Coal and biomass blending combustion double-layer optimization operation method considering dynamic carbon absorption and economical efficiency cooperation

Abstract

The invention discloses a coal and biomass blending combustion double-layer optimization operation method considering dynamic carbon absorption and economy cooperation. The method comprises the steps that coal and biomass fuel data and operation parameters are obtained; based on the fuel data and the operation parameters, a double-layer optimization model is constructed, the double-layer optimization model comprises an upper layer model and a lower layer model, the upper layer model is used for minimizing net carbon emission, and the lower layer model is used for minimizing the total operation cost of the system; based on the fuel data and the operation parameters, obtaining constraint conditions of the double-layer optimization model; according to the double-layer optimization model and the constraint conditions, a dynamic carbon absorption-economical efficiency collaborative model is constructed, the dynamic carbon absorption-economical efficiency collaborative model is used for achieving dynamic balance between carbon emission minimization and the total operation cost, and a scheduling optimization scheme of coal and biomass blending combustion is obtained. The problem that only mixed combustion of biomass fuel and coal with a fixed blending combustion ratio can be adopted when a coal-fired unit carries out blending combustion on biomass for power generation at present can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-carbon dispatching of power systems, and in particular relates to a double-layer optimized operation method of coal and biomass blending that takes into account dynamic carbon absorption and economic synergy. Background Art

[0002] As one of the main sources of carbon emissions, the power industry faces enormous pressure to reduce emissions. Traditional coal-fired power generation units have a high carbon emission intensity. How to achieve a low-carbon transformation while ensuring power supply is a major technical challenge facing the current power system. Guided by the "dual carbon" strategic goal, the "Action Plan for the Low-Carbon Transformation and Construction of Coal-fired Power (2024-2027)" (National Development and Reform Commission, Environmental Protection Bureau

[2024] No. 894) was formulated. The action plan clearly proposes three transformation and construction methods, including biomass co-firing, green ammonia co-firing, and carbon capture, utilization and storage, and biomass co-firing technology is placed at the top of the three transformation and construction methods. Therefore, in recent years, biomass co-firing technology has been widely used in coal-fired power plants.

[0003] As a renewable resource, biomass has the potential to be carbon neutral. Biomass blending technology involves mixing biomass fuel with coal in a certain ratio to reduce coal usage and thus carbon emissions. However, most current biomass blending technologies use a fixed blending ratio (such as 10% or 20%), which has the following limitations:

[0004] Unable to adapt to changes in grid load: The grid load demand changes dynamically, and the fixed co-combustion ratio cannot be adjusted in real time according to the load demand. This may result in the biomass co-combustion ratio being too high at low load, incomplete combustion, and low efficiency; while at high load, the biomass co-combustion ratio is too low, and the emission reduction advantages of biomass cannot be fully utilized.

[0005] Ignoring the dynamic changes in carbon emissions: The fixed blending ratio cannot be adjusted in real time according to carbon emissions, and precise control of carbon emissions cannot be achieved.

[0006] Although there are some models for biomass co-combustion optimization in existing research, most of them have the following problems. (1) Only considering carbon emissions in the combustion stage: Traditional carbon emission models only consider carbon emissions in the biomass combustion stage, ignoring the carbon absorption capacity of biomass throughout its life cycle from planting to combustion. This model cannot fully evaluate the net carbon effect of biomass co-combustion. (2) Not considering the impact of load fluctuations: Existing models usually do not consider the quantitative impact of load fluctuations on biomass combustion efficiency. Load fluctuations may lead to incomplete biomass combustion and reduce carbon absorption efficiency. (3) Simplifying the relationship between boiler efficiency and co-combustion ratio: In existing research, the nonlinear relationship between boiler efficiency and biomass co-combustion ratio is usually simplified to a fixed value or linear relationship, which deviates from the actual combustion characteristics. (4) Not considering the impact of fuel composition: Traditional models usually rely only on standard emission factors and do not refine the implicit impact of fuel composition (such as the chlorine content in coal and the volatile matter in biomass) on emissions. (5) Single-objective optimization: Most existing optimization methods only focus on a single objective, such as optimizing only carbon emissions or only optimizing economic efficiency, making it difficult to achieve coordinated optimization of carbon absorption and economic efficiency.

