Power distribution network carbon power cooperative scheduling method and device considering new energy bearing capacity improvement

Abstract

The invention provides a power distribution network carbon power cooperative scheduling method and device considering new energy bearing capacity improvement, and relates to the technical field of power distribution network optimization, and the method comprises the steps: building a first target function with the minimization of the operation cost of a power distribution network and the minimization of the carbon emission as targets, setting a safety constraint condition, introducing a plurality of power distribution network distributed new energy bearing capacity improvement means, and constructing a day-ahead optimal scheduling model to obtain a day-ahead scheduling scheme; and establishing a second objective function by taking minimization of the operation cost of the power distribution network as an objective, and constructing an intra-day optimal scheduling model to obtain an intra-day scheduling scheme. The day-ahead and intra-day two-stage scheduling is comprehensively considered, a day-ahead dual-target scheduling optimization model which comprehensively considers the operation cost of the power distribution network and the carbon emission of the system is constructed, the economy is considered, the distributed new energy consumption of the power distribution network is improved, and the carbon emission reduction is promoted. And multiple new energy operation bearing capacity improving means are introduced into the day-ahead and intra-day scheduling optimization models, so that the distributed new energy bearing capacity of the power distribution network is improved.

Description

Technical Field

[0001] This invention relates to the field of distribution network optimization technology, and in particular to a distribution network carbon-electricity coordinated dispatch method that takes into account the improvement of new energy carrying capacity. Background Technology

[0002] As a crucial component of the new power system, the distribution system is key to promoting the grid integration and consumption of renewable energy. At the distribution network operation and dispatch level, increasing the distributed renewable energy consumption rate and minimizing carbon emissions are the clear directions for the development of distribution network dispatch technology. In recent years, energy storage systems, reactive power compensators, and other new power equipment, as well as PQ control of photovoltaic inverters, have provided powerful means to promote the consumption of distributed renewable energy in the distribution network. However, active control equipment such as energy storage systems typically incurs certain operating costs, and the backflow phenomenon caused by large-scale renewable energy grid integration increases system losses and worsens the economics of distribution network operation.

[0003] Therefore, how to improve the absorption of distributed renewable energy in the distribution network and reduce carbon emissions while taking into account the operating costs of the distribution network has become a major problem that urgently needs to be solved. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a carbon-electricity coordinated dispatch method for distribution networks that considers enhancing the carrying capacity of new energy sources, thereby balancing economic efficiency with improving the absorption of distributed new energy sources in the distribution network and promoting carbon emission reduction.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, this invention provides a method for coordinated carbon-electricity dispatching of distribution networks that considers enhancing the carrying capacity of new energy sources, comprising: S1. Establish the first objective function with the goals of minimizing the operating cost of the power distribution network and minimizing carbon emissions; S2. Set safety constraints, introduce several methods to enhance the carrying capacity of distributed new energy in the distribution network, and establish a mathematical model for each enhancement method; S3. Construct an active distribution network day-ahead optimization scheduling model based on the first objective function, and use a mathematical model of security constraints and several improvement methods as the first overall constraint. S4. Solve the day-ahead optimization scheduling model of the active distribution network to obtain the day-ahead scheduling scheme; S5. Establish a second objective function with the goal of minimizing the operating cost of the distribution network; S6. Construct an active distribution network intraday optimization scheduling model based on the second objective function, and use a mathematical model of safety constraints and several improvement methods as the second overall constraint. S7. Solve the intraday optimal scheduling model of the active distribution network to obtain the intraday scheduling scheme.

[0006] The present invention provides a preferred embodiment in the first aspect, wherein the distribution network operation cost in S1 includes: network loss cost, curtailment penalty cost, distributed power generation operation cost, cost of purchasing electricity from the upper-level grid, and demand response cost.

[0007] In a first aspect, the present invention provides a preferred embodiment, S1, in which the improvement means include: photovoltaic inverter PQ control, static compensator reactive power compensation, energy storage regulation, and demand response.

