Power distribution network electricity-carbon coupling scheduling method, device and equipment and storage medium

Abstract

The invention discloses a power distribution network electricity-carbon coupling scheduling method, device and equipment and a storage medium, and is applied to the technical field of power distribution network scheduling. Processing the data to obtain source load power prediction data and initial node carbon potential; constructing a double-layer coupling scheduling architecture comprising a producer and consumer carbon flow density calculation layer and an electricity-carbon multi-objective optimization layer; inputting the initial node carbon potential into an electricity-carbon multi-objective optimization layer for processing to obtain a power flow distribution result and net injection power data; based on the power flow distribution result and the net injection power data, the initial node carbon potential is updated through a producer carbon flow density calculation layer, and node updated carbon potential is obtained; and judging whether the node updating carbon potential and the node carbon potential of the last iteration meet a convergence condition or not, and determining an electricity-carbon coupling scheduling scheme. According to the method provided by the invention, the technical problem that a traditional scheduling method only focuses on cost minimization and neglects a carbon transaction market mechanism and a carbon emission reduction target can be solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network dispatching technology, and in particular to a power-carbon coupled dispatching method, apparatus, equipment and storage medium for power distribution networks. Background Technology

[0002] The energy sector is a key area for achieving significant emission reductions. As an important component of the new power system, the power distribution network is a crucial link in promoting the grid integration and consumption of new energy sources.

[0003] Traditional dispatching methods typically aim to minimize costs. Under constraints such as load demand, generator output limits, and line transmission capacity, they optimize the output allocation of each generator unit, considering only the balance of power. However, this method cannot adapt to the operating mode under the carbon trading market mechanism, cannot reduce carbon trading costs through optimized dispatching, and is difficult to achieve carbon emission reduction targets while ensuring the safe and stable operation of the system, thus hindering the low-carbon transformation of the power system. Summary of the Invention

[0004] This invention provides a power-carbon coupled scheduling method, device, equipment, and storage medium for power distribution networks. It addresses the technical problem that traditional scheduling methods focus solely on cost minimization while neglecting carbon trading market mechanisms and carbon emission reduction targets. This invention achieves precise matching of renewable energy grid connection and consumption needs. Through multi-objective collaborative optimization of power and carbon, it balances the operational safety of the power distribution network, the economic efficiency of scheduling costs, and the environmental friendliness of carbon emission reduction, thereby promoting the transformation of the power distribution network towards low-carbon and high-efficiency operation.

[0005] To address the aforementioned technical problems, this invention provides a power-carbon coupled dispatching method for distribution networks, the method comprising: Obtain real-time operation data and day-ahead carbon emission intensity data of the target distribution network; The real-time operating data is subjected to rolling prediction processing based on error correction technology to obtain the source-load power prediction data of the target distribution network; the day-ahead carbon emission intensity data is initialized to obtain the initial nodal carbon potential. Construct a two-layer coupled scheduling architecture that includes a producer-consumer carbon flux density calculation layer and an electro-carbon multi-objective optimization layer; With the goal of minimizing the overall scheduling cost of the target distribution network, the initial node carbon potential is input into the electricity-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing, to obtain the power flow distribution results and net injected power data of the target distribution network; the overall scheduling cost includes at least operating costs and carbon trading costs; Based on the power flow distribution results and the net injected power data, the initial node carbon potential is updated through the producer-consumer carbon flow density calculation layer in the dual-layer coupled scheduling architecture to obtain the node updated carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process. Determine whether the node's updated carbon potential and the node's carbon potential from the previous iteration meet the convergence condition; if not, return the node's updated carbon potential as the current node carbon potential to the electric-carbon multi-objective optimization layer for iterative processing; if the convergence condition is met, determine the electric-carbon coupled scheduling scheme based on the power flow distribution results and the net injected power data.

[0006] As one preferred embodiment, the step of performing rolling prediction processing on the real-time operating data based on error correction technology to obtain source-load power prediction data for the target distribution network includes: The real-time operating data includes voltage data and power output data; The voltage data and the output data are processed to obtain the real-time prediction error value; The source-load power data of the target distribution network are obtained by using the error continuous extrapolation technique to perform deviation superposition correction on the real-time prediction error value.

[0007] As one preferred embodiment, the step of using error continuous extrapolation technology to perform deviation superposition correction processing on the real-time prediction error value to obtain the source-load power data of the target distribution network includes: The real-time prediction error value is extrapolated using the error persistence extrapolation technique to obtain the extrapolation error sequence; The extrapolation error sequence is subjected to deviation superposition correction processing to obtain the source-load power data of the target distribution network.

[0008] As one preferred embodiment, the step of inputting the initial node carbon potential into the electric-carbon multi-objective optimization layer of the two-layer coupled scheduling architecture for processing to obtain the power flow distribution results and net injected power data of the target distribution network includes: The initial node carbon potential is processed using the nodal carbon potential-source load power mapping modeling technique to obtain the source load output constraint set; The source load output constraint set is input into the electric-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing to obtain the power flow distribution results and net injected power data of the target distribution network.

