Comprehensive energy system modeling method and device based on hierarchical network structure

Abstract

The invention relates to the technical field of energy management and control, and relates to an integrated energy system modeling method and device based on a hierarchical network structure, a computer program product and electronic equipment. The method comprises the following steps: acquiring equipment information in an integrated energy system and energy information in an energy process; energy related to the integrated energy system is used as nodes, a target directed multi-graph is constructed according to the equipment information and the energy information, and the target directed multi-graph is used for reflecting the coupling relation between different kinds of energy in the integrated energy system; a layered network structure is constructed based on the target directed multi-graph, an interlayer matrix between every two layers in the layered network structure is determined, and each interlayer matrix is used for describing the energy conversion relation of one device; and performing matrix coupling on the inter-layer matrixes to obtain a coupling matrix model of the integrated energy system. According to the invention, dynamic modeling of multiple energy sources in the energy process of the integrated energy system can be realized, and model support is provided for optimization control of the integrated energy system.

Description

Technical Field

[0001] This disclosure relates to the field of energy management technology, and more specifically, to a method for modeling integrated energy systems based on a hierarchical network structure, a device for modeling integrated energy systems based on a hierarchical network structure, a computer program product, and an electronic device. Background Technology

[0002] With the rapid development of new energy and digital technologies, integrated energy systems, as key nodes in the energy internet, undertake the tasks of integrating, converting, and coordinating the control of various energy forms such as electricity, heat, cooling, and natural gas. Traditional integrated energy system modeling methods are mostly based on static input-output relationships, failing to fully depict the dynamic interaction relationships between various energy sources and making it difficult to adapt to the optimization control requirements under complex operating conditions.

[0003] Currently, various technologies have emerged to explore the modeling and optimization of integrated energy systems, such as the introduction of AI (Artificial Intelligence), digital twins, and edge computing. However, there are still many shortcomings in dynamic modeling, the clarity of data-driven modeling structures, real-time solvability, and the accuracy of equipment performance expression, which to some extent affect the optimization of automated control of integrated energy systems based on modeling.

[0004] It should be noted that the information in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a modeling method, a modeling device, a computer program product, and an electronic device for integrated energy systems based on a hierarchical network structure, thereby enabling dynamic modeling of multiple energy sources in the energy process of an integrated energy system, improving the accuracy of dynamic modeling, and providing model support for the optimal control of integrated energy systems.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part by practice of this disclosure.

[0007] According to one aspect of this disclosure, a method for modeling an integrated energy system based on a hierarchical network structure is provided, comprising: acquiring equipment information and energy information in energy processes within the integrated energy system, wherein the energy processes include at least one of energy conversion, energy transfer, and energy distribution; constructing a target directed multigraph based on the energy involved in the integrated energy system as nodes, wherein the target directed multigraph reflects the coupling relationship between different energies in the integrated energy system; constructing a hierarchical network structure based on the target directed multigraph, and determining the inter-layer matrix between every two layers in the hierarchical network structure, wherein each inter-layer matrix describes the energy conversion relationship of a device; and coupling the inter-layer matrices to obtain a coupling matrix model of the integrated energy system.

[0008] In one exemplary embodiment of this disclosure, a target directed multigraph is constructed using the energy involved in the integrated energy system as nodes, based on equipment information and energy information. This includes: determining virtual input nodes and virtual output nodes, and representing the energy involved in the integrated energy system as energy nodes, to determine graph nodes based on the virtual input nodes, virtual output nodes, and energy nodes; determining directed edges between graph nodes based on energy information to obtain a first directed multigraph, wherein the directed edges of the first directed multigraph are labeled with equipment information and energy information involved in the graph nodes at both ends of the directed edge; simplifying the first directed multigraph based on energy information to obtain a second directed multigraph; and merging the graph nodes in the second directed multigraph according to energy category to obtain the target directed multigraph.

[0009] In an exemplary embodiment of this disclosure, if the energy output direction of a target graph node is connected to multiple directed edges, a sub-loop is added to the target graph node in the same number as the number of directed edges, and the energy allocation parameters of the directed edges corresponding to each sub-loop are labeled; the first directed multigraph is simplified according to the energy information to obtain a second directed multigraph, including: starting from a virtual input node, moving the energy information labeled on the directed edges of the first directed multigraph along the direction of the directed edges until it is impossible to move to a new directed edge; wherein, if the tail graph node of the target directed edge has a sub-loop, the energy information labeled on the target directed edge is no longer moved; if a target directed edge exists, the energy allocation parameters corresponding to the sub-loop of the target directed edge are added to the directed edge of the energy output direction of the tail graph node, and the sub-loop is deleted; device information is added to each directed edge to indicate the energy conversion medium between the graph nodes connected by the directed edge.

[0010] In one exemplary embodiment of this disclosure, the graph nodes in the second directed multigraph are merged according to the energy category to obtain a target directed multigraph, including: merging graph nodes with the same energy category in the second directed multigraph to obtain a merged node; and switching all directed edges connected to graph nodes with the same energy category to the merged node to obtain the target directed multigraph.

[0011] In one exemplary embodiment of this disclosure, constructing a hierarchical network structure based on a target directed multigraph includes: determining an energy input layer based on virtual input nodes and an energy output layer based on virtual output nodes; constructing an intermediate layer between the energy input layer and the energy output layer based on the number N of devices corresponding to the target directed multigraph, wherein the number of intermediate layers is equal to N+1, wherein each layer includes the same energy nodes as the target directed multigraph, and the energy categories of the energy nodes in each layer are ordered in the same way; constructing inter-layer connections between the energy nodes in each layer based on the target directed multigraph, and determining the hierarchical network structure based on the obtained inter-layer connections, wherein horizontal lines between layers represent the transfer of the same type of energy, and diagonal lines represent the conversion or transfer between different types of energy.

[0012] In one exemplary embodiment of this disclosure, determining the interlayer matrix between every two layers in a hierarchical network structure includes: constructing an empty matrix based on the energy nodes of the target directed multigraph; determining at least one target position in the empty matrix based on the interlayer connection for every two layers, and using the energy information between the two layers as elements of the at least one target position to obtain the interlayer matrix between the two layers.

[0013] In one exemplary embodiment of this disclosure, matrix coupling is performed between the interlayer matrices to obtain a coupled matrix model of the integrated energy system, including: multiplying the interlayer matrices sequentially in the direction from the energy output layer to the energy input layer to obtain the coupled matrix model.

[0014] In one exemplary embodiment of this disclosure, the energy information includes energy conversion efficiency, and the method further includes: performing characteristic fitting based on the rated energy information in the device information to obtain the energy efficiency function model of the device; and determining the energy conversion efficiency based on the energy efficiency function model.

[0015] According to one aspect of this disclosure, a modeling apparatus for an integrated energy system based on a hierarchical network structure is provided, comprising: an information acquisition module for acquiring equipment information and energy information in energy processes within the integrated energy system, wherein the energy processes include at least one of energy conversion, energy transfer, and energy distribution; a graph construction module for constructing a target directed multigraph based on the energy involved in the integrated energy system as nodes, according to the equipment information and energy information, wherein the target directed multigraph reflects the coupling relationship between different energies in the integrated energy system; a network construction module for constructing a hierarchical network structure based on the target directed multigraph and determining the inter-layer matrix between every two layers in the hierarchical network structure, wherein each inter-layer matrix describes the energy conversion relationship of a device; and a model construction module for matrix coupling of the inter-layer matrices to obtain a coupled matrix model of the integrated energy system.

[0016] According to one aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any of the above methods.

[0017] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the above methods by executing the executable instructions.

[0018] The exemplary embodiment of this disclosure describes a method for modeling an integrated energy system based on a hierarchical network structure. This method acquires equipment information and energy information from energy processes within the integrated energy system. Using the energy involved in the integrated energy system as nodes, it constructs a target directed multigraph based on the equipment and energy information. Then, it constructs a hierarchical network structure based on the target directed multigraph and determines the inter-layer matrix between each pair of layers in the hierarchical network structure. Each inter-layer matrix describes the energy conversion relationship of a device. Finally, it performs matrix coupling on the inter-layer matrices to obtain a coupled matrix model of the integrated energy system.

