Electric power system optimization planning method and device based on seasonal heat storage modeling

Abstract

The invention relates to the technical field of electric power systems, in particular to an electric power system optimization planning method and device based on seasonal heat storage modeling, and the method comprises the steps: carrying out the linear processing of a pre-established seasonal heat storage hydrothermal dynamic model according to the operation characteristics of seasonal heat storage, a linear programming model of seasonal heat storage is obtained; based on the linear programming model and the initial optimization programming model of the target power system, establishing a power optimization programming model considering seasonal heat storage modeling; and solving the power optimization planning model to obtain seasonal heat storage capacity configuration and optimization operation planning of the target power system. According to the method, the change condition of the mass of each temperature layer in seasonal heat storage operation can be effectively modeled, the time-varying heat loss characteristics of seasonal heat storage can be accurately described, scientific configuration of seasonal heat storage capacity and long-time scale movement of energy in a power system can be realized, the capacity and output of a renewable energy unit can be reasonably arranged, and the efficiency of seasonal heat storage is improved. And the consumption level of the power system is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and particularly relates to a power system optimization planning method and device based on seasonal heat storage modeling. Background Technique

[0002] With the continuous increase in the proportion of renewable energy in the power system and the gradual deepening of the replacement of electric energy in the heating link, the contradiction between seasonal heat load demand and the output of renewable energy, such as seasonal peak shifting and difficulty in matching, has become increasingly serious. The seasonal mismatch between power sources and loads brings long-term and seasonal risks of supply-demand imbalance to the future energy and power system with deep coupling of electricity and heat, and poses huge challenges to the construction and safe operation of the new power system. Seasonal Thermal Storage (STS) can realize cross-seasonal storage of heat and participate in heat supply during the heating season, which is an ideal method to achieve large-scale consumption of renewable energy and suppress the risk of seasonal heating imbalance.

[0003] In related technologies, in the research on the participation of seasonal heat storage in the power system planning and operation, current researchers have carried out certain research on aspects such as SOC modeling, annual balance, and electricity-heat coupling of seasonal heat storage.

[0004] However, in related technologies, the research on the participation of seasonal heat storage in the power system planning and operation rarely considers the dynamic physical process during the operation of seasonal heat storage, and the analysis and research on the time-varying heat dissipation characteristics of seasonal heat storage throughout the year are also insufficient. Therefore, it is impossible to analyze the impact of the time-varying heat dissipation characteristics of seasonal heat storage in different time periods on the power system operation and the consumption of renewable energy, which urgently needs to be solved. Summary of the Invention

[0005] The present application provides a power system optimization planning method and device based on seasonal heat storage modeling to solve the problems in related technologies that the research on the participation of seasonal heat storage in the power system planning and operation rarely considers the dynamic physical process during the operation of seasonal heat storage, and the analysis and research on the time-varying heat dissipation characteristics of seasonal heat storage throughout the year are also insufficient, so that it is impossible to analyze the impact of the time-varying heat dissipation characteristics of seasonal heat storage in different time periods on the power system operation and the consumption of renewable energy.

[0006] The first aspect embodiment of the present application provides a power system optimization planning method based on seasonal heat storage modeling, including the following steps: linearize the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage to obtain a linear programming model of the seasonal heat storage; establish a power optimization planning model considering seasonal heat storage modeling based on the linear programming model and the initial optimization planning model of the target power system; solve the power optimization planning model to obtain the seasonal heat storage capacity configuration and optimized operation planning of the target power system.

[0007] Optionally, in an embodiment of the present application, before performing the linearization process on the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage, it further includes: analyzing the coupling characteristics between multiple water layers of the heat storage device corresponding to the seasonal heat storage based on the dynamic operation process of the seasonal heat storage; determining the operating characteristics of the seasonal heat storage according to the coupling characteristics between the multiple water layers.

[0008] Optionally, in an embodiment of the present application, performing the linearization process on the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage to obtain the linear programming model of the seasonal heat storage includes: determining the vertical distribution law of the multiple water layers according to the operating characteristics of the seasonal heat storage, and dividing corresponding temperature intervals for each water layer in the multiple water layers according to the vertical distribution law; when the heat storage medium in each water layer of the multiple water layers is at a fixed temperature, determining the mass change of each water layer in the multiple water layers corresponding to the temperature interval during the heat energy charging and discharging process; determining the heat storage amount change of each water layer in the multiple water layers during the heat energy charging and discharging process according to the mass change to complete the linearization process and obtain the linear programming model of the seasonal heat storage.

[0009] Optionally, in an embodiment of the present application, the expression for the mass change of each water layer in the multiple water layers during the heat energy charging and discharging process is:

[0010]

[0011] where m h,i,t+1 represents the mass of water layer i at time t + 1, m h,i,t represents the mass of water layer i at time t, Δm h,i,t is the mass change of water layer i caused by energy charging and discharging during the time period t, represents the mass equivalently transferred from water layer i to water layer i + 1 under the action of natural heat loss, represents the mass equivalently transferred from water layer i - 1 to water layer i under the action of natural heat loss, and respectively represent the charging water flow rate and discharging water flow rate of water layer i, and are natural loss transfer coefficients, and m h,i-1,t represents the mass of water layer i at time t - 1.

[0012] Optionally, in an embodiment of the present application, establishing a power optimization planning model considering seasonal heat storage modeling based on the linear programming model and the initial optimization planning model of the target power system includes: obtaining multiple operation constraints of the target power system, and based on the linear programming model, obtaining multiple heat storage constraints of the seasonal heat storage; establishing the power optimization planning model considering seasonal heat storage modeling according to the multiple operation constraints of the target power system, the multiple heat storage constraints of the seasonal heat storage, and the initial optimization planning model.

[0013] Optionally, in an embodiment of the present application, the expression of the pre-established hydrothermal dynamic model of the seasonal heat storage is:

[0014]

[0015] Where, ΔQ h,i,t is the total heat obtained by water layer i within time period t, is the heat absorbed by water layer i from the adjacent water layer within time period t, is the heat accumulated by charging and discharging energy by water layer i within time period t, is the heat obtained by water layer i from natural heat loss within time period t.

