A power distribution network source-network-load-storage coordinated optimization scheduling method and system containing a heat storage type industrial load

Abstract

This invention provides a method and system for coordinated optimization scheduling of power distribution networks, grids, loads, and storage systems, including thermal storage industrial loads. The method includes: acquiring historical prediction error data of new energy sources and loads; establishing a mathematical model of a gas turbine equipped with a flexible carbon capture device, and performing convex relaxation processing on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable; constructing a mathematical model of the demand response of thermal storage industrial loads, considering daily regulation capacity, daily regulation frequency, and daily output constraints, and performing piecewise linearization processing on the nonlinear constraints of the daily production tasks of thermal storage industrial loads; obtaining a set of typical prediction error scenarios based on the acquired historical prediction error data of new energy sources and loads; and constructing a coordinated optimization scheduling model of power distribution networks, grids, loads, and storage systems, including the demand response of thermal storage industrial loads. This invention leverages flexible resources across multiple stages of the power distribution network, improving the absorption rate of new energy sources and the economic efficiency of system operation, and reducing carbon emissions, thereby contributing to the achievement of dual-carbon goals and sustainable social development.

Description

Technical Field

[0001] This invention belongs to the field of power systems and relates to distribution network dispatching technology. Specifically, it relates to a method and system for coordinated and optimized dispatching of power generation, grid, load and storage in a distribution network that includes thermal storage industrial loads. Background Technology

[0002] The power industry is a major source of carbon emissions. Adopting carbon capture technology on the power generation side to reduce direct CO2 emissions and utilizing renewable energy to replace fossil fuel power generation is of significant emission reduction importance. However, renewable energy output is uncertain and even exhibits some anti-peak-shaving characteristics, meaning that renewable energy generation is low during peak load periods and high during off-peak periods, resulting in low renewable energy utilization. Furthermore, if renewable energy generation is low during peak load periods, it inevitably leads to increased power generation and carbon emissions from fossil fuel units, requiring the capture and treatment of more CO2. This also means increased power consumption of the carbon capture unit, creating an unfavorable "peak-on-peak" phenomenon and placing enormous pressure on system operation for peak shaving. Adding storage tanks can decouple CO2 capture and treatment, increasing the flexibility of the carbon capture unit and thus alleviating this situation. Meanwhile, thermal storage-type industrial load resources have high thermal inertia, and after adding energy storage equipment, they have great potential to participate in the flexible regulation of the system. If their participation in demand response plans can be promoted, the system's peak-shaving pressure can be greatly alleviated. However, the large-scale exploitation of flexible source-load-storage resources will inevitably lead to an increase in the complexity of the system's optimal scheduling model. Inappropriate modeling methods and optimal scheduling algorithms will not only fail to improve the economy of the scheduling scheme, but may even threaten the safety of system operation and load power consumption.

[0003] In recent years, energy conservation, carbon reduction, and clean energy use have gradually become a consensus. Therefore, the power system needs to exert efforts from multiple perspectives simultaneously: source, load, and storage. At the source, carbon capture devices can be used to store and utilize CO2 emitted by traditional fossil fuel units, and new energy power generation should be vigorously promoted. At the load side, loads, especially the potentially huge thermal storage industrial loads, can be fully mobilized to participate in demand response projects. At the storage side, mechanisms such as two-part tariffs can promote the installation and utilization of user-side energy storage, optimize electricity consumption curves, and facilitate the local consumption of new energy power. However, the mathematical models for flexible carbon capture devices and thermal storage industrial loads participating in demand response both exhibit varying degrees of non-convexity and nonlinearity, making direct inclusion in the distribution network optimization scheduling model difficult to solve. Furthermore, the uncertainty in distribution network operation brought about by the increased penetration of new energy also needs to be considered in its optimization scheduling model.

[0004] CN202011490794.6A discloses a coordinated control method for wind power consumption based on fused magnesium load. The method includes: predicting the day-ahead wind power output curve and considering the actual production and operation status of the fused magnesium load; treating the fused magnesium load as a controllable load during day-ahead scheduling; regulating high-energy-consuming loads during wind curtailment periods; minimizing the total wind curtailment for the following day as the optimization objective; introducing upper and lower limits for fused magnesium load power, regulation duration constraints, and regulation frequency constraints, as well as operational constraints for wind and thermal power; and calculating the regulation amount of the fused magnesium load for each period to achieve wind power consumption. However, this method does not consider the actual situation of adding carbon capture devices to fossil fuel generators in the construction of low-carbon power systems, and therefore fails to account for this in the scheduling model. Furthermore, it considers the prediction errors of new energy generation and load power by reserving reserve capacity, which does not provide a sufficiently refined characterization of the uncertainty of power system sources and loads.

[0005] CN202110554949.6A discloses a source-load interaction peak-shaving strategy based on active control of fused magnesium, including: predicting the wind power output curve and conventional load power curve for the next day; for conventional units, operating them at minimum output; and considering the actual operating characteristics of the fused magnesium load, treating it as a controllable load. By adjusting the fused magnesium load power and conventional unit output during wind curtailment periods, the goal of wind power absorption and alleviating grid peak-shaving pressure can be achieved. However, this invention does not consider the actual situation of adding carbon capture devices to fossil fuel generators in the construction of low-carbon power systems, and therefore fails to take this into account in the dispatch model. The method uses a deterministic optimization model, which does not consider the prediction errors of renewable energy generation and load power, and is not directly applicable to the optimization dispatch problem of high-uncertainty power systems. Furthermore, it does not consider the impact of fused magnesium load power adjustment on its yield and output.

[0006] CN202211267772.2A proposes an integrated wind-solar-hydrogen energy optimization method considering methanation and carbon capture, belonging to the field of energy optimization. The method includes the following steps: establishing an integrated wind-solar-hydrogen energy architecture considering carbon capture and methanation; based on this architecture, establishing mathematical models for each energy flow conversion and storage device; based on these models, establishing objective functions for scheduling and capacity allocation, and defining constraints for these functions. The objective function for scheduling is the minimum sum of energy purchase cost, wind and solar curtailment, and operation and maintenance cost; the objective function for capacity allocation is the minimum sum of construction, operation and maintenance cost, and carbon emission cost; and finally, using the CPLEX solver, obtaining the optimal scheduling and capacity allocation schemes.