[0019] On the one hand, by constructing a target directed multigraph, various energy forms and their conversion / transfer processes are abstracted into nodes and edges, intuitively presenting the coupled topology of complex heterogeneous energy networks and avoiding the problem that traditional single-energy modeling cannot represent across dimensions. On the other hand, the hierarchical network structure divides the integrated energy system into different levels, quantifies device-level energy conversion relationships through inter-layer matrices, and achieves modular decomposition of high-dimensional nonlinear systems, significantly reducing computational complexity. Furthermore, by generating a global coupled matrix model through inter-layer matrix coupling, physical relationships such as device information (e.g., device parameters), energy flow constraints, and energy information (e.g., energy conversion efficiency) are encoded into matrix operations, facilitating rapid simulation analysis and providing a computable model foundation for optimized scheduling. Therefore, the modeling method of the exemplary embodiments of this disclosure can balance clear structure, dynamic scalability, and accurate representation of device performance, providing model support for the automatic control optimization of integrated energy systems using multiple energy sources.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which:

[0022] Figure 1 A flowchart illustrating an exemplary embodiment of the present disclosure of a method for modeling an integrated energy system based on a hierarchical network structure is shown.

[0023] Figure 2 A flowchart illustrating a construction of a target directed multigraph according to an exemplary embodiment of the present disclosure is shown.

[0024] Figure 3 An input-output block diagram of an exemplary system according to an exemplary embodiment of the present disclosure is shown.

[0025] Figure 4 Exemplary embodiments according to this disclosure are shown. Figure 3 The system corresponds to a first type of directed multigraph.

[0026] Figure 5 A schematic diagram of obtaining a second directed multigraph is shown according to an exemplary embodiment of the present disclosure.

[0027] Figure 6 A flowchart illustrating an exemplary embodiment of the present disclosure for constructing a hierarchical network structure is shown.

[0028] Figure 7A schematic diagram of a layered network structure constructed according to an exemplary embodiment of the present disclosure is shown.

[0029] Figure 8 A flowchart illustrating an example of constructing an interlayer matrix according to an exemplary embodiment of this disclosure is shown.

[0030] Figure 9 A schematic diagram of a coupling matrix model according to an exemplary embodiment of the present disclosure is shown.

[0031] Figure 10 A structural diagram of a university laboratory hall energy supply system according to an exemplary embodiment of the present disclosure is shown.

[0032] Figure 10 A structural diagram of a university laboratory hall energy supply system according to an exemplary embodiment of the present disclosure is shown.

[0033] Figure 11 A schematic diagram of a system represented using a node-based structure according to an exemplary embodiment of the present disclosure is shown.

[0034] Figure 12 An exemplary embodiment according to this disclosure is shown. Figure 11 A schematic diagram of the state variables in the corresponding node-based structure block diagram.

[0035] Figure 13 A fitting curve of the electrical efficiency performance of a CHP according to an exemplary embodiment of the present disclosure is shown.

[0036] Figure 14 A fitting curve of the thermal efficiency performance of a CHP according to an exemplary embodiment of the present disclosure is shown.

[0037] Figure 15 A GB thermal efficiency performance fitting curve is shown according to an exemplary embodiment of the present disclosure.

[0038] Figure 16 A COP performance fitting curve of an electric refrigerator according to an exemplary embodiment of the present disclosure is shown.

[0039] Figure 17 A COP performance fitting curve of an absorption cooler according to an exemplary embodiment of the present disclosure is shown.

[0040] Figure 18 A schematic diagram of a directed multigraph according to an exemplary embodiment of the present disclosure is shown.

[0041] Figure 19 A hierarchical network structure corresponding to a system according to an exemplary embodiment of the present disclosure is shown.

[0042] Figure 20An exemplary embodiment of the present disclosure is shown. Figure 19 A schematic diagram of the inter-layer matrices in a hierarchical network structure.

[0043] Figure 21 A schematic diagram is shown of a coupling matrix model of an integrated energy system obtained by coupling based on an interlayer matrix according to an exemplary embodiment of the present disclosure.

[0044] Figure 22 A daily (summer) load diagram of an energy supply system for a university laboratory hall according to an exemplary embodiment of the present disclosure is shown.

[0045] Figure 23 A daily (winter) load diagram of an energy supply system for a university laboratory hall according to an exemplary embodiment of the present disclosure is shown.

[0046] Figure 24 The diagram shows the dynamic optimization control parameter variation curve of a power supply system for a university experimental hall according to an exemplary embodiment of the present disclosure on a summer day.

[0047] Figure 25 The diagram shows the dynamic optimization control parameter variation curve of a power supply system for a university experimental hall according to an exemplary embodiment of the present disclosure on a winter day.

[0048] Figure 26 A diagram showing the energy consumption distribution of an energy supply system for a university experimental hall according to an exemplary embodiment of the present disclosure is provided.

[0049] Figure 27 A comparison chart showing the overall efficiency of all equipment (under dynamic control and non-dynamic control) in a power supply system for a university experimental hall according to an exemplary embodiment of the present disclosure is provided.

[0050] Figure 28 A schematic diagram of the composition of an integrated energy system modeling apparatus based on a hierarchical network structure according to an exemplary embodiment of the present disclosure is shown.

[0051] Figure 29 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown.

[0052] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0054] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0055] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0056] With the rapid development of new energy and digital technologies, integrated energy systems, as key nodes in the energy internet, undertake the tasks of integrating, converting, and coordinating the control of various energy forms such as electricity, heat, cooling, and natural gas. Traditional integrated energy system modeling methods are mostly based on static input-output relationships, failing to fully depict the dynamic interactions between multiple energy sources and making it difficult to adapt to the optimization and control needs under complex operating conditions. Currently, various technologies are emerging to explore integrated energy system modeling and optimization, such as the introduction of AI, digital twins, and edge computing. However, there are still many shortcomings in dynamic modeling, data-driven modeling structure clarity, real-time solvability, and accuracy of equipment performance representation. These shortcomings, to some extent, affect the automated control optimization of integrated energy systems based on modeling.

[0057] The technical solution provided by the exemplary embodiments of this disclosure introduces the concept of energy synchronization and hierarchical structure to construct a hierarchical network structure of the integrated energy system, and uses the inter-layer matrix of each hierarchical network structure to couple and obtain the coupling matrix model of the integrated energy system. This enables high-precision dynamic modeling of the entire process of flow, conversion and distribution of multiple energy sources in the integrated energy system, and is applicable to programmed dynamic optimization control.

[0058] It should be noted that the integrated energy system modeling method based on a hierarchical network structure of the exemplary embodiments of this disclosure can be applied to the planning, management, and design of regional integrated energy systems (such as parks, communities, and cities); multi-energy flow system collaborative optimization scheduling (such as dynamically adjusting the production and distribution of various energy sources such as electricity, heat, and cooling based on load demand, renewable energy output (such as wind power and photovoltaics), and energy prices according to microgrids or regional energy stations); renewable energy consumption and energy storage system optimization; carbon emission reduction and energy efficiency assessment scenarios, etc., without limiting the specific application scenarios. The exemplary embodiments of this disclosure solve the problems of high complexity and low computational efficiency of traditional integrated energy system models through a matrix-based and hierarchical modeling framework, and are suitable for rapid analysis and decision support in multi-energy collaborative scenarios.

[0059] like Figure 1 The diagram shown is a flowchart of an exemplary embodiment of the integrated energy system modeling method based on a hierarchical network structure of this disclosure, with reference to... Figure 1 As shown, the method includes steps S110 to S140, as detailed below:

[0060] Step S110: Obtain equipment information and energy information in the energy process of the integrated energy system. The energy process includes at least one of energy conversion, energy transfer and energy distribution.

