CN113313410B - Multi-energy coupling modeling evaluation method and device and terminal equipment - Google Patents
Multi-energy coupling modeling evaluation method and device and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of multi-energy coupling modeling, and provides a multi-energy coupling modeling evaluation method, a device and terminal equipment, wherein the method comprises the following steps: acquiring use information of various energy systems and electricity utilization information of a target area; establishing a multi-energy coupling model of the multi-energy comprehensive system based on the use information of the multiple energy systems and the electricity utilization information of the target area; based on an energy hub theory, performing layered modeling solution on the multi-energy coupling model to obtain a plurality of equipment site selection, volume fixing and operation scheduling schemes corresponding to the target area; and performing load prediction and economy on each equipment locating sizing and operation scheduling scheme, and selecting an optimal equipment locating sizing and operation scheduling scheme corresponding to the target area according to an evaluation result. The scheme provided by the application can facilitate analysis and comparison among different coupling schemes, so that the optimal multi-energy coupling scheme is obtained, and the energy utilization rate is improved.
Description
Technical Field
The invention belongs to the technical field of multi-energy coupling modeling, and particularly relates to a multi-energy coupling modeling evaluation method and device and terminal equipment.
Background
From a system perspective, coupling different energy carriers presents a number of potential advantages over conventional decoupled energy supply networks, with the degree of freedom provided by the redundant energy flow paths providing room for multi-energy co-optimization. Through interconnection and intercommunication of energy systems, the space-time imbalance of different energy sources under different supply and demand backgrounds is improved, and the purposes of reducing the energy consumption cost of the system, improving the energy consumption efficiency and enhancing the reliability of energy supply are achieved. The operating characteristics of different energy supply systems are different, and through mutual coordination, the uncertainty of an energy supply link can be reduced or eliminated, so that the safe consumption of renewable energy sources is facilitated.
The comprehensive energy is an organic whole consisting of various energy conversion equipment, energy storage equipment, energy supply pipelines, users and the like, and the coupling mechanism of various forms of energy is the first problem of realizing comprehensive energy planning. However, the existing multi-energy coupling modes are different, and the superiority of the multi-energy coupling system cannot be reflected.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, and a terminal device for evaluating multi-energy coupling modeling, so as to solve the problem in the prior art that the utilization rate of a multi-energy coupling system is low.
The first aspect of the embodiments of the present invention provides a multi-energy coupling modeling evaluation method, including:
acquiring use information of various energy systems and electricity utilization information of a target area;
establishing a multi-energy coupling model of the multi-energy comprehensive system based on the use information of the multiple energy systems and the electricity utilization information of the target area, wherein the multi-energy coupling model comprises a multi-energy coupling static linear model and a multi-energy complementary dynamic model;
based on an energy hub theory, performing layered modeling solution on the multi-energy coupling model to obtain a plurality of equipment site selection, volume fixing and operation scheduling schemes corresponding to the target area;
and carrying out load prediction and economy on each equipment location capacity and operation scheduling scheme, and selecting an optimal equipment location capacity and operation scheduling scheme corresponding to the target area according to an evaluation result.
