Multifunctional heat storage equipment planning and configuration method considering dynamic environment capacity constraint of comprehensive energy system

Abstract

The invention discloses a multifunctional heat storage equipment planning and configuration method considering dynamic environment capacity constraint of a comprehensive energy system, and belongs to the field of power system planning. Aiming at the defects of an existing method in the aspects of heat storage equipment modeling, pollution diffusion analysis, environmental constraint construction and the like, the method realizes energy-environment collaborative planning through the following steps: constructing an active-reactive and multi-regulation capacity coupling model of novel multifunctional heat storage equipment; establishing a pollutant space-time diffusion and multi-energy flow coupling model based on a Gaussian plume model; constructing a multi-element geographic data set and a gridding information matrix; adopting an analytic hierarchy process and clustering to realize environment capacity differential partitioning; and a multi-target planning model is established and solved. According to the method, deep fusion of the energy system and environmental constraints is realized, the space-time pollution control capability is improved, the economical efficiency, the environmental protection property and the renewable energy consumption rate of the system are optimized, and a scientific scheme is provided for comprehensive energy system equipment planning.

Description

Technical Field

[0001] This invention belongs to the field of power system planning, specifically relating to a planning and configuration method for multifunctional thermal storage equipment that considers the dynamic environmental capacity constraints of a comprehensive energy system. Background Technology

[0002] The existing planning and design methods for integrated energy systems have the following shortcomings: (1) Traditional methods do not adequately characterize the flexibility of thermal storage equipment, often simplifying it into a single electro-thermal coupling model, which is difficult to reflect the multi-functional regulation capabilities of current new thermal storage equipment. This development status has not been given sufficient attention, resulting in significant deficiencies in the modeling of existing thermal storage equipment in terms of flexibility exploration and regulation capability description. (2) Traditional methods do not adequately consider air pollutants, mostly focusing on total quantity control, but the concentration and spatial distribution characteristics of pollutants are often simplified. Factors such as regional diffusion paths and spatiotemporal sedimentation differences are often not adequately characterized, making it difficult for pollutant control to truly reflect the actual impact of different regions. (3) Existing integrated energy system planning methods mainly focus on the economics of the power supply side and the security of the power grid. Although some studies consider integrated energy system modeling and multi-energy complementarity, they do not adequately consider the planning of thermal storage devices with multi-functional regulation capabilities. (4) Traditional methods, when constructing environmental constraints for energy systems, only adopt a uniform total carbon emission limit or a single pollutant emission ceiling, treating the entire region as a whole with consistent environmental carrying capacity. They fail to construct differentiated environmental capacity zoning models, making it difficult to effectively integrate environmental constraints into the spatiotemporal coupling optimization of multi-energy systems. (5) Although current research has introduced regional division attempts, most of them use population density or a single socioeconomic indicator as the sole basis for division, ignoring natural factors such as meteorological conditions that have a decisive impact on the ability of pollution diffusion and dilution. This makes it difficult to support the refined modeling and decision optimization of environmental differences in the planning stage of large-scale multi-energy systems. (6) Current research has begun to explore incorporating environmental constraints into power system planning to achieve the green transformation of power systems. However, these methods are difficult to solve the problems of complex coupling between environment and system, strong time variability, and significant spatial heterogeneity in actual systems, and cannot effectively adjust system planning and design.

[0003] In summary, while existing methods have achieved certain results in areas such as thermal energy storage equipment modeling, pollutant diffusion analysis, multi-energy system modeling, environmental constraint construction, regional division, and environmentally driven planning of power systems, current research still suffers from problems such as coarse equipment models, limited applicability of spatiotemporal pollution diffusion models, insufficient description of multi-energy coupling, inadequate characterization of environmental capacity, coarse regional zoning criteria, and insufficient environmental feedback response of planning strategies. These limitations make it difficult to meet the comprehensive needs of integrated energy systems for decarbonization, safety, and sustainable development under the background of high-proportion renewable energy integration. Therefore, it is necessary to construct an energy-environment collaborative planning method for integrated energy systems. This method should introduce differentiated environmental capacity constraints, refine the spatiotemporal diffusion characteristics of pollutants, strengthen multi-energy flow coupling modeling, introduce novel thermal energy storage equipment, and construct an environmentally driven optimization planning mechanism to achieve efficient utilization, clean development, and sustainable evolution of integrated energy systems. Summary of the Invention

[0004] To address the aforementioned issues, this paper proposes a multifunctional thermal storage equipment planning and configuration method that considers the dynamic environmental capacity constraints of integrated energy systems. This method solves the problem of insufficient multi-energy system coupling modeling in integrated energy systems and improves the ability to control the spatiotemporal distribution of air pollution under environmental capacity constraints.

[0005] The technical solution adopted in this invention is as follows: A method for planning and configuring multifunctional thermal storage equipment considering the dynamic environmental capacity constraints of a comprehensive energy system, comprising the following steps:

[0006] S1. Constructing a novel multi-functional thermal storage equipment model: First, establish an active-reactive coupling modeling method for the novel multi-functional thermal storage equipment to characterize its operating boundary and apparent power constraints during grid voltage support and power regulation. Second, construct a multi-type regulation capability coupling model from three dimensions: active power, frequency regulation capacity, and energy state. Through constraint equations, characterize the shared occupation relationship between frequency regulation, standby, and heating services on thermal storage state and power margin, thereby achieving a unified characterization of the multi-type regulation capabilities of the novel thermal storage equipment and sustainable regulation capability constraints.

[0007] S2. Constructing an integrated energy system energy-environment coupling model: First, based on the Pasquill–Gifford diffuse parameterized Gaussian plume model, establish... , A spatiotemporal diffusion emission model for particulate matter pollutants is developed to achieve quantitative correlation between emissions and environmental concentrations and to enable refined gaseous pollution control. Subsequently, by combining the operating characteristics of power systems and heating systems with the operating model of new multi-functional thermal storage equipment, energy constraints such as power balance are characterized, and an energy coupling mechanism for power conversion and heating is introduced to achieve integrated energy system coupling modeling.

