Multi-agent participated distributed state estimation method and device for multi-energy system

Abstract

The invention provides a distributed state estimation method and device for a multi-energy system with multi-agent participation. The method comprises the following steps: respectively constructing a gas pipe network dynamic model, a power grid AC power flow model and a coupling equipment model; the method comprises the following steps: by taking boundary information of an exchange energy network as a constraint condition, correcting local state estimation results in a power system and a gas system on the basis of constrained Kalman filtering to form consistent state estimation; according to the method, the privacy protection problem between energy management subjects is solved, and the risk of exposing detail sensitive data in the consistency estimation process is avoided; meanwhile, the method has the advantages of being simple and efficient, and a consistent estimation result can be achieved without a tedious iteration process.

Description

Technical Field

[0001] The present invention belongs to the field of multi-energy system monitoring and state estimation, and in particular relates to a distributed state estimation method and device for a multi-energy system with multi-agent participation. Background Art

[0002] With the large-scale grid connection of renewable energy, natural gas is gradually replacing coal as the preferred fossil fuel; on the one hand, gas generators have the ability to start and stop quickly, which can effectively balance the intermittent output of renewable energy; on the other hand, compared with coal, the combustion of natural gas produces less carbon dioxide; however, most natural gas is transported through pipelines, so it is necessary to study the natural gas network and the power network as a unified system.

[0003] In the electric-gas integrated energy system, in order to ensure the safe and reliable operation of the system, state estimation can provide accurate system status for monitoring and control of the system, and plays an important role in the Energy Management System (EMS); however, in actual applications, the electric power system and the gas system may belong to different management entities, and there is an information gap between different energy systems, that is, it is impossible for the gas system administrator to provide detailed network status information to the electric power system administrator, and vice versa; in addition, compared with a single energy system, the number of sensors deployed and the sampling frequency in the electric-gas integrated energy system have increased significantly. Under such large-scale data collection, the traditional centralized state estimation framework is bound to cause serious transmission delays; currently, the distributed estimation schemes proposed in some studies usually require multiple iterations, and considering the nonlinear characteristics of the system model, it is usually difficult to achieve consistency in the state estimation of the entire integrated energy system.

[0004] Therefore, it is urgent to develop an efficient distributed estimation method. Summary of the invention

[0005] In response to the above challenges, the purpose of the present invention is to propose a distributed state estimation method and device for a multi-energy system involving multiple subjects, aiming to solve the problem in the prior art that the distributed estimation scheme requires multiple iterations and is difficult to achieve consistency in the state estimation of the integrated energy system.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A first aspect of an embodiment of the present application provides a distributed state estimation method for a multi-energy system in which multiple agents participate, the method comprising:

[0008] S1. Obtain the gas pipeline network topology and pipeline parameters in the multi-energy system, and construct a gas pipeline network dynamic model based on the gas pipeline network topology and pipeline parameters;

[0009] S2. Acquire the grid topology and line parameters in the multi-energy system, and construct a grid AC power flow model based on the grid topology and line parameters;

[0010] S3, constructing a coupling device model in the multi-energy system;

[0011] S4, obtaining the measured data of the gas system and the power system, and performing local state estimation of the power system and the gas system respectively according to the gas network dynamic model and the power grid AC power flow model;

[0012] S5. The power system and the gas system exchange boundary information without exposing specific network data;

[0013] S6. Based on the boundary information, a constrained Kalman filtering method is used to correct the local state estimate to form a consistent state estimate.

[0014] Optionally, the step of obtaining the gas network topology and pipeline parameters in the multi-energy system and constructing a gas network dynamic model based on the gas network topology and pipeline parameters includes:

[0015] Acquire the gas network topology and pipeline parameters of the gas system;

[0016] According to the gas pipeline network topology, a gas pipeline network line diagram structure is constructed;

[0017] A gas pipeline network dynamic model is constructed based on the gas pipeline network line diagram structure.

[0018] Optionally, the step of constructing the AC power flow model of the power grid of the multi-energy system includes:

[0019] Obtaining grid topology and line parameters of the power system;

[0020] According to the grid topology and line parameters of the power system, the grid AC power flow model is constructed based on a two-parameter exponential smoothing method.

[0021] Optionally, the coupling device is a gas generator or a P2G device.

[0022] The steps of acquiring the measured data of the gas system and the power system and respectively performing local state estimation of the gas system and the power system according to the gas network dynamic model and the power grid AC power flow model include:

[0023] Acquiring measurement data of the gas system;

[0024] According to the gas network dynamic model, a Kalman filter algorithm is used to calculate a local state estimate of the gas system;

[0025] Acquiring measurement data of the power grid;

[0026] Based on the AC power flow model of the power grid, an unscented Kalman filter algorithm is used to calculate the local state estimation of the power system.

