Integrated energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change

Abstract

The invention discloses an integrated energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change, belongs to the field of power system environmental economic scheduling, and solves the problems that in an existing integrated energy system scheduling method, environmental constraint expression is rough, and pollutant diffusion characteristics and environmental capacity space-time difference are difficult to reflect. According to the method, by fusing multi-source geographic information and meteorological conditions, an environment capacity dynamic partition model and an air pollutant diffusion model are constructed, and the models are introduced into a power, thermal and natural gas system coupled optimal scheduling framework, so that collaborative optimization of energy system operation cost and pollutant emission influence is realized; according to the method, the operation strategy of the energy system can be adaptively adjusted according to the dynamic change of the meteorological conditions and the environment bearing capacity, the safe and stable operation of the comprehensive energy system is guaranteed, the air quality of the environment sensitive area is effectively improved, the spatial configuration reasonability of pollutant emission and the overall operation economy of the system are improved, and the economic benefit is increased. Good application prospects are realized.

Description

Technical Field

[0001] This invention belongs to the field of environmental and economic dispatch of power systems, and specifically relates to a comprehensive energy system dispatching method that considers the dynamic changes in pollutant diffusion and environmental capacity. Background Technology

[0002] Cities, as concentrated areas of energy consumption and pollutant emissions, experience a continuous increase in demand for various energy sources, including electricity, heating, and natural gas. Fossil fuels still occupy a significant position in urban integrated energy systems, and the energy production and conversion processes inevitably generate carbon dioxide, PM2.5, SO2, and NO. x Air pollutants such as these have an adverse impact on urban air quality and public health.

[0003] To balance economic operation and environmental protection goals, existing research on integrated energy system scheduling mostly employs total emission control or emission cost constraints, limiting system operation by setting uniform upper limits on pollutant or carbon emissions. While these methods are simple to implement and ensure overall emission compliance, they are typically based on static assumptions, ignoring significant spatial differences in the urban environment and potentially leading to localized pollution accumulation or overly conservative operational constraints. To improve the precision of environmental constraints, some studies have introduced pollutant diffusion models, considering meteorological factors such as wind speed and direction to characterize the spatial transport and concentration distribution of pollutants, thereby achieving emission control based on spatial concentration. However, existing research primarily focuses on the power system level, and its applicability to urban integrated energy systems with highly coupled multi-energy subsystems such as electricity, heat, and gas remains limited.

[0004] On the other hand, environmental conditions themselves constrain the operation of energy systems through environmental capacity. Urban areas exhibit significant spatial heterogeneity in terms of population density, land use, economic activity, and meteorological conditions, with different regions showing marked differences in their tolerance for pollutant emissions. Existing studies often use preset fixed regional thresholds or expert experience to represent environmental capacity. While this method has some practicality, it struggles to reflect the dynamic changes in wind fields and meteorological conditions on hourly scales, potentially leading to insufficient environmental protection in dispatching decisions during certain periods.

[0005] In summary, existing integrated energy system scheduling methods still have shortcomings in considering environmental impact. There is an urgent need for an integrated energy system scheduling method that can simultaneously consider pollutant diffusion characteristics and dynamic changes in environmental capacity, so as to achieve synergistic optimization of efficient operation of urban energy systems and improvement of air quality. Summary of the Invention

[0006] Based on the above shortcomings, this invention provides a comprehensive energy system scheduling method that considers the dynamic changes in pollutant diffusion and environmental capacity, in order to solve the problems of the coarse expression of environmental constraints and the inability to reflect the characteristics of pollutant diffusion and the spatiotemporal differences in environmental capacity in existing comprehensive energy system scheduling methods.

[0007] The technical solution adopted in this invention is as follows: A comprehensive energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity, comprising the following steps:

[0008] S1: Constructing a multi-source geographic information gridded database: Collect multi-source data related to environmental capacity and energy system operation within the study area, and perform unified spatial reference and temporal scale processing on the multi-source data to construct a multi-source geographic information gridded database; through spatial interpolation and grid division methods, map the multi-source data to grid cells with unified spatial resolution to form a gridded attribute dataset that is updated hourly; wherein, the multi-source data includes hourly changing meteorological data and static spatial data that remain unchanged within the scheduling cycle, the meteorological data includes temperature, humidity, wind speed, air pressure and solar radiation parameters, and the static spatial data includes topographic elevation, population density and GDP;

[0009] S2: Dynamic clustering of environmental capacity based on hybrid weights: Based on the multi-source geographic information gridded database, dynamic clustering analysis is performed on environmental-related indicators of each grid unit in the study area, using hours as the time scale, to construct environmental capacity zoning results that change over time; by executing the clustering process hour by hour, a dynamic zoning sequence of environmental capacity reflecting the spatiotemporal differences in environmental carrying capacity is obtained; wherein, the dynamic clustering analysis adopts a hybrid weighting method that integrates subjective weights and objective weights, assigning comprehensive weights to the multidimensional indicators participating in the clustering, wherein the subjective weights are determined based on the analytic hierarchy process, and the objective weights are determined based on the information entropy method;

[0010] S3: Construct an air pollutant diffusion model: Based on the dynamic zoning results of the environmental capacity, construct an air pollutant diffusion model to describe the impact of pollution source emissions on the air quality of each grid unit; wherein, the air pollutant diffusion model includes a Gaussian plume model and a decay model to characterize the effects of dry and wet deposition of pollutants, and is used to calculate the pollutant concentration distribution at different times and spatial locations;