[0061] Step S120: Using the energy involved in the integrated energy system as nodes, construct a target directed multigraph based on equipment information and energy information. The target directed multigraph is used to reflect the coupling relationship between different energies in the integrated energy system.

[0062] Step S130: Construct a hierarchical network structure based on the target directed multigraph, and determine the inter-layer matrix between every two layers in the hierarchical network structure. Each inter-layer matrix is ​​used to describe the energy conversion relationship of a device.

[0063] Step S140: Perform matrix coupling between the matrices of each layer to obtain the coupled matrix model of the integrated energy system.

[0064] Steps S110 to S140 will be described in more detail below.

[0065] In step S110, equipment information and energy information in the energy process of the integrated energy system are obtained. The energy process includes at least one of energy conversion, energy transfer and energy distribution.

[0066] In the exemplary embodiments of this disclosure, equipment information refers to the specific information of the equipment included in the integrated energy system, including but not limited to equipment type and model, and rated energy information of the equipment (such as rated power of electrical energy used, rated electrical efficiency, and thermal efficiency). Energy process refers to the dynamic behaviors involving energy conversion, transmission, and distribution within the integrated energy system. Its function is to describe the flow and interaction relationships of different forms of energy (such as electricity, heat, cold, and gas) in the system. Energy information is the information describing the energy process, including but not limited to energy category, energy conversion efficiency, and energy distribution parameters. In actual implementation, equipment information and energy information in the energy process of the integrated energy system can be collected. For example, the electrical energy distribution coefficient is implemented by the power electronic controller that distributes electrical energy, referred to as an energy router; the natural gas distribution coefficient is implemented by the valve controller that distributes natural gas; the heat energy distribution coefficient is implemented by the valve controller that controls the flow rate of hot water pipes; the cold energy distribution coefficient is implemented by the valve controller that controls the flow rate of cold air pipes; and the coefficient of performance of a refrigerator, etc. These coefficients can be flexibly determined according to the actual needs of the scenario, and the exemplary embodiments of this disclosure do not impose any limitations on them.

[0067] In some alternative embodiments, characteristic fitting can be performed based on the rated energy information in the device information to obtain the device's energy efficiency function model, which can then be used to determine the energy conversion efficiency. This can be understood as the device's energy conversion efficiency not being a constant, but rather established by collecting relevant device data and employing a fitting method.

[0068] As an example, if an integrated energy system includes a combined heat and power (CHP) unit, which is the main energy source of the system, it needs to guarantee electricity demand when there is no other power supply. Taking a CHP unit with a rated power of 120 kW, a rated electrical efficiency of 36.00%, a thermal efficiency of 51.00%, and a fitting coefficient of determination (R-square) of 0.9992 as an example, an energy efficiency function model relative to the electrical efficiency of the natural gas input to the CHP can be constructed through linear fitting. Accordingly, Equation 1 shows the energy efficiency function model relative to the electrical efficiency of the natural gas input to the CHP:

[0069] η CHPe (Ng) = a4·(Ng) 4 +a3·(Ng) 3 +a2·(Ng) 2 +a1·Ng+a0

[0070]

[0071] In Equation 1, Ng is the amount of natural gas input to CHP, and η CHPe(·) represents the energy efficiency function model relative to the electrical efficiency of CHP input natural gas, and a0-a4 are the coefficients of the fitting formula. The fitting determination coefficient used in the fitting process is a standard for judging the fitting accuracy, which can be taken as 0.9992. When the fitting result is greater than the fitting determination coefficient, it means that the fitting result meets the requirements.

[0072] It should be understood that similar fitting methods can be used to obtain corresponding energy efficiency function models for other equipment in the integrated energy system, and then the energy conversion efficiency can be determined based on the energy efficiency function model. These will not be listed here.

[0073] By combining "characteristic fitting" with "energy efficiency function model" to determine the dynamic energy conversion efficiency of equipment, the limitations of traditional static efficiency model are broken through. This enables the modeling of integrated energy system to more accurately reflect the dynamic characteristics of equipment in actual operation, and to be closer to actual operating data. It avoids the distortion of optimization results caused by the deviation of efficiency assumptions. Moreover, for equipment from different manufacturers or models, exclusive energy efficiency functions can be obtained through independent fitting, avoiding the errors caused by "one-size-fits-all" parameterization. This provides high-accuracy underlying equipment model support for subsequent modeling.

[0074] In step S120, using the energy involved in the integrated energy system as nodes, a target directed multigraph is constructed based on equipment information and energy information. The target directed multigraph is used to reflect the coupling relationship between different energies in the integrated energy system.

[0075] In the exemplary embodiments of this disclosure, the target directed multigraph is the core data structure for modeling integrated energy systems, used to formally represent the coupling relationships between different energy forms (such as electricity, heat, cold, gas, etc.). Its core feature is that it uses energy as nodes and energy processes (conversion / transfer / distribution) as edges, and describes complex energy interaction processes through directionality and multiplicity.

[0076] like Figure 2 The diagram shows a flowchart for constructing a target directed multigraph, which includes:

[0077] Step S210: Determine the virtual input nodes and virtual output nodes, and represent the energy involved in the integrated energy system as energy nodes, so as to determine the graph nodes based on the virtual input nodes, virtual output nodes and energy nodes.

[0078] The virtual input node serves as the input side (i.e., the source side) and is connected to the energy network of the integrated energy system. The virtual input node may include at least one type of energy, such as natural gas, or it may include both electricity and natural gas. In other words, the integrated energy system may be supplied with electricity and natural gas by the power grid and the natural gas grid. Of course, in some scenarios, it may also include photovoltaic equipment supplying electricity, etc. The exemplary embodiments of this disclosure do not impose any special limitation on the number of virtual input nodes.

[0079] The virtual output node acts as the output side (i.e., the load), serving as the output side of the integrated energy system. For example, the output side of the integrated energy system connects to the experimental hall, providing the necessary energy to the experimental hall. The experimental hall may also include laboratories, control rooms, experimental platforms, power distribution control equipment, and lighting equipment, etc. The exemplary embodiments of this disclosure do not specifically limit the specific composition of the output side.

[0080] As an example, such as Figure 3 The diagram shown is an input-output block diagram of an exemplary system, such as... Figure 3 As shown, this integrated energy system includes a combined heat and power (CHP) unit and a compression electric refrigeration group (CERG) (also known as an electric refrigeration unit), involving four categories of energy: natural gas, electricity, heat, and cold energy. Virtual input nodes V(I) and virtual output nodes V(O) can be determined, and the energy output from all devices is represented as energy nodes. Based on the virtual input nodes, virtual output nodes, and energy nodes, the graph nodes of the directed multigraph are determined.

[0081] Step S220: Determine the directed edges between graph nodes based on the energy information to obtain a first directed multigraph. The directed edges of the first directed multigraph are labeled with the device information and energy information involved in the graph nodes at both ends of the directed edges.

[0082] The energy information includes energy conversion relationships, thus allowing directed edges to be generated between graph nodes, resulting in a first directed multigraph. The direction of the directed edges indicates the energy flow. Each directed edge is labeled with information about the equipment and energy involved, such as equipment name and energy conversion efficiency, on both ends of the graph node.

[0083] In some optional embodiments, if the energy output direction of a target graph node is connected to multiple directed edges, then a number of sub-loops equal to the number of directed edges are added to the target graph node, and the energy allocation parameters corresponding to the directed edges of each sub-loop are labeled. That is, when the energy of a target graph node will be allocated to directed edges of different branches, a number of sub-loops equal to the number of branches can be added to the target graph node, and the energy allocation parameters corresponding to their respective branches can be labeled for the sub-loops.

