Distributed solving method considering carbon capture-energy storage collaborative multi-region interconnection system
By decoupling the carbon capture power plant from electricity and introducing an energy storage system, a carbon capture-energy storage collaborative model was constructed. A distributed scheduling optimization method was adopted to solve the problem of carbon capture intensity fluctuation in the carbon capture device, improve the low-carbon performance of the power system and the effect of new energy consumption, and achieve improved economic benefits.
Patent Information
- Application Number
- CN202411313445.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-20
- Publication Date
- 2025-01-10
AI Technical Summary
Existing research has failed to effectively address the fluctuations in carbon capture intensity of carbon capture devices when considering the flexibility of carbon capture unit release, resulting in the power system's low-carbon performance not being fully realized and the effect of new energy consumption being poor.
By decoupling the electricity and carbon in carbon capture power plants, an energy storage system is introduced to construct a carbon capture-energy storage collaborative model. A distributed scheduling optimization method is adopted, and real-time low-carbon economic scheduling is carried out based on a rolling optimization strategy. This decouples the energy consumption and net output of the carbon capture device, and combines an information network topology and a distributed iterative optimization solution framework.
This has improved the stability of carbon capture intensity and the flexibility of the system, promoted the consumption of new energy sources, reduced the carbon emissions of the system, and improved economic efficiency.
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Abstract
Description
Technical Field
[0001] The present invention belongs to the field of power system dispatch optimization, and in particular relates to a distributed solution method for a multi-region interconnected system considering carbon capture-energy storage synergy. Specifically, a carbon capture-energy storage synergy model is used to fully explore the low-carbon operation of the interconnected power system, and a distributed real-time low-carbon dispatch optimization of the multi-region interconnected system is achieved based on a rolling optimization strategy. Background Art
[0002] As the main source of carbon emissions, the power industry has important significance for promoting the low-carbon transformation of the power system to achieve sustainable development of the power grid. Carbon capture technology is a key means to reduce carbon dioxide emissions and solve the problem of global warming. It will play a vital role in the deep emission reduction of the power industry in the later stage. Gradually increasing the proportion of new energy and carrying out low-carbon transformation of conventional units are important ways to promote the low-carbon transformation of the power system. After the traditional thermal power units are transformed into carbon capture units using carbon capture technology, the carbon capture device can be regarded as an adjustable load in the plant, which can reduce the carbon emission intensity of the unit and improve the flexibility of the unit. However, most of the existing studies have ignored the problem of carbon capture intensity fluctuation of the carbon capture device when the carbon capture unit releases its flexibility, and cannot exert the optimal low-carbon performance of the system. Taking into account the carbon capture intensity and the new energy consumption effect to achieve low-carbon economic dispatch of the power system has become a problem to be solved in the current research on carbon capture power plants for new energy consumption. Summary of the invention
[0003] In view of the above problems, the present invention provides a scientific, reasonable, highly applicable and effective distributed solution method for a multi-region interconnected system that considers carbon capture-energy storage synergy. Its purpose is to use the energy storage system to supply energy to the carbon capture device when the carbon capture unit releases its flexibility, so as to avoid the fluctuation of the carbon capture intensity of the carbon capture device caused by the coupling relationship between the energy consumption of the carbon capture device and the net output of the carbon capture unit, and improve the low-carbon and economic operation of the system. The method first decouples the carbon capture unit from electricity, and introduces energy storage to construct a carbon capture-energy storage synergy model; then, the distributed scheduling optimization is applied to the multi-region interconnected system containing carbon capture-energy storage, and based on the rolling optimization strategy, the distributed real-time low-carbon economic scheduling model considering the carbon capture-energy storage synergy model is solved.
[0004] In order to solve the existing problems, the technical solution adopted by the present invention is: a distributed solution method considering the carbon capture-energy storage coordinated multi-regional interconnected system, which includes: electric-carbon decoupling of carbon capture power plants, construction of distributed economic dispatching models for carbon capture-energy storage coordinated multi-regional interconnected systems, construction of information network topology structure, design of distributed iterative optimization solution framework and establishment of evaluation indicators.
[0005] Furthermore, the carbon capture power plant electricity-carbon decoupling is as follows:
[0006] Equations (1) and (2) are mathematical models for the operation of a carbon capture power plant in split-flow operation mode.