A second aspect of an embodiment of the present invention provides a multi-energy coupling modeling and evaluating apparatus, including:
the information acquisition module is used for acquiring the use information of various energy systems and the electricity utilization information of a target area;
the model establishing module is used for establishing a multi-energy coupling model of the multi-energy comprehensive system based on the use information of various energy systems and the electricity utilization information of the target area, and the multi-energy coupling model comprises a multi-energy coupling static linear model and a multi-energy complementary dynamic model;
the solving module is used for carrying out layered modeling solving on the multi-energy coupling model based on an energy hub theory to obtain a plurality of equipment locating, sizing and operation scheduling schemes corresponding to the target area;
and the optimal scheme acquisition module is used for carrying out load prediction and economy on the locating, sizing and operation scheduling schemes of each device and selecting the optimal device locating, sizing and operation scheduling scheme corresponding to the target area according to the evaluation result.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the multi-energy coupling modeling and evaluating method when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the multi-energy coupling modeling evaluation method as described above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the method comprises the steps of firstly acquiring use information of various energy systems and electricity utilization information of a target area; then establishing a multi-energy coupling model of the multi-energy comprehensive system based on the use information of the multiple energy systems and the electricity utilization information of the target area, wherein the multi-energy coupling model comprises a multi-energy coupling static linear model and a multi-energy complementary dynamic model; based on an energy hub theory, performing layered modeling solution on the multi-energy coupling model to obtain a plurality of equipment locating, sizing and operation scheduling schemes corresponding to the target area; and finally, carrying out load prediction and economy on each equipment locating sizing and operation scheduling scheme, and selecting an optimal equipment locating sizing and operation scheduling scheme corresponding to the target area according to an evaluation result. The scheme provided by the embodiment can facilitate analysis and comparison among different coupling schemes, so that an optimal multi-energy coupling scheme is obtained, and the energy utilization rate is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic diagram of an implementation of a multi-energy coupling modeling evaluation method provided by an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a multi-energy monitoring system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a multi-energy coupling modeling and evaluation apparatus according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to illustrate the technical means of the present invention, the following description is given by way of specific examples.
In one embodiment, as shown in fig. 1, fig. 1 shows a flow of implementing a multi-energy coupling modeling evaluation method provided by this embodiment, which includes:
s101: acquiring use information of various energy systems and electricity utilization information of a target area;
s102: establishing a multi-energy coupling model of the multi-energy comprehensive system based on the use information of the multiple energy systems and the electricity utilization information of the target area, wherein the multi-energy coupling model comprises a multi-energy coupling static linear model and a multi-energy complementary dynamic model;
s103: based on an energy hub theory, performing layered modeling solution on the multi-energy coupling model to obtain a plurality of equipment site selection, volume fixing and operation scheduling schemes corresponding to the target area;
s104: and carrying out load prediction and economy on each equipment location capacity and operation scheduling scheme, and selecting an optimal equipment location capacity and operation scheduling scheme corresponding to the target area according to an evaluation result.
In one embodiment, the energy system comprises a solar photovoltaic power generation system, an energy storage system, a ground source heat pump system, an ice cold storage system, a solar water heating system, a solar air conditioning system, a regenerative electric boiler system, a municipal heat supply network and a solar water heating system;
the use information comprises energy types, existence modes, use modes and flow information among all links of the area network;
the electricity utilization information of the target area comprises the energy consumption user energy demand sum and the energy consumption user energy demand of each energy consumption user in the target area.
In this embodiment, firstly, the types, the existence modes and the use modes of the energy sources in the target area are investigated in various modes, and meanwhile, the flowing and transferring modes of the various energy sources among various links such as area sources, networks, loads, storages and the like are investigated and researched. And obtaining the use information of the multi-energy system.
The energy demand of the comprehensive energy is the sum of the energy demands of all energy consuming users scattered in the region, when the data acquisition template is used for carrying out data investigation on the usage of the comprehensive energy, the data investigation needs to be carried out from the usage of the region total amount and the user units, and the data acquisition module obtains the energy demand sum of the energy consuming users in the target region and the energy demands of all energy consuming users through the mutual cooperation of the self control system and the operation monitoring system of the energy systems such as a ground source heat pump, ice storage, a heat storage electric boiler, a solar air conditioner, hot water, photovoltaic, energy storage and the like, and uploads the collected data to the database.
In one embodiment, the specific implementation flow of S102 in fig. 1 includes:
and establishing a multi-energy coupling static linear model and a multi-energy complementary dynamic model of four regional network links including a source, a network, a load and a storage based on the use information of the various energy systems and the electricity utilization information of the target region and considering the non-linearity, the dynamic characteristics, the multi-time scale and the uncertainty factors among the various energy systems.