[0027] Optionally, the measurement data of the gas system are the gas network node pressure and the gas network pipeline flow; the measurement data of the power system are the voltage amplitude of the power grid bus, and the active power and reactive power of the power grid branch.

[0028] Optionally, the boundary information is energy coupling constraint information.

[0029] Optionally, the method further includes:

[0030] The measurement data of the gas system and the power system at the next moment are obtained, and the above steps S4 to S6 are repeated until the last moment.

[0031] A second aspect of an embodiment of the present application provides a distributed state estimation device for a multi-energy system in which multiple agents participate, including:

[0032] CPU, memory, input and output interfaces, wired or wireless network interfaces, and power supply;

[0033] The memory is a transient storage memory or a persistent storage memory;

[0034] The central processing unit is configured to communicate with the memory and execute instruction operations in the memory to perform the distributed state estimation method of the multi-energy system described in the first aspect.

[0035] A third aspect of an embodiment of the present application provides a computer-readable storage medium, which includes instructions. When the instructions are executed on a computer, the computer executes the distributed state estimation method for a multi-energy system described in the first aspect.

[0036] A fourth aspect of an embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed on a computer, the computer executes the distributed state estimation method for a multi-energy system described in the first aspect.

[0037] Compared with the existing technology, in this method, the state estimation of the power system and the natural gas system is first performed separately in the local estimator, and then a small amount of boundary information is exchanged to further correct the system state without exposing the detailed network data. This method is not only simple to calculate, but also more efficient. It can achieve consistent estimation results without tedious iterative process. Therefore, this method solves the privacy protection problem between energy management entities and avoids the risk of exposing detailed sensitive data during the consistent estimation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 A schematic flow chart of a distributed state estimation method for a multi-energy system involving multiple agents disclosed in an embodiment of the present application;

[0040] Figure 2 A flow chart of another method for distributed state estimation of a multi-energy system involving multiple agents disclosed in an embodiment of the present application;

[0041] Figure 3 A flowchart of another method for distributed state estimation of a multi-energy system involving multiple agents disclosed in an embodiment of the present application;

[0042] Figure 4 This is a schematic diagram of the topological structure of the electricity-gas integrated energy system;

[0043] Figure 5 It is a schematic diagram of the line diagram model corresponding to the gas pipeline network of the electricity-gas integrated energy system;

[0044] Figure 6 It is a schematic diagram of the line diagram structure of a single pipeline;

[0045] Figure 7 This is a schematic diagram of the structure of a distributed state estimation device for a multi-energy system involving multiple agents disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, but not necessarily to describe a specific order or sequence; it should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein; in addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0047] It should be noted that the descriptions involving "first", "second", etc. in the present application are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features; therefore, the features specified as "first" and "second" may explicitly or implicitly include at least one of such features; in addition, the technical solutions between the various embodiments can be combined with each other, but must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0048] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments; based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0049] See also Figure 1 , Figure 1 A flowchart of a distributed state estimation method for a multi-energy system involving multiple agents disclosed in an embodiment of the present application includes steps 110 to 160.

[0050] Step S110: Obtain the gas network topology and pipeline parameters in the multi-energy system, and construct a gas network dynamic model based on the gas network topology and pipeline parameters.

[0051] Through systematic management and optimization, the multi-energy system achieves efficient conversion, storage and distribution of energy, reduces energy costs and reduces energy waste; it comprehensively utilizes multiple energy sources, improves energy utilization, enhances system flexibility, reliability and economy, making the application of multi-energy systems more and more extensive.

[0052] A multi-energy system generally refers to a new energy system formed by coupling multiple energy systems such as cold, heat, electricity, gas, and water in the energy production, transmission, and consumption links. In this embodiment, the multi-energy system is formed by coupling electricity and gas as the two energy sources for consumption links. If there are similar energy sources, this solution can also be applied and is not limited here.

[0053] This distributed state estimation method is applicable to a multi-energy system, wherein the gas network topology and pipeline parameters in the multi-energy system are obtained, and a gas network dynamic model is constructed based on the gas network topology and pipeline parameters; topology represents the relationship between multiple objects within a certain range in the form of a graph, that is, the number of nodes of the gas network in the multi-energy system and the relationship between each node are obtained, thereby forming a gas network topology; pipeline parameters include parameters such as the cross-sectional inner diameter of the pipeline, the cross-sectional area of ​​the pipeline and the friction coefficient, which are used to construct a gas network dynamic model. Different gas network topologies and pipeline parameters establish different gas network dynamic models. For specific model establishment, see below.

