Power system pollution and carbon reduction method based on source-network interaction

Abstract

The invention relates to the field of cooperative control of atmospheric pollutant and carbon dioxide emission of a power system, and discloses a power system pollution and carbon reduction method based on source-network interaction, which relates to an analysis method and specifically comprises four steps. The method comprises the following steps: 1, establishing an atmospheric pollutant and CO2 emission model; 2, space-time diffusion characteristics of atmospheric pollutants are described through a Gaussian plume diffusion model, and the influence of the space-time diffusion characteristics on a typical load area is analyzed; 3, establishing a power plant coal blending and power grid dispatching combined optimization model containing pollution reduction and carbon reduction constraints; and 4, establishing a collaborative optimization framework and dividing responsibilities of a source network. Compared with a power coal blending and power dispatching technology adopted by a current power plant and a power grid, the method has the advantages that on the basis of daily load prediction, collaborative interaction and joint response between the power grid and the power plant are enhanced through refined decision making, and emission of atmospheric pollutants and CO2 in power generation and transmission links is reduced to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the field of power system operation control and atmospheric pollutants and carbon emission technology, and in particular to a method for reducing pollution and carbon emissions in a power system based on source-grid interaction. Background Art

[0002] Global warming and air pollution are two major environmental issues that affect human survival and development. In the production of electricity, a large amount of CO2, SO2 and NO are generated. x Gases such as carbon dioxide and carbon monoxide are harmful to the global environment. Therefore, the power industry should play a pioneering role in the current development of reducing carbon emissions and reducing air pollution. In recent years, with the national carbon peak and carbon neutrality, pollution reduction and carbon reduction synergy efficiency strategy, the low-carbon and low-atmospheric pollutant development of the power system has become a focus. Therefore, under the new economic situation, it is particularly important to study its reasonable planning and scheduling.

[0003] For the methods of low carbon and low atmospheric pollutants in power systems, some research results have been accumulated at home and abroad over the years. According to various research results, there are many patent applications involving low carbon and low atmospheric pollutants. Patent CN118863368A "Low-carbon economic dispatching method and system for electric-thermal integrated energy system" specifically relates to a low-carbon economic dispatching method and system for electric-thermal integrated energy system based on a step-type carbon trading mechanism. First, a carbon trading model for the integrated energy system is established based on the step-type carbon trading mechanism to constrain the carbon emissions of the system; then, on the basis of the carbon trading model, considering the sharing of electric-thermal multi-energy, a low-carbon economic dispatching model for the integrated energy system is constructed with the minimum cost of electricity purchase and sales, gas purchase, equipment operation and maintenance costs and carbon trading costs as the objective function; finally, the alternating direction multiplier method is used to realize the distributed trading of multiple integrated energy systems, and the electric-thermal hydrogen trading strategy of the integrated energy system is obtained. The invention uses the complementarity of energy differences within the integrated energy system to share electric-thermal multi-energy, effectively reducing the operating cost and carbon emissions of the integrated energy system. Compared with the traditional carbon trading mechanism, the step-type carbon trading mechanism is more conducive to carbon emission reduction in the integrated energy system.

[0004] Patent CN112417652B "An Optimization and Scheduling Method and System for Electricity-Gas-Heat Integrated Energy System" proposes a coordinated optimization and scheduling method based on scenario analysis of an electricity-gas-heat integrated energy system. An optimization and scheduling method for an electricity-gas-heat integrated energy system, characterized in that, first, an electricity-gas-heat low-carbon economic scheduling model is constructed based on a step-by-step carbon trading mechanism. Then, the objective function of the low-carbon optimization model is constructed based on the electricity-gas-heat low-carbon economic scheduling model. Finally, the constraints of the low-carbon optimization model are constructed, and the low-carbon optimization model is solved to obtain low-carbon optimization parameters. The low-carbon optimization parameters include: coal-fired unit output, gas-fired unit output, power-to-gas equipment output, and wind turbine unit output.

[0005] Patent CN115099467A "A distributed robust dispatching method for power systems targeting at the uncertainty of atmospheric pollutant diffusion" provides a distributed robust dispatching model for power systems that takes into account the uncertainty of meteorological conditions in the diffusion process of atmospheric pollutant emissions. First, a refined diffusion control mode for atmospheric pollutant emissions is constructed to establish a distribution set of the uncertainty of meteorological conditions in the diffusion process of atmospheric pollutant emissions. Then, a day-ahead unit combination dispatching model for the power system that takes into account the uncertainty of meteorological conditions is established, and then a two-stage distributed robust optimization method for the day-ahead unit combination dispatching model for the power system is established. This invention belongs to the field of day-ahead dispatching technology on the transmission side of the power system, specifically, it can finely describe the contribution of unit pollutant emissions to the concentration of atmospheric pollutants after a period of diffusion, thereby providing a basis for the control of atmospheric pollutants in the power system; at the same time, the unit combination dispatching takes into account the uncertainty of meteorological conditions in the diffusion process, so that the unit combination dispatching plan has a certain green robustness, that is, no matter how the meteorological conditions change, the pollutant control effect can be guaranteed.