[0007] The above patents do not consider the adjustable capacity brought about by the participation of thermal storage industrial loads in demand response in the integrated energy optimization scheduling model, nor do they consider the adjustable flue gas diversion ratio of carbon capture devices and the flexible adjustment capability brought about by the installation of liquid storage tanks.

[0008] Therefore, there is an urgent need for a distribution network optimization and scheduling method that takes into account the adjustable capacity brought about by the participation of thermal storage industrial loads in demand response, while also taking into account the uncertainty of distribution network operation caused by the increase in the penetration rate of new energy sources. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a method for coordinated and optimized scheduling of power distribution networks, grids, loads, and storage systems that includes thermal storage industrial loads. By establishing a mathematical model of the demand response of flexible carbon capture gas turbines and thermal storage industrial loads, a coordinated and optimized scheduling model and solution algorithm for power distribution networks, including thermal storage industrial loads, are developed, providing theoretical and technical support for the optimized operation of future power distribution networks.

[0010] The present invention adopts the following technical solution.

[0011] A method for coordinated and optimized scheduling of power distribution networks, grids, loads, and energy storage, including industrial loads with thermal storage, comprising the following steps:

[0012] S1, acquire historical forecast error data for new energy sources and load;

[0013] S2. Establish a mathematical model of a gas turbine equipped with a flexible carbon capture device, and perform convex relaxation treatment on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable.

[0014] S3. Construct a mathematical model for the demand response of thermal storage industrial loads that takes into account daily regulation capacity, daily regulation frequency, and daily output constraints, and perform piecewise linearization on the nonlinear constraints of daily production tasks of thermal storage industrial loads.

[0015] S4. Based on the historical prediction error data of new energy sources and loads obtained in S1, obtain a set of typical prediction error scenarios.

[0016] S5, based on the day-ahead forecast curves of new energy sources and load power provided by the distribution network dispatch center and the typical forecast error scenario set obtained in S4, constructs a coordinated optimization dispatch model for distribution network sources, grids, loads and storage that includes demand response of thermal storage type industrial loads.

[0017] The present invention further includes the following preferred embodiments:

[0018] In S1, the historical prediction error data for new energy sources and loads includes hourly power prediction error data for each of them, which are taken back at least one year from the date of dispatch.

[0019] In S2, the flexible carbon capture device installed on the gas turbine includes an absorption tower, a storage tank, and a regeneration tower. During peak load periods, the storage tank stores the CO2 absorbed by the absorption tower and does not process it temporarily, reducing the carbon capture power of the gas turbine during peak load periods and increasing the grid connection power. During off-peak periods, the storage tank releases the stored CO2 and sends it to the regeneration tower, consuming the power of the gas turbine for subsequent carbon capture processing.

[0020] In S2, the mathematical model for a gas turbine equipped with a flexible carbon capture device is constructed as follows:

[0021]

[0022] Among them, P t CCGT The total output power of the gas turbine including the flexible carbon capture device during time period t. P is the on-grid power of the gas turbine during time period t. t CC P represents the carbon capture energy consumption of the carbon capture equipment during time period t. t Y Let t represent the fixed energy consumption of the carbon capture equipment during time period t, and χ represent the operating energy consumption of the carbon capture equipment per unit of CO2 captured. Let t be the total mass of CO2 captured during time period t. Let t represent the mass of CO2 entering the carbon capture device after being diverted by the flue gas bypass system during time period t. The CO2 supply to the rich liquid tank equipped with the carbon capture device during time period t. δ represents the CO2 absorption rate of the rich liquid tank during time period t, and δ represents the flue gas split ratio. For maximum carbon capture level, The total mass of CO2 actually produced by the gas turbine during time period t γ CCGT CO2 produced per unit of electricity by a gas turbine This represents the net carbon emissions of the gas turbine, which is the mass of CO2 emitted into the air after being diverted by the flue gas bypass system during time period t.

[0023] In S2, the convex relaxation treatment includes the constraint conditions for constructing the mathematical model of the gas turbine according to the following formula:

[0024]

[0025] Where δ represents the flue gas split ratio, P t CCGT This represents the total output power of the carbon capture gas turbine during time period t. These represent the lower and upper limits of the output of the carbon capture gas turbine, respectively, and ω represents the value used to replace δ. t P t CCGTAuxiliary variables for bilinear terms.

[0026] In S3, piecewise linearization of the nonlinear constraints of production tasks involves introducing auxiliary variables for piecewise linearization transformation using the following formula:

[0027]

[0028] Where x1, x2, and x3 are three 0-1 auxiliary variables introduced for piecewise linearization, O m,t,k For the output of the m-th thermal storage industrial load at time t in the k-th prediction error scenario, O m Let λ1, λ2, and λ3 represent the output demand of the m-th thermal storage industrial load, and λ1, λ2, and λ3 represent the product yield under the following conditions: power reduction, rated power, and power increase of the thermal storage industrial load equipment, respectively. This serves as the reference power for the operation of thermal storage industrial load equipment. Let be the optimization variables corresponding to the upward and downward adjustment of the power of the m-th thermal storage industrial load at time t under the scenario of the k-th day-ahead prediction error.

[0029] In S4, based on the historical prediction error data of new energy sources and loads obtained in S1, a typical prediction error scenario set is obtained, including:

[0030] Step S401: Calculate the Euclidean distance between any two source load historical prediction error vectors;

[0031] Step S402: Calculate the local density of the source load historical prediction error data corresponding to the historical prediction error vector;

[0032] Step S403: Determine the cluster center distance of the historical prediction error data of the source load;

[0033] Step S404: Using the normalized values ​​of the local density and cluster center distance as the horizontal and vertical axes, traverse all historical prediction error data of new energy sources and loads, and plot the corresponding points of the historical prediction error data of source and load in two-dimensional space.