[0007]
[0008] in, is the total output and net output of the power plant in region i during time period t; Respectively represent the operating energy consumption and fixed energy consumption of the carbon capture device; λ C The energy consumption coefficient of the CO2 carbon capture device for the capture unit; is the amount of CO2 captured by the carbon capture device; θ C is the carbon capture efficiency, which is taken as 0.9; is the carbon emission coefficient of the corresponding power plant; δ C is the flue gas split ratio, It is the smoke split ratio at the corresponding time; They represent the total carbon emissions and actual carbon emissions of the unit in period t respectively.
[0009] The electricity-carbon decoupling of the carbon capture power plant is to break down the coupling relationship between the operating energy consumption of the carbon capture device and the net output of the power plant. The carbon capture device can be regarded as a load unit with a negative output. The fixed energy consumption of the carbon capture device is negligible, that is, its upper limit of output is 0. Unlike other traditional adjustable equipment, the lower limit of the output of the carbon capture device is not a fixed value. In the carbon capture power plant, the carbon capture device can only absorb and fix the flue gas emitted by the power plant. Therefore, the lower limit of the energy consumption of the carbon capture device depends on the flue gas emissions of the corresponding carbon capture power plant. Therefore, its lower limit of output is related to the flue gas diversion ratio and the power plant output. According to equations (1) and (2), the lower limit of output can be obtained, and its output constraint condition is shown in (3).
[0010]
[0011] For the carbon capture power plant after electricity-carbon decoupling, the carbon capture device is now an independent unit, and the net output of the power plant is the total output of the power plant, which is no longer coupled with the carbon capture energy consumption. Here, electricity-carbon decoupling only decouples the energy of the carbon capture power plant, and there is still a certain connection between the unit output and the control parameters such as the flue gas diversion ratio of the carbon capture device.
[0012] The unit cost of the carbon capture device is only the carbon trading cost, as shown in formula (4).
[0013]
[0014] in, Carbon trading cost benefits brought by carbon capture devices to process CO2; C The carbon trading price.
[0015] Furthermore, the distributed economic dispatch model of the carbon capture-energy storage coordinated multi-region interconnected system is constructed as follows:
[0016] With the goal of minimizing the overall operating cost of the multi-region interconnected system, the economic dispatch model related to the interconnected regions can be expressed as a linear programming problem as shown in Equation (5) and Equation (6), defined as Model A:
[0017]
[0018] stAx∝b(6)
[0019] Among them, f is the overall operating cost of the regional interconnected system; c is the column vector composed of the marginal power generation cost coefficients of the output units in each region; x is the column vector composed of the power output power decisions of each output unit; A and b are constant matrices and column vectors formed by constraint conditions; "∝" represents the size relationship between matrices, where the "∝" related to the power balance constraint is "=", and the "∝" related to the other constraints is "≤", and the forms of c, A, and b are as follows:
[0020]
[0021] Where n is the number of regions, let the set of regions be v = {v1,...,v n}, the set of cells in region i is v i ={1,...,m i};c i is the marginal generation cost coefficient matrix given for the ith region; is the coefficient matrix related to the global constraints; A i 、b i is the coefficient matrix associated with the local constraints.
[0022] Step 1: Find the dual problem of model A, as shown in equations (8) and (9):
[0023]
[0024] Where λ is the multiplier associated with each constraint in constraint equation (6), is the multiplier associated with the global constraint, λ i is the multiplier associated with the local constraint of the i-th region. λ is i The value range of the multiplier related to the equality constraint is (-∞,+∞), and the value range of the multiplier related to other constraints is (-∞,0).
[0025] Step 2: Right The decomposition of is defined as model B;
[0026]
[0027] Among them, λ z 、b z , A z 、c z The form is as follows:
[0028]
[0029] in, The decomposed elements must satisfy For the general The local variables associated with the ith region after decomposition; The decomposed elements must satisfy is the global inequality constraint value associated with region i,
[0030]
[0031]
[0032] 1) If That is, j is a controllable device, then
[0033] Among them, the marginal power generation cost of the output unit j in the i-th region is divided into: Segment; 1 T ∈R T is a column vector whose elements are all 1; They are respectively the power generation and marginal operating cost of the kth section of the unit in the jth output unit in region i.
[0034]
[0035] in, Represented as the identity matrix, D∈R T×T .
