Branch heat network energy storage automatic evaluation and regulation method and device for integrated energy system

Abstract

The application provides a kind of dendritic heat network energy storage automatic evaluation and regulation method and device of integrated energy system, method includes: based on the simulation result of dendritic heat network, the heat storage state of dendritic heat network is quantitatively evaluated, and the energy storage power limit and energy storage capacity are obtained;Wherein, the simulation result is obtained by simulating dendritic heat network through the heat flow model established in advance, and the heat flow model is established based on each node type, parent-child relationship and traversal order of dendritic heat network;According to the energy storage power limit and the energy storage capacity, the rolling optimization of target period is carried out to realize the regulation of integrated energy system.The method provided by the application directly generates the heat flow model of heat network, without artificially abstracting the model and then building, effectively avoiding errors caused by human error;After automatically modeling and simulating the heat network, the energy storage state is quantitatively evaluated, and the regulation of integrated energy system is realized.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system regulation technology, and in particular to an automatic assessment and regulation method and device for the energy storage of a branched heat network in an integrated energy system. Background Technology

[0002] A branched heating network refers to a network layout where the water supply originates from the heat source, branches along the main trunk line to various user inlets, and after heat exchange at the users, the return water returns to the heat source along the same route. Network topology refers to the physical layout of various devices connected by a medium; it describes the specific physical (real) or logical (virtual) arrangement of the members constituting the network. Its characteristics vary across different networks. For example, in computer networks, nodes generally have distinctive features, while branches typically do not; in electrical networks, however, components on branches usually have distinctive features, while nodes do not.

[0003] In existing technologies, thermal network simulation is achieved using existing electrical network simulation software. Through thermoelectric analogy, heat flow is likened to electric current, and thermal potential (temperature) is likened to electric potential. Electrical laws are then used to construct thermal network constraints. However, the existing model construction method involves manually identifying each component and circuit, and then using software to construct its thermal network model. This process is complex and prone to errors.

[0004] How to achieve automatic assessment and control of dendritic heating network energy storage and effectively avoid errors caused by human mistakes is a technical problem that needs to be solved. Summary of the Invention

[0005] This invention provides an automatic assessment and control method and apparatus for dendritic heat network energy storage in an integrated energy system, in order to overcome the deficiencies in the prior art.

[0006] This invention provides an automatic assessment and control method for energy storage in a branched heat network of an integrated energy system, comprising the following steps:

[0007] The thermal storage state of the branched thermal network is quantitatively evaluated based on the simulation results, and the energy storage and release power limit and energy storage and release capacity are obtained. The simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network.

[0008] Based on the energy storage and release power limit and the energy storage and release capacity, rolling optimization is performed for the target period to achieve regulation of the integrated energy system.

[0009] According to the present invention, an automatic assessment and control method for energy storage in a branched heat network of a combined energy system is provided, wherein the simulation process of the branched heat network includes:

[0010] Based on the pre-obtained node connection matrix of the branched heating network, the node type, parent-child relationship, and traversal order of the branched heating network are determined; wherein, the node connection matrix is ​​used to describe the association between nodes and pipe segments in the branched heating network;

[0011] A heat flow model of the branched heating network is established based on the node types, parent-child relationships, and traversal order of the branched heating network. The network simulation of the branched heating network is then performed based on the heat flow model to obtain the simulation results.

[0012] According to the present invention, an automatic assessment and control method for energy storage in a branched thermal network of a combined energy system is provided. The step of determining the node type, parent-child relationship, and traversal order of the branched thermal network based on a pre-acquired node connection matrix includes:

[0013] The topology of the branched heating network is determined based on the pre-obtained node connection matrix of the branched heating network.

[0014] Based on the topological structure, the topological relationships are determined. Then, a hierarchical traversal method is used to determine the node types, parent-child relationships, and traversal order of the branched heat network.

[0015] According to the present invention, an automatic assessment and control method for energy storage in a branched thermal network of a combined energy system is provided. The step of establishing a heat flow model of the branched thermal network based on the node types, parent-child relationships, and traversal order of the network includes:

[0016] Based on the node types, parent-child relationships, and traversal order of the branched heating network, the water supply section and the return section are modeled respectively to obtain the heat flow model of the branched heating network.

[0017] According to the present invention, an automatic assessment and control method for energy storage in a branched heat network of a combined energy system is provided. The node types include heat source nodes, branch source nodes and heat user nodes. The heat source nodes are nodes without parent nodes, the branch source nodes are nodes with parent nodes and child nodes, and the heat user nodes are nodes with parent nodes and no child nodes.

[0018] The modeling of the water supply section based on the node types, parent-child relationships, and traversal order of the branched heating network includes:

[0019] Starting from the first node, determine each node of the branched heating network. If the node is a heat source node, traverse at least one child node of the heat source node to obtain the water supply temperature of each child node in the at least one child node.

[0020] When the node is a branch node, traverse at least one child node of the branch node to obtain the water supply temperature of each child node in the at least one child node;

[0021] When the node is a heat user node, the heat exchange process between the heat exchange station and the heat user is calculated to obtain heat exchange data.

