CN-122022293-A - Comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change

CN122022293ACN 122022293 ACN122022293 ACN 122022293ACN-122022293-A

Abstract

The invention discloses a comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change, belongs to the field of power system environmental economy scheduling, and solves the problems that in the existing comprehensive energy system scheduling method, environmental constraint expression is coarse, pollutant diffusion characteristics and environmental capacity space-time difference are difficult to reflect; the method can adaptively adjust the operation strategy of the energy system according to the weather conditions and the dynamic changes of the environmental bearing capacity, effectively improve the air quality of an environmental sensitive area while ensuring the safe and stable operation of the comprehensive energy system, and improve the space configuration rationality of pollutant emission and the overall operation economy of the system, thereby having good application prospect.

Inventors

XU YING
XIE YILIN
YI ZHONGKAI
TU ZHENGHONG
RONG SHUANG
ZHAO HAOTIAN

Assignees

哈尔滨工业大学
国网黑龙江省电力有限公司
清华大学

Dates

Publication Date: 20260512
Application Date: 20260119

Claims (9)

1. The comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change is characterized by comprising the following steps: S1, constructing a multisource geographic information gridding database, namely collecting multisource data related to environment capacity and energy system operation in a research area, and performing unified space reference and time scale processing on the multisource data to construct the multisource geographic information gridding database; mapping the multi-source data into grid cells with uniform spatial resolution through a spatial interpolation and grid division method to form a grid attribute data set updated time by time, wherein the multi-source data comprises weather data which changes in an hour and static space data which is kept unchanged in a scheduling period, the weather data comprises temperature, humidity, wind speed, air pressure and solar radiation parameters, and the static space data comprises topography elevation, population density and domestic production total value; S2, carrying out dynamic clustering on the environment capacity based on mixed weights, namely carrying out dynamic clustering analysis on environment related indexes of grid units in a research area by taking hours as time scales on the basis of the multisource geographic information grid database, constructing an environment capacity partitioning result which changes with time, and carrying out a clustering process every hour to obtain an environment capacity dynamic partitioning sequence reflecting space-time difference of the environment bearing capacity, wherein the dynamic clustering analysis adopts a mixed weighting method integrating subjective weights and objective weights to give comprehensive weights to multidimensional indexes participating in clustering, the subjective weights are determined on the basis of a hierarchical analysis method, and the objective weights are determined on the basis of an information entropy method; s3, constructing an air pollutant diffusion model, namely constructing an air pollutant diffusion model for describing the influence of pollutant source emission on the air quality of each grid unit based on the environmental capacity dynamic partition result, wherein the air pollutant diffusion model comprises a Gaussian plume model and an attenuation model for describing the influence of dry sedimentation and wet sedimentation of pollutants, and the air pollutant diffusion model is used for calculating the pollutant concentration distribution at different time and space positions; And S4, constructing an urban comprehensive energy system optimization scheduling model oriented to air quality improvement, wherein the urban comprehensive energy system optimization scheduling model comprises an urban comprehensive energy system cost calculation model, an electric-thermal-gas multi-energy system coupling model and a system operation constraint model, the urban comprehensive energy system cost calculation model is used for calculating comprehensive cost of energy production, energy conversion and energy storage operation in a scheduling period, a pollutant environmental fine model based on an environmental capacity dynamic partitioning result is introduced, cost quantization is carried out on pollutant emission exceeding a corresponding space-time environmental capacity threshold, and the electric-thermal-gas multi-energy system coupling model is used for describing energy conversion and transmission relations among an electric power system, a thermodynamic system and a natural gas system.
2. The comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change according to claim 1, wherein in step S1, the constructed multisource geographic information gridding database comprises performing common kriging interpolation processing on the multisource geographic information data, as shown in formulas (1) - (2); wherein: A predicted value for the target location; is the first Observations of individual sample locations; The total number of observation points; Is that Observations at the location; is the first Points and the first Covariance between individual points; is the first Individual points and target points Covariance between; Is a lagrangian multiplier associated with unbiased constraints; dividing a research area into grid cells according to a preset spatial resolution, so that various data have consistency in space; And mapping the processed meteorological data and static space data into each grid cell to form a grid attribute data set updated according to the hours.