[0008] The present invention provides a preferred embodiment in a first aspect, wherein the safety constraints include: power flow constraints, which calculate the power flow of branches through a branch power flow model and require that the power flow of each branch in the distribution network meets the safe operating conditions during operation; node voltage constraints, which require that the node voltage of each node in the distribution network does not exceed the limit during operation; line thermal stability constraints, which require that the reverse load rate index of each line in the distribution network does not exceed the limit during operation; branch current constraints, which require that the current of each branch in the distribution network system does not exceed the limit during operation; and renewable energy operation carrying capacity constraints, which require that the renewable energy operation carrying capacity is not lower than the lower limit during operation.

[0009] In a first aspect, the present invention provides a preferred embodiment in which the branch power flow model adopts the Distflow branch power flow model, which simplifies the original branch power flow model by omitting the phase angle of voltage and current, and is applicable to the branch power flow calculation of radial distribution networks.

[0010] In the first aspect, the present invention provides a preferred solution, in S4, where the day-ahead optimization scheduling model of the active distribution network is a biobjective function problem, and the NNC method is used to obtain a series of relatively uniformly distributed Pareto solutions, from which the optimal solution is selected.

[0011] The present invention provides a preferred embodiment in the first aspect, wherein the distribution network operating cost in S5 includes: network loss cost, curtailment penalty cost, distributed power generation operating cost, cost of purchasing electricity from the upper-level grid, demand response cost, and interruptible load cost.

[0012] In the first aspect, the present invention provides a preferred solution, in S4, by solving the day-ahead optimization scheduling model of the active distribution network based on the first overall constraint condition and the fixed new energy output and load forecast data, and obtaining the scheduling scheme for each time period of the day, which is the day-ahead scheduling scheme.

[0013] In the first aspect, the present invention provides a preferred solution. In S6, the active distribution network intraday optimization scheduling model adopts an intraday rolling optimization model, sets a time scale, and solves once for each time scale. The day-ahead scheduling scheme output by the active distribution network day-ahead optimization scheduling model is used as the input for the initial period of the active distribution network intraday optimization scheduling model. The measured system data under each period state is fed back to the intraday rolling optimization model. Combined with the new energy output and load forecast data of the set time scale in the future period, the optimal control sequence is solved as the intraday scheduling scheme.

[0014] The present invention provides, in a first aspect, a distribution network carbon-electricity coordinated dispatching device considering the enhancement of new energy carrying capacity, for executing the aforementioned method, comprising: a first objective establishment module, used to establish a first objective function with the objectives of minimizing distribution network operating costs and minimizing carbon emissions; a constraint setting module, used to set safety constraints, introduce several means of enhancing the distributed new energy carrying capacity of the distribution network, and establish a mathematical model for each enhancement means; a day-ahead optimization dispatching model construction module, used to construct an active distribution network day-ahead optimization dispatching model based on the first objective function, and using the safety constraints and the mathematical models of several enhancement means as the first overall constraint; a day-ahead optimization solution module, used to solve the active distribution network day-ahead optimization dispatching model to obtain a day-ahead dispatching scheme; an intraday optimization dispatching model construction module, used to establish a second objective function with the objective of minimizing distribution network operating costs; a second objective establishment module, used to construct an active distribution network intraday optimization dispatching model based on the second objective function, and using the safety constraints and the mathematical models of several enhancement means as the second overall constraint; and an intraday optimization solution module, used to solve the active distribution network intraday optimization dispatching model to obtain an intraday dispatching scheme.