[0022] According to the present invention, an automatic assessment and control method for energy storage in a branched thermal network of a combined energy system is provided. The step of modeling the return water portion based on the node types, parent-child relationships, and traversal order of the branched thermal network includes:

[0023] Starting from the last node, each node of the branched heating network is determined in reverse order. If the node is a heat source node, at least one child node of the heat source node is traversed to obtain the return water temperature and the supply water temperature of each child node in the at least one child node at the next moment.

[0024] When the node is a branch node, traverse at least one child node of the branch node to obtain the return water temperature of each child node in the at least one child node;

[0025] When the node is a heat user node, the return water temperature of the heat user node is obtained based on the heat exchange data.

[0026] According to the present invention, an automatic assessment and control method for energy storage in a branched thermal network of an integrated energy system is provided, wherein the method involves performing rolling optimization for a target period based on the energy storage and release power limit and the energy storage and release capacity to achieve control of the integrated energy system, comprising:

[0027] Based on the energy storage and release power limit and the energy storage and release capacity, the boundary conditions for rolling optimization are determined, and the boundary conditions are used as the dynamic thermal storage characteristics of the integrated energy system.

[0028] Based on the dynamic thermal storage characteristics, the target time interval within the target period is optimized on a rolling basis to obtain the target day-ahead plan, so as to realize the regulation and control of the integrated energy system based on the target day-ahead plan.

[0029] This invention also provides an automatic assessment and control device for dendritic heat network energy storage in an integrated energy system, comprising the following modules:

[0030] An evaluation module is used to quantitatively evaluate the thermal storage state of the branched thermal network based on the simulation results of the branched thermal network, and obtain the energy storage and release power limit and energy storage and release capacity; wherein, the simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network.

[0031] The control module is used to perform rolling optimization for a target period based on the energy storage and release power limit and the energy storage and release capacity, so as to realize the control of the branched heat network of the integrated energy system.

[0032] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the automatic assessment and control method for the energy storage of the branched heat network of the integrated energy system as described above.

[0033] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an automatic assessment and control method for the energy storage of a branched heat network in an integrated energy system as described above.

[0034] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the automatic assessment and control method for the energy storage of the branched heat network in the integrated energy system as described above.

[0035] The present invention provides an automatic assessment and control method and apparatus for energy storage in a branched heat network of an integrated energy system. This method quantitatively assesses the thermal storage state of the branched heat network based on simulation results, obtaining the energy storage / release power limit and energy storage / release capacity. The simulation results are obtained by simulating the branched heat network using a pre-established heat flow model, which is based on the node types, parent-child relationships, and traversal order of the branched heat network. Based on the energy storage / release power limit and energy storage / release capacity, rolling optimization is performed over a target period to achieve control of the integrated energy system. Therefore, the present invention directly generates the heat flow model of the heat network, eliminating the need for manual model abstraction and reconstruction, effectively avoiding errors caused by human error. After automatically modeling and simulating the heat network, its energy storage state is quantitatively assessed, enabling control of the integrated energy system. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the automatic assessment and control method for branched heat network energy storage in integrated energy systems provided by the present invention.

[0038] Figure 2 This is a schematic diagram of a branched heat network in an integrated energy system to which the method provided by this invention is applicable.

[0039] Figure 3 This is a schematic diagram of a bus-type heating network of an integrated energy system to which the method provided by this invention is applicable for comparison.

[0040] Figure 4 This is the overall flowchart of the automatic assessment and control method for branched heat network energy storage in integrated energy systems provided by the present invention.

[0041] Figure 5 This is a flowchart of the water supply automation modeling logic provided by the present invention.

[0042] Figure 6 This is a schematic diagram of the heat flow model of each module in the water supply automation modeling provided by the present invention.

[0043] Figure 7 This is the logic flowchart for automated modeling of water return provided by the present invention.

[0044] Figure 8 This is a schematic diagram of the heat flow model of the automated water return modeling module provided by the present invention.

[0045] Figure 9 This is a flowchart of the real-time capacity quantification evaluation strategy for thermal storage that combines time-domain simulation and the bisection method, provided by this invention.

[0046] Figure 10 This is a flowchart of the rolling optimization strategy provided by the present invention.

[0047] Figure 11 This is a schematic diagram illustrating the change of indoor temperature of each heat user over time under the heat storage conditions provided by the present invention.

[0048] Figure 12 This is a schematic diagram illustrating the change of indoor temperature of each heat user over time under heat release conditions provided by the present invention.

[0049] Figure 13 This is a schematic diagram of the power output of the CHP unit and wind power consumption provided by the present invention.

[0050] Figure 14 This is a schematic diagram of the structure of the automatic assessment and control device for the branched heat network energy storage of the integrated energy system provided by the present invention.

[0051] Figure 15 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0053] The following is combined Figures 1-15 The present invention describes an automatic assessment and control method and apparatus for the energy storage of a branched heat network in an integrated energy system.

[0054] Figure 1 This is a flowchart illustrating the automatic assessment and control method for dendritic heat network energy storage in an integrated energy system provided by the present invention, as shown below. Figure 1 As shown, the method includes the following:

[0055] Step 100: Quantitatively evaluate the thermal storage state of the branched thermal network based on the simulation results to obtain the energy storage and release power limit and energy storage and release capacity; wherein, the simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network.