[0011] S4: Constructing an Optimized Scheduling Model for Urban Integrated Energy Systems to Improve Air Quality: Based on the air pollutant diffusion model and the dynamic zoning results of environmental capacity, an optimized scheduling model for urban integrated energy systems to improve air quality is constructed. This model aims to comprehensively optimize the operating costs of energy systems and the impact of pollutant emissions while meeting the safety constraints of the power, heat, and natural gas systems. The optimized scheduling model includes an urban integrated energy system cost calculation model, an electricity-heat-gas multi-energy system coupling model, and a system operation constraint model. The cost calculation model calculates the comprehensive costs of energy production, energy conversion, and energy storage operation within the scheduling cycle. It also introduces a pollutant environmental penalty model based on the dynamic zoning results of environmental capacity to quantify the costs of pollutant emissions exceeding the corresponding spatiotemporal environmental capacity threshold. The electricity-heat-gas multi-energy system coupling model describes the energy conversion and transmission relationships between the power, heat, and natural gas systems. The system operation constraint model includes energy supply and demand balance constraints, equipment output constraints, energy conversion efficiency constraints, equipment ramp-up constraints, and energy storage equipment operation constraints to ensure the safe, stable, and feasible operation of the urban integrated energy system within the scheduling cycle.

[0012] Furthermore, the constructed multi-source geographic information gridded database includes: performing ordinary kriging interpolation on the multi-source geographic information data, as shown in equations (1)-(2);

[0013]

[0014]

[0015] In the formula: The predicted value for the target location; For the first Observations at each sample location; This represents the total number of observation points. for Observations at the location; For the first The point and the first Covariance between points; For the first Points and target points Covariance between them; For Lagrange multipliers associated with unbiasedness constraints;

[0016] The study area was then divided into grid cells according to the preset spatial resolution to ensure spatial consistency of various types of data.

[0017] The processed meteorological data and static spatial data are mapped to each grid cell to form a gridded attribute dataset that is updated hourly, providing a data foundation for subsequent dynamic clustering of environmental capacity and air pollutant diffusion models.

[0018] Furthermore, in step S2, the hybrid weight calculation formula based on the analytic hierarchy process and the entropy weight method is shown in equation (3):

[0019]

[0020] In the formula: Weights for the analytic hierarchy process (AHP); For information entropy weights; These are the weighting coefficients;

[0021] The method for establishing the judgment matrix in the analytic hierarchy process is shown in equation (4):

[0022]

[0023] In the formula: As an indicator relative to indicators The importance of; The total number of evaluation indicators;

[0024] The formula for calculating subjective weights based on the analytic hierarchy process is shown in equation (5):

[0025]

[0026] In the formula: For the first The weights of each evaluation indicator using the analytic hierarchy process;

[0027] The formulas for calculating the objective weights based on the entropy weight method are shown in equations (6)-(7):

[0028]

[0029]

[0030] In the formula: For the first Normalized information entropy of each variable; For grid Medium variables Relative to the proportion of all grids, The total number of grid cells. Normalization factor;

[0031] To eliminate the impact of inconsistent dimensions of different indicators on the analysis results, the multidimensional indicators are normalized so that their values ​​are limited to the range of [0,1].

[0032] At each hourly scale, a weighted attribute matrix is ​​constructed based on the mixed weights, and the weighted attribute matrix is ​​input into the k-means clustering algorithm to perform spatial clustering of the study area, dividing the area into a preset number of environmental capacity categories to characterize different environmental sensitivities and pollutant emission tolerance levels.

[0033] The clustering process is repeated throughout the day's scheduling cycle to obtain an hourly updated spatial partition sequence of environmental capacity, which is used to dynamically reflect the temporal changes in meteorological conditions and environmental status.

[0034] Furthermore, in step S3, the Gaussian plume model for the diffusion of air pollutants is shown in equation (8):

[0035]

[0036] In the formula: Represents the effective emission height of pollution sources;

[0037] The air pollutant concentration decay model considering the dry and wet deposition removal mechanisms is shown in equation (9):

[0038]

[0039] By combining the air pollutant attenuation model with the Gaussian plume model, an air pollutant diffusion model is obtained, as shown in equation (10):

[0040]

[0041] Furthermore, in step S4, the cost calculation model for the urban integrated energy system includes operating costs and pollutant fine costs, with the operating costs shown in equation (11):

[0042]

[0043] In the formula: A collection of energy production equipment; A collection of energy conversion devices; For equipment At any moment fuel costs; For equipment At any moment Operating costs;

[0044] The cost of pollutant fines is shown in equation (12):

[0045]

[0046] In the formula: For partitioned sets; For partitioning The 24-hour average concentration of pollutants in the medium; For partitioning The corresponding pollutant concentration emission threshold; For category Additional fines for pollutant emissions exceeding the corresponding threshold;

[0047] The 24-hour average pollutant concentration for each category is calculated using Equation (13):

[0048]

[0049] In the formula: A set of scheduling times; A collection of grids; For grid In time The concentration of pollutants below;

[0050] The active power balance model of the bus is shown in equation (14):

[0051]

[0052] In the formula: Indicates the connection with the busbar The set of directly connected adjacent busbars; Indicates connecting busbar and The susceptance of the branch circuit; It is a busbar Voltage phase angle at the point;

[0053] The branch DC power flow model is shown in equation (15):

[0054]

[0055] In the formula: This is the phase angle difference; For branch circuit reactance;

[0056] The active power transmission constraint of the branch is shown in equation (16):

[0057]