[0007] Therefore, there is an urgent need for a dual-layer optimized operation method for coal and biomass co-combustion that takes into account the synergy of dynamic carbon absorption and economic efficiency. Summary of the Invention

[0008] In order to solve the above technical problems, the present invention proposes a two-layer optimized operation method of coal and biomass co-combustion that takes into account dynamic carbon absorption and economic synergy. It can solve the problem that when coal-fired units co-combust biomass for power generation, they can only use a fixed co-combustion ratio of biomass fuel and coal for mixed combustion, and cannot adjust this co-combustion ratio in real time as the power grid load demand changes and carbon emissions change.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] The present invention provides a dual-layer optimized operation method for coal and biomass co-combustion considering dynamic carbon absorption and economic synergy, comprising:

[0011] Obtain coal and biomass fuel data and operating parameters;

[0012] Based on the fuel data and operating parameters, a two-layer optimization model is constructed, wherein the two-layer optimization model includes an upper model and a lower model, the upper model is used to minimize net carbon emissions, and the lower model is used to minimize total system operating costs;

[0013] Obtaining constraints of the two-level optimization model based on the fuel data and operating parameters;

[0014] According to the two-layer optimization model and constraints, a dynamic carbon absorption-economic synergy model is constructed, wherein the dynamic carbon absorption-economic synergy model is used to achieve a dynamic balance between minimizing carbon emissions and minimizing total operating costs, and obtain a scheduling optimization plan for the blended combustion of coal and biomass.

[0015] Optionally, the fuel data includes: carbon emission factors of different types of fuels, mass percentages of volatile components in different biomasses, correction coefficients for chlorine content in different types of coal, chlorine content in different types of coal, regional carbon sink factors of different biomasses, maximum carbon sequestration capacity of biomass, biomass sequestration rate coefficient, biomass growth time, calorific value of different types of fuels, boiler efficiency of uncombusted biomass, prices of different types of fuels, minimum and maximum calorific value of mixed fuels, minimum and maximum mass of volatile matter in mixed fuels, minimum and maximum mass of moisture in mixed fuels, minimum and maximum mass of ash in mixed fuels, minimum and maximum ash melting point of mixed fuels, maximum mass of sulfur in mixed fuels, maximum mass of chlorine in mixed fuels, maximum allowable blending ratio of different types of fuels, basic blending ratio of different types of biomass, minimum and maximum total inventory of different types of biomass, gain coefficient of biomass blending ratio on boiler efficiency, and attenuation coefficient of biomass blending ratio on boiler efficiency.

[0016] Optionally, the operating parameters include: load-sensitive attenuation coefficient, power load rate at different times, boiler efficiency loss conversion coefficient, increased co-combustion ratio of different types of biomass during the night-time load trough period, increased co-combustion ratio of different types of biomass after triggering the carbon emission feedback mechanism, minimum and maximum power load rate, shortage penalty coefficients of different types of biomass, excess inventory penalty coefficients of different types of biomass, carbon prices in different ranges, carbon emission baseline value of the unit, annual carbon emission reduction coefficient, time attenuation factor, and regional factor correction coefficient.

[0017] Optionally, the upper-level model minimizes net carbon emissions through an upper-level objective function, and the lower-level model minimizes total system operating costs through a lower-level objective function.

[0018] Optionally, the upper layer objective function is:

[0019]

[0020] Among them, D Carbon For the upper level goal, Carbon emissions from direct coal combustion and direct biomass combustion, is the amount of dynamic carbon absorption by biomass, is the indirect carbon emission from boiler efficiency loss, I is the total number of fuel types of biomass and coal, I = {1, 2, L n}, which can be queried by establishing a mapping relationship between numbers in the set and fuels, such as 1 for straw, 2 for rice husk, 3 for cow dung, 4 for lignite, n represents multiple fuels that can be expanded, T is the number of time periods in the scheduling cycle, x i,t is the blending ratio of the i-th fuel at time t, m i,t is the blending mass of the i-th fuel at time t, is the carbon emission factor of the i-th fuel, φ i is the coupling factor of the i-th fuel characteristic, γ i is the regional carbon sink factor of biomass i, CSF(t) is the carbon sequestration model, is the load reduction term, λ is the load sensitivity reduction coefficient, L t is the power load rate at time t, ζ is the boiler efficiency loss conversion coefficient, Δη t is the change in boiler efficiency at time t.