[0056] It should be noted that, before step 100, the simulation process of the branched heating network provided in this embodiment will be explained first, specifically including:

[0057] Step 100a: Based on the pre-acquired node connection matrix of the branched heating network, determine the node type, parent-child relationship, and traversal order of the branched heating network; wherein, the node connection matrix is ​​used to describe the association between nodes and pipe segments in the branched heating network.

[0058] Step 100a specifically includes:

[0059] The topology of the branched heating network is determined based on the pre-obtained node connection matrix of the branched heating network.

[0060] Based on the topological structure, the topological relationships are determined. Then, a hierarchical traversal method is used to determine the node types, parent-child relationships, and traversal order of the branched heat network.

[0061] It should be noted that, for the integrated electric-thermal energy system, under a given connection matrix of each node in the branched heat network, the heat network topology is determined, and the topological relationships between each node are clarified. At this point, it is assumed that the flow velocity at all points in the primary network remains constant, and the ambient temperature changes uniformly at all points. The delay and energy storage effect of the secondary network are ignored, and the heat exchange station and the heat user are considered as a single node. The simulation time interval used in the example is 18 seconds, with 4800 time points per day.

[0062] Figure 2 This is a schematic diagram of a branched heat network in an integrated energy system to which the method provided by this invention is applicable, as shown below. Figure 2 As shown, starting from the CHP unit, the primary heating network of the heating system branches out, and after diversion, it reaches n heat users. Figure 2 For example, the topology is determined based on the following node connection matrix:

[0063]

[0064] Figure 3 This is a schematic diagram of a bus-type heating network for comparison, applicable to an integrated energy system, as described in this invention. Figure 3 For example, the topology is determined based on the following node connection matrix:

[0065]

[0066] Among them, the diagonal elements of the matrix These represent the nodes A, B, C, D, etc. (in alphabetical order). If it is not 0, it means that node i and node j are connected, and the water flow direction is from node j to node i under water supply conditions.

[0067] Once the heating network topology is determined, the topological relationships between nodes can be clarified. A level-order traversal method is used to determine the node type, parent-child relationships, and traversal order. The node classification and judgment criteria are as follows:

[0068] 1. Heat source (thermal power plant) node: A node without a parent node;

[0069] 2. Branching node: A node that has both a parent node and child nodes;

[0070] 3. Hot user node: A node that has a parent node but no child nodes.

[0071] See also Figure 2 Node A is the heat source (thermal power plant) node, B, E, H, and K are the distribution nodes, and C, D, F, G, I, J, L, and M are the heat user nodes. The traversal order is A→B→C→D→E→F→G→H→I→J→K→L→M.

[0072] See also Figure 3 Node A is the heat source (thermal power plant) node, B, D, F, H, J, L, N, and P are the distribution nodes, and C, E, G, I, K, M, O, and Q are the heat user nodes. The traversal order is A→B→C→D→E→F→G→H→I→J→K→L→M→N→O→P→Q.

[0073] It should be noted that, Figure 3 The number of heat exchange stations shown is Figure 2 The branched thermal networks shown are identical, but due to differences in topology, the simulation time varies. Figure 2 The example shown took 2335.24 seconds. Figure 3 The example shown takes 2832.14 seconds, indicating that the complexity of the topology has a significant impact on the simulation time.

[0074] Step 100b: Establish a heat flow model of the branched heating network based on the node types, parent-child relationships, and traversal order of each node, and perform a pipeline simulation of the branched heating network based on the heat flow model to obtain the simulation results.

[0075] Figure 4 This is a flowchart of the automatic evaluation and control method for branched heat network energy storage in integrated energy systems provided by the present invention. The automatic evaluation and control method for branched heat network energy storage in integrated energy systems provided by the present invention generally includes four steps: determining the topology, modeling and simulating, quantitative evaluation, and rolling optimization.

[0076] It should be noted that the simulation is based on the heat flow method. The heat flow method uses thermoelectric analogy, treating the heat transfer flow as an electric current, thus allowing the application of universal electrical laws (such as Kirchhoff's laws) to construct overall constraints on the thermodynamic system, comprehensively considering the nonlinear constraints of the heat transfer process. The heat flow method is widely used in the analysis and optimization of heat transfer processes in refrigeration systems, energy storage systems, combined heat and power plants, and thermodynamic systems under supercritical conditions, laying the foundation for analyzing heat transport constraints in integrated electric-thermal energy systems. The time intervals and number of time points in the simulation are merely examples; the values ​​are interchangeable.

[0077] It should be noted that the modeling is mainly divided into the water supply section and the water return section.

[0078] Specifically, the water supply section is modeled based on the node types, parent-child relationships, and traversal order of the dendritic heating network, including:

[0079] Starting from the first node, determine each node of the branched heating network. If the node is a heat source node, traverse at least one child node of the heat source node to obtain the water supply temperature of each child node in the at least one child node.

[0080] When the node is a branch node, traverse at least one child node of the branch node to obtain the water supply temperature of each child node in the at least one child node;

[0081] When the node is a heat user node, the heat exchange process between the heat exchange station and the heat user is calculated to obtain heat exchange data.