[0006] However, the above-mentioned traditional methods have the following defects:

[0007] 1. The calculation of atmospheric pollutants and carbon dioxide emissions is limited to coefficients and is too rough, resulting in insufficient accuracy of the constructed model.

[0008] 2. The control object should consider multiple aspects such as environmental protection and economy. There are conflicts between some control objectives. From the decision-making perspective, the contradictions between various decision-making objectives cannot be well resolved.

[0009] 3. Current related model research only emphasizes "pollution reduction" or "carbon reduction", and no research has considered the centralized control of both pollution reduction and carbon reduction. However, in fact, the emissions of atmospheric pollutants and carbon dioxide in the power production process are strongly correlated. Summary of the invention

[0010] In order to solve the problem of pollutant and greenhouse gas emissions in existing power generation and transmission technologies, the present invention provides a method for reducing pollution and carbon emissions in an electric power system based on source-grid interaction.

[0011] The present invention provides a method for reducing pollution and carbon emissions in a power system based on source-grid interaction, comprising the following steps:

[0012] S101, Establishment of atmospheric pollutant and carbon dioxide emission model. NO directly emitted by coal-fired power generation x , SO2, and CO2 mainly depend on the coal consumption and the N, S, and C content of coal quality. By analyzing the transfer process of elements in each link of coal-fired power generation, the NO generated per unit of coal combustion can be obtained. x , SO2, CO2 gas models.

[0013] S102, using a Gaussian plume diffusion model to analyze the spatiotemporal diffusion characteristics of atmospheric pollutants, thereby analyzing the impact of atmospheric pollutant diffusion on typical load areas (residential areas, office areas, etc.).

[0014] S103, establishment of a collaborative optimization model for power plant power coal matching and power grid dispatching with pollution reduction and carbon reduction constraints. First, the power plant power coal matching model and the power grid dispatching model are combined for collaborative optimization, and the emissions of atmospheric pollutants and carbon dioxide are restricted in the optimization model to form atmospheric pollutant emission constraints and carbon emission constraints. The objective function is to minimize the total cost of power generation, and through linearization and second-order cone relaxation methods, various non-convex related constraints are converted into solvable second-order cone constraints. Finally, a mixed integer second-order cone optimization problem with multiple objective functions is solved to obtain the output value of each generator set.

[0015] S104, for the scenario dominated by coal-fired power generation, a collaborative optimization method for power coal distribution and power generation scheduling is proposed, which is of great significance to achieving the goal of synergistic efficiency improvement of pollution reduction and carbon reduction. In order to ensure the normal operation of the collaborative optimization model, the main framework of collaborative optimization is proposed, and the responsibilities of the two main entities, power plants and power grids, are divided in detail.

[0016] Furthermore, the method for establishing the atmospheric pollutant and CO2 emission model in step S101 includes: analyzing the conversion process of N, S, and C elements in each production link of coal combustion, and then using the coefficient method to analyze the NO generated by each unit of coal-fired power generation in each production link. x , SO2, CO2 gas volume.

[0017] Furthermore, in step S102, the Gaussian plume diffusion model is first introduced, and the complex and changeable wind speed and wind direction of 24 hours are introduced as meteorological conditions. The Gaussian plume diffusion model is used to describe the spatiotemporal diffusion characteristics of atmospheric pollutants and analyze the specific impact on specific residential areas.

[0018] Furthermore, the specific steps of constructing the joint optimization model in S103 include: 1) establishing a power coal blending model that describes the mixed coal of the power plant; 2) establishing a unit combination model that describes the active power flow; 3) restricting the emissions of atmospheric pollutants and CO2 to form a set of pollution reduction and carbon reduction constraints, which are introduced into the active power flow model; 4) transforming various types of non-convex constraints through linearization and second-order cone relaxation methods, and solving them using the commercial solver GUROBI.

[0019] The objective function of the joint optimization of power coal distribution and power generation dispatch in S103 is as follows:

[0020]

[0021] Where, T is the total duration, the scheduling interval is 1h, so T is taken as 24h; G is the number of coal-fired units; C cost is the total cost of electricity production in the region; c k is the price of coal type k; N r B is the type of coal; g,k,t is the consumption of coal type k by coal-fired unit g in 1 hour; c new,i It represents the maintenance cost of the unit output of the new energy; It is the power generation capacity of the new energy unit.