[0084] As an example, Figure 4 It shows Figure 3 The system corresponds to the first directed multigraph, where I is a virtual input node, O is a virtual output node, and Ng I For natural gas nodes, Ee CHP As an energy node, He CHP For thermal nodes, Ce CERG For cold energy nodes, directed edges are added between graph nodes to indicate the direction of energy flow. These directed edges also indicate the equipment and energy information involved in the graph nodes at both ends of the edge. For example, for Ng... I Nodes and Ee CHP The directed edges between nodes are reinforced with device information (CHP) and energy information (η). Ee (Energy conversion efficiency), Ee CHP Nodes and Ce CERG Add device information CERG and energy information COP to the directed edges between nodes. CERG (Coefficient of performance). In addition, due to Ee CHP If a node's energy output direction is connected to two directed edges, then for that node's energy output direction... CHP Add two sub-rings to the node and label the energy allocation parameters (such as ρ1 and ρ2) of the directed edges corresponding to each sub-ring.

[0085] It should be understood that virtual input nodes and virtual output nodes do not involve energy conversion between devices, so no device information or energy information is added to the directed edges connecting these two nodes.

[0086] Step S230: Simplify the first directed multigraph based on the energy information to obtain the second directed multigraph.

[0087] Considering that in an integrated energy system, each form of energy can flow along a specific path, and that energy flow includes energy conversion, efficiency changes, and energy losses, the parameters on each path can be represented as the product of the parameters of each directed edge in that path. This means that the parameters on the directed edges can flow along the direction of energy flow, while the product of the parameters along the entire path remains unchanged. Based on this, an exemplary embodiment of this disclosure simplifies the first directed multigraph by utilizing the parameter flow of a directed multigraph. Specifically, the process includes:

[0088] First, starting from the virtual input node, the energy information marked on the directed edges of the first directed multigraph is moved along the direction of the directed edge until it is impossible to move to a new directed edge.

[0089] Specifically, if a tail graph node of a target directed edge has a sub-cycle, the energy information marked on the target directed edge will no longer be moved. Furthermore, if a target directed edge exists, the energy allocation parameters corresponding to the sub-cycle of the target directed edge will be added to the directed edge in the energy output direction of the tail graph node, and the sub-cycle will be deleted.

[0090] This can be understood as meaning that "until it can no longer move to a new directed edge" refers to the energy information moving to a directed edge connected to the virtual output node, or the tail graph node of the energy information having a sub-cycle. For a target directed edge, the sub-cycle on the corresponding tail graph node is deleted, and the energy allocation parameter corresponding to the sub-cycle is added to the directed edge in the energy output direction of the tail graph node.

[0091] Secondly, device information is added to each directed edge to represent the energy conversion medium between the graph nodes connected by the directed edge.

[0092] As an example, Figure 5 A schematic diagram of obtaining a second directed multigraph is shown, and the simplified process is explained in conjunction with the first directed multigraph described above.

[0093] Starting with the virtual input node, the parameters (energy information) on each directed edge flow along the outflow direction of the graph node at the tail of the edge. If the graph node at the tail of the directed edge has a child cycle, the parameters do not flow. For example, edge (Ng) I -He CHP) The parameters (CHP, ηHe) are derived from the tail graph node (He) of this edge. CHP The outflow direction is towards the edge (He) CHP -O). But there are directed edges (Ng) I -Ee CHP The parameters of ) cannot flow because of the tail graph node (Ee) CHP It has sub-rings.

[0094] Since the number of sub-loops is the same as the number of directed edges in the branches of the tail graph nodes, the energy allocation parameters on the sub-loops of the tail graph nodes are assigned to the directed edges in the output direction of the tail graph nodes, for example, node (Ee). CHP It has two sub-rings with energy allocation parameters (ρ1) and (ρ2) respectively, which are allocated to the edge (Ee). CHP -Ce CERG ) and (Ee CHP After -O), these sub-rings can be deleted.

[0095] Furthermore, after completing the parameter flow, device information can be added to each directed edge to indicate which devices (i.e., energy conversion media) are involved in the energy conversion. For example, (CHP-CERG) can be added to the edge connecting Ee and Ce, and (CHP-O) can be added to the edge connecting Ee and O to obtain a second directed multigraph.

[0096] By using parameter flow, sub-cycles in the first directed multigraph can be removed, simplifying the modeling process and making it clearer.

[0097] Step S240: Merge the graph nodes in the second directed multigraph according to the energy category to obtain the target directed multigraph.

[0098] Considering that integrated energy systems may obtain the same type of energy from different devices—for example, thermal energy may be output from both a combined heat and power (CHP) unit and an auxiliary boiler unit—there may be duplicate energy nodes in the directed multigraph. Therefore, such nodes need to be merged.

[0099] First, the graph nodes with the same energy category in the second directed multigraph can be merged to obtain a merged node. Then, all directed edges connected to the graph nodes with the same energy category can be switched to the merged node to obtain the target directed multigraph.

[0100] It should be noted that the above examples are for illustrative purposes only. The number and types of graph nodes in the directed multigraph of the exemplary embodiments of this disclosure may be more complex and are determined according to the actual scenario.

[0101] The exemplary embodiments of this disclosure clarify the system input / output boundaries by defining virtual input nodes and virtual output nodes. This means that external energy supply and internal demand are incorporated into a unified topology through virtual nodes, avoiding the problem of ambiguous boundary conditions in modeling and facilitating global energy flow analysis. Furthermore, each directed edge precisely corresponds to a physical device or transmission path, preserving the original data granularity. Multiple devices with the same energy conversion direction are independently expressed through multiple edges (sub-loops), avoiding information loss and further refining device-level modeling. Simplifying the process reduces the number of sub-loops, improving computational speed. Merging graph nodes according to energy categories avoids graph size explosion due to energy subdivision while preserving multi-energy coupling relationships. Simultaneously, the merged nodes directly correspond to the "energy layer" in the hierarchical model (i.e., adapting to the hierarchical network structure), facilitating subsequent generation of inter-layer matrices.

[0102] As can be seen, the exemplary embodiments of this disclosure achieve a high-precision, low-redundancy, and highly computable multi-energy coupling representation in the modeling of integrated energy systems through a phased directed multigraph construction method of virtual node introduction → preliminary graph construction → simplification → node merging.

[0103] In step S130, a hierarchical network structure is constructed based on the target directed multigraph, and the inter-layer matrix between every two layers in the hierarchical network structure is determined. Each inter-layer matrix is ​​used to describe the energy conversion relationship of a device.

[0104] In the exemplary embodiments of this disclosure, the hierarchical network structure is used to transform complex multi-energy coupling relationships into a hierarchical, matrix-based computable model. The inter-layer matrix describes the energy conversion relationship between adjacent layers. Specifically, the hierarchical network structure of the integrated energy system includes two types of layers: one is the input layer and the output layer, which are independent of the devices in the system and determined by the set virtual nodes; the other is the intermediate layer, where each pair of layers contains the energy conversion relationship of one device. Energy other than this does not undergo energy conversion between the two layers, but is only transferred and distributed.

[0105] Specifically, such as Figure 6 The diagram shows a flowchart of constructing a hierarchical network structure, which includes:

[0106] Step S610: Determine the energy input layer based on the virtual input nodes, and determine the energy output layer based on the virtual output nodes.

[0107] The input layer represents energy input, and the output layer represents energy output, which is also the load of the integrated energy system. The input layer and the output layer are the base layers of the hierarchical network structure.

[0108] Step S620: Based on the number of devices N corresponding to the target directed multigraph, construct an intermediate layer between the energy input layer and the energy output layer. The number of intermediate layers is equal to N+1. Each layer includes the same energy nodes as the target directed multigraph, and the energy categories of the energy nodes in each layer are ordered in the same way.

[0109] Continuing with the example above, the input layer is denoted as "S" and the output layer as "L". Since this example includes two devices (a combined heat and power unit and an electric chiller unit), the number of intermediate layers is determined to be three, denoted as "M1", "M2", and "M3" respectively. Each layer includes the same energy nodes as the target directed multigraph. This can be understood as follows: after constructing each layer, to facilitate subsequent inter-layer connections, identical energy nodes are set in each layer with the same energy category order, such as... Figure 7 The diagram shows a hierarchical network structure. Since this example includes electrical energy nodes, natural gas nodes, thermal energy nodes, and cold energy nodes, each layer has corresponding energy nodes with the same energy category order, which facilitates the establishment of inter-layer connections.