[0016] An embodiment of the second aspect of the present application provides a power system optimization planning device based on seasonal heat storage modeling, including: a processing module, configured to linearize the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage to obtain a linear programming model of the seasonal heat storage; a building module, configured to establish a power optimization planning model considering seasonal heat storage modeling based on the linear programming model and the initial optimization planning model of the target power system; a solving module, configured to solve the power optimization planning model to obtain the seasonal heat storage capacity configuration and optimized operation planning of the target power system.

[0017] Optionally, in an embodiment of the present application, it further includes: an analysis module, configured to analyze the coupling characteristics between multiple water layers of the heat storage device corresponding to the seasonal heat storage based on the dynamic operation process of the seasonal heat storage before linearizing the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage; a determination module, configured to determine the operating characteristics of the seasonal heat storage according to the coupling characteristics between the multiple water layers.

[0018] Optionally, in an embodiment of the present application, the processing module includes: a dividing unit, configured to determine the vertical distribution law of the plurality of water layers according to the operating characteristics of the seasonal heat storage, and divide a corresponding temperature range for each of the plurality of water layers according to the vertical distribution law; a determining unit, configured to determine the mass change of each of the plurality of water layers in the process of heat energy charging and discharging when the heat storage medium of each of the plurality of water layers is at a fixed temperature; and a processing unit, configured to determine the change in the heat storage amount of each of the plurality of water layers in the process of heat energy charging and discharging according to the mass change, so as to complete the linearization process and obtain a linear programming model of the seasonal heat storage.

[0019] Optionally, in an embodiment of the present application, the expression of the mass change of each of the plurality of water layers in the process of heat energy charging and discharging is:

[0020]

[0021] where m h,i,t+1 represents the mass of the water layer i at the moment t + 1, m h,i,t represents the mass of the water layer i at the moment t, Δm h,i,t is the mass change of the water layer i caused by energy charging and discharging within the time period t, represents the mass equivalently transferred from the water layer i to the water layer i + 1 under the action of natural heat loss, represents the mass equivalently transferred from the water layer i - 1 to the water layer i under the action of natural heat loss, and respectively represent the charging water flow rate and discharging water flow rate of the water layer i, and are natural heat loss transfer coefficients, and m h,i-1,t represents the mass of the water layer i at the moment t - 1.

[0022] Optionally, in an embodiment of the present application, the establishing module includes: an obtaining unit, configured to obtain a plurality of operation constraints of the target power system, and obtain a plurality of heat storage constraints of the seasonal heat storage based on the linear programming model; and an establishing unit, configured to establish a power optimization planning model considering seasonal heat storage modeling according to the plurality of operation constraints of the target power system, the plurality of heat storage constraints of the seasonal heat storage, and the initial optimization planning model.

[0023] Optionally, in an embodiment of the present application, the expression of the pre-established hydrothermal dynamic model of the seasonal heat storage is:

[0024]

[0025] where ΔQ h,i,tis the total heat obtained by water layer i within time period t, is the heat absorbed by water layer i from adjacent water layers within time period t, is the heat accumulated by water layer i through charge and discharge energy storage within time period t, is the heat obtained by water layer i from natural heat loss within time period t.

[0026] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement the power system optimization planning method based on seasonal heat storage modeling as described in the above embodiments.

[0027] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the above-mentioned power system optimization planning method based on seasonal heat storage modeling.

[0028] An embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, which when executed is used to implement the above-mentioned power system optimization planning method based on seasonal heat storage modeling.

[0029] Embodiments of the present application can perform refined modeling of seasonal heat storage according to the operating characteristics of seasonal heat storage, linearize the non-linear constraints in the model to obtain a linear programming model of seasonal heat storage, embed it into the initial optimization planning model of the target power system, and then solve it to obtain the seasonal heat storage capacity configuration and optimized operation plan of the target power system. Thus, on the basis of considering the hydrothermal dynamic process of seasonal heat storage, the time-varying heat dissipation characteristics of seasonal heat storage and its impact on the optimization planning of the power system are analyzed according to the coupling characteristics of each water layer, so as to obtain a refined linear programming model of seasonal heat storage and an optimization planning model of the target power system considering refined modeling of seasonal heat storage. A reasonable power system planning scheme is obtained by solving the optimization planning model; and the power system optimization planning model with refined modeling operation constraints of seasonal heat storage added in the present application can effectively describe the actual operation of seasonal heat storage in the power system, so that the planning scheme can take into account the operating characteristics of seasonal heat storage such as time-varying heat dissipation characteristics and seasonal characteristics of renewable energy output, effectively improving the analysis ability of high-proportion renewable energy power systems and providing support for the dispatching operation of future high-proportion renewable energy power systems. Thus, it solves the problems in the related technologies that the research on the participation of seasonal heat storage in the power system planning operation rarely considers the dynamic physical process in the operation process of seasonal heat storage, and the analysis and research on the time-varying heat dissipation characteristics of seasonal heat storage throughout the year are also insufficient, so that the impact of the time-varying heat dissipation characteristics of seasonal heat storage on the power system operation and renewable energy consumption in different time periods cannot be analyzed.

[0030] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Brief Description of the Drawings

[0031] The above-mentioned and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, wherein:

[0032] Figure 1 It is a flowchart of a method for optimizing the planning of a power system based on seasonal heat storage modeling according to an embodiment of the present application;

[0033] Figure 2 It is a flowchart of a method for optimizing the planning of a power system based on seasonal heat storage modeling according to an embodiment of the present application;

[0034] Figure 3 It is a schematic structural diagram of a device for optimizing the planning of a power system based on seasonal heat storage modeling according to an embodiment of the present application;

[0035] Figure 4 It is a schematic structural diagram of an electronic device according to an embodiment of the present application.