[0034] Step S405: Arrange the products of the horizontal and vertical coordinates drawn in the two-dimensional space from high to low in step S404, and take the set of samples corresponding to the top 5% of the sorted points as the typical prediction error scenario set.

[0035] In S5, the construction of a coordinated optimization scheduling model for power grid, source, load, and storage systems that includes thermal storage-based industrial load demand response includes:

[0036] Step S501: Construct the objective function of a power grid optimization scheduling model that includes flexible carbon capture gas turbines, thermal storage industrial load demand response, and energy storage devices;

[0037] Step S502: Construct the constraints of the coordinated optimization scheduling model of power grid source, grid, load and storage for distribution network containing thermal storage industrial loads.

[0038] In step S501, the objective function of the distribution network optimization scheduling model containing flexible carbon capture gas turbines, thermal storage industrial load demand response, and energy storage devices is constructed as follows:

[0039]

[0040] Among them, C CCGT This indicates the start-up and shutdown cost of a gas turbine. This represents the operating cost of the carbon capture gas turbine in the k-th typical scenario. This represents the energy purchase cost of the distribution network in the k-th typical scenario. This represents the subsidy cost for the distribution network to pay for thermal storage-type industrial loads to participate in demand response in the k-th typical scenario. This represents the carbon emission cost in the k-th typical scenario. This represents the penalty cost for wind and solar power curtailment in the k-th typical scenario. This represents the load shedding cost in the k-th typical scenario.

[0041] In step S502, the constraints for constructing the coordinated optimization scheduling model of power grid, source, load and storage for distribution network containing thermal storage industrial loads include: constructing power balance constraints; constructing line transmission capacity constraints; constructing wind and solar curtailment and load shedding constraints; constructing carbon capture gas turbine output constraints, ramping constraints and minimum start-stop time constraints; and constructing power and energy constraints for energy storage devices.

[0042] Meanwhile, the present invention also provides a power grid source-grid-load-storage optimization scheduling system for industrial loads with thermal storage, the system including a data acquisition module, a gas turbine mathematical model construction module, a demand response mathematical model construction module, a prediction error scenario set acquisition module, and an optimization scheduling model construction module;

[0043] The data acquisition module is used to acquire historical data on new energy sources and loads, obtain historical prediction error data on new energy sources and loads based on the acquired historical data, and cluster the historical prediction error data on new energy sources and loads to obtain a set of typical prediction error scenarios.

[0044] The gas turbine mathematical model building module is used to build a mathematical model of a gas turbine equipped with a flexible carbon capture device, and to perform convex relaxation processing on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable.

[0045] The demand response mathematical model construction module is used to construct a mathematical model of demand response for thermal storage industrial loads that takes into account the constraints of daily regulation capacity and daily regulation frequency, and to perform piecewise linearization on the nonlinear constraints of its daily production tasks.

[0046] The prediction error scenario set acquisition module is used to acquire typical prediction error scenario sets based on historical prediction error data of new energy and load.

[0047] The optimized scheduling model construction module is used to construct a coordinated and optimized scheduling model for power generation, grid, load and storage of distribution networks, based on the day-ahead forecast curves of new energy sources and load power provided by the distribution network dispatch center and the typical forecast error scenario set obtained.

[0048] A terminal includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform steps according to any one of the methods.

[0049] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the methods described.

[0050] The beneficial effects of this invention are as follows: Compared with the prior art, this invention first establishes a mathematical model for the participation of flexible carbon capture devices and thermal storage industrial loads in demand response, and then performs convex transformation and linearization on it; then, it uses a fast search density clustering algorithm to extract typical prediction error scenarios from historical samples of source-load day-ahead prediction errors, and combines this with day-ahead prediction basic data to construct a set of typical day-ahead operation scenarios; finally, based on the above, it constructs and solves a coordinated optimization scheduling model for distribution network sources, grids, loads, and storage that includes thermal storage industrial loads. The method proposed in this invention can improve the renewable energy absorption rate and the economic efficiency of system operation, and reduce carbon emissions, while fully considering the uncertainty of renewable energy load prediction and tapping into flexible source-load-storage resources. Attached Figure Description

[0051] Figure 1 This is a flowchart of a method for coordinated and optimized scheduling of power distribution networks, grids, loads, and storage that includes thermal storage industrial loads, according to the present invention.

[0052] Figure 2 This is a schematic diagram of a test distribution network designed based on the IEEE-33 node standard system in one embodiment of the present invention;

[0053] Figure 3 This is a wind power and load forecast power curve diagram in one embodiment of the present invention;

[0054] Figure 4 This is a block diagram of a power distribution network source-grid-load-storage coordinated optimization scheduling system for industrial loads with thermal storage, according to the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0056] This application provides a method for coordinated and optimized scheduling of power distribution networks, including industrial loads with thermal storage capabilities, such as... Figure 1 The diagram shows a flowchart of a coordinated and optimized scheduling method for power distribution networks containing thermal storage industrial loads, according to the present invention. The method includes the following steps:

[0057] S1, obtain historical prediction error data for new energy sources and load.

[0058] In one embodiment of the present invention, in S1, the historical data of new energy sources and loads include their respective hourly power prediction error data for at least one year prior to the date of dispatch.

[0059] S2. A mathematical model of a gas turbine equipped with a flexible carbon capture device is established, and convex relaxation treatment is performed on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable.

[0060] In one embodiment of the present invention, in S2, the flexible carbon capture device installed on the gas turbine includes an absorption tower, a storage tank, and a regeneration tower. During peak load periods, the storage tank stores the CO2 absorbed by the absorption tower and does not process it temporarily, thereby reducing the carbon capture power of the gas turbine during peak load periods and increasing the grid connection power. During off-peak load periods, the storage tank releases the stored CO2 and sends it to the regeneration tower, consuming the power of the gas turbine for subsequent carbon capture processing.