[0082] In one embodiment, Figure 5 This is a flowchart of the water supply automation modeling logic provided by the present invention, such as... Figure 5 As shown, each node is processed according to the order obtained by hierarchical traversal and the node type. If it is a heat source (thermal power plant) node, its child nodes are traversed, and the pipeline flow loss is considered to obtain the water supply temperature of each child node. If it is a branch node, its child nodes are traversed in the same way, and the pipeline flow loss is considered to obtain the water supply temperature of each child node. If it is a heat user node, the heat exchange process between the heat exchange station and the heat user is calculated.

[0083] Figure 6 This is a schematic diagram of the heat flow model of each module in the automated water supply modeling provided by the present invention, as shown below. Figure 6 As shown, this model follows Kirchhoff's laws, which are consistent with... Figure 5 The three node processing methods provided correspond to the same method for heat source nodes and branch nodes, and the heat flow model is basically the same.

[0084] (1) From arrive This refers to the process of heat exchange between the medium flowing through the pipeline from the current node to its child nodes and the external environment, where the heating part is only present at the heat source node.

[0085] (2) From arrive This refers to the heat exchange process between the heat exchange station and the environment, including the heat exchange process from the heat exchange station to the heat user and the heat exchange process from the heat user to the environment. The heat exchange process from the heat exchange station to the heat user involves the thermal resistance of the heat exchange station. Thermal resistance of heat exchangers for heat users and a thermodynamic potential (This represents the change in secondary network water temperature after heat exchange). The inlet temperature of the primary network at the heat exchange station is now known. Initial indoor temperature of heat users The following governing equations can be established using the heat flow method:

[0086]

[0087]

[0088]

[0089]

[0090] The heat flux is obtained after solving, which is then used for subsequent calculations. Among these, This refers to the outlet temperature of the secondary network side of the heat exchange station. This refers to the primary network outlet temperature of the heat exchange station (return water temperature at the heat user node). For heat exchange heat flow, For the heat capacity flow of the fluid on the primary network side of the heat exchange station, This refers to the heat capacity flow of the fluid on the secondary network side of the heat exchange station.

[0091] The heat exchange process from the heat user to the environment.

[0092]

[0093]

[0094]

[0095]

[0096] in, For indoor air heat capacity, For the heat capacity of the internal enclosure structure, For the heat capacity of the central enclosure structure. For the heat capacity of the external enclosure structure. For the total heat capacity of the building envelope, and Q represents heat flux. For the internal enclosure temperature, Temperature of the central building envelope. For the temperature of the external building envelope, This refers to the convective thermal resistance between the internal air and the building envelope. For the internal thermal resistance of the building envelope, External thermal resistance of the building envelope. For the convective thermal resistance between the external environment and the building envelope, For heat dissipation power of indoor lights and electrical appliances, This represents solar radiation power.

[0097] Specifically, the return water section is modeled based on the node types, parent-child relationships, and traversal order of the dendritic heating network, including:

[0098] Starting from the last node, each node of the branched heating network is determined in reverse order. If the node is a heat source node, at least one child node of the heat source node is traversed to obtain the return water temperature and the supply water temperature of each child node in the at least one child node at the next moment.

[0099] When the node is a branch node, traverse at least one child node of the branch node to obtain the return water temperature of each child node in the at least one child node;

[0100] When the node is a heat user node, the return water temperature of the heat user node is obtained based on the heat exchange data.

[0101] In one embodiment, Figure 7 This is a flowchart of the automated modeling logic for water return provided by the present invention, such as... Figure 7 As shown, following the order obtained from the hierarchical traversal and the node category, each node is processed in reverse order from the last node. If it is a heat source (thermal power plant) node, its child nodes are traversed, and after considering the pipeline flow, a mixing calculation (if any) is performed to obtain the return water temperature of the node. Then, the supply water temperature of the node at the next moment is obtained after heating. If it is a branch node, its child nodes are traversed in the same way. After considering the pipeline flow, a mixing calculation (if any) is performed to obtain the return water temperature of the node. If it is a heat user node, the return water temperature of the node is obtained by calculating the data obtained from the heat exchange with the secondary network.

[0102] Figure 8 This is a schematic diagram of the heat flow model of the automated water return modeling module provided by the present invention, as shown below. Figure 8 As shown, this model is based on Kirchhoff's laws. For both the heat source node and the branch node, the heat flow model is consistent and includes both mixed and unmixed cases. The return water situation for the heat user node has been given in the supply water section. The temperature relationship between the two mixed fluids during the mixing process can be expressed based on the energy conservation principle as follows:

[0103]

[0104] in,

[0105]

[0106]

[0107] in, The temperature of fluid 1 The temperature of fluid 2, Let be the mass flow rate of fluid 1. The mass flow rate of fluid 2, It is the mixing thermodynamic potential of fluid 1. It is the mixing thermodynamic potential of fluid 2. The temperature after mixing is given, and heat loss is not considered.

[0108] Furthermore, it should be noted that because the thermal storage characteristics of the integrated electric-thermal energy system change in real time, it is necessary to evaluate them in real time during scheduling. Based on the real-time capacity quantification evaluation strategy of thermal storage combining time-domain simulation and the bisection method, the thermal storage status of the pipeline network can be quantitatively evaluated, and the average peak-shaving capacity and energy storage and release power limit of the system under different operating conditions can be determined within a given simulation time.