[0015] Compared with the prior art, the present invention has the following advantages: This invention presents a carbon-electricity coordinated dispatching method for distribution networks that considers enhancing the carrying capacity of renewable energy sources. It comprehensively considers both day-ahead and intraday dispatching phases. In the day-ahead dispatching phase, a day-ahead dual-objective dispatching optimization model is constructed, comprehensively considering both distribution network operating costs and system carbon emissions. This model balances economic and low-carbon indicators, avoiding bias from a single objective, and achieving a balance between economic efficiency and improved absorption of distributed renewable energy in the distribution network, thereby promoting carbon emission reduction. Furthermore, this invention introduces various renewable energy carrying capacity enhancement measures as constraints into both the day-ahead dual-objective dispatching optimization model and the intraday dispatching optimization model, which can significantly improve the carrying capacity of distributed renewable energy in the distribution network. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 A flowchart of a distribution network carbon-electricity coordinated dispatching method considering the improvement of new energy carrying capacity, provided as a specific embodiment of the present invention; Figure 2 A schematic diagram of the power flow model of the distribution network branch in the distribution network carbon-electricity coordinated scheduling method for considering the improvement of new energy carrying capacity provided in a specific embodiment of the present invention; Figure 3 This is a standard solution space diagram of the dual-objective optimization problem in the distribution network carbon-electricity coordinated dispatching method considering the improvement of new energy carrying capacity provided in a specific embodiment of the present invention; Figure 4 This is a block diagram of a distribution network carbon-electricity coordinated dispatching device that takes into account the improvement of new energy carrying capacity, provided for a specific embodiment of the present invention.

[0018] Attached reference numerals: First objective establishment module 1, constraint setting module 2, day-ahead optimization scheduling model construction module 3, day-ahead optimization solution module 4, intraday optimization scheduling model construction module 5, second objective establishment module 6, intraday optimization solution module 7. Detailed Implementation

[0019] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please refer to Figure 1 In one specific implementation, the distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity is mainly divided into a day-ahead phase and an intraday phase, and is mainly implemented through the following steps: (a) The period from S1 to S4. S1. Establish the first objective function with the goals of minimizing the operating cost of the power distribution network and minimizing carbon emissions; S2. Set safety constraints, introduce several methods to enhance the carrying capacity of distributed new energy in the distribution network, and establish a mathematical model for each enhancement method; S3. Construct an active distribution network day-ahead optimization scheduling model based on the first objective function, and use a mathematical model of security constraints and several improvement methods as the first overall constraint. S4. Solve the day-ahead optimization scheduling model of the active distribution network to obtain the day-ahead scheduling scheme; In one implementation, the renewable energy operating capacity is defined as the proportion of the actual total output of renewable energy to the total installed capacity when the renewable energy installed capacity reaches its upper limit, as follows: (1) In the formula, To enhance the system's capacity for new energy operation, Contribute to new energy at all times and in all stages. The total installed capacity of new energy at each node. T It is a collection of all the times of the day (a total of 24 times a day). It is the set of all nodes.

[0021] In one implementation, in step S1, the distribution network operating cost includes: network loss cost, curtailment penalty cost, distributed generation operating cost, cost of purchasing electricity from the upstream grid, and demand response cost. The first objective function is as shown in equation (2): (2) In the formula, For the operating costs of the distribution network, For system carbon emissions, for t Real-time network loss cost for t The cost of constantly abandoning light For the operating cost of distributed power sources, for t The cost of electricity purchased by the distribution network from the superior grid at all times. Cost of responding to load demand. The carbon emission intensity of each carbon emission source. For each carbon emission source at any time t of efforts, T This is a collection of all times in a day (a total of 24 times in a day), and the same applies below. The specific expressions for each cost are as shown in equation (3): (3) In the formula, , , , , , These are the grid loss cost coefficient, wind and solar curtailment cost coefficient, gas turbine operation cost coefficient, photovoltaic operation and maintenance cost coefficient, electricity purchase price from the upstream power grid, and demand response cost coefficient. for t Time Branch ij The effective value of the current, branch road ij The resistance value. , , , They are respectively t The system measures the amount of curtailed solar power, gas turbine output, photovoltaic output, and the amount of electricity purchased from the upstream power grid. and They are nodes i (Each bus node in the power system) at time t The increase or decrease in load.

[0022] In one implementation, step S2 introduces several methods to enhance the carrying capacity of distributed renewable energy in the distribution network, mainly including the following types of methods: 1) The photovoltaic inverter PQ control detects the amplitude and phase of the grid voltage, follows the grid voltage, and controls the output three-phase current to achieve constant active and reactive power control. The specific mathematical model is shown in equation (4): (4) In the formula, For photovoltaic installed capacity, , Photovoltaic time t Those who contribute effort and those who contribute effort but do not.

