of the embodiments of the application described and shown in the drawings here can be arranged and designed in various different configurations.

[0041] 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, the function of the part of the modules or sub-modules is called by the processor, and the other part of the modules or sub-modules is implemented by hardware, for example, by hardware circuit. In addition, part or all of the modules can be selected to achieve the purpose of the application scheme according to actual needs.

[0042] Please refer toFigs. 1-2 The application provides a non-iterative low-carbon scheduling method for a power system based on carbon emission flow calculation and power flow decomposition, comprising the following steps:

[0043] S101: Obtain basic parameters of the power system, and construct a power carbon emission flow model based on power flow direction decomposition, wherein the power carbon emission flow model comprises a node carbon emission factor model and a carbon flow balance model; the node carbon emission factor model is used to solve node carbon emission factors; and the carbon flow balance model is used to solve power flow and carbon flow;

[0044] S102: Establish a low-carbon scheduling optimization model for the power system with the minimum total operation cost of system units as the target, establish an economic scheduling constraint set for the power system based on the basic parameters, and establish a low-carbon scheduling constraint set for the power system based on the power carbon emission flow model;

[0045] S103: Solve the low-carbon scheduling optimization model for the power system based on the economic scheduling constraint set and the low-carbon scheduling constraint set for the power system, and output a solution.

[0046] The solution of the target function can be obtained by using the Lagrange multiplier method to integrate the constraint into the target function, and converting it into an unconstrained problem. KKT condition: generalized Lagrange multiplier method, processing inequality constraint. Projection gradient method, projecting back to the feasible region after gradient descent. Interior point method, converting the constraint optimization into a sequence of unconstrained problems through the barrier function, and suitable for convex optimization.

[0047] The solution includes unit output plan, system power flow result, node carbon emission factor and the like.

[0048] The method provided by the application comprises the following steps: firstly, obtaining various input data such as network topology, source load prediction data, generator set parameters and line parameters in the power system; secondly, constructing a power carbon emission flow model based on power flow direction decomposition, and then obtaining a node carbon emission factor real-time calculation method and a carbon flow balance model suitable for non-iterative optimization solution; and finally, establishing a low-carbon scheduling optimization model for the power system, embedding the load node carbon emission amount in the form of constraint into the model, and performing calculation and solution, so as to finally obtain unit output plan, system power flow result, node carbon emission factor and the like. By using 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 scheduling model, thereby providing a new technical path for realizing fine low-carbon scheduling of the power system.

[0049] In one example embodiment of the present 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.

[0050] Specifically, the network topology includes:

[0051] 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 ; the load set is .

[0052] The load prediction data includes:

[0053] The load prediction data of the power system at period t is , . is a column vector containing D elements, and each element is the predicted value of load d at period t.

[0054] The new energy output prediction data includes:

[0055] The new energy output prediction data at period t is , . is a column vector containing R elements, and each element is the predicted output per unit value of new energy unit r at period t.

[0056] The generator set parameters include:

[0057] The generator set output upper and lower limit vectors , , , .

[0058] and are column vectors containing G elements, and each element and represent the output upper and lower limits of the gth unit, respectively.

[0059] The generator set first-order term and constant term bid price cost vectors , , , . , are column vectors of length G, each element , is the constant term offer cost of unit g.

[0060] are column vectors of length G, each element , , , . , are column vectors of length G, each element , is the up, down reserve offer cost of unit g.

[0061] are column vectors of length G, each element , . is a column vector of length G, each element is the fuel emission factor of unit g.

[0062] are column vectors of length G, each element , , , . and are column vectors of length G, each element and represent the maximum up, down ramp rate of the gth unit, respectively.

[0063] are column vectors of length G, each element , , , . and are column vectors of length G, each element and represent the maximum output of the gth unit at the initial, final start-up period, respectively.

[0064] are column vectors of length G, each element , , , . and are column vectors of length G, each element and represent the minimum start-up, shut-down time of the gth unit, respectively.

[0065] Line parameters:

[0066] Line transmission power upper bound vector , . is a column vector containing L elements, each element denotes the transmission power upper bound of the th line.

[0067] Generator-to-node affiliation matrix:

[0068] Generator-to-node affiliation matrix , . is an N x G matrix, each element is 1 if unit g belongs to node i, and 0 otherwise.