[0054] Step S120: Obtain the grid topology and line parameters in the multi-energy system, and construct a grid AC power flow model based on the grid topology and line parameters.

[0055] The grid topology and line parameters in the multi-energy system are obtained, and the grid AC power flow model is constructed based on the grid topology and line parameters; specifically, the number of nodes in the grid in the multi-energy system and the relationship between each node are obtained to form the grid topology; the line parameters include parameters such as voltage amplitude, active power and reactive power, which are used to construct the grid AC power flow model; different grid topologies and line parameters are used to establish different grid AC power flow models; the gas pipeline network dynamic model and the grid AC power flow model are constructed independently to reduce the impact on each other; the gas pipeline network can also be called a natural gas system or a gas system, and the grid can also be called a power system.

[0056] Step S130: construct a coupled device model in a multi-energy system.

[0057] In this embodiment, the coupling device model of the multi-energy system is determined by the specific coupling device performance. The coupling device mainly includes a gas generator or a P2G device.

[0058] Step S140: Acquire the measured data of the gas system and the power system, and perform local state estimation of the power system and the gas system respectively based on the gas network dynamic model and the power grid AC flow model.

[0059] Among them, the measurement data of the gas system and the power system are obtained. The measurement data refers to the data obtained by means of certain measurement tools or certain measurement standards, that is, the data that can be measured. In this embodiment, the measurement data of the gas system are parameters such as the gas network node pressure and the gas network pipeline flow; the measurement data of the power system are parameters such as the voltage amplitude of the power grid bus, the active power and reactive power of the power grid branch, etc. Through the above parameters, combined with the gas network dynamic model and the power grid AC flow model, the local state estimation of the gas system and the power system can be obtained respectively.

[0060] Step S150: The power system and the gas system exchange boundary information without exposing specific network data.

[0061] Boundary information refers to the elements used to distinguish the relationship between a system and other systems, especially adjacent systems, in an information system. It clarifies which elements belong to the system and which elements belong to adjacent systems or the external environment. By exchanging boundary information, detailed network data in the multi-energy system can be kept from being exposed, thus ensuring the data security of the multi-energy system. In this embodiment, the boundary information is energy coupling constraint information. Specifically, the boundary information is the number of gas generators or P2G devices, the number of electric generators, or energy conversion efficiency.

[0062] Step S160: Based on the boundary information, a constrained Kalman filtering method is used to correct the local state estimate to form a consistent state estimate.

[0063] Based on the boundary information and the coupled device model, the acquired boundary information is used as a constraint condition and the constrained Kalman filtering algorithm is used to correct the local state estimation of the gas system and the power system, thereby completing the consistent state estimation of the coupled device model.

[0064] In this embodiment, in this method, the state estimation of the power system and the gas system is first performed separately in the local estimator, and then a small amount of boundary information is exchanged to further correct the system state without exposing the detailed network data. This method is not only simple to calculate, but also more efficient, and consistent estimation results can be achieved without tedious iterative processes; therefore, this method solves the privacy protection problem between energy management entities and avoids the risk of exposing detailed sensitive data during the consistency estimation process.

[0065] See also Figure 2 , Figure 2 A flow chart of another distributed state estimation method for a multi-energy system involving multiple agents disclosed in an embodiment of the present application.

[0066] Specifically, step S110 includes:

[0067] Step S111: Acquire the topology and pipeline parameters of the gas pipeline network of the gas system.

[0068] In this embodiment, the multi-energy system includes a gas system and an electric power system, which are independent energy systems respectively. For the gas system, the number of nodes of the gas pipeline network in the gas system and the relationship between each node are obtained to form the gas pipeline network topology. Similarly, for the electrical system, the number of nodes of the power grid of the electrical system and the relationship between each node are obtained to form the power grid topology.

[0069] In this embodiment, the Figure 4 The electric-gas integrated energy system shown is used as an example for illustration. The electric-gas integrated energy system of the present invention comprises 11 natural gas nodes and 10 busbars, wherein the natural gas network and the power grid are connected via a gas generator deployed on busbar 4, and natural gas node 1 and busbar 1 are the gas source node and balancing node of the natural gas system and the electric power system, respectively.

[0070] Step S112: constructing a gas pipeline network line diagram model according to the gas pipeline network topology.

[0071] Before constructing the gas pipeline network line graph model, the line graph theory is introduced. The transmission process of natural gas in the pipeline follows the fluid mechanics equations, and large-scale natural gas networks must satisfy Kirchhoff's first and second laws. Using line graph theory modeling can unify the above process and extract the corresponding state space model.