[0061] In one embodiment of the present invention, step S2 further includes:

[0062] Step S201: Construct a mathematical model of the gas turbine equipped with flexible carbon capture according to the following formula:

[0063]

[0064] Among them, P t CCGT The total output power of the gas turbine including the flexible carbon capture device during time period t. P is the on-grid power of the gas turbine during time period t. t CC P represents the carbon capture energy consumption of the carbon capture equipment during time period t. t Y Let t represent the fixed energy consumption of the carbon capture equipment during time period t, and χ represent the operating energy consumption of the carbon capture equipment per unit of CO2 captured. Let t be the total mass of CO2 captured during time period t. Let t represent the mass of CO2 entering the carbon capture device after being diverted by the flue gas bypass system during time period t. The CO2 supply to the rich liquid tank equipped with the carbon capture device during time period t; δ represents the CO2 absorption rate of the rich liquid tank during time period t, and δ represents the flue gas split ratio. min δ max These represent the minimum and maximum values ​​of the flue gas split ratio, respectively. For maximum carbon capture level, The total mass of CO2 actually produced by the gas turbine during time period t γ CCGT CO2 produced per unit of electricity by a gas turbine This represents the net carbon emissions of the gas turbine, which is the mass of CO2 emitted into the air after being diverted by the flue gas bypass system during time period t.

[0065] Step S202 further supplements the model of the key equipment of the flexible carbon capture device—the storage tank; the flue gas generated by the carbon capture gas turbine enters the absorption tower at a certain split ratio and undergoes an absorption reaction with the ethanolamine solution. The ethanolamine solution absorbs a larger proportion of CO2 in the flue gas through rinsing, forming a rich solution with a higher CO2 concentration. Subsequently, the rich solution is pumped to the regeneration tower, where a reverse reaction occurs to achieve CO2 desorption and ethanolamine solution regeneration. The desorbed CO2 is sent to the compressor for reuse or storage, while the regenerated lean solution is returned to the absorption tower to complete the solution recycling; the above process follows the constraints shown in the following formula:

[0066]

[0067] Where η is the maximum operating efficiency of the regeneration tower and the compressor. This represents the upper limit of the total output power of carbon capture gas turbines. To represent a 0-1 variable reflecting the changes in the capacity of the lean and rich liquid tanks during time period t, when the capacity of the lean liquid tank increases, the corresponding... The value is positive; when the capacity of the rich liquid tank increases, the corresponding value is... It is positive.

[0068] It should be noted that since CO2 exists in the ethanolamine solution in the form of a compound, the relationship between the stored volume of ethanolamine solution and the mass of CO2 must be taken into account, as shown in the following expression:

[0069]

[0070] Among them, V t in V is the volume of solution required for the rich liquid tank to absorb CO2 during time period t. tout M is the volume of solution required for CO2 to be released from the rich liquid tank during time period t. MEA , Here, denoted as the molar mass of MEA and CO2, respectively, and ψ represents the amount of CO2 that 1 mol of MEA solution can absorb, in mol / mol. These represent the solution concentration and solution density at time t, respectively.

[0071] The model expression for the liquid storage tank is as follows:

[0072]

[0073] Among them, V t FL V t WL These represent the solution volumes in the rich and lean solution tanks, respectively, during time period t. These represent the solution volumes in the rich and lean solution tanks, respectively, during time period t-1. These represent the initial solution volumes in the rich and lean solution tanks, respectively. V represents the solution volume in the rich solution tank and the lean solution tank after one scheduling cycle. t FL V t WL V represents the volume of solution in the rich and lean tanks at time t, respectively. L This represents the volume of the storage tank.

[0074] Step S203: The model of the flexible carbon capture gas turbine is non-convex, and there exists an adjustable flue gas split ratio δ and the total output power P of the carbon capture gas turbine at time t. t CHP The multiplicative bilinear terms. To ensure the solvability of the model, the McCormick envelope is used to relax this non-convex problem into a convex problem. The relaxed constraints are:

[0075]

[0076] Substituting equation (5) into equation (1) allows for the convex relaxation of the flexible carbon capture gas turbine model.

[0077] S3. Construct a mathematical model for the demand response of thermal storage industrial loads that takes into account daily regulation capacity, daily regulation frequency, and daily output constraints, and perform piecewise linearization on the nonlinear constraints of daily production tasks of thermal storage industrial loads.

[0078] In one embodiment of the present invention, in step S3, constructing a mathematical model for the demand response of thermal storage industrial load that takes into account constraints such as daily adjustment capacity and daily adjustment frequency includes: constructing a daily adjustment capacity constraint for thermal storage industrial load; constructing a daily adjustment frequency constraint for thermal storage industrial load; and constructing a daily output constraint for thermal storage industrial load.

[0079] The daily load regulation capacity constraint formula for thermal storage industrial loads is shown below:

[0080]

[0081] Among them, P m,t,k Let m be the operating power of the thermal storage industrial load after adjustment at time t under the scenario of prediction error a day before the kth day. This serves as the reference power for thermal storage industrial load operation. Let su be the optimization variable corresponding to the upward and downward adjustment of the power of the m-th thermal storage industrial load at time t under the scenario of the k-th day-ahead prediction error. m,t,k sd m,t,k To represent the 0-1 variables reflecting whether the m-th thermal storage industrial load device is in a power adjustment state at time t under the prediction error scenario of the k-th day, This refers to the maximum upward and downward power adjustment for heat storage industrial loads under the premise of ensuring safe operation.

[0082] The daily adjustment frequency constraint formula for thermal storage industrial loads is shown below:

[0083]

[0084] Among them, y m,t,k and z m,t,k These are the state transition variables for the power adjustment (upward or downward) of the m-th thermal storage industrial load device at time t under the prediction error scenario of the k-th day; i.e., y m,t,k (z m,t,k When the value is 1, it represents the m-th thermal storage industrial load device changing from other states to a power up (down) state under the k-th prediction error scenario; when the value is 0, it represents that no such state transition has occurred; M is the maximum number of adjustments set for the thermal storage industrial load; su m,(t-1),k This represents a 0-1 variable indicating that the m-th thermal storage industrial load device is in a power-up state at time (t-1) under the prediction error scenario of the k-th day.