[0036]
[0037] Among them, 0∈R T is a column vector whose elements are all 0; is the lower limit of the output of the jth output unit in area i; Δt is the time period, which is 5 minutes; It is the initial output of the unit.
[0038]
[0039] (1) If That is, j is the AGC unit, then
[0040]
[0041] (2) If
[0042]
[0043] (3) If j is wind power, then
[0044]
[0045] in, is the predicted output of wind power unit j in region i at time t;
[0046] (4) If j is a carbon capture-storage synergistic unit, then
[0047]
[0048] in, It is the rated power of energy storage charging and discharging; are the upper and lower limits of energy storage capacity respectively; is the remaining capacity corresponding to time t; is the rated capacity of the energy storage.
[0049] The augmented Lagrangian function corresponding to model B is shown in formula (29):
[0050]
[0051] Among them, σ is a constant penalty factor, that is, the iteration step length; W T For and Constraints The associated multiplier vector is of the form:
[0052]
[0053] Among them, w (i,h) is the multiplier related to the association constraints between the interconnected region optimization subproblems; w i It is a multiplier related to the local constraints of a single regional optimization subproblem, which is equivalent to the output matrix of each regional unit.
[0054] By rearranging equation (29), we can obtain the Lagrangian function corresponding to the single regional optimization subproblem:
[0055]
[0056] Among them, Hi , It can be calculated based on the local information of region i and the associated information of adjacent regions. i , With λ z,i The form is as follows:
[0057]
[0058] So far, the Lagrangian function corresponding to the single regional optimization subproblem is obtained.
[0059] Furthermore, the information network topology is constructed
[0060] The information network topology is represented as a directed graph between nodes: G = (v, ε), where v is a set of points, representing each region, v = {1, 2, ..., n}; ε = v × v is a set of edges, representing the information topological structure relationship between regions; the set of points adjacent to point i is represented by N i Indicates that N i = {N i + , N i -}, where N i + is the set of points connected to and pointed by point i; N i - is the set of points connected to point i and pointing to point i; G = (v, ε) The point-edge association matrix is represented by the matrix E, which divides all regions into two categories. The set of the first type of regions is P1, and the set of the second type of regions is P2. N = {P1∪P2} and Adjacent areas are required to be classified differently, and this requirement can be met by rationally planning the information network topology.
[0061] Furthermore, the distributed real-time iterative optimization solution framework is designed
[0062] (a) Initialize the data of each region, λ z,i =0, w (i,h) =0, w (h,i) =0, w i =0;
[0063] (b) Solve the optimization model for each region and obtain the kth iteration related to each region.
[0064] (c) Information transmission between adjacent regions to update
[0065] (d) determining whether the number of iterations meets the initial setting, if not, returning to step (b), if it does, the iteration ends;
[0066] (e) Determine whether it is the last scroll. If not, record the energy storage capacity. And the unit output value, as the initial value of the next rolling energy storage capacity and the initial output of the unit; if yes, store the current energy storage capacity and exit the iteration.
[0067] in, is the w of the kth iteration i , w (i,h) , w (h,i) .
[0068] Further, the establishment of evaluation indicators
[0069]
[0070] In the formula, r (k) 、s (k) are the original residual and dual residual of the kth iteration respectively;
[0071] kind is equal to 1 or 2; Respectively represent the kth and k+1th iterations The algorithm convergence and system emission reduction effect are evaluated.