[0009] As a preferred embodiment, the step of updating the initial node carbon potential based on the power flow distribution results and the net injected power data through the producer-consumer carbon flow density calculation layer in the two-layer coupled scheduling architecture to obtain the node updated carbon potential includes: The carbon flow tracing technology is used to process the power flow distribution results and the net injection power data to obtain the carbon flow density distribution data of each node's producers and consumers; The carbon flow density distribution data of each node producer-consumer is input into the producer-consumer carbon flow density calculation layer in the two-layer coupled scheduling architecture to update the initial node carbon potential, thereby obtaining the updated node carbon potential.

[0010] As one preferred embodiment, determining whether the node's updated carbon potential and the node's carbon potential from the previous iteration satisfy the convergence condition includes: The relative change in the overall dispatch cost of the target distribution network is used as a criterion to determine whether the master-slave game iteration converges. If convergence is not achieved, the carbon potential is updated using nodes to enter the next round of producer-consumer carbon flow density calculation layer optimization until the system power flow distribution and carbon flow distribution converge together.

[0011] As one preferred embodiment, after determining the electricity-carbon coupling scheduling scheme, the distribution network electricity-carbon coupling scheduling method further includes: The electric-carbon coupled scheduling scheme is subjected to power flow convergence verification and N-1 security verification in sequence.

[0012] The present invention also provides a power distribution network electricity-carbon coupling dispatching device, comprising: The acquisition module is used to acquire real-time operating data and day-ahead carbon emission intensity data of the target distribution network; The prediction module is used to perform rolling prediction processing on the real-time operating data based on error correction technology to obtain the source-load power prediction data of the target distribution network; and to perform initialization processing on the day-ahead carbon emission intensity data to obtain the initial nodal carbon potential. The building module is used to construct a two-layer coupled scheduling architecture that includes a producer-consumer carbon flux density calculation layer and an electro-carbon multi-objective optimization layer; The optimization module is used to minimize the overall scheduling cost of the target distribution network. It inputs the initial node carbon potential into the electricity-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing, and obtains the power flow distribution results and net injected power data of the target distribution network. The overall scheduling cost includes at least operating costs and carbon trading costs. The update module is used to update the initial node carbon potential based on the power flow distribution results and the net injected power data through the producer-consumer carbon flow density calculation layer in the dual-layer coupled scheduling architecture to obtain the node updated carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process. The judgment module is used to determine whether the node updated carbon potential and the node carbon potential of the previous iteration meet the convergence condition; if not, the node updated carbon potential is returned as the current node carbon potential to the electric-carbon multi-objective optimization layer for iterative processing; if the convergence condition is met, the electric-carbon coupling scheduling scheme is determined based on the power flow distribution results and the net injected power data.

[0013] The present invention also provides a power distribution network electricity-carbon coupling scheduling device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power distribution network electricity-carbon coupling scheduling method as described above.

[0014] The present invention further provides a computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the power-carbon coupling scheduling method for the power distribution network as described above.

[0015] Compared with the prior art, the beneficial effects of the present invention are at least one of the following: This invention acquires real-time operational data and day-ahead carbon emission intensity data of a target distribution network; performs rolling forecasting processing on the real-time operational data based on error correction technology to obtain source-load power forecasting data for the target distribution network; initializes the day-ahead carbon emission intensity data to obtain initial node carbon potential; constructs a two-layer coupled scheduling architecture including a producer-consumer carbon flow density calculation layer and an electricity-carbon multi-objective optimization layer; with the goal of minimizing the overall scheduling cost of the target distribution network, the initial node carbon potential is input into the electricity-carbon multi-objective optimization layer of the two-layer coupled scheduling architecture for processing to obtain the power flow distribution results and net injected power data of the target distribution network; the overall scheduling cost includes at least... This includes operating costs and carbon trading costs; based on the power flow distribution results and the net injected power data, the initial node carbon potential is updated through the producer-consumer carbon flow density calculation layer in the two-layer coupled scheduling architecture to obtain the updated node carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process; it is determined whether the updated node carbon potential and the node carbon potential of the previous iteration meet the convergence condition; if not, the updated node carbon potential is returned as the current node carbon potential to the electric-carbon multi-objective optimization layer for iterative processing; if the convergence condition is met, the electric-carbon coupled scheduling scheme is determined based on the power flow distribution results and the net injected power data.