[0023] 2) Static compensator reactive power compensation: Static compensators are dynamic voltage regulating devices that have been widely used in actual power grids in recent years. They can absorb or generate reactive power according to the operating status of the distribution network to regulate the system voltage level, thereby alleviating the problem of node voltage exceeding the limit caused by large-scale distributed photovoltaic grid-connected operation, and thus improving the photovoltaic absorption capacity. Its corresponding mathematical model is shown in equation (5): (5) In the formula, This indicates the maximum adjustable reactive power of the static var generator. Indicates time t Reactive power output of static var generator 。

[0024] 3) Energy storage regulation: Energy storage devices (ESS) serve as flexible regulation resources, absorbing some of the active power generated by photovoltaics during periods of strong sunlight to alleviate voltage overruns at nodes and thermal stability overruns on lines within the distribution network, thereby enhancing the system's distributed photovoltaic operating capacity. Energy storage devices need to meet charging and discharging power constraints as well as energy constraints; their corresponding mathematical models are shown in equations (6), (7), and (8): (6) (7) (8) In the formula, and 0-1 variables are used to represent t The charging and discharging state of the ESS at all times. When it is 0, When the value is 1, the ESS is in a discharging state. When it is 1, When the value is 0, the ESS is in a charging state; This represents the upper limit of the active power of the ESS; and They represent t ESS's charging and discharging power at all times; and These are the charging efficiency and discharging efficiency of ESS, respectively. , These represent the upper and lower limits of ESS battery capacity, respectively. This represents the initial charge level of the ESS. Furthermore, to ensure the sustainable operation of the ESS, after completing a day's operation, the ESS needs to return to its initial charge level the following day. .

[0025] 4) Demand response: Incentive-based demand response is preferred. This is a key means for the power system to guide users to proactively adjust their electricity consumption behavior through economic compensation or reward mechanisms, thereby balancing supply and demand and improving grid reliability and economy. During the day-ahead phase, dispatching transferable loads must meet the maximum transfer amount constraint and maintain a constant total load, as detailed below: (9) In the formula, , These are the discrimination nodes. i exist t A binary variable that is constantly in a state of increasing or decreasing load. , These are the nodes before and after the demand response. i At any moment tThe formula constrains the adjustable proportion of node load to 20% and ensures that the system load before and after the response remains unchanged throughout the entire planning period.

[0026] In one implementation, step S2. mainly introduces the following types of constraints for the operation of the distribution network: 1) Power flow constraints: Branch power flow is calculated using a branch power flow model, requiring that the power flow of each branch in the distribution network meets safe operating conditions after photovoltaic (PV) grid integration. Distribution network planning is based on "closed-loop design, open-loop operation," and is generally a radial network structure. For example... Figure 2 As shown, the Distflow branch power flow model simplifies the original branch power flow model by omitting the phase angle of voltage and current. It is also applicable to radial branch power flow calculations, as detailed below: (10) In the formula, i For the first node of the branch road, j This is the end node. , They are respectively i, j The square of the voltage amplitude, branch road ij Complex impedance, Representative node j The injected active power, Representative node j Injected reactive power, branch road ij since i Flow direction j The square of the current amplitude, branch road ij since i Flow direction j active power, branch road ij since i Flow direction j reactive power, branch road jk since j Flow direction k active power, branch road jk since j Flow direction k The reactive power. Additionally... Figure 2 middle, For nodes i Injection complex power, Representative node i The injected active power, Representative node i Injected reactive power, Similarly, For nodes j Injection complex power, Representative node j The injected active power, Representative node j Injected reactive power; branch road ij upper self-node i Send to j The complex power.

[0027] 2) Node voltage constraints require that during distribution network operation, the node voltage of each node in the distribution network must not exceed the limit. Specifically: (11) In the formula, for t time i Node voltage, and These represent the upper and lower limits of the node voltage, respectively.