[0021] Optionally, the lower layer objective function is:

[0022]

[0023] Among them, C Total For the lower level target, For fuel costs, For biomass fuel supply stability cost, C Trade is the carbon trading cost, They are the dynamic weight coefficients of fuel cost, biomass fuel supply stability cost, and carbon trading cost, respectively. i is the price of the i-th fuel, is the penalty coefficient for the shortage of the i-th biomass, is the penalty coefficient for excess inventory of the i-th biomass, R i,t is the actual inventory of the i-th type of biomass in period t, R i,min The safety threshold set for the total biomass inventory, R i,max is the maximum value of the total biomass inventory, r is the interval limit of the zone pricing, J is the index of all carbon price intervals, θ j is the carbon price in the jth interval, L j is the limit of the j-th carbon price interval, and Z(·) is the indicator function.

[0024] Optionally, the constraints of the two-layer optimization model include:

[0025] System power balance constraints, fuel cost constraints, fuel characteristics constraints, fuel blending ratio constraints, biomass dynamic blending ratio constraints, power load rate constraints, biomass inventory dynamic change constraints, total biomass inventory constraints, unit output constraints, unit climbing constraints and power balance constraints.

[0026] Compared with the prior art, the present invention has the following advantages and technical effects:

[0027] Based on the actual situation of existing coal and biomass co-firing for power generation, the present invention considers the carbon absorption of biomass throughout its life cycle on the basis of traditional carbon emission models, establishes a carbon sequestration model, and converts the carbon absorption capacity of biomass throughout its life cycle from planting to combustion into a time function of carbon sequestration, breaking through the limitation of traditional models that only consider carbon emissions during the combustion stage; in view of the fact that traditional models ignore the quantitative impact of load fluctuations on biomass combustion efficiency, an exponential decay model is established to couple load fluctuations with carbon absorption efficiency, and quantitatively analyze the efficiency drop problem caused by insufficient combustion under low load; an indirect carbon emission model caused by nonlinear efficiency loss of boiler efficiency and co-firing ratio is proposed, which is close to reality. Based on the actual combustion characteristics, the nonlinear relationship between boiler efficiency and biomass blending ratio is modeled using a quadratic function. This addresses the problem of existing studies directly assuming this nonlinear relationship as a fixed value or simplifying it using linear transformation methods. A dynamic coupling mechanism for coal and biomass blending is established, dynamically adjusting the blending ratio based on the economic operation of the system and minimizing carbon emissions. This improves the biomass blending method that relies entirely on manual experience and adopts a fixed ratio. To address the implicit impact of fuel chlorine content (coal) and volatile matter (biomass) on emissions, compensation factors are designed to correct direct emission calculations, addressing the traditional model's reliance on standard emission factors without detailing the local impact of fuel composition. A two-layer optimization operation model is also adopted. The upper layer establishes direct carbon emission models for coal and biomass, a dynamic carbon absorption model for biomass, and an indirect carbon emission model for boiler efficiency loss, with the goal of minimizing net carbon emissions. The lower layer aims to minimize the sum of fuel costs, carbon trading costs, and fuel supply stability costs. The upper and lower layers exchange information through net carbon emissions, blending ratio, and fuel budget, and optimize and solve the model with the blending ratio and fuel budget as the iteration stop conditions, realizing the coordinated optimization of dynamic carbon absorption and economic efficiency, and providing a dynamic blending ratio adjustment method for coal and biomass blending. While ensuring that the power plant meets the load demand of the power grid, it can not only reduce carbon emissions, but also achieve cost optimization, thereby helping the low-carbon transformation of coal-fired power. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0029] Figure 1 This is a flow chart of a dual-layer optimized operation method for coal and biomass blending that considers dynamic carbon absorption and economic synergy according to an embodiment of the present invention;

[0030] Figure 2 It is a schematic diagram of the structure of coal and biomass co-combustion in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0032] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] This embodiment proposes a two-layer optimization operation method for coal and biomass co-combustion considering dynamic carbon absorption and economic synergy, such as Figure 1 As shown, the specific steps include:

[0034] Obtain coal and biomass fuel data and operating parameters;

[0035] Based on fuel data and operating parameters, a two-layer optimization model is constructed, wherein the two-layer optimization model includes an upper model and a lower model, the upper model is used to minimize net carbon emissions, and the lower model is used to minimize the total operating cost of the system;

[0036] Obtain constraints for the two-level optimization model based on fuel data and operating parameters;

[0037] Based on the two-layer optimization model and constraints, a dynamic carbon absorption-economic synergy model is constructed. The dynamic carbon absorption-economic synergy model is used to achieve a dynamic balance between minimizing carbon emissions and minimizing total operating costs, and to obtain a scheduling optimization plan for coal and biomass blending.