[0072] The distributed solution method of the carbon capture-energy storage coordinated multi-region interconnected system of the present invention has the following advantages: including: electricity-carbon decoupling of carbon capture power plants, construction of distributed economic dispatch model of carbon capture-energy storage coordinated multi-region interconnected systems, construction of information network topology, design of distributed iterative optimization solution framework and carbon capture intensity comparison verification. Electricity-carbon decoupling of carbon capture power plants is carried out to ensure the flexibility of carbon capture unit operation while making the net output of the power plant no longer coupled with the operating energy consumption of the carbon capture device; then, a carbon capture-energy storage coordinated model is constructed, and the coordinated model makes reasonable action responses according to the actual operation of the system. The carbon capture-energy storage coordinated model promotes the consumption of new energy, releases the flexibility of the system, ensures the carbon capture intensity, and reduces the carbon emissions of the system. BRIEF DESCRIPTION OF THE DRAWINGS
[0073] Figure 1 Flowchart of the distributed solution method for multi-region interconnected system considering carbon capture-energy storage synergy;
[0074] Figure 2 Provide IEEE-39 node system partition and information network topology diagram;
[0075] Figure 3 Regulate the equipment output decision curve for scenario 1;
[0076] Figure 4 This is the wind turbine abandonment situation in scenario 1;
[0077] Figure 5 Regulate the equipment output decision curve for scenario 2;
[0078] Figure 6 Regulate the equipment output decision curve for scenario three;
[0079] Figure 7 It is the carbon capture intensity comparison curve and SOC curve of the carbon capture device;
[0080] Figure 8 is the convergence curve diagram; DETAILED DESCRIPTION
[0081] In order to better understand the purpose, structure and function of the present invention, the following is a further detailed description of a distributed solution method for a multi-region interconnected system considering carbon capture-energy storage collaboration in the present invention in conjunction with the accompanying drawings.
[0082] In order to solve the existing problems, the technical solution adopted by the present invention is: a distributed solution method considering the carbon capture-energy storage coordinated multi-regional interconnected system, which includes: electric-carbon decoupling of carbon capture power plants, construction of distributed economic dispatching models for carbon capture-energy storage coordinated multi-regional interconnected systems, construction of information network topology structure, design of distributed iterative optimization solution framework and establishment of evaluation indicators.
[0083] In this embodiment, the carbon capture power plant is decoupled from electricity to carbon as follows:
[0084] Equations (1) and (2) are mathematical models for the operation of a carbon capture power plant in split-flow operation mode.
[0085]
[0086]
[0087] in, is the total output and net output of the power plant in region i during time period t; Respectively represent the operating energy consumption and fixed energy consumption of the carbon capture device; λ C The energy consumption coefficient of the CO2 carbon capture device for the capture unit; is the amount of CO2 captured by the carbon capture device; θ C is the carbon capture efficiency, which is taken as 0.9; is the carbon emission coefficient of the corresponding power plant; δ C is the flue gas split ratio, It is the flue gas split ratio at the corresponding time; They represent the total carbon emissions and actual carbon emissions of the unit in period t respectively.
[0088] The electricity-carbon decoupling of the carbon capture power plant is to break down the coupling relationship between the operating energy consumption of the carbon capture device and the net output of the power plant. The carbon capture device can be regarded as a load unit with a negative output. The fixed energy consumption of the carbon capture device is negligible, that is, its upper limit of output is 0. Unlike other traditional adjustable equipment, the lower limit of the output of the carbon capture device is not a fixed value. In the carbon capture power plant, the carbon capture device can only absorb and fix the flue gas emitted by the power plant. Therefore, the lower limit of the energy consumption of the carbon capture device depends on the flue gas emissions of the corresponding carbon capture power plant. Therefore, its lower limit of output is related to the flue gas diversion ratio and the power plant output. According to equations (1) and (2), the lower limit of output can be obtained, and its output constraint condition is shown in (3).
[0089]
[0090] For the carbon capture power plant after electricity-carbon decoupling, the carbon capture device is now an independent unit, and the net output of the power plant is the total output of the power plant, which is no longer coupled with the carbon capture energy consumption. Here, electricity-carbon decoupling only decouples the energy of the carbon capture power plant, and there is still a certain connection between the unit output and the control parameters such as the flue gas diversion ratio of the carbon capture device.
[0091] The unit cost of the carbon capture device is only the carbon trading cost, as shown in formula (4).
[0092]
[0093] in, Carbon trading cost benefits brought by carbon capture devices to process CO2; C The carbon trading price.
[0094] In this embodiment, the distributed economic dispatch model of the carbon capture-energy storage coordinated multi-region interconnected system is constructed as follows:
[0095] With the goal of minimizing the overall operating cost of the multi-region interconnected system, the economic dispatch model related to the interconnected regions can be expressed as a linear programming problem as shown in Equation (5) and Equation (6), defined as Model A:
[0096]
[0097] stAx∝b(6)
[0098] Among them, f is the overall operating cost of the regional interconnected system; c is the column vector composed of the marginal power generation cost coefficients of the output units in each region; x is the column vector composed of the power output power decisions of each output unit; A and b are constant matrices and column vectors formed by constraint conditions; "∝" represents the size relationship between matrices, where the "∝" related to the power balance constraint is "=", and the "∝" related to the other constraints is "≤", and the forms of c, A, and b are as follows:
[0099]
[0100] Where n is the number of regions, let the set of regions be v = {v1,...,v n}, the set of cells in region i is v i ={1,...,m i};c i is the marginal generation cost coefficient matrix given for the ith region; is the coefficient matrix related to the global constraints; A i 、b i is the coefficient matrix associated with the local constraints.