[0058] In the formula: and They represent branches respectively Minimum and maximum permissible power transfer;

[0059] The coupled thermal system model of the urban integrated energy system is shown in equation (17):

[0060]

[0061] In the formula: This is the association matrix from nodes to pipelines; Let this be the water flow vector in each pipe; The input vector for each node; This is the pressure drop vector; This is a diagonal matrix of hydraulic resistance coefficients; The correlation matrix for loop branches;

[0062] The calculation method for the heat energy transported at each node is shown in equation (18):

[0063]

[0064] In the formula: The density of water; This is the specific heat capacity of water; and These are the input and output temperatures at the node, respectively. For the heat power delivered;

[0065] The calculation method for the pipe outlet temperature after considering heat loss is shown in equation (19):

[0066]

[0067] In the formula: The temperature at the end of the pipe; The total heat transfer coefficient of the pipe insulation layer; The length of the pipe section. Ambient temperature;

[0068] The thermal energy conservation model of the mixing node of the thermodynamic system is shown in equation (20):

[0069]

[0070] In the formula: Outflow; The outlet mixing temperature; For inflow quality; Inlet temperature;

[0071] The Weymouth model describing the volumetric flow rate of natural gas pipelines is shown in equation (21):

[0072]

[0073] In the formula: For pipelines The Weymouth coefficient; , They are nodes and Pressure at the location; Pipeline section The inner diameter; The length of the pipe; , For standard reference temperature and pressure; , These are the average temperature and the drag coefficient, respectively.

[0074] The nodal gas balance equations for the natural gas network are shown in equation (22):

[0075]

[0076] In the formula: This is the association matrix from nodes to pipelines; This is the vector representing the gas flow rate in the pipe; The gas demand vector for each node;

[0077] The constraints of the energy conversion equipment are shown in equation (23):

[0078]

[0079] In the formula: , respectively equipment At any moment Input energy and output energy; It is equipment Energy conversion efficiency;

[0080] The output constraint of the equipment is shown in equation (24):

[0081]

[0082] In the formula: For equipment At any moment ; output power; , respectively equipment At any moment The minimum and maximum allowable output power;

[0083] The equipment ramp rate constraint is shown in equation (25):

[0084]

[0085] In the formula: For equipment In time The output; , These are the equipment The minimum and maximum climbing rates;

[0086] The energy state evolution model of the energy storage device is shown in equation (26):

[0087]

[0088] In the formula: and These are the equipment At any moment and time Stored energy; , They are time points The charging power and discharging power; , These are charging efficiency and discharging efficiency, respectively. It is a binary variable that indicates the storage mode;

[0089] The energy capacity constraint model for energy storage devices is shown in equation (27):

[0090]

[0091] In the formula: , These are the minimum and maximum storage capacities, respectively.

[0092] The power constraint model for charging and discharging of energy storage devices is shown in equation (28):

[0093]

[0094] In the formula: , These are the rated charging power and the rated discharging power, respectively.

[0095] This invention also provides a comprehensive energy system dispatching system that considers pollutant diffusion and dynamic changes in environmental capacity, comprising:

[0096] The system comprises a data acquisition and gridding module, an environmental capacity dynamic clustering module, a pollutant diffusion model construction module, and an integrated energy system optimization scheduling module. These modules interact sequentially to collaboratively achieve dynamic optimization scheduling of the integrated energy system. This system is used to implement the integrated energy system scheduling method described above, which considers the dynamic changes in pollutant diffusion and environmental capacity.

[0097] The data acquisition and gridding processing module is used to collect multi-source data related to environmental capacity and energy system operation within the study area, perform unified spatial reference and time scale processing on the multi-source data, and map the multi-source data to grid cells with unified spatial resolution through spatial interpolation and gridding methods to form a gridded attribute dataset that is updated hourly; wherein, the multi-source data includes meteorological data that changes hourly and static spatial data that remains unchanged within the scheduling cycle;

[0098] The dynamic clustering module for environmental capacity is used to assign comprehensive weights to environmental-related indicators of each grid unit based on the gridded attribute dataset, using hours as the time scale, and employing a hybrid weighting method that integrates subjective and objective weights. This constructs a weighted attribute matrix, which is then input into the k-means clustering algorithm to perform spatial clustering of the study area to obtain environmental capacity zoning results. By repeatedly executing the clustering process hourly, a dynamic zoning sequence reflecting the spatiotemporal differences in environmental carrying capacity is generated. The subjective weights are determined based on the analytic hierarchy process (AHP), and the objective weights are determined based on the information entropy method.

[0099] The pollutant diffusion model construction module is used to construct an air pollutant diffusion model based on the environmental capacity dynamic zoning results. The air pollutant diffusion model includes a Gaussian plume model and a decay model that characterizes the effects of dry and wet deposition of pollutants. The two are combined to calculate the pollutant concentration distribution at different times and spatial locations.

[0100] The integrated energy system optimization scheduling module is used to construct an optimized scheduling model for improving air quality based on the air pollutant diffusion model and the dynamic zoning results of environmental capacity. The optimized scheduling model integrates the urban integrated energy system cost calculation model, the electric-heat-gas multi-energy system coupling model, and the system operation constraint model. Under the premise of meeting the safety operation constraints of the electric, heat, and natural gas systems, it realizes the comprehensive optimization of energy system operating costs and pollutant emission impacts, and outputs a scheduling scheme.

[0101] The present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the comprehensive energy system scheduling method described above, which considers the dynamic changes in pollutant diffusion and environmental capacity.