[0038] Specifically, step 101: obtaining coal and biomass fuel data and related operating parameters;

[0039] Step 102: Based on the acquired coal and biomass fuel data and related operating parameters, a two-layer optimization model is constructed. The upper layer of the two-layer optimization model is a carbon emission model for the blending of coal and biomass, and the lower layer is a system operation economic model. The scheduling of the blending of coal and biomass is optimized, so that the biomass blending ratio is adjusted in real time according to load changes.

[0040] Step 103: Establish constraints of the two-layer optimization model based on the acquired data and parameters;

[0041] Step 104: Based on the two-level optimization model and the constraint condition relationship, the two-level optimization model is solved using a variety of solving tools such as CPLEX, GUROBI, and heuristic algorithms, and a scheduling optimization plan for the blended combustion of coal and biomass is determined based on the solution results.

[0042] Furthermore, the fuel data includes: the carbon emission factor of the i-th fuel The mass percentage v of volatile components in biomass i i , the correction coefficient of chlorine content of the i-th coal θ i , the chlorine content of the i-th coal is Cl i , regional carbon sink factor γ of biomass i i , the maximum carbon storage capacity of biomass α, the biomass storage rate coefficient β, the biomass growth time τ1, the calorific value Q of the i-th fuel i , the boiler efficiency η0 without biomass, the price of the i-th fuel f i , the minimum calorific value of the mixed fuel Q min and maximum Q max , the minimum value of the volatile matter mass of the mixed fuel V min and maximum value V max , the minimum value of the water mass of the mixed fuel A min and the maximum value A max , the minimum value of the mixed fuel ash mass M min and the maximum value M max , the minimum value of the mixed fuel ash melting point ST min and maximum ST max , the maximum sulfur content of the mixed fuel S max , the maximum value of the chlorine content of the mixed fuel Cl max , the maximum allowed blending ratio of the i-th fuel x i,max , the basic ratio of the i-th biomass blend The minimum value R of the total amount of biomass inventory of type i i,min and the maximum value R i,max , the gain coefficient κ1 of the biomass blending ratio on the boiler efficiency, and the attenuation coefficient κ2 of the biomass blending ratio on the boiler efficiency. The above coefficients κ1 and κ2 can be determined by fitting using the actual operation data of the boiler.

[0043] Operating parameters: load sensitive attenuation coefficient λ, power load rate L at time t t , boiler efficiency loss conversion coefficient ζ, the ratio of the i-th biomass to be mixed during the nighttime low load period The increased proportion of biomass blending after the carbon emission feedback mechanism is triggered Minimum value of power load factor L min and the maximum value L max, the i-th biomass shortage penalty coefficient Penalty coefficient for excess biomass inventory of type i Carbon price θ in the jth interval j , the unit's carbon emission baseline value D Bench , annual carbon emission reduction coefficient ω, time attenuation factor τ2, regional factor correction coefficient δ,

[0044] Specifically, the technical solution provided in this embodiment can acquire the fuel data and related operating parameters required for the blending of coal and biomass by triggering scheduled collection according to a preset time scale or real-time collection triggered by an immediate instruction.

[0045] Furthermore, the upper-level model minimizes the net carbon emissions through the upper-level objective function, and the lower-level model minimizes the total system operating cost through the lower-level objective function.

[0046] Specifically, the double-layer optimization model constructed in this embodiment has the upper layer target D Carbon To minimize net carbon emissions, the lower level target C Total In order to minimize the total operating cost of the system, the upper objective function mainly includes: carbon emissions from direct combustion of coal and carbon emissions from direct combustion of biomass Dynamic carbon uptake by biomass Indirect carbon emissions from boiler efficiency loss The lower objective function mainly includes: fuel cost Biomass fuel supply stability costs Carbon trading cost C Trade , the two-layer objective function is shown in the following formula:

[0047] Upper objective function:

[0048]

[0049] Direct carbon emissions from coal and biomass fuels at time t As shown in the following formula:

[0050]