[0022] Furthermore, the specific contents of the centralized optimization model framework of power coal blending and power generation dispatching constructed by S104 include: first, establish a centralized optimization framework of power coal blending and low-carbon economic dispatching under a production scenario dominated by coal-fired power, and its objective function is to minimize the total economic cost, and the economic cost includes the cost of purchasing coal and the maintenance cost of wind power and photovoltaic units. The constraints mainly include two parts, among which the constraints of coal blending optimization mainly include the constraints of mixed coal quality indicators and the constraints of atmospheric pollutant emission intensity, and the constraints of power generation dispatching in the power system mainly include energy balance, flow constraints, and operation constraints of wind power, photovoltaic, and thermal power units. There is a certain coupling relationship between the mathematical models of coal blending and dispatching, and the coupling constraints include the maximum and minimum constraints of the total daily coal consumption, coal-to-electricity conversion constraints, atmospheric quality constraints in typical load areas, and direct carbon emissions constraints. Through the centralized optimization of power coal blending and power generation dispatching, the unified and reasonable allocation of coal resources within the region can be achieved while ensuring the economic production of the power system, reducing the direct carbon emissions of the power system, and improving the air quality in the typical load area within the system.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] (1) By analyzing the coupling relationship between the two production links of coal blending and dispatching, the present invention innovatively proposes to collaboratively model power coal blending and power generation dispatching with optimization space in the entire power production process, thus forming a centralized optimization production strategy.

[0025] (2) The Gaussian plume diffusion model is introduced in the present invention to form air quality constraints in typical load areas, and at the same time, the direct carbon emissions of the power system are limited. From the perspective of optimizing the coal ratio and consumption at the same time, the environmental impact of coal-fired power units is reduced, and on the basis of ensuring the economic and environmental protection of power system power coal matching and power generation scheduling, the reasonable allocation of different types of coal resources and unit output is completed.

[0026] (3) The present invention can adjust the coal-fired power generation ratio, unit start-up and shutdown, and output according to variable meteorological conditions (wind speed, wind direction, and atmospheric stability), thereby comprehensively reducing pollution and carbon emissions and improving the environmental friendliness of power system production. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the process of the present invention;

[0028] Figure 2 This is a schematic diagram of the Gaussian plume diffusion model;

[0029] Figure 3 This is a centralized optimization framework diagram for power coal blending and power generation dispatching in the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0031] As process Figure 1 As shown in the figure, a method for reducing pollution and carbon emissions in a power system based on source-grid interaction is used to adjust the coal ratio, unit start-stop time and unit output of thermal power units according to complex and changeable meteorological conditions (wind speed, wind direction and atmospheric stability) through multi-party cooperation and progressive optimization. The method mainly includes the following steps:

[0032] Step S101, the carbon emissions of the power system mainly come from the carbon emissions of the two links of coal combustion in the furnace and flue gas desulfurization. In my country, about 80% of thermal power plants use the limestone-gypsum wet method for flue gas desulfurization. The absorption of SO2 during the desulfurization process will produce a certain amount of CO2 emissions. This part of carbon emissions should be included in the measurement range of carbon emissions in the combustion link. The limestone-gypsum wet method mainly uses limestone as an absorbent to react chemically with SO2 in the flue gas. The chemical reaction equation is shown below. It can be seen that 1 mol of SO2 absorbed in the chemical reaction will produce 1 mol of CO2.

[0033] CaCO3=CaO+CO2↑

[0034]

[0035] Finally, based on the elemental analysis of each type of coal, the carbon emissions of coal type k in boiler combustion and flue gas desulfurization can be calculated as shown below.

[0036]

[0037] in, is the content of C and S in coal type k; M C 、M S are the relative atomic masses of C and S respectively; is the relative molecular mass of CO2; η b is the combustion efficiency of the thermal power plant boiler; η s,1is the conversion rate of S element in coal into SO2; η s,2 It is the absorption rate of SO2 by the desulfurization device.

[0038] After desulfurization and denitrification, SO2 and NO are discharged into the atmosphere. x The concentration can be calculated by the following formula:

[0039]

[0040] in, is the nitrogen content of coal type k; β N is the nitrogen conversion rate in coal type k; η N is the denitration rate of the denitration device; is the relative molecular mass of SO2.

[0041] Step S102, construct a calculation model for atmospheric pollutants and carbon emissions from coal combustion that takes into account the spatiotemporal diffusion characteristics. In order to control the air quality in typical load areas, it is necessary to accurately calculate the direct emissions of atmospheric pollutants caused by coal combustion. x and SO2) mainly depends on the coal consumption and the N and S content of coal quality. In order to reduce the SO2, NO x To prevent the pollution of the atmosphere caused by gases, most thermal power plants need to install desulfurization and denitrification equipment to absorb atmospheric pollutants. According to the analysis in S101, after limestone-gypsum wet desulfurization and ammonia denitrification, the SO2 and NO2 emitted directly into the atmosphere per unit mass of coal combustion can be calculated. x concentration.