[0016] Compared with existing technologies, this invention addresses the pain point of traditional scheduling focusing only on cost minimization while neglecting carbon trading mechanisms and carbon emission reduction targets. By supplementing day-ahead carbon emission intensity data and combining error correction technology, it achieves accurate prediction of source-load power. With operating costs and carbon trading costs as comprehensive optimization objectives, it constructs a two-layer coupled architecture that takes into account the carbon flow transfer characteristics of energy storage, namely a producer-consumer carbon flow density calculation layer and an electricity-carbon multi-objective optimization layer. This forms a two-way feedback mechanism of carbon potential constraint input, power optimization solution, and dynamic carbon potential update. The feasibility of the scheme is then ensured through iterative convergence verification. Ultimately, while meeting the safety constraints of distribution network operation and adapting to the grid connection and consumption needs of new energy, it achieves multi-objective synergy of operation economy and carbon emission reduction and environmental protection. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the power-carbon coupling scheduling method for a power distribution network in one embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a power distribution network electricity-carbon coupling dispatching device in one embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a power distribution network electricity-carbon coupling dispatching device in one embodiment of the present invention; Figure label: Among them, 11. Acquisition module; 12. Prediction module; 13. Construction module; 14. Optimization module; 15. Update module; 16. Judgment module; 21. Processor; 22. Memory. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] One embodiment of the present invention provides a power-carbon coupled dispatching method for distribution networks. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shown is a flowchart of a power-carbon coupled dispatching method for a distribution network in one embodiment of the present invention. The method includes: S1: Obtain real-time operation data and day-ahead carbon emission intensity data of the target distribution network; S2: Based on error correction technology, the real-time operating data is subjected to rolling prediction processing to obtain the source-load power prediction data of the target distribution network; the day-ahead carbon emission intensity data is initialized to obtain the initial node carbon potential; S3: Construct a two-layer coupled scheduling architecture that includes a producer-consumer carbon flux density calculation layer and an electro-carbon multi-objective optimization layer; S4: With the goal of minimizing the overall scheduling cost of the target distribution network, the initial node carbon potential is input into the electricity-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing, to obtain the power flow distribution results and net injected power data of the target distribution network; the overall scheduling cost includes at least operating costs and carbon trading costs; S5: Based on the power flow distribution results and the net injected power data, the initial node carbon potential is updated through the producer-consumer carbon flow density calculation layer in the dual-layer coupled scheduling architecture to obtain the node updated carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process. S6: Determine whether the node updated carbon potential and the node carbon potential of the previous iteration meet the convergence condition; if not, return the node updated carbon potential as the current node carbon potential to the electric-carbon multi-objective optimization layer for iterative processing; if the convergence condition is met, determine the electric-carbon coupled scheduling scheme based on the power flow distribution results and the net injected power data.

[0022] Specifically, real-time operating data includes at least voltage data and real-time output data.

[0023] Specifically, voltage and output data can be directly collected through distribution network monitoring terminals, while day-ahead carbon emission intensity data can be obtained by logging into the greenhouse gas emission factor database jointly built by the Ministry of Ecology and Environment and the National Bureau of Statistics to query indirect emission factors such as fuel combustion and purchased electricity in the power industry, or by extracting the day-ahead planned output structure of the upper-level power grid, the installed capacity and planned output ratio of each power source type, and the planned power exchange of tie lines from the distribution network dispatching system, EMS / SCADA, power acquisition terminals, and ledger database. The corresponding emission factors are matched according to the unit type (coal power, gas power, new energy, energy storage), and the day-ahead average carbon emission intensity at the regional / node level is calculated by combining day-ahead load forecasts and tie line plans.

[0024] Alternatively, a bottom-up emission factor method can be used, where the total carbon emissions are obtained by multiplying the fuel consumption by the corresponding emission factor, and then divided by the planned power generation / electricity consumption to obtain the emission intensity per unit of electricity.

[0025] In step S2, rolling prediction processing is performed on the real-time operating data based on error correction technology to obtain source-load power prediction data of the target distribution network. This includes: the real-time operating data includes voltage data and output data; prediction error processing is performed on the voltage data and the output data to obtain a real-time prediction error value; and deviation superposition correction processing is performed on the real-time prediction error value using continuous error extrapolation technology to obtain the source-load power data of the target distribution network. Specifically, the deviation superposition correction processing of the real-time prediction error value using continuous error extrapolation technology to obtain the source-load power data of the target distribution network includes: extrapolating the real-time prediction error value using continuous error extrapolation technology to obtain an extrapolation error sequence; and deviation superposition correction processing is performed on the extrapolation error sequence to obtain the source-load power data of the target distribution network.

[0026] Specifically, first, extract the voltage and output data of the target distribution network at the current time and in the short historical period. At the same time, retrieve the voltage and output prediction values ​​for the corresponding time period. By calculating the difference between the real-time data and the corresponding prediction value at each time node, obtain the voltage prediction error and output prediction error of each node at different times. Integrate these error data to form a set of real-time prediction error values.

[0027] Next, trend analysis is performed on the acquired set of real-time prediction error values ​​to identify the pattern of error change over time, including the frequency of error fluctuation, amplitude range, and direction of change trend. Based on this pattern, an extrapolation prediction model is constructed. The model takes historical and current real-time prediction error values ​​as input and calculates the error prediction sequence within a short future time window, i.e., the extrapolation error sequence.

[0028] The initial source load power prediction data of the target distribution network is obtained. Then, the error values ​​at each time point in the extrapolation error sequence are superimposed with the initial source load power prediction data at the corresponding time point. At the same time, the rationality of the superimposed values ​​is verified in combination with the operating constraints of the distribution network, and outliers that exceed the safe operating range are eliminated. Finally, the corrected source load power data of each node is obtained.