[0051] Where: I is the total number of biomass and coal fuel types, I = {1, 2, L n}, which can be queried by establishing a mapping relationship between numbers in the set and fuels, such as 1 for straw, 2 for rice husk, 3 for cow dung, 4 for lignite, n represents multiple fuels that can be expanded, T is the number of time periods in the scheduling cycle, x i,t is the blending ratio of the i-th fuel at time t, m i,t is the blending mass of the i-th fuel at time t, is the carbon emission factor of the i-th fuel, which reflects the carbon emission intensity of unit fuel i, φ iis the coupling factor of the i-th fuel characteristic, which is used to correct the actual emissions during fuel combustion and reflect the nonlinear effect of fuel on carbon emissions. i is the mass percentage of volatile components in biomass i. Biomass with high volatile content is easier to ignite and burn more completely, reducing unburned carbon loss and thus reducing the actual carbon emissions per unit fuel. i is the correction coefficient of chlorine content in type i coal, Cl i is the chlorine content of the i-th type of coal.

[0052] In order to consider the carbon emissions of biomass throughout its life cycle from growth to combustion, dynamic carbon absorption is used to understand the net carbon effect of biomass during its life cycle. Since biomass absorbs CO2 from the atmosphere through photosynthesis during its growth phase and releases CO2 during combustion, ideally, the amount of absorption and release is equal, achieving "carbon neutrality". However, in practice, it is affected by factors such as time delay, combustion efficiency, and regional carbon sink differences. Therefore, dynamic corrections are needed, and the dynamic carbon absorption of biomass is not necessarily the same as the actual carbon absorption. As shown in the following formula:

[0053]

[0054] CSF(t)=α(1-e -βτ ) (6)

[0055] Where: γ i is the regional carbon sink factor of biomass i, CSF(t) is the carbon sequestration model, which reflects the temporal variation of carbon absorption during the biomass growth cycle. is the load reduction term, λ is the load sensitivity reduction coefficient, L t is the power load rate at time t, α is the maximum carbon sequestration capacity of biomass, β is the biomass sequestration rate coefficient, and τ1 is the biomass growth time.

[0056] During the co-firing of coal and biomass, the change in the co-firing ratio will lead to a decrease in boiler efficiency, which will consume more fuel to maintain the same energy output, and indirectly increase carbon emissions. Therefore, it is necessary to quantify the nonlinear effect of the co-firing ratio on combustion efficiency and its indirect increase in carbon emissions. As shown in the following formula:

[0057]

[0058] Where: ζ is the boiler efficiency loss conversion coefficient. If the boiler efficiency decreases, more fuel needs to be burned to compensate for the energy gap. The boiler efficiency loss conversion coefficient ζ is used to quantify the coal compensation carbon emissions corresponding to the unit efficiency loss, converting the efficiency change into equivalent carbon emissions. Δη tis the change in boiler efficiency at time t; the gain coefficient κ1 of the biomass blending ratio on boiler efficiency represents the positive effect of biomass volatile matter in promoting combustion when the ratio is low; the attenuation coefficient κ2 of the biomass blending ratio on boiler efficiency represents the negative effect of incomplete combustion or increased ash when the ratio is high.

[0059] Lower layer objective function:

[0060]

[0061] Where: They are the dynamic weight coefficient of fuel cost, the dynamic weight coefficient of biomass fuel supply stability cost, and the dynamic weight coefficient of carbon trading cost.

[0062] Fuel cost at each time period t As shown in the following formula:

[0063]

[0064] Where: f i is the price of the i-th fuel.

[0065] In order to consider the economic impact of biomass raw material supply interruption on power plant operation, it is necessary to calculate the cost of biomass supply stability, that is, to ensure that the total inventory of the i-th type of biomass is not less than the set safety threshold R i,min , to prevent the unit from shutting down or being forced to increase the combustion ratio due to biomass fuel shortage, and to balance the inventory cost and supply reliability by dynamically adjusting the biomass raw material procurement plan and the blending ratio; at the same time, to prevent inventory overload operation. Since the inventory volume is affected by the maximum storage capacity of investment and construction, therefore, when the total amount of the stored i-th biomass inventory is greater than the maximum value R i,max , it is also necessary to take penalties for excess inventory and biomass fuel supply stability costs As shown in the following formula:

[0066]

[0067] Where: is the penalty coefficient for the shortage of the i-th biomass, is the penalty coefficient for excess inventory of the i-th biomass, R i,t is the actual inventory of the i-th biomass in period t.