[0110] Step S630: Construct inter-layer connections between energy nodes in each layer according to the target directed multigraph, and determine the hierarchical network structure based on the obtained inter-layer connections. Horizontal connections between layers represent the transfer of the same type of energy, while diagonal connections represent the conversion or transfer between different types of energy.

[0111] Since the directed multigraph reflects the coupling relationships between different energies in an integrated energy system, inter-layer connections can be constructed between energy nodes in each layer to obtain a hierarchical network structure. (See also...) Figure 7 In the diagram, directed lines marked with red arrows represent interlayer connections. For example, the horizontal line between the input layer "S" and layer "M1" represents the transfer of the same type of energy, namely natural gas (N2). S →N g 1 The transfer of energy between these two layers does not involve the conversion of other types of energy. For example, the diagonal line connecting layers "M1" and "M2" represents the conversion of natural gas into electrical and thermal energy (N). g 1 →E e 2 N g 1 →H e 2 The diagonal line between layers "M2" and "M3" represents the conversion and transfer of electrical energy (E). e 2 →C e 3 E e 2 →E e 3 ), and the transfer of heat energy (H e 2 →H e 3 Other inter-layer connections are similar and will not be listed one by one.

[0112] In addition, energy information (such as energy conversion efficiency, energy distribution parameters, etc.) can be added to the connections between layers, such as... Figure 7 The η shown He wait.

[0113] By constructing a hierarchical network structure, each device's energy conversion relationship occupies a dedicated layer, preserving its independent conversion characteristics. Furthermore, by intuitively distinguishing between energy conversion and transmission, the hierarchical network structure possesses physical interpretability, facilitating the construction of inter-layer matrices and simplifying fault diagnosis. In addition, it is scalable; if new devices are added, no global adjustments are required, only the new layer needs to be expanded.

[0114] After constructing a hierarchical network structure, an inter-layer matrix can be built between every two layers. This inter-layer matrix serves as the basic unit of the hierarchical network structure and is used to describe the energy conversion relationship of a device. For example... Figure 8 The diagram shows a flowchart for constructing an inter-layer matrix, which includes:

[0115] Step S810: Construct an empty matrix based on the energy nodes of the target directed multigraph.

[0116] The energy nodes in a directed multigraph can also be understood as the energy nodes contained in each layer of a hierarchical network structure, such as... Figure 7 The hierarchical network structure shown can be used to determine a 4×4 empty matrix based on the electrical energy node, natural gas node, thermal energy node, and cold energy node.

[0117] Step S820: For each pair of layers, determine at least one target position in the empty matrix based on the interlayer connection, and use the energy information between the two layers as elements of at least one target position to obtain the interlayer matrix between the two layers.

[0118] Interlayer connections can reflect the direction of energy flow. Based on this, the target position in the empty matrix can be determined according to the energy nodes before and after the connection of the interlayer connection, and the energy information corresponding to the interlayer connection can be used as the element of the target position.

[0119] As an example, see Figure 7 The layered network structure shown, taking layers "M2" and "M3" as examples, is based on the conversion of electrical energy E... e 2 →E e 3 E e 2 →C e 3 From one energy node to another, the first row and first column of the empty matrix can be determined as the target location based on the inter-layer connection. Therefore, ρ2 is used as the element of this target location. Similarly, based on the energy conversion E... e 2 →C e 3 From the power node to the cold node, the target location can be determined by the fourth row and first column of the empty matrix based on this interlayer connection. Therefore, ρ1COP... CERG As an element at that target location. For the transfer of thermal energy in this layer (H... e 2 →H e 3From thermal node to thermal node, the third row and third column of the empty matrix are determined according to the inter-layer connection relationship, and the target position is set as "1" as the element of the target position. Apart from this, there is no other energy conversion or transfer between layers "M2" and "M3", so the other elements in the empty matrix are padded with zeros. Thus, the inter-layer matrix between layers "M2" and "M3", i.e., "H3", is obtained.

[0120]

[0121] It is understandable that, for any inter-layer connection between two adjacent layers, the row number of the target position is determined by the energy node sorted by energy category according to the output end of the inter-layer connection, and the column number of the target position is determined by the energy node sorted by energy category according to the input end of the inter-layer connection, thus determining the target position in the empty matrix.

[0122] Similarly, the inter-layer matrix between every two layers in the hierarchical network structure can be obtained, which will not be elaborated further.

[0123] In the exemplary embodiments of this disclosure, an empty matrix is ​​constructed based on the energy nodes of the target directed multigraph, ensuring that the matrix dimension strictly corresponds to the system energy type. The matrix element positions are precisely located through inter-layer connections, avoiding errors caused by manual mapping. In other words, the accuracy of modeling is ensured through structured matrix construction. Furthermore, energy information (such as static parameters (fixed efficiency) or dynamic functions) is carried by the matrix elements. This dynamic parameter embedding facilitates application in complex scenarios of integrated energy systems.

[0124] In step S140, the inter-layer matrices are coupled to obtain the coupled matrix model of the integrated energy system.

[0125] In an exemplary embodiment of this disclosure, the inter-layer matrices can be multiplied sequentially in the direction from the energy output layer to the energy input layer to obtain the coupling matrix model. Here, the direction from the energy output layer to the energy input layer refers to multiplying the inter-layer matrices determined from the back (output layer) to the front (input layer) to obtain the coupling matrix model.

[0126] As an example, such as Figure 9 The diagram shown above illustrates how the coupling matrix model is determined in the example above. Figure 9 ,correspond Figure 7 The layered network structure shown is multiplied sequentially from H4 to H1 to obtain the coupling matrix model, where matrix S is the matrix composed of source inputs (virtual input nodes). This leads to the coupling matrix model on the right-hand side of the equation.

[0127] By employing the technical approach of inverse interlayer matrix multiplication, the integration of global coupling relationships is achieved in the modeling of integrated energy systems. Matrix multiplication automatically accumulates energy conversion losses, enabling precise quantification of multi-energy flow coupling. Furthermore, the elements in this coupling matrix model can represent the total conversion relationship from a certain input energy to a certain output energy, and thus, rapid evaluation and optimization of system-level energy efficiency can be performed based on the input vector and the coupling matrix model.

[0128] The following uses the integrated energy system as an example of the energy supply system for a university experimental hall (a building-type integrated energy system) to illustrate the modeling method of the integrated energy system based on a hierarchical network structure of the exemplary embodiments of this disclosure.

[0129] like Figure 10 The diagram shows the structure of the power supply system for the university's experimental hall. The system's external connections include an input side (source side) and an output side (load). The input side connects to the energy network, receiving electricity and natural gas from the power grid and natural gas network. Additionally, the input side includes photovoltaic equipment to supply electricity. The output side connects to the experimental hall, which covers approximately 1062 square meters and consists of three sub-laboratories and a control room. The main energy-consuming units within the experimental hall include: ① three sets of water pump experimental platforms; ② one set of water quality monitoring experimental platforms; ③ one set of circulating water experimental platforms; ④ power distribution and control equipment; and ⑤ the lighting and staff power consumption of the experimental hall.

[0130] The system's internal components include various energy conversion devices, specifically: ① a combined heat and power (CHP) unit, which uses a micro gas turbine to generate electricity and a heat recovery device to produce heat, enabling the conversion of natural gas into electricity and heat; ② a gas boiler (GB), which uses gas combustion to heat water, enabling the conversion of natural gas into heat; ③ an electric refrigerator (EC), which uses electricity to start a compressor for refrigeration, enabling the conversion of electricity into cold energy, with air as the primary carrier; and ④ an absorption refrigerator (AC), which uses refrigerant to absorb heat and generate cold energy, enabling the conversion of heat into cold energy. The parameters for these devices are obtained based on equipment selection and dynamic fitting in the actual scenario.