[0042] The analysis is conducted on the two optimizable links of power coal blending and power generation dispatching, aiming to reasonably plan the coal ratio of each thermal power unit and formulate the start-up and shutdown and output plan of the thermal power unit under the premise of ensuring the cleanness of power production. In order to match the above-mentioned coal-fired air pollutant emission model, only the direct carbon emissions of the two production links of coal blending and dispatching are considered in this chapter.

[0043] SO2 and NO in the air x Excessive levels of SO2 and NO in the air of a particular area can harm health. It is necessary to establish air quality constraints in densely populated areas to reduce the impact of coal-fired air pollutants on the air quality in the above areas. x The concentration of pollutants such as carbon dioxide and carbon dioxide not only depends on the total amount of direct emissions from pollution sources, but also on the meteorological conditions of the system at the current moment. Therefore, to reduce air pollution caused by thermal power, it is not meaningful to limit the total amount of pollutant emissions. We should also analyze the diffusion process of pollutants under different meteorological conditions in detail and improve the spatiotemporal distribution characteristics of air pollutants from the root.

[0044] This patent introduces the Gaussian plume diffusion model to describe the diffusion characteristics of atmospheric pollutants. The emission location of atmospheric pollutants from a thermal power unit that continuously burns coal for power generation can be regarded as a continuously emitting elevated emission point source. Under ideal conditions of uniform, steady turbulent fields, the diffusion mode of pollutants continuously emitted from an elevated point source in the atmosphere can be modeled as a Gaussian plume model. The schematic diagram of the Gaussian plume diffusion model is shown in the attached figure. Figure 2 shown.

[0045] The mathematical model of Gaussian plume diffusion under finite space-time boundaries is as follows:

[0046]

[0047] Where C(x,y,z,H) is the concentration of air pollutants at the coordinate (x,y,z); Q gas is the emission intensity of air pollutants at the chimney outlet of thermal power plants; is the average wind speed; H is the effective height of the emission point source; and σ y and σ z are the horizontal and vertical diffusion parameters of atmospheric pollutants, respectively.

[0048] Each coal-fired unit is an emission source of atmospheric pollutants, and the load points in residential and industrial areas are sampling locations for atmospheric pollutant concentrations. By analyzing the influence of multiple factors such as meteorological conditions such as atmospheric stability, wind speed, wind direction, and the coordinates of the sampling location (specific load point) relative to the emission source, the average concentration of atmospheric pollutants in residential areas for 1 hour is finally obtained as shown in the following formula.

[0049]

[0050] in, is the concentration of atmospheric pollutants caused by coal-fired unit g at load point d; is the average wind speed at time t; is the amount of air pollutants emitted by unit g during coal combustion within 1 hour; y,t and σ z,t are the horizontal and vertical atmospheric pollutant diffusion parameters within 1 hour; y g,d,t H is the crosswind distance from the typical load point d to the thermal power unit g; g The effective source height.

[0051] The above Gaussian plume diffusion model is incorporated into power generation scheduling to calculate the diffusion concentration of atmospheric pollutants at typical load points within 1 hour, as shown below.

[0052]

[0053] in, is the amount of atmospheric pollutants generated by the combustion of unit mass of coal type k; K(g,d,t) is the concentration calculation coefficient of atmospheric pollutants diffused from coal-fired power units to residential area d at time t.

[0054] σ y and σ z As a horizontal and vertical atmospheric diffusion parameter, its value is related to many atmospheric conditions such as atmospheric turbulence structure, ground roughness, and duration of pollutant emissions. In order to accurately calculate the values ​​of the two diffusion parameters, it is necessary to analyze the atmospheric stability of the target power system. According to the Pasquill classification method, the atmospheric stability can be divided into six categories from extremely unstable to stable: A, B, C, D, E, and F. Among them, A, B, and C indicate that the current meteorological conditions are unstable, E and F indicate that the meteorological conditions are stable, and D indicates neutral meteorological conditions, that is, the meteorological conditions are between stable and unstable. Among the three types of stability A, B, and C, A indicates extremely unstable meteorological conditions, B indicates moderately unstable meteorological conditions, and C indicates weakly unstable meteorological conditions. Among the two types of stability E and F, E indicates weakly stable meteorological conditions, and F indicates moderately stable meteorological conditions. For specific judgment methods, please refer to Tables 1 and 2.

[0055] Tab.1 Judgment of sunshine intensity

[0056]

[0057] Tab.2 Judgment of Pasquill's atmospheric stability

[0058]

[0059]

[0060] Table 1 determines the current sunshine intensity based on the cloud conditions and sunshine angle. Table 2 determines the atmospheric stability at different times based on meteorological conditions such as light intensity and wind speed. Corresponding to Table 3, the calculation method of the diffusion coefficients of the y-axis and z-axis is obtained.

[0061] Tab.3 Calculation method of diffusion coefficient

[0062]

[0063] Where x represents the load point position corresponding to the wind direction axis coordinate.