[0068] When calculating the carbon trading cost, the higher the carbon emissions, the higher the penalty price per unit carbon emissions, thereby punishing the power generation enterprises for carbon over-emissions; conversely, when the carbon emissions are lower than the quota, the reward price per unit carbon emissions decreases step by step (the reward price is negative), and the power generation enterprises are rewarded for reducing carbon emissions. The specific carbon trading cost C Trade , as shown in the following formula:

[0069]

[0070] Where r is the interval limit of the zone pricing, r can change with the interval of the carbon quota, J is the index of all carbon price intervals, θ j is the carbon price in the jth interval, when D Carbon ≤L j When θ j is a negative value, otherwise, D Carbon >L j When θ j is a positive value, D C arbon≤L0 This range is the negative carbon price range, and quotas can be sold to obtain income. L0<D Carbon ≤L1 This interval is the free quota interval, with no penalty and no benefit. Generally, L1 can be equal to the carbon quota, L1=D Alloc , L1<D Carbon ≤L j This interval is the penalty interval, and Z(g) is the indicator function, that is, a binary variable, which is 1 when the condition is met and 0 otherwise.

[0071] The carbon quota adopts the baseline quota allocation method, and on this basis introduces the annual emission reduction coefficient and time decay factor, and takes into account the actual situation of peak-shaving units in different regions, considers the impact of different regions on the quota, and adopts dynamic regional factors for correction. The specific carbon quota D Alloc , as shown in the following formula:

[0072]

[0073] Where: D Bench is the carbon emission baseline value of the unit, ω is the annual emission reduction coefficient, τ2 is the time attenuation factor, and δ is the regional factor correction coefficient.

[0074] Furthermore, the constraints of the two-level optimization model include:

[0075] System power balance constraints, fuel cost constraints, fuel characteristics constraints, fuel blending ratio constraints, biomass dynamic blending ratio constraints, power load rate constraints, biomass inventory dynamic change constraints, total biomass inventory constraints, unit output constraints, unit climbing constraints and power balance constraints.

[0076] Specifically, the system power balance constraint is shown in the following formula:

[0077]

[0078] Where: E t is the power generation of the unit after coal is mixed with biomass, η tis the boiler efficiency model at time t, and η0 is the boiler efficiency without biomass combustion.

[0079] In order to ensure the economical operation of the unit and maximize the advantages of biomass fuel economy and low carbon emissions, the fuel cost used for system power generation is restricted. The fuel cost constraint is shown in the following formula:

[0080]

[0081] Where: is the maximum fuel cost.

[0082] Fuel property constraints mainly consider the calorific value Q of the mixed fuel i Range, volatile matter mass V i , water quality A i , ash mass M i , ash melting point ST i , pollutant emission limit sulfur S i With chlorine Cl i The emission is shown in the following formula:

[0083]

[0084] Where: Q min and Q max are the minimum and maximum calorific values ​​of the mixed fuel, V min and V max are the minimum and maximum values ​​of the volatile matter mass of the mixed fuel, A min and A max are the minimum and maximum values ​​of the water mass of the mixed fuel, M min and M max are the minimum and maximum values ​​of the ash content of the mixed fuel, ST min and ST max are the minimum and maximum values ​​of the ash melting point of the mixed fuel, S max is the maximum sulfur content of the mixed fuel, Cl max It is the maximum value of the chlorine content in the mixed fuel.

[0085] The fuel blending ratio constraint mainly considers that when blending biomass, scientific blending rules must be followed. Unrestricted blending of one fuel may reduce the unit's power output, reduce the unit's operational stability, increase the risk of coking and slagging, and may also lead to increased carbon emissions, failing to achieve energy conservation and environmental protection goals. The blending ratio of coal and biomass is 100%, as shown in the following formula:

[0086]

[0087] Where: x i,maxis the maximum blending ratio allowed for the i-th fuel.

[0088] The dynamic biomass blending ratio constraint mainly considers that the change of biomass blending ratio should be consistent with the actual scenario of power generation and carbon emissions. Scenario 1: During the nighttime low load period (t∈[0:00,6:00]), the unit output is reduced. At this time, the blending ratio can be appropriately adjusted to ensure that the blending ratio is above the basic ratio, and the corresponding blending ratio can be appropriately increased according to the degree of decrease in the low load. Scenario 2: In the case of carbon emissions, the adjustment of the blending ratio can be triggered by carbon feedback, that is, when direct carbon emissions China's coal carbon emissions exceed the limit The biomass blending ratio can be appropriately increased on top of the original ratio, as shown in the following formula:

[0089]

[0090] Where: is the basic ratio of the i-th biomass blend, is the ratio of the i-th biomass to be mixed during the low-load period at night, The increased co-combustion ratio of the i-th biomass after the carbon emission feedback mechanism is triggered.