[0036] Reference Signs:

[0037] 10 - Device for optimizing the planning of a power system based on seasonal heat storage modeling: 100 - Processing module, 200 - Establishing module, and 300 - Solving module; 401 - Memory, 402 - Processor, and 403 - Communication interface. Detailed Description of the Embodiments

[0038] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0039] The following describes the power system optimization planning method and device based on seasonal heat storage modeling according to the embodiments of the present application. In view of the problem in the related technology mentioned in the above background art that the research on the participation of seasonal heat storage in the power system planning operation rarely considers the dynamic physical process during the operation of seasonal heat storage, and the analysis and research on the time-varying heat dissipation characteristics of seasonal heat storage throughout the year are also insufficient, so that the impact of the time-varying heat dissipation characteristics of seasonal heat storage on the power system operation and renewable energy consumption in different time periods cannot be analyzed, the present application provides a power system optimization planning method based on seasonal heat storage modeling. In this method, the seasonal heat storage can be finely modeled according to the operation characteristics of seasonal heat storage, and the non-linear constraints in the model are linearized to obtain a linear programming model of seasonal heat storage, which is embedded in the initial optimization planning model of the target power system, and then solved to obtain the seasonal heat storage capacity configuration and optimized operation plan of the target power system. Thus, on the basis of considering the hydrothermal dynamic process of seasonal heat storage, the time-varying heat dissipation characteristics of seasonal heat storage and its impact on the power system optimization planning are analyzed according to the coupling characteristics of each water layer, so as to obtain a refined linear programming model of seasonal heat storage and an optimization planning model of the target power system considering the refined modeling of seasonal heat storage. By solving the optimization planning model, a reasonable power system planning scheme is obtained; and the power system optimization planning model with refined modeling operation constraints of seasonal heat storage added in the present application can effectively describe the actual operation of seasonal heat storage in the power system, so that the planning scheme can take into account the operation characteristics of seasonal heat storage such as time-varying heat dissipation characteristics and the seasonal characteristics of renewable energy output, effectively improving the analysis ability of high-proportion renewable energy power systems and providing support for the dispatching operation of future high-proportion renewable energy power systems. Thus, the problems in the related technology that the research on the participation of seasonal heat storage in the power system planning operation rarely considers the dynamic physical process during the operation of seasonal heat storage, and the analysis and research on the time-varying heat dissipation characteristics of seasonal heat storage throughout the year are also insufficient, so that the impact of the time-varying heat dissipation characteristics of seasonal heat storage on the power system operation and renewable energy consumption in different time periods cannot be analyzed, etc. are solved.

[0040] Specifically, Figure 1 is a flowchart of a power system optimization planning method based on seasonal heat storage modeling provided by an embodiment of the present application.

[0041] As Figure 1 shown, the power system optimization planning method based on seasonal heat storage modeling includes the following steps:

[0042] In step S101, the hydrothermal dynamic model of the pre-established seasonal heat storage is linearized according to the operation characteristics of the seasonal heat storage to obtain a linear programming model of the seasonal heat storage. Among them, the expression of the pre-established hydrothermal dynamic model of the seasonal heat storage can be but is not limited to:

[0043]

[0044] Among them, ΔQ h,i,t is the total heat obtained by water layer i within time period t, is the heat absorbed by water layer i from adjacent water layers within time period t, is the heat accumulated by charging and discharging of water layer i within time period t, is the heat obtained by water layer i from natural heat loss within time period t.

[0045] Those skilled in the art of this technology can understand that seasonal heat storage can store excess thermal energy during periods of heat surplus such as summer, and release it during peak heating demand periods such as winter, realizing the transfer of thermal energy over time, solving the problem of the mismatch between thermal energy production and consumption in time, improving the utilization efficiency of energy, reducing energy waste, and thus improving the optimal planning of the operation of the power system.

[0046] In some embodiments, the present application can accurately describe the coupling relationship between the heat storage system and different energy systems such as electricity and heat by performing refined modeling on seasonal heat storage, promote the collaborative optimization of various energy forms such as electricity and heat, enable the reasonable flow and conversion of energy between power systems, and thereby improve the operation efficiency of the entire power system.

[0047] When performing refined modeling on seasonal heat storage, the embodiments of the present application can, but are not limited to, linearize the pre-established hydrothermal dynamic model according to the operating characteristics of seasonal heat storage, thereby constituting a linear programming model of seasonal heat storage, which can make the processed seasonal heat storage planning model applicable to the optimal solution of the power system optimization planning model.

[0048] Among them, the pre-established hydrothermal dynamic model of seasonal heat storage can be understood here as a model established in advance according to the heat change of the water layer in the heat storage device of seasonal heat storage, and the expression can be, but is not limited to, represented as follows:

[0049]

[0050] Among them, ΔQ h,i,t is the total heat obtained by water layer i within time period t; is the heat absorbed by water layer i from adjacent water layers within time period t, that is, the interlayer exchange heat; is the heat accumulated by charging and discharging of water layer i within time period t, that is, the charging and discharging heat; is the heat obtained by water layer i from natural heat loss within time period t, that is, the natural loss heat.

[0051] And, the expressions of these physical quantities can be, but are not limited to, represented as:

[0052]

[0053] where m h,i,t represents the mass of water layer i at time t, C h represents the specific heat capacity of water, T h,i,t represents the temperature of water layer i at time t, T h,i,t+1 represents the temperature of water layer i at time t + 1, k h represents the thermal conductivity of water, A h,c represents the cross-sectional area of the heat storage tank, T h,i+1,t represents the temperature of water layer i + 1 at time t, T h,i-1,t represents the temperature of water layer i - 1 at time t, Δx h,i-1,i represents the central distance between the (i - 1)-th layer and the i-th layer of the heat storage medium, Δx h,i+1,i represents the central distance between the (i + 1)-th layer and the i-th layer of the heat storage medium, represents the heat obtained by water layer i from charging at time t, represents the heat lost by water layer i from discharging at time t, and respectively represent the charging water flow rate and discharging water flow rate of water layer i, U h,s,i represents the side heat transfer coefficient of water layer i, A h,s,i represents the side area of the i-th layer of the heat storage tank, represents the ambient temperature of the heat storage tank at time t, and Δt represents the time length of a single time period.

[0054] Optionally, in an embodiment of the present application, before linearizing the pre-established hydrothermal dynamic model of seasonal heat storage according to the operating characteristics of seasonal heat storage, it further includes: analyzing the coupling characteristics between multiple water layers of the heat storage device corresponding to seasonal heat storage based on the dynamic operation process of seasonal heat storage; determining the operating characteristics of seasonal heat storage according to the coupling characteristics between multiple water layers.