3. The comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change according to claim 2, wherein in step S2, a mixed weight calculation formula based on a hierarchical analysis method and an entropy weight method is shown as formula (3): wherein: Is the analytic hierarchy process weight; the information entropy weight; Is a weight coefficient; the method for establishing the judgment matrix in the analytic hierarchy process is shown in the formula (4): wherein: Is taken as an index Relative to the index Is of importance of (2); The total number of the evaluation indexes is calculated; The calculation formula of subjective weight based on analytic hierarchy process is shown in formula (5): wherein: is the first The analytic hierarchy process weights of the individual evaluation indexes; The calculation formulas of the objective weights based on the entropy weight method are shown in formulas (6) - (7): wherein: is the first Normalized information entropy of each variable; is a grid Medium variable With respect to the proportion of all the grids, As a total number of grids, Is a normalization factor; In order to eliminate the influence of different index dimension inconsistencies on an analysis result, carrying out normalization processing on the multidimensional index to limit the value of the multidimensional index to the interval [0,1 ]; Under each hour scale, constructing a weighted attribute matrix based on the mixed weight, inputting the weighted attribute matrix into a k-means clustering algorithm, performing spatial clustering on a researched area, and dividing the area into a preset number of environment capacity categories so as to represent different environment sensitivities and pollutant emission tolerance degrees; And repeatedly executing the clustering process in the whole-day scheduling period to obtain an environment capacity space partition sequence updated hour by hour, wherein the space partition sequence is used for dynamically reflecting time sequence changes of meteorological conditions and environment states.
4. The comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change according to claim 1, wherein in step S3, a gaussian plume model of air pollutant diffusion is shown as formula (8): wherein: Representing the effective emission height of the pollution source; The air pollutant concentration decay model taking the air pollutant dry sedimentation and wet sedimentation removal mechanism into consideration is shown in the formula (9): combining the air pollutant attenuation model with the Gaussian plume model to obtain an air pollutant diffusion model, wherein the air pollutant diffusion model is shown as a formula (10):
5. The integrated energy system scheduling method considering pollutant diffusion and dynamic change of environmental capacity according to claim 1, wherein in step S4, the city integrated energy system cost calculation model includes an operation cost and a pollutant penalty cost, and the operation cost is represented by formula (11): wherein: is a collection of energy production devices; is a collection of energy conversion devices; is a device At the moment of time Is a fuel cost of (a); is a device At the moment of time Is not limited by the operating cost of (a); The penalty cost of the pollutant is shown in formula (12): wherein: Is a set of partitions; For partitioning Average concentration of medium contaminants for 24 hours; For partitioning A corresponding pollutant concentration emission threshold; Is of the category Additional emission fines for contaminants exceeding the corresponding threshold; the 24 hour average contaminant concentration calculation for each category is shown in formula (13): wherein: Is a set of scheduling times; Is a collection of grids; is a grid At the time of The concentration of contaminants below; The bus active power balance model is shown as (14): wherein: Representation and bus bar A collection of directly connected adjacent bus bars; indicating connection bus And Susceptances of the branches of (2); Is a bus bar A voltage phase angle at; the branch direct current power flow model is shown as (15): wherein: is the phase angle difference; is the branch reactance; The branch active power transfer constraints are shown in equation (16): wherein: And Respectively represent through branches Minimum and maximum allowed power transmission of (a); the thermodynamic system coupling model in the urban comprehensive energy system is shown as a formula (17): wherein: an association matrix for node-to-pipe; a water flow vector in each pipeline; an input vector for each node; is a pressure drop vector; Is a diagonal matrix of hydraulic resistance coefficients; The loop branch incidence matrix is adopted; The calculation method of the heat energy transmitted by each node is shown as a formula (18): wherein: Is the density of water; is the specific heat capacity of water; And Input and output temperatures at the nodes, respectively; for the thermal power delivered; the method for calculating the outlet temperature of the pipeline after considering the heat loss is shown in the formula (19): wherein: Temperature at the end of the pipe; The total heat transfer coefficient of the insulating layer of the pipeline; For the length of the pipe section, Is ambient temperature; The thermal energy