[0109] Figure 9 This is a flowchart of the real-time capacity quantification assessment strategy for thermal energy storage combining time-domain simulation and the bisection method provided by the present invention. The real-time capacity quantification assessment strategy for thermal energy storage combining time-domain simulation and the bisection method involved in step 100 is as follows: Figure 9 As shown, the time intervals and number of time points involved are merely examples, and the selection of each value is interchangeable.

[0110] Based on the current operating status of the heating system, and considering the energy storage and release conditions for a given duration, firstly, the average peak-shaving capacity is set. Initial upper and lower limits of the bisection iteration and And obtain the mean from the upper and lower limits. The mean The heat production power of the CHP unit, which increases or decreases during the energy storage and release period, is used to perform time-domain simulation of the heating system in MATLAB software.

[0111] When operating under energy storage conditions, obtain the maximum indoor air temperature during the duration of energy storage and within several hours after energy storage. and the maximum outlet temperature of the CHP unit Determine whether the maximum indoor air temperature exceeds the upper limit for indoor air temperature operation and whether the CHP unit outlet temperature exceeds the upper limit. If neither exceeds, it indicates that the current average value is within acceptable limits. The energy storage capacity is too small, failing to reach the maximum capacity of the heating system. The current average value will be used instead. As the lower bound of the bisection method, the lower bound is updated; if any temperature exceeds the constraint, it indicates that the current mean is... The value is too high, exceeding the energy storage capacity of the heating system, so the current average value will be adjusted accordingly. As the upper bound of the bisection method, the upper bound is updated. When the upper bound and the lower bound are sufficiently close, the iteration converges, and the final result is obtained. This refers to the maximum average peak-shaving capacity under a given energy storage duration based on the current system operating state. .

[0112] Similarly, when the energy release is in operation, the duration of the energy release and the minimum indoor air temperature in the hours following the energy release are obtained based on the time-domain simulation results. As mentioned earlier, since the model considers the heat transfer constraints of the entire heating system, the lower limit constraint of the CHP unit outlet temperature is automatically satisfied, so there is no need to discuss the lower limit of the CHP unit outlet temperature here. We determine whether the minimum indoor air temperature is lower than the lower limit of the indoor air temperature operating range. If it does not exceed the lower limit, it indicates that the current average... The absolute value is too small, failing to bring the heating system to the lower limit of energy release. The current average value... As the upper limit of the dichotomy method, the upper limit is updated. In the energy release condition, both the upper and lower limits of the dichotomy method are negative, with the absolute value of the lower limit greater than the absolute value of the upper limit. If the minimum indoor air temperature is already lower than the lower limit of the indoor air temperature operating range, it indicates that the current average... The absolute value is too large, exceeding the lower limit of energy release of the heating system, so the current average value is... As the lower bound of the bisection method, the lower bound is updated. When the upper bound and the lower bound are sufficiently close, the iteration converges, and the final result is obtained. This refers to the maximum average peak-shaving capability under a given energy release duration based on the current system operating state. .

[0113] Step 200: Based on the energy storage and release power limit and the energy storage and release capacity, perform rolling optimization for the target period to achieve regulation of the integrated energy system.

[0114] Specifically, step 200 includes:

[0115] Based on the energy storage and release power limit and the energy storage and release capacity, the boundary conditions for rolling optimization are determined, and the boundary conditions are used as the dynamic thermal storage characteristics of the integrated energy system.

[0116] Based on the dynamic thermal storage characteristics, the target time interval within the target period is optimized on a rolling basis to obtain the target day-ahead plan, so as to realize the regulation and control of the integrated energy system based on the target day-ahead plan.

[0117] It should be noted that by conducting real-time quantitative evaluation of energy storage, the boundary conditions for rolling optimization can be obtained. These conditions can be used as the dynamic thermal storage characteristics of the system for rolling optimization at 1-hour intervals. After repeated iterations, an excellent day-ahead plan for thermal output and wind power consumption can be obtained, thereby guiding the integrated energy system to operate more efficiently and energy-savingly.

[0118] In one embodiment, Figure 10 This is a flowchart of the rolling optimization strategy provided by the present invention. The time intervals and number of time points involved are merely examples, and the selection of each value is replaceable. The specific process includes:

[0119] (1) The entire operation cycle is 24 hours, and the scheduling time scale is 1 hour. Starting from the initial moment, =0, quantifying the flexibility of the heating system under four conditions: energy storage and release scenarios lasting 1 hour, obtaining its average peak-shaving capacity. The available energy storage and release capacity is obtained under the energy storage and release scenario lasting for 24 hours. .

[0120] (2) The obtained average peak shaving capability The upper and lower limits of the CHP unit's thermal output power are set for the first hour, while there are no corresponding limits for the CHP thermal output power during the following 23 hours. Meanwhile, during the 24-hour operation period, the available stored energy capacity will be... The application is to determine the upper and lower limits of energy storage capacity. Based on these constraints, the LP problem is solved to obtain the output of each unit over 24 hours.

[0121] (3) Apply the optimized output of each unit for the first hour and discard the results for the remaining hours. Then, shift the optimized time domain forward by 1 hour, i.e., the current time. Scroll to the next hour And requantify the flexibility of heating systems under four energy storage and release scenarios to update and .