[0102] The present invention also provides a computer program product, which, when executed by a processor, implements the comprehensive energy system scheduling method as described above, taking into account the dynamic changes in pollutant diffusion and environmental capacity.

[0103] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is used to implement the integrated energy system scheduling method described above, taking into account the dynamic changes in pollutant diffusion and environmental capacity.

[0104] The beneficial effects and advantages of this invention are as follows: This invention achieves synergistic optimization of energy system operation and air quality improvement. On the one hand, by introducing a dynamic environmental capacity zoning method based on mixed weights, it can characterize the spatial and temporal differences in urban environmental carrying capacity, avoiding local pollution accumulation or overly conservative scheduling caused by traditional total emission or static emission constraints. On the other hand, by embedding the Gaussian plume model and pollutant attenuation model into the coupled scheduling process of power, heat, and natural gas systems, it realizes the mapping of energy-related emissions to spatially resolved pollutant concentrations, enabling environmental constraints to adaptively adjust with changes in meteorological conditions, thereby effectively reducing pollutant concentration levels in environmentally sensitive areas. Under the premise of ensuring the stable operation of the integrated energy system, this invention promotes the rational allocation of pollutant emissions at the regional scale, improves the economy and flexibility of energy system operation, and enhances adaptability to environmental heterogeneity and uncertain meteorological conditions. Attached Figure Description

[0105] Figure 1 This is an overall flowchart of the present invention;

[0106] Figure 2 This is a schematic diagram of the environmental capacity zoning process;

[0107] Figure 3 This is a schematic diagram of the power balance of electricity, heat, and gas in the urban integrated energy system in Example 3;

[0108] Figure 4 This is a schematic diagram of the pollutant distribution results under the dynamic zoning strategy and total pollutant control strategy at 18h in Example 3. Detailed Implementation

[0109] The present invention will be specifically described below with reference to the embodiments, but the scope of protection of the present invention is not limited to the embodiments described.

[0110] Example 1

[0111] A comprehensive energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity, the specific process of which is as follows: Figure 1 As shown, it includes the following steps:

[0112] S1: Construct a multi-source geographic information gridded database: Collect multi-source geographic information data for environmental capacity assessment within the study area, and perform unified spatial reference and time scale processing on the multi-source geographic information data to construct a multi-source geographic information database; wherein, the multi-source geographic information data includes hourly changing meteorological data and static spatial data that remain unchanged within the scheduling cycle, the meteorological data includes temperature, humidity, wind speed, air pressure and solar radiation parameters, and the static spatial data includes topographic elevation, population density and GDP; specifically as follows: Perform ordinary kriging interpolation processing on the multi-source geographic information data, as shown in formulas (1)-(2):

[0113]

[0114]

[0115] In the formula: The predicted value for the target location; For the first Observations at each sample location; This represents the total number of observation points. for Observations at the location; For the first The point and the first Covariance between points; For the first Points and target points Covariance between them; For Lagrange multipliers associated with unbiasedness constraints;

[0116] The study area was then divided into grid cells according to the preset spatial resolution to ensure spatial consistency of various types of data.

[0117] The processed meteorological data and static spatial data are mapped to each grid cell to form a gridded attribute dataset that is updated hourly, providing a data foundation for subsequent dynamic clustering of environmental capacity and air pollutant diffusion models.

[0118] S2: Dynamic Clustering of Environmental Capacity Based on Hybrid Weights: Based on the aforementioned gridded database, dynamic clustering analysis is performed on environmental-related indicators of each grid unit within the study area, using hours as the time scale, to construct time-varying environmental capacity partitioning results. The specific data collection and clustering process is as follows: Figure 2 As shown;

[0119] The formula for calculating the hybrid weight based on the analytic hierarchy process and the entropy weight method is shown in (3):

[0120]

[0121] In the formula: Weights for the analytic hierarchy process (AHP); For information entropy weights; These are the weighting coefficients;

[0122] Formula (4) is the method for establishing the judgment matrix in the analytic hierarchy process:

[0123]

[0124] In the formula: As an indicator relative to indicators The importance of; The total number of evaluation indicators;

[0125] Formula (5) is the method for calculating subjective weights based on the analytic hierarchy process:

[0126]

[0127] In the formula: For the first The weights of each evaluation indicator using the analytic hierarchy process;

[0128] Formulas (6)-(7) are the methods for calculating objective weights based on the entropy weight method:

[0129]

[0130]

[0131] In the formula: For the first Normalized information entropy of each variable; For grid Medium variables Relative to the proportion of all grids, The total number of grid cells. Normalization factor;

[0132] To eliminate the impact of inconsistent dimensions of different indicators on the analysis results, the multidimensional indicators are normalized so that their values ​​are limited to the range of [0,1].

[0133] At each hourly scale, a weighted attribute matrix is ​​constructed based on the mixed weights, and the weighted attribute matrix is ​​input into the k-means clustering algorithm to perform spatial clustering of the study area, dividing the area into a preset number of environmental capacity categories to characterize different environmental sensitivities and pollutant emission tolerance levels.

[0134] The clustering process is repeated throughout the day's scheduling cycle to obtain an hourly updated spatial partition sequence of environmental capacity, which is used to dynamically reflect the temporal changes in meteorological conditions and environmental status.