[0029] The carbon potential is initialized by performing carbon potential initialization processing on the current carbon emission intensity data to obtain the initial node carbon potential.

[0030] It should be noted that the carbon potential at a node refers to the carbon emissions on the generation side corresponding to the consumption of a unit of electricity at that node.

[0031] Specifically, the topology of the target distribution network is divided into three core types according to the functional attributes and power supply type of the nodes: power supply nodes, load nodes, and interconnection nodes.

[0032] The predefined node types are matched one-to-one with day-ahead carbon intensity data. For power generation nodes, the day-ahead carbon intensity value of the corresponding power generation type is directly used as the base carbon intensity. For load nodes, the composite day-ahead carbon intensity of its power supply source is used as the base carbon intensity. If the load is jointly supplied by multiple power sources, the calculation is weighted according to the power supply ratio of each power source. For interconnection nodes, the day-ahead average carbon intensity of the upstream power grid or adjacent distribution network is used as the base carbon intensity.

[0033] Based on the safety constraints and low-carbon dispatch requirements of power distribution network operation, weight coefficients are set for different types of nodes.

[0034] The setting of weighting coefficients needs to consider three core factors: the importance of the node, the proportion of new energy grid connection, and the carbon emission reduction target requirements.

[0035] A carbon potential calculation model is constructed, with the formula being: Initial node carbon potential = Node basic carbon intensity × Node weight coefficient. The initial carbon potential of each node is calculated using this formula.

[0036] A two-layer coupled scheduling architecture is constructed, which includes a producer-consumer carbon flux density calculation layer and an electricity-carbon multi-objective optimization layer.

[0037] With the goal of minimizing the overall scheduling cost of the target distribution network, the initial node carbon potential is input into the electric-carbon multi-objective optimization layer of the two-layer coupled scheduling architecture for processing to obtain the power flow distribution results and net injected power data of the target distribution network. This includes: processing the initial node carbon potential using node carbon potential-source-load power mapping modeling technology to obtain a source-load output constraint set; and inputting the source-load output constraint set into the electric-carbon multi-objective optimization layer of the two-layer coupled scheduling architecture for processing to obtain the power flow distribution results and net injected power data of the target distribution network.

[0038] Based on the inherent correlation between nodal carbon potential and source-load power, a nodal carbon potential-source-load power mapping model is constructed. This model uses the initial nodal carbon potential as the core input parameter, and combines the carbon emission intensity characteristics of different types of power sources and the electricity consumption characteristics of loads to quantitatively define the output range of various power sources and the adjustment range of flexible loads. For example, for nodes with high carbon potential, the model will correspondingly compress the maximum allowable output of high-carbon power sources, while relaxing the output restrictions for low-carbon renewable energy sources; for nodes with low carbon potential, the constraint threshold can be appropriately adjusted. After model calculation, a source-load output constraint set covering all power sources and loads is finally integrated.

[0039] The source-load output constraint set is input into the multi-objective optimization layer for carbon emissions. This optimization layer takes minimizing the overall dispatch cost of the target distribution network as its core objective, while incorporating traditional constraints on distribution network operation. These traditional constraints include line transmission capacity constraints, generator output upper and lower limits constraints, and system power supply and demand balance constraints. The optimization layer uses a multi-objective optimization algorithm to couple the source-load output constraint set with traditional operational constraints for solution. During the calculation process, it considers the balance between operating costs and carbon trading costs, while simulating the power transmission process between various branches and nodes of the distribution network, quantifying the power injection and outflow at each node.

[0040] It should be noted that the comprehensive scheduling cost includes at least operating costs and carbon trading costs.

[0041] Based on the power flow distribution results and the net injected power data, the initial node carbon potential is updated through the producer-consumer carbon flow density calculation layer in the two-layer coupled scheduling architecture to obtain the node updated carbon potential. This includes: processing the power flow distribution results and the net injected power data using carbon flow tracing technology to obtain the producer-consumer carbon flow density distribution data for each node; and inputting the producer-consumer carbon flow density distribution data for each node into the producer-consumer carbon flow density calculation layer in the two-layer coupled scheduling architecture to update the initial node carbon potential to obtain the node updated carbon potential.

[0042] It should be noted that the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics that include the energy storage charging and discharging process.

[0043] Based on the basic principles of carbon flow tracing technology, and combined with the power flow direction and amplitude parameters of each branch in the power flow distribution results of the distribution network, as well as the net injected power data of each node, the carbon flow input and output paths of each node's producers and consumers are clarified.

[0044] Simultaneously, the basic carbon emission intensity data of each power source is correlated, the carbon emission of each node producer-consumer during unit power transmission is quantitatively calculated, and the calculation results of all nodes are integrated to form the carbon flow density distribution data of each node producer-consumer.

[0045] The carbon flow density distribution data of each node's prosumer is input into the prosumer carbon flow density calculation layer. This calculation layer pre-embeds a carbon flow transfer characteristic model that includes the energy storage charging and discharging process. Based on the input carbon flow density distribution data, the calculation layer analyzes the dynamic change pattern of carbon flow at each node, compares it with the initial node carbon potential set value, and corrects the mismatch between the initial node carbon potential and the actual carbon flow distribution through quantitative calculation. Finally, it obtains the node updated carbon potential that can reflect the true state of carbon flow in the current distribution network.