[0091] The power load rate constraint is one of the important parameters in the co-firing model. When the load is low at night, L t When the unit operates in an inefficient state, it will lead to incomplete biomass combustion, reduced carbon absorption efficiency, and an increase in the corresponding unit power generation cost. At this time, it is necessary to balance economic and environmental issues by adjusting the blending ratio, as shown in the following formula:

[0092]

[0093] Where: L min and L max are the minimum and maximum power load rates respectively.

[0094] The dynamic change constraint of biomass inventory is shown in the following formula:

[0095]

[0096] Where: G i,t is the arrival quantity of the i-th type of biomass at time t.

[0097] The total biomass inventory constraint is as follows:

[0098]

[0099] Where: R i,min and R i,maxis the minimum and maximum total inventory of the i-th biomass at time t.

[0100] The unit output constraint must meet the minimum and maximum output limits of the unit, as shown in the following formula:

[0101]

[0102] Where: P min and P max It is the minimum output value and maximum output value of the unit.

[0103] The unit climbing constraint is shown in the following formula:

[0104]

[0105] Where: P Up and P Down is the upward and downward climbing rate of the unit.

[0106] The power balance constraint is shown in the following formula:

[0107]

[0108] Where: is the grid load demand at time t.

[0109] More specifically, in step 104, the two-level optimization model and the constraint relationship are solved using a variety of solving tools such as CPLEX, GUROBI, and heuristic algorithms, and a scheduling optimization plan for the blended combustion of coal and biomass is determined based on the solution results.

[0110] Finally, the dynamic carbon absorption-economic synergy model established based on the above-mentioned two-level optimization framework for coal and biomass blending is constructed, including the upper-level objective function (Formulas 1-8) and the lower-level objective function (Formulas 9-13), the upper-level constraints (Formulas 14-21) and the lower-level constraint formulas (Formulas 22-26). The applicable solving tools include, but are not limited to, optimization solvers such as CPLEX and GUROBI, or heuristic algorithms such as, but not limited to, genetic algorithms, particle swarm optimization, and simulated annealing algorithms to handle nonlinear coupling problems. Through two-level iterative optimization, a dynamic balance between carbon emission minimization and total cost economics is achieved, ultimately outputting the optimal blending ratio and unit scheduling plan.

[0111] Please refer to the schematic diagram of the coal and biomass co-combustion structure in this embodiment. Figure 2Compared with existing combustion optimization technologies that use only coal or only biomass as raw materials, this solution constructs a two-layer collaborative optimization model based on the coupling characteristics of coal and biomass fuels, with dynamic carbon absorption and economy as the goals. Under the premise of ensuring stable combustion in the boiler, the blending ratio of coal and biomass is dynamically adjusted. When the grid load demand is small, the biomass blending ratio can be increased. At this time, it can not only meet the grid load demand, but also reduce carbon emissions and system operating costs; when the grid load demand is large, the biomass blending ratio can be reduced and the coal ratio can be increased to meet the grid load demand as much as possible. This method provides a new idea for the optimized operation of traditional coal-fired units after upgrading and transformation. It can not only ensure the grid load demand, but also optimize carbon emissions. It also effectively utilizes low-cost or "zero-cost" waste biomass raw materials from farmland, enhancing the low-carbon and economic level of power plant operation.

[0112] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A dual-layer optimized operation method for coal and biomass blending considering dynamic carbon absorption and economic synergy, characterized in that: include: Obtain coal and biomass fuel data and operating parameters; Based on the fuel data and operating parameters, a two-layer optimization model is constructed, wherein the two-layer optimization model includes an upper model and a lower model, the upper model is used to minimize net carbon emissions, and the lower model is used to minimize total system operating costs; Obtaining constraints of the two-level optimization model based on the fuel data and operating parameters; According to the two-layer optimization model and constraints, a dynamic carbon absorption-economic synergy model is constructed, wherein the dynamic carbon absorption-economic synergy model is used to achieve a dynamic balance between minimizing carbon emissions and minimizing total operating costs, and obtain a scheduling optimization plan for the blended combustion of coal and biomass.