[0055] Based on the related descriptions of other embodiments, it can be understood that the present application can linearize the pre-established hydrothermal dynamic model of seasonal heat storage according to the operating characteristics of seasonal heat storage.

[0056] In some embodiments, the present application can analyze the coupling characteristics between multiple water layers of the heat storage device corresponding to seasonal heat storage by analyzing the dynamic operation process of seasonal heat storage; thereby determining the operating characteristics of seasonal heat storage according to the coupling characteristics between multiple water layers, so as to linearize the pre-established hydrothermal dynamic model of seasonal heat storage according to the operating characteristics of seasonal heat storage to obtain a refined linear programming model of seasonal heat storage.

[0057] That is, the embodiments of the present application fully consider the dynamic physical processes during seasonal heat storage operation, and through the coupling characteristics between multiple water layers of the heat storage device corresponding to seasonal heat storage, fully study the time-varying heat dissipation characteristics of seasonal heat storage throughout the year, which helps to analyze the impact of the time-varying heat dissipation characteristics of seasonal heat storage on the operation of the power system and the consumption of renewable energy during different periods.

[0058] Specifically, the dynamic operation process of seasonal heat storage is mainly based on the heat storage device, using water as the heat storage medium. Since the water temperature at different positions will vary due to factors such as heat transfer during the heat storage process, multiple water layers with different characteristics such as different temperatures are formed. For example, in a large cylindrical hot water storage tank, the water in the upper and lower parts may show temperature stratification and form different water layers due to factors such as different heat exchange conditions with the external environment and the position of the heating source.

[0059] Among them, the coupling characteristics between multiple water layers can be understood here as the characteristics of interaction and influence between different water layers. It includes heat conduction between water layers, mass exchange between water layers (such as the mixing of water in different water layers due to the flow of water caused by convection, etc.), and the interaction caused by the pressure difference between water layers. For example, the water layer with a higher temperature will transfer heat to the adjacent water layer with a lower temperature through heat conduction. At the same time, the density difference caused by the temperature difference may cause changes in the mass of the water layer, etc.

[0060] According to various factors such as the structure of the heat storage device, the properties of the heat storage medium, and the coupling characteristics between water layers, the embodiments of the present application can obtain the operating characteristics of seasonal heat storage, such as the heat storage efficiency, heat release rate, the range of heat storage temperature that the system can maintain, and the heat storage and heat release capabilities in different seasons.

[0061] Optionally, in an embodiment of the present application, linearization processing is performed on the pre-established hydrothermal dynamic model of seasonal heat storage according to the operating characteristics of seasonal heat storage to obtain a linear programming model of seasonal heat storage, including: determining the vertical distribution law of multiple water layers according to the operating characteristics of seasonal heat storage, and dividing corresponding temperature intervals for each water layer in the multiple water layers according to the vertical distribution law; when the heat storage medium in each water layer of the multiple water layers is at a fixed temperature, determining the mass change of each water layer in the corresponding temperature interval during the charging and discharging process of thermal energy; determining the change in the heat storage amount of each water layer in the multiple water layers during the charging and discharging process of thermal energy according to the mass change to complete the linearization processing and obtain the linear programming model of seasonal heat storage. The expression of the mass change of each water layer in the multiple water layers during the charging and discharging process of thermal energy can be but is not limited to being expressed as:

[0062]

[0063] Among them, mh,i,t+1 Denote the mass of water layer i at time t+1 as m h,i,t Denote the mass of water layer i at time t as Δm h,i,t is the mass change of water layer i during time period t due to energy charging and discharging Denote the mass equivalently transferred from water layer i to water layer i+1 under the action of natural heat loss Denote the mass equivalently transferred from water layer i-1 to water layer i under the action of natural heat loss and respectively denote the charging water flow rate and discharging water flow rate of water layer i and is the natural heat loss transfer coefficient, m h,i-1,t Denote the mass of water layer i at time t-1

[0064] In the actual implementation process, when the present application linearizes the pre-established hydrothermal dynamic model of seasonal heat storage according to the operating characteristics of seasonal heat storage, considering that there are upper and lower limits for the temperature range of the actual operation of seasonal heat storage, therefore, the present application can determine the vertical distribution law of multiple water layers according to the operating characteristics of seasonal heat storage, divide the corresponding temperature range for each water layer in the multiple water layers according to the vertical distribution law, and then, assuming that the temperature of each layer of heat storage medium is fixed, use the water layer mass as the optimization variable to intuitively reflect the change of the heat energy storage during the charging and discharging process through the change of the water layer mass corresponding to each temperature, and further obtain the linear programming model of seasonal heat storage for the optimal planning of the power system

[0065] Specifically, the embodiment of the present application can first determine the vertical distribution law of multiple water layers according to the operating characteristics of seasonal heat storage, and divide the corresponding temperature range for each water layer in the multiple water layers according to the vertical distribution law; then, when the heat storage medium of each water layer in the multiple water layers is at a fixed temperature, determine the mass change of each water layer in the multiple water layers corresponding to the temperature range during the heat energy charging and discharging process; finally, determine the change of the heat storage amount of each water layer in the multiple water layers during the heat energy charging and discharging process according to the mass change, so as to complete the linearization process of the hydrothermal dynamic model of seasonal heat storage and obtain the linear programming model of seasonal heat storage

[0066] For example, when the heat storage medium of each water layer in the multiple water layers is at a fixed temperature, the specific analysis process of the mass change and heat storage amount change of each water layer in the multiple water layers during the heat energy charging and discharging process, that is, the linearization process of the hydrothermal dynamic model of seasonal heat storage can but is not limited to be expressed as follows