[0046] Next, it is determined whether the node updated carbon potential and the node carbon potential of the previous iteration meet the convergence condition. If not, the node updated carbon potential is returned as the current node carbon potential to the electric-carbon multi-objective optimization layer for iterative processing. If the convergence condition is met, the electric-carbon coupled scheduling scheme is determined based on the power flow distribution results and the net injected power data.

[0047] The relative change in the comprehensive scheduling cost of the target distribution network is used as a criterion to determine whether the master-slave game iteration has converged. If it has not converged, the carbon potential of the nodes is updated to enter the next round of producer-consumer carbon flow density calculation layer optimization until the system power flow distribution and carbon flow distribution converge together.

[0048] Specifically, the relative change in the comprehensive dispatch cost of the target distribution network is used as the convergence criterion for the master-slave game iteration. First, the difference in comprehensive dispatch cost between the current iteration round and the previous iteration round is calculated. Then, the difference is divided by the comprehensive dispatch cost of the previous iteration round to obtain the relative change. At the same time, a very small convergence threshold is set in combination with the actual engineering needs. Then, the calculated relative change is compared with the preset threshold to determine whether the convergence condition is met. In this process, the stability of the power flow distribution and carbon flow distribution of the system can also be checked simultaneously.

[0049] If the relative change in overall scheduling cost exceeds the preset convergence threshold, it is determined that the convergence condition is not met. In this case, the latest node carbon potential is directly used as the current node carbon potential, and the system returns to the electric carbon multi-objective optimization layer to start a new round of iteration. The electric carbon multi-objective optimization layer uses this updated carbon potential as the new constraint, reconstructs the source load output constraint set, and performs multi-objective optimization calculations to generate new power flow distribution results and net injected power data. These new data are then input into the producer-consumer carbon flow density calculation layer for the next round of node carbon potential updates. This process is repeated, thereby promoting the formation of a closed-loop iterative optimization mechanism in the two-layer coupled scheduling architecture, continuously correcting the node carbon potential and scheduling scheme, and causing the system power flow distribution and carbon flow distribution to gradually approach the co-optimal state.

[0050] If the relative change in the overall scheduling cost is less than or equal to the preset convergence threshold, it is determined that the convergence condition is met, and the iteration process is stopped. The power flow distribution results and net injected power data obtained in the current iteration are extracted. Based on these data, the final electric-carbon coupling scheduling scheme is formed. The scheme needs to specify the output plan of each power source, the adjustment strategy of flexible load, the charging and discharging sequence of energy storage equipment, and the power transmission allocation of each line.

[0051] In another embodiment, a producer-consumer response model considering the dynamic carbon emission characteristics of energy storage is constructed; the model is used to characterize how a producer-consumer adjusts its internal energy storage charging and discharging behavior and load demand based on the received node carbon potential signal, and calculates the resulting dynamic electricity sales carbon flow density; wherein, the carbon flow density of energy storage discharge depends on the ratio of the cumulative carbon emission intensity of its historical charging sources to the current stored electricity.

[0052] A master-slave game-theoretic collaborative optimization framework is constructed, including response optimization at the producer-consumer level and collaborative scheduling at the distribution network operator level. First, the initial node carbon potential is distributed to the producer-consumer level. Producers and consumers aim to minimize the sum of electricity cost and carbon cost, and calculate the optimal net injected power and the carbon flow density of electricity sales, feeding it back to the distribution network operator. Second, the distribution network operator aims to minimize the overall scheduling cost and performs optimal power flow calculation based on the boundary conditions fed back by the producer-consumers.

[0053] Based on the optimal power flow calculation results of the distribution network operator, the carbon potential of all nodes in the network is updated using carbon flow tracing theory, and the updated node carbon potential is redistributed to the producer-consumer layer.

[0054] The relative change in the overall dispatch cost of the distribution network operator is used as a criterion to determine whether the master-slave game iteration has converged. If it has not converged, the updated node carbon potential is used to enter the next round of producer-consumer layer optimization until the system power flow distribution and carbon flow distribution converge in tandem.

[0055] In another embodiment, based on the comprehensive scheduling cost, the convergence of the updated carbon potential of nodes in the target distribution network is determined to identify the electricity-carbon coupled scheduling scheme.

[0056] Specifically, the relative change in the comprehensive dispatch cost of the power distribution system operator is used as the convergence criterion for the iteration. When the relative deviation of the objective function value changes between two adjacent iterations is less than a preset threshold, the two-layer model (producer-consumer carbon flow density model and electricity-carbon multi-objective optimization model) is considered to have converged.

[0057] If the above convergence conditions are met, or the number of iterations reaches the preset maximum number of iterations, the iteration process is terminated, and the power flow solution, node carbon potential distribution, and decision results of each participating entity obtained in the current iteration are taken as the optimal power-carbon coordinated scheduling scheme within the rolling window; otherwise, the iteration continues, and the updated node carbon potential is redistributed to the producer-consumer layer to continue the next round of master-slave game iteration, so as to achieve coordinated convergence of power flow distribution and carbon flow distribution in the distribution network.