[0008] S3. Constructing a Multi-Element Geographic Dataset and a Gridded Geographic Information Matrix: First, collect natural geographic and socioeconomic factors related to air pollutant diffusion and environmental sensitivity in the region where the integrated energy system is located, including precipitation, temperature, wind speed, topography, land use type, population density, and regional GDP, to construct a multi-element geographic dataset. Furthermore, based on GIS, divide the study area into equal-scale grids, mapping the multi-element data to each grid cell to obtain a multi-dimensional geographic information matrix. Perform annual or multi-year average processing on natural factors that change over time to meet medium- and long-term planning needs. Through the aforementioned multi-dimensional information matrix, achieve detailed characterization and information extraction of the geographic environmental elements of the study area.

[0009] S4. A geographic regional zoning model considering differences in environmental carrying capacity is proposed: The importance of natural and socio-economic indicators is compared in pairs using the analytic hierarchy process (AHP), a judgment matrix is ​​constructed, and the weights of each indicator are calculated through eigenvectors to complete the consistency test; the multidimensional geographic information matrix is ​​standardized by Z-score, and the grid cells are clustered using a hierarchical clustering method based on the Ward minimum variance criterion to obtain several environmental carrying capacity type zones. Each type zone has similar environmental carrying capacity and ecological sensitivity, thus realizing differentiated environmental zoning of the integrated energy system region.

[0010] S5. Establish and solve a multi-objective planning model for energy-environment coordination in an integrated energy system: Within a given planning period, with the objectives of minimizing total system cost, minimizing environmental impact, and maximizing renewable energy absorption rate, a multi-objective coordinated planning model is constructed. Decision variables include the planning strategy design for new multi-functional thermal storage equipment in each environmental zone. Combining the results of static environmental zoning, emission limits for different types of zones are embedded into the model in the form of economic penalties, forming a coupled model of integrated energy system and environmental capacity. A multi-objective problem weighted sum method is adopted to solve the model. By setting different weights for the objective function, multiple candidate schemes are provided for the capacity planning and spatial layout of integrated energy system equipment within the planning period.

[0011] Furthermore, in step S1, the novel multifunctional thermal storage equipment model includes active-reactive coupling constraints, which are constructed as shown in formula (1), and the equipment energy power constraints are shown in formulas (2)-(3); for the equipment energy change level, thermal energy output cascade utilization equations and energy dynamic equations are constructed as shown in formulas (4)-(5); the coupling relationship is analyzed, and the corresponding constraint equations are constructed as shown in formulas (6)-(10).

[0012] Formula (1) is the active-reactive coupling constraint equation for multifunctional thermal storage equipment;

[0013] Formulas (2)-(3) are the upper and lower limit constraint equations for the energy power of multifunctional thermal storage equipment;

[0014] Formula (4) is the equation for the cascade utilization of thermal energy output from multifunctional thermal storage equipment;

[0015] Formula (5) is the dynamic update equation for the energy state of multifunctional thermal storage equipment;

[0016] Formulas (6) to (8) are the constraint equations for the available range of frequency regulation capacity of multifunctional thermal storage equipment;

[0017] Formulas (9)-(10) are the constraint equations for the sustainable development of the regulating capacity of multifunctional thermal storage equipment based on energy margin;

[0018] (1)

[0019] (2)

[0020] (3)

[0021] (4)

[0022] (5)

[0023] (6)

[0024] (7)

[0025] (8)

[0026] (9)

[0027] (10)

[0028] In the formula: , These represent the charging and discharging power of the i-th thermal storage device at time t; , , , These represent the power output and utilization of thermal energy at different temperatures of the equipment. For time t, the reactive power output of the i-th thermal storage device; Let i be the apparent power capacity of the i-th thermal storage device; This represents the maximum energy capacity of the i-th thermal storage device. Let be the upper limit of energy release of the i-th thermal storage device; For the i-th heat storage equipment in Energy state at any given moment; , These are the upper and lower limits of the capacity of the i-th thermal storage unit, respectively; , These represent charge / discharge efficiencies, respectively. This represents the proportion of energy lost through self-dissipation. This represents the simulation time. The frequency regulation capacity of the i-th thermal storage device at time t; Set an upper limit for the frequency regulation capacity of the i-th thermal storage device; , These are the upper and lower limits of the charging capacity of the i-th thermal storage device, respectively. The minimum number of consecutive hours for which frequency modulation service is to be provided.

[0029] Furthermore, in step S2, the atmospheric pollutant diffusion model is constructed as shown in formulas (11)-(13); at the system modeling level, the system energy balance is constrained, and a new type of multifunctional heat storage equipment is introduced as shown in formulas (14)-(15).

[0030] Formulas (11)-(13) are Gaussian plume models with Pasquill–Gifford dispersion parameterization;

[0031] Formula (14) is the power balance equation of the integrated energy system;

[0032] Formula (15) is the thermal power balance equation of the integrated energy system;

[0033] (11)

[0034] (12)

[0035] (13)

[0036] (14)

[0037] (15)

[0038] In the formula: Represents ground point The concentration of pollutants at the location; Indicates the strength of the pollutant source; This represents the average wind speed at the height of the emission source; Indicates crosswind diffusion parameters; Indicates the vertical diffusion parameter; Indicates the effective emission height of pollutants; Indicates the distance downwind; This represents the linear coefficient of the crosswind diffusion parameter; This represents the exponential coefficient of the crosswind diffusion parameter; This represents the linear coefficient of the vertical diffusion parameter; This represents the exponential coefficient of the vertical diffusion parameter; The node at time t With nodes Power transmitted between lines; For nodes The voltage phase angle vector at time t; For nodes The voltage phase angle vector at time t; For nodes With nodes Reactance of the line between them; , , These represent the heat production of the CHP unit, the electric boiler, and the thermal storage equipment at time t, respectively. This represents the heat load at time t.

[0039] Furthermore, in step S3, a multi-element geographic dataset and a gridded geographic information matrix are constructed as shown in formulas (16)-(18);

[0040] Formula (16) is the geographic region gridding processing matrix;

[0041] Formula (17) is the equation for constructing a multi-factor geographic dataset of the study area;

[0042] Formula (18) is used to extract stable representative values ​​that reflect long-term characteristics in unidimensional studies;

[0043] (16)

[0044] (17)

[0045] (18)

[0046] In the formula: A gridded matrix representing the geographic region; A multi-element geographic dataset representing the study area; This is single-dimensional research data; Represents the time set of the study area; This represents the average distribution of single-dimensional research data over a time scale.