[0122] (4) Solve the LP problem again to obtain the output of all units during the remaining operating cycle. When If the time does not exceed 23, continue to repeat step 2.

[0123] Thus, the rolling optimization for the entire 24-hour operation cycle was completed. Taking into account the dynamic energy storage characteristics of the heating system, the operation of the integrated energy system was optimized. The rolling optimization strategy decomposes a large problem into smaller sub-problems, which also separates the linear and nonlinear parts of the optimization problem.

[0124] Furthermore, Figure 11 This is a schematic diagram illustrating the change of indoor temperature for each heat user over time under heat storage conditions, as provided by the present invention. Figure 12 This is a schematic diagram illustrating the change of indoor temperature of each heat user over time under heat release conditions, provided by the present invention. Specifically, it illustrates the change of indoor temperature of each heat user over time under two operating conditions: heat storage and release time of 1 hour. This ensures that the indoor temperature of the heat user remains within the specified upper and lower limits under all conditions; reaching these limits indicates that the energy storage and release has reached its limit.

[0125] Figure 13 This is a schematic diagram of the power output of the CHP unit and wind power consumption provided by the present invention, as shown below. Figure 13 As shown, the output of CHP units and wind power is obtained through a rolling day-ahead plan. This day-ahead plan can be used to obtain the optimal output of each CHP unit and to regulate the operation of the integrated energy system.

[0126] The present invention proposes an automatic assessment and control technology for the energy storage of the heating network in an integrated energy system that considers the passive energy storage characteristics and regulation capabilities of the heating system. This technology makes the modeling of the heating network in the integrated energy system more automated and standardized. This automatic assessment and control technology, which considers the characteristics of the electricity and heat transport process, can maximize the energy storage potential and regulation capabilities of the heating system. The resulting day-ahead plan can improve the economic efficiency of the integrated energy system operation and has value in promoting the consumption of renewable energy and driving dual-carbon energy conservation.

[0127] The above describes the steps of the automatic evaluation and control method for energy storage in a branched heat network of an integrated energy system provided by this invention. As can be seen from the above description, the automatic evaluation and control method for energy storage in a branched heat network of an integrated energy system provided by this invention quantitatively evaluates the thermal storage state of the branched heat network based on simulation results, obtaining the energy storage and release power limit and energy storage and release capacity. The simulation results are obtained by simulating the branched heat network using a pre-established heat flow model, which is established based on the node types, parent-child relationships, and traversal order of the branched heat network. Based on the energy storage and release power limit and energy storage and release capacity, rolling optimization is performed over a target period to achieve control of the integrated energy system. Therefore, this invention directly generates the heat flow model of the heat network, eliminating the need for manual model abstraction and reconstruction, effectively avoiding errors caused by human error. After automatically modeling and simulating the heat network, its energy storage state is quantitatively evaluated, achieving control of the integrated energy system.

[0128] The automatic assessment and control device for branched heating network energy storage in an integrated energy system provided by the present invention is described below. The automatic assessment and control device for branched heating network energy storage in an integrated energy system described below can be referred to in correspondence with the automatic assessment and control method for branched heating network energy storage in an integrated energy system described above.

[0129] Figure 14 This is a schematic diagram of the structure of the automatic assessment and control device for branched heat network energy storage in the integrated energy system provided by the present invention, as shown below. Figure 14 As shown, the automatic assessment and control device for dendritic heat network energy storage in an integrated energy system provided by the present invention includes:

[0130] Evaluation module 1401 is used to quantitatively evaluate the thermal storage state of the branched thermal network based on the simulation results of the branched thermal network, and obtain the energy storage and release power limit and energy storage and release capacity; wherein, the simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, and the heat flow model is established based on the node types, parent-child relationships and traversal order of the branched thermal network.

[0131] The control module 1402 is used to perform rolling optimization of the target period based on the energy storage and release power limit and the energy storage and release capacity, so as to realize the control of the branched heat network of the integrated energy system.

[0132] The present invention provides an automatic assessment and control method for energy storage in a branched heat network of an integrated energy system. This method quantifies the thermal storage state of the branched heat network based on simulation results, obtaining the energy storage / release power limit and energy storage / release capacity. The simulation results are obtained by simulating the branched heat network using a pre-established heat flow model, which is based on the node types, parent-child relationships, and traversal order of the branched heat network. Based on the energy storage / release power limit and energy storage / release capacity, rolling optimization is performed over a target period to achieve control over the integrated energy system. Therefore, the present invention directly generates the heat flow model of the heat network, eliminating the need for manual model abstraction and reconstruction, effectively avoiding errors caused by human negligence. After automatically modeling and simulating the heat network, its energy storage state is quantified, enabling control over the integrated energy system.

[0133] Based on the above embodiments, in this embodiment, the device further includes a simulation module, specifically used for:

[0134] Based on the pre-obtained node connection matrix of the branched heating network, the node type, parent-child relationship, and traversal order of the branched heating network are determined; wherein, the node connection matrix is ​​used to describe the association between nodes and pipe segments in the branched heating network;

[0135] A heat flow model of the branched heating network is established based on the node types, parent-child relationships, and traversal order of the branched heating network. The network simulation of the branched heating network is then performed based on the heat flow model to obtain the simulation results.