[0064] In the smoke diffusion model, the effective chimney height H g It is the sum of the entity height and the plume lift height, denoted by H1 and H2 respectively.

[0065] H g =H1+H2

[0066] Step S103, to ensure the economic efficiency of power production, centralized optimization is performed with the minimum total cost of power production as the objective function.

[0067]

[0068] Where, T is the total duration, the scheduling interval is 1h, so T is taken as 24h; G is the number of coal-fired units; C cost is the total cost of electricity production in the region; c k is the price of coal type k; N r B is the type of coal; g,k,t is the consumption of coal type k by coal-fired unit g in 1 hour; c new,i It represents the maintenance cost of the unit output of the new energy; It is the power generation capacity of the new energy unit.

[0069] 1) Daily coal consumption constraints

[0070] Considering that my country's high-quality coal reserves are limited, we cannot blindly consume high-quality coal, which will not only lead to excessive consumption of high-quality coal, but also waste of low-quality coal resources. At the same time, based on the limited coal storage space in the coal yard of thermal power plants and the limited amount of available coal resources, it is necessary to impose certain restrictions on the daily consumption of each type of coal. The daily consumption of different types of coal must meet certain maximum and minimum consumption constraints as shown in the following formula.

[0071]

[0072] in, is the upper limit of daily consumption of coal type k; It is the lower limit of daily consumption of coal type k.

[0073] 2) Boiler coal quality index constraints

[0074] In order to ensure the safety and efficiency of boiler combustion, various indicators of mixed coal quality must be constrained as shown below.

[0075]

[0076] Among them, M ad,k , A ad,k 、V daf,k , Q net,k They represent the moisture, ash, volatile matter and lower calorific value of unit mass of coal type k respectively; It represents the upper and lower limits of moisture, ash, volatile matter and low calorific value respectively.

[0077] 3) Constraints on the intensity of air pollutant emissions

[0078] Mixed coal combustion will produce SO2 and NO x To prevent harmful gases from polluting the atmosphere, it is necessary to restrict the concentration of air pollutants emitted by unit mixed coal combustion, as shown below.

[0079]

[0080] in, and They are respectively the SO2 and NO emitted into the atmosphere per unit mass of coal type k after combustion through the desulfurization and denitrification device x quantity; and SO2 and NO x The upper limit of the emission volume specified; V y is the volume of flue gas produced by the combustion of unit mass of coal, generally taken as 10m 3 / kg.

[0081] 4) Coal-electricity conversion constraints

[0082] There is a corresponding relationship between thermal power output power and coal consumption. The standard coal consumption function of thermal power units can be obtained by polynomial fitting, as shown below.

[0083]

[0084] Among them, p k It represents the ratio of the lower calorific value of coal type k to standard coal, and according to p k The coal types of different coal qualities can be converted into the mass of standard coal; It is the low calorific value of standard coal; P g,t is the power generation capacity of the thermal power unit; a g , b g and c g is the fitting coefficient of the quadratic, linear and constant terms of the standard coal consumption function; S g Indicates the standard coal consumption when the unit is started; Indicates the startup variable of the thermal power unit. It is 1 when it is started, otherwise it is 0.

[0085] 5) Coal ratio constraints

[0086] Thermal power plants generally optimize coal blending on the day before and determine the ratio of each type of coal, which remains unchanged during the day. Therefore, the coal blending constraint is shown in the following formula.

[0087]

[0088] Among them, v g,k Q is the coal type k ratio of unit g; g,t is the coal consumption of unit g.

[0089] 6) Collaborative constraints on pollution reduction and carbon reduction

[0090] The coordinated constraints on pollution reduction and carbon reduction include constraints on the system's direct CO2 emissions and constraints on air quality in typical load areas, as shown below.

[0091] Direct CO2 emissions constraints:

[0092] In order to reduce the direct carbon emissions caused by the system's electricity production, it is necessary to set an upper limit on the system's CO2 emissions to constrain the system's carbon emissions.

[0093]

[0094] in, It is the upper limit of direct CO2 emissions of the system.

[0095] Air quality constraints for typical load areas:

[0096] Considering that the long-term cumulative effect of atmospheric pollutants in the air will cause serious harm to the human body, it is necessary to control the daily average concentration of atmospheric pollutants in densely populated areas.

[0097]

[0098] in, and Load zone NO x and the upper limit of the daily average concentration of SO2.

[0099] 7) Power system energy balance constraints

[0100] The energy balance constraint of the power system is shown in the following equation.

[0101]

[0102] is the active power of thermal power; G is the set of thermal power units; P ij,t is the transmission power of line ij; J n are the collections of thermal power units and branches connected to node n respectively; is the load power.

[0103] 8) Output constraints of thermal power units

[0104] The output, ramp power, start and stop time of coal-fired power units also need to meet certain constraints, as shown below.