[0047] Furthermore, in step S4, the importance of each indicator is quantitatively weighted based on the analytic hierarchy process, and the objectivity and consistency of the weight allocation are ensured by combining qualitative judgment and quantitative calculation multi-criteria decision-making method. The specific process is shown in formulas (19)-(21); the data is standardized by Z-score, as shown in formulas (22) and (23); the weighted hierarchical clustering method based on Ward's minimum variance criterion is used to realize the geographical region partitioning, as shown in formulas (24)-(26).

[0048] Formula (19) is the equation for constructing a pairwise comparison matrix;

[0049] Formula (20) is the equation for calculating the weight vector of each indicator;

[0050] Formula (21) is the consistency test equation for pairwise comparison matrices;

[0051] Formulas (22) and (23) are the Z-score standardization of the original data matrix;

[0052] Formula (24) is the equation for calculating the distance metric between samples;

[0053] Formula (25) is the equation for calculating the sum of squares within a class;

[0054] Formula (26) is the equation for the Ward minimum variance method merging criterion;

[0055] (19)

[0056] (20)

[0057] (twenty one)

[0058] (twenty two)

[0059] (twenty three)

[0060] (twenty four)

[0061] (25)

[0062] (26)

[0063] In the formula: Indicators relative to indicators The relative importance score; For matrix The largest eigenvalue; This is the final normalized weight vector; For the number of indicators, As a consistency indicator; The consistency ratio, It is a random consistency indicator; For the sample In features The original index values ​​below; For the first The mean of each feature; For the first Standard deviation of each feature; These are the standardized indicator values; For data volume; This represents the weighted feature vector of samples within the study area; , Samples and The weighted eigenvectors; express and The square Euclidean distance between them; For the first A cluster; Let be the centroid of the cluster; Represents the sum of squares within a class; Cluster and The increase in the sum of squares within the class after merging; , These represent the number of samples in the cluster; , These are two clusters of centroids.

[0064] Furthermore, in step S5, the multi-objective energy-environment collaborative planning model of the integrated energy system is shown in formulas (27)-(29). Based on the various optimization objectives of the integrated energy system, the operating cost, carbon trading cost and atmospheric pollutant emission penalty cost of the system are weighted; a differentiated pollutant penalty mechanism model is constructed to map the pollutant concentration in different regions to the system operating cost in order to achieve the regulation of pollutant emissions.

[0065] Formula (27) is the objective function of the multi-objective programming problem;

[0066] Formulas (28)-(29) are equations for the differential pollutant penalty mechanism;

[0067] (27)

[0068] (28)

[0069] (29)

[0070] In the formula: Costs related to system operation; Costs related to carbon trading; Costs related to air pollutant emissions; Indicates the first The weights of each objective function; These represent the first, second, and third units in the coal-fired, combined heat and power, and gas-fired power unit systems, respectively. Taiwan unit for the first Each monitoring point at time The concentration of harmful gases produced at that time; To set the penalty coefficient for pollutant gas emissions; , , , They are respectively the first time at time t Electricity output of coal-fired power units, electricity output of combined heat and power (CHP) units, heat output of CHP units, and electricity output of gas-fired power units; This represents the correspondence matrix between pollutant gas sources and acceptors; it is derived from the Gaussian plume formula and the first... The static zoning results of each monitoring point determine the outcome. For the first The air quality requirements for each monitoring point's location are defined by the corresponding regional air quality standards.

[0071] This invention also provides a multifunctional thermal storage equipment planning and configuration system that considers the dynamic environmental capacity constraints of an integrated energy system. The system implements the above-described process of a multifunctional thermal storage equipment planning and configuration method considering the dynamic environmental capacity constraints of an integrated energy system through a modular program, including:

[0072] Multifunctional thermal storage equipment modeling module: used to construct active-reactive coupling model and multi-type regulation capability coupling model of new multifunctional thermal storage equipment. The active-reactive coupling model describes the operating boundary and apparent power constraint of the equipment's grid voltage support and power regulation. The multi-type regulation capability coupling model describes the common occupation relationship of frequency regulation, standby and heating service on thermal storage state and power margin from three dimensions: active power, frequency regulation capacity and energy state.

[0073] Energy-Environment Coupling Modeling Module: Used to construct coupled models of integrated energy systems and the environment, including models based on the Pasquill–Gifford dispersed parameterized Gaussian plume model. , A spatiotemporal diffusion emission model for particulate matter pollutants, and a multi-energy flow coupling model that integrates the operating characteristics of power systems, heating systems and new multi-functional thermal storage equipment. The multi-energy flow coupling model includes power balance constraints and an energy coupling mechanism for power conversion and heating.

[0074] Geographic Information Processing Module: Used to construct multi-element geographic datasets and gridded geographic information matrices, collect natural geographic and socio-economic factors including precipitation, temperature, wind speed, topography, land use type, population density, and regional GDP, divide the study area into equal-scale grids based on GIS, map multi-element data to grid cells to form a multi-dimensional geographic information matrix, and perform annual or multi-year average processing on natural factors that change over time.

[0075] Environmental capacity zoning module: It is used to realize the zoning of geographical regions that take into account the differences in environmental capacity. It uses the analytic hierarchy process to determine the weight of each geographical element indicator and complete the consistency test. It performs Z-score standardization on the multidimensional geographic information matrix and clusters the grid cells using the hierarchical clustering method based on the Ward minimum variance criterion to obtain several environmental capacity type zones with similar environmental carrying capacity and ecological sensitivity.

[0076] Multi-objective programming solution module: Used to establish and solve the energy-environment coordinated multi-objective programming model of the integrated energy system. With the objectives of minimizing the total system cost, minimizing environmental impact, and maximizing the renewable energy absorption rate, the emission limits of each environmental capacity type zone are embedded into the model in the form of economic penalties. The multi-objective weighted sum method is used to solve the model and output the capacity planning and spatial layout candidate schemes of new multi-functional thermal storage equipment in each environmental zone.