[0085] The daily output constraint formula for heat storage industrial loads is shown below:

[0086]

[0087] Among them, O m P represents the target output value for heat storage industrial loads. m,t,k Let λ(P) be the adjusted operating power of the m-th thermal storage industrial load device at time t under the scenario of prediction error a day before the k-th day. m,t,k ) is a piecewise linear function of product yield with respect to power, i.e.:

[0088]

[0089] Wherein, λ1, λ2, and λ3 represent the product yield of the thermal storage industrial load under the reduced load power, rated power, and increased load power states, respectively.

[0090] In one embodiment of the present invention, the production constraint constituted by equations (8) and (9) is a conditional constraint, which makes the model unable to be directly solved by commonly used commercial solvers; the piecewise linearization of the nonlinear constraint of the production task includes introducing auxiliary variables for piecewise linearization transformation according to the following formula:

[0091]

[0092] Where x1, x2, and x3 are three 0-1 auxiliary variables introduced for piecewise linearization, O m,t,k Let m be the output of the m-th thermal storage industrial equipment at time t in the k-th prediction error scenario.

[0093] S4: Based on the historical prediction error data of new energy sources and loads obtained in S1, obtain a set of typical prediction error scenarios.

[0094] In the process embodiments of the present invention, obtaining a typical prediction error scenario set includes:

[0095] Step S401: Calculate the Euclidean distance between any two source load historical prediction error vectors.

[0096] First, record the historical prediction error data for any new energy source and any load as follows:

[0097] v sl =[v1,…,v x ,…,v y ,…v N (11)

[0098] In the formula, v sl A matrix composed of historical data of source load prediction errors; v1, v x v y v N These are the 1st, xth, yth, and Nth source load day-ahead prediction error historical samples, respectively; N is the total number of source load prediction error historical samples.

[0099] Then, with v x and v y For example, the Euclidean distance between two prediction error samples is given:

[0100] d xy =||v x -v y ||2 (12)

[0101] In the formula, d xy For v x and v y The Euclidean distance between them, ||·||2 represents the L2 norm.

[0102] Step S402: Calculate the local density of the source load historical prediction error data corresponding to the historical prediction error vector.

[0103] In one embodiment of the present invention, the Gaussian kernel function method is used to calculate sample v. x Local density δ x The specific formula is shown below:

[0104]

[0105] In the formula, δ x For sample v x Local density; d c The interruption distance is selected according to a set standard, which is to ensure that the distance from each data point to other data points is less than d. c The average number is approximately 1% to 2% of the total data.

[0106] Step S403: Determine the cluster center distance of the historical prediction error data of the source load.

[0107] In one embodiment of the present invention, sample v x The cluster center distance is defined as v x Compared to v x The minimum Euclidean distance among all historical samples with high local density can be expressed as:

[0108]

[0109] Among them, v z For the z-th historical prediction error sample, μ x For sample v x The distance between cluster centers.

[0110] Step S404: Using the normalized values ​​of the local density and cluster center distance as the horizontal and vertical axes, traverse all historical prediction error data of new energy sources and loads, and plot the corresponding points of the historical prediction error data of source and load in two-dimensional space.

[0111] In one embodiment of the present invention, after all sample points have been drawn, the products of the horizontal and vertical coordinates of all points in the graph are arranged from high to low. The set of samples corresponding to the top 5% of the sorted points is taken as typical prediction error scenarios. The remaining samples are assigned to the cluster of the nearest typical scenario based on their Euclidean distance from these typical scenarios. After all samples have been assigned, the probability of any typical scenario is calculated as follows:

[0112]

[0113] Where, p k Let n(k) be the probability corresponding to the kth typical scenario, and n(k) be the number of all samples belonging to the cluster to which the kth typical scenario belongs.

[0114] S5, based on the day-ahead forecast curves of new energy sources and load power provided by the distribution network dispatch center and the typical forecast error scenario set obtained in S4, constructs an optimized dispatch model for distribution network sources, grids, loads and storage that includes thermal storage industrial loads.

[0115] In one embodiment of the present invention, constructing a coordinated optimization scheduling model for power distribution network sources, grids, loads, and storage that includes thermal storage industrial loads includes:

[0116] Step S501: Add the typical prediction error scenarios of all new energy sources and loads to the basic scenarios represented by the day-ahead prediction curves to obtain a set of typical day-ahead operating scenarios. Based on this, construct the objective function of the distribution network source-grid-load-storage coordinated optimization scheduling model for industrial loads with thermal storage as follows:

[0117]

[0118] Where, n sce C represents the total number of typical day-ahead operating scenarios. CCGT It is the start-up and shutdown cost of the gas turbine. This represents the operating cost of the carbon capture gas turbine in the k-th typical scenario. This represents the energy purchase cost of the power distribution network in the k-th typical scenario. The cost of subsidies for the distribution network to support the participation of thermal storage industrial loads in demand response under the k-th typical scenario. For the carbon emission cost in the k-th typical scenario, Let $\frac{ ... Let $\frac{k}{k}$ be the load shedding cost in the kth typical scenario.

[0119] In this embodiment, the formula for calculating the start-up and shutdown cost of the gas turbine is as follows:

[0120]

[0121] Where, N CCGT Number of carbon capture gas turbines; SU i SD i These represent the start-up and shutdown costs of the i-th generating unit, respectively; I i,t I i,t-1 These are the start / stop state variables of the i-th unit at time t and time t-1, respectively.

[0122] In this embodiment, the formula for calculating the operating cost of the carbon capture gas turbine in the kth typical scenario is as follows:

[0123]

[0124] Among them, c CCGT c ccus These are the unit operating costs of the carbon capture gas turbine power generation equipment and the carbon capture equipment, respectively. Let i be the electrical power produced by the i-th carbon capture gas turbine at time t in the k-th typical scenario; Let t represent the operating energy consumption of the carbon capture device at time t under the k-th typical scenario.