[0067] (1) Heat exchange between layers

[0068] For sensible heat storage (i.e., seasonal heat storage stores the excess heat in a certain season for use in other seasons, stores heat by raising the temperature of a substance, and releases heat by lowering the temperature of the substance when needed), due to the vertical temperature distribution inside the heat storage device and maintaining a relatively stable state over a long period of time, that is, the heat exchanged between layers in sensible heat storage is much smaller than the heat charging / discharging and natural heat loss. Therefore, in the embodiments of the present application, the ratio of the heat exchanged between layers to the natural heat loss can be calculated, and its formula can be but not limited to expressed as follows:

[0069]

[0070] Among them, R h is the radius of the heat storage device, represents the heat absorbed by the i-th water layer from the adjacent water layer within the time period t, represents the heat obtained by the i-th water layer from natural heat loss within the time period t, k h represents the thermal conductivity of water, U h,s,i represents the lateral heat transfer coefficient of the i-th water layer, represents the ambient temperature of the heat storage tank at time t, T h,i,t represents the temperature of the i-th water layer at time t, Δx h,i+1,t represents the central distance between the (i + 1)-th heat storage medium layer and the i-th heat storage medium layer, Δx h,i-1,t represents the central distance between the (i - 1)-th heat storage medium layer and the i-th heat storage medium layer.

[0071] Compared with the temperature difference between the high-temperature heat storage medium layer and the ambient temperature, the temperature difference between adjacent water layers can be ignored. Therefore, the absolute values of the two terms in the brackets in formula (3) are similar, and the overall value tends to be a very small value. This indicates that in the modeling process, compared with the natural heat loss of each heat storage medium layer, the heat exchanged between layers can be ignored.

[0072] (2) Heat charging / discharging

[0073] In the embodiments of the present application, for the heat charging / discharging behavior of the heat storage device, it can be directly modeled as the mass change of the heat storage medium layer, that is, each water layer, and its formula can be but not limited to expressed as follows:

[0074]

[0075] Among them, Δm h,i,t is the mass change of the i-th water layer caused by heat charging / discharging within the time period t, and respectively represent the flowing water flow rates from the bottom layer to the top layer / from the top layer to the bottom layer.

[0076] (3) Natural heat loss

[0077] For the natural loss of heat, the water temperature of each layer decreases, that is, the mass of the water layer corresponding to the high-temperature layer continuously decreases, and the mass of the water layer corresponding to the low-temperature layer increases. Therefore, for the natural heat loss of water layer i within time period t, the embodiments of the present application can equivalently consider it as if has transferred to the (i + 1)-th layer, and its formula can be but is not limited to being expressed as follows:

[0078]

[0079] From this, Equation (6) can be obtained:

[0080]

[0081] Among them, represents the mass equivalently transferred from water layer i to water layer i + 1 under the action of natural heat loss; ρ h is the density of the heat storage medium. Denote the constant coefficient on the right side of the equation as the natural loss transfer coefficient Then, the natural loss of the planning model can be but is not limited to being modeled as Equation (7):

[0082]

[0083] Among them, the natural loss transfer coefficient represents the proportion of the mass transfer amount of the heat storage medium in the i-th layer to the adjacent lower layer due to natural heat loss in the total mass within time period t.

[0084] In summary, by integrating each model, a linear programming model for seasonal heat storage that can describe the mass dynamic changes between different water layers during the charging and discharging processes can be obtained, and it can be but is not limited to being expressed as follows:

[0085]

[0086] Step S102: Based on the linear programming model and the initial optimization planning model of the target power system, establish a power optimization planning model considering seasonal heat storage modeling.

[0087] As a possible implementation manner, after establishing the refined model of seasonal heat storage, that is, the linear programming model of seasonal heat storage, the embodiments of the present application can embed this model into the initial optimization planning model of the target power system to establish a power optimization planning model considering refined modeling of seasonal heat storage, so as to solve the multi-faceted optimization planning of the power system based on this power optimization planning model.

[0088] Among them, the target power system can be understood here as a power system with seasonal heat storage and embedded with the linear programming model of seasonal heat storage.

[0089] Next, the process of establishing a refined power optimization planning model considering seasonal heat storage in the embodiments of the present application will be further explained.

[0090] Optionally, in an embodiment of the present application, based on a linear programming model and an initial optimization planning model of a target power system, a power optimization planning model considering seasonal heat storage modeling is established, including: obtaining multiple operating constraints of the target power system, and based on the linear programming model, obtaining multiple heat storage constraints of seasonal heat storage; establishing a power optimization planning model considering seasonal heat storage modeling according to the multiple operating constraints of the target power system, the multiple heat storage constraints of seasonal heat storage, and the initial optimization planning model.

[0091] Those skilled in the art can understand that in the actual operation processes of the power system and seasonal heat storage, certain constraints will be imposed. For example, the cost constraint of the power system, the capacity constraint of the heat storage device of seasonal heat storage, etc.

[0092] Based on this, in some embodiments, when establishing a refined power optimization planning model considering seasonal heat storage modeling, the present application can obtain multiple operating constraints of the target power system, and based on the linear programming model, obtain multiple heat storage constraints of seasonal heat storage, and then establish a power optimization planning model considering seasonal heat storage modeling according to the multiple operating constraints of the target power system, the multiple heat storage constraints of seasonal heat storage, and the initial optimization planning model of the target power system.

[0093] For example, let the objective function of the initial optimization planning model of the target power system be:

[0094] minC ove =C inv +C ope (9)

[0095] Wherein, C ove is the annualized total cost, C inv is the annualized investment and construction cost of the system, and C ope is the annual operating cost. The specific expressions of each cost are as follows

[0096] Furthermore, the annualized investment and construction cost C inv of the target power system can be but is not limited to expressed as follows:

[0097]

[0098] Wherein, represents the unit annualized investment cost of the generator set g, represents the investment capacity of the generator set, and the superscript G represents various types of unit types, specifically including thermal power TG, wind power WG, photovoltaic PV, hydropower HY, etc.; Denotes the annualized investment cost per unit of battery energy storage b, B b Denotes the installed capacity of battery energy storage; And Respectively denote the annualized investment cost per unit of the heat pump and seasonal heat storage, And Respectively denote the installed capacities of the heat pump and seasonal heat storage; Is the annualized investment cost of the to-be-built line l, Is the investment capacity of the to-be-built line l, Is a 0-1 variable representing whether the to-be-built line l is to be built or not.