In the embodiment, the multi-energy coupling modeling is performed under the condition that the influence of various factors such as nonlinearity, dynamic characteristics, multi-time scale, uncertainty and the like among the multi-energy sources of the multi-energy comprehensive system is considered, a multi-energy coupling static linear model comprising various links such as sources, networks, loads, storages and the like is completely constructed, and meanwhile, the multi-energy complementary dynamic model is established to meet the modeling requirements in various aspects such as static, dynamic and time delay characteristics and the like.
And further, after the model is established, the established model is subjected to layered modeling solution through Energy Hub theory, so that the cooperative optimization of the equipment type selection, site selection, volume determination and operation scheduling scheme is realized.
In one embodiment, S104 in fig. 1 includes:
analyzing parameters of economy, climate, building layout and population density in the target area to obtain a plurality of influence constants, and predicting or predicting combinations of loads and mutual coupling conditions of various energy system types in the multi-energy coupling model according to the plurality of influence constants;
and acquiring energy flow data of the multi-energy system, and acquiring multi-complex coupled load data according to the energy flow data of each energy system, the load of each energy system type and the prediction or combined prediction result of the mutual coupling condition of the load.
In an embodiment, the specific process of acquiring the energy flow data of the multi-energy system includes:
establishing a hybrid model of the power-natural gas system, establishing a power flow equation containing a plurality of energy network states under a unified framework, and solving the comprehensive power flow of the system.
In an embodiment, the specific process of acquiring energy flow data of the multi-energy system may further include:
and analyzing the coupling relation of the multiple energy systems in different modes, and performing decoupling calculation on each energy system to obtain energy flow data of the multiple energy systems.
In one embodiment, the multi-energy complementary dynamic model comprises a dynamic energy hub and a dynamic energy connector model, wherein the dynamic energy hub considers the dynamic characteristics of the energy conversion unit on the basis of a traditional hub model; the dynamic energy connector describes the static characteristics and the dynamic change rule of the electric energy, liquid working medium or gaseous fuel conveying link.
In this embodiment, data is analyzed by an evaluation module, the evaluation module is divided into load prediction and economy evaluation, the load prediction includes a macro-type prediction method and a micro-type prediction method, before prediction, parameter analysis needs to be performed on various factors such as economy, climate, building layout, population density and the like, various influence constants are obtained after a large amount of data is sorted and analyzed, prediction or combined prediction of electricity, gas, heat, cold and other types of loads and mutual coupling conditions thereof in a multi-energy coupling model is performed, and collected energy flow data is calculated, so that multi-complex coupled load data is obtained.
Specifically, the economic assessment is based on a first law and a second law of thermodynamics, and an engineering economic theory is introduced to perform key assessment on economic benefit, energy conservation and emission reduction.
In the embodiment, collected data are uploaded to a database through a self control system and an operation monitoring system of energy subsystems such as a ground source heat pump, an ice cold accumulation system, a heat accumulation type electric boiler, a solar air conditioner, a hot water system, a photovoltaic system, an energy storage system and the like, so that a coupling model is established, energy flows generated by various energy control systems in the model such as a solar photovoltaic power generation system, an energy storage system, a ground source heat pump system, an ice cold accumulation system, a solar water heating system, a solar air conditioner system, a heat accumulation type electric boiler system, a municipal heat supply network, a solar water heating system and the like are counted and calculated, the obtained data are substituted into a multi-element composite prediction method, and meanwhile, the economy of the model is researched based on engineering economics. Therefore, various multi-energy coupling models are evaluated and analyzed, the optimal model suitable for the region is selected, and the construction of a comprehensive energy system is facilitated.
The energy system comprises an on-site system, and the on-site system comprises a solar photovoltaic power generation system, an energy storage system, a ground source heat pump system, an ice cold storage system, a solar water heating system, a solar air conditioning system, a heat accumulation type electric boiler system, a municipal heat supply network and a solar water heating system.