[0135] S3: Construct an air pollutant diffusion model: Based on the dynamic zoning results of the environmental capacity, construct an air pollutant diffusion model to describe the impact of pollution source emissions on the air quality of each grid unit;

[0136] Formula (8) is the Gaussian plume model for the diffusion of atmospheric pollutants:

[0137]

[0138] In the formula: Represents the effective emission height of pollution sources;

[0139] Equation (9) is an atmospheric pollutant concentration decay model that considers both dry and wet deposition removal mechanisms:

[0140]

[0141] By combining the atmospheric pollutant attenuation model with the Gaussian plume model, an atmospheric pollutant diffusion model is obtained, as shown in equation (10):

[0142]

[0143] S4: Construct an optimized scheduling model for urban integrated energy systems aimed at improving air quality: Based on the air pollutant diffusion model and the results of dynamic environmental capacity zoning, construct an optimized scheduling model for urban integrated energy systems aimed at improving air quality. This model is used to achieve comprehensive optimization of energy system operating costs and pollutant emission impacts while meeting the safety operation constraints of electricity, heat, and natural gas systems.

[0144] The cost calculation model for the urban integrated energy system includes operating costs and pollutant fines. Formula (11) represents the operating costs:

[0145]

[0146] In the formula: A collection of energy production equipment; A collection of energy conversion devices; For equipment At any moment fuel costs; For equipment At any moment Operating costs;

[0147] Formula (12) represents the cost of pollutant fines:

[0148]

[0149] In the formula: For partitioned sets; For partitioning The 24-hour average concentration of pollutants in the medium; For partitioning The corresponding pollutant concentration emission threshold; For category Additional fines for pollutant emissions exceeding the corresponding threshold;

[0150] Formula (13) for calculating the 24-hour average pollutant concentration for each category:

[0151]

[0152] In the formula: A set of scheduling times; A collection of grids; For grid In time The concentration of pollutants below;

[0153] Formula (14) is the active power balance model for the bus:

[0154]

[0155] In the formula: Indicates the connection with the busbar The set of directly connected adjacent busbars; Indicates connecting busbar and The susceptance of the branch circuit; It is a busbar Voltage phase angle at the point;

[0156] Formula (15) is the branch DC power flow model:

[0157]

[0158] In the formula: This is the phase angle difference; For branch circuit reactance;

[0159] Formula (16) represents the active power transmission constraint of the branch:

[0160]

[0161] In the formula: and They represent branches respectively Minimum and maximum permissible power transfer;

[0162] Formula (17) is the coupled thermal system model of the urban integrated energy system:

[0163]

[0164] In the formula: This is the association matrix from nodes to pipelines; Let this be the water flow vector in each pipe; The input vector for each node; This is the pressure drop vector; This is a diagonal matrix of hydraulic resistance coefficients; The correlation matrix for loop branches;

[0165] Formula (18) is the method for calculating the heat energy transported at each node:

[0166]

[0167] In the formula: The density of water; This is the specific heat capacity of water; and These are the input and output temperatures at the node, respectively. For the heat power delivered;

[0168] Formula (19) is the method for calculating the pipe outlet temperature after considering heat loss:

[0169]

[0170] In the formula: The temperature at the end of the pipe; The total heat transfer coefficient of the pipe insulation layer; The length of the pipe section. Ambient temperature;

[0171] Equation (20) is the thermal energy conservation model for the mixing nodes of a thermodynamic system:

[0172]

[0173] In the formula: Outflow; The outlet mixing temperature; For inflow quality; Inlet temperature;

[0174] Equation (21) is the Weymouth model describing the volumetric flow rate of natural gas pipelines:

[0175]

[0176] In the formula: For pipelines The Weymouth coefficient; and They are nodes and Pressure at the location; Pipeline section The inner diameter; The length of the pipe; and For standard reference temperature and pressure; and The average temperature and drag coefficient;

[0177] Equation (22) is the nodal gas balance equation for the natural gas network:

[0178]

[0179] In the formula: This is the association matrix from nodes to pipelines; This is the vector representing the gas flow rate in the pipe; The gas demand vector for each node;

[0180] Formula (23) represents the constraints for energy conversion equipment:

[0181]

[0182] In the formula: and respectively equipment At any moment Input energy and output energy; It is equipment Energy conversion efficiency;

[0183] Formula (24) represents the equipment output constraint:

[0184]

[0185] In the formula: For equipment At any moment ; output power; and respectively equipment At any moment The minimum and maximum allowable output power;

[0186] Formula (25) is the constraint on the equipment ramp rate;

[0187]

[0188] In the formula: For equipment In time The output; and These are the equipment The minimum and maximum climbing rates;

[0189] Formula (26) is the energy state evolution model for energy storage devices:

[0190]

[0191] In the formula: and These are the equipment At any moment and time Stored energy; and They are time points The charging power and discharging power; and These are charging efficiency and discharging efficiency, respectively. It is a binary variable that indicates the storage mode;

[0192] Formula (27) is the energy capacity constraint model for energy storage devices:

[0193]

[0194] In the formula: and These are the minimum and maximum storage capacities, respectively.

[0195] Formula (28) is the power constraint model for the charging and discharging of energy storage devices:

[0196]

[0197] In the formula: and These are the rated charging power and the rated discharging power, respectively.

[0198] Example 2

[0199] This embodiment provides a comprehensive energy system dispatching system that considers pollutant diffusion and dynamic changes in environmental capacity, including:

[0200] The system comprises a data acquisition and gridding module, an environmental capacity dynamic clustering module, a pollutant diffusion model construction module, and an integrated energy system optimization scheduling module. These modules interact sequentially to collaboratively achieve dynamic optimization scheduling of the integrated energy system. This system is used to implement the integrated energy system scheduling method that considers the dynamic changes in pollutant diffusion and environmental capacity, as described in Example 1.