[0099] The annual operating cost of the target power system can be but is not limited to being expressed as follows:

[0100]

[0101] Among them, δ s Is the corresponding weight for each scenario, Is the variable operating cost of thermal power unit g, Is the hourly output of thermal power unit g; c cur Is the unit load shedding penalty, Is the load shedding electricity; c fuel Represents the unit cost based on traditional fossil energy for heating, Is the heat quantity based on traditional fossil energy for heating.

[0102] Transform the operating constraints of the target power system into the constraint conditions of the initial optimization planning model, including but not limited to the following constraint conditions:

[0103] (1) Power network operation constraints:

[0104] The power network operation constraints specifically include power system operation constraints such as node power balance, traditional thermal power operation, wind power / solar power operation, and line power flow. Considering that the operating conditions of different power systems are different, the specific constraint conditions can be set by professionals in this field according to the actual situation and actual needs of the target power system. In the embodiments of this application, only a brief exemplary description is given and no specific limitations are made.

[0105]

[0106] (2) Seasonal heat storage constraints:

[0107] (2-1) The maximum capacity constraint can be but is not limited to being expressed as:

[0108]

[0109] Among them, And respectively represent the upper limits of the installed capacities of the heat pump and seasonal heat storage. That is, equations (13) and (14) respectively set the upper limits of the heat pump and the plan for seasonal heat storage.

[0110] (2-2) The electrothermal conversion relationship can be but is not limited to being expressed as:

[0111]

[0112] where represents the electric heating power, respectively represent the heat flow rate stored in the heat storage tank and the direct heating load, is the energy conversion efficiency of the heat pump. That is, equation (15) can ensure the power balance of the heating power.

[0113] (2-3) The heat load balance constraint can be but is not limited to being expressed as:

[0114]

[0115] where respectively represent the heat release from the heat storage tank, the direct heating by the heat pump, and the heating by traditional fossil energy, is the hourly heat load demand. That is, equation (16) describes the heat power balance constraint conditions in the region.

[0116] (2-4) The charging and discharging energy constraint can be but is not limited to being expressed as:

[0117]

[0118] where and respectively represent the flowing water flow rate from the bottom layer to the top layer / from the top layer to the bottom layer. That is, equations (17) and (18) respectively give the physical relationship constraints between the charging and discharging of the heat storage tank and the water layer temperature.

[0119] (2-5) The operation constraint can be but is not limited to being expressed as:

[0120]

[0121] 0 ≤ m h,i,s,t ≤ m h (22)

[0122] 0 ≤ ∑ i m h,i,s,t ≤ m h (23)

[0123]

[0124] Equations (19)-(25) thus construct an operation model of seasonal heat storage for the power system, where:

[0125] Formula (19) limits the power consumption of the electric heat pump connected to the seasonal heat storage; Formula (20) is the upper and lower limit constraints on the quality of hot water charged and discharged from the storage tank; Formula (21) describes the quality change of each water layer in the seasonal heat storage; Formula (22) is the upper and lower limit constraints on the quality of the single-layer medium; Formula (23) constrains the total water storage quality of the heat storage tank; Formula (24) is the water flow equation constraint on the top and bottom layers of the heat storage tank; Formula (25) is the annual balance constraint on the water volume of each water layer in the seasonal heat storage.

[0126] After incorporating these constraints into the initial optimization model of the target power system, a power optimization planning model that takes into account the refined modeling of seasonal heat storage can be obtained.

[0127] Step S103, solving the power optimization planning model to obtain the seasonal heat storage capacity configuration and optimized operation plan of the target power system.

[0128] In other embodiments, after obtaining the power optimization planning model considering the refined modeling of seasonal heat storage, the present application can input the obtained mathematical model and boundary conditions of the power optimization planning model into a certain solution software for optimization solution, thereby obtaining the optimization calculation results, such as the investment capacity of the generator set. Installed capacity of electric heat pumps and seasonal thermal storage and and the mass m of water layer i in heat storage tank h at time t in scenario s h,i,s,t Etc., that is, the planned capacity configuration of each heat storage device and the quality of the water layer for seasonal heat storage, so as to provide a reference for the formulation of the power system planning scheme and obtain the system optimization planning scheme for the target power system.

[0129] Figure 2 This is a flow chart of a method for optimizing power system planning based on seasonal heat storage modeling according to an embodiment of the present application. The overall process can be as follows: Figure 2 As shown:

[0130] Step S201, analyzing the hydrothermal dynamic model of seasonal heat storage, and analyzing the coupling characteristics between various water layers during the seasonal heat storage operation;

[0131] Step S202, taking the water layer quality as an optimization variable, linearizing the nonlinear constraints of seasonal heat storage operation, and forming a linear programming model of seasonal heat storage, so that the processed linear programming model of seasonal heat storage is suitable for optimizing and solving the power system optimization planning model;

[0132] Step S203, embedding the proposed seasonal heat storage refinement model (i.e., seasonal heat storage linear programming model) into the power system optimization planning model;

[0133] Step S204: Based on the model solution, obtain the seasonal heat storage capacity configuration and operation strategy that conform to the actual operation conditions.

[0134] In the embodiment of the present application, the seasonal heat storage capacity configuration and optimal operation plan of the target power system can be obtained by solving the power optimization planning model. Power design and operation scheduling personnel can carry out reasonable capacity planning configuration of the power system according to the solution results, scientifically configure the seasonal heat storage capacity, effectively analyze the power system dispatching operation of seasonal heat storage in the power system, provide a method basis for the formulation of the planning scheme of the actual power system, realize the long-term and large-scale transfer of energy, and thus ensure the renewable energy consumption level in the power system, reduce system wind and light curtailment, improve the energy utilization value and the economy and environmental protection of the operation of the target power system by considering the operation conditions of seasonal heat storage.