When the cold, heat, electricity and hot water daily predicted load data are counted, the daily generated energy of the solar photovoltaic power generation system is predicted through a GA-BP comprehensive algorithm; the solar air-conditioning system refrigerates and heats every day; solar hot water produces hot water every day; based on the above, the optimized proportion criterion is selected, and the energy production of the solar photovoltaic power generation, the solar air conditioner and the solar water heating system is closely related to the solar radiation amount and the illumination hours, so that the replacement of the energy is gradually an important mode of comprehensive demand response, the replacement of the energy can reduce the energy consumption cost on the user side, the scheduling expectation of each energy system is responded on the premise of meeting the energy consumption demand, and the considerable response income provides sufficient driving force for the corresponding behavior of the user.
As shown in fig. 2, fig. 2 shows a system structure provided by an embodiment of the present invention, which includes an operation monitoring system, a power grid detection device, solar hot water, a solar air conditioner, a ground source heat pump control system, a heat storage boiler control system, an ice storage control system, and a photovoltaic power generation control system, where the solar hot water, the solar air conditioner, the ground source heat pump control system, the heat storage boiler control system, the ice storage control system, and the photovoltaic power generation control system are energy systems, and the power grid detection device is used for associating each energy system. The system comprises a power grid and a photovoltaic power generation system, and transmits collected data to an operation monitoring system. The operation monitoring system is the brain of the whole energy network dispatching control system, completes the analysis, mining and prediction of historical data, and works out an energy production proportioning plan according to load side prediction data and a selected criterion, thereby establishing an optimal multi-energy coupling model.
The power flow solving algorithm used in the energy flow calculation of the energy system in the multi-energy coupling static linear model can be divided into a unified solution and a decoupling solution. When a unified solution is adopted, a hybrid model of the power-natural gas system needs to be established, then a power flow equation comprising a plurality of energy network states is established under a unified framework, the comprehensive power flow of the system is solved, and the decoupling solution needs to analyze the coupling relation of the systems under different modes and decouple and calculate the power flow, the natural gas and the thermodynamic system, so that the power flow, the gas and the thermal coupling analysis modules can be added on an original independent power flow calculation module to realize the purpose.
The multi-energy complementary dynamic model comprises a dynamic energy hub and a dynamic energy connector model. The dynamic energy concentrator considers the dynamic characteristics of an energy conversion unit on the basis of a traditional concentrator model, and the dynamic energy connector describes the static characteristics and the dynamic change rule of an electric energy, liquid working medium or gaseous fuel conveying link.
In the embodiment, the regional comprehensive energy system is firstly investigated and analyzed, then the planned optimization model is used for performing complementary modeling on the energy sources in the system, load prediction combining macroscopic modeling and microscopic modeling is performed by using obtained data and a solution obtained by layered modeling, meanwhile, the economy of the model is evaluated in two aspects of improvement of energy utilization efficiency and improvement of energy grade, a researcher can conveniently analyze and compare different models, an optimal model is obtained, and various energy sources can be utilized at the maximum efficiency when the comprehensive energy system is built.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In one embodiment, as shown in fig. 3, fig. 3 shows a structure of the multi-energy coupling modeling and evaluating apparatus 100 provided by the present embodiment, which includes:
the information acquisition module 110 is used for acquiring the use information of various energy systems and the electricity utilization information of a target area;
the model establishing module 120 is configured to establish a multi-energy coupling model of the multi-energy comprehensive system based on the usage information of the multiple energy systems and the electricity utilization information of the target area, where the multi-energy coupling model includes a multi-energy coupling static linear model and a multi-energy complementary dynamic model;
the solving module 130 is configured to perform layered modeling solution on the multi-energy coupling model based on an energy hub theory to obtain a multiple device locating, sizing and operation scheduling scheme corresponding to the target area;
and the optimal scheme obtaining module 140 is configured to perform load prediction and economy on each device location capacity and operation scheduling scheme, and select an optimal device location capacity and operation scheduling scheme corresponding to the target area according to the evaluation result.