[0069] Load-to-node affiliation matrix:

[0070] Load-to-node affiliation matrix , . is an N x D matrix, each element is 1 if load d belongs to node i, and 0 otherwise.

[0071] Line-to-node affiliation matrix:

[0072] Line-to-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, and 0 if node i has no connection with line l.

[0073] PTDF matrix:

[0074] Power transfer distribution factor matrix , . is an N x L matrix, each element denotes the power transfer distribution factor of node i to line l.

[0075] In one example embodiment of the present invention, carbon emission flow is a responsibility allocation framework that conceptualizes carbon emission as virtual tags attached to power flow. This approach enables explicit tracking of emission transmission and accumulation process within the grid. Traditional regional carbon accounting methods allocate carbon responsibility uniformly 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, CEF decouples carbon responsibility from subjective regional division by calculating based on the actual physical distribution of power flow.

[0076] A node power balance model and a carbon emission flow balance model are established.

[0077] Since carbon emission flow strictly follows the active power distribution law, to improve computational efficiency, the power loss caused by network loss is ignored, and a direct current flow is used to establish a node power balance model as follows:

[0078]

[0079] In the formula, , Pi(n) and Pj(n) represent the adjacent node set of injecting power into node n and flowing out power from node n through a single line, respectively; , G(n) and D(n) represent the set of generator units and loads on node n, respectively; , Pi(n) and Pj(n) represent the power flowing from node i to node n and from node n to node j, respectively; , Pg(n) and Pd(n) represent the injection power of generator unit g and the outflow power of load d on node n, respectively.

[0080] Under the framework of virtual material flow, the inflow and outflow of carbon emission of a certain node must also follow the law of conservation of mass. The carbon emission flow balance model is as follows

[0081]

[0082] In the formula, , Pi(n) and Pj(n) represent the carbon flow from node i to node n and from node n to node j, respectively; , Pg(n) and Pd(n) represent the injection carbon flow of generator unit g and the outflow carbon flow of load d on node n, respectively.

[0083] According to the principle of fair mixing, all carbon emissions of a certain node have the same carbon intensity, which is equal to the carbon emission factor of the node. Therefore, the node carbon emission flow balance model can be rewritten as:

[0084]

[0085] The node carbon emission factor model is derived by combining the node power balance model and the node carbon emission flow balance model.

[0086]

[0087] In the formula, This is the generator set fuel emission factor vector. , Representing nodes respectively ,node The node carbon emission factor.

[0088] In one exemplary embodiment of the present invention, the line carbon flow is obtained by multiplying the nodal carbon emission factor of the line power flow source node by the line power flow. Therefore, the calculation of carbon flow depends on the determination of the line power flow direction. However, the system cannot determine the specific power flow direction of each line before power flow calculation. Therefore, the nodal carbon flow balance equation as shown in the nodal carbon emission flow balance model cannot be directly embedded into the optimization scheduling model. This is also the reason why traditional research adopts the serial solution mode of "power flow calculation first, then carbon flow calculation".

[0089] To address this issue, this invention proposes a power flow direction decomposition method that eliminates the dependence of carbon flow calculation on the power flow direction of the line, thereby enabling simultaneous non-iterative solutions for power flow calculation and carbon flow calculation. The specific implementation method is as follows: For a given line... ,line The starting and ending nodes are respectively , , defined from arrive The current direction is positive, and the line Actual power flow To perform forward and reverse decomposition, that is, to define... and Let be the forward and reverse power flow components of line l, respectively, satisfying the following constraints:

[0090]

[0091]

[0092] Establish a carbon flow balance model suitable for non-iterative solutions;

[0093] After decomposition by tidal current direction, the carbon flow balance model can be rewritten from the nodal carbon emission flow balance model in the following two forms:

[0094]

[0095] wherein, is a set of nodes adjacent to node , is a carbon emission factor of node , is a power flow component from node k to node n, is a power flow component from node n to node k, is a power outflow of node , is a power outflow of node

[0096] Compared with the node carbon emission flow balance model and the carbon flow balance model, the node carbon emission flow balance model needs to screen the actual power flow injection and the lines of the power flow outflow node n and the corresponding adjacent nodes, so as to write the carbon flow balance equation, and cannot be programmed to solve. The carbon flow balance model only needs to be aimed at the adjacent lines of node n and the nodes, so as to write the carbon flow balance equation, and can be directly embedded into the power system optimization scheduling model to participate in solving.