[0072] In the line graph theory, the flow and pressure of the basic variables in the gas transmission process are expressed as a set of generalized system variables - flow variables, denoted as f(t); and effort variables, denoted as e(t); according to the different types of relationships between variables, the system elements can be divided into energy source S, energy dissipation R and energy storage elements; among them, the energy source S is divided into flow source S f and span source S e , energy storage can be divided into flow storage C and force storage L; the dissipative element R is defined as:

[0073] e(t)=R·f(t)

[0074] The stream storage C is defined as:

[0075]

[0076] The force storage L is defined as:

[0077]

[0078] Specifically, in this embodiment, according to the above relationship, the pipeline flow rate m (kg / s) and the node pressure p (Pa) can be defined as the generalized flow variable f and the force variable e, respectively. The former represents the flux of the energy flow, and the latter represents the span of the energy flow. The product of the two represents the instantaneous power. Compared with the direction along the pipeline axis, the rate of change of the gas characteristics perpendicular to the axis can be ignored. Therefore, the control equation of the airflow in the pipeline can be simplified to a function of the time t and the length x along the pipeline axis; that is:

[0079]

[0080] Where A represents the cross-sectional area of ​​the pipe (m 2 ), λ represents the friction coefficient, c represents the speed of sound (m / s), and D represents the inner diameter of the pipe (m); In this embodiment, the linear method is used to discretize the partial derivative of the space in the above equation, and the result is:

[0081]

[0082] Among them, the subscripts in and out respectively mark the inlet and outlet of the pipe section after spatial discretization, and Δx represents the length of the pipe section. The gas load is usually relatively stable. Based on Taylor expansion, the above formula can be approximated as:

[0083]

[0084] Among them, the fluctuation variable Δe out and Δf in Corresponding to the force variable e and flow variable f in the line graph theory, the relevant expressions of the flow storage C, dissipative element R, and force storage L of the gas pipeline in this embodiment are obtained as follows:

[0085] C = (ΔxA) / c 2

[0086]

[0087] L=Δx / A

[0088] According to the principle of series voltage division and parallel current division, the defined basic elements can be connected one by one to construct the line graph structure; Figure 4 The schematic diagram of the line diagram model of the topological structure of the natural gas network is as follows: Figure 5 As shown, there is a directed edge S e ,R, L and C represent the power source, dissipation, power storage and flow storage elements respectively, and R' represents the gas load. The nodes in the figure have no practical meaning.

[0089] Based on the line graph theory, it can be known that the dissipative element R and the force storage L share the loss of the force variable, and the dissipative element R and the flow storage C share the loss of the flow variable; based on the principle of "series pressure division, parallel flow division", R and L are in series, and R and C are in parallel; thus, a line graph structure diagram of a single gas pipeline can be constructed (such as Figure 6 to facilitate understanding of the model building.

[0090] In this embodiment, based on the above line graph theory, it can be known that the gas pipeline network line graph model that can be constructed by 11 natural gas nodes in the electricity-gas integrated energy system of the present invention is as follows: Figure 5 shown.

[0091] Step S113: constructing a gas network dynamic model based on the gas network pipeline parameters and the gas network line diagram model.

[0092] The schematic diagram of the gas pipe network model in this embodiment is as follows Figure 5 As shown; the solid line represents the spanning tree and the dotted line represents the residual tree; Figure 5 The directed edges S, R, L and C represent the force source, dissipation, force storage and flow storage elements respectively, and R' represents the gas load; except for the node d representing the atmospheric pressure (i.e., the reference node), the other nodes have no practical meaning.

[0093] It can be seen from step S112 that in the online graph theory, the network equations of these system variables (Kirchhoff's first and second laws) can be uniformly described by the basic cut set matrix.

[0094] Among them, the cut set matrix is ​​a matrix that describes the association between cut sets and branches. In a directed graph, the relationship between cut set j and branch k is: if branch k is not an element of cut set j, then branch k is said to be unassociated with cut set j; if branch k is an element of cut set j, then branch k is said to be associated with cut set j; assuming that a connected directed graph has n nodes and b branches, the number of independent cut sets is n-1, and the cut set matrix is ​​an (n-1)×b-order matrix; the rows of the cut set matrix correspond to the cut sets, and the columns correspond to the branches; the association between the cut sets and the branches can be expressed by any of its elements q jk Indicates; where q jk The specific meanings of the values ​​are as follows:

[0095] (1)q jk =+1, indicating that the cut set is associated with the branch and has the same direction.