[0077] 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 a multifunctional thermal storage equipment planning and configuration method considering the dynamic environmental capacity constraints of a comprehensive energy system as described above.

[0078] The present invention also provides a computer program product, which, when executed by a processor, implements a multifunctional thermal storage equipment planning and configuration method that takes into account the dynamic environmental capacity constraints of a comprehensive energy system, as described above.

[0079] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the program is executed by a processor to implement a multifunctional thermal storage equipment planning and configuration method considering the dynamic environmental capacity constraints of an integrated energy system as described above.

[0080] Advantages and beneficial effects of this invention: This invention achieves deep integration of energy system planning and environmental constraints by constructing a multifunctional thermal storage equipment model, an integrated energy system energy-environment coupling model, and an environmental capacity zoning mechanism based on multi-element geographic data. By accurately characterizing the spatiotemporal diffusion characteristics of pollutants using a Gaussian plume diffusion model and introducing a differentiated pollution penalty mechanism based on regional environmental capacity differences, the system can formulate adaptive emission regulation strategies for areas with different environmental carrying capacities during the planning stage. This invention optimizes the refinement and robustness of multi-energy system planning and operation strategies, achieving the unified goals of energy supply and demand coordination, environmental quality assurance, and multi-energy flow synergistic optimization. It promotes the construction and development of environment-driven planning mechanisms, strengthens pollution diffusion regulation and optimization capabilities, and comprehensively improves the system's cleanliness, operational efficiency, and sustainable development capabilities. Attached Figure Description

[0081] Figure 1 This is a flowchart of the quantization method of the present invention.

[0082] Figure 2 This is a model diagram of the multifunctional thermal storage equipment constructed according to the present invention.

[0083] Figure 3 This is a flowchart of the weighted hierarchical clustering method for regional partitioning in this invention.

[0084] Figure 4 It is a spatiotemporal distribution diagram of pollution when the system's average output is before and after optimization.

[0085] Figure 5 This is a spatiotemporal distribution diagram of pollution at maximum system output before and after optimization. Detailed Implementation

[0086] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0087] Example 1

[0088] like Figure 1 As shown, a method for planning and configuring multifunctional thermal storage equipment considering the dynamic environmental capacity constraints of an integrated energy system includes the following steps:

[0089] S1. Construct a model of a new multifunctional thermal storage equipment (such as...) Figure 2 (As shown): First, focusing on the coordinated output of active and reactive power in equipment, a coupled active and reactive power modeling method for novel multifunctional thermal storage equipment is established to characterize its operating boundaries and apparent power constraints during grid voltage support and power regulation, thereby achieving the synergistic utilization of multiple regulation capabilities. Second, starting from the three dimensions of active power, frequency regulation capacity, and energy state, a coupling model between multiple types of regulation capabilities is constructed to finely characterize the shared occupation relationship between frequency regulation, standby, and heating services on thermal storage state and power margin, thus achieving a unified characterization of the multiple types of regulation capabilities of the novel thermal storage equipment and sustainable regulation capability constraints.

[0090] Among them, the active-reactive coupling constraint of the new multifunctional thermal storage equipment model is constructed as shown in formula (1), and the equipment energy power constraint is shown in formula (2)-formula (3). For the equipment energy change level, the thermal energy output cascade utilization equation and energy dynamic equation are constructed as shown in formula (4)-formula (5). In order to give full play to the multi-type adjustment capability of the equipment, the coupling relationship is analyzed and the corresponding constraint equation is constructed as shown in formula (6)-formula (10).

[0091] Formula (1) is the active-reactive coupling constraint equation for multifunctional thermal storage equipment.

[0092] Formulas (2)-(3) are the upper and lower limit constraint equations for the energy power of multifunctional thermal storage equipment.

[0093] Formula (4) is the equation for the cascade utilization of thermal energy output from multifunctional thermal storage equipment.

[0094] Formula (5) is the dynamic update equation for the energy state of multifunctional thermal storage equipment.

[0095] Formulas (6) to (8) are the constraint equations for the available range of frequency regulation capacity of multifunctional thermal storage equipment.

[0096] Formulas (9)-(10) are the constraint equations for the sustainable development of the regulating capacity of multifunctional thermal storage equipment based on energy margin.

[0097] (1)

[0098] (2)

[0099] (3)

[0100] (4)

[0101] (5)

[0102] (6)

[0103] (7)

[0104] (8)

[0105] (9)

[0106] (10)

[0107] In the formula: and These represent the charging and discharging power of the i-th thermal storage device at time t; , , , These represent the power output and utilization of thermal energy at different temperatures of the equipment. For time t, the reactive power output of the i-th thermal storage device; Let i be the apparent power capacity of the i-th thermal storage device; This represents the maximum energy capacity of the i-th thermal storage device. Let be the upper limit of energy release of the i-th thermal storage device; For the i-th heat storage equipment in Energy state at any given moment; , These are the upper and lower limits of the capacity of the i-th thermal storage unit, respectively. , These are the charging and discharging efficiencies, respectively. This represents the proportion of energy lost through self-dissipation. This represents the simulation time. The frequency regulation capacity of the i-th thermal storage device at time t; Set an upper limit for the frequency regulation capacity of the i-th thermal storage device; and These are the upper and lower limits of the energy charging for the i-th thermal storage device, respectively. The minimum number of consecutive hours for which frequency modulation service is to be provided.

[0108] S2. Constructing an integrated energy system energy-environment coupling model: First, for the atmospheric pollutant diffusion model, establish... , A spatiotemporal diffusion emission model for pollutants such as particulate matter is developed, mapping the output and emission intensity of emission sources such as coal-fired power units and combined heat and power units to environmental receptor points, achieving a quantitative correlation between emissions and environmental concentrations and enabling refined gaseous pollution control. Subsequently, the power system and heating system within the integrated energy system are analyzed, considering the operational characteristics of new multi-functional thermal storage equipment, and characterizing energy constraints such as power balance required for safe and stable operation within the integrated energy system. An energy coupling mechanism for the conversion of electrical energy into heat is introduced to achieve coupled modeling of the integrated energy system.