[0028] 3) Line thermal stability constraints require that during distribution network operation, the reverse load rate index of each line in the distribution network does not exceed the limit. This embodiment uses the reverse load rate index. The following measures are used to measure the thermal stability of the lines during the operation calculation of the distribution network: (12) In the formula, for t Total active power output of photovoltaic systems within the power supply range of the power line at any given time. For equivalent electrical load within the same power supply range, This represents the operating limits for transmission lines. Therefore, the following must be met during the operation of the distribution network: (13) In the formula, This represents the maximum reverse load rate of the line.

[0029] 4) Branch current constraints require that the branch currents of the distribution network system do not exceed the limits during operation. Specifically: (14) In the formula, for t Time Branch ij The current, These are the branch current limits for the distribution network.

[0030] 5) Constraints on the carrying capacity of new energy sources: When the distribution network is in operation, the carrying capacity of new energy sources must be within a certain range. h Not lower than the lower limit: (15) Thus, taking the safety constraints and the mathematical model of the above-mentioned improvement methods as the first overall constraints, namely equations (3)-(15), and according to the first objective function, the day-ahead optimization scheduling model of the active distribution network can be obtained as follows: equation (16): (16) In one implementation, step S4. solves the current day-optimal scheduling model as a biobjective problem, i.e., solving equation (16). The NNC method (Normalized Normal Constraint, NNC) is used to obtain a series of relatively uniform Pareto solutions, from which a suitable compromise solution is selected as the optimal solution, as follows: S41. Order , The multi-objective optimization model shown in equation (16) is transformed into a model based on... For a single-objective optimization problem with objective function, keeping constraints (3)-(15) unchanged, the minimum value of the distribution network operating cost is obtained by solving the day-ahead scheduling optimization model. At the same time, the current carbon emissions of the system are recorded as The solution is obtained. .

[0031] S42. Referring to the process in step S41, transform equation (16) into... This is a single-objective optimization problem with a given objective function. Keeping other constraints constant, the goal is to minimize the system's carbon emissions. Meanwhile, the current operating cost of the distribution network is recorded as The solution is obtained. .

[0032] S43. The two sets of solutions obtained in the preceding two steps , These are the two extreme points of the Pareto front in equation (16). Considering the different dimensions and orders of magnitude of the two objective functions, the solution space is normalized according to equation (17) to improve the uniformity of the subsequent Pareto front distribution: (17) In the formula, For a variable matrix, For the objective function At this point, it should be treated as a variable. For the objective function In this case, it is treated as a variable.

[0033] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the standardization space for a bi-objective optimization problem. and Let x and y be the x and y coordinates of the graph, respectively. After normalization, the range of the two objective functions is [0,1]. Connect the two sets of extreme points obtained in steps S41 and S42. and The utopian line can then be obtained. The line connecting the optimal solutions obtained with different objective functions as a single objective function is the utopian line.

[0034] S44. Define the Utopia line as having extreme points. Pointing to extreme points The vector is Divide the Utopia Line into equal parts N Small segments can generate a total of N+1 There are 1 segmentation point, and the coordinates of each segmentation point are: (18) In the formula, .

[0035] S45. At the dividing point The normal line intersects the Pareto front at the point where the Utopia line is drawn. A single-objective optimization model (19) with the objective function of minimizing the operation of the distribution network is constructed. The dividing point can be obtained by solving model (19). The optimal solution on the corresponding Pareto front .

[0036] (19) S46. Repeat step S45, The dividing point By substituting these solutions into model (19) sequentially, a series of relatively uniformly distributed Pareto solutions can be obtained. A suitable compromise solution can be selected as the optimal solution based on actual needs.

[0037] (ii) Intraday phase S5 to S7. S5. Establish a second objective function with the goal of minimizing the operating cost of the distribution network; S6. Construct an active distribution network intraday optimization scheduling model based on the second objective function, and use a mathematical model of safety constraints and several improvement methods as the second overall constraint. S7. Solve the intraday optimal scheduling model of the active distribution network to obtain the intraday scheduling scheme.