[0125] In this embodiment, the formula for calculating the energy purchase cost of the distribution network in the k-th typical scenario is as follows:

[0126]

[0127] Among them, c grid,t The price at which electricity is purchased from the main grid at time t within the dispatch cycle. For the k-th typical scenario, at time t, electricity is purchased from the main network.

[0128] In this embodiment, the formula for calculating the subsidy cost for the distribution network to pay for the participation of thermal storage industrial loads in demand response under the k-th typical scenario is as follows:

[0129]

[0130] Among them, c m1 c m2 These are the unit response electricity subsidies for a single upward or downward adjustment of the load of thermal storage industrial plants; For the unit upward and downward power adjustment of the m-th thermal storage industrial load equipment at time t in the k-th typical scenario; These represent the charging and discharging power of the energy storage device installed at the m-th thermal storage industrial load device during time period t in the k-th typical scenario.

[0131] In this embodiment, the formula for calculating the carbon emission cost under the k-th typical scenario is as follows:

[0132]

[0133] in, Cost per unit of carbon emissions λ represents the net carbon emissions during time period t in the k-th typical scenario; e Carbon emission factor per unit of electricity purchased.

[0134] In this embodiment, the formula for calculating the penalty cost of wind and solar power curtailment in the kth typical scenario is as follows:

[0135]

[0136] Among them, c cur The penalty coefficient per unit of wind and solar power curtailment. These represent the amount of abandoned power generated during time period t for the w-th wind farm and the v-th photovoltaic power station in the k-th typical scenario, respectively.

[0137]

[0138] In this embodiment, the formula for calculating the load shedding cost in the kth typical scenario is as follows:

[0139] Among them, c L The unit load shedding penalty factor; Let be the load shedding amount at node b during time period t in the k-th typical scenario.

[0140] Step S502, the constraints of the constructed distribution network source-grid-load-storage coordinated optimization scheduling model containing thermal storage industrial loads are as follows:

[0141] 1) The power balance constraints are as follows:

[0142]

[0143] in, These represent the predicted power generation of the w-th wind farm and the v-th photovoltaic power station connected to the system during time period t in the k-th typical scenario, respectively. Let t be the total system load at node b during time period t.

[0144] 2) The line transmission capacity constraints are as follows:

[0145]

[0146] Among them, K lb f is the power flow distribution transfer factor of system node b with respect to line l. lmax Let be the maximum transmission power of line l. i∈b, w∈b, v∈b, and m∈b represent that unit i, wind farm w, photovoltaic power station v, thermal storage industrial equipment m are connected to system node b to access the distribution network.

[0147] 3) The constraints on wind and solar curtailment and load shedding are as follows:

[0148]

[0149] 4) The output constraints of the carbon capture gas turbine are as follows:

[0150]

[0151] in, and These are the lower and upper limits of the output of the i-th carbon capture gas turbine, respectively.

[0152] 5) The ramp-up constraints for carbon capture gas turbines are as follows:

[0153]

[0154] Among them, DR i UR i These represent the uphill and downhill ramp rates of carbon capture gas turbine i, respectively.

[0155] 6) The minimum start-stop time constraints for carbon capture gas turbines are as follows:

[0156]

[0157] in, These represent the durations during which carbon capture gas turbine i has been continuously started and shut down during time period t-1. These represent the minimum start-up and shutdown durations for carbon capture gas turbine i, respectively.

[0158] 7) The power and energy constraint formulas for energy storage devices are shown below:

[0159]

[0160] in, These are the charging and discharging status indicators of the energy storage device installed at the m-th thermal storage industrial load equipment in the k-th typical scenario during time period t, and both are 0-1 variables. η m The maximum charging and discharging power, maximum energy storage capacity, and charging and discharging efficiency of the energy storage device installed at the m-th thermal storage industrial load equipment; S represents the real-time stored energy of the energy storage device installed at the m-th thermal storage industrial load equipment during time period t in the k-th typical scenario; mmin S mmax These are the minimum and maximum state of charge coefficients of the energy storage device installed at the m-th thermal storage industrial load equipment, respectively.

[0161] Equations (17)-(23) are regarded as objective functions, and equations (1)-(7), (10), and (24)-(31) are regarded as constraints. This constitutes the coordinated optimization scheduling model of power grid source, grid, load and storage for distribution network containing heat storage type industrial load proposed in this application.

[0162] In one embodiment of the present invention, after step 5, the model algorithm is solved using the CPLEX commercial solver.

[0163] In one embodiment of the present invention, using the design example of an improved IEEE-33 node distribution network system standard test system, the network structure is as follows: Figure 2 As shown in the table, this power distribution network connects to three identical gas turbines, GT1-GT3, connected to nodes 22, 25, and 33 respectively. Specific technical parameters are shown in Table 1. All three gas turbines are equipped with identical carbon capture devices, and relevant parameters are shown in Table 2. A wind farm is connected to node 18, and its power output prediction curve is shown in Table 2. Figure 3 As shown, the unit wind curtailment penalty cost is 700 yuan / MW·h; the proportion of active load at each node to the total active load of the distribution network is consistent with the IEEE-33 node standard system; the load of the fused magnesium enterprise is selected as a representative of the thermal storage industrial load. This enterprise is connected to node 18. Without participating in demand response, its daily power curve is a horizontal line with a value equal to the rated power. Its relevant parameters are shown in Table 3; the predicted total active power curve of all loads in the distribution network other than the aforementioned fused magnesium enterprise load is shown in Table 3. Figure 3 As shown. The electricity price purchased by the distribution network from the main grid adopts a time-of-use pricing system, based on... Figure 3 The load curve shown is divided into peak, flat, and valley periods, and the corresponding electricity prices for each period are shown in Table 4; the average carbon emission factor for purchased electricity is 0.65 t / MW·h.