[0101] Step 1: Find the dual problem of model A, as shown in equations (8) and (9):
[0102]
[0103] Where λ is the multiplier associated with each constraint in constraint equation (6), is the multiplier associated with the global constraint, λ i is the multiplier associated with the local constraint of the i-th region. λ is i The value range of the multiplier related to the equality constraint is (-∞,+∞), and the value range of the multiplier related to other constraints is (-∞,0).
[0104] Step 2: Right The decomposition of is defined as model B;
[0105]
[0106] Among them, λ z 、b z , A z 、c z The form is as follows:
[0107]
[0108] in, The decomposed elements must satisfy For the general The local variables associated with the ith region after decomposition; The decomposed elements must satisfy is the global inequality constraint value associated with region i,
[0109]
[0110] 2) If That is, j is a controllable device, then
[0111]
[0112] Among them, the marginal power generation cost of the output unit j in the i-th region is divided into: Segment; 1 T ∈R T is a column vector whose elements are all 1; They are respectively the power generation and marginal operating cost of the kth section of the unit in the jth output unit in region i.
[0113]
[0114] in, Represented as the identity matrix, D∈R T×T .
[0115]
[0116] Among them, 0∈R T is a column vector whose elements are all 0; is the lower limit of the output of the jth output unit in area i; Δt is the time period, which is 5 minutes; It is the initial output of the unit.
[0117]
[0118] (1) If That is, j is the AGC unit, then
[0119]
[0120] (2) If
[0121]
[0122] (3) If j is wind power, then
[0123]
[0124] in, is the predicted output of wind power unit j in region i at time t;
[0125] (4) If j is a carbon capture-storage synergistic unit, then
[0126]
[0127] in, It is the rated power of energy storage charging and discharging; are the upper and lower limits of energy storage capacity respectively; is the remaining capacity corresponding to time t; is the rated capacity of the energy storage.
[0128] The augmented Lagrangian function corresponding to model B is shown in formula (29):
[0129]
[0130] Among them, σ is a constant penalty factor, that is, the iteration step length; W T For and Constraints The associated multiplier vector is of the form:
[0131]
[0132] Among them, w (i,h) is the multiplier related to the association constraints between the interconnected region optimization subproblems; w i It is a multiplier related to the local constraints of a single regional optimization subproblem, which is equivalent to the output matrix of each regional unit.
[0133] By rearranging equation (29), we can obtain the Lagrangian function corresponding to the single regional optimization subproblem:
[0134]
[0135] Among them, H i , It can be calculated based on the local information of region i and the associated information of adjacent regions. i , With λ z,i The form is as follows:
[0136]
[0137] So far, the Lagrangian function corresponding to the single regional optimization subproblem is obtained.
[0138] In this embodiment, the information network topology structure is constructed
[0139] The information network topology is represented as a directed graph between nodes: G = (v, ε), where v is a set of points, representing each region, v = {1, 2, ..., n}; ε = v × v is a set of edges, representing the information topological structure relationship between regions; the set of points adjacent to point i is represented by N i Indicates that N i = {N i + , N i -}, where N i + is the set of points connected to and pointed by point i; N i - is the set of points connected to point i and pointing to point j; G = (v, ε) The point-edge association matrix is represented by the matrix E, which divides all regions into two categories. The set of the first type of regions is P1, and the set of the second type of regions is P2. N = {P1∪P2} and Adjacent areas are required to be classified differently, and this requirement can be met by rationally planning the information network topology.
[0140] In this embodiment, the distributed real-time iterative optimization solution framework is designed
[0141] (a) Initialize the data of each region, λ z,i =0, w (i,h) =0, w (h,i) =0, w i =0;
[0142] (b) Solve the optimization model for each region and obtain the kth iteration related to each region.