[0136] Based on the above embodiments, in this embodiment, the device further includes a determining module, specifically used for:

[0137] The topology of the branched heating network is determined based on the pre-obtained node connection matrix of the branched heating network.

[0138] Based on the topological structure, the topological relationships are determined. Then, a hierarchical traversal method is used to determine the node types, parent-child relationships, and traversal order of the branched heat network.

[0139] Based on the above embodiments, in this embodiment, the device further includes a modeling module, specifically used for:

[0140] Based on the node types, parent-child relationships, and traversal order of the branched heating network, the water supply section and the return section are modeled respectively to obtain the heat flow model of the branched heating network.

[0141] Based on the above embodiments, in this embodiment, the node types include heat source nodes, distribution nodes, and heat user nodes. The heat source node is a node without a parent node, the distribution node is a node with a parent node and child nodes, and the heat user node is a node with a parent node but no child nodes.

[0142] The modeling module is specifically used for:

[0143] Starting from the first node, determine each node of the branched heating network. If the node is a heat source node, traverse at least one child node of the heat source node to obtain the water supply temperature of each child node in the at least one child node.

[0144] When the node is a branch node, traverse at least one child node of the branch node to obtain the water supply temperature of each child node in the at least one child node;

[0145] When the node is a heat user node, the heat exchange process between the heat exchange station and the heat user is calculated to obtain heat exchange data.

[0146] Based on the above embodiments, in this embodiment, the modeling module is further specifically used for:

[0147] Starting from the last node, each node of the branched heating network is determined in reverse order. If the node is a heat source node, at least one child node of the heat source node is traversed to obtain the return water temperature and the supply water temperature of each child node in the at least one child node at the next moment.

[0148] When the node is a branch node, traverse at least one child node of the branch node to obtain the return water temperature of each child node in the at least one child node;

[0149] When the node is a heat user node, the return water temperature of the heat user node is obtained based on the heat exchange data.

[0150] Based on the above embodiments, in this embodiment, the control module 1402 is specifically used for:

[0151] Based on the energy storage and release power limit and the energy storage and release capacity, the boundary conditions for rolling optimization are determined, and the boundary conditions are used as the dynamic thermal storage characteristics of the integrated energy system.

[0152] Based on the dynamic thermal storage characteristics, the target time interval within the target period is optimized on a rolling basis to obtain the target day-ahead plan, so as to realize the regulation and control of the integrated energy system based on the target day-ahead plan.

[0153] Figure 15 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 15As shown, the electronic device may include: a processor 1510, a communications interface 1520, a memory 1530, and a communication bus 1540. The processor 1510, communications interface 1520, and memory 1530 communicate with each other via the communication bus 1540. The processor 1510 can call logical instructions from the memory 1530 to execute an automatic assessment and control method for the dendritic heat network energy storage of an integrated energy system. This method includes:

[0154] The thermal storage state of the branched thermal network is quantitatively evaluated based on the simulation results, and the energy storage and release power limit and energy storage and release capacity are obtained. The simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network.

[0155] Based on the energy storage and release power limit and the energy storage and release capacity, rolling optimization is performed for the target period to achieve regulation of the integrated energy system.

[0156] Furthermore, the logical instructions in the aforementioned memory 1530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0157] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the automatic assessment and control method for dendritic heat network energy storage in the integrated energy system provided by the above methods. The method includes:

[0158] The thermal storage state of the branched thermal network is quantitatively evaluated based on the simulation results, and the energy storage and release power limit and energy storage and release capacity are obtained. The simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network.

[0159] Based on the energy storage and release power limit and the energy storage and release capacity, rolling optimization is performed for the target period to achieve regulation of the integrated energy system.

[0160] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for automatic assessment and control of dendritic heat network energy storage in an integrated energy system provided by the above methods, the method comprising:

[0161] The thermal storage state of the branched thermal network is quantitatively evaluated based on the simulation results, and the energy storage and release power limit and energy storage and release capacity are obtained. The simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network.

[0162] Based on the energy storage and release power limit and the energy storage and release capacity, rolling optimization is performed for the target period to achieve regulation of the integrated energy system.