conservation model of the thermodynamic system mixing node is shown in formula (20): wherein: is the outflow flow; mixing temperature for outlet; is inflow mass; is the inlet temperature; The Weymouth model describing the volumetric flow of the natural gas pipeline is shown in formula (21): wherein: Is a pipeline Weymouth coefficients of (a); 、 Respectively nodes And Pressure at; Is a pipeline section Is a diameter of (2); is the length of the pipeline; 、 is a standard reference temperature and pressure; 、 average temperature and drag coefficient, respectively; The node gas balance equation of the natural gas network is shown in formula (22): wherein: an association matrix for node-to-pipe; is a vector of the gas flow in the pipeline; a gas demand vector for each node; the energy conversion device constraint is as shown in formula (23): wherein: 、 Respectively devices At the moment of time Is a combination of the input energy and the output energy of the power supply; Is an apparatus Energy conversion efficiency of (a); the device force constraint is as shown in equation (24): wherein: is a device At the moment of time Output power of (2); 、 Respectively devices At the moment of time Minimum and maximum allowable output power of (a); The equipment climbing rate constraint is as shown in formula (25): wherein: is a device At the time of An output of (2); 、 Devices respectively Minimum and maximum ramp rates of (a); The energy state evolution model of the energy storage device is shown as (26): wherein: And Devices respectively At the moment of time And time of day Stored energy; 、 Respectively the time of day Charging power and discharging power of (a); 、 Charging efficiency and discharging efficiency, respectively; is a binary variable indicating a storage mode; The energy storage device energy capacity constraint model is shown as (27): wherein: 、 minimum and maximum storage capacity, respectively; The energy storage device charge and discharge power constraint model is shown as (28): wherein: 、 the rated charge power and the rated discharge power, respectively.
6. An integrated energy system scheduling system that accounts for contaminant diffusion and dynamic changes in environmental capacity, comprising: The comprehensive energy system scheduling method is used for realizing the comprehensive energy system scheduling method which considers the pollutant diffusion and the dynamic change of the environmental capacity according to any one of claims 1-5; The data acquisition and gridding processing module is used for collecting multi-source data related to environment capacity and energy system operation in a research area, carrying out unified space reference and time scale processing on the multi-source data, and mapping the multi-source data into grid cells with unified space resolution through a spatial interpolation and gridding dividing method to form a gridding attribute data set updated time by time, wherein the multi-source data comprises meteorological data changing according to hours and static space data which keeps unchanged in a scheduling period; The environment capacity dynamic clustering module is used for assigning comprehensive weights to environment related indexes of each grid unit by adopting a mixed weighting method of fusing subjective weights and objective weights based on the grid attribute dataset and taking hours as a time scale, constructing a weighted attribute matrix, inputting a k-means clustering algorithm, and carrying out spatial clustering on a research area to obtain an environment capacity partitioning result; The pollutant diffusion model construction module is used for constructing an air pollutant diffusion model based on the environmental capacity dynamic partitioning result, wherein the air pollutant diffusion model comprises a Gaussian smoke plume model and an attenuation model for describing the influence of dry sedimentation and wet sedimentation of pollutants, and pollutant concentration distribution at different time and space positions is calculated through the combination of the Gaussian smoke plume model and the attenuation model; The comprehensive energy system optimizing and scheduling module is used for constructing an optimizing and scheduling model for improving air quality on the basis of the air pollutant diffusion model and the environmental capacity dynamic partitioning result, integrating a city comprehensive energy system cost calculation model, an electric-thermal-gas multi-energy system coupling model and a system operation constraint model, realizing comprehensive optimization of energy system operation cost and pollutant emission influence on the premise of meeting the electric power, thermal power and natural gas system safe operation constraint, and outputting a scheduling scheme.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the integrated energy system scheduling method of any one of claims 1-5 that takes into account contaminant diffusion and dynamic changes in environmental capacity.
8. A computer program product, characterized in that the computer program/instructions, when executed by a processor, implements the integrated energy system scheduling method according to any one of claims 1-5, taking into account contaminant diffusion and dynamic changes in environmental capacity.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, is adapted to implement the integrated energy system scheduling method according to any one of claims 1-5, which takes into account contaminant diffusion and dynamic changes in environmental capacity.