[0038] In a preferred embodiment, the objective function (second objective function) of the intraday phase mathematical model includes minimizing the distribution network operating cost, and increasing the interruptible load compensation cost compared to the day-ahead phase. The remaining terms of the second objective function are expressed in the same way as in equation (3), specifically as shown in equation (20): (20) (twenty one) In the formula, To compensate for interruptible load costs, , These are the interruptible load cost factor and the load reduction amount, respectively.

[0039] In step (2), among the methods for improving the mathematical model during the intraday phase, the mathematical model for demand response is improved by adding interruptible load constraints compared to the day-ahead phase, as shown in equation (22): (twenty two) Increasing the participation of interruptible loads in intraday scheduling can further improve the flexibility and economy of scheduling, and can also reduce carbon emissions to some extent.

[0040] Therefore, taking the intraday stage safety constraints and the mathematical model of the above-mentioned improvement measures as the second overall constraints, namely equations (3)-(8), (10)-(15) and (21)-(22), and according to the second objective function, the intraday optimization scheduling model of the active distribution network can be obtained as shown in equation (23): (twenty three) Solving equation (23) yields the intraday scheduling scheme: the output of distributed new energy power generation at each time and the load size at each time.

[0041] In step S4, the day-ahead optimization scheduling model of the active distribution network is solved based on the first overall constraint condition and the fixed renewable energy output and load forecast data to obtain the scheduling scheme for each time of day, which is the day-ahead scheduling scheme. Specifically, the day-ahead optimization is the overall solution obtained by solving the scheduling scheme for each time of day based on the first overall constraint condition and the fixed renewable energy output and load forecast curves (data predicted the previous day).

[0042] In S6, the active distribution network intraday optimization scheduling model adopts an intraday rolling optimization model, setting a time scale / time resolution, such as 15 minutes, and solving once for each time scale. The unit operating status in the day-ahead scheduling scheme output by the active distribution network day-ahead optimization scheduling model is used as the input of the active distribution network intraday optimization scheduling model for the initial period. Real-time system data (line power flow, node voltage amplitude and phase angle, and actual output of gas turbines in operation during each period (15 minutes as one period) are fed back to the intraday rolling optimization model. Combined with the renewable energy output and load forecast data with a time scale of 15 minutes within a future period, such as within 4 hours, the optimal control sequence (the scheduling scheme sequence with a time scale of 15 minutes within the next 4 hours, with 15 minutes corresponding to one sequence) is solved as the intraday scheduling scheme. Specifically, intraday rolling optimization is based on the day-ahead scheduling scheme. Some variables are already determined day-ahead, while others are determined in real-time during the intraday phase using real-time data (i.e., the ultra-short-term forecast curves of renewable energy output and load) at each time resolution (15 minutes). That is, it needs to be solved once for each time period, for a total of 96 times per day. This embodiment uses a 15-minute rolling window, dynamically updating the scheduling plan for the next 4 hours based on the latest forecast data. After executing the first 15-minute instruction, it performs rolling optimization again, improving plan accuracy through feedback correction. More specifically, the input and output of the day-ahead optimization scheduling model and the intraday optimization scheduling model are as follows: (1) Day-ahead optimized scheduling model: Inputs: system topology, line impedance, various cost coefficients, carbon emission intensity of each generator, unit emission of each generator, upper and lower limits of each variable, fixed renewable energy output and load forecast curves.

[0043] Output: Daily optimized scheduling determinations, such as power purchase plans, unit operating status, and energy storage operating status.

[0044] (2) Intraday Optimized Scheduling Model: Inputs for each time period: day-ahead optimized scheduling determination, power flow of the line during the time period, voltage amplitude and phase angle of nodes, actual output of gas turbines in operation, output of new energy sources and ultra-short-term load forecast curves and forecast errors.

[0045] Output at different times: line power flow, node voltage, and output of each gas turbine unit in operation within the next time window.