[0109] Among them, the atmospheric pollutant diffusion model is constructed as shown in formulas (11)-(13), thereby realizing refined gas pollution control. Then, at the system modeling level, the system energy balance is constrained, and a new type of multifunctional heat storage equipment is introduced to realize the flexible operation of the system and improve the effectiveness and economy of the system planning process, as shown in formulas (14)-(15).

[0110] Formulas (11)-(13) are Gaussian plume models with Pasquill–Gifford (PG) dispersion parameterization.

[0111] Formula (14) is the power balance equation of the integrated energy system.

[0112] Formula (15) is the thermal power balance equation of the integrated energy system.

[0113] (11)

[0114] (12)

[0115] (13)

[0116] (14)

[0117] (15)

[0118] In the formula: Represents ground point The concentration of pollutants at the location; Indicates the strength of the pollutant source; This represents the average wind speed at the height of the emission source; Indicates crosswind diffusion parameters; Indicates the vertical diffusion parameter; Indicates the effective emission height of pollutants; Indicates the distance downwind; This represents the linear coefficient of the crosswind diffusion parameter; This represents the exponential coefficient of the crosswind diffusion parameter; This represents the linear coefficient of the vertical diffusion parameter; This represents the exponential coefficient of the vertical diffusion parameter; The node at time t With nodes Power transmitted between lines; For nodes The voltage phase angle vector at time t; For nodes The voltage phase angle vector at time t; For nodes With nodes Reactance of the line between them; , , These represent the heat production of the CHP unit, the electric boiler, and the thermal storage equipment at time t, respectively. This represents the heat load at time t.

[0119] S3. Constructing a Multi-Factor Geographic Dataset and a Gridded Geographic Information Matrix: First, natural geographic factors and socioeconomic factors related to air pollutant diffusion and environmental sensitivity in the region where the integrated energy system is located are collected, including but not limited to precipitation, temperature, wind speed, topography, land use type, population density, and regional GDP, to construct a multi-factor geographic dataset. Furthermore, based on GIS, the study area is divided into equal-scale grids, and the multi-factor data is mapped to each grid cell to obtain a multi-dimensional geographic information matrix. Time-varying natural factors are processed using annual or multi-year averages to meet medium- and long-term planning needs. Through this information matrix, a detailed characterization and information extraction of the geographic environmental elements of the study area are achieved.

[0120] Among them, the construction of multi-element geographic datasets and gridded geographic information matrices is shown in formulas (16)-(18).

[0121] Formula (16) is the geographic region gridding processing matrix.

[0122] Formula (17) is the equation for constructing a multi-factor geographic dataset for the study area.

[0123] Formula (18) is a stable representative value that is extracted from a single-dimensional study to reflect long-term characteristics.

[0124] (16)

[0125] (17)

[0126] (18)

[0127] In the formula: A gridded matrix representing the geographic region; A multi-element geographic dataset representing the study area; The data is for single-dimensional research, including natural geographical factors such as precipitation and temperature, which have a significant impact on gas diffusion and deposition processes; and socio-economic factors such as population density and regional GDP, which are used to characterize the size of the population affected by pollution and the level of economic development. Represents the time set of the study area; This represents the average distribution of single-dimensional research data over a time scale.

[0128] S4. Propose a geographical regional zoning model that considers differences in environmental capacity: To reasonably quantify the differences in environmental capacity between different regions, such as... Figure 3 As shown, the Analytic Hierarchy Process (AHP) is used to compare the importance of natural and socioeconomic indicators in pairs, constructing a judgment matrix and calculating the weights of each indicator through eigenvectors to complete the consistency test. Furthermore, based on this, the multidimensional geographic information matrix is ​​standardized, and a hierarchical clustering method based on the Ward minimum variance criterion is used to perform cluster analysis on the grid cells, resulting in several environmental capacity type zones. Each type zone has similar environmental carrying capacity and ecological sensitivity, achieving differentiated environmental zoning for the integrated energy system region.

[0129] Among them, the importance of each indicator is quantitatively weighted based on the analytic hierarchy process, and the multi-criteria decision-making method combining qualitative judgment and quantitative calculation is used to ensure the objectivity and consistency of weight allocation. The specific process is shown in formulas (19)-(21). Then, the data is Z-score standardized, as shown in formulas (22) and (23). After that, the weighted hierarchical clustering method based on the Ward minimum variance criterion is used to realize the geographical region partitioning, as shown in formulas (24)-(26).

[0130] Formula (19) is a pairwise comparison matrix equation.

[0131] Formula (20) is the equation for calculating the weight vector of each indicator.

[0132] Formula (21) is the consistency test equation for pairwise comparison matrices.

[0133] Formulas (22) and (23) are the Z-score standardization of the original data matrix.

[0134] Formula (24) is the equation for calculating the distance between samples.

[0135] Formula (25) is the equation for calculating the sum of squares within a class.

[0136] Formula (26) is the equation for the Ward minimum variance method merging criterion.

[0137] (19)

[0138] (20)

[0139] (twenty one)

[0140] (twenty two)

[0141] (twenty three)

[0142] (twenty four)

[0143] (25)

[0144] (26)

[0145] In the formula: Indicators relative to indicators The relative importance score; For matrix The largest eigenvalue; This is the final normalized weight vector; For the number of indicators; As a consistency indicator; The consistency ratio; It is a random consistency indicator; For the sample In features The original index values ​​below; For the first The mean of each feature; For the first Standard deviation of each feature; These are the standardized indicator values; For data volume; This represents the weighted feature vector of samples within the study area; , Samples and The weighted eigenvectors; Indicates sample and The square Euclidean distance between them; For the first A cluster; Let be the centroid of the cluster; Represents the sum of squares within a class; Cluster and The increase in the sum of squares within the class after merging; , These represent the number of samples in the cluster; , These are two clusters of centroids.

[0146] S5. Establish and solve a multi-objective planning model for the integrated energy system that coordinates energy and environment: Within a given planning period, a multi-objective collaborative planning model is constructed with the objectives of minimizing total system cost, minimizing environmental impact, and maximizing renewable energy absorption rate. Decision variables include the planning strategy design for facilities such as new multi-functional thermal storage equipment within each environmental zone. Combined with the results of static environmental zoning, emission limits for different types of zones are embedded into the model in the form of economic penalties, forming a coupled model of the integrated energy system and environmental capacity. A weighted sum method algorithm for solving multi-objective problems is adopted. By setting different weights for the objective function, multiple candidate schemes are provided for the capacity planning and spatial layout of integrated energy system equipment within the planning period.