[0105] Minimum start and stop time constraints for thermal power units:

[0106]

[0107] Among them, y g,t is the online state variable of the thermal power unit, which is 1 when online, otherwise 0; v g,t It is the shutdown action variable of the thermal power unit.

[0108] Output constraints of thermal power units:

[0109]

[0110] in, and It is the maximum and minimum value of thermal power output.

[0111] Thermal power unit climbing constraints:

[0112]

[0113] Among them, RU g With RD g They are the climbing power and downhill power of the thermal power unit respectively.

[0114] Constraints on the on / off action of thermal power units:

[0115]

[0116] Among them, MU g With MD g It is the minimum startup and minimum shutdown time of thermal power units.

[0117] The constraints of the above coal blending and scheduling centralized optimization model contain quadratic variables and the product of integer variables and continuous variables. Therefore, it cannot be solved directly using commercial solvers. It is necessary to relax and linearize the above constraints. To linearize the model, the constraints contain the product of integer variables and continuous variables, which need to be linearized. The method is as follows: First, introduce a set of binary variables For integer variable v g,k Discretization is performed, and the product of the integer variable and the continuous variable can be converted into the sum of the products of multiple binary quantities and continuous quantities, as shown below. The large M method is used to process the product of each binary quantity and the continuous quantity to form multiple sets of linear constraints, where m∈{1,2,3,4,5,6,7}, and M is a large real number. is a set of continuous variables introduced by the Big M method. Finally, the nonlinear constraints are transformed into linear constraints.

[0118]

[0119] Perform second-order cone relaxation on the model, use the second-order cone relaxation method to process the quadratic variables in the constraints, and define the variable P g2,t As an intermediate variable, the equality constraints containing quadratic variables are transformed into second-order cone constraints, as shown in the following equation.

[0120]

[0121] Step S104, centralized optimization framework and division of responsibilities of the dispatch center. The centralized optimization framework for power coal distribution and low-carbon economic dispatch in the production scenario dominated by coal-fired power is shown in the attached figure. Figure 3 As shown in the figure. The objective function of the centralized optimization model is to minimize the total economic cost, which includes the cost of purchasing coal and the maintenance cost of wind power and photovoltaic units. The constraints mainly include two parts. The constraints of coal blending optimization mainly include the constraints of mixed coal quality indicators and the constraints of atmospheric pollutant emission intensity. The constraints of power system dispatching mainly include energy balance, flow constraints and operation constraints of wind power, photovoltaic and thermal power units. There is a certain coupling relationship between the mathematical models of coal blending and dispatching. The coupling constraints include the maximum and minimum constraints of daily coal consumption, coal-to-electricity conversion constraints, atmospheric quality constraints in typical load areas and direct carbon emissions constraints. Through the centralized optimization of coal blending and dispatching, the unified and reasonable allocation of coal resources within the region can be achieved while ensuring the economic production of the power system, reducing the direct carbon emissions of the power system and improving the air quality in the typical load area of ​​the system.

[0122] This centralized optimization method involves the cooperation of the dispatching center, thermal power plants, coal distribution yards, and new energy units, among which the dispatching center is the hub for the coordination of multiple equipment. The coal distribution yard always provides coal consumption support for load demand. In the centralized optimization model, each coal yard will send the coal quality parameters of the stored coal to the dispatching center, and at the same time, each new energy equipment will send its operating parameters to the dispatching center. The dispatching center will perform centralized optimization based on the day-ahead load forecast value, combined with the sent thermal power plant coal yard and the operating parameters of each power equipment, to obtain the coal ratio, thermal power unit start and stop, and output plan of each thermal power unit, and then send the coal ratio and the output plan of each unit to the thermal power plant coal distribution yard and the operation control center of each power equipment, as shown in the attached figure. Figure 3 shown.

Claims

1. A method for reducing pollution and carbon emissions in a power system based on source-grid interaction, characterized in that: The steps include: S101, build an atmospheric pollutant and carbon dioxide emission model, and obtain the NO generated per unit of coal combustion by analyzing the transfer process of elements in each link of coal-fired power generation. x , SO2, CO2 gas volume; S102, using the Gaussian plume diffusion model to describe the spatiotemporal diffusion characteristics of atmospheric pollutants; S103, combines the atmospheric pollutant and CO2 emission models constructed in S101 with the Gaussian plume diffusion model established in S102 to form atmospheric pollutant emission constraints and carbon emission constraints, with the minimization of the total power generation cost as the objective function; through the linearization and second-order cone relaxation methods, various non-convex related constraints are converted into solvable second-order cone constraints, and finally a mixed integer second-order cone optimization problem of a multi-objective function is solved to obtain the output value of each generator set; S104, for the scenario dominated by coal-fired power generation, a collaborative optimization method for power coal distribution and power generation dispatching is proposed, and a main framework of collaborative optimization is proposed to divide the rights and responsibilities of the two main bodies, the power plant and the power grid. Among them, the dispatching center is the hub for coordination among multiple parties, and the coal distribution yard always provides coal consumption support for load demand. In this joint optimization model, each coal yard will send the coal quality parameters of the stored coal to the dispatching center, and each equipment will send its operating parameters to the dispatching center. The dispatching center will perform joint optimization based on the load forecast value, the operating parameters of the coal yard of the thermal power plant and each power equipment to obtain the coal ratio, start and stop and output plan of each thermal power unit, and then send the coal ratio and the output plan of each unit to the coal distribution yard of the thermal power plant and the operation control center of each power equipment respectively.