[0143] (c) Information transmission between adjacent regions to update
[0144] (d) determining whether the number of iterations meets the initial setting, if not, returning to step (b), if it does, the iteration ends;
[0145] (e) Determine whether it is the last scroll. If not, record the energy storage capacity. And the unit output value, as the initial value of the next rolling energy storage capacity and the initial output of the unit; if yes, store the current energy storage capacity and exit the iteration.
[0146] in, is the w of the kth iteration i , w (i,h) , w (h,i) .
[0147] In this embodiment, the establishment of evaluation indicators
[0148]
[0149] In the formula, r (k) 、s (k) are the original residual and dual residual of the kth iteration respectively;
[0150] kind is equal to 1 or 2; Respectively represent the kth and k+1th iterations The algorithm convergence and system emission reduction effect are also evaluated.
[0151] Embodiment 1:
[0152] Analysis of the effect of distributed real-time low-carbon economic dispatch
[0153] This study conducts a case analysis on the improved IEEE-39 node power grid model, in which 200MW wind farms are introduced at nodes 9, 19, and 22 respectively; units 31, 34, and 39 are carbon capture units, and the system structure is as follows: Figure 2-5 As shown. The IEEE-39 power-saving system is divided into regions, and the 39 nodes are divided into 4 regional interconnected systems. The scheduling period is selected as 1 day, the time period Δt is selected as 5 minutes, and the carbon trading cost is 0.042$ / kg; the interconnected systems are geographically close, and a uniform electricity price is set in the present invention.
[0154] Table 1 shows the operating parameters of each unit.
[0155] Table 1 IEEE-39 bus system power generation parameters
[0156]
[0157] Figure 3 This is the output decision curve of the available equipment for scenario 1 (excluding energy storage). Figure 4 This is the situation of wind turbine abandonment in scenario 1. Due to the low carbon and economical nature of wind power, wind power is preferred for energy supply. In the 0-5h period, the wind turbine output is high, and there is a source-load mismatch phenomenon. The total wind power abandonment is 752.63MW·h. During the period of wind abandonment, due to the restriction of flue gas diversion ratio, the carbon capture group operates at the lower limit of output, and the carbon capture intensity of the carbon capture device is low.
[0158] Figure 5This is the output decision curve of the callable equipment for scenario 2 (including energy storage but not collaborative). The consumption of new energy is achieved through the time-space translation function of energy storage on electric energy. During the period of high wind power generation, energy storage is charged to absorb excess electricity; during the peak load period, energy storage supplies power to the external power grid. In the 9-12h period, the energy storage supplies energy to the load, which leads to a decrease in the output decision of the carbon capture unit. The carbon capture intensity of the carbon capture unit is closely related to the unit output, so the carbon capture intensity is reduced during this period.
[0159] Figure 6 This is the output decision curve of the available equipment for scenario 2 (including the carbon capture-energy storage synergy model). During the high wind power output period of 0-5h, the carbon capture-energy storage model absorbs excess electricity and consumes the abandoned wind power; during the peak load period of 9-13h, the energy storage begins to release its electricity and supply power to the grid. It is worth noting that part of the energy storage discharge is supplied to the load, and the other part is to supply power to the carbon capture device to ensure its carbon capture intensity.
[0160] The operation of the system under the three scenarios was compared and analyzed. The penalty cost of wind curtailment in scenarios 2 and 3 was reduced to 0 due to the effect of energy storage, achieving wind curtailment. The carbon capture income of scenario 3 increased by 24.15% and 7.46% respectively compared with scenarios 1 and 2; the total operating costs were reduced by 8.40% and 1.76% respectively; energy storage not only promoted the consumption of new energy, but also enhanced the carbon capture intensity of carbon capture units. In this case, due to the error problem of distributed economic dispatch results, scenario 3 resulted in the failure of wind power output to fully supply the load, resulting in wind curtailment, but the error was 0.07%, which met the actual engineering needs.
[0161] Figure 7 The carbon capture intensity comparison curve and SOC curve of the carbon capture device in three scenarios; in the period of high wind power generation from 0 to 5 hours, the carbon capture unit is operated at a lower output decision, and the carbon capture intensity of scenario 3 is the same as that of scenario 2 due to the limitation of flue gas emissions. However, in the peak load period from 9 to 12 hours, in scenario 3, the carbon capture energy consumption is decoupled from the net output of the carbon capture unit. At this time, when the carbon capture unit releases its flexibility, the energy storage supplies power to the carbon capture device, alleviating the impact of the net output of the carbon capture unit on the carbon capture energy consumption.