Description

Comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change Technical Field The invention belongs to the field of environmental economic dispatching of power systems, and particularly relates to a comprehensive energy system dispatching method considering pollutant diffusion and environmental capacity dynamic change. Background Urban energy consumption and pollutant emission centralized areas are continuously increased, various energy demands such as electric power, heat supply and fuel gas are continuously increased, fossil energy still occupies an important position in urban comprehensive energy systems, carbon dioxide and air pollutants such as PM2.5 and SO 2、NOx are inevitably generated in the energy production and conversion process, and adverse effects are caused on urban air quality and public health. In order to balance economic operation and environmental protection targets, the existing comprehensive energy system scheduling research mostly adopts a method of total emission control or emission cost constraint, and limits the system operation by setting a unified upper limit on pollutant or carbon emission. The method is simple to implement, can ensure the overall emission compliance, but usually ignores the obvious difference of urban environments in space based on static assumption, and easily causes local pollution accumulation or over-conservation of operation constraint. In order to improve the refinement level of environmental constraint, a pollutant diffusion model is introduced in part of research, and the spatial transmission and concentration distribution of pollutants are described by considering meteorological factors such as wind speed, wind direction and the like, so that emission control based on spatial concentration is realized. However, the related researches in the prior art mainly focus on the aspect of an electric power system, and the applicability of the urban comprehensive energy system with high coupling of multi-energy subsystems such as electricity, heat, gas and the like is still limited. On the other hand, the environmental conditions themselves also constrain the energy system to operate by environmental capacity. Urban areas have significant spatial heterogeneity in population density, land utilization, economic activity, meteorological conditions, and the like, and the tolerance of different areas to pollutant emissions varies significantly. The existing research mostly shows the environment capacity through presetting a fixed area threshold value or expert experience, and the method has certain practicability, but the dynamic change of wind fields and meteorological conditions on an hour scale is difficult to reflect, and the scheduling decision may be insufficient for protecting the environment in part of time periods. In view of the foregoing, the existing comprehensive energy system scheduling method still has a defect in consideration of environmental impact, and there is a need for a comprehensive energy system scheduling method capable of simultaneously considering the pollutant diffusion characteristic and the dynamic change of the environmental capacity, so as to realize the collaborative optimization of the efficient operation and the air quality improvement of the urban energy system. Disclosure of Invention Based on the defects, the invention provides a comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change, which aims to solve the problems that the environment constraint expression is rough and the pollutant diffusion characteristic and the environmental capacity space-time difference are difficult to reflect in the existing comprehensive energy system scheduling method. The technical scheme adopted by the invention is that the comprehensive energy system scheduling method considering pollutant diffusion and environmental capacity dynamic change comprises the following steps: S1, constructing a multisource geographic information gridding database, namely collecting multisource data related to environment capacity and energy system operation in a research area, and performing unified space reference and time scale processing on the multisource data to construct the multisource geographic information gridding database; mapping the multi-source data into grid cells with uniform spatial resolution through a spatial interpolation and grid division method to form a grid attribute data set updated time by time, wherein the multi-source data comprises weather data which changes in an hour and static space data which is kept unchanged in a scheduling period, the weather data comprises temperature, humidity, wind speed, air pressure and solar radiation parameters, and the static space data comprises topography elevation, population density and domestic production total value; S2, carrying out dynamic clustering on the environment capacity ba