[0096] (2)q jk =-1, indicating that the cut set is associated with the branch in the opposite direction.

[0097] (3)q jk =0, indicating that the cut set is not associated with the branch.

[0098] Based on this, the cut set matrix of the gas pipeline network line diagram model in this embodiment can be divided into an association matrix of a spanning tree and an association matrix of a co-tree, wherein the association matrix of the spanning tree and the association matrix of the co-tree are calculated based on the actual topology; for the convenience of calculation, the association matrix will also remove the reference node, thereby forming a reduction matrix after removing the reference node; specifically as follows:

[0099] In this embodiment, the cut set matrix H defining the gas network line diagram model can be calculated by the following formula:

[0100]

[0101] Among them, B tr and B co denote the incidence matrices of the spanning tree and the co-tree, respectively. represents the reduction matrix after removing the reference node; for ease of understanding, Figure 6 Taking the line graph of a single pipeline in as an example, the corresponding correlation matrix B is expressed as:

[0102]

[0103] Among them, a, b, c, and d represent nodes in the line graph, respectively, and S e , R, L, C1, C1 and R' represent the corresponding edges; the incidence matrix B can be decomposed into B tr and B co ; After removing the reference node d, Figure 6 The corresponding cut set matrix H can be calculated by the following formula:

[0104]

[0105] According to the cut set matrix obtained from the above definition, the network equations of the system variables of the spanning tree and co-tree in the line graph model can be described as:

[0106] f tr =-H×f co

[0107] e co =H T ×e tr

[0108] Among them, f co and e co denote the set of flow variables and force variables in the co-tree, respectively, and f tr and e tr Respectively represent the set of flow variables and force variables in the spanning tree; the above system variable set can be further decomposed into:

[0109]

[0110] The subscripts S, R, L, and C represent the force source, dissipation, force storage, and flow storage elements, respectively; for example, f C,tr Represents the set of flow variables corresponding to the flow storage elements in the spanning tree; the other representations are similar; N s ,N L ,N C ,N R and N R’ Then they represent the number of each system element respectively; further, according to the division of the system variable set, the cut set matrix H can be expressed in the form of a block matrix:

[0111]

[0112] Furthermore, from the definition of system elements, the interaction between flow variables and force variables can be rewritten into matrix form as follows:

[0113]

[0114]

[0115] in, and They are all diagonal matrices, and the elements on the diagonal represent the flow storage element vectors in the spanning tree and the co-tree respectively; is a diagonal matrix, and the elements on the diagonal correspond to the force storage element vectors in the cotree; and It is a diagonal matrix, and the diagonal elements represent the dissipation element vector in the spanning tree and the load element vector in the co-tree.

[0116] Finally, combining the system element equation and network equation of the gas pipeline network, the spatial expression equation of the dynamic model of the gas pipeline network can be obtained:

[0117]

[0118] See also Figure 4 , Figure 4 Schematic diagram of the topological structure of the electricity-gas integrated energy system.

[0119] Specifically, step S120: obtaining the grid topology and line parameters of the power system;

[0120] According to the grid topology and line parameters of the power system, the AC power flow model of the power grid is constructed based on the two-parameter exponential smoothing method.

[0121] In this embodiment, according to the power grid topology (see Figure 4), construct a corresponding AC power flow model of the power grid; in this embodiment, the corresponding AC power flow model of the power grid, that is, the AC power flow model, can be constructed based on the topology; in power engineering, "power flow" specifically refers to the distribution of voltage (including amplitude and phase angle), active power, reactive power, etc. at various locations in the power grid; the distribution of power flow is something that the operation and dispatching units and maintenance departments must know.

[0122] According to the power grid line parameters and the power grid line parameters, wherein the line parameters include parameters such as voltage amplitude, active power and reactive power; the line parameters of this embodiment are provided by the SCADA system; wherein the measurement data is provided by the SCADA system; further, the SCADA (Supervisory Control And Data Acquisition) system, that is, the data acquisition and monitoring control system; the SCADA system is a computer-based DCS and power automation monitoring system; it has a wide range of applications and can be applied to data acquisition and monitoring control and process control in the fields of electricity, metallurgy, petroleum, chemical industry, gas, railways, etc.