[0163] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0164] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automatic assessment and control method for energy storage in a branched heat network of an integrated energy system, characterized in that, include: The thermal storage state of the branched thermal network is quantitatively evaluated based on the simulation results, and the energy storage and release power limit and energy storage and release capacity are obtained. The simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network. Based on the energy storage and release power limit and the energy storage and release capacity, rolling optimization is performed for the target period to achieve regulation of the integrated energy system; the simulation process of the dendritic heat network includes: Based on the pre-obtained node connection matrix of the branched heating network, the node type, parent-child relationship, and traversal order of the branched heating network are determined; wherein, the node connection matrix is ​​used to describe the association between nodes and pipe segments in the branched heating network; A heat flow model of the branched heating network is established based on the node types, parent-child relationships, and traversal order of the branched heating network. The network simulation of the branched heating network is then performed based on the heat flow model to obtain the simulation results. The step of determining the node type, parent-child relationship, and traversal order of the branched heating network based on the pre-acquired node connection matrix includes: The topology of the branched heating network is determined based on the pre-obtained node connection matrix of the branched heating network. Based on the topological structure, the topological relationships are determined, and a hierarchical traversal method is used to determine the node types, parent-child relationships, and traversal order of the branched heat network. The step of establishing a heat flow model for the branched heat network based on the node types, parent-child relationships, and traversal order of the branched heat network includes: Based on the node types, parent-child relationships, and traversal order of the branched heating network, the water supply section and the return section are modeled respectively to obtain the heat flow model of the branched heating network; The node types include heat source nodes, distribution nodes, and heat user nodes. A heat source node is a node without a parent node, a distribution node is a node with a parent node and child nodes, and a heat user node is a node with a parent node but no child nodes. The modeling of the water supply section based on the node types, parent-child relationships, and traversal order of the branched heating network includes: Starting from the first node, determine each node of the branched heating network. If the node is a heat source node, traverse at least one child node of the heat source node to obtain the water supply temperature of each child node in the at least one child node. When the node is a branch node, traverse at least one child node of the branch node to obtain the water supply temperature of each child node in the at least one child node; When the node is a heat user node, the heat exchange process between the heat exchange station and the heat user is calculated to obtain heat exchange data; The modeling of the return water section based on the node types, parent-child relationships, and traversal order of the branched heating network includes: Starting from the last node, each node of the branched heating network is determined in reverse order. If the node is a heat source node, at least one child node of the heat source node is traversed to obtain the return water temperature and the supply water temperature of each child node in the at least one child node at the next moment. When the node is a branch node, traverse at least one child node of the branch node to obtain the return water temperature of each child node in the at least one child node; When the node is a heat user node, the return water temperature of the heat user node is obtained based on the heat exchange data.

2. The automatic assessment and control method for dendritic heat network energy storage in an integrated energy system according to claim 1, characterized in that, The step of performing rolling optimization for a target period based on the energy storage and release power limit and the energy storage and release capacity to achieve regulation of the integrated energy system includes: Based on the energy storage and release power limit and the energy storage and release capacity, the boundary conditions for rolling optimization are determined, and the boundary conditions are used as the dynamic thermal storage characteristics of the integrated energy system. Based on the dynamic thermal storage characteristics, the target time interval within the target period is optimized on a rolling basis to obtain the target day-ahead plan, so as to realize the regulation and control of the integrated energy system based on the target day-ahead plan.

3. An automatic assessment and control device for dendritic heat network energy storage in an integrated energy system, characterized in that, include: An evaluation module is used to quantitatively evaluate the thermal storage state of the branched thermal network based on the simulation results of the branched thermal network, and obtain the energy storage and release power limit and energy storage and release capacity; wherein, the simulation results are obtained by simulating the branched thermal network through a pre-established heat flow model, which is established based on the node types, parent-child relationships and traversal order of the branched thermal network. The control module is used to perform rolling optimization of the target period based on the energy storage and release power limit and the energy storage and release capacity, so as to realize the control of the branched heat network of the integrated energy system. The device also includes a simulation module, specifically used for: Based on the pre-obtained node connection matrix of the branched heating network, the node type, parent-child relationship, and traversal order of the branched heating network are determined; wherein, the node connection matrix is ​​used to describe the association between nodes and pipe segments in the branched heating network; A heat flow model of the branched heating network is established based on the node types, parent-child relationships, and traversal order of the branched heating network. The network simulation of the branched heating network is then performed based on the heat flow model to obtain the simulation results. The device further includes a determining module, specifically used for: The topology of the branched heating network is determined based on the pre-obtained node connection matrix of the branched heating network. Based on the topological structure, the topological relationships are determined, and a hierarchical traversal method is used to determine the node types, parent-child relationships, and traversal order of the branched heat network. The device also includes a modeling module, specifically used for: Based on the node types, parent-child relationships, and traversal order of the branched heating network, the water supply section and the return section are modeled respectively to obtain the heat flow model of the branched heating network; The node types include heat source nodes, distribution nodes, and heat user nodes. A heat source node is a node without a parent node, a distribution node is a node with a parent node and child nodes, and a heat user node is a node with a parent node but no child nodes. The modeling module is specifically used for: Starting from the first node, determine each node of the branched heating network. If the node is a heat source node, traverse at least one child node of the heat source node to obtain the water supply temperature of each child node in the at least one child node. When the node is a branch node, traverse at least one child node of the branch node to obtain the water supply temperature of each child node in the at least one child node; When the node is a heat user node, the heat exchange process between the heat exchange station and the heat user is calculated to obtain heat exchange data; The modeling module is further used for: Starting from the last node, each node of the branched heating network is determined in reverse order. If the node is a heat source node, at least one child node of the heat source node is traversed to obtain the return water temperature and the supply water temperature of each child node in the at least one child node at the next moment. When the node is a branch node, traverse at least one child node of the branch node to obtain the return water temperature of each child node in the at least one child node; When the node is a heat user node, the return water temperature of the heat user node is obtained based on the heat exchange data.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the automatic assessment and control method for the energy storage of the branched heating network in the integrated energy system as described in any one of claims 1 to 2.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the automatic assessment and control method for the energy storage of the branched heating network in the integrated energy system as described in any one of claims 1 to 2.

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