In one embodiment, the energy system comprises a solar photovoltaic power generation system, an energy storage system, a ground source heat pump system, an ice cold storage system, a solar water heating system, a solar air conditioning system, a heat accumulation type electric boiler system, a municipal heat supply network and a solar water heating system;
the use information comprises energy types, existence modes, use modes and flow information among all links of the area network;
the electricity utilization information of the target area comprises the energy consumption user energy demand sum and the energy consumption user energy demand of each energy consumption user in the target area.
In one embodiment, the model building module 120 is specifically configured to:
and establishing a multi-energy coupling static linear model and a multi-energy complementary dynamic model of four regional network links including a source, a network, a load and a storage based on the use information of the various energy systems and the electricity utilization information of the target region and considering the non-linearity, the dynamic characteristics, the multi-time scale and the uncertainty factors among the various energy systems.
In one embodiment, the solving module 130 includes:
the parameter analysis unit is used for carrying out parameter analysis on economy, climate, building layout and population density in the target area to obtain a plurality of influence constants, and predicting or combining and predicting the loads of various energy system types and the mutual coupling conditions of the energy system types in the multi-energy coupling model according to the plurality of influence constants;
and the load data acquisition unit is used for acquiring energy flow data of the multi-energy system and acquiring multi-complex coupled load data according to the energy flow data of each energy system, the load of various energy system types and the prediction or combined prediction result of the mutual coupling condition of the load.
In an embodiment, the load data obtaining unit is specifically configured to:
establishing a hybrid model of the power-natural gas system, establishing a power flow equation containing a plurality of energy network states under a unified framework, and solving the comprehensive power flow of the system.
In an embodiment, the load data obtaining unit is specifically configured to:
and analyzing the coupling relation of the multiple energy systems in different modes, and performing decoupling calculation on each energy system to obtain energy flow data of the multiple energy systems.
In one embodiment, the multi-energy complementary dynamic model comprises a dynamic energy hub and a dynamic energy connector model, wherein the dynamic energy hub considers the dynamic characteristics of the energy conversion unit on the basis of a traditional hub model; the dynamic energy connector describes the static characteristics and the dynamic change rule of the electric energy, liquid working medium or gaseous fuel conveying link.
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40, when executing the computer program 42, implements the steps of the various embodiments of the multi-energy coupling modeling and evaluation method described above, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 110 to 140 shown in fig. 3.
The computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 42 in the terminal device 4.
The terminal device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 4 and does not constitute a limitation of terminal device 4 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (7)
1. A multi-energy coupling modeling evaluation method is characterized by comprising the following steps:
acquiring use information of various energy systems and power utilization information of a target area;
establishing a multi-energy coupling model of the multi-energy comprehensive system based on the use information of the multiple energy systems and the electricity utilization information of the target area, wherein the multi-energy coupling model comprises a multi-energy coupling static linear model and a multi-energy complementary dynamic model;
based on an energy hub theory, performing layered modeling solution on the multi-energy coupling model to obtain a plurality of equipment site selection, volume fixing and operation scheduling schemes corresponding to the target area;
load prediction and economy are carried out on each equipment locating, sizing and operation scheduling scheme, and an optimal equipment locating, sizing and operation scheduling scheme corresponding to the target area is selected according to an evaluation result;
the method for establishing the multi-energy coupling model of the multi-energy comprehensive system based on the use information of the multiple energy systems and the electricity utilization information of the target area comprises the following steps:
based on the use information of various energy systems and the electricity utilization information of the target area, considering the non-linearity, dynamic characteristics, multi-time scale and uncertainty factors among the various energy systems, and establishing a multi-energy coupling static linear model and a multi-energy complementary dynamic model of four area network links including a source, a network, a load and a storage;
the load prediction of the location determination capacity and operation scheduling scheme of each device comprises the following steps:
carrying out parameter analysis on economy, climate, building layout and population density in the target area to obtain a plurality of influence constants, and carrying out prediction or combined prediction on loads of various energy system types and mutual coupling conditions thereof in the multi-energy coupling model according to the plurality of influence constants;
acquiring energy flow data of a multi-energy system, and acquiring multi-complex coupled load data according to the energy flow data of each energy system, the load of each energy system type and the prediction or combined prediction result of the mutual coupling condition of the load;
the multi-energy complementary dynamic model comprises a dynamic energy concentrator and a dynamic energy connector model, and the dynamic energy concentrator considers the dynamic characteristics of the energy conversion unit on the basis of the traditional concentrator model; the dynamic energy connector describes the static characteristics and the dynamic change rule of the electric energy, liquid working medium or gaseous fuel conveying link.