[0123] Based on the two-parameter exponential smoothing technology, the two-parameter exponential smoothing method is a time series prediction method, which is mainly used to process data with linear trend and temporal characteristics. It is an extension of the exponential smoothing method, which introduces two parameters to deal with linear trend and temporal effects respectively, thereby improving the accuracy of prediction; the state transition model of the power system (i.e., the power grid) in the multi-energy system can be defined as:

[0124]

[0125] in, Indicates k e The state vector of the power system at time and They correspond to the voltage phase angle and voltage amplitude of busbar i, respectively, with subscript N e Represents the total number of buses; Note: It is assumed here that bus 1 is a balanced node. It represents the system Gaussian noise with mean 0 and covariance Q; and It can be calculated by the following formula:

[0126]

[0127] α and β represent two different smoothing parameter coefficients; and They are parameters in the calculation process, which are only for the convenience of calculation and have no specific meaning.

[0128] In this embodiment, the measurement data is provided by the SCADA system, and the measurement vector includes voltage amplitude, injection power and line power. Then the measurement model of the power system can be defined as:

[0129]

[0130] in, Indicates k e The measurement vector of the power system at time instant, Voltage magnitude vector, and Indicates active injection and reactive injection, and Indicates branch active power measurement and branch reactive power measurement; represents the system Gaussian noise with mean 0 and covariance R, where R represents the covariance of the measurement noise. In the actual implementation of the filter, the measurement noise covariance R can generally be observed and is a known condition of the filter.

[0131] Specifically, step S130 includes:

[0132] Step 3A: Obtain performance parameters of coupled devices in the multi-energy system.

[0133] The coupling equipment mainly includes gas generators and P2G equipment; P2G stands for Power to Gas, which can be renewable energy power generation equipment or power-to-gas equipment; the concept originates from the expansion of renewable energy import in Germany, using the surplus electricity from wind power generation, solar power generation, etc. to electrolyze water to generate hydrogen, which is then supplied to the existing gas pipeline network; or using electricity, water and CO2 in the atmosphere to produce methane through methanation reaction and provide gas, all of which are aimed at the utilization of hydrogen; these devices play a key role in the integrated energy system, by converting one form of energy into another form of energy, realizing the mutual conversion and cascade utilization of energy, thereby improving energy utilization efficiency and reducing pollutant emissions.

[0134] The physical system of the integrated energy system is an energy supply system formed by coupling the power subsystem and the natural gas subsystem through coupling equipment; the coupling equipment converts a certain form of energy into another form of energy, which can specifically include gas generators, power-to-gas equipment, electric boilers, gas boilers, etc.; the conversion equipment in the integrated energy system includes independent equipment and coupled equipment. Coupling equipment is the key unit for realizing energy conversion in the integrated energy system and is the coupling node between different energy systems, including coupling equipment, electric-thermal coupling, gas-thermal coupling, gas-electric-thermal coupling and other forms.

[0135] This embodiment adopts the coupling device method. When the coupling device is selected, its performance parameters are also determined. When selecting the coupling device of this embodiment, it is necessary to consider ignoring the transient process in the operation of the device (Note: the network level state estimation can ignore the transient characteristics of the device) and assume that the energy conversion relationship is linear.

[0136] Step 3B: Construct a coupling device model based on the performance parameters of the coupling device.

[0137] The coupling device is a gas generator or a renewable energy generator, such as Figure 5 As shown, taking the gas generator as an example, the transient process in the operation of the equipment can be ignored, and assuming that the energy conversion relationship is linear, the coupling model of the coupling device can be uniformly expressed as the following formula:

[0138] P c =η i M c

[0139] Among them, P c Indicates the active power produced or consumed by the equipment, M c Indicates the gas mass flow rate consumed or generated, η i represents the energy conversion efficiency of coupled device i, i∈(P2G,GFG); where GFG is a high-efficiency boiling dryer; therefore, assuming that the coupled device model is accurate, the state estimation result of the integrated energy network must meet the output constraints defined by the coupled device model of the coupled device.

[0140] Specifically, step S140 includes:

[0141] Step 4A: Obtain gas system measurement data.

[0142] Step 4B: Based on the dynamic model of the gas network, the Kalman filter algorithm is used to calculate the local state estimation of the gas system.

[0143] Step 5A: Obtaining measurement data of the power grid;

[0144] Step 5B: Based on the AC power flow model of the power grid, the unscented Kalman filter algorithm is used to calculate the local state estimation of the power system.

[0145] In this embodiment, step 4A and step 5A are performed in different subsystems, and may be performed synchronously or asynchronously.

[0146] Specifically, the measurement data of the power system and the natural gas system are obtained. The measurement data of the gas network are the gas network node pressure and the gas network pipeline flow; the measurement data of the power grid are the voltage amplitude of the power grid bus, the active power and reactive power of the power grid branch; specifically including the bus voltage amplitude, branch active and reactive power measurement, gas network node pressure, pipeline flow, etc.; based on the obtained measurement data, the state estimation of the power grid and the natural gas network is performed respectively, among which the power system adopts the unscented Kalman filtering algorithm, and the natural gas system adopts the Kalman filtering algorithm.