[0058] After determining the power-carbon coupled scheduling scheme, the power-carbon coupled scheduling method for the distribution network further includes: performing power flow convergence verification processing and N-1 security verification processing on the power-carbon coupled scheduling scheme in sequence.

[0059] The core of power flow convergence verification is to check whether the power flow distribution of the distribution network corresponding to the electric-carbon coupled dispatch scheme meets the physical operating laws. The specific operation process is as follows: Substitute the source-load power allocation data and power output adjustment strategies from the determined electric-carbon coupled dispatch scheme into the distribution network power flow calculation model. Use either the forward-backward substitution method or the Newton-Raphson method to perform power flow calculations, iteratively solving for the voltage amplitude and phase of each node, as well as the active power, reactive power transmission, and power loss of each branch. During the calculation, continuously monitor the residual changes during the iteration process. When the residual is less than the preset convergence threshold and no longer changes significantly, the power flow calculation is considered converged.

[0060] If the residual still fails to meet the convergence requirements after multiple iterations, it indicates that the current scheduling scheme has unreasonable parameter settings. It is necessary to readjust the source-load power allocation ratio or constraint boundary conditions until the power flow calculation converges.

[0061] The core of the N-1 safety verification process is to test the safe and stable operation capability of the electric-carbon coupled dispatch scheme under the fault of a single component in the distribution network. The specific operation procedure is as follows: A list of key components in the target distribution network is compiled, covering all transmission branches, transformers, generator sets, and other core equipment. For each component in the list, a fault scenario of that component being taken out of service is simulated sequentially. Under the fault scenario, the source-load power data of the electric-carbon coupled dispatch scheme is substituted into the power flow calculation model to recalculate the power flow distribution of the distribution network. The focus is on monitoring whether the voltage of each node is within the allowable deviation range after the fault, whether the transmission power of the remaining branches exceeds the capacity limit, and whether the generator output is within the rated range. If all operating parameters of the distribution network meet the safety constraints under all single-component fault scenarios, the dispatch scheme is deemed to have passed the N-1 safety verification.

[0062] If a fault scenario results in parameter exceeding the limit, the scheduling scheme needs to be optimized, such as adjusting the power output or load distribution of relevant nodes, to improve the fault response capability of the distribution network.

[0063] Another embodiment of the present invention provides a power-carbon coupling dispatching device for a distribution network. For details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The diagram shown is a structural schematic of a power distribution network electricity-carbon coupling dispatching device according to one embodiment of the present invention. The device includes: The acquisition module 11 is used to acquire real-time operating data and day-ahead carbon emission intensity data of the target distribution network; Prediction module 12 is used to perform rolling prediction processing on the real-time operating data based on error correction technology to obtain source-load power prediction data of the target distribution network; and to perform initialization processing on the day-ahead carbon emission intensity data to obtain the initial nodal carbon potential. Module 13 is used to construct a two-layer coupled scheduling architecture that includes a producer-consumer carbon flux density calculation layer and an electro-carbon multi-objective optimization layer; Optimization module 14 is used to input the initial node carbon potential into the electricity-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing, with the goal of minimizing the overall scheduling cost of the target distribution network, to obtain the power flow distribution results and net injected power data of the target distribution network; the overall scheduling cost includes at least operating costs and carbon trading costs; The update module 15 is used to update the initial node carbon potential based on the power flow distribution results and the net injected power data through the producer-consumer carbon flow density calculation layer in the dual-layer coupled scheduling architecture to obtain the node updated carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process. The judgment module 16 is used to determine whether the node updated carbon potential and the node carbon potential of the previous iteration meet the convergence condition; if not, the node updated carbon potential is returned as the current node carbon potential to the electric-carbon multi-objective optimization layer for iterative processing; if the convergence condition is met, the electric-carbon coupling scheduling scheme is determined based on the power flow distribution results and the net injected power data.

[0064] See Figure 3 This is a schematic diagram of the structure of a power distribution network electricity-carbon coupling scheduling device provided in an embodiment of the present invention. The power distribution network electricity-carbon coupling scheduling device provided in this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, it implements the steps as described in the above embodiment of the power distribution network electricity-carbon coupling scheduling method, for example... Figure 1 The steps S1 to S6 described above; or, when the processor 21 executes the computer program, it implements the functions of each module in the above-described device embodiments, such as the acquisition module 11.