[0135] According to the power system optimization planning method based on seasonal heat storage modeling proposed in the embodiment of the present application, the seasonal heat storage can be finely modeled according to the operation characteristics of seasonal heat storage, and the nonlinear constraints in the model are linearized to obtain the linear programming model of seasonal heat storage, which is embedded in the initial optimization planning model of the target power system, and then solved to obtain the seasonal heat storage capacity configuration and optimal operation plan of the target power system. Thus, on the basis of considering the hydrothermal dynamic process of seasonal heat storage, the time-varying heat dissipation characteristics of seasonal heat storage and its influence on the power system optimization planning are analyzed according to the coupling characteristics of each water layer, so as to obtain the refined linear programming model of seasonal heat storage and the optimization planning model of the target power system considering the fine modeling of seasonal heat storage, and a reasonable power system planning scheme is obtained by solving the optimization planning model; moreover, the power system optimization planning model with refined modeling operation constraints of seasonal heat storage added in the present application can effectively describe the actual operation of seasonal heat storage in the power system, so that the planning scheme can take into account the operation characteristics of seasonal heat storage such as time-varying heat dissipation characteristics and the seasonal characteristics of renewable energy output, effectively improving the analysis ability of high-proportion renewable energy power systems and providing support for the dispatching operation of future high-proportion renewable energy power systems. Thus, the problems in the related technology are solved, such as that the research on the participation of seasonal heat storage in the power system planning operation rarely considers the dynamic physical process in the operation process of seasonal heat storage, and the analysis and research on the time-varying heat dissipation characteristics of seasonal heat storage throughout the year are also insufficient, so that the influence of the time-varying heat dissipation characteristics of seasonal heat storage on the power system operation and renewable energy consumption in different periods cannot be analyzed.

[0136] Next, describe the power system optimization planning device based on seasonal heat storage modeling proposed in the embodiment of the present application with reference to the accompanying drawings.

[0137] Figure 3It is a schematic structural diagram of a power system optimization planning device based on seasonal heat storage modeling according to an embodiment of the present application.

[0138] As Figure 3 shown, the power system optimization planning device 10 based on seasonal heat storage modeling includes: a processing module 100, a building module 200, and a solving module 300.

[0139] Among them, the processing module 100 is used to linearize a pre-established hydrothermal dynamic model of seasonal heat storage according to the operating characteristics of seasonal heat storage to obtain a linear programming model of seasonal heat storage.

[0140] The building module 200 is used to build a power optimization planning model considering seasonal heat storage modeling based on the linear programming model and the initial optimization planning model of the target power system.

[0141] The solving module 300 is used to solve the power optimization planning model to obtain the seasonal heat storage capacity configuration and optimized operation plan of the target power system.

[0142] Optionally, in an embodiment of the present application, it further includes: an analysis module and a determination module.

[0143] Among them, the analysis module is used to analyze the coupling characteristics between multiple water layers of the heat storage device corresponding to seasonal heat storage based on the dynamic operation process of seasonal heat storage before linearizing the pre-established hydrothermal dynamic model of seasonal heat storage according to the operating characteristics of seasonal heat storage.

[0144] The determination module is used to determine the operating characteristics of seasonal heat storage according to the coupling characteristics between multiple water layers.

[0145] Optionally, in an embodiment of the present application, the processing module 100 includes: a division unit, a determination unit, and a processing unit.

[0146] Among them, the division unit is used to determine the vertical distribution law of multiple water layers according to the operating characteristics of seasonal heat storage, and divide corresponding temperature intervals for each water layer in the multiple water layers according to the vertical distribution law.

[0147] The determination unit is used to determine the mass change of each water layer in the multiple water layers corresponding to the temperature interval during the heat energy charging and discharging process when the heat storage medium of each water layer in the multiple water layers is at a fixed temperature.

[0148] The processing unit is used to determine the heat storage change of each water layer in the multiple water layers during the heat energy charging and discharging process according to the mass change to complete the linearization process and obtain a linear programming model of seasonal heat storage.

[0149] Optionally, in an embodiment of the present application, the expression of the mass change of each water layer among multiple water layers during the thermal energy charging and discharging process can be but is not limited to being expressed as:

[0150]

[0151] Wherein, m h,i,t+1 represents the mass of the water layer i at the moment t + 1, m h,i,t represents the mass of the water layer i at the moment t, and Δm h,i,t is the mass change of the water layer i caused by energy charging and discharging within the time period t. represents the mass equivalently transferred from the water layer i to the water layer i + 1 under the action of natural heat loss, represents the mass equivalently transferred from the water layer i - 1 to the water layer i under the action of natural heat loss, and respectively represent the charging water flow rate and discharging water flow rate of the water layer i, and are the natural loss transfer coefficients, and m h,i-1,t represents the mass of the water layer i at the moment t - 1.

[0152] Optionally, in an embodiment of the present application, the establishing module 200 includes: an obtaining unit and an establishing unit.

[0153] Wherein, the obtaining unit is configured to obtain multiple operation constraints of the target power system and, based on a linear programming model, obtain multiple heat storage constraints for seasonal heat storage.

[0154] The establishing unit is configured to establish a power optimization planning model considering seasonal heat storage modeling according to multiple operation constraints of the target power system, multiple heat storage constraints for seasonal heat storage, and an initial optimization planning model.

[0155] Optionally, in an embodiment of the present application, the expression of the pre-established hydrothermal dynamic model of seasonal heat storage can be but is not limited to being expressed as:

[0156]

[0157] Wherein, ΔQ h,i,t is the total heat obtained by the water layer i within the time period t, is the heat absorbed by the water layer i from the adjacent water layer within the time period t, is the heat accumulated by the water layer i due to energy charging and discharging within the time period t, is the heat obtained by the water layer i from natural heat loss within the time period t.

[0158] It should be noted that the foregoing explanation of the embodiments of the power system optimization planning method based on seasonal heat storage modeling is also applicable to the power system optimization planning device based on seasonal heat storage modeling of this embodiment, and will not be elaborated here.