[0162] Figure 8 is the convergence curve. It can be seen from the dual residual and original residual curves that when the number of iterations reaches 127, the distributed iterative solution reaches the convergence requirement. The iteration time is 10.1s (the time of a single rolling iteration). In addition, the system operation cost obtained by the distributed economic dispatch solution is 786
[0163] 730.02$, with a relative error of 0.002% compared with the centralized economic dispatch cost ($786713.09), which meets the actual engineering needs.
[0164] Based on the above situation, the method described in the present invention is used to solve the problem of loss of carbon capture intensity in releasing flexibility of traditional carbon capture units to assist new energy consumption in multi-regional interconnected systems, decouple electricity and carbon from carbon capture power plants, build a carbon capture-energy storage synergy model, and build a distributed real-time economic scheduling iterative solution framework based on rolling optimization. The example shows that compared with the independent modes with and without energy storage systems, the benefits of the carbon capture-energy storage synergy mode are improved, and the error with the centralized economic scheduling results is small. While ensuring the carbon capture intensity, distributed real-time economic scheduling of multi-regional interconnected systems is achieved. This proves that the distributed solution method of the present invention considering carbon capture-energy storage synergy in multi-regional interconnected systems is real and effective.
[0165] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.
Claims
1. A distributed solution method for a multi-region interconnected system considering carbon capture and energy storage synergy, characterized in that: It includes electricity-carbon decoupling of carbon capture power plants, construction of distributed economic dispatch models for multi-regional interconnected systems with carbon capture and energy storage collaboration, construction of information network topology, design of distributed iterative optimization solution framework and establishment of evaluation indicators.
2. The distributed solution method for a multi-region interconnected system considering carbon capture and energy storage coordination according to claim 1 is characterized in that: The carbon capture power plant electricity-carbon decoupling is as follows: Formulas (1) and (2) are the mathematical models of the carbon capture power plant operation in split-flow operation mode: in, is the total output and net output of the power plant in region i during time period t; Respectively represent the operating energy consumption and fixed energy consumption of the carbon capture device; λ C The energy consumption coefficient of the CO2 carbon capture device for the capture unit; is the amount of CO2 captured by the carbon capture device; θ C is the carbon capture efficiency, which is taken as 0.9; is the carbon emission coefficient of the corresponding power plant; δ C is the flue gas split ratio, It is the smoke split ratio at the corresponding time; They represent the total carbon emissions and actual carbon emissions of the unit in period t respectively; According to equations (1) and (2), the output lower limit is obtained, and the output constraint condition is shown in (3): The unit cost of the carbon capture device is only the carbon trading cost, as shown in formula (4): in, Carbon trading cost benefits for carbon capture devices to process CO2; C The carbon trading price.
3. The distributed solution method for a multi-region interconnected system considering carbon capture and energy storage coordination according to claim 1 is characterized in that: The distributed economic dispatch model of the carbon capture-energy storage coordinated multi-region interconnected system is constructed as follows: With the goal of minimizing the overall operating cost of the multi-region interconnected system, the economic dispatch model related to the interconnected regions can be expressed as a linear programming problem as shown in Equation (5) and Equation (6), defined as Model A: stAx∝b(6) Among them, f is the overall operating cost of the regional interconnected system; c is the column vector composed of the marginal power generation cost coefficients of the output units in each region; x is the column vector composed of the power output power decisions of each output unit; A and b are constant matrices and column vectors formed by constraint conditions; "∝" represents the size relationship between matrices, where "∝" related to power balance constraints is "=", and "∝" related to other constraints is "≤", and the forms of c, A, and b are as follows: Where n is the number of regions, let the set of regions be v = {v1,...,v n }, the set of cells in region i is v i ={1,...,m i };c i is the marginal generation cost coefficient matrix given for the ith region; is the coefficient matrix related to the global constraints; A i 、b i is the coefficient matrix associated with the local constraints.