[0147] Among them, the multi-objective energy-environment collaborative planning model of the integrated energy system is shown in formulas (27)-(29). Based on the various optimization objectives of the integrated energy system, the operating cost, carbon trading cost and atmospheric pollutant emission penalty cost of the system are weighted. In addition, in order to effectively control the spatiotemporal distribution of pollutants and make reasonable use of the environmental capacity margin of the system's location, a differentiated pollutant penalty mechanism model is proposed, which maps the pollutant concentration in different regions to the system operating cost, so as to achieve the regulation of pollutant emissions.

[0148] Formula (27) is the objective function of the multi-objective programming problem.

[0149] Formulas (28)-(29) are equations for the differential pollutant punishment mechanism.

[0150] (27)

[0151] (28)

[0152] (29)

[0153] In the formula: For system operating costs, Costs related to carbon trading, Costs related to air pollutant emissions, Indicates the first The weights of each objective function, These represent the first, second, and third units in the coal-fired, combined heat and power, and gas-fired power unit systems, respectively. Taiwan unit for the first Each monitoring point at time The concentration of harmful gases produced at that time To establish a penalty coefficient for pollutant gas emissions in this paper, , , ,and They are respectively the first time at time t Electricity output of coal-fired power units, electricity output of combined heat and power (CHP) units, thermal output of CHP units, and electricity output of gas-fired power units. This represents the correspondence matrix between pollutant gas sources and acceptors, derived from the Gaussian plume formula and the first... The static zoning results of each monitoring point determine the outcome. For the first The air quality requirements for each monitoring point's location are defined by the corresponding regional air quality standards.

[0154] like Figure 4-5The optimization results shown indicate that, regardless of average or maximum output conditions, the peak concentration of pollutants in the system significantly decreases, the range of pollution hotspots shrinks significantly, and the spatiotemporal distribution of pollution becomes more uniform than before, transforming it from highly concentrated. Pollution levels in sensitive areas are greatly improved, and environmental pressure is significantly reduced. This demonstrates that the present invention can effectively suppress the concentrated operation of high-emission units during unfavorable periods and in unfavorable areas, enhance the absorption capacity of new energy sources through the multi-faceted adjustment capabilities of thermal storage equipment, reduce the demand for high-emission power output, and achieve synergistic optimization between energy utilization, environmental quality, and renewable energy consumption while maintaining system energy supply security and economy. Therefore, the energy system not only exhibits better environmental friendliness under normal operating conditions but also maintains a low pollution level under extreme conditions such as high loads, showcasing the significant advantages and application value of the present invention in the context of complex energy-environment coupling.

[0155] Example 2

[0156] A multifunctional thermal storage equipment planning and configuration system considering the dynamic environmental capacity constraints of an integrated energy system, implements the process of a multifunctional thermal storage equipment planning and configuration method considering the dynamic environmental capacity constraints of an integrated energy system as described in Example 1 through a modular program, including:

[0157] Multifunctional thermal storage equipment modeling module: used to construct active-reactive coupling model and multi-type regulation capability coupling model of new multifunctional thermal storage equipment. The active-reactive coupling model describes the operating boundary and apparent power constraint of the equipment's grid voltage support and power regulation. The multi-type regulation capability coupling model describes the common occupation relationship of frequency regulation, standby and heating service on thermal storage state and power margin from three dimensions: active power, frequency regulation capacity and energy state.

[0158] Energy-Environment Coupling Modeling Module: Used to construct coupled models of integrated energy systems and the environment, including models based on the Pasquill–Gifford dispersed parameterized Gaussian plume model. , A spatiotemporal diffusion emission model for particulate matter pollutants, and a multi-energy flow coupling model that integrates the operating characteristics of power systems, heating systems and new multi-functional thermal storage equipment. The multi-energy flow coupling model includes power balance constraints and an energy coupling mechanism for power conversion and heating.

[0159] Geographic Information Processing Module: Used to construct multi-element geographic datasets and gridded geographic information matrices, collect natural geographic and socio-economic factors including precipitation, temperature, wind speed, topography, land use type, population density, and regional GDP, divide the study area into equal-scale grids based on GIS, map multi-element data to grid cells to form a multi-dimensional geographic information matrix, and perform annual or multi-year average processing on natural factors that change over time.

[0160] Environmental capacity zoning module: It is used to realize the zoning of geographical regions that take into account the differences in environmental capacity. It uses the analytic hierarchy process to determine the weight of each geographical element indicator and complete the consistency test. It performs Z-score standardization on the multidimensional geographic information matrix and clusters the grid cells using the hierarchical clustering method based on the Ward minimum variance criterion to obtain several environmental capacity type zones with similar environmental carrying capacity and ecological sensitivity.

[0161] Multi-objective programming solution module: Used to establish and solve the energy-environment coordinated multi-objective programming model of the integrated energy system. With the objectives of minimizing the total system cost, minimizing environmental impact, and maximizing the renewable energy absorption rate, the emission limits of each environmental capacity type zone are embedded into the model in the form of economic penalties. The multi-objective weighted sum method is used to solve the model and output the capacity planning and spatial layout candidate schemes of new multi-functional thermal storage equipment in each environmental zone.