[0046] Additionally, it should be noted that an active distribution network refers to a network structure that coordinates the operation of distributed generation, active loads, and energy storage. The scheduling model of this invention encompasses distributed generation, load response, and energy storage devices. Furthermore, the distribution network can exchange information through communication equipment and issue control commands to each component. Therefore, the day-ahead optimization scheduling model and the intraday optimization scheduling model are defined as the active distribution network day-ahead optimization scheduling model and the active distribution network intraday optimization scheduling model, respectively.

[0047] Please refer to Figure 4 In one embodiment, a distribution network carbon-electricity coordinated dispatching device considering the enhancement of renewable energy carrying capacity is provided. This device executes the distribution network carbon-electricity coordinated dispatching method considering the enhancement of renewable energy carrying capacity described in the above embodiments. Firstly, it executes steps S1 to S4 of the day-ahead stage and mainly consists of the following modules: a first objective establishment module 1, used to establish a first objective function with the objectives of minimizing distribution network operating costs and minimizing carbon emissions; a constraint setting module 2, used to set safety constraints, introduce several distributed renewable energy carrying capacity enhancement methods for the distribution network, and establish a mathematical model for each enhancement method; a day-ahead optimization dispatching model construction module 3, used to construct an active distribution network day-ahead optimization dispatching model based on the first objective function, using the safety constraints and the mathematical models of several enhancement methods as the first overall constraint; and a day-ahead optimization solution module 4, used to solve the active distribution network day-ahead optimization dispatching model to obtain the day-ahead dispatching scheme. Secondly, the steps S5 to S7 of the above-mentioned intraday phase are mainly composed of the following modules: intraday optimization scheduling model construction module 5, which is used to establish a second objective function with the goal of minimizing the operating cost of the distribution network; second objective establishment module 6, which is used to construct an active distribution network intraday optimization scheduling model based on the second objective function, and uses a mathematical model of safety constraints and several improvement methods as the second overall constraint; intraday optimization solution module 7, which is used to solve the active distribution network intraday optimization scheduling model to obtain the intraday scheduling scheme.

[0048] Based on the above embodiments, the present invention can achieve the following beneficial technical effects: This invention comprehensively considers both day-ahead and intraday scheduling. Compared to traditional day-ahead scheduling, it overcomes the static decision-making deficiencies of day-ahead scheduling through high-frequency, small-step rolling optimization, making it particularly suitable for new power systems with high volatility and uncertainty. During the day-ahead scheduling phase, various methods to enhance the carrying capacity of renewable energy sources are employed, along with incentive-based demand response mechanisms, further improving the flexibility of the distribution system and promoting the integration of distributed renewable energy. The day-ahead scheduling phase also constructs a dual-objective optimization problem that comprehensively considers distribution network operating costs and system carbon emissions, balancing economic and low-carbon indicators. During the intraday rolling optimization phase, scheduling results are corrected in real time, improving the accuracy of the scheduling scheme. Therefore, the distribution network operation optimization scheduling scheme formed by the method proposed in this invention can improve system operating economy while ensuring the safe operation of the active distribution network and promoting the integration of distributed renewable energy, demonstrating significant engineering practicality.

[0049] 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.

[0050] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Furthermore, the above embodiments only illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A method for coordinated carbon-electricity dispatching of distribution networks considering the enhancement of renewable energy carrying capacity, characterized in that, include: S1. Establish the first objective function with the goals of minimizing the operating cost of the power distribution network and minimizing carbon emissions; S2. Set safety constraints, introduce several methods to enhance the carrying capacity of distributed new energy in the distribution network, and establish a mathematical model for each enhancement method; S3. Construct an active distribution network day-ahead optimization scheduling model based on the first objective function, and use a mathematical model of security constraints and several improvement methods as the first overall constraint. S4. Solve the day-ahead optimization scheduling model of the active distribution network to obtain the day-ahead scheduling scheme; S5. Establish a second objective function with the goal of minimizing the operating cost of the distribution network; S6. Construct an active distribution network intraday optimization scheduling model based on the second objective function, and use a mathematical model of safety constraints and several improvement methods as the second overall constraint. S7. Solve the intraday optimal scheduling model of the active distribution network to obtain the intraday scheduling scheme.