[0164] Table 1 Gas Turbine Parameter Table

[0165]

[0166] Table 2 Parameters of Carbon Capture Device

[0167]

[0168] Table 3 Relevant parameters for fused magnesium enterprises

[0169]

[0170]

[0171] Table 4 Time Period Division and Electricity Price

[0172]

[0173] Based on the above example parameters, to verify the ability of introducing gas turbines with flexible carbon capture devices, fused magnesium demand response, and user-side energy storage devices to improve the economic efficiency, low carbon emissions, and flexibility of distribution network operation, three different scenarios were set up for comparative analysis. The scenarios are as follows:

[0174] 1) Scenario 1: GT1-GT3 are equipped with traditional carbon capture devices (no storage tank, fixed flue gas split ratio), without considering the participation of fused magnesium enterprises in demand response.

[0175] 2) Scenario 2: GT1-GT3 are modified into gas turbines with adjustable flue gas split ratio and flexible carbon capture devices, without considering the participation of fused magnesium enterprises in demand response.

[0176] 3) Scenario 3: Convert GT1-GT3+ into gas turbines with adjustable flue gas split ratio and flexible carbon capture devices, and consider the participation of fused magnesium enterprises in demand response.

[0177] Scenario 3 corresponds to the optimized scheduling method proposed in this invention. All three scenarios were constructed by calling the YALMIP toolbox using MATLAB software and solved using the CPLEX solver. The results are shown in Table 5.

[0178] Table 5 Comparison of Distribution Network Operating Costs under Different Scenarios

[0179]

[0180] As shown in Table 5, Scenario 1 has the highest overall cost, carbon emissions, and load shedding, followed by Scenario 2, and Scenario 3 has the lowest. From the comparative analysis of the scheduling results, the gas turbine with adjustable flue gas diversion ratio and flexible carbon capture device, the participation of fused magnesium load in demand response, and the installation of energy storage devices on the user side can each have beneficial effects on total carbon emissions, renewable energy consumption, and system scheduling costs.

[0181] Meanwhile, this invention also provides a coordinated and optimized dispatching system for power distribution networks, grids, loads, and storage that includes thermal storage-type industrial loads, such as... Figure 4 The diagram shown is a block diagram of a power grid source-grid-load-storage optimization scheduling system for industrial loads with thermal storage. The system includes a data acquisition module, a gas turbine mathematical model construction module, a demand response mathematical model construction module, a prediction error scenario set acquisition module, and an optimization scheduling model construction module.

[0182] The data acquisition module is used to acquire historical data on new energy sources and loads;

[0183] The gas turbine mathematical model construction module is used to establish a mathematical model of a gas turbine equipped with a flexible carbon capture device, and to perform convex relaxation processing on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable.

[0184] The demand response mathematical model construction module is used to construct a thermal storage industrial load demand response mathematical model that takes into account the constraints of daily adjustment capacity and daily adjustment frequency, and to perform piecewise linearization processing on the nonlinear constraints of its daily production tasks.

[0185] The prediction error scenario set acquisition module is used to acquire a typical prediction error scenario set based on historical prediction error data of new energy sources and loads.

[0186] The optimized scheduling model construction module is used to construct a coordinated optimized scheduling model and solution algorithm for power generation, grid, load and storage of distribution network, based on the day-ahead forecast curves of new energy and load power provided by the distribution network dispatch center and the obtained typical forecast error scenario set.

[0187] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0188] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0189] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0190] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for coordinated and optimized scheduling of power distribution networks, loads, and energy storage, including industrial loads with thermal storage capabilities, characterized in that... The method includes the following steps: S1, acquire historical forecast error data for new energy sources and load; S2. Establish a mathematical model of a gas turbine equipped with a flexible carbon capture device, and perform convex relaxation treatment on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable. S3. Construct a mathematical model for the demand response of thermal storage industrial loads that takes into account daily regulation capacity, daily regulation frequency, and daily output constraints, and perform piecewise linearization on the nonlinear constraint of daily output of thermal storage industrial loads. S4. Based on the historical prediction error data of new energy sources and loads obtained in S1, obtain a set of typical prediction error scenarios. S5, based on the day-ahead forecast curves of new energy and load power provided by the distribution network dispatch center and the typical forecast error scenario set obtained by S4, constructs a coordinated optimization dispatch model of distribution network source-grid-load-storage with demand response of thermal storage industrial load; The construction of a coordinated and optimized scheduling model for power grid, source, load, and storage systems that includes thermal storage-based industrial load demand response includes: Step S501: Construct the objective function of a power grid optimization scheduling model that includes flexible carbon capture gas turbines, thermal storage industrial load demand response, and energy storage devices; Step S502: Construct the constraints of the coordinated optimization scheduling model of power grid source, grid, load and storage for distribution network containing thermal storage industrial loads.

2. The method for coordinated and optimized scheduling of power distribution network sources, grid, load, and storage including thermal storage industrial loads according to claim 1, characterized in that: In S1, the historical prediction error data for new energy sources and loads includes hourly power prediction error data for each of them, which are taken back at least one year from the date of dispatch.

3. The method for coordinated and optimized scheduling of power distribution network sources, grid, load, and storage including thermal storage industrial loads according to claim 1, characterized in that: In S2, the flexible carbon capture device installed on the gas turbine includes an absorption tower, a storage tank, and a regeneration tower. During peak load periods, the storage tank stores the CO2 absorbed by the absorption tower and does not process it temporarily, reducing the carbon capture power of the gas turbine during peak load periods and increasing the grid connection power. During off-peak periods, the storage tank releases the stored CO2 and sends it to the regeneration tower, consuming the power of the gas turbine for subsequent carbon capture processing.

4. The method for coordinated and optimized scheduling of power distribution network sources, grid, load, and storage for industrial loads containing thermal storage as described in claim 1, characterized in that: In S2, the mathematical model for a gas turbine equipped with a flexible carbon capture device is constructed as follows: in, for t The total output power of the gas turbine including the flexible carbon capture device during the time period. for t The on-grid power of the gas turbine during the time period, for t Energy consumption of carbon capture equipment during carbon capture operation. for t Fixed energy consumption of time-of-use carbon capture equipment The energy consumption of a carbon capture device for capturing one unit of CO2. for t Total mass of CO2 captured during the time period for t The mass of CO2 entering the carbon capture device after being diverted by the flue gas bypass system during a given period. The rich liquid tank equipped for the carbon capture unit t CO2 supply during the period For the rich liquid tank in t CO2 absorption during a given period The flue gas split ratio, For maximum carbon capture level, for t The total mass of CO2 actually produced by the gas turbine during the time period. CO2 produced per unit of electricity by a gas turbine The net carbon emissions of a gas turbine are... t The amount of CO2 emitted into the air after being diverted by the flue gas bypass system during a given period.