Claims

1. A method for planning and configuring multi-functional thermal storage equipment considering dynamic environmental capacity constraints of integrated energy systems, characterized in that, The method comprises the following steps: S1, constructing a new multifunctional heat storage equipment model: first, an active-reactive coupling modeling method of the new multifunctional heat storage equipment is established, the operation boundary and apparent power constraint of the new multifunctional heat storage equipment in the process of power grid voltage support and power regulation are described; second, a multi-type regulation capacity coupling model is constructed from the three dimensions of active power, frequency modulation capacity and energy state, the common occupation relationship of frequency modulation, standby and heating services to the heat storage state and power margin is described through constraint equations, so that the unified description and sustainable regulation capacity constraint of the multi-type regulation capacity of the new heat storage equipment are realized; S2, Build an energy-environment coupling model of integrated energy system: First, based on the Pasquill-Gifford Gaussian plume model with high dispersion parameters, the pollutant emission model of particulate matter is established 、 , to realize the quantitative correlation of emission to environmental concentration and fine gas pollution control. Then, combined with the operation characteristics of power system and heating system and the operation model of new multi-functional heat storage equipment, the energy coupling mechanism of power-to-heat is introduced to realize the coupling modeling of integrated energy system. S3, constructing a multi-element geographic data set and a grid geographic information matrix: first, the natural geographic factors and social and economic factors related to atmospheric pollutant diffusion and environmental sensitivity in the region where the integrated energy system is located are collected, including precipitation, temperature, wind speed, terrain, land use type, population density, regional gross product, etc., to construct a multi-element geographic data set; in addition, the research area is divided into equal-scale grids based on GIS, and the multi-element data is mapped to each grid unit to obtain a multi-dimensional geographic information matrix; the natural factors changing with time are processed as annual average or multi-year average to meet the needs of medium and long-term planning; through the multi-dimensional information matrix, the detailed description and information extraction of the geographical environmental factors of the research area are realized; S4, a geographical area partitioning model considering the difference in environmental capacity is proposed: the importance of natural and social economic indicators is compared by pair using the analytic hierarchy process, a judgment matrix is constructed, and the weight of each indicator is calculated through the characteristic vector to complete the consistency test; the multi-dimensional geographic information matrix is standardized by Z-score, and the grid units are analyzed by hierarchical clustering method based on Ward minimum variance criterion to obtain several environmental capacity type areas, each type area has similar environmental carrying capacity and ecological sensitivity, and the differentiated environmental partitioning of the integrated energy system region is realized; S5, establishing an integrated energy system energy-environment collaborative multi-objective planning model and solving: in the given planning period, the minimum total cost, the minimum environmental impact and the maximum renewable energy consumption rate are taken as the objectives, a multi-objective collaborative planning model is constructed, the decision variables include the planning strategy design of the new multifunctional heat storage equipment in each environmental partition; and combining the environmental static partitioning results, the emission limits of different type areas are embedded in the model in the form of economic punishment to form a coupling model of the integrated energy system and the environmental capacity; a multi-objective problem weighted sum method is used to solve the algorithm, and by setting different weights of the objective functions, multiple candidate schemes are provided for the capacity planning and spatial layout of the integrated energy system equipment in the planning period.

2. The method of claim 1, wherein, In step S1, the new multifunctional heat storage equipment model includes active-reactive coupling constraints, which are constructed as shown in formula (1), and the equipment energy power constraints are shown in formula (2)-formula (3); for the equipment energy change level, the heat energy output step utilization equation and the energy dynamic equation are constructed as shown in formula (4)-formula (5); the corresponding constraint equations are constructed by analyzing the coupling relationship as shown in formula (6)-formula (10); Formula (1) is the active-reactive coupling constraint equation of multifunctional thermal storage equipment; Formula (2) and (3) are the upper and lower limit constraint equations of energy power of multifunctional thermal storage equipment; Formula (4) is the thermal energy output step utilization equation of multifunctional thermal storage equipment; Formula (5) is the energy state dynamic updating equation of multifunctional thermal storage equipment; Formula (6) to (8) are the available range constraint equations of frequency modulation capacity of multifunctional thermal storage equipment; Formula (9) and (10) are the sustainability constraint equations of regulation capacity of multifunctional thermal storage equipment based on energy margin; （1） （2） （3） （4） （5） （6） （7） （8） （9） （10） In the formula: , are the charging and discharging power of the i-th heat storage equipment at time t, respectively; , , , are the heat energy output utilization power at different temperatures of the equipment, respectively; is the reactive power output of the i-th heat storage equipment at time t; is the apparent power capacity of the i-th heat storage equipment; is the upper limit of the energy charging of the i-th heat storage equipment; is the upper limit of the energy discharging of the i-th heat storage equipment; is the energy state of the i-th heat storage equipment at time t; , , are the upper and lower limits of the capacity of the i-th heat storage equipment, respectively; , represent the charging and discharging efficiency, respectively; is the energy self-discharge ratio; is the simulation time number; is the frequency modulation capacity of the i-th heat storage equipment at time t; is the upper limit of the frequency modulation capacity of the i-th heat storage equipment; , are the upper and lower limits of the energy charging of the i-th heat storage device, respectively; is the minimum number of consecutive time slots for which the frequency modulation service is provided.

3. The method of claim 1, wherein, In step S2, the construction of the atmospheric pollutant diffusion model is shown in formula (11) to (13); at the system modeling level, the system energy balance is constrained, and a new type of multifunctional thermal storage equipment is introduced, as shown in formula (14) to (15); Formula (11) to (13) are Gaussian plume models using Pasquill-Gifford dispersion parameterization; Formula (14) is the electric power balance equation of the integrated energy system; Formula (15) is the thermal power balance equation of the integrated energy system; （11） （12） （13） （14） （15） wherein: represents the pollutant concentration at the ground point ; represents the pollutant source strength; represents the average wind speed at the height of the emission source; represents the cross-wind diffusion parameter; represents the vertical diffusion parameter; represents the effective emission height of the pollutant; represents the downwind distance; represents the linear coefficient of the cross-wind diffusion parameter; represents the exponential coefficient of the cross-wind diffusion parameter; represents the linear coefficient of the vertical diffusion parameter; represents the exponential coefficient of the vertical diffusion parameter; is the line transmission power between the node and the node at the time t; is the voltage phase angle vector of the node at the time t; is the voltage phase angle vector of the node at the time t; is the reactance of the line between the node and the node ; , , respectively represent the heat produced by the CHP unit, the electric boiler and the heat storage equipment at the time t, represents the heat load at the time t.

4. The method of claim 1, wherein, In step S3, the construction of the multi-element geographic data set and the grid geographic information matrix is shown in formula (16) to (18); Formula (16) is the grid processing matrix of geographic areas; Formula (17) is the construction equation of the multi-element geographic data set of the study area; Formula (18) is to extract a single-dimensional stable representative value reflecting long-term characteristics; （16） （17） （18） wherein: is a geographical area gridded matrix; denotes a multi-element geographical dataset of the study area; is a single-dimensional study data; denotes a time set of the study area; is a single-dimensional study data averaged over time scale result.