[0065] For example, the computer program can be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the power distribution network's electricity-carbon coupling dispatching equipment. For example, the computer program can be divided into an acquisition module 11, a prediction module 12, a construction module 13, etc., with the specific functions of each module as follows: The acquisition module 11 is used to acquire real-time operating data and day-ahead carbon emission intensity data of the target distribution network; Prediction module 12 is used to perform rolling prediction processing on the real-time operating data based on error correction technology to obtain source-load power prediction data of the target distribution network; and to perform initialization processing on the day-ahead carbon emission intensity data to obtain the initial nodal carbon potential. Module 13 is used to construct a two-layer coupled scheduling architecture that includes a producer-consumer carbon flux density calculation layer and an electro-carbon multi-objective optimization layer; Optimization module 14 is used to input the initial node carbon potential into the electricity-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing, with the goal of minimizing the overall scheduling cost of the target distribution network, to obtain the power flow distribution results and net injected power data of the target distribution network; the overall scheduling cost includes at least operating costs and carbon trading costs; The update module 15 is used to update the initial node carbon potential based on the power flow distribution results and the net injected power data through the producer-consumer carbon flow density calculation layer in the dual-layer coupled scheduling architecture to obtain the node updated carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process. The judgment module 16 is used to determine whether the node updated carbon potential and the node carbon potential of the previous iteration meet the convergence condition; if not, the node updated carbon potential is returned as the current node carbon potential to the electric-carbon multi-objective optimization layer for iterative processing; if the convergence condition is met, the electric-carbon coupling scheduling scheme is determined based on the power flow distribution results and the net injected power data.

[0066] The power distribution network electricity-carbon coupling scheduling device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of a power distribution network electricity-carbon coupling scheduling device and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the power distribution network electricity-carbon coupling scheduling device may also include input / output devices, network access devices, buses, etc.

[0067] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the power distribution network's electricity-carbon coupling dispatching equipment, connecting various parts of the equipment via various interfaces and lines.

[0068] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the power distribution network electricity-carbon coupling dispatching equipment by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0069] If the integrated module of the power distribution network electricity-carbon coupling dispatching equipment is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0071] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform steps in the power distribution network electricity-carbon coupling scheduling method of the above embodiments, for example... Figure 1 Steps S1 to S6 as described above.

[0072] This invention addresses the core pain points of traditional scheduling methods through a technical approach involving carbon-dimensional data fusion, a two-layer coupled architecture design, multi-objective collaborative optimization, and iterative convergence verification. Simultaneously, it adapts to the grid connection and consumption needs of new energy sources, achieving synergistic effects across three objectives: First, traditional dispatching relies solely on power operation data, lacking supporting carbon emission data and thus failing to connect with carbon trading market mechanisms. This method, in addition to acquiring real-time distribution network operation data, introduces day-ahead carbon emission intensity data and uses error correction techniques to perform rolling forecasts of source-load power. This not only solves the source-load matching problem caused by the volatility of renewable energy output but also provides a quantitative basis for initializing nodal carbon potential, thus addressing the carbon dimension shortcomings of traditional dispatching from the data source.

[0073] Secondly, traditional dispatching employs a single-objective, single-level optimization model, which cannot simultaneously address power security constraints and carbon emission reduction requirements. This proposed method uses a two-layer coupled architecture. The electricity-carbon multi-objective optimization layer aims to minimize the overall dispatching cost, expanding the single operating cost of traditional dispatching to a composite objective of operating cost and carbon trading cost. It directly incorporates the carbon trading market mechanism into the optimization constraints, breaking the limitation of traditional dispatching that only focuses on power balance. The producer-consumer carbon flow density calculation layer focuses on carbon flow transmission characteristics, especially incorporating the carbon flow impact of energy storage charging and discharging processes. It processes power flow distribution and net injected power data through carbon flow tracking technology, updating node carbon potential in reverse, forming a two-way linkage mechanism of power optimization-carbon potential feedback-re-optimization. This ensures that carbon emission reduction targets do not deviate from the constraints of distribution network safety operation.

[0074] Ultimately, traditional static optimization schemes for power dispatch cannot adapt to the dynamic changes in renewable energy output and carbon potential, easily leading to suboptimal carbon emission reduction or power system operational risks in actual operation. This new method drives a two-layer architecture through iterative iteration by determining whether the updated carbon potential of a node meets the convergence condition of the carbon potential from the previous iteration. This allows the power flow distribution results and carbon potential calculation results to gradually approach the optimal solution, ensuring the safety of the distribution network operation and accurately matching the source-load fluctuation characteristics during renewable energy grid integration and consumption through dynamic carbon potential correction, thus avoiding carbon flow calculation errors caused by unstable renewable energy output.

[0075] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A power-carbon coupled dispatching method for a distribution network, characterized in that, include: Obtain real-time operation data and day-ahead carbon emission intensity data of the target distribution network; Based on error correction technology, the real-time operating data is subjected to rolling prediction processing to obtain the source-load power prediction data of the target distribution network. The current day carbon emission intensity data is initialized to obtain the initial node carbon potential; Construct a two-layer coupled scheduling architecture that includes a producer-consumer carbon flux density calculation layer and an electro-carbon multi-objective optimization layer; With the goal of minimizing the overall scheduling cost of the target distribution network, the initial node carbon potential is input into the electricity-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing, to obtain the power flow distribution results and net injected power data of the target distribution network; the overall scheduling cost includes at least operating costs and carbon trading costs; Based on the power flow distribution results and the net injected power data, the initial node carbon potential is updated through the producer-consumer carbon flow density calculation layer in the dual-layer coupled scheduling architecture to obtain the node updated carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process. Determine whether the updated carbon potential of the node satisfies the convergence condition compared with the carbon potential of the node in the previous iteration; If the conditions are not met, the updated carbon potential of the node is returned to the electro-carbon multi-objective optimization layer as the current node carbon potential for iterative processing. If the convergence condition is met, an electric-carbon coupled scheduling scheme is determined based on the power flow distribution results and the net injected power data.