2. According to claim 1, a method for reducing pollution and carbon emissions in a power system based on source-grid interaction is characterized in that: The method for calculating the atmospheric pollutants and CO2 emissions in step S101 includes calculating the atmospheric pollutant SO2 emissions, the atmospheric pollutant NO x Emissions calculation, CO2 emissions calculation, the specific methods are as follows: The carbon emissions of the power system mainly come from the carbon emissions from coal combustion in the furnace and flue gas desulfurization. The carbon emissions of coal type k in boiler combustion and flue gas desulfurization are shown below. in, is the content of C and S in coal type k; M C 、M S are the relative atomic masses of C and S respectively; is the relative molecular mass of CO2; η b is the combustion efficiency of the thermal power plant boiler; η s,1 is the conversion rate of S element in coal into SO2; η s,2 is the absorption rate of SO2 by the desulfurization device; After desulfurization and denitrification, SO2 and NO are discharged into the atmosphere. x The concentration is calculated by the following formula: in, is the nitrogen content of coal type k; β N is the nitrogen conversion rate in coal type k; η N is the denitration rate of the denitration device; M SO2 is the relative molecular mass of SO2.

3. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 1, characterized in that: The Gaussian plume model is shown in the following formula: Where C(x,y,z,H) is the concentration of air pollutants at the coordinate (x,y,z); Q gas is the emission intensity of air pollutants at the chimney outlet of thermal power plants; is the average wind speed; H is the effective height of the emission point source; and σ y and σ z are the horizontal and vertical diffusion parameters of atmospheric pollutants, respectively.

4. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 3, characterized in that: Each coal-fired unit is a point source of atmospheric pollutant emissions. The load points in residential and industrial areas are set as sampling locations for atmospheric pollutant concentrations. By analyzing the influence of multiple factors such as atmospheric stability, wind speed, wind direction and other meteorological conditions and the coordinates of the sampling location relative to the emission source, combined with the Gaussian plume model, the average concentration of atmospheric pollutants in a typical load area for 1 hour is finally obtained, as shown in the following formula: in, is the concentration of atmospheric pollutants caused by coal-fired unit g at load point d; is the average wind speed at time t; is the amount of air pollutants emitted by unit g during coal combustion within 1 hour; y,t and σ z,t are the horizontal and vertical atmospheric pollutant diffusion parameters within 1 hour; y g,d,t H is the crosswind distance from the typical load point d to the thermal power unit g; g The effective source height.

5. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 1, characterized in that: To ensure the economy of power generation, collaborative optimization is carried out with the goal of minimizing the total cost of electricity production. The time resolution of the collaborative optimization model is 1 hour, and the optimization is carried out based on the daily load forecast value. Atmospheric pollutant emission constraints and total carbon emission constraints are added to the optimization model. While ensuring the economy of power generation, it also takes the environmental protection of power generation into consideration.

6. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 1, characterized in that: The air pollutant emission constraints and carbon emission constraints are as follows: 1) Daily coal consumption constraints Considering the limited reserves of high-quality coal in my country, it is necessary to impose certain restrictions on the daily consumption of each type of coal; the daily consumption of different types of coal must meet certain maximum and minimum consumption constraints as shown in the following formula: in, is the upper limit of daily consumption of coal type k; is the lower limit of daily consumption of coal type k; 2) Boiler coal quality index constraints In order to ensure the safety and efficiency of boiler combustion, various indicators of mixed coal quality should be constrained as follows: Among them, M ad,k , A ad,k 、V daf,k , Q net,k They represent the moisture, ash, volatile matter and lower calorific value of unit mass of coal type k respectively; It represents the upper and lower limits of moisture, ash, volatile matter and low calorific value respectively; 3) Constraints on the intensity of air pollutant emissions Mixed coal combustion will produce SO2 and NO x To prevent harmful gases from polluting the atmosphere, it is necessary to restrict the concentration of air pollutants emitted by the combustion of mixed coal per unit, as shown below: in, and They are respectively the SO2 and NO emitted into the atmosphere per unit mass of coal type k after combustion through the desulfurization and denitrification device x quantity; and SO2 and NO x The upper limit of the emission volume specified; V y is the volume of flue gas produced by the combustion of unit mass of coal, generally taken as 10m 3 / kg; 4) Coal-electricity conversion constraints There is a corresponding relationship between thermal power output power and coal consumption. The standard coal consumption function of thermal power units can be obtained by polynomial fitting, as shown below: Among them, p k It represents the ratio of the lower calorific value of coal type k to standard coal, and according to p k The coal types of different coal qualities can be converted into the mass of standard coal; It is the low calorific value of standard coal; P g,t is the power generation capacity of the thermal power unit; a g , b g and c g is the fitting coefficient of the quadratic, linear and constant terms of the standard coal consumption function; S g Indicates the standard coal consumption when the unit is started; Indicates the startup variable of the thermal power unit, which is 1 if started, otherwise 0; 5) Coal ratio constraints The coal ratio constraint is as follows: Among them, v g,k Q is the coal type k ratio of unit g; g,t is the coal consumption of unit g; 6) Collaborative constraints on pollution reduction and carbon reduction The coordinated constraints of pollution reduction and carbon reduction include constraints on the direct CO2 emissions of the system and constraints on the air quality in typical load areas, as shown below: Direct CO2 emissions constraints: In order to reduce the direct carbon emissions caused by the power generation of the system, it is necessary to set an upper limit for the system's CO2 emissions to constrain the system's carbon emissions: in, is the upper limit of the direct CO2 emissions of the system; 7) Air quality constraints in typical load areas: Considering that the long-term cumulative effect of atmospheric pollutants in the air will cause serious harm to the human body, it is necessary to restrict the daily average concentration of atmospheric pollutants in densely populated areas, as follows: in, and Load zone NO x and the upper limit of the daily average concentration of SO2; 8) Power system energy balance constraints The energy balance constraint of the power system is as follows: in, is the active power of thermal power; G is the set of thermal power units; P ij,t is the transmission power of line ij; J n are the collections of thermal power units and branches connected to node n respectively; is the load power; 9) Output constraints of thermal power units The output, ramp power, start and stop time of coal-fired power units also need to meet certain constraints, as shown below: Minimum start and stop time constraints for thermal power units: Among them, y g,t is the online state variable of the thermal power unit, which is 1 when online, otherwise 0; v g,t It is the shutdown action variable of the thermal power unit. Output constraints of thermal power units: in, and The maximum and minimum values ​​of thermal power output; Thermal power unit climbing constraints: Among them, RU g With RD g They are the climbing power and downhill power of the thermal power unit respectively; Constraints on the on / off action of thermal power units: Among them, MU g With MD g It is the minimum startup and minimum shutdown time of thermal power units.

7. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 1, characterized in that: In S102, the linearization method is used to transform the constraints that cannot be solved by the commercial solver. The non-convex constraints that cannot be solved in this model include the air quality constraints and the direct CO2 emission constraints in the typical load area. There are quadratic variables and the product of integer variables and continuous variables. Therefore, they cannot be solved directly using commercial solvers. It is necessary to linearize the above constraints and linearize the model. The constraints contain the product of integer variables and continuous variables, which need to be linearized. The method is as follows: First, a set of binary variables are introduced. For integer variable v g,k Discretization is performed, and the product of the integer variable and the continuous variable can be converted into the sum of the products of multiple binary quantities and continuous quantities, as shown below. The product of each binary quantity and the continuous quantity is processed using the big M method to form multiple sets of linear constraints, where m = {1, 2, 3, 4, 5, 6, 7}, and M is a large real number. is a set of continuous variables introduced by the Big M method. Finally, the nonlinear constraints are transformed into linear constraints. The specific calculation is as follows:

8. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 1, characterized in that: In S103, the energy balance constraint of the power system in the proposed model is subjected to second-order cone relaxation. The second-order cone relaxation method is used to process the secondary variables in the constraint conditions. The variable P is defined as g2,t As an intermediate variable, the equality constraints containing quadratic variables are transformed into second-order cone constraints, as shown below:

9. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 1, characterized in that: The objective function of the optimization method in S103 is as follows: Where, T is the total duration, the scheduling interval is 1h, so T is taken as 24h; G is the number of coal-fired units; C cost is the total cost of electricity production in the region; c k is the price of coal type k; N r B is the type of coal; g,k,t is the consumption of coal type k by coal-fired unit g in 1 hour; c new,i It represents the maintenance cost of the unit output of the new energy; It is the power generation capacity of the new energy unit.

10. The method for reducing pollution and carbon emissions in a power system based on source-grid interaction according to claim 1, characterized in that: The collaborative optimization model of power coal blending and power grid dispatching constructed by S104 integrates the power plant power coal blending optimization model and the power grid power generation dispatching optimization model. The two cooperate with each other and use MATLAB to directly call the commercial solver GUROBI for optimization and solution to obtain the unit power generation output and coal ratio. On the basis of ensuring the economic efficiency of power generation, it takes into account the reduction of pollution and carbon in the power generation process.

Images