4. The distributed solution method for a multi-region interconnected system considering carbon capture and energy storage coordination according to claim 1 is characterized in that: The construction of the distributed economic dispatch model of the carbon capture-storage coordinated multi-region interconnected system is detailed. The following steps are involved: Step 1: Find the dual problem of model A, as shown in equations (8) and (9): Where λ is the multiplier associated with each constraint in constraint equation (6), is the multiplier associated with the global constraint, λ i is the multiplier associated with the local constraint of the i-th region; Ω λ is i Value range: the value range of multipliers related to equality constraints is (-∞,+∞), and the value range of multipliers related to other constraints is (-∞,0]; Step 2: Right The decomposition of is defined as model B; Among them, λ z 、b z , A z 、c z The form is as follows: in, The decomposed elements must satisfy (i=1,...,n) is The local variables associated with the ith region after decomposition; The decomposed elements must satisfy (i=1,...,n) is the global inequality constraint value associated with region i, 1) If That is, j is a controllable device, then Among them, the marginal power generation cost of the output unit j in the i-th region is divided into: Segment; 1 T ∈R T is a column vector whose elements are all 1; are the power generation and marginal operating cost of power generation of the kth section of the unit in the jth output unit in region i, respectively; in, Represented as the identity matrix, D∈R T×T ; Among them, 0∈R T is a column vector whose elements are all 0; is the lower limit of the output of the jth output unit in area i; Δt is the time period, which is 5 minutes; is the initial output of the unit; (1) If That is, j is the AGC unit, then (2) If (3) If j is wind power, then in, is the predicted output of wind power unit j in region i at time t; (4) If j is a carbon capture-storage synergistic unit, then in, It is the rated power of energy storage charging and discharging; are the upper and lower limits of energy storage capacity respectively; is the remaining capacity corresponding to time t; is the rated capacity of the energy storage; The augmented Lagrangian function corresponding to model B is shown in formula (29): Among them, σ is a constant penalty factor, that is, the iteration step length; W T For and Constraints The associated multiplier vector is of the form: Among them, w (i,h) is the multiplier related to the association constraints between the interconnected region optimization subproblems; w i is the multiplier related to the local constraints of the single regional optimization subproblem, which is equivalent to the output matrix of each regional unit; By rearranging equation (29), we can obtain the Lagrangian function corresponding to the single regional optimization subproblem: Among them, H i , It can be calculated based on the local information of region i and the associated information of adjacent regions; i , With λ z,i The form is as follows: So far, the Lagrangian function corresponding to the single regional optimization subproblem is obtained.
5. The distributed solution method for a multi-region interconnected system considering carbon capture and energy storage coordination according to claim 1 is characterized in that: The information network topology is constructed as follows: The information network topology is represented as a directed graph between nodes: G = (v, ε), where v is a set of points, representing each region, v = {1, 2, ..., n}; ε = v × v is a set of edges, representing the information topological structure relationship between regions; the set of points adjacent to point i is represented by N i Indicates that N i = {N i + , N i - }, where N i + is the set of points connected to and pointed by point i; N i - is the set of points connected to point i and pointing to point i; G = (v, ε) The point-edge association matrix is represented by the matrix E, which divides all regions into two categories. The set of the first type of regions is P1, and the set of the second type of regions is P2. N = {P1∪P2} and Adjacent areas are required to be classified differently, and this requirement can be met by rationally planning the information network topology.
6. The distributed solution method for a multi-region interconnected system considering carbon capture and energy storage coordination according to claim 1 is characterized in that: The distributed real-time iterative optimization solution framework is designed as follows: (a) Initialize the data of each region, λ z,i =0, w (i,h) =0, w (h,i) =0, w i =0; (b) Solve the optimization model for each region and obtain the kth iteration related to each region. (c) Information transmission between adjacent regions to update (d) determining whether the number of iterations meets the initial setting, if not, returning to step (b), if it does, the iteration ends; (e) Determine whether it is the last scroll. If not, record the energy storage capacity. And the unit output value, which will serve as the initial value of the next rolling energy storage capacity and the initial output of the unit; If yes, store the energy storage capacity and exit the iteration; in, is the w of the kth iteration i , w (i,h) , w (h,i) .
7. The distributed solution method for a multi-region interconnected system considering carbon capture and energy storage coordination according to claim 1 is characterized in that: The evaluation indicators are as follows: In the formula, r (k) 、s (k) are the original residual and dual residual of the kth iteration respectively; kind is equal to 1 or 2; Respectively represent the kth and k+1th iterations The algorithm convergence and system emission reduction effect are evaluated.