2. The multi-energy coupling modeling evaluation method of claim 1,
the energy system comprises a solar photovoltaic power generation system, an energy storage system, a ground source heat pump system, an ice cold storage system, a solar water heating system, a solar air conditioning system, a heat accumulating type electric boiler system, a municipal heat supply network and a solar water heating system;
the use information comprises energy types, existence modes, use modes and flow information among all links of the area network;
the electricity utilization information of the target area comprises the energy consumption user energy demand sum and the energy consumption user energy demand of each energy consumption user in the target area.
3. The multi-energy coupling modeling and evaluation method of claim 1, wherein the obtaining energy flow data for the multi-energy system comprises:
establishing a hybrid model of the power-natural gas system, establishing a power flow equation containing a plurality of energy network states under a unified framework, and solving the comprehensive power flow of the system.
4. The multi-energy coupling modeling and evaluation method of claim 1, wherein the obtaining energy flow data for the multi-energy system comprises:
and analyzing the coupling relation of the multiple energy systems in different modes, and performing decoupling calculation on each energy system to obtain energy flow data of the multiple energy systems.
5. A multi-energy coupling modeling and evaluation apparatus, comprising:
the information acquisition module is used for acquiring the use information of various energy systems and the electricity utilization information of a target area;
the model establishing module is used for establishing a multi-energy coupling model of the multi-energy comprehensive system based on the use information of the multiple energy systems and the electricity utilization information of the target area, and the multi-energy coupling model comprises a multi-energy coupling static linear model and a multi-energy complementary dynamic model;
the solving module is used for carrying out layered modeling solving on the multi-energy coupling model based on an energy hub theory to obtain a plurality of equipment locating, sizing and operation scheduling schemes corresponding to the target area;
the optimal scheme acquisition module is used for carrying out load prediction and economy on each equipment locating capacity and operation scheduling scheme, and selecting the optimal equipment locating capacity and operation scheduling scheme corresponding to the target area according to the evaluation result;
the model building module comprises:
based on the use information of various energy systems and the electricity utilization information of the target area, considering the non-linearity, dynamic characteristics, multi-time scale and uncertainty factors among the various energy systems, and establishing a multi-energy coupling static linear model and a multi-energy complementary dynamic model of four area network links including a source, a network, a load and a storage;
the optimal solution acquisition module comprises:
carrying out parameter analysis on economy, climate, building layout and population density in the target area to obtain a plurality of influence constants, and carrying out prediction or combined prediction on loads of various energy system types and mutual coupling conditions thereof in the multi-energy coupling model according to the plurality of influence constants;
acquiring energy flow data of a multi-energy system, and acquiring multi-complex coupled load data according to the energy flow data of each energy system, the load of each energy system type and the prediction or combined prediction result of the mutual coupling condition of the load;
the multi-energy complementary dynamic model comprises a dynamic energy concentrator and a dynamic energy connector model, and the dynamic energy concentrator considers the dynamic characteristics of the energy conversion unit on the basis of the traditional concentrator model; the dynamic energy connector describes the static characteristics and the dynamic change rule of the electric energy, liquid working medium or gaseous fuel conveying link.
6. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor realizes the steps of the method according to any of claims 1 to 4 when executing the computer program.
7. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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