[0131] The system can achieve cascaded energy utilization during operation. Its operation process includes: ① Natural gas combustion drives a micro gas turbine to generate electricity, which is directly supplied to the output side and electric chiller for cooling; ② The waste heat of the micro gas turbine is absorbed to heat water, and the hot water is supplied to the output side and absorption refrigeration unit for cooling; ③ The gas boiler serves as an auxiliary heating device to supplement heat energy.

[0132] For ease of understanding, the system is represented using a node-based structure, such as... Figure 11 As shown in the node-based block diagram, there are 14 paths, representing the input and output paths of four types of energy, named LIE, LOE, LIN, LON, LIH, LOH, LIC, and LOC. Four sets of energy control parameters are also set: ρEe, ρNg, ρHe, and ρCe, which are control variable parameters generated by the four energy controllers, respectively represented as: ① ρEe: Electricity distribution coefficient, implemented by the power electronic controller distributing electrical energy, called the energy router; ② ρNg: Natural gas distribution coefficient, implemented by the valve controller distributing natural gas; ③ ρHe: Heat energy distribution coefficient, implemented by the valve controller controlling the flow rate of hot water pipes; ④ ρCe: Cold energy distribution coefficient, implemented by the valve controller controlling the flow rate of cold air pipes. Specifically, the state variables in this node-based block diagram are as follows: Figure 12 As shown.

[0133] First, the energy conversion efficiency of the equipment can be obtained by fitting the rated energy information in the equipment information. As shown in Formula 1 above, this is the energy function model for the electrical efficiency of the CHP input natural gas corresponding to the cogeneration unit, and its corresponding fitting curve is as follows... Figure 13 As shown below, the process of determining the energy efficiency function models for other types of energy in this system will also be explained.

[0134] For combined heat and power (CHP) units, taking a unit with a rated electrical power of 120 kW, a rated electrical efficiency of 36.00%, and a thermal efficiency of 51.00% as an example, and a fitting coefficient of determination (R-square) of 0.9992 as an example, an energy efficiency function model relative to the thermal efficiency of the CHP input natural gas can be constructed through linear fitting. Specifically, Figure 14 The fitted curve of CHP thermal efficiency performance is shown. Correspondingly, Equation 3 shows the energy efficiency function model relative to the thermal efficiency of CHP input natural gas:

[0135]

[0136] In Equation 3, Ng is the amount of natural gas input to CHP, and η CHPh (·) represents the energy efficiency function model relative to the thermal efficiency of natural gas input to CHP, where b0-b1 and c0-c1 are the coefficients of the fitting formula. The fitting determination coefficient used in the fitting process is a standard for judging the accuracy of the fitting, which can be taken as 0.9992. When the fitting result is greater than the fitting determination coefficient, it means that the fitting result meets the requirements.

[0137] For a natural gas hot water boiler (GB), taking a rated capacity of 50kW and a rated thermal efficiency of 85.00% as an example, the GB thermal efficiency performance fitting curve is as follows: Figure 15 As shown in Equation 4, the energy efficiency function model for the thermal efficiency of GB input natural gas obtained by fitting is as follows:

[0138]

[0139] {d2=92.07,d1=170.74,d0=0.10 Formula 4}

[0140] In Equation 4, Ng is the amount of natural gas input to CHP, and η GB (·) represents the energy efficiency function model of GB input natural gas, and d0-d2 are the coefficients of the fitting formula. The fitting decision coefficient used in the fitting process can be 0.9992. When the fitting result is greater than the fitting decision coefficient, it means that the fitting result meets the requirements.

[0141] For an electric chiller (EC), which is an energy transfer device, electrical energy is used to drive a compressor to transfer the cooling energy of chilled water to the system. Taking a rated COP of 3 and a rated power of 100kW as an example, the COP performance fitting curve of the electric chiller is as follows: Figure 16 As shown in Equation 5, the fitted energy efficiency function model related to the EC input power is as follows:

[0142]

[0143] In Equation 5, Ee represents the amount of input electrical energy, and COP represents the amount of input electrical energy. EC (·) represents the energy efficiency function model related to the energy efficiency ratio of EC input power, and e0-e3 are the coefficients of the fitting formula. The fitting decision coefficient used in the fitting process can be 0.9992. When the fitting result is greater than the fitting decision coefficient, it means that the fitting result meets the requirements.

[0144] For a lithium bromide absorption chiller (AC), with hot water input as the heat energy, taking an AC rated COP of 0.83 and a rated power of 110kW as an example, the COP performance fitting curve of the absorption chiller is as follows: Figure 17 As shown in Equation 6, the fitted energy efficiency function model related to AC input power is as follows:

[0145] COP AC (He)=(g2·(He) 2 +g1·He+g0) / 100

[0146] {g2=-1.75·10 -3 ,g1=0.72,g0=19.64 Formula 6

[0147] In Equation 6, He represents the amount of input thermal energy, and COP represents the amount of input thermal energy. AC(·) represents the energy efficiency function model related to AC input electrical energy, and g0-g2 are the coefficients of the fitting formula. The fitting decision coefficient used in the fitting process can be 0.9728. When the fitting result is greater than the fitting decision coefficient, it means that the fitting result meets the requirements.

[0148] Furthermore, for electrical lines, taking low-voltage cables used in transmission lines as an example with a loss of 5%, the transmission coefficient is 95%, and the energy efficiency function model of the electrical line is as shown in Equation 7:

[0149]

[0150] In Equation 7, each α represents the energy efficiency function model of different electrical circuits in the system.

[0151] Similarly, taking a natural gas pipeline with a diameter of DN600 as an example, and using the 'Panhandel b formula' to calculate the pipeline's gas transmission efficiency as 89%, the energy efficiency function model of the natural gas pipeline is as shown in Equation 8:

[0152]

[0153] In Equation 8, each α represents the energy efficiency function model of different electrical circuits in the system.

[0154] If the temperature difference between the inlet and outlet of the hot water pipe is less than 1℃, and taking a heat loss of 4% as an example, the energy efficiency function model of the hot water pipe is as shown in Equation 9:

[0155]

[0156] In Equation 8, each α represents the energy efficiency function model of different hot water pipes in the system.

[0157] Assuming the loss coefficient of the cold water pipe is the same as that of the hot water pipe, the energy efficiency function model of the hot water pipe is as shown in Equation 10:

[0158]

[0159] In Equation 10, each α represents the energy efficiency function model of different cold water pipes in the system.

[0160] After acquiring the device information and energy information in the energy process of the system, a directed multigraph is constructed according to the method of the exemplary embodiment described above. For example, Figure 18 This is a schematic diagram of a directed multigraph, in which each energy node represents a type of energy, each directed edge represents the transfer of energy, the parameters on the directed edges represent the energy conversion relationship, and the output degree of the node represents the distribution of energy. That is, on each output edge, the output energy needs to be multiplied by the distribution coefficient (i.e., the energy distribution parameter).

[0161] Based on this directed multigraph, the longest path from the virtual input node "S" to the virtual output node "L" passes through three energy nodes, while the shortest path passes through only one. This indicates that the energy flow is asynchronous, allowing for the construction of a hierarchical network structure based on the directed multigraph. This can be understood as dividing the directed multigraph structure into several layers, each with various energy nodes, and incorporating multiple virtual flow steps during energy flow to synchronize the energy flow throughout the system. The specific process of constructing the hierarchical network structure includes:

[0162] First, the input layer and output layer are determined. The input layer S represents the system's energy input, and the output layer L represents the system's load.

[0163] Secondly, based on the number of devices N corresponding to the directed multigraph, an intermediate layer is constructed between the energy input layer and the energy output layer. This system includes four sets of devices, resulting in five intermediate layers, denoted by M1-M5.