2. The power-carbon coupling dispatching method for distribution networks as described in claim 1, characterized in that, The rolling prediction processing of the real-time operating data based on error correction technology to obtain the source-load power prediction data of the target distribution network includes: The real-time operating data includes voltage data and power output data; The voltage data and the output data are processed to obtain the real-time prediction error value; The source-load power data of the target distribution network are obtained by using the error continuous extrapolation technique to perform deviation superposition correction on the real-time prediction error value.

3. The power-carbon coupling dispatching method for distribution networks as described in claim 2, characterized in that, The method of using continuous error extrapolation to perform deviation superposition correction on the real-time prediction error value to obtain the source-load power data of the target distribution network includes: The real-time prediction error value is extrapolated using the error persistence extrapolation technique to obtain the extrapolation error sequence; The extrapolation error sequence is subjected to deviation superposition correction processing to obtain the source-load power data of the target distribution network.

4. The power-carbon coupling dispatching method for distribution networks as described in claim 1, characterized in that, The process of inputting the initial node carbon potential into the electric-carbon multi-objective optimization layer of the two-layer coupled scheduling architecture for processing to obtain the power flow distribution results and net injected power data of the target distribution network includes: The initial node carbon potential is processed using the nodal carbon potential-source load power mapping modeling technique to obtain the source load output constraint set; The source load output constraint set is input into the electric-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing to obtain the power flow distribution results and net injected power data of the target distribution network.

5. The power-carbon coupling dispatching method for distribution networks as described in claim 1, characterized in that, The process of updating the initial node carbon potential based on the power flow distribution results and the net injected power data, through the producer-consumer carbon flow density calculation layer in the two-layer coupled scheduling architecture, to obtain the node updated carbon potential includes: The carbon flow tracing technology is used to process the power flow distribution results and the net injection power data to obtain the carbon flow density distribution data of each node's producers and consumers; The carbon flow density distribution data of each node producer-consumer is input into the producer-consumer carbon flow density calculation layer in the two-layer coupled scheduling architecture to update the initial node carbon potential, thereby obtaining the updated node carbon potential.

6. The power-carbon coupling dispatching method for distribution networks as described in claim 1, characterized in that, The step of determining whether the updated carbon potential of the node satisfies the convergence condition with the node carbon potential of the previous iteration includes: The relative change in the overall dispatch cost of the target distribution network is used as a criterion to determine whether the master-slave game iteration converges. If convergence is not achieved, the carbon potential is updated using nodes to enter the next round of producer-consumer carbon flow density calculation layer optimization until the system power flow distribution and carbon flow distribution converge together.

7. The power-carbon coupling dispatching method for distribution networks as described in claim 1, characterized in that, After determining the electricity-carbon coupling scheduling scheme, the distribution network electricity-carbon coupling scheduling method further includes: The electric-carbon coupled scheduling scheme is subjected to power flow convergence verification and N-1 security verification in sequence.

8. A power-carbon coupling dispatching device for a distribution network, characterized in that, include: The acquisition module is used to acquire real-time operating data and day-ahead carbon emission intensity data of the target distribution network; The prediction module is used to perform rolling prediction processing on the real-time operating data based on error correction technology to obtain the source-load power prediction data of the target distribution network. The current day carbon emission intensity data is initialized to obtain the initial node carbon potential; The building module is used to construct a two-layer coupled scheduling architecture that includes a producer-consumer carbon flux density calculation layer and an electro-carbon multi-objective optimization layer; The optimization module is used to minimize the overall scheduling cost of the target distribution network. It inputs the initial node carbon potential into the electricity-carbon multi-objective optimization layer in the two-layer coupled scheduling architecture for processing, and obtains the power flow distribution results and net injected power data of the target distribution network. The overall scheduling cost includes at least operating costs and carbon trading costs. The update module is used to update the initial node carbon potential based on the power flow distribution results and the net injected power data through the producer-consumer carbon flow density calculation layer in the dual-layer coupled scheduling architecture to obtain the node updated carbon potential; the producer-consumer carbon flow density calculation layer is used to characterize the carbon flow transfer characteristics including the energy storage charging and discharging process. The judgment module is used to determine whether the updated carbon potential of the node and the carbon potential of the node in the previous iteration meet the convergence condition. If the conditions are not met, the updated carbon potential of the node is returned to the electro-carbon multi-objective optimization layer as the current node carbon potential for iterative processing. If the convergence condition is met, an electric-carbon coupled scheduling scheme is determined based on the power flow distribution results and the net injected power data.

9. A power distribution network electricity-carbon coupling dispatching device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the power-carbon coupling scheduling method for a distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the power-carbon coupling scheduling method for the distribution network as described in any one of claims 1 to 7.

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