2. A two-layer optimized operation method for coal and biomass blending considering dynamic carbon absorption and economic synergy according to claim 1, characterized in that: The fuel data include: carbon emission factors of different types of fuels, mass percentages of volatile components in different biomasses, correction coefficients for chlorine content of different types of coal, chlorine content of different types of coal, regional carbon sink factors of different biomasses, maximum carbon sequestration capacity of biomass, biomass sequestration rate coefficient, biomass growth time, calorific value of different types of fuels, boiler efficiency of uncombusted biomass, prices of different types of fuels, minimum and maximum calorific value of mixed fuels, minimum and maximum volatile matter mass of mixed fuels, minimum and maximum moisture mass of mixed fuels, minimum and maximum ash mass of mixed fuels, minimum and maximum ash melting point of mixed fuels, maximum sulfur mass of mixed fuels, maximum chlorine mass of mixed fuels, maximum allowable blending ratio of different types of fuels, basic blending ratio of different types of biomass, minimum and maximum total inventory of different types of biomass, gain coefficient of biomass blending ratio on boiler efficiency, and attenuation coefficient of biomass blending ratio on boiler efficiency.

3. The method for optimizing the operation of coal and biomass blended combustion considering dynamic carbon absorption and economic synergy according to claim 1, characterized in that: The operating parameters include: load-sensitive attenuation coefficient, power load rate at different times, boiler efficiency loss conversion coefficient, increased co-combustion ratio of different types of biomass during the night-time load trough period, increased co-combustion ratio of different types of biomass after triggering the carbon emission feedback mechanism, minimum and maximum power load rate, shortage penalty coefficients of different types of biomass, excess inventory penalty coefficients of different types of biomass, carbon prices in different ranges, carbon emission baseline value of the unit, annual carbon emission reduction coefficient, time attenuation factor, and regional factor correction coefficient.

4. The method of optimizing the operation of coal and biomass blended combustion considering dynamic carbon absorption and economic synergy according to claim 1, characterized in that: The upper-level model minimizes net carbon emissions through an upper-level objective function, and the lower-level model minimizes total system operating costs through a lower-level objective function.

5. A dual-layer optimized operation method for coal and biomass blending considering dynamic carbon absorption and economic synergy according to claim 4, characterized in that: The upper objective function is: Among them, D Carbon For the upper level goal, Carbon emissions from direct coal combustion and direct biomass combustion, is the amount of dynamic carbon absorption by biomass, is the indirect carbon emission from boiler efficiency loss, I is the total number of fuel types of biomass and coal, T is the number of time periods in the scheduling cycle, and x i,t is the blending ratio of the i-th fuel at time t, m i,t is the blending mass of the i-th fuel at time t, is the carbon emission factor of the i-th fuel, φ i is the coupling factor of the i-th fuel characteristic, γ i is the regional carbon sink factor of biomass i, CSF(t) is the carbon sequestration model, e -λLt is the load reduction term, λ is the load sensitivity reduction coefficient, L t is the power load rate at time t, ζ is the boiler efficiency loss conversion coefficient, Δη t is the change in boiler efficiency at time t.

6. A dual-layer optimized operation method for coal and biomass blending considering dynamic carbon absorption and economic synergy according to claim 5, characterized in that: The lower layer objective function is: Among them, C Total For the lower level target, C t Fuel For fuel costs, For biomass fuel supply stability cost, C Trade is the carbon trading cost, They are the dynamic weight coefficients of fuel cost, biomass fuel supply stability cost, and carbon trading cost, respectively. i is the price of the i-th fuel, is the penalty coefficient for the shortage of the i-th biomass, is the penalty coefficient for excess inventory of the i-th biomass, R i,t is the actual inventory of the i-th biomass in period t, R i,min The safety threshold set for the total biomass inventory, R i,max is the maximum value of the total biomass inventory, r is the interval limit of the zone pricing, J is the index of all carbon price intervals, θ j is the carbon price in the jth interval, L j is the limit of the j-th carbon price interval, and Z(·) is the indicator function.

7. The method of claim 1 for optimizing the operation of coal and biomass blended combustion in a dual-layer manner considering dynamic carbon absorption and economic synergy, characterized in that: The constraints of the two-level optimization model include: System power balance constraints, fuel cost constraints, fuel characteristics constraints, fuel blending ratio constraints, biomass dynamic blending ratio constraints, power load rate constraints, biomass inventory dynamic change constraints, total biomass inventory constraints, unit output constraints, unit climbing constraints and power balance constraints.

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