[0201] The data acquisition and gridding processing module is used to collect multi-source data related to environmental capacity and energy system operation within the study area, perform unified spatial reference and time scale processing on the multi-source data, and map the multi-source data to grid cells with unified spatial resolution through spatial interpolation and gridding methods to form a gridded attribute dataset that is updated hourly; wherein, the multi-source data includes meteorological data that changes hourly and static spatial data that remains unchanged within the scheduling cycle;

[0202] The dynamic clustering module for environmental capacity is used to assign comprehensive weights to environmental-related indicators of each grid unit based on the gridded attribute dataset, using hours as the time scale, and employing a hybrid weighting method that integrates subjective and objective weights. This constructs a weighted attribute matrix, which is then input into the k-means clustering algorithm to perform spatial clustering of the study area to obtain environmental capacity zoning results. By repeatedly executing the clustering process hourly, a dynamic zoning sequence reflecting the spatiotemporal differences in environmental carrying capacity is generated. The subjective weights are determined based on the analytic hierarchy process (AHP), and the objective weights are determined based on the information entropy method.

[0203] The pollutant diffusion model construction module is used to construct an air pollutant diffusion model based on the environmental capacity dynamic zoning results. The air pollutant diffusion model includes a Gaussian plume model and a decay model that characterizes the effects of dry and wet deposition of pollutants. The two are combined to calculate the pollutant concentration distribution at different times and spatial locations.

[0204] The integrated energy system optimization scheduling module is used to construct an optimized scheduling model for improving air quality based on the air pollutant diffusion model and the dynamic zoning results of environmental capacity. The optimized scheduling model integrates the urban integrated energy system cost calculation model, the electric-heat-gas multi-energy system coupling model, and the system operation constraint model. Under the premise of meeting the safety operation constraints of the electric, heat, and natural gas systems, it realizes the comprehensive optimization of energy system operating costs and pollutant emission impacts, and outputs a scheduling scheme.

[0205] Example 3

[0206] In this embodiment, the total emission control strategy and the emission control strategy based on dynamic zoning of environmental capacity were compared and evaluated to verify the impact of the method of the present invention on the operation of urban integrated energy system and the improvement of air quality under different meteorological conditions.

[0207] like Figure 3 As shown, the power output of pollutant sources and the power balance of the electrical, thermal, and gas subsystems were analyzed under two emission control strategies. Wind direction, as a key influencing factor on pollutant diffusion, plays an important role in the real-time operation and regulation of the system. When adopting the emission control strategy based on dynamic zoning, the energy output of high-emission units exhibits stronger responsiveness, especially those located near environmentally sensitive areas, whose output can be dynamically adjusted according to changes in environmental capacity.

[0208] During the second to fifth hour of the scheduling cycle, influenced by southeasterly winds, pollutant dispersion is mainly directed towards the northwest area of ​​the city. Since the dispersion plume is mainly distributed in the Class 3 to Class 5 areas with higher environmental margins, the dynamic emission control strategy allows relevant emission units to maintain a high output level while meeting environmental constraints.

[0209] Between the 6th and 19th hours, the prevailing wind shifted to the southwest, and pollutants began to disperse towards the northeast of the city. To prevent pollutant concentrations from exceeding standards in environmentally sensitive areas, the dynamic emission control strategy imposed stricter restrictions on the output of relevant emission units compared to the overall emission control strategy.

[0210] like Figure 4As shown, a comparative analysis of the spatial distribution of pollutant concentrations under two emission control strategies at 18 hours was conducted. The results indicate that under southwesterly wind conditions, pollutants mainly diffuse to areas with low environmental carrying capacity (Category 1 and Category 2). The emission control strategy based on dynamic zoning effectively suppressed the increase in pollutant concentrations in local areas by reducing the emission levels of corresponding pollution sources. Simultaneously, some pollutant emissions were guided to areas with higher environmental carrying capacity, achieving a rational spatial redistribution of pollutant loads.

[0211] In summary, this embodiment demonstrates that the emission control strategy based on dynamic zoning of environmental capacity proposed in this invention can effectively improve air quality in key sensitive areas while ensuring the safe operation of the urban integrated energy system. It also enhances the system's adaptability to regional environmental heterogeneity and meteorological changes, showcasing the significant advantages of introducing spatially defined environmental constraints into the operational decision-making of the integrated energy system.