[0159] The power system optimization planning device based on seasonal heat storage modeling proposed according to the embodiments of the present application can perform refined modeling of seasonal heat storage according to the operating characteristics of seasonal heat storage, linearize the non-linear constraints in the model to obtain a linear programming model of seasonal heat storage, embed it into the initial optimization planning model of the target power system, and then solve it to obtain the seasonal heat storage capacity configuration and optimized operation plan of the target power system. Thus, on the basis of considering the hydrothermal dynamic process of seasonal heat storage, the time-varying heat dissipation characteristics of seasonal heat storage and its impact on the power system optimization planning are analyzed according to the coupling characteristics of each water layer, so as to obtain a refined linear programming model of seasonal heat storage and an optimization planning model of the target power system considering refined modeling of seasonal heat storage. A reasonable power system planning scheme is obtained by solving the optimization planning model; moreover, the power system optimization planning model with refined modeling operation constraints of seasonal heat storage added in the present application can effectively describe the actual operation of seasonal heat storage in the power system, so that the planning scheme can take into account the operating characteristics of seasonal heat storage such as time-varying heat dissipation characteristics and the seasonal characteristics of renewable energy output, effectively improving the analysis ability of high-proportion renewable energy power systems and providing support for the dispatching operation of future high-proportion renewable energy power systems. Thus, it solves the problems in the related technologies that the research on the participation of seasonal heat storage in the power system planning operation rarely considers the dynamic physical process in the operation process of seasonal heat storage, and the analysis and research on the time-varying heat dissipation characteristics of seasonal heat storage throughout the year are also insufficient, so that the impact of the time-varying heat dissipation characteristics of seasonal heat storage on the power system operation and renewable energy consumption in different periods cannot be analyzed.

[0160] Figure 4 The structural schematic diagram of the electronic device provided by the embodiments of the present application. The electronic device may include:

[0161] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0162] When the processor 402 executes the program, it implements the power system optimization planning method based on seasonal heat storage modeling provided in the above embodiments.

[0163] Furthermore, the electronic device further includes:

[0164] A communication interface 403 for communication between the memory 401 and the processor 402.

[0165] A memory 401 for storing a computer program that can run on a processor 402.

[0166] The memory 401 may include a high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0167] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0168] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 can communicate with each other through an internal interface.

[0169] The processor 402 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0170] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned power system optimization planning method based on seasonal heat storage modeling is implemented.

[0171] The embodiments of the present application also provide a computer program product, including a computer program, which can run computer instructions, and when the computer instructions are executed by a processor, the power system optimization planning method based on seasonal heat storage modeling provided by the embodiments of the present application is implemented.

[0172] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0173] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0174] Any process or method description shown in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0175] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection part (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0176] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0177] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above-described embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0178] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, may exist separately physically for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0179] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An optimization planning method for a power system based on seasonal heat storage modeling, characterized in that It includes the following steps: Linearize the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage to obtain the linear programming model of the seasonal heat storage; Based on the linear programming model and the initial optimization planning model of the target power system, establish a power optimization planning model considering seasonal heat storage modeling; Solve the power optimization planning model to obtain the seasonal heat storage capacity configuration and optimized operation plan of the target power system.

2. The method according to claim 1, wherein Before linearizing the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage, it further includes: Based on the dynamic operation process of the seasonal heat storage, analyze the coupling characteristics between multiple water layers of the heat storage device corresponding to the seasonal heat storage; Determine the operating characteristics of the seasonal heat storage according to the coupling characteristics between the multiple water layers.

3. The method according to claim 2, wherein The linearizing the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage to obtain the linear programming model of the seasonal heat storage includes: Determine the vertical distribution law of the multiple water layers according to the operating characteristics of the seasonal heat storage, and divide corresponding temperature intervals for each of the multiple water layers according to the vertical distribution law; When the heat storage medium of each of the multiple water layers is at a fixed temperature, determine the mass change of each of the multiple water layers corresponding to the temperature interval during the heat energy charging and discharging process; Determine the change in heat storage capacity of each of the multiple water layers during the heat energy charging and discharging process according to the mass change to complete the linearization process and obtain the linear programming model of the seasonal heat storage.

4. The method according to claim 3, wherein The expression for the mass change of each of the multiple water layers during the heat energy charging and discharging process is: where, m h,i,t+1 represents the mass of water layer i at time t+1, m h,i,t represents the mass of water layer i at time t, Δm h,i,t is the mass change of water layer i caused by charging and discharging energy during time period t, represents the mass equivalently transferred from water layer i to water layer i+1 under the action of natural heat loss, represents the mass equivalently transferred from water layer i-1 to water layer i under the action of natural heat loss, and respectively represent the charging water flow rate and discharging water flow rate of water layer i, and are the natural loss transfer coefficients, m h,i-1,t represents the mass of water layer i at time t-1.

5. The method according to claim 1, characterized in that, The establishing a power optimization planning model considering seasonal heat storage modeling based on the linear programming model and the initial optimization planning model of the target power system includes: Obtain multiple operating constraints of the target power system, and based on the linear programming model, obtain multiple heat storage constraints of the seasonal heat storage; Establish the power optimization planning model considering seasonal heat storage modeling according to the multiple operating constraints of the target power system, the multiple heat storage constraints of the seasonal heat storage, and the initial optimization planning model.

6. The method according to claim 1, wherein The expression of the pre-established hydrothermal dynamic model of the seasonal heat storage is: Among them, ΔQ h,i,t is the total heat obtained by water layer i within time period t, is the heat absorbed by water layer i from the adjacent water layer within time period t, is the heat accumulated by charge and discharge of water layer i within time period t, is the heat obtained by water layer i from natural heat loss within time period t.

7. An optimization planning device for a power system based on seasonal heat storage modeling, characterized in that, It includes: A processing module for linearizing the pre-established hydrothermal dynamic model of the seasonal heat storage according to the operating characteristics of the seasonal heat storage to obtain the linear programming model of the seasonal heat storage; A establishing module for establishing a power optimization planning model considering seasonal heat storage modeling based on the linear programming model and the initial optimization planning model of the target power system; A solving module for solving the power optimization planning model to obtain the seasonal heat storage capacity configuration and optimized operation plan of the target power system.

8. An electronic device, characterized in that, It includes: 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 method for optimizing the planning of a power system based on seasonal heat storage modeling as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for optimizing the planning of a power system based on seasonal heat storage modeling as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the method for optimizing the planning of a power system based on seasonal heat storage modeling as described in any one of claims 1-6.

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