[0097] In an example embodiment of the present application, the establishment of the low-carbon scheduling optimization model of the power system comprises:

[0098]

[0099] wherein, is a scheduling period, is a generator unit primary term offering cost vector, is a generator unit output column vector at t moment, is a generator unit constant term offering cost vector, is a generator unit start-stop state column vector at t moment, is a generator unit on reserve offering cost vector, is a generator unit on reserve column vector at t moment, is a generator unit off reserve offering cost vector, is a generator unit off reserve column vector at t moment.

[0100] Secondly, the power system economic scheduling 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 scheduling constraint set comprises total carbon emission amount constraints of the power system, carbon emission factor constraints of load nodes and carbon emission amount constraints of load nodes.

[0101] Specifically, the generator unit output upper and lower limit constraints are:

[0102]

[0103] Specifically, for new energy generating units:

[0104]

[0105] In the formula: " indicates the matrix operation of element-wise multiplication. This represents the lower limit of the power output of new energy generating units. The upper limit of power output for new energy units, Let t be the column vector of the start-up and shutdown status of the new energy generating units.

[0106] Generator set ramping constraints:

[0107] Generator sets, especially coal-fired units, cannot have their power output change drastically within a certain period of time due to physical limitations such as boiler heating and turbine inertia. Therefore, it is necessary to impose ramp rate constraints on output changes.

[0108]

[0109] Minimum start-stop time constraints for generator sets:

[0110] Due to the technical requirements of equipment such as boilers and steam turbines, once the unit is started, it must run continuously for a period of time and cannot be stopped immediately. Similarly, after the unit is stopped, it must be kept in a certain downtime before it can be restarted. Therefore, the power generation scheduling of the generator unit must meet the minimum start-up time and downtime constraints.

[0111]

[0112] In the formula: This represents the column vector of generator unit start-up or shutdown times during time period t. ,element This indicates the time during which unit g has been started or stopped in time period t, with positive for starting and negative for stopping.

[0113] Line transmission power constraints:

[0114]

[0115] In the formula: This represents the column vector of line transmission power during time period t. ,element This represents the transmission power of the l-th line during time period t.

[0116] PTDF constraints:

[0117] Since carbon emission flows are transmitted only along with active power flow, the DC power flow calculation method based on PTDF can simplify the network parameters of the power grid, thereby enabling rapid solutions for power flow and carbon flow.

[0118]

[0119] In the formula: represents the injection power column vector of the system at time t.

[0120] System power balance constraint:

[0121]

[0122] System reserve constraint:

[0123]

[0124] 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 t.

[0125] The present application proposes three low-carbon scheduling constraints for the carbon reduction demand of the power system, which are respectively total carbon emission of the power system constraint, load node carbon emission factor constraint and load node carbon emission constraint. In actual application, appropriate constraints and constraint strength can be selected according to specific conditions.

[0126] The total carbon emission of the power system constraint includes:

[0127]

[0128] In the formula, is the maximum carbon emission of the system in the scheduling period T.

[0129] The load node carbon emission factor constraint includes:

[0130]

[0131] In the formula, is the load and node affiliation matrix, is the node carbon emission factor column vector of all nodes at time t, is the node carbon emission factor column vector of all load nodes at time t, is the node carbon emission factor upper limit column vector of the load node at time t.

[0132] The load node carbon emission constraint: in actual industrial production, the regulatory policy usually has requirements for the total carbon emission of the production enterprise. The corresponding node-level total carbon emission constraint is:

[0133]

[0134] In the formula, a column vector of upper limits of node carbon emissions of load nodes at a time period t, power system load prediction data of t time period, a matrix operation of element-wise multiplication.

[0135] A power system non-iterative low-carbon scheduling system based on carbon emission flow calculation and power flow decomposition, comprising:

[0136] 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;

[0137] a target function module configured to take the minimum total operation cost of system units as a 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 scheduling constraint set based on the power carbon emission flow model;

[0138] 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 scheduling constraint set, and output a solving result.

[0139] In addition, each of the functional units 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.