[0164] Next, inter-layer connections are constructed between energy nodes in each layer based on the directed multigraph, and the hierarchical network structure is determined based on the obtained inter-layer connections. Horizontal connections between layers represent the transfer of the same type of energy, while diagonal connections represent the conversion or transfer between different types of energy. For example... Figure 19 This is the hierarchical network structure corresponding to the system, where the red directed lines represent inter-layer connections.

[0165] Furthermore, the inter-layer matrix between every two layers in the hierarchical network structure is determined, and each inter-layer matrix describes the energy conversion relationship of a device. For example, Figure 20 The image shown corresponds to Figure 19 The diagram shows the inter-layer matrices in the hierarchical network structure, resulting in a total of 6 inter-layer matrices. Figure 21 This is a schematic diagram of the coupling matrix model of the integrated energy system obtained by coupling based on the inter-layer matrix.

[0166] It should be noted that the detailed specifications of this example have already been described in the exemplary embodiments described above, and will not be repeated here.

[0167] The following section uses the aforementioned energy supply system of a university experimental hall as an example to illustrate the dynamic optimization control of the energy system's energy based on a coupled matrix model of the integrated energy system. To adapt to programmed processing, the coupled matrix model of this system (e.g., Figure 21 Convert to the nested structure of Formula 11:

[0168] L=H6(H5(H4(H3(H2(H1(S))))))

[0169]

[0170] In Equation 11, see Figures 19-21 H1-H6 are the inter-layer matrices between every two layers, S is the matrix composed of source inputs (virtual input nodes), and S1-S5 are the nesting results of nested inter-layer matrices of different types. The operation rules shown in Formula 12 are used during the nesting process:

[0171]

[0172] The symbol (.) represents a function operation in the dynamic model.

[0173] Although nested structures are slightly more complex in expression, they can be flexibly applied to programmatic processing. After obtaining the nested structure, each layer is dynamically optimized. Since energy conversion occurs at most once at a time, the calculations are not overly complex.

[0174] Optionally, different optimization objectives can be set for different layers. Specifically, the processing procedure for each layer includes:

[0175] 1) Energy Output Layer: Since the scene load does not consider the distribution of cold energy, ρ can be taken as... Ce =1. S5 can be directly calculated using formula 13, yielding the result shown in formula 14:

[0176]

[0177] Where L is the matrix corresponding to the energy output layer (virtual output node), and represents the load side.

[0178] 2) Intermediate Layer: Since the intermediate layer contains control parameters (energy information, such as energy distribution coefficient, energy conversion efficiency, etc.), an optimization method is used to calculate the control parameter values ​​of the input energy of each layer. The total input energy of each intermediate layer is used as the optimization objective, and the objective function is shown in Equation 15:

[0179] f(ρ,S i ) = S i

[0180]

[0181] The established constraints are as follows:

[0182] (a) The inputs and outputs of each layer must satisfy the coupling matrix relationship of the hierarchical network structure.

[0183] (b) The control parameters generated by the same energy controller must satisfy the allocation relationship.

[0184] (c) Control parameter constraints determined by the performance of the control equipment.

[0185] (d) Since the equipment is most efficient and has the longest service life when it is working within the rated range, the working efficiency of each piece of equipment meets the constraint of the rated working range.

[0186] It should be noted that constraints (a) to (d) can be flexibly determined for different integrated energy systems, and the exemplary embodiments of this disclosure do not specifically limit their details.

[0187] Based on the above optimization objectives and constraints, an optimizer is established as shown in Equation 16:

[0188]

[0189] Among them, S i Refers to the inter-layer matrix of the i-th layer.

[0190] 3) Energy Input Layer: The energy input layer S is connected to S1, and S1 can be directly obtained from formula 17:

[0191]

[0192] Furthermore, unless otherwise specified, the parameter values ​​shall still be based on the examples above, and the integrated energy system shall be controlled once per hour from 8:00 to 17:00, with hourly load data as follows: Figure 22 and Figure 23 As shown, Figure 22 The simulation results for this system on a summer day are as follows. Figure 23 This is a simulation result of the system on a winter day.

[0193] Based on the coupling matrix model of the integrated energy system, the values ​​of the seven control variables are obtained through hierarchical network structure control, as follows: Figure 24 and 25 As shown, where, Figure 24 It is the curve of the dynamic optimization control parameters of the system on a certain day in summer. Figure 25 This is the dynamic optimization control parameter change curve of the system on a certain day in winter. Comparing it with the control variable values ​​in static energy management (where only a single control variable participates in control), we can see that the equipment control is more stable in dynamic optimization, with all control variables participating in the control, and it does not rely on individual control variables when controlling the system's energy flow. By adjusting the control variables, the energy consumption distribution of the system is as follows: Figure 26 As shown, Figure (a) is the energy consumption distribution of the system under dynamic optimization control on a certain day in summer, and Figure (b) is the energy consumption distribution of the system under dynamic optimization control on a certain day in winter.

[0194] Correspondingly, the comparison of the overall efficiency of all devices in the system is as follows: Figure 27As shown, Figure (a) compares the overall equipment efficiency of the system under dynamic control and energy management (and without dynamic control) on a summer day, and Figure (b) compares the overall equipment efficiency of the system under dynamic control and energy management (and without dynamic control) on a winter day. Figure 27 It can be seen that dynamic optimization control can maintain the overall efficiency above 65% in summer and above 60% in winter, ensuring that the equipment is in good condition. Compared with the equipment efficiency of static energy management, the efficiency is improved by 26.71% and 53.73% respectively, achieving the effect of efficiency enhancement.

[0195] The hierarchical network structure-based integrated energy system modeling method in the exemplary embodiments of this disclosure, on the one hand, abstracts various energy forms and their conversion / transfer processes into nodes and edges by constructing a target directed multigraph, intuitively presenting the coupled topology of complex heterogeneous energy networks and avoiding the problem of traditional single-energy modeling being unable to represent across dimensions. On the other hand, the hierarchical network structure divides the integrated energy system into different levels, quantifies device-level energy conversion relationships through inter-layer matrices, realizes the modular decomposition of high-dimensional nonlinear systems, and significantly reduces computational complexity. Furthermore, a global coupled matrix model is generated through inter-layer matrix coupling, encoding physical relationships such as device information (e.g., device parameters), energy flow constraints, and energy information (e.g., energy conversion efficiency) into matrix operations, facilitating rapid simulation analysis and providing a computable model foundation for optimized scheduling. Therefore, the modeling method in the exemplary embodiments of this disclosure can balance clear structure, dynamic scalability, and accurate representation of device performance, providing model support for the automatic control optimization of integrated energy systems using multiple energy sources.

[0196] In an exemplary embodiment of this disclosure, a comprehensive energy system modeling apparatus based on a hierarchical network structure is also provided. (See reference...) Figure 28 As shown, the device 2800 may include an information acquisition module 2810, a graph construction module 2820, a network construction module 2830, and a model construction module 2840. Specifically:

[0197] The information acquisition module 2810 is used to acquire equipment information and energy information in the energy process of the integrated energy system. The energy process includes at least one of energy conversion, energy transfer and energy distribution.

[0198] The graph construction module 2820 is used to construct a target directed multigraph based on the energy involved in the integrated energy system as nodes and the equipment information and energy information. The target directed multigraph is used to reflect the coupling relationship between different energies in the integrated energy system.

[0199] The network construction module 2830 is used to construct a hierarchical network structure based on the target directed multigraph and determine the inter-layer matrix between every two layers in the hierarchical network structure. Each inter-layer matrix is ​​used to describe the energy conversion relationship of a device.

[0200] Model building module 2840 is used to perform matrix coupling between the matrices of each layer to obtain the coupled matrix model of the integrated energy system.

[0201] Since the details of each functional module of the integrated energy system modeling apparatus based on hierarchical network structure in the exemplary embodiments of this disclosure have been described in the exemplary embodiments of the integrated energy system modeling method based on hierarchical network structure described above, they will not be repeated here.