2. The distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity according to claim 1, characterized in that, The distribution network operating costs in S1 include: network loss costs, curtailment penalty costs, distributed generation operating costs, costs of purchasing electricity from the upstream grid, and demand response costs.

3. The distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity according to claim 1, characterized in that, In S1, the improvement methods include: photovoltaic inverter PQ control, static compensator reactive power compensation, energy storage regulation, and demand response.

4. The distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity according to claim 1, characterized in that, The security constraints include: Power flow constraints are calculated by branch power flow model, requiring that the power flow of each branch in the distribution network meets the safe operation conditions during operation; Node voltage constraints require that during operation, the node voltage of each node in the distribution network does not exceed the limit; Line thermal stability constraints require that during operation, the reverse load rate index of each line in the distribution network does not exceed the limit; Branch current constraints require that during operation, the current in each branch of the distribution network system does not exceed the limit; The constraint on the carrying capacity of new energy operation requires that the carrying capacity of new energy operation should not be lower than the lower limit during operation.

5. The distribution network carbon-electricity coordinated dispatch method considering the improvement of new energy carrying capacity according to claim 4, characterized in that, The branch power flow model adopts the Distflow branch power flow model, which simplifies the original branch power flow model by omitting the phase angle of voltage and current, and is applicable to the branch power flow calculation of radial distribution networks.

6. The distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity according to claim 1, characterized in that, In S4, the day-ahead optimization scheduling model of the active distribution network is a biobjective function problem. The NNC method is used to obtain a series of relatively uniform Pareto solutions, from which the optimal solution is selected.

7. The distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity according to claim 1, characterized in that, The distribution network operating costs in S5 include: network loss costs, curtailment penalty costs, distributed generation operating costs, costs of purchasing electricity from the upstream grid, demand response costs, and interruptible load costs.

8. The distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity according to claim 1, characterized in that, In S4, the day-ahead optimization scheduling model of the active distribution network is solved based on the first overall constraint condition, fixed new energy output, and load forecast data to obtain the scheduling scheme for each time of day, which is the day-ahead scheduling scheme.

9. The distribution network carbon-electricity coordinated dispatch method considering the enhancement of new energy carrying capacity according to claim 1, characterized in that, In S6, the active distribution network intraday optimization scheduling model adopts an intraday rolling optimization model, which sets a time scale and solves once for each time scale. The day-ahead scheduling scheme output by the active distribution network day-ahead optimization scheduling model is used as the input for the initial period of the active distribution network intraday optimization scheduling model. The measured system data under each period state is fed back to the intraday rolling optimization model. Combined with the new energy output and load forecast data of the set time scale in the future period, the optimal control sequence is solved as the intraday scheduling scheme.

10. A distribution network carbon-electricity coordinated dispatching device considering the enhancement of new energy carrying capacity, used to execute the method described in any one of claims 1 to 9, characterized in that, include: The first objective establishment module is used to establish the first objective function with the objectives of minimizing the operating cost of the distribution network and minimizing carbon emissions; The constraint setting module is used to set safety constraints, introduce several methods to improve the carrying capacity of distributed new energy in the distribution network, and establish a mathematical model for each method. The day-ahead optimization scheduling model construction module is used to construct an active distribution network day-ahead optimization scheduling model based on the first objective function, and uses a mathematical model of security constraints and several improvement methods as the first overall constraint. The day-ahead optimization solution module is used to solve the day-ahead optimization scheduling model of the active distribution network to obtain the day-ahead scheduling scheme; The intraday optimization scheduling model construction module is used to establish a second objective function with the goal of minimizing the operating cost of the distribution network. The second objective establishment module is used to construct an active distribution network intraday optimization scheduling model based on the second objective function, and uses a mathematical model of safety constraints and several improvement methods as the second overall constraint. The intraday optimization solution module is used to solve the intraday optimization scheduling model of the active distribution network to obtain the intraday scheduling scheme.

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