5. A method for coordinated and optimized scheduling of power distribution networks, loads, and energy storage systems, including industrial loads with thermal storage, as described in claim 1 or 4, characterized in that: In S2, the convex relaxation treatment includes the constraint conditions for constructing the mathematical model of the gas turbine according to the following formula: in, Indicates the flue gas split ratio. express t Total output power of the carbon capture gas turbine during the time period , These represent the lower and upper limits of the output power of the carbon capture gas turbine, respectively. Indicates used as a substitute Auxiliary variables for bilinear terms, , These represent the minimum and maximum values ​​of the flue gas split ratio, respectively.

6. The method for coordinated and optimized scheduling of power distribution network sources, grid, load, and storage including thermal storage industrial loads according to claim 1, characterized in that: In S3, piecewise linearization of the daily output nonlinear constraint involves introducing auxiliary variables for piecewise linearization transformation using the following formula: in, x 1. x 2. x 3 represents the three 0-1 auxiliary variables introduced for piecewise linearization. For the first k Prediction error scenarios t Time of the first m The output of a heat storage type industrial load, O m For the first m The output demand of a heat storage type industrial load. λ 1. λ 2. λ 3 represents the product yield of the thermal storage industrial load equipment under the following conditions: power reduction, rated power, and power increase. This serves as the reference power for the operation of thermal storage industrial load equipment. , This refers to the maximum upward and downward power adjustment for heat storage industrial loads under the premise of ensuring safe operation.

7. The method for coordinated and optimized scheduling of power distribution network sources, grid, load, and storage for industrial loads containing thermal storage as described in claim 1, characterized in that: In S4, based on the historical prediction error data of new energy sources and loads obtained in S1, a typical prediction error scenario set is obtained, including: Step S401: Calculate the Euclidean distance between any two source load historical prediction error vectors; Step S402: Calculate the local density of the source load historical prediction error data corresponding to the historical prediction error vector; Step S403: Determine the cluster center distance of the historical prediction error data of the source load; Step S404: Using the normalized values ​​of the local density and cluster center distance as the horizontal and vertical axes, traverse all historical prediction error data of new energy sources and loads, and plot the corresponding points of the historical prediction error data of source and load in two-dimensional space. Step S405: Arrange the products of the horizontal and vertical coordinates drawn in the two-dimensional space from high to low in step S404, and take the set of samples corresponding to the top 5% of the points as the typical prediction error scenario set.

8. The method for coordinated and optimized scheduling of power distribution networks, sources, grids, loads, and storage for industrial loads containing thermal storage, as described in claim 1, is characterized in that: In step S501, the objective function of the distribution network optimization scheduling model containing flexible carbon capture gas turbines, thermal storage industrial load demand response, and energy storage devices is constructed as follows: in, This indicates the start-up and shutdown cost of a gas turbine. Indicates that the carbon capture gas turbine is in the first k Operating costs in a typical scenario Indicates the distribution network in the first k Energy purchase costs in a typical scenario Indicates the distribution network in the first k The cost of subsidies for thermal storage industrial loads participating in demand response in a typical scenario. Indicates the first k Carbon emission costs in a typical scenario Indicates the first k The penalty cost of wind and solar power curtailment in a typical scenario Indicates the first k The cost of load shedding in a typical scenario.

9. The method for coordinated and optimized scheduling of power distribution network sources, grid, load, and storage for industrial loads containing thermal storage as described in claim 1, characterized in that: In step S502, the constraints for constructing the coordinated optimization scheduling model of the power distribution network source-grid-load-storage system containing thermal storage industrial loads include: Construct power balance constraints; Construct line transmission capacity constraints; Construct constraints on wind and solar curtailment and load shedding; Construct constraints on the output of the carbon capture gas turbine, the ramping constraint, and the minimum start-stop time constraint. And the power and energy constraints for constructing energy storage devices.

10. A power grid source-grid-load-storage optimization scheduling system for industrial loads with thermal storage using the method described in any one of claims 1-9, the system comprising a data acquisition module, a gas turbine mathematical model construction module, a demand response mathematical model construction module, a prediction error scenario set acquisition module, and an optimization scheduling model construction module, characterized in that: The data acquisition module is used to acquire historical data on new energy sources and loads, obtain historical prediction error data on new energy sources and loads based on the acquired historical data, and cluster the historical prediction error data on new energy sources and loads to obtain a set of typical prediction error scenarios. The gas turbine mathematical model building module is used to build a mathematical model of a gas turbine equipped with a flexible carbon capture device, and to perform convex relaxation processing on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable. The demand response mathematical model construction module is used to construct a mathematical model of demand response for thermal storage industrial loads that takes into account the constraints of daily regulation capacity and daily regulation frequency, and to perform piecewise linearization on the nonlinear constraints of its daily output. The prediction error scenario set acquisition module is used to acquire typical prediction error scenario sets based on historical prediction error data of new energy and load. The optimized scheduling model construction module is used to construct a coordinated and optimized scheduling model for power generation, grid, load and storage of distribution network, based on the day-ahead forecast curves of new energy sources and load power provided by the distribution network dispatch center and the typical forecast error scenario set obtained. The construction of a coordinated and optimized scheduling model for power grid, source, load, and storage systems that includes thermal storage-based industrial load demand response includes: The objective function of a distribution network optimization scheduling model incorporating flexible carbon capture gas turbines, thermal storage industrial load demand response, and energy storage devices is constructed; the constraints of a distribution network source-grid-load-storage coordinated optimization scheduling model incorporating thermal storage industrial loads are also constructed.

11. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-9.

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