Claims

1. A comprehensive energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity, characterized in that, Includes the following steps: S1: Constructing a multi-source geographic information gridded database: Collect multi-source data related to environmental capacity and energy system operation within the study area, and perform unified spatial reference and temporal scale processing on the multi-source data to construct a multi-source geographic information gridded database; through spatial interpolation and grid division methods, map the multi-source data to grid cells with unified spatial resolution to form a gridded attribute dataset that is updated hourly; wherein, the multi-source data includes hourly changing meteorological data and static spatial data that remain unchanged within the scheduling cycle, the meteorological data includes temperature, humidity, wind speed, air pressure and solar radiation parameters, and the static spatial data includes topographic elevation, population density and GDP; S2: Dynamic clustering of environmental capacity based on hybrid weights: Based on the multi-source geographic information gridded database, dynamic clustering analysis is performed on environmental-related indicators of each grid unit within the study area, using hours as the time scale, to construct environmental capacity zoning results that change over time; by executing the clustering process hourly, a dynamic zoning sequence of environmental capacity reflecting the spatiotemporal differences in environmental carrying capacity is obtained; wherein, the dynamic clustering analysis adopts a hybrid weighting method that integrates subjective and objective weights, assigning comprehensive weights to the multidimensional indicators participating in the clustering; the subjective weights are determined based on the analytic hierarchy process, and the objective weights are determined based on the information entropy method; S3: Construct an air pollutant diffusion model: Based on the dynamic zoning results of the environmental capacity, construct an air pollutant diffusion model to describe the impact of pollution source emissions on the air quality of each grid unit; wherein, the air pollutant diffusion model includes a Gaussian plume model and a decay model to characterize the effects of dry and wet deposition of pollutants, and is used to calculate the pollutant concentration distribution at different times and spatial locations; S4: Constructing an Optimized Scheduling Model for Urban Integrated Energy Systems to Improve Air Quality: Based on the air pollutant diffusion model and the dynamic zoning results of environmental capacity, an optimized scheduling model for urban integrated energy systems to improve air quality is constructed. This model aims to comprehensively optimize the operating costs of energy systems and the impact of pollutant emissions while meeting the safety constraints of the power, heat, and natural gas systems. The optimized scheduling model includes an urban integrated energy system cost calculation model, an electricity-heat-gas multi-energy system coupling model, and a system operation constraint model. The urban integrated energy system cost calculation model calculates the comprehensive costs of energy production, energy conversion, and energy storage operation within the scheduling cycle. It also introduces a pollutant environmental penalty model based on the dynamic zoning results of environmental capacity to quantify the costs of pollutant emissions exceeding the corresponding spatiotemporal environmental capacity threshold. The electricity-heat-gas multi-energy system coupling model describes the energy conversion and transmission relationships between the power system, heat system, and natural gas system. The system operation constraint model includes energy supply and demand balance constraints, equipment output constraints, energy conversion efficiency constraints, equipment ramp-up constraints, and energy storage equipment operation constraints.

2. The integrated energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity according to claim 1, characterized in that, In step S1, the constructed multi-source geographic information gridded database includes: performing ordinary kriging interpolation on the multi-source geographic information data, as shown in equations (1)-(2); In the formula: The predicted value for the target location; For the first Observations at each sample location; This represents the total number of observation points. for Observations at the location; For the first The point and the first Covariance between points; For the first Points and target points Covariance between them; For Lagrange multipliers associated with unbiasedness constraints; The study area was then divided into grid cells according to the preset spatial resolution to ensure spatial consistency of various types of data. The processed meteorological data and static spatial data are mapped to each grid cell to form a gridded attribute dataset that is updated hourly.

3. The integrated energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity according to claim 2, characterized in that, In step S2, the hybrid weight calculation formula based on the analytic hierarchy process and the entropy weight method is shown in equation (3): In the formula: Weights for the analytic hierarchy process (AHP); For information entropy weights; These are the weighting coefficients; The method for establishing the judgment matrix in the analytic hierarchy process is shown in equation (4): In the formula: As an indicator relative to indicators The importance of; The total number of evaluation indicators; The formula for calculating subjective weights based on the analytic hierarchy process is shown in equation (5): In the formula: For the first The weights of each evaluation indicator using the analytic hierarchy process; The formulas for calculating the objective weights based on the entropy weight method are shown in equations (6)-(7): In the formula: For the first Normalized information entropy of each variable; For grid Medium variables Relative to the proportion of all grids, The total number of grid cells. Normalization factor; To eliminate the impact of inconsistent dimensions of different indicators on the analysis results, the multidimensional indicators are normalized so that their values ​​are limited to the range of [0,1]. At each hourly scale, a weighted attribute matrix is ​​constructed based on the mixed weights, and the weighted attribute matrix is ​​input into the k-means clustering algorithm to perform spatial clustering of the study area, dividing the area into a preset number of environmental capacity categories to characterize different environmental sensitivities and pollutant emission tolerance levels. The clustering process is repeated throughout the day's scheduling cycle to obtain an hourly updated spatial partition sequence of environmental capacity, which is used to dynamically reflect the temporal changes in meteorological conditions and environmental status.

4. The integrated energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity according to claim 1, characterized in that, In step S3, the Gaussian plume model for the diffusion of air pollutants is shown in equation (8): In the formula: Represents the effective emission height of pollution sources; The air pollutant concentration decay model considering the dry and wet deposition removal mechanisms is shown in equation (9): By combining the air pollutant attenuation model with the Gaussian plume model, an air pollutant diffusion model is obtained, as shown in equation (10):