[0140] The embodiment of the present application takes a modified version of the PJM-5 node system as an implementation object, and a schematic diagram of the modified version of the PJM-5 node system is as shown in Fig. 2The system includes five units, wherein G1 is a wind turbine unit, G2 is a photovoltaic turbine unit, G3 is a gas turbine unit, G4 and G5 are coal-fired turbine units, and the parameters of the turbine units are shown in Table 1 and Table 2. The system includes loads D1-D3, wherein D1 is an industrial high-energy-consumption load. The system source load prediction data are shown in Table 3. Three running scenarios are set in the embodiment of the present application, wherein scenario A is a benchmark scenario, i.e., traditional economic dispatch without considering carbon emission constraints; scenario B considers the total system carbon emission constraints for low-carbon economic dispatch, and the carbon emission reduction intensity is set to 1%-6% of the total system carbon emission; and scenario C considers the node carbon emission constraints for low-carbon economic dispatch, 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 a mature commercial solver is used for optimization solution.

[0141] Table 1 Reference table of power generation cost and direct carbon emission factor of turbine units

[0142]

[0143] Table 2 Table of technical parameters of turbine units

[0144]

[0145] The optimization dispatching 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 solution 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 that the precise carbon control target can be realized.

[0146] 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 dispatching 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.

[0147] Table 3 System source load prediction data table

[0148]

[0149] Comparing scenarios B and C, from a system-wide perspective, as the carbon emission reduction ratio increases, the total cost of scenario B gradually increases while the total carbon emissions gradually decrease. The total carbon emission reduction strictly meets the system's carbon emission constraints, exhibiting a linear downward trend. The maximum total carbon emission reduction is 750.44 tons, and the maximum carbon emission reduction cost is $42.77 per ton. From the perspective of load 3, as the carbon emission reduction ratio increases, the carbon emission reduction of load 3 generally shows an increasing trend, but the proportion of carbon emission reduction of load 3 to total carbon emission reduction ranges from 23.59% to 42.25%.

[0150] Table 4 Reference Table of Optimized Scheduling Results for Each Scenario

[0151]

[0152] Table 5. Reference Table of Carbon Emissions for Loads 1-3 under Various Scenarios

[0153]

[0154] By comparing the scheduling results of scenario A and scenario B, it can be found that implementing system-wide carbon emission constraints cannot achieve precise carbon reduction at the node level, nor can it achieve synchronous emission reduction across all nodes. In fact, as carbon emission reduction efforts increase, in order to reduce system-wide carbon emissions, the proportion of low-carbon or zero-carbon emission units in total power generation will increase. This means that nodes near renewable energy units (such as the node where load 2 is located in this embodiment) may absorb more low-carbon emission electricity, thereby reducing their node carbon emission factor. Meanwhile, the node carbon emission factor of other nodes (such as the node where load 1 is located in this embodiment) may increase due to changes in power flow. Therefore, when implementing system-wide carbon emission constraints, the carbon emission quotas of all enterprises within the scheduling area must be fully considered to avoid unreasonable transfer of carbon emission responsibility. When implementing single-node carbon emission constraints, it can be observed that the carbon emissions of load 3 gradually decrease, while the carbon emissions of load 1 and load 3 remain basically unchanged after the carbon emission reduction ratio reaches 3%. The total system carbon emissions remain unchanged first and then decrease. This is because the system adjusts the output power of the generator sets, redirecting the renewable carbon flow that was originally prioritized for load 2 to load 3. This is the opposite of the situation where all generator sets are required to prioritize power supply to neighboring nodes under the constraints of the entire system.

[0155] 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.

[0156] 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. 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 from 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.​ 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 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.​​​​ 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 establishment of the power system low-carbon dispatch optimization model comprises: wherein, is the dispatch period, is the generator set constant term offer cost vector, is the generator set output column vector at time t, is the generator set constant term offer cost vector, is the generator set start-up and shut-down state column vector at time t, is the generator set on-standby offer cost vector, is the generator set on-standby column vector at time t, is the generator set off-standby offer cost vector, is the generator set off-standby column vector at time t.

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 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.

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 total power system carbon emission amount constraint comprises: wherein is the maximum carbon emission preset by the system within the dispatch period T, is the generator set fuel emission factor vector.

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 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.

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 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. 9.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 for performing the non-iterative low-carbon dispatch of a power system based on carbon emission flow calculation and power flow decomposition according to any one of claims 1-8 comprises: 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.

Images