[0202] It should be noted that although several modules or units of the integrated energy system modeling device based on a hierarchical network structure have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0203] Exemplary embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the above-described integrated energy system modeling method based on a hierarchical network structure.

[0204] In one embodiment, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing the computer program, such as read-only memory, NAND flash memory, etc.

[0205] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0206] Computer program code can be written in one or more programming languages. The program code can execute entirely on the user's computing device, or partially on the user's computing device, or as a standalone software package, or partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device through any type of network, such as a local area network (LAN), a wide area network (WAN), etc., or it can be connected to an external computing device (e.g., through an internet connection provided by a mobile network operator).

[0207] Computer programs can be carried or transmitted via signals such as electricity, magnetism, light, electromagnetic fields, and infrared radiation. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to be executed by the processor of the electronic device) the method steps of various exemplary embodiments of this disclosure, such as the steps of the above-described integrated energy system modeling method based on a hierarchical network structure.

[0208] Furthermore, in exemplary embodiments of this disclosure, an electronic device capable of implementing the above-described methods is also provided. Those skilled in the art will understand that various aspects of this disclosure can be implemented as systems, methods, or program products. Therefore, various aspects of this disclosure can be specifically implemented as entirely hardware embodiments, entirely software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, collectively referred to herein as "circuit," "module," or "system."

[0209] The following reference Figure 29 To describe an electronic device 2900 according to such an embodiment of the present disclosure. Figure 29 The electronic device 2900 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0210] like Figure 29 As shown, the electronic device 2900 is manifested in the form of a general-purpose computing device. The components of the electronic device 2900 may include, but are not limited to: at least one processing unit 2910, at least one storage unit 2920, a bus 2930 connecting different system components (including storage unit 2920 and processing unit 2910), and a display unit 2940.

[0211] The storage unit stores program code that can be executed by the processing unit 2910, causing the processing unit 2910 to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0212] Storage unit 2920 may include readable media in the form of volatile storage units, such as random access memory (RAM) 2921 and / or cache memory 2922, and may further include read-only memory (ROM) 2930.

[0213] Storage unit 2920 may also include a program / utility 2924 having a set (at least one) of program modules 2925, such program modules 2925 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0214] Bus 2930 can represent one or more of several types of bus structures, including memory cell bus or memory cell controller, peripheral bus, graphics acceleration port, processing unit, or local bus using any of the multiple bus structures.

[0215] Electronic device 2900 can also communicate with one or more external devices 3000 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 2900, and / or any device that enables electronic device 2900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 2950. Furthermore, electronic device 2900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 2960. As shown, network adapter 2960 communicates with other modules of electronic device 2900 via bus 3030. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 2900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0216] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0217] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0218] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A modeling method for integrated energy systems based on a hierarchical network structure, characterized in that, include: Acquire equipment information and energy information in energy processes within an integrated energy system, wherein the energy processes include at least one of energy conversion, energy transfer, and energy distribution; Using the energy involved in the integrated energy system as nodes, a target directed multigraph is constructed based on the equipment information and the energy information. The target directed multigraph is used to reflect the coupling relationship between different energies in the integrated energy system. A hierarchical network structure is constructed based on the target directed multigraph, and the inter-layer matrix between every two layers in the hierarchical network structure is determined. Each inter-layer matrix is ​​used to describe the energy conversion relationship of a device. The interlayer matrices are coupled to obtain the coupled matrix model of the integrated energy system.

2. The method according to claim 1, characterized in that, The construction of a target directed multigraph, using the energy involved in the integrated energy system as nodes and based on the equipment information and the energy information, includes: Virtual input nodes and virtual output nodes are determined, and the energy involved in the integrated energy system is represented as energy nodes, so as to determine graph nodes based on the virtual input nodes, the virtual output nodes, and the energy nodes; Based on the energy information, directed edges between graph nodes are determined to obtain a first directed multigraph, wherein the directed edges of the first directed multigraph are labeled with device information and energy information related to the graph nodes at both ends of the directed edges; The first directed multigraph is simplified based on the energy information to obtain the second directed multigraph; The graph nodes in the second directed multigraph are merged according to the energy category to obtain the target directed multigraph.

3. The method according to claim 2, characterized in that, If the energy output direction of a target graph node is connected to multiple directed edges, then add a sub-loop to the target graph node in the same number as the number of directed edges, and label the energy allocation parameters of the directed edges corresponding to each sub-loop; The step of simplifying the first directed multigraph based on the energy information to obtain the second directed multigraph includes: Starting from the virtual input node, the energy information marked on the directed edge of the first directed multigraph is moved along the direction of the directed edge until it is impossible to move to a new directed edge; wherein, if there is a target directed edge whose tail graph node has a sub-cycle, the energy information marked on the target directed edge is no longer moved. If the target directed edge exists, the energy allocation parameters corresponding to the sub-loop of the target directed edge are added to the directed edge in the energy output direction of the tail graph node, and the sub-loop is deleted. Add device information to each directed edge to represent the energy conversion medium between the graph nodes connected by the directed edge.

4. The method according to claim 2, characterized in that, The step of merging the graph nodes in the second directed multigraph according to energy category to obtain the target directed multigraph includes: Merge the graph nodes with the same energy category in the second directed multigraph to obtain merged nodes; All directed edges connected to the graph nodes with the same energy category are switched to the merge node to obtain the target directed multigraph.

5. The method according to claim 2, characterized in that, The construction of a hierarchical network structure based on the target directed multigraph includes: The energy input layer is determined based on the virtual input nodes, and the energy output layer is determined based on the virtual output nodes; Based on the number of devices N corresponding to the target directed multigraph, an intermediate layer is constructed between the energy input layer and the energy output layer. The number of intermediate layers is equal to N+1. Each layer includes the same energy nodes as the target directed multigraph, and the energy categories of the energy nodes in each layer are ordered in the same way. Based on the target directed multigraph, inter-layer connections are constructed between energy nodes in each layer, and the hierarchical network structure is determined based on the obtained inter-layer connections. Horizontal connections between layers represent the transfer of the same type of energy, while diagonal connections represent the conversion or transfer between different types of energy.

6. The method according to claim 5, characterized in that, Determining the inter-layer matrix between every two layers in the hierarchical network structure includes: Construct an empty matrix based on the energy nodes of the target directed multigraph; For each pair of layers, at least one target position in the empty matrix is ​​determined based on the interlayer connection, and the energy information between the two layers is used as the element of the at least one target position to obtain the interlayer matrix between the two layers.

7. The method according to claim 5, characterized in that, The step of coupling the inter-layer matrices to obtain the coupled matrix model of the integrated energy system includes: The interlayer matrices are multiplied sequentially in the direction from the energy output layer to the energy input layer to obtain the coupling matrix model.

8. The method according to any one of claims 1 to 7, characterized in that, The energy information includes energy conversion efficiency, and the method further includes: Based on the rated energy information in the equipment information, characteristic fitting is performed to obtain the energy efficiency function model of the equipment; The energy conversion efficiency is determined based on the energy efficiency function model.

9. A modeling device for integrated energy systems based on a hierarchical network structure, characterized in that, include: The information acquisition module is used to acquire equipment information and energy information in the energy process of the integrated energy system, wherein the energy process includes at least one of energy conversion, energy transfer and energy distribution. The graph construction module is used to construct a target directed multigraph based on the energy involved in the integrated energy system as nodes, according to the equipment information and the energy information. The target directed multigraph is used to reflect the coupling relationship between different energies in the integrated energy system. The network construction module is used to construct a hierarchical network structure based on the target directed multigraph and determine the inter-layer matrix between every two layers in the hierarchical network structure. Each inter-layer matrix is ​​used to describe the energy conversion relationship of a device. The model building module is used to perform matrix coupling on the inter-layer matrices to obtain the coupled matrix model of the integrated energy system.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

11. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 8 by executing the executable instructions.

Images