[0147] First, according to the spatial model of gas network state estimation established in step S113, the Kalman filter algorithm is used to perform local state estimation of the gas system and the power system respectively, which can also be called local state estimation.

[0148] For the convenience of expression, the spatial model for estimating the gas network state established in step S113 can be rewritten as:

[0149] x g,k =A g x g,k-1 +B g u g,k +w k

[0150] z g,k =C g x g,k +v k

[0151] in, Indicates the status of the gas system. represents input, z g represents the system output, assuming that all system states are measurable, C g is the identity matrix, w k and v k Represent system noise and measurement noise respectively, satisfying Gaussian distribution A g , B g and C g Respectively represent matrices of appropriate dimensions; According to the above spatial model, the classic Kalman filtering algorithm is used to estimate the operating state of the natural gas system.

[0152] In addition, considering the nonlinear characteristics of the power system flow model in step S213, the unscented Kalman filter is used to estimate the operating state of the power system; the local state estimation results of the natural gas system and the power system are marked as and Among them, the unscented Kalman filter is a method of applying the Kalman filter to a nonlinear system. It is a mature algorithm and will not be described in detail here.

[0153] It can be seen from step S130 that the gas generator model constructed by step S130, P c and M c All are measurable. Combined with the gas system and power system models established in steps S110 and S120, the coupling relationship of the gas-electricity integrated energy system can be expressed as the following formula:

[0154]

[0155] in, They represent the measurement equations of the coupled equipment of the power system and the natural gas system respectively. e (·) is a nonlinear function. To improve computational efficiency, the state estimation result at time k is Performing Taylor expansion at , we can obtain the linear expression of the coupling constraint:

[0156]

[0157] in,

[0158]

[0159] Further, D k =[D e,k ,D g,k ], represents the measurement equation of the natural system coupled device, then the coupling constraint is rewritten as follows

[0160] D k x k =d k

[0161] Among them, x k =[x e,k ,x g,k ] T Represents system variables, x e and,x g They represent the operating status of the power system and the gas system at time k respectively.

[0162] This constraint can reflect the mutual influence between the power system and the natural gas system. In the centralized state estimation framework, the Kalman filter problem with system state equality constraint can be corrected by the following formula:

[0163]

[0164] in, Indicates the system status after correction, represents the covariance matrix.

[0165] However, the centralized framework needs to expose the current system state and the corresponding covariance matrix. This information is relatively sensitive because it fully reflects the operating state of the energy system, such as node voltage, node gas pressure, etc. In order to maintain the consistency of the state estimation of the gas-electric integrated energy system and protect the information privacy of each management subject, the boundary information exchanged at time k includes α i,k ,β i,k ,x i,k , that is, the corresponding number of gas generators, number of electric generators and energy conversion efficiency, which are calculated as follows:

[0166]

[0167] Where, subscript i∈(e,g).

[0168] Finally, define the integrated energy system S = {e, g}, that is, only the gas-electric energy system coupling scenario is considered in the system, then for i∈S, represents the set of systems coupled with system i (note: including system i), we can get:

[0169]

[0170] in, represents the corrected state of system i at time k, Represents the result of local estimation. Among them,

[0171]

[0172]

[0173] See also Figure 3 , Figure 3 A flow chart of another distributed state estimation method for a multi-energy system involving multiple agents disclosed in an embodiment of the present application.

[0174] The steps of this embodiment are basically the same as those of the first embodiment, and are not described in detail here. Among them, this embodiment basically includes:

[0175] It is determined whether the acquisition time is the last time. If not, the measurement data of the gas pipeline network and the power grid at the next time are acquired, and the above steps S4 to S6 are repeated.

[0176] Specifically, in the above step S140, the measurement data of the gas system and the measurement data of the electrical system are both obtained at time T. After step S160 is completed, it is determined whether time T is the last time. If not, the above steps S140 to S160 are repeated at the next time until time T is the last time.

[0177] In this embodiment, within a predetermined time, the distributed state estimation method of the multi-energy system is iteratively calculated for a preset time to ensure the stability and reliability of the system.

[0178] In summary, in this method, the state estimation of the power system and the gas system is first performed separately in the local estimator, and then a small amount of boundary information is exchanged to further correct the system state without exposing the detailed network data. This method is not only simple to calculate, but also more efficient. It can achieve consistent estimation results without tedious iterative processes. Therefore, this method solves the privacy protection problem between energy management entities and avoids the risk of exposing detailed sensitive data during the consistency estimation process.