5. The integrated energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity according to claim 1, characterized in that, In step S4, the cost calculation model for the urban integrated energy system includes operating costs and pollutant fines. The operating costs are shown in equation (11): In the formula: A collection of energy production equipment; A collection of energy conversion devices; For equipment At any moment fuel costs; For equipment At any moment Operating costs; The cost of pollutant fines is shown in equation (12): In the formula: For partitioned sets; For partitioning The 24-hour average concentration of pollutants in the medium; For partitioning The corresponding pollutant concentration emission threshold; For category Additional fines for pollutant emissions exceeding the corresponding threshold; The 24-hour average pollutant concentration for each category is calculated using Equation (13): In the formula: A set of scheduling times; A collection of grids; For grid In time The concentration of pollutants below; The active power balance model of the bus is shown in equation (14): In the formula: Indicates the connection with the busbar The set of directly connected adjacent busbars; Indicates connecting busbar and The susceptance of the branch circuit; It is a busbar Voltage phase angle at the point; The branch DC power flow model is shown in equation (15): In the formula: This is the phase angle difference; For branch circuit reactance; The active power transmission constraint of the branch is shown in equation (16): In the formula: and They represent branches respectively Minimum and maximum permissible power transfer; The coupled thermal system model of the urban integrated energy system is shown in equation (17): In the formula: This is the association matrix from nodes to pipelines; Let this be the water flow vector in each pipe; The input vector for each node; This is the pressure drop vector; This is a diagonal matrix of hydraulic resistance coefficients; The correlation matrix for loop branches; The calculation method for the heat energy transported at each node is shown in equation (18): In the formula: The density of water; This is the specific heat capacity of water; and These are the input and output temperatures at the node, respectively. For the heat power delivered; The calculation method for the pipe outlet temperature after considering heat loss is shown in equation (19): In the formula: The temperature at the end of the pipe; The total heat transfer coefficient of the pipe insulation layer; The length of the pipe section. Ambient temperature; The thermal energy conservation model of the mixing node of the thermodynamic system is shown in equation (20): In the formula: Outflow; The outlet mixing temperature; For inflow quality; Inlet temperature; The Weymouth model describing the volumetric flow rate of natural gas pipelines is shown in equation (21): In the formula: For pipelines The Weymouth coefficient; , They are nodes and Pressure at the location; Pipeline section The inner diameter; The length of the pipe; , For standard reference temperature and pressure; , These are the average temperature and the drag coefficient, respectively. The nodal gas balance equations for the natural gas network are shown in equation (22): In the formula: This is the association matrix from nodes to pipelines; This is the vector representing the gas flow rate in the pipe; The gas demand vector for each node; The constraints of the energy conversion equipment are shown in equation (23): In the formula: , respectively equipment At any moment Input energy and output energy; It is equipment Energy conversion efficiency; The output constraint of the equipment is shown in equation (24): In the formula: For equipment At any moment ; output power; , respectively equipment At any moment The minimum and maximum allowable output power; The equipment ramp rate constraint is shown in equation (25): In the formula: For equipment In time The output; , These are the equipment The minimum and maximum climbing rates; The energy state evolution model of the energy storage device is shown in equation (26): In the formula: and These are the equipment At any moment and time Stored energy; , They are time points The charging power and discharging power; , These are charging efficiency and discharging efficiency, respectively. It is a binary variable that indicates the storage mode; The energy capacity constraint model for energy storage devices is shown in equation (27): In the formula: , These are the minimum and maximum storage capacities, respectively. The power constraint model for charging and discharging of energy storage devices is shown in equation (28): In the formula: , These are the rated charging power and the rated discharging power, respectively.

6. A comprehensive energy system dispatching system considering the dynamic changes in pollutant diffusion and environmental capacity, characterized in that, include: The system comprises a data acquisition and gridding processing module, an environmental capacity dynamic clustering module, a pollutant diffusion model construction module, and an integrated energy system optimization scheduling module. These modules interact sequentially to collaboratively achieve dynamic optimization scheduling of the integrated energy system. This system is used to implement the integrated energy system scheduling method that considers the dynamic changes in pollutant diffusion and environmental capacity as described in any one of claims 1-5. The data acquisition and gridding processing module is used to collect multi-source data related to environmental capacity and energy system operation within the study area, perform unified spatial reference and time scale processing on the multi-source data, and map the multi-source data to grid cells with unified spatial resolution through spatial interpolation and gridding methods to form a gridded attribute dataset that is updated hourly; wherein, the multi-source data includes meteorological data that changes hourly and static spatial data that remains unchanged within the scheduling cycle; The dynamic clustering module for environmental capacity is used to assign comprehensive weights to environmental-related indicators of each grid unit based on the gridded attribute dataset, using hours as the time scale, and employing a hybrid weighting method that integrates subjective and objective weights. This constructs a weighted attribute matrix, which is then input into the k-means clustering algorithm to perform spatial clustering of the study area to obtain environmental capacity zoning results. By repeatedly executing the clustering process hourly, a dynamic zoning sequence reflecting the spatiotemporal differences in environmental carrying capacity is generated. The subjective weights are determined based on the analytic hierarchy process (AHP), and the objective weights are determined based on the information entropy method. The pollutant diffusion model construction module is used to construct an air pollutant diffusion model based on the environmental capacity dynamic zoning results. The air pollutant diffusion model includes a Gaussian plume model and a decay model that characterizes the effects of dry and wet deposition of pollutants. The two are combined to calculate the pollutant concentration distribution at different times and spatial locations. The integrated energy system optimization scheduling module is used to construct an optimized scheduling model for improving air quality based on the air pollutant diffusion model and the dynamic zoning results of environmental capacity. The optimized scheduling model integrates the urban integrated energy system cost calculation model, the electric-heat-gas multi-energy system coupling model, and the system operation constraint model. Under the premise of meeting the safety operation constraints of the electric, heat, and natural gas systems, it realizes the comprehensive optimization of energy system operating costs and pollutant emission impacts, and outputs a scheduling scheme.

7. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the integrated energy system scheduling method considering pollutant diffusion and dynamic changes in environmental capacity as described in any one of claims 1-5.

8. A computer program product, characterized in that: When the computer program / instruction is executed by the processor, it implements the integrated energy system scheduling method as described in any one of claims 1-5, which takes into account the dynamic changes in pollutant diffusion and environmental capacity.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by the processor, the program is used to implement the integrated energy system scheduling method as described in any one of claims 1-5, which takes into account the dynamic changes in pollutant diffusion and environmental capacity.

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