5. The method of claim 1, wherein, In step S4, the importance of each index is quantitatively weighted based on the analytic hierarchy process, and a multi-criteria decision-making method combining qualitative judgment and quantitative calculation is used to ensure the objectivity and consistency of weight distribution, as shown in formula (19) to (21); the data is standardized by Z-score, as shown in formula (22) and (23); the weighted hierarchical clustering method based on Ward minimum variance criterion is used to realize the geographic regionalization, as shown in formula (24) to (26); Formula (19) is the construction of a pair comparison matrix equation; Formula (20) is the weight vector calculation equation of each index; Formula (21) is the consistency test equation of the pair comparison matrix; Formula (22) and (23) are the Z-score standardization processing of the original data matrix; Formula (24) is the sample distance measurement calculation equation; Formula (25) is the intra-class sum of squares calculation equation; Formula (26) is the Ward minimum variance method merging criterion equation; （19） （20） （21） （22） （23） （24） （25） （26） In the formula: Indicators relative to indicators The relative importance score; For matrix The largest eigenvalue; This is the final normalized weight vector; For the number of indicators, As a consistency indicator; The consistency ratio, It is a random consistency indicator; For the sample In features The original index values ​​below; For the first The mean of each feature; For the first Standard deviation of each feature; These are the standardized indicator values; For data volume; This represents the weighted feature vector of samples within the study area; , Samples and The weighted eigenvectors; express and The square Euclidean distance between them; For the first A cluster; Let be the centroid of the cluster; Represents the sum of squares within a class; Cluster and The increase in the sum of squares within the class after merging; , These represent the number of samples in the cluster; , These are two clusters of centroids.

6. The method of claim 1, wherein, In step S5, the multi-objective energy-environment collaborative planning model of the integrated energy system is shown in formula (27) to (29), based on the demand for multiple optimization objectives of the integrated energy system, the operation cost, carbon trading cost and atmospheric pollutant emission penalty cost of the system operation are weighted; the differential pollutant penalty mechanism model is constructed to map the pollutant concentration in different regions to the system operation cost to realize the adjustment of pollutant emission; Formula (27) is the objective function of the multi-objective planning problem; Formula (28) and (29) are the differential pollutant penalty mechanism equations; （27） （28） （29） In the formula: is the system operation related cost; is the carbon trading related cost; is the atmospheric pollutant emission related cost; represents the weight of the th objective function; respectively represent the harmful gas concentration value generated by the th unit in the coal-fired unit, combined heat and power unit, and gas unit system at the th monitoring point at time ; is the set pollutant gas emission penalty coefficient; , , , respectively are the coal-fired unit electric output, combined heat and power unit electric output, combined heat and power unit heat output, and gas unit electric output of the th unit at time t; represents the corresponding relationship matrix between the pollutant gas source and the receptor; it is determined by the Gaussian plume formula and the static zoning result of the th monitoring point; is the regional zoning air quality requirement index of the th monitoring point.

7. A multi-functional thermal storage equipment planning and configuration system considering dynamic environment capacity constraints of a comprehensive energy system, characterized in that, The process of a multifunctional thermal storage equipment planning and configuration method considering dynamic environmental capacity constraints of a comprehensive energy system according to any one of claims 1-6 is implemented through a modular program, comprising: a multifunctional thermal storage equipment modeling module: for constructing an active-reactive coupling model and a multi-type regulation capacity coupling model of the new multifunctional thermal storage equipment, the active-reactive coupling model depicts the operating boundary and apparent power constraint of the equipment power grid voltage support and power regulation, and the multi-type regulation capacity coupling model depicts the common occupation relationship of frequency modulation, standby and heating service on the thermal storage state and power margin from the three dimensions of active power, frequency modulation capacity and energy state; Energy-environment coupling modeling module: for building a coupling model of integrated energy system and environment, including a Pasquill-Gifford dispersion parameterized Gaussian plume model-based , , a space-time diffusion emission model of particulate matter pollutants, and a multi-energy flow coupling model fusing operation characteristics of power systems, heating systems and new multi-functional heat storage equipment, the multi-energy flow coupling model containing power balance constraints and energy coupling mechanisms of electric energy conversion for heating; a geographic information processing module: for constructing a multi-element geographic data set and a grid geographic information matrix, collecting natural geographic and social economic factors: including precipitation, temperature, wind speed, terrain, land use type, population density, and regional gross domestic product, performing equal-scale grid division on the research area based on GIS, mapping the multi-element data to the grid cells to form a multi-dimensional geographic information matrix, and performing annual average or multi-year average processing on the time-varying natural factors; an environmental capacity partitioning module: for realizing geographic region partitioning considering environmental capacity differences, determining the index weight of each geographic element by using the analytic hierarchy process and completing consistency test, performing Z-score standardization processing on the multi-dimensional geographic information matrix, and clustering the grid cells by using the hierarchical clustering method based on the Ward minimum variance criterion to obtain several environmental capacity type regions with similar environmental bearing capacity and ecological sensitivity; a multi-objective planning solving module: for establishing and solving a comprehensive energy system energy-environment coordination multi-objective planning model, taking the minimum total cost, the minimum environmental impact and the maximum renewable energy consumption rate as the target, embedding the emission limit value of each environmental capacity type region in the form of economic penalty, solving by using the multi-objective weighted sum method, and outputting the capacity planning and spatial layout candidate scheme of the new multifunctional thermal storage equipment in each environmental partition.

8. An electronic device, comprising: comprise: 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 multifunctional thermal storage equipment planning and configuration method considering dynamic environmental capacity constraints of a comprehensive energy system according to any one of claims 1-6.

9. A computer program product, characterised in that, The computer program / instructions are executed by the processor to implement the multifunctional thermal storage equipment planning and configuration method considering dynamic environmental capacity constraints of a comprehensive energy system according to any one of claims 1-6.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the multifunctional thermal storage equipment planning and configuration method considering dynamic environmental capacity constraints of a comprehensive energy system according to any one of claims 1-6.

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