[0179] See below Figure 7 , a structural diagram of a distributed state estimation device for a multi-energy system with multi-agent participation disclosed in an embodiment of the present application includes:

[0180] CPU 1101, memory 1105, input / output interface 1104, wired or wireless network interface 1103 and power supply 1102;

[0181] The memory 1105 is a temporary storage memory or a permanent storage memory;

[0182] The CPU 1101 is configured to communicate with the memory 1105 and execute the instructions in the memory 1105 to perform the aforementioned Figures 2 to 6 A distributed state estimation method for a multi-energy system in any of the illustrated embodiments.

[0183] The embodiment of the present application also provides a chip system, characterized in that the chip system includes at least one processor and a communication interface, the communication interface and the at least one processor are interconnected through a line, and the at least one processor is used to run a computer program or instruction to execute the aforementioned Figures 1 to 5 A distributed state estimation method for a multi-energy system in any of the illustrated embodiments.

[0184] The embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium includes instructions, when the instructions are executed on a computer, the computer executes the aforementioned Figures 2 to 6 A distributed state estimation method for a multi-energy system in any of the illustrated embodiments.

[0185] The present application also provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the aforementioned Figures 2 to 6 A distributed state estimation method for a multi-energy system in any of the illustrated embodiments.

[0186] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0187] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways; for example, the device embodiments described above are merely schematic, for example, the division of the units is merely a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed; another point, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0188] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0189] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit; the above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium; based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk, etc. Various media that can store program codes.

Claims

1. A distributed state estimation method for a multi-energy system with multi-agent participation, characterized in that: The method comprises: S1. Obtain the gas pipeline network topology and pipeline parameters in the multi-energy system, and construct a gas pipeline network dynamic model based on the gas pipeline network topology and pipeline parameters; S2. Acquire the grid topology and line parameters in the multi-energy system, and construct a grid AC power flow model based on the grid topology and line parameters; S3, constructing a coupling device model in the multi-energy system; S4, obtaining the measured data of the gas system and the power system, and performing local state estimation of the power system and the gas system respectively according to the gas network dynamic model and the power grid AC power flow model; S5. The power system and the gas system exchange boundary information without exposing specific network data; S6. Based on the boundary information, a constrained Kalman filtering method is used to correct the local state estimate to form a consistent state estimate.

2. The distributed state estimation method according to claim 1, characterized in that: The step of obtaining the gas network topology and pipeline parameters in the multi-energy system and constructing a gas network dynamic model based on the gas network topology and pipeline parameters comprises: Acquire the gas network topology and pipeline parameters of the gas system; According to the gas pipeline network topology, a gas pipeline network line diagram structure is constructed; A gas pipeline network dynamic model is constructed based on the gas pipeline network line diagram structure.

3. The distributed state estimation method according to claim 2, characterized in that: The steps of constructing the AC power flow model of the multi-energy system include: Obtaining grid topology and line parameters of the power system; According to the grid topology and line parameters of the power system, the grid AC power flow model is constructed based on a two-parameter exponential smoothing method.

4. The distributed state estimation method according to claim 3, characterized in that: The coupling device is a gas generator or a P2G device.

5. The distributed state estimation method according to claim 3, characterized in that: The steps of respectively acquiring the measurement data of the gas system and the power system and performing local state estimation of the gas system and the power system according to the gas network dynamic model and the power grid AC flow model include: Acquiring measurement data of the gas system; According to the gas network dynamic model, a Kalman filter algorithm is used to calculate a local state estimate of the gas system; Acquiring measurement data of the power grid; Based on the AC power flow model of the power grid, an unscented Kalman filter algorithm is used to calculate the local state estimation of the power system.

6. The distributed state estimation method according to claim 5, characterized in that: The measured data of the gas system are the gas network node pressure and the gas network pipeline flow.

7. The distributed state estimation method according to claim 5, characterized in that: The measured data of the power system are the voltage amplitude of the power grid bus, and the active power and reactive power of the power grid branches.

8. The distributed state estimation method according to claim 1, characterized in that: The boundary information is energy coupling constraint information.

9. The distributed state estimation method according to claim 1, characterized in that: The method further comprises: It is determined whether the acquisition time is the last time. If not, the measurement data of the gas system and the power system at the next time are acquired, and the above steps S4 to S6 are repeated.

10. A distributed state estimation device for a multi-energy system with multi-agent participation, characterized in that: The device comprises: CPU, memory, input and output interfaces, wired or wireless network interfaces, and power supply; The memory is a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instruction operations in the memory to perform the state estimation method according